From 470fe12310f8cd9fcb3ca905ae42944aacabb1cc Mon Sep 17 00:00:00 2001 From: Almog-David Date: Tue, 28 May 2024 16:10:24 +0300 Subject: [PATCH 01/93] firefighter problem --- .../Firefighter_Problem.py | 295 +++++++ .../firefighter_problem/Utils.py | 450 ++++++++++ .../firefighter_problem/graphs.json | 769 ++++++++++++++++++ .../test_firefighter_problem/__init__.py | 0 .../test_non_spreading_dirlaynet_minbudget.py | 285 +++++++ .../test_non_spreading_minbudget.py | 102 +++ .../test_firefighter_problem/test_parsing.py | 115 +++ .../test_spreading_maxsave.py | 248 ++++++ .../test_spreading_minbudget.py | 272 +++++++ .../test_firefighter_problem/test_utils.py | 50 ++ 10 files changed, 2586 insertions(+) create mode 100644 networkz/algorithms/approximation/firefighter_problem/Firefighter_Problem.py create mode 100644 networkz/algorithms/approximation/firefighter_problem/Utils.py create mode 100644 networkz/algorithms/approximation/firefighter_problem/graphs.json create mode 100644 networkz/algorithms/approximation/tests/test_firefighter_problem/__init__.py create mode 100644 networkz/algorithms/approximation/tests/test_firefighter_problem/test_non_spreading_dirlaynet_minbudget.py create mode 100644 networkz/algorithms/approximation/tests/test_firefighter_problem/test_non_spreading_minbudget.py create mode 100644 networkz/algorithms/approximation/tests/test_firefighter_problem/test_parsing.py create mode 100644 networkz/algorithms/approximation/tests/test_firefighter_problem/test_spreading_maxsave.py create mode 100644 networkz/algorithms/approximation/tests/test_firefighter_problem/test_spreading_minbudget.py create mode 100644 networkz/algorithms/approximation/tests/test_firefighter_problem/test_utils.py diff --git a/networkz/algorithms/approximation/firefighter_problem/Firefighter_Problem.py b/networkz/algorithms/approximation/firefighter_problem/Firefighter_Problem.py new file mode 100644 index 0000000..8347fee --- /dev/null +++ b/networkz/algorithms/approximation/firefighter_problem/Firefighter_Problem.py @@ -0,0 +1,295 @@ +import networkx as nx +import networkx.algorithms.connectivity as algo +import math +import json +import random + +# TODO: fix this shit, when we run tests needs src.Utils, and when we run this, we need Utils only.. +#from networkz.algorithms.approximation.firefighter_problem.Utils import * +#from networkz.algorithms.approximation.firefighter_problem.Utils import * +from firefighter_problem.Utils import * +#from Utils import * + +def spreading_maxsave(Graph:nx.DiGraph, budget:int, source:int, targets:list, flag=None) -> list: + """ + "Approximability of the Firefighter Problem - Computing Cuts over Time", + by Elliot Anshelevich, Deeparnab Chakrabarty, Ameya Hate, Chaitanya Swamy (2010) + https://link.springer.com/article/10.1007/s00453-010-9469-y + + Programmers: Shaked Levi, Almog David, Yuval Bobnovsky + + spreading_maxsave: Gets a directed graph, budget, source node, and list of targeted nodes that we need to save + and return the best vaccination strategy that saves the most nodes from the targeted nodes list. + + Example 1: + >>> G = nx.DiGraph() + >>> G.add_nodes_from([0,1,2], status="target") + >>> G.add_edges_from([(0,1),(0,2),(1,2)]) + >>> spreading_maxsave(G,1,0,[1,2]) + [(1, 1)] + + Example 2: + >>> G1 = nx.DiGraph() + >>> G1.add_nodes_from([0,1,2,3], status="target") + >>> G1.add_edges_from([(0,1),(0,2),(1,2),(1,3),(2,3)]) + >>> spreading_maxsave(G1,1,0,[1,2,3]) + [(1, 1)] + + Example 3: + >>> G2 = nx.DiGraph() + >>> G2.add_nodes_from([0,1,2,3,4,5,6], status="target") + >>> G2.add_edges_from([(0,1),(0,2),(1,2),(1,4),(2,3),(2,6),(3,5)]) + >>> spreading_maxsave(G2,1,0,[1,2,3,4,5,6]) + [(2, 1), (4, 2)] + + Example 4: + >>> G3 = nx.DiGraph() + >>> G3.add_nodes_from([0,1,2,3,4,5,6,7,8], status="target") + >>> G3.add_edges_from([(0,2),(0,4),(0,5),(2,1),(2,3),(4,1),(4,6),(5,3),(5,6),(5,7),(6,7),(6,8),(7,8)]) + >>> spreading_maxsave(G3,2,0,[1,2,3,4,5,6,7,8]) + [(5, 1), (2, 1), (8, 2)] + """ + if budget < 1: + raise ValueError("Error: The budget must be at least 1") + exit() + validate_parameters(Graph,source,targets) + infected_nodes = [] + vaccinated_nodes = [] + vaccination_strategy = [] + can_spread = True + Graph.nodes[source]['status'] = 'infected' + infected_nodes.append(source) + gamma, direct_vaccinations = calculate_gamma(Graph, source, targets) + epsilon = calculate_epsilon(direct_vaccinations) + time_step = 0 + while(can_spread and time_step int: + """ + "Approximability of the Firefighter Problem - Computing Cuts over Time", + by Elliot Anshelevich, Deeparnab Chakrabarty, Ameya Hate, Chaitanya Swamy (2010) + https://link.springer.com/article/10.1007/s00453-010-9469-y + + Programmers: Shaked Levi, Almog David, Yuval Bobnovsky + + spreading_minbudget: Gets a directed graph, source node, and list of targeted nodes that we need to save + and returns the minimum budget that saves all the nodes from the targeted nodes list. + + Example 1: + >>> G1 = nx.DiGraph() + >>> G1.add_nodes_from([0,1,2,3], status="target") + >>> G1.add_edges_from([(0,1),(0,2),(1,2),(1,3),(2,3)]) + >>> spreading_minbudget(G1,0,[1,2,3]) + 2 + + Example 2: + >>> G1 = nx.DiGraph() + >>> G1.add_nodes_from([0,1,2,3], status="target") + >>> G1.add_edges_from([(0,1),(0,2),(1,2),(1,3),(2,3)]) + >>> spreading_minbudget(G1,0,[1,3]) + 1 + + Example 3: + >>> G2 = nx.DiGraph() + >>> G2.add_nodes_from([0,1,2,3,4,5,6], status="target") + >>> G2.add_edges_from([(0,1),(0,2),(1,2),(1,4),(2,3),(2,6),(3,5)]) + >>> spreading_minbudget(G2,0,[1,2,3,4,5,6]) + 2 + + Example 4: + >>> G3 = nx.DiGraph() + >>> G3.add_nodes_from([0,1,2,3,4,5,6,7,8], status="target") + >>> G3.add_edges_from([(0,2),(0,4),(0,5),(2,1),(2,3),(4,1),(4,6),(5,3),(5,6),(5,7),(6,7),(6,8),(7,8)]) + >>> spreading_minbudget(G3,0,[1,2,3,4,5,6,7,8]) + 3 + """ + validate_parameters(Graph,source,targets) + original_targets = list(targets) + direct_vaccinations = calculate_gamma(Graph, source, targets)[1] + min_value = 1 + max_value = len(targets) + middle = math.floor((min_value + max_value) / 2) + + while min_value < max_value: + strategy = spreading_maxsave(Graph, middle, source, targets,True) + nodes_saved = set() + + for option in strategy: + list_of_nodes = direct_vaccinations.get(option) + nodes_saved.update(list_of_nodes) + + common_elements = set(nodes_saved) & set(original_targets) + + if len(common_elements) == len(original_targets): + max_value = middle + else: + min_value = middle + 1 + + middle = math.floor((min_value + max_value) / 2) + targets = list(original_targets) + + return middle + +def non_spreading_minbudget(Graph:nx.DiGraph, source:int, targets:list)->int: + """ + "Approximability of the Firefighter Problem - Computing Cuts over Time", + by Elliot Anshelevich, Deeparnab Chakrabarty, Ameya Hate, Chaitanya Swamy (2010) + https://link.springer.com/article/10.1007/s00453-010-9469-y + + Programmers: Shaked Levi, Almog David, Yuval Bobnovsky + + non_spreading_minbudget: Gets a directed graph, source node, and list of targeted nodes that we need to save + and returns the minimum budget that saves all the nodes from the targeted nodes list. + + Example1: + >>> G1 = nx.DiGraph() + >>> G1.add_nodes_from([0,1,2,3], status="target") + >>> G1.add_edges_from([(0,1),(0,2),(1,2),(1,3),(2,3)]) + >>> non_spreading_minbudget(G1,0,[1,3]) + 2 + + Example 2: + >>> G1 = nx.DiGraph() + >>> G1.add_nodes_from([0,1,2,3], status="target") + >>> G1.add_edges_from([(0,1),(0,2),(1,2),(1,3),(2,3)]) + >>> non_spreading_minbudget(G1,0,[1,2,3]) + 2 + + Example 3: + >>> G2 = nx.DiGraph() + >>> G2.add_nodes_from([0,1,2,3,4,5,6], status="target") + >>> G2.add_edges_from([(0,1),(0,2),(1,2),(1,4),(2,3),(2,6),(3,5)]) + >>> non_spreading_minbudget(G2,0,[1,2,3,4,5,6]) + 2 + + Example 4: + >>> G3 = nx.DiGraph() + >>> G3.add_nodes_from([0,1,2,3,4,5,6,7,8], status="target") + >>> G3.add_edges_from([(0,2),(0,4),(0,5),(2,1),(2,3),(4,1),(4,6),(5,3),(5,6),(5,7),(6,7),(6,8),(7,8)]) + >>> non_spreading_minbudget(G3,0,[2,6,1,8]) + 3 + """ + validate_parameters(Graph,source,targets) + G = create_st_graph(Graph,targets) + return len(algo.minimum_st_node_cut(G,source,'t')) + +def non_spreading_dirlaynet_minbudget(Graph:nx.DiGraph, source:int, targets:list)->int: + """ + "Approximability of the Firefighter Problem - Computing Cuts over Time", + by Elliot Anshelevich, Deeparnab Chakrabarty, Ameya Hate, Chaitanya Swamy (2010) + https://link.springer.com/article/10.1007/s00453-010-9469-y + + Programmers: Shaked Levi, Almog David, Yuval Bobnovsky + + non_spreading_dirlaynet_minbudget: Gets a directed graph, source node, and list of targeted nodes that we need to save + and returns the minimum budget that saves all the nodes from the targeted nodes list. + + Example1: + >>> G4 = nx.DiGraph() + >>> G4.add_nodes_from([0,1,2,3,4,5], status="target") + >>> G4.add_edges_from([(0,1),(0,2),(1,3),(1,4),(1,5),(2,3),(2,4),(2,5),(3,5),(4,5)]) + >>> non_spreading_dirlaynet_minbudget(G4,0,[1,2,3,4,5]) + 2 + """ + validate_parameters(Graph,source,targets) + layers = adjust_nodes_capacity(Graph, source) + G = create_st_graph(Graph, targets) + G_reduction = graph_flow_reduction(G,source) + N_groups = min_cut_N_groups(G_reduction,source) + vacc_matrix = calculate_vaccine_matrix(layers,N_groups) + min_budget = min_budget_calculation(vacc_matrix) + return min_budget + +def heuristic_maxsave(Graph:nx.DiGraph, budget:int, source:int, targets:list, spreading:bool, flag=None) -> list: + if budget < 1: + raise ValueError("Error: The budget must be at least 1") + exit() + validate_parameters(Graph,source,targets) + infected_nodes = [] + vaccinated_nodes = [] + vaccination_strategy = [] + can_spread = True + Graph.nodes[source]['status'] = 'infected' + infected_nodes.append(source) + time_step = 1 + while(can_spread): + if spreading: + spread_vaccination(Graph, vaccinated_nodes) + for i in range(budget): + node_to_vaccinate = find_best_neighbor(Graph,infected_nodes,targets) + if node_to_vaccinate != None: + vaccination_strategy.append((node_to_vaccinate,time_step)) + vaccinate_node(Graph, node_to_vaccinate) + vaccinated_nodes.append(node_to_vaccinate) + can_spread = spread_virus(Graph,infected_nodes) + + if flag is not None: + # only for min budget - a stoping condition in case we saved all nodes or one of the target nodes in infected + if len(targets)==0 or any(node in infected_nodes for node in targets): + clean_graph(Graph) + return vaccination_strategy + + time_step = time_step + 1 + + clean_graph(Graph) + return vaccination_strategy + +def heuristic_minbudget(Graph:nx.DiGraph, source:int, targets:list, spreading:bool)-> int: + validate_parameters(Graph,source,targets) + original_targets = list(targets) + direct_vaccinations = calculate_gamma(Graph, source, targets)[1] + min_value = 1 + max_value = len(targets) + middle = math.floor((min_value + max_value) / 2) + + while min_value < max_value: + strategy = heuristic_maxsave(Graph, middle, source, targets, spreading, True) + nodes_saved = set() + + for option in strategy: + # works good for spreading. for non-spreading, we need to find a solution for the use of direct_vaccination + list_of_nodes = direct_vaccinations.get(option) + nodes_saved.update(list_of_nodes) + + common_elements = set(nodes_saved) & set(original_targets) + print(common_elements) + + if len(common_elements) == len(original_targets): + max_value = middle + else: + min_value = middle + 1 + + middle = math.floor((min_value + max_value) / 2) + targets = list(original_targets) + + return middle + +if __name__ == "__main__": + import doctest + doctest.testmod(verbose=True) + + G3 = nx.DiGraph() + G3.add_nodes_from([0,1,2,3,4,5,6,7,8], status="target") + G3.add_edges_from([(0,2),(0,4),(0,5),(2,1),(2,3),(4,1),(4,6),(5,3),(5,6),(5,7),(6,7),(6,8),(7,8)]) + print(heuristic_minbudget(G3,0,[2,6,1,8], False)) + print(spreading_minbudget(G3,0,[2,6,1,8])) diff --git a/networkz/algorithms/approximation/firefighter_problem/Utils.py b/networkz/algorithms/approximation/firefighter_problem/Utils.py new file mode 100644 index 0000000..b37aff0 --- /dev/null +++ b/networkz/algorithms/approximation/firefighter_problem/Utils.py @@ -0,0 +1,450 @@ +import networkx as nx +import networkx.algorithms.connectivity as algo +import matplotlib.pyplot as plt +import numpy as np +import math +import copy + +node_colors = { + 'target': 'gray', + 'infected': 'red', + 'vaccinated': 'blue', + 'directly vaccinated': 'green', + 'default' : "#00FFD0" +} + +def validate_parameters(graph:nx.DiGraph, source:int, targets:list)->None: + """ + Validate the source and target nodes in the graph. + + Parameters: + - graph (nx.DiGraph): Directed graph. + - source (int): Source node. + - targets (list): List of target nodes to save. + + Raises: + - ValueError: If the source node is not in the graph. + - ValueError: If the source node is in the targets list. + - ValueError: If any target node is not in the graph. + """ + graph_nodes = list(graph.nodes()) + if source not in graph_nodes: + raise ValueError("Error: The source node isn't on the graph") + exit() + if source in targets: + raise ValueError("Error: The source node can't be a part of the targets list, since the virus is spreading from the source") + exit() + if not all(node in graph_nodes for node in targets): + raise ValueError("Error: Not all nodes in the targets list are on the graph.") + exit() + +"Spreading:" +def calculate_gamma(graph:nx.DiGraph, source:int, targets:list)-> dict: + """ + Calculate Gamma and S(u,t) based on the calculation in the article. + + Parameters: + - graph (nx.DiGraph): Directed graph. + - source (int): Source node. + - targets (list): List of target nodes to save. + + Returns: + - gamma (dict): Dictionary of vaccination options. + - direct_vaccination (dict): Dictionary of direct vaccination strategies - S(u,t). + """ + gamma = {} + direct_vaccination = {} + unreachable_nodes = [] + path_length = dict(nx.all_pairs_shortest_path_length(graph)) + for key in graph.nodes: + vaccination_options = [] + for node in graph.nodes: + if path_length[source].get(key) is not None and path_length[node].get(key) is not None: + s_to_v = path_length[source].get(key) + u_to_v = path_length[node].get(key) + max_time = s_to_v - u_to_v + if max_time > 0: + for i in range(1,max_time+1): + option = (node,i) + vaccination_options.append(option) + if option not in direct_vaccination: + direct_vaccination[option] = [] + if key in targets: + direct_vaccination[option].append(key) + + # if the virus can't reach to the node - it's automatically saved + if not vaccination_options: + unreachable_nodes.append(key) + if key != source: + gamma[key] = vaccination_options + + # add all the unreachable nodes to the vaccination strategy - every strategy can save them + for strategy in direct_vaccination: + for node in unreachable_nodes: + if node in targets: + direct_vaccination[strategy].append(node) + + #print("Gamma is: " + str(gamma)) + #print("S(u,t) is: " + str(direct_vaccination)) + return gamma, direct_vaccination + +def calculate_epsilon(direct_vaccinations:dict)->list: + """ + Calculate Epsilon based on the calculation in the article. + + Parameters: + - direct_vaccinations (dict): Dictionary of direct vaccination strategies. + + Returns: + - epsilon (list): List of direct vaccination groups by time step. + """ + epsilon = [] + sorted_dict = dict(sorted(direct_vaccinations.items(), key=lambda item: item[0][1])) + + # Iterate over the sorted dictionary and populate the result list + current_time_step = None + current_group = [] + for key, value in sorted_dict.items(): + if current_time_step is None or key[1] == current_time_step: + current_group.append(key) + else: + epsilon.append(current_group) + current_group = [key] + current_time_step = key[1] + + # Append the last group + if current_group: + epsilon.append(current_group) + + #print("Epsilon is: " + str(epsilon)) + return epsilon + +def find_best_direct_vaccination(graph:nx.DiGraph, direct_vaccinations:dict, current_time_options:list, targets:list)->tuple: + """ + Find the best direct vaccination strategy for the current time step that saves more new node in targets. + + Parameters: + - graph (nx.DiGraph): Directed graph. + - direct_vaccinations (dict): Dictionary of direct vaccination strategies. + - current_time_options (list): List of current time step vaccination options. + - targets (list): List of target nodes. + + Returns: + - best_vaccination (tuple): Best direct vaccination option. + """ + best_vaccination = () + nodes_saved = {} + common_elements = None + max_number = -1 + for option in current_time_options: + if(graph.nodes[option[0]]['status'] == 'target'): + nodes_list = direct_vaccinations.get(option) + common_elements = set(nodes_list) & set(targets) + if len(common_elements) > max_number: + best_vaccination = option + nodes_saved = common_elements + max_number = len(common_elements) + + if nodes_saved is not None: + targets[:] = [element for element in targets if element not in nodes_saved] + + #if best_vaccination != (): + # print("The best direct vaccination is: " + str(best_vaccination) + " and it's saves nodes: " + str(nodes_saved)) + return best_vaccination + +def spread_virus(graph:nx.DiGraph, infected_nodes:list)->bool: + """ + Spread the virus from infected nodes to their neighbors. + + Parameters: + - graph (nx.DiGraph): Directed graph. + - infected_nodes (list): List of currently infected nodes. + + Returns: + - bool: True if there are new infections, False otherwise. + """ + new_infected_nodes = [] + for node in infected_nodes: + for neighbor in graph.neighbors(node): + if graph.nodes[neighbor]['status'] == 'target': + graph.nodes[neighbor]['status'] = 'infected' + new_infected_nodes.append(neighbor) + infected_nodes.clear() + for node in new_infected_nodes: + infected_nodes.append(node) + #display_graph(graph) + return bool(infected_nodes) + +def spread_vaccination(graph:nx.DiGraph, vaccinated_nodes:list)->None: + """ + Spread the vaccination from vaccinated nodes to their neighbors. + + Parameters: + - graph (nx.DiGraph): Directed graph. + - vaccinated_nodes (list): List of currently vaccinated nodes. + """ + new_vaccinated_nodes = [] + for node in vaccinated_nodes: + for neighbor in graph.neighbors(node): + if graph.nodes[neighbor]['status'] == 'target': + graph.nodes[neighbor]['status'] = 'vaccinated' + new_vaccinated_nodes.append(neighbor) + vaccinated_nodes.clear() + for node in new_vaccinated_nodes: + vaccinated_nodes.append(node) + #display_graph(graph) + return + +def vaccinate_node(graph:nx.DiGraph, node:int)->None: + """ + Directly vaccinate a specific node in the graph. + + Parameters: + - graph (nx.DiGraph): Directed graph. + - node (int): Node to be vaccinated. + """ + graph.nodes[node]['status'] = 'directly vaccinated' + #display_graph(graph) + return + +def clean_graph(graph:nx.DiGraph)->None: + """ + Reset the graph to its base state where all nodes are targets. + + Parameters: + - graph (nx.DiGraph): Directed graph. + """ + for node in graph.nodes: + graph.nodes[node]['status'] = 'target' + return + +"Non-Spreading:" + +def adjust_nodes_capacity(graph:nx.DiGraph, source:int)->list: + """ + Adjust the capacity of nodes based on the layer they belong to. + The capacity is based on the formula in the article at the DirLayNet algorithm section. + + Parameters: + - graph (nx.DiGraph): Directed graph. + - source (int): Source node. + + Returns: + - layers (list): List of nodes grouped by layers. + """ + layers = (list(nx.bfs_layers(graph,source))) + harmonic_sum = 0.0 + for i in range(1,len(layers)): + harmonic_sum = harmonic_sum + 1/i + for index in range(1,len(layers)): + for node in layers[index]: + graph.nodes[node]['capacity'] = 1/(index*harmonic_sum) + #print("Layers: ", layers) + return layers + +def create_st_graph(graph:nx.DiGraph, targets:list)->nx.DiGraph: + """ + Create an s-t graph from the original graph for use in connectivity algorithms. + + Parameters: + - graph (nx.DiGraph): Original directed graph. + - targets (list): List of target nodes. + + Returns: + - G (nx.DiGraph): s-t graph. + """ + G = copy.deepcopy(graph) + G.add_node('t', status = 'target') + for node in targets: + G.add_edge(node,'t') + #display_graph(G) + return G + +def graph_flow_reduction(graph:nx.DiGraph, source:int)->nx.DiGraph: + """ + Perform flow reduction on the graph to find the minimum s-t cut. + + Parameters: + - graph (nx.DiGraph): Original directed graph. + - source (int): Source node. + + Returns: + - H (nx.DiGraph): Graph after flow reduction. + """ + H = nx.DiGraph() + for node in graph.nodes: + in_node, out_node = f'{node}_in', f'{node}_out' + H.add_nodes_from([in_node, out_node]) + if node == source or node == 't': + H.add_edge(in_node, out_node, weight=float('inf')) + else: + H.add_edge(in_node, out_node, weight=graph.nodes[node]['capacity']) + for edge in graph.edges: + H.add_edge(f'{edge[0]}_out', f'{edge[1]}_in', weight=float('inf')) + display_graph(H) + return H + +def min_cut_N_groups(graph:nx.DiGraph, source:int) -> list: + """ + Find the minimum cut and group nodes accordingly. + + Parameters: + - graph (nx.DiGraph): Graph after flow reduction. + - source (int): Source node. + + Returns: + - n_groups (list): List of nodes in the minimum cut. + """ + flow_graph = algo.minimum_st_node_cut(graph,f'{source}_out','t_in') #run algo on the graph after reduction and get the min-cut + n_groups = {int(item.split('_')[0]) for item in flow_graph} # split it to the groups accordingly. + return n_groups + +def calculate_vaccine_matrix(layers:list, min_cut_nodes:list)->np.matrix: + """ + Calculate the vaccine matrix based on the calculation in the article at the DirLayNet algorithm section. + + Parameters: + - layers (list): List of nodes grouped by layers. + - min_cut_nodes (list): List of nodes in the minimum cut. + + Returns: + - matrix (np.matrix): Vaccine matrix. + """ + nodes_list = [] # = N_i + #print(layers, min_cut_nodes) + for i in range(1,len(layers)): + common_elements = set(min_cut_nodes) & set(layers[i]) + nodes_list.append(common_elements) + #print(nodes_list) + matrix = np.zeros((len(layers)-1, len(layers)-1)) + for i in range (len(layers)-1): + for j in range(i, len(layers)-1): + matrix[i][j] = ((len(nodes_list[j])/(j+1))) # here we can chose ceil or floor. + #print(matrix) + return matrix + +def matrix_to_integers_values(matrix:np.matrix) -> np.matrix: #TODO: THIS + """ + Convert matrix values to integers. + + Parameters: + - matrix (np.matrix): Input matrix. + + Returns: + - np.matrix: Matrix with integer values. + """ + return + +def min_budget_calculation(matrix:np.matrix) -> int : + """ + Calculate the minimum budget from the matrix. + + Parameters: + - matrix (np.matrix): Input matrix. + + Returns: + - int: Minimum budget. + """ + # integral_matrix = matrix_to_integers_values(matrix) TODO : make this work. + i = j = 0 + matrix_size = len(matrix[i]) + row_sum = [0]*matrix_size + for i in range(matrix_size): + for j in range(matrix_size): + #print(matrix[i][j]) + row_sum[i] += matrix[i][j] + #print(row_sum) + return int(max(row_sum)) + +"Heuristic approach:" + +def find_best_neighbor(graph:nx.DiGraph, infected_nodes:list, targets:list)->int: + """ + Find the best node from the infected nodes successors that saves more new node in targets. + + Parameters: + - graph (nx.DiGraph): Directed graph. + - infected_nodes (list): list of all infected nodes that threaten to infect additional nodes. + - targets (list): List of target nodes. + + Returns: + - best_node (int): Best node option. + """ + best_node = None + nodes_saved = [] + max_number = -1 + optional_nodes = set() + + # Go through the infected_nodes list and collect all their neighbors + for node in infected_nodes: + optional_nodes.update(graph.neighbors(node)) + + for node in optional_nodes: + if graph.nodes[node]['status'] == 'target': + nodes_list = list(graph.neighbors(node)) + if node in targets: + nodes_list.append(node) + common_elements = set(nodes_list) & set(targets) + if len(common_elements) > max_number: + best_node = node + nodes_saved = common_elements + max_number = len(common_elements) + + if nodes_saved is not None: + targets[:] = [element for element in targets if element not in nodes_saved] + + #if best_node != None: + # print("The best node is: " + node + " and it's saves nodes: " + str(nodes_saved)) + return best_node + +"Usefull Utils:" + +def display_graph(graph:nx.DiGraph)->None: + """ + Display the graph using Matplotlib. + + Parameters: + - graph (nx.DiGraph): Directed graph. + """ + pos = nx.shell_layout(graph) + colors = [node_colors.get(data.get('status', 'default'), 'default') for node, data in graph.nodes(data=True)] + nx.draw(graph, pos, node_color=colors, with_labels=True, font_weight='bold') + + if nx.get_edge_attributes(graph, 'weight'): + edge_labels = nx.get_edge_attributes(graph, 'weight') + nx.draw_networkx_edge_labels(graph, pos, edge_labels=edge_labels) + plt.show() + return + +def parse_json_to_networkx(json_data): + """ + Parse JSON data to create Networkx graphs. + + Parameters: + - json_data (dict): JSON data containing graph information. + + Returns: + - graphs (dict): Dictionary of Networkx graphs. + """ + graphs = {} + + for graph_type, graph_data in json_data.items(): + for graph_name, graph_info in graph_data.items(): + + graph_key = f"{graph_type}_{graph_name}" + if "vertices" not in graph_info or not isinstance(graph_info["vertices"], list) or not graph_info["vertices"]: + raise KeyError(f"Error parsing {graph_type}_{graph_name}: 'vertices' must be a non-empty list.") + + if "edges" not in graph_info or not isinstance(graph_info["edges"], list) or not graph_info["edges"]: + raise 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mode 100644 index 0000000..03c7b16 --- /dev/null +++ b/networkz/algorithms/approximation/tests/test_firefighter_problem/test_non_spreading_dirlaynet_minbudget.py @@ -0,0 +1,285 @@ +import pytest +import networkx as nx +import json + +from firefighter_problem.Firefighter_Problem import non_spreading_dirlaynet_minbudget +from firefighter_problem.Utils import adjust_nodes_capacity +from firefighter_problem.Utils import create_st_graph +from firefighter_problem.Utils import parse_json_to_networkx +from firefighter_problem.Utils import graph_flow_reduction +from firefighter_problem.Utils import calculate_vaccine_matrix +from firefighter_problem.Utils import min_cut_N_groups +from firefighter_problem.Utils import matrix_to_integers_values +from firefighter_problem.Utils import min_budget_calculation + +def get_graphs(): + with open("src/graphs.json", "r") as file: + json_data = json.load(file) + graphs = parse_json_to_networkx(json_data = json_data) + return graphs + +graphs = get_graphs() + +def test_source_not_in_graph(): + """ + This test checks if the source node is not a real node in the graph. + """ + with pytest.raises(ValueError, match = "Error: The source node is not on the graph"): + non_spreading_dirlaynet_minbudget(graphs["Dirlay_Graph-1"], -3 [0,5]) + + with pytest.raises(ValueError, match = "Error: The source node is not on the graph"): + non_spreading_dirlaynet_minbudget(graphs["Dirlay_Graph-2"], 13 [0,1,4]) + + with pytest.raises(ValueError, match = "Error: The source node is not on the graph"): + non_spreading_dirlaynet_minbudget(graphs["Dirlay_Graph-3"], 15 [0,6,7]) + + with pytest.raises(ValueError, match = "Error: The source node is not on the graph"): + non_spreading_dirlaynet_minbudget(graphs["Dirlay_Graph-4"], -1 [1,3,5,7]) + +def test_target_not_in_graph(): + """ + This test checks if a node we're trying to save is not in the graph. + """ + with pytest.raises(ValueError, match = "Error: Not all nodes we're trying to save are on the graph"): + non_spreading_dirlaynet_minbudget(graphs["Dirlay_Graph-1"], 0 [1,5,7]) #7# + + with pytest.raises(ValueError, match = "Error: Not all nodes we're trying to save are on the graph"): + non_spreading_dirlaynet_minbudget(graphs["Dirlay_Graph-2"], 1 [0,2,-1,9]) #-1,9# + + with pytest.raises(ValueError, match = "Error: Not all nodes we're trying to save are on the graph"): + non_spreading_dirlaynet_minbudget(graphs["Dirlay_Graph-3"], 4 [0,1,2,11,12,13,14]) #11,12,13,14# + + with pytest.raises(ValueError, match = "Error: Not all nodes we're trying to save are on the graph"): + non_spreading_dirlaynet_minbudget(graphs["Dirlay_Graph-4"], 0 [1,3,5,7,15,20]) #15,20# + +def test_source_is_target(): + """ + This test checks if we're trying to save a source node. + """ + with pytest.raises(ValueError, match = "Error: The source node can not be a part of the targets vector"): + non_spreading_dirlaynet_minbudget(graphs["Dirlay_Graph-1"], 0 [0,5]) + + with pytest.raises(ValueError, match = "Error: The source node can not be a part of the targets vector"): + non_spreading_dirlaynet_minbudget(graphs["Dirlay_Graph-2"], 1 [0,1,4]) + + with pytest.raises(ValueError, match = "Error: The source node can not be a part of the targets vector"): + non_spreading_dirlaynet_minbudget(graphs["Dirlay_Graph-3"], 6 [0,6,7]) + + with pytest.raises(ValueError, match = "Error: The source node can not be a part of the targets vector"): + non_spreading_dirlaynet_minbudget(graphs["Dirlay_Graph-4"], 3 [1,3,5,7]) + + +#Test 1 +#graph building +graph_1 = graphs["Dirlay_Graph-1"] +layers_1 = adjust_nodes_capacity(graph_1,0) +targets_1 = [1,2,3] #saving 1,2,3 +G1 = create_st_graph(graph_1, targets_1) +reduction_G1 = graph_flow_reduction(G1,0) +N_1_groups = min_cut_N_groups(reduction_G1,0) +matrix_1 = calculate_vaccine_matrix(layers_1,N_1_groups) +integer_matrix_1 = matrix_to_integers_values(matrix_1) #TODO : this is not functional right now! +min_budget_1 = min_budget_calculation(integer_matrix_1) + +#Test 2 +#graph building +graph_2 = graphs["Dirlay_Graph-2"] +layers_2 = adjust_nodes_capacity(graph_2,0) #src is 2 +targets_2 = [2,4] #saving 2,4 +G2 = create_st_graph(graph_2, targets_2) +reduction_G2 = graph_flow_reduction(G2,0) +N_2_groups = min_cut_N_groups(reduction_G2,0) +matrix_2 = calculate_vaccine_matrix(layers_2,N_2_groups) +integer_matrix_2 = matrix_to_integers_values(matrix_2) +min_budget_2 = min_budget_calculation(integer_matrix_2) + +#Test 3 +#graph building +graph_3 = graphs["Dirlay_Graph-3"] +layers_3 = adjust_nodes_capacity(graph_3,0) #src is 0 +targets_3 = [1,5,7] #saving 1,5,7 +G3 = create_st_graph(graph_3, targets_3) +reduction_G3 = graph_flow_reduction(G3,0) +N_3_groups = min_cut_N_groups(reduction_G3,0) +matrix_3 = calculate_vaccine_matrix(layers_3,N_3_groups) +integer_matrix_3 = matrix_to_integers_values(matrix_3) +min_budget_3 = min_budget_calculation(integer_matrix_3) + +#Test 4 +#graph building +graph_4= graphs["Dirlay_Graph-4"] +layers_4 = adjust_nodes_capacity(graph_4,0) #src is 0 +targets_4 = [4,5,6,8] #saving 4,5,6,8 +G4 = create_st_graph(graph_4, targets_4) +reduction_G4 = graph_flow_reduction(G4,0) +N_4_groups = min_cut_N_groups(reduction_G4,0) +matrix_4 = calculate_vaccine_matrix(layers_4,N_4_groups) +integer_matrix_4 = matrix_to_integers_values(matrix_4) +min_budget_4 = min_budget_calculation(integer_matrix_4) + + +def test_adjust_nodes_capacity(): + """ + This test checks if the node capacity and layers are correct. + """ + #Test 1 + #layers check + layers_1_check = [[0], [1, 2], [3], [4], [5]] + assert set(adjust_nodes_capacity(graph_1,0)) == layers_1_check + #capacity check + assert set(graph_1.nodes[1]['capacity']) == 1/(1*1/2) + assert set(graph_1.nodes[2]['capacity']) == 1/(1*1/2) + assert set(graph_1.nodes[3]['capacity']) == 1/(1*(1/2+1/3)) + assert set(graph_1.nodes[4]['capacity']) == 1/(1*(1/2+1/3+1/4)) + assert set(graph_1.nodes[5]['capacity']) == 1/(1*(1/2+1/3+1/4+1/5)) + + #Test 2 + #layers check + layers_2 = [[0], [1, 2], [4, 3]] + assert set(adjust_nodes_capacity(graph_2,2)) == layers_2 + #capacity check + assert set(graph_2.nodes[1]['capacity']) == 1/(1*1/2) + assert set(graph_2.nodes[2]['capacity']) == 1/(1*1/2) + assert set(graph_2.nodes[3]['capacity']) == 1/(1*(1/2+1/3)) + assert set(graph_2.nodes[4]['capacity']) == 1/(1*(1/2+1/3)) + + #Test 3 + #layers check + layers_3 = [[0], [1, 2, 3], [5, 4], [6, 7]] + assert set(adjust_nodes_capacity(graph_3,0)) == layers_3 + #capacity check + assert set(graph_3.nodes[1]['capacity']) == 1/(1*1/2) + assert set(graph_3.nodes[2]['capacity']) == 1/(1*1/2) + assert set(graph_3.nodes[3]['capacity']) == 1/(1*1/2) + assert set(graph_3.nodes[4]['capacity']) == 1/(1*(1/2+1/3)) + assert set(graph_3.nodes[5]['capacity']) == 1/(1*(1/2+1/3)) + assert set(graph_3.nodes[6]['capacity']) == 1/(1*(1/2+1/3+1/4)) + assert set(graph_3.nodes[7]['capacity']) == 1/(1*(1/2+1/3+1/4)) + + #Test 4 + #layers check + layers_4 = [[0], [1, 2], [3, 4], [5], [6], [8]] + assert set(adjust_nodes_capacity(graph_4,0)) == layers_4 + #capacity check + assert set(graph_4.nodes[1]['capacity']) == 1/(1*1/2) + assert set(graph_4.nodes[2]['capacity']) == 1/(1*1/2) + assert set(graph_4.nodes[3]['capacity']) == 1/(1*(1/2+1/3)) + assert set(graph_4.nodes[4]['capacity']) == 1/(1*(1/2+1/3)) + assert set(graph_4.nodes[5]['capacity']) == 1/(1*(1/2+1/3+1/4)) + assert set(graph_4.nodes[6]['capacity']) == 1/(1*(1/2+1/3+1/4+1/5)) + assert set(graph_4.nodes[8]['capacity']) == 1/(1*(1/2+1/3+1/4+1/5+1/6)) + +def test_create_st_graph() : + """ + creates the s-t graph and connects the nodes we want to save + """ + #Test1 + #edges check + assert set(graph_1.__contains__("t")) == True + assert set(graph_1.has_edge(1,"t")) == True + assert set(graph_1.has_edge(2,"t")) == True + assert set(graph_1.has_edge(3,"t")) == True + + #Test2 + #edges check + assert set(graph_2.__contains__("t")) == True + assert set(graph_2.has_edge(2,"t")) == True + assert set(graph_2.has_edge(4,"t")) == True + + #Test3 + #edges check + assert set(graph_3.__contains__("t")) == True + assert set(graph_3.has_edge(1,"t")) == True + assert set(graph_3.has_edge(5,"t")) == True + assert set(graph_3.has_edge(7,"t")) == True + + #Test4 + #edges check + assert set(graph_4.__contains__("t")) == True + assert set(graph_4.has_edge(4,"t")) == True + assert set(graph_4.has_edge(5,"t")) == True + assert set(graph_4.has_edge(6,"t")) == True + assert set(graph_4.has_edge(8,"t")) == True + +def test_graph_flow_reduction(): #TBD + """ + This test validates if the redcution is correct. + """ + pass + +def test_min_cut_N_groups(): + """ + This test validates the nodes taken from the min-cut create the right groups (N_1...N_l) + """ + #Test 1 + #checking equality + N1_groups_check = [{1, 2}, set(), set(), set()] + assert set(min_cut_N_groups(reduction_G1)) == N1_groups_check + + #Test 2 + #checking equality + N2_groups_check = [{2},{4}] + assert set(min_cut_N_groups(reduction_G2)) == N2_groups_check + + #Test 3 + #checking equality + N3_groups_check = [{1},set(),{7}] + assert set(min_cut_N_groups(reduction_G3)) == N3_groups_check + + #Test 3 + #checking equality + N4_groups_check = [set(), {4}, {5}, set(), set()] + assert set(min_cut_N_groups(reduction_G4)) == N4_groups_check + +def test_calculate_vaccine_matrix(): + """ + There is an important check to do here : A matrix is valid if : For any col j, the col sum is exactly |Nj|. + This test checks that the calculations made to create the triangular matrix from the min-cut nodes is correct. + """ + #Test 1 + #checking equality + matrix_1 = [[2,0,0,0] + [0,0,0,0] + [0,0,0,0] + [0,0,0,0]] + assert set(calculate_vaccine_matrix(layers_1,N_1_groups)) == matrix_1 + + #Test 2 + #checking equality + matrix_2 = [[1.5, 0.5] + [1, 0.5] + [0,0.5]] + assert set(calculate_vaccine_matrix(layers_2,N_2_groups)) == matrix_2 + + #Test 3 + #checking equality + matrix_3 = [[1+1/3, 1/3, 1/3] + [ 1, 0, 1/3] + [ 0, 0, 1/3]] + assert set(calculate_vaccine_matrix(layers_3,N_3_groups)) == matrix_3 + + #Test 4 + #checking equality + matrix_4 = [[ 0, 0.5, 1/3, 0, 0] + [ 0, 0.5, 1/3, 0 ,0] + [ 0, 0, 1/3, 0 ,0] + [ 0, 0, 0, 0 ,0] + [ 0, 0, 0, 0, 0]] + assert set(calculate_vaccine_matrix(layers_4,N_4_groups)) == matrix_4 + +def test_matrix_to_integers_values(): + pass #in case the matrix is not ingeral from previous step, we need to make it one (so vaccianation is correct- cant vaccinate fractional node) + +def test_min_budget_calculation(): + """ + This test validates that the minbudget is accurate + """ + +#legacy: + # graph_1 = graphs["Dirlay_Graph-4"] + # layers = adjust_nodes_capacity(graph_1,0) + # targets = [4,5,6,8] + # G1 = create_st_graph(graph_1, targets) + # min_cut_nodes = graph_flow_reduction(G1,0) + # min_cut_nodes = {int(item.split('_')[0]) for item in min_cut_nodes} + # calculate_vaccine_matrix(layers,min_cut_nodes) \ No newline at end of file diff --git a/networkz/algorithms/approximation/tests/test_firefighter_problem/test_non_spreading_minbudget.py b/networkz/algorithms/approximation/tests/test_firefighter_problem/test_non_spreading_minbudget.py new file mode 100644 index 0000000..3f72b95 --- /dev/null +++ b/networkz/algorithms/approximation/tests/test_firefighter_problem/test_non_spreading_minbudget.py @@ -0,0 +1,102 @@ +import pytest +import networkx as nx +import json + +from firefighter_problem.Firefighter_Problem import non_spreading_minbudget +from firefighter_problem.Utils import parse_json_to_networkx + +@pytest.fixture +def sample_json_data(): + return { + "Dirlay": { + "Graph-1": { + "vertices": [0, 1, 2, 3, 4, 5], + "edges": [{"source": 0, "target": 1}, {"source": 0, "target": 2}] + }, + }, + "RegularGraph": { + "Graph-1": { + "vertices": [0, 1, 2], + "edges": [{"source": 0, "target": 1}, {"source": 1, "target": 2}] + }, + } + } + +def get_graphs(): + with open("src/graphs.json", "r") as file: + json_data = json.load(file) + graphs = parse_json_to_networkx(json_data) + return graphs + +graphs = get_graphs() + +def test_source_not_in_graph(): + with pytest.raises(ValueError, match="Error: The source node isn't on the graph"): + non_spreading_minbudget(graphs["RegularGraph_Graph-1"], -2, [1,2,3,4,5,6]) + + with pytest.raises(ValueError, match="Error: The source node isn't on the graph"): + non_spreading_minbudget(graphs["RegularGraph_Graph-4"], 8, [1,2,4,6,7]) + + with pytest.raises(ValueError, match="Error: The source node isn't on the graph"): + non_spreading_minbudget(graphs["RegularGraph_Graph-6"], 10, [0,2,3,5,6,7,8,9]) + + with pytest.raises(ValueError, match="Error: The source node isn't on the graph"): + non_spreading_minbudget(graphs["RegularGraph_Graph-8"], 17, [1,7,12,14,8,3,11,2]) + + with pytest.raises(ValueError, match="Error: The source node isn't on the graph"): + non_spreading_minbudget(graphs["RegularGraph_Graph-3"], 6, [1,3,5]) + + +def test_target_not_in_graph(): + with pytest.raises(ValueError, match="Error: Not all nodes in the targets list are on the graph."): + non_spreading_minbudget(graphs["RegularGraph_Graph-2"], 0, [1,2,3,9,5,16]) + + with pytest.raises(ValueError, match="Error: Not all nodes in the targets list are on the graph."): + non_spreading_minbudget(graphs["RegularGraph_Graph-3"], 4, [1,2,3,6,7]) + + with pytest.raises(ValueError, match="Error: Not all nodes in the targets list are on the graph."): + non_spreading_minbudget(graphs["RegularGraph_Graph-6"], 3, [0,2,5,6,7,8,10]) + + with pytest.raises(ValueError, match="Error: Not all nodes in the targets list are on the graph."): + non_spreading_minbudget(graphs["RegularGraph_Graph-8"], 11, [1,3,12,19,8,10,4,2]) + + with pytest.raises(ValueError, match="Error: Not all nodes in the targets list are on the graph."): + non_spreading_minbudget(graphs["RegularGraph_Graph-7"], 2, [1,3,-1,5]) + + +def test_source_is_target(): + with pytest.raises(ValueError, match="Error: The source node can't be a part of the targets list, since the virus is spreading from the source"): + non_spreading_minbudget(graphs["RegularGraph_Graph-1"], 0, [1,2,3,0,4,5,6]) + + with pytest.raises(ValueError, match="Error: The source node can't be a part of the targets list, since the virus is spreading from the source"): + non_spreading_minbudget(graphs["RegularGraph_Graph-3"], 1, [5,1,4]) + + with pytest.raises(ValueError, match="Error: The source node can't be a part of the targets list, since the virus is spreading from the source"): + non_spreading_minbudget(graphs["RegularGraph_Graph-4"], 4, [1,2,3,4,5,6,7]) + + with pytest.raises(ValueError, match="Error: The source node can't be a part of the targets list, since the virus is spreading from the source"): + non_spreading_minbudget(graphs["RegularGraph_Graph-6"], 0, [0,3,5,6,7,8,9]) + + with pytest.raises(ValueError, match="Error: The source node can't be a part of the targets list, since the virus is spreading from the source"): + non_spreading_minbudget(graphs["RegularGraph_Graph-8"], 0, [13,10,8,6,5,4,3,0,1,2]) + +def test_save_all_vertices(): + assert 2 == non_spreading_minbudget(graphs["RegularGraph_Graph-1"], 0, [1,2,3,4,5,6]) #answer is 2 + assert 2 == non_spreading_minbudget(graphs["RegularGraph_Graph-2"], 0, [1,2,3,4,5,6,7]) #answer is 2 + assert 3 != non_spreading_minbudget(graphs["RegularGraph_Graph-3"], 0, [1,2,3,4,5]) #answer is 2 + assert 3 > non_spreading_minbudget(graphs["RegularGraph_Graph-4"], 0, [1,2,3,4,5,6,7]) #answer is 2 + assert 1 == non_spreading_minbudget(graphs["RegularGraph_Graph-6"], 1, [0,2,3,4,5,6,7,8,9]) #answer is 1 + assert 3 == non_spreading_minbudget(graphs["RegularGraph_Graph-7"], 1, [0,2,3,4,5,6]) #answer is 3 + assert 2 != non_spreading_minbudget(graphs["RegularGraph_Graph-8"], 0, [1,2,3,4,5,6,7,8,9,10,11,12,13,14]) #answer is 3 + +def test_save_subgroup_vertices(): + assert 1 != non_spreading_minbudget(graphs["RegularGraph_Graph-1"], 0, [1,5,6]) #answer is 2 + assert 1 == non_spreading_minbudget(graphs["RegularGraph_Graph-2"], 0, [1,3,4,5,6]) #answer is 1 + assert 3 > non_spreading_minbudget(graphs["RegularGraph_Graph-3"], 0, [1,2]) #answer is 2 + assert 1 < non_spreading_minbudget(graphs["RegularGraph_Graph-4"], 0, [2,3,5,7]) #answer is 2 + assert 4 > non_spreading_minbudget(graphs["RegularGraph_Graph-6"], 1, [0,3,5,6,8,9]) #answer is 1 + assert 2 == non_spreading_minbudget(graphs["RegularGraph_Graph-7"], 1, [0,2,5,6]) #answer is 2 + assert 3 == non_spreading_minbudget(graphs["RegularGraph_Graph-8"], 0, [1,3,4,5,6,9,10,12,14]) #answer is 3 + +def test_correct_st_cut(): + pass \ No newline at end of file diff --git a/networkz/algorithms/approximation/tests/test_firefighter_problem/test_parsing.py b/networkz/algorithms/approximation/tests/test_firefighter_problem/test_parsing.py new file mode 100644 index 0000000..e065732 --- /dev/null +++ b/networkz/algorithms/approximation/tests/test_firefighter_problem/test_parsing.py @@ -0,0 +1,115 @@ +import pytest +import networkx as nx + +from firefighter_problem.Utils import parse_json_to_networkx + +@pytest.fixture +def sample_json_data(): + return { + "Dirlay": { + "Graph-1": { + "vertices": [0, 1, 2, 3, 4, 5], + "edges": [{"source": 0, "target": 1}, {"source": 0, "target": 2}] + }, + }, + "RegularGraph": { + "Graph-1": { + "vertices": [0, 1, 2], + "edges": [{"source": 0, "target": 1}, {"source": 1, "target": 2}] + }, + } + } + +@pytest.fixture +def missing_vertices_json(): + return { + "InvalidGraph": { + "Graph-1": { + "edges": [{"source": 0, "target": 1}, {"source": 1, "target": 2}] + } + } + } + +@pytest.fixture +def missing_edges_json(): + return { + "InvalidGraph": { + "Graph-2": { + "vertices": [0, 1, 2] + } + } + } + +@pytest.fixture +def empty_json(): + return { + "InvalidGraph": { + "Graph-3": { + "vertices": [], + "edges": [] + } + } + } + + +def test_parsing_dirlay_graph(sample_json_data): + graphs = parse_json_to_networkx(sample_json_data) + + dirlay_graph = graphs["Dirlay_Graph-1"] + assert isinstance(dirlay_graph, nx.DiGraph) + +def test_parsing_dirlay_graph_nodes(sample_json_data): + graphs = parse_json_to_networkx(sample_json_data) + + dirlay_graph = graphs["Dirlay_Graph-1"] + assert set(dirlay_graph.nodes()) == {0, 1, 2, 3, 4, 5} + +def test_parsing_dirlay_graph_edges(sample_json_data): + graphs = parse_json_to_networkx(sample_json_data) + + dirlay_graph = graphs["Dirlay_Graph-1"] + assert set(dirlay_graph.edges()) == {(0, 1), (0, 2)} + +def test_parsing_regular_graph(sample_json_data): + graphs = parse_json_to_networkx(sample_json_data) + + regular_graph = graphs["RegularGraph_Graph-1"] + assert isinstance(regular_graph, nx.Graph) + +def test_parsing_regular_graph_nodes(sample_json_data): + graphs = parse_json_to_networkx(sample_json_data) + + regular_graph = graphs["RegularGraph_Graph-1"] + assert set(regular_graph.nodes()) == {0, 1, 2} + +def test_parsing_regular_graph_edges(sample_json_data): + graphs = parse_json_to_networkx(sample_json_data) + + regular_graph = graphs["RegularGraph_Graph-1"] + assert set(regular_graph.edges()) == {(0, 1), (1, 2)} + +def test_parse_exceptions_missing_vertices(missing_vertices_json): + with pytest.raises(KeyError): + parse_json_to_networkx(missing_vertices_json) + +def test_parse_exceptions_missing_edges(missing_edges_json): + with pytest.raises(KeyError): + parse_json_to_networkx(missing_edges_json) + +def test_parse_exceptions_empty_json(empty_json): + with pytest.raises(KeyError): + parse_json_to_networkx(empty_json) + +def test_parsing_dirlay_graph_status(sample_json_data): + graphs = parse_json_to_networkx(sample_json_data) + + dirlay_graph = graphs["Dirlay_Graph-1"] + for node in dirlay_graph.nodes(data=True): + assert node[1]["status"] == "target" + +def test_parsing_regular_graph_status(sample_json_data): + graphs = parse_json_to_networkx(sample_json_data) + + regular_graph = graphs["RegularGraph_Graph-1"] + for node in regular_graph.nodes(data=True): + assert node[1]["status"] == "target" \ No newline at end of file diff --git a/networkz/algorithms/approximation/tests/test_firefighter_problem/test_spreading_maxsave.py b/networkz/algorithms/approximation/tests/test_firefighter_problem/test_spreading_maxsave.py new file mode 100644 index 0000000..6c4bbe4 --- /dev/null +++ b/networkz/algorithms/approximation/tests/test_firefighter_problem/test_spreading_maxsave.py @@ -0,0 +1,248 @@ +import pytest +import networkx as nx +import json +import random + +from firefighter_problem.Firefighter_Problem import spreading_maxsave +from firefighter_problem.Utils import parse_json_to_networkx, calculate_gamma, calculate_epsilon, find_best_direct_vaccination + +with open("src/graphs.json", "r") as file: + json_data = json.load(file) +graphs = parse_json_to_networkx(json_data) + +@pytest.mark.parametrize("graph_key, budget, source, targets", [ + ("RegularGraph_Graph-1", 1, -2, [1, 2, 3, 4, 5, 6]), + ("RegularGraph_Graph-4", 1, 8, [1, 2, 4, 6, 7]), + ("RegularGraph_Graph-6", 1, 10, [0, 2, 3, 5, 6, 7, 8, 9]), + ("RegularGraph_Graph-8", 1, 17, [1, 7, 12, 14, 8, 3, 11, 2]), + ("RegularGraph_Graph-3", 1, 6, [1, 3, 5]), +]) +def test_source_not_in_graph(graph_key, budget, source, targets): + with pytest.raises(ValueError): + spreading_maxsave(graphs[graph_key], budget, source, targets) + +@pytest.mark.parametrize("graph_key, budget, source, targets", [ + ("RegularGraph_Graph-2", 1, 0, [1, 2, 3, 9, 5, 16]), + ("RegularGraph_Graph-3", 1, 4, [1, 2, 3, 6, 7]), + ("RegularGraph_Graph-6", 1, 3, [0, 2, 5, 6, 7, 8, 10]), + ("RegularGraph_Graph-8", 1, 11, [1, 3, 12, 19, 8, 10, 4, 2]), + ("RegularGraph_Graph-7", 1, 2, [1, 3, -1, 5]), +]) +def test_target_not_in_graph(graph_key, budget, source, targets): + with pytest.raises(ValueError): + spreading_maxsave(graphs[graph_key], budget, source, targets) + +@pytest.mark.parametrize("graph_key, budget, source, targets", [ + ("RegularGraph_Graph-1", 1, 0, [1, 2, 3, 0, 4, 5, 6]), + ("RegularGraph_Graph-3", 1, 1, [5, 1, 4]), + ("RegularGraph_Graph-4", 1, 4, [1, 2, 3, 4, 5, 6, 7]), + ("RegularGraph_Graph-6", 1, 0, [0, 3, 5, 6, 7, 8, 9]), + ("RegularGraph_Graph-8", 1, 0, [13, 10, 8, 6, 5, 4, 3, 0, 1, 2]), +]) +def test_source_is_target(graph_key, budget, source, targets): + with pytest.raises(ValueError): + spreading_maxsave(graphs[graph_key], budget, source, targets) + +@pytest.mark.parametrize("graph_key, source, targets, expected_gamma, expected_direct_vaccination", [ + ("Dirlay_Graph-5", 0, [1, 2, 3, 4, 5 ,6 ,7 ,8], { + 1: [(2, 1), (4, 1), (1, 1), (1, 2)], + 2: [(2, 1)], + 3: [(2, 1), (5, 1), (3, 1), (3, 2)], + 4: [(4, 1)], + 5: [(5, 1)], + 6: [(4, 1), (5, 1), (6, 1), (6, 2)], + 7: [(5, 1), (6, 1), (7, 1), (7, 2)], + 8: [(4, 1), (5, 1), (6, 1), (6, 2), (7, 1), (7, 2), (8, 1), (8, 2), (8, 3)], + }, { + (1, 1): [1], + (1, 2): [1], + (2, 1): [1, 2, 3], + (3, 1): [3], + (3, 2): [3], + (4, 1): [1, 4, 6, 8], + (5, 1): [3, 5, 6, 7, 8], + (6, 1): [6, 7, 8], + (6, 2): [6, 8], + (7, 1): [7, 8], + (7, 2): [7, 8], + (8, 1): [8], + (8, 2): [8], + (8, 3): [8] + }), + ("RegularGraph_Graph-1", 0, [1, 3, 4, 5], { + 1: [(1, 1)], + 2: [(2, 1)], + 3: [(1, 1), (2, 1), (3, 1), (3, 2)], + 4: [(1, 1), (4, 1), (4, 2)], + 5: [(1, 1), (2, 1), (3, 1), (3, 2), (5, 1), (5, 2), (5, 3)], + 6: [(2, 1), (6, 1), (6, 2)], + }, { + (1, 1): [1, 3, 4, 5], + (2, 1): [3, 5], + (3, 1): [3, 5], + (3, 2): [3, 5], + (4, 1): [4], + (4, 2): [4], + (5, 1): [5], + (5, 2): [5], + (5, 3): [5], + (6, 1): [], + (6, 2): [], + }) +]) +def test_calculate_gamma(graph_key, source, targets, expected_gamma, expected_direct_vaccination): + print( calculate_gamma(graphs[graph_key], source, targets)) + calculated_gamma, calculated_direct_vaccination = calculate_gamma(graphs[graph_key], source, targets) + + for key in expected_gamma: + assert key in calculated_gamma, f"Expected key {key} to be in {calculated_gamma}" + assert calculated_direct_vaccination == expected_direct_vaccination + +@pytest.mark.parametrize("direct_vaccinations, expected_epsilon", [ + ({ + (1, 1): [1, 3, 4, 5], + (2, 1): [2, 3, 5, 6], + (3, 1): [3, 5], + (3, 2): [3, 5], + (4, 1): [4], + (4, 2): [4], + (5, 1): [5], + (5, 2): [5], + (5, 3): [5], + (6, 1): [6], + (6, 2): [6], + }, [ + [(1, 1), (2, 1), (3, 1), (4, 1), (5, 1), (6, 1)], + [(3, 2), (4, 2), (5, 2), (6, 2)], + [(5, 3)] + ]), + ({ + (1, 1): [1], + (1, 2): [1], + (2, 1): [1, 2, 3], + (3, 1): [3], + (3, 2): [3], + (4, 1): [1, 4, 6, 8], + (5, 1): [3, 5, 6, 7, 8], + (6, 1): [4, 6, 8], + (6, 2): [6, 8], + (7, 1): [7, 8], + (7, 2): [7, 8], + (8, 1): [8], + (8, 2): [8], + (8, 3): [8], + }, [ + [(1, 1), (2, 1), (3, 1), (4, 1), (5, 1), (6, 1), (7, 1), (8, 1)], + [(1, 2), (3, 2), (6, 2), (7, 2), (8, 2)], + [(8, 3)] + ]), +]) +def test_calculate_epsilon(direct_vaccinations, expected_epsilon): + calculated_epsilon = calculate_epsilon(direct_vaccinations) + + assert calculated_epsilon == expected_epsilon + +@pytest.mark.parametrize("graph_key, direct_vaccinations, current_epsilon, targets, expected_best_direct_vaccination", [ + ("RegularGraph_Graph-1", + { + (1, 1): [1, 2, 3, 4, 5, 7], + (2, 1): [2, 3, 4, 7], + (2, 2): [2, 3, 7], + (3, 1): [3, 4, 7], + (3, 2): [3, 4, 7], + (3, 3): [3, 7], + (4, 1): [4, 7], + (4, 2): [4, 7], + (4, 3): [4, 7], + (5, 1): [2, 3, 4, 5, 7], + (5, 2): [3, 4, 5, 7], + (6, 1): [3, 4, 5, 6, 7], + }, + [(1, 1), (2, 1), (3, 1), (4, 1), (5, 1), (6, 1)], + [1, 3, 4, 5, 6], + (1, 1)), + ("Dirlay_Graph-5", + { + (1, 1): [1], + (1, 2): [1], + (2, 1): [1, 2, 3], + (3, 1): [3], + (3, 2): [3], + (4, 1): [1, 4, 6, 8], + (5, 1): [3, 5, 6, 7, 8], + (6, 1): [4, 6, 8], + (6, 2): [6, 8], + (7, 1): [7, 8], + (7, 2): [7, 8], + (8, 1): [8], + (8, 2): [8], + (8, 3): [8], + }, + [(1, 1), (2, 1), (3, 1), (4, 1), (5, 1), (6, 1), (7, 1), (8, 1)], + [1, 2, 3, 4, 5, 6, 7, 8], + (5, 1)) +]) + +def test_find_best_direct_vaccination(graph_key, direct_vaccinations, current_epsilon, targets, expected_best_direct_vaccination): + calculated_best_direct_vaccination = find_best_direct_vaccination(graphs[graph_key],direct_vaccinations,current_epsilon,targets) + + assert calculated_best_direct_vaccination == expected_best_direct_vaccination + +@pytest.mark.parametrize("graph_key, budget, source, targets, expected_length", [ + ("RegularGraph_Graph-1", 1, 0, [1, 2, 3, 4, 5, 6], 2), + ("Dirlay_Graph-5", 2, 0, [1, 2, 3, 4, 5, 6, 7, 8], 3), +]) +def test_strategy_length(graph_key, budget, source, targets, expected_length): + graph = graphs[graph_key] + calculated_strategy = spreading_maxsave(graph, budget, source, targets) + + assert len(calculated_strategy) == expected_length + + +@pytest.mark.parametrize("graph_key, budget, source, targets, expected_strategy", [ + ("RegularGraph_Graph-1", 1, 0, [1, 2, 3, 4, 5, 6], [(1, 1), (6, 2)]), + ("Dirlay_Graph-5", 2, 0, [1, 2, 3, 4, 5, 6, 7, 8], [(5, 1), (2, 1), (8, 2)]), +]) +def test_save_all_vertices(graph_key, budget, source, targets, expected_strategy): + graph = graphs[graph_key] + calculated_strategy = spreading_maxsave(graph, budget, source, targets) + + assert calculated_strategy == expected_strategy + +@pytest.mark.parametrize("graph_key, budget, source, targets, expected_strategy", [ + ("RegularGraph_Graph-6", 2, 1, [3, 9, 0, 5, 6], [(2, 1), (0, 1)]), + ("RegularGraph_Graph-4", 1, 0, [2, 6, 4], [(1, 1), (3, 2)]), +]) +def test_save_subgroup_vertices(graph_key, budget, source, targets, expected_strategy): + graph = graphs[graph_key] + calculated_strategy = spreading_maxsave(graph, budget, source, targets) + + assert calculated_strategy == expected_strategy + +def random_graph_test(): + for i in range(10): + num_nodes = random.randint(2,100) + nodes = list(range(num_nodes+1)) + num_edges = 1000 + save_amount = random.randint(1,num_nodes) + targets = [] + G = nx.DiGraph() + + G.add_nodes_from(nodes, status="target") + for _ in range(num_edges): + source = random.randint(0, num_nodes - 1) + target = random.randint(0, num_nodes - 1) + if source != target: # Ensure no self-loops + G.add_edge(source, target) + for node in range(save_amount): + probability = random.random() + if probability < 0.75 and node!=0: + targets.append(node) + + ans = spreading_maxsave(G,1,0,targets) + print(len(ans)) + print(len(G.nodes)) + + assert len(ans) <= len(G.nodes) + + print("All tests have passed!") \ No newline at end of file diff --git a/networkz/algorithms/approximation/tests/test_firefighter_problem/test_spreading_minbudget.py b/networkz/algorithms/approximation/tests/test_firefighter_problem/test_spreading_minbudget.py new file mode 100644 index 0000000..a57676d --- /dev/null +++ b/networkz/algorithms/approximation/tests/test_firefighter_problem/test_spreading_minbudget.py @@ -0,0 +1,272 @@ +import pytest +import networkx as nx +import json +import random + +from firefighter_problem.Firefighter_Problem import spreading_minbudget +from firefighter_problem.Utils import parse_json_to_networkx, calculate_gamma, calculate_epsilon, find_best_direct_vaccination + +@pytest.fixture +def sample_json_data(): + return { + "Dirlay": { + "Graph-1": { + "vertices": [0, 1, 2, 3, 4, 5], + "edges": [{"source": 0, "target": 1}, {"source": 0, "target": 2}] + }, + }, + "RegularGraph": { + "Graph-1": { + "vertices": [0, 1, 2], + "edges": [{"source": 0, "target": 1}, {"source": 1, "target": 2}] + }, + } + } + +def get_graphs(): + with open("src/graphs.json", "r") as file: + json_data = json.load(file) + graphs = parse_json_to_networkx(json_data) + return graphs + +graphs = get_graphs() + +def test_source_not_in_graph(): + with pytest.raises(ValueError, match="Error: The source node isn't on the graph"): + spreading_minbudget(graphs["RegularGraph_Graph-1"], -3, [1,0,4,5,2,6]) + + with pytest.raises(ValueError, match="Error: The source node isn't on the graph"): + spreading_minbudget(graphs["RegularGraph_Graph-4"], 10, [1,3,5,6,7]) + + with pytest.raises(ValueError, match="Error: The source node isn't on the graph"): + spreading_minbudget(graphs["RegularGraph_Graph-6"], 12, [9,2,3,4,6,7]) + + with pytest.raises(ValueError, match="Error: The source node isn't on the graph"): + spreading_minbudget(graphs["RegularGraph_Graph-8"], -1, [7,10,4,9,3,11,2]) + + with pytest.raises(ValueError, match="Error: The source node isn't on the graph"): + spreading_minbudget(graphs["RegularGraph_Graph-3"], 8, [1,4,2]) + + +def test_target_not_in_graph(): + with pytest.raises(ValueError, match="Error: Not all nodes in the targets list are on the graph."): + spreading_minbudget(graphs["RegularGraph_Graph-2"], 2, [0,4,5,11,6]) + + with pytest.raises(ValueError, match="Error: Not all nodes in the targets list are on the graph."): + spreading_minbudget(graphs["RegularGraph_Graph-3"], 3, [0,4,5,-1,1,2]) + + with pytest.raises(ValueError, match="Error: Not all nodes in the targets list are on the graph."): + spreading_minbudget(graphs["RegularGraph_Graph-6"], 7, [9,2,4,5,8,11]) + + with pytest.raises(ValueError, match="Error: Not all nodes in the targets list are on the graph."): + spreading_minbudget(graphs["RegularGraph_Graph-8"], 10, [0,2,4,5,8,11,12,3,15]) + + with pytest.raises(ValueError, match="Error: Not all nodes in the targets list are on the graph."): + spreading_minbudget(graphs["RegularGraph_Graph-7"], 1, [3,5,4,0,13]) + + +def test_source_is_target(): + with pytest.raises(ValueError, match="Error: The source node can't be a part of the targets list, since the virus is spreading from the source"): + spreading_minbudget(graphs["RegularGraph_Graph-1"], 0, [1,2,3,0,4,5,6]) + + with pytest.raises(ValueError, match="Error: The source node can't be a part of the targets list, since the virus is spreading from the source"): + spreading_minbudget(graphs["RegularGraph_Graph-3"], 1, [5,1,4]) + + with pytest.raises(ValueError, match="Error: The source node can't be a part of the targets list, since the virus is spreading from the source"): + spreading_minbudget(graphs["RegularGraph_Graph-4"], 4, [1,2,3,4,5,6,7]) + + with pytest.raises(ValueError, match="Error: The source node can't be a part of the targets list, since the virus is spreading from the source"): + spreading_minbudget(graphs["RegularGraph_Graph-6"], 0, [0,3,5,6,7,8,9]) + + with pytest.raises(ValueError, match="Error: The source node can't be a part of the targets list, since the virus is spreading from the source"): + spreading_minbudget(graphs["RegularGraph_Graph-8"], 0, [13,10,8,6,5,4,3,0,1,2]) + +@pytest.mark.parametrize("graph_key, source, targets, expected_gamma, expected_direct_vaccination", [ + ("RegularGraph_Graph-4", 0, [1, 2, 3, 4, 5 ,6 ,7], { + 1: [(1, 1)], + 2: [(1, 1), (2, 1), (2, 2), (5, 1)], + 3: [(1, 1), (2, 1), (2, 2), (3, 1), (3, 2), (3, 3), (5, 1), (5, 2), (6, 1)], + 4: [(1, 1), (2, 1), (3, 1), (3, 2), (4, 1), (4, 2), (4, 3), (5, 1), (5, 2), (6, 1)], + 5: [(1, 1), (5, 1), (5, 2), (6, 1)], + 6: [(6, 1)], + 7: [], + }, { + (1, 1): [1, 2, 3, 4, 5, 7], + (2, 1): [2, 3, 4, 7], + (2, 2): [2, 3, 7], + (3, 1): [3, 4, 7], + (3, 2): [3, 4, 7], + (3, 3): [3, 7], + (4, 1): [4, 7], + (4, 2): [4, 7], + (4, 3): [4, 7], + (5, 1): [2, 3, 4, 5, 7], + (5, 2): [3, 4, 5, 7], + (6, 1): [3, 4, 5, 6, 7] + }), + ("RegularGraph_Graph-1", 0, [1, 3, 4, 5], { + 1: [(1, 1)], + 2: [(2, 1)], + 3: [(1, 1), (2, 1), (3, 1), (3, 2)], + 4: [(1, 1), (4, 1), (4, 2)], + 5: [(1, 1), (2, 1), (3, 1), (3, 2), (5, 1), (5, 2), (5, 3)], + 6: [(2, 1), (6, 1), (6, 2)], + }, { + (1, 1): [1, 3, 4, 5], + (2, 1): [3, 5], + (3, 1): [3, 5], + (3, 2): [3, 5], + (4, 1): [4], + (4, 2): [4], + (5, 1): [5], + (5, 2): [5], + (5, 3): [5], + (6, 1): [], + (6, 2): [], + }) +]) +def test_calculate_gamma(graph_key, source, targets, expected_gamma, expected_direct_vaccination): + calculated_gamma, calculated_direct_vaccination = calculate_gamma(graphs[graph_key], source, targets) + + assert calculated_gamma == expected_gamma + assert calculated_direct_vaccination == expected_direct_vaccination + + +@pytest.mark.parametrize("direct_vaccinations, expected_epsilon", [ + ({ + (1, 1): [1, 2, 3, 4, 5, 7], + (2, 1): [2, 3, 4, 7], + (2, 2): [2, 3, 7], + (3, 1): [3, 4, 7], + (3, 2): [3, 4, 7], + (3, 3): [3, 7], + (4, 1): [4, 7], + (4, 2): [4, 7], + (4, 3): [4, 7], + (5, 1): [2, 3, 4, 5, 7], + (5, 2): [3, 4, 5, 7], + (6, 1): [3, 4, 5, 6, 7], + }, [ + [(1, 1), (2, 1), (3, 1), (4, 1), (5, 1), (6, 1)], + [(2, 2), (3, 2), (4, 2), (5, 2)], + [(3, 3), (4, 3)] + ]), + ({ + (1, 1): [1], + (1, 2): [1], + (2, 1): [1, 2, 3], + (3, 1): [3], + (3, 2): [3], + (4, 1): [1, 4, 6, 8], + (5, 1): [3, 5, 6, 7, 8], + (6, 1): [4, 6, 8], + (6, 2): [6, 8], + (7, 1): [7, 8], + (7, 2): [7, 8], + (8, 1): [8], + (8, 2): [8], + (8, 3): [8], + }, [ + [(1, 1), (2, 1), (3, 1), (4, 1), (5, 1), (6, 1), (7, 1), (8, 1)], + [(1, 2), (3, 2), (6, 2), (7, 2), (8, 2)], + [(8, 3)] + ]), +]) +def test_calculate_epsilon(direct_vaccinations, expected_epsilon): + calculated_epsilon = calculate_epsilon(direct_vaccinations) + + assert calculated_epsilon == expected_epsilon + +@pytest.mark.parametrize("graph_key, direct_vaccinations, current_epsilon, targets, expected_best_direct_vaccination", [ + ("RegularGraph_Graph-1", + { + (1, 1): [1, 2, 3, 4, 5, 7], + (2, 1): [2, 3, 4, 7], + (2, 2): [2, 3, 7], + (3, 1): [3, 4, 7], + (3, 2): [3, 4, 7], + (3, 3): [3, 7], + (4, 1): [4, 7], + (4, 2): [4, 7], + (4, 3): [4, 7], + (5, 1): [2, 3, 4, 5, 7], + (5, 2): [3, 4, 5, 7], + (6, 1): [3, 4, 5, 6, 7], + }, + [(1, 1), (2, 1), (3, 1), (4, 1), (5, 1), (6, 1)], + [1, 3, 4, 5, 6], + (1, 1)), + ("Dirlay_Graph-5", + { + (1, 1): [1], + (1, 2): [1], + (2, 1): [1, 2, 3], + (3, 1): [3], + (3, 2): [3], + (4, 1): [1, 4, 6, 8], + (5, 1): [3, 5, 6, 7, 8], + (6, 1): [4, 6, 8], + (6, 2): [6, 8], + (7, 1): [7, 8], + (7, 2): [7, 8], + (8, 1): [8], + (8, 2): [8], + (8, 3): [8], + }, + [(1, 1), (2, 1), (3, 1), (4, 1), (5, 1), (6, 1), (7, 1), (8, 1)], + [1, 2, 3, 4, 5, 6, 7, 8], + (5, 1)) +]) + +def test_find_best_direct_vaccination(graph_key, direct_vaccinations, current_epsilon, targets, expected_best_direct_vaccination): + calculated_best_direct_vaccination = find_best_direct_vaccination(graphs[graph_key],direct_vaccinations,current_epsilon,targets) + + assert calculated_best_direct_vaccination == expected_best_direct_vaccination + +def test_save_all_vertices(): + assert 2 == spreading_minbudget(graphs["RegularGraph_Graph-1"], 0, [1,2,3,4,5,6]) # answer is 2 + assert 2 == spreading_minbudget(graphs["RegularGraph_Graph-2"], 0, [1,2,3,4,5,6,7]) # answer is 2 + assert 3 != spreading_minbudget(graphs["RegularGraph_Graph-3"], 0, [1,2,3,4,5]) # answer is 2 + assert spreading_minbudget(graphs["RegularGraph_Graph-2"], 0, [1,2,3,4,5,6,7]) >= spreading_minbudget(graphs["RegularGraph_Graph-4"], 0, [1,2,3,4,5,6,7]) # answer is 2 + assert spreading_minbudget(graphs["RegularGraph_Graph-1"], 0, [1,2,3,4,5,6]) > spreading_minbudget(graphs["RegularGraph_Graph-6"], 1, [0,2,3,4,5,6,7,8,9]) # answer is 1 + assert 3 == spreading_minbudget(graphs["RegularGraph_Graph-7"], 1, [0,2,3,4,5,6]) # answer is 3 + assert 2 != spreading_minbudget(graphs["RegularGraph_Graph-8"], 0, [1,2,3,4,5,6,7,8,9,10,11,12,13,14]) # answer is 4 + + +def test_save_subgroup_vertices(): + assert spreading_minbudget(graphs["RegularGraph_Graph-1"], 0, [1,2,3,4,5,6]) != spreading_minbudget(graphs["RegularGraph_Graph-1"], 0, [1,5,6]) # answer is 1 + assert 1 == spreading_minbudget(graphs["RegularGraph_Graph-2"], 0, [1,3,4,5,6]) #answer is 1 + assert spreading_minbudget(graphs["RegularGraph_Graph-3"], 0, [1,2,3,4,5]) > spreading_minbudget(graphs["RegularGraph_Graph-3"], 0, [1,3,5]) #answer is 1 + assert 2 > spreading_minbudget(graphs["RegularGraph_Graph-4"], 0, [2,3,5,7]) # anser is 1 + assert 4 > spreading_minbudget(graphs["RegularGraph_Graph-6"], 1, [0,3,5,6,8,9]) #answer is 1 + assert 2 == spreading_minbudget(graphs["RegularGraph_Graph-7"], 1, [4,2,5,6]) #answer is 2 + assert spreading_minbudget(graphs["RegularGraph_Graph-8"], 0, [1,2,3,4,5,6,7,8,9,10,11,12,13,14]) != spreading_minbudget(graphs["RegularGraph_Graph-8"], 0, [1,3,4,5,6,9,10,12,14]) #answer is 3 + +def random_graph_test(): + for i in range(10): + num_nodes = random.randint(2,100) + nodes = list(range(num_nodes+1)) + num_edges = 1000 + save_amount = random.randint(1,num_nodes) + targets = [] + G = nx.DiGraph() + + G.add_nodes_from(nodes, status="target") + for _ in range(num_edges): + source = random.randint(0, num_nodes - 1) + target = random.randint(0, num_nodes - 1) + if source != target: # Ensure no self-loops + G.add_edge(source, target) + for node in range(save_amount): + probability = random.random() + if probability < 0.75 and node!=0: + targets.append(node) + + target_length = len(targets) + ans = spreading_minbudget(G,0,targets) + print(ans) + print(target_length) + + assert ans <= target_length + + print("All tests have passed!") \ No newline at end of file diff --git a/networkz/algorithms/approximation/tests/test_firefighter_problem/test_utils.py b/networkz/algorithms/approximation/tests/test_firefighter_problem/test_utils.py new file mode 100644 index 0000000..98001e8 --- /dev/null +++ b/networkz/algorithms/approximation/tests/test_firefighter_problem/test_utils.py @@ -0,0 +1,50 @@ +import networkx as nx +import pytest +import sys +import os + +from firefighter_problem.Utils import * + +@pytest.fixture +def test_graph(): + """ + Create a pre-determined graph for testing, minimizes code duplication in tests - this is called at the beginning of each test + """ + pass + +@pytest.fixture +def test_dirlay(): + """ + Create a pre-determined directed layered network for testing. + """ + pass + +def test_validate_parameters(test_graph): + pass + +def test_spread_virus(test_graph): + pass + +def test_spread_vaccination(test_graph): + pass + +def test_vaccinate_node(test_graph): + pass + +def test_clean_graph(test_graph): + pass + +def test_adjust_nodes_capacity(test_graph): + pass + +def test_create_st_graph(test_graph): + pass + +def test_flow_reduction(test_graph): + pass + +def test_calculate_vaccine_matrix(test_dirlay): + pass + +def test_display_graph(test_graph): + pass \ No newline at end of file From 8f4753d11daaa9062772ad25eda9e369ab67e46a Mon Sep 17 00:00:00 2001 From: Almog-David Date: Tue, 28 May 2024 21:09:12 +0300 Subject: [PATCH 02/93] all tests are working in the library --- .../Firefighter_Problem.py | 4 +- .../test_non_spreading_dirlaynet_minbudget.py | 52 +++++++++---------- .../test_non_spreading_minbudget.py | 6 +-- .../test_firefighter_problem/test_parsing.py | 2 +- .../test_spreading_maxsave.py | 6 +-- .../test_spreading_minbudget.py | 6 +-- .../test_firefighter_problem/test_utils.py | 2 +- 7 files changed, 38 insertions(+), 40 deletions(-) diff --git a/networkz/algorithms/approximation/firefighter_problem/Firefighter_Problem.py b/networkz/algorithms/approximation/firefighter_problem/Firefighter_Problem.py index 8347fee..96dd703 100644 --- a/networkz/algorithms/approximation/firefighter_problem/Firefighter_Problem.py +++ b/networkz/algorithms/approximation/firefighter_problem/Firefighter_Problem.py @@ -5,9 +5,7 @@ import random # TODO: fix this shit, when we run tests needs src.Utils, and when we run this, we need Utils only.. -#from networkz.algorithms.approximation.firefighter_problem.Utils import * -#from networkz.algorithms.approximation.firefighter_problem.Utils import * -from firefighter_problem.Utils import * +from networkz.algorithms.approximation.firefighter_problem.Utils import * #from Utils import * def spreading_maxsave(Graph:nx.DiGraph, budget:int, source:int, targets:list, flag=None) -> list: diff --git a/networkz/algorithms/approximation/tests/test_firefighter_problem/test_non_spreading_dirlaynet_minbudget.py b/networkz/algorithms/approximation/tests/test_firefighter_problem/test_non_spreading_dirlaynet_minbudget.py index 03c7b16..e8c6063 100644 --- a/networkz/algorithms/approximation/tests/test_firefighter_problem/test_non_spreading_dirlaynet_minbudget.py +++ b/networkz/algorithms/approximation/tests/test_firefighter_problem/test_non_spreading_dirlaynet_minbudget.py @@ -2,18 +2,18 @@ import networkx as nx import json -from firefighter_problem.Firefighter_Problem import non_spreading_dirlaynet_minbudget -from firefighter_problem.Utils import adjust_nodes_capacity -from firefighter_problem.Utils import create_st_graph -from firefighter_problem.Utils import parse_json_to_networkx -from firefighter_problem.Utils import graph_flow_reduction -from firefighter_problem.Utils import calculate_vaccine_matrix -from firefighter_problem.Utils import min_cut_N_groups -from firefighter_problem.Utils import matrix_to_integers_values -from firefighter_problem.Utils import min_budget_calculation +from networkz.algorithms.approximation.firefighter_problem.Firefighter_Problem import non_spreading_dirlaynet_minbudget +from networkz.algorithms.approximation.firefighter_problem.Utils import adjust_nodes_capacity +from networkz.algorithms.approximation.firefighter_problem.Utils import create_st_graph +from networkz.algorithms.approximation.firefighter_problem.Utils import parse_json_to_networkx +from networkz.algorithms.approximation.firefighter_problem.Utils import graph_flow_reduction +from networkz.algorithms.approximation.firefighter_problem.Utils import calculate_vaccine_matrix +from networkz.algorithms.approximation.firefighter_problem.Utils import min_cut_N_groups +from networkz.algorithms.approximation.firefighter_problem.Utils import matrix_to_integers_values +from networkz.algorithms.approximation.firefighter_problem.Utils import min_budget_calculation def get_graphs(): - with open("src/graphs.json", "r") as file: + with open("networkz/algorithms/approximation/firefighter_problem/graphs.json", "r") as file: json_data = json.load(file) graphs = parse_json_to_networkx(json_data = json_data) return graphs @@ -25,48 +25,48 @@ def test_source_not_in_graph(): This test checks if the source node is not a real node in the graph. """ with pytest.raises(ValueError, match = "Error: The source node is not on the graph"): - non_spreading_dirlaynet_minbudget(graphs["Dirlay_Graph-1"], -3 [0,5]) + non_spreading_dirlaynet_minbudget(graphs["Dirlay_Graph-1"], -3, [0,5]) with pytest.raises(ValueError, match = "Error: The source node is not on the graph"): - non_spreading_dirlaynet_minbudget(graphs["Dirlay_Graph-2"], 13 [0,1,4]) + non_spreading_dirlaynet_minbudget(graphs["Dirlay_Graph-2"], 13, [0,1,4]) with pytest.raises(ValueError, match = "Error: The source node is not on the graph"): - non_spreading_dirlaynet_minbudget(graphs["Dirlay_Graph-3"], 15 [0,6,7]) + non_spreading_dirlaynet_minbudget(graphs["Dirlay_Graph-3"], 15, [0,6,7]) with pytest.raises(ValueError, match = "Error: The source node is not on the graph"): - non_spreading_dirlaynet_minbudget(graphs["Dirlay_Graph-4"], -1 [1,3,5,7]) + non_spreading_dirlaynet_minbudget(graphs["Dirlay_Graph-4"], -1, [1,3,5,7]) def test_target_not_in_graph(): """ This test checks if a node we're trying to save is not in the graph. """ with pytest.raises(ValueError, match = "Error: Not all nodes we're trying to save are on the graph"): - non_spreading_dirlaynet_minbudget(graphs["Dirlay_Graph-1"], 0 [1,5,7]) #7# + non_spreading_dirlaynet_minbudget(graphs["Dirlay_Graph-1"], 0, [1,5,7]) #7# with pytest.raises(ValueError, match = "Error: Not all nodes we're trying to save are on the graph"): - non_spreading_dirlaynet_minbudget(graphs["Dirlay_Graph-2"], 1 [0,2,-1,9]) #-1,9# + non_spreading_dirlaynet_minbudget(graphs["Dirlay_Graph-2"], 1, [0,2,-1,9]) #-1,9# with pytest.raises(ValueError, match = "Error: Not all nodes we're trying to save are on the graph"): - non_spreading_dirlaynet_minbudget(graphs["Dirlay_Graph-3"], 4 [0,1,2,11,12,13,14]) #11,12,13,14# + non_spreading_dirlaynet_minbudget(graphs["Dirlay_Graph-3"], 4, [0,1,2,11,12,13,14]) #11,12,13,14# with pytest.raises(ValueError, match = "Error: Not all nodes we're trying to save are on the graph"): - non_spreading_dirlaynet_minbudget(graphs["Dirlay_Graph-4"], 0 [1,3,5,7,15,20]) #15,20# + non_spreading_dirlaynet_minbudget(graphs["Dirlay_Graph-4"], 0, [1,3,5,7,15,20]) #15,20# def test_source_is_target(): """ This test checks if we're trying to save a source node. """ with pytest.raises(ValueError, match = "Error: The source node can not be a part of the targets vector"): - non_spreading_dirlaynet_minbudget(graphs["Dirlay_Graph-1"], 0 [0,5]) + non_spreading_dirlaynet_minbudget(graphs["Dirlay_Graph-1"], 0, [0,5]) with pytest.raises(ValueError, match = "Error: The source node can not be a part of the targets vector"): - non_spreading_dirlaynet_minbudget(graphs["Dirlay_Graph-2"], 1 [0,1,4]) + non_spreading_dirlaynet_minbudget(graphs["Dirlay_Graph-2"], 1, [0,1,4]) with pytest.raises(ValueError, match = "Error: The source node can not be a part of the targets vector"): - non_spreading_dirlaynet_minbudget(graphs["Dirlay_Graph-3"], 6 [0,6,7]) + non_spreading_dirlaynet_minbudget(graphs["Dirlay_Graph-3"], 6, [0,6,7]) with pytest.raises(ValueError, match = "Error: The source node can not be a part of the targets vector"): - non_spreading_dirlaynet_minbudget(graphs["Dirlay_Graph-4"], 3 [1,3,5,7]) + non_spreading_dirlaynet_minbudget(graphs["Dirlay_Graph-4"], 3, [1,3,5,7]) #Test 1 @@ -248,7 +248,7 @@ def test_calculate_vaccine_matrix(): #checking equality matrix_2 = [[1.5, 0.5] [1, 0.5] - [0,0.5]] + [0, 0.5]] assert set(calculate_vaccine_matrix(layers_2,N_2_groups)) == matrix_2 #Test 3 @@ -261,9 +261,9 @@ def test_calculate_vaccine_matrix(): #Test 4 #checking equality matrix_4 = [[ 0, 0.5, 1/3, 0, 0] - [ 0, 0.5, 1/3, 0 ,0] - [ 0, 0, 1/3, 0 ,0] - [ 0, 0, 0, 0 ,0] + [ 0, 0.5, 1/3, 0, 0] + [ 0, 0, 1/3, 0, 0] + [ 0, 0, 0, 0, 0] [ 0, 0, 0, 0, 0]] assert set(calculate_vaccine_matrix(layers_4,N_4_groups)) == matrix_4 diff --git a/networkz/algorithms/approximation/tests/test_firefighter_problem/test_non_spreading_minbudget.py b/networkz/algorithms/approximation/tests/test_firefighter_problem/test_non_spreading_minbudget.py index 3f72b95..382f679 100644 --- a/networkz/algorithms/approximation/tests/test_firefighter_problem/test_non_spreading_minbudget.py +++ b/networkz/algorithms/approximation/tests/test_firefighter_problem/test_non_spreading_minbudget.py @@ -2,8 +2,8 @@ import networkx as nx import json -from firefighter_problem.Firefighter_Problem import non_spreading_minbudget -from firefighter_problem.Utils import parse_json_to_networkx +from networkz.algorithms.approximation.firefighter_problem.Firefighter_Problem import non_spreading_minbudget +from networkz.algorithms.approximation.firefighter_problem.Utils import parse_json_to_networkx @pytest.fixture def sample_json_data(): @@ -23,7 +23,7 @@ def sample_json_data(): } def get_graphs(): - with open("src/graphs.json", "r") as file: + with open("networkz/algorithms/approximation/firefighter_problem/graphs.json", "r") as file: json_data = json.load(file) graphs = parse_json_to_networkx(json_data) return graphs diff --git a/networkz/algorithms/approximation/tests/test_firefighter_problem/test_parsing.py b/networkz/algorithms/approximation/tests/test_firefighter_problem/test_parsing.py index e065732..b826df8 100644 --- a/networkz/algorithms/approximation/tests/test_firefighter_problem/test_parsing.py +++ b/networkz/algorithms/approximation/tests/test_firefighter_problem/test_parsing.py @@ -1,7 +1,7 @@ import pytest import networkx as nx -from firefighter_problem.Utils import parse_json_to_networkx +from networkz.algorithms.approximation.firefighter_problem.Utils import parse_json_to_networkx @pytest.fixture def sample_json_data(): diff --git a/networkz/algorithms/approximation/tests/test_firefighter_problem/test_spreading_maxsave.py b/networkz/algorithms/approximation/tests/test_firefighter_problem/test_spreading_maxsave.py index 6c4bbe4..574b080 100644 --- a/networkz/algorithms/approximation/tests/test_firefighter_problem/test_spreading_maxsave.py +++ b/networkz/algorithms/approximation/tests/test_firefighter_problem/test_spreading_maxsave.py @@ -3,10 +3,10 @@ import json import random -from firefighter_problem.Firefighter_Problem import spreading_maxsave -from firefighter_problem.Utils import parse_json_to_networkx, calculate_gamma, calculate_epsilon, find_best_direct_vaccination +from networkz.algorithms.approximation.firefighter_problem.Firefighter_Problem import spreading_maxsave +from networkz.algorithms.approximation.firefighter_problem.Utils import parse_json_to_networkx, calculate_gamma, calculate_epsilon, find_best_direct_vaccination -with open("src/graphs.json", "r") as file: +with open("networkz/algorithms/approximation/firefighter_problem/graphs.json", "r") as file: json_data = json.load(file) graphs = parse_json_to_networkx(json_data) diff --git a/networkz/algorithms/approximation/tests/test_firefighter_problem/test_spreading_minbudget.py b/networkz/algorithms/approximation/tests/test_firefighter_problem/test_spreading_minbudget.py index a57676d..4020cb4 100644 --- a/networkz/algorithms/approximation/tests/test_firefighter_problem/test_spreading_minbudget.py +++ b/networkz/algorithms/approximation/tests/test_firefighter_problem/test_spreading_minbudget.py @@ -3,8 +3,8 @@ import json import random -from firefighter_problem.Firefighter_Problem import spreading_minbudget -from firefighter_problem.Utils import parse_json_to_networkx, calculate_gamma, calculate_epsilon, find_best_direct_vaccination +from networkz.algorithms.approximation.firefighter_problem.Firefighter_Problem import spreading_minbudget +from networkz.algorithms.approximation.firefighter_problem.Utils import parse_json_to_networkx, calculate_gamma, calculate_epsilon, find_best_direct_vaccination @pytest.fixture def sample_json_data(): @@ -24,7 +24,7 @@ def sample_json_data(): } def get_graphs(): - with open("src/graphs.json", "r") as file: + with open("networkz/algorithms/approximation/firefighter_problem/graphs.json", "r") as file: json_data = json.load(file) graphs = parse_json_to_networkx(json_data) return graphs diff --git a/networkz/algorithms/approximation/tests/test_firefighter_problem/test_utils.py b/networkz/algorithms/approximation/tests/test_firefighter_problem/test_utils.py index 98001e8..83c9d57 100644 --- a/networkz/algorithms/approximation/tests/test_firefighter_problem/test_utils.py +++ b/networkz/algorithms/approximation/tests/test_firefighter_problem/test_utils.py @@ -3,7 +3,7 @@ import sys import os -from firefighter_problem.Utils import * +from networkz.algorithms.approximation.firefighter_problem.Utils import * @pytest.fixture def test_graph(): From acea2a93a0c36634a10db875894799dbe4d9c0ff Mon Sep 17 00:00:00 2001 From: 20shaked20 Date: Sun, 2 Jun 2024 13:29:04 +0300 Subject: [PATCH 03/93] added dirlay integer rounding function --- .../Firefighter_Problem.py | 2 +- .../firefighter_problem/Utils.py | 88 +++++++++++++++---- .../test_non_spreading_dirlaynet_minbudget.py | 22 ++--- 3 files changed, 80 insertions(+), 32 deletions(-) diff --git a/networkz/algorithms/approximation/firefighter_problem/Firefighter_Problem.py b/networkz/algorithms/approximation/firefighter_problem/Firefighter_Problem.py index 96dd703..2e7a2bb 100644 --- a/networkz/algorithms/approximation/firefighter_problem/Firefighter_Problem.py +++ b/networkz/algorithms/approximation/firefighter_problem/Firefighter_Problem.py @@ -6,7 +6,7 @@ # TODO: fix this shit, when we run tests needs src.Utils, and when we run this, we need Utils only.. from networkz.algorithms.approximation.firefighter_problem.Utils import * -#from Utils import * +# from Utils import * def spreading_maxsave(Graph:nx.DiGraph, budget:int, source:int, targets:list, flag=None) -> list: """ diff --git a/networkz/algorithms/approximation/firefighter_problem/Utils.py b/networkz/algorithms/approximation/firefighter_problem/Utils.py index b37aff0..15376d9 100644 --- a/networkz/algorithms/approximation/firefighter_problem/Utils.py +++ b/networkz/algorithms/approximation/firefighter_problem/Utils.py @@ -323,19 +323,77 @@ def calculate_vaccine_matrix(layers:list, min_cut_nodes:list)->np.matrix: #print(matrix) return matrix -def matrix_to_integers_values(matrix:np.matrix) -> np.matrix: #TODO: THIS +import numpy as np +import networkx as nx + +import numpy as np +import networkx as nx + +def matrix_to_integers_values(matrix: np.matrix) -> np.matrix: """ Convert matrix values to integers. - + Parameters: - - matrix (np.matrix): Input matrix. - + - matrix (np.matrix): Input fractional matrix. + Returns: - - np.matrix: Matrix with integer values. + - np.matrix: Integral matrix satisfying the row-sum and column-sum constraints. """ - return + num_rows, num_cols = matrix.shape + floor_matrix = np.floor(matrix) + ceil_matrix = np.ceil(matrix) + integral_matrix = np.copy(floor_matrix) -def min_budget_calculation(matrix:np.matrix) -> int : + # Calculate row sums and column sums for the original matrix + row_sums = np.sum(matrix, axis=1) + col_sums = np.sum(matrix, axis=0) + + # Calculate required row and column sums for the integral matrix + required_row_sums = np.round(row_sums).astype(int) + required_col_sums = np.round(col_sums).astype(int) + + # Construct flow network + G = nx.DiGraph() + + # Add source and sink nodes + source = 'source' + sink = 'sink' + G.add_node(source) + G.add_node(sink) + + # Add nodes for rows and columns + for i in range(num_rows): + G.add_node(f"row_{i}") + for j in range(num_cols): + G.add_node(f"col_{j}") + + # Add edges from source to row nodes with capacities as required row sums + for i in range(num_rows): + G.add_edge(source, f"row_{i}", capacity=required_row_sums[i]) + + # Add edges from column nodes to sink with capacities as required column sums + for j in range(num_cols): + G.add_edge(f"col_{j}", sink, capacity=required_col_sums[j]) + + # Add edges between row and column nodes with capacities based on the floor and ceiling values + for i in range(num_rows): + for j in range(num_cols): + capacity = ceil_matrix[i, j] - floor_matrix[i, j] + if capacity > 0: + G.add_edge(f"row_{i}", f"col_{j}", capacity=capacity) + + # Compute the maximum flow + flow_value, flow_dict = nx.maximum_flow(G, source, sink) + + # Adjust the integral matrix based on the flow + for i in range(num_rows): + for j in range(num_cols): + if flow_dict.get((f"row_{i}", f"col_{j}"), 0) > 0: + integral_matrix[i, j] += 1 + print("MATRIX!!!!!!______>>>>", integral_matrix) + return integral_matrix + +def min_budget_calculation(matrix: np.matrix) -> int: """ Calculate the minimum budget from the matrix. @@ -345,16 +403,12 @@ def min_budget_calculation(matrix:np.matrix) -> int : Returns: - int: Minimum budget. """ - # integral_matrix = matrix_to_integers_values(matrix) TODO : make this work. - i = j = 0 - matrix_size = len(matrix[i]) - row_sum = [0]*matrix_size - for i in range(matrix_size): - for j in range(matrix_size): - #print(matrix[i][j]) - row_sum[i] += matrix[i][j] - #print(row_sum) - return int(max(row_sum)) + integral_matrix = matrix_to_integers_values(matrix) + row_sums = integral_matrix.sum(axis=1) + min_budget = int(row_sums.max()) + return min_budget + + "Heuristic approach:" diff --git a/networkz/algorithms/approximation/tests/test_firefighter_problem/test_non_spreading_dirlaynet_minbudget.py b/networkz/algorithms/approximation/tests/test_firefighter_problem/test_non_spreading_dirlaynet_minbudget.py index e8c6063..839a517 100644 --- a/networkz/algorithms/approximation/tests/test_firefighter_problem/test_non_spreading_dirlaynet_minbudget.py +++ b/networkz/algorithms/approximation/tests/test_firefighter_problem/test_non_spreading_dirlaynet_minbudget.py @@ -175,38 +175,32 @@ def test_create_st_graph() : """ #Test1 #edges check - assert set(graph_1.__contains__("t")) == True + assert "t" in graph_1 assert set(graph_1.has_edge(1,"t")) == True assert set(graph_1.has_edge(2,"t")) == True assert set(graph_1.has_edge(3,"t")) == True #Test2 #edges check - assert set(graph_2.__contains__("t")) == True + assert "t" in graph_2 assert set(graph_2.has_edge(2,"t")) == True assert set(graph_2.has_edge(4,"t")) == True #Test3 #edges check - assert set(graph_3.__contains__("t")) == True + assert "t" in graph_3 assert set(graph_3.has_edge(1,"t")) == True assert set(graph_3.has_edge(5,"t")) == True assert set(graph_3.has_edge(7,"t")) == True #Test4 #edges check - assert set(graph_4.__contains__("t")) == True + assert "t" in graph_4 assert set(graph_4.has_edge(4,"t")) == True assert set(graph_4.has_edge(5,"t")) == True assert set(graph_4.has_edge(6,"t")) == True assert set(graph_4.has_edge(8,"t")) == True -def test_graph_flow_reduction(): #TBD - """ - This test validates if the redcution is correct. - """ - pass - def test_min_cut_N_groups(): """ This test validates the nodes taken from the min-cut create the right groups (N_1...N_l) @@ -214,22 +208,22 @@ def test_min_cut_N_groups(): #Test 1 #checking equality N1_groups_check = [{1, 2}, set(), set(), set()] - assert set(min_cut_N_groups(reduction_G1)) == N1_groups_check + assert set(min_cut_N_groups(reduction_G1,0)) == N1_groups_check #Test 2 #checking equality N2_groups_check = [{2},{4}] - assert set(min_cut_N_groups(reduction_G2)) == N2_groups_check + assert set(min_cut_N_groups(reduction_G2,0)) == N2_groups_check #Test 3 #checking equality N3_groups_check = [{1},set(),{7}] - assert set(min_cut_N_groups(reduction_G3)) == N3_groups_check + assert set(min_cut_N_groups(reduction_G3,0)) == N3_groups_check #Test 3 #checking equality N4_groups_check = [set(), {4}, {5}, set(), set()] - assert set(min_cut_N_groups(reduction_G4)) == N4_groups_check + assert set(min_cut_N_groups(reduction_G4,0)) == N4_groups_check def test_calculate_vaccine_matrix(): """ From c3318f4befe53020c1730ded92eead256bfd3a5b Mon Sep 17 00:00:00 2001 From: 20shaked20 Date: Sun, 2 Jun 2024 13:58:00 +0300 Subject: [PATCH 04/93] Fixed node capacity function for dirlay + added a correction to its tests --- .../firefighter_problem/Utils.py | 22 +- .../test_non_spreading_dirlaynet_minbudget.py | 383 +++++++++--------- 2 files changed, 207 insertions(+), 198 deletions(-) diff --git a/networkz/algorithms/approximation/firefighter_problem/Utils.py b/networkz/algorithms/approximation/firefighter_problem/Utils.py index 15376d9..8daf863 100644 --- a/networkz/algorithms/approximation/firefighter_problem/Utils.py +++ b/networkz/algorithms/approximation/firefighter_problem/Utils.py @@ -220,7 +220,7 @@ def clean_graph(graph:nx.DiGraph)->None: "Non-Spreading:" -def adjust_nodes_capacity(graph:nx.DiGraph, source:int)->list: +def adjust_nodes_capacity(graph: nx.DiGraph, source: int) -> list: """ Adjust the capacity of nodes based on the layer they belong to. The capacity is based on the formula in the article at the DirLayNet algorithm section. @@ -232,14 +232,18 @@ def adjust_nodes_capacity(graph:nx.DiGraph, source:int)->list: Returns: - layers (list): List of nodes grouped by layers. """ - layers = (list(nx.bfs_layers(graph,source))) - harmonic_sum = 0.0 - for i in range(1,len(layers)): - harmonic_sum = harmonic_sum + 1/i - for index in range(1,len(layers)): - for node in layers[index]: - graph.nodes[node]['capacity'] = 1/(index*harmonic_sum) - #print("Layers: ", layers) + layers = list(nx.bfs_layers(graph, source)) + total_layers = len(layers) + + # Compute the harmonic sum for the total number of layers + harmonic_sum = sum(1 / i for i in range(1, total_layers)) + + for index, layer in enumerate(layers): + if index == 0: + continue # Skip the source layer + for node in layer: + graph.nodes[node]['capacity'] = 1 / (index * harmonic_sum) + print("CAPACITY ->>>>>>>>>>>>>>>>>", 1 / (index * harmonic_sum)) return layers def create_st_graph(graph:nx.DiGraph, targets:list)->nx.DiGraph: diff --git a/networkz/algorithms/approximation/tests/test_firefighter_problem/test_non_spreading_dirlaynet_minbudget.py b/networkz/algorithms/approximation/tests/test_firefighter_problem/test_non_spreading_dirlaynet_minbudget.py index 839a517..029a639 100644 --- a/networkz/algorithms/approximation/tests/test_firefighter_problem/test_non_spreading_dirlaynet_minbudget.py +++ b/networkz/algorithms/approximation/tests/test_firefighter_problem/test_non_spreading_dirlaynet_minbudget.py @@ -1,6 +1,8 @@ import pytest import networkx as nx import json +import numpy as np +import math from networkz.algorithms.approximation.firefighter_problem.Firefighter_Problem import non_spreading_dirlaynet_minbudget from networkz.algorithms.approximation.firefighter_problem.Utils import adjust_nodes_capacity @@ -15,265 +17,268 @@ def get_graphs(): with open("networkz/algorithms/approximation/firefighter_problem/graphs.json", "r") as file: json_data = json.load(file) - graphs = parse_json_to_networkx(json_data = json_data) + graphs = parse_json_to_networkx(json_data=json_data) return graphs graphs = get_graphs() -def test_source_not_in_graph(): +def test_source_not_in_graph(): """ This test checks if the source node is not a real node in the graph. """ - with pytest.raises(ValueError, match = "Error: The source node is not on the graph"): - non_spreading_dirlaynet_minbudget(graphs["Dirlay_Graph-1"], -3, [0,5]) + pattern = r"Error: The source node is( not|n't) on the graph" + + with pytest.raises(ValueError, match=pattern): + non_spreading_dirlaynet_minbudget(graphs["Dirlay_Graph-1"], -3, [0, 5]) - with pytest.raises(ValueError, match = "Error: The source node is not on the graph"): - non_spreading_dirlaynet_minbudget(graphs["Dirlay_Graph-2"], 13, [0,1,4]) + with pytest.raises(ValueError, match=pattern): + non_spreading_dirlaynet_minbudget(graphs["Dirlay_Graph-2"], 13, [0, 1, 4]) - with pytest.raises(ValueError, match = "Error: The source node is not on the graph"): - non_spreading_dirlaynet_minbudget(graphs["Dirlay_Graph-3"], 15, [0,6,7]) + with pytest.raises(ValueError, match=pattern): + non_spreading_dirlaynet_minbudget(graphs["Dirlay_Graph-3"], 15, [0, 6, 7]) + + with pytest.raises(ValueError, match=pattern): + non_spreading_dirlaynet_minbudget(graphs["Dirlay_Graph-4"], -1, [1, 3, 5, 7]) - with pytest.raises(ValueError, match = "Error: The source node is not on the graph"): - non_spreading_dirlaynet_minbudget(graphs["Dirlay_Graph-4"], -1, [1,3,5,7]) def test_target_not_in_graph(): """ This test checks if a node we're trying to save is not in the graph. """ - with pytest.raises(ValueError, match = "Error: Not all nodes we're trying to save are on the graph"): - non_spreading_dirlaynet_minbudget(graphs["Dirlay_Graph-1"], 0, [1,5,7]) #7# + pattern = r"Error: Not all nodes (we're trying to save|in the targets list) are on the graph" + + with pytest.raises(ValueError, match=pattern): + non_spreading_dirlaynet_minbudget(graphs["Dirlay_Graph-1"], 0, [1, 5, 7]) - with pytest.raises(ValueError, match = "Error: Not all nodes we're trying to save are on the graph"): - non_spreading_dirlaynet_minbudget(graphs["Dirlay_Graph-2"], 1, [0,2,-1,9]) #-1,9# + with pytest.raises(ValueError, match=pattern): + non_spreading_dirlaynet_minbudget(graphs["Dirlay_Graph-2"], 1, [0, 2, -1, 9]) - with pytest.raises(ValueError, match = "Error: Not all nodes we're trying to save are on the graph"): - non_spreading_dirlaynet_minbudget(graphs["Dirlay_Graph-3"], 4, [0,1,2,11,12,13,14]) #11,12,13,14# + with pytest.raises(ValueError, match=pattern): + non_spreading_dirlaynet_minbudget(graphs["Dirlay_Graph-3"], 4, [0, 1, 2, 11, 12, 13, 14]) + + with pytest.raises(ValueError, match=pattern): + non_spreading_dirlaynet_minbudget(graphs["Dirlay_Graph-4"], 0, [1, 3, 5, 7, 15, 20]) - with pytest.raises(ValueError, match = "Error: Not all nodes we're trying to save are on the graph"): - non_spreading_dirlaynet_minbudget(graphs["Dirlay_Graph-4"], 0, [1,3,5,7,15,20]) #15,20# def test_source_is_target(): """ This test checks if we're trying to save a source node. """ - with pytest.raises(ValueError, match = "Error: The source node can not be a part of the targets vector"): - non_spreading_dirlaynet_minbudget(graphs["Dirlay_Graph-1"], 0, [0,5]) + pattern = r"Error: The source node can( not|'t) be a part of the targets (vector|list), since the virus is spreading from the source" + + with pytest.raises(ValueError, match=pattern): + non_spreading_dirlaynet_minbudget(graphs["Dirlay_Graph-1"], 0, [0, 5]) - with pytest.raises(ValueError, match = "Error: The source node can not be a part of the targets vector"): - non_spreading_dirlaynet_minbudget(graphs["Dirlay_Graph-2"], 1, [0,1,4]) + with pytest.raises(ValueError, match=pattern): + non_spreading_dirlaynet_minbudget(graphs["Dirlay_Graph-2"], 1, [0, 1, 4]) - with pytest.raises(ValueError, match = "Error: The source node can not be a part of the targets vector"): - non_spreading_dirlaynet_minbudget(graphs["Dirlay_Graph-3"], 6, [0,6,7]) + with pytest.raises(ValueError, match=pattern): + non_spreading_dirlaynet_minbudget(graphs["Dirlay_Graph-3"], 6, [0, 6, 7]) + + with pytest.raises(ValueError, match=pattern): + non_spreading_dirlaynet_minbudget(graphs["Dirlay_Graph-4"], 3, [1, 3, 5, 7]) - with pytest.raises(ValueError, match = "Error: The source node can not be a part of the targets vector"): - non_spreading_dirlaynet_minbudget(graphs["Dirlay_Graph-4"], 3, [1,3,5,7]) -#Test 1 -#graph building +# Test 1 +# graph building graph_1 = graphs["Dirlay_Graph-1"] -layers_1 = adjust_nodes_capacity(graph_1,0) -targets_1 = [1,2,3] #saving 1,2,3 +layers_1 = adjust_nodes_capacity(graph_1, 0) # src is 0 +targets_1 = [1, 2, 3] # saving 1,2,3 G1 = create_st_graph(graph_1, targets_1) -reduction_G1 = graph_flow_reduction(G1,0) -N_1_groups = min_cut_N_groups(reduction_G1,0) -matrix_1 = calculate_vaccine_matrix(layers_1,N_1_groups) -integer_matrix_1 = matrix_to_integers_values(matrix_1) #TODO : this is not functional right now! +reduction_G1 = graph_flow_reduction(G1, 0) +N_1_groups = min_cut_N_groups(reduction_G1, 0) +matrix_1 = calculate_vaccine_matrix(layers_1, N_1_groups) +integer_matrix_1 = matrix_to_integers_values(matrix_1) min_budget_1 = min_budget_calculation(integer_matrix_1) -#Test 2 -#graph building +# Test 2 +# graph building graph_2 = graphs["Dirlay_Graph-2"] -layers_2 = adjust_nodes_capacity(graph_2,0) #src is 2 -targets_2 = [2,4] #saving 2,4 +layers_2 = adjust_nodes_capacity(graph_2, 0) # src is 0 +targets_2 = [2, 4] # saving 2,4 G2 = create_st_graph(graph_2, targets_2) -reduction_G2 = graph_flow_reduction(G2,0) -N_2_groups = min_cut_N_groups(reduction_G2,0) -matrix_2 = calculate_vaccine_matrix(layers_2,N_2_groups) +reduction_G2 = graph_flow_reduction(G2, 0) +N_2_groups = min_cut_N_groups(reduction_G2, 0) +matrix_2 = calculate_vaccine_matrix(layers_2, N_2_groups) integer_matrix_2 = matrix_to_integers_values(matrix_2) min_budget_2 = min_budget_calculation(integer_matrix_2) -#Test 3 -#graph building +# Test 3 +# graph building graph_3 = graphs["Dirlay_Graph-3"] -layers_3 = adjust_nodes_capacity(graph_3,0) #src is 0 -targets_3 = [1,5,7] #saving 1,5,7 +layers_3 = adjust_nodes_capacity(graph_3, 0) # src is 0 +targets_3 = [1, 5, 7] # saving 1,5,7 G3 = create_st_graph(graph_3, targets_3) -reduction_G3 = graph_flow_reduction(G3,0) -N_3_groups = min_cut_N_groups(reduction_G3,0) -matrix_3 = calculate_vaccine_matrix(layers_3,N_3_groups) +reduction_G3 = graph_flow_reduction(G3, 0) +N_3_groups = min_cut_N_groups(reduction_G3, 0) +matrix_3 = calculate_vaccine_matrix(layers_3, N_3_groups) integer_matrix_3 = matrix_to_integers_values(matrix_3) min_budget_3 = min_budget_calculation(integer_matrix_3) -#Test 4 -#graph building -graph_4= graphs["Dirlay_Graph-4"] -layers_4 = adjust_nodes_capacity(graph_4,0) #src is 0 -targets_4 = [4,5,6,8] #saving 4,5,6,8 +# Test 4 +# graph building +graph_4 = graphs["Dirlay_Graph-4"] +layers_4 = adjust_nodes_capacity(graph_4, 0) # src is 0 +targets_4 = [4, 5, 6, 8] # saving 4,5,6,8 G4 = create_st_graph(graph_4, targets_4) -reduction_G4 = graph_flow_reduction(G4,0) -N_4_groups = min_cut_N_groups(reduction_G4,0) -matrix_4 = calculate_vaccine_matrix(layers_4,N_4_groups) +reduction_G4 = graph_flow_reduction(G4, 0) +N_4_groups = min_cut_N_groups(reduction_G4, 0) +matrix_4 = calculate_vaccine_matrix(layers_4, N_4_groups) integer_matrix_4 = matrix_to_integers_values(matrix_4) min_budget_4 = min_budget_calculation(integer_matrix_4) - -def test_adjust_nodes_capacity(): +def test_adjust_nodes_capacity(): """ This test checks if the node capacity and layers are correct. """ - #Test 1 - #layers check + # Tolerance for floating point comparisons, this is simply to not inturrpt in the after point shenanings. + tolerance = 1e-6 + + # Test 1 layers_1_check = [[0], [1, 2], [3], [4], [5]] - assert set(adjust_nodes_capacity(graph_1,0)) == layers_1_check - #capacity check - assert set(graph_1.nodes[1]['capacity']) == 1/(1*1/2) - assert set(graph_1.nodes[2]['capacity']) == 1/(1*1/2) - assert set(graph_1.nodes[3]['capacity']) == 1/(1*(1/2+1/3)) - assert set(graph_1.nodes[4]['capacity']) == 1/(1*(1/2+1/3+1/4)) - assert set(graph_1.nodes[5]['capacity']) == 1/(1*(1/2+1/3+1/4+1/5)) - - #Test 2 - #layers check - layers_2 = [[0], [1, 2], [4, 3]] - assert set(adjust_nodes_capacity(graph_2,2)) == layers_2 - #capacity check - assert set(graph_2.nodes[1]['capacity']) == 1/(1*1/2) - assert set(graph_2.nodes[2]['capacity']) == 1/(1*1/2) - assert set(graph_2.nodes[3]['capacity']) == 1/(1*(1/2+1/3)) - assert set(graph_2.nodes[4]['capacity']) == 1/(1*(1/2+1/3)) - - #Test 3 - #layers check - layers_3 = [[0], [1, 2, 3], [5, 4], [6, 7]] - assert set(adjust_nodes_capacity(graph_3,0)) == layers_3 - #capacity check - assert set(graph_3.nodes[1]['capacity']) == 1/(1*1/2) - assert set(graph_3.nodes[2]['capacity']) == 1/(1*1/2) - assert set(graph_3.nodes[3]['capacity']) == 1/(1*1/2) - assert set(graph_3.nodes[4]['capacity']) == 1/(1*(1/2+1/3)) - assert set(graph_3.nodes[5]['capacity']) == 1/(1*(1/2+1/3)) - assert set(graph_3.nodes[6]['capacity']) == 1/(1*(1/2+1/3+1/4)) - assert set(graph_3.nodes[7]['capacity']) == 1/(1*(1/2+1/3+1/4)) - - #Test 4 - #layers check - layers_4 = [[0], [1, 2], [3, 4], [5], [6], [8]] - assert set(adjust_nodes_capacity(graph_4,0)) == layers_4 - #capacity check - assert set(graph_4.nodes[1]['capacity']) == 1/(1*1/2) - assert set(graph_4.nodes[2]['capacity']) == 1/(1*1/2) - assert set(graph_4.nodes[3]['capacity']) == 1/(1*(1/2+1/3)) - assert set(graph_4.nodes[4]['capacity']) == 1/(1*(1/2+1/3)) - assert set(graph_4.nodes[5]['capacity']) == 1/(1*(1/2+1/3+1/4)) - assert set(graph_4.nodes[6]['capacity']) == 1/(1*(1/2+1/3+1/4+1/5)) - assert set(graph_4.nodes[8]['capacity']) == 1/(1*(1/2+1/3+1/4+1/5+1/6)) + assert adjust_nodes_capacity(graph_1, 0) == layers_1_check + assert math.isclose(graph_1.nodes[1]['capacity'], 1 / (1 * (1/1 + 1/2 + 1/3 + 1/4)), rel_tol=tolerance) + assert math.isclose(graph_1.nodes[2]['capacity'], 1 / (1 * (1/1 + 1/2 + 1/3 + 1/4)), rel_tol=tolerance) + assert math.isclose(graph_1.nodes[3]['capacity'], 1 / (2 * (1/1 + 1/2 + 1/3 + 1/4)), rel_tol=tolerance) + assert math.isclose(graph_1.nodes[4]['capacity'], 1 / (3 * (1/1 + 1/2 + 1/3 + 1/4)), rel_tol=tolerance) + assert math.isclose(graph_1.nodes[5]['capacity'], 1 / (4 * (1/1 + 1/2 + 1/3 + 1/4)), rel_tol=tolerance) + + # Test 2 + layers_2_check = [[0], [1, 2], [4, 3]] + assert adjust_nodes_capacity(graph_2, 0) == layers_2_check + assert math.isclose(graph_2.nodes[1]['capacity'], 1 / (1 * (1/1 + 1/2)), rel_tol=tolerance) + assert math.isclose(graph_2.nodes[2]['capacity'], 1 / (1 * (1/1 + 1/2)), rel_tol=tolerance) + assert math.isclose(graph_2.nodes[3]['capacity'], 1 / (2 * (1/1 + 1/2)), rel_tol=tolerance) + assert math.isclose(graph_2.nodes[4]['capacity'], 1 / (2 * (1/1 + 1/2)), rel_tol=tolerance) + + # Test 3 + layers_3_check = [[0], [1, 2, 3], [5, 4], [6, 7]] + assert adjust_nodes_capacity(graph_3, 0) == layers_3_check + assert math.isclose(graph_3.nodes[1]['capacity'], 1 / (1 * (1/1 + 1/2 + 1/3)), rel_tol=tolerance) + assert math.isclose(graph_3.nodes[2]['capacity'], 1 / (1 * (1/1 + 1/2 + 1/3)), rel_tol=tolerance) + assert math.isclose(graph_3.nodes[3]['capacity'], 1 / (1 * (1/1 + 1/2 + 1/3)), rel_tol=tolerance) + assert math.isclose(graph_3.nodes[4]['capacity'], 1 / (2 * (1/1 + 1/2 + 1/3)), rel_tol=tolerance) + assert math.isclose(graph_3.nodes[5]['capacity'], 1 / (2 * (1/1 + 1/2 + 1/3)), rel_tol=tolerance) + assert math.isclose(graph_3.nodes[6]['capacity'], 1 / (3 * (1/1 + 1/2 + 1/3)), rel_tol=tolerance) + assert math.isclose(graph_3.nodes[7]['capacity'], 1 / (3 * (1/1 + 1/2 + 1/3)), rel_tol=tolerance) + + # Test 4 + layers_4_check = [[0], [1, 2], [3, 4], [5], [6], [8]] + assert adjust_nodes_capacity(graph_4, 0) == layers_4_check + assert math.isclose(graph_4.nodes[1]['capacity'], 1 / (1 * (1/1 + 1/2 + 1/3 + 1/4 + 1/5)), rel_tol=tolerance) + assert math.isclose(graph_4.nodes[2]['capacity'], 1 / (1 * (1/1 + 1/2 + 1/3 + 1/4 + 1/5)), rel_tol=tolerance) + assert math.isclose(graph_4.nodes[3]['capacity'], 1 / (2 * (1/1 + 1/2 + 1/3 + 1/4 + 1/5)), rel_tol=tolerance) + assert math.isclose(graph_4.nodes[4]['capacity'], 1 / (2 * (1/1 + 1/2 + 1/3 + 1/4 + 1/5)), rel_tol=tolerance) + assert math.isclose(graph_4.nodes[5]['capacity'], 1 / (3 * (1/1 + 1/2 + 1/3 + 1/4 + 1/5)), rel_tol=tolerance) + assert math.isclose(graph_4.nodes[6]['capacity'], 1 / (4 * (1/1 + 1/2 + 1/3 + 1/4 + 1/5)), rel_tol=tolerance) + assert math.isclose(graph_4.nodes[8]['capacity'], 1 / (5 * (1/1 + 1/2 + 1/3 + 1/4 + 1/5)), rel_tol=tolerance) + + def test_create_st_graph() : """ - creates the s-t graph and connects the nodes we want to save + Creates the s-t graph and connects the nodes we want to save. """ - #Test1 - #edges check - assert "t" in graph_1 - assert set(graph_1.has_edge(1,"t")) == True - assert set(graph_1.has_edge(2,"t")) == True - assert set(graph_1.has_edge(3,"t")) == True - - #Test2 - #edges check - assert "t" in graph_2 - assert set(graph_2.has_edge(2,"t")) == True - assert set(graph_2.has_edge(4,"t")) == True - - #Test3 - #edges check - assert "t" in graph_3 - assert set(graph_3.has_edge(1,"t")) == True - assert set(graph_3.has_edge(5,"t")) == True - assert set(graph_3.has_edge(7,"t")) == True - - #Test4 - #edges check - assert "t" in graph_4 - assert set(graph_4.has_edge(4,"t")) == True - assert set(graph_4.has_edge(5,"t")) == True - assert set(graph_4.has_edge(6,"t")) == True - assert set(graph_4.has_edge(8,"t")) == True + # Test1 + # edges check + assert "t" in G1 + assert G1.has_edge(1, "t") + assert G1.has_edge(2, "t") + assert G1.has_edge(3, "t") + + # Test2 + # edges check + assert "t" in G2 + assert G2.has_edge(2, "t") + assert G2.has_edge(4, "t") + + # Test3 + # edges check + assert "t" in G3 + assert G3.has_edge(1, "t") + assert G3.has_edge(5, "t") + assert G3.has_edge(7, "t") + + # Test4 + # edges check + assert "t" in G4 + assert G4.has_edge(4, "t") + assert G4.has_edge(5, "t") + assert G4.has_edge(6, "t") + assert G4.has_edge(8, "t") def test_min_cut_N_groups(): """ - This test validates the nodes taken from the min-cut create the right groups (N_1...N_l) + This test validates the nodes taken from the min-cut create the right groups (N_1...N_l). """ - #Test 1 - #checking equality + # Test 1 + # checking equality N1_groups_check = [{1, 2}, set(), set(), set()] - assert set(min_cut_N_groups(reduction_G1,0)) == N1_groups_check + assert min_cut_N_groups(reduction_G1, 0) == N1_groups_check - #Test 2 - #checking equality - N2_groups_check = [{2},{4}] - assert set(min_cut_N_groups(reduction_G2,0)) == N2_groups_check + # Test 2 + # checking equality + N2_groups_check = [{2}, {4}] + assert min_cut_N_groups(reduction_G2, 0) == N2_groups_check - #Test 3 - #checking equality - N3_groups_check = [{1},set(),{7}] - assert set(min_cut_N_groups(reduction_G3,0)) == N3_groups_check + # Test 3 + # checking equality + N3_groups_check = [{1}, set(), {7}] + assert min_cut_N_groups(reduction_G3, 0) == N3_groups_check - #Test 3 - #checking equality + # Test 4 + # checking equality N4_groups_check = [set(), {4}, {5}, set(), set()] - assert set(min_cut_N_groups(reduction_G4,0)) == N4_groups_check + assert min_cut_N_groups(reduction_G4, 0) == N4_groups_check def test_calculate_vaccine_matrix(): """ - There is an important check to do here : A matrix is valid if : For any col j, the col sum is exactly |Nj|. - This test checks that the calculations made to create the triangular matrix from the min-cut nodes is correct. + This test checks that the calculations made to create the triangular matrix from the min-cut nodes are correct. + A matrix is valid if, for any column j, the column sum is exactly |Nj|. """ - #Test 1 - #checking equality - matrix_1 = [[2,0,0,0] - [0,0,0,0] - [0,0,0,0] - [0,0,0,0]] - assert set(calculate_vaccine_matrix(layers_1,N_1_groups)) == matrix_1 - - #Test 2 - #checking equality - matrix_2 = [[1.5, 0.5] - [1, 0.5] - [0, 0.5]] - assert set(calculate_vaccine_matrix(layers_2,N_2_groups)) == matrix_2 - - #Test 3 - #checking equality - matrix_3 = [[1+1/3, 1/3, 1/3] - [ 1, 0, 1/3] - [ 0, 0, 1/3]] - assert set(calculate_vaccine_matrix(layers_3,N_3_groups)) == matrix_3 - - #Test 4 - #checking equality - matrix_4 = [[ 0, 0.5, 1/3, 0, 0] - [ 0, 0.5, 1/3, 0, 0] - [ 0, 0, 1/3, 0, 0] - [ 0, 0, 0, 0, 0] - [ 0, 0, 0, 0, 0]] - assert set(calculate_vaccine_matrix(layers_4,N_4_groups)) == matrix_4 + # Test 1 + # checking equality + matrix_1_check = np.array([[2, 0, 0, 0], + [0, 0, 0, 0], + [0, 0, 0, 0], + [0, 0, 0, 0]]) + np.testing.assert_array_equal(calculate_vaccine_matrix(layers_1, N_1_groups), matrix_1_check) + + # Test 2 + # checking equality + matrix_2_check = np.array([[1.5, 0.5], + [1, 0.5], + [0, 0.5]]) + np.testing.assert_array_equal(calculate_vaccine_matrix(layers_2, N_2_groups), matrix_2_check) + + # Test 3 + # checking equality + matrix_3_check = np.array([[4/3, 1/3, 1/3], + [1, 0, 1/3], + [0, 0, 1/3]]) + np.testing.assert_array_equal(calculate_vaccine_matrix(layers_3, N_3_groups), matrix_3_check) + + # Test 4 + # checking equality + matrix_4_check = np.array([[0, 0.5, 1/3, 0, 0], + [0, 0.5, 1/3, 0, 0], + [0, 0, 1/3, 0, 0], + [0, 0, 0, 0, 0], + [0, 0, 0, 0, 0]]) + np.testing.assert_array_equal(calculate_vaccine_matrix(layers_4, N_4_groups), matrix_4_check) def test_matrix_to_integers_values(): - pass #in case the matrix is not ingeral from previous step, we need to make it one (so vaccianation is correct- cant vaccinate fractional node) + """ + Test the matrix to integers values function. + """ + matrix = np.matrix([[1.5, 0.5], [1, 0.5], [0, 0.5]]) + expected_matrix = np.matrix([[2, 0], [1, 1], [0, 1]]) + np.testing.assert_array_equal(matrix_to_integers_values(matrix), expected_matrix) def test_min_budget_calculation(): """ - This test validates that the minbudget is accurate + This test validates that the minimum budget is accurate. """ - -#legacy: - # graph_1 = graphs["Dirlay_Graph-4"] - # layers = adjust_nodes_capacity(graph_1,0) - # targets = [4,5,6,8] - # G1 = create_st_graph(graph_1, targets) - # min_cut_nodes = graph_flow_reduction(G1,0) - # min_cut_nodes = {int(item.split('_')[0]) for item in min_cut_nodes} - # calculate_vaccine_matrix(layers,min_cut_nodes) \ No newline at end of file + matrix = np.matrix([[2, 0], [1, 1], [0, 1]]) + assert min_budget_calculation(matrix) == 2 From 525d52f4496f8cec0e0f86a6b82f42f45b64226a Mon Sep 17 00:00:00 2001 From: Almog-David Date: Sun, 2 Jun 2024 14:36:56 +0300 Subject: [PATCH 05/93] fixed heuristic functions + added print that give the user more information --- .../Firefighter_Problem.py | 37 +++++++++---------- .../firefighter_problem/Utils.py | 30 ++++++++++----- .../test_non_spreading_minbudget.py | 5 +-- 3 files changed, 38 insertions(+), 34 deletions(-) diff --git a/networkz/algorithms/approximation/firefighter_problem/Firefighter_Problem.py b/networkz/algorithms/approximation/firefighter_problem/Firefighter_Problem.py index 96dd703..94d60b1 100644 --- a/networkz/algorithms/approximation/firefighter_problem/Firefighter_Problem.py +++ b/networkz/algorithms/approximation/firefighter_problem/Firefighter_Problem.py @@ -5,8 +5,8 @@ import random # TODO: fix this shit, when we run tests needs src.Utils, and when we run this, we need Utils only.. -from networkz.algorithms.approximation.firefighter_problem.Utils import * -#from Utils import * +#from networkz.algorithms.approximation.firefighter_problem.Utils import * +from Utils import * def spreading_maxsave(Graph:nx.DiGraph, budget:int, source:int, targets:list, flag=None) -> list: """ @@ -219,10 +219,13 @@ def non_spreading_dirlaynet_minbudget(Graph:nx.DiGraph, source:int, targets:list return min_budget def heuristic_maxsave(Graph:nx.DiGraph, budget:int, source:int, targets:list, spreading:bool, flag=None) -> list: + """ This heuristic approach is based on the local search problem. + We will select the best neighbor that save the most nodes from targets""" if budget < 1: raise ValueError("Error: The budget must be at least 1") exit() validate_parameters(Graph,source,targets) + clean_graph(Graph) infected_nodes = [] vaccinated_nodes = [] vaccination_strategy = [] @@ -244,35 +247,29 @@ def heuristic_maxsave(Graph:nx.DiGraph, budget:int, source:int, targets:list, sp if flag is not None: # only for min budget - a stoping condition in case we saved all nodes or one of the target nodes in infected if len(targets)==0 or any(node in infected_nodes for node in targets): - clean_graph(Graph) return vaccination_strategy time_step = time_step + 1 - clean_graph(Graph) return vaccination_strategy def heuristic_minbudget(Graph:nx.DiGraph, source:int, targets:list, spreading:bool)-> int: validate_parameters(Graph,source,targets) original_targets = list(targets) - direct_vaccinations = calculate_gamma(Graph, source, targets)[1] min_value = 1 max_value = len(targets) middle = math.floor((min_value + max_value) / 2) + saved_everyone = True while min_value < max_value: - strategy = heuristic_maxsave(Graph, middle, source, targets, spreading, True) - nodes_saved = set() + heuristic_maxsave(Graph, middle, source, targets, spreading, True) - for option in strategy: - # works good for spreading. for non-spreading, we need to find a solution for the use of direct_vaccination - list_of_nodes = direct_vaccinations.get(option) - nodes_saved.update(list_of_nodes) - - common_elements = set(nodes_saved) & set(original_targets) - print(common_elements) - - if len(common_elements) == len(original_targets): + for node in original_targets: + if Graph.nodes[node]['status'] == 'infected': + saved_everyone = False + break + + if saved_everyone: max_value = middle else: min_value = middle + 1 @@ -283,11 +280,11 @@ def heuristic_minbudget(Graph:nx.DiGraph, source:int, targets:list, spreading:bo return middle if __name__ == "__main__": - import doctest - doctest.testmod(verbose=True) + #import doctest + #doctest.testmod(verbose=True) G3 = nx.DiGraph() G3.add_nodes_from([0,1,2,3,4,5,6,7,8], status="target") G3.add_edges_from([(0,2),(0,4),(0,5),(2,1),(2,3),(4,1),(4,6),(5,3),(5,6),(5,7),(6,7),(6,8),(7,8)]) - print(heuristic_minbudget(G3,0,[2,6,1,8], False)) - print(spreading_minbudget(G3,0,[2,6,1,8])) + print(heuristic_minbudget(G3,0,[2,6,1,8], True)) + #print(spreading_minbudget(G3,0,[2,6,1,8])) diff --git a/networkz/algorithms/approximation/firefighter_problem/Utils.py b/networkz/algorithms/approximation/firefighter_problem/Utils.py index b37aff0..b4f16ff 100644 --- a/networkz/algorithms/approximation/firefighter_problem/Utils.py +++ b/networkz/algorithms/approximation/firefighter_problem/Utils.py @@ -140,6 +140,7 @@ def find_best_direct_vaccination(graph:nx.DiGraph, direct_vaccinations:dict, cur if(graph.nodes[option[0]]['status'] == 'target'): nodes_list = direct_vaccinations.get(option) common_elements = set(nodes_list) & set(targets) + print("the direct vaccination " + str(option) + " is saving the nodes " + str(common_elements)) if len(common_elements) > max_number: best_vaccination = option nodes_saved = common_elements @@ -148,8 +149,8 @@ def find_best_direct_vaccination(graph:nx.DiGraph, direct_vaccinations:dict, cur if nodes_saved is not None: targets[:] = [element for element in targets if element not in nodes_saved] - #if best_vaccination != (): - # print("The best direct vaccination is: " + str(best_vaccination) + " and it's saves nodes: " + str(nodes_saved)) + if best_vaccination != (): + print("The best direct vaccination is: " + str(best_vaccination) + " and it's saves nodes: " + str(nodes_saved)) return best_vaccination def spread_virus(graph:nx.DiGraph, infected_nodes:list)->bool: @@ -169,10 +170,11 @@ def spread_virus(graph:nx.DiGraph, infected_nodes:list)->bool: if graph.nodes[neighbor]['status'] == 'target': graph.nodes[neighbor]['status'] = 'infected' new_infected_nodes.append(neighbor) + print("node " + f'{neighbor}' + " has been infected from node " + f'{node}') infected_nodes.clear() for node in new_infected_nodes: infected_nodes.append(node) - #display_graph(graph) + display_graph(graph) return bool(infected_nodes) def spread_vaccination(graph:nx.DiGraph, vaccinated_nodes:list)->None: @@ -189,10 +191,11 @@ def spread_vaccination(graph:nx.DiGraph, vaccinated_nodes:list)->None: if graph.nodes[neighbor]['status'] == 'target': graph.nodes[neighbor]['status'] = 'vaccinated' new_vaccinated_nodes.append(neighbor) + print("node " + f'{neighbor}' + " has been vaccinated from node " + f'{node}') vaccinated_nodes.clear() for node in new_vaccinated_nodes: vaccinated_nodes.append(node) - #display_graph(graph) + display_graph(graph) return def vaccinate_node(graph:nx.DiGraph, node:int)->None: @@ -204,7 +207,8 @@ def vaccinate_node(graph:nx.DiGraph, node:int)->None: - node (int): Node to be vaccinated. """ graph.nodes[node]['status'] = 'directly vaccinated' - #display_graph(graph) + print("node " + f'{node}' + " has been directly vaccinated") + display_graph(graph) return def clean_graph(graph:nx.DiGraph)->None: @@ -381,10 +385,16 @@ def find_best_neighbor(graph:nx.DiGraph, infected_nodes:list, targets:list)->int for node in optional_nodes: if graph.nodes[node]['status'] == 'target': - nodes_list = list(graph.neighbors(node)) + # for each node that is target, we will add only his nighbors that are target as well + neighbors_list = list(graph.neighbors(node)) + target_neighbors = set() + for neighbor in neighbors_list: + if graph.nodes[neighbor]['status'] == 'target': + target_neighbors.add(neighbor) if node in targets: - nodes_list.append(node) - common_elements = set(nodes_list) & set(targets) + target_neighbors.add(node) + common_elements = set(target_neighbors) & set(targets) + print("node " + f'{node}' + " is saving the nodes " + str(common_elements)) if len(common_elements) > max_number: best_node = node nodes_saved = common_elements @@ -393,8 +403,8 @@ def find_best_neighbor(graph:nx.DiGraph, infected_nodes:list, targets:list)->int if nodes_saved is not None: targets[:] = [element for element in targets if element not in nodes_saved] - #if best_node != None: - # print("The best node is: " + node + " and it's saves nodes: " + str(nodes_saved)) + if best_node != None: + print("The best node is: " + f'{best_node}' + " and it's saves nodes: " + str(nodes_saved)) return best_node "Usefull Utils:" diff --git a/networkz/algorithms/approximation/tests/test_firefighter_problem/test_non_spreading_minbudget.py b/networkz/algorithms/approximation/tests/test_firefighter_problem/test_non_spreading_minbudget.py index 382f679..09eab2c 100644 --- a/networkz/algorithms/approximation/tests/test_firefighter_problem/test_non_spreading_minbudget.py +++ b/networkz/algorithms/approximation/tests/test_firefighter_problem/test_non_spreading_minbudget.py @@ -96,7 +96,4 @@ def test_save_subgroup_vertices(): assert 1 < non_spreading_minbudget(graphs["RegularGraph_Graph-4"], 0, [2,3,5,7]) #answer is 2 assert 4 > non_spreading_minbudget(graphs["RegularGraph_Graph-6"], 1, [0,3,5,6,8,9]) #answer is 1 assert 2 == non_spreading_minbudget(graphs["RegularGraph_Graph-7"], 1, [0,2,5,6]) #answer is 2 - assert 3 == non_spreading_minbudget(graphs["RegularGraph_Graph-8"], 0, [1,3,4,5,6,9,10,12,14]) #answer is 3 - -def test_correct_st_cut(): - pass \ No newline at end of file + assert 3 == non_spreading_minbudget(graphs["RegularGraph_Graph-8"], 0, [1,3,4,5,6,9,10,12,14]) #answer is 3 \ No newline at end of file From cabc9ed45c6dab217066722620a29e23c72248cc Mon Sep 17 00:00:00 2001 From: Almog-David Date: Sun, 2 Jun 2024 14:55:01 +0300 Subject: [PATCH 06/93] add a lot of prints for the user to understand what happening --- .../firefighter_problem/Firefighter_Problem.py | 11 +++++++++++ 1 file changed, 11 insertions(+) diff --git a/networkz/algorithms/approximation/firefighter_problem/Firefighter_Problem.py b/networkz/algorithms/approximation/firefighter_problem/Firefighter_Problem.py index 94d60b1..4da998d 100644 --- a/networkz/algorithms/approximation/firefighter_problem/Firefighter_Problem.py +++ b/networkz/algorithms/approximation/firefighter_problem/Firefighter_Problem.py @@ -51,6 +51,8 @@ def spreading_maxsave(Graph:nx.DiGraph, budget:int, source:int, targets:list, fl raise ValueError("Error: The budget must be at least 1") exit() validate_parameters(Graph,source,targets) + print("starting the spreading maxsave function with a source node " + f'{source}' + ", the budget is " + f'{budget}' +", and the targets are: " + str(targets)) + infected_nodes = [] vaccinated_nodes = [] vaccination_strategy = [] @@ -122,6 +124,8 @@ def spreading_minbudget(Graph:nx.DiGraph, source:int, targets:list)-> int: 3 """ validate_parameters(Graph,source,targets) + print("starting the spreading minbudget function with a source node " + f'{source}' + ", and the targets are: " + str(targets)) + original_targets = list(targets) direct_vaccinations = calculate_gamma(Graph, source, targets)[1] min_value = 1 @@ -139,8 +143,10 @@ def spreading_minbudget(Graph:nx.DiGraph, source:int, targets:list)-> int: common_elements = set(nodes_saved) & set(original_targets) if len(common_elements) == len(original_targets): + print("the current budget " + f'{middle}' + " has saved all the targets!") max_value = middle else: + print("the current budget " + f'{middle}' + " didn't saved all the targets!") min_value = middle + 1 middle = math.floor((min_value + max_value) / 2) @@ -225,6 +231,8 @@ def heuristic_maxsave(Graph:nx.DiGraph, budget:int, source:int, targets:list, sp raise ValueError("Error: The budget must be at least 1") exit() validate_parameters(Graph,source,targets) + print("starting the heuristic maxsave function with a source node " + f'{source}' + ", the budget is " + f'{budget}' +", the targets are: " + str(targets) + ", and the function is spreading=" + str(spreading)) + clean_graph(Graph) infected_nodes = [] vaccinated_nodes = [] @@ -255,6 +263,7 @@ def heuristic_maxsave(Graph:nx.DiGraph, budget:int, source:int, targets:list, sp def heuristic_minbudget(Graph:nx.DiGraph, source:int, targets:list, spreading:bool)-> int: validate_parameters(Graph,source,targets) + print("starting the heuristic minbudget function with a source node " + f'{source}' + ", the targets are: " + str(targets) + ", and the function is spreading=" + str(spreading)) original_targets = list(targets) min_value = 1 max_value = len(targets) @@ -270,8 +279,10 @@ def heuristic_minbudget(Graph:nx.DiGraph, source:int, targets:list, spreading:bo break if saved_everyone: + print("the current budget " + f'{middle}' + " has saved all the targets!") max_value = middle else: + print("the current budget " + f'{middle}' + " didn't saved all the targets!") min_value = middle + 1 middle = math.floor((min_value + max_value) / 2) From 9b5fba365a83046e39e044f86d65c52dd5c6dbe0 Mon Sep 17 00:00:00 2001 From: 20shaked20 Date: Sun, 2 Jun 2024 14:58:52 +0300 Subject: [PATCH 07/93] Fixed how the Mincut returns the nodes and groups them into N groups and added corrections to its corresponding tests --- .../firefighter_problem/Utils.py | 47 ++++++++++++------- .../test_non_spreading_dirlaynet_minbudget.py | 31 +++++++----- 2 files changed, 48 insertions(+), 30 deletions(-) diff --git a/networkz/algorithms/approximation/firefighter_problem/Utils.py b/networkz/algorithms/approximation/firefighter_problem/Utils.py index 8daf863..6043131 100644 --- a/networkz/algorithms/approximation/firefighter_problem/Utils.py +++ b/networkz/algorithms/approximation/firefighter_problem/Utils.py @@ -220,7 +220,7 @@ def clean_graph(graph:nx.DiGraph)->None: "Non-Spreading:" -def adjust_nodes_capacity(graph: nx.DiGraph, source: int) -> list: +def adjust_nodes_capacity(graph:nx.DiGraph, source:int)->list: """ Adjust the capacity of nodes based on the layer they belong to. The capacity is based on the formula in the article at the DirLayNet algorithm section. @@ -232,18 +232,14 @@ def adjust_nodes_capacity(graph: nx.DiGraph, source: int) -> list: Returns: - layers (list): List of nodes grouped by layers. """ - layers = list(nx.bfs_layers(graph, source)) - total_layers = len(layers) - - # Compute the harmonic sum for the total number of layers - harmonic_sum = sum(1 / i for i in range(1, total_layers)) - - for index, layer in enumerate(layers): - if index == 0: - continue # Skip the source layer - for node in layer: - graph.nodes[node]['capacity'] = 1 / (index * harmonic_sum) - print("CAPACITY ->>>>>>>>>>>>>>>>>", 1 / (index * harmonic_sum)) + layers = (list(nx.bfs_layers(graph,source))) + harmonic_sum = 0.0 + for i in range(1,len(layers)): + harmonic_sum = harmonic_sum + 1/i + for index in range(1,len(layers)): + for node in layers[index]: + graph.nodes[node]['capacity'] = 1/(index*harmonic_sum) + # print("Layers: ", layers) return layers def create_st_graph(graph:nx.DiGraph, targets:list)->nx.DiGraph: @@ -288,20 +284,35 @@ def graph_flow_reduction(graph:nx.DiGraph, source:int)->nx.DiGraph: display_graph(H) return H -def min_cut_N_groups(graph:nx.DiGraph, source:int) -> list: +def min_cut_N_groups(graph: nx.DiGraph, source: int, layers: list) -> dict: """ Find the minimum cut and group nodes accordingly. Parameters: - graph (nx.DiGraph): Graph after flow reduction. - source (int): Source node. + - layers (list): List of lists, where each sublist contains nodes belonging to that layer. Returns: - - n_groups (list): List of nodes in the minimum cut. + - groups (dict): Dictionary with layers as keys and lists of nodes in the minimum cut as values. """ - flow_graph = algo.minimum_st_node_cut(graph,f'{source}_out','t_in') #run algo on the graph after reduction and get the min-cut - n_groups = {int(item.split('_')[0]) for item in flow_graph} # split it to the groups accordingly. - return n_groups + # Compute the minimum cut + flow_graph = algo.minimum_st_node_cut(graph, f'{source}_out', 't_in') + + # Initialize the groups dictionary with empty lists for each layer index + groups = {i+1: [] for i in range(len(layers)-1)} + + # Populate the groups dictionary + for item in flow_graph: + node = item.split('_') + node = int(node) + for i, layer_nodes in enumerate(layers): + if node in layer_nodes: + groups[i].append(node) + break + + return groups + def calculate_vaccine_matrix(layers:list, min_cut_nodes:list)->np.matrix: """ diff --git a/networkz/algorithms/approximation/tests/test_firefighter_problem/test_non_spreading_dirlaynet_minbudget.py b/networkz/algorithms/approximation/tests/test_firefighter_problem/test_non_spreading_dirlaynet_minbudget.py index 029a639..5b9a70a 100644 --- a/networkz/algorithms/approximation/tests/test_firefighter_problem/test_non_spreading_dirlaynet_minbudget.py +++ b/networkz/algorithms/approximation/tests/test_firefighter_problem/test_non_spreading_dirlaynet_minbudget.py @@ -87,7 +87,7 @@ def test_source_is_target(): targets_1 = [1, 2, 3] # saving 1,2,3 G1 = create_st_graph(graph_1, targets_1) reduction_G1 = graph_flow_reduction(G1, 0) -N_1_groups = min_cut_N_groups(reduction_G1, 0) +N_1_groups = min_cut_N_groups(reduction_G1, 0,layers_1) matrix_1 = calculate_vaccine_matrix(layers_1, N_1_groups) integer_matrix_1 = matrix_to_integers_values(matrix_1) min_budget_1 = min_budget_calculation(integer_matrix_1) @@ -99,7 +99,7 @@ def test_source_is_target(): targets_2 = [2, 4] # saving 2,4 G2 = create_st_graph(graph_2, targets_2) reduction_G2 = graph_flow_reduction(G2, 0) -N_2_groups = min_cut_N_groups(reduction_G2, 0) +N_2_groups = min_cut_N_groups(reduction_G2, 0, layers_2) matrix_2 = calculate_vaccine_matrix(layers_2, N_2_groups) integer_matrix_2 = matrix_to_integers_values(matrix_2) min_budget_2 = min_budget_calculation(integer_matrix_2) @@ -111,7 +111,7 @@ def test_source_is_target(): targets_3 = [1, 5, 7] # saving 1,5,7 G3 = create_st_graph(graph_3, targets_3) reduction_G3 = graph_flow_reduction(G3, 0) -N_3_groups = min_cut_N_groups(reduction_G3, 0) +N_3_groups = min_cut_N_groups(reduction_G3, 0, layers_3) matrix_3 = calculate_vaccine_matrix(layers_3, N_3_groups) integer_matrix_3 = matrix_to_integers_values(matrix_3) min_budget_3 = min_budget_calculation(integer_matrix_3) @@ -123,7 +123,7 @@ def test_source_is_target(): targets_4 = [4, 5, 6, 8] # saving 4,5,6,8 G4 = create_st_graph(graph_4, targets_4) reduction_G4 = graph_flow_reduction(G4, 0) -N_4_groups = min_cut_N_groups(reduction_G4, 0) +N_4_groups = min_cut_N_groups(reduction_G4, 0, layers_4) matrix_4 = calculate_vaccine_matrix(layers_4, N_4_groups) integer_matrix_4 = matrix_to_integers_values(matrix_4) min_budget_4 = min_budget_calculation(integer_matrix_4) @@ -212,25 +212,32 @@ def test_min_cut_N_groups(): """ This test validates the nodes taken from the min-cut create the right groups (N_1...N_l). """ + def sort_dict_values(d): #| this is for sorting purposes , cases where [1,2] or [2,1] it does not really matters as we need to vaccinate them. + return {k: sorted(v) for k, v in d.items()} + # Test 1 # checking equality - N1_groups_check = [{1, 2}, set(), set(), set()] - assert min_cut_N_groups(reduction_G1, 0) == N1_groups_check + N1_groups_check = {1: [1, 2], 2: [], 3: [], 4: []} + result_1 = min_cut_N_groups(reduction_G1, 0, layers_1) + assert sort_dict_values(result_1) == sort_dict_values(N1_groups_check) # Test 2 # checking equality - N2_groups_check = [{2}, {4}] - assert min_cut_N_groups(reduction_G2, 0) == N2_groups_check + N2_groups_check = {1: [2], 2: [4]} + result_2 = min_cut_N_groups(reduction_G2, 0, layers_2) + assert sort_dict_values(result_2) == sort_dict_values(N2_groups_check) # Test 3 # checking equality - N3_groups_check = [{1}, set(), {7}] - assert min_cut_N_groups(reduction_G3, 0) == N3_groups_check + N3_groups_check = {1: [1], 2: [], 3: [7]} + result_3 = min_cut_N_groups(reduction_G3, 0, layers_3) + assert sort_dict_values(result_3) == sort_dict_values(N3_groups_check) # Test 4 # checking equality - N4_groups_check = [set(), {4}, {5}, set(), set()] - assert min_cut_N_groups(reduction_G4, 0) == N4_groups_check + N4_groups_check = {1: [], 2: [4], 3: [5], 4: [], 5: []} + result_4 = min_cut_N_groups(reduction_G4, 0, layers_4) + assert sort_dict_values(result_4) == sort_dict_values(N4_groups_check) def test_calculate_vaccine_matrix(): """ From 415e82166583b5557e7bfb89443d73909745d93e Mon Sep 17 00:00:00 2001 From: 20shaked20 Date: Sun, 2 Jun 2024 15:15:22 +0300 Subject: [PATCH 08/93] Minor fix --- networkz/algorithms/approximation/firefighter_problem/Utils.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/networkz/algorithms/approximation/firefighter_problem/Utils.py b/networkz/algorithms/approximation/firefighter_problem/Utils.py index 6043131..0a892a8 100644 --- a/networkz/algorithms/approximation/firefighter_problem/Utils.py +++ b/networkz/algorithms/approximation/firefighter_problem/Utils.py @@ -304,7 +304,7 @@ def min_cut_N_groups(graph: nx.DiGraph, source: int, layers: list) -> dict: # Populate the groups dictionary for item in flow_graph: - node = item.split('_') + node , suffix = item.split('_') node = int(node) for i, layer_nodes in enumerate(layers): if node in layer_nodes: From 7707972bb4c62bbe4c35fea884ee7e8072e2f05a Mon Sep 17 00:00:00 2001 From: Almog-David Date: Sun, 2 Jun 2024 16:38:50 +0300 Subject: [PATCH 09/93] minimal changes --- .../firefighter_problem/Firefighter_Problem.py | 4 ++-- .../algorithms/approximation/firefighter_problem/Utils.py | 6 +++--- 2 files changed, 5 insertions(+), 5 deletions(-) diff --git a/networkz/algorithms/approximation/firefighter_problem/Firefighter_Problem.py b/networkz/algorithms/approximation/firefighter_problem/Firefighter_Problem.py index 4da998d..716cdfb 100644 --- a/networkz/algorithms/approximation/firefighter_problem/Firefighter_Problem.py +++ b/networkz/algorithms/approximation/firefighter_problem/Firefighter_Problem.py @@ -5,8 +5,8 @@ import random # TODO: fix this shit, when we run tests needs src.Utils, and when we run this, we need Utils only.. -#from networkz.algorithms.approximation.firefighter_problem.Utils import * -from Utils import * +from networkz.algorithms.approximation.firefighter_problem.Utils import * +#from Utils import * def spreading_maxsave(Graph:nx.DiGraph, budget:int, source:int, targets:list, flag=None) -> list: """ diff --git a/networkz/algorithms/approximation/firefighter_problem/Utils.py b/networkz/algorithms/approximation/firefighter_problem/Utils.py index b4f16ff..fcbc6f3 100644 --- a/networkz/algorithms/approximation/firefighter_problem/Utils.py +++ b/networkz/algorithms/approximation/firefighter_problem/Utils.py @@ -174,7 +174,7 @@ def spread_virus(graph:nx.DiGraph, infected_nodes:list)->bool: infected_nodes.clear() for node in new_infected_nodes: infected_nodes.append(node) - display_graph(graph) + #display_graph(graph) return bool(infected_nodes) def spread_vaccination(graph:nx.DiGraph, vaccinated_nodes:list)->None: @@ -195,7 +195,7 @@ def spread_vaccination(graph:nx.DiGraph, vaccinated_nodes:list)->None: vaccinated_nodes.clear() for node in new_vaccinated_nodes: vaccinated_nodes.append(node) - display_graph(graph) + #display_graph(graph) return def vaccinate_node(graph:nx.DiGraph, node:int)->None: @@ -208,7 +208,7 @@ def vaccinate_node(graph:nx.DiGraph, node:int)->None: """ graph.nodes[node]['status'] = 'directly vaccinated' print("node " + f'{node}' + " has been directly vaccinated") - display_graph(graph) + #display_graph(graph) return def clean_graph(graph:nx.DiGraph)->None: From a0e0ca41d0a98ae1b570452b0964f8b153f86759 Mon Sep 17 00:00:00 2001 From: 20shaked20 Date: Sun, 2 Jun 2024 16:39:19 +0300 Subject: [PATCH 10/93] Matrix_to_integers should be ok, also added few tests and minor tweaks to it --- .../firefighter_problem/Utils.py | 131 +++++++++--------- .../test_non_spreading_dirlaynet_minbudget.py | 2 +- 2 files changed, 66 insertions(+), 67 deletions(-) diff --git a/networkz/algorithms/approximation/firefighter_problem/Utils.py b/networkz/algorithms/approximation/firefighter_problem/Utils.py index 0a892a8..db96eac 100644 --- a/networkz/algorithms/approximation/firefighter_problem/Utils.py +++ b/networkz/algorithms/approximation/firefighter_problem/Utils.py @@ -281,7 +281,7 @@ def graph_flow_reduction(graph:nx.DiGraph, source:int)->nx.DiGraph: H.add_edge(in_node, out_node, weight=graph.nodes[node]['capacity']) for edge in graph.edges: H.add_edge(f'{edge[0]}_out', f'{edge[1]}_in', weight=float('inf')) - display_graph(H) + # display_graph(H) return H def min_cut_N_groups(graph: nx.DiGraph, source: int, layers: list) -> dict: @@ -314,99 +314,99 @@ def min_cut_N_groups(graph: nx.DiGraph, source: int, layers: list) -> dict: return groups -def calculate_vaccine_matrix(layers:list, min_cut_nodes:list)->np.matrix: +def calculate_vaccine_matrix(layers:list, min_cut_nodes_grouped:dict)->np.matrix: + "TODO: Fix this to work, its not working for now with tests.." + """ Calculate the vaccine matrix based on the calculation in the article at the DirLayNet algorithm section. Parameters: - layers (list): List of nodes grouped by layers. - - min_cut_nodes (list): List of nodes in the minimum cut. + - min_cut_nodes_group (list): List of nodes in the minimum cut grouped into layers of them. Returns: - matrix (np.matrix): Vaccine matrix. """ - nodes_list = [] # = N_i - #print(layers, min_cut_nodes) - for i in range(1,len(layers)): - common_elements = set(min_cut_nodes) & set(layers[i]) - nodes_list.append(common_elements) - #print(nodes_list) - matrix = np.zeros((len(layers)-1, len(layers)-1)) - for i in range (len(layers)-1): - for j in range(i, len(layers)-1): - matrix[i][j] = ((len(nodes_list[j])/(j+1))) # here we can chose ceil or floor. - #print(matrix) - return matrix - -import numpy as np -import networkx as nx -import numpy as np -import networkx as nx + print("Layers:", layers) + print("Min cut nodes grouped:", min_cut_nodes_grouped) + # Mij ': = Nj j , 1≤i≤j≤l + # Determine the size of the matrix based on the number of groups + matrix_length = max(min_cut_nodes_grouped.keys()) + matrix = np.zeros((matrix_length, matrix_length)) # Use a tuple for the shape + + for j in range(matrix_length): + for i in range(j+1): + N_j = len(min_cut_nodes_grouped[j+1]) + value = N_j / (j + 1) + matrix[j][i] = value + + print("MATRIX IS ->>>>>>>>>", matrix) + return matrix def matrix_to_integers_values(matrix: np.matrix) -> np.matrix: + "TODO: keep testing this, it should be ok, but only for sure after done with the above method" """ - Convert matrix values to integers. - + Convert a matrix with fractional entries to an integral matrix such that + the row and column sums are either the floor or ceiling of the original sums. + Parameters: - - matrix (np.matrix): Input fractional matrix. - + matrix (np.matrix): The input matrix with fractional entries. + Returns: - - np.matrix: Integral matrix satisfying the row-sum and column-sum constraints. + np.matrix: The converted integral matrix. """ - num_rows, num_cols = matrix.shape - floor_matrix = np.floor(matrix) - ceil_matrix = np.ceil(matrix) - integral_matrix = np.copy(floor_matrix) - - # Calculate row sums and column sums for the original matrix - row_sums = np.sum(matrix, axis=1) - col_sums = np.sum(matrix, axis=0) + # dimensions of the matrix + rows, cols = matrix.shape + + # row and column sums + row_sums = np.array(matrix.sum(axis=1)).flatten() + col_sums = np.array(matrix.sum(axis=0)).flatten() - # Calculate required row and column sums for the integral matrix - required_row_sums = np.round(row_sums).astype(int) - required_col_sums = np.round(col_sums).astype(int) + print("Row sums:", row_sums) + print("Column sums:", col_sums) - # Construct flow network G = nx.DiGraph() - # Add source and sink nodes - source = 'source' - sink = 'sink' + # add source and sink nodes + source = 's' + sink = 't' G.add_node(source) G.add_node(sink) - # Add nodes for rows and columns - for i in range(num_rows): - G.add_node(f"row_{i}") - for j in range(num_cols): - G.add_node(f"col_{j}") + # add nodes for rows and columns + row_nodes = ['r{}'.format(i) for i in range(rows)] + col_nodes = ['c{}'.format(j) for j in range(cols)] + G.add_nodes_from(row_nodes) + G.add_nodes_from(col_nodes) - # Add edges from source to row nodes with capacities as required row sums - for i in range(num_rows): - G.add_edge(source, f"row_{i}", capacity=required_row_sums[i]) + # add edges from source to row nodes with capacities as the ceiling of row sums + for i in range(rows): + G.add_edge(source, row_nodes[i], capacity=np.ceil(row_sums[i])) - # Add edges from column nodes to sink with capacities as required column sums - for j in range(num_cols): - G.add_edge(f"col_{j}", sink, capacity=required_col_sums[j]) + # add edges from column nodes to sink with capacities as the ceiling of column sums + for j in range(cols): + G.add_edge(col_nodes[j], sink, capacity=np.ceil(col_sums[j])) - # Add edges between row and column nodes with capacities based on the floor and ceiling values - for i in range(num_rows): - for j in range(num_cols): - capacity = ceil_matrix[i, j] - floor_matrix[i, j] - if capacity > 0: - G.add_edge(f"row_{i}", f"col_{j}", capacity=capacity) + # add edges from row nodes to column nodes with capacity 1 + for i in range(rows): + for j in range(cols): + G.add_edge(row_nodes[i], col_nodes[j], capacity=1) - # Compute the maximum flow + # computes the maximum flow flow_value, flow_dict = nx.maximum_flow(G, source, sink) - # Adjust the integral matrix based on the flow - for i in range(num_rows): - for j in range(num_cols): - if flow_dict.get((f"row_{i}", f"col_{j}"), 0) > 0: - integral_matrix[i, j] += 1 - print("MATRIX!!!!!!______>>>>", integral_matrix) - return integral_matrix + # builds the integral matrix + integral_matrix = np.zeros_like(matrix, dtype=int) + for i in range(rows): + for j in range(cols): + if flow_dict[row_nodes[i]][col_nodes[j]] > 0: + integral_matrix[i, j] = np.ceil(matrix[i, j]) + else: + integral_matrix[i, j] = np.floor(matrix[i, j]) + print("MATRIXXXX->>>>", integral_matrix) + return np.matrix(integral_matrix) + def min_budget_calculation(matrix: np.matrix) -> int: """ @@ -424,7 +424,6 @@ def min_budget_calculation(matrix: np.matrix) -> int: return min_budget - "Heuristic approach:" def find_best_neighbor(graph:nx.DiGraph, infected_nodes:list, targets:list)->int: diff --git a/networkz/algorithms/approximation/tests/test_firefighter_problem/test_non_spreading_dirlaynet_minbudget.py b/networkz/algorithms/approximation/tests/test_firefighter_problem/test_non_spreading_dirlaynet_minbudget.py index 5b9a70a..a7ec00d 100644 --- a/networkz/algorithms/approximation/tests/test_firefighter_problem/test_non_spreading_dirlaynet_minbudget.py +++ b/networkz/algorithms/approximation/tests/test_firefighter_problem/test_non_spreading_dirlaynet_minbudget.py @@ -239,7 +239,7 @@ def sort_dict_values(d): #| this is for sorting purposes , cases where [1,2] or result_4 = min_cut_N_groups(reduction_G4, 0, layers_4) assert sort_dict_values(result_4) == sort_dict_values(N4_groups_check) -def test_calculate_vaccine_matrix(): +def test_calculate_vaccine_matrix(): #TODO: This tets is not working for now, need to fix its imeplemntation! """ This test checks that the calculations made to create the triangular matrix from the min-cut nodes are correct. A matrix is valid if, for any column j, the column sum is exactly |Nj|. From f1219fd108a0efc3bd52f5142677411fb0df3130 Mon Sep 17 00:00:00 2001 From: Almog-David Date: Mon, 3 Jun 2024 12:03:02 +0300 Subject: [PATCH 11/93] move the test to the correct location --- .../tests/test_coalition_formation.py | 168 ------------------ .../test_firefighter_problem/__init__.py | 0 .../test_firefighter_problem}/__init__.py | 0 .../test_non_spreading_dirlaynet_minbudget.py | 0 .../test_non_spreading_minbudget.py | 0 .../test_firefighter_problem/test_parsing.py | 0 .../test_spreading_maxsave.py | 0 .../test_spreading_minbudget.py | 0 .../test_firefighter_problem/test_utils.py | 0 9 files changed, 168 deletions(-) delete mode 100644 networkz/algorithms/approximation/tests/test_coalition_formation.py delete mode 100644 networkz/algorithms/approximation/tests/test_firefighter_problem/__init__.py rename networkz/algorithms/{approximation/tests => tests/test_firefighter_problem}/__init__.py (100%) rename networkz/algorithms/{approximation => }/tests/test_firefighter_problem/test_non_spreading_dirlaynet_minbudget.py (100%) rename networkz/algorithms/{approximation => }/tests/test_firefighter_problem/test_non_spreading_minbudget.py (100%) rename networkz/algorithms/{approximation => }/tests/test_firefighter_problem/test_parsing.py (100%) rename networkz/algorithms/{approximation => }/tests/test_firefighter_problem/test_spreading_maxsave.py (100%) rename networkz/algorithms/{approximation => }/tests/test_firefighter_problem/test_spreading_minbudget.py (100%) rename networkz/algorithms/{approximation => }/tests/test_firefighter_problem/test_utils.py (100%) diff --git a/networkz/algorithms/approximation/tests/test_coalition_formation.py b/networkz/algorithms/approximation/tests/test_coalition_formation.py deleted file mode 100644 index cf39fd1..0000000 --- a/networkz/algorithms/approximation/tests/test_coalition_formation.py +++ /dev/null @@ -1,168 +0,0 @@ -""" -Testing the :mod:`networkx.algorithms.approximation.coalition_formation` module. - -Which is the implementation of the Social Aware Assignment of Passengers in Ridesharing -The social aware assignment problem belongs to the field of coalition formation, which is an important research branch -within multiagent systems. It analyses the outcome that results when a set of agents is partitioned into coalitions. -Actually, Match_And_Merge model is a special case of simple Additively Separable Hedonic Games (ASHGs). - -Which was described in the article: -Levinger C., Hazon N., Azaria A. Social Aware Assignment of Passengers in Ridesharing. - 2022, https://github.com/VictoKu1/ResearchAlgorithmsCourse1/raw/main/Article/2022%2C%20Chaya%20Amos%20Noam%2C%20Socially%20aware%20assignment%20of%20passengers%20in%20ride%20sharing.pdf. - -The match_and_merge algorithm is based on the pseudocode from the article -which is written (as well as the tests) by Victor Kushnir. -""" -import math -import random - -import pytest - -import networkx as nx -from networkz.algorithms.approximation.coalition_formation import match_and_merge - - -def small_chain_graph(): - G = nx.Graph() - list_of_edges = [(1, 2), (2, 3), (3, 4), (4, 5), (4, 6)] - G.add_edges_from(list_of_edges) - return G - - -def clique_graph_of_size_3(): - G = nx.Graph() - list_of_edges = [(1, 2), (2, 3), (3, 1)] - G.add_edges_from(list_of_edges) - return G - - -class Test_coalition_formation: - def test_empty_graph_returns_empty_list(self): - G_empty = nx.Graph() - assert match_and_merge(G_empty, k=0) == [] - - def test_small_chain_graph_with_k_4_returns_correct_partition(self): - G_1 = small_chain_graph() - assert match_and_merge(G_1, k=4) == [[1, 2], [3, 4, 5, 6]] - - def test_small_chain_graph_with_k_3_returns_correct_partition(self): - G_1 = small_chain_graph() - assert match_and_merge(G_1, k=3) == [[1, 2], [3, 4, 6], [5]] - - def test_small_chain_graph_with_k_2_returns_correct_partition(self): - G_1 = small_chain_graph() - assert match_and_merge(G_1, k=2) == [[1, 2], [3, 4], [5], [6]] - - def test_clique_graph_of_size_3_with_k_3_returns_correct_partition(self): - G_clique_3 = clique_graph_of_size_3() - assert match_and_merge(G_clique_3, k=3) == [[1, 2, 3]] - - def test_approximation_ratio(self): - # For each n between 5 and 15, generate a clique graph with n nodes and check for 5= 0.99999 - - def test_clique_graph_with_k_in_range_every_node_in_exactly_one_partition(self): - # Check that every node is in exactly one partition - for n in range(5, 15): - G = nx.complete_graph(n) - for k in range(5, 15): - if k <= n: - P = match_and_merge(G, k) - assert [len([p for p in P if n in p]) == 1 for n in G.nodes()] - - def test_clique_graph_with_k_in_range_number_of_partitions_at_most_ceil_n_2(self): - # Check that the number of partitions is at most ceil(n/2) - for n in range(5, 15): - G = nx.complete_graph(n) - for k in range(5, 15): - if k <= n: - P = match_and_merge(G, k) - assert len(P) <= math.ceil(G.number_of_nodes() / 2) - - def test_k_greater_than_n_raises_error(self): - # Check that it raises an error when k>n - for n in range(5, 15): - G = nx.complete_graph(n) - for k in range(5, 15): - if k > n: - with pytest.raises(nx.NetworkXError): - match_and_merge(G, k) - - def test_random_graph_with_k_in_range_returns_correct_partition(self): - # For each n between 5 and 15 (inclusive), generate a random graph with n nodes and check for 5 1] - G_test = nx.Graph() - G_test.add_nodes_from(sorted((G.nodes()), reverse=True)) - G_test.add_edges_from(sorted((G.edges()), reverse=True)) - assert len(P) == len( - [ - tuple(sorted(p)) - for p in sorted(nx.max_weight_matching(G_test, weight=1)) - ] - ) - - def test_partition_is_a_social_aware_assignment_check_size_of_subset(self): - for n in range(5, 15): - p = 0.5 - G = nx.gnp_random_graph(n, p) - for k in range(5, n): - P = match_and_merge(G, k) - # Check that every partition has at most k nodes - assert [len(p) <= k for p in P] - - def test_partition_is_a_social_aware_assignment_V_P(self): - for n in range(5, 15): - p = 0.5 - G = nx.gnp_random_graph(n, p) - P = match_and_merge(G, 2) - # Check that the value of V_P is maximized - V_P = 0 - for p in P: - for i in p: - for j in p: - if G.has_edge(i, j): - V_P += 1 - assert V_P >= len( - [tuple(sorted(p)) for p in sorted(nx.max_weight_matching(G, weight=1))] - ) - - def test_disconnected_components_weighted_graph_with_k_5_returns_correct_partition( - self, - ): - # Check that the partition of graph G is correct for a disconnected graph - G = small_chain_graph() - G.add_edges_from([(7, 8), (8, 9), (9, 7)]) - # Add random weights to the edges - for u, v in G.edges(): - G[u][v]["weight"] = random.randint(1, 205) - assert match_and_merge(G, k=5) == [[1, 2], [3, 4, 5, 6], [7, 8, 9]] diff --git a/networkz/algorithms/approximation/tests/test_firefighter_problem/__init__.py b/networkz/algorithms/approximation/tests/test_firefighter_problem/__init__.py deleted file mode 100644 index e69de29..0000000 diff --git a/networkz/algorithms/approximation/tests/__init__.py b/networkz/algorithms/tests/test_firefighter_problem/__init__.py similarity index 100% rename from networkz/algorithms/approximation/tests/__init__.py rename to networkz/algorithms/tests/test_firefighter_problem/__init__.py diff --git a/networkz/algorithms/approximation/tests/test_firefighter_problem/test_non_spreading_dirlaynet_minbudget.py b/networkz/algorithms/tests/test_firefighter_problem/test_non_spreading_dirlaynet_minbudget.py similarity index 100% rename from networkz/algorithms/approximation/tests/test_firefighter_problem/test_non_spreading_dirlaynet_minbudget.py rename to networkz/algorithms/tests/test_firefighter_problem/test_non_spreading_dirlaynet_minbudget.py diff --git a/networkz/algorithms/approximation/tests/test_firefighter_problem/test_non_spreading_minbudget.py b/networkz/algorithms/tests/test_firefighter_problem/test_non_spreading_minbudget.py similarity index 100% rename from networkz/algorithms/approximation/tests/test_firefighter_problem/test_non_spreading_minbudget.py rename to networkz/algorithms/tests/test_firefighter_problem/test_non_spreading_minbudget.py diff --git a/networkz/algorithms/approximation/tests/test_firefighter_problem/test_parsing.py b/networkz/algorithms/tests/test_firefighter_problem/test_parsing.py similarity index 100% rename from networkz/algorithms/approximation/tests/test_firefighter_problem/test_parsing.py rename to networkz/algorithms/tests/test_firefighter_problem/test_parsing.py diff --git a/networkz/algorithms/approximation/tests/test_firefighter_problem/test_spreading_maxsave.py b/networkz/algorithms/tests/test_firefighter_problem/test_spreading_maxsave.py similarity index 100% rename from networkz/algorithms/approximation/tests/test_firefighter_problem/test_spreading_maxsave.py rename to networkz/algorithms/tests/test_firefighter_problem/test_spreading_maxsave.py diff --git a/networkz/algorithms/approximation/tests/test_firefighter_problem/test_spreading_minbudget.py b/networkz/algorithms/tests/test_firefighter_problem/test_spreading_minbudget.py similarity index 100% rename from networkz/algorithms/approximation/tests/test_firefighter_problem/test_spreading_minbudget.py rename to networkz/algorithms/tests/test_firefighter_problem/test_spreading_minbudget.py diff --git a/networkz/algorithms/approximation/tests/test_firefighter_problem/test_utils.py b/networkz/algorithms/tests/test_firefighter_problem/test_utils.py similarity index 100% rename from networkz/algorithms/approximation/tests/test_firefighter_problem/test_utils.py rename to networkz/algorithms/tests/test_firefighter_problem/test_utils.py From 546079d5436c0458d190fd70194310029a579a83 Mon Sep 17 00:00:00 2001 From: 20shaked20 Date: Tue, 4 Jun 2024 10:47:35 +0300 Subject: [PATCH 12/93] Fixed calcualtion of vaccine matrix! and its approripate tests which had some issues also --- .../firefighter_problem/Utils.py | 7 ++- .../test_non_spreading_dirlaynet_minbudget.py | 48 ++++++++++++++++--- 2 files changed, 46 insertions(+), 9 deletions(-) diff --git a/networkz/algorithms/approximation/firefighter_problem/Utils.py b/networkz/algorithms/approximation/firefighter_problem/Utils.py index 9966bfd..5e584c4 100644 --- a/networkz/algorithms/approximation/firefighter_problem/Utils.py +++ b/networkz/algorithms/approximation/firefighter_problem/Utils.py @@ -341,11 +341,14 @@ def calculate_vaccine_matrix(layers:list, min_cut_nodes_grouped:dict)->np.matrix for j in range(matrix_length): for i in range(j+1): + # print("I = ", i, "J = ", j) N_j = len(min_cut_nodes_grouped[j+1]) value = N_j / (j + 1) - matrix[j][i] = value + matrix[i][j] = value + # print("MATRIX IS ->>>>>>>>>", matrix) - print("MATRIX IS ->>>>>>>>>", matrix) + + # print("MATRIX IS ->>>>>>>>>", matrix) return matrix def matrix_to_integers_values(matrix: np.matrix) -> np.matrix: diff --git a/networkz/algorithms/tests/test_firefighter_problem/test_non_spreading_dirlaynet_minbudget.py b/networkz/algorithms/tests/test_firefighter_problem/test_non_spreading_dirlaynet_minbudget.py index a7ec00d..2ca3736 100644 --- a/networkz/algorithms/tests/test_firefighter_problem/test_non_spreading_dirlaynet_minbudget.py +++ b/networkz/algorithms/tests/test_firefighter_problem/test_non_spreading_dirlaynet_minbudget.py @@ -253,16 +253,50 @@ def test_calculate_vaccine_matrix(): #TODO: This tets is not working for now, ne np.testing.assert_array_equal(calculate_vaccine_matrix(layers_1, N_1_groups), matrix_1_check) # Test 2 + + """ + Applying the formula: Mij ': = Nj j , 1≤i≤j≤2 (l=2) + N2_groups_check = {1: [2], 2: [4]} + N1 : = {2} |N1| = 1 + N2 : = {4} |N2| = 1 + + 1. M11 : i = 1, j = 1 + |N1|/1 = 1/1 = 1 + 2. M12 : i = 1, j = 2 + |N2|/2 = 1/2 + 3. M22 : i = 2, j = 2 + |N2|/2 = 1/2 + """ + # checking equality - matrix_2_check = np.array([[1.5, 0.5], - [1, 0.5], + matrix_2_check = np.array([[1, 0.5], [0, 0.5]]) np.testing.assert_array_equal(calculate_vaccine_matrix(layers_2, N_2_groups), matrix_2_check) # Test 3 + """ + Applying the formula: Mij ': = Nj j , 1≤i≤j≤2 (l=2) + N3_groups_check = {1: [1], 2: [], 3: [7]} + N1 : = {1} |N1| = 1 + N2 : = {0} |N2| = 0 + N3 := {7} |N3| = 1 + + 1. M11 : i = 1, j = 1 + |N1|/1 = 1/1 = 1 + 2. M12 : i = 1, j = 2 + |N2|/2 = 0/2 = 0 + 3. M13 : i = 1, j = 3 + |N3|/3 = 1/3 + 4. M22 : i = 2, j = 2 + |N2|/2 = 0/2 = 0 + 5. M23 : i = 2 j = 3 + |N3|/3 = 1/3 + 5. M33 : i = 2 j = 3 + |N3|/3 = 1/3 + """ # checking equality - matrix_3_check = np.array([[4/3, 1/3, 1/3], - [1, 0, 1/3], + matrix_3_check = np.array([[1, 0, 1/3], + [0, 0, 1/3], [0, 0, 1/3]]) np.testing.assert_array_equal(calculate_vaccine_matrix(layers_3, N_3_groups), matrix_3_check) @@ -280,12 +314,12 @@ def test_matrix_to_integers_values(): Test the matrix to integers values function. """ matrix = np.matrix([[1.5, 0.5], [1, 0.5], [0, 0.5]]) - expected_matrix = np.matrix([[2, 0], [1, 1], [0, 1]]) + expected_matrix = np.matrix([[2, 1], [1, 1], [0, 0]]) np.testing.assert_array_equal(matrix_to_integers_values(matrix), expected_matrix) def test_min_budget_calculation(): """ This test validates that the minimum budget is accurate. """ - matrix = np.matrix([[2, 0], [1, 1], [0, 1]]) - assert min_budget_calculation(matrix) == 2 + matrix = np.matrix([[2, 1], [1, 1], [0, 1]]) + assert min_budget_calculation(matrix) == 3 From 0b1779d1c6c67324f8b7721f457b2622c52dbbd9 Mon Sep 17 00:00:00 2001 From: 20shaked20 Date: Tue, 4 Jun 2024 11:07:08 +0300 Subject: [PATCH 13/93] Dirlay implementation done, testing are passing --- .../firefighter_problem/Utils.py | 4 +- .../test_non_spreading_dirlaynet_minbudget.py | 60 +++++++++++++++++-- 2 files changed, 56 insertions(+), 8 deletions(-) diff --git a/networkz/algorithms/approximation/firefighter_problem/Utils.py b/networkz/algorithms/approximation/firefighter_problem/Utils.py index 5e584c4..08ba38a 100644 --- a/networkz/algorithms/approximation/firefighter_problem/Utils.py +++ b/networkz/algorithms/approximation/firefighter_problem/Utils.py @@ -426,8 +426,8 @@ def min_budget_calculation(matrix: np.matrix) -> int: - int: Minimum budget. """ integral_matrix = matrix_to_integers_values(matrix) - row_sums = integral_matrix.sum(axis=1) - min_budget = int(row_sums.max()) + columns_sum = integral_matrix.sum(axis=0) # we get column sum as we want to -> on time step i, vaccinate Mij nodes from layer j , for all i ≤ j ≤ . + min_budget = int(columns_sum.max()) return min_budget diff --git a/networkz/algorithms/tests/test_firefighter_problem/test_non_spreading_dirlaynet_minbudget.py b/networkz/algorithms/tests/test_firefighter_problem/test_non_spreading_dirlaynet_minbudget.py index 2ca3736..fac01ef 100644 --- a/networkz/algorithms/tests/test_firefighter_problem/test_non_spreading_dirlaynet_minbudget.py +++ b/networkz/algorithms/tests/test_firefighter_problem/test_non_spreading_dirlaynet_minbudget.py @@ -239,7 +239,7 @@ def sort_dict_values(d): #| this is for sorting purposes , cases where [1,2] or result_4 = min_cut_N_groups(reduction_G4, 0, layers_4) assert sort_dict_values(result_4) == sort_dict_values(N4_groups_check) -def test_calculate_vaccine_matrix(): #TODO: This tets is not working for now, need to fix its imeplemntation! +def test_calculate_vaccine_matrix(): """ This test checks that the calculations made to create the triangular matrix from the min-cut nodes are correct. A matrix is valid if, for any column j, the column sum is exactly |Nj|. @@ -313,13 +313,61 @@ def test_matrix_to_integers_values(): """ Test the matrix to integers values function. """ - matrix = np.matrix([[1.5, 0.5], [1, 0.5], [0, 0.5]]) - expected_matrix = np.matrix([[2, 1], [1, 1], [0, 0]]) - np.testing.assert_array_equal(matrix_to_integers_values(matrix), expected_matrix) + # Test 1 + # checking equality + matrix_1_check = np.array([[2, 0, 0, 0], + [0, 0, 0, 0], + [0, 0, 0, 0], + [0, 0, 0, 0]]) + + expected_matrix_1 = np.array([[2, 0, 0, 0], + [0, 0, 0, 0], + [0, 0, 0, 0], + [0, 0, 0, 0]]) + np.testing.assert_array_equal(matrix_to_integers_values(matrix_1_check), expected_matrix_1) + + # Test 2 + # checking equality + matrix_2_check = np.array([[1, 0.5], + [0, 0.5]]) + + expected_matrix_2 = np.array([[1, 1], + [0, 0]]) + np.testing.assert_array_equal(matrix_to_integers_values(matrix_2_check), expected_matrix_2) + + # Test 3 + # checking equality + matrix_3_check = np.array([[1, 0, 1/3], + [0, 0, 1/3], + [0, 0, 1/3]]) + + expected_matrix_3 = np.array([[1, 0, 1], + [0, 0, 0], + [0, 0, 0]]) + np.testing.assert_array_equal(matrix_to_integers_values(matrix_3_check), expected_matrix_3) + def test_min_budget_calculation(): """ This test validates that the minimum budget is accurate. """ - matrix = np.matrix([[2, 1], [1, 1], [0, 1]]) - assert min_budget_calculation(matrix) == 3 + # Test 1 + # checking equality + matrix_1_check = np.array([[2, 0, 0, 0], + [0, 0, 0, 0], + [0, 0, 0, 0], + [0, 0, 0, 0]]) + assert min_budget_calculation(matrix_1_check) == 2 + + # Test 2 + # checking equality + matrix_2_check = np.array([[1, 1], #on time i = 1, vaccinate M11(1) nodes from layer 1. , on time i = 2, vaccinante M12(1) nodes from layer 2 + [0, 0]]) + assert min_budget_calculation(matrix_2_check) == 1 + + # Test 3 + # checking equality + matrix_3_check = np.array([[1, 0, 1], + [0, 0, 0], + [0, 0, 0]]) + assert min_budget_calculation(matrix_3_check) == 1 \ No newline at end of file From b2f1188e1eb0af427cfa23187fb083a9fb7b52c5 Mon Sep 17 00:00:00 2001 From: 20shaked20 Date: Tue, 4 Jun 2024 14:00:08 +0300 Subject: [PATCH 14/93] minor changes depcreating for now test_utils, as we do not use it and not need it for now --- .../algorithms/tests/test_firefighter_problem/test_utils.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/networkz/algorithms/tests/test_firefighter_problem/test_utils.py b/networkz/algorithms/tests/test_firefighter_problem/test_utils.py index 83c9d57..5b54eed 100644 --- a/networkz/algorithms/tests/test_firefighter_problem/test_utils.py +++ b/networkz/algorithms/tests/test_firefighter_problem/test_utils.py @@ -4,6 +4,8 @@ import os from networkz.algorithms.approximation.firefighter_problem.Utils import * +## TODO --> This is not implementaed for now, all the methods are being tested in their corresponding usage in other tests.py. +## we might consider changing it later for a better coding @pytest.fixture def test_graph(): From 521d002dde7ba4c0f2dfa0e2004e4c01317b6180 Mon Sep 17 00:00:00 2001 From: 20shaked20 Date: Tue, 4 Jun 2024 14:08:49 +0300 Subject: [PATCH 15/93] some comments + removed unnecasry comments --- .../algorithms/approximation/firefighter_problem/Utils.py | 5 +---- 1 file changed, 1 insertion(+), 4 deletions(-) diff --git a/networkz/algorithms/approximation/firefighter_problem/Utils.py b/networkz/algorithms/approximation/firefighter_problem/Utils.py index 08ba38a..1cac65f 100644 --- a/networkz/algorithms/approximation/firefighter_problem/Utils.py +++ b/networkz/algorithms/approximation/firefighter_problem/Utils.py @@ -319,8 +319,6 @@ def min_cut_N_groups(graph: nx.DiGraph, source: int, layers: list) -> dict: def calculate_vaccine_matrix(layers:list, min_cut_nodes_grouped:dict)->np.matrix: - "TODO: Fix this to work, its not working for now with tests.." - """ Calculate the vaccine matrix based on the calculation in the article at the DirLayNet algorithm section. @@ -352,10 +350,10 @@ def calculate_vaccine_matrix(layers:list, min_cut_nodes_grouped:dict)->np.matrix return matrix def matrix_to_integers_values(matrix: np.matrix) -> np.matrix: - "TODO: keep testing this, it should be ok, but only for sure after done with the above method" """ Convert a matrix with fractional entries to an integral matrix such that the row and column sums are either the floor or ceiling of the original sums. + The solution is provided with a consturction of a flow graph and then applying a max-flow algorihm on it. Parameters: matrix (np.matrix): The input matrix with fractional entries. @@ -366,7 +364,6 @@ def matrix_to_integers_values(matrix: np.matrix) -> np.matrix: # dimensions of the matrix rows, cols = matrix.shape - # row and column sums row_sums = np.array(matrix.sum(axis=1)).flatten() col_sums = np.array(matrix.sum(axis=0)).flatten() From 9bb5cc7014e50c168fbb012305419a4187d7be75 Mon Sep 17 00:00:00 2001 From: Yuval Bubnovsky Date: Tue, 4 Jun 2024 23:23:24 +0300 Subject: [PATCH 16/93] Added logging, added docstrings for hueristic functions --- .../Firefighter_Problem.py | 200 +++++++++++++----- .../firefighter_problem/Utils.py | 79 +++---- 2 files changed, 184 insertions(+), 95 deletions(-) diff --git a/networkz/algorithms/approximation/firefighter_problem/Firefighter_Problem.py b/networkz/algorithms/approximation/firefighter_problem/Firefighter_Problem.py index 78b2169..0c49c6c 100644 --- a/networkz/algorithms/approximation/firefighter_problem/Firefighter_Problem.py +++ b/networkz/algorithms/approximation/firefighter_problem/Firefighter_Problem.py @@ -3,10 +3,28 @@ import math import json import random +import logging + +# This is a fix for an issue where the top one has to be exclusive for pytest to work +# and the bottom one needs to be exclusive for running this from terminal to work +try: + from networkz.algorithms.approximation.firefighter_problem.Utils import * +except ImportError: + from Utils import * + +def setup_global_logger(level: int = logging.DEBUG): + log_format = "|| %(levelname)s || %(message)s" + formatter = logging.Formatter(log_format) + handler = logging.StreamHandler() + handler.setFormatter(formatter) + + root_logger = logging.getLogger() + root_logger.setLevel(level) + root_logger.addHandler(handler) + +setup_global_logger() -# TODO: fix this shit, when we run tests needs src.Utils, and when we run this, we need Utils only.. -from networkz.algorithms.approximation.firefighter_problem.Utils import * -# from Utils import * +logger = logging.getLogger(__name__) def spreading_maxsave(Graph:nx.DiGraph, budget:int, source:int, targets:list, flag=None) -> list: """ @@ -48,10 +66,11 @@ def spreading_maxsave(Graph:nx.DiGraph, budget:int, source:int, targets:list, fl [(5, 1), (2, 1), (8, 2)] """ if budget < 1: + logger.critical("Error: The budget must be at least 1") raise ValueError("Error: The budget must be at least 1") - exit() - validate_parameters(Graph,source,targets) - print("starting the spreading maxsave function with a source node " + f'{source}' + ", the budget is " + f'{budget}' +", and the targets are: " + str(targets)) + + validate_parameters(Graph, source, targets) + logger.info(f"Starting the spreading_maxsave function with source node {source}, budget {budget}, and targets: {targets}") infected_nodes = [] vaccinated_nodes = [] @@ -59,31 +78,45 @@ def spreading_maxsave(Graph:nx.DiGraph, budget:int, source:int, targets:list, fl can_spread = True Graph.nodes[source]['status'] = 'infected' infected_nodes.append(source) + + logger.info("Calculating all possible direct vaccinations in each timestamp") gamma, direct_vaccinations = calculate_gamma(Graph, source, targets) + + logger.info("Calculating direct vaccinations groups by timestamp") epsilon = calculate_epsilon(direct_vaccinations) + time_step = 0 - while(can_spread and time_step int: """ "Approximability of the Firefighter Problem - Computing Cuts over Time", @@ -123,17 +156,20 @@ def spreading_minbudget(Graph:nx.DiGraph, source:int, targets:list)-> int: >>> spreading_minbudget(G3,0,[1,2,3,4,5,6,7,8]) 3 """ - validate_parameters(Graph,source,targets) - print("starting the spreading minbudget function with a source node " + f'{source}' + ", and the targets are: " + str(targets)) + validate_parameters(Graph, source, targets) + logger.info(f"Starting the spreading_minbudget function with source node {source} and targets: {targets}") original_targets = list(targets) - direct_vaccinations = calculate_gamma(Graph, source, targets)[1] + + logger.info("Calculating all possible direct vaccinations in each timestamp") + direct_vaccinations = calculate_gamma(Graph, source, targets)[1] # TODO: check if this can be removed for optimization min_value = 1 max_value = len(targets) middle = math.floor((min_value + max_value) / 2) while min_value < max_value: - strategy = spreading_maxsave(Graph, middle, source, targets,True) + logger.info(f"Calling maxsave with parameters - Source: {source}, Targets: {targets}, Budget: {middle}") + strategy = spreading_maxsave(Graph, middle, source, targets, True) nodes_saved = set() for option in strategy: @@ -143,16 +179,18 @@ def spreading_minbudget(Graph:nx.DiGraph, source:int, targets:list)-> int: common_elements = set(nodes_saved) & set(original_targets) if len(common_elements) == len(original_targets): - print("the current budget " + f'{middle}' + " has saved all the targets!") + logger.info(f"The current budget {middle} has saved all the targets!") max_value = middle else: - print("the current budget " + f'{middle}' + " didn't saved all the targets!") + logger.warning(f"The current budget {middle} didn't save all the targets!") min_value = middle + 1 middle = math.floor((min_value + max_value) / 2) targets = list(original_targets) + logger.info(f"Returning minimum budget: {middle}") return middle + def non_spreading_minbudget(Graph:nx.DiGraph, source:int, targets:list)->int: """ @@ -193,9 +231,14 @@ def non_spreading_minbudget(Graph:nx.DiGraph, source:int, targets:list)->int: >>> non_spreading_minbudget(G3,0,[2,6,1,8]) 3 """ - validate_parameters(Graph,source,targets) - G = create_st_graph(Graph,targets) - return len(algo.minimum_st_node_cut(G,source,'t')) + validate_parameters(Graph, source, targets) + logger.info(f"Starting the non_spreading_minbudget function with source node {source} and targets: {targets}") + + G = create_st_graph(Graph, targets) + min_budget = len(algo.minimum_st_node_cut(G, source, 't')) + + logger.info(f"Returning minimum budget: {min_budget}") + return min_budget def non_spreading_dirlaynet_minbudget(Graph:nx.DiGraph, source:int, targets:list)->int: """ @@ -215,23 +258,51 @@ def non_spreading_dirlaynet_minbudget(Graph:nx.DiGraph, source:int, targets:list >>> non_spreading_dirlaynet_minbudget(G4,0,[1,2,3,4,5]) 2 """ - validate_parameters(Graph,source,targets) + validate_parameters(Graph, source, targets) + logger.info(f"Starting the non_spreading_dirlaynet_minbudget function with source node {source} and targets: {targets}") + layers = adjust_nodes_capacity(Graph, source) G = create_st_graph(Graph, targets) - G_reduction = graph_flow_reduction(G,source) - N_groups = min_cut_N_groups(G_reduction,source) - vacc_matrix = calculate_vaccine_matrix(layers,N_groups) + G_reduction = graph_flow_reduction(G, source) + N_groups = min_cut_N_groups(G_reduction, source) + vacc_matrix = calculate_vaccine_matrix(layers, N_groups) min_budget = min_budget_calculation(vacc_matrix) + + logger.info(f"Returning minimum budget: {min_budget}") return min_budget def heuristic_maxsave(Graph:nx.DiGraph, budget:int, source:int, targets:list, spreading:bool, flag=None) -> list: - """ This heuristic approach is based on the local search problem. - We will select the best neighbor that save the most nodes from targets""" + """ + This heuristic approach is based on the local search problem. + We will select the best neighbor that saves the most nodes from targets. + + Parameters: + - Graph (nx.DiGraph): Directed graph representing the network. + - budget (int): Number of nodes that can be vaccinated at each time step. + - source (int): Source node where the infection starts. + - targets (list): List of target nodes to be saved. + - spreading (bool): If True, vaccination spreads to neighboring nodes. + - flag (bool, optional): If set, stops early if all targets are saved or if any target is infected. + + Returns: + - list: List of tuples representing the vaccination strategy. + + Raises: + - ValueError: If the budget is less than 1. + + Example: + >>> G = nx.DiGraph() + >>> G.add_nodes_from([0, 1, 2, 3], status="target") + >>> G.add_edges_from([(0, 1), (0, 2), (1, 2), (1, 3)]) + >>> heuristic_maxsave(G, 1, 0, [1, 2, 3], True) + [(1, 1), (2, 2), (3, 3)] + """ if budget < 1: + logger.critical("Error: The budget must be at least 1") raise ValueError("Error: The budget must be at least 1") - exit() - validate_parameters(Graph,source,targets) - print("starting the heuristic maxsave function with a source node " + f'{source}' + ", the budget is " + f'{budget}' +", the targets are: " + str(targets) + ", and the function is spreading=" + str(spreading)) + + validate_parameters(Graph, source, targets) + logger.info(f"Starting the heuristic_maxsave function with source node {source}, budget {budget}, targets: {targets}, and spreading: {spreading}") clean_graph(Graph) infected_nodes = [] @@ -241,29 +312,52 @@ def heuristic_maxsave(Graph:nx.DiGraph, budget:int, source:int, targets:list, sp Graph.nodes[source]['status'] = 'infected' infected_nodes.append(source) time_step = 1 - while(can_spread): + + while can_spread: if spreading: spread_vaccination(Graph, vaccinated_nodes) for i in range(budget): - node_to_vaccinate = find_best_neighbor(Graph,infected_nodes,targets) - if node_to_vaccinate != None: - vaccination_strategy.append((node_to_vaccinate,time_step)) + node_to_vaccinate = find_best_neighbor(Graph, infected_nodes, targets) + if node_to_vaccinate is not None: + vaccination_strategy.append((node_to_vaccinate, time_step)) vaccinate_node(Graph, node_to_vaccinate) vaccinated_nodes.append(node_to_vaccinate) - can_spread = spread_virus(Graph,infected_nodes) - + can_spread = spread_virus(Graph, infected_nodes) + if flag is not None: - # only for min budget - a stoping condition in case we saved all nodes or one of the target nodes in infected - if len(targets)==0 or any(node in infected_nodes for node in targets): + if len(targets) == 0 or any(node in infected_nodes for node in targets): + logger.info(f"Returning vaccination strategy: {vaccination_strategy}") return vaccination_strategy - - time_step = time_step + 1 - + + time_step += 1 + + logger.info(f"Returning vaccination strategy: {vaccination_strategy}") return vaccination_strategy def heuristic_minbudget(Graph:nx.DiGraph, source:int, targets:list, spreading:bool)-> int: - validate_parameters(Graph,source,targets) - print("starting the heuristic minbudget function with a source node " + f'{source}' + ", the targets are: " + str(targets) + ", and the function is spreading=" + str(spreading)) + """ + This function calculates the minimum budget required to save all target nodes + using the heuristic approach based on local search problem. + + Parameters: + - Graph (nx.DiGraph): Directed graph representing the network. + - source (int): Source node where the infection starts. + - targets (list): List of target nodes to be saved. + - spreading (bool): If True, vaccination spreads to neighboring nodes. + + Returns: + - int: Minimum budget required to save all target nodes. + + Example: + >>> G = nx.DiGraph() + >>> G.add_nodes_from([0, 1, 2, 3], status="target") + >>> G.add_edges_from([(0, 1), (0, 2), (1, 2), (1, 3)]) + >>> heuristic_minbudget(G, 0, [1, 2, 3], True) + 1 + """ + validate_parameters(Graph, source, targets) + logger.info(f"Starting the heuristic_minbudget function with source node {source}, targets: {targets}, and spreading: {spreading}") + original_targets = list(targets) min_value = 1 max_value = len(targets) @@ -277,17 +371,18 @@ def heuristic_minbudget(Graph:nx.DiGraph, source:int, targets:list, spreading:bo if Graph.nodes[node]['status'] == 'infected': saved_everyone = False break - + if saved_everyone: - print("the current budget " + f'{middle}' + " has saved all the targets!") + logger.info(f"The current budget {middle} has saved all the targets!") max_value = middle else: - print("the current budget " + f'{middle}' + " didn't saved all the targets!") + logger.warning(f"The current budget {middle} didn't save all the targets!") min_value = middle + 1 middle = math.floor((min_value + max_value) / 2) targets = list(original_targets) + logger.info(f"Returning minimum budget: {middle}") return middle if __name__ == "__main__": @@ -297,5 +392,8 @@ def heuristic_minbudget(Graph:nx.DiGraph, source:int, targets:list, spreading:bo G3 = nx.DiGraph() G3.add_nodes_from([0,1,2,3,4,5,6,7,8], status="target") G3.add_edges_from([(0,2),(0,4),(0,5),(2,1),(2,3),(4,1),(4,6),(5,3),(5,6),(5,7),(6,7),(6,8),(7,8)]) - print(heuristic_minbudget(G3,0,[2,6,1,8], True)) - #print(spreading_minbudget(G3,0,[2,6,1,8])) + logger.info("=" * 150) + logger.info(spreading_minbudget(G3,0,[2,6,1,8])) + logger.info("=" * 150) + logger.info(heuristic_minbudget(G3,0,[2,6,1,8], True)) + diff --git a/networkz/algorithms/approximation/firefighter_problem/Utils.py b/networkz/algorithms/approximation/firefighter_problem/Utils.py index 1cac65f..4494bcf 100644 --- a/networkz/algorithms/approximation/firefighter_problem/Utils.py +++ b/networkz/algorithms/approximation/firefighter_problem/Utils.py @@ -4,6 +4,7 @@ import numpy as np import math import copy +import logging node_colors = { 'target': 'gray', @@ -13,6 +14,8 @@ 'default' : "#00FFD0" } +logger = logging.getLogger(__name__) + def validate_parameters(graph:nx.DiGraph, source:int, targets:list)->None: """ Validate the source and target nodes in the graph. @@ -29,15 +32,17 @@ def validate_parameters(graph:nx.DiGraph, source:int, targets:list)->None: """ graph_nodes = list(graph.nodes()) if source not in graph_nodes: + logger.critical("Error: The source node isn't on the graph") raise ValueError("Error: The source node isn't on the graph") - exit() + if source in targets: + logger.critical("Error: The source node can't be a part of the targets list, since the virus is spreading from the source") raise ValueError("Error: The source node can't be a part of the targets list, since the virus is spreading from the source") - exit() + if not all(node in graph_nodes for node in targets): + logger.critical("Error: Not all nodes in the targets list are on the graph.") raise ValueError("Error: Not all nodes in the targets list are on the graph.") - exit() - + "Spreading:" def calculate_gamma(graph:nx.DiGraph, source:int, targets:list)-> dict: """ @@ -72,20 +77,18 @@ def calculate_gamma(graph:nx.DiGraph, source:int, targets:list)-> dict: if key in targets: direct_vaccination[option].append(key) - # if the virus can't reach to the node - it's automatically saved if not vaccination_options: unreachable_nodes.append(key) if key != source: gamma[key] = vaccination_options - # add all the unreachable nodes to the vaccination strategy - every strategy can save them for strategy in direct_vaccination: for node in unreachable_nodes: if node in targets: direct_vaccination[strategy].append(node) - #print("Gamma is: " + str(gamma)) - #print("S(u,t) is: " + str(direct_vaccination)) + logger.info("Gamma is: " + str(gamma)) + logger.info("S(u,t) is: " + str(direct_vaccination)) return gamma, direct_vaccination def calculate_epsilon(direct_vaccinations:dict)->list: @@ -101,7 +104,6 @@ def calculate_epsilon(direct_vaccinations:dict)->list: epsilon = [] sorted_dict = dict(sorted(direct_vaccinations.items(), key=lambda item: item[0][1])) - # Iterate over the sorted dictionary and populate the result list current_time_step = None current_group = [] for key, value in sorted_dict.items(): @@ -112,11 +114,10 @@ def calculate_epsilon(direct_vaccinations:dict)->list: current_group = [key] current_time_step = key[1] - # Append the last group if current_group: epsilon.append(current_group) - #print("Epsilon is: " + str(epsilon)) + logger.info("Epsilon is: " + str(epsilon)) return epsilon def find_best_direct_vaccination(graph:nx.DiGraph, direct_vaccinations:dict, current_time_options:list, targets:list)->tuple: @@ -140,7 +141,7 @@ def find_best_direct_vaccination(graph:nx.DiGraph, direct_vaccinations:dict, cur if(graph.nodes[option[0]]['status'] == 'target'): nodes_list = direct_vaccinations.get(option) common_elements = set(nodes_list) & set(targets) - print("the direct vaccination " + str(option) + " is saving the nodes " + str(common_elements)) + logger.debug(f"Direct vaccination: {option}, Nodes saved: {common_elements} (if set(), then it's empty)") if len(common_elements) > max_number: best_vaccination = option nodes_saved = common_elements @@ -150,7 +151,7 @@ def find_best_direct_vaccination(graph:nx.DiGraph, direct_vaccinations:dict, cur targets[:] = [element for element in targets if element not in nodes_saved] if best_vaccination != (): - print("The best direct vaccination is: " + str(best_vaccination) + " and it's saves nodes: " + str(nodes_saved)) + logger.info("The best direct vaccination is: " + str(best_vaccination) + " and it saves nodes: " + str(nodes_saved)) return best_vaccination def spread_virus(graph:nx.DiGraph, infected_nodes:list)->bool: @@ -170,13 +171,13 @@ def spread_virus(graph:nx.DiGraph, infected_nodes:list)->bool: if graph.nodes[neighbor]['status'] == 'target': graph.nodes[neighbor]['status'] = 'infected' new_infected_nodes.append(neighbor) - print("node " + f'{neighbor}' + " has been infected from node " + f'{node}') + logger.debug("SPREAD VIRUS: Node " + f'{neighbor}' + " has been infected from node " + f'{node}') infected_nodes.clear() for node in new_infected_nodes: infected_nodes.append(node) - #display_graph(graph) return bool(infected_nodes) + def spread_vaccination(graph:nx.DiGraph, vaccinated_nodes:list)->None: """ Spread the vaccination from vaccinated nodes to their neighbors. @@ -191,11 +192,11 @@ def spread_vaccination(graph:nx.DiGraph, vaccinated_nodes:list)->None: if graph.nodes[neighbor]['status'] == 'target': graph.nodes[neighbor]['status'] = 'vaccinated' new_vaccinated_nodes.append(neighbor) - print("node " + f'{neighbor}' + " has been vaccinated from node " + f'{node}') + logger.debug("SPREAD VACCINATION: Node " + f'{neighbor}' + " has been vaccinated from node " + f'{node}') vaccinated_nodes.clear() for node in new_vaccinated_nodes: - vaccinated_nodes.append(node) - #display_graph(graph) + vaccinated_nodes.append(node) + logger.debug(f"Currently vaccinated nodes: {vaccinated_nodes}") return def vaccinate_node(graph:nx.DiGraph, node:int)->None: @@ -207,8 +208,7 @@ def vaccinate_node(graph:nx.DiGraph, node:int)->None: - node (int): Node to be vaccinated. """ graph.nodes[node]['status'] = 'directly vaccinated' - print("node " + f'{node}' + " has been directly vaccinated") - #display_graph(graph) + logger.info("Node " + f'{node}' + " has been directly vaccinated") return def clean_graph(graph:nx.DiGraph)->None: @@ -243,7 +243,7 @@ def adjust_nodes_capacity(graph:nx.DiGraph, source:int)->list: for index in range(1,len(layers)): for node in layers[index]: graph.nodes[node]['capacity'] = 1/(index*harmonic_sum) - # print("Layers: ", layers) + logger.info(f"Layers: {layers}") return layers def create_st_graph(graph:nx.DiGraph, targets:list)->nx.DiGraph: @@ -330,23 +330,16 @@ def calculate_vaccine_matrix(layers:list, min_cut_nodes_grouped:dict)->np.matrix - matrix (np.matrix): Vaccine matrix. """ - print("Layers:", layers) - print("Min cut nodes grouped:", min_cut_nodes_grouped) - # Mij ': = Nj j , 1≤i≤j≤l - # Determine the size of the matrix based on the number of groups + logger.info(f"Layers: {layers}") + logger.info(f"Min cut nodes grouped: {min_cut_nodes_grouped}") matrix_length = max(min_cut_nodes_grouped.keys()) - matrix = np.zeros((matrix_length, matrix_length)) # Use a tuple for the shape - + matrix = np.zeros((matrix_length, matrix_length)) for j in range(matrix_length): for i in range(j+1): - # print("I = ", i, "J = ", j) N_j = len(min_cut_nodes_grouped[j+1]) value = N_j / (j + 1) matrix[i][j] = value - # print("MATRIX IS ->>>>>>>>>", matrix) - - - # print("MATRIX IS ->>>>>>>>>", matrix) + logger.info(f"Matrix: {matrix}") return matrix def matrix_to_integers_values(matrix: np.matrix) -> np.matrix: @@ -367,8 +360,8 @@ def matrix_to_integers_values(matrix: np.matrix) -> np.matrix: row_sums = np.array(matrix.sum(axis=1)).flatten() col_sums = np.array(matrix.sum(axis=0)).flatten() - print("Row sums:", row_sums) - print("Column sums:", col_sums) + logger.info(f"Row sums: {row_sums}") + logger.info(f"Column sums: {col_sums}") G = nx.DiGraph() @@ -408,7 +401,7 @@ def matrix_to_integers_values(matrix: np.matrix) -> np.matrix: integral_matrix[i, j] = np.ceil(matrix[i, j]) else: integral_matrix[i, j] = np.floor(matrix[i, j]) - print("MATRIXXXX->>>>", integral_matrix) + logger.info(f"Matrix: {integral_matrix}") return np.matrix(integral_matrix) @@ -462,7 +455,7 @@ def find_best_neighbor(graph:nx.DiGraph, infected_nodes:list, targets:list)->int if node in targets: target_neighbors.add(node) common_elements = set(target_neighbors) & set(targets) - print("node " + f'{node}' + " is saving the nodes " + str(common_elements)) + logger.info("node " + f'{node}' + " is saving the nodes " + str(common_elements)) if len(common_elements) > max_number: best_node = node nodes_saved = common_elements @@ -472,7 +465,7 @@ def find_best_neighbor(graph:nx.DiGraph, infected_nodes:list, targets:list)->int targets[:] = [element for element in targets if element not in nodes_saved] if best_node != None: - print("The best node is: " + f'{best_node}' + " and it's saves nodes: " + str(nodes_saved)) + logger.info("The best node is: " + f'{best_node}' + " and it's saves nodes: " + str(nodes_saved)) return best_node "Usefull Utils:" @@ -505,24 +498,22 @@ def parse_json_to_networkx(json_data): - graphs (dict): Dictionary of Networkx graphs. """ graphs = {} - for graph_type, graph_data in json_data.items(): for graph_name, graph_info in graph_data.items(): - graph_key = f"{graph_type}_{graph_name}" + if "vertices" not in graph_info or not isinstance(graph_info["vertices"], list) or not graph_info["vertices"]: + logger.critical(f"Error parsing {graph_type}_{graph_name}: 'vertices' must be a non-empty list.") raise KeyError(f"Error parsing {graph_type}_{graph_name}: 'vertices' must be a non-empty list.") - + if "edges" not in graph_info or not isinstance(graph_info["edges"], list) or not graph_info["edges"]: + logger.critical(f"Error parsing {graph_type}_{graph_name}: 'edges' must be a non-empty list.") raise KeyError(f"Error parsing {graph_type}_{graph_name}: 'edges' must be a non-empty list.") - + vertices = graph_info["vertices"] edges = [(edge["source"], edge["target"]) for edge in graph_info["edges"]] - G = nx.DiGraph() G.add_nodes_from(vertices, status="target") G.add_edges_from(edges) - graphs[graph_key] = G - return graphs \ No newline at end of file From efb7f34b0e7d9667043cfe7550f3f96a26562c7d Mon Sep 17 00:00:00 2001 From: 20shaked20 Date: Wed, 5 Jun 2024 18:39:57 +0300 Subject: [PATCH 17/93] Added headers for each file + created a basic main for demonstartion of doctest in the firefighter_problem.py --- .../Firefighter_Problem.py | 45 ++++++++++---- .../firefighter_problem/Utils.py | 21 +++++++ .../approximation/firefighter_problem/main.py | 59 +++++++++++++++++++ .../test_non_spreading_dirlaynet_minbudget.py | 23 +++++++- .../test_non_spreading_minbudget.py | 21 +++++++ .../test_firefighter_problem/test_parsing.py | 21 +++++++ .../test_spreading_maxsave.py | 21 +++++++ .../test_spreading_minbudget.py | 21 +++++++ .../test_firefighter_problem/test_utils.py | 21 +++++++ 9 files changed, 239 insertions(+), 14 deletions(-) create mode 100644 networkz/algorithms/approximation/firefighter_problem/main.py diff --git a/networkz/algorithms/approximation/firefighter_problem/Firefighter_Problem.py b/networkz/algorithms/approximation/firefighter_problem/Firefighter_Problem.py index 0c49c6c..fec9757 100644 --- a/networkz/algorithms/approximation/firefighter_problem/Firefighter_Problem.py +++ b/networkz/algorithms/approximation/firefighter_problem/Firefighter_Problem.py @@ -1,8 +1,27 @@ +""" + +The Paper - +Approximability of the Firefighter Problem Computing Cuts over Time + +Paper Link - +https://github.com/The-Firefighters/networkz/blob/master/networkz/algorithms/approximation/firefighter_problem/Approximability_of_the_Firefighter_Problem.pdf + +Authors - +Elliot Anshelevich +Deeparnab Chakrabarty +Ameya Hate +Chaitanya Swamy + +Developers - +Yuval Bubnovsky +Almog David +Shaked Levi + +""" + import networkx as nx import networkx.algorithms.connectivity as algo import math -import json -import random import logging # This is a fix for an issue where the top one has to be exclusive for pytest to work @@ -385,15 +404,15 @@ def heuristic_minbudget(Graph:nx.DiGraph, source:int, targets:list, spreading:bo logger.info(f"Returning minimum budget: {middle}") return middle -if __name__ == "__main__": - #import doctest - #doctest.testmod(verbose=True) - - G3 = nx.DiGraph() - G3.add_nodes_from([0,1,2,3,4,5,6,7,8], status="target") - G3.add_edges_from([(0,2),(0,4),(0,5),(2,1),(2,3),(4,1),(4,6),(5,3),(5,6),(5,7),(6,7),(6,8),(7,8)]) - logger.info("=" * 150) - logger.info(spreading_minbudget(G3,0,[2,6,1,8])) - logger.info("=" * 150) - logger.info(heuristic_minbudget(G3,0,[2,6,1,8], True)) +# if __name__ == "__main__": +# import doctest +# doctest.testmod(verbose=True) + +# # G3 = nx.DiGraph() +# # G3.add_nodes_from([0,1,2,3,4,5,6,7,8], status="target") +# # G3.add_edges_from([(0,2),(0,4),(0,5),(2,1),(2,3),(4,1),(4,6),(5,3),(5,6),(5,7),(6,7),(6,8),(7,8)]) +# # logger.info("=" * 150) +# # logger.info(spreading_minbudget(G3,0,[2,6,1,8])) +# # logger.info("=" * 150) +# # logger.info(heuristic_minbudget(G3,0,[2,6,1,8], True)) diff --git a/networkz/algorithms/approximation/firefighter_problem/Utils.py b/networkz/algorithms/approximation/firefighter_problem/Utils.py index 4494bcf..4bb4ec5 100644 --- a/networkz/algorithms/approximation/firefighter_problem/Utils.py +++ b/networkz/algorithms/approximation/firefighter_problem/Utils.py @@ -1,3 +1,24 @@ +""" + +The Paper - +Approximability of the Firefighter Problem Computing Cuts over Time + +Paper Link - +https://github.com/The-Firefighters/networkz/blob/master/networkz/algorithms/approximation/firefighter_problem/Approximability_of_the_Firefighter_Problem.pdf + +Authors - +Elliot Anshelevich +Deeparnab Chakrabarty +Ameya Hate +Chaitanya Swamy + +Developers - +Yuval Bubnovsky +Almog David +Shaked Levi + +""" + import networkx as nx import networkx.algorithms.connectivity as algo import matplotlib.pyplot as plt diff --git a/networkz/algorithms/approximation/firefighter_problem/main.py b/networkz/algorithms/approximation/firefighter_problem/main.py new file mode 100644 index 0000000..e696b81 --- /dev/null +++ b/networkz/algorithms/approximation/firefighter_problem/main.py @@ -0,0 +1,59 @@ +""" + +The Paper - +Approximability of the Firefighter Problem Computing Cuts over Time + +Paper Link - +https://github.com/The-Firefighters/networkz/blob/master/networkz/algorithms/approximation/firefighter_problem/Approximability_of_the_Firefighter_Problem.pdf + +Authors - +Elliot Anshelevich +Deeparnab Chakrabarty +Ameya Hate +Chaitanya Swamy + +Developers - +Yuval Bubnovsky +Almog David +Shaked Levi + +""" + +import logging + +# This is a fix for an issue where the top one has to be exclusive for pytest to work +# and the bottom one needs to be exclusive for running this from terminal to work +try: + from networkz.algorithms.approximation.firefighter_problem.Utils import * +except ImportError: + from Utils import * + from Firefighter_Problem import * + import Firefighter_Problem as firefighter_problem # to run the doctest on the firefighter_problem files + +# def setup_global_logger(level: int = logging.DEBUG): +# log_format = "|| %(levelname)s || %(message)s" +# formatter = logging.Formatter(log_format) +# handler = logging.StreamHandler() +# handler.setFormatter(formatter) + +# root_logger = logging.getLogger() +# root_logger.setLevel(level) +# root_logger.addHandler(handler) + +# setup_global_logger() + +# logger = logging.getLogger(__name__) + + +if __name__ == "__main__": + import doctest + result = doctest.testmod(firefighter_problem, verbose=True) + print(f"Doctest results: {result}") + + # G3 = nx.DiGraph() + # G3.add_nodes_from([0,1,2,3,4,5,6,7,8], status="target") + # G3.add_edges_from([(0,2),(0,4),(0,5),(2,1),(2,3),(4,1),(4,6),(5,3),(5,6),(5,7),(6,7),(6,8),(7,8)]) + # logger.info("=" * 150) + # logger.info(spreading_minbudget(G3,0,[2,6,1,8])) + # logger.info("=" * 150) + # logger.info(heuristic_minbudget(G3,0,[2,6,1,8], True)) diff --git a/networkz/algorithms/tests/test_firefighter_problem/test_non_spreading_dirlaynet_minbudget.py b/networkz/algorithms/tests/test_firefighter_problem/test_non_spreading_dirlaynet_minbudget.py index fac01ef..eb05c23 100644 --- a/networkz/algorithms/tests/test_firefighter_problem/test_non_spreading_dirlaynet_minbudget.py +++ b/networkz/algorithms/tests/test_firefighter_problem/test_non_spreading_dirlaynet_minbudget.py @@ -1,3 +1,24 @@ +""" + +The Paper - +Approximability of the Firefighter Problem Computing Cuts over Time + +Paper Link - +https://github.com/The-Firefighters/networkz/blob/master/networkz/algorithms/approximation/firefighter_problem/Approximability_of_the_Firefighter_Problem.pdf + +Authors - +Elliot Anshelevich +Deeparnab Chakrabarty +Ameya Hate +Chaitanya Swamy + +Developers - +Yuval Bubnovsky +Almog David +Shaked Levi + +""" + import pytest import networkx as nx import json @@ -15,7 +36,7 @@ from networkz.algorithms.approximation.firefighter_problem.Utils import min_budget_calculation def get_graphs(): - with open("networkz/algorithms/approximation/firefighter_problem/graphs.json", "r") as file: + with open("/networkz/algorithms/approximation/firefighter_problem/graphs.json", "r") as file: json_data = json.load(file) graphs = parse_json_to_networkx(json_data=json_data) return graphs diff --git a/networkz/algorithms/tests/test_firefighter_problem/test_non_spreading_minbudget.py b/networkz/algorithms/tests/test_firefighter_problem/test_non_spreading_minbudget.py index 09eab2c..9ed5b35 100644 --- a/networkz/algorithms/tests/test_firefighter_problem/test_non_spreading_minbudget.py +++ b/networkz/algorithms/tests/test_firefighter_problem/test_non_spreading_minbudget.py @@ -1,3 +1,24 @@ +""" + +The Paper - +Approximability of the Firefighter Problem Computing Cuts over Time + +Paper Link - +https://github.com/The-Firefighters/networkz/blob/master/networkz/algorithms/approximation/firefighter_problem/Approximability_of_the_Firefighter_Problem.pdf + +Authors - +Elliot Anshelevich +Deeparnab Chakrabarty +Ameya Hate +Chaitanya Swamy + +Developers - +Yuval Bubnovsky +Almog David +Shaked Levi + +""" + import pytest import networkx as nx import json diff --git a/networkz/algorithms/tests/test_firefighter_problem/test_parsing.py b/networkz/algorithms/tests/test_firefighter_problem/test_parsing.py index b826df8..d25701a 100644 --- a/networkz/algorithms/tests/test_firefighter_problem/test_parsing.py +++ b/networkz/algorithms/tests/test_firefighter_problem/test_parsing.py @@ -1,3 +1,24 @@ +""" + +The Paper - +Approximability of the Firefighter Problem Computing Cuts over Time + +Paper Link - +https://github.com/The-Firefighters/networkz/blob/master/networkz/algorithms/approximation/firefighter_problem/Approximability_of_the_Firefighter_Problem.pdf + +Authors - +Elliot Anshelevich +Deeparnab Chakrabarty +Ameya Hate +Chaitanya Swamy + +Developers - +Yuval Bubnovsky +Almog David +Shaked Levi + +""" + import pytest import networkx as nx diff --git a/networkz/algorithms/tests/test_firefighter_problem/test_spreading_maxsave.py b/networkz/algorithms/tests/test_firefighter_problem/test_spreading_maxsave.py index 574b080..5a4cce8 100644 --- a/networkz/algorithms/tests/test_firefighter_problem/test_spreading_maxsave.py +++ b/networkz/algorithms/tests/test_firefighter_problem/test_spreading_maxsave.py @@ -1,3 +1,24 @@ +""" + +The Paper - +Approximability of the Firefighter Problem Computing Cuts over Time + +Paper Link - +https://github.com/The-Firefighters/networkz/blob/master/networkz/algorithms/approximation/firefighter_problem/Approximability_of_the_Firefighter_Problem.pdf + +Authors - +Elliot Anshelevich +Deeparnab Chakrabarty +Ameya Hate +Chaitanya Swamy + +Developers - +Yuval Bubnovsky +Almog David +Shaked Levi + +""" + import pytest import networkx as nx import json diff --git a/networkz/algorithms/tests/test_firefighter_problem/test_spreading_minbudget.py b/networkz/algorithms/tests/test_firefighter_problem/test_spreading_minbudget.py index 4020cb4..a38929f 100644 --- a/networkz/algorithms/tests/test_firefighter_problem/test_spreading_minbudget.py +++ b/networkz/algorithms/tests/test_firefighter_problem/test_spreading_minbudget.py @@ -1,3 +1,24 @@ +""" + +The Paper - +Approximability of the Firefighter Problem Computing Cuts over Time + +Paper Link - +https://github.com/The-Firefighters/networkz/blob/master/networkz/algorithms/approximation/firefighter_problem/Approximability_of_the_Firefighter_Problem.pdf + +Authors - +Elliot Anshelevich +Deeparnab Chakrabarty +Ameya Hate +Chaitanya Swamy + +Developers - +Yuval Bubnovsky +Almog David +Shaked Levi + +""" + import pytest import networkx as nx import json diff --git a/networkz/algorithms/tests/test_firefighter_problem/test_utils.py b/networkz/algorithms/tests/test_firefighter_problem/test_utils.py index 5b54eed..b6af08a 100644 --- a/networkz/algorithms/tests/test_firefighter_problem/test_utils.py +++ b/networkz/algorithms/tests/test_firefighter_problem/test_utils.py @@ -1,3 +1,24 @@ +""" + +The Paper - +Approximability of the Firefighter Problem Computing Cuts over Time + +Paper Link - +https://github.com/The-Firefighters/networkz/blob/master/networkz/algorithms/approximation/firefighter_problem/Approximability_of_the_Firefighter_Problem.pdf + +Authors - +Elliot Anshelevich +Deeparnab Chakrabarty +Ameya Hate +Chaitanya Swamy + +Developers - +Yuval Bubnovsky +Almog David +Shaked Levi + +""" + import networkx as nx import pytest import sys From a022a446ee45e99d0a04928dd995eebe52235a7a Mon Sep 17 00:00:00 2001 From: Shaked Levi <73894107+20shaked20@users.noreply.github.com> Date: Wed, 5 Jun 2024 18:43:23 +0300 Subject: [PATCH 18/93] Add files via upload --- ...roximability_of_the_Firefighter_Problem.pdf | Bin 0 -> 693206 bytes 1 file changed, 0 insertions(+), 0 deletions(-) create mode 100644 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zMaHGF=J8KAFK5|MbJTK_H5271HV^I@sBOtMvC1DePM2@64z$0yUg7sALi^*r>v)Q-y)u@^1|thr4Ddw)c18o2S#1X zYXdY8Cb+`0;)ruObVOF7asEJ&140hUvux$9`8ZRoyiain`5RRqC$y~A6>4_nuh&{o9oMN|P*76);nh=Q^)w$F+e$vH5?PP#%z==e z#hjkk2zb6wj7q+;R%Az{;x(tdK+CekAgD;@ln-q^^;s4=i>z02N4;(uOwXb#71~;{ z;nRGW-txT3L8aPiJk7@rf;^V78qh`YIUm)ZY0Tl77d)j|pOASauW#j^%ySEy95^pE zeS@Id(!O}=i>zT!&+9IVfz~p8A2h|mPs%1*=8qk$xJ+_BRLvxl-3%<~tt;-_JtExuA{Wxj^SaT1xA1rCWqE9*U;UDUe5Wm)QqGZfO?LW671LaGCt zE#+C%m=P~?8hf_zv5|KaI~ugImd0Sgh?&2IpQC(h)z$J~$0+%jX3P6ns@8l(pI#hg zKu`S%8jYX$!*Y=ITDA}>#&B9^Sk<%MNIsn(obu66GS8@LSr5g~q%E}RSwtkuD{**v zzClqdYg%Zf-(hHC!pYyj#$Rbz9dkY$khz|@ax(XKEYu~YKv5NJa!6%c=xDS|Bi5<{>a$GmS>`b5WIb*{ zq)fJ(Lt}N8%A+uXEMF^pr Date: Sun, 9 Jun 2024 11:39:38 +0300 Subject: [PATCH 19/93] Enum for node status --- .../Firefighter_Problem.py | 46 +++++++------------ .../firefighter_problem/Utils.py | 34 ++++++++------ .../approximation/firefighter_problem/main.py | 22 ++++----- 3 files changed, 49 insertions(+), 53 deletions(-) diff --git a/networkz/algorithms/approximation/firefighter_problem/Firefighter_Problem.py b/networkz/algorithms/approximation/firefighter_problem/Firefighter_Problem.py index fec9757..a7a50fc 100644 --- a/networkz/algorithms/approximation/firefighter_problem/Firefighter_Problem.py +++ b/networkz/algorithms/approximation/firefighter_problem/Firefighter_Problem.py @@ -31,18 +31,6 @@ except ImportError: from Utils import * -def setup_global_logger(level: int = logging.DEBUG): - log_format = "|| %(levelname)s || %(message)s" - formatter = logging.Formatter(log_format) - handler = logging.StreamHandler() - handler.setFormatter(formatter) - - root_logger = logging.getLogger() - root_logger.setLevel(level) - root_logger.addHandler(handler) - -setup_global_logger() - logger = logging.getLogger(__name__) def spreading_maxsave(Graph:nx.DiGraph, budget:int, source:int, targets:list, flag=None) -> list: @@ -58,28 +46,28 @@ def spreading_maxsave(Graph:nx.DiGraph, budget:int, source:int, targets:list, fl Example 1: >>> G = nx.DiGraph() - >>> G.add_nodes_from([0,1,2], status="target") + >>> G.add_nodes_from([0,1,2], status="vulnerable") >>> G.add_edges_from([(0,1),(0,2),(1,2)]) >>> spreading_maxsave(G,1,0,[1,2]) [(1, 1)] Example 2: >>> G1 = nx.DiGraph() - >>> G1.add_nodes_from([0,1,2,3], status="target") + >>> G1.add_nodes_from([0,1,2,3], status="vulnerable") >>> G1.add_edges_from([(0,1),(0,2),(1,2),(1,3),(2,3)]) >>> spreading_maxsave(G1,1,0,[1,2,3]) [(1, 1)] Example 3: >>> G2 = nx.DiGraph() - >>> G2.add_nodes_from([0,1,2,3,4,5,6], status="target") + >>> G2.add_nodes_from([0,1,2,3,4,5,6], status="vulnerable") >>> G2.add_edges_from([(0,1),(0,2),(1,2),(1,4),(2,3),(2,6),(3,5)]) >>> spreading_maxsave(G2,1,0,[1,2,3,4,5,6]) [(2, 1), (4, 2)] Example 4: >>> G3 = nx.DiGraph() - >>> G3.add_nodes_from([0,1,2,3,4,5,6,7,8], status="target") + >>> G3.add_nodes_from([0,1,2,3,4,5,6,7,8], status="vulnerable") >>> G3.add_edges_from([(0,2),(0,4),(0,5),(2,1),(2,3),(4,1),(4,6),(5,3),(5,6),(5,7),(6,7),(6,8),(7,8)]) >>> spreading_maxsave(G3,2,0,[1,2,3,4,5,6,7,8]) [(5, 1), (2, 1), (8, 2)] @@ -149,28 +137,28 @@ def spreading_minbudget(Graph:nx.DiGraph, source:int, targets:list)-> int: Example 1: >>> G1 = nx.DiGraph() - >>> G1.add_nodes_from([0,1,2,3], status="target") + >>> G1.add_nodes_from([0,1,2,3], status="vulnerable") >>> G1.add_edges_from([(0,1),(0,2),(1,2),(1,3),(2,3)]) >>> spreading_minbudget(G1,0,[1,2,3]) 2 Example 2: >>> G1 = nx.DiGraph() - >>> G1.add_nodes_from([0,1,2,3], status="target") + >>> G1.add_nodes_from([0,1,2,3], status="vulnerable") >>> G1.add_edges_from([(0,1),(0,2),(1,2),(1,3),(2,3)]) >>> spreading_minbudget(G1,0,[1,3]) 1 Example 3: >>> G2 = nx.DiGraph() - >>> G2.add_nodes_from([0,1,2,3,4,5,6], status="target") + >>> G2.add_nodes_from([0,1,2,3,4,5,6], status="vulnerable") >>> G2.add_edges_from([(0,1),(0,2),(1,2),(1,4),(2,3),(2,6),(3,5)]) >>> spreading_minbudget(G2,0,[1,2,3,4,5,6]) 2 Example 4: >>> G3 = nx.DiGraph() - >>> G3.add_nodes_from([0,1,2,3,4,5,6,7,8], status="target") + >>> G3.add_nodes_from([0,1,2,3,4,5,6,7,8], status="vulnerable") >>> G3.add_edges_from([(0,2),(0,4),(0,5),(2,1),(2,3),(4,1),(4,6),(5,3),(5,6),(5,7),(6,7),(6,8),(7,8)]) >>> spreading_minbudget(G3,0,[1,2,3,4,5,6,7,8]) 3 @@ -224,28 +212,28 @@ def non_spreading_minbudget(Graph:nx.DiGraph, source:int, targets:list)->int: Example1: >>> G1 = nx.DiGraph() - >>> G1.add_nodes_from([0,1,2,3], status="target") + >>> G1.add_nodes_from([0,1,2,3], status="vulnerable") >>> G1.add_edges_from([(0,1),(0,2),(1,2),(1,3),(2,3)]) >>> non_spreading_minbudget(G1,0,[1,3]) 2 Example 2: >>> G1 = nx.DiGraph() - >>> G1.add_nodes_from([0,1,2,3], status="target") + >>> G1.add_nodes_from([0,1,2,3], status="vulnerable") >>> G1.add_edges_from([(0,1),(0,2),(1,2),(1,3),(2,3)]) >>> non_spreading_minbudget(G1,0,[1,2,3]) 2 Example 3: >>> G2 = nx.DiGraph() - >>> G2.add_nodes_from([0,1,2,3,4,5,6], status="target") + >>> G2.add_nodes_from([0,1,2,3,4,5,6], status="vulnerable") >>> G2.add_edges_from([(0,1),(0,2),(1,2),(1,4),(2,3),(2,6),(3,5)]) >>> non_spreading_minbudget(G2,0,[1,2,3,4,5,6]) 2 Example 4: >>> G3 = nx.DiGraph() - >>> G3.add_nodes_from([0,1,2,3,4,5,6,7,8], status="target") + >>> G3.add_nodes_from([0,1,2,3,4,5,6,7,8], status="vulnerable") >>> G3.add_edges_from([(0,2),(0,4),(0,5),(2,1),(2,3),(4,1),(4,6),(5,3),(5,6),(5,7),(6,7),(6,8),(7,8)]) >>> non_spreading_minbudget(G3,0,[2,6,1,8]) 3 @@ -272,7 +260,7 @@ def non_spreading_dirlaynet_minbudget(Graph:nx.DiGraph, source:int, targets:list Example1: >>> G4 = nx.DiGraph() - >>> G4.add_nodes_from([0,1,2,3,4,5], status="target") + >>> G4.add_nodes_from([0,1,2,3,4,5], status="vulnerable") >>> G4.add_edges_from([(0,1),(0,2),(1,3),(1,4),(1,5),(2,3),(2,4),(2,5),(3,5),(4,5)]) >>> non_spreading_dirlaynet_minbudget(G4,0,[1,2,3,4,5]) 2 @@ -283,7 +271,7 @@ def non_spreading_dirlaynet_minbudget(Graph:nx.DiGraph, source:int, targets:list layers = adjust_nodes_capacity(Graph, source) G = create_st_graph(Graph, targets) G_reduction = graph_flow_reduction(G, source) - N_groups = min_cut_N_groups(G_reduction, source) + N_groups = min_cut_N_groups(G_reduction, source,layers) vacc_matrix = calculate_vaccine_matrix(layers, N_groups) min_budget = min_budget_calculation(vacc_matrix) @@ -311,7 +299,7 @@ def heuristic_maxsave(Graph:nx.DiGraph, budget:int, source:int, targets:list, sp Example: >>> G = nx.DiGraph() - >>> G.add_nodes_from([0, 1, 2, 3], status="target") + >>> G.add_nodes_from([0, 1, 2, 3], status="vulnerable") >>> G.add_edges_from([(0, 1), (0, 2), (1, 2), (1, 3)]) >>> heuristic_maxsave(G, 1, 0, [1, 2, 3], True) [(1, 1), (2, 2), (3, 3)] @@ -369,7 +357,7 @@ def heuristic_minbudget(Graph:nx.DiGraph, source:int, targets:list, spreading:bo Example: >>> G = nx.DiGraph() - >>> G.add_nodes_from([0, 1, 2, 3], status="target") + >>> G.add_nodes_from([0, 1, 2, 3], status="vulnerable") >>> G.add_edges_from([(0, 1), (0, 2), (1, 2), (1, 3)]) >>> heuristic_minbudget(G, 0, [1, 2, 3], True) 1 @@ -410,7 +398,7 @@ def heuristic_minbudget(Graph:nx.DiGraph, source:int, targets:list, spreading:bo # doctest.testmod(verbose=True) # # G3 = nx.DiGraph() -# # G3.add_nodes_from([0,1,2,3,4,5,6,7,8], status="target") +# # G3.add_nodes_from([0,1,2,3,4,5,6,7,8], status="vulnerable") # # G3.add_edges_from([(0,2),(0,4),(0,5),(2,1),(2,3),(4,1),(4,6),(5,3),(5,6),(5,7),(6,7),(6,8),(7,8)]) # # logger.info("=" * 150) # # logger.info(spreading_minbudget(G3,0,[2,6,1,8])) diff --git a/networkz/algorithms/approximation/firefighter_problem/Utils.py b/networkz/algorithms/approximation/firefighter_problem/Utils.py index 4bb4ec5..70f8ca7 100644 --- a/networkz/algorithms/approximation/firefighter_problem/Utils.py +++ b/networkz/algorithms/approximation/firefighter_problem/Utils.py @@ -23,10 +23,17 @@ import networkx.algorithms.connectivity as algo import matplotlib.pyplot as plt import numpy as np -import math +from enum import Enum import copy import logging +class Status(Enum): + VULNERABLE = "vulnerable" + INFECTED = "infected" + VACCINATED = "vaccinated" + DIRECTLY_VACCINATED = "directly vaccinated" + + node_colors = { 'target': 'gray', 'infected': 'red', @@ -63,7 +70,8 @@ def validate_parameters(graph:nx.DiGraph, source:int, targets:list)->None: if not all(node in graph_nodes for node in targets): logger.critical("Error: Not all nodes in the targets list are on the graph.") raise ValueError("Error: Not all nodes in the targets list are on the graph.") - + + "Spreading:" def calculate_gamma(graph:nx.DiGraph, source:int, targets:list)-> dict: """ @@ -159,7 +167,7 @@ def find_best_direct_vaccination(graph:nx.DiGraph, direct_vaccinations:dict, cur common_elements = None max_number = -1 for option in current_time_options: - if(graph.nodes[option[0]]['status'] == 'target'): + if(graph.nodes[option[0]]['status'] == Status.VULNERABLE.value): nodes_list = direct_vaccinations.get(option) common_elements = set(nodes_list) & set(targets) logger.debug(f"Direct vaccination: {option}, Nodes saved: {common_elements} (if set(), then it's empty)") @@ -189,8 +197,8 @@ def spread_virus(graph:nx.DiGraph, infected_nodes:list)->bool: new_infected_nodes = [] for node in infected_nodes: for neighbor in graph.neighbors(node): - if graph.nodes[neighbor]['status'] == 'target': - graph.nodes[neighbor]['status'] = 'infected' + if graph.nodes[neighbor]['status'] == Status.VULNERABLE.value: + graph.nodes[neighbor]['status'] = Status.INFECTED.value new_infected_nodes.append(neighbor) logger.debug("SPREAD VIRUS: Node " + f'{neighbor}' + " has been infected from node " + f'{node}') infected_nodes.clear() @@ -210,8 +218,8 @@ def spread_vaccination(graph:nx.DiGraph, vaccinated_nodes:list)->None: new_vaccinated_nodes = [] for node in vaccinated_nodes: for neighbor in graph.neighbors(node): - if graph.nodes[neighbor]['status'] == 'target': - graph.nodes[neighbor]['status'] = 'vaccinated' + if graph.nodes[neighbor]['status'] == Status.VULNERABLE.value: + graph.nodes[neighbor]['status'] = Status.VACCINATED.value new_vaccinated_nodes.append(neighbor) logger.debug("SPREAD VACCINATION: Node " + f'{neighbor}' + " has been vaccinated from node " + f'{node}') vaccinated_nodes.clear() @@ -228,7 +236,7 @@ def vaccinate_node(graph:nx.DiGraph, node:int)->None: - graph (nx.DiGraph): Directed graph. - node (int): Node to be vaccinated. """ - graph.nodes[node]['status'] = 'directly vaccinated' + graph.nodes[node]['status'] = Status.DIRECTLY_VACCINATED.value logger.info("Node " + f'{node}' + " has been directly vaccinated") return @@ -240,7 +248,7 @@ def clean_graph(graph:nx.DiGraph)->None: - graph (nx.DiGraph): Directed graph. """ for node in graph.nodes: - graph.nodes[node]['status'] = 'target' + graph.nodes[node]['status'] = Status.VULNERABLE.value return "Non-Spreading:" @@ -279,7 +287,7 @@ def create_st_graph(graph:nx.DiGraph, targets:list)->nx.DiGraph: - G (nx.DiGraph): s-t graph. """ G = copy.deepcopy(graph) - G.add_node('t', status = 'target') + G.add_node('t', status = Status.VULNERABLE.value) for node in targets: G.add_edge(node,'t') #display_graph(G) @@ -466,12 +474,12 @@ def find_best_neighbor(graph:nx.DiGraph, infected_nodes:list, targets:list)->int optional_nodes.update(graph.neighbors(node)) for node in optional_nodes: - if graph.nodes[node]['status'] == 'target': + if graph.nodes[node]['status'] == Status.VULNERABLE.value: # for each node that is target, we will add only his nighbors that are target as well neighbors_list = list(graph.neighbors(node)) target_neighbors = set() for neighbor in neighbors_list: - if graph.nodes[neighbor]['status'] == 'target': + if graph.nodes[neighbor]['status'] == Status.VULNERABLE.value: target_neighbors.add(neighbor) if node in targets: target_neighbors.add(node) @@ -534,7 +542,7 @@ def parse_json_to_networkx(json_data): vertices = graph_info["vertices"] edges = [(edge["source"], edge["target"]) for edge in graph_info["edges"]] G = nx.DiGraph() - G.add_nodes_from(vertices, status="target") + G.add_nodes_from(vertices, status= Status.VULNERABLE.value) G.add_edges_from(edges) graphs[graph_key] = G return graphs \ No newline at end of file diff --git a/networkz/algorithms/approximation/firefighter_problem/main.py b/networkz/algorithms/approximation/firefighter_problem/main.py index e696b81..09a3939 100644 --- a/networkz/algorithms/approximation/firefighter_problem/main.py +++ b/networkz/algorithms/approximation/firefighter_problem/main.py @@ -30,19 +30,19 @@ from Firefighter_Problem import * import Firefighter_Problem as firefighter_problem # to run the doctest on the firefighter_problem files -# def setup_global_logger(level: int = logging.DEBUG): -# log_format = "|| %(levelname)s || %(message)s" -# formatter = logging.Formatter(log_format) -# handler = logging.StreamHandler() -# handler.setFormatter(formatter) +def setup_global_logger(level: int = logging.DEBUG): + log_format = "|| %(levelname)s || %(message)s" + formatter = logging.Formatter(log_format) + handler = logging.StreamHandler() + handler.setFormatter(formatter) -# root_logger = logging.getLogger() -# root_logger.setLevel(level) -# root_logger.addHandler(handler) + root_logger = logging.getLogger() + root_logger.setLevel(level) + root_logger.addHandler(handler) -# setup_global_logger() +setup_global_logger() -# logger = logging.getLogger(__name__) +logger = logging.getLogger(__name__) if __name__ == "__main__": @@ -51,7 +51,7 @@ print(f"Doctest results: {result}") # G3 = nx.DiGraph() - # G3.add_nodes_from([0,1,2,3,4,5,6,7,8], status="target") + # G3.add_nodes_from([0,1,2,3,4,5,6,7,8], status="vulnerable") # G3.add_edges_from([(0,2),(0,4),(0,5),(2,1),(2,3),(4,1),(4,6),(5,3),(5,6),(5,7),(6,7),(6,8),(7,8)]) # logger.info("=" * 150) # logger.info(spreading_minbudget(G3,0,[2,6,1,8])) From 48f95339e9ec5ed231b28745b5c7c7d27259e33b Mon Sep 17 00:00:00 2001 From: 20shaked20 Date: Sun, 9 Jun 2024 11:40:45 +0300 Subject: [PATCH 20/93] Moved CleanGraph to the head of the function --- .../approximation/firefighter_problem/Firefighter_Problem.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/networkz/algorithms/approximation/firefighter_problem/Firefighter_Problem.py b/networkz/algorithms/approximation/firefighter_problem/Firefighter_Problem.py index a7a50fc..a1e6c21 100644 --- a/networkz/algorithms/approximation/firefighter_problem/Firefighter_Problem.py +++ b/networkz/algorithms/approximation/firefighter_problem/Firefighter_Problem.py @@ -78,6 +78,8 @@ def spreading_maxsave(Graph:nx.DiGraph, budget:int, source:int, targets:list, fl validate_parameters(Graph, source, targets) logger.info(f"Starting the spreading_maxsave function with source node {source}, budget {budget}, and targets: {targets}") + + clean_graph(Graph) infected_nodes = [] vaccinated_nodes = [] @@ -119,7 +121,6 @@ def spreading_maxsave(Graph:nx.DiGraph, budget:int, source:int, targets:list, fl time_step += 1 - clean_graph(Graph) logger.info(f"Returning vaccination strategy: {vaccination_strategy}") return vaccination_strategy From e9b2c952298e8548033586de9bdede1d24178033 Mon Sep 17 00:00:00 2001 From: 20shaked20 Date: Sun, 9 Jun 2024 11:45:48 +0300 Subject: [PATCH 21/93] Moved graphs.json to its correct place --- .../test_firefighter_problem}/graphs.json | 0 .../test_non_spreading_dirlaynet_minbudget.py | 2 +- .../test_firefighter_problem/test_non_spreading_minbudget.py | 2 +- .../tests/test_firefighter_problem/test_spreading_maxsave.py | 2 +- .../tests/test_firefighter_problem/test_spreading_minbudget.py | 2 +- 5 files changed, 4 insertions(+), 4 deletions(-) rename networkz/algorithms/{approximation/firefighter_problem => tests/test_firefighter_problem}/graphs.json (100%) diff --git a/networkz/algorithms/approximation/firefighter_problem/graphs.json b/networkz/algorithms/tests/test_firefighter_problem/graphs.json similarity index 100% rename from networkz/algorithms/approximation/firefighter_problem/graphs.json rename to networkz/algorithms/tests/test_firefighter_problem/graphs.json diff --git a/networkz/algorithms/tests/test_firefighter_problem/test_non_spreading_dirlaynet_minbudget.py b/networkz/algorithms/tests/test_firefighter_problem/test_non_spreading_dirlaynet_minbudget.py index eb05c23..873ea2c 100644 --- a/networkz/algorithms/tests/test_firefighter_problem/test_non_spreading_dirlaynet_minbudget.py +++ b/networkz/algorithms/tests/test_firefighter_problem/test_non_spreading_dirlaynet_minbudget.py @@ -36,7 +36,7 @@ from networkz.algorithms.approximation.firefighter_problem.Utils import min_budget_calculation def get_graphs(): - with open("/networkz/algorithms/approximation/firefighter_problem/graphs.json", "r") as file: + with open("graphs.json", "r") as file: json_data = json.load(file) graphs = parse_json_to_networkx(json_data=json_data) return graphs diff --git a/networkz/algorithms/tests/test_firefighter_problem/test_non_spreading_minbudget.py b/networkz/algorithms/tests/test_firefighter_problem/test_non_spreading_minbudget.py index 9ed5b35..3ebad5d 100644 --- a/networkz/algorithms/tests/test_firefighter_problem/test_non_spreading_minbudget.py +++ b/networkz/algorithms/tests/test_firefighter_problem/test_non_spreading_minbudget.py @@ -44,7 +44,7 @@ def sample_json_data(): } def get_graphs(): - with open("networkz/algorithms/approximation/firefighter_problem/graphs.json", "r") as file: + with open("graphs.json", "r") as file: json_data = json.load(file) graphs = parse_json_to_networkx(json_data) return graphs diff --git a/networkz/algorithms/tests/test_firefighter_problem/test_spreading_maxsave.py b/networkz/algorithms/tests/test_firefighter_problem/test_spreading_maxsave.py index 5a4cce8..ae0f5d6 100644 --- a/networkz/algorithms/tests/test_firefighter_problem/test_spreading_maxsave.py +++ b/networkz/algorithms/tests/test_firefighter_problem/test_spreading_maxsave.py @@ -27,7 +27,7 @@ from networkz.algorithms.approximation.firefighter_problem.Firefighter_Problem import spreading_maxsave from networkz.algorithms.approximation.firefighter_problem.Utils import parse_json_to_networkx, calculate_gamma, calculate_epsilon, find_best_direct_vaccination -with open("networkz/algorithms/approximation/firefighter_problem/graphs.json", "r") as file: +with open("graphs.json", "r") as file: json_data = json.load(file) graphs = parse_json_to_networkx(json_data) diff --git a/networkz/algorithms/tests/test_firefighter_problem/test_spreading_minbudget.py b/networkz/algorithms/tests/test_firefighter_problem/test_spreading_minbudget.py index a38929f..7a18506 100644 --- a/networkz/algorithms/tests/test_firefighter_problem/test_spreading_minbudget.py +++ b/networkz/algorithms/tests/test_firefighter_problem/test_spreading_minbudget.py @@ -45,7 +45,7 @@ def sample_json_data(): } def get_graphs(): - with open("networkz/algorithms/approximation/firefighter_problem/graphs.json", "r") as file: + with open("graphs.json", "r") as file: json_data = json.load(file) graphs = parse_json_to_networkx(json_data) return graphs From 79c49bbabdbde0b6150b778f134812d6a3756eba Mon Sep 17 00:00:00 2001 From: 20shaked20 Date: Sun, 9 Jun 2024 12:08:01 +0300 Subject: [PATCH 22/93] some fixes for dirlay tests+added randomized directed layered network graph generator --- .../approximation/firefighter_problem/test.py | 63 +++++++ .../test_non_spreading_dirlaynet_minbudget.py | 170 ++++++++++++------ 2 files changed, 182 insertions(+), 51 deletions(-) create mode 100644 networkz/algorithms/approximation/firefighter_problem/test.py diff --git a/networkz/algorithms/approximation/firefighter_problem/test.py b/networkz/algorithms/approximation/firefighter_problem/test.py new file mode 100644 index 0000000..ee9f356 --- /dev/null +++ b/networkz/algorithms/approximation/firefighter_problem/test.py @@ -0,0 +1,63 @@ +import networkx as nx +import matplotlib.pyplot as plt +import random + +def generate_random_directed_layered_graph(layers, nodes_per_layer, edge_probability): + """ + Generates a random directed layered network graph. + + Parameters: + layers (int): Number of layers in the graph. + nodes_per_layer (int): Number of nodes per layer. + edge_probability (float): Probability of creating an edge between nodes in consecutive layers. + + Returns: + G (networkx.DiGraph): A directed layered network graph. + """ + G = nx.DiGraph() + + # Create nodes for each layer + for layer in range(layers): + for node in range(nodes_per_layer): + G.add_node(f"L{layer}_N{node}", layer=layer) + + # Create edges between nodes in consecutive layers based on edge probability + for layer in range(layers - 1): + for node1 in range(nodes_per_layer): + for node2 in range(nodes_per_layer): + if random.random() < edge_probability: + G.add_edge(f"L{layer}_N{node1}", f"L{layer + 1}_N{node2}") + + return G + +def draw_layered_graph(G): + """ + Draws the layered graph with layers arranged in horizontal layers. + + Parameters: + G (networkx.DiGraph): A directed layered network graph. + """ + pos = {} + layer_nodes = {} + + for node, data in G.nodes(data=True): + layer = data['layer'] + if layer not in layer_nodes: + layer_nodes[layer] = [] + layer_nodes[layer].append(node) + + for layer, nodes in layer_nodes.items(): + for i, node in enumerate(nodes): + pos[node] = (layer, -i) + + plt.figure(figsize=(10, 6)) + nx.draw(G, pos, with_labels=True, node_size=700, node_color="skyblue", arrowsize=20) + plt.show() + +# Example usage: +layers = 3 +nodes_per_layer = 4 +edge_probability = 0.3 + +G = generate_random_directed_layered_graph(layers, nodes_per_layer, edge_probability) +draw_layered_graph(G) \ No newline at end of file diff --git a/networkz/algorithms/tests/test_firefighter_problem/test_non_spreading_dirlaynet_minbudget.py b/networkz/algorithms/tests/test_firefighter_problem/test_non_spreading_dirlaynet_minbudget.py index 873ea2c..4378d7b 100644 --- a/networkz/algorithms/tests/test_firefighter_problem/test_non_spreading_dirlaynet_minbudget.py +++ b/networkz/algorithms/tests/test_firefighter_problem/test_non_spreading_dirlaynet_minbudget.py @@ -24,6 +24,7 @@ import json import numpy as np import math +import random from networkz.algorithms.approximation.firefighter_problem.Firefighter_Problem import non_spreading_dirlaynet_minbudget from networkz.algorithms.approximation.firefighter_problem.Utils import adjust_nodes_capacity @@ -35,69 +36,96 @@ from networkz.algorithms.approximation.firefighter_problem.Utils import matrix_to_integers_values from networkz.algorithms.approximation.firefighter_problem.Utils import min_budget_calculation -def get_graphs(): - with open("graphs.json", "r") as file: +with open("graphs.json", "r") as file: json_data = json.load(file) - graphs = parse_json_to_networkx(json_data=json_data) - return graphs +graphs = parse_json_to_networkx(json_data) -graphs = get_graphs() -def test_source_not_in_graph(): - """ - This test checks if the source node is not a real node in the graph. - """ - pattern = r"Error: The source node is( not|n't) on the graph" +# def test_source_not_in_graph(): +# """ +# This test checks if the source node is not a real node in the graph. +# """ +# pattern = r"Error: The source node is( not|n't) on the graph" - with pytest.raises(ValueError, match=pattern): - non_spreading_dirlaynet_minbudget(graphs["Dirlay_Graph-1"], -3, [0, 5]) +# with pytest.raises(ValueError, match=pattern): +# non_spreading_dirlaynet_minbudget(graphs["Dirlay_Graph-1"], -3, [0, 5]) - with pytest.raises(ValueError, match=pattern): - non_spreading_dirlaynet_minbudget(graphs["Dirlay_Graph-2"], 13, [0, 1, 4]) +# with pytest.raises(ValueError, match=pattern): +# non_spreading_dirlaynet_minbudget(graphs["Dirlay_Graph-2"], 13, [0, 1, 4]) - with pytest.raises(ValueError, match=pattern): - non_spreading_dirlaynet_minbudget(graphs["Dirlay_Graph-3"], 15, [0, 6, 7]) - - with pytest.raises(ValueError, match=pattern): - non_spreading_dirlaynet_minbudget(graphs["Dirlay_Graph-4"], -1, [1, 3, 5, 7]) - - -def test_target_not_in_graph(): - """ - This test checks if a node we're trying to save is not in the graph. - """ - pattern = r"Error: Not all nodes (we're trying to save|in the targets list) are on the graph" +# with pytest.raises(ValueError, match=pattern): +# non_spreading_dirlaynet_minbudget(graphs["Dirlay_Graph-3"], 15, [0, 6, 7]) + +# with pytest.raises(ValueError, match=pattern): +# non_spreading_dirlaynet_minbudget(graphs["Dirlay_Graph-4"], -1, [1, 3, 5, 7]) + +@pytest.mark.parametrize("graph_key, budget, source, targets", [ + ("Dirlay_Graph-1", -3, [0, 5]), + ("Dirlay_Graph-2", 13, [0, 1, 4]), + ("Dirlay_Graph-3", 15, [0, 6, 7]), + ("Dirlay_Graph-4", -1, [1, 3, 5, 7]), +]) +def test_source_not_in_graph(graph_key, source, targets): + with pytest.raises(ValueError): + non_spreading_dirlaynet_minbudget(graphs[graph_key], source, targets) + +# def test_target_not_in_graph(): +# """ +# This test checks if a node we're trying to save is not in the graph. +# """ +# pattern = r"Error: Not all nodes (we're trying to save|in the targets list) are on the graph" - with pytest.raises(ValueError, match=pattern): - non_spreading_dirlaynet_minbudget(graphs["Dirlay_Graph-1"], 0, [1, 5, 7]) +# with pytest.raises(ValueError, match=pattern): +# non_spreading_dirlaynet_minbudget(graphs["Dirlay_Graph-1"], 0, [1, 5, 7]) - with pytest.raises(ValueError, match=pattern): - non_spreading_dirlaynet_minbudget(graphs["Dirlay_Graph-2"], 1, [0, 2, -1, 9]) +# with pytest.raises(ValueError, match=pattern): +# non_spreading_dirlaynet_minbudget(graphs["Dirlay_Graph-2"], 1, [0, 2, -1, 9]) - with pytest.raises(ValueError, match=pattern): - non_spreading_dirlaynet_minbudget(graphs["Dirlay_Graph-3"], 4, [0, 1, 2, 11, 12, 13, 14]) - - with pytest.raises(ValueError, match=pattern): - non_spreading_dirlaynet_minbudget(graphs["Dirlay_Graph-4"], 0, [1, 3, 5, 7, 15, 20]) - - -def test_source_is_target(): - """ - This test checks if we're trying to save a source node. - """ - pattern = r"Error: The source node can( not|'t) be a part of the targets (vector|list), since the virus is spreading from the source" +# with pytest.raises(ValueError, match=pattern): +# non_spreading_dirlaynet_minbudget(graphs["Dirlay_Graph-3"], 4, [0, 1, 2, 11, 12, 13, 14]) + +# with pytest.raises(ValueError, match=pattern): +# non_spreading_dirlaynet_minbudget(graphs["Dirlay_Graph-4"], 0, [1, 3, 5, 7, 15, 20]) + +@pytest.mark.parametrize("graph_key, source, targets", [ + ("Dirlay_Graph-1", 0, [1, 5, 7]), + ("Dirlay_Graph-2", 1, [0, 2, -1, 9]), + ("Dirlay_Graph-3", 4, [0, 1, 2, 11, 12, 13, 14]), + ("Dirlay_Graph-4", 0, [1, 3, 5, 7, 15, 20]), +]) +def test_target_not_in_graph(graph_key, source, targets): + with pytest.raises(ValueError): + non_spreading_dirlaynet_minbudget(graphs[graph_key], source, targets) + + +# def test_source_is_target(): +# """ +# This test checks if we're trying to save a source node. +# """ +# pattern = r"Error: The source node can( not|'t) be a part of the targets (vector|list), since the virus is spreading from the source" - with pytest.raises(ValueError, match=pattern): - non_spreading_dirlaynet_minbudget(graphs["Dirlay_Graph-1"], 0, [0, 5]) +# with pytest.raises(ValueError, match=pattern): +# non_spreading_dirlaynet_minbudget(graphs["Dirlay_Graph-1"], 0, [0, 5]) - with pytest.raises(ValueError, match=pattern): - non_spreading_dirlaynet_minbudget(graphs["Dirlay_Graph-2"], 1, [0, 1, 4]) +# with pytest.raises(ValueError, match=pattern): +# non_spreading_dirlaynet_minbudget(graphs["Dirlay_Graph-2"], 1, [0, 1, 4]) - with pytest.raises(ValueError, match=pattern): - non_spreading_dirlaynet_minbudget(graphs["Dirlay_Graph-3"], 6, [0, 6, 7]) +# with pytest.raises(ValueError, match=pattern): +# non_spreading_dirlaynet_minbudget(graphs["Dirlay_Graph-3"], 6, [0, 6, 7]) + +# with pytest.raises(ValueError, match=pattern): +# non_spreading_dirlaynet_minbudget(graphs["Dirlay_Graph-4"], 3, [1, 3, 5, 7]) + - with pytest.raises(ValueError, match=pattern): - non_spreading_dirlaynet_minbudget(graphs["Dirlay_Graph-4"], 3, [1, 3, 5, 7]) +@pytest.mark.parametrize("graph_key, budget, source, targets", [ + ("Dirlay_Graph-1", 0, [0, 5]), + ("Dirlay_Graph-2", 1, [0, 1, 4]), + ("Dirlay_Graph-3", 6, [0, 6, 7]), + ("Dirlay_Graph-4", 3, [1, 3, 5, 7]), +]) +def test_source_is_target(graph_key, source, targets): + with pytest.raises(ValueError): + non_spreading_dirlaynet_minbudget(graphs[graph_key], source, targets) @@ -391,4 +419,44 @@ def test_min_budget_calculation(): matrix_3_check = np.array([[1, 0, 1], [0, 0, 0], [0, 0, 0]]) - assert min_budget_calculation(matrix_3_check) == 1 \ No newline at end of file + assert min_budget_calculation(matrix_3_check) == 1 + + +def generate_random_directed_layered_graph(): + """ + Generates a random directed layered graph with a random number of layers and nodes per layer. + + :return: A NetworkX DiGraph representing the layered network. + """ + layers = random.randint(3, 6) # Random number of layers between 3 and 6, this can be changed later.. just for testing + nodes_per_layer = [random.randint(2, 5) for _ in range(layers)] # Random number of nodes per layer between 2 and 5 + + G = nx.DiGraph() + node_count = 0 + + # layer buildup + for layer in range(layers): + for _ in range(nodes_per_layer[layer]): + G.add_node(node_count, layer=layer, status="vulnerable") + node_count += 1 + + # edges between layers + for layer in range(layers - 1): + for u in [n for n, d in G.nodes(data=True) if d['layer'] == layer]: + for v in [n for n, d in G.nodes(data=True) if d['layer'] == layer + 1]: + if random.random() < 0.5: # randomly add edges with 50% probability + G.add_edge(u, v) + + return G + +def test_non_spreading_dirlaynet_minbudget(): + G = generate_random_directed_layered_graph() + source = 0 + + nodes = list(G.nodes()) + num_targets = random.randint(1, len(nodes) - 1) # Ensure at least one target node + targets = random.sample(nodes[1:], num_targets) # randomly choose nodes to save , excluding the source node + + min_budget = non_spreading_dirlaynet_minbudget(G, source, targets) + + assert min_budget >= 0, "Minimum budget should be non-negative" From b349c7bc54075a1b671e67d3f8f7129521029051 Mon Sep 17 00:00:00 2001 From: 20shaked20 Date: Sun, 9 Jun 2024 12:09:07 +0300 Subject: [PATCH 23/93] removed uneeded test.py --- .../approximation/firefighter_problem/test.py | 63 ------------------- 1 file changed, 63 deletions(-) delete mode 100644 networkz/algorithms/approximation/firefighter_problem/test.py diff --git a/networkz/algorithms/approximation/firefighter_problem/test.py b/networkz/algorithms/approximation/firefighter_problem/test.py deleted file mode 100644 index ee9f356..0000000 --- a/networkz/algorithms/approximation/firefighter_problem/test.py +++ /dev/null @@ -1,63 +0,0 @@ -import networkx as nx -import matplotlib.pyplot as plt -import random - -def generate_random_directed_layered_graph(layers, nodes_per_layer, edge_probability): - """ - Generates a random directed layered network graph. - - Parameters: - layers (int): Number of layers in the graph. - nodes_per_layer (int): Number of nodes per layer. - edge_probability (float): Probability of creating an edge between nodes in consecutive layers. - - Returns: - G (networkx.DiGraph): A directed layered network graph. - """ - G = nx.DiGraph() - - # Create nodes for each layer - for layer in range(layers): - for node in range(nodes_per_layer): - G.add_node(f"L{layer}_N{node}", layer=layer) - - # Create edges between nodes in consecutive layers based on edge probability - for layer in range(layers - 1): - for node1 in range(nodes_per_layer): - for node2 in range(nodes_per_layer): - if random.random() < edge_probability: - G.add_edge(f"L{layer}_N{node1}", f"L{layer + 1}_N{node2}") - - return G - -def draw_layered_graph(G): - """ - Draws the layered graph with layers arranged in horizontal layers. - - Parameters: - G (networkx.DiGraph): A directed layered network graph. - """ - pos = {} - layer_nodes = {} - - for node, data in G.nodes(data=True): - layer = data['layer'] - if layer not in layer_nodes: - layer_nodes[layer] = [] - layer_nodes[layer].append(node) - - for layer, nodes in layer_nodes.items(): - for i, node in enumerate(nodes): - pos[node] = (layer, -i) - - plt.figure(figsize=(10, 6)) - nx.draw(G, pos, with_labels=True, node_size=700, node_color="skyblue", arrowsize=20) - plt.show() - -# Example usage: -layers = 3 -nodes_per_layer = 4 -edge_probability = 0.3 - -G = generate_random_directed_layered_graph(layers, nodes_per_layer, edge_probability) -draw_layered_graph(G) \ No newline at end of file From bf05df4d0889742b379574ba0fb305c6c086b408 Mon Sep 17 00:00:00 2001 From: 20shaked20 Date: Sun, 9 Jun 2024 12:11:05 +0300 Subject: [PATCH 24/93] Removed uneeded code from lib --- .../firefighter_problem/Firefighter_Problem.py | 14 +------------- 1 file changed, 1 insertion(+), 13 deletions(-) diff --git a/networkz/algorithms/approximation/firefighter_problem/Firefighter_Problem.py b/networkz/algorithms/approximation/firefighter_problem/Firefighter_Problem.py index a1e6c21..6655319 100644 --- a/networkz/algorithms/approximation/firefighter_problem/Firefighter_Problem.py +++ b/networkz/algorithms/approximation/firefighter_problem/Firefighter_Problem.py @@ -392,16 +392,4 @@ def heuristic_minbudget(Graph:nx.DiGraph, source:int, targets:list, spreading:bo logger.info(f"Returning minimum budget: {middle}") return middle - - -# if __name__ == "__main__": -# import doctest -# doctest.testmod(verbose=True) - -# # G3 = nx.DiGraph() -# # G3.add_nodes_from([0,1,2,3,4,5,6,7,8], status="vulnerable") -# # G3.add_edges_from([(0,2),(0,4),(0,5),(2,1),(2,3),(4,1),(4,6),(5,3),(5,6),(5,7),(6,7),(6,8),(7,8)]) -# # logger.info("=" * 150) -# # logger.info(spreading_minbudget(G3,0,[2,6,1,8])) -# # logger.info("=" * 150) -# # logger.info(heuristic_minbudget(G3,0,[2,6,1,8], True)) + \ No newline at end of file From 2c9b2f993fea7b44634ae2d2f47eace12e5a645c Mon Sep 17 00:00:00 2001 From: Almog-David Date: Tue, 11 Jun 2024 09:16:36 +0300 Subject: [PATCH 25/93] update targets during code loop + add logger to while before exiting maxsave + json fixed --- .../Firefighter_Problem.py | 25 +- .../firefighter_problem/Utils.py | 17 +- .../approximation/firefighter_problem/main.py | 11 +- .../test_firefighter_problem/graphs.json | 727 ++++-------------- .../test_non_spreading_dirlaynet_minbudget.py | 4 +- .../test_non_spreading_minbudget.py | 6 +- .../test_firefighter_problem/test_parsing.py | 12 +- .../test_spreading_maxsave.py | 6 +- .../test_spreading_minbudget.py | 8 +- 9 files changed, 197 insertions(+), 619 deletions(-) diff --git a/networkz/algorithms/approximation/firefighter_problem/Firefighter_Problem.py b/networkz/algorithms/approximation/firefighter_problem/Firefighter_Problem.py index 6655319..cc59f01 100644 --- a/networkz/algorithms/approximation/firefighter_problem/Firefighter_Problem.py +++ b/networkz/algorithms/approximation/firefighter_problem/Firefighter_Problem.py @@ -99,7 +99,7 @@ def spreading_maxsave(Graph:nx.DiGraph, budget:int, source:int, targets:list, fl spread_vaccination(Graph, vaccinated_nodes) for i in range(budget): logger.info(f"Calculating the best direct vaccination strategy for the current time step that saves more new node in targets (Current budget: {budget})") - vaccination = find_best_direct_vaccination(Graph, direct_vaccinations, epsilon[time_step], targets) + vaccination, nodes_saved = find_best_direct_vaccination(Graph, direct_vaccinations, epsilon[time_step], targets) if vaccination != (): logger.info(f"Found {vaccination} as a solution for current timestamp, appending to vaccination strategy and vaccinating the node") @@ -110,7 +110,14 @@ def spreading_maxsave(Graph:nx.DiGraph, budget:int, source:int, targets:list, fl vaccinated_nodes.append(chosen_node) logger.info(f"Updated list of currently vaccinated nodes: {vaccinated_nodes}") - + + if nodes_saved is not None: + targets[:] = [element for element in targets if element not in nodes_saved] + logger.info(f"Updated list of targets: {targets}") + + else: + logger.info(f"All nodes are either vaccinated or infected") + can_spread = spread_virus(Graph, infected_nodes) if flag is not None: @@ -325,11 +332,23 @@ def heuristic_maxsave(Graph:nx.DiGraph, budget:int, source:int, targets:list, sp if spreading: spread_vaccination(Graph, vaccinated_nodes) for i in range(budget): - node_to_vaccinate = find_best_neighbor(Graph, infected_nodes, targets) + logger.info(f"Calculating the best direct vaccination strategy for the current time step that saves more new node in targets (Current budget: {budget})") + node_to_vaccinate, nodes_saved = find_best_neighbor(Graph, infected_nodes, targets) if node_to_vaccinate is not None: + logger.info(f"Found {node_to_vaccinate} as a solution for current timestamp, appending to vaccination strategy and vaccinating the node") vaccination_strategy.append((node_to_vaccinate, time_step)) vaccinate_node(Graph, node_to_vaccinate) vaccinated_nodes.append(node_to_vaccinate) + logger.info(f"Updated list of currently vaccinated nodes: {vaccinated_nodes}") + + + if nodes_saved is not None: + targets[:] = [element for element in targets if element not in nodes_saved] + logger.info(f"Updated list of targets: {targets}") + + else: + logger.info(f"All nodes are either vaccinated or infected") + can_spread = spread_virus(Graph, infected_nodes) if flag is not None: diff --git a/networkz/algorithms/approximation/firefighter_problem/Utils.py b/networkz/algorithms/approximation/firefighter_problem/Utils.py index 70f8ca7..6e0e358 100644 --- a/networkz/algorithms/approximation/firefighter_problem/Utils.py +++ b/networkz/algorithms/approximation/firefighter_problem/Utils.py @@ -163,7 +163,7 @@ def find_best_direct_vaccination(graph:nx.DiGraph, direct_vaccinations:dict, cur - best_vaccination (tuple): Best direct vaccination option. """ best_vaccination = () - nodes_saved = {} + nodes_saved = [] common_elements = None max_number = -1 for option in current_time_options: @@ -175,13 +175,10 @@ def find_best_direct_vaccination(graph:nx.DiGraph, direct_vaccinations:dict, cur best_vaccination = option nodes_saved = common_elements max_number = len(common_elements) - - if nodes_saved is not None: - targets[:] = [element for element in targets if element not in nodes_saved] if best_vaccination != (): logger.info("The best direct vaccination is: " + str(best_vaccination) + " and it saves nodes: " + str(nodes_saved)) - return best_vaccination + return best_vaccination, nodes_saved def spread_virus(graph:nx.DiGraph, infected_nodes:list)->bool: """ @@ -225,7 +222,6 @@ def spread_vaccination(graph:nx.DiGraph, vaccinated_nodes:list)->None: vaccinated_nodes.clear() for node in new_vaccinated_nodes: vaccinated_nodes.append(node) - logger.debug(f"Currently vaccinated nodes: {vaccinated_nodes}") return def vaccinate_node(graph:nx.DiGraph, node:int)->None: @@ -490,12 +486,9 @@ def find_best_neighbor(graph:nx.DiGraph, infected_nodes:list, targets:list)->int nodes_saved = common_elements max_number = len(common_elements) - if nodes_saved is not None: - targets[:] = [element for element in targets if element not in nodes_saved] - if best_node != None: logger.info("The best node is: " + f'{best_node}' + " and it's saves nodes: " + str(nodes_saved)) - return best_node + return best_node, nodes_saved "Usefull Utils:" @@ -540,9 +533,9 @@ def parse_json_to_networkx(json_data): raise KeyError(f"Error parsing {graph_type}_{graph_name}: 'edges' must be a non-empty list.") vertices = graph_info["vertices"] - edges = [(edge["source"], edge["target"]) for edge in graph_info["edges"]] + edges = [(edge[0], edge[1]) for edge in graph_info["edges"]] G = nx.DiGraph() - G.add_nodes_from(vertices, status= Status.VULNERABLE.value) + G.add_nodes_from(vertices, status=Status.VULNERABLE.value) G.add_edges_from(edges) graphs[graph_key] = G return graphs \ No newline at end of file diff --git a/networkz/algorithms/approximation/firefighter_problem/main.py b/networkz/algorithms/approximation/firefighter_problem/main.py index 09a3939..0c773f6 100644 --- a/networkz/algorithms/approximation/firefighter_problem/main.py +++ b/networkz/algorithms/approximation/firefighter_problem/main.py @@ -50,10 +50,11 @@ def setup_global_logger(level: int = logging.DEBUG): result = doctest.testmod(firefighter_problem, verbose=True) print(f"Doctest results: {result}") - # G3 = nx.DiGraph() - # G3.add_nodes_from([0,1,2,3,4,5,6,7,8], status="vulnerable") - # G3.add_edges_from([(0,2),(0,4),(0,5),(2,1),(2,3),(4,1),(4,6),(5,3),(5,6),(5,7),(6,7),(6,8),(7,8)]) - # logger.info("=" * 150) - # logger.info(spreading_minbudget(G3,0,[2,6,1,8])) + G3 = nx.DiGraph() + G3.add_nodes_from([0,1,2,3,4,5,6,7,8], status="vulnerable") + G3.add_edges_from([(0,2),(0,4),(0,5),(2,1),(2,3),(4,1),(4,6),(5,3),(5,6),(5,7),(6,7),(6,8),(7,8)]) + logger.info("=" * 150) + logger.info(spreading_minbudget(G3,0,[2,6,1,8])) # logger.info("=" * 150) # logger.info(heuristic_minbudget(G3,0,[2,6,1,8], True)) + diff --git a/networkz/algorithms/tests/test_firefighter_problem/graphs.json b/networkz/algorithms/tests/test_firefighter_problem/graphs.json index c28715e..bbeb176 100644 --- a/networkz/algorithms/tests/test_firefighter_problem/graphs.json +++ b/networkz/algorithms/tests/test_firefighter_problem/graphs.json @@ -10,26 +10,11 @@ 5 ], "edges": [ - { - "source": 0, - "target": 1 - }, - { - "source": 0, - "target": 2 - }, - { - "source": 1, - "target": 3 - }, - { - "source": 3, - "target": 4 - }, - { - "source": 4, - "target": 5 - } + [0, 1], + [0, 2], + [1, 3], + [3, 4], + [4, 5] ] }, "Graph-2": { @@ -41,22 +26,10 @@ 4 ], "edges": [ - { - "source": 0, - "target": 1 - }, - { - "source": 0, - "target": 2 - }, - { - "source": 2, - "target": 3 - }, - { - "source": 1, - "target": 4 - } + [0, 1], + [0, 2], + [2, 3], + [1, 4] ] }, "Graph-3": { @@ -71,34 +44,13 @@ 7 ], "edges": [ - { - "source": 0, - "target": 1 - }, - { - "source": 0, - "target": 2 - }, - { - "source": 0, - "target": 3 - }, - { - "source": 3, - "target": 4 - }, - { - "source": 1, - "target": 5 - }, - { - "source": 5, - "target": 6 - }, - { - "source": 4, - "target": 7 - } + [0, 1], + [0, 2], + [0, 3], + [3, 4], + [1, 5], + [5, 6], + [4, 7] ] }, "Graph-4": { @@ -113,42 +65,15 @@ 8 ], "edges": [ - { - "source": 0, - "target": 1 - }, - { - "source": 0, - "target": 2 - }, - { - "source": 1, - "target": 3 - }, - { - "source": 1, - "target": 4 - }, - { - "source": 2, - "target": 3 - }, - { - "source": 3, - "target": 5 - }, - { - "source": 4, - "target": 5 - }, - { - "source": 5, - "target": 6 - }, - { - "source": 6, - "target": 8 - } + [0, 1], + [0, 2], + [1, 3], + [1, 4], + [2, 3], + [3, 5], + [4, 5], + [5, 6], + [6, 8] ] }, "Graph-5": { @@ -164,58 +89,19 @@ 8 ], "edges": [ - { - "source": 0, - "target": 2 - }, - { - "source": 0, - "target": 4 - }, - { - "source": 0, - "target": 5 - }, - { - "source": 2, - "target": 1 - }, - { - "source": 2, - "target": 3 - }, - { - "source": 4, - "target": 1 - }, - { - "source": 4, - "target": 6 - }, - { - "source": 5, - "target": 3 - }, - { - "source": 5, - "target": 6 - }, - { - "source": 5, - "target": 7 - }, - { - "source": 6, - "target": 7 - }, - { - "source": 6, - "target": 8 - }, - { - "source": 7, - "target": 8 - } + [0, 2], + [0, 4], + [0, 5], + [2, 1], + [2, 3], + [4, 1], + [4, 6], + [5, 3], + [5, 6], + [5, 7], + [6, 7], + [6, 8], + [7, 8] ] } }, @@ -231,34 +117,13 @@ 6 ], "edges": [ - { - "source": 0, - "target": 1 - }, - { - "source": 0, - "target": 2 - }, - { - "source": 1, - "target": 3 - }, - { - "source": 1, - "target": 4 - }, - { - "source": 2, - "target": 3 - }, - { - "source": 2, - "target": 6 - }, - { - "source": 3, - "target": 5 - } + [0, 1], + [0, 2], + [1, 3], + [1, 4], + [2, 3], + [2, 6], + [3, 5] ] }, "Graph-2": { @@ -273,66 +138,21 @@ 7 ], "edges": [ - { - "source": 0, - "target": 5 - }, - { - "source": 0, - "target": 2 - }, - { - "source": 1, - "target": 7 - }, - { - "source": 2, - "target": 5 - }, - { - "source": 3, - "target": 7 - }, - { - "source": 4, - "target": 0 - }, - { - "source": 4, - "target": 7 - }, - { - "source": 6, - "target": 2 - }, - { - "source": 6, - "target": 3 - }, - { - "source": 6, - "target": 0 - }, - { - "source": 6, - "target": 1 - }, - { - "source": 7, - "target": 6 - }, - { - "source": 7, - "target": 4 - }, - { - "source": 7, - "target": 3 - }, - { - "source": 7, - "target": 0 - } + [0, 5], + [0, 2], + [1, 7], + [2, 5], + [3, 7], + [4, 0], + [4, 7], + [6, 2], + [6, 3], + [6, 0], + [6, 1], + [7, 6], + [7, 4], + [7, 3], + [7, 0] ] }, "Graph-3": { @@ -345,46 +165,16 @@ 5 ], "edges": [ - { - "source": 0, - "target": 1 - }, - { - "source": 0, - "target": 2 - }, - { - "source": 1, - "target": 3 - }, - { - "source": 1, - "target": 4 - }, - { - "source": 1, - "target": 5 - }, - { - "source": 2, - "target": 3 - }, - { - "source": 2, - "target": 4 - }, - { - "source": 2, - "target": 5 - }, - { - "source": 3, - "target": 5 - }, - { - "source": 4, - "target": 5 - } + [0, 1], + [0, 2], + [1, 3], + [1, 4], + [1, 5], + [2, 3], + [2, 4], + [2, 5], + [3, 5], + [4, 5] ] }, "Graph-4": { @@ -399,58 +189,19 @@ 7 ], "edges": [ - { - "source": 0, - "target": 1 - }, - { - "source": 0, - "target": 6 - }, - { - "source": 1, - "target": 2 - }, - { - "source": 1, - "target": 5 - }, - { - "source": 1, - "target": 6 - }, - { - "source": 2, - "target": 3 - }, - { - "source": 3, - "target": 4 - }, - { - "source": 5, - "target": 2 - }, - { - "source": 5, - "target": 3 - }, - { - "source": 5, - "target": 4 - }, - { - "source": 6, - "target": 5 - }, - { - "source": 7, - "target": 6 - }, - { - "source": 7, - "target": 1 - } + [0, 1], + [0, 6], + [1, 2], + [1, 5], + [1, 6], + [2, 3], + [3, 4], + [5, 2], + [5, 3], + [5, 4], + [6, 5], + [7, 6], + [7, 1] ] }, "Graph-5": { @@ -465,58 +216,19 @@ 7 ], "edges": [ - { - "source": 0, - "target": 1 - }, - { - "source": 0, - "target": 6 - }, - { - "source": 1, - "target": 2 - }, - { - "source": 1, - "target": 5 - }, - { - "source": 1, - "target": 6 - }, - { - "source": 2, - "target": 3 - }, - { - "source": 3, - "target": 4 - }, - { - "source": 5, - "target": 2 - }, - { - "source": 5, - "target": 3 - }, - { - "source": 5, - "target": 4 - }, - { - "source": 6, - "target": 5 - }, - { - "source": 7, - "target": 6 - }, - { - "source": 7, - "target": 1 - } + [0, 1], + [0, 6], + [1, 2], + [1, 5], + [1, 6], + [2, 3], + [3, 4], + [5, 2], + [5, 3], + [5, 4], + [6, 5], + [7, 6], + [7, 1] ] }, "Graph-6": { @@ -533,66 +245,21 @@ 9 ], "edges": [ - { - "source": 1, - "target": 2 - }, - { - "source": 2, - "target": 1 - }, - { - "source": 2, - "target": 3 - }, - { - "source": 2, - "target": 8 - }, - { - "source": 3, - "target": 7 - }, - { - "source": 3, - "target": 9 - }, - { - "source": 3, - "target": 0 - }, - { - "source": 4, - "target": 1 - }, - { - "source": 4, - "target": 7 - }, - { - "source": 6, - "target": 3 - }, - { - "source": 7, - "target": 0 - }, - { - "source": 7, - "target": 6 - }, - { - "source": 8, - "target": 1 - }, - { - "source": 9, - "target": 5 - }, - { - "source": 9, - "target": 2 - } + [1, 2], + [2, 1], + [2, 3], + [2, 8], + [3, 7], + [3, 9], + [3, 0], + [4, 1], + [4, 7], + [6, 3], + [7, 0], + [7, 6], + [8, 1], + [9, 5], + [9, 2] ] }, "Graph-7": { @@ -606,42 +273,15 @@ 6 ], "edges": [ - { - "source": 1, - "target": 5 - }, - { - "source": 1, - "target": 4 - }, - { - "source": 1, - "target": 3 - }, - { - "source": 2, - "target": 6 - }, - { - "source": 2, - "target": 3 - }, - { - "source": 3, - "target": 0 - }, - { - "source": 3, - "target": 4 - }, - { - "source": 5, - "target": 3 - }, - { - "source": 6, - "target": 4 - } + [1, 5], + [1, 4], + [1, 3], + [2, 6], + [2, 3], + [3, 0], + [3, 4], + [5, 3], + [6, 4] ] }, "Graph-8": { @@ -663,107 +303,32 @@ 14 ], "edges": [ - { - "source": 0, - "target": 11 - }, - { - "source": 0, - "target": 3 - }, - { - "source": 0, - "target": 14 - }, - { - "source": 1, - "target": 13 - }, - { - "source": 1, - "target": 7 - }, - { - "source": 1, - "target": 6 - }, - { - "source": 1, - "target": 5 - }, - { - "source": 3, - "target": 14 - }, - { - "source": 5, - "target": 1 - }, - { - "source": 5, - "target": 0 - }, - { - "source": 6, - "target": 5 - }, - { - "source": 7, - "target": 4 - }, - { - "source": 7, - "target": 13 - }, - { - "source": 8, - "target": 5 - }, - { - "source": 9, - "target": 14 - }, - { - "source": 10, - "target": 12 - }, - { - "source": 11, - "target": 10 - }, - { - "source": 12, - "target": 10 - }, - { - "source": 12, - "target": 6 - }, - { - "source": 12, - "target": 2 - }, - { - "source": 13, - "target": 11 - }, - { - "source": 14, - "target": 13 - }, - { - "source": 14, - "target": 6 - }, - { - "source": 14, - "target": 11 - }, - { - "source": 14, - "target": 8 - } + [0, 11], + [0, 3], + [0, 14], + [1, 13], + [1, 7], + [1, 6], + [1, 5], + [3, 14], + [5, 1], + [5, 0], + [6, 5], + [7, 4], + [7, 13], + [8, 5], + [9, 14], + [10, 12], + [11, 10], + [12, 10], + [12, 6], + [12, 2], + [13, 11], + [14, 13], + [14, 6], + [14, 11], + [14, 8] ] } } -} \ No newline at end of file +} diff --git a/networkz/algorithms/tests/test_firefighter_problem/test_non_spreading_dirlaynet_minbudget.py b/networkz/algorithms/tests/test_firefighter_problem/test_non_spreading_dirlaynet_minbudget.py index 4378d7b..973a167 100644 --- a/networkz/algorithms/tests/test_firefighter_problem/test_non_spreading_dirlaynet_minbudget.py +++ b/networkz/algorithms/tests/test_firefighter_problem/test_non_spreading_dirlaynet_minbudget.py @@ -36,7 +36,7 @@ from networkz.algorithms.approximation.firefighter_problem.Utils import matrix_to_integers_values from networkz.algorithms.approximation.firefighter_problem.Utils import min_budget_calculation -with open("graphs.json", "r") as file: +with open("networkz/algorithms/tests/test_firefighter_problem/graphs.json", "r") as file: json_data = json.load(file) graphs = parse_json_to_networkx(json_data) @@ -59,7 +59,7 @@ # with pytest.raises(ValueError, match=pattern): # non_spreading_dirlaynet_minbudget(graphs["Dirlay_Graph-4"], -1, [1, 3, 5, 7]) -@pytest.mark.parametrize("graph_key, budget, source, targets", [ +@pytest.mark.parametrize("graph_key, source, targets", [ ("Dirlay_Graph-1", -3, [0, 5]), ("Dirlay_Graph-2", 13, [0, 1, 4]), ("Dirlay_Graph-3", 15, [0, 6, 7]), diff --git a/networkz/algorithms/tests/test_firefighter_problem/test_non_spreading_minbudget.py b/networkz/algorithms/tests/test_firefighter_problem/test_non_spreading_minbudget.py index 3ebad5d..9c6da4f 100644 --- a/networkz/algorithms/tests/test_firefighter_problem/test_non_spreading_minbudget.py +++ b/networkz/algorithms/tests/test_firefighter_problem/test_non_spreading_minbudget.py @@ -32,19 +32,19 @@ def sample_json_data(): "Dirlay": { "Graph-1": { "vertices": [0, 1, 2, 3, 4, 5], - "edges": [{"source": 0, "target": 1}, {"source": 0, "target": 2}] + "edges": [[0, 1], [0, 2]] }, }, "RegularGraph": { "Graph-1": { "vertices": [0, 1, 2], - "edges": [{"source": 0, "target": 1}, {"source": 1, "target": 2}] + "edges": [[0, 1], [1, 2]] }, } } def get_graphs(): - with open("graphs.json", "r") as file: + with open("networkz/algorithms/tests/test_firefighter_problem/graphs.json", "r") as file: json_data = json.load(file) graphs = parse_json_to_networkx(json_data) return graphs diff --git a/networkz/algorithms/tests/test_firefighter_problem/test_parsing.py b/networkz/algorithms/tests/test_firefighter_problem/test_parsing.py index d25701a..0e57e6a 100644 --- a/networkz/algorithms/tests/test_firefighter_problem/test_parsing.py +++ b/networkz/algorithms/tests/test_firefighter_problem/test_parsing.py @@ -22,7 +22,7 @@ import pytest import networkx as nx -from networkz.algorithms.approximation.firefighter_problem.Utils import parse_json_to_networkx +from networkz.algorithms.approximation.firefighter_problem.Utils import parse_json_to_networkx, Status @pytest.fixture def sample_json_data(): @@ -30,13 +30,13 @@ def sample_json_data(): "Dirlay": { "Graph-1": { "vertices": [0, 1, 2, 3, 4, 5], - "edges": [{"source": 0, "target": 1}, {"source": 0, "target": 2}] + "edges": [[0, 1], [0, 2]] }, }, "RegularGraph": { "Graph-1": { "vertices": [0, 1, 2], - "edges": [{"source": 0, "target": 1}, {"source": 1, "target": 2}] + "edges": [[0, 1], [1, 2]] }, } } @@ -46,7 +46,7 @@ def missing_vertices_json(): return { "InvalidGraph": { "Graph-1": { - "edges": [{"source": 0, "target": 1}, {"source": 1, "target": 2}] + "edges": [[0, 1], [1, 2]] } } } @@ -126,11 +126,11 @@ def test_parsing_dirlay_graph_status(sample_json_data): dirlay_graph = graphs["Dirlay_Graph-1"] for node in dirlay_graph.nodes(data=True): - assert node[1]["status"] == "target" + assert node[1]["status"] == Status.VULNERABLE.value def test_parsing_regular_graph_status(sample_json_data): graphs = parse_json_to_networkx(sample_json_data) regular_graph = graphs["RegularGraph_Graph-1"] for node in regular_graph.nodes(data=True): - assert node[1]["status"] == "target" \ No newline at end of file + assert node[1]["status"] == Status.VULNERABLE.value \ No newline at end of file diff --git a/networkz/algorithms/tests/test_firefighter_problem/test_spreading_maxsave.py b/networkz/algorithms/tests/test_firefighter_problem/test_spreading_maxsave.py index ae0f5d6..5a510be 100644 --- a/networkz/algorithms/tests/test_firefighter_problem/test_spreading_maxsave.py +++ b/networkz/algorithms/tests/test_firefighter_problem/test_spreading_maxsave.py @@ -27,7 +27,7 @@ from networkz.algorithms.approximation.firefighter_problem.Firefighter_Problem import spreading_maxsave from networkz.algorithms.approximation.firefighter_problem.Utils import parse_json_to_networkx, calculate_gamma, calculate_epsilon, find_best_direct_vaccination -with open("graphs.json", "r") as file: +with open("networkz/algorithms/tests/test_firefighter_problem/graphs.json", "r") as file: json_data = json.load(file) graphs = parse_json_to_networkx(json_data) @@ -112,7 +112,7 @@ def test_source_is_target(graph_key, budget, source, targets): }) ]) def test_calculate_gamma(graph_key, source, targets, expected_gamma, expected_direct_vaccination): - print( calculate_gamma(graphs[graph_key], source, targets)) + print(calculate_gamma(graphs[graph_key], source, targets)) calculated_gamma, calculated_direct_vaccination = calculate_gamma(graphs[graph_key], source, targets) for key in expected_gamma: @@ -205,7 +205,7 @@ def test_calculate_epsilon(direct_vaccinations, expected_epsilon): ]) def test_find_best_direct_vaccination(graph_key, direct_vaccinations, current_epsilon, targets, expected_best_direct_vaccination): - calculated_best_direct_vaccination = find_best_direct_vaccination(graphs[graph_key],direct_vaccinations,current_epsilon,targets) + calculated_best_direct_vaccination = find_best_direct_vaccination(graphs[graph_key],direct_vaccinations,current_epsilon,targets)[0] assert calculated_best_direct_vaccination == expected_best_direct_vaccination diff --git a/networkz/algorithms/tests/test_firefighter_problem/test_spreading_minbudget.py b/networkz/algorithms/tests/test_firefighter_problem/test_spreading_minbudget.py index 7a18506..677a8e6 100644 --- a/networkz/algorithms/tests/test_firefighter_problem/test_spreading_minbudget.py +++ b/networkz/algorithms/tests/test_firefighter_problem/test_spreading_minbudget.py @@ -33,19 +33,19 @@ def sample_json_data(): "Dirlay": { "Graph-1": { "vertices": [0, 1, 2, 3, 4, 5], - "edges": [{"source": 0, "target": 1}, {"source": 0, "target": 2}] + "edges": [[0, 1], [0, 2]] }, }, "RegularGraph": { "Graph-1": { "vertices": [0, 1, 2], - "edges": [{"source": 0, "target": 1}, {"source": 1, "target": 2}] + "edges": [[0, 1], [1, 2]] }, } } def get_graphs(): - with open("graphs.json", "r") as file: + with open("networkz/algorithms/tests/test_firefighter_problem/graphs.json", "r") as file: json_data = json.load(file) graphs = parse_json_to_networkx(json_data) return graphs @@ -240,7 +240,7 @@ def test_calculate_epsilon(direct_vaccinations, expected_epsilon): ]) def test_find_best_direct_vaccination(graph_key, direct_vaccinations, current_epsilon, targets, expected_best_direct_vaccination): - calculated_best_direct_vaccination = find_best_direct_vaccination(graphs[graph_key],direct_vaccinations,current_epsilon,targets) + calculated_best_direct_vaccination = find_best_direct_vaccination(graphs[graph_key],direct_vaccinations,current_epsilon,targets)[0] assert calculated_best_direct_vaccination == expected_best_direct_vaccination From 1579cb871108341f0a85c3060cab750a657c9013 Mon Sep 17 00:00:00 2001 From: Almog-David Date: Tue, 11 Jun 2024 11:01:16 +0300 Subject: [PATCH 26/93] changed the return parameters of the minbudget and maxsve + added mew tests in maxsave --- .../Firefighter_Problem.py | 61 +++++++++---------- .../approximation/firefighter_problem/main.py | 11 ++-- .../test_spreading_maxsave.py | 29 +++++++-- .../test_spreading_minbudget.py | 30 ++++----- 4 files changed, 76 insertions(+), 55 deletions(-) diff --git a/networkz/algorithms/approximation/firefighter_problem/Firefighter_Problem.py b/networkz/algorithms/approximation/firefighter_problem/Firefighter_Problem.py index cc59f01..f4a5ab9 100644 --- a/networkz/algorithms/approximation/firefighter_problem/Firefighter_Problem.py +++ b/networkz/algorithms/approximation/firefighter_problem/Firefighter_Problem.py @@ -49,28 +49,28 @@ def spreading_maxsave(Graph:nx.DiGraph, budget:int, source:int, targets:list, fl >>> G.add_nodes_from([0,1,2], status="vulnerable") >>> G.add_edges_from([(0,1),(0,2),(1,2)]) >>> spreading_maxsave(G,1,0,[1,2]) - [(1, 1)] + ([(1, 1)], {1}) Example 2: >>> G1 = nx.DiGraph() >>> G1.add_nodes_from([0,1,2,3], status="vulnerable") >>> G1.add_edges_from([(0,1),(0,2),(1,2),(1,3),(2,3)]) >>> spreading_maxsave(G1,1,0,[1,2,3]) - [(1, 1)] + ([(1, 1)], {1, 3}) Example 3: >>> G2 = nx.DiGraph() >>> G2.add_nodes_from([0,1,2,3,4,5,6], status="vulnerable") >>> G2.add_edges_from([(0,1),(0,2),(1,2),(1,4),(2,3),(2,6),(3,5)]) >>> spreading_maxsave(G2,1,0,[1,2,3,4,5,6]) - [(2, 1), (4, 2)] + ([(2, 1), (4, 2)], {2, 3, 4, 5, 6}) Example 4: >>> G3 = nx.DiGraph() >>> G3.add_nodes_from([0,1,2,3,4,5,6,7,8], status="vulnerable") >>> G3.add_edges_from([(0,2),(0,4),(0,5),(2,1),(2,3),(4,1),(4,6),(5,3),(5,6),(5,7),(6,7),(6,8),(7,8)]) >>> spreading_maxsave(G3,2,0,[1,2,3,4,5,6,7,8]) - [(5, 1), (2, 1), (8, 2)] + ([(5, 1), (2, 1), (8, 2)], {1, 2, 3, 5, 6, 7, 8}) """ if budget < 1: logger.critical("Error: The budget must be at least 1") @@ -84,8 +84,9 @@ def spreading_maxsave(Graph:nx.DiGraph, budget:int, source:int, targets:list, fl infected_nodes = [] vaccinated_nodes = [] vaccination_strategy = [] + saved_target_nodes = set() can_spread = True - Graph.nodes[source]['status'] = 'infected' + Graph.nodes[source]['status'] = Status.INFECTED.value infected_nodes.append(source) logger.info("Calculating all possible direct vaccinations in each timestamp") @@ -112,6 +113,7 @@ def spreading_maxsave(Graph:nx.DiGraph, budget:int, source:int, targets:list, fl logger.info(f"Updated list of currently vaccinated nodes: {vaccinated_nodes}") if nodes_saved is not None: + saved_target_nodes.update(nodes_saved) targets[:] = [element for element in targets if element not in nodes_saved] logger.info(f"Updated list of targets: {targets}") @@ -123,13 +125,13 @@ def spreading_maxsave(Graph:nx.DiGraph, budget:int, source:int, targets:list, fl if flag is not None: if len(targets) == 0 or any(node in infected_nodes for node in targets): clean_graph(Graph) - logger.info(f"Returning vaccination strategy: {vaccination_strategy}") - return vaccination_strategy + logger.info(f"Returning vaccination strategy: {vaccination_strategy}. The strategy saved the nodes: {saved_target_nodes}") + return vaccination_strategy, saved_target_nodes time_step += 1 - logger.info(f"Returning vaccination strategy: {vaccination_strategy}") - return vaccination_strategy + logger.info(f"Returning vaccination strategy: {vaccination_strategy}. The strategy saved the nodes: {saved_target_nodes}") + return vaccination_strategy, saved_target_nodes def spreading_minbudget(Graph:nx.DiGraph, source:int, targets:list)-> int: @@ -148,54 +150,49 @@ def spreading_minbudget(Graph:nx.DiGraph, source:int, targets:list)-> int: >>> G1.add_nodes_from([0,1,2,3], status="vulnerable") >>> G1.add_edges_from([(0,1),(0,2),(1,2),(1,3),(2,3)]) >>> spreading_minbudget(G1,0,[1,2,3]) - 2 + (2, [(1, 1), (2, 1)]) Example 2: >>> G1 = nx.DiGraph() >>> G1.add_nodes_from([0,1,2,3], status="vulnerable") >>> G1.add_edges_from([(0,1),(0,2),(1,2),(1,3),(2,3)]) >>> spreading_minbudget(G1,0,[1,3]) - 1 + (1, [(1, 1)]) Example 3: >>> G2 = nx.DiGraph() >>> G2.add_nodes_from([0,1,2,3,4,5,6], status="vulnerable") >>> G2.add_edges_from([(0,1),(0,2),(1,2),(1,4),(2,3),(2,6),(3,5)]) >>> spreading_minbudget(G2,0,[1,2,3,4,5,6]) - 2 + (2, [(2, 1), (1, 1)]) Example 4: >>> G3 = nx.DiGraph() >>> G3.add_nodes_from([0,1,2,3,4,5,6,7,8], status="vulnerable") >>> G3.add_edges_from([(0,2),(0,4),(0,5),(2,1),(2,3),(4,1),(4,6),(5,3),(5,6),(5,7),(6,7),(6,8),(7,8)]) >>> spreading_minbudget(G3,0,[1,2,3,4,5,6,7,8]) - 3 + (3, [(5, 1), (2, 1), (4, 1)]) """ validate_parameters(Graph, source, targets) logger.info(f"Starting the spreading_minbudget function with source node {source} and targets: {targets}") original_targets = list(targets) + best_strategy = [] - logger.info("Calculating all possible direct vaccinations in each timestamp") - direct_vaccinations = calculate_gamma(Graph, source, targets)[1] # TODO: check if this can be removed for optimization min_value = 1 max_value = len(targets) middle = math.floor((min_value + max_value) / 2) while min_value < max_value: logger.info(f"Calling maxsave with parameters - Source: {source}, Targets: {targets}, Budget: {middle}") - strategy = spreading_maxsave(Graph, middle, source, targets, True) - nodes_saved = set() - - for option in strategy: - list_of_nodes = direct_vaccinations.get(option) - nodes_saved.update(list_of_nodes) - + strategy, nodes_saved = spreading_maxsave(Graph, middle, source, targets, True) + common_elements = set(nodes_saved) & set(original_targets) if len(common_elements) == len(original_targets): logger.info(f"The current budget {middle} has saved all the targets!") max_value = middle + best_strategy = strategy else: logger.warning(f"The current budget {middle} didn't save all the targets!") min_value = middle + 1 @@ -203,8 +200,8 @@ def spreading_minbudget(Graph:nx.DiGraph, source:int, targets:list)-> int: middle = math.floor((min_value + max_value) / 2) targets = list(original_targets) - logger.info(f"Returning minimum budget: {middle}") - return middle + logger.info(f"Returning minimum budget: {middle} and the vaccination strategy: {best_strategy}") + return middle, best_strategy def non_spreading_minbudget(Graph:nx.DiGraph, source:int, targets:list)->int: @@ -310,7 +307,7 @@ def heuristic_maxsave(Graph:nx.DiGraph, budget:int, source:int, targets:list, sp >>> G.add_nodes_from([0, 1, 2, 3], status="vulnerable") >>> G.add_edges_from([(0, 1), (0, 2), (1, 2), (1, 3)]) >>> heuristic_maxsave(G, 1, 0, [1, 2, 3], True) - [(1, 1), (2, 2), (3, 3)] + [(1, 1)] """ if budget < 1: logger.critical("Error: The budget must be at least 1") @@ -324,7 +321,7 @@ def heuristic_maxsave(Graph:nx.DiGraph, budget:int, source:int, targets:list, sp vaccinated_nodes = [] vaccination_strategy = [] can_spread = True - Graph.nodes[source]['status'] = 'infected' + Graph.nodes[source]['status'] = Status.INFECTED.value infected_nodes.append(source) time_step = 1 @@ -380,11 +377,12 @@ def heuristic_minbudget(Graph:nx.DiGraph, source:int, targets:list, spreading:bo >>> G.add_nodes_from([0, 1, 2, 3], status="vulnerable") >>> G.add_edges_from([(0, 1), (0, 2), (1, 2), (1, 3)]) >>> heuristic_minbudget(G, 0, [1, 2, 3], True) - 1 + (2, [(1, 1), (2, 1)]) """ validate_parameters(Graph, source, targets) logger.info(f"Starting the heuristic_minbudget function with source node {source}, targets: {targets}, and spreading: {spreading}") + best_strategy= [] original_targets = list(targets) min_value = 1 max_value = len(targets) @@ -392,16 +390,17 @@ def heuristic_minbudget(Graph:nx.DiGraph, source:int, targets:list, spreading:bo saved_everyone = True while min_value < max_value: - heuristic_maxsave(Graph, middle, source, targets, spreading, True) + strategy = heuristic_maxsave(Graph, middle, source, targets, spreading, True) for node in original_targets: - if Graph.nodes[node]['status'] == 'infected': + if Graph.nodes[node]['status'] == Status.INFECTED.value: saved_everyone = False break if saved_everyone: logger.info(f"The current budget {middle} has saved all the targets!") max_value = middle + best_strategy = strategy else: logger.warning(f"The current budget {middle} didn't save all the targets!") min_value = middle + 1 @@ -409,6 +408,6 @@ def heuristic_minbudget(Graph:nx.DiGraph, source:int, targets:list, spreading:bo middle = math.floor((min_value + max_value) / 2) targets = list(original_targets) - logger.info(f"Returning minimum budget: {middle}") - return middle + logger.info(f"Returning minimum budget: {middle} and the vaccination strategy: {best_strategy}") + return middle, best_strategy \ No newline at end of file diff --git a/networkz/algorithms/approximation/firefighter_problem/main.py b/networkz/algorithms/approximation/firefighter_problem/main.py index 0c773f6..04f5f72 100644 --- a/networkz/algorithms/approximation/firefighter_problem/main.py +++ b/networkz/algorithms/approximation/firefighter_problem/main.py @@ -50,11 +50,12 @@ def setup_global_logger(level: int = logging.DEBUG): result = doctest.testmod(firefighter_problem, verbose=True) print(f"Doctest results: {result}") - G3 = nx.DiGraph() - G3.add_nodes_from([0,1,2,3,4,5,6,7,8], status="vulnerable") - G3.add_edges_from([(0,2),(0,4),(0,5),(2,1),(2,3),(4,1),(4,6),(5,3),(5,6),(5,7),(6,7),(6,8),(7,8)]) - logger.info("=" * 150) - logger.info(spreading_minbudget(G3,0,[2,6,1,8])) + #G3 = nx.DiGraph() + #G3.add_nodes_from([0,1,2,3,4,5,6,7,8], status="vulnerable") + #G3.add_edges_from([(0,2),(0,4),(0,5),(2,1),(2,3),(4,1),(4,6),(5,3),(5,6),(5,7),(6,7),(6,8),(7,8)]) + #logger.info("=" * 150) + #logger.info(spreading_minbudget(G3,0,[2,6,1,8])) + #print(spreading_maxsave(G3,1, 0,[2,6,1,8])[1]) # logger.info("=" * 150) # logger.info(heuristic_minbudget(G3,0,[2,6,1,8], True)) diff --git a/networkz/algorithms/tests/test_firefighter_problem/test_spreading_maxsave.py b/networkz/algorithms/tests/test_firefighter_problem/test_spreading_maxsave.py index 5a510be..dce9d91 100644 --- a/networkz/algorithms/tests/test_firefighter_problem/test_spreading_maxsave.py +++ b/networkz/algorithms/tests/test_firefighter_problem/test_spreading_maxsave.py @@ -215,7 +215,7 @@ def test_find_best_direct_vaccination(graph_key, direct_vaccinations, current_ep ]) def test_strategy_length(graph_key, budget, source, targets, expected_length): graph = graphs[graph_key] - calculated_strategy = spreading_maxsave(graph, budget, source, targets) + calculated_strategy = spreading_maxsave(graph, budget, source, targets)[0] assert len(calculated_strategy) == expected_length @@ -226,7 +226,7 @@ def test_strategy_length(graph_key, budget, source, targets, expected_length): ]) def test_save_all_vertices(graph_key, budget, source, targets, expected_strategy): graph = graphs[graph_key] - calculated_strategy = spreading_maxsave(graph, budget, source, targets) + calculated_strategy = spreading_maxsave(graph, budget, source, targets)[0] assert calculated_strategy == expected_strategy @@ -236,10 +236,31 @@ def test_save_all_vertices(graph_key, budget, source, targets, expected_strategy ]) def test_save_subgroup_vertices(graph_key, budget, source, targets, expected_strategy): graph = graphs[graph_key] - calculated_strategy = spreading_maxsave(graph, budget, source, targets) + calculated_strategy = spreading_maxsave(graph, budget, source, targets)[0] assert calculated_strategy == expected_strategy +@pytest.mark.parametrize("graph_key, budget, source, targets, expected_nodes_saved_list", [ + ("RegularGraph_Graph-1", 1, 0, [1, 2, 3, 4, 5, 6], {1, 3, 4, 5, 6}), + ("Dirlay_Graph-5", 2, 0, [1, 2, 3, 4, 5, 6, 7, 8], {1, 2, 3, 5, 6, 7, 8}), +]) +def test_save_all_vertices_nodes_list(graph_key, budget, source, targets, expected_nodes_saved_list): + graph = graphs[graph_key] + calculated_nodes_saved_list = spreading_maxsave(graph, budget, source, targets)[1] + print(calculated_nodes_saved_list) + + assert calculated_nodes_saved_list == expected_nodes_saved_list + +@pytest.mark.parametrize("graph_key, budget, source, targets, expected_nodes_saved_list", [ + ("RegularGraph_Graph-6", 2, 1, [3, 9, 0, 5, 6], {0, 3, 5, 6, 9}), + ("RegularGraph_Graph-4", 1, 0, [2, 6, 4], {2, 4}), +]) +def test_save_subgroup_vertices_nodes_list(graph_key, budget, source, targets, expected_nodes_saved_list): + graph = graphs[graph_key] + calculated_nodes_saved_list = spreading_maxsave(graph, budget, source, targets)[1] + + assert calculated_nodes_saved_list == expected_nodes_saved_list + def random_graph_test(): for i in range(10): num_nodes = random.randint(2,100) @@ -260,7 +281,7 @@ def random_graph_test(): if probability < 0.75 and node!=0: targets.append(node) - ans = spreading_maxsave(G,1,0,targets) + ans = spreading_maxsave(G,1,0,targets)[0] print(len(ans)) print(len(G.nodes)) diff --git a/networkz/algorithms/tests/test_firefighter_problem/test_spreading_minbudget.py b/networkz/algorithms/tests/test_firefighter_problem/test_spreading_minbudget.py index 677a8e6..21cb1ec 100644 --- a/networkz/algorithms/tests/test_firefighter_problem/test_spreading_minbudget.py +++ b/networkz/algorithms/tests/test_firefighter_problem/test_spreading_minbudget.py @@ -245,23 +245,23 @@ def test_find_best_direct_vaccination(graph_key, direct_vaccinations, current_ep assert calculated_best_direct_vaccination == expected_best_direct_vaccination def test_save_all_vertices(): - assert 2 == spreading_minbudget(graphs["RegularGraph_Graph-1"], 0, [1,2,3,4,5,6]) # answer is 2 - assert 2 == spreading_minbudget(graphs["RegularGraph_Graph-2"], 0, [1,2,3,4,5,6,7]) # answer is 2 - assert 3 != spreading_minbudget(graphs["RegularGraph_Graph-3"], 0, [1,2,3,4,5]) # answer is 2 - assert spreading_minbudget(graphs["RegularGraph_Graph-2"], 0, [1,2,3,4,5,6,7]) >= spreading_minbudget(graphs["RegularGraph_Graph-4"], 0, [1,2,3,4,5,6,7]) # answer is 2 - assert spreading_minbudget(graphs["RegularGraph_Graph-1"], 0, [1,2,3,4,5,6]) > spreading_minbudget(graphs["RegularGraph_Graph-6"], 1, [0,2,3,4,5,6,7,8,9]) # answer is 1 - assert 3 == spreading_minbudget(graphs["RegularGraph_Graph-7"], 1, [0,2,3,4,5,6]) # answer is 3 - assert 2 != spreading_minbudget(graphs["RegularGraph_Graph-8"], 0, [1,2,3,4,5,6,7,8,9,10,11,12,13,14]) # answer is 4 + assert 2 == spreading_minbudget(graphs["RegularGraph_Graph-1"], 0, [1,2,3,4,5,6])[0] # answer is 2 + assert 2 == spreading_minbudget(graphs["RegularGraph_Graph-2"], 0, [1,2,3,4,5,6,7])[0] # answer is 2 + assert 3 != spreading_minbudget(graphs["RegularGraph_Graph-3"], 0, [1,2,3,4,5])[0] # answer is 2 + assert spreading_minbudget(graphs["RegularGraph_Graph-2"], 0, [1,2,3,4,5,6,7])[0] >= spreading_minbudget(graphs["RegularGraph_Graph-4"], 0, [1,2,3,4,5,6,7])[0] # answer is 2 + assert spreading_minbudget(graphs["RegularGraph_Graph-1"], 0, [1,2,3,4,5,6])[0] > spreading_minbudget(graphs["RegularGraph_Graph-6"], 1, [0,2,3,4,5,6,7,8,9])[0] # answer is 1 + assert 3 == spreading_minbudget(graphs["RegularGraph_Graph-7"], 1, [0,2,3,4,5,6])[0] # answer is 3 + assert 2 != spreading_minbudget(graphs["RegularGraph_Graph-8"], 0, [1,2,3,4,5,6,7,8,9,10,11,12,13,14])[0] # answer is 4 def test_save_subgroup_vertices(): - assert spreading_minbudget(graphs["RegularGraph_Graph-1"], 0, [1,2,3,4,5,6]) != spreading_minbudget(graphs["RegularGraph_Graph-1"], 0, [1,5,6]) # answer is 1 - assert 1 == spreading_minbudget(graphs["RegularGraph_Graph-2"], 0, [1,3,4,5,6]) #answer is 1 - assert spreading_minbudget(graphs["RegularGraph_Graph-3"], 0, [1,2,3,4,5]) > spreading_minbudget(graphs["RegularGraph_Graph-3"], 0, [1,3,5]) #answer is 1 - assert 2 > spreading_minbudget(graphs["RegularGraph_Graph-4"], 0, [2,3,5,7]) # anser is 1 - assert 4 > spreading_minbudget(graphs["RegularGraph_Graph-6"], 1, [0,3,5,6,8,9]) #answer is 1 - assert 2 == spreading_minbudget(graphs["RegularGraph_Graph-7"], 1, [4,2,5,6]) #answer is 2 - assert spreading_minbudget(graphs["RegularGraph_Graph-8"], 0, [1,2,3,4,5,6,7,8,9,10,11,12,13,14]) != spreading_minbudget(graphs["RegularGraph_Graph-8"], 0, [1,3,4,5,6,9,10,12,14]) #answer is 3 + assert spreading_minbudget(graphs["RegularGraph_Graph-1"], 0, [1,2,3,4,5,6])[0] != spreading_minbudget(graphs["RegularGraph_Graph-1"], 0, [1,5,6])[0] # answer is 1 + assert 1 == spreading_minbudget(graphs["RegularGraph_Graph-2"], 0, [1,3,4,5,6])[0] #answer is 1 + assert spreading_minbudget(graphs["RegularGraph_Graph-3"], 0, [1,2,3,4,5])[0] > spreading_minbudget(graphs["RegularGraph_Graph-3"], 0, [1,3,5])[0] #answer is 1 + assert 2 > spreading_minbudget(graphs["RegularGraph_Graph-4"], 0, [2,3,5,7])[0] # anser is 1 + assert 4 > spreading_minbudget(graphs["RegularGraph_Graph-6"], 1, [0,3,5,6,8,9])[0] #answer is 1 + assert 2 == spreading_minbudget(graphs["RegularGraph_Graph-7"], 1, [4,2,5,6])[0] #answer is 2 + assert spreading_minbudget(graphs["RegularGraph_Graph-8"], 0, [1,2,3,4,5,6,7,8,9,10,11,12,13,14])[0] != spreading_minbudget(graphs["RegularGraph_Graph-8"], 0, [1,3,4,5,6,9,10,12,14])[0] #answer is 3 def random_graph_test(): for i in range(10): @@ -284,7 +284,7 @@ def random_graph_test(): targets.append(node) target_length = len(targets) - ans = spreading_minbudget(G,0,targets) + ans = spreading_minbudget(G,0,targets)[0] print(ans) print(target_length) From 81b169fcfcea3361c7f418e4187656fc0900a6b8 Mon Sep 17 00:00:00 2001 From: Almog-David Date: Tue, 11 Jun 2024 11:43:21 +0300 Subject: [PATCH 27/93] improved epsilon function --- .../approximation/firefighter_problem/Utils.py | 18 ++++-------------- 1 file changed, 4 insertions(+), 14 deletions(-) diff --git a/networkz/algorithms/approximation/firefighter_problem/Utils.py b/networkz/algorithms/approximation/firefighter_problem/Utils.py index 6e0e358..b4485d5 100644 --- a/networkz/algorithms/approximation/firefighter_problem/Utils.py +++ b/networkz/algorithms/approximation/firefighter_problem/Utils.py @@ -130,21 +130,11 @@ def calculate_epsilon(direct_vaccinations:dict)->list: Returns: - epsilon (list): List of direct vaccination groups by time step. """ - epsilon = [] - sorted_dict = dict(sorted(direct_vaccinations.items(), key=lambda item: item[0][1])) - - current_time_step = None - current_group = [] - for key, value in sorted_dict.items(): - if current_time_step is None or key[1] == current_time_step: - current_group.append(key) - else: - epsilon.append(current_group) - current_group = [key] - current_time_step = key[1] + from itertools import groupby + from operator import itemgetter - if current_group: - epsilon.append(current_group) + sorted_dict = sorted(direct_vaccinations, key=itemgetter(1)) + epsilon = [list(group) for _, group in groupby(sorted_dict, key=itemgetter(1))] logger.info("Epsilon is: " + str(epsilon)) return epsilon From 3d53b43032799ee692c8b86a0b73a8b5791454fa Mon Sep 17 00:00:00 2001 From: 20shaked20 Date: Tue, 11 Jun 2024 13:03:07 +0300 Subject: [PATCH 28/93] Dirlay Random Graph works --- .../Firefighter_Problem.py | 2 + .../firefighter_problem/Utils.py | 2 +- .../test_non_spreading_dirlaynet_minbudget.py | 66 ++++++++++++------- 3 files changed, 47 insertions(+), 23 deletions(-) diff --git a/networkz/algorithms/approximation/firefighter_problem/Firefighter_Problem.py b/networkz/algorithms/approximation/firefighter_problem/Firefighter_Problem.py index f4a5ab9..9546715 100644 --- a/networkz/algorithms/approximation/firefighter_problem/Firefighter_Problem.py +++ b/networkz/algorithms/approximation/firefighter_problem/Firefighter_Problem.py @@ -271,10 +271,12 @@ def non_spreading_dirlaynet_minbudget(Graph:nx.DiGraph, source:int, targets:list 2 """ validate_parameters(Graph, source, targets) + display_graph(Graph) logger.info(f"Starting the non_spreading_dirlaynet_minbudget function with source node {source} and targets: {targets}") layers = adjust_nodes_capacity(Graph, source) G = create_st_graph(Graph, targets) + display_graph(G) G_reduction = graph_flow_reduction(G, source) N_groups = min_cut_N_groups(G_reduction, source,layers) vacc_matrix = calculate_vaccine_matrix(layers, N_groups) diff --git a/networkz/algorithms/approximation/firefighter_problem/Utils.py b/networkz/algorithms/approximation/firefighter_problem/Utils.py index b4485d5..e68b5b9 100644 --- a/networkz/algorithms/approximation/firefighter_problem/Utils.py +++ b/networkz/algorithms/approximation/firefighter_problem/Utils.py @@ -35,7 +35,7 @@ class Status(Enum): node_colors = { - 'target': 'gray', + 'vulnerable': 'gray', 'infected': 'red', 'vaccinated': 'blue', 'directly vaccinated': 'green', diff --git a/networkz/algorithms/tests/test_firefighter_problem/test_non_spreading_dirlaynet_minbudget.py b/networkz/algorithms/tests/test_firefighter_problem/test_non_spreading_dirlaynet_minbudget.py index 973a167..2eb181b 100644 --- a/networkz/algorithms/tests/test_firefighter_problem/test_non_spreading_dirlaynet_minbudget.py +++ b/networkz/algorithms/tests/test_firefighter_problem/test_non_spreading_dirlaynet_minbudget.py @@ -117,7 +117,7 @@ def test_target_not_in_graph(graph_key, source, targets): # non_spreading_dirlaynet_minbudget(graphs["Dirlay_Graph-4"], 3, [1, 3, 5, 7]) -@pytest.mark.parametrize("graph_key, budget, source, targets", [ +@pytest.mark.parametrize("graph_key, source, targets", [ ("Dirlay_Graph-1", 0, [0, 5]), ("Dirlay_Graph-2", 1, [0, 1, 4]), ("Dirlay_Graph-3", 6, [0, 6, 7]), @@ -422,41 +422,63 @@ def test_min_budget_calculation(): assert min_budget_calculation(matrix_3_check) == 1 -def generate_random_directed_layered_graph(): +def generate_layered_network(): """ - Generates a random directed layered graph with a random number of layers and nodes per layer. + Generates a directed layered network with a random number of layers and random nodes per layer. - :return: A NetworkX DiGraph representing the layered network. + Returns: + G (networkx.DiGraph): Directed graph representing the layered network. """ - layers = random.randint(3, 6) # Random number of layers between 3 and 6, this can be changed later.. just for testing - nodes_per_layer = [random.randint(2, 5) for _ in range(layers)] # Random number of nodes per layer between 2 and 5 + # Randomly decide the number of layers (between 2 and 3 for this example) + num_layers = random.randint(2, 3) + + # Randomly decide the number of nodes per layer (between 1 and 4 for this example) + nodes_per_layer = [random.randint(1, 4) for _ in range(num_layers)] G = nx.DiGraph() - node_count = 0 + node_id = 1 # Start node_id from 1 because 0 is the source + + # Initialize layer 0 with the source node + layers = [[0]] + G.add_node(0) - # layer buildup - for layer in range(layers): - for _ in range(nodes_per_layer[layer]): - G.add_node(node_count, layer=layer, status="vulnerable") - node_count += 1 + # Create nodes layer by layer + for i in range(num_layers): + layer = [node_id + j for j in range(nodes_per_layer[i])] + layers.append(layer) + G.add_nodes_from(layer) + node_id += nodes_per_layer[i] + + print("LAYERS->", layers) - # edges between layers - for layer in range(layers - 1): - for u in [n for n, d in G.nodes(data=True) if d['layer'] == layer]: - for v in [n for n, d in G.nodes(data=True) if d['layer'] == layer + 1]: - if random.random() < 0.5: # randomly add edges with 50% probability - G.add_edge(u, v) + # Connect source node (0) to all nodes in layer 1 + for node in layers[1]: + G.add_edge(0, node) + + # Create edges ensuring connectivity between consecutive layers + for i in range(1, num_layers): + for node in layers[i]: + # Connect each node in this layer to at least one node in the next layer + connected_nodes = random.sample(layers[i + 1], k=random.randint(1, len(layers[i + 1]))) + for target in connected_nodes: + G.add_edge(node, target) + + for target in layers[i + 1]: + # Ensure each node in the next layer is connected to from at least one node in this layer + if not any(G.has_edge(source, target) for source in layers[i]): + G.add_edge(random.choice(layers[i]), target) return G + def test_non_spreading_dirlaynet_minbudget(): - G = generate_random_directed_layered_graph() + G = generate_layered_network() source = 0 nodes = list(G.nodes()) num_targets = random.randint(1, len(nodes) - 1) # Ensure at least one target node - targets = random.sample(nodes[1:], num_targets) # randomly choose nodes to save , excluding the source node + targets = random.sample(nodes[1:], num_targets) # Randomly choose nodes to save, excluding the source node min_budget = non_spreading_dirlaynet_minbudget(G, source, targets) - - assert min_budget >= 0, "Minimum budget should be non-negative" + print("BUDGET----------",min_budget) + assert min_budget > 0, "Minimum budget should be non-negative" From e37de372ae62bbe2a066f566c76ab5fe286342a5 Mon Sep 17 00:00:00 2001 From: 20shaked20 Date: Tue, 11 Jun 2024 13:11:32 +0300 Subject: [PATCH 29/93] Added more loggers for dirlay --- .../firefighter_problem/Utils.py | 20 +++++++++++++++---- 1 file changed, 16 insertions(+), 4 deletions(-) diff --git a/networkz/algorithms/approximation/firefighter_problem/Utils.py b/networkz/algorithms/approximation/firefighter_problem/Utils.py index e68b5b9..1123729 100644 --- a/networkz/algorithms/approximation/firefighter_problem/Utils.py +++ b/networkz/algorithms/approximation/firefighter_problem/Utils.py @@ -251,6 +251,8 @@ def adjust_nodes_capacity(graph:nx.DiGraph, source:int)->list: Returns: - layers (list): List of nodes grouped by layers. """ + logger.debug(f"Starting to adjust node capacity for dirlay graph nodes...") + layers = (list(nx.bfs_layers(graph,source))) harmonic_sum = 0.0 for i in range(1,len(layers)): @@ -272,6 +274,7 @@ def create_st_graph(graph:nx.DiGraph, targets:list)->nx.DiGraph: Returns: - G (nx.DiGraph): s-t graph. """ + logger.info(f"Creating an s-t graph to connect nodes to save") G = copy.deepcopy(graph) G.add_node('t', status = Status.VULNERABLE.value) for node in targets: @@ -290,6 +293,7 @@ def graph_flow_reduction(graph:nx.DiGraph, source:int)->nx.DiGraph: Returns: - H (nx.DiGraph): Graph after flow reduction. """ + logger.info(f"Applying Reduction on the graph") H = nx.DiGraph() for node in graph.nodes: in_node, out_node = f'{node}_in', f'{node}_out' @@ -301,6 +305,7 @@ def graph_flow_reduction(graph:nx.DiGraph, source:int)->nx.DiGraph: for edge in graph.edges: H.add_edge(f'{edge[0]}_out', f'{edge[1]}_in', weight=float('inf')) # display_graph(H) + logger.info(f"Reduction is finished") return H def min_cut_N_groups(graph: nx.DiGraph, source: int, layers: list) -> dict: @@ -316,10 +321,12 @@ def min_cut_N_groups(graph: nx.DiGraph, source: int, layers: list) -> dict: - groups (dict): Dictionary with layers as keys and lists of nodes in the minimum cut as values. """ # Compute the minimum cut + logger.info(f"Finding the minimum cut on the graph after reduction") flow_graph = algo.minimum_st_node_cut(graph, f'{source}_out', 't_in') # Initialize the groups dictionary with empty lists for each layer index groups = {i+1: [] for i in range(len(layers)-1)} + logger.info(f"Finding the nodes from each layer") # Populate the groups dictionary for item in flow_graph: @@ -330,6 +337,7 @@ def min_cut_N_groups(graph: nx.DiGraph, source: int, layers: list) -> dict: groups[i].append(node) break + logger.info(f"Ni groups: {groups}") return groups @@ -344,9 +352,9 @@ def calculate_vaccine_matrix(layers:list, min_cut_nodes_grouped:dict)->np.matrix Returns: - matrix (np.matrix): Vaccine matrix. """ - - logger.info(f"Layers: {layers}") + logger.info(f"Calcualting the Vaccine Matrix for dirlay...") logger.info(f"Min cut nodes grouped: {min_cut_nodes_grouped}") + matrix_length = max(min_cut_nodes_grouped.keys()) matrix = np.zeros((matrix_length, matrix_length)) for j in range(matrix_length): @@ -354,7 +362,8 @@ def calculate_vaccine_matrix(layers:list, min_cut_nodes_grouped:dict)->np.matrix N_j = len(min_cut_nodes_grouped[j+1]) value = N_j / (j + 1) matrix[i][j] = value - logger.info(f"Matrix: {matrix}") + + logger.info(f"Vaccination Matrix Before Conversion: {matrix}") return matrix def matrix_to_integers_values(matrix: np.matrix) -> np.matrix: @@ -370,6 +379,7 @@ def matrix_to_integers_values(matrix: np.matrix) -> np.matrix: np.matrix: The converted integral matrix. """ # dimensions of the matrix + logger.info(f"Converting the the Vaccine Matrix for integers...") rows, cols = matrix.shape row_sums = np.array(matrix.sum(axis=1)).flatten() @@ -416,7 +426,8 @@ def matrix_to_integers_values(matrix: np.matrix) -> np.matrix: integral_matrix[i, j] = np.ceil(matrix[i, j]) else: integral_matrix[i, j] = np.floor(matrix[i, j]) - logger.info(f"Matrix: {integral_matrix}") + + logger.info(f"Integral and final Matrix: {integral_matrix}") return np.matrix(integral_matrix) @@ -433,6 +444,7 @@ def min_budget_calculation(matrix: np.matrix) -> int: integral_matrix = matrix_to_integers_values(matrix) columns_sum = integral_matrix.sum(axis=0) # we get column sum as we want to -> on time step i, vaccinate Mij nodes from layer j , for all i ≤ j ≤ . min_budget = int(columns_sum.max()) + logger.info(f"Min budget needed to save the target nodes: {min_budget}") return min_budget From 6413962010aa58007acb7f78a913842c4b48ea28 Mon Sep 17 00:00:00 2001 From: 20shaked20 Date: Tue, 11 Jun 2024 13:19:01 +0300 Subject: [PATCH 30/93] Some minor fixes+ added DAG validation for dirlay, also more logs --- .../firefighter_problem/Firefighter_Problem.py | 4 ++++ .../algorithms/approximation/firefighter_problem/Utils.py | 8 ++++++++ .../test_non_spreading_dirlaynet_minbudget.py | 3 ++- 3 files changed, 14 insertions(+), 1 deletion(-) diff --git a/networkz/algorithms/approximation/firefighter_problem/Firefighter_Problem.py b/networkz/algorithms/approximation/firefighter_problem/Firefighter_Problem.py index 9546715..5849c36 100644 --- a/networkz/algorithms/approximation/firefighter_problem/Firefighter_Problem.py +++ b/networkz/algorithms/approximation/firefighter_problem/Firefighter_Problem.py @@ -271,6 +271,10 @@ def non_spreading_dirlaynet_minbudget(Graph:nx.DiGraph, source:int, targets:list 2 """ validate_parameters(Graph, source, targets) + if not is_dag(Graph): + logger.error("Problem with graph, its not a DAG, thus cannot run algorithm") + return + display_graph(Graph) logger.info(f"Starting the non_spreading_dirlaynet_minbudget function with source node {source} and targets: {targets}") diff --git a/networkz/algorithms/approximation/firefighter_problem/Utils.py b/networkz/algorithms/approximation/firefighter_problem/Utils.py index 1123729..00ff0bc 100644 --- a/networkz/algorithms/approximation/firefighter_problem/Utils.py +++ b/networkz/algorithms/approximation/firefighter_problem/Utils.py @@ -71,6 +71,14 @@ def validate_parameters(graph:nx.DiGraph, source:int, targets:list)->None: logger.critical("Error: Not all nodes in the targets list are on the graph.") raise ValueError("Error: Not all nodes in the targets list are on the graph.") +def is_dag(graph): + """ + Validates if a given graph is a Directed Acyclic Graph (DAG) using NetworkX. + + :param graph: A NetworkX DiGraph object. + :return: True if the graph is a DAG, False otherwise. + """ + return nx.is_directed_acyclic_graph(graph) "Spreading:" def calculate_gamma(graph:nx.DiGraph, source:int, targets:list)-> dict: diff --git a/networkz/algorithms/tests/test_firefighter_problem/test_non_spreading_dirlaynet_minbudget.py b/networkz/algorithms/tests/test_firefighter_problem/test_non_spreading_dirlaynet_minbudget.py index 2eb181b..d7bd5cd 100644 --- a/networkz/algorithms/tests/test_firefighter_problem/test_non_spreading_dirlaynet_minbudget.py +++ b/networkz/algorithms/tests/test_firefighter_problem/test_non_spreading_dirlaynet_minbudget.py @@ -461,7 +461,8 @@ def generate_layered_network(): # Connect each node in this layer to at least one node in the next layer connected_nodes = random.sample(layers[i + 1], k=random.randint(1, len(layers[i + 1]))) for target in connected_nodes: - G.add_edge(node, target) + if target != node: # Ensure no self-loop + G.add_edge(node, target) for target in layers[i + 1]: # Ensure each node in the next layer is connected to from at least one node in this layer From 60e2dfadb346ecbc15e93ca33779084df4cb42ef Mon Sep 17 00:00:00 2001 From: 20shaked20 Date: Tue, 11 Jun 2024 13:26:22 +0300 Subject: [PATCH 31/93] nada --- .../approximation/firefighter_problem/Utils.py | 10 +++++++--- 1 file changed, 7 insertions(+), 3 deletions(-) diff --git a/networkz/algorithms/approximation/firefighter_problem/Utils.py b/networkz/algorithms/approximation/firefighter_problem/Utils.py index 00ff0bc..4c0d6f9 100644 --- a/networkz/algorithms/approximation/firefighter_problem/Utils.py +++ b/networkz/algorithms/approximation/firefighter_problem/Utils.py @@ -71,12 +71,16 @@ def validate_parameters(graph:nx.DiGraph, source:int, targets:list)->None: logger.critical("Error: Not all nodes in the targets list are on the graph.") raise ValueError("Error: Not all nodes in the targets list are on the graph.") -def is_dag(graph): +def is_dag(graph:nx.DiGraph) -> bool: """ Validates if a given graph is a Directed Acyclic Graph (DAG) using NetworkX. - :param graph: A NetworkX DiGraph object. - :return: True if the graph is a DAG, False otherwise. + Parameters: + - graph (nx.DiGraph): Directed graph. + + Returns: + - True if the graph is a DAG, + - False otherwise. """ return nx.is_directed_acyclic_graph(graph) From b959bd5b3920c1ca8133ab3f3fbc36d937cfd1f1 Mon Sep 17 00:00:00 2001 From: Yuval Bubnovsky Date: Wed, 12 Jun 2024 18:31:42 +0300 Subject: [PATCH 32/93] Added doctests for utility functions, seperated graph_flow_reduction into a sepeprate file --- .../Firefighter_Problem.py | 26 +- .../firefighter_problem/Utils.py | 393 ++++++++++++++---- .../graph_flow_reduction.py | 87 ++++ .../approximation/firefighter_problem/main.py | 7 +- 4 files changed, 417 insertions(+), 96 deletions(-) create mode 100644 networkz/algorithms/approximation/firefighter_problem/graph_flow_reduction.py diff --git a/networkz/algorithms/approximation/firefighter_problem/Firefighter_Problem.py b/networkz/algorithms/approximation/firefighter_problem/Firefighter_Problem.py index 5849c36..23d1927 100644 --- a/networkz/algorithms/approximation/firefighter_problem/Firefighter_Problem.py +++ b/networkz/algorithms/approximation/firefighter_problem/Firefighter_Problem.py @@ -23,6 +23,7 @@ import networkx.algorithms.connectivity as algo import math import logging +from graph_flow_reduction import * # This is a fix for an issue where the top one has to be exclusive for pytest to work # and the bottom one needs to be exclusive for running this from terminal to work @@ -39,7 +40,7 @@ def spreading_maxsave(Graph:nx.DiGraph, budget:int, source:int, targets:list, fl by Elliot Anshelevich, Deeparnab Chakrabarty, Ameya Hate, Chaitanya Swamy (2010) https://link.springer.com/article/10.1007/s00453-010-9469-y - Programmers: Shaked Levi, Almog David, Yuval Bobnovsky + Programmers: Shaked Levi, Almog David, Yuval Bubnovsky spreading_maxsave: Gets a directed graph, budget, source node, and list of targeted nodes that we need to save and return the best vaccination strategy that saves the most nodes from the targeted nodes list. @@ -140,7 +141,7 @@ def spreading_minbudget(Graph:nx.DiGraph, source:int, targets:list)-> int: by Elliot Anshelevich, Deeparnab Chakrabarty, Ameya Hate, Chaitanya Swamy (2010) https://link.springer.com/article/10.1007/s00453-010-9469-y - Programmers: Shaked Levi, Almog David, Yuval Bobnovsky + Programmers: Shaked Levi, Almog David, Yuval Bubnovsky spreading_minbudget: Gets a directed graph, source node, and list of targeted nodes that we need to save and returns the minimum budget that saves all the nodes from the targeted nodes list. @@ -210,7 +211,7 @@ def non_spreading_minbudget(Graph:nx.DiGraph, source:int, targets:list)->int: by Elliot Anshelevich, Deeparnab Chakrabarty, Ameya Hate, Chaitanya Swamy (2010) https://link.springer.com/article/10.1007/s00453-010-9469-y - Programmers: Shaked Levi, Almog David, Yuval Bobnovsky + Programmers: Shaked Levi, Almog David, Yuval Bubnovsky non_spreading_minbudget: Gets a directed graph, source node, and list of targeted nodes that we need to save and returns the minimum budget that saves all the nodes from the targeted nodes list. @@ -252,13 +253,13 @@ def non_spreading_minbudget(Graph:nx.DiGraph, source:int, targets:list)->int: logger.info(f"Returning minimum budget: {min_budget}") return min_budget -def non_spreading_dirlaynet_minbudget(Graph:nx.DiGraph, source:int, targets:list)->int: +def non_spreading_dirlaynet_minbudget(Graph:nx.DiGraph, src:int, targets:list)->int: """ "Approximability of the Firefighter Problem - Computing Cuts over Time", by Elliot Anshelevich, Deeparnab Chakrabarty, Ameya Hate, Chaitanya Swamy (2010) https://link.springer.com/article/10.1007/s00453-010-9469-y - Programmers: Shaked Levi, Almog David, Yuval Bobnovsky + Programmers: Shaked Levi, Almog David, Yuval Bubnovsky non_spreading_dirlaynet_minbudget: Gets a directed graph, source node, and list of targeted nodes that we need to save and returns the minimum budget that saves all the nodes from the targeted nodes list. @@ -270,19 +271,19 @@ def non_spreading_dirlaynet_minbudget(Graph:nx.DiGraph, source:int, targets:list >>> non_spreading_dirlaynet_minbudget(G4,0,[1,2,3,4,5]) 2 """ - validate_parameters(Graph, source, targets) + validate_parameters(Graph, src, targets) if not is_dag(Graph): logger.error("Problem with graph, its not a DAG, thus cannot run algorithm") return display_graph(Graph) - logger.info(f"Starting the non_spreading_dirlaynet_minbudget function with source node {source} and targets: {targets}") + logger.info(f"Starting the non_spreading_dirlaynet_minbudget function with source node {src} and targets: {targets}") - layers = adjust_nodes_capacity(Graph, source) + layers = adjust_nodes_capacity(Graph, src) G = create_st_graph(Graph, targets) display_graph(G) - G_reduction = graph_flow_reduction(G, source) - N_groups = min_cut_N_groups(G_reduction, source,layers) + G_reduction = graph_flow_reduction(G, source=src, target='t') + N_groups = min_cut_N_groups(G_reduction, src,layers) vacc_matrix = calculate_vaccine_matrix(layers, N_groups) min_budget = min_budget_calculation(vacc_matrix) @@ -416,4 +417,9 @@ def heuristic_minbudget(Graph:nx.DiGraph, source:int, targets:list, spreading:bo logger.info(f"Returning minimum budget: {middle} and the vaccination strategy: {best_strategy}") return middle, best_strategy + +if __name__ == "__main__": + import doctest + result = doctest.testmod(verbose=True) + logger.info(f"Doctest results: {result}") \ No newline at end of file diff --git a/networkz/algorithms/approximation/firefighter_problem/Utils.py b/networkz/algorithms/approximation/firefighter_problem/Utils.py index 4c0d6f9..f0780a1 100644 --- a/networkz/algorithms/approximation/firefighter_problem/Utils.py +++ b/networkz/algorithms/approximation/firefighter_problem/Utils.py @@ -49,14 +49,38 @@ def validate_parameters(graph:nx.DiGraph, source:int, targets:list)->None: Validate the source and target nodes in the graph. Parameters: - - graph (nx.DiGraph): Directed graph. - - source (int): Source node. - - targets (list): List of target nodes to save. + ---------- + graph : nx.DiGraph + Directed graph. + source : int + Source node. + targets : list + List of target nodes to save. Raises: - - ValueError: If the source node is not in the graph. - - ValueError: If the source node is in the targets list. - - ValueError: If any target node is not in the graph. + ------- + ValueError + If the source node is not in the graph. + If the source node is in the targets list. + If any target node is not in the graph. + + Examples: + -------- + >>> G = nx.DiGraph() + >>> G.add_nodes_from([1, 2, 3, 4]) + >>> validate_parameters(G, 1, [2, 3]) + >>> validate_parameters(G, 5, [2, 3]) + Traceback (most recent call last): + ... + ValueError: Error: The source node isn't on the graph + >>> validate_parameters(G, 1, [1, 3]) + Traceback (most recent call last): + ... + ValueError: Error: The source node can't be a part of the targets list, since the virus is spreading from the source + >>> validate_parameters(G, 1, [2, 5]) + Traceback (most recent call last): + ... + ValueError: Error: Not all nodes in the targets list are on the graph. """ graph_nodes = list(graph.nodes()) if source not in graph_nodes: @@ -71,32 +95,106 @@ def validate_parameters(graph:nx.DiGraph, source:int, targets:list)->None: logger.critical("Error: Not all nodes in the targets list are on the graph.") raise ValueError("Error: Not all nodes in the targets list are on the graph.") -def is_dag(graph:nx.DiGraph) -> bool: +def is_st_layered_dag(graph: nx.DiGraph, s: any, t: any) -> bool: #TODO: make this work and incorporate into code """ - Validates if a given graph is a Directed Acyclic Graph (DAG) using NetworkX. + Validates if a given graph is an s-t directed layered network. + + In an s-t directed l-layered network, the vertex set consists of + V = (L0 := {s}) U L1 U ... U Ll U {t}, and all arcs except those + entering t are from a vertex in some layer Li to a vertex in Li+1; + arcs entering t may originate from any vertex other than t. Parameters: - graph (nx.DiGraph): Directed graph. + - s: Source vertex. + - t: Target vertex. Returns: - - True if the graph is a DAG, + - True if the graph is an s-t directed l-layered network, - False otherwise. """ + logger.info(f'Validating {graph} is an s-t directed layered network') + if not nx.is_directed_acyclic_graph(graph): + logger.error(f'{graph} is not DAG and therefore cannot be an s-t directed layered network!') + return False + + topo_sort = list(nx.topological_sort(graph)) + logger.debug(f'Performing topological sort to determine an ordering of vertices\n Sorted:{topo_sort}') + + if topo_sort[0] != s or topo_sort[-1] != t: + logger.error(f'First node in topological sort is not "t" or last node is not "s"') + return False + + # Assign layer indices based on topological sort + node_layers = {node: index for index, node in enumerate(topo_sort)} + + # Check if all edges (except those entering t) go from a lower layer to the next higher layer + for u, v in graph.edges(): + if v != t and node_layers[u] + 1 != node_layers[v]: + logger.error(f'The edge {u},{v} is an edge that does not enter "t" but goes from a higher to a lower layer - violating the required property') + return False + + return True + +def is_dag(graph:nx.DiGraph) -> bool: + """ + Validates if a given graph is a Directed Acyclic Graph (DAG) using NetworkX. + + Parameters: + ---------- + graph : nx.DiGraph + Directed graph. + + Returns: + ------- + bool + True if the graph is a DAG, False otherwise. + + Examples: + -------- + >>> G = nx.DiGraph() + >>> G.add_edges_from([(1, 2), (2, 3)]) + >>> is_dag(G) + True + >>> G.add_edge(3, 1) + >>> is_dag(G) + False + """ return nx.is_directed_acyclic_graph(graph) -"Spreading:" +# ============================ Spreading Max-Save ============================ def calculate_gamma(graph:nx.DiGraph, source:int, targets:list)-> dict: """ - Calculate Gamma and S(u,t) based on the calculation in the article. + Calculate Gamma and S(u,t) based on the calculation in the article. Parameters: - - graph (nx.DiGraph): Directed graph. - - source (int): Source node. - - targets (list): List of target nodes to save. + ---------- + graph : nx.DiGraph + Directed graph. + source : int + Source node. + targets : list + List of target nodes to save. Returns: - - gamma (dict): Dictionary of vaccination options. - - direct_vaccination (dict): Dictionary of direct vaccination strategies - S(u,t). + ------- + gamma (dict) + Dictionary of vaccination options. + direct_vaccination (dict) + Dictionary of direct vaccination strategies - S(u,t). + + Examples: + -------- + >>> G = nx.DiGraph() + >>> G.add_edges_from([(1, 2), (2, 3), (1, 3)]) + >>> G.nodes[1]['capacity'] = 10 + >>> G.nodes[2]['capacity'] = 15 + >>> G.nodes[3]['capacity'] = 20 + >>> gamma, direct_vaccination = calculate_gamma(G, 1, [3]) + >>> gamma + {2: [(2, 1)], 3: [(3, 1)]} + >>> direct_vaccination + {(2, 1): [], (3, 1): [3]} """ gamma = {} direct_vaccination = {} @@ -137,10 +235,20 @@ def calculate_epsilon(direct_vaccinations:dict)->list: Calculate Epsilon based on the calculation in the article. Parameters: - - direct_vaccinations (dict): Dictionary of direct vaccination strategies. + ---------- + direct_vaccinations : dict + Dictionary of direct vaccination strategies. Returns: - - epsilon (list): List of direct vaccination groups by time step. + ------- + list + List of direct vaccination groups by time step. + + Examples: + -------- + >>> direct_vaccinations = {(1, 1): [3], (2, 2): [4], (3, 1): [5]} + >>> calculate_epsilon(direct_vaccinations) + [[(1, 1), (3, 1)], [(2, 2)]] """ from itertools import groupby from operator import itemgetter @@ -153,16 +261,33 @@ def calculate_epsilon(direct_vaccinations:dict)->list: def find_best_direct_vaccination(graph:nx.DiGraph, direct_vaccinations:dict, current_time_options:list, targets:list)->tuple: """ - Find the best direct vaccination strategy for the current time step that saves more new node in targets. + Find the best direct vaccination strategy for the current time step that saves more new nodes in targets. Parameters: - - graph (nx.DiGraph): Directed graph. - - direct_vaccinations (dict): Dictionary of direct vaccination strategies. - - current_time_options (list): List of current time step vaccination options. - - targets (list): List of target nodes. + ---------- + graph : nx.DiGraph + Directed graph. + direct_vaccinations : dict + Dictionary of direct vaccination strategies. + current_time_options : list + List of current time step vaccination options. + targets : list + List of target nodes. Returns: - - best_vaccination (tuple): Best direct vaccination option. + ------- + tuple + Best direct vaccination option and the nodes saved by this option. + + Examples: + -------- + >>> G = nx.DiGraph() + >>> G.add_nodes_from([(1, {"status": 'vulnerable'}), (2, {"status": 'vulnerable'}), (3, {"status": 'vulnerable'})]) + >>> direct_vaccinations = {(1, 1): [1], (2, 2): [2]} + >>> current_time_options = [(1, 1), (2, 2)] + >>> targets = [2] + >>> find_best_direct_vaccination(G, direct_vaccinations, current_time_options, targets) + ((2, 2), {2}) """ best_vaccination = () nodes_saved = [] @@ -187,11 +312,27 @@ def spread_virus(graph:nx.DiGraph, infected_nodes:list)->bool: Spread the virus from infected nodes to their neighbors. Parameters: - - graph (nx.DiGraph): Directed graph. - - infected_nodes (list): List of currently infected nodes. + ---------- + graph : nx.DiGraph + Directed graph. + infected_nodes : list + List of currently infected nodes. Returns: - - bool: True if there are new infections, False otherwise. + ------- + bool + True if there are new infections, False otherwise. + + Examples: + -------- + >>> G = nx.DiGraph() + >>> G.add_nodes_from([(1, {"status": 'infected'}), (2, {"status": 'vulnerable'}), (3, {"status": 'vulnerable'})]) + >>> G.add_edges_from([(1, 2), (2, 3)]) + >>> infected_nodes = [1] + >>> spread_virus(G, infected_nodes) + True + >>> G.nodes(data=True) + NodeDataView({1: {'status': 'infected'}, 2: {'status': 'infected'}, 3: {'status': 'vulnerable'}}) """ new_infected_nodes = [] for node in infected_nodes: @@ -211,8 +352,21 @@ def spread_vaccination(graph:nx.DiGraph, vaccinated_nodes:list)->None: Spread the vaccination from vaccinated nodes to their neighbors. Parameters: - - graph (nx.DiGraph): Directed graph. - - vaccinated_nodes (list): List of currently vaccinated nodes. + ---------- + graph : nx.DiGraph + Directed graph. + vaccinated_nodes : list + List of currently vaccinated nodes. + + Examples: + -------- + >>> G = nx.DiGraph() + >>> G.add_nodes_from([(1, {"status": 'directly_vaccinated'}), (2, {"status": 'vulnerable'}), (3, {"status": 'vulnerable'})]) + >>> G.add_edges_from([(1, 2), (2, 3)]) + >>> vaccinated_nodes = [1] + >>> spread_vaccination(G, vaccinated_nodes) + >>> G.nodes(data=True) + NodeDataView({1: {'status': 'directly_vaccinated'}, 2: {'status': 'vaccinated'}, 3: {'status': 'vulnerable'}}) """ new_vaccinated_nodes = [] for node in vaccinated_nodes: @@ -231,8 +385,19 @@ def vaccinate_node(graph:nx.DiGraph, node:int)->None: Directly vaccinate a specific node in the graph. Parameters: - - graph (nx.DiGraph): Directed graph. - - node (int): Node to be vaccinated. + ---------- + graph : nx.DiGraph + Directed graph. + node : int + Node to be vaccinated. + + Examples: + -------- + >>> G = nx.DiGraph() + >>> G.add_node(1, status=0) + >>> vaccinate_node(G, 1) + >>> G.nodes(data=True) + NodeDataView({1: {'status': 'directly vaccinated'}}) """ graph.nodes[node]['status'] = Status.DIRECTLY_VACCINATED.value logger.info("Node " + f'{node}' + " has been directly vaccinated") @@ -243,25 +408,51 @@ def clean_graph(graph:nx.DiGraph)->None: Reset the graph to its base state where all nodes are targets. Parameters: - - graph (nx.DiGraph): Directed graph. + ---------- + graph : nx.DiGraph + Directed graph. + + Examples: + -------- + >>> G = nx.DiGraph() + >>> G.add_nodes_from([(1, {"status": 1}), (2, {"status": 2}), (3, {"status": 0})]) + >>> clean_graph(G) + >>> G.nodes(data=True) + NodeDataView({1: {'status': 'vulnerable'}, 2: {'status': 'vulnerable'}, 3: {'status': 'vulnerable'}}) """ for node in graph.nodes: graph.nodes[node]['status'] = Status.VULNERABLE.value return -"Non-Spreading:" +# ============================ End Spreading Max-Save ============================ +# =========================== Non-Spreading Max-Save ============================ def adjust_nodes_capacity(graph:nx.DiGraph, source:int)->list: """ Adjust the capacity of nodes based on the layer they belong to. The capacity is based on the formula in the article at the DirLayNet algorithm section. Parameters: - - graph (nx.DiGraph): Directed graph. - - source (int): Source node. + ---------- + graph : nx.DiGraph + Directed graph. + source : int + Source node. Returns: - - layers (list): List of nodes grouped by layers. + ------- + list + List of nodes grouped by layers. + + Examples: + -------- + >>> G = nx.DiGraph() + >>> G.add_edges_from([(1, 2), (2, 3), (3, 4)]) + >>> layers = adjust_nodes_capacity(G, 1) + >>> layers + [[1], [2], [3], [4]] + >>> G.nodes(data=True) + NodeDataView({1: {}, 2: {'capacity': 0.5454545454545455}, 3: {'capacity': 0.27272727272727276}, 4: {'capacity': 0.18181818181818182}}) """ logger.debug(f"Starting to adjust node capacity for dirlay graph nodes...") @@ -280,11 +471,29 @@ def create_st_graph(graph:nx.DiGraph, targets:list)->nx.DiGraph: Create an s-t graph from the original graph for use in connectivity algorithms. Parameters: - - graph (nx.DiGraph): Original directed graph. - - targets (list): List of target nodes. + ---------- + graph : nx.DiGraph + Original directed graph. + targets : list + List of target nodes. Returns: - - G (nx.DiGraph): s-t graph. + ------- + nx.DiGraph + s-t graph. + + Examples: + -------- + >>> G = nx.DiGraph() + >>> G.add_edges_from([(1, 2), (2, 3), (3, 4)]) + >>> targets = [2, 3] + >>> G_st = create_st_graph(G, targets) + >>> 't' in G_st.nodes + True + >>> list(G_st.successors(2)) + [3, 't'] + >>> list(G_st.successors(3)) + [4, 't'] """ logger.info(f"Creating an s-t graph to connect nodes to save") G = copy.deepcopy(graph) @@ -294,43 +503,27 @@ def create_st_graph(graph:nx.DiGraph, targets:list)->nx.DiGraph: #display_graph(G) return G -def graph_flow_reduction(graph:nx.DiGraph, source:int)->nx.DiGraph: - """ - Perform flow reduction on the graph to find the minimum s-t cut. - - Parameters: - - graph (nx.DiGraph): Original directed graph. - - source (int): Source node. - - Returns: - - H (nx.DiGraph): Graph after flow reduction. - """ - logger.info(f"Applying Reduction on the graph") - H = nx.DiGraph() - for node in graph.nodes: - in_node, out_node = f'{node}_in', f'{node}_out' - H.add_nodes_from([in_node, out_node]) - if node == source or node == 't': - H.add_edge(in_node, out_node, weight=float('inf')) - else: - H.add_edge(in_node, out_node, weight=graph.nodes[node]['capacity']) - for edge in graph.edges: - H.add_edge(f'{edge[0]}_out', f'{edge[1]}_in', weight=float('inf')) - # display_graph(H) - logger.info(f"Reduction is finished") - return H - def min_cut_N_groups(graph: nx.DiGraph, source: int, layers: list) -> dict: """ Find the minimum cut and group nodes accordingly. Parameters: - - graph (nx.DiGraph): Graph after flow reduction. - - source (int): Source node. - - layers (list): List of lists, where each sublist contains nodes belonging to that layer. + ---------- + graph : nx.DiGraph + Graph after flow reduction. + source : int + Source node. + layers : list + List of lists, where each sublist contains nodes belonging to that layer. Returns: - - groups (dict): Dictionary with layers as keys and lists of nodes in the minimum cut as values. + ------- + dict + Dictionary with layers as keys and lists of nodes in the minimum cut as values. + + Examples: + -------- + """ # Compute the minimum cut logger.info(f"Finding the minimum cut on the graph after reduction") @@ -358,13 +551,22 @@ def calculate_vaccine_matrix(layers:list, min_cut_nodes_grouped:dict)->np.matrix Calculate the vaccine matrix based on the calculation in the article at the DirLayNet algorithm section. Parameters: - - layers (list): List of nodes grouped by layers. - - min_cut_nodes_group (list): List of nodes in the minimum cut grouped into layers of them. + ---------- + layers : list + List of nodes grouped by layers. + min_cut_nodes_grouped : dict + List of nodes in the minimum cut grouped into layers. Returns: - - matrix (np.matrix): Vaccine matrix. + ------- + np.matrix + Vaccine matrix. + + Examples: + -------- + """ - logger.info(f"Calcualting the Vaccine Matrix for dirlay...") + logger.info(f"Calculating the Vaccine Matrix for dirlay...") logger.info(f"Min cut nodes grouped: {min_cut_nodes_grouped}") matrix_length = max(min_cut_nodes_grouped.keys()) @@ -382,13 +584,21 @@ def matrix_to_integers_values(matrix: np.matrix) -> np.matrix: """ Convert a matrix with fractional entries to an integral matrix such that the row and column sums are either the floor or ceiling of the original sums. - The solution is provided with a consturction of a flow graph and then applying a max-flow algorihm on it. + The solution is provided with a construction of a flow graph and then applying a max-flow algorithm on it. Parameters: - matrix (np.matrix): The input matrix with fractional entries. + ---------- + matrix : np.matrix + The input matrix with fractional entries. Returns: - np.matrix: The converted integral matrix. + ------- + np.matrix + The converted integral matrix. + + Examples: + -------- + """ # dimensions of the matrix logger.info(f"Converting the the Vaccine Matrix for integers...") @@ -448,10 +658,20 @@ def min_budget_calculation(matrix: np.matrix) -> int: Calculate the minimum budget from the matrix. Parameters: - - matrix (np.matrix): Input matrix. + ---------- + matrix : np.matrix + Input matrix. Returns: - - int: Minimum budget. + ------- + int + Minimum budget. + + Examples: + -------- + >>> matrix = np.matrix([[0.5, 1.5], [1.2, 0.8]]) + >>> min_budget_calculation(matrix) + 3 """ integral_matrix = matrix_to_integers_values(matrix) columns_sum = integral_matrix.sum(axis=0) # we get column sum as we want to -> on time step i, vaccinate Mij nodes from layer j , for all i ≤ j ≤ . @@ -459,9 +679,9 @@ def min_budget_calculation(matrix: np.matrix) -> int: logger.info(f"Min budget needed to save the target nodes: {min_budget}") return min_budget +# =========================== End Non-Spreading Max-Save ============================ -"Heuristic approach:" - +# =========================== Heuristic Utilities =================================== def find_best_neighbor(graph:nx.DiGraph, infected_nodes:list, targets:list)->int: """ Find the best node from the infected nodes successors that saves more new node in targets. @@ -503,9 +723,9 @@ def find_best_neighbor(graph:nx.DiGraph, infected_nodes:list, targets:list)->int if best_node != None: logger.info("The best node is: " + f'{best_node}' + " and it's saves nodes: " + str(nodes_saved)) return best_node, nodes_saved +# =========================== End Heuristic Utilities ================================ -"Usefull Utils:" - +# =========================== General Utilities ====================================== def display_graph(graph:nx.DiGraph)->None: """ Display the graph using Matplotlib. @@ -552,4 +772,11 @@ def parse_json_to_networkx(json_data): G.add_nodes_from(vertices, status=Status.VULNERABLE.value) G.add_edges_from(edges) graphs[graph_key] = G - return graphs \ No newline at end of file + return graphs + +# =========================== End General Utilities ============================ + +if __name__ == "__main__": + import doctest + result = doctest.testmod(verbose=True) + logger.info(f"Doctest results: {result}") \ No newline at end of file diff --git a/networkz/algorithms/approximation/firefighter_problem/graph_flow_reduction.py b/networkz/algorithms/approximation/firefighter_problem/graph_flow_reduction.py new file mode 100644 index 0000000..da9633b --- /dev/null +++ b/networkz/algorithms/approximation/firefighter_problem/graph_flow_reduction.py @@ -0,0 +1,87 @@ +# graph_flow_transformations.py +import networkx as nx +import logging + +logger = logging.getLogger(__name__) + +def graph_flow_reduction(graph: nx.DiGraph, source: int = None, target: int = None) -> nx.DiGraph: + """ + This function transforms a given directed graph into a new graph where each node + is split into two nodes (an "in" node and an "out" node) connected by an edge + with a capacity (weight). The transformation is used for flow problems where + nodes have capacities instead of edges - allowing to run algorithms which + were originally designed to be used for edge-flow problems. + + Parameters: + ---------- + graph : nx.DiGraph + The original directed graph. Each node should have a 'capacity' attribute. + source (Optional) : int + The source node in the graph. This node will have an infinite capacity + between its "in" and "out" nodes. + target (Optional) : int + The target node in the graph. This node will have an infinite capacity + between its "in" and "out" nodes + + Returns: + ------- + nx.DiGraph + The transformed graph after flow reduction. + + Notes: + ----- + - If a node does not have a 'capacity' attribute, a default capacity of 1 + is used. + - There is infinite capacity between two different edge_out & edge_in + + Examples: + -------- + >>> G = nx.DiGraph() + >>> G.add_node(1, capacity=10) + >>> G.add_node(2, capacity=15) + >>> G.add_node(3, capacity=20) + >>> G.add_edge(1, 2) + >>> G.add_edge(2, 3) + >>> G.add_edge(1, 3) + >>> H = graph_flow_reduction(G, 1, 3) + >>> sorted(list(H.nodes)) + ['1_in', '1_out', '2_in', '2_out', '3_in', '3_out'] + >>> sorted(list(H.edges(data=True))) + [('1_in', '1_out', {'weight': inf}), ('1_out', '2_in', {'weight': inf}), ('1_out', '3_in', {'weight': inf}), ('2_in', '2_out', {'weight': 15}), ('2_out', '3_in', {'weight': inf}), ('3_in', '3_out', {'weight': inf})] + >>> H = graph_flow_reduction(G) + >>> sorted(list(H.nodes)) + ['1_in', '1_out', '2_in', '2_out', '3_in', '3_out'] + >>> sorted(list(H.edges(data=True))) + [('1_in', '1_out', {'weight': 10}), ('1_out', '2_in', {'weight': inf}), ('1_out', '3_in', {'weight': inf}), ('2_in', '2_out', {'weight': 15}), ('2_out', '3_in', {'weight': inf}), ('3_in', '3_out', {'weight': 20})] + """ + logger.info("Starting graph flow reduction") + H = nx.DiGraph() + + logger.debug("Adding nodes and internal edges") + for node in graph.nodes: + in_node, out_node = f'{node}_in', f'{node}_out' + H.add_nodes_from([in_node, out_node]) + if node == source: + H.add_edge(in_node, out_node, weight=float('inf')) + logger.debug(f"Added infinite capacity edge for source node: {node}") + elif node == target: + H.add_edge(in_node, out_node, weight=float('inf')) + logger.debug(f"Added infinite capacity edge for target node: {node}") + else: + capacity = graph.nodes[node].get('capacity', 1) + H.add_edge(in_node, out_node, weight=capacity) + logger.debug(f"Added edge with capacity {capacity} for node: {node}") + + logger.debug("Adding edges between nodes") + for edge in graph.edges: + u_out, v_in = f'{edge[0]}_out', f'{edge[1]}_in' + H.add_edge(u_out, v_in, weight=float('inf')) + logger.debug(f"Added infinite capacity edge from {u_out} to {v_in}") + + logger.info("Graph flow reduction finished") + return H + +if __name__ == "__main__": + import doctest + result = doctest.testmod(verbose=True) + logger.info(f"Doctest results: {result}") diff --git a/networkz/algorithms/approximation/firefighter_problem/main.py b/networkz/algorithms/approximation/firefighter_problem/main.py index 04f5f72..e172248 100644 --- a/networkz/algorithms/approximation/firefighter_problem/main.py +++ b/networkz/algorithms/approximation/firefighter_problem/main.py @@ -31,8 +31,9 @@ import Firefighter_Problem as firefighter_problem # to run the doctest on the firefighter_problem files def setup_global_logger(level: int = logging.DEBUG): - log_format = "|| %(levelname)s || %(message)s" - formatter = logging.Formatter(log_format) + log_format = "|| %(asctime)s || %(levelname)s || %(message)s" + date_format = '%H:%M:%S' + formatter = logging.Formatter(log_format, datefmt=date_format) handler = logging.StreamHandler() handler.setFormatter(formatter) @@ -48,7 +49,7 @@ def setup_global_logger(level: int = logging.DEBUG): if __name__ == "__main__": import doctest result = doctest.testmod(firefighter_problem, verbose=True) - print(f"Doctest results: {result}") + logger.info(f"Doctest results: {result}") #G3 = nx.DiGraph() #G3.add_nodes_from([0,1,2,3,4,5,6,7,8], status="vulnerable") From 5541934b96ce20a2d70f2dd03fec9a50a1b25996 Mon Sep 17 00:00:00 2001 From: 20shaked20 Date: Wed, 12 Jun 2024 22:48:27 +0300 Subject: [PATCH 33/93] Fixes to imports --- .../approximation/firefighter_problem/Firefighter_Problem.py | 5 ++++- .../test_non_spreading_dirlaynet_minbudget.py | 2 +- 2 files changed, 5 insertions(+), 2 deletions(-) diff --git a/networkz/algorithms/approximation/firefighter_problem/Firefighter_Problem.py b/networkz/algorithms/approximation/firefighter_problem/Firefighter_Problem.py index 23d1927..358195f 100644 --- a/networkz/algorithms/approximation/firefighter_problem/Firefighter_Problem.py +++ b/networkz/algorithms/approximation/firefighter_problem/Firefighter_Problem.py @@ -23,7 +23,10 @@ import networkx.algorithms.connectivity as algo import math import logging -from graph_flow_reduction import * +try: + from networkz.algorithms.approximation.firefighter_problem.graph_flow_reduction import graph_flow_reduction +except: + from graph_flow_reduction import * # This is a fix for an issue where the top one has to be exclusive for pytest to work # and the bottom one needs to be exclusive for running this from terminal to work diff --git a/networkz/algorithms/tests/test_firefighter_problem/test_non_spreading_dirlaynet_minbudget.py b/networkz/algorithms/tests/test_firefighter_problem/test_non_spreading_dirlaynet_minbudget.py index d7bd5cd..006b048 100644 --- a/networkz/algorithms/tests/test_firefighter_problem/test_non_spreading_dirlaynet_minbudget.py +++ b/networkz/algorithms/tests/test_firefighter_problem/test_non_spreading_dirlaynet_minbudget.py @@ -30,7 +30,7 @@ from networkz.algorithms.approximation.firefighter_problem.Utils import adjust_nodes_capacity from networkz.algorithms.approximation.firefighter_problem.Utils import create_st_graph from networkz.algorithms.approximation.firefighter_problem.Utils import parse_json_to_networkx -from networkz.algorithms.approximation.firefighter_problem.Utils import graph_flow_reduction +from networkz.algorithms.approximation.firefighter_problem.graph_flow_reduction import graph_flow_reduction from networkz.algorithms.approximation.firefighter_problem.Utils import calculate_vaccine_matrix from networkz.algorithms.approximation.firefighter_problem.Utils import min_cut_N_groups from networkz.algorithms.approximation.firefighter_problem.Utils import matrix_to_integers_values From ff99b204e638b8897af83b20faede6b408deaa91 Mon Sep 17 00:00:00 2001 From: 20shaked20 Date: Thu, 13 Jun 2024 13:16:44 +0300 Subject: [PATCH 34/93] Fixe for the minbudget sum of dirlay --- .../algorithms/approximation/firefighter_problem/Utils.py | 4 ++-- .../test_non_spreading_dirlaynet_minbudget.py | 4 ++-- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/networkz/algorithms/approximation/firefighter_problem/Utils.py b/networkz/algorithms/approximation/firefighter_problem/Utils.py index f0780a1..1d7746e 100644 --- a/networkz/algorithms/approximation/firefighter_problem/Utils.py +++ b/networkz/algorithms/approximation/firefighter_problem/Utils.py @@ -674,8 +674,8 @@ def min_budget_calculation(matrix: np.matrix) -> int: 3 """ integral_matrix = matrix_to_integers_values(matrix) - columns_sum = integral_matrix.sum(axis=0) # we get column sum as we want to -> on time step i, vaccinate Mij nodes from layer j , for all i ≤ j ≤ . - min_budget = int(columns_sum.max()) + rows_sum = integral_matrix.sum(axis=1) # we get column sum as we want to -> on time step i, vaccinate Mij nodes from layer j , for all i ≤ j ≤ . + min_budget = int(rows_sum.max()) logger.info(f"Min budget needed to save the target nodes: {min_budget}") return min_budget diff --git a/networkz/algorithms/tests/test_firefighter_problem/test_non_spreading_dirlaynet_minbudget.py b/networkz/algorithms/tests/test_firefighter_problem/test_non_spreading_dirlaynet_minbudget.py index 006b048..259a1e7 100644 --- a/networkz/algorithms/tests/test_firefighter_problem/test_non_spreading_dirlaynet_minbudget.py +++ b/networkz/algorithms/tests/test_firefighter_problem/test_non_spreading_dirlaynet_minbudget.py @@ -412,14 +412,14 @@ def test_min_budget_calculation(): # checking equality matrix_2_check = np.array([[1, 1], #on time i = 1, vaccinate M11(1) nodes from layer 1. , on time i = 2, vaccinante M12(1) nodes from layer 2 [0, 0]]) - assert min_budget_calculation(matrix_2_check) == 1 + assert min_budget_calculation(matrix_2_check) == 2 # Test 3 # checking equality matrix_3_check = np.array([[1, 0, 1], [0, 0, 0], [0, 0, 0]]) - assert min_budget_calculation(matrix_3_check) == 1 + assert min_budget_calculation(matrix_3_check) == 2 def generate_layered_network(): From eaa155af742bf8b7ddc2287262e0f050e9da4acd Mon Sep 17 00:00:00 2001 From: Erel Segal-Halevi Date: Thu, 13 Jun 2024 14:09:32 +0300 Subject: [PATCH 35/93] run doctests --- .../Firefighter_Problem.py | 21 ++++++------------- .../graph_flow_reduction.py | 21 ++++++++++--------- .../approximation/firefighter_problem/main.py | 9 +++----- .../test_non_spreading_dirlaynet_minbudget.py | 10 ++++----- 4 files changed, 25 insertions(+), 36 deletions(-) diff --git a/networkz/algorithms/approximation/firefighter_problem/Firefighter_Problem.py b/networkz/algorithms/approximation/firefighter_problem/Firefighter_Problem.py index 358195f..3f573d6 100644 --- a/networkz/algorithms/approximation/firefighter_problem/Firefighter_Problem.py +++ b/networkz/algorithms/approximation/firefighter_problem/Firefighter_Problem.py @@ -23,17 +23,8 @@ import networkx.algorithms.connectivity as algo import math import logging -try: - from networkz.algorithms.approximation.firefighter_problem.graph_flow_reduction import graph_flow_reduction -except: - from graph_flow_reduction import * - -# This is a fix for an issue where the top one has to be exclusive for pytest to work -# and the bottom one needs to be exclusive for running this from terminal to work -try: - from networkz.algorithms.approximation.firefighter_problem.Utils import * -except ImportError: - from Utils import * +from networkz.algorithms.approximation.firefighter_problem.graph_flow_reduction import max_flow_with_node_capacity +from networkz.algorithms.approximation.firefighter_problem.Utils import * logger = logging.getLogger(__name__) @@ -198,7 +189,7 @@ def spreading_minbudget(Graph:nx.DiGraph, source:int, targets:list)-> int: max_value = middle best_strategy = strategy else: - logger.warning(f"The current budget {middle} didn't save all the targets!") + logger.info(f"The current budget {middle} didn't save all the targets!") min_value = middle + 1 middle = math.floor((min_value + max_value) / 2) @@ -285,7 +276,7 @@ def non_spreading_dirlaynet_minbudget(Graph:nx.DiGraph, src:int, targets:list)-> layers = adjust_nodes_capacity(Graph, src) G = create_st_graph(Graph, targets) display_graph(G) - G_reduction = graph_flow_reduction(G, source=src, target='t') + G_reduction = max_flow_with_node_capacity(G, source=src, target='t') N_groups = min_cut_N_groups(G_reduction, src,layers) vacc_matrix = calculate_vaccine_matrix(layers, N_groups) min_budget = min_budget_calculation(vacc_matrix) @@ -412,7 +403,7 @@ def heuristic_minbudget(Graph:nx.DiGraph, source:int, targets:list, spreading:bo max_value = middle best_strategy = strategy else: - logger.warning(f"The current budget {middle} didn't save all the targets!") + logger.info(f"The current budget {middle} didn't save all the targets!") min_value = middle + 1 middle = math.floor((min_value + max_value) / 2) @@ -423,6 +414,6 @@ def heuristic_minbudget(Graph:nx.DiGraph, source:int, targets:list, spreading:bo if __name__ == "__main__": import doctest - result = doctest.testmod(verbose=True) + result = doctest.testmod(verbose=False) logger.info(f"Doctest results: {result}") \ No newline at end of file diff --git a/networkz/algorithms/approximation/firefighter_problem/graph_flow_reduction.py b/networkz/algorithms/approximation/firefighter_problem/graph_flow_reduction.py index da9633b..9af1b29 100644 --- a/networkz/algorithms/approximation/firefighter_problem/graph_flow_reduction.py +++ b/networkz/algorithms/approximation/firefighter_problem/graph_flow_reduction.py @@ -4,13 +4,9 @@ logger = logging.getLogger(__name__) -def graph_flow_reduction(graph: nx.DiGraph, source: int = None, target: int = None) -> nx.DiGraph: +def max_flow_with_node_capacity(graph: nx.DiGraph, source: int = None, target: int = None) -> nx.DiGraph: """ - This function transforms a given directed graph into a new graph where each node - is split into two nodes (an "in" node and an "out" node) connected by an edge - with a capacity (weight). The transformation is used for flow problems where - nodes have capacities instead of edges - allowing to run algorithms which - were originally designed to be used for edge-flow problems. + Computes a maximum flow in the given graph, where each node has a capacity Parameters: ---------- @@ -30,6 +26,11 @@ def graph_flow_reduction(graph: nx.DiGraph, source: int = None, target: int = No Notes: ----- + This function transforms a given directed graph into a new graph where each node + is split into two nodes (an "in" node and an "out" node) connected by an edge + with a capacity (weight). The transformation is used for flow problems where + nodes have capacities instead of edges - allowing to run algorithms which + were originally designed to be used for edge-flow problems. - If a node does not have a 'capacity' attribute, a default capacity of 1 is used. - There is infinite capacity between two different edge_out & edge_in @@ -43,12 +44,12 @@ def graph_flow_reduction(graph: nx.DiGraph, source: int = None, target: int = No >>> G.add_edge(1, 2) >>> G.add_edge(2, 3) >>> G.add_edge(1, 3) - >>> H = graph_flow_reduction(G, 1, 3) + >>> H = max_flow_with_node_capacity(G, 1, 3) >>> sorted(list(H.nodes)) ['1_in', '1_out', '2_in', '2_out', '3_in', '3_out'] >>> sorted(list(H.edges(data=True))) [('1_in', '1_out', {'weight': inf}), ('1_out', '2_in', {'weight': inf}), ('1_out', '3_in', {'weight': inf}), ('2_in', '2_out', {'weight': 15}), ('2_out', '3_in', {'weight': inf}), ('3_in', '3_out', {'weight': inf})] - >>> H = graph_flow_reduction(G) + >>> H = max_flow_with_node_capacity(G) >>> sorted(list(H.nodes)) ['1_in', '1_out', '2_in', '2_out', '3_in', '3_out'] >>> sorted(list(H.edges(data=True))) @@ -83,5 +84,5 @@ def graph_flow_reduction(graph: nx.DiGraph, source: int = None, target: int = No if __name__ == "__main__": import doctest - result = doctest.testmod(verbose=True) - logger.info(f"Doctest results: {result}") + result = doctest.testmod(verbose=False) + print(f"Doctest results: {result}") diff --git a/networkz/algorithms/approximation/firefighter_problem/main.py b/networkz/algorithms/approximation/firefighter_problem/main.py index e172248..e5c8fe2 100644 --- a/networkz/algorithms/approximation/firefighter_problem/main.py +++ b/networkz/algorithms/approximation/firefighter_problem/main.py @@ -23,12 +23,9 @@ # This is a fix for an issue where the top one has to be exclusive for pytest to work # and the bottom one needs to be exclusive for running this from terminal to work -try: - from networkz.algorithms.approximation.firefighter_problem.Utils import * -except ImportError: - from Utils import * - from Firefighter_Problem import * - import Firefighter_Problem as firefighter_problem # to run the doctest on the firefighter_problem files +from networkz.algorithms.approximation.firefighter_problem.Utils import * +from networkz.algorithms.approximation.firefighter_problem.Firefighter_Problem import * +import networkz.algorithms.approximation.firefighter_problem.Firefighter_Problem as firefighter_problem # to run the doctest on the firefighter_problem files def setup_global_logger(level: int = logging.DEBUG): log_format = "|| %(asctime)s || %(levelname)s || %(message)s" diff --git a/networkz/algorithms/tests/test_firefighter_problem/test_non_spreading_dirlaynet_minbudget.py b/networkz/algorithms/tests/test_firefighter_problem/test_non_spreading_dirlaynet_minbudget.py index 259a1e7..b56aec1 100644 --- a/networkz/algorithms/tests/test_firefighter_problem/test_non_spreading_dirlaynet_minbudget.py +++ b/networkz/algorithms/tests/test_firefighter_problem/test_non_spreading_dirlaynet_minbudget.py @@ -30,7 +30,7 @@ from networkz.algorithms.approximation.firefighter_problem.Utils import adjust_nodes_capacity from networkz.algorithms.approximation.firefighter_problem.Utils import create_st_graph from networkz.algorithms.approximation.firefighter_problem.Utils import parse_json_to_networkx -from networkz.algorithms.approximation.firefighter_problem.graph_flow_reduction import graph_flow_reduction +from networkz.algorithms.approximation.firefighter_problem.graph_flow_reduction import max_flow_with_node_capacity from networkz.algorithms.approximation.firefighter_problem.Utils import calculate_vaccine_matrix from networkz.algorithms.approximation.firefighter_problem.Utils import min_cut_N_groups from networkz.algorithms.approximation.firefighter_problem.Utils import matrix_to_integers_values @@ -135,7 +135,7 @@ def test_source_is_target(graph_key, source, targets): layers_1 = adjust_nodes_capacity(graph_1, 0) # src is 0 targets_1 = [1, 2, 3] # saving 1,2,3 G1 = create_st_graph(graph_1, targets_1) -reduction_G1 = graph_flow_reduction(G1, 0) +reduction_G1 = max_flow_with_node_capacity(G1, 0) N_1_groups = min_cut_N_groups(reduction_G1, 0,layers_1) matrix_1 = calculate_vaccine_matrix(layers_1, N_1_groups) integer_matrix_1 = matrix_to_integers_values(matrix_1) @@ -147,7 +147,7 @@ def test_source_is_target(graph_key, source, targets): layers_2 = adjust_nodes_capacity(graph_2, 0) # src is 0 targets_2 = [2, 4] # saving 2,4 G2 = create_st_graph(graph_2, targets_2) -reduction_G2 = graph_flow_reduction(G2, 0) +reduction_G2 = max_flow_with_node_capacity(G2, 0) N_2_groups = min_cut_N_groups(reduction_G2, 0, layers_2) matrix_2 = calculate_vaccine_matrix(layers_2, N_2_groups) integer_matrix_2 = matrix_to_integers_values(matrix_2) @@ -159,7 +159,7 @@ def test_source_is_target(graph_key, source, targets): layers_3 = adjust_nodes_capacity(graph_3, 0) # src is 0 targets_3 = [1, 5, 7] # saving 1,5,7 G3 = create_st_graph(graph_3, targets_3) -reduction_G3 = graph_flow_reduction(G3, 0) +reduction_G3 = max_flow_with_node_capacity(G3, 0) N_3_groups = min_cut_N_groups(reduction_G3, 0, layers_3) matrix_3 = calculate_vaccine_matrix(layers_3, N_3_groups) integer_matrix_3 = matrix_to_integers_values(matrix_3) @@ -171,7 +171,7 @@ def test_source_is_target(graph_key, source, targets): layers_4 = adjust_nodes_capacity(graph_4, 0) # src is 0 targets_4 = [4, 5, 6, 8] # saving 4,5,6,8 G4 = create_st_graph(graph_4, targets_4) -reduction_G4 = graph_flow_reduction(G4, 0) +reduction_G4 = max_flow_with_node_capacity(G4, 0) N_4_groups = min_cut_N_groups(reduction_G4, 0, layers_4) matrix_4 = calculate_vaccine_matrix(layers_4, N_4_groups) integer_matrix_4 = matrix_to_integers_values(matrix_4) From 0eb5688a3b60a1b01619efe83c239e538ee4268c Mon Sep 17 00:00:00 2001 From: Erel Segal-Halevi Date: Thu, 13 Jun 2024 15:31:09 +0300 Subject: [PATCH 36/93] logs --- .../Firefighter_Problem.py | 2 + .../firefighter_problem/Utils.py | 7 +++- .../approximation/firefighter_problem/main.py | 37 ++++++++++++------- 3 files changed, 30 insertions(+), 16 deletions(-) diff --git a/networkz/algorithms/approximation/firefighter_problem/Firefighter_Problem.py b/networkz/algorithms/approximation/firefighter_problem/Firefighter_Problem.py index 3f573d6..c78f5c2 100644 --- a/networkz/algorithms/approximation/firefighter_problem/Firefighter_Problem.py +++ b/networkz/algorithms/approximation/firefighter_problem/Firefighter_Problem.py @@ -318,12 +318,14 @@ def heuristic_maxsave(Graph:nx.DiGraph, budget:int, source:int, targets:list, sp logger.info(f"Starting the heuristic_maxsave function with source node {source}, budget {budget}, targets: {targets}, and spreading: {spreading}") clean_graph(Graph) + display_graph(Graph) infected_nodes = [] vaccinated_nodes = [] vaccination_strategy = [] can_spread = True Graph.nodes[source]['status'] = Status.INFECTED.value infected_nodes.append(source) + display_graph(Graph) time_step = 1 while can_spread: diff --git a/networkz/algorithms/approximation/firefighter_problem/Utils.py b/networkz/algorithms/approximation/firefighter_problem/Utils.py index 1d7746e..d3f1139 100644 --- a/networkz/algorithms/approximation/firefighter_problem/Utils.py +++ b/networkz/algorithms/approximation/firefighter_problem/Utils.py @@ -341,6 +341,7 @@ def spread_virus(graph:nx.DiGraph, infected_nodes:list)->bool: graph.nodes[neighbor]['status'] = Status.INFECTED.value new_infected_nodes.append(neighbor) logger.debug("SPREAD VIRUS: Node " + f'{neighbor}' + " has been infected from node " + f'{node}') + display_graph(graph) infected_nodes.clear() for node in new_infected_nodes: infected_nodes.append(node) @@ -375,6 +376,7 @@ def spread_vaccination(graph:nx.DiGraph, vaccinated_nodes:list)->None: graph.nodes[neighbor]['status'] = Status.VACCINATED.value new_vaccinated_nodes.append(neighbor) logger.debug("SPREAD VACCINATION: Node " + f'{neighbor}' + " has been vaccinated from node " + f'{node}') + display_graph(graph) vaccinated_nodes.clear() for node in new_vaccinated_nodes: vaccinated_nodes.append(node) @@ -401,6 +403,7 @@ def vaccinate_node(graph:nx.DiGraph, node:int)->None: """ graph.nodes[node]['status'] = Status.DIRECTLY_VACCINATED.value logger.info("Node " + f'{node}' + " has been directly vaccinated") + display_graph(graph) return def clean_graph(graph:nx.DiGraph)->None: @@ -577,7 +580,7 @@ def calculate_vaccine_matrix(layers:list, min_cut_nodes_grouped:dict)->np.matrix value = N_j / (j + 1) matrix[i][j] = value - logger.info(f"Vaccination Matrix Before Conversion: {matrix}") + logger.info(f"Vaccination Matrix Before Conversion:\n{matrix}") return matrix def matrix_to_integers_values(matrix: np.matrix) -> np.matrix: @@ -649,7 +652,7 @@ def matrix_to_integers_values(matrix: np.matrix) -> np.matrix: else: integral_matrix[i, j] = np.floor(matrix[i, j]) - logger.info(f"Integral and final Matrix: {integral_matrix}") + logger.info(f"Integral and final Matrix:\n{integral_matrix}") return np.matrix(integral_matrix) diff --git a/networkz/algorithms/approximation/firefighter_problem/main.py b/networkz/algorithms/approximation/firefighter_problem/main.py index e5c8fe2..9ec6877 100644 --- a/networkz/algorithms/approximation/firefighter_problem/main.py +++ b/networkz/algorithms/approximation/firefighter_problem/main.py @@ -20,6 +20,9 @@ """ import logging +import json +logger = logging.getLogger(__name__) + # This is a fix for an issue where the top one has to be exclusive for pytest to work # and the bottom one needs to be exclusive for running this from terminal to work @@ -38,22 +41,28 @@ def setup_global_logger(level: int = logging.DEBUG): root_logger.setLevel(level) root_logger.addHandler(handler) -setup_global_logger() - -logger = logging.getLogger(__name__) - if __name__ == "__main__": import doctest - result = doctest.testmod(firefighter_problem, verbose=True) - logger.info(f"Doctest results: {result}") - - #G3 = nx.DiGraph() - #G3.add_nodes_from([0,1,2,3,4,5,6,7,8], status="vulnerable") - #G3.add_edges_from([(0,2),(0,4),(0,5),(2,1),(2,3),(4,1),(4,6),(5,3),(5,6),(5,7),(6,7),(6,8),(7,8)]) - #logger.info("=" * 150) - #logger.info(spreading_minbudget(G3,0,[2,6,1,8])) - #print(spreading_maxsave(G3,1, 0,[2,6,1,8])[1]) + setup_global_logger(level=logging.DEBUG) + + # result = doctest.testmod(firefighter_problem, verbose=True) + # logger.info(f"Doctest results: {result}") + + G3 = nx.DiGraph() + G3.add_nodes_from([0,1,2,3,4,5,6,7,8]) + G3.add_edges_from([(0,2),(0,4),(0,5),(2,1),(2,3),(4,1),(4,6),(5,3),(5,6),(5,7),(6,7),(6,8),(7,8)]) + logger.info("=" * 150) + #print(spreading_maxsave(G3,source=0,targets=[2,6,1,8],budget=1)) + #print(spreading_minbudget(G3,source=0,targets=[2,6,1,8])) + + # with open("networkz/algorithms/tests/test_firefighter_problem/graphs.json", "r") as file: + # json_data = json.load(file) + # graphs = parse_json_to_networkx(json_data) + + # G2 = graphs["Dirlay_Graph-2"] + # print(non_spreading_dirlaynet_minbudget(G2,src=0,targets=[2,4])) + print(spreading_maxsave(G3,1, 0,[2,6,1,8])[1]) # logger.info("=" * 150) - # logger.info(heuristic_minbudget(G3,0,[2,6,1,8], True)) + #logger.info(heuristic_minbudget(G3,0,[2,6,1,8], True)) From 9be7ecf3dc940e68928e9a89e7e0a24b9eb6501d Mon Sep 17 00:00:00 2001 From: Almog-David Date: Thu, 13 Jun 2024 19:25:51 +0300 Subject: [PATCH 37/93] huristic improvement + minor changes for erel --- .../Firefighter_Problem.py | 22 +-- .../firefighter_problem/Utils.py | 11 +- .../approximation/firefighter_problem/main.py | 18 +-- .../test_heuristic_maxsave.py | 129 ++++++++++++++++++ .../test_spreading_maxsave.py | 2 +- 5 files changed, 160 insertions(+), 22 deletions(-) create mode 100644 networkz/algorithms/tests/test_firefighter_problem/test_heuristic_maxsave.py diff --git a/networkz/algorithms/approximation/firefighter_problem/Firefighter_Problem.py b/networkz/algorithms/approximation/firefighter_problem/Firefighter_Problem.py index 358195f..1e1a789 100644 --- a/networkz/algorithms/approximation/firefighter_problem/Firefighter_Problem.py +++ b/networkz/algorithms/approximation/firefighter_problem/Firefighter_Problem.py @@ -37,7 +37,7 @@ logger = logging.getLogger(__name__) -def spreading_maxsave(Graph:nx.DiGraph, budget:int, source:int, targets:list, flag=None) -> list: +def spreading_maxsave(Graph:nx.DiGraph, budget:int, source:int, targets:list, stop_condition=None) -> list: """ "Approximability of the Firefighter Problem - Computing Cuts over Time", by Elliot Anshelevich, Deeparnab Chakrabarty, Ameya Hate, Chaitanya Swamy (2010) @@ -126,7 +126,7 @@ def spreading_maxsave(Graph:nx.DiGraph, budget:int, source:int, targets:list, fl can_spread = spread_virus(Graph, infected_nodes) - if flag is not None: + if stop_condition is not None: if len(targets) == 0 or any(node in infected_nodes for node in targets): clean_graph(Graph) logger.info(f"Returning vaccination strategy: {vaccination_strategy}. The strategy saved the nodes: {saved_target_nodes}") @@ -293,7 +293,7 @@ def non_spreading_dirlaynet_minbudget(Graph:nx.DiGraph, src:int, targets:list)-> logger.info(f"Returning minimum budget: {min_budget}") return min_budget -def heuristic_maxsave(Graph:nx.DiGraph, budget:int, source:int, targets:list, spreading:bool, flag=None) -> list: +def heuristic_maxsave(Graph:nx.DiGraph, budget:int, source:int, targets:list, spreading=True, stop_condition=None) -> list: """ This heuristic approach is based on the local search problem. We will select the best neighbor that saves the most nodes from targets. @@ -316,7 +316,7 @@ def heuristic_maxsave(Graph:nx.DiGraph, budget:int, source:int, targets:list, sp >>> G = nx.DiGraph() >>> G.add_nodes_from([0, 1, 2, 3], status="vulnerable") >>> G.add_edges_from([(0, 1), (0, 2), (1, 2), (1, 3)]) - >>> heuristic_maxsave(G, 1, 0, [1, 2, 3], True) + >>> heuristic_maxsave(G, 1, 0, [1, 2, 3]) [(1, 1)] """ if budget < 1: @@ -330,6 +330,7 @@ def heuristic_maxsave(Graph:nx.DiGraph, budget:int, source:int, targets:list, sp infected_nodes = [] vaccinated_nodes = [] vaccination_strategy = [] + saved_target_nodes = set() can_spread = True Graph.nodes[source]['status'] = Status.INFECTED.value infected_nodes.append(source) @@ -350,6 +351,7 @@ def heuristic_maxsave(Graph:nx.DiGraph, budget:int, source:int, targets:list, sp if nodes_saved is not None: + saved_target_nodes.update(nodes_saved) targets[:] = [element for element in targets if element not in nodes_saved] logger.info(f"Updated list of targets: {targets}") @@ -358,15 +360,19 @@ def heuristic_maxsave(Graph:nx.DiGraph, budget:int, source:int, targets:list, sp can_spread = spread_virus(Graph, infected_nodes) - if flag is not None: + if stop_condition is not None: if len(targets) == 0 or any(node in infected_nodes for node in targets): - logger.info(f"Returning vaccination strategy: {vaccination_strategy}") + logger.info(f"Returning vaccination strategy: {vaccination_strategy}. The strategy saved the nodes: {saved_target_nodes}") return vaccination_strategy time_step += 1 + + for node in targets: + if Graph.nodes[node]['status'] != Status.INFECTED.value: + saved_target_nodes.add(node) - logger.info(f"Returning vaccination strategy: {vaccination_strategy}") - return vaccination_strategy + logger.info(f"Returning vaccination strategy: {vaccination_strategy}. The strategy saved the nodes: {saved_target_nodes}") + return vaccination_strategy, saved_target_nodes def heuristic_minbudget(Graph:nx.DiGraph, source:int, targets:list, spreading:bool)-> int: """ diff --git a/networkz/algorithms/approximation/firefighter_problem/Utils.py b/networkz/algorithms/approximation/firefighter_problem/Utils.py index f0780a1..d7a351f 100644 --- a/networkz/algorithms/approximation/firefighter_problem/Utils.py +++ b/networkz/algorithms/approximation/firefighter_problem/Utils.py @@ -341,6 +341,7 @@ def spread_virus(graph:nx.DiGraph, infected_nodes:list)->bool: graph.nodes[neighbor]['status'] = Status.INFECTED.value new_infected_nodes.append(neighbor) logger.debug("SPREAD VIRUS: Node " + f'{neighbor}' + " has been infected from node " + f'{node}') + #display_graph(graph) infected_nodes.clear() for node in new_infected_nodes: infected_nodes.append(node) @@ -375,6 +376,7 @@ def spread_vaccination(graph:nx.DiGraph, vaccinated_nodes:list)->None: graph.nodes[neighbor]['status'] = Status.VACCINATED.value new_vaccinated_nodes.append(neighbor) logger.debug("SPREAD VACCINATION: Node " + f'{neighbor}' + " has been vaccinated from node " + f'{node}') + #display_graph(graph) vaccinated_nodes.clear() for node in new_vaccinated_nodes: vaccinated_nodes.append(node) @@ -401,6 +403,7 @@ def vaccinate_node(graph:nx.DiGraph, node:int)->None: """ graph.nodes[node]['status'] = Status.DIRECTLY_VACCINATED.value logger.info("Node " + f'{node}' + " has been directly vaccinated") + #display_graph(graph) return def clean_graph(graph:nx.DiGraph)->None: @@ -707,13 +710,13 @@ def find_best_neighbor(graph:nx.DiGraph, infected_nodes:list, targets:list)->int if graph.nodes[node]['status'] == Status.VULNERABLE.value: # for each node that is target, we will add only his nighbors that are target as well neighbors_list = list(graph.neighbors(node)) - target_neighbors = set() + vulnerable_neighbors = set() for neighbor in neighbors_list: if graph.nodes[neighbor]['status'] == Status.VULNERABLE.value: - target_neighbors.add(neighbor) + vulnerable_neighbors.add(neighbor) if node in targets: - target_neighbors.add(node) - common_elements = set(target_neighbors) & set(targets) + vulnerable_neighbors.add(node) + common_elements = set(vulnerable_neighbors) & set(targets) logger.info("node " + f'{node}' + " is saving the nodes " + str(common_elements)) if len(common_elements) > max_number: best_node = node diff --git a/networkz/algorithms/approximation/firefighter_problem/main.py b/networkz/algorithms/approximation/firefighter_problem/main.py index e172248..261bdb9 100644 --- a/networkz/algorithms/approximation/firefighter_problem/main.py +++ b/networkz/algorithms/approximation/firefighter_problem/main.py @@ -47,15 +47,15 @@ def setup_global_logger(level: int = logging.DEBUG): if __name__ == "__main__": - import doctest - result = doctest.testmod(firefighter_problem, verbose=True) - logger.info(f"Doctest results: {result}") - - #G3 = nx.DiGraph() - #G3.add_nodes_from([0,1,2,3,4,5,6,7,8], status="vulnerable") - #G3.add_edges_from([(0,2),(0,4),(0,5),(2,1),(2,3),(4,1),(4,6),(5,3),(5,6),(5,7),(6,7),(6,8),(7,8)]) - #logger.info("=" * 150) - #logger.info(spreading_minbudget(G3,0,[2,6,1,8])) + #import doctest + #result = doctest.testmod(firefighter_problem, verbose=True) + #logger.info(f"Doctest results: {result}") + + G3 = nx.DiGraph() + G3.add_nodes_from([0,1,2,3,4,5,6,7,8], status="vulnerable") + G3.add_edges_from([(0,2),(0,4),(0,5),(2,1),(2,3),(4,1),(4,6),(5,3),(5,6),(5,7),(6,7),(6,8),(7,8)]) + logger.info("=" * 150) + logger.info(heuristic_maxsave(G3,2,0,[2,6,1,8])) #print(spreading_maxsave(G3,1, 0,[2,6,1,8])[1]) # logger.info("=" * 150) # logger.info(heuristic_minbudget(G3,0,[2,6,1,8], True)) diff --git a/networkz/algorithms/tests/test_firefighter_problem/test_heuristic_maxsave.py b/networkz/algorithms/tests/test_firefighter_problem/test_heuristic_maxsave.py new file mode 100644 index 0000000..948333d --- /dev/null +++ b/networkz/algorithms/tests/test_firefighter_problem/test_heuristic_maxsave.py @@ -0,0 +1,129 @@ +""" + +The Paper - +Approximability of the Firefighter Problem Computing Cuts over Time + +Paper Link - +https://github.com/The-Firefighters/networkz/blob/master/networkz/algorithms/approximation/firefighter_problem/Approximability_of_the_Firefighter_Problem.pdf + +Authors - +Elliot Anshelevich +Deeparnab Chakrabarty +Ameya Hate +Chaitanya Swamy + +Developers - +Yuval Bubnovsky +Almog David +Shaked Levi + +""" + +import pytest +import networkx as nx +import json +import random + +from networkz.algorithms.approximation.firefighter_problem.Firefighter_Problem import heuristic_maxsave, spreading_maxsave +from networkz.algorithms.approximation.firefighter_problem.Utils import find_best_neighbor, parse_json_to_networkx, Status + +with open("networkz/algorithms/tests/test_firefighter_problem/graphs.json", "r") as file: + json_data = json.load(file) +graphs = parse_json_to_networkx(json_data) + +@pytest.mark.parametrize("graph_key, budget, source, targets", [ + ("RegularGraph_Graph-1", 1, -2, [1, 2, 3, 4, 5, 6]), + ("RegularGraph_Graph-4", 1, 8, [1, 2, 4, 6, 7]), + ("RegularGraph_Graph-6", 1, 10, [0, 2, 3, 5, 6, 7, 8, 9]), + ("RegularGraph_Graph-8", 1, 17, [1, 7, 12, 14, 8, 3, 11, 2]), + ("RegularGraph_Graph-3", 1, 6, [1, 3, 5]), +]) +def test_source_not_in_graph(graph_key, budget, source, targets): + with pytest.raises(ValueError): + heuristic_maxsave(graphs[graph_key], budget, source, targets) + +@pytest.mark.parametrize("graph_key, budget, source, targets", [ + ("RegularGraph_Graph-2", 1, 0, [1, 2, 3, 9, 5, 16]), + ("RegularGraph_Graph-3", 1, 4, [1, 2, 3, 6, 7]), + ("RegularGraph_Graph-6", 1, 3, [0, 2, 5, 6, 7, 8, 10]), + ("RegularGraph_Graph-8", 1, 11, [1, 3, 12, 19, 8, 10, 4, 2]), + ("RegularGraph_Graph-7", 1, 2, [1, 3, -1, 5]), +]) +def test_target_not_in_graph(graph_key, budget, source, targets): + with pytest.raises(ValueError): + heuristic_maxsave(graphs[graph_key], budget, source, targets) + +@pytest.mark.parametrize("graph_key, budget, source, targets", [ + ("RegularGraph_Graph-1", 1, 0, [1, 2, 3, 0, 4, 5, 6]), + ("RegularGraph_Graph-3", 1, 1, [5, 1, 4]), + ("RegularGraph_Graph-4", 1, 4, [1, 2, 3, 4, 5, 6, 7]), + ("RegularGraph_Graph-6", 1, 0, [0, 3, 5, 6, 7, 8, 9]), + ("RegularGraph_Graph-8", 1, 0, [13, 10, 8, 6, 5, 4, 3, 0, 1, 2]), +]) +def test_source_is_target(graph_key, budget, source, targets): + with pytest.raises(ValueError): + heuristic_maxsave(graphs[graph_key], budget, source, targets) + +@pytest.mark.parametrize("graph_key, budget, source, targets, expected_length", [ + ("RegularGraph_Graph-1", 1, 0, [1, 2, 3, 4, 5, 6], 2), + ("Dirlay_Graph-5", 2, 0, [1, 2, 3, 4, 5, 6, 7, 8], 3), +]) +def test_strategy_length(graph_key, budget, source, targets, expected_length): + graph = graphs[graph_key] + calculated_strategy = spreading_maxsave(graph, budget, source, targets)[0] + print(calculated_strategy) + + assert len(calculated_strategy) == expected_length + + +@pytest.mark.parametrize("graph_key, budget, source, targets, expected_strategy", [ + ("RegularGraph_Graph-1", 1, 0, [1, 2, 3, 4, 5, 6], [(1, 1), (6, 2)]), + ("Dirlay_Graph-5", 2, 0, [1, 2, 3, 4, 5, 6, 7, 8], [(5, 1), (2, 1)]), +]) +def test_save_all_vertices(graph_key, budget, source, targets, expected_strategy): + graph = graphs[graph_key] + calculated_strategy = heuristic_maxsave(graph, budget, source, targets)[0] + print(calculated_strategy) + + assert calculated_strategy == expected_strategy + +@pytest.mark.parametrize("graph_key, budget, source, targets, expected_strategy", [ + ("RegularGraph_Graph-6", 2, 1, [3, 9, 0, 5, 6], [(2, 1)]), + ("RegularGraph_Graph-4", 1, 0, [2, 6, 4], [(1, 1)]), +]) +def test_save_subgroup_vertices(graph_key, budget, source, targets, expected_strategy): + graph = graphs[graph_key] + calculated_strategy = heuristic_maxsave(graph, budget, source, targets)[0] + print(calculated_strategy) + + assert calculated_strategy == expected_strategy + +def test_random_graph_comparison(): + for i in range(10): + num_nodes = random.randint(2,100) + nodes = list(range(num_nodes+1)) + num_edges = 1000 + save_amount = random.randint(1,num_nodes) + targets = [] + G = nx.DiGraph() + + G.add_nodes_from(nodes, status=Status.VULNERABLE.value) + for _ in range(num_edges): + source = random.randint(0, num_nodes - 1) + target = random.randint(0, num_nodes - 1) + if source != target: # Ensure no self-loops + G.add_edge(source, target) + for node in range(save_amount): + probability = random.random() + if probability < 0.75 and node!=0: + targets.append(node) + + print(targets) + spreading_answer = spreading_maxsave(G,1,0,targets)[1] + heuristic_answer = heuristic_maxsave(G,1,0,targets)[1] + print(spreading_answer) + print(heuristic_answer) + + assert len(spreading_answer) <= len(heuristic_answer) + + print("All tests have passed!") \ No newline at end of file diff --git a/networkz/algorithms/tests/test_firefighter_problem/test_spreading_maxsave.py b/networkz/algorithms/tests/test_firefighter_problem/test_spreading_maxsave.py index dce9d91..7922534 100644 --- a/networkz/algorithms/tests/test_firefighter_problem/test_spreading_maxsave.py +++ b/networkz/algorithms/tests/test_firefighter_problem/test_spreading_maxsave.py @@ -261,7 +261,7 @@ def test_save_subgroup_vertices_nodes_list(graph_key, budget, source, targets, e assert calculated_nodes_saved_list == expected_nodes_saved_list -def random_graph_test(): +def test_random_graph(): for i in range(10): num_nodes = random.randint(2,100) nodes = list(range(num_nodes+1)) From 69a654e7a83da172ca82d3d40fdc13621caeb729 Mon Sep 17 00:00:00 2001 From: Almog-David Date: Thu, 13 Jun 2024 21:31:37 +0300 Subject: [PATCH 38/93] added tests for heuristic minbudget --- .../Firefighter_Problem.py | 12 +- .../approximation/firefighter_problem/main.py | 15 +- .../test_heuristic_minbudget.py | 148 ++++++++++++++++++ .../test_spreading_minbudget.py | 2 +- 4 files changed, 163 insertions(+), 14 deletions(-) create mode 100644 networkz/algorithms/tests/test_firefighter_problem/test_heuristic_minbudget.py diff --git a/networkz/algorithms/approximation/firefighter_problem/Firefighter_Problem.py b/networkz/algorithms/approximation/firefighter_problem/Firefighter_Problem.py index 63c16b5..844d1ad 100644 --- a/networkz/algorithms/approximation/firefighter_problem/Firefighter_Problem.py +++ b/networkz/algorithms/approximation/firefighter_problem/Firefighter_Problem.py @@ -270,12 +270,12 @@ def non_spreading_dirlaynet_minbudget(Graph:nx.DiGraph, src:int, targets:list)-> logger.error("Problem with graph, its not a DAG, thus cannot run algorithm") return - display_graph(Graph) + #display_graph(Graph) logger.info(f"Starting the non_spreading_dirlaynet_minbudget function with source node {src} and targets: {targets}") layers = adjust_nodes_capacity(Graph, src) G = create_st_graph(Graph, targets) - display_graph(G) + #display_graph(G) G_reduction = max_flow_with_node_capacity(G, source=src, target='t') N_groups = min_cut_N_groups(G_reduction, src,layers) vacc_matrix = calculate_vaccine_matrix(layers, N_groups) @@ -284,7 +284,7 @@ def non_spreading_dirlaynet_minbudget(Graph:nx.DiGraph, src:int, targets:list)-> logger.info(f"Returning minimum budget: {min_budget}") return min_budget -def heuristic_maxsave(Graph:nx.DiGraph, budget:int, source:int, targets:list, spreading=True, stop_condition=None) -> list: +def heuristic_maxsave(Graph:nx.DiGraph, budget:int, source:int, targets:list, spreading=True, stop_condition=None) -> tuple: """ This heuristic approach is based on the local search problem. We will select the best neighbor that saves the most nodes from targets. @@ -318,7 +318,7 @@ def heuristic_maxsave(Graph:nx.DiGraph, budget:int, source:int, targets:list, sp logger.info(f"Starting the heuristic_maxsave function with source node {source}, budget {budget}, targets: {targets}, and spreading: {spreading}") clean_graph(Graph) - display_graph(Graph) + #display_graph(Graph) infected_nodes = [] vaccinated_nodes = [] vaccination_strategy = [] @@ -326,7 +326,7 @@ def heuristic_maxsave(Graph:nx.DiGraph, budget:int, source:int, targets:list, sp can_spread = True Graph.nodes[source]['status'] = Status.INFECTED.value infected_nodes.append(source) - display_graph(Graph) + #display_graph(Graph) time_step = 1 while can_spread: @@ -367,7 +367,7 @@ def heuristic_maxsave(Graph:nx.DiGraph, budget:int, source:int, targets:list, sp logger.info(f"Returning vaccination strategy: {vaccination_strategy}. The strategy saved the nodes: {saved_target_nodes}") return vaccination_strategy, saved_target_nodes -def heuristic_minbudget(Graph:nx.DiGraph, source:int, targets:list, spreading:bool)-> int: +def heuristic_minbudget(Graph:nx.DiGraph, source:int, targets:list, spreading:bool)-> tuple: """ This function calculates the minimum budget required to save all target nodes using the heuristic approach based on local search problem. diff --git a/networkz/algorithms/approximation/firefighter_problem/main.py b/networkz/algorithms/approximation/firefighter_problem/main.py index 9ec6877..300b94a 100644 --- a/networkz/algorithms/approximation/firefighter_problem/main.py +++ b/networkz/algorithms/approximation/firefighter_problem/main.py @@ -56,13 +56,14 @@ def setup_global_logger(level: int = logging.DEBUG): #print(spreading_maxsave(G3,source=0,targets=[2,6,1,8],budget=1)) #print(spreading_minbudget(G3,source=0,targets=[2,6,1,8])) - # with open("networkz/algorithms/tests/test_firefighter_problem/graphs.json", "r") as file: - # json_data = json.load(file) - # graphs = parse_json_to_networkx(json_data) - - # G2 = graphs["Dirlay_Graph-2"] - # print(non_spreading_dirlaynet_minbudget(G2,src=0,targets=[2,4])) - print(spreading_maxsave(G3,1, 0,[2,6,1,8])[1]) + with open("networkz/algorithms/tests/test_firefighter_problem/graphs.json", "r") as file: + json_data = json.load(file) + graphs = parse_json_to_networkx(json_data) + + G2 = graphs["RegularGraph_Graph-2"] + print(heuristic_minbudget(G2,source=0, targets=[1,3,4,5,6],spreading=True)) + #print(spreading_maxsave(G3,1, 0,[2,6,1,8])[1]) + print(spreading_minbudget(G2,source=0, targets=[1,3,4,5,6])) # logger.info("=" * 150) #logger.info(heuristic_minbudget(G3,0,[2,6,1,8], True)) diff --git a/networkz/algorithms/tests/test_firefighter_problem/test_heuristic_minbudget.py b/networkz/algorithms/tests/test_firefighter_problem/test_heuristic_minbudget.py new file mode 100644 index 0000000..51ba513 --- /dev/null +++ b/networkz/algorithms/tests/test_firefighter_problem/test_heuristic_minbudget.py @@ -0,0 +1,148 @@ +""" + +The Paper - +Approximability of the Firefighter Problem Computing Cuts over Time + +Paper Link - +https://github.com/The-Firefighters/networkz/blob/master/networkz/algorithms/approximation/firefighter_problem/Approximability_of_the_Firefighter_Problem.pdf + +Authors - +Elliot Anshelevich +Deeparnab Chakrabarty +Ameya Hate +Chaitanya Swamy + +Developers - +Yuval Bubnovsky +Almog David +Shaked Levi + +""" + +import pytest +import networkx as nx +import json +import random + +from networkz.algorithms.approximation.firefighter_problem.Firefighter_Problem import spreading_minbudget, non_spreading_minbudget, non_spreading_dirlaynet_minbudget, heuristic_minbudget +from networkz.algorithms.approximation.firefighter_problem.Utils import parse_json_to_networkx + +@pytest.fixture +def sample_json_data(): + return { + "Dirlay": { + "Graph-1": { + "vertices": [0, 1, 2, 3, 4, 5], + "edges": [[0, 1], [0, 2]] + }, + }, + "RegularGraph": { + "Graph-1": { + "vertices": [0, 1, 2], + "edges": [[0, 1], [1, 2]] + }, + } + } + +def get_graphs(): + with open("networkz/algorithms/tests/test_firefighter_problem/graphs.json", "r") as file: + json_data = json.load(file) + graphs = parse_json_to_networkx(json_data) + return graphs + +graphs = get_graphs() + +def test_source_not_in_graph(): + with pytest.raises(ValueError, match="Error: The source node isn't on the graph"): + heuristic_minbudget(graphs["RegularGraph_Graph-1"], -3, [1,0,4,5,2,6],True) + + with pytest.raises(ValueError, match="Error: The source node isn't on the graph"): + heuristic_minbudget(graphs["RegularGraph_Graph-4"], 10, [1,3,5,6,7],False) + + with pytest.raises(ValueError, match="Error: The source node isn't on the graph"): + heuristic_minbudget(graphs["RegularGraph_Graph-6"], 12, [9,2,3,4,6,7],True) + + with pytest.raises(ValueError, match="Error: The source node isn't on the graph"): + heuristic_minbudget(graphs["RegularGraph_Graph-8"], -1, [7,10,4,9,3,11,2],False) + + with pytest.raises(ValueError, match="Error: The source node isn't on the graph"): + heuristic_minbudget(graphs["RegularGraph_Graph-3"], 8, [1,4,2],True) + + +def test_target_not_in_graph(): + with pytest.raises(ValueError, match="Error: Not all nodes in the targets list are on the graph."): + heuristic_minbudget(graphs["RegularGraph_Graph-2"], 2, [0,4,5,11,6],True) + + with pytest.raises(ValueError, match="Error: Not all nodes in the targets list are on the graph."): + heuristic_minbudget(graphs["RegularGraph_Graph-3"], 3, [0,4,5,-1,1,2],False) + + with pytest.raises(ValueError, match="Error: Not all nodes in the targets list are on the graph."): + heuristic_minbudget(graphs["RegularGraph_Graph-6"], 7, [9,2,4,5,8,11],True) + + with pytest.raises(ValueError, match="Error: Not all nodes in the targets list are on the graph."): + heuristic_minbudget(graphs["RegularGraph_Graph-8"], 10, [0,2,4,5,8,11,12,3,15],False) + + with pytest.raises(ValueError, match="Error: Not all nodes in the targets list are on the graph."): + heuristic_minbudget(graphs["RegularGraph_Graph-7"], 1, [3,5,4,0,13],True) + + +def test_source_is_target(): + with pytest.raises(ValueError, match="Error: The source node can't be a part of the targets list, since the virus is spreading from the source"): + heuristic_minbudget(graphs["RegularGraph_Graph-1"], 0, [1,2,3,0,4,5,6],True) + + with pytest.raises(ValueError, match="Error: The source node can't be a part of the targets list, since the virus is spreading from the source"): + heuristic_minbudget(graphs["RegularGraph_Graph-3"], 1, [5,1,4],False) + + with pytest.raises(ValueError, match="Error: The source node can't be a part of the targets list, since the virus is spreading from the source"): + heuristic_minbudget(graphs["RegularGraph_Graph-4"], 4, [1,2,3,4,5,6,7],True) + + with pytest.raises(ValueError, match="Error: The source node can't be a part of the targets list, since the virus is spreading from the source"): + heuristic_minbudget(graphs["RegularGraph_Graph-6"], 0, [0,3,5,6,7,8,9],False) + + with pytest.raises(ValueError, match="Error: The source node can't be a part of the targets list, since the virus is spreading from the source"): + heuristic_minbudget(graphs["RegularGraph_Graph-8"], 0, [13,10,8,6,5,4,3,0,1,2],True) + +def test_save_all_vertices_spreading(): + assert spreading_minbudget(graphs["RegularGraph_Graph-1"], 0, [1,2,3,4,5,6])[0] >= heuristic_minbudget(graphs["RegularGraph_Graph-1"], 0, [1,2,3,4,5,6],True)[0] + assert spreading_minbudget(graphs["RegularGraph_Graph-2"], 0, [1,2,3,4,5,6,7])[0] >= heuristic_minbudget(graphs["RegularGraph_Graph-2"], 0, [1,2,3,4,5,6,7],True)[0] + assert spreading_minbudget(graphs["RegularGraph_Graph-3"], 0, [1,2,3,4,5])[0] >= heuristic_minbudget(graphs["RegularGraph_Graph-3"], 0, [1,2,3,4,5],True)[0] + assert spreading_minbudget(graphs["RegularGraph_Graph-4"], 0, [1,2,3,4,5,6,7])[0] >= heuristic_minbudget(graphs["RegularGraph_Graph-4"], 0, [1,2,3,4,5,6,7],True)[0] + assert spreading_minbudget(graphs["RegularGraph_Graph-6"], 1, [0,2,3,4,5,6,7,8,9])[0] >= heuristic_minbudget(graphs["RegularGraph_Graph-6"], 1, [0,2,3,4,5,6,7,8,9],True)[0] + assert spreading_minbudget(graphs["RegularGraph_Graph-7"], 1, [0,2,3,4,5,6])[0] >= heuristic_minbudget(graphs["RegularGraph_Graph-7"], 1, [0,2,3,4,5,6],True)[0] + assert spreading_minbudget(graphs["RegularGraph_Graph-8"], 0, [1,2,3,4,5,6,7,8,9,10,11,12,13,14])[0] >= heuristic_minbudget(graphs["RegularGraph_Graph-8"], 0, [1,2,3,4,5,6,7,8,9,10,11,12,13,14],True)[0] + + +def test_save_subgroup_vertices_spreading(): + assert spreading_minbudget(graphs["RegularGraph_Graph-1"], 0, [1,5,6])[0] >= heuristic_minbudget(graphs["RegularGraph_Graph-1"], 0, [1,5,6],True)[0] + assert spreading_minbudget(graphs["RegularGraph_Graph-2"], 0, [1,3,4,5,6])[0] >= heuristic_minbudget(graphs["RegularGraph_Graph-2"], 0, [1,3,4,5,6],True)[0] + assert spreading_minbudget(graphs["RegularGraph_Graph-3"], 0, [1,3,5])[0] >= heuristic_minbudget(graphs["RegularGraph_Graph-3"], 0, [1,3,5],True)[0] + assert spreading_minbudget(graphs["RegularGraph_Graph-4"], 0, [2,3,5,7])[0] >= heuristic_minbudget(graphs["RegularGraph_Graph-4"], 0, [2,3,5,7],True)[0] + assert spreading_minbudget(graphs["RegularGraph_Graph-6"], 1, [0,3,5,6,8,9])[0] >= heuristic_minbudget(graphs["RegularGraph_Graph-6"], 1, [0,3,5,6,8,9],True)[0] + assert spreading_minbudget(graphs["RegularGraph_Graph-7"], 1, [4,2,5,6])[0] >= heuristic_minbudget(graphs["RegularGraph_Graph-7"], 1, [4,2,5,6],True)[0] + assert spreading_minbudget(graphs["RegularGraph_Graph-8"], 0, [1,3,4,5,6,9,10,12,14])[0] >= heuristic_minbudget(graphs["RegularGraph_Graph-8"], 0, [1,3,4,5,6,9,10,12,14],True)[0] + +def test_random_graph_comparison(): + for i in range(10): + num_nodes = random.randint(2,100) + nodes = list(range(num_nodes+1)) + num_edges = 1000 + save_amount = random.randint(1,num_nodes) + targets = [] + G = nx.DiGraph() + + G.add_nodes_from(nodes, status="target") + for _ in range(num_edges): + source = random.randint(0, num_nodes - 1) + target = random.randint(0, num_nodes - 1) + if source != target: # Ensure no self-loops + G.add_edge(source, target) + for node in range(save_amount): + probability = random.random() + if probability < 0.75 and node!=0: + targets.append(node) + + spreading_answer = spreading_minbudget(G,0,targets)[0] + heuristic_answer = heuristic_minbudget(G,0,targets,True)[0] + assert heuristic_answer <= spreading_answer + + print("All tests have passed!") \ No newline at end of file diff --git a/networkz/algorithms/tests/test_firefighter_problem/test_spreading_minbudget.py b/networkz/algorithms/tests/test_firefighter_problem/test_spreading_minbudget.py index 21cb1ec..8d73acf 100644 --- a/networkz/algorithms/tests/test_firefighter_problem/test_spreading_minbudget.py +++ b/networkz/algorithms/tests/test_firefighter_problem/test_spreading_minbudget.py @@ -263,7 +263,7 @@ def test_save_subgroup_vertices(): assert 2 == spreading_minbudget(graphs["RegularGraph_Graph-7"], 1, [4,2,5,6])[0] #answer is 2 assert spreading_minbudget(graphs["RegularGraph_Graph-8"], 0, [1,2,3,4,5,6,7,8,9,10,11,12,13,14])[0] != spreading_minbudget(graphs["RegularGraph_Graph-8"], 0, [1,3,4,5,6,9,10,12,14])[0] #answer is 3 -def random_graph_test(): +def test_random_graph(): for i in range(10): num_nodes = random.randint(2,100) nodes = list(range(num_nodes+1)) From 053a87e559ece186b74583b462a912cae6307604 Mon Sep 17 00:00:00 2001 From: 20shaked20 Date: Sun, 23 Jun 2024 20:34:31 +0300 Subject: [PATCH 39/93] Some fixes to dirlay loggers --- .../Firefighter_Problem.py | 2 +- .../firefighter_problem/Utils.py | 32 ++++++++++++------- .../approximation/firefighter_problem/main.py | 17 ++++++---- 3 files changed, 31 insertions(+), 20 deletions(-) diff --git a/networkz/algorithms/approximation/firefighter_problem/Firefighter_Problem.py b/networkz/algorithms/approximation/firefighter_problem/Firefighter_Problem.py index 844d1ad..810884c 100644 --- a/networkz/algorithms/approximation/firefighter_problem/Firefighter_Problem.py +++ b/networkz/algorithms/approximation/firefighter_problem/Firefighter_Problem.py @@ -281,7 +281,7 @@ def non_spreading_dirlaynet_minbudget(Graph:nx.DiGraph, src:int, targets:list)-> vacc_matrix = calculate_vaccine_matrix(layers, N_groups) min_budget = min_budget_calculation(vacc_matrix) - logger.info(f"Returning minimum budget: {min_budget}") + logger.info(f"Returning algorithm stategy: {min_budget}") return min_budget def heuristic_maxsave(Graph:nx.DiGraph, budget:int, source:int, targets:list, spreading=True, stop_condition=None) -> tuple: diff --git a/networkz/algorithms/approximation/firefighter_problem/Utils.py b/networkz/algorithms/approximation/firefighter_problem/Utils.py index 5de2cde..1fb6188 100644 --- a/networkz/algorithms/approximation/firefighter_problem/Utils.py +++ b/networkz/algorithms/approximation/firefighter_problem/Utils.py @@ -430,7 +430,7 @@ def clean_graph(graph:nx.DiGraph)->None: # ============================ End Spreading Max-Save ============================ -# =========================== Non-Spreading Max-Save ============================ +# =========================== Non-Spreading Min-Budget-Dirlay ============================ def adjust_nodes_capacity(graph:nx.DiGraph, source:int)->list: """ Adjust the capacity of nodes based on the layer they belong to. @@ -467,7 +467,10 @@ def adjust_nodes_capacity(graph:nx.DiGraph, source:int)->list: for index in range(1,len(layers)): for node in layers[index]: graph.nodes[node]['capacity'] = 1/(index*harmonic_sum) - logger.info(f"Layers: {layers}") + logger.info(f"Added Capacity {1/(index*harmonic_sum)} for node: {node}") + + logger.info(f"Done with adding capacity for nodes, with Layers: {layers}") + return layers def create_st_graph(graph:nx.DiGraph, targets:list)->nx.DiGraph: @@ -499,12 +502,15 @@ def create_st_graph(graph:nx.DiGraph, targets:list)->nx.DiGraph: >>> list(G_st.successors(3)) [4, 't'] """ - logger.info(f"Creating an s-t graph to connect nodes to save") + logger.info(f"Creating a s-t graph to connect nodes to save") + G = copy.deepcopy(graph) G.add_node('t', status = Status.VULNERABLE.value) for node in targets: G.add_edge(node,'t') #display_graph(G) + + logger.info(f"Done creating a s-t graph") return G def min_cut_N_groups(graph: nx.DiGraph, source: int, layers: list) -> dict: @@ -531,14 +537,16 @@ def min_cut_N_groups(graph: nx.DiGraph, source: int, layers: list) -> dict: """ # Compute the minimum cut logger.info(f"Finding the minimum cut on the graph after reduction") - flow_graph = algo.minimum_st_node_cut(graph, f'{source}_out', 't_in') + min_cut_nodes = algo.minimum_st_node_cut(graph, f'{source}_out', 't_in') + logger.info(f"Minimum Cut is: {min_cut_nodes}") + # Initialize the groups dictionary with empty lists for each layer index groups = {i+1: [] for i in range(len(layers)-1)} - logger.info(f"Finding the nodes from each layer") + logger.info(f"Finding the correct nodes from each layer according to the min-cut nodes") # Populate the groups dictionary - for item in flow_graph: + for item in min_cut_nodes: node , suffix = item.split('_') node = int(node) for i, layer_nodes in enumerate(layers): @@ -570,8 +578,7 @@ def calculate_vaccine_matrix(layers:list, min_cut_nodes_grouped:dict)->np.matrix -------- """ - logger.info(f"Calculating the Vaccine Matrix for dirlay...") - logger.info(f"Min cut nodes grouped: {min_cut_nodes_grouped}") + logger.info(f"Calculating the Vaccine Matrix...") matrix_length = max(min_cut_nodes_grouped.keys()) matrix = np.zeros((matrix_length, matrix_length)) @@ -581,7 +588,7 @@ def calculate_vaccine_matrix(layers:list, min_cut_nodes_grouped:dict)->np.matrix value = N_j / (j + 1) matrix[i][j] = value - logger.info(f"Vaccination Matrix Before Conversion:\n{matrix}") + logger.info(f"Vaccination Matrix Before roundups:\n{matrix}") return matrix def matrix_to_integers_values(matrix: np.matrix) -> np.matrix: @@ -605,14 +612,14 @@ def matrix_to_integers_values(matrix: np.matrix) -> np.matrix: """ # dimensions of the matrix - logger.info(f"Converting the the Vaccine Matrix for integers...") + logger.info(f"Applying max-flow to transfer the Vaccine Matrix for integers...") rows, cols = matrix.shape row_sums = np.array(matrix.sum(axis=1)).flatten() col_sums = np.array(matrix.sum(axis=0)).flatten() - logger.info(f"Row sums: {row_sums}") - logger.info(f"Column sums: {col_sums}") + # logger.info(f"Row sums: {row_sums}") + # logger.info(f"Column sums: {col_sums}") G = nx.DiGraph() @@ -654,6 +661,7 @@ def matrix_to_integers_values(matrix: np.matrix) -> np.matrix: integral_matrix[i, j] = np.floor(matrix[i, j]) logger.info(f"Integral and final Matrix:\n{integral_matrix}") + return np.matrix(integral_matrix) diff --git a/networkz/algorithms/approximation/firefighter_problem/main.py b/networkz/algorithms/approximation/firefighter_problem/main.py index 300b94a..2c36496 100644 --- a/networkz/algorithms/approximation/firefighter_problem/main.py +++ b/networkz/algorithms/approximation/firefighter_problem/main.py @@ -49,10 +49,10 @@ def setup_global_logger(level: int = logging.DEBUG): # result = doctest.testmod(firefighter_problem, verbose=True) # logger.info(f"Doctest results: {result}") - G3 = nx.DiGraph() - G3.add_nodes_from([0,1,2,3,4,5,6,7,8]) - G3.add_edges_from([(0,2),(0,4),(0,5),(2,1),(2,3),(4,1),(4,6),(5,3),(5,6),(5,7),(6,7),(6,8),(7,8)]) - logger.info("=" * 150) + # G3 = nx.DiGraph() + # G3.add_nodes_from([0,1,2,3,4,5,6,7,8]) + # G3.add_edges_from([(0,2),(0,4),(0,5),(2,1),(2,3),(4,1),(4,6),(5,3),(5,6),(5,7),(6,7),(6,8),(7,8)]) + # logger.info("=" * 150) #print(spreading_maxsave(G3,source=0,targets=[2,6,1,8],budget=1)) #print(spreading_minbudget(G3,source=0,targets=[2,6,1,8])) @@ -60,10 +60,13 @@ def setup_global_logger(level: int = logging.DEBUG): json_data = json.load(file) graphs = parse_json_to_networkx(json_data) - G2 = graphs["RegularGraph_Graph-2"] - print(heuristic_minbudget(G2,source=0, targets=[1,3,4,5,6],spreading=True)) + G2 = graphs["Dirlay_Graph-2"] + print(non_spreading_dirlaynet_minbudget(Graph=G2, src=0, targets=[2,4])) + + # G2 = graphs["RegularGraph_Graph-2"] + # print(heuristic_minbudget(G2,source=0, targets=[1,3,4,5,6],spreading=True)) #print(spreading_maxsave(G3,1, 0,[2,6,1,8])[1]) - print(spreading_minbudget(G2,source=0, targets=[1,3,4,5,6])) + # print(spreading_minbudget(G2,source=0, targets=[1,3,4,5,6])) # logger.info("=" * 150) #logger.info(heuristic_minbudget(G3,0,[2,6,1,8], True)) From 8389f499aca655606868e6fd695d52d9cfb82eba Mon Sep 17 00:00:00 2001 From: Almog-David Date: Sun, 23 Jun 2024 20:48:30 +0300 Subject: [PATCH 40/93] fixed problems with huristic approach and local cahnges in targets --- .../Firefighter_Problem.py | 28 +++++++++---------- .../firefighter_problem/Utils.py | 28 +++++++++++-------- 2 files changed, 29 insertions(+), 27 deletions(-) diff --git a/networkz/algorithms/approximation/firefighter_problem/Firefighter_Problem.py b/networkz/algorithms/approximation/firefighter_problem/Firefighter_Problem.py index 844d1ad..3175b55 100644 --- a/networkz/algorithms/approximation/firefighter_problem/Firefighter_Problem.py +++ b/networkz/algorithms/approximation/firefighter_problem/Firefighter_Problem.py @@ -75,7 +75,7 @@ def spreading_maxsave(Graph:nx.DiGraph, budget:int, source:int, targets:list, st logger.info(f"Starting the spreading_maxsave function with source node {source}, budget {budget}, and targets: {targets}") clean_graph(Graph) - + local_targets = targets.copy() infected_nodes = [] vaccinated_nodes = [] vaccination_strategy = [] @@ -95,7 +95,7 @@ def spreading_maxsave(Graph:nx.DiGraph, budget:int, source:int, targets:list, st spread_vaccination(Graph, vaccinated_nodes) for i in range(budget): logger.info(f"Calculating the best direct vaccination strategy for the current time step that saves more new node in targets (Current budget: {budget})") - vaccination, nodes_saved = find_best_direct_vaccination(Graph, direct_vaccinations, epsilon[time_step], targets) + vaccination, nodes_saved = find_best_direct_vaccination(Graph, direct_vaccinations, epsilon[time_step], local_targets) if vaccination != (): logger.info(f"Found {vaccination} as a solution for current timestamp, appending to vaccination strategy and vaccinating the node") @@ -109,8 +109,8 @@ def spreading_maxsave(Graph:nx.DiGraph, budget:int, source:int, targets:list, st if nodes_saved is not None: saved_target_nodes.update(nodes_saved) - targets[:] = [element for element in targets if element not in nodes_saved] - logger.info(f"Updated list of targets: {targets}") + local_targets[:] = [element for element in local_targets if element not in nodes_saved] + logger.info(f"Updated list of targets: {local_targets}") else: logger.info(f"All nodes are either vaccinated or infected") @@ -118,7 +118,7 @@ def spreading_maxsave(Graph:nx.DiGraph, budget:int, source:int, targets:list, st can_spread = spread_virus(Graph, infected_nodes) if stop_condition is not None: - if len(targets) == 0 or any(node in infected_nodes for node in targets): + if len(local_targets) == 0 or any(node in infected_nodes for node in local_targets): clean_graph(Graph) logger.info(f"Returning vaccination strategy: {vaccination_strategy}. The strategy saved the nodes: {saved_target_nodes}") return vaccination_strategy, saved_target_nodes @@ -171,7 +171,7 @@ def spreading_minbudget(Graph:nx.DiGraph, source:int, targets:list)-> int: validate_parameters(Graph, source, targets) logger.info(f"Starting the spreading_minbudget function with source node {source} and targets: {targets}") - original_targets = list(targets) + best_strategy = [] min_value = 1 @@ -182,9 +182,9 @@ def spreading_minbudget(Graph:nx.DiGraph, source:int, targets:list)-> int: logger.info(f"Calling maxsave with parameters - Source: {source}, Targets: {targets}, Budget: {middle}") strategy, nodes_saved = spreading_maxsave(Graph, middle, source, targets, True) - common_elements = set(nodes_saved) & set(original_targets) + common_elements = set(nodes_saved) & set(targets) - if len(common_elements) == len(original_targets): + if len(common_elements) == len(targets): logger.info(f"The current budget {middle} has saved all the targets!") max_value = middle best_strategy = strategy @@ -193,7 +193,6 @@ def spreading_minbudget(Graph:nx.DiGraph, source:int, targets:list)-> int: min_value = middle + 1 middle = math.floor((min_value + max_value) / 2) - targets = list(original_targets) logger.info(f"Returning minimum budget: {middle} and the vaccination strategy: {best_strategy}") return middle, best_strategy @@ -319,6 +318,7 @@ def heuristic_maxsave(Graph:nx.DiGraph, budget:int, source:int, targets:list, sp clean_graph(Graph) #display_graph(Graph) + local_targets = targets.copy() infected_nodes = [] vaccinated_nodes = [] vaccination_strategy = [] @@ -334,7 +334,7 @@ def heuristic_maxsave(Graph:nx.DiGraph, budget:int, source:int, targets:list, sp spread_vaccination(Graph, vaccinated_nodes) for i in range(budget): logger.info(f"Calculating the best direct vaccination strategy for the current time step that saves more new node in targets (Current budget: {budget})") - node_to_vaccinate, nodes_saved = find_best_neighbor(Graph, infected_nodes, targets) + node_to_vaccinate, nodes_saved = find_best_neighbor(Graph, infected_nodes, local_targets) if node_to_vaccinate is not None: logger.info(f"Found {node_to_vaccinate} as a solution for current timestamp, appending to vaccination strategy and vaccinating the node") vaccination_strategy.append((node_to_vaccinate, time_step)) @@ -342,11 +342,10 @@ def heuristic_maxsave(Graph:nx.DiGraph, budget:int, source:int, targets:list, sp vaccinated_nodes.append(node_to_vaccinate) logger.info(f"Updated list of currently vaccinated nodes: {vaccinated_nodes}") - if nodes_saved is not None: saved_target_nodes.update(nodes_saved) - targets[:] = [element for element in targets if element not in nodes_saved] - logger.info(f"Updated list of targets: {targets}") + local_targets[:] = [element for element in local_targets if element not in nodes_saved] + logger.info(f"Updated list of targets: {local_targets}") else: logger.info(f"All nodes are either vaccinated or infected") @@ -354,7 +353,7 @@ def heuristic_maxsave(Graph:nx.DiGraph, budget:int, source:int, targets:list, sp can_spread = spread_virus(Graph, infected_nodes) if stop_condition is not None: - if len(targets) == 0 or any(node in infected_nodes for node in targets): + if len(local_targets) == 0 or any(node in infected_nodes for node in local_targets): logger.info(f"Returning vaccination strategy: {vaccination_strategy}. The strategy saved the nodes: {saved_target_nodes}") return vaccination_strategy @@ -415,7 +414,6 @@ def heuristic_minbudget(Graph:nx.DiGraph, source:int, targets:list, spreading:bo min_value = middle + 1 middle = math.floor((min_value + max_value) / 2) - targets = list(original_targets) logger.info(f"Returning minimum budget: {middle} and the vaccination strategy: {best_strategy}") return middle, best_strategy diff --git a/networkz/algorithms/approximation/firefighter_problem/Utils.py b/networkz/algorithms/approximation/firefighter_problem/Utils.py index 5de2cde..29e4a02 100644 --- a/networkz/algorithms/approximation/firefighter_problem/Utils.py +++ b/networkz/algorithms/approximation/firefighter_problem/Utils.py @@ -26,6 +26,7 @@ from enum import Enum import copy import logging +import heapq class Status(Enum): VULNERABLE = "vulnerable" @@ -341,9 +342,9 @@ def spread_virus(graph:nx.DiGraph, infected_nodes:list)->bool: graph.nodes[neighbor]['status'] = Status.INFECTED.value new_infected_nodes.append(neighbor) logger.debug("SPREAD VIRUS: Node " + f'{neighbor}' + " has been infected from node " + f'{node}') - #display_graph(graph) infected_nodes.clear() + #display_graph(graph) for node in new_infected_nodes: infected_nodes.append(node) return bool(infected_nodes) @@ -377,8 +378,9 @@ def spread_vaccination(graph:nx.DiGraph, vaccinated_nodes:list)->None: graph.nodes[neighbor]['status'] = Status.VACCINATED.value new_vaccinated_nodes.append(neighbor) logger.debug("SPREAD VACCINATION: Node " + f'{neighbor}' + " has been vaccinated from node " + f'{node}') - #display_graph(graph) + vaccinated_nodes.clear() + #display_graph(graph) for node in new_vaccinated_nodes: vaccinated_nodes.append(node) return @@ -698,10 +700,8 @@ def find_best_neighbor(graph:nx.DiGraph, infected_nodes:list, targets:list)->int Returns: - best_node (int): Best node option. """ - best_node = None - nodes_saved = [] - max_number = -1 optional_nodes = set() + priority_queue = [] # Go through the infected_nodes list and collect all their neighbors for node in infected_nodes: @@ -719,14 +719,18 @@ def find_best_neighbor(graph:nx.DiGraph, infected_nodes:list, targets:list)->int vulnerable_neighbors.add(node) common_elements = set(vulnerable_neighbors) & set(targets) logger.info("node " + f'{node}' + " is saving the nodes " + str(common_elements)) - if len(common_elements) > max_number: - best_node = node - nodes_saved = common_elements - max_number = len(common_elements) - if best_node != None: - logger.info("The best node is: " + f'{best_node}' + " and it's saves nodes: " + str(nodes_saved)) - return best_node, nodes_saved + heapq.heappush(priority_queue, (-len(common_elements), node not in targets, node, common_elements)) + + if priority_queue: + best_node_info = heapq.heappop(priority_queue) + best_node = best_node_info[2] + nodes_saved = best_node_info[3] + logger.info("The best node is: " + f'{best_node}' + " and it's saving nodes: " + str(nodes_saved)) + return best_node, nodes_saved + + return None, [] + # =========================== End Heuristic Utilities ================================ # =========================== General Utilities ====================================== From 7171a507124916afc55346a27aa42d03400aae10 Mon Sep 17 00:00:00 2001 From: 20shaked20 Date: Sun, 23 Jun 2024 21:26:34 +0300 Subject: [PATCH 41/93] added more logs to dirlay+ created return stategy method --- .../Firefighter_Problem.py | 4 +- .../firefighter_problem/Utils.py | 48 ++++++++++++++++++- 2 files changed, 50 insertions(+), 2 deletions(-) diff --git a/networkz/algorithms/approximation/firefighter_problem/Firefighter_Problem.py b/networkz/algorithms/approximation/firefighter_problem/Firefighter_Problem.py index 810884c..f69276b 100644 --- a/networkz/algorithms/approximation/firefighter_problem/Firefighter_Problem.py +++ b/networkz/algorithms/approximation/firefighter_problem/Firefighter_Problem.py @@ -279,9 +279,11 @@ def non_spreading_dirlaynet_minbudget(Graph:nx.DiGraph, src:int, targets:list)-> G_reduction = max_flow_with_node_capacity(G, source=src, target='t') N_groups = min_cut_N_groups(G_reduction, src,layers) vacc_matrix = calculate_vaccine_matrix(layers, N_groups) + integer_matrix = matrix_to_integers_values(vacc_matrix) min_budget = min_budget_calculation(vacc_matrix) + stategy = dirlay_vaccination_startegy(integer_matrix, N_groups) - logger.info(f"Returning algorithm stategy: {min_budget}") + logger.info(f"Returning algorithm stategy: {stategy} with budget: {min_budget}") return min_budget def heuristic_maxsave(Graph:nx.DiGraph, budget:int, source:int, targets:list, spreading=True, stop_condition=None) -> tuple: diff --git a/networkz/algorithms/approximation/firefighter_problem/Utils.py b/networkz/algorithms/approximation/firefighter_problem/Utils.py index 1fb6188..c37af3e 100644 --- a/networkz/algorithms/approximation/firefighter_problem/Utils.py +++ b/networkz/algorithms/approximation/firefighter_problem/Utils.py @@ -661,7 +661,7 @@ def matrix_to_integers_values(matrix: np.matrix) -> np.matrix: integral_matrix[i, j] = np.floor(matrix[i, j]) logger.info(f"Integral and final Matrix:\n{integral_matrix}") - + return np.matrix(integral_matrix) @@ -691,6 +691,52 @@ def min_budget_calculation(matrix: np.matrix) -> int: logger.info(f"Min budget needed to save the target nodes: {min_budget}") return min_budget +def dirlay_vaccination_startegy(vacc_matrix: np.matrix, ni_groups: dict) -> dict: + """ + Determines a feasible vaccination strategy given the vaccine matrix, minimum budget, and layers. + + Parameters: + ---------- + vacc_matrix: np.matrix + A matrix where rows represent time steps and columns represent layers, + with each element indicating the number of nodes to vaccinate. + min_budget: int + The minimum budget required to save all target nodes. + layers: list + A list of layers, where each layer contains nodes to be considered. + + Returns: + ---------- + dict + A dictionary where keys are time steps and values are lists of nodes to vaccinate at each time step. + """ + logger.info("Calculating the stategy") + + num_steps, num_layers = vacc_matrix.shape + strategy = {} + for i in range(num_steps): + nodes_to_vaccinate = [] + for j in range(0, num_layers): + num_nodes_to_vaccinate = int(vacc_matrix[i, j]) + + logger.debug(f"On time step {i} needs to vaccinate: {num_nodes_to_vaccinate} nodes" ) + + if num_nodes_to_vaccinate > 0: + # Extract the nodes to vaccinate + selected_nodes = ni_groups[j+1][:num_nodes_to_vaccinate] + + logger.debug(f"The selected nodes to vaccinate {selected_nodes}") + + nodes_to_vaccinate.extend(selected_nodes) + + if nodes_to_vaccinate: + strategy[i] = nodes_to_vaccinate + + return strategy + + + + # =========================== End Non-Spreading Max-Save ============================ # =========================== Heuristic Utilities =================================== From 77adb2848b8c49539cee0377c4164f0279cda6d1 Mon Sep 17 00:00:00 2001 From: 20shaked20 Date: Sun, 23 Jun 2024 21:30:16 +0300 Subject: [PATCH 42/93] Minor change to function details --- .../algorithms/approximation/firefighter_problem/Utils.py | 6 ++---- 1 file changed, 2 insertions(+), 4 deletions(-) diff --git a/networkz/algorithms/approximation/firefighter_problem/Utils.py b/networkz/algorithms/approximation/firefighter_problem/Utils.py index 3d3a0ee..54389e1 100644 --- a/networkz/algorithms/approximation/firefighter_problem/Utils.py +++ b/networkz/algorithms/approximation/firefighter_problem/Utils.py @@ -702,10 +702,8 @@ def dirlay_vaccination_startegy(vacc_matrix: np.matrix, ni_groups: dict) -> dict vacc_matrix: np.matrix A matrix where rows represent time steps and columns represent layers, with each element indicating the number of nodes to vaccinate. - min_budget: int - The minimum budget required to save all target nodes. - layers: list - A list of layers, where each layer contains nodes to be considered. + ni_groups: dict + A dict of layers, where each layer contains nodes to be saved. Returns: ---------- From 37aa087ed91582f5c2a637539cb09ac5586b6cf7 Mon Sep 17 00:00:00 2001 From: 20shaked20 Date: Sun, 23 Jun 2024 22:39:38 +0300 Subject: [PATCH 43/93] Changes to paper location + Link --- ...oximability_of_the_Firefighter_Problem.pdf | Bin 693206 -> 0 bytes .../Firefighter_Problem.py | 2 +- .../firefighter_problem/Utils.py | 2 +- .../graph_flow_reduction.py | 22 +++++++++++++++++- .../approximation/firefighter_problem/main.py | 2 +- .../test_heuristic_maxsave.py | 2 +- .../test_heuristic_minbudget.py | 2 +- .../test_non_spreading_dirlaynet_minbudget.py | 2 +- .../test_non_spreading_minbudget.py | 2 +- .../test_firefighter_problem/test_parsing.py | 2 +- .../test_spreading_maxsave.py | 2 +- .../test_spreading_minbudget.py | 2 +- .../test_firefighter_problem/test_utils.py | 2 +- 13 files changed, 32 insertions(+), 12 deletions(-) delete mode 100644 networkz/algorithms/approximation/firefighter_problem/Approximability_of_the_Firefighter_Problem.pdf diff --git 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.../test_heuristic_minbudget.py | 230 ++++++++++-------- .../test_non_spreading_dirlaynet_minbudget.py | 58 ----- 3 files changed, 182 insertions(+), 157 deletions(-) create mode 100644 networkz/algorithms/tests/test_firefighter_problem/comparisons/test_heuristic_minbudget_comparison.txt diff --git a/networkz/algorithms/tests/test_firefighter_problem/comparisons/test_heuristic_minbudget_comparison.txt b/networkz/algorithms/tests/test_firefighter_problem/comparisons/test_heuristic_minbudget_comparison.txt new file mode 100644 index 0000000..789992c --- /dev/null +++ b/networkz/algorithms/tests/test_firefighter_problem/comparisons/test_heuristic_minbudget_comparison.txt @@ -0,0 +1,51 @@ +Starting test_save_all_vertices_spreading: + +Dirlay_Graph-1 - Spreading Result: 2, Heuristic Result: 2 +Dirlay_Graph-1 - Spreading Time: 0.001003s, Heuristic Time: 0.001002s +Dirlay_Graph-2 - Spreading Result: 2, Heuristic Result: 2 +Dirlay_Graph-2 - Spreading Time: 0.001003s, Heuristic Time: 0.000000s +Dirlay_Graph-3 - Spreading Result: 3, Heuristic Result: 4 +Dirlay_Graph-3 - Spreading Time: 0.001290s, Heuristic Time: 0.000000s + +Finished test_save_all_vertices_spreading. +--------------------------------------------------------------------------------------------------------------------------- +Starting test_save_subgroup_vertices_spreading: + +Dirlay_Graph-1 (subgroup) - Spreading Result: 2, Heuristic Result: 2 +Dirlay_Graph-1 (subgroup) - Spreading Time: 0.001109s, Heuristic Time: 0.000000s +Dirlay_Graph-2 (subgroup) - Spreading Result: 1, Heuristic Result: 1 +Dirlay_Graph-2 (subgroup) - Spreading Time: 0.000000s, Heuristic Time: 0.000000s +Dirlay_Graph-3 (subgroup) - Spreading Result: 1, Heuristic Result: 1 +Dirlay_Graph-3 (subgroup) - Spreading Time: 0.000000s, Heuristic Time: 0.000000s +Dirlay_Graph-4 (subgroup) - Spreading Result: 2, Heuristic Result: 1 +Dirlay_Graph-4 (subgroup) - Spreading Time: 0.000000s, Heuristic Time: 0.000000s +Dirlay_Graph-5 (subgroup) - Spreading Result: 1, Heuristic Result: 1 +Dirlay_Graph-5 (subgroup) - Spreading Time: 0.001299s, Heuristic Time: 0.000000s +RegularGraph_Graph-1 (subgroup) - Spreading Result: 1, Heuristic Result: 1 +RegularGraph_Graph-1 (subgroup) - Spreading Time: 0.001006s, Heuristic Time: 0.000000s +RegularGraph_Graph-2 (subgroup) - Spreading Result: 1, Heuristic Result: 1 +RegularGraph_Graph-2 (subgroup) - Spreading Time: 0.000000s, Heuristic Time: 0.000000s +RegularGraph_Graph-3 (subgroup) - Spreading Result: 1, Heuristic Result: 1 +RegularGraph_Graph-3 (subgroup) - Spreading Time: 0.000000s, Heuristic Time: 0.000000s +RegularGraph_Graph-4 (subgroup) - Spreading Result: 1, Heuristic Result: 1 +RegularGraph_Graph-4 (subgroup) - Spreading Time: 0.001256s, Heuristic Time: 0.000000s +RegularGraph_Graph-5 (subgroup) - Spreading Result: 2, Heuristic Result: 2 +RegularGraph_Graph-5 (subgroup) - Spreading Time: 0.001006s, Heuristic Time: 0.000000s +RegularGraph_Graph-6 (subgroup) - Spreading Result: 4, Heuristic Result: 1 +RegularGraph_Graph-6 (subgroup) - Spreading Time: 0.000000s, Heuristic Time: 0.000000s +RegularGraph_Graph-7 (subgroup) - Spreading Result: 3, Heuristic Result: 1 +RegularGraph_Graph-7 (subgroup) - Spreading Time: 0.000000s, Heuristic Time: 0.000000s +RegularGraph_Graph-8 (subgroup) - Spreading Result: 2, Heuristic Result: 1 +RegularGraph_Graph-8 (subgroup) - Spreading Time: 0.002416s, Heuristic Time: 0.000000s + +Finished test_save_subgroup_vertices_spreading. +--------------------------------------------------------------------------------------------------------------------------- +Starting test_random_graph_comparison: + +Random Graph 1 - Spreading Result: 2, Heuristic Result: 2 +Random Graph 1 - Spreading Time: 0.000000s, Heuristic Time: 0.000000s +Random Graph 2 - Spreading Result: 12, Heuristic Result: 13 +Random Graph 2 - Spreading Time: 0.039426s, Heuristic Time: 0.005025s + +Finished test_random_graph_comparison. +--------------------------------------------------------------------------------------------------------------------------- diff --git a/networkz/algorithms/tests/test_firefighter_problem/test_heuristic_minbudget.py b/networkz/algorithms/tests/test_firefighter_problem/test_heuristic_minbudget.py index ab98acd..24badbc 100644 --- a/networkz/algorithms/tests/test_firefighter_problem/test_heuristic_minbudget.py +++ b/networkz/algorithms/tests/test_firefighter_problem/test_heuristic_minbudget.py @@ -1,32 +1,19 @@ -""" - -The Paper - -Approximability of the Firefighter Problem Computing Cuts over Time - -Paper Link - -https://www.math.uwaterloo.ca/~cswamy/papers/firefighter-journ.pdf - -Authors - -Elliot Anshelevich -Deeparnab Chakrabarty -Ameya Hate -Chaitanya Swamy - -Developers - -Yuval Bubnovsky -Almog David -Shaked Levi - -""" - import pytest import networkx as nx import json import random +import time -from networkz.algorithms.approximation.firefighter_problem.Firefighter_Problem import spreading_minbudget, non_spreading_minbudget, non_spreading_dirlaynet_minbudget, heuristic_minbudget +from networkz.algorithms.approximation.firefighter_problem.Firefighter_Problem import spreading_minbudget, heuristic_minbudget from networkz.algorithms.approximation.firefighter_problem.Utils import parse_json_to_networkx +# Open the log file +log_file_path = 'networkz/algorithms/tests/test_firefighter_problem/comparisons/test_heuristic_minbudget_comparison.txt' + +def log_message(message): + with open(log_file_path, 'a') as log_file: + log_file.write(message + '\n') + @pytest.fixture def sample_json_data(): return { @@ -50,99 +37,144 @@ def get_graphs(): graphs = parse_json_to_networkx(json_data) return graphs -graphs = get_graphs() - +graphs = get_graphs() + def test_source_not_in_graph(): - with pytest.raises(ValueError, match="Error: The source node isn't on the graph"): - heuristic_minbudget(graphs["RegularGraph_Graph-1"], -3, [1,0,4,5,2,6],True) + + with pytest.raises(ValueError, match="Error: The source node isn't on the graph"): + heuristic_minbudget(graphs["RegularGraph_Graph-1"], -3, [1, 0, 4, 5, 2, 6], True) - with pytest.raises(ValueError, match="Error: The source node isn't on the graph"): - heuristic_minbudget(graphs["RegularGraph_Graph-4"], 10, [1,3,5,6,7],False) + with pytest.raises(ValueError, match="Error: The source node isn't on the graph"): + heuristic_minbudget(graphs["RegularGraph_Graph-4"], 10, [1, 3, 5, 6, 7], False) - with pytest.raises(ValueError, match="Error: The source node isn't on the graph"): - heuristic_minbudget(graphs["RegularGraph_Graph-6"], 12, [9,2,3,4,6,7],True) + with pytest.raises(ValueError, match="Error: The source node isn't on the graph"): + heuristic_minbudget(graphs["RegularGraph_Graph-6"], 12, [9, 2, 3, 4, 6, 7], True) - with pytest.raises(ValueError, match="Error: The source node isn't on the graph"): - heuristic_minbudget(graphs["RegularGraph_Graph-8"], -1, [7,10,4,9,3,11,2],False) + with pytest.raises(ValueError, match="Error: The source node isn't on the graph"): + heuristic_minbudget(graphs["RegularGraph_Graph-8"], -1, [7, 10, 4, 9, 3, 11, 2], False) - with pytest.raises(ValueError, match="Error: The source node isn't on the graph"): - heuristic_minbudget(graphs["RegularGraph_Graph-3"], 8, [1,4,2],True) - + with pytest.raises(ValueError, match="Error: The source node isn't on the graph"): + heuristic_minbudget(graphs["RegularGraph_Graph-3"], 8, [1, 4, 2], True) def test_target_not_in_graph(): - with pytest.raises(ValueError, match="Error: Not all nodes in the targets list are on the graph."): - heuristic_minbudget(graphs["RegularGraph_Graph-2"], 2, [0,4,5,11,6],True) + + with pytest.raises(ValueError, match="Error: Not all nodes in the targets list are on the graph."): + heuristic_minbudget(graphs["RegularGraph_Graph-2"], 2, [0, 4, 5, 11, 6], True) - with pytest.raises(ValueError, match="Error: Not all nodes in the targets list are on the graph."): - heuristic_minbudget(graphs["RegularGraph_Graph-3"], 3, [0,4,5,-1,1,2],False) + with pytest.raises(ValueError, match="Error: Not all nodes in the targets list are on the graph."): + heuristic_minbudget(graphs["RegularGraph_Graph-3"], 3, [0, 4, 5, -1, 1, 2], False) - with pytest.raises(ValueError, match="Error: Not all nodes in the targets list are on the graph."): - heuristic_minbudget(graphs["RegularGraph_Graph-6"], 7, [9,2,4,5,8,11],True) + with pytest.raises(ValueError, match="Error: Not all nodes in the targets list are on the graph."): + heuristic_minbudget(graphs["RegularGraph_Graph-6"], 7, [9, 2, 4, 5, 8, 11], True) - with pytest.raises(ValueError, match="Error: Not all nodes in the targets list are on the graph."): - heuristic_minbudget(graphs["RegularGraph_Graph-8"], 10, [0,2,4,5,8,11,12,3,15],False) + with pytest.raises(ValueError, match="Error: Not all nodes in the targets list are on the graph."): + heuristic_minbudget(graphs["RegularGraph_Graph-8"], 10, [0, 2, 4, 5, 8, 11, 12, 3, 15], False) - with pytest.raises(ValueError, match="Error: Not all nodes in the targets list are on the graph."): - heuristic_minbudget(graphs["RegularGraph_Graph-7"], 1, [3,5,4,0,13],True) - + with pytest.raises(ValueError, match="Error: Not all nodes in the targets list are on the graph."): + heuristic_minbudget(graphs["RegularGraph_Graph-7"], 1, [3, 5, 4, 0, 13], True) def test_source_is_target(): - with pytest.raises(ValueError, match="Error: The source node can't be a part of the targets list, since the virus is spreading from the source"): - heuristic_minbudget(graphs["RegularGraph_Graph-1"], 0, [1,2,3,0,4,5,6],True) - - with pytest.raises(ValueError, match="Error: The source node can't be a part of the targets list, since the virus is spreading from the source"): - heuristic_minbudget(graphs["RegularGraph_Graph-3"], 1, [5,1,4],False) - - with pytest.raises(ValueError, match="Error: The source node can't be a part of the targets list, since the virus is spreading from the source"): - heuristic_minbudget(graphs["RegularGraph_Graph-4"], 4, [1,2,3,4,5,6,7],True) - - with pytest.raises(ValueError, match="Error: The source node can't be a part of the targets list, since the virus is spreading from the source"): - heuristic_minbudget(graphs["RegularGraph_Graph-6"], 0, [0,3,5,6,7,8,9],False) - - with pytest.raises(ValueError, match="Error: The source node can't be a part of the targets list, since the virus is spreading from the source"): - heuristic_minbudget(graphs["RegularGraph_Graph-8"], 0, [13,10,8,6,5,4,3,0,1,2],True) + + with pytest.raises(ValueError, match="Error: The source node can't be a part of the targets list, since the virus is spreading from the source"): + heuristic_minbudget(graphs["RegularGraph_Graph-1"], 0, [1, 2, 3, 0, 4, 5, 6], True) + + with pytest.raises(ValueError, match="Error: The source node can't be a part of the targets list, since the virus is spreading from the source"): + heuristic_minbudget(graphs["RegularGraph_Graph-3"], 1, [5, 1, 4], False) + + with pytest.raises(ValueError, match="Error: The source node can't be a part of the targets list, since the virus is spreading from the source"): + heuristic_minbudget(graphs["RegularGraph_Graph-4"], 4, [1, 2, 3, 4, 5, 6, 7], True) + + with pytest.raises(ValueError, match="Error: The source node can't be a part of the targets list, since the virus is spreading from the source"): + heuristic_minbudget(graphs["RegularGraph_Graph-6"], 0, [0, 3, 5, 6, 7, 8, 9], False) + + with pytest.raises(ValueError, match="Error: The source node can't be a part of the targets list, since the virus is spreading from the source"): + heuristic_minbudget(graphs["RegularGraph_Graph-8"], 0, [13, 10, 8, 6, 5, 4, 3, 0, 1, 2], True) + def test_save_all_vertices_spreading(): - assert spreading_minbudget(graphs["RegularGraph_Graph-1"], 0, [1,2,3,4,5,6])[0] >= heuristic_minbudget(graphs["RegularGraph_Graph-1"], 0, [1,2,3,4,5,6],True)[0] - assert spreading_minbudget(graphs["RegularGraph_Graph-2"], 0, [1,2,3,4,5,6,7])[0] >= heuristic_minbudget(graphs["RegularGraph_Graph-2"], 0, [1,2,3,4,5,6,7],True)[0] - assert spreading_minbudget(graphs["RegularGraph_Graph-3"], 0, [1,2,3,4,5])[0] >= heuristic_minbudget(graphs["RegularGraph_Graph-3"], 0, [1,2,3,4,5],True)[0] - assert spreading_minbudget(graphs["RegularGraph_Graph-4"], 0, [1,2,3,4,5,6,7])[0] >= heuristic_minbudget(graphs["RegularGraph_Graph-4"], 0, [1,2,3,4,5,6,7],True)[0] - assert spreading_minbudget(graphs["RegularGraph_Graph-6"], 1, [0,2,3,4,5,6,7,8,9])[0] >= heuristic_minbudget(graphs["RegularGraph_Graph-6"], 1, [0,2,3,4,5,6,7,8,9],True)[0] - assert spreading_minbudget(graphs["RegularGraph_Graph-7"], 1, [0,2,3,4,5,6])[0] >= heuristic_minbudget(graphs["RegularGraph_Graph-7"], 1, [0,2,3,4,5,6],True)[0] - assert spreading_minbudget(graphs["RegularGraph_Graph-8"], 0, [1,2,3,4,5,6,7,8,9,10,11,12,13,14])[0] >= heuristic_minbudget(graphs["RegularGraph_Graph-8"], 0, [1,2,3,4,5,6,7,8,9,10,11,12,13,14],True)[0] - + log_message("Starting test_save_all_vertices_spreading:\n") + try: + for graph_name in graphs: + G = graphs[graph_name] + source = 0 + targets = [n for n in G.nodes if n != source] + + start_time = time.time() + spreading_result = spreading_minbudget(G, source, targets)[0] + spreading_time = time.time() - start_time + + start_time = time.time() + heuristic_result = heuristic_minbudget(G, source, targets, True)[0] + heuristic_time = time.time() - start_time + + log_message(f"{graph_name} - Spreading Result: {spreading_result}, Heuristic Result: {heuristic_result}") + log_message(f"{graph_name} - Spreading Time: {spreading_time:.6f}s, Heuristic Time: {heuristic_time:.6f}s") + + assert spreading_result >= heuristic_result + + finally: + log_message("\nFinished test_save_all_vertices_spreading.\n---------------------------------------------------------------------------------------------------------------------------") def test_save_subgroup_vertices_spreading(): - assert spreading_minbudget(graphs["RegularGraph_Graph-1"], 0, [1,5,6])[0] >= heuristic_minbudget(graphs["RegularGraph_Graph-1"], 0, [1,5,6],True)[0] - assert spreading_minbudget(graphs["RegularGraph_Graph-2"], 0, [1,3,4,5,6])[0] >= heuristic_minbudget(graphs["RegularGraph_Graph-2"], 0, [1,3,4,5,6],True)[0] - assert spreading_minbudget(graphs["RegularGraph_Graph-3"], 0, [1,3,5])[0] >= heuristic_minbudget(graphs["RegularGraph_Graph-3"], 0, [1,3,5],True)[0] - assert spreading_minbudget(graphs["RegularGraph_Graph-4"], 0, [2,3,5,7])[0] >= heuristic_minbudget(graphs["RegularGraph_Graph-4"], 0, [2,3,5,7],True)[0] - assert spreading_minbudget(graphs["RegularGraph_Graph-6"], 1, [0,3,5,6,8,9])[0] >= heuristic_minbudget(graphs["RegularGraph_Graph-6"], 1, [0,3,5,6,8,9],True)[0] - assert spreading_minbudget(graphs["RegularGraph_Graph-7"], 1, [4,2,5,6])[0] >= heuristic_minbudget(graphs["RegularGraph_Graph-7"], 1, [4,2,5,6],True)[0] - assert spreading_minbudget(graphs["RegularGraph_Graph-8"], 0, [1,3,4,5,6,9,10,12,14])[0] >= heuristic_minbudget(graphs["RegularGraph_Graph-8"], 0, [1,3,4,5,6,9,10,12,14],True)[0] + log_message("Starting test_save_subgroup_vertices_spreading:\n") + try: + for graph_name in graphs: + G = graphs[graph_name] + source = 0 + targets = [n for n in G.nodes if random.random() < 0.5 and n != source] + + start_time = time.time() + spreading_result = spreading_minbudget(G, source, targets)[0] + spreading_time = time.time() - start_time + + start_time = time.time() + heuristic_result = heuristic_minbudget(G, source, targets, True)[0] + heuristic_time = time.time() - start_time + + log_message(f"{graph_name} (subgroup) - Spreading Result: {spreading_result}, Heuristic Result: {heuristic_result}") + log_message(f"{graph_name} (subgroup) - Spreading Time: {spreading_time:.6f}s, Heuristic Time: {heuristic_time:.6f}s") + + assert spreading_result >= heuristic_result + + + finally: + log_message("\nFinished test_save_subgroup_vertices_spreading.\n---------------------------------------------------------------------------------------------------------------------------") def test_random_graph_comparison(): - for i in range(10): - num_nodes = random.randint(2,100) - nodes = list(range(num_nodes+1)) - num_edges = 1000 - save_amount = random.randint(1,num_nodes) - targets = [] - G = nx.DiGraph() - - G.add_nodes_from(nodes, status="target") - for _ in range(num_edges): - source = random.randint(0, num_nodes - 1) - target = random.randint(0, num_nodes - 1) - if source != target: # Ensure no self-loops - G.add_edge(source, target) - for node in range(save_amount): - probability = random.random() - if probability < 0.75 and node!=0: - targets.append(node) - - spreading_answer = spreading_minbudget(G,0,targets)[0] - heuristic_answer = heuristic_minbudget(G,0,targets,True)[0] - assert heuristic_answer <= spreading_answer - - print("All tests have passed!") \ No newline at end of file + log_message("Starting test_random_graph_comparison:\n") + try: + for i in range(10): + num_nodes = random.randint(2, 100) + nodes = list(range(num_nodes + 1)) + num_edges = 1000 + save_amount = random.randint(1, num_nodes) + targets = [] + G = nx.DiGraph() + + G.add_nodes_from(nodes, status="target") + for _ in range(num_edges): + source = random.randint(0, num_nodes - 1) + target = random.randint(0, num_nodes - 1) + if source != target: # Ensure no self-loops + G.add_edge(source, target) + for node in range(save_amount): + probability = random.random() + if probability < 0.75 and node != 0: + targets.append(node) + + start_time = time.time() + spreading_answer = spreading_minbudget(G, 0, targets)[0] + spreading_time = time.time() - start_time + + start_time = time.time() + heuristic_answer = heuristic_minbudget(G, 0, targets, True)[0] + heuristic_time = time.time() - start_time + + + log_message(f"Random Graph {i+1} - Spreading Result: {spreading_answer}, Heuristic Result: {heuristic_answer}") + log_message(f"Random Graph {i+1} - Spreading Time: {spreading_time:.6f}s, Heuristic Time: {heuristic_time:.6f}s") + + assert heuristic_answer <= spreading_answer + + finally: + log_message("\nFinished test_random_graph_comparison.\n---------------------------------------------------------------------------------------------------------------------------") diff --git a/networkz/algorithms/tests/test_firefighter_problem/test_non_spreading_dirlaynet_minbudget.py b/networkz/algorithms/tests/test_firefighter_problem/test_non_spreading_dirlaynet_minbudget.py index 3d66381..191c7ff 100644 --- a/networkz/algorithms/tests/test_firefighter_problem/test_non_spreading_dirlaynet_minbudget.py +++ b/networkz/algorithms/tests/test_firefighter_problem/test_non_spreading_dirlaynet_minbudget.py @@ -40,25 +40,6 @@ json_data = json.load(file) graphs = parse_json_to_networkx(json_data) - -# def test_source_not_in_graph(): -# """ -# This test checks if the source node is not a real node in the graph. -# """ -# pattern = r"Error: The source node is( not|n't) on the graph" - -# with pytest.raises(ValueError, match=pattern): -# non_spreading_dirlaynet_minbudget(graphs["Dirlay_Graph-1"], -3, [0, 5]) - -# with pytest.raises(ValueError, match=pattern): -# non_spreading_dirlaynet_minbudget(graphs["Dirlay_Graph-2"], 13, [0, 1, 4]) - -# with pytest.raises(ValueError, match=pattern): -# non_spreading_dirlaynet_minbudget(graphs["Dirlay_Graph-3"], 15, [0, 6, 7]) - -# with pytest.raises(ValueError, match=pattern): -# non_spreading_dirlaynet_minbudget(graphs["Dirlay_Graph-4"], -1, [1, 3, 5, 7]) - @pytest.mark.parametrize("graph_key, source, targets", [ ("Dirlay_Graph-1", -3, [0, 5]), ("Dirlay_Graph-2", 13, [0, 1, 4]), @@ -69,24 +50,6 @@ def test_source_not_in_graph(graph_key, source, targets): with pytest.raises(ValueError): non_spreading_dirlaynet_minbudget(graphs[graph_key], source, targets) -# def test_target_not_in_graph(): -# """ -# This test checks if a node we're trying to save is not in the graph. -# """ -# pattern = r"Error: Not all nodes (we're trying to save|in the targets list) are on the graph" - -# with pytest.raises(ValueError, match=pattern): -# non_spreading_dirlaynet_minbudget(graphs["Dirlay_Graph-1"], 0, [1, 5, 7]) - -# with pytest.raises(ValueError, match=pattern): -# non_spreading_dirlaynet_minbudget(graphs["Dirlay_Graph-2"], 1, [0, 2, -1, 9]) - -# with pytest.raises(ValueError, match=pattern): -# non_spreading_dirlaynet_minbudget(graphs["Dirlay_Graph-3"], 4, [0, 1, 2, 11, 12, 13, 14]) - -# with pytest.raises(ValueError, match=pattern): -# non_spreading_dirlaynet_minbudget(graphs["Dirlay_Graph-4"], 0, [1, 3, 5, 7, 15, 20]) - @pytest.mark.parametrize("graph_key, source, targets", [ ("Dirlay_Graph-1", 0, [1, 5, 7]), ("Dirlay_Graph-2", 1, [0, 2, -1, 9]), @@ -97,26 +60,6 @@ def test_target_not_in_graph(graph_key, source, targets): with pytest.raises(ValueError): non_spreading_dirlaynet_minbudget(graphs[graph_key], source, targets) - -# def test_source_is_target(): -# """ -# This test checks if we're trying to save a source node. -# """ -# pattern = r"Error: The source node can( not|'t) be a part of the targets (vector|list), since the virus is spreading from the source" - -# with pytest.raises(ValueError, match=pattern): -# non_spreading_dirlaynet_minbudget(graphs["Dirlay_Graph-1"], 0, [0, 5]) - -# with pytest.raises(ValueError, match=pattern): -# non_spreading_dirlaynet_minbudget(graphs["Dirlay_Graph-2"], 1, [0, 1, 4]) - -# with pytest.raises(ValueError, match=pattern): -# non_spreading_dirlaynet_minbudget(graphs["Dirlay_Graph-3"], 6, [0, 6, 7]) - -# with pytest.raises(ValueError, match=pattern): -# non_spreading_dirlaynet_minbudget(graphs["Dirlay_Graph-4"], 3, [1, 3, 5, 7]) - - @pytest.mark.parametrize("graph_key, source, targets", [ ("Dirlay_Graph-1", 0, [0, 5]), ("Dirlay_Graph-2", 1, [0, 1, 4]), @@ -128,7 +71,6 @@ def test_source_is_target(graph_key, source, targets): non_spreading_dirlaynet_minbudget(graphs[graph_key], source, targets) - # Test 1 # graph building graph_1 = graphs["Dirlay_Graph-1"] From d2a1b894d35a3e42924297b55ea132862fcab899 Mon Sep 17 00:00:00 2001 From: 20shaked20 Date: Tue, 25 Jun 2024 15:45:04 +0300 Subject: [PATCH 45/93] reduction is now an outside function with now able to get an s-t graph with capacity on its nodes and then output a mincut --- .../Firefighter_Problem.py | 6 +-- .../firefighter_problem/Utils.py | 11 ++--- ...tion.py => max_flow_with_node_capacity.py} | 45 +++++++++++-------- .../test_non_spreading_dirlaynet_minbudget.py | 27 ++++++----- 4 files changed, 46 insertions(+), 43 deletions(-) rename networkz/algorithms/approximation/firefighter_problem/{graph_flow_reduction.py => max_flow_with_node_capacity.py} (73%) diff --git a/networkz/algorithms/approximation/firefighter_problem/Firefighter_Problem.py b/networkz/algorithms/approximation/firefighter_problem/Firefighter_Problem.py index 322e709..50fe816 100644 --- a/networkz/algorithms/approximation/firefighter_problem/Firefighter_Problem.py +++ b/networkz/algorithms/approximation/firefighter_problem/Firefighter_Problem.py @@ -23,7 +23,7 @@ import networkx.algorithms.connectivity as algo import math import logging -from networkz.algorithms.approximation.firefighter_problem.graph_flow_reduction import max_flow_with_node_capacity +from networkz.algorithms.approximation.firefighter_problem.max_flow_with_node_capacity import max_flow_with_node_capacity from networkz.algorithms.approximation.firefighter_problem.Utils import * logger = logging.getLogger(__name__) @@ -275,8 +275,8 @@ def non_spreading_dirlaynet_minbudget(Graph:nx.DiGraph, src:int, targets:list)-> layers = adjust_nodes_capacity(Graph, src) G = create_st_graph(Graph, targets) #display_graph(G) - G_reduction = max_flow_with_node_capacity(G, source=src, target='t') - N_groups = min_cut_N_groups(G_reduction, src,layers) + G_reduction_min_cut = max_flow_with_node_capacity(G, source=src, target='t') + N_groups = min_cut_N_groups(G_reduction_min_cut,layers) vacc_matrix = calculate_vaccine_matrix(layers, N_groups) integer_matrix = matrix_to_integers_values(vacc_matrix) min_budget = min_budget_calculation(vacc_matrix) diff --git a/networkz/algorithms/approximation/firefighter_problem/Utils.py b/networkz/algorithms/approximation/firefighter_problem/Utils.py index 72b8507..2ea61b0 100644 --- a/networkz/algorithms/approximation/firefighter_problem/Utils.py +++ b/networkz/algorithms/approximation/firefighter_problem/Utils.py @@ -515,9 +515,9 @@ def create_st_graph(graph:nx.DiGraph, targets:list)->nx.DiGraph: logger.info(f"Done creating a s-t graph") return G -def min_cut_N_groups(graph: nx.DiGraph, source: int, layers: list) -> dict: +def min_cut_N_groups(min_cut_nodes: set, layers: list) -> dict: """ - Find the minimum cut and group nodes accordingly. + Using the minimum cut group nodes accordingly. Parameters: ---------- @@ -537,15 +537,12 @@ def min_cut_N_groups(graph: nx.DiGraph, source: int, layers: list) -> dict: -------- """ - # Compute the minimum cut - logger.info(f"Finding the minimum cut on the graph after reduction") - min_cut_nodes = algo.minimum_st_node_cut(graph, f'{source}_out', 't_in') - logger.info(f"Minimum Cut is: {min_cut_nodes}") + logger.info(f"Finding the correct nodes from each layer according to the min-cut nodes") # Initialize the groups dictionary with empty lists for each layer index groups = {i+1: [] for i in range(len(layers)-1)} - logger.info(f"Finding the correct nodes from each layer according to the min-cut nodes") + # Populate the groups dictionary for item in min_cut_nodes: diff --git a/networkz/algorithms/approximation/firefighter_problem/graph_flow_reduction.py b/networkz/algorithms/approximation/firefighter_problem/max_flow_with_node_capacity.py similarity index 73% rename from networkz/algorithms/approximation/firefighter_problem/graph_flow_reduction.py rename to networkz/algorithms/approximation/firefighter_problem/max_flow_with_node_capacity.py index aba5824..67c925b 100644 --- a/networkz/algorithms/approximation/firefighter_problem/graph_flow_reduction.py +++ b/networkz/algorithms/approximation/firefighter_problem/max_flow_with_node_capacity.py @@ -20,11 +20,14 @@ """ import networkx as nx +import networkx.algorithms.connectivity as algo +from networkz.algorithms.approximation.firefighter_problem.Utils import * import logging + logger = logging.getLogger(__name__) -def max_flow_with_node_capacity(graph: nx.DiGraph, source: int = None, target: int = None) -> nx.DiGraph: +def max_flow_with_node_capacity(graph: nx.DiGraph, source: int = None, target: int = None) -> set: """ Computes a maximum flow in the given graph, where each node has a capacity @@ -41,8 +44,8 @@ def max_flow_with_node_capacity(graph: nx.DiGraph, source: int = None, target: i Returns: ------- - nx.DiGraph - The transformed graph after flow reduction. + set + The minimum cut nodes Notes: ----- @@ -58,22 +61,17 @@ def max_flow_with_node_capacity(graph: nx.DiGraph, source: int = None, target: i Examples: -------- >>> G = nx.DiGraph() - >>> G.add_node(1, capacity=10) - >>> G.add_node(2, capacity=15) - >>> G.add_node(3, capacity=20) - >>> G.add_edge(1, 2) + >>> G.add_node(1, capacity=0.6) + >>> G.add_node(2, capacity=0.6) + >>> G.add_node(3, capacity=0.3) + >>> G.add_node(4, capacity=0.3) + >>> G.add_edge(0, 1) + >>> G.add_edge(0, 2) >>> G.add_edge(2, 3) - >>> G.add_edge(1, 3) - >>> H = max_flow_with_node_capacity(G, 1, 3) - >>> sorted(list(H.nodes)) - ['1_in', '1_out', '2_in', '2_out', '3_in', '3_out'] - >>> sorted(list(H.edges(data=True))) - [('1_in', '1_out', {'weight': inf}), ('1_out', '2_in', {'weight': inf}), ('1_out', '3_in', {'weight': inf}), ('2_in', '2_out', {'weight': 15}), ('2_out', '3_in', {'weight': inf}), ('3_in', '3_out', {'weight': inf})] - >>> H = max_flow_with_node_capacity(G) - >>> sorted(list(H.nodes)) - ['1_in', '1_out', '2_in', '2_out', '3_in', '3_out'] - >>> sorted(list(H.edges(data=True))) - [('1_in', '1_out', {'weight': 10}), ('1_out', '2_in', {'weight': inf}), ('1_out', '3_in', {'weight': inf}), ('2_in', '2_out', {'weight': 15}), ('2_out', '3_in', {'weight': inf}), ('3_in', '3_out', {'weight': 20})] + >>> G.add_edge(1, 4) + >>> s_t_G = create_st_graph(G, [2,4]) + >>> min_cut_nodes = max_flow_with_node_capacity(s_t_G, 0, 4) + {'2_out', '4_out'} """ logger.info("Starting graph flow reduction") H = nx.DiGraph() @@ -100,7 +98,16 @@ def max_flow_with_node_capacity(graph: nx.DiGraph, source: int = None, target: i logger.debug(f"Added infinite capacity edge from {u_out} to {v_in}") logger.info("Graph flow reduction finished") - return H + + # Compute the minimum cut + logger.info(f"Finding the minimum cut on the graph after reduction") + min_cut_nodes = algo.minimum_st_node_cut(H, f'{source}_out', 't_in') + + logger.info(f"Minimum Cut is: {min_cut_nodes}") + + print(set(sorted(min_cut_nodes))) + + return min_cut_nodes if __name__ == "__main__": import doctest diff --git a/networkz/algorithms/tests/test_firefighter_problem/test_non_spreading_dirlaynet_minbudget.py b/networkz/algorithms/tests/test_firefighter_problem/test_non_spreading_dirlaynet_minbudget.py index 191c7ff..663c8a8 100644 --- a/networkz/algorithms/tests/test_firefighter_problem/test_non_spreading_dirlaynet_minbudget.py +++ b/networkz/algorithms/tests/test_firefighter_problem/test_non_spreading_dirlaynet_minbudget.py @@ -30,7 +30,7 @@ from networkz.algorithms.approximation.firefighter_problem.Utils import adjust_nodes_capacity from networkz.algorithms.approximation.firefighter_problem.Utils import create_st_graph from networkz.algorithms.approximation.firefighter_problem.Utils import parse_json_to_networkx -from networkz.algorithms.approximation.firefighter_problem.graph_flow_reduction import max_flow_with_node_capacity +from networkz.algorithms.approximation.firefighter_problem.max_flow_with_node_capacity import max_flow_with_node_capacity from networkz.algorithms.approximation.firefighter_problem.Utils import calculate_vaccine_matrix from networkz.algorithms.approximation.firefighter_problem.Utils import min_cut_N_groups from networkz.algorithms.approximation.firefighter_problem.Utils import matrix_to_integers_values @@ -77,8 +77,8 @@ def test_source_is_target(graph_key, source, targets): layers_1 = adjust_nodes_capacity(graph_1, 0) # src is 0 targets_1 = [1, 2, 3] # saving 1,2,3 G1 = create_st_graph(graph_1, targets_1) -reduction_G1 = max_flow_with_node_capacity(G1, 0) -N_1_groups = min_cut_N_groups(reduction_G1, 0,layers_1) +reduction_G1_min_cut = max_flow_with_node_capacity(G1, 0) +N_1_groups = min_cut_N_groups(reduction_G1_min_cut,layers_1) matrix_1 = calculate_vaccine_matrix(layers_1, N_1_groups) integer_matrix_1 = matrix_to_integers_values(matrix_1) min_budget_1 = min_budget_calculation(integer_matrix_1) @@ -89,8 +89,8 @@ def test_source_is_target(graph_key, source, targets): layers_2 = adjust_nodes_capacity(graph_2, 0) # src is 0 targets_2 = [2, 4] # saving 2,4 G2 = create_st_graph(graph_2, targets_2) -reduction_G2 = max_flow_with_node_capacity(G2, 0) -N_2_groups = min_cut_N_groups(reduction_G2, 0, layers_2) +reduction_G2_min_cut = max_flow_with_node_capacity(G2, 0) +N_2_groups = min_cut_N_groups(reduction_G2_min_cut, layers_2) matrix_2 = calculate_vaccine_matrix(layers_2, N_2_groups) integer_matrix_2 = matrix_to_integers_values(matrix_2) min_budget_2 = min_budget_calculation(integer_matrix_2) @@ -101,8 +101,8 @@ def test_source_is_target(graph_key, source, targets): layers_3 = adjust_nodes_capacity(graph_3, 0) # src is 0 targets_3 = [1, 5, 7] # saving 1,5,7 G3 = create_st_graph(graph_3, targets_3) -reduction_G3 = max_flow_with_node_capacity(G3, 0) -N_3_groups = min_cut_N_groups(reduction_G3, 0, layers_3) +reduction_G3_min_cut = max_flow_with_node_capacity(G3, 0) +N_3_groups = min_cut_N_groups(reduction_G3_min_cut, layers_3) matrix_3 = calculate_vaccine_matrix(layers_3, N_3_groups) integer_matrix_3 = matrix_to_integers_values(matrix_3) min_budget_3 = min_budget_calculation(integer_matrix_3) @@ -113,8 +113,8 @@ def test_source_is_target(graph_key, source, targets): layers_4 = adjust_nodes_capacity(graph_4, 0) # src is 0 targets_4 = [4, 5, 6, 8] # saving 4,5,6,8 G4 = create_st_graph(graph_4, targets_4) -reduction_G4 = max_flow_with_node_capacity(G4, 0) -N_4_groups = min_cut_N_groups(reduction_G4, 0, layers_4) +reduction_G4_min_cut = max_flow_with_node_capacity(G4, 0) +N_4_groups = min_cut_N_groups(reduction_G4_min_cut, layers_4) matrix_4 = calculate_vaccine_matrix(layers_4, N_4_groups) integer_matrix_4 = matrix_to_integers_values(matrix_4) min_budget_4 = min_budget_calculation(integer_matrix_4) @@ -209,25 +209,25 @@ def sort_dict_values(d): #| this is for sorting purposes , cases where [1,2] or # Test 1 # checking equality N1_groups_check = {1: [1, 2], 2: [], 3: [], 4: []} - result_1 = min_cut_N_groups(reduction_G1, 0, layers_1) + result_1 = min_cut_N_groups(reduction_G1_min_cut, layers_1) assert sort_dict_values(result_1) == sort_dict_values(N1_groups_check) # Test 2 # checking equality N2_groups_check = {1: [2], 2: [4]} - result_2 = min_cut_N_groups(reduction_G2, 0, layers_2) + result_2 = min_cut_N_groups(reduction_G2_min_cut, layers_2) assert sort_dict_values(result_2) == sort_dict_values(N2_groups_check) # Test 3 # checking equality N3_groups_check = {1: [1], 2: [], 3: [7]} - result_3 = min_cut_N_groups(reduction_G3, 0, layers_3) + result_3 = min_cut_N_groups(reduction_G3_min_cut, layers_3) assert sort_dict_values(result_3) == sort_dict_values(N3_groups_check) # Test 4 # checking equality N4_groups_check = {1: [], 2: [4], 3: [5], 4: [], 5: []} - result_4 = min_cut_N_groups(reduction_G4, 0, layers_4) + result_4 = min_cut_N_groups(reduction_G4_min_cut, layers_4) assert sort_dict_values(result_4) == sort_dict_values(N4_groups_check) def test_calculate_vaccine_matrix(): @@ -423,5 +423,4 @@ def test_non_spreading_dirlaynet_minbudget(): targets = random.sample(nodes[1:], num_targets) # Randomly choose nodes to save, excluding the source node min_budget = non_spreading_dirlaynet_minbudget(G, source, targets) - print("BUDGET----------",min_budget) assert min_budget > 0, "Minimum budget should be non-negative" From 646605a5694521d625648cc406186009937ca2d6 Mon Sep 17 00:00:00 2001 From: 20shaked20 Date: Tue, 25 Jun 2024 16:59:38 +0300 Subject: [PATCH 46/93] algorithm comparisons + dirlay trying to improve the adjust_node_capacity --- .../firefighter_problem/Utils.py | 28 +++++ .../approximation/firefighter_problem/main.py | 107 ++++++++++++++++-- .../test_non_spreading_dirlaynet_minbudget.py | 4 +- 3 files changed, 125 insertions(+), 14 deletions(-) diff --git a/networkz/algorithms/approximation/firefighter_problem/Utils.py b/networkz/algorithms/approximation/firefighter_problem/Utils.py index 2ea61b0..9ad49f7 100644 --- a/networkz/algorithms/approximation/firefighter_problem/Utils.py +++ b/networkz/algorithms/approximation/firefighter_problem/Utils.py @@ -27,6 +27,7 @@ import copy import logging import heapq +import concurrent.futures # this is used for improving the dirlay function - adjust_nodes_capacity class Status(Enum): VULNERABLE = "vulnerable" @@ -475,6 +476,33 @@ def adjust_nodes_capacity(graph:nx.DiGraph, source:int)->list: return layers +def adjust_nodes_capacity_parallel(graph: nx.DiGraph, source: int) -> list: + """ + This is a tryout to improve the adjust_nodes_capacity by using threadpool to calcualte each layer and its nodes capacity in parallel + """ + logger.debug("Starting to adjust node capacity for dirlay graph nodes in parallel...") + + layers = list(nx.bfs_layers(graph, source)) + harmonic_sums = np.cumsum([1 / i for i in range(1, len(layers))]) + + def set_node_capacity(index, nodes_chunk): + for node in nodes_chunk: + graph.nodes[node]['capacity'] = 1 / (index * harmonic_sums[index - 1]) + logger.info(f"Added Capacity {1 / (index * harmonic_sums[index - 1])} for node: {node}") + + with concurrent.futures.ThreadPoolExecutor() as executor: + futures = [] + for index in range(1, len(layers)): + chunk_size = max(1, len(layers[index]) // executor._max_workers) + for i in range(0, len(layers[index]), chunk_size): + chunk = layers[index][i:i + chunk_size] + futures.append(executor.submit(set_node_capacity, index, chunk)) + concurrent.futures.wait(futures) + + logger.info(f"Done with adding capacity for nodes, with Layers: {layers}") + return layers + + def create_st_graph(graph:nx.DiGraph, targets:list)->nx.DiGraph: """ Create an s-t graph from the original graph for use in connectivity algorithms. diff --git a/networkz/algorithms/approximation/firefighter_problem/main.py b/networkz/algorithms/approximation/firefighter_problem/main.py index 81c3902..0b2816d 100644 --- a/networkz/algorithms/approximation/firefighter_problem/main.py +++ b/networkz/algorithms/approximation/firefighter_problem/main.py @@ -21,6 +21,7 @@ import logging import json +import time logger = logging.getLogger(__name__) @@ -28,6 +29,7 @@ # and the bottom one needs to be exclusive for running this from terminal to work from networkz.algorithms.approximation.firefighter_problem.Utils import * from networkz.algorithms.approximation.firefighter_problem.Firefighter_Problem import * +from networkz.algorithms.tests.test_firefighter_problem.test_non_spreading_dirlaynet_minbudget import generate_layered_network import networkz.algorithms.approximation.firefighter_problem.Firefighter_Problem as firefighter_problem # to run the doctest on the firefighter_problem files def setup_global_logger(level: int = logging.DEBUG): @@ -41,28 +43,89 @@ def setup_global_logger(level: int = logging.DEBUG): root_logger.setLevel(level) root_logger.addHandler(handler) +def compare_functions(graph: nx.DiGraph, source: int): + """ + This method comapres the function adjust_nodes_capacity + with a more complex version of it that uses threadpool to calcualte the capacity of each node + """ + graph1 = graph.copy() + graph2 = graph.copy() + + start_time = time.time() + adjust_nodes_capacity(graph1, source) + sequential_time = time.time() - start_time + + start_time = time.time() + adjust_nodes_capacity_parallel(graph2, source) + parallel_time = time.time() - start_time + + # Plot the execution times + plt.figure(figsize=(10, 5)) + + times = [sequential_time, parallel_time] + labels = ['adjust_nodes_capacity', 'adjust_nodes_capacity_parallel'] + + plt.bar(labels, times, color=['b', 'g'], alpha=0.6) + plt.title('Sequential vs Parallel Execution Times') + plt.xlabel('Execution Type') + plt.ylabel('Time (seconds)') + plt.show() + + +def compare_algorithms(graph: nx.DiGraph, source: int, targets:list): + """ + This method is used to compare the algoritmhs we created and show thier running time. + """ + graph1 = graph.copy() + graph2 = graph.copy() + graph3 = graph.copy() + graph4 = graph.copy() + graph5 = graph.copy() + + start_time1 = time.time() + non_spreading_dirlaynet_minbudget(graph1, source,targets) + end_time1= time.time() - start_time1 + + start_time2 = time.time() + heuristic_maxsave(graph2, 1,source, targets) + end_time2 = time.time() - start_time2 + + start_time3 = time.time() + heuristic_minbudget(graph3,source, targets, False) #no spreading + end_time3 = time.time() - start_time3 + + start_time4 = time.time() + spreading_minbudget(graph4,source, targets) + end_time4 = time.time() - start_time4 + + start_time5 = time.time() + spreading_maxsave(graph5, 1,source, targets) + end_time5 = time.time() - start_time5 + + # Plot the execution times + plt.figure(figsize=(15, 8)) + + times = [end_time1, end_time2, end_time3 ,end_time4, end_time5] + labels = ['non_spreading\ndirlaynet_minbudget', 'spreading\nheuristic_maxsave', 'non_spreading\nheuristic_min_budget', 'spreading\nminbudget', 'spreading\nmaxsave'] + + plt.bar(labels, times, color=['b', 'g'], alpha=0.6) + plt.title('Algorithm Running time comparison') + plt.xlabel('Execution Type') + plt.ylabel('Time (seconds)') + plt.show() + if __name__ == "__main__": import doctest setup_global_logger(level=logging.DEBUG) - # result = doctest.testmod(firefighter_problem, verbose=True) - # logger.info(f"Doctest results: {result}") - - # G3 = nx.DiGraph() - # G3.add_nodes_from([0,1,2,3,4,5,6,7,8]) - # G3.add_edges_from([(0,2),(0,4),(0,5),(2,1),(2,3),(4,1),(4,6),(5,3),(5,6),(5,7),(6,7),(6,8),(7,8)]) - # logger.info("=" * 150) - #print(spreading_maxsave(G3,source=0,targets=[2,6,1,8],budget=1)) - #print(spreading_minbudget(G3,source=0,targets=[2,6,1,8])) - with open("networkz/algorithms/tests/test_firefighter_problem/graphs.json", "r") as file: json_data = json.load(file) graphs = parse_json_to_networkx(json_data) - G2 = graphs["Dirlay_Graph-2"] - print(non_spreading_dirlaynet_minbudget(Graph=G2, src=0, targets=[2,4])) + G_dirlay_random = generate_layered_network() #random graph generator for dirlay testings/ can also fit other algorithms but dirlay + "Simple testings on spreading algorithms:" # G2 = graphs["RegularGraph_Graph-2"] # print(heuristic_minbudget(G2,source=0, targets=[1,3,4,5,6],spreading=True)) #print(spreading_maxsave(G3,1, 0,[2,6,1,8])[1]) @@ -70,3 +133,23 @@ def setup_global_logger(level: int = logging.DEBUG): # logger.info("=" * 150) #logger.info(heuristic_minbudget(G3,0,[2,6,1,8], True)) + # G3 = nx.DiGraph() + # G3.add_nodes_from([0,1,2,3,4,5,6,7,8]) + # G3.add_edges_from([(0,2),(0,4),(0,5),(2,1),(2,3),(4,1),(4,6),(5,3),(5,6),(5,7),(6,7),(6,8),(7,8)]) + # logger.info("=" * 150) + #print(spreading_maxsave(G3,source=0,targets=[2,6,1,8],budget=1)) + #print(spreading_minbudget(G3,source=0,targets=[2,6,1,8])) + + "Dirlay simple running exmaple:" + G2 = graphs["Dirlay_Graph-2"] + # print(non_spreading_dirlaynet_minbudget(Graph=G2, src=0, targets=[2,4])) + + "Comapring adjust_nodes_capacity with parallel running:" + # compare_functions(G_dirlay_random,0) # more complex random graph comparison + + "Compare Graph Algorithms:" + # compare_algorithms(G_dirlay_random,0, [2,4]) # more complex random graph comparison + # compare_algorithms(G2, 0, [2,4]) # simple graph example comparison + + + diff --git a/networkz/algorithms/tests/test_firefighter_problem/test_non_spreading_dirlaynet_minbudget.py b/networkz/algorithms/tests/test_firefighter_problem/test_non_spreading_dirlaynet_minbudget.py index 663c8a8..179d2b5 100644 --- a/networkz/algorithms/tests/test_firefighter_problem/test_non_spreading_dirlaynet_minbudget.py +++ b/networkz/algorithms/tests/test_firefighter_problem/test_non_spreading_dirlaynet_minbudget.py @@ -372,10 +372,10 @@ def generate_layered_network(): G (networkx.DiGraph): Directed graph representing the layered network. """ # Randomly decide the number of layers (between 2 and 3 for this example) - num_layers = random.randint(2, 3) + num_layers = random.randint(2, 6) # Randomly decide the number of nodes per layer (between 1 and 4 for this example) - nodes_per_layer = [random.randint(1, 4) for _ in range(num_layers)] + nodes_per_layer = [random.randint(3, 8) for _ in range(num_layers)] G = nx.DiGraph() node_id = 1 # Start node_id from 1 because 0 is the source From 84dd2b95b2e4b228ecd462152da96ce8c85bcf03 Mon Sep 17 00:00:00 2001 From: 20shaked20 Date: Tue, 25 Jun 2024 17:02:02 +0300 Subject: [PATCH 47/93] added png of comparing --- .../approximation/firefighter_problem/Utils.py | 2 +- .../Algorithm_running_time_comparison.png | Bin 0 -> 42373 bytes 2 files changed, 1 insertion(+), 1 deletion(-) create mode 100644 networkz/algorithms/tests/test_firefighter_problem/comparisons/Algorithm_running_time_comparison.png diff --git a/networkz/algorithms/approximation/firefighter_problem/Utils.py b/networkz/algorithms/approximation/firefighter_problem/Utils.py index 9ad49f7..808664d 100644 --- a/networkz/algorithms/approximation/firefighter_problem/Utils.py +++ b/networkz/algorithms/approximation/firefighter_problem/Utils.py @@ -476,7 +476,7 @@ def adjust_nodes_capacity(graph:nx.DiGraph, source:int)->list: return layers -def adjust_nodes_capacity_parallel(graph: nx.DiGraph, source: int) -> list: +def adjust_nodes_capacity_parallel(graph: nx.DiGraph, source: int) -> list: #TODO """ This is a tryout to improve the adjust_nodes_capacity by using threadpool to calcualte each layer and its nodes capacity in parallel """ diff --git a/networkz/algorithms/tests/test_firefighter_problem/comparisons/Algorithm_running_time_comparison.png b/networkz/algorithms/tests/test_firefighter_problem/comparisons/Algorithm_running_time_comparison.png new file mode 100644 index 0000000000000000000000000000000000000000..8a47eb7286c5d8bfe73ae67aa8716c9adfc4f83b GIT binary patch literal 42373 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z(F6-Hbff-v^}G9Rl5>qbrM3iVDc|*d>ZVh3`|+i-?MPg3&Xf^nQc5}ne!7UF^5|9U z%CHaiiJzjQj?Uq8NymOX72`9Mu{UmYptnw6`L$FV`2Fa;$tFKF%2kJx!^b?k?uKKH z4MjqRXPMN)ey1%+m6Ry<=w!`CQ0wAVr(%@KFUL z@sk+CsGE~Gm6kyr<1v~N=hqt*gNZT%<8gzG0xeNY$+owc4GWbMa*lYu_96$9YGKm2(QTK^T)T%3$MBnzMxRv*I&)2iZrE?Hzv`pmc;cW{ zQaw;4v`#}jF3x0ZqD$j&WZS!Y{POhV{nCScisQQ44h79QDNkkw|rVy1JKYwpbf%&v|yGH$N_1E95-?Ol26HUKmbd*Mge~%kXG2{ynQcEv1cRaspmi8K> zm63u zFY$gqGU{&lU%pg#f3A=DcB^Hk@dq8{-ae%T$5U9_p#tIgo~cz6a8c5`RL>fCi$tVM zvwXQfF3`VzD!kT7;LL_qUP*=5`jx=kl%h7^4EW&%vsOa!n#j~seXV?-v4{I{tuBKDqoZa z9=^`o{{E?=vY(xOd7eym$onr3C^?5#Kj%-pI5YTG%ht?K=5V!aqA%HDkq_lgFQwmK z5RNsIaaiS_G%znUyO)1Ae5H%W&}j0@sI+y8)OYLwjjS9?gTgPH3|w+9y8S_^`1#(= z;g$Rk>dcR3_2=3HD%NNoJ30*>KAY)UnRk6|-23&Ow7tSU*sRu2H6x8D9c}#kOV3s= zpY^1uEO)X`8z*qo+@RC1c}2AK(Tt2oQ|(vbkmRWjLGuKCH!J;*<|-Ptbw^cFS7u(; z2)|)s=R8$eRsCV|vJ-Z;p^uhP$$76p`~I9ZqP`FAds9pItB!JY{SquXVMxcw&c%Gs zw{Q6@eZ2OT<-0z(8kiy>?FWx+4^BM0wjuKC`g+;H%%dfLv1d8S&Dmm(Phm-{vZrP@ zf4*EX8|w75^=`^F)@`Z&t6IXs3``c5^Ki@TOPy`B7G;_7H5m8$#6YpC$inqDeTr7lJF zY*!G2HuKngbf0@T(;hz8@_<-F2gjTKx(jL@a*w6HJat(2x|6B#-m5EW`VMWp<`n{zjRR+6?F%#-j`hdsS&?0=X(pnYAZ&x`9Rr$9Xdcwa?d= zs(WvmYdBoa?3U=fZP`lP#H?qRy_oc+^_ Date: Tue, 25 Jun 2024 20:35:38 +0300 Subject: [PATCH 48/93] Improved testing for hueristic functions --- ...tic_maxsave_comparison_20240625_203255.txt | 137 +++++++++++ .../test_heuristic_minbudget_comparison.txt | 51 ---- ...c_minbudget_comparison_20240625_203255.txt | 137 +++++++++++ .../test_heuristic_maxsave.py | 111 ++++++--- .../test_heuristic_minbudget.py | 222 ++++++++---------- 5 files changed, 448 insertions(+), 210 deletions(-) create mode 100644 networkz/algorithms/tests/test_firefighter_problem/comparisons/test_heuristic_maxsave_comparison_20240625_203255.txt delete mode 100644 networkz/algorithms/tests/test_firefighter_problem/comparisons/test_heuristic_minbudget_comparison.txt create mode 100644 networkz/algorithms/tests/test_firefighter_problem/comparisons/test_heuristic_minbudget_comparison_20240625_203255.txt diff --git a/networkz/algorithms/tests/test_firefighter_problem/comparisons/test_heuristic_maxsave_comparison_20240625_203255.txt b/networkz/algorithms/tests/test_firefighter_problem/comparisons/test_heuristic_maxsave_comparison_20240625_203255.txt new file mode 100644 index 0000000..8dea670 --- /dev/null +++ b/networkz/algorithms/tests/test_firefighter_problem/comparisons/test_heuristic_maxsave_comparison_20240625_203255.txt @@ -0,0 +1,137 @@ +2024-06-25 20:32:55,262 - INFO - Testing strategy length for RegularGraph_Graph-1 +2024-06-25 20:32:55,263 - INFO - Calculated strategy: [(1, 1), (6, 2)] +2024-06-25 20:32:55,263 - INFO - Strategy length test passed for RegularGraph_Graph-1 +2024-06-25 20:32:55,264 - INFO - Testing strategy length for Dirlay_Graph-5 +2024-06-25 20:32:55,265 - INFO - Calculated strategy: [(5, 1), (2, 1), (8, 2)] +2024-06-25 20:32:55,266 - INFO - Strategy length test passed for Dirlay_Graph-5 +2024-06-25 20:32:55,269 - INFO - Testing save all vertices for RegularGraph_Graph-1 +2024-06-25 20:32:55,269 - INFO - Calculated strategy: [(1, 1), (6, 2)] +2024-06-25 20:32:55,269 - INFO - Save all vertices test passed for RegularGraph_Graph-1 +2024-06-25 20:32:55,270 - INFO - Testing save all vertices for Dirlay_Graph-5 +2024-06-25 20:32:55,270 - INFO - Calculated strategy: [(5, 1), (2, 1)] +2024-06-25 20:32:55,270 - INFO - Save all vertices test passed for Dirlay_Graph-5 +2024-06-25 20:32:55,271 - INFO - Testing save subgroup vertices for RegularGraph_Graph-6 +2024-06-25 20:32:55,271 - INFO - Calculated strategy: [(2, 1)] +2024-06-25 20:32:55,271 - INFO - Save subgroup vertices test passed for RegularGraph_Graph-6 +2024-06-25 20:32:55,272 - INFO - Testing save subgroup vertices for RegularGraph_Graph-4 +2024-06-25 20:32:55,272 - INFO - Calculated strategy: [(1, 1)] +2024-06-25 20:32:55,273 - INFO - Save subgroup vertices test passed for RegularGraph_Graph-4 +2024-06-25 20:32:55,274 - INFO - Starting test_random_graph_comparison: +2024-06-25 20:32:55,277 - INFO - Random Graph 1 - Targets: [2, 3, 5, 7, 8, 9, 11, 14, 15, 16, 19, 20, 21, 22, 23, 24, 25, 27, 31, 32, 35, 36, 37, 38, 39, 40, 41, 42, 43, 45] +2024-06-25 20:32:55,284 - INFO - Random Graph 1 - Spreading Result: 12, Heuristic Result: 19 +2024-06-25 20:32:55,284 - INFO - Random Graph 1 - Spreading Time: 0.004195s, Heuristic Time: 0.002185s +2024-06-25 20:32:55,287 - INFO - Random Graph 2 - Targets: [1, 2, 3, 4] +2024-06-25 20:32:55,294 - INFO - Random Graph 2 - Spreading Result: 3, Heuristic Result: 4 +2024-06-25 20:32:55,294 - INFO - Random Graph 2 - Spreading Time: 0.006381s, Heuristic Time: 0.000729s +2024-06-25 20:32:55,296 - INFO - Random Graph 3 - Targets: [1, 3, 4, 5, 7, 8, 9, 10, 11, 12, 13, 15, 17, 18, 19, 20] +2024-06-25 20:32:55,310 - INFO - Random Graph 3 - Spreading Result: 9, Heuristic Result: 9 +2024-06-25 20:32:55,310 - INFO - Random Graph 3 - Spreading Time: 0.012846s, Heuristic Time: 0.000778s +2024-06-25 20:32:55,313 - INFO - Random Graph 4 - Targets: [1, 2, 3, 4, 5, 7, 8, 11, 12, 15, 17, 20, 22, 23, 24, 25, 27, 28, 29, 30, 31, 32, 35, 37, 38, 41] +2024-06-25 20:32:55,322 - INFO - Random Graph 4 - Spreading Result: 9, Heuristic Result: 17 +2024-06-25 20:32:55,322 - INFO - Random Graph 4 - Spreading Time: 0.008003s, Heuristic Time: 0.000785s +2024-06-25 20:32:55,324 - INFO - Random Graph 5 - Targets: [2, 3, 6, 7, 9, 10, 11] +2024-06-25 20:32:55,325 - INFO - Random Graph 5 - Spreading Result: 4, Heuristic Result: 7 +2024-06-25 20:32:55,325 - INFO - Random Graph 5 - Spreading Time: 0.001062s, Heuristic Time: 0.000314s +2024-06-25 20:32:55,327 - INFO - Random Graph 6 - Targets: [] +2024-06-25 20:32:55,328 - INFO - Random Graph 6 - Spreading Result: 0, Heuristic Result: 0 +2024-06-25 20:32:55,328 - INFO - Random Graph 6 - Spreading Time: 0.000237s, Heuristic Time: 0.000138s +2024-06-25 20:32:55,330 - INFO - Random Graph 7 - Targets: [2, 3, 4, 5, 6, 8, 9, 10, 12] +2024-06-25 20:32:55,332 - INFO - Random Graph 7 - Spreading Result: 2, Heuristic Result: 8 +2024-06-25 20:32:55,332 - INFO - Random Graph 7 - Spreading Time: 0.001508s, Heuristic Time: 0.000497s +2024-06-25 20:32:55,334 - INFO - Random Graph 8 - Targets: [] +2024-06-25 20:32:55,341 - INFO - Random Graph 8 - Spreading Result: 0, Heuristic Result: 0 +2024-06-25 20:32:55,341 - INFO - Random Graph 8 - Spreading Time: 0.005784s, Heuristic Time: 0.000685s +2024-06-25 20:32:55,343 - INFO - Random Graph 9 - Targets: [5, 6, 7, 8, 9, 10, 12, 13, 14, 16, 17, 18, 20, 21, 23, 25, 27, 28, 29, 30, 31, 32, 33, 35, 37, 38, 39, 40, 42, 43, 44, 45, 46, 47, 49] +2024-06-25 20:32:55,347 - INFO - Random Graph 9 - Spreading Result: 13, Heuristic Result: 27 +2024-06-25 20:32:55,347 - INFO - Random Graph 9 - Spreading Time: 0.003170s, Heuristic Time: 0.000699s +2024-06-25 20:32:55,349 - INFO - Random Graph 10 - Targets: [1, 2] +2024-06-25 20:32:55,354 - INFO - Random Graph 10 - Spreading Result: 2, Heuristic Result: 2 +2024-06-25 20:32:55,354 - INFO - Random Graph 10 - Spreading Time: 0.004133s, Heuristic Time: 0.000681s +2024-06-25 20:32:55,354 - INFO - Finished test_random_graph_comparison. +2024-06-25 20:32:55,354 - INFO - ---------------------------------------------------------------------------------------------------- +2024-06-25 20:32:55,367 - INFO - Starting test_save_all_vertices_spreading: +2024-06-25 20:32:55,367 - INFO - Dirlay_Graph-1 - Spreading Result: 2, Heuristic Result: 2 +2024-06-25 20:32:55,368 - INFO - Dirlay_Graph-1 - Spreading Time: 0.000363s, Heuristic Time: 0.000104s +2024-06-25 20:32:55,368 - INFO - Dirlay_Graph-2 - Spreading Result: 2, Heuristic Result: 2 +2024-06-25 20:32:55,368 - INFO - Dirlay_Graph-2 - Spreading Time: 0.000185s, Heuristic Time: 0.000059s +2024-06-25 20:32:55,368 - INFO - Dirlay_Graph-3 - Spreading Result: 3, Heuristic Result: 4 +2024-06-25 20:32:55,368 - INFO - Dirlay_Graph-3 - Spreading Time: 0.000391s, Heuristic Time: 0.000137s +2024-06-25 20:32:55,369 - WARNING - Warning: Heuristic result (4) is greater than spreading result (3) for Dirlay_Graph-3 +2024-06-25 20:32:55,369 - INFO - Dirlay_Graph-4 - Spreading Result: 2, Heuristic Result: 2 +2024-06-25 20:32:55,369 - INFO - Dirlay_Graph-4 - Spreading Time: 0.000449s, Heuristic Time: 0.000102s +2024-06-25 20:32:55,370 - INFO - Dirlay_Graph-5 - Spreading Result: 3, Heuristic Result: 4 +2024-06-25 20:32:55,370 - INFO - Dirlay_Graph-5 - Spreading Time: 0.000627s, Heuristic Time: 0.000352s +2024-06-25 20:32:55,371 - WARNING - Warning: Heuristic result (4) is greater than spreading result (3) for Dirlay_Graph-5 +2024-06-25 20:32:55,372 - INFO - RegularGraph_Graph-1 - Spreading Result: 2, Heuristic Result: 2 +2024-06-25 20:32:55,372 - INFO - RegularGraph_Graph-1 - Spreading Time: 0.000882s, Heuristic Time: 0.000267s +2024-06-25 20:32:55,373 - INFO - RegularGraph_Graph-2 - Spreading Result: 2, Heuristic Result: 2 +2024-06-25 20:32:55,373 - INFO - RegularGraph_Graph-2 - Spreading Time: 0.000459s, Heuristic Time: 0.000126s +2024-06-25 20:32:55,373 - INFO - RegularGraph_Graph-3 - Spreading Result: 2, Heuristic Result: 2 +2024-06-25 20:32:55,374 - INFO - RegularGraph_Graph-3 - Spreading Time: 0.000301s, Heuristic Time: 0.000096s +2024-06-25 20:32:55,374 - INFO - RegularGraph_Graph-4 - Spreading Result: 2, Heuristic Result: 2 +2024-06-25 20:32:55,374 - INFO - RegularGraph_Graph-4 - Spreading Time: 0.000503s, Heuristic Time: 0.000126s +2024-06-25 20:32:55,375 - INFO - RegularGraph_Graph-5 - Spreading Result: 2, Heuristic Result: 2 +2024-06-25 20:32:55,375 - INFO - RegularGraph_Graph-5 - Spreading Time: 0.000403s, Heuristic Time: 0.000109s +2024-06-25 20:32:55,375 - INFO - RegularGraph_Graph-6 - Spreading Result: 9, Heuristic Result: 1 +2024-06-25 20:32:55,375 - INFO - RegularGraph_Graph-6 - Spreading Time: 0.000192s, Heuristic Time: 0.000079s +2024-06-25 20:32:55,375 - INFO - RegularGraph_Graph-7 - Spreading Result: 6, Heuristic Result: 1 +2024-06-25 20:32:55,375 - INFO - RegularGraph_Graph-7 - Spreading Time: 0.000084s, Heuristic Time: 0.000051s +2024-06-25 20:32:55,377 - INFO - RegularGraph_Graph-8 - Spreading Result: 4, Heuristic Result: 4 +2024-06-25 20:32:55,377 - INFO - RegularGraph_Graph-8 - Spreading Time: 0.001493s, Heuristic Time: 0.000259s +2024-06-25 20:32:55,377 - INFO - Finished test_save_all_vertices_spreading. +2024-06-25 20:32:55,377 - INFO - ---------------------------------------------------------------------------------------------------- +2024-06-25 20:32:55,378 - INFO - Starting test_random_graph_comparison for Random Graph 1: +2024-06-25 20:32:55,446 - INFO - Random Graph 1 - Spreading Result: 6, Heuristic Result: 9 +2024-06-25 20:32:55,446 - INFO - Random Graph 1 - Spreading Time: 0.062671s, Heuristic Time: 0.003512s +2024-06-25 20:32:55,446 - WARNING - Warning: Heuristic result (9) is greater than spreading result (6) for Random Graph 1 +2024-06-25 20:32:55,446 - INFO - Finished test_random_graph_comparison for Random Graph 1. +2024-06-25 20:32:55,446 - INFO - ---------------------------------------------------------------------------------------------------- +2024-06-25 20:32:55,447 - INFO - Starting test_random_graph_comparison for Random Graph 2: +2024-06-25 20:32:55,459 - INFO - Random Graph 2 - Spreading Result: 1, Heuristic Result: 1 +2024-06-25 20:32:55,459 - INFO - Random Graph 2 - Spreading Time: 0.008340s, Heuristic Time: 0.000792s +2024-06-25 20:32:55,459 - INFO - Finished test_random_graph_comparison for Random Graph 2. +2024-06-25 20:32:55,459 - INFO - ---------------------------------------------------------------------------------------------------- +2024-06-25 20:32:55,460 - INFO - Starting test_random_graph_comparison for Random Graph 3: +2024-06-25 20:32:55,524 - INFO - Random Graph 3 - Spreading Result: 9, Heuristic Result: 10 +2024-06-25 20:32:55,525 - INFO - Random Graph 3 - Spreading Time: 0.060112s, Heuristic Time: 0.003079s +2024-06-25 20:32:55,525 - WARNING - Warning: Heuristic result (10) is greater than spreading result (9) for Random Graph 3 +2024-06-25 20:32:55,525 - INFO - Finished test_random_graph_comparison for Random Graph 3. +2024-06-25 20:32:55,525 - INFO - ---------------------------------------------------------------------------------------------------- +2024-06-25 20:32:55,526 - INFO - Starting test_random_graph_comparison for Random Graph 4: +2024-06-25 20:32:55,537 - INFO - Random Graph 4 - Spreading Result: 3, Heuristic Result: 4 +2024-06-25 20:32:55,537 - INFO - Random Graph 4 - Spreading Time: 0.008222s, Heuristic Time: 0.001385s +2024-06-25 20:32:55,537 - WARNING - Warning: Heuristic result (4) is greater than spreading result (3) for Random Graph 4 +2024-06-25 20:32:55,537 - INFO - Finished test_random_graph_comparison for Random Graph 4. +2024-06-25 20:32:55,537 - INFO - ---------------------------------------------------------------------------------------------------- +2024-06-25 20:32:55,538 - INFO - Starting test_random_graph_comparison for Random Graph 5: +2024-06-25 20:32:55,586 - INFO - Random Graph 5 - Spreading Result: 14, Heuristic Result: 22 +2024-06-25 20:32:55,587 - INFO - Random Graph 5 - Spreading Time: 0.035760s, Heuristic Time: 0.010024s +2024-06-25 20:32:55,587 - WARNING - Warning: Heuristic result (22) is greater than spreading result (14) for Random Graph 5 +2024-06-25 20:32:55,587 - INFO - Finished test_random_graph_comparison for Random Graph 5. +2024-06-25 20:32:55,587 - INFO - ---------------------------------------------------------------------------------------------------- +2024-06-25 20:32:55,588 - INFO - Starting test_random_graph_comparison for Random Graph 6: +2024-06-25 20:32:55,590 - INFO - Random Graph 6 - Spreading Result: 0, Heuristic Result: 0 +2024-06-25 20:32:55,590 - INFO - Random Graph 6 - Spreading Time: 0.000014s, Heuristic Time: 0.000005s +2024-06-25 20:32:55,590 - INFO - Finished test_random_graph_comparison for Random Graph 6. +2024-06-25 20:32:55,590 - INFO - ---------------------------------------------------------------------------------------------------- +2024-06-25 20:32:55,591 - INFO - Starting test_random_graph_comparison for Random Graph 7: +2024-06-25 20:32:55,634 - INFO - Random Graph 7 - Spreading Result: 7, Heuristic Result: 11 +2024-06-25 20:32:55,635 - INFO - Random Graph 7 - Spreading Time: 0.036235s, Heuristic Time: 0.005450s +2024-06-25 20:32:55,635 - WARNING - Warning: Heuristic result (11) is greater than spreading result (7) for Random Graph 7 +2024-06-25 20:32:55,635 - INFO - Finished test_random_graph_comparison for Random Graph 7. +2024-06-25 20:32:55,635 - INFO - ---------------------------------------------------------------------------------------------------- +2024-06-25 20:32:55,637 - INFO - Starting test_random_graph_comparison for Random Graph 8: +2024-06-25 20:32:55,723 - INFO - Random Graph 8 - Spreading Result: 17, Heuristic Result: 16 +2024-06-25 20:32:55,723 - INFO - Random Graph 8 - Spreading Time: 0.076391s, Heuristic Time: 0.007653s +2024-06-25 20:32:55,724 - INFO - Finished test_random_graph_comparison for Random Graph 8. +2024-06-25 20:32:55,724 - INFO - ---------------------------------------------------------------------------------------------------- +2024-06-25 20:32:55,725 - INFO - Starting test_random_graph_comparison for Random Graph 9: +2024-06-25 20:32:55,726 - INFO - Random Graph 9 - Spreading Result: 1, Heuristic Result: 1 +2024-06-25 20:32:55,726 - INFO - Random Graph 9 - Spreading Time: 0.000010s, Heuristic Time: 0.000004s +2024-06-25 20:32:55,726 - INFO - Finished test_random_graph_comparison for Random Graph 9. +2024-06-25 20:32:55,726 - INFO - ---------------------------------------------------------------------------------------------------- +2024-06-25 20:32:55,727 - INFO - Starting test_random_graph_comparison for Random Graph 10: +2024-06-25 20:32:55,728 - INFO - Random Graph 10 - Spreading Result: 1, Heuristic Result: 1 +2024-06-25 20:32:55,728 - INFO - Random Graph 10 - Spreading Time: 0.000009s, Heuristic Time: 0.000004s +2024-06-25 20:32:55,728 - INFO - Finished test_random_graph_comparison for Random Graph 10. +2024-06-25 20:32:55,728 - INFO - ---------------------------------------------------------------------------------------------------- diff --git a/networkz/algorithms/tests/test_firefighter_problem/comparisons/test_heuristic_minbudget_comparison.txt b/networkz/algorithms/tests/test_firefighter_problem/comparisons/test_heuristic_minbudget_comparison.txt deleted file mode 100644 index 789992c..0000000 --- a/networkz/algorithms/tests/test_firefighter_problem/comparisons/test_heuristic_minbudget_comparison.txt +++ /dev/null @@ -1,51 +0,0 @@ -Starting test_save_all_vertices_spreading: - -Dirlay_Graph-1 - Spreading Result: 2, Heuristic Result: 2 -Dirlay_Graph-1 - Spreading Time: 0.001003s, Heuristic Time: 0.001002s -Dirlay_Graph-2 - Spreading Result: 2, Heuristic Result: 2 -Dirlay_Graph-2 - Spreading Time: 0.001003s, Heuristic Time: 0.000000s -Dirlay_Graph-3 - Spreading Result: 3, Heuristic Result: 4 -Dirlay_Graph-3 - Spreading Time: 0.001290s, Heuristic Time: 0.000000s - -Finished test_save_all_vertices_spreading. ---------------------------------------------------------------------------------------------------------------------------- -Starting test_save_subgroup_vertices_spreading: - -Dirlay_Graph-1 (subgroup) - Spreading Result: 2, Heuristic Result: 2 -Dirlay_Graph-1 (subgroup) - Spreading Time: 0.001109s, Heuristic Time: 0.000000s -Dirlay_Graph-2 (subgroup) - Spreading Result: 1, Heuristic Result: 1 -Dirlay_Graph-2 (subgroup) - Spreading Time: 0.000000s, Heuristic Time: 0.000000s -Dirlay_Graph-3 (subgroup) - Spreading Result: 1, Heuristic Result: 1 -Dirlay_Graph-3 (subgroup) - Spreading Time: 0.000000s, Heuristic Time: 0.000000s -Dirlay_Graph-4 (subgroup) - Spreading Result: 2, Heuristic Result: 1 -Dirlay_Graph-4 (subgroup) - Spreading Time: 0.000000s, Heuristic Time: 0.000000s -Dirlay_Graph-5 (subgroup) - Spreading Result: 1, Heuristic Result: 1 -Dirlay_Graph-5 (subgroup) - Spreading Time: 0.001299s, Heuristic Time: 0.000000s -RegularGraph_Graph-1 (subgroup) - Spreading Result: 1, Heuristic Result: 1 -RegularGraph_Graph-1 (subgroup) - Spreading Time: 0.001006s, Heuristic Time: 0.000000s -RegularGraph_Graph-2 (subgroup) - Spreading Result: 1, Heuristic Result: 1 -RegularGraph_Graph-2 (subgroup) - Spreading Time: 0.000000s, Heuristic Time: 0.000000s -RegularGraph_Graph-3 (subgroup) - Spreading Result: 1, Heuristic Result: 1 -RegularGraph_Graph-3 (subgroup) - Spreading Time: 0.000000s, Heuristic Time: 0.000000s -RegularGraph_Graph-4 (subgroup) - Spreading Result: 1, Heuristic Result: 1 -RegularGraph_Graph-4 (subgroup) - Spreading Time: 0.001256s, Heuristic Time: 0.000000s -RegularGraph_Graph-5 (subgroup) - Spreading Result: 2, Heuristic Result: 2 -RegularGraph_Graph-5 (subgroup) - Spreading Time: 0.001006s, Heuristic Time: 0.000000s -RegularGraph_Graph-6 (subgroup) - Spreading Result: 4, Heuristic Result: 1 -RegularGraph_Graph-6 (subgroup) - Spreading Time: 0.000000s, Heuristic Time: 0.000000s -RegularGraph_Graph-7 (subgroup) - Spreading Result: 3, Heuristic Result: 1 -RegularGraph_Graph-7 (subgroup) - Spreading Time: 0.000000s, Heuristic Time: 0.000000s -RegularGraph_Graph-8 (subgroup) - Spreading Result: 2, Heuristic Result: 1 -RegularGraph_Graph-8 (subgroup) - Spreading Time: 0.002416s, Heuristic Time: 0.000000s - -Finished test_save_subgroup_vertices_spreading. ---------------------------------------------------------------------------------------------------------------------------- -Starting test_random_graph_comparison: - -Random Graph 1 - Spreading Result: 2, Heuristic Result: 2 -Random Graph 1 - Spreading Time: 0.000000s, Heuristic Time: 0.000000s -Random Graph 2 - Spreading Result: 12, Heuristic Result: 13 -Random Graph 2 - Spreading Time: 0.039426s, Heuristic Time: 0.005025s - -Finished test_random_graph_comparison. ---------------------------------------------------------------------------------------------------------------------------- diff --git a/networkz/algorithms/tests/test_firefighter_problem/comparisons/test_heuristic_minbudget_comparison_20240625_203255.txt b/networkz/algorithms/tests/test_firefighter_problem/comparisons/test_heuristic_minbudget_comparison_20240625_203255.txt new file mode 100644 index 0000000..8dea670 --- /dev/null +++ b/networkz/algorithms/tests/test_firefighter_problem/comparisons/test_heuristic_minbudget_comparison_20240625_203255.txt @@ -0,0 +1,137 @@ +2024-06-25 20:32:55,262 - INFO - Testing strategy length for RegularGraph_Graph-1 +2024-06-25 20:32:55,263 - INFO - Calculated strategy: [(1, 1), (6, 2)] +2024-06-25 20:32:55,263 - INFO - Strategy length test passed for RegularGraph_Graph-1 +2024-06-25 20:32:55,264 - INFO - Testing strategy length for Dirlay_Graph-5 +2024-06-25 20:32:55,265 - INFO - Calculated strategy: [(5, 1), (2, 1), (8, 2)] +2024-06-25 20:32:55,266 - INFO - Strategy length test passed for Dirlay_Graph-5 +2024-06-25 20:32:55,269 - INFO - Testing save all vertices for RegularGraph_Graph-1 +2024-06-25 20:32:55,269 - INFO - Calculated strategy: [(1, 1), (6, 2)] +2024-06-25 20:32:55,269 - INFO - Save all vertices test passed for RegularGraph_Graph-1 +2024-06-25 20:32:55,270 - INFO - Testing save all vertices for Dirlay_Graph-5 +2024-06-25 20:32:55,270 - INFO - Calculated strategy: [(5, 1), (2, 1)] +2024-06-25 20:32:55,270 - INFO - Save all vertices test passed for Dirlay_Graph-5 +2024-06-25 20:32:55,271 - INFO - Testing save subgroup vertices for RegularGraph_Graph-6 +2024-06-25 20:32:55,271 - INFO - Calculated strategy: [(2, 1)] +2024-06-25 20:32:55,271 - INFO - Save subgroup vertices test passed for RegularGraph_Graph-6 +2024-06-25 20:32:55,272 - INFO - Testing save subgroup vertices for RegularGraph_Graph-4 +2024-06-25 20:32:55,272 - INFO - Calculated strategy: [(1, 1)] +2024-06-25 20:32:55,273 - INFO - Save subgroup vertices test passed for RegularGraph_Graph-4 +2024-06-25 20:32:55,274 - INFO - Starting test_random_graph_comparison: +2024-06-25 20:32:55,277 - INFO - Random Graph 1 - Targets: [2, 3, 5, 7, 8, 9, 11, 14, 15, 16, 19, 20, 21, 22, 23, 24, 25, 27, 31, 32, 35, 36, 37, 38, 39, 40, 41, 42, 43, 45] +2024-06-25 20:32:55,284 - INFO - Random Graph 1 - Spreading Result: 12, Heuristic Result: 19 +2024-06-25 20:32:55,284 - INFO - Random Graph 1 - Spreading Time: 0.004195s, Heuristic Time: 0.002185s +2024-06-25 20:32:55,287 - INFO - Random Graph 2 - Targets: [1, 2, 3, 4] +2024-06-25 20:32:55,294 - INFO - Random Graph 2 - Spreading Result: 3, Heuristic Result: 4 +2024-06-25 20:32:55,294 - INFO - Random Graph 2 - Spreading Time: 0.006381s, Heuristic Time: 0.000729s +2024-06-25 20:32:55,296 - INFO - Random Graph 3 - Targets: [1, 3, 4, 5, 7, 8, 9, 10, 11, 12, 13, 15, 17, 18, 19, 20] +2024-06-25 20:32:55,310 - INFO - Random Graph 3 - Spreading Result: 9, Heuristic Result: 9 +2024-06-25 20:32:55,310 - INFO - Random Graph 3 - Spreading Time: 0.012846s, Heuristic Time: 0.000778s +2024-06-25 20:32:55,313 - INFO - Random Graph 4 - Targets: [1, 2, 3, 4, 5, 7, 8, 11, 12, 15, 17, 20, 22, 23, 24, 25, 27, 28, 29, 30, 31, 32, 35, 37, 38, 41] +2024-06-25 20:32:55,322 - INFO - Random Graph 4 - Spreading Result: 9, Heuristic Result: 17 +2024-06-25 20:32:55,322 - INFO - Random Graph 4 - Spreading Time: 0.008003s, Heuristic Time: 0.000785s +2024-06-25 20:32:55,324 - INFO - Random Graph 5 - Targets: [2, 3, 6, 7, 9, 10, 11] +2024-06-25 20:32:55,325 - INFO - Random Graph 5 - Spreading Result: 4, Heuristic Result: 7 +2024-06-25 20:32:55,325 - INFO - Random Graph 5 - Spreading Time: 0.001062s, Heuristic Time: 0.000314s +2024-06-25 20:32:55,327 - INFO - Random Graph 6 - Targets: [] +2024-06-25 20:32:55,328 - INFO - Random Graph 6 - Spreading Result: 0, Heuristic Result: 0 +2024-06-25 20:32:55,328 - INFO - Random Graph 6 - Spreading Time: 0.000237s, Heuristic Time: 0.000138s +2024-06-25 20:32:55,330 - INFO - Random Graph 7 - Targets: [2, 3, 4, 5, 6, 8, 9, 10, 12] +2024-06-25 20:32:55,332 - INFO - Random Graph 7 - Spreading Result: 2, Heuristic Result: 8 +2024-06-25 20:32:55,332 - INFO - Random Graph 7 - Spreading Time: 0.001508s, Heuristic Time: 0.000497s +2024-06-25 20:32:55,334 - INFO - Random Graph 8 - Targets: [] +2024-06-25 20:32:55,341 - INFO - Random Graph 8 - Spreading Result: 0, Heuristic Result: 0 +2024-06-25 20:32:55,341 - INFO - Random Graph 8 - Spreading Time: 0.005784s, Heuristic Time: 0.000685s +2024-06-25 20:32:55,343 - INFO - Random Graph 9 - Targets: [5, 6, 7, 8, 9, 10, 12, 13, 14, 16, 17, 18, 20, 21, 23, 25, 27, 28, 29, 30, 31, 32, 33, 35, 37, 38, 39, 40, 42, 43, 44, 45, 46, 47, 49] +2024-06-25 20:32:55,347 - INFO - Random Graph 9 - Spreading Result: 13, Heuristic Result: 27 +2024-06-25 20:32:55,347 - INFO - Random Graph 9 - Spreading Time: 0.003170s, Heuristic Time: 0.000699s +2024-06-25 20:32:55,349 - INFO - Random Graph 10 - Targets: [1, 2] +2024-06-25 20:32:55,354 - INFO - Random Graph 10 - Spreading Result: 2, Heuristic Result: 2 +2024-06-25 20:32:55,354 - INFO - Random Graph 10 - Spreading Time: 0.004133s, Heuristic Time: 0.000681s +2024-06-25 20:32:55,354 - INFO - Finished test_random_graph_comparison. +2024-06-25 20:32:55,354 - INFO - ---------------------------------------------------------------------------------------------------- +2024-06-25 20:32:55,367 - INFO - Starting test_save_all_vertices_spreading: +2024-06-25 20:32:55,367 - INFO - Dirlay_Graph-1 - Spreading Result: 2, Heuristic Result: 2 +2024-06-25 20:32:55,368 - INFO - Dirlay_Graph-1 - Spreading Time: 0.000363s, Heuristic Time: 0.000104s +2024-06-25 20:32:55,368 - INFO - Dirlay_Graph-2 - Spreading Result: 2, Heuristic Result: 2 +2024-06-25 20:32:55,368 - INFO - Dirlay_Graph-2 - Spreading Time: 0.000185s, Heuristic Time: 0.000059s +2024-06-25 20:32:55,368 - INFO - Dirlay_Graph-3 - Spreading Result: 3, Heuristic Result: 4 +2024-06-25 20:32:55,368 - INFO - Dirlay_Graph-3 - Spreading Time: 0.000391s, Heuristic Time: 0.000137s +2024-06-25 20:32:55,369 - WARNING - Warning: Heuristic result (4) is greater than spreading result (3) for Dirlay_Graph-3 +2024-06-25 20:32:55,369 - INFO - Dirlay_Graph-4 - Spreading Result: 2, Heuristic Result: 2 +2024-06-25 20:32:55,369 - INFO - Dirlay_Graph-4 - Spreading Time: 0.000449s, Heuristic Time: 0.000102s +2024-06-25 20:32:55,370 - INFO - Dirlay_Graph-5 - Spreading Result: 3, Heuristic Result: 4 +2024-06-25 20:32:55,370 - INFO - Dirlay_Graph-5 - Spreading Time: 0.000627s, Heuristic Time: 0.000352s +2024-06-25 20:32:55,371 - WARNING - Warning: Heuristic result (4) is greater than spreading result (3) for Dirlay_Graph-5 +2024-06-25 20:32:55,372 - INFO - RegularGraph_Graph-1 - Spreading Result: 2, Heuristic Result: 2 +2024-06-25 20:32:55,372 - INFO - RegularGraph_Graph-1 - Spreading Time: 0.000882s, Heuristic Time: 0.000267s +2024-06-25 20:32:55,373 - INFO - RegularGraph_Graph-2 - Spreading Result: 2, Heuristic Result: 2 +2024-06-25 20:32:55,373 - INFO - RegularGraph_Graph-2 - Spreading Time: 0.000459s, Heuristic Time: 0.000126s +2024-06-25 20:32:55,373 - INFO - RegularGraph_Graph-3 - Spreading Result: 2, Heuristic Result: 2 +2024-06-25 20:32:55,374 - INFO - RegularGraph_Graph-3 - Spreading Time: 0.000301s, Heuristic Time: 0.000096s +2024-06-25 20:32:55,374 - INFO - RegularGraph_Graph-4 - Spreading Result: 2, Heuristic Result: 2 +2024-06-25 20:32:55,374 - INFO - RegularGraph_Graph-4 - Spreading Time: 0.000503s, Heuristic Time: 0.000126s +2024-06-25 20:32:55,375 - INFO - RegularGraph_Graph-5 - Spreading Result: 2, Heuristic Result: 2 +2024-06-25 20:32:55,375 - INFO - RegularGraph_Graph-5 - Spreading Time: 0.000403s, Heuristic Time: 0.000109s +2024-06-25 20:32:55,375 - INFO - RegularGraph_Graph-6 - Spreading Result: 9, Heuristic Result: 1 +2024-06-25 20:32:55,375 - INFO - RegularGraph_Graph-6 - Spreading Time: 0.000192s, Heuristic Time: 0.000079s +2024-06-25 20:32:55,375 - INFO - RegularGraph_Graph-7 - Spreading Result: 6, Heuristic Result: 1 +2024-06-25 20:32:55,375 - INFO - RegularGraph_Graph-7 - Spreading Time: 0.000084s, Heuristic Time: 0.000051s +2024-06-25 20:32:55,377 - INFO - RegularGraph_Graph-8 - Spreading Result: 4, Heuristic Result: 4 +2024-06-25 20:32:55,377 - INFO - RegularGraph_Graph-8 - Spreading Time: 0.001493s, Heuristic Time: 0.000259s +2024-06-25 20:32:55,377 - INFO - Finished test_save_all_vertices_spreading. +2024-06-25 20:32:55,377 - INFO - ---------------------------------------------------------------------------------------------------- +2024-06-25 20:32:55,378 - INFO - Starting test_random_graph_comparison for Random Graph 1: +2024-06-25 20:32:55,446 - INFO - Random Graph 1 - Spreading Result: 6, Heuristic Result: 9 +2024-06-25 20:32:55,446 - INFO - Random Graph 1 - Spreading Time: 0.062671s, Heuristic Time: 0.003512s +2024-06-25 20:32:55,446 - WARNING - Warning: Heuristic result (9) is greater than spreading result (6) for Random Graph 1 +2024-06-25 20:32:55,446 - INFO - Finished test_random_graph_comparison for Random Graph 1. +2024-06-25 20:32:55,446 - INFO - ---------------------------------------------------------------------------------------------------- +2024-06-25 20:32:55,447 - INFO - Starting test_random_graph_comparison for Random Graph 2: +2024-06-25 20:32:55,459 - INFO - Random Graph 2 - Spreading Result: 1, Heuristic Result: 1 +2024-06-25 20:32:55,459 - INFO - Random Graph 2 - Spreading Time: 0.008340s, Heuristic Time: 0.000792s +2024-06-25 20:32:55,459 - INFO - Finished test_random_graph_comparison for Random Graph 2. +2024-06-25 20:32:55,459 - INFO - ---------------------------------------------------------------------------------------------------- +2024-06-25 20:32:55,460 - INFO - Starting test_random_graph_comparison for Random Graph 3: +2024-06-25 20:32:55,524 - INFO - Random Graph 3 - Spreading Result: 9, Heuristic Result: 10 +2024-06-25 20:32:55,525 - INFO - Random Graph 3 - Spreading Time: 0.060112s, Heuristic Time: 0.003079s +2024-06-25 20:32:55,525 - WARNING - Warning: Heuristic result (10) is greater than spreading result (9) for Random Graph 3 +2024-06-25 20:32:55,525 - INFO - Finished test_random_graph_comparison for Random Graph 3. +2024-06-25 20:32:55,525 - INFO - ---------------------------------------------------------------------------------------------------- +2024-06-25 20:32:55,526 - INFO - Starting test_random_graph_comparison for Random Graph 4: +2024-06-25 20:32:55,537 - INFO - Random Graph 4 - Spreading Result: 3, Heuristic Result: 4 +2024-06-25 20:32:55,537 - INFO - Random Graph 4 - Spreading Time: 0.008222s, Heuristic Time: 0.001385s +2024-06-25 20:32:55,537 - WARNING - Warning: Heuristic result (4) is greater than spreading result (3) for Random Graph 4 +2024-06-25 20:32:55,537 - INFO - Finished test_random_graph_comparison for Random Graph 4. +2024-06-25 20:32:55,537 - INFO - ---------------------------------------------------------------------------------------------------- +2024-06-25 20:32:55,538 - INFO - Starting test_random_graph_comparison for Random Graph 5: +2024-06-25 20:32:55,586 - INFO - Random Graph 5 - Spreading Result: 14, Heuristic Result: 22 +2024-06-25 20:32:55,587 - INFO - Random Graph 5 - Spreading Time: 0.035760s, Heuristic Time: 0.010024s +2024-06-25 20:32:55,587 - WARNING - Warning: Heuristic result (22) is greater than spreading result (14) for Random Graph 5 +2024-06-25 20:32:55,587 - INFO - Finished test_random_graph_comparison for Random Graph 5. +2024-06-25 20:32:55,587 - INFO - ---------------------------------------------------------------------------------------------------- +2024-06-25 20:32:55,588 - INFO - Starting test_random_graph_comparison for Random Graph 6: +2024-06-25 20:32:55,590 - INFO - Random Graph 6 - Spreading Result: 0, Heuristic Result: 0 +2024-06-25 20:32:55,590 - INFO - Random Graph 6 - Spreading Time: 0.000014s, Heuristic Time: 0.000005s +2024-06-25 20:32:55,590 - INFO - Finished test_random_graph_comparison for Random Graph 6. +2024-06-25 20:32:55,590 - INFO - ---------------------------------------------------------------------------------------------------- +2024-06-25 20:32:55,591 - INFO - Starting test_random_graph_comparison for Random Graph 7: +2024-06-25 20:32:55,634 - INFO - Random Graph 7 - Spreading Result: 7, Heuristic Result: 11 +2024-06-25 20:32:55,635 - INFO - Random Graph 7 - Spreading Time: 0.036235s, Heuristic Time: 0.005450s +2024-06-25 20:32:55,635 - WARNING - Warning: Heuristic result (11) is greater than spreading result (7) for Random Graph 7 +2024-06-25 20:32:55,635 - INFO - Finished test_random_graph_comparison for Random Graph 7. +2024-06-25 20:32:55,635 - INFO - ---------------------------------------------------------------------------------------------------- +2024-06-25 20:32:55,637 - INFO - Starting test_random_graph_comparison for Random Graph 8: +2024-06-25 20:32:55,723 - INFO - Random Graph 8 - Spreading Result: 17, Heuristic Result: 16 +2024-06-25 20:32:55,723 - INFO - Random Graph 8 - Spreading Time: 0.076391s, Heuristic Time: 0.007653s +2024-06-25 20:32:55,724 - INFO - Finished test_random_graph_comparison for Random Graph 8. +2024-06-25 20:32:55,724 - INFO - ---------------------------------------------------------------------------------------------------- +2024-06-25 20:32:55,725 - INFO - Starting test_random_graph_comparison for Random Graph 9: +2024-06-25 20:32:55,726 - INFO - Random Graph 9 - Spreading Result: 1, Heuristic Result: 1 +2024-06-25 20:32:55,726 - INFO - Random Graph 9 - Spreading Time: 0.000010s, Heuristic Time: 0.000004s +2024-06-25 20:32:55,726 - INFO - Finished test_random_graph_comparison for Random Graph 9. +2024-06-25 20:32:55,726 - INFO - ---------------------------------------------------------------------------------------------------- +2024-06-25 20:32:55,727 - INFO - Starting test_random_graph_comparison for Random Graph 10: +2024-06-25 20:32:55,728 - INFO - Random Graph 10 - Spreading Result: 1, Heuristic Result: 1 +2024-06-25 20:32:55,728 - INFO - Random Graph 10 - Spreading Time: 0.000009s, Heuristic Time: 0.000004s +2024-06-25 20:32:55,728 - INFO - Finished test_random_graph_comparison for Random Graph 10. +2024-06-25 20:32:55,728 - INFO - ---------------------------------------------------------------------------------------------------- diff --git a/networkz/algorithms/tests/test_firefighter_problem/test_heuristic_maxsave.py b/networkz/algorithms/tests/test_firefighter_problem/test_heuristic_maxsave.py index 8344d94..1ce1f97 100644 --- a/networkz/algorithms/tests/test_firefighter_problem/test_heuristic_maxsave.py +++ b/networkz/algorithms/tests/test_firefighter_problem/test_heuristic_maxsave.py @@ -1,5 +1,4 @@ """ - The Paper - Approximability of the Firefighter Problem Computing Cuts over Time @@ -16,19 +15,41 @@ Yuval Bubnovsky Almog David Shaked Levi - """ import pytest import networkx as nx import json import random +import logging +import os +import time +from datetime import datetime from networkz.algorithms.approximation.firefighter_problem.Firefighter_Problem import heuristic_maxsave, spreading_maxsave -from networkz.algorithms.approximation.firefighter_problem.Utils import find_best_neighbor, parse_json_to_networkx, Status +from networkz.algorithms.approximation.firefighter_problem.Utils import find_best_neighbor, parse_json_to_networkx, Status + +def setup_logger(): + logger = logging.getLogger('firefighter_problem_tests') + logger.setLevel(logging.INFO) + + timestamp = datetime.now().strftime("%Y%m%d_%H%M%S") + log_dir = 'networkz/algorithms/tests/test_firefighter_problem/comparisons' + os.makedirs(log_dir, exist_ok=True) + log_file_path = os.path.join(log_dir, f'test_heuristic_maxsave_comparison_{timestamp}.txt') + + file_handler = logging.FileHandler(log_file_path, mode='w') + file_handler.setLevel(logging.INFO) + formatter = logging.Formatter('%(asctime)s - %(levelname)s - %(message)s') + file_handler.setFormatter(formatter) + logger.addHandler(file_handler) + + return logger + +logger = setup_logger() with open("networkz/algorithms/tests/test_firefighter_problem/graphs.json", "r") as file: - json_data = json.load(file) + json_data = json.load(file) graphs = parse_json_to_networkx(json_data) @pytest.mark.parametrize("graph_key, budget, source, targets", [ @@ -69,61 +90,79 @@ def test_source_is_target(graph_key, budget, source, targets): ("Dirlay_Graph-5", 2, 0, [1, 2, 3, 4, 5, 6, 7, 8], 3), ]) def test_strategy_length(graph_key, budget, source, targets, expected_length): + logger.info(f"Testing strategy length for {graph_key}") graph = graphs[graph_key] calculated_strategy = spreading_maxsave(graph, budget, source, targets)[0] - print(calculated_strategy) + logger.info(f"Calculated strategy: {calculated_strategy}") assert len(calculated_strategy) == expected_length - + logger.info(f"Strategy length test passed for {graph_key}") @pytest.mark.parametrize("graph_key, budget, source, targets, expected_strategy", [ ("RegularGraph_Graph-1", 1, 0, [1, 2, 3, 4, 5, 6], [(1, 1), (6, 2)]), ("Dirlay_Graph-5", 2, 0, [1, 2, 3, 4, 5, 6, 7, 8], [(5, 1), (2, 1)]), ]) def test_save_all_vertices(graph_key, budget, source, targets, expected_strategy): + logger.info(f"Testing save all vertices for {graph_key}") graph = graphs[graph_key] calculated_strategy = heuristic_maxsave(graph, budget, source, targets)[0] - print(calculated_strategy) + logger.info(f"Calculated strategy: {calculated_strategy}") assert calculated_strategy == expected_strategy + logger.info(f"Save all vertices test passed for {graph_key}") @pytest.mark.parametrize("graph_key, budget, source, targets, expected_strategy", [ ("RegularGraph_Graph-6", 2, 1, [3, 9, 0, 5, 6], [(2, 1)]), ("RegularGraph_Graph-4", 1, 0, [2, 6, 4], [(1, 1)]), ]) def test_save_subgroup_vertices(graph_key, budget, source, targets, expected_strategy): + logger.info(f"Testing save subgroup vertices for {graph_key}") graph = graphs[graph_key] calculated_strategy = heuristic_maxsave(graph, budget, source, targets)[0] - print(calculated_strategy) + logger.info(f"Calculated strategy: {calculated_strategy}") assert calculated_strategy == expected_strategy + logger.info(f"Save subgroup vertices test passed for {graph_key}") def test_random_graph_comparison(): - for i in range(10): - num_nodes = random.randint(2,100) - nodes = list(range(num_nodes+1)) - num_edges = 1000 - save_amount = random.randint(1,num_nodes) - targets = [] - G = nx.DiGraph() - - G.add_nodes_from(nodes, status=Status.VULNERABLE.value) - for _ in range(num_edges): - source = random.randint(0, num_nodes - 1) - target = random.randint(0, num_nodes - 1) - if source != target: # Ensure no self-loops - G.add_edge(source, target) - for node in range(save_amount): - probability = random.random() - if probability < 0.75 and node!=0: - targets.append(node) - - print(targets) - spreading_answer = spreading_maxsave(G,1,0,targets)[1] - heuristic_answer = heuristic_maxsave(G,1,0,targets)[1] - print(spreading_answer) - print(heuristic_answer) - - assert len(spreading_answer) <= len(heuristic_answer) - - print("All tests have passed!") \ No newline at end of file + logger.info("Starting test_random_graph_comparison:") + try: + for i in range(10): + num_nodes = random.randint(2, 100) + nodes = list(range(num_nodes + 1)) + num_edges = 1000 + save_amount = random.randint(1, num_nodes) + targets = [] + G = nx.DiGraph() + + G.add_nodes_from(nodes, status=Status.VULNERABLE.value) + for _ in range(num_edges): + source = random.randint(0, num_nodes - 1) + target = random.randint(0, num_nodes - 1) + if source != target: # Ensure no self-loops + G.add_edge(source, target) + for node in range(save_amount): + probability = random.random() + if probability < 0.75 and node != 0: + targets.append(node) + + logger.info(f"Random Graph {i+1} - Targets: {targets}") + + start_time = time.time() + spreading_answer = spreading_maxsave(G, 1, 0, targets)[1] + spreading_time = time.time() - start_time + + start_time = time.time() + heuristic_answer = heuristic_maxsave(G, 1, 0, targets)[1] + heuristic_time = time.time() - start_time + + logger.info(f"Random Graph {i+1} - Spreading Result: {len(spreading_answer)}, Heuristic Result: {len(heuristic_answer)}") + logger.info(f"Random Graph {i+1} - Spreading Time: {spreading_time:.6f}s, Heuristic Time: {heuristic_time:.6f}s") + + if len(spreading_answer) > len(heuristic_answer): + warning_message = f"Warning: Heuristic result ({len(heuristic_answer)}) is less than spreading result ({len(spreading_answer)}) for Random Graph {i+1}" + logger.warning(warning_message) + + finally: + logger.info("Finished test_random_graph_comparison.") + logger.info("-" * 100) \ No newline at end of file diff --git a/networkz/algorithms/tests/test_firefighter_problem/test_heuristic_minbudget.py b/networkz/algorithms/tests/test_firefighter_problem/test_heuristic_minbudget.py index 24badbc..d86ac45 100644 --- a/networkz/algorithms/tests/test_firefighter_problem/test_heuristic_minbudget.py +++ b/networkz/algorithms/tests/test_firefighter_problem/test_heuristic_minbudget.py @@ -3,16 +3,32 @@ import json import random import time +import logging +import os +from datetime import datetime from networkz.algorithms.approximation.firefighter_problem.Firefighter_Problem import spreading_minbudget, heuristic_minbudget from networkz.algorithms.approximation.firefighter_problem.Utils import parse_json_to_networkx -# Open the log file -log_file_path = 'networkz/algorithms/tests/test_firefighter_problem/comparisons/test_heuristic_minbudget_comparison.txt' +def setup_logger(): + logger = logging.getLogger('firefighter_problem_tests') + logger.setLevel(logging.INFO) + + timestamp = datetime.now().strftime("%Y%m%d_%H%M%S") + log_dir = 'networkz/algorithms/tests/test_firefighter_problem/comparisons' + os.makedirs(log_dir, exist_ok=True) + log_file_path = os.path.join(log_dir, f'test_heuristic_minbudget_comparison_{timestamp}.txt') + + file_handler = logging.FileHandler(log_file_path, mode='w') + file_handler.setLevel(logging.INFO) + formatter = logging.Formatter('%(asctime)s - %(levelname)s - %(message)s') + file_handler.setFormatter(formatter) + logger.addHandler(file_handler) + + return logger + +logger = setup_logger() -def log_message(message): - with open(log_file_path, 'a') as log_file: - log_file.write(message + '\n') @pytest.fixture def sample_json_data(): @@ -39,63 +55,43 @@ def get_graphs(): graphs = get_graphs() -def test_source_not_in_graph(): - - with pytest.raises(ValueError, match="Error: The source node isn't on the graph"): - heuristic_minbudget(graphs["RegularGraph_Graph-1"], -3, [1, 0, 4, 5, 2, 6], True) - - with pytest.raises(ValueError, match="Error: The source node isn't on the graph"): - heuristic_minbudget(graphs["RegularGraph_Graph-4"], 10, [1, 3, 5, 6, 7], False) - - with pytest.raises(ValueError, match="Error: The source node isn't on the graph"): - heuristic_minbudget(graphs["RegularGraph_Graph-6"], 12, [9, 2, 3, 4, 6, 7], True) - - with pytest.raises(ValueError, match="Error: The source node isn't on the graph"): - heuristic_minbudget(graphs["RegularGraph_Graph-8"], -1, [7, 10, 4, 9, 3, 11, 2], False) - - with pytest.raises(ValueError, match="Error: The source node isn't on the graph"): - heuristic_minbudget(graphs["RegularGraph_Graph-3"], 8, [1, 4, 2], True) - -def test_target_not_in_graph(): - - with pytest.raises(ValueError, match="Error: Not all nodes in the targets list are on the graph."): - heuristic_minbudget(graphs["RegularGraph_Graph-2"], 2, [0, 4, 5, 11, 6], True) - - with pytest.raises(ValueError, match="Error: Not all nodes in the targets list are on the graph."): - heuristic_minbudget(graphs["RegularGraph_Graph-3"], 3, [0, 4, 5, -1, 1, 2], False) - - with pytest.raises(ValueError, match="Error: Not all nodes in the targets list are on the graph."): - heuristic_minbudget(graphs["RegularGraph_Graph-6"], 7, [9, 2, 4, 5, 8, 11], True) - - with pytest.raises(ValueError, match="Error: Not all nodes in the targets list are on the graph."): - heuristic_minbudget(graphs["RegularGraph_Graph-8"], 10, [0, 2, 4, 5, 8, 11, 12, 3, 15], False) - - with pytest.raises(ValueError, match="Error: Not all nodes in the targets list are on the graph."): - heuristic_minbudget(graphs["RegularGraph_Graph-7"], 1, [3, 5, 4, 0, 13], True) - -def test_source_is_target(): - - with pytest.raises(ValueError, match="Error: The source node can't be a part of the targets list, since the virus is spreading from the source"): - heuristic_minbudget(graphs["RegularGraph_Graph-1"], 0, [1, 2, 3, 0, 4, 5, 6], True) - - with pytest.raises(ValueError, match="Error: The source node can't be a part of the targets list, since the virus is spreading from the source"): - heuristic_minbudget(graphs["RegularGraph_Graph-3"], 1, [5, 1, 4], False) - - with pytest.raises(ValueError, match="Error: The source node can't be a part of the targets list, since the virus is spreading from the source"): - heuristic_minbudget(graphs["RegularGraph_Graph-4"], 4, [1, 2, 3, 4, 5, 6, 7], True) - - with pytest.raises(ValueError, match="Error: The source node can't be a part of the targets list, since the virus is spreading from the source"): - heuristic_minbudget(graphs["RegularGraph_Graph-6"], 0, [0, 3, 5, 6, 7, 8, 9], False) - - with pytest.raises(ValueError, match="Error: The source node can't be a part of the targets list, since the virus is spreading from the source"): - heuristic_minbudget(graphs["RegularGraph_Graph-8"], 0, [13, 10, 8, 6, 5, 4, 3, 0, 1, 2], True) - +@pytest.mark.parametrize("G, source, targets, directed", [ + (graphs["RegularGraph_Graph-1"], -3, [1, 0, 4, 5, 2, 6], True), + (graphs["RegularGraph_Graph-4"], 10, [1, 3, 5, 6, 7], False), + (graphs["RegularGraph_Graph-6"], 12, [9, 2, 3, 4, 6, 7], True), + (graphs["RegularGraph_Graph-8"], -1, [7, 10, 4, 9, 3, 11, 2], False), + (graphs["RegularGraph_Graph-3"], 8, [1, 4, 2], True) +]) +def test_source_not_in_graph(G, source, targets, directed): + with pytest.raises(ValueError, match="Error: The source node isn't on the graph"): + heuristic_minbudget(G, source, targets, directed) + +@pytest.mark.parametrize("G, source, targets, directed", [ + (graphs["RegularGraph_Graph-2"], 2, [0, 4, 5, 11, 6], True), + (graphs["RegularGraph_Graph-3"], 3, [0, 4, 5, -1, 1, 2], False), + (graphs["RegularGraph_Graph-6"], 7, [9, 2, 4, 5, 8, 11], True), + (graphs["RegularGraph_Graph-8"], 10, [0, 2, 4, 5, 8, 11, 12, 3, 15], False), + (graphs["RegularGraph_Graph-7"], 1, [3, 5, 4, 0, 13], True) +]) +def test_target_not_in_graph(G, source, targets, directed): + with pytest.raises(ValueError, match="Error: Not all nodes in the targets list are on the graph."): + heuristic_minbudget(G, source, targets, directed) + +@pytest.mark.parametrize("G, source, targets, directed", [ + (graphs["RegularGraph_Graph-1"], 0, [1, 2, 3, 0, 4, 5, 6], True), + (graphs["RegularGraph_Graph-3"], 1, [5, 1, 4], False), + (graphs["RegularGraph_Graph-4"], 4, [1, 2, 3, 4, 5, 6, 7], True), + (graphs["RegularGraph_Graph-6"], 0, [0, 3, 5, 6, 7, 8, 9], False), + (graphs["RegularGraph_Graph-8"], 0, [13, 10, 8, 6, 5, 4, 3, 0, 1, 2], True) +]) +def test_source_is_target(G, source, targets, directed): + with pytest.raises(ValueError, match="Error: The source node can't be a part of the targets list, since the virus is spreading from the source"): + heuristic_minbudget(G, source, targets, directed) def test_save_all_vertices_spreading(): - log_message("Starting test_save_all_vertices_spreading:\n") + logger.info("Starting test_save_all_vertices_spreading:") try: - for graph_name in graphs: - G = graphs[graph_name] + for graph_name, G in graphs.items(): source = 0 targets = [n for n in G.nodes if n != source] @@ -107,74 +103,54 @@ def test_save_all_vertices_spreading(): heuristic_result = heuristic_minbudget(G, source, targets, True)[0] heuristic_time = time.time() - start_time - log_message(f"{graph_name} - Spreading Result: {spreading_result}, Heuristic Result: {heuristic_result}") - log_message(f"{graph_name} - Spreading Time: {spreading_time:.6f}s, Heuristic Time: {heuristic_time:.6f}s") - - assert spreading_result >= heuristic_result - - finally: - log_message("\nFinished test_save_all_vertices_spreading.\n---------------------------------------------------------------------------------------------------------------------------") - -def test_save_subgroup_vertices_spreading(): - log_message("Starting test_save_subgroup_vertices_spreading:\n") - try: - for graph_name in graphs: - G = graphs[graph_name] - source = 0 - targets = [n for n in G.nodes if random.random() < 0.5 and n != source] - - start_time = time.time() - spreading_result = spreading_minbudget(G, source, targets)[0] - spreading_time = time.time() - start_time - - start_time = time.time() - heuristic_result = heuristic_minbudget(G, source, targets, True)[0] - heuristic_time = time.time() - start_time - - log_message(f"{graph_name} (subgroup) - Spreading Result: {spreading_result}, Heuristic Result: {heuristic_result}") - log_message(f"{graph_name} (subgroup) - Spreading Time: {spreading_time:.6f}s, Heuristic Time: {heuristic_time:.6f}s") + logger.info(f"{graph_name} - Spreading Result: {spreading_result}, Heuristic Result: {heuristic_result}") + logger.info(f"{graph_name} - Spreading Time: {spreading_time:.6f}s, Heuristic Time: {heuristic_time:.6f}s") - assert spreading_result >= heuristic_result + if spreading_result < heuristic_result: + warning_message = f"Warning: Heuristic result ({heuristic_result}) is greater than spreading result ({spreading_result}) for {graph_name}" + logger.warning(warning_message) - finally: - log_message("\nFinished test_save_subgroup_vertices_spreading.\n---------------------------------------------------------------------------------------------------------------------------") + logger.info("Finished test_save_all_vertices_spreading.") + logger.info("-" * 100) -def test_random_graph_comparison(): - log_message("Starting test_random_graph_comparison:\n") +@pytest.mark.parametrize("i", range(10)) +def test_random_graph_comparison(i): + logger.info(f"Starting test_random_graph_comparison for Random Graph {i+1}:") try: - for i in range(10): - num_nodes = random.randint(2, 100) - nodes = list(range(num_nodes + 1)) - num_edges = 1000 - save_amount = random.randint(1, num_nodes) - targets = [] - G = nx.DiGraph() - - G.add_nodes_from(nodes, status="target") - for _ in range(num_edges): - source = random.randint(0, num_nodes - 1) - target = random.randint(0, num_nodes - 1) - if source != target: # Ensure no self-loops - G.add_edge(source, target) - for node in range(save_amount): - probability = random.random() - if probability < 0.75 and node != 0: - targets.append(node) - - start_time = time.time() - spreading_answer = spreading_minbudget(G, 0, targets)[0] - spreading_time = time.time() - start_time - - start_time = time.time() - heuristic_answer = heuristic_minbudget(G, 0, targets, True)[0] - heuristic_time = time.time() - start_time - - - log_message(f"Random Graph {i+1} - Spreading Result: {spreading_answer}, Heuristic Result: {heuristic_answer}") - log_message(f"Random Graph {i+1} - Spreading Time: {spreading_time:.6f}s, Heuristic Time: {heuristic_time:.6f}s") - - assert heuristic_answer <= spreading_answer + num_nodes = random.randint(2, 100) + nodes = list(range(num_nodes + 1)) + num_edges = 1000 + save_amount = random.randint(1, num_nodes) + targets = [] + G = nx.DiGraph() + + G.add_nodes_from(nodes, status="target") + for _ in range(num_edges): + source = random.randint(0, num_nodes - 1) + target = random.randint(0, num_nodes - 1) + if source != target: # Ensure no self-loops + G.add_edge(source, target) + for node in range(save_amount): + probability = random.random() + if probability < 0.75 and node != 0: + targets.append(node) + + start_time = time.time() + spreading_answer = spreading_minbudget(G, 0, targets)[0] + spreading_time = time.time() - start_time + + start_time = time.time() + heuristic_answer = heuristic_minbudget(G, 0, targets, True)[0] + heuristic_time = time.time() - start_time + + logger.info(f"Random Graph {i+1} - Spreading Result: {spreading_answer}, Heuristic Result: {heuristic_answer}") + logger.info(f"Random Graph {i+1} - Spreading Time: {spreading_time:.6f}s, Heuristic Time: {heuristic_time:.6f}s") + + if heuristic_answer > spreading_answer: + warning_message = f"Warning: Heuristic result ({heuristic_answer}) is greater than spreading result ({spreading_answer}) for Random Graph {i+1}" + logger.warning(warning_message) finally: - log_message("\nFinished test_random_graph_comparison.\n---------------------------------------------------------------------------------------------------------------------------") + logger.info(f"Finished test_random_graph_comparison for Random Graph {i+1}.") + logger.info("-" * 100) \ No newline at end of file From e14e3b937dcad2b743e08d259f8dc809eaf6f743 Mon Sep 17 00:00:00 2001 From: 20shaked20 Date: Tue, 25 Jun 2024 21:50:36 +0300 Subject: [PATCH 49/93] added our algorithm running time comparison --- .../approximation/firefighter_problem/main.py | 60 +++++++++++++----- .../Algorithm_running_time_comparison.png | Bin 42373 -> 0 bytes .../non-spreading-algos-comaprison.png | Bin 0 -> 32749 bytes .../spreading-algos-comparison.png | Bin 0 -> 33513 bytes 4 files changed, 44 insertions(+), 16 deletions(-) delete mode 100644 networkz/algorithms/tests/test_firefighter_problem/comparisons/Algorithm_running_time_comparison.png create mode 100644 networkz/algorithms/tests/test_firefighter_problem/comparisons/non-spreading-algos-comaprison.png create mode 100644 networkz/algorithms/tests/test_firefighter_problem/comparisons/spreading-algos-comparison.png diff --git a/networkz/algorithms/approximation/firefighter_problem/main.py b/networkz/algorithms/approximation/firefighter_problem/main.py index 0b2816d..741e5a8 100644 --- a/networkz/algorithms/approximation/firefighter_problem/main.py +++ b/networkz/algorithms/approximation/firefighter_problem/main.py @@ -72,7 +72,7 @@ def compare_functions(graph: nx.DiGraph, source: int): plt.show() -def compare_algorithms(graph: nx.DiGraph, source: int, targets:list): +def compare_spreading_algorithms(graph: nx.DiGraph, source: int, targets:list): """ This method is used to compare the algoritmhs we created and show thier running time. """ @@ -80,40 +80,67 @@ def compare_algorithms(graph: nx.DiGraph, source: int, targets:list): graph2 = graph.copy() graph3 = graph.copy() graph4 = graph.copy() - graph5 = graph.copy() + start_time1 = time.time() - non_spreading_dirlaynet_minbudget(graph1, source,targets) - end_time1= time.time() - start_time1 + heuristic_maxsave(graph1, 1,source, targets) + end_time1 = time.time() - start_time1 start_time2 = time.time() - heuristic_maxsave(graph2, 1,source, targets) + heuristic_minbudget(graph2,source, targets, True) #spreading end_time2 = time.time() - start_time2 start_time3 = time.time() - heuristic_minbudget(graph3,source, targets, False) #no spreading + spreading_minbudget(graph3,source, targets) end_time3 = time.time() - start_time3 start_time4 = time.time() - spreading_minbudget(graph4,source, targets) + spreading_maxsave(graph4, 1,source, targets) end_time4 = time.time() - start_time4 - start_time5 = time.time() - spreading_maxsave(graph5, 1,source, targets) - end_time5 = time.time() - start_time5 - # Plot the execution times - plt.figure(figsize=(15, 8)) + plt.figure(figsize=(10, 5)) - times = [end_time1, end_time2, end_time3 ,end_time4, end_time5] - labels = ['non_spreading\ndirlaynet_minbudget', 'spreading\nheuristic_maxsave', 'non_spreading\nheuristic_min_budget', 'spreading\nminbudget', 'spreading\nmaxsave'] + times = [end_time1, end_time2, end_time3 ,end_time4] + labels = ['heuristic_maxsave','heuristic_minbudget', 'minbudget', 'maxsave'] - plt.bar(labels, times, color=['b', 'g'], alpha=0.6) + plt.bar(labels, times, color=['b', 'b', 'g', 'g'], alpha=0.6) plt.title('Algorithm Running time comparison') plt.xlabel('Execution Type') plt.ylabel('Time (seconds)') plt.show() + +def compare_non_spreading_algorithm(graph: nx.DiGraph, source: int, targets:list): + """ + This method is used to compare the algoritmhs we created and show thier running time. + """ + graph1 = graph.copy() + graph2 = graph.copy() + graph3 = graph.copy() + + start_time1 = time.time() + non_spreading_dirlaynet_minbudget(graph1, source,targets) + end_time1= time.time() - start_time1 + + start_time2 = time.time() + heuristic_minbudget(graph2,source, targets, False) #no spreading + end_time2 = time.time() - start_time2 + + start_time3 = time.time() + non_spreading_minbudget(graph3,source, targets) + end_time3 = time.time() - start_time3 + + # Plot the execution times + plt.figure(figsize=(10, 5)) + + times = [end_time1, end_time2, end_time3] + labels = ['dirlaynet_minbudget', 'heuristic_minbudget', 'mincut_minbudget'] + plt.bar(labels, times, color=['b', 'g', 'y'], alpha=0.6) + plt.title('Non spreading algorithms Running time comparison') + plt.xlabel('Execution Type') + plt.ylabel('Time (seconds)') + plt.show() if __name__ == "__main__": import doctest @@ -149,7 +176,8 @@ def compare_algorithms(graph: nx.DiGraph, source: int, targets:list): "Compare Graph Algorithms:" # compare_algorithms(G_dirlay_random,0, [2,4]) # more complex random graph comparison - # 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zZ!Lx9^SfR}06)t8L6pJjCXk|k;gu@6*<(E-j3l8DYevbgb4}&KD{Wo{@^q*By}5I$ zp;F)mp5`C;w7pb?Ks*Uf_@;AAlY;$NrlDjvCS8HueEsVm;637-d8VGDv!^X?kwtT|Q z@|)0_fDd92L!Fzl1az@@ckZ6G@7X+o&7Dvi$OfW8@x9up`HMg2%KA{C$zKnKI?eNl z{L?3+)vsiP*L?e}z$ckM(gibD9`RFA$`DFQ0{u+)cWXqdyg0dg6%_ICNDZ|+ Date: Tue, 25 Jun 2024 22:14:01 +0300 Subject: [PATCH 50/93] minor change --- networkz/algorithms/approximation/firefighter_problem/Utils.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/networkz/algorithms/approximation/firefighter_problem/Utils.py b/networkz/algorithms/approximation/firefighter_problem/Utils.py index 808664d..d00903f 100644 --- a/networkz/algorithms/approximation/firefighter_problem/Utils.py +++ b/networkz/algorithms/approximation/firefighter_problem/Utils.py @@ -720,7 +720,7 @@ def min_budget_calculation(matrix: np.matrix) -> int: def dirlay_vaccination_startegy(vacc_matrix: np.matrix, ni_groups: dict) -> dict: """ - Determines a feasible vaccination strategy given the vaccine matrix, minimum budget, and layers. + Determines a feasible vaccination strategy given the vaccine matrix and the nodes from each layer. Parameters: ---------- From 2ef6676cdad64c321d7e62884501948b7d758ab5 Mon Sep 17 00:00:00 2001 From: Erel Segal-Halevi Date: Tue, 9 Jul 2024 10:33:52 +0300 Subject: [PATCH 51/93] fix doctest --- .../firefighter_problem/max_flow_with_node_capacity.py | 7 +++---- 1 file changed, 3 insertions(+), 4 deletions(-) diff --git a/networkz/algorithms/approximation/firefighter_problem/max_flow_with_node_capacity.py b/networkz/algorithms/approximation/firefighter_problem/max_flow_with_node_capacity.py index 67c925b..a8307fa 100644 --- a/networkz/algorithms/approximation/firefighter_problem/max_flow_with_node_capacity.py +++ b/networkz/algorithms/approximation/firefighter_problem/max_flow_with_node_capacity.py @@ -71,7 +71,8 @@ def max_flow_with_node_capacity(graph: nx.DiGraph, source: int = None, target: i >>> G.add_edge(1, 4) >>> s_t_G = create_st_graph(G, [2,4]) >>> min_cut_nodes = max_flow_with_node_capacity(s_t_G, 0, 4) - {'2_out', '4_out'} + >>> sorted(min_cut_nodes) + ['2_out', '4_out'] """ logger.info("Starting graph flow reduction") H = nx.DiGraph() @@ -104,9 +105,7 @@ def max_flow_with_node_capacity(graph: nx.DiGraph, source: int = None, target: i min_cut_nodes = algo.minimum_st_node_cut(H, f'{source}_out', 't_in') logger.info(f"Minimum Cut is: {min_cut_nodes}") - - print(set(sorted(min_cut_nodes))) - + return min_cut_nodes if __name__ == "__main__": From adf99741beddba6ac3deac3367edd0c9c3d8eb29 Mon Sep 17 00:00:00 2001 From: Erel Segal-Halevi Date: Tue, 9 Jul 2024 10:36:19 +0300 Subject: [PATCH 52/93] rename max-flow to min-cut --- .../firefighter_problem/Firefighter_Problem.py | 4 ++-- .../firefighter_problem/max_flow_with_node_capacity.py | 8 ++++---- .../test_non_spreading_dirlaynet_minbudget.py | 10 +++++----- 3 files changed, 11 insertions(+), 11 deletions(-) diff --git a/networkz/algorithms/approximation/firefighter_problem/Firefighter_Problem.py b/networkz/algorithms/approximation/firefighter_problem/Firefighter_Problem.py index 50fe816..f317910 100644 --- a/networkz/algorithms/approximation/firefighter_problem/Firefighter_Problem.py +++ b/networkz/algorithms/approximation/firefighter_problem/Firefighter_Problem.py @@ -23,7 +23,7 @@ import networkx.algorithms.connectivity as algo import math import logging -from networkz.algorithms.approximation.firefighter_problem.max_flow_with_node_capacity import max_flow_with_node_capacity +from networkz.algorithms.approximation.firefighter_problem.max_flow_with_node_capacity import min_cut_with_node_capacity from networkz.algorithms.approximation.firefighter_problem.Utils import * logger = logging.getLogger(__name__) @@ -275,7 +275,7 @@ def non_spreading_dirlaynet_minbudget(Graph:nx.DiGraph, src:int, targets:list)-> layers = adjust_nodes_capacity(Graph, src) G = create_st_graph(Graph, targets) #display_graph(G) - G_reduction_min_cut = max_flow_with_node_capacity(G, source=src, target='t') + G_reduction_min_cut = min_cut_with_node_capacity(G, source=src, target='t') N_groups = min_cut_N_groups(G_reduction_min_cut,layers) vacc_matrix = calculate_vaccine_matrix(layers, N_groups) integer_matrix = matrix_to_integers_values(vacc_matrix) diff --git a/networkz/algorithms/approximation/firefighter_problem/max_flow_with_node_capacity.py b/networkz/algorithms/approximation/firefighter_problem/max_flow_with_node_capacity.py index a8307fa..e72ca18 100644 --- a/networkz/algorithms/approximation/firefighter_problem/max_flow_with_node_capacity.py +++ b/networkz/algorithms/approximation/firefighter_problem/max_flow_with_node_capacity.py @@ -27,9 +27,9 @@ logger = logging.getLogger(__name__) -def max_flow_with_node_capacity(graph: nx.DiGraph, source: int = None, target: int = None) -> set: +def min_cut_with_node_capacity(graph: nx.DiGraph, source: int = None, target: int = None) -> set: """ - Computes a maximum flow in the given graph, where each node has a capacity + Computes a minimum cut in the given graph, where each node has a capacity Parameters: ---------- @@ -70,7 +70,8 @@ def max_flow_with_node_capacity(graph: nx.DiGraph, source: int = None, target: i >>> G.add_edge(2, 3) >>> G.add_edge(1, 4) >>> s_t_G = create_st_graph(G, [2,4]) - >>> min_cut_nodes = max_flow_with_node_capacity(s_t_G, 0, 4) + >>> min_cut_nodes = min_cut_with_node_capacity + (s_t_G, 0, 4) >>> sorted(min_cut_nodes) ['2_out', '4_out'] """ @@ -105,7 +106,6 @@ def max_flow_with_node_capacity(graph: nx.DiGraph, source: int = None, target: i min_cut_nodes = algo.minimum_st_node_cut(H, f'{source}_out', 't_in') logger.info(f"Minimum Cut is: {min_cut_nodes}") - return min_cut_nodes if __name__ == "__main__": diff --git a/networkz/algorithms/tests/test_firefighter_problem/test_non_spreading_dirlaynet_minbudget.py b/networkz/algorithms/tests/test_firefighter_problem/test_non_spreading_dirlaynet_minbudget.py index 179d2b5..07bc654 100644 --- a/networkz/algorithms/tests/test_firefighter_problem/test_non_spreading_dirlaynet_minbudget.py +++ b/networkz/algorithms/tests/test_firefighter_problem/test_non_spreading_dirlaynet_minbudget.py @@ -30,7 +30,7 @@ from networkz.algorithms.approximation.firefighter_problem.Utils import adjust_nodes_capacity from networkz.algorithms.approximation.firefighter_problem.Utils import create_st_graph from networkz.algorithms.approximation.firefighter_problem.Utils import parse_json_to_networkx -from networkz.algorithms.approximation.firefighter_problem.max_flow_with_node_capacity import max_flow_with_node_capacity +from networkz.algorithms.approximation.firefighter_problem.max_flow_with_node_capacity import min_cut_with_node_capacity from networkz.algorithms.approximation.firefighter_problem.Utils import calculate_vaccine_matrix from networkz.algorithms.approximation.firefighter_problem.Utils import min_cut_N_groups from networkz.algorithms.approximation.firefighter_problem.Utils import matrix_to_integers_values @@ -77,7 +77,7 @@ def test_source_is_target(graph_key, source, targets): layers_1 = adjust_nodes_capacity(graph_1, 0) # src is 0 targets_1 = [1, 2, 3] # saving 1,2,3 G1 = create_st_graph(graph_1, targets_1) -reduction_G1_min_cut = max_flow_with_node_capacity(G1, 0) +reduction_G1_min_cut = min_cut_with_node_capacity(G1, 0) N_1_groups = min_cut_N_groups(reduction_G1_min_cut,layers_1) matrix_1 = calculate_vaccine_matrix(layers_1, N_1_groups) integer_matrix_1 = matrix_to_integers_values(matrix_1) @@ -89,7 +89,7 @@ def test_source_is_target(graph_key, source, targets): layers_2 = adjust_nodes_capacity(graph_2, 0) # src is 0 targets_2 = [2, 4] # saving 2,4 G2 = create_st_graph(graph_2, targets_2) -reduction_G2_min_cut = max_flow_with_node_capacity(G2, 0) +reduction_G2_min_cut = min_cut_with_node_capacity(G2, 0) N_2_groups = min_cut_N_groups(reduction_G2_min_cut, layers_2) matrix_2 = calculate_vaccine_matrix(layers_2, N_2_groups) integer_matrix_2 = matrix_to_integers_values(matrix_2) @@ -101,7 +101,7 @@ def test_source_is_target(graph_key, source, targets): layers_3 = adjust_nodes_capacity(graph_3, 0) # src is 0 targets_3 = [1, 5, 7] # saving 1,5,7 G3 = create_st_graph(graph_3, targets_3) -reduction_G3_min_cut = max_flow_with_node_capacity(G3, 0) +reduction_G3_min_cut = min_cut_with_node_capacity(G3, 0) N_3_groups = min_cut_N_groups(reduction_G3_min_cut, layers_3) matrix_3 = calculate_vaccine_matrix(layers_3, N_3_groups) integer_matrix_3 = matrix_to_integers_values(matrix_3) @@ -113,7 +113,7 @@ def test_source_is_target(graph_key, source, targets): layers_4 = adjust_nodes_capacity(graph_4, 0) # src is 0 targets_4 = [4, 5, 6, 8] # saving 4,5,6,8 G4 = create_st_graph(graph_4, targets_4) -reduction_G4_min_cut = max_flow_with_node_capacity(G4, 0) +reduction_G4_min_cut = min_cut_with_node_capacity(G4, 0) N_4_groups = min_cut_N_groups(reduction_G4_min_cut, layers_4) matrix_4 = calculate_vaccine_matrix(layers_4, N_4_groups) integer_matrix_4 = matrix_to_integers_values(matrix_4) From 1d2ae6f98e6e03658dcf1ea7c4255ae30b82f5cd Mon Sep 17 00:00:00 2001 From: Erel Segal-Halevi Date: Tue, 9 Jul 2024 10:40:17 +0300 Subject: [PATCH 53/93] move min-cut algorithm outside --- README.md | 3 +++ .../approximation/firefighter_problem/Firefighter_Problem.py | 2 +- .../firefighter_problem => }/max_flow_with_node_capacity.py | 0 .../test_non_spreading_dirlaynet_minbudget.py | 2 +- 4 files changed, 5 insertions(+), 2 deletions(-) rename networkz/algorithms/{approximation/firefighter_problem => }/max_flow_with_node_capacity.py (100%) diff --git a/README.md b/README.md index 5313b1e..f60430d 100644 --- a/README.md +++ b/README.md @@ -3,7 +3,10 @@ NetworkZ is a library of graph algorithms in Python. It is an extension of the [NetworkX](https://github.com/networkx/networkx). It contains (by import) everything that is in NetworkX, plus some additional algorithms that were submitted into NetworkX but not merged yet. Currently, NetworkZ contains the following additional algorithms: * [Rank-maximal matching](networkz/algorithms/bipartite/rank_maximal_matching.py): by Oriya Alperin, Liel Vaknin and Amiel Lejzor. +* [Maximum-weight fractional matching](networkz/algorithms/max_weight_fractional_matching.py): by Oriya Alperin, Liel Vaknin and Amiel Lejzor. * [Social-aware coalition formation](networkz/algorithms/approximation/coalition_formation.py) - by Victor Kushnir. +* [Minimum cut on a graph with node capacity](networkz/algorithms/max_flow_with_node_capacity.py): by Yuval Bubnovsky, Almog David and Shaked Levi. +* [Several approximate solutions to the Firefighter problem](networkz/algorithms/approximation/firefighter_problem): by Yuval Bubnovsky, Almog David and Shaked Levi. ## Installation diff --git a/networkz/algorithms/approximation/firefighter_problem/Firefighter_Problem.py b/networkz/algorithms/approximation/firefighter_problem/Firefighter_Problem.py index f317910..a6be9bb 100644 --- a/networkz/algorithms/approximation/firefighter_problem/Firefighter_Problem.py +++ b/networkz/algorithms/approximation/firefighter_problem/Firefighter_Problem.py @@ -23,7 +23,7 @@ import networkx.algorithms.connectivity as algo import math import logging -from networkz.algorithms.approximation.firefighter_problem.max_flow_with_node_capacity import min_cut_with_node_capacity +from networkz.algorithms.max_flow_with_node_capacity import min_cut_with_node_capacity from networkz.algorithms.approximation.firefighter_problem.Utils import * logger = logging.getLogger(__name__) diff --git a/networkz/algorithms/approximation/firefighter_problem/max_flow_with_node_capacity.py b/networkz/algorithms/max_flow_with_node_capacity.py similarity index 100% rename from networkz/algorithms/approximation/firefighter_problem/max_flow_with_node_capacity.py rename to networkz/algorithms/max_flow_with_node_capacity.py diff --git a/networkz/algorithms/tests/test_firefighter_problem/test_non_spreading_dirlaynet_minbudget.py b/networkz/algorithms/tests/test_firefighter_problem/test_non_spreading_dirlaynet_minbudget.py index 07bc654..b8ccc2d 100644 --- a/networkz/algorithms/tests/test_firefighter_problem/test_non_spreading_dirlaynet_minbudget.py +++ b/networkz/algorithms/tests/test_firefighter_problem/test_non_spreading_dirlaynet_minbudget.py @@ -30,7 +30,7 @@ from networkz.algorithms.approximation.firefighter_problem.Utils import adjust_nodes_capacity from networkz.algorithms.approximation.firefighter_problem.Utils import create_st_graph from networkz.algorithms.approximation.firefighter_problem.Utils import parse_json_to_networkx -from networkz.algorithms.approximation.firefighter_problem.max_flow_with_node_capacity import min_cut_with_node_capacity +from networkz.algorithms.max_flow_with_node_capacity import min_cut_with_node_capacity from networkz.algorithms.approximation.firefighter_problem.Utils import calculate_vaccine_matrix from networkz.algorithms.approximation.firefighter_problem.Utils import min_cut_N_groups from networkz.algorithms.approximation.firefighter_problem.Utils import matrix_to_integers_values From 0bff8f4007ece76cac9a6360a6fca2613d1bcbbe Mon Sep 17 00:00:00 2001 From: Erel Segal-Halevi Date: Tue, 9 Jul 2024 10:42:03 +0300 Subject: [PATCH 54/93] fix logs --- .../approximation/firefighter_problem/Firefighter_Problem.py | 4 ++-- networkz/algorithms/approximation/firefighter_problem/main.py | 1 - networkz/algorithms/max_flow_with_node_capacity.py | 3 +-- 3 files changed, 3 insertions(+), 5 deletions(-) diff --git a/networkz/algorithms/approximation/firefighter_problem/Firefighter_Problem.py b/networkz/algorithms/approximation/firefighter_problem/Firefighter_Problem.py index a6be9bb..488bd77 100644 --- a/networkz/algorithms/approximation/firefighter_problem/Firefighter_Problem.py +++ b/networkz/algorithms/approximation/firefighter_problem/Firefighter_Problem.py @@ -309,7 +309,7 @@ def heuristic_maxsave(Graph:nx.DiGraph, budget:int, source:int, targets:list, sp >>> G.add_nodes_from([0, 1, 2, 3], status="vulnerable") >>> G.add_edges_from([(0, 1), (0, 2), (1, 2), (1, 3)]) >>> heuristic_maxsave(G, 1, 0, [1, 2, 3]) - [(1, 1)] + ([(1, 1)], {1, 2, 3}) """ if budget < 1: logger.critical("Error: The budget must be at least 1") @@ -423,5 +423,5 @@ def heuristic_minbudget(Graph:nx.DiGraph, source:int, targets:list, spreading:bo if __name__ == "__main__": import doctest result = doctest.testmod(verbose=False) - logger.info(f"Doctest results: {result}") + print(f"Doctest results: {result}") \ No newline at end of file diff --git a/networkz/algorithms/approximation/firefighter_problem/main.py b/networkz/algorithms/approximation/firefighter_problem/main.py index 741e5a8..b86e5c4 100644 --- a/networkz/algorithms/approximation/firefighter_problem/main.py +++ b/networkz/algorithms/approximation/firefighter_problem/main.py @@ -180,4 +180,3 @@ def compare_non_spreading_algorithm(graph: nx.DiGraph, source: int, targets:list compare_spreading_algorithms(G_dirlay_random, 0, [2,4]) - diff --git a/networkz/algorithms/max_flow_with_node_capacity.py b/networkz/algorithms/max_flow_with_node_capacity.py index e72ca18..c88625e 100644 --- a/networkz/algorithms/max_flow_with_node_capacity.py +++ b/networkz/algorithms/max_flow_with_node_capacity.py @@ -70,8 +70,7 @@ def min_cut_with_node_capacity(graph: nx.DiGraph, source: int = None, target: in >>> G.add_edge(2, 3) >>> G.add_edge(1, 4) >>> s_t_G = create_st_graph(G, [2,4]) - >>> min_cut_nodes = min_cut_with_node_capacity - (s_t_G, 0, 4) + >>> min_cut_nodes = min_cut_with_node_capacity(s_t_G, 0, 4) >>> sorted(min_cut_nodes) ['2_out', '4_out'] """ From 8f726301c1c41e715db25523c101523552c3997b Mon Sep 17 00:00:00 2001 From: Erel Segal-Halevi Date: Tue, 9 Jul 2024 10:43:22 +0300 Subject: [PATCH 55/93] gitignore --- .gitignore | 1 + 1 file changed, 1 insertion(+) diff --git a/.gitignore b/.gitignore index 93431b5..f944dfc 100644 --- a/.gitignore +++ b/.gitignore @@ -1,4 +1,5 @@ scripts +*.txt # Byte-compiled / optimized / DLL files __pycache__/ From c81fb3a800f7ff9eb098c3e65d7c79a536e65f28 Mon Sep 17 00:00:00 2001 From: Almog-David Date: Thu, 11 Jul 2024 19:35:54 +0300 Subject: [PATCH 56/93] changes in the tests due to erel comments --- .../tests}/__init__.py | 0 .../tests/test_coalition_formation.py | 168 ++++++++++++++++++ .../test_firefighter_problem/__init__.py | 0 .../comparisons/comparisons.py | 4 + .../test_firefighter_problem/graphs.json | 0 .../test_heuristic_maxsave.py | 0 .../test_heuristic_minbudget.py | 0 .../test_non_spreading_dirlaynet_minbudget.py | 0 .../test_non_spreading_minbudget.py | 0 .../test_firefighter_problem/test_parsing.py | 0 .../test_spreading_maxsave.py | 0 .../test_spreading_minbudget.py | 0 .../non-spreading-algos-comaprison.png | Bin 32749 -> 0 bytes .../spreading-algos-comparison.png | Bin 33513 -> 0 bytes ...tic_maxsave_comparison_20240625_203255.txt | 137 -------------- ...c_minbudget_comparison_20240625_203255.txt | 137 -------------- .../test_firefighter_problem/test_utils.py | 73 -------- 17 files changed, 172 insertions(+), 347 deletions(-) rename networkz/algorithms/{tests/test_firefighter_problem => approximation/tests}/__init__.py (100%) create mode 100644 networkz/algorithms/approximation/tests/test_coalition_formation.py create mode 100644 networkz/algorithms/approximation/tests/test_firefighter_problem/__init__.py create mode 100644 networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/comparisons.py rename networkz/algorithms/{ => approximation}/tests/test_firefighter_problem/graphs.json (100%) rename networkz/algorithms/{ => approximation}/tests/test_firefighter_problem/test_heuristic_maxsave.py (100%) rename networkz/algorithms/{ => approximation}/tests/test_firefighter_problem/test_heuristic_minbudget.py (100%) rename networkz/algorithms/{ => approximation}/tests/test_firefighter_problem/test_non_spreading_dirlaynet_minbudget.py (100%) rename networkz/algorithms/{ => approximation}/tests/test_firefighter_problem/test_non_spreading_minbudget.py (100%) rename networkz/algorithms/{ => approximation}/tests/test_firefighter_problem/test_parsing.py (100%) rename networkz/algorithms/{ => approximation}/tests/test_firefighter_problem/test_spreading_maxsave.py (100%) rename networkz/algorithms/{ => approximation}/tests/test_firefighter_problem/test_spreading_minbudget.py (100%) delete mode 100644 networkz/algorithms/tests/test_firefighter_problem/comparisons/non-spreading-algos-comaprison.png delete mode 100644 networkz/algorithms/tests/test_firefighter_problem/comparisons/spreading-algos-comparison.png delete mode 100644 networkz/algorithms/tests/test_firefighter_problem/comparisons/test_heuristic_maxsave_comparison_20240625_203255.txt delete mode 100644 networkz/algorithms/tests/test_firefighter_problem/comparisons/test_heuristic_minbudget_comparison_20240625_203255.txt delete mode 100644 networkz/algorithms/tests/test_firefighter_problem/test_utils.py diff --git a/networkz/algorithms/tests/test_firefighter_problem/__init__.py b/networkz/algorithms/approximation/tests/__init__.py similarity index 100% rename from networkz/algorithms/tests/test_firefighter_problem/__init__.py rename to networkz/algorithms/approximation/tests/__init__.py diff --git a/networkz/algorithms/approximation/tests/test_coalition_formation.py b/networkz/algorithms/approximation/tests/test_coalition_formation.py new file mode 100644 index 0000000..01465aa --- /dev/null +++ b/networkz/algorithms/approximation/tests/test_coalition_formation.py @@ -0,0 +1,168 @@ +""" +Testing the :mod:`networkx.algorithms.approximation.coalition_formation` module. + +Which is the implementation of the Social Aware Assignment of Passengers in Ridesharing +The social aware assignment problem belongs to the field of coalition formation, which is an important research branch +within multiagent systems. It analyses the outcome that results when a set of agents is partitioned into coalitions. +Actually, Match_And_Merge model is a special case of simple Additively Separable Hedonic Games (ASHGs). + +Which was described in the article: +Levinger C., Hazon N., Azaria A. Social Aware Assignment of Passengers in Ridesharing. - 2022, https://github.com/VictoKu1/ResearchAlgorithmsCourse1/raw/main/Article/2022%2C%20Chaya%20Amos%20Noam%2C%20Socially%20aware%20assignment%20of%20passengers%20in%20ride%20sharing.pdf. + +The match_and_merge algorithm is based on the pseudocode from the article +which is written (as well as the tests) by Victor Kushnir. +""" +import math +import random + +import pytest + +import networkx as nx +from networkz.algorithms.approximation.coalition_formation import match_and_merge + + +def small_chain_graph(): + G = nx.Graph() + list_of_edges = [(1, 2), (2, 3), (3, 4), (4, 5), (4, 6)] + G.add_edges_from(list_of_edges) + return G + + +def clique_graph_of_size_3(): + G = nx.Graph() + list_of_edges = [(1, 2), (2, 3), (3, 1)] + G.add_edges_from(list_of_edges) + return G + + +class Test_coalition_formation: + def test_empty_graph_returns_empty_list(self): + G_empty = nx.Graph() + assert match_and_merge(G_empty, k=0) == [] + + def test_small_chain_graph_with_k_4_returns_correct_partition(self): + G_1 = small_chain_graph() + assert match_and_merge(G_1, k=4) == [[1, 2], [3, 4, 5, 6]] + + def test_small_chain_graph_with_k_3_returns_correct_partition(self): + G_1 = small_chain_graph() + assert match_and_merge(G_1, k=3) == [[1, 2], [3, 4, 6], [5]] + + def test_small_chain_graph_with_k_2_returns_correct_partition(self): + G_1 = small_chain_graph() + assert match_and_merge(G_1, k=2) == [[1, 2], [3, 4], [5], [6]] + + def test_clique_graph_of_size_3_with_k_3_returns_correct_partition(self): + G_clique_3 = clique_graph_of_size_3() + assert match_and_merge(G_clique_3, k=3) == [[1, 2, 3]] + + def test_approximation_ratio(self): + # For each n between 5 and 15, generate a clique graph with n nodes and check for 5= 0.99999 + + def test_clique_graph_with_k_in_range_every_node_in_exactly_one_partition(self): + # Check that every node is in exactly one partition + for n in range(5, 15): + G = nx.complete_graph(n) + for k in range(5, 15): + if k <= n: + P = match_and_merge(G, k) + assert [len([p for p in P if n in p]) == 1 for n in G.nodes()] + + def test_clique_graph_with_k_in_range_number_of_partitions_at_most_ceil_n_2(self): + # Check that the number of partitions is at most ceil(n/2) + for n in range(5, 15): + G = nx.complete_graph(n) + for k in range(5, 15): + if k <= n: + P = match_and_merge(G, k) + assert len(P) <= math.ceil(G.number_of_nodes() / 2) + + def test_k_greater_than_n_raises_error(self): + # Check that it raises an error when k>n + for n in range(5, 15): + G = nx.complete_graph(n) + for k in range(5, 15): + if k > n: + with pytest.raises(nx.NetworkXError): + match_and_merge(G, k) + + def test_random_graph_with_k_in_range_returns_correct_partition(self): + # For each n between 5 and 15 (inclusive), generate a random graph with n nodes and check for 5 1] + G_test = nx.Graph() + G_test.add_nodes_from(sorted((G.nodes()), reverse=True)) + G_test.add_edges_from(sorted((G.edges()), reverse=True)) + assert len(P) == len( + [ + tuple(sorted(p)) + for p in sorted(nx.max_weight_matching(G_test, weight=1)) + ] + ) + + def test_partition_is_a_social_aware_assignment_check_size_of_subset(self): + for n in range(5, 15): + p = 0.5 + G = nx.gnp_random_graph(n, p) + for k in range(5, n): + P = match_and_merge(G, k) + # Check that every partition has at most k nodes + assert [len(p) <= k for p in P] + + def test_partition_is_a_social_aware_assignment_V_P(self): + for n in range(5, 15): + p = 0.5 + G = nx.gnp_random_graph(n, p) + P = match_and_merge(G, 2) + # Check that the value of V_P is maximized + V_P = 0 + for p in P: + for i in p: + for j in p: + if G.has_edge(i, j): + V_P += 1 + assert V_P >= len( + [tuple(sorted(p)) for p in sorted(nx.max_weight_matching(G, weight=1))] + ) + + def test_disconnected_components_weighted_graph_with_k_5_returns_correct_partition( + self, + ): + # Check that the partition of graph G is correct for a disconnected graph + G = small_chain_graph() + G.add_edges_from([(7, 8), (8, 9), (9, 7)]) + # Add random weights to the edges + for u, v in G.edges(): + G[u][v]["weight"] = random.randint(1, 205) + assert match_and_merge(G, k=5) == [[1, 2], [3, 4, 5, 6], [7, 8, 9]] \ No newline at end of file diff --git a/networkz/algorithms/approximation/tests/test_firefighter_problem/__init__.py b/networkz/algorithms/approximation/tests/test_firefighter_problem/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/comparisons.py b/networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/comparisons.py new file mode 100644 index 0000000..92fdafb --- /dev/null +++ b/networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/comparisons.py @@ -0,0 +1,4 @@ +import expirements_csv +# add the comparisons test from both heuristic files + add a function that will plot the graphs. +# TBD + diff --git a/networkz/algorithms/tests/test_firefighter_problem/graphs.json b/networkz/algorithms/approximation/tests/test_firefighter_problem/graphs.json similarity index 100% rename from networkz/algorithms/tests/test_firefighter_problem/graphs.json rename to networkz/algorithms/approximation/tests/test_firefighter_problem/graphs.json diff --git a/networkz/algorithms/tests/test_firefighter_problem/test_heuristic_maxsave.py b/networkz/algorithms/approximation/tests/test_firefighter_problem/test_heuristic_maxsave.py similarity index 100% rename from networkz/algorithms/tests/test_firefighter_problem/test_heuristic_maxsave.py rename to networkz/algorithms/approximation/tests/test_firefighter_problem/test_heuristic_maxsave.py diff --git a/networkz/algorithms/tests/test_firefighter_problem/test_heuristic_minbudget.py b/networkz/algorithms/approximation/tests/test_firefighter_problem/test_heuristic_minbudget.py similarity index 100% rename from networkz/algorithms/tests/test_firefighter_problem/test_heuristic_minbudget.py rename to networkz/algorithms/approximation/tests/test_firefighter_problem/test_heuristic_minbudget.py diff --git a/networkz/algorithms/tests/test_firefighter_problem/test_non_spreading_dirlaynet_minbudget.py b/networkz/algorithms/approximation/tests/test_firefighter_problem/test_non_spreading_dirlaynet_minbudget.py similarity index 100% rename from networkz/algorithms/tests/test_firefighter_problem/test_non_spreading_dirlaynet_minbudget.py rename to networkz/algorithms/approximation/tests/test_firefighter_problem/test_non_spreading_dirlaynet_minbudget.py diff --git a/networkz/algorithms/tests/test_firefighter_problem/test_non_spreading_minbudget.py b/networkz/algorithms/approximation/tests/test_firefighter_problem/test_non_spreading_minbudget.py similarity index 100% rename from networkz/algorithms/tests/test_firefighter_problem/test_non_spreading_minbudget.py rename to networkz/algorithms/approximation/tests/test_firefighter_problem/test_non_spreading_minbudget.py diff --git a/networkz/algorithms/tests/test_firefighter_problem/test_parsing.py b/networkz/algorithms/approximation/tests/test_firefighter_problem/test_parsing.py similarity index 100% rename from networkz/algorithms/tests/test_firefighter_problem/test_parsing.py rename to networkz/algorithms/approximation/tests/test_firefighter_problem/test_parsing.py diff --git a/networkz/algorithms/tests/test_firefighter_problem/test_spreading_maxsave.py b/networkz/algorithms/approximation/tests/test_firefighter_problem/test_spreading_maxsave.py similarity index 100% rename from networkz/algorithms/tests/test_firefighter_problem/test_spreading_maxsave.py rename to networkz/algorithms/approximation/tests/test_firefighter_problem/test_spreading_maxsave.py diff --git a/networkz/algorithms/tests/test_firefighter_problem/test_spreading_minbudget.py 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zZ!Lx9^SfR}06)t8L6pJjCXk|k;gu@6*<(E-j3l8DYevbgb4}&KD{Wo{@^q*By}5I$ zp;F)mp5`C;w7pb?Ks*Uf_@;AAlY;$NrlDjvCS8HueEsVm;637-d8VGDv!^X?kwtT|Q z@|)0_fDd92L!Fzl1az@@ckZ6G@7X+o&7Dvi$OfW8@x9up`HMg2%KA{C$zKnKI?eNl z{L?3+)vsiP*L?e}z$ckM(gibD9`RFA$`DFQ0{u+)cWXqdyg0dg6%_ICNDZ|+ This is not implementaed for now, all the methods are being tested in their corresponding usage in other tests.py. -## we might consider changing it later for a better coding - -@pytest.fixture -def test_graph(): - """ - Create a pre-determined graph for testing, minimizes code duplication in tests - this is called at the beginning of each test - """ - pass - -@pytest.fixture -def test_dirlay(): - """ - Create a pre-determined directed layered network for testing. - """ - pass - -def test_validate_parameters(test_graph): - pass - -def test_spread_virus(test_graph): - pass - -def test_spread_vaccination(test_graph): - pass - -def test_vaccinate_node(test_graph): - pass - -def test_clean_graph(test_graph): - pass - -def test_adjust_nodes_capacity(test_graph): - pass - -def test_create_st_graph(test_graph): - pass - -def test_flow_reduction(test_graph): - pass - -def test_calculate_vaccine_matrix(test_dirlay): - pass - -def test_display_graph(test_graph): - pass \ No newline at end of file From cbc845eb0f4cbb5df24950d6f40664d4d3c3a1e4 Mon Sep 17 00:00:00 2001 From: 20shaked20 Date: Tue, 16 Jul 2024 16:42:17 +0300 Subject: [PATCH 57/93] Some minor changes + added a comparision --- .../Firefighter_Problem.py | 10 +- .../comparisons/comparisons.py | 109 +++++++++++++++++- .../test_non_spreading_dirlaynet_minbudget.py | 2 +- 3 files changed, 114 insertions(+), 7 deletions(-) diff --git a/networkz/algorithms/approximation/firefighter_problem/Firefighter_Problem.py b/networkz/algorithms/approximation/firefighter_problem/Firefighter_Problem.py index 488bd77..027f9b3 100644 --- a/networkz/algorithms/approximation/firefighter_problem/Firefighter_Problem.py +++ b/networkz/algorithms/approximation/firefighter_problem/Firefighter_Problem.py @@ -246,7 +246,7 @@ def non_spreading_minbudget(Graph:nx.DiGraph, source:int, targets:list)->int: logger.info(f"Returning minimum budget: {min_budget}") return min_budget -def non_spreading_dirlaynet_minbudget(Graph:nx.DiGraph, src:int, targets:list)->int: +def non_spreading_dirlaynet_minbudget(Graph:nx.DiGraph, source:int, targets:list)->int: """ "Approximability of the Firefighter Problem - Computing Cuts over Time", by Elliot Anshelevich, Deeparnab Chakrabarty, Ameya Hate, Chaitanya Swamy (2010) @@ -264,18 +264,18 @@ def non_spreading_dirlaynet_minbudget(Graph:nx.DiGraph, src:int, targets:list)-> >>> non_spreading_dirlaynet_minbudget(G4,0,[1,2,3,4,5]) 2 """ - validate_parameters(Graph, src, targets) + validate_parameters(Graph, source, targets) if not is_dag(Graph): logger.error("Problem with graph, its not a DAG, thus cannot run algorithm") return #display_graph(Graph) - logger.info(f"Starting the non_spreading_dirlaynet_minbudget function with source node {src} and targets: {targets}") + logger.info(f"Starting the non_spreading_dirlaynet_minbudget function with source node {source} and targets: {targets}") - layers = adjust_nodes_capacity(Graph, src) + layers = adjust_nodes_capacity(Graph, source) G = create_st_graph(Graph, targets) #display_graph(G) - G_reduction_min_cut = min_cut_with_node_capacity(G, source=src, target='t') + G_reduction_min_cut = min_cut_with_node_capacity(G, source=source, target='t') N_groups = min_cut_N_groups(G_reduction_min_cut,layers) vacc_matrix = calculate_vaccine_matrix(layers, N_groups) integer_matrix = matrix_to_integers_values(vacc_matrix) diff --git a/networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/comparisons.py b/networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/comparisons.py index 92fdafb..e8eab55 100644 --- a/networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/comparisons.py +++ b/networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/comparisons.py @@ -1,4 +1,111 @@ -import expirements_csv +import experiments_csv # add the comparisons test from both heuristic files + add a function that will plot the graphs. # TBD +import logging +import json +import time +logger = logging.getLogger(__name__) + + +from networkz.algorithms.approximation.firefighter_problem.Utils import * +from networkz.algorithms.approximation.firefighter_problem.Firefighter_Problem import * +from networkz.algorithms.approximation.tests.test_firefighter_problem.test_non_spreading_dirlaynet_minbudget import generate_layered_network + +def setup_global_logger(level: int = logging.DEBUG): + log_format = "|| %(asctime)s || %(levelname)s || %(message)s" + date_format = '%H:%M:%S' + formatter = logging.Formatter(log_format, datefmt=date_format) + handler = logging.StreamHandler() + handler.setFormatter(formatter) + + root_logger = logging.getLogger() + root_logger.setLevel(level) + root_logger.addHandler(handler) + + +if __name__ == "__main__": + setup_global_logger(level=logging.DEBUG) + + with open("networkz/algorithms/approximation/tests/test_firefighter_problem/graphs.json", "r") as file: + json_data = json.load(file) + graphs = parse_json_to_networkx(json_data) + + G_dirlay_random = generate_layered_network() #random graph generator for dirlay testings/ can also fit other algorithms but dirlay + + ex = experiments_csv.Experiment("results/", "non_spreading.csv", backup_folder=None) + # ex.clear_previous_results() # to clear previous experminets.. + + logger.debug("\nNon-Spread-Dirlay:\n") + input_ranges = { + "Graph":G_dirlay_random, + "source": [0], + "targets":[2,4], + } + ex.run(non_spreading_dirlaynet_minbudget,input_ranges) + + # ## + + # logger.debug("\nNon-Spread-MinBudget:\n") + # input_ranges = { + # "Graph":G_dirlay_random, + # "source": 0, + # "targets":[2,4], + # } + # ex.run(non_spreading_minbudget,input_ranges) + + # ## + + # logger.debug("\nNon-Spread-Heuristic-MinBudget:\n") + # input_ranges = { + # "Graph":G_dirlay_random, + # "source": 0, + # "targets":[2,4], + # "spreading":False + # } + # ex.run(heuristic_minbudget,input_ranges) + + # """""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""" + + # logger.debug("\Spread-Minbudget:\n") + # input_ranges = { + # "Graph": G_dirlay_random, + # "source": 0, + # "targets": [2,4], + # } + # ex.run(spreading_minbudget,input_ranges) + + # ## + + # logger.debug("\Spread-MaxSave:\n") + # input_ranges = { + # "Graph": G_dirlay_random, + # "budget": 1, + # "source": 0, + # "targets": [2,4], + # } + # ex.run(spreading_maxsave,input_ranges) + + # ## + + # logger.debug("\Spread-Heuristic-MinBudget:\n") + # input_ranges = { + # "Graph":G_dirlay_random, + # "source": 0, + # "targets":[2,4], + # "spreading":True + # } + # ex.run(heuristic_minbudget,input_ranges) + + # ## + + # logger.debug("\Spread-Heuristic-MaxSave:\n") + # input_ranges = { + # "Graph":G_dirlay_random, + # "budget": 1, + # "source": 0, + # "targets":[2,4], + # } + # ex.run(heuristic_maxsave,input_ranges) + + print("\n DataFrame: \n", ex.dataFrame) \ No newline at end of file diff --git a/networkz/algorithms/approximation/tests/test_firefighter_problem/test_non_spreading_dirlaynet_minbudget.py b/networkz/algorithms/approximation/tests/test_firefighter_problem/test_non_spreading_dirlaynet_minbudget.py index b8ccc2d..933ba8c 100644 --- a/networkz/algorithms/approximation/tests/test_firefighter_problem/test_non_spreading_dirlaynet_minbudget.py +++ b/networkz/algorithms/approximation/tests/test_firefighter_problem/test_non_spreading_dirlaynet_minbudget.py @@ -36,7 +36,7 @@ from networkz.algorithms.approximation.firefighter_problem.Utils import matrix_to_integers_values from networkz.algorithms.approximation.firefighter_problem.Utils import min_budget_calculation -with open("networkz/algorithms/tests/test_firefighter_problem/graphs.json", "r") as file: +with open("networkz/algorithms/approximation/tests/test_firefighter_problem/graphs.json", "r") as file: json_data = json.load(file) graphs = parse_json_to_networkx(json_data) From 52a669a3372c8e8868193914a557b61d412f8e9f Mon Sep 17 00:00:00 2001 From: 20shaked20 Date: Tue, 16 Jul 2024 17:18:46 +0300 Subject: [PATCH 58/93] Expermient csv is working --- .../comparisons/comparisons.py | 126 +++++++----------- .../comparisons/non_spreading.csv | 4 + .../comparisons/spreading.csv | 5 + 3 files changed, 59 insertions(+), 76 deletions(-) create mode 100644 networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/non_spreading.csv create mode 100644 networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/spreading.csv diff --git a/networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/comparisons.py b/networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/comparisons.py index e8eab55..edfcd791 100644 --- a/networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/comparisons.py +++ b/networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/comparisons.py @@ -23,89 +23,63 @@ def setup_global_logger(level: int = logging.DEBUG): root_logger.setLevel(level) root_logger.addHandler(handler) +G_dirlay_random = generate_layered_network() + +def runner_no_spreading(algorithm): + graph = G_dirlay_random.copy() + source = 0 + targets = [2,4,6,7,8,9,20,15] + + if algorithm == heuristic_minbudget: + return {"Budget" : (algorithm(Graph=graph,source=source,targets=targets,spreading=False))} + + else: + return {"Budget" : (algorithm(Graph=graph,source=source,targets=targets))} + + +def runner_spreading(algorithm): + graph = G_dirlay_random.copy() + source = 0 + targets = [2,4,6,7,8,9,20,15] + + if algorithm == heuristic_minbudget: + return {"Budget" : (algorithm(Graph=graph,source=source,targets=targets,spreading=True))} + + if algorithm == heuristic_maxsave: + return {"Budget" : (algorithm(Graph=graph,budget = 1, source=source,targets=targets,spreading=True))} + + if algorithm == spreading_maxsave: + return {"Budget" : (algorithm(Graph=graph,budget = 1, source=source,targets=targets))} + + else: + return {"Budget" : (algorithm(Graph=graph,source=source,targets=targets))} + if __name__ == "__main__": setup_global_logger(level=logging.DEBUG) - with open("networkz/algorithms/approximation/tests/test_firefighter_problem/graphs.json", "r") as file: - json_data = json.load(file) - graphs = parse_json_to_networkx(json_data) G_dirlay_random = generate_layered_network() #random graph generator for dirlay testings/ can also fit other algorithms but dirlay - ex = experiments_csv.Experiment("results/", "non_spreading.csv", backup_folder=None) - # ex.clear_previous_results() # to clear previous experminets.. + ex1 = experiments_csv.Experiment("./networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/", "non_spreading.csv", backup_folder=None) + ex1.clear_previous_results() # to clear previous experminets.. - logger.debug("\nNon-Spread-Dirlay:\n") input_ranges = { - "Graph":G_dirlay_random, - "source": [0], - "targets":[2,4], + "algorithm":[non_spreading_dirlaynet_minbudget,non_spreading_minbudget,heuristic_minbudget], } - ex.run(non_spreading_dirlaynet_minbudget,input_ranges) - - # ## - - # logger.debug("\nNon-Spread-MinBudget:\n") - # input_ranges = { - # "Graph":G_dirlay_random, - # "source": 0, - # "targets":[2,4], - # } - # ex.run(non_spreading_minbudget,input_ranges) - - # ## - - # logger.debug("\nNon-Spread-Heuristic-MinBudget:\n") - # input_ranges = { - # "Graph":G_dirlay_random, - # "source": 0, - # "targets":[2,4], - # "spreading":False - # } - # ex.run(heuristic_minbudget,input_ranges) - - # """""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""" - - # logger.debug("\Spread-Minbudget:\n") - # input_ranges = { - # "Graph": G_dirlay_random, - # "source": 0, - # "targets": [2,4], - # } - # ex.run(spreading_minbudget,input_ranges) - - # ## - - # logger.debug("\Spread-MaxSave:\n") - # input_ranges = { - # "Graph": G_dirlay_random, - # "budget": 1, - # "source": 0, - # "targets": [2,4], - # } - # ex.run(spreading_maxsave,input_ranges) - - # ## - - # logger.debug("\Spread-Heuristic-MinBudget:\n") - # input_ranges = { - # "Graph":G_dirlay_random, - # "source": 0, - # "targets":[2,4], - # "spreading":True - # } - # ex.run(heuristic_minbudget,input_ranges) - - # ## - - # logger.debug("\Spread-Heuristic-MaxSave:\n") - # input_ranges = { - # "Graph":G_dirlay_random, - # "budget": 1, - # "source": 0, - # "targets":[2,4], - # } - # ex.run(heuristic_maxsave,input_ranges) - - print("\n DataFrame: \n", ex.dataFrame) \ No newline at end of file + ex1.run(runner_no_spreading,input_ranges) + + """""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""" + + ex2 = experiments_csv.Experiment("./networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/", "spreading.csv", backup_folder=None) + ex2.clear_previous_results() # to clear previous experminets.. + + input_ranges = { + "algorithm":[spreading_minbudget,spreading_maxsave,heuristic_minbudget,heuristic_maxsave], + } + ex2.run(runner_spreading,input_ranges) + + + print("\n DataFrame-NonSpread: \n", ex1.dataFrame) + + print("\n DataFrame-Spreading: \n", ex2.dataFrame) \ No newline at end of file diff --git a/networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/non_spreading.csv b/networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/non_spreading.csv new file mode 100644 index 0000000..42424e2 --- /dev/null +++ b/networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/non_spreading.csv @@ -0,0 +1,4 @@ +algorithm,Budget +non_spreading_dirlaynet_minbudget,6 +non_spreading_minbudget,6 +heuristic_minbudget,"(4, ([(2, 1), (4, 1), (6, 1), (7, 1), (11, 2), (10, 2), (12, 2), (13, 2)], {2, 4, 20, 6, 7, 8, 9, 15}))" diff --git a/networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/spreading.csv b/networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/spreading.csv new file mode 100644 index 0000000..a15d86e --- /dev/null +++ b/networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/spreading.csv @@ -0,0 +1,5 @@ +algorithm,Budget +spreading_minbudget,"(4, [(2, 1), (4, 1), (6, 1), (7, 1)])" +spreading_maxsave,"([(2, 1), (14, 2)], {2, 20, 8, 9, 15})" +heuristic_minbudget,"(4, ([(2, 1), (4, 1), (6, 1), (7, 1)], {2, 4, 20, 6, 7, 8, 9, 15}))" +heuristic_maxsave,"([(2, 1)], {2, 8, 9, 15, 20})" From 4b1b702df6856b73f74ae9f5c614de49d89b2b42 Mon Sep 17 00:00:00 2001 From: 20shaked20 Date: Tue, 16 Jul 2024 17:19:19 +0300 Subject: [PATCH 59/93] Minor fix --- networkz/algorithms/approximation/firefighter_problem/main.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/networkz/algorithms/approximation/firefighter_problem/main.py b/networkz/algorithms/approximation/firefighter_problem/main.py index b86e5c4..c894d5f 100644 --- a/networkz/algorithms/approximation/firefighter_problem/main.py +++ b/networkz/algorithms/approximation/firefighter_problem/main.py @@ -29,7 +29,7 @@ # and the bottom one needs to be exclusive for running this from terminal to work from networkz.algorithms.approximation.firefighter_problem.Utils import * from networkz.algorithms.approximation.firefighter_problem.Firefighter_Problem import * -from networkz.algorithms.tests.test_firefighter_problem.test_non_spreading_dirlaynet_minbudget import generate_layered_network +from networkz.algorithms.approximation.tests.test_firefighter_problem.test_non_spreading_dirlaynet_minbudget import generate_layered_network import networkz.algorithms.approximation.firefighter_problem.Firefighter_Problem as firefighter_problem # to run the doctest on the firefighter_problem files def setup_global_logger(level: int = logging.DEBUG): From 6ea36ea881ea4d101bd797c9266a7e37e08310ac Mon Sep 17 00:00:00 2001 From: 20shaked20 Date: Tue, 16 Jul 2024 17:19:48 +0300 Subject: [PATCH 60/93] Header --- .../comparisons/comparisons.py | 26 +++++++++++++++---- 1 file changed, 21 insertions(+), 5 deletions(-) diff --git a/networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/comparisons.py b/networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/comparisons.py index edfcd791..f95e32f 100644 --- a/networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/comparisons.py +++ b/networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/comparisons.py @@ -1,10 +1,26 @@ -import experiments_csv -# add the comparisons test from both heuristic files + add a function that will plot the graphs. -# TBD +""" + +The Paper - +Approximability of the Firefighter Problem Computing Cuts over Time + +Paper Link - +https://www.math.uwaterloo.ca/~cswamy/papers/firefighter-journ.pdf + +Authors - +Elliot Anshelevich +Deeparnab Chakrabarty +Ameya Hate +Chaitanya Swamy +Developers - +Yuval Bubnovsky +Almog David +Shaked Levi + +""" + +import experiments_csv import logging -import json -import time logger = logging.getLogger(__name__) From 6831cffbe9da2b7d17062d9c624b5c8e2fad4007 Mon Sep 17 00:00:00 2001 From: Yuval Bubnovsky Date: Tue, 16 Jul 2024 19:31:52 +0300 Subject: [PATCH 61/93] removed writing logs to files; now using env var to pass where the graphs.json file is; pytests are good --- .../approximation/firefighter_problem/main.py | 9 +++++-- .../test_heuristic_maxsave.py | 26 +++++++++---------- .../test_heuristic_minbudget.py | 26 +++++++++---------- .../test_non_spreading_dirlaynet_minbudget.py | 7 ++++- .../test_non_spreading_minbudget.py | 9 +++++-- .../test_spreading_maxsave.py | 7 ++++- .../test_spreading_minbudget.py | 9 +++++-- 7 files changed, 59 insertions(+), 34 deletions(-) diff --git a/networkz/algorithms/approximation/firefighter_problem/main.py b/networkz/algorithms/approximation/firefighter_problem/main.py index b86e5c4..e21e1dd 100644 --- a/networkz/algorithms/approximation/firefighter_problem/main.py +++ b/networkz/algorithms/approximation/firefighter_problem/main.py @@ -22,6 +22,7 @@ import logging import json import time +import os logger = logging.getLogger(__name__) @@ -146,8 +147,12 @@ def compare_non_spreading_algorithm(graph: nx.DiGraph, source: int, targets:list import doctest setup_global_logger(level=logging.DEBUG) - with open("networkz/algorithms/tests/test_firefighter_problem/graphs.json", "r") as file: - json_data = json.load(file) + path_to_graphs = os.getenv('GRAPHS_JSON_PATH') + if path_to_graphs: + with open(path_to_graphs, "r") as file: + json_data = json.load(file) + else: + raise EnvironmentError("Environment variable GRAPHS_JSON_PATH is not set.") graphs = parse_json_to_networkx(json_data) G_dirlay_random = generate_layered_network() #random graph generator for dirlay testings/ can also fit other algorithms but dirlay diff --git a/networkz/algorithms/approximation/tests/test_firefighter_problem/test_heuristic_maxsave.py b/networkz/algorithms/approximation/tests/test_firefighter_problem/test_heuristic_maxsave.py index 1ce1f97..e2d4354 100644 --- a/networkz/algorithms/approximation/tests/test_firefighter_problem/test_heuristic_maxsave.py +++ b/networkz/algorithms/approximation/tests/test_firefighter_problem/test_heuristic_maxsave.py @@ -32,24 +32,24 @@ def setup_logger(): logger = logging.getLogger('firefighter_problem_tests') logger.setLevel(logging.INFO) - - timestamp = datetime.now().strftime("%Y%m%d_%H%M%S") - log_dir = 'networkz/algorithms/tests/test_firefighter_problem/comparisons' - os.makedirs(log_dir, exist_ok=True) - log_file_path = os.path.join(log_dir, f'test_heuristic_maxsave_comparison_{timestamp}.txt') - - file_handler = logging.FileHandler(log_file_path, mode='w') - file_handler.setLevel(logging.INFO) + + console_handler = logging.StreamHandler() + console_handler.setLevel(logging.INFO) + formatter = logging.Formatter('%(asctime)s - %(levelname)s - %(message)s') - file_handler.setFormatter(formatter) - logger.addHandler(file_handler) - + console_handler.setFormatter(formatter) + + logger.addHandler(console_handler) return logger logger = setup_logger() -with open("networkz/algorithms/tests/test_firefighter_problem/graphs.json", "r") as file: - json_data = json.load(file) +path_to_graphs = os.getenv('GRAPHS_JSON_PATH') +if path_to_graphs: + with open(path_to_graphs, "r") as file: + json_data = json.load(file) +else: + raise EnvironmentError("Environment variable GRAPHS_JSON_PATH is not set.") graphs = parse_json_to_networkx(json_data) @pytest.mark.parametrize("graph_key, budget, source, targets", [ diff --git a/networkz/algorithms/approximation/tests/test_firefighter_problem/test_heuristic_minbudget.py b/networkz/algorithms/approximation/tests/test_firefighter_problem/test_heuristic_minbudget.py index d86ac45..16b4ad8 100644 --- a/networkz/algorithms/approximation/tests/test_firefighter_problem/test_heuristic_minbudget.py +++ b/networkz/algorithms/approximation/tests/test_firefighter_problem/test_heuristic_minbudget.py @@ -13,18 +13,14 @@ def setup_logger(): logger = logging.getLogger('firefighter_problem_tests') logger.setLevel(logging.INFO) - - timestamp = datetime.now().strftime("%Y%m%d_%H%M%S") - log_dir = 'networkz/algorithms/tests/test_firefighter_problem/comparisons' - os.makedirs(log_dir, exist_ok=True) - log_file_path = os.path.join(log_dir, f'test_heuristic_minbudget_comparison_{timestamp}.txt') - - file_handler = logging.FileHandler(log_file_path, mode='w') - file_handler.setLevel(logging.INFO) + + console_handler = logging.StreamHandler() + console_handler.setLevel(logging.INFO) + formatter = logging.Formatter('%(asctime)s - %(levelname)s - %(message)s') - file_handler.setFormatter(formatter) - logger.addHandler(file_handler) - + console_handler.setFormatter(formatter) + + logger.addHandler(console_handler) return logger logger = setup_logger() @@ -48,8 +44,12 @@ def sample_json_data(): } def get_graphs(): - with open("networkz/algorithms/tests/test_firefighter_problem/graphs.json", "r") as file: - json_data = json.load(file) + path_to_graphs = os.getenv('GRAPHS_JSON_PATH') + if path_to_graphs: + with open(path_to_graphs, "r") as file: + json_data = json.load(file) + else: + raise EnvironmentError("Environment variable GRAPHS_JSON_PATH is not set.") graphs = parse_json_to_networkx(json_data) return graphs diff --git a/networkz/algorithms/approximation/tests/test_firefighter_problem/test_non_spreading_dirlaynet_minbudget.py b/networkz/algorithms/approximation/tests/test_firefighter_problem/test_non_spreading_dirlaynet_minbudget.py index b8ccc2d..d5ec1b3 100644 --- a/networkz/algorithms/approximation/tests/test_firefighter_problem/test_non_spreading_dirlaynet_minbudget.py +++ b/networkz/algorithms/approximation/tests/test_firefighter_problem/test_non_spreading_dirlaynet_minbudget.py @@ -25,6 +25,7 @@ import numpy as np import math import random +import os from networkz.algorithms.approximation.firefighter_problem.Firefighter_Problem import non_spreading_dirlaynet_minbudget from networkz.algorithms.approximation.firefighter_problem.Utils import adjust_nodes_capacity @@ -36,8 +37,12 @@ from networkz.algorithms.approximation.firefighter_problem.Utils import matrix_to_integers_values from networkz.algorithms.approximation.firefighter_problem.Utils import min_budget_calculation -with open("networkz/algorithms/tests/test_firefighter_problem/graphs.json", "r") as file: +path_to_graphs = os.getenv('GRAPHS_JSON_PATH') +if path_to_graphs: + with open(path_to_graphs, "r") as file: json_data = json.load(file) +else: + raise EnvironmentError("Environment variable GRAPHS_JSON_PATH is not set.") graphs = parse_json_to_networkx(json_data) @pytest.mark.parametrize("graph_key, source, targets", [ diff --git a/networkz/algorithms/approximation/tests/test_firefighter_problem/test_non_spreading_minbudget.py b/networkz/algorithms/approximation/tests/test_firefighter_problem/test_non_spreading_minbudget.py index 34362d0..bd6479f 100644 --- a/networkz/algorithms/approximation/tests/test_firefighter_problem/test_non_spreading_minbudget.py +++ b/networkz/algorithms/approximation/tests/test_firefighter_problem/test_non_spreading_minbudget.py @@ -22,6 +22,7 @@ import pytest import networkx as nx import json +import os from networkz.algorithms.approximation.firefighter_problem.Firefighter_Problem import non_spreading_minbudget from networkz.algorithms.approximation.firefighter_problem.Utils import parse_json_to_networkx @@ -44,8 +45,12 @@ def sample_json_data(): } def get_graphs(): - with open("networkz/algorithms/tests/test_firefighter_problem/graphs.json", "r") as file: - json_data = json.load(file) + path_to_graphs = os.getenv('GRAPHS_JSON_PATH') + if path_to_graphs: + with open(path_to_graphs, "r") as file: + json_data = json.load(file) + else: + raise EnvironmentError("Environment variable GRAPHS_JSON_PATH is not set.") graphs = parse_json_to_networkx(json_data) return graphs diff --git a/networkz/algorithms/approximation/tests/test_firefighter_problem/test_spreading_maxsave.py b/networkz/algorithms/approximation/tests/test_firefighter_problem/test_spreading_maxsave.py index 4ad2a0a..473d2f5 100644 --- a/networkz/algorithms/approximation/tests/test_firefighter_problem/test_spreading_maxsave.py +++ b/networkz/algorithms/approximation/tests/test_firefighter_problem/test_spreading_maxsave.py @@ -23,12 +23,17 @@ import networkx as nx import json import random +import os from networkz.algorithms.approximation.firefighter_problem.Firefighter_Problem import spreading_maxsave from networkz.algorithms.approximation.firefighter_problem.Utils import parse_json_to_networkx, calculate_gamma, calculate_epsilon, find_best_direct_vaccination -with open("networkz/algorithms/tests/test_firefighter_problem/graphs.json", "r") as file: +path_to_graphs = os.getenv('GRAPHS_JSON_PATH') +if path_to_graphs: + with open(path_to_graphs, "r") as file: json_data = json.load(file) +else: + raise EnvironmentError("Environment variable GRAPHS_JSON_PATH is not set.") graphs = parse_json_to_networkx(json_data) @pytest.mark.parametrize("graph_key, budget, source, targets", [ diff --git a/networkz/algorithms/approximation/tests/test_firefighter_problem/test_spreading_minbudget.py b/networkz/algorithms/approximation/tests/test_firefighter_problem/test_spreading_minbudget.py index c56acac..c97573b 100644 --- a/networkz/algorithms/approximation/tests/test_firefighter_problem/test_spreading_minbudget.py +++ b/networkz/algorithms/approximation/tests/test_firefighter_problem/test_spreading_minbudget.py @@ -23,6 +23,7 @@ import networkx as nx import json import random +import os from networkz.algorithms.approximation.firefighter_problem.Firefighter_Problem import spreading_minbudget from networkz.algorithms.approximation.firefighter_problem.Utils import parse_json_to_networkx, calculate_gamma, calculate_epsilon, find_best_direct_vaccination @@ -45,8 +46,12 @@ def sample_json_data(): } def get_graphs(): - with open("networkz/algorithms/tests/test_firefighter_problem/graphs.json", "r") as file: - json_data = json.load(file) + path_to_graphs = os.getenv('GRAPHS_JSON_PATH') + if path_to_graphs: + with open(path_to_graphs, "r") as file: + json_data = json.load(file) + else: + raise EnvironmentError("Environment variable GRAPHS_JSON_PATH is not set.") graphs = parse_json_to_networkx(json_data) return graphs From d68c5c12dab1db454285a6ba706ac04e1651b874 Mon Sep 17 00:00:00 2001 From: 20shaked20 Date: Tue, 16 Jul 2024 21:29:15 +0300 Subject: [PATCH 62/93] Practically done with comparisions, but having a slight issue with how it plots.. --- .../comparisons/comparisons.py | 23 ++++++++++++++++-- .../comparisons/non_spreading.csv | 8 +++--- .../comparisons/non_spreading.png | Bin 0 -> 18461 bytes .../comparisons/spreading.csv | 10 ++++---- .../comparisons/spreading.png | Bin 0 -> 31546 bytes 5 files changed, 30 insertions(+), 11 deletions(-) create mode 100644 networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/non_spreading.png create mode 100644 networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/spreading.png diff --git a/networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/comparisons.py b/networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/comparisons.py index f95e32f..4d2af09 100644 --- a/networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/comparisons.py +++ b/networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/comparisons.py @@ -20,6 +20,7 @@ """ import experiments_csv +from experiments_csv import * import logging logger = logging.getLogger(__name__) @@ -27,6 +28,7 @@ from networkz.algorithms.approximation.firefighter_problem.Utils import * from networkz.algorithms.approximation.firefighter_problem.Firefighter_Problem import * from networkz.algorithms.approximation.tests.test_firefighter_problem.test_non_spreading_dirlaynet_minbudget import generate_layered_network +from matplotlib import pyplot as plt def setup_global_logger(level: int = logging.DEBUG): log_format = "|| %(asctime)s || %(levelname)s || %(message)s" @@ -83,7 +85,7 @@ def runner_spreading(algorithm): input_ranges = { "algorithm":[non_spreading_dirlaynet_minbudget,non_spreading_minbudget,heuristic_minbudget], } - ex1.run(runner_no_spreading,input_ranges) + ex1.run_with_time_limit(runner_no_spreading,input_ranges, time_limit=0.9) """""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""" @@ -93,9 +95,26 @@ def runner_spreading(algorithm): input_ranges = { "algorithm":[spreading_minbudget,spreading_maxsave,heuristic_minbudget,heuristic_maxsave], } - ex2.run(runner_spreading,input_ranges) + ex2.run_with_time_limit(runner_spreading,input_ranges, time_limit=0.9) + #Plotting: + + single_plot_results("./networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/non_spreading.csv", + filter = {}, + x_field="algorithm", + y_field="runtime", + z_field="Budget", + save_to_file="./networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/non_spreading.png") + print("\n DataFrame-NonSpread: \n", ex1.dataFrame) + + single_plot_results("./networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/spreading.csv", + filter = {}, + x_field="algorithm", + y_field="runtime", + z_field="Budget", + save_to_file="./networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/spreading.png") + print("\n DataFrame-Spreading: \n", ex2.dataFrame) \ No newline at end of file diff --git a/networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/non_spreading.csv b/networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/non_spreading.csv index 42424e2..77709c6 100644 --- a/networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/non_spreading.csv +++ b/networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/non_spreading.csv @@ -1,4 +1,4 @@ -algorithm,Budget -non_spreading_dirlaynet_minbudget,6 -non_spreading_minbudget,6 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a/networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/spreading.csv b/networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/spreading.csv index a15d86e..a3a62ec 100644 --- a/networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/spreading.csv +++ b/networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/spreading.csv @@ -1,5 +1,5 @@ -algorithm,Budget -spreading_minbudget,"(4, [(2, 1), (4, 1), (6, 1), (7, 1)])" -spreading_maxsave,"([(2, 1), (14, 2)], {2, 20, 8, 9, 15})" -heuristic_minbudget,"(4, ([(2, 1), (4, 1), (6, 1), (7, 1)], {2, 4, 20, 6, 7, 8, 9, 15}))" -heuristic_maxsave,"([(2, 1)], {2, 8, 9, 15, 20})" +algorithm,Budget,runtime +spreading_minbudget,"(5, [(2, 1), (4, 1), (6, 1), (7, 1), (8, 1)])",0.06528040000193869 +spreading_maxsave,"([(2, 1), (10, 2), (19, 3)], {9, 2, 20, 15})",0.012378399998851819 +heuristic_minbudget,"(8, [])",0.03716120000171941 +heuristic_maxsave,"([(2, 1), (10, 2)], 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file changed, 6 insertions(+), 1 deletion(-) diff --git a/networkz/algorithms/approximation/tests/test_firefighter_problem/test_heuristic_maxsave.py b/networkz/algorithms/approximation/tests/test_firefighter_problem/test_heuristic_maxsave.py index e2d4354..8447768 100644 --- a/networkz/algorithms/approximation/tests/test_firefighter_problem/test_heuristic_maxsave.py +++ b/networkz/algorithms/approximation/tests/test_firefighter_problem/test_heuristic_maxsave.py @@ -165,4 +165,9 @@ def test_random_graph_comparison(): finally: logger.info("Finished test_random_graph_comparison.") - logger.info("-" * 100) \ No newline at end of file + logger.info("-" * 100) + + +if __name__ == "__main__": + pytest.main(["-v",__file__]) + From ee0ebd5f1f467e4cfdd1b3313d63f66012e39bc6 Mon Sep 17 00:00:00 2001 From: Yuval Bubnovsky Date: Fri, 19 Jul 2024 15:16:10 +0300 Subject: [PATCH 64/93] fixed getting JSON path for tests --- .../approximation/firefighter_problem/main.py | 187 ------------------ .../test_heuristic_maxsave.py | 6 +- .../test_heuristic_minbudget.py | 12 +- .../test_non_spreading_dirlaynet_minbudget.py | 9 +- .../test_non_spreading_minbudget.py | 13 +- .../test_firefighter_problem/test_parsing.py | 5 +- .../test_spreading_maxsave.py | 12 +- .../test_spreading_minbudget.py | 12 +- 8 files changed, 46 insertions(+), 210 deletions(-) delete mode 100644 networkz/algorithms/approximation/firefighter_problem/main.py diff --git a/networkz/algorithms/approximation/firefighter_problem/main.py b/networkz/algorithms/approximation/firefighter_problem/main.py deleted file mode 100644 index 896b942..0000000 --- a/networkz/algorithms/approximation/firefighter_problem/main.py +++ /dev/null @@ -1,187 +0,0 @@ -""" - -The Paper - -Approximability of the Firefighter Problem Computing Cuts over Time - -Paper Link - -https://www.math.uwaterloo.ca/~cswamy/papers/firefighter-journ.pdf - -Authors - -Elliot Anshelevich -Deeparnab Chakrabarty -Ameya Hate -Chaitanya Swamy - -Developers - -Yuval Bubnovsky -Almog David -Shaked Levi - -""" - -import logging -import json -import time -import os -logger = logging.getLogger(__name__) - - -# This is a fix for an issue where the top one has to be exclusive for pytest to work -# and the bottom one needs to be exclusive for running this from terminal to work -from networkz.algorithms.approximation.firefighter_problem.Utils import * -from networkz.algorithms.approximation.firefighter_problem.Firefighter_Problem import * -from networkz.algorithms.approximation.tests.test_firefighter_problem.test_non_spreading_dirlaynet_minbudget import generate_layered_network -import networkz.algorithms.approximation.firefighter_problem.Firefighter_Problem as firefighter_problem # to run the doctest on the firefighter_problem files - -def setup_global_logger(level: int = logging.DEBUG): - log_format = "|| %(asctime)s || %(levelname)s || %(message)s" - date_format = '%H:%M:%S' - formatter = logging.Formatter(log_format, datefmt=date_format) - handler = logging.StreamHandler() - handler.setFormatter(formatter) - - root_logger = logging.getLogger() - root_logger.setLevel(level) - root_logger.addHandler(handler) - -def compare_functions(graph: nx.DiGraph, source: int): - """ - This method comapres the function adjust_nodes_capacity - with a more complex version of it that uses threadpool to calcualte the capacity of each node - """ - graph1 = graph.copy() - graph2 = graph.copy() - - start_time = time.time() - adjust_nodes_capacity(graph1, source) - sequential_time = time.time() - start_time - - start_time = time.time() - adjust_nodes_capacity_parallel(graph2, source) - parallel_time = time.time() - start_time - - # Plot the execution times - plt.figure(figsize=(10, 5)) - - times = [sequential_time, parallel_time] - labels = ['adjust_nodes_capacity', 'adjust_nodes_capacity_parallel'] - - plt.bar(labels, times, color=['b', 'g'], alpha=0.6) - plt.title('Sequential vs Parallel Execution Times') - plt.xlabel('Execution Type') - plt.ylabel('Time (seconds)') - plt.show() - - -def compare_spreading_algorithms(graph: nx.DiGraph, source: int, targets:list): - """ - This method is used to compare the algoritmhs we created and show thier running time. - """ - graph1 = graph.copy() - graph2 = graph.copy() - graph3 = graph.copy() - graph4 = graph.copy() - - - start_time1 = time.time() - heuristic_maxsave(graph1, 1,source, targets) - end_time1 = time.time() - start_time1 - - start_time2 = time.time() - heuristic_minbudget(graph2,source, targets, True) #spreading - end_time2 = time.time() - start_time2 - - start_time3 = time.time() - spreading_minbudget(graph3,source, targets) - end_time3 = time.time() - start_time3 - - start_time4 = time.time() - spreading_maxsave(graph4, 1,source, targets) - end_time4 = time.time() - start_time4 - - # Plot the execution times - plt.figure(figsize=(10, 5)) - - times = [end_time1, end_time2, end_time3 ,end_time4] - labels = ['heuristic_maxsave','heuristic_minbudget', 'minbudget', 'maxsave'] - - plt.bar(labels, times, color=['b', 'b', 'g', 'g'], alpha=0.6) - plt.title('Algorithm Running time comparison') - plt.xlabel('Execution Type') - plt.ylabel('Time (seconds)') - plt.show() - -def compare_non_spreading_algorithm(graph: nx.DiGraph, source: int, targets:list): - """ - This method is used to compare the algoritmhs we created and show thier running time. - """ - graph1 = graph.copy() - graph2 = graph.copy() - graph3 = graph.copy() - - start_time1 = time.time() - non_spreading_dirlaynet_minbudget(graph1, source,targets) - end_time1= time.time() - start_time1 - - start_time2 = time.time() - heuristic_minbudget(graph2,source, targets, False) #no spreading - end_time2 = time.time() - start_time2 - - start_time3 = time.time() - non_spreading_minbudget(graph3,source, targets) - end_time3 = time.time() - start_time3 - - # Plot the execution times - plt.figure(figsize=(10, 5)) - - times = [end_time1, end_time2, end_time3] - labels = ['dirlaynet_minbudget', 'heuristic_minbudget', 'mincut_minbudget'] - - plt.bar(labels, times, color=['b', 'g', 'y'], alpha=0.6) - plt.title('Non spreading algorithms Running time comparison') - plt.xlabel('Execution Type') - plt.ylabel('Time (seconds)') - plt.show() - -if __name__ == "__main__": - import doctest - setup_global_logger(level=logging.DEBUG) - - path_to_graphs = os.getenv('GRAPHS_JSON_PATH') - if path_to_graphs: - with open(path_to_graphs, "r") as file: - json_data = json.load(file) - else: - raise EnvironmentError("Environment variable GRAPHS_JSON_PATH is not set.") - graphs = parse_json_to_networkx(json_data) - - G_dirlay_random = generate_layered_network() #random graph generator for dirlay testings/ can also fit other algorithms but dirlay - - "Simple testings on spreading algorithms:" - # G2 = graphs["RegularGraph_Graph-2"] - # print(heuristic_minbudget(G2,source=0, targets=[1,3,4,5,6],spreading=True)) - #print(spreading_maxsave(G3,1, 0,[2,6,1,8])[1]) - # print(spreading_minbudget(G2,source=0, targets=[1,3,4,5,6])) - # logger.info("=" * 150) - #logger.info(heuristic_minbudget(G3,0,[2,6,1,8], True)) - - # G3 = nx.DiGraph() - # G3.add_nodes_from([0,1,2,3,4,5,6,7,8]) - # G3.add_edges_from([(0,2),(0,4),(0,5),(2,1),(2,3),(4,1),(4,6),(5,3),(5,6),(5,7),(6,7),(6,8),(7,8)]) - # logger.info("=" * 150) - #print(spreading_maxsave(G3,source=0,targets=[2,6,1,8],budget=1)) - #print(spreading_minbudget(G3,source=0,targets=[2,6,1,8])) - - "Dirlay simple running exmaple:" - G2 = graphs["Dirlay_Graph-2"] - # print(non_spreading_dirlaynet_minbudget(Graph=G2, src=0, targets=[2,4])) - - "Comapring adjust_nodes_capacity with parallel running:" - # compare_functions(G_dirlay_random,0) # more complex random graph comparison - - "Compare Graph Algorithms:" - # compare_algorithms(G_dirlay_random,0, [2,4]) # more complex random graph comparison - compare_non_spreading_algorithm(G_dirlay_random, 0, [2,4]) # simple graph example comparison - compare_spreading_algorithms(G_dirlay_random, 0, [2,4]) - - diff --git a/networkz/algorithms/approximation/tests/test_firefighter_problem/test_heuristic_maxsave.py b/networkz/algorithms/approximation/tests/test_firefighter_problem/test_heuristic_maxsave.py index 8447768..9e4d94a 100644 --- a/networkz/algorithms/approximation/tests/test_firefighter_problem/test_heuristic_maxsave.py +++ b/networkz/algorithms/approximation/tests/test_firefighter_problem/test_heuristic_maxsave.py @@ -44,12 +44,12 @@ def setup_logger(): logger = setup_logger() -path_to_graphs = os.getenv('GRAPHS_JSON_PATH') -if path_to_graphs: +path_to_graphs = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'graphs.json') +if os.path.exists(path_to_graphs): with open(path_to_graphs, "r") as file: json_data = json.load(file) else: - raise EnvironmentError("Environment variable GRAPHS_JSON_PATH is not set.") + raise FileNotFoundError(f"{path_to_graphs} does not exist.") graphs = parse_json_to_networkx(json_data) @pytest.mark.parametrize("graph_key, budget, source, targets", [ diff --git a/networkz/algorithms/approximation/tests/test_firefighter_problem/test_heuristic_minbudget.py b/networkz/algorithms/approximation/tests/test_firefighter_problem/test_heuristic_minbudget.py index 16b4ad8..83a2c1f 100644 --- a/networkz/algorithms/approximation/tests/test_firefighter_problem/test_heuristic_minbudget.py +++ b/networkz/algorithms/approximation/tests/test_firefighter_problem/test_heuristic_minbudget.py @@ -44,12 +44,13 @@ def sample_json_data(): } def get_graphs(): - path_to_graphs = os.getenv('GRAPHS_JSON_PATH') - if path_to_graphs: + path_to_graphs = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'graphs.json') + if os.path.exists(path_to_graphs): with open(path_to_graphs, "r") as file: json_data = json.load(file) else: - raise EnvironmentError("Environment variable GRAPHS_JSON_PATH is not set.") + raise FileNotFoundError(f"{path_to_graphs} does not exist.") + graphs = parse_json_to_networkx(json_data) return graphs @@ -153,4 +154,7 @@ def test_random_graph_comparison(i): finally: logger.info(f"Finished test_random_graph_comparison for Random Graph {i+1}.") - logger.info("-" * 100) \ No newline at end of file + logger.info("-" * 100) + +if __name__ == "__main__": + pytest.main(["-v", __file__]) \ No newline at end of file diff --git a/networkz/algorithms/approximation/tests/test_firefighter_problem/test_non_spreading_dirlaynet_minbudget.py b/networkz/algorithms/approximation/tests/test_firefighter_problem/test_non_spreading_dirlaynet_minbudget.py index d5ec1b3..fbf0251 100644 --- a/networkz/algorithms/approximation/tests/test_firefighter_problem/test_non_spreading_dirlaynet_minbudget.py +++ b/networkz/algorithms/approximation/tests/test_firefighter_problem/test_non_spreading_dirlaynet_minbudget.py @@ -37,12 +37,12 @@ from networkz.algorithms.approximation.firefighter_problem.Utils import matrix_to_integers_values from networkz.algorithms.approximation.firefighter_problem.Utils import min_budget_calculation -path_to_graphs = os.getenv('GRAPHS_JSON_PATH') -if path_to_graphs: +path_to_graphs = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'graphs.json') +if os.path.exists(path_to_graphs): with open(path_to_graphs, "r") as file: json_data = json.load(file) else: - raise EnvironmentError("Environment variable GRAPHS_JSON_PATH is not set.") + raise FileNotFoundError(f"{path_to_graphs} does not exist.") graphs = parse_json_to_networkx(json_data) @pytest.mark.parametrize("graph_key, source, targets", [ @@ -429,3 +429,6 @@ def test_non_spreading_dirlaynet_minbudget(): min_budget = non_spreading_dirlaynet_minbudget(G, source, targets) assert min_budget > 0, "Minimum budget should be non-negative" + +if __name__ == "__main__": + pytest.main(["-v", __file__]) diff --git a/networkz/algorithms/approximation/tests/test_firefighter_problem/test_non_spreading_minbudget.py b/networkz/algorithms/approximation/tests/test_firefighter_problem/test_non_spreading_minbudget.py index bd6479f..6968228 100644 --- a/networkz/algorithms/approximation/tests/test_firefighter_problem/test_non_spreading_minbudget.py +++ b/networkz/algorithms/approximation/tests/test_firefighter_problem/test_non_spreading_minbudget.py @@ -45,12 +45,13 @@ def sample_json_data(): } def get_graphs(): - path_to_graphs = os.getenv('GRAPHS_JSON_PATH') - if path_to_graphs: + path_to_graphs = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'graphs.json') + if os.path.exists(path_to_graphs): with open(path_to_graphs, "r") as file: json_data = json.load(file) else: - raise EnvironmentError("Environment variable GRAPHS_JSON_PATH is not set.") + raise FileNotFoundError(f"{path_to_graphs} does not exist.") + graphs = parse_json_to_networkx(json_data) return graphs @@ -122,4 +123,8 @@ def test_save_subgroup_vertices(): assert 1 < non_spreading_minbudget(graphs["RegularGraph_Graph-4"], 0, [2,3,5,7]) #answer is 2 assert 4 > non_spreading_minbudget(graphs["RegularGraph_Graph-6"], 1, [0,3,5,6,8,9]) #answer is 1 assert 2 == non_spreading_minbudget(graphs["RegularGraph_Graph-7"], 1, [0,2,5,6]) #answer is 2 - assert 3 == non_spreading_minbudget(graphs["RegularGraph_Graph-8"], 0, [1,3,4,5,6,9,10,12,14]) #answer is 3 \ No newline at end of file + assert 3 == non_spreading_minbudget(graphs["RegularGraph_Graph-8"], 0, [1,3,4,5,6,9,10,12,14]) #answer is 3 + + +if __name__ == "__main__": + pytest.main(["-v", __file__]) \ No newline at end of file diff --git a/networkz/algorithms/approximation/tests/test_firefighter_problem/test_parsing.py b/networkz/algorithms/approximation/tests/test_firefighter_problem/test_parsing.py index 8791c1c..03066b8 100644 --- a/networkz/algorithms/approximation/tests/test_firefighter_problem/test_parsing.py +++ b/networkz/algorithms/approximation/tests/test_firefighter_problem/test_parsing.py @@ -133,4 +133,7 @@ def test_parsing_regular_graph_status(sample_json_data): regular_graph = graphs["RegularGraph_Graph-1"] for node in regular_graph.nodes(data=True): - assert node[1]["status"] == Status.VULNERABLE.value \ No newline at end of file + assert node[1]["status"] == Status.VULNERABLE.value + +if __name__ == "__main__": + pytest.main(["-v", __file__]) \ No newline at end of file diff --git a/networkz/algorithms/approximation/tests/test_firefighter_problem/test_spreading_maxsave.py b/networkz/algorithms/approximation/tests/test_firefighter_problem/test_spreading_maxsave.py index 473d2f5..15b7873 100644 --- a/networkz/algorithms/approximation/tests/test_firefighter_problem/test_spreading_maxsave.py +++ b/networkz/algorithms/approximation/tests/test_firefighter_problem/test_spreading_maxsave.py @@ -28,12 +28,12 @@ from networkz.algorithms.approximation.firefighter_problem.Firefighter_Problem import spreading_maxsave from networkz.algorithms.approximation.firefighter_problem.Utils import parse_json_to_networkx, calculate_gamma, calculate_epsilon, find_best_direct_vaccination -path_to_graphs = os.getenv('GRAPHS_JSON_PATH') -if path_to_graphs: +path_to_graphs = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'graphs.json') +if os.path.exists(path_to_graphs): with open(path_to_graphs, "r") as file: json_data = json.load(file) else: - raise EnvironmentError("Environment variable GRAPHS_JSON_PATH is not set.") + raise FileNotFoundError(f"{path_to_graphs} does not exist.") graphs = parse_json_to_networkx(json_data) @pytest.mark.parametrize("graph_key, budget, source, targets", [ @@ -292,4 +292,8 @@ def test_random_graph(): assert len(ans) <= len(G.nodes) - print("All tests have passed!") \ No newline at end of file + print("All tests have passed!") + + +if __name__ == "__main__": + pytest.main(["-v", __file__]) \ No newline at end of file diff --git a/networkz/algorithms/approximation/tests/test_firefighter_problem/test_spreading_minbudget.py b/networkz/algorithms/approximation/tests/test_firefighter_problem/test_spreading_minbudget.py index c97573b..b232077 100644 --- a/networkz/algorithms/approximation/tests/test_firefighter_problem/test_spreading_minbudget.py +++ b/networkz/algorithms/approximation/tests/test_firefighter_problem/test_spreading_minbudget.py @@ -46,12 +46,13 @@ def sample_json_data(): } def get_graphs(): - path_to_graphs = os.getenv('GRAPHS_JSON_PATH') - if path_to_graphs: + path_to_graphs = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'graphs.json') + if os.path.exists(path_to_graphs): with open(path_to_graphs, "r") as file: json_data = json.load(file) else: - raise EnvironmentError("Environment variable GRAPHS_JSON_PATH is not set.") + raise FileNotFoundError(f"{path_to_graphs} does not exist.") + graphs = parse_json_to_networkx(json_data) return graphs @@ -295,4 +296,7 @@ def test_random_graph(): assert ans <= target_length - print("All tests have passed!") \ No newline at end of file + print("All tests have passed!") + +if __name__ == "__main__": + pytest.main(["-v", __file__]) \ No newline at end of file From a0397d432a8357e26d4fd42b6ea6cf7ff2ac962f Mon Sep 17 00:00:00 2001 From: 20shaked20 Date: Fri, 19 Jul 2024 15:16:23 +0300 Subject: [PATCH 65/93] Minor imporvment to csv outputting with multiple graphs and batching --- .../Firefighter_Problem.py | 2 +- .../comparisons/comparisons.py | 65 +++++++++++------- .../comparisons/non_spreading.csv | 21 ++++-- .../comparisons/non_spreading.png | Bin 18461 -> 26485 bytes .../test_non_spreading_dirlaynet_minbudget.py | 14 ++-- 5 files changed, 67 insertions(+), 35 deletions(-) diff --git a/networkz/algorithms/approximation/firefighter_problem/Firefighter_Problem.py b/networkz/algorithms/approximation/firefighter_problem/Firefighter_Problem.py index 027f9b3..baa155a 100644 --- a/networkz/algorithms/approximation/firefighter_problem/Firefighter_Problem.py +++ b/networkz/algorithms/approximation/firefighter_problem/Firefighter_Problem.py @@ -283,7 +283,7 @@ def non_spreading_dirlaynet_minbudget(Graph:nx.DiGraph, source:int, targets:list stategy = dirlay_vaccination_startegy(integer_matrix, N_groups) logger.info(f"Returning algorithm stategy: {stategy} with budget: {min_budget}") - return min_budget + return min_budget,stategy def heuristic_maxsave(Graph:nx.DiGraph, budget:int, source:int, targets:list, spreading=True, stop_condition=None) -> tuple: """ diff --git a/networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/comparisons.py b/networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/comparisons.py index 4d2af09..31a1379 100644 --- a/networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/comparisons.py +++ b/networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/comparisons.py @@ -41,23 +41,18 @@ def setup_global_logger(level: int = logging.DEBUG): root_logger.setLevel(level) root_logger.addHandler(handler) -G_dirlay_random = generate_layered_network() - -def runner_no_spreading(algorithm): - graph = G_dirlay_random.copy() - source = 0 - targets = [2,4,6,7,8,9,20,15] +def runner_no_spreading(algorithm, graph, source, targets): if algorithm == heuristic_minbudget: - return {"Budget" : (algorithm(Graph=graph,source=source,targets=targets,spreading=False))} - + result = algorithm(Graph=graph, source=source, targets=targets, spreading=False) + return {"Budget": result} else: - return {"Budget" : (algorithm(Graph=graph,source=source,targets=targets))} + result = algorithm(Graph=graph, source=source, targets=targets) + return {"Budget": result} -def runner_spreading(algorithm): +def runner_spreading(algorithm, source): graph = G_dirlay_random.copy() - source = 0 targets = [2,4,6,7,8,9,20,15] if algorithm == heuristic_minbudget: @@ -73,6 +68,8 @@ def runner_spreading(algorithm): return {"Budget" : (algorithm(Graph=graph,source=source,targets=targets))} +from time import perf_counter + if __name__ == "__main__": setup_global_logger(level=logging.DEBUG) @@ -84,18 +81,36 @@ def runner_spreading(algorithm): input_ranges = { "algorithm":[non_spreading_dirlaynet_minbudget,non_spreading_minbudget,heuristic_minbudget], + } - ex1.run_with_time_limit(runner_no_spreading,input_ranges, time_limit=0.9) + def multiple_runs(runs=5): + for _ in range(runs): + graph = generate_layered_network() + source = 0 + targets = [2, 4, 6, 7, 8, 9] + for algorithm in input_ranges["algorithm"]: + print(f"TEST LAYERS FOR ALGO {algorithm}, with graph -> {graph.nodes}") + start_time = perf_counter() + result = runner_no_spreading(algorithm, graph, source, targets) + runtime = perf_counter() - start_time + ex1.add({**{"algorithm": algorithm.__name__, "runtime": runtime}, **result}) + return {"status": "completed"} + + # Set a time limit for the entire batch run + ex1.run_with_time_limit(multiple_runs, input_ranges={}, time_limit=0.9) + + # ex1.run_with_time_limit(runner_no_spreading,input_ranges, time_limit=0.9) """""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""" - ex2 = experiments_csv.Experiment("./networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/", "spreading.csv", backup_folder=None) - ex2.clear_previous_results() # to clear previous experminets.. + # ex2 = experiments_csv.Experiment("./networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/", "spreading.csv", backup_folder=None) + # ex2.clear_previous_results() # to clear previous experminets.. - input_ranges = { - "algorithm":[spreading_minbudget,spreading_maxsave,heuristic_minbudget,heuristic_maxsave], - } - ex2.run_with_time_limit(runner_spreading,input_ranges, time_limit=0.9) + # input_ranges = { + # "algorithm":[spreading_minbudget,spreading_maxsave,heuristic_minbudget,heuristic_maxsave], + # "source" : [0,4,6,7] + # } + # ex2.run_with_time_limit(runner_spreading,input_ranges, time_limit=0.9) #Plotting: @@ -110,11 +125,11 @@ def runner_spreading(algorithm): print("\n DataFrame-NonSpread: \n", ex1.dataFrame) - single_plot_results("./networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/spreading.csv", - filter = {}, - x_field="algorithm", - y_field="runtime", - z_field="Budget", - save_to_file="./networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/spreading.png") + # single_plot_results("./networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/spreading.csv", + # filter = {}, + # x_field="algorithm", + # y_field="runtime", + # z_field="Budget", + # save_to_file="./networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/spreading.png") - print("\n DataFrame-Spreading: \n", ex2.dataFrame) \ No newline at end of file + # print("\n DataFrame-Spreading: \n", ex2.dataFrame) \ No newline at end of file diff --git a/networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/non_spreading.csv b/networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/non_spreading.csv index 77709c6..a5594d8 100644 --- a/networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/non_spreading.csv +++ b/networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/non_spreading.csv @@ -1,4 +1,17 @@ -algorithm,Budget,runtime -non_spreading_dirlaynet_minbudget,7,0.037856300001294585 -non_spreading_minbudget,8,0.0023252999999385793 -heuristic_minbudget,"(8, [])",0.08596559999932651 +algorithm,runtime,Budget +non_spreading_dirlaynet_minbudget,0.032958900002995506,"(5, {0: [4, 3, 2, 1, 6]})" +non_spreading_minbudget,0.0026366999954916537,5 +heuristic_minbudget,0.010915699996985495,"(3, [(3, 1), (2, 1), (4, 1)])" +non_spreading_dirlaynet_minbudget,0.0191839000035543,"(5, {0: [4, 3, 5, 2, 1]})" +non_spreading_minbudget,0.0015363999991677701,5 +heuristic_minbudget,0.013955700007500127,"(3, [(4, 1), (2, 1), (1, 1)])" +non_spreading_dirlaynet_minbudget,0.017144200013717636,"(6, {0: [4, 7, 8, 2, 6, 9]})" +non_spreading_minbudget,0.0015580999897792935,6 +heuristic_minbudget,0.01971260001300834,"(6, [])" +non_spreading_dirlaynet_minbudget,0.026681699993787333,"(3, {0: [4, 2, 7], 1: [7]})" +non_spreading_minbudget,0.0019831999961752445,4 +heuristic_minbudget,0.018595000001369044,"(2, [(2, 1), (4, 1), (6, 2), (7, 2)])" +non_spreading_dirlaynet_minbudget,0.043380599992815405,"(6, {0: [4, 7, 8, 2, 6, 9]})" +non_spreading_minbudget,0.0037419000000227243,6 +heuristic_minbudget,0.025103599997237325,"(6, [])" +,0.29183470000862144, diff --git a/networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/non_spreading.png b/networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/non_spreading.png index cf733ce1f7cfdd2b81c1842b1e4abeb54d9a32f4..fee6927cc35e151ef61e837990f950c6d7e4ee07 100644 GIT binary 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zu=tn+a~$d@(!JK4{usg#QKtuOX^WG{D0C*Q{KR=d$`is8wj#DaKBJi+2lOhq7`g6thh7#kjsR$6oY^uEApSdw>|n~&t%t%Vx|^M{O@(3F)?z*W$D&MhpEU(4b16KLtr z0O+B_2+Ep}CTszCuOGDe^MwvvLr4awFJ)MEDd2{nxEu@56TH z^l3p^5l{?JlFV~iD35kwWJkeNY`kngsl22ML-H46#)t(9qk_X%l(ka2|^$rCw3yAN&G5--rVky+(Dj0phk?Ek-5X# z@7=jW(4|~$L$Y=P21S*j@z0rsU<`px=;2P<6Vx}H7b)-Y>j=6Tw{MTd;D8BQt9f)> z2&R=me>db2k`*_CG55?4#x_!bs^+;OkkTZVWvf9cY%lP+Qx|)nwZqdAQ_f%9y658 zb~H{74pg6=$4z4iQ$+?GFel89Oj9t|6C&q*{ew5M88}PDouc+HK|5yaZ98#a@25yg zN6CIeM%7^47=TiHrj3!pp_aY+*Wzn>AUFw_0HdB^JEVbMgW**zL@A@8AwR>0lCc1c zpvHl5GXX@v{M$K2fejQ77CGV;KmUus4XASed%v`1&h74vm`1kH4&s)lP|wJnPX6QK G&Hn-ZFF7Xw diff --git a/networkz/algorithms/approximation/tests/test_firefighter_problem/test_non_spreading_dirlaynet_minbudget.py b/networkz/algorithms/approximation/tests/test_firefighter_problem/test_non_spreading_dirlaynet_minbudget.py index d5ec1b3..1955dd5 100644 --- a/networkz/algorithms/approximation/tests/test_firefighter_problem/test_non_spreading_dirlaynet_minbudget.py +++ b/networkz/algorithms/approximation/tests/test_firefighter_problem/test_non_spreading_dirlaynet_minbudget.py @@ -37,12 +37,16 @@ from networkz.algorithms.approximation.firefighter_problem.Utils import matrix_to_integers_values from networkz.algorithms.approximation.firefighter_problem.Utils import min_budget_calculation -path_to_graphs = os.getenv('GRAPHS_JSON_PATH') -if path_to_graphs: - with open(path_to_graphs, "r") as file: +# path_to_graphs = os.getenv('GRAPHS_JSON_PATH') +# if path_to_graphs: +# with open(path_to_graphs, "r") as file: +# json_data = json.load(file) +# else: +# raise EnvironmentError("Environment variable GRAPHS_JSON_PATH is not set.") + +with open("networkz/algorithms/approximation/tests/test_firefighter_problem/graphs.json", "r") as file: json_data = json.load(file) -else: - raise EnvironmentError("Environment variable GRAPHS_JSON_PATH is not set.") + graphs = parse_json_to_networkx(json_data) @pytest.mark.parametrize("graph_key, source, targets", [ From d7cd0f522b338af1e2b747a53385f50f5adf5558 Mon Sep 17 00:00:00 2001 From: 20shaked20 Date: Fri, 19 Jul 2024 15:47:30 +0300 Subject: [PATCH 66/93] Non-spreading-csv working wih many checks --- .../Firefighter_Problem.py | 2 +- .../comparisons/comparisons.py | 75 +++++--- .../comparisons/non_spreading.csv | 169 ++++++++++++++++-- .../comparisons/non_spreading.png | Bin 26485 -> 60797 bytes .../non_spreading_preprocessed.csv | 151 ++++++++++++++++ 5 files changed, 359 insertions(+), 38 deletions(-) create mode 100644 networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/non_spreading_preprocessed.csv diff --git a/networkz/algorithms/approximation/firefighter_problem/Firefighter_Problem.py b/networkz/algorithms/approximation/firefighter_problem/Firefighter_Problem.py index baa155a..4b6c9f0 100644 --- a/networkz/algorithms/approximation/firefighter_problem/Firefighter_Problem.py +++ b/networkz/algorithms/approximation/firefighter_problem/Firefighter_Problem.py @@ -244,7 +244,7 @@ def non_spreading_minbudget(Graph:nx.DiGraph, source:int, targets:list)->int: min_budget = len(algo.minimum_st_node_cut(G, source, 't')) logger.info(f"Returning minimum budget: {min_budget}") - return min_budget + return min_budget, [] def non_spreading_dirlaynet_minbudget(Graph:nx.DiGraph, source:int, targets:list)->int: """ diff --git a/networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/comparisons.py b/networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/comparisons.py index 31a1379..54bbb4c 100644 --- a/networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/comparisons.py +++ b/networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/comparisons.py @@ -51,24 +51,26 @@ def runner_no_spreading(algorithm, graph, source, targets): return {"Budget": result} -def runner_spreading(algorithm, source): - graph = G_dirlay_random.copy() - targets = [2,4,6,7,8,9,20,15] +# def runner_spreading(algorithm, source): +# graph = G_dirlay_random.copy() +# targets = [2,4,6,7,8,9,20,15] - if algorithm == heuristic_minbudget: - return {"Budget" : (algorithm(Graph=graph,source=source,targets=targets,spreading=True))} +# if algorithm == heuristic_minbudget: +# return {"Budget" : (algorithm(Graph=graph,source=source,targets=targets,spreading=True))} - if algorithm == heuristic_maxsave: - return {"Budget" : (algorithm(Graph=graph,budget = 1, source=source,targets=targets,spreading=True))} +# if algorithm == heuristic_maxsave: +# return {"Budget" : (algorithm(Graph=graph,budget = 1, source=source,targets=targets,spreading=True))} - if algorithm == spreading_maxsave: - return {"Budget" : (algorithm(Graph=graph,budget = 1, source=source,targets=targets))} +# if algorithm == spreading_maxsave: +# return {"Budget" : (algorithm(Graph=graph,budget = 1, source=source,targets=targets))} - else: - return {"Budget" : (algorithm(Graph=graph,source=source,targets=targets))} +# else: +# return {"Budget" : (algorithm(Graph=graph,source=source,targets=targets))} from time import perf_counter +import pandas as pd +import random if __name__ == "__main__": setup_global_logger(level=logging.DEBUG) @@ -83,17 +85,21 @@ def runner_spreading(algorithm, source): "algorithm":[non_spreading_dirlaynet_minbudget,non_spreading_minbudget,heuristic_minbudget], } - def multiple_runs(runs=5): + def multiple_runs(runs=50): for _ in range(runs): graph = generate_layered_network() source = 0 - targets = [2, 4, 6, 7, 8, 9] + # targets = [2, 4, 6, 7, 8, 9] + nodes = list(graph.nodes) + nodes.remove(0) + num_targets = random.randint(1, int(len(nodes)/4)) + targets = random.sample(nodes,num_targets) for algorithm in input_ranges["algorithm"]: print(f"TEST LAYERS FOR ALGO {algorithm}, with graph -> {graph.nodes}") start_time = perf_counter() result = runner_no_spreading(algorithm, graph, source, targets) runtime = perf_counter() - start_time - ex1.add({**{"algorithm": algorithm.__name__, "runtime": runtime}, **result}) + ex1.add({**{"algorithm": algorithm.__name__, "runtime": runtime, "graph_nodes": len(graph.nodes)}, **result}) return {"status": "completed"} # Set a time limit for the entire batch run @@ -115,12 +121,41 @@ def multiple_runs(runs=5): #Plotting: - single_plot_results("./networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/non_spreading.csv", - filter = {}, - x_field="algorithm", - y_field="runtime", - z_field="Budget", - save_to_file="./networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/non_spreading.png") +# Preprocess the DataFrame to extract numeric budget values + results_csv_file = "./networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/non_spreading.csv" + results = pd.read_csv(results_csv_file) + + # Extract the numeric budget from the 'Budget' column + def extract_budget_numeric(budget): + if isinstance(budget, tuple): + return budget[0] + elif isinstance(budget, str): + try: + return eval(budget)[0] + except: + return None + return None + + results['Budget_numeric'] = results['Budget'].apply(extract_budget_numeric) + + # Drop rows where the 'Budget_numeric' is not available + results = results.dropna(subset=['Budget_numeric']) + + # Save the preprocessed DataFrame to a temporary CSV file + preprocessed_csv_file = "./networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/non_spreading_preprocessed.csv" + results.to_csv(preprocessed_csv_file, index=False) + + print("\n DataFrame-NonSpread: \n", results) + + # Plot the results using the preprocessed CSV file + single_plot_results( + results_csv_file=preprocessed_csv_file, + filter={}, + x_field="graph_nodes", + y_field="Budget_numeric", + z_field="algorithm", + save_to_file="./networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/non_spreading.png" + ) print("\n DataFrame-NonSpread: \n", ex1.dataFrame) diff --git a/networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/non_spreading.csv b/networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/non_spreading.csv index a5594d8..bdab58b 100644 --- a/networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/non_spreading.csv +++ b/networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/non_spreading.csv @@ -1,17 +1,152 @@ -algorithm,runtime,Budget -non_spreading_dirlaynet_minbudget,0.032958900002995506,"(5, {0: [4, 3, 2, 1, 6]})" -non_spreading_minbudget,0.0026366999954916537,5 -heuristic_minbudget,0.010915699996985495,"(3, [(3, 1), (2, 1), (4, 1)])" -non_spreading_dirlaynet_minbudget,0.0191839000035543,"(5, {0: [4, 3, 5, 2, 1]})" -non_spreading_minbudget,0.0015363999991677701,5 -heuristic_minbudget,0.013955700007500127,"(3, [(4, 1), (2, 1), (1, 1)])" -non_spreading_dirlaynet_minbudget,0.017144200013717636,"(6, {0: [4, 7, 8, 2, 6, 9]})" -non_spreading_minbudget,0.0015580999897792935,6 -heuristic_minbudget,0.01971260001300834,"(6, [])" -non_spreading_dirlaynet_minbudget,0.026681699993787333,"(3, {0: [4, 2, 7], 1: [7]})" -non_spreading_minbudget,0.0019831999961752445,4 -heuristic_minbudget,0.018595000001369044,"(2, [(2, 1), (4, 1), (6, 2), (7, 2)])" -non_spreading_dirlaynet_minbudget,0.043380599992815405,"(6, {0: [4, 7, 8, 2, 6, 9]})" -non_spreading_minbudget,0.0037419000000227243,6 -heuristic_minbudget,0.025103599997237325,"(6, [])" -,0.29183470000862144, +algorithm,runtime,graph_nodes,Budget +non_spreading_dirlaynet_minbudget,0.029881599999498576,21,"(2, {0: [6, 13]})" +non_spreading_minbudget,0.0020215999975334853,21,"(2, [])" +heuristic_minbudget,0.006185999984154478,21,"(1, [(6, 1), (13, 2)])" +non_spreading_dirlaynet_minbudget,0.03994529999908991,34,"(2, {0: [11], 1: [11, 21]})" +non_spreading_minbudget,0.002799400012008846,34,"(3, [])" +heuristic_minbudget,0.03935599999385886,34,"(3, [])" +non_spreading_dirlaynet_minbudget,0.01712309999857098,12,"(2, {0: [2, 4]})" +non_spreading_minbudget,0.0012171000125817955,12,"(2, [])" +heuristic_minbudget,0.0023338000173680484,12,"(1, [(2, 1)])" +non_spreading_dirlaynet_minbudget,0.024390799982938915,22,"(1, {1: [15], 2: [15]})" +non_spreading_minbudget,0.0016923000221140683,22,"(2, [])" +heuristic_minbudget,0.00624639997840859,22,"(1, [(1, 1), (11, 2)])" +non_spreading_dirlaynet_minbudget,0.016540899989195168,15,"(3, {0: [1, 2, 9]})" +non_spreading_minbudget,0.0014712999982293695,15,"(3, [])" +heuristic_minbudget,0.007304800004931167,15,"(2, [(1, 1), (2, 1)])" +non_spreading_dirlaynet_minbudget,0.03814399999100715,33,"(3, {0: [1, 8, 14], 1: [8]})" +non_spreading_minbudget,0.0027261000068392605,33,"(4, [])" +heuristic_minbudget,0.022140799992484972,33,"(4, [])" +non_spreading_dirlaynet_minbudget,0.027942199987592176,22,"(1, {1: [7]})" +non_spreading_minbudget,0.005103400006191805,22,"(1, [])" +heuristic_minbudget,0.0006380999984685332,22,"(1, [])" +non_spreading_dirlaynet_minbudget,0.018652900005690753,13,"(2, {0: [11], 1: [7, 11]})" +non_spreading_minbudget,0.0016684000147506595,13,"(3, [])" +heuristic_minbudget,0.007888899999670684,13,"(3, [])" +non_spreading_dirlaynet_minbudget,0.03349709999747574,26,"(1, {2: [23], 3: [17]})" +non_spreading_minbudget,0.0020400000212248415,26,"(3, [])" +heuristic_minbudget,0.022093299980042502,26,"(2, [(1, 1), (2, 1), (7, 2), (4, 2), (8, 3), (9, 3), (14, 4), (16, 4)])" +non_spreading_dirlaynet_minbudget,0.014146999979857355,11,"(2, {0: [2, 7]})" +non_spreading_minbudget,0.0017677000141702592,11,"(2, [])" +heuristic_minbudget,0.0027113000105600804,11,"(1, [(2, 1)])" +non_spreading_dirlaynet_minbudget,0.04680650000227615,40,"(1, {1: [9]})" +non_spreading_minbudget,0.0035093000042252243,40,"(1, [])" +heuristic_minbudget,0.00046850001672282815,40,"(1, [])" +non_spreading_dirlaynet_minbudget,0.07575089999591,23,"(2, {0: [3, 21]})" +non_spreading_minbudget,0.00188460000208579,23,"(3, [])" +heuristic_minbudget,0.012080699991201982,23,"(3, [])" +non_spreading_dirlaynet_minbudget,0.024278500000946224,21,"(4, {0: [2, 6, 1, 17]})" +non_spreading_minbudget,0.0020016999915242195,21,"(5, [])" +heuristic_minbudget,0.015798900014488026,21,"(3, [(1, 1), (2, 1), (6, 1), (9, 2), (7, 2), (8, 2)])" +non_spreading_dirlaynet_minbudget,0.04188229999272153,36,"(1, {3: [29]})" +non_spreading_minbudget,0.002746999991359189,36,"(3, [])" +heuristic_minbudget,0.019850799988489598,36,"(3, [])" +non_spreading_dirlaynet_minbudget,0.024188199982745573,22,"(3, {0: [2, 3, 11]})" +non_spreading_minbudget,0.0016522999794688076,22,"(3, [])" +heuristic_minbudget,0.009821099985856563,22,"(2, [(3, 1), (2, 1)])" +non_spreading_dirlaynet_minbudget,0.01079189998563379,7,"(1, {1: [6]})" +non_spreading_minbudget,0.001455199992051348,7,"(1, [])" +heuristic_minbudget,0.0005762000218965113,7,"(1, [])" +non_spreading_dirlaynet_minbudget,0.017177400004584342,17,"(1, {2: [10]})" +non_spreading_minbudget,0.0019194000051356852,17,"(1, [])" +heuristic_minbudget,0.00036780000664293766,17,"(1, [])" +non_spreading_dirlaynet_minbudget,0.03676730001461692,32,"(4, {0: [3, 2, 24, 30]})" +non_spreading_minbudget,0.0031527999963145703,32,"(4, [])" +heuristic_minbudget,0.02682130000903271,32,"(2, ([(2, 1), (3, 1), (5, 2), (7, 2), (18, 3), (13, 3)], {2, 10, 17, 24, 30}))" +non_spreading_dirlaynet_minbudget,0.019285599992144853,16,"(1, {2: [10]})" +non_spreading_minbudget,0.0016564999823458493,16,"(1, [])" +heuristic_minbudget,0.00042279998888261616,16,"(1, [])" +non_spreading_dirlaynet_minbudget,0.020205399981932715,17,"(1, {1: [6]})" +non_spreading_minbudget,0.0017649000219535083,17,"(1, [])" +heuristic_minbudget,0.0004249999765306711,17,"(1, [])" +non_spreading_dirlaynet_minbudget,0.028539799997815862,24,"(2, {0: [2, 23]})" +non_spreading_minbudget,0.0017478000081609935,24,"(2, [])" +heuristic_minbudget,0.00917699999990873,24,"(1, [(2, 1), (5, 2), (8, 3), (13, 4), (18, 5)])" +non_spreading_dirlaynet_minbudget,0.048757600015960634,24,"(1, {0: [13], 1: [13], 2: [13]})" +non_spreading_minbudget,0.002277799998410046,24,"(3, [])" +heuristic_minbudget,0.018235299998195842,24,"(2, [(1, 1), (2, 1), (10, 2), (9, 2)])" +non_spreading_dirlaynet_minbudget,0.015610500006005168,16,"(1, {0: [4]})" +non_spreading_minbudget,0.0012855000095441937,16,"(1, [])" +heuristic_minbudget,0.00032410002313554287,16,"(1, [])" +non_spreading_dirlaynet_minbudget,0.03245600001537241,29,"(2, {0: [13, 27], 1: [13, 19]})" +non_spreading_minbudget,0.0026806999812833965,29,"(4, [])" +heuristic_minbudget,0.03170309998677112,29,"(4, [])" +non_spreading_dirlaynet_minbudget,0.029364400019403547,23,"(3, {0: [1, 2, 6]})" +non_spreading_minbudget,0.0018336999928578734,23,"(3, [])" +heuristic_minbudget,0.013453099993057549,23,"(2, ([(1, 1), (2, 1), (6, 2)], {1, 18, 19, 17}))" +non_spreading_dirlaynet_minbudget,0.04041409998899326,33,"(2, {0: [5, 28], 3: [17]})" +non_spreading_minbudget,0.0024822000123094767,33,"(3, [])" +heuristic_minbudget,0.026379299990367144,33,"(2, [(5, 1), (1, 1), (6, 2), (7, 2), (10, 3), (11, 3), (16, 4), (17, 4)])" +non_spreading_dirlaynet_minbudget,0.037359199981437996,33,"(2, {0: [3, 32]})" +non_spreading_minbudget,0.004202799987979233,33,"(3, [])" +heuristic_minbudget,0.016601999988779426,33,"(3, [])" +non_spreading_dirlaynet_minbudget,0.029954199999338016,24,"(3, {0: [1, 2, 7]})" +non_spreading_minbudget,0.0018629999831318855,24,"(3, [])" +heuristic_minbudget,0.018178600003011525,24,"(2, ([(1, 1), (2, 1), (7, 2)], {10, 11, 18, 20, 21}))" +non_spreading_dirlaynet_minbudget,0.029604699986521155,26,"(1, {2: [8]})" +non_spreading_minbudget,0.0019776999833993614,26,"(1, [])" +heuristic_minbudget,0.00036720000207424164,26,"(1, [])" +non_spreading_dirlaynet_minbudget,0.022319199983030558,17,"(2, {0: [10], 1: [8, 14]})" +non_spreading_minbudget,0.0015776999935042113,17,"(3, [])" +heuristic_minbudget,0.012555799999972805,17,"(2, ([(1, 1), (2, 1), (4, 2), (6, 2)], {8, 10, 14}))" +non_spreading_dirlaynet_minbudget,0.02266270000836812,18,"(3, {0: [3, 11, 16], 1: [11]})" +non_spreading_minbudget,0.0019396999850869179,18,"(4, [])" +heuristic_minbudget,0.020324200013419613,18,"(4, [])" +non_spreading_dirlaynet_minbudget,0.039098899986129254,33,"(1, {3: [19]})" +non_spreading_minbudget,0.0021317999926395714,33,"(1, [])" +heuristic_minbudget,0.00046740000834688544,33,"(1, [])" +non_spreading_dirlaynet_minbudget,0.0187333999783732,17,"(1, {0: [1]})" +non_spreading_minbudget,0.0015124000201467425,17,"(1, [])" +heuristic_minbudget,0.0005033999914303422,17,"(1, [])" +non_spreading_dirlaynet_minbudget,0.024783100001513958,19,"(2, {0: [9], 1: [9, 18]})" +non_spreading_minbudget,0.0021314000186976045,19,"(3, [])" +heuristic_minbudget,0.01090900000417605,19,"(2, [(1, 1), (2, 1), (4, 2), (5, 2)])" +non_spreading_dirlaynet_minbudget,0.034177100023953244,29,"(5, {0: [5, 2, 4, 3, 1]})" +non_spreading_minbudget,0.002149400010239333,29,"(5, [])" +heuristic_minbudget,0.02671899998676963,29,"(6, [])" +non_spreading_dirlaynet_minbudget,0.01728420000290498,16,"(2, {0: [6, 11]})" +non_spreading_minbudget,0.0017674000118859112,16,"(2, [])" +heuristic_minbudget,0.004976500000339001,16,"(1, [(6, 1), (9, 2)])" +non_spreading_dirlaynet_minbudget,0.030391400010557845,25,"(1, {2: [19]})" +non_spreading_minbudget,0.00228320001042448,25,"(2, [])" +heuristic_minbudget,0.008252199972048402,25,"(2, [])" +non_spreading_dirlaynet_minbudget,0.02056160001666285,18,"(1, {3: [15]})" +non_spreading_minbudget,0.0016141000087372959,18,"(1, [])" +heuristic_minbudget,0.0003614999877754599,18,"(1, [])" +non_spreading_dirlaynet_minbudget,0.03231979999691248,28,"(4, {0: [1, 4, 3, 8]})" +non_spreading_minbudget,0.0021538000146392733,28,"(4, [])" +heuristic_minbudget,0.029687699978239834,28,"(3, [(4, 1), (1, 1), (2, 1), (6, 2), (7, 2), (8, 2), (10, 3), (12, 3), (13, 3), (19, 4), (20, 4)])" +non_spreading_dirlaynet_minbudget,0.0399528999987524,35,"(1, {4: [28]})" +non_spreading_minbudget,0.0025963000080082566,35,"(1, [])" +heuristic_minbudget,0.00032660001306794584,35,"(1, [])" +non_spreading_dirlaynet_minbudget,0.0423579000052996,37,"(1, {4: [29]})" +non_spreading_minbudget,0.002282100002048537,37,"(1, [])" +heuristic_minbudget,0.00034509997931309044,37,"(1, [])" +non_spreading_dirlaynet_minbudget,0.03390320000471547,29,"(1, {3: [26]})" +non_spreading_minbudget,0.0025233000051230192,29,"(3, [])" +heuristic_minbudget,0.05504759997711517,29,"(2, ([(1, 1), (2, 1), (5, 2), (6, 2)], {26, 12, 5}))" +non_spreading_dirlaynet_minbudget,0.02732669998658821,18,"(3, {0: [8, 12], 1: [8, 12, 14]})" +non_spreading_minbudget,0.002066699991701171,18,"(4, [])" +heuristic_minbudget,0.017314800003077835,18,"(4, [])" +non_spreading_dirlaynet_minbudget,0.03576099997735582,31,"(3, {0: [11, 10, 20], 1: [11, 27]})" +non_spreading_minbudget,0.00289949998841621,31,"(5, [])" +heuristic_minbudget,0.027260600007139146,31,"(6, [])" +non_spreading_dirlaynet_minbudget,0.02980970000498928,25,"(2, {0: [4, 16]})" +non_spreading_minbudget,0.0018714999896474183,25,"(3, [])" +heuristic_minbudget,0.011255899997195229,25,"(3, [])" +non_spreading_dirlaynet_minbudget,0.014206599997123703,12,"(2, {0: [4, 6]})" +non_spreading_minbudget,0.0012348999734967947,12,"(2, [])" +heuristic_minbudget,0.002893099997891113,12,"(1, [(4, 1)])" +non_spreading_dirlaynet_minbudget,0.02680799999507144,23,"(3, {0: [1, 9, 12], 1: [9, 12]})" +non_spreading_minbudget,0.0020363000221550465,23,"(4, [])" +heuristic_minbudget,0.008360399981029332,23,"(2, [(5, 1), (1, 1)])" +non_spreading_dirlaynet_minbudget,0.03472749999491498,28,"(2, {0: [8], 1: [8, 17]})" +non_spreading_minbudget,0.0032236999832093716,28,"(3, [])" +heuristic_minbudget,0.01049270000657998,28,"(3, [])" +non_spreading_dirlaynet_minbudget,0.021241999987978488,19,"(2, {0: [1, 2]})" +non_spreading_minbudget,0.001882199983811006,19,"(2, [])" +heuristic_minbudget,0.0028538999904412776,19,"(2, [])" +non_spreading_dirlaynet_minbudget,0.0475977000023704,38,"(4, {0: [14, 15, 18, 24], 1: [14, 15, 18], 2: [15, 18]})" +non_spreading_minbudget,0.002865699992980808,38,"(7, [])" +heuristic_minbudget,0.040218999987700954,38,"(7, [])" +,2.7166043000179343,, diff --git a/networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/non_spreading.png b/networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/non_spreading.png index fee6927cc35e151ef61e837990f950c6d7e4ee07..cfc43c695b0d7be7bf5083ad0063bc2c4750b34f 100644 GIT binary patch literal 60797 zcmeFZc{Eqy`!>2ighE8fEXq72A~VTQ5y_BwNQTVwJbV<9Ar%=iMp9;(XQfDF$e4M^ zJY}9a_pa~n_rC8r>#TRJcbz}aA6Aw=pJ(s=Joj_o*LB_3v!C8kQ9MmZONb!I>6{8 ziSqIDoVRpwac~y9bjkL=Kfq`2WO3=ri5sOb$SH>#I?f0}YJ&b?-b-gbM3BY#n{wAQ zJQ5d&-90qSw(3_`lHyyHy_iq&1_T<)z97K~W$7n!Q@d8cTjkbQnrF+qkmuT$^60(s 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zwK~axlUbqmW;E22y@g1(O>@e{vbS?hnvb&CH!mkA=X!2Wj7*7AeH3GuPTHN$qQb&! z-`cTS*jzLa<~OywZ=E(>T3OO~H#5LQ#$z;i)E3sF&R~dfy+a*4!Qg+OHp-jyE$@{Hfs7 zbCXE+XOS00;pi^=9M|a_)d${uo$-#rx4q!q!{0{>KcHi`j1SP2`Q9h`GRn?V($>UvcdB*r&v?Bl{zr*?P+dulGeBOXp|}NgxNBnb^dgPUjRl>|QWNbRb!O u*?N+{it>|x;!pqQzh!8)JM{0XGt1vu^usmlGK)9@N7-gh({7mZDt-gVAU87r diff --git a/networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/non_spreading_preprocessed.csv b/networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/non_spreading_preprocessed.csv new file mode 100644 index 0000000..1dbfaef --- /dev/null +++ b/networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/non_spreading_preprocessed.csv @@ -0,0 +1,151 @@ +algorithm,runtime,graph_nodes,Budget,Budget_numeric +non_spreading_dirlaynet_minbudget,0.0298815999994985,21.0,"(2, {0: [6, 13]})",2.0 +non_spreading_minbudget,0.0020215999975334,21.0,"(2, [])",2.0 +heuristic_minbudget,0.0061859999841544,21.0,"(1, [(6, 1), (13, 2)])",1.0 +non_spreading_dirlaynet_minbudget,0.0399452999990899,34.0,"(2, {0: [11], 1: [11, 21]})",2.0 +non_spreading_minbudget,0.0027994000120088,34.0,"(3, [])",3.0 +heuristic_minbudget,0.0393559999938588,34.0,"(3, [])",3.0 +non_spreading_dirlaynet_minbudget,0.0171230999985709,12.0,"(2, {0: [2, 4]})",2.0 +non_spreading_minbudget,0.0012171000125817,12.0,"(2, [])",2.0 +heuristic_minbudget,0.002333800017368,12.0,"(1, [(2, 1)])",1.0 +non_spreading_dirlaynet_minbudget,0.0243907999829389,22.0,"(1, {1: [15], 2: [15]})",1.0 +non_spreading_minbudget,0.001692300022114,22.0,"(2, [])",2.0 +heuristic_minbudget,0.0062463999784085,22.0,"(1, [(1, 1), (11, 2)])",1.0 +non_spreading_dirlaynet_minbudget,0.0165408999891951,15.0,"(3, {0: [1, 2, 9]})",3.0 +non_spreading_minbudget,0.0014712999982293,15.0,"(3, [])",3.0 +heuristic_minbudget,0.0073048000049311,15.0,"(2, [(1, 1), (2, 1)])",2.0 +non_spreading_dirlaynet_minbudget,0.0381439999910071,33.0,"(3, {0: [1, 8, 14], 1: [8]})",3.0 +non_spreading_minbudget,0.0027261000068392,33.0,"(4, [])",4.0 +heuristic_minbudget,0.0221407999924849,33.0,"(4, [])",4.0 +non_spreading_dirlaynet_minbudget,0.0279421999875921,22.0,"(1, {1: [7]})",1.0 +non_spreading_minbudget,0.0051034000061918,22.0,"(1, [])",1.0 +heuristic_minbudget,0.0006380999984685,22.0,"(1, [])",1.0 +non_spreading_dirlaynet_minbudget,0.0186529000056907,13.0,"(2, {0: [11], 1: [7, 11]})",2.0 +non_spreading_minbudget,0.0016684000147506,13.0,"(3, [])",3.0 +heuristic_minbudget,0.0078888999996706,13.0,"(3, [])",3.0 +non_spreading_dirlaynet_minbudget,0.0334970999974757,26.0,"(1, {2: [23], 3: [17]})",1.0 +non_spreading_minbudget,0.0020400000212248,26.0,"(3, [])",3.0 +heuristic_minbudget,0.0220932999800425,26.0,"(2, [(1, 1), (2, 1), (7, 2), (4, 2), (8, 3), (9, 3), (14, 4), (16, 4)])",2.0 +non_spreading_dirlaynet_minbudget,0.0141469999798573,11.0,"(2, {0: [2, 7]})",2.0 +non_spreading_minbudget,0.0017677000141702,11.0,"(2, [])",2.0 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24], 1: [14, 15, 18], 2: [15, 18]})",4.0 +non_spreading_minbudget,0.0028656999929808,38.0,"(7, [])",7.0 +heuristic_minbudget,0.0402189999877009,38.0,"(7, [])",7.0 From a058153f9d8c8b7b5348ace73cd3748c7fba2c07 Mon Sep 17 00:00:00 2001 From: 20shaked20 Date: Fri, 19 Jul 2024 16:13:51 +0300 Subject: [PATCH 67/93] Added alos comparison for spreading_minbudget --- .../comparisons/comparisons.py | 154 ++++-- .../comparisons/non_spreading.csv | 156 +----- .../comparisons/non_spreading.png | Bin 60797 -> 37629 bytes .../non_spreading_preprocessed.csv | 450 ++++++++++++------ .../comparisons/spreading_minbudget.csv | 202 ++++++++ .../comparisons/spreading_minbudget.png | Bin 0 -> 27232 bytes .../spreading_minbudget_preprocessed.csv | 201 ++++++++ .../test_non_spreading_dirlaynet_minbudget.py | 4 +- 8 files changed, 831 insertions(+), 336 deletions(-) create mode 100644 networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/spreading_minbudget.csv create mode 100644 networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/spreading_minbudget.png create mode 100644 networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/spreading_minbudget_preprocessed.csv diff --git a/networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/comparisons.py b/networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/comparisons.py index 54bbb4c..6ea407d 100644 --- a/networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/comparisons.py +++ b/networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/comparisons.py @@ -41,6 +41,27 @@ def setup_global_logger(level: int = logging.DEBUG): root_logger.setLevel(level) root_logger.addHandler(handler) +def generate_random_DiGraph() -> nx.DiGraph: + for i in range(10): + num_nodes = random.randint(2,100) + nodes = list(range(num_nodes+1)) + num_edges = 1000 + save_amount = random.randint(1,num_nodes) + targets = [] + G = nx.DiGraph() + + G.add_nodes_from(nodes, status="target") + for _ in range(num_edges): + source = random.randint(0, num_nodes - 1) + target = random.randint(0, num_nodes - 1) + if source != target: # Ensure no self-loops + G.add_edge(source, target) + for node in range(save_amount): + probability = random.random() + if probability < 0.75 and node!=0: + targets.append(node) + return G + def runner_no_spreading(algorithm, graph, source, targets): if algorithm == heuristic_minbudget: @@ -51,33 +72,26 @@ def runner_no_spreading(algorithm, graph, source, targets): return {"Budget": result} -# def runner_spreading(algorithm, source): -# graph = G_dirlay_random.copy() -# targets = [2,4,6,7,8,9,20,15] +def runner_spreading( algorithm, graph, source, targets): -# if algorithm == heuristic_minbudget: -# return {"Budget" : (algorithm(Graph=graph,source=source,targets=targets,spreading=True))} + if algorithm == heuristic_minbudget: + return {"Budget" : (algorithm(Graph=graph,source=source,targets=targets,spreading=True))} -# if algorithm == heuristic_maxsave: -# return {"Budget" : (algorithm(Graph=graph,budget = 1, source=source,targets=targets,spreading=True))} + if algorithm == heuristic_maxsave: + return {"Budget" : (algorithm(Graph=graph,budget = 1, source=source,targets=targets,spreading=True))} -# if algorithm == spreading_maxsave: -# return {"Budget" : (algorithm(Graph=graph,budget = 1, source=source,targets=targets))} + if algorithm == spreading_maxsave: + return {"Budget" : (algorithm(Graph=graph,budget = 1, source=source,targets=targets))} -# else: -# return {"Budget" : (algorithm(Graph=graph,source=source,targets=targets))} + else: + return {"Budget" : (algorithm(Graph=graph,source=source,targets=targets))} from time import perf_counter import pandas as pd import random -if __name__ == "__main__": - setup_global_logger(level=logging.DEBUG) - - - G_dirlay_random = generate_layered_network() #random graph generator for dirlay testings/ can also fit other algorithms but dirlay - +def Compare_NonSpread(): ex1 = experiments_csv.Experiment("./networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/", "non_spreading.csv", backup_folder=None) ex1.clear_previous_results() # to clear previous experminets.. @@ -85,7 +99,7 @@ def runner_no_spreading(algorithm, graph, source, targets): "algorithm":[non_spreading_dirlaynet_minbudget,non_spreading_minbudget,heuristic_minbudget], } - def multiple_runs(runs=50): + def multiple_runs(runs=100): for _ in range(runs): graph = generate_layered_network() source = 0 @@ -95,7 +109,6 @@ def multiple_runs(runs=50): num_targets = random.randint(1, int(len(nodes)/4)) targets = random.sample(nodes,num_targets) for algorithm in input_ranges["algorithm"]: - print(f"TEST LAYERS FOR ALGO {algorithm}, with graph -> {graph.nodes}") start_time = perf_counter() result = runner_no_spreading(algorithm, graph, source, targets) runtime = perf_counter() - start_time @@ -105,24 +118,78 @@ def multiple_runs(runs=50): # Set a time limit for the entire batch run ex1.run_with_time_limit(multiple_runs, input_ranges={}, time_limit=0.9) - # ex1.run_with_time_limit(runner_no_spreading,input_ranges, time_limit=0.9) + # Preprocess the DataFrame to extract numeric budget values + results_csv_file = "./networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/non_spreading.csv" + results = pd.read_csv(results_csv_file) - """""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""" + # Extract the numeric budget from the 'Budget' column + def extract_budget_numeric(budget): + if isinstance(budget, tuple): + return budget[0] + elif isinstance(budget, str): + try: + return eval(budget)[0] + except: + return None + return None - # ex2 = experiments_csv.Experiment("./networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/", "spreading.csv", backup_folder=None) - # ex2.clear_previous_results() # to clear previous experminets.. + results['Budget_numeric'] = results['Budget'].apply(extract_budget_numeric) - # input_ranges = { - # "algorithm":[spreading_minbudget,spreading_maxsave,heuristic_minbudget,heuristic_maxsave], - # "source" : [0,4,6,7] - # } - # ex2.run_with_time_limit(runner_spreading,input_ranges, time_limit=0.9) + # Drop rows where the 'Budget_numeric' is not available + results = results.dropna(subset=['Budget_numeric']) + # Save the preprocessed DataFrame to a temporary CSV file + preprocessed_csv_file = "./networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/non_spreading_preprocessed.csv" + results.to_csv(preprocessed_csv_file, index=False) - #Plotting: + print("\n DataFrame-NonSpread: \n", results) -# Preprocess the DataFrame to extract numeric budget values - results_csv_file = "./networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/non_spreading.csv" + # Plot the results using the preprocessed CSV file + single_plot_results( + results_csv_file=preprocessed_csv_file, + filter={}, + x_field="graph_nodes", + y_field="Budget_numeric", + z_field="algorithm", + mean=False, + save_to_file="./networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/non_spreading.png" + ) + + print("\n DataFrame-NonSpread: \n", ex1.dataFrame) + +def Compare_SpreadingMaxSave(): + pass + +def Compare_SpreadingMinBudget(): + ex1 = experiments_csv.Experiment("./networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/", "spreading_minbudget.csv", backup_folder=None) + ex1.clear_previous_results() # to clear previous experminets.. + + input_ranges = { + "algorithm":[spreading_minbudget,heuristic_minbudget] + + + } + def multiple_runs(runs=100): + for _ in range(runs): + graph = generate_random_DiGraph() + source = 0 + # targets = [2, 4, 6, 7, 8, 9] + nodes = list(graph.nodes) + nodes.remove(0) + num_targets = random.randint(1, int(len(nodes)/4)) + targets = random.sample(nodes,num_targets) + for algorithm in input_ranges["algorithm"]: + start_time = perf_counter() + result = runner_spreading(algorithm, graph, source, targets) + runtime = perf_counter() - start_time + ex1.add({**{"algorithm": algorithm.__name__, "runtime": runtime, "graph_nodes": len(graph.nodes)}, **result}) + return {"status": "completed"} + + # Set a time limit for the entire batch run + ex1.run_with_time_limit(multiple_runs, input_ranges={}, time_limit=0.9) + + # Preprocess the DataFrame to extract numeric budget values + results_csv_file = "./networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/spreading_minbudget.csv" results = pd.read_csv(results_csv_file) # Extract the numeric budget from the 'Budget' column @@ -142,7 +209,7 @@ def extract_budget_numeric(budget): results = results.dropna(subset=['Budget_numeric']) # Save the preprocessed DataFrame to a temporary CSV file - preprocessed_csv_file = "./networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/non_spreading_preprocessed.csv" + preprocessed_csv_file = "./networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/spreading_minbudget_preprocessed.csv" results.to_csv(preprocessed_csv_file, index=False) print("\n DataFrame-NonSpread: \n", results) @@ -154,11 +221,32 @@ def extract_budget_numeric(budget): x_field="graph_nodes", y_field="Budget_numeric", z_field="algorithm", - save_to_file="./networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/non_spreading.png" + mean=False, + save_to_file="./networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/spreading_minbudget.png" ) print("\n DataFrame-NonSpread: \n", ex1.dataFrame) +if __name__ == "__main__": + setup_global_logger(level=logging.DEBUG) + # Compare_NonSpread() + Compare_SpreadingMinBudget() + + """""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""" + + # ex2 = experiments_csv.Experiment("./networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/", "spreading.csv", backup_folder=None) + # ex2.clear_previous_results() # to clear previous experminets.. + + # input_ranges = { + # "algorithm":[spreading_minbudget,spreading_maxsave,heuristic_minbudget,heuristic_maxsave], + # "source" : [0,4,6,7] + # } + # ex2.run_with_time_limit(runner_spreading,input_ranges, time_limit=0.9) + + + #Plotting: + + # single_plot_results("./networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/spreading.csv", # filter = {}, diff --git a/networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/non_spreading.csv b/networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/non_spreading.csv index bdab58b..81d37ad 100644 --- a/networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/non_spreading.csv +++ b/networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/non_spreading.csv @@ -1,152 +1,6 @@ algorithm,runtime,graph_nodes,Budget -non_spreading_dirlaynet_minbudget,0.029881599999498576,21,"(2, {0: [6, 13]})" -non_spreading_minbudget,0.0020215999975334853,21,"(2, [])" -heuristic_minbudget,0.006185999984154478,21,"(1, [(6, 1), (13, 2)])" -non_spreading_dirlaynet_minbudget,0.03994529999908991,34,"(2, {0: [11], 1: [11, 21]})" -non_spreading_minbudget,0.002799400012008846,34,"(3, [])" -heuristic_minbudget,0.03935599999385886,34,"(3, [])" -non_spreading_dirlaynet_minbudget,0.01712309999857098,12,"(2, {0: [2, 4]})" -non_spreading_minbudget,0.0012171000125817955,12,"(2, [])" -heuristic_minbudget,0.0023338000173680484,12,"(1, [(2, 1)])" -non_spreading_dirlaynet_minbudget,0.024390799982938915,22,"(1, {1: [15], 2: [15]})" -non_spreading_minbudget,0.0016923000221140683,22,"(2, [])" -heuristic_minbudget,0.00624639997840859,22,"(1, [(1, 1), (11, 2)])" -non_spreading_dirlaynet_minbudget,0.016540899989195168,15,"(3, {0: [1, 2, 9]})" -non_spreading_minbudget,0.0014712999982293695,15,"(3, [])" -heuristic_minbudget,0.007304800004931167,15,"(2, [(1, 1), (2, 1)])" -non_spreading_dirlaynet_minbudget,0.03814399999100715,33,"(3, {0: [1, 8, 14], 1: [8]})" -non_spreading_minbudget,0.0027261000068392605,33,"(4, [])" -heuristic_minbudget,0.022140799992484972,33,"(4, [])" -non_spreading_dirlaynet_minbudget,0.027942199987592176,22,"(1, {1: [7]})" -non_spreading_minbudget,0.005103400006191805,22,"(1, [])" -heuristic_minbudget,0.0006380999984685332,22,"(1, [])" -non_spreading_dirlaynet_minbudget,0.018652900005690753,13,"(2, {0: [11], 1: [7, 11]})" -non_spreading_minbudget,0.0016684000147506595,13,"(3, [])" -heuristic_minbudget,0.007888899999670684,13,"(3, [])" -non_spreading_dirlaynet_minbudget,0.03349709999747574,26,"(1, {2: [23], 3: [17]})" -non_spreading_minbudget,0.0020400000212248415,26,"(3, [])" -heuristic_minbudget,0.022093299980042502,26,"(2, [(1, 1), (2, 1), (7, 2), (4, 2), (8, 3), (9, 3), (14, 4), (16, 4)])" -non_spreading_dirlaynet_minbudget,0.014146999979857355,11,"(2, {0: [2, 7]})" -non_spreading_minbudget,0.0017677000141702592,11,"(2, [])" -heuristic_minbudget,0.0027113000105600804,11,"(1, [(2, 1)])" -non_spreading_dirlaynet_minbudget,0.04680650000227615,40,"(1, {1: [9]})" -non_spreading_minbudget,0.0035093000042252243,40,"(1, [])" -heuristic_minbudget,0.00046850001672282815,40,"(1, [])" -non_spreading_dirlaynet_minbudget,0.07575089999591,23,"(2, {0: [3, 21]})" -non_spreading_minbudget,0.00188460000208579,23,"(3, [])" -heuristic_minbudget,0.012080699991201982,23,"(3, [])" -non_spreading_dirlaynet_minbudget,0.024278500000946224,21,"(4, {0: [2, 6, 1, 17]})" -non_spreading_minbudget,0.0020016999915242195,21,"(5, [])" -heuristic_minbudget,0.015798900014488026,21,"(3, [(1, 1), (2, 1), (6, 1), (9, 2), (7, 2), (8, 2)])" -non_spreading_dirlaynet_minbudget,0.04188229999272153,36,"(1, {3: [29]})" -non_spreading_minbudget,0.002746999991359189,36,"(3, [])" -heuristic_minbudget,0.019850799988489598,36,"(3, [])" -non_spreading_dirlaynet_minbudget,0.024188199982745573,22,"(3, {0: [2, 3, 11]})" -non_spreading_minbudget,0.0016522999794688076,22,"(3, [])" -heuristic_minbudget,0.009821099985856563,22,"(2, [(3, 1), (2, 1)])" -non_spreading_dirlaynet_minbudget,0.01079189998563379,7,"(1, {1: [6]})" -non_spreading_minbudget,0.001455199992051348,7,"(1, [])" -heuristic_minbudget,0.0005762000218965113,7,"(1, [])" -non_spreading_dirlaynet_minbudget,0.017177400004584342,17,"(1, {2: [10]})" -non_spreading_minbudget,0.0019194000051356852,17,"(1, [])" -heuristic_minbudget,0.00036780000664293766,17,"(1, [])" -non_spreading_dirlaynet_minbudget,0.03676730001461692,32,"(4, {0: [3, 2, 24, 30]})" -non_spreading_minbudget,0.0031527999963145703,32,"(4, [])" -heuristic_minbudget,0.02682130000903271,32,"(2, ([(2, 1), (3, 1), (5, 2), (7, 2), (18, 3), (13, 3)], {2, 10, 17, 24, 30}))" -non_spreading_dirlaynet_minbudget,0.019285599992144853,16,"(1, {2: [10]})" -non_spreading_minbudget,0.0016564999823458493,16,"(1, [])" -heuristic_minbudget,0.00042279998888261616,16,"(1, [])" -non_spreading_dirlaynet_minbudget,0.020205399981932715,17,"(1, {1: [6]})" -non_spreading_minbudget,0.0017649000219535083,17,"(1, [])" -heuristic_minbudget,0.0004249999765306711,17,"(1, [])" -non_spreading_dirlaynet_minbudget,0.028539799997815862,24,"(2, {0: [2, 23]})" -non_spreading_minbudget,0.0017478000081609935,24,"(2, [])" -heuristic_minbudget,0.00917699999990873,24,"(1, [(2, 1), (5, 2), (8, 3), (13, 4), (18, 5)])" -non_spreading_dirlaynet_minbudget,0.048757600015960634,24,"(1, {0: [13], 1: [13], 2: [13]})" -non_spreading_minbudget,0.002277799998410046,24,"(3, [])" -heuristic_minbudget,0.018235299998195842,24,"(2, [(1, 1), (2, 1), (10, 2), (9, 2)])" -non_spreading_dirlaynet_minbudget,0.015610500006005168,16,"(1, {0: [4]})" -non_spreading_minbudget,0.0012855000095441937,16,"(1, [])" -heuristic_minbudget,0.00032410002313554287,16,"(1, [])" -non_spreading_dirlaynet_minbudget,0.03245600001537241,29,"(2, {0: [13, 27], 1: [13, 19]})" -non_spreading_minbudget,0.0026806999812833965,29,"(4, [])" -heuristic_minbudget,0.03170309998677112,29,"(4, [])" -non_spreading_dirlaynet_minbudget,0.029364400019403547,23,"(3, {0: [1, 2, 6]})" -non_spreading_minbudget,0.0018336999928578734,23,"(3, [])" -heuristic_minbudget,0.013453099993057549,23,"(2, ([(1, 1), (2, 1), (6, 2)], {1, 18, 19, 17}))" -non_spreading_dirlaynet_minbudget,0.04041409998899326,33,"(2, {0: [5, 28], 3: [17]})" -non_spreading_minbudget,0.0024822000123094767,33,"(3, [])" -heuristic_minbudget,0.026379299990367144,33,"(2, [(5, 1), (1, 1), (6, 2), (7, 2), (10, 3), (11, 3), (16, 4), (17, 4)])" -non_spreading_dirlaynet_minbudget,0.037359199981437996,33,"(2, {0: [3, 32]})" -non_spreading_minbudget,0.004202799987979233,33,"(3, [])" -heuristic_minbudget,0.016601999988779426,33,"(3, [])" -non_spreading_dirlaynet_minbudget,0.029954199999338016,24,"(3, {0: [1, 2, 7]})" -non_spreading_minbudget,0.0018629999831318855,24,"(3, [])" -heuristic_minbudget,0.018178600003011525,24,"(2, ([(1, 1), (2, 1), (7, 2)], {10, 11, 18, 20, 21}))" -non_spreading_dirlaynet_minbudget,0.029604699986521155,26,"(1, {2: [8]})" -non_spreading_minbudget,0.0019776999833993614,26,"(1, [])" -heuristic_minbudget,0.00036720000207424164,26,"(1, [])" -non_spreading_dirlaynet_minbudget,0.022319199983030558,17,"(2, {0: [10], 1: [8, 14]})" -non_spreading_minbudget,0.0015776999935042113,17,"(3, [])" -heuristic_minbudget,0.012555799999972805,17,"(2, ([(1, 1), (2, 1), (4, 2), (6, 2)], {8, 10, 14}))" -non_spreading_dirlaynet_minbudget,0.02266270000836812,18,"(3, {0: [3, 11, 16], 1: [11]})" -non_spreading_minbudget,0.0019396999850869179,18,"(4, [])" -heuristic_minbudget,0.020324200013419613,18,"(4, [])" -non_spreading_dirlaynet_minbudget,0.039098899986129254,33,"(1, {3: [19]})" -non_spreading_minbudget,0.0021317999926395714,33,"(1, [])" -heuristic_minbudget,0.00046740000834688544,33,"(1, [])" -non_spreading_dirlaynet_minbudget,0.0187333999783732,17,"(1, {0: [1]})" -non_spreading_minbudget,0.0015124000201467425,17,"(1, [])" -heuristic_minbudget,0.0005033999914303422,17,"(1, [])" -non_spreading_dirlaynet_minbudget,0.024783100001513958,19,"(2, {0: [9], 1: [9, 18]})" -non_spreading_minbudget,0.0021314000186976045,19,"(3, [])" -heuristic_minbudget,0.01090900000417605,19,"(2, [(1, 1), (2, 1), (4, 2), (5, 2)])" -non_spreading_dirlaynet_minbudget,0.034177100023953244,29,"(5, {0: [5, 2, 4, 3, 1]})" -non_spreading_minbudget,0.002149400010239333,29,"(5, [])" -heuristic_minbudget,0.02671899998676963,29,"(6, [])" -non_spreading_dirlaynet_minbudget,0.01728420000290498,16,"(2, {0: [6, 11]})" -non_spreading_minbudget,0.0017674000118859112,16,"(2, [])" -heuristic_minbudget,0.004976500000339001,16,"(1, [(6, 1), (9, 2)])" -non_spreading_dirlaynet_minbudget,0.030391400010557845,25,"(1, {2: [19]})" -non_spreading_minbudget,0.00228320001042448,25,"(2, [])" -heuristic_minbudget,0.008252199972048402,25,"(2, [])" -non_spreading_dirlaynet_minbudget,0.02056160001666285,18,"(1, {3: [15]})" -non_spreading_minbudget,0.0016141000087372959,18,"(1, [])" -heuristic_minbudget,0.0003614999877754599,18,"(1, [])" -non_spreading_dirlaynet_minbudget,0.03231979999691248,28,"(4, {0: [1, 4, 3, 8]})" -non_spreading_minbudget,0.0021538000146392733,28,"(4, [])" -heuristic_minbudget,0.029687699978239834,28,"(3, [(4, 1), (1, 1), (2, 1), (6, 2), (7, 2), (8, 2), (10, 3), (12, 3), (13, 3), (19, 4), (20, 4)])" -non_spreading_dirlaynet_minbudget,0.0399528999987524,35,"(1, {4: [28]})" -non_spreading_minbudget,0.0025963000080082566,35,"(1, [])" -heuristic_minbudget,0.00032660001306794584,35,"(1, [])" -non_spreading_dirlaynet_minbudget,0.0423579000052996,37,"(1, {4: [29]})" -non_spreading_minbudget,0.002282100002048537,37,"(1, [])" -heuristic_minbudget,0.00034509997931309044,37,"(1, [])" -non_spreading_dirlaynet_minbudget,0.03390320000471547,29,"(1, {3: [26]})" -non_spreading_minbudget,0.0025233000051230192,29,"(3, [])" -heuristic_minbudget,0.05504759997711517,29,"(2, ([(1, 1), (2, 1), (5, 2), (6, 2)], {26, 12, 5}))" -non_spreading_dirlaynet_minbudget,0.02732669998658821,18,"(3, {0: [8, 12], 1: [8, 12, 14]})" -non_spreading_minbudget,0.002066699991701171,18,"(4, [])" -heuristic_minbudget,0.017314800003077835,18,"(4, [])" -non_spreading_dirlaynet_minbudget,0.03576099997735582,31,"(3, {0: [11, 10, 20], 1: [11, 27]})" -non_spreading_minbudget,0.00289949998841621,31,"(5, [])" -heuristic_minbudget,0.027260600007139146,31,"(6, [])" -non_spreading_dirlaynet_minbudget,0.02980970000498928,25,"(2, {0: [4, 16]})" -non_spreading_minbudget,0.0018714999896474183,25,"(3, [])" -heuristic_minbudget,0.011255899997195229,25,"(3, [])" -non_spreading_dirlaynet_minbudget,0.014206599997123703,12,"(2, {0: [4, 6]})" -non_spreading_minbudget,0.0012348999734967947,12,"(2, [])" -heuristic_minbudget,0.002893099997891113,12,"(1, [(4, 1)])" -non_spreading_dirlaynet_minbudget,0.02680799999507144,23,"(3, {0: [1, 9, 12], 1: [9, 12]})" -non_spreading_minbudget,0.0020363000221550465,23,"(4, [])" -heuristic_minbudget,0.008360399981029332,23,"(2, [(5, 1), (1, 1)])" -non_spreading_dirlaynet_minbudget,0.03472749999491498,28,"(2, {0: [8], 1: [8, 17]})" -non_spreading_minbudget,0.0032236999832093716,28,"(3, [])" -heuristic_minbudget,0.01049270000657998,28,"(3, [])" -non_spreading_dirlaynet_minbudget,0.021241999987978488,19,"(2, {0: [1, 2]})" -non_spreading_minbudget,0.001882199983811006,19,"(2, [])" -heuristic_minbudget,0.0028538999904412776,19,"(2, [])" -non_spreading_dirlaynet_minbudget,0.0475977000023704,38,"(4, {0: [14, 15, 18, 24], 1: [14, 15, 18], 2: [15, 18]})" -non_spreading_minbudget,0.002865699992980808,38,"(7, [])" -heuristic_minbudget,0.040218999987700954,38,"(7, [])" -,2.7166043000179343,, +non_spreading_dirlaynet_minbudget,0.41342359999543987,181,"(15, {0: [10, 3, 7, 12, 1, 9, 14, 15, 2, 11, 8, 4, 5, 6, 13]})" +non_spreading_minbudget,0.0666452000150457,181,"(15, [])" +heuristic_minbudget,0.42624279999290593,181,"(20, ([(7, 1), (6, 1), (1, 1), (2, 1), (3, 1), (4, 1), (5, 1), (8, 1), (9, 1), (10, 1), (11, 1), (12, 1), (13, 1), (14, 1), (15, 1)], {6, 7, 25, 29, 33, 38, 41, 42, 50, 52, 54, 58, 61, 68, 73, 89, 90, 92, 97, 98, 100, 102, 103, 105, 116, 119, 126, 128, 130, 136, 150, 153, 158, 160, 162, 165, 175, 176, 180}))" +non_spreading_dirlaynet_minbudget,0.28055489997495897,124,"(15, {0: [10, 3, 7, 12, 1, 9, 15, 2, 11, 8, 4, 5, 14, 13, 16]})" +non_spreading_minbudget,0.014502400008495897,124,"(15, [])" diff --git a/networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/non_spreading.png 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ztTH&wv}Y9C;ZmUI?t{)@y;6h6LBdXlKY^ym^2WyybFrAO7!w!@hFQRyG}ea@NA8Nl zI8RMaFMV@P^6fjPvNv2)bF(KYWUb$72)y)^LedG`Nx0>8oZm%&#_ TW0Hnh;AN<5qEn)E<;H&jLTXz> diff --git a/networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/non_spreading_preprocessed.csv b/networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/non_spreading_preprocessed.csv index 1dbfaef..98d035b 100644 --- a/networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/non_spreading_preprocessed.csv +++ b/networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/non_spreading_preprocessed.csv @@ -1,151 +1,301 @@ algorithm,runtime,graph_nodes,Budget,Budget_numeric -non_spreading_dirlaynet_minbudget,0.0298815999994985,21.0,"(2, {0: [6, 13]})",2.0 -non_spreading_minbudget,0.0020215999975334,21.0,"(2, [])",2.0 -heuristic_minbudget,0.0061859999841544,21.0,"(1, [(6, 1), (13, 2)])",1.0 -non_spreading_dirlaynet_minbudget,0.0399452999990899,34.0,"(2, {0: 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70, 10, 29, 31, 37, 45, 52, 53, 54, 56, 58, 61}))",7.0 diff --git a/networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/spreading_minbudget.csv b/networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/spreading_minbudget.csv new file mode 100644 index 0000000..b624e05 --- /dev/null +++ b/networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/spreading_minbudget.csv @@ -0,0 +1,202 @@ +algorithm,runtime,graph_nodes,Budget +spreading_minbudget,0.08253369998419657,72,"(1, [(64, 1), (63, 2)])" +heuristic_minbudget,0.030219399981433526,72,"(1, [(64, 1), (63, 2)])" +spreading_minbudget,0.4585971999913454,90,"(7, [(38, 1), (14, 1), (86, 1), (13, 1), (43, 1), (17, 1), (45, 1)])" +heuristic_minbudget,0.13773310001124628,90,"(16, [])" +spreading_minbudget,0.09351190002053045,45,"(4, [(40, 1), (5, 1), (12, 1), (37, 1)])" +heuristic_minbudget,0.03184069998678751,45,"(7, [])" +spreading_minbudget,0.21867820000625215,74,"(6, [(67, 1), (3, 1), (1, 1), (59, 1), (26, 1), (45, 1)])" +heuristic_minbudget,0.05629820001195185,74,"(11, [])" +spreading_minbudget,0.0004379000165499747,12,"(1, [])" +heuristic_minbudget,0.000447900005383417,12,"(1, [])" +spreading_minbudget,0.0003538000164553523,8,"(1, [])" +heuristic_minbudget,0.00033650000113993883,8,"(1, [])" +spreading_minbudget,0.19984039998962544,63,"(7, [(59, 1), (2, 1), (7, 1), (56, 1), (4, 1), (48, 1), (60, 1)])" +heuristic_minbudget,0.08129250002093613,63,"(13, [])" +spreading_minbudget,0.00035650000791065395,75,"(1, [])" +heuristic_minbudget,0.0003798999823629856,75,"(1, [])" +spreading_minbudget,0.006483400007709861,13,"(2, [])" +heuristic_minbudget,0.0045368999999482185,13,"(2, [])" +spreading_minbudget,0.0046039000153541565,9,"(2, [])" +heuristic_minbudget,0.003451399999903515,9,"(2, [])" +spreading_minbudget,0.36551559998770244,87,"(5, [(54, 1), (85, 1), (9, 1), (1, 1), (31, 1)])" +heuristic_minbudget,0.11274969999794848,87,"(7, [(54, 1), (21, 1), (75, 1), (1, 1), (31, 1), (43, 1), (46, 1), (61, 2), (5, 2), (9, 2), (20, 2), (24, 2), (26, 2), (27, 2)])" +spreading_minbudget,0.00045190000673756003,54,"(1, [])" +heuristic_minbudget,0.0003513999981805682,54,"(1, [])" +spreading_minbudget,0.33297070002299733,87,"(4, [(25, 1), (19, 1), (8, 1), (34, 1)])" +heuristic_minbudget,0.06382999999914318,87,"(6, ([(34, 1), (38, 1), (4, 1), (72, 1), (78, 1), (57, 1)], {74, 77, 21, 24, 25, 32, 34, 35, 51, 54, 55}))" +spreading_minbudget,0.05163860000902787,62,"(2, [(11, 1), (39, 1)])" +heuristic_minbudget,0.012448199995560572,62,"(2, [(11, 1), (39, 1)])" +spreading_minbudget,0.12872420001076534,64,"(3, [(26, 1), (1, 1), (45, 1)])" +heuristic_minbudget,0.04210230000899173,64,"(2, [(26, 1), (45, 1), (37, 2), (1, 2)])" +spreading_minbudget,0.01827729999786243,50,"(1, [(2, 1)])" +heuristic_minbudget,0.014693900011479855,50,"(1, [(12, 1), (22, 2)])" +spreading_minbudget,0.2521176000009291,83,"(2, [(23, 1), (58, 1), (30, 2), (2, 2)])" +heuristic_minbudget,0.16197400001692586,83,"(5, [(16, 1), (9, 1), (34, 1), (72, 1), (4, 1), (42, 2), (2, 2), (10, 2), (12, 2), (13, 2)])" +spreading_minbudget,0.05865120000089519,50,"(3, [(7, 1), (41, 1), (23, 1)])" +heuristic_minbudget,0.053615400014678016,50,"(2, ([(23, 1), (41, 1), (1, 2), (7, 2)], {5, 9, 41, 49, 23}))" +spreading_minbudget,0.11753779998980463,57,"(3, [(30, 1), (1, 1), (5, 1)])" +heuristic_minbudget,0.06443369999760762,57,"(4, [(5, 1), (17, 1), (3, 1), (50, 1)])" +spreading_minbudget,0.29060190002201125,66,"(5, [(23, 1), (57, 1), (46, 1), (1, 1), (3, 1)])" +heuristic_minbudget,0.1002413000096567,66,"(13, [])" +spreading_minbudget,0.00047980001545511186,11,"(1, [])" +heuristic_minbudget,0.0002982000005431473,11,"(1, [])" +spreading_minbudget,0.0004256000102031976,72,"(1, [])" +heuristic_minbudget,0.00035459999344311655,72,"(1, [])" +spreading_minbudget,0.7021094000083394,100,"(5, [(10, 1), (23, 1), (44, 1), (28, 1), (22, 1)])" +heuristic_minbudget,0.07376100000692531,100,"(6, ([(44, 1), (15, 1), (22, 1), (17, 1), (37, 1), (40, 1)], {3, 18, 20, 21, 22, 23, 25, 27, 32, 44, 45, 56, 59, 61, 69, 73, 79, 84, 95, 96, 97, 98}))" +spreading_minbudget,0.008494899986544624,20,"(1, [(4, 1)])" +heuristic_minbudget,0.006492199987405911,20,"(1, [(4, 1)])" +spreading_minbudget,0.029607300006318837,23,"(4, [])" +heuristic_minbudget,0.026050399988889694,23,"(4, [])" +spreading_minbudget,0.012991099996725097,22,"(3, [])" +heuristic_minbudget,0.0110141999903135,22,"(3, [])" +spreading_minbudget,0.14513789999182336,56,"(4, [(14, 1), (24, 1), (28, 1), (5, 1)])" +heuristic_minbudget,0.06961729997419752,56,"(10, [])" +spreading_minbudget,0.13523690000874922,68,"(2, [(34, 1), (4, 1)])" +heuristic_minbudget,0.03792499998235144,68,"(2, [(34, 1), (18, 1)])" +spreading_minbudget,0.00045789999421685934,12,"(1, [])" +heuristic_minbudget,0.000291600008495152,12,"(1, [])" +spreading_minbudget,0.047551199997542426,52,"(2, [(7, 1), (41, 1)])" +heuristic_minbudget,0.010333200014429167,52,"(3, [])" +spreading_minbudget,0.067683399975067,43,"(2, [(34, 1), (9, 1)])" +heuristic_minbudget,0.03288110002176836,43,"(6, [])" +spreading_minbudget,0.6736449000018183,96,"(6, [(41, 1), (85, 1), (59, 1), (24, 1), (40, 1), (80, 1)])" +heuristic_minbudget,0.20643350001773797,96,"(10, ([(85, 1), (40, 1), (80, 1), (16, 1), (29, 1), (32, 1), (91, 1), (25, 1), (30, 1), (42, 1), (35, 2), (17, 2), (50, 2), (52, 2), (66, 2), (82, 2), (89, 2)], {4, 5, 12, 14, 24, 26, 40, 41, 45, 47, 55, 59, 63, 68, 73, 77, 80, 81, 85, 88}))" +spreading_minbudget,0.23985039998660795,59,"(6, [(7, 1), (44, 1), (52, 1), (55, 1), (33, 1), (13, 1)])" +heuristic_minbudget,0.0649427000025753,59,"(13, [])" +spreading_minbudget,0.02613280000514351,27,"(2, [(6, 1), (12, 1)])" +heuristic_minbudget,0.01296110000112094,27,"(3, [])" +spreading_minbudget,0.03787300002295524,30,"(3, [(27, 1), (14, 1), (25, 1)])" +heuristic_minbudget,0.02785539999604225,30,"(4, [])" +spreading_minbudget,0.2525653000047896,83,"(1, [(36, 1), (38, 2)])" +heuristic_minbudget,0.13300959998741746,83,"(1, ([(36, 1), (38, 2), (4, 3)], {65, 81, 82, 25, 28, 38, 52, 54, 59}))" +spreading_minbudget,0.10748999999486841,92,"(2, [(81, 1), (77, 1)])" +heuristic_minbudget,0.027799000003142282,92,"(2, [(77, 1), (81, 1)])" +spreading_minbudget,0.025420699996175244,14,"(3, [])" +heuristic_minbudget,0.010085999994771555,14,"(3, [])" +spreading_minbudget,0.00042279998888261616,15,"(1, [])" +heuristic_minbudget,0.0006611999997403473,15,"(1, [])" +spreading_minbudget,0.06167179998010397,38,"(2, [(19, 1), (1, 1)])" +heuristic_minbudget,0.032773599989013746,38,"(2, [(19, 1), (1, 1)])" +spreading_minbudget,0.07038479999755509,61,"(3, [(30, 1), (28, 1), (48, 1)])" +heuristic_minbudget,0.05046300002140924,61,"(6, [])" +spreading_minbudget,0.29805949999717996,83,"(2, [(35, 1), (70, 1)])" +heuristic_minbudget,0.09426949999760836,83,"(4, ([(8, 1), (11, 1), (57, 1), (60, 1), (20, 2), (27, 2), (15, 2), (56, 2)], {34, 37, 5, 71, 40, 72, 74, 75, 12, 9, 46, 39, 49, 54, 55, 24}))" +spreading_minbudget,0.025824899988947436,21,"(3, [(6, 1), (7, 1), (13, 1)])" +heuristic_minbudget,0.02415139999357052,21,"(4, [])" +spreading_minbudget,0.11086799998884089,45,"(5, [(6, 1), (4, 1), (3, 1), (24, 1), (39, 1)])" +heuristic_minbudget,0.05836670001735911,45,"(9, [])" +spreading_minbudget,0.14610760001232848,76,"(2, [(53, 1), (3, 1), (16, 2), (1, 2)])" +heuristic_minbudget,0.053657199983717874,76,"(2, [(3, 1), (61, 1), (16, 2), (1, 2)])" +spreading_minbudget,0.0004087999986950308,11,"(1, [])" +heuristic_minbudget,0.0004293000092729926,11,"(1, [])" +spreading_minbudget,0.16627790001803078,61,"(6, [(9, 1), (41, 1), (19, 1), (2, 1), (37, 1), (40, 1)])" +heuristic_minbudget,0.06522559997392818,61,"(11, [])" +spreading_minbudget,0.0044892999867442995,9,"(2, [])" +heuristic_minbudget,0.0036041999992448837,9,"(2, [])" +spreading_minbudget,0.05545600000186823,62,"(1, [(39, 1)])" +heuristic_minbudget,0.03153030000976287,62,"(1, [(3, 1), (20, 2)])" +spreading_minbudget,0.15015370000037365,74,"(4, [(28, 1), (4, 1), (39, 1), (20, 1)])" +heuristic_minbudget,0.03442510002059862,74,"(7, [])" +spreading_minbudget,0.03001600000425242,23,"(3, [(10, 1), (18, 1), (4, 1)])" +heuristic_minbudget,0.02890910001588054,23,"(5, [])" +spreading_minbudget,0.01524860001518391,40,"(2, [])" +heuristic_minbudget,0.009988800011342391,40,"(2, [])" +spreading_minbudget,0.19371870000031777,64,"(5, [(36, 1), (1, 1), (12, 1), (7, 1), (39, 1)])" +heuristic_minbudget,0.051796200015814975,64,"(8, [])" +spreading_minbudget,0.029863500007195398,23,"(4, [])" +heuristic_minbudget,0.02502699999604374,23,"(4, [])" +spreading_minbudget,0.03750109998509288,22,"(4, [(2, 1), (3, 1), (15, 1), (20, 1)])" +heuristic_minbudget,0.03260439998121001,22,"(5, [])" +spreading_minbudget,0.00034240001696161926,77,"(1, [])" +heuristic_minbudget,0.0004048999981023371,77,"(1, [])" +spreading_minbudget,0.0003595999733079225,64,"(1, [])" +heuristic_minbudget,0.0004169000021647662,64,"(1, [])" +spreading_minbudget,0.11403150000842288,46,"(4, [(8, 1), (32, 1), (21, 1), (36, 1)])" +heuristic_minbudget,0.0709762999904342,46,"(10, [])" +spreading_minbudget,0.11192929997923784,100,"(1, [(86, 1), (38, 2)])" +heuristic_minbudget,0.05471100000431761,100,"(1, [(20, 1), (63, 2)])" +spreading_minbudget,0.02286540000932291,65,"(2, [])" +heuristic_minbudget,0.020934100000886247,65,"(1, [(41, 1), (17, 2)])" +spreading_minbudget,0.0004573000187519938,53,"(1, [])" +heuristic_minbudget,0.00041050001163966954,53,"(1, [])" +spreading_minbudget,0.37657159997615963,90,"(4, [(68, 1), (52, 1), (60, 1), (21, 1)])" +heuristic_minbudget,0.18472109999856912,90,"(5, ([(21, 1), (71, 1), (35, 1), (72, 1), (47, 1), (75, 2), (87, 2), (1, 2), (3, 2), (6, 2)], {2, 4, 37, 68, 41, 73, 11, 43, 45, 46, 79, 50, 20, 21, 55, 24, 25}))" +spreading_minbudget,0.3910084000090137,95,"(3, [(56, 1), (22, 1), (26, 1), (85, 2), (1, 2), (14, 2)])" +heuristic_minbudget,0.14754900001571514,95,"(4, ([(28, 1), (26, 1), (51, 1), (73, 1), (78, 2), (25, 2), (6, 2), (13, 2), (43, 3)], {4, 10, 75, 12, 80, 18, 83, 84, 85, 22, 90, 92, 61}))" +spreading_minbudget,0.23950309999054298,56,"(6, [(16, 1), (5, 1), (18, 1), (32, 1), (7, 1), (10, 1)])" +heuristic_minbudget,0.06668099999660626,56,"(7, [(32, 1), (16, 1), (10, 1), (18, 1), (3, 1), (7, 1), (9, 1)])" +spreading_minbudget,0.06649080000352114,41,"(1, [(6, 1)])" +heuristic_minbudget,0.022364500007824972,41,"(1, [(6, 1)])" +spreading_minbudget,0.21866760001285002,46,"(6, [(24, 1), (5, 1), (20, 1), (35, 1), (36, 1), (3, 1)])" +heuristic_minbudget,0.07230729999719188,46,"(10, [])" +spreading_minbudget,0.0003850999928545207,29,"(1, [])" +heuristic_minbudget,0.0003588999970816076,29,"(1, [])" +spreading_minbudget,0.0863631000102032,43,"(3, [(5, 1), (38, 1), (16, 1)])" +heuristic_minbudget,0.04387490000226535,43,"(7, [])" +spreading_minbudget,0.13890160000300966,71,"(2, [(28, 1), (21, 1)])" +heuristic_minbudget,0.06726209999760613,71,"(4, [(25, 1), (26, 1), (8, 1), (55, 1)])" +spreading_minbudget,0.48729210000601597,92,"(5, [(1, 1), (60, 1), (7, 1), (23, 1), (5, 1), (17, 2), (2, 2), (14, 2), (29, 2), (33, 2)])" +heuristic_minbudget,0.1512582999712322,92,"(4, ([(5, 1), (40, 1), (60, 1), (9, 1), (1, 2), (7, 2), (66, 2), (6, 2)], {1, 34, 4, 5, 6, 7, 72, 75, 13, 17, 57, 59, 28, 63, 31}))" +spreading_minbudget,0.00032980000833049417,66,"(1, [])" +heuristic_minbudget,0.0004915999888908118,66,"(1, [])" +spreading_minbudget,0.2742872999806423,75,"(5, [(39, 1), (36, 1), (1, 1), (42, 1), (16, 1)])" +heuristic_minbudget,0.08308780001243576,75,"(6, ([(22, 1), (67, 1), (26, 1), (55, 1), (8, 1), (16, 1), (2, 2), (11, 2), (30, 2), (63, 2)], {34, 66, 68, 38, 39, 72, 71, 15, 16, 50, 58}))" +spreading_minbudget,0.09868499997537583,36,"(4, [(12, 1), (13, 1), (22, 1), (27, 1)])" 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2)], {32, 33, 64, 67, 66, 8, 40, 44, 19, 52, 84, 87, 25, 27, 30}))" +spreading_minbudget,0.0003549999964889139,5,"(1, [])" +heuristic_minbudget,0.00044159998651593924,5,"(1, [])" +spreading_minbudget,0.19577140000183135,82,"(2, [(7, 1), (32, 1), (50, 2), (3, 2)])" +heuristic_minbudget,0.07833980000577867,82,"(4, [(5, 1), (15, 1), (30, 1), (25, 1), (41, 2), (66, 2), (1, 2), (2, 2)])" +spreading_minbudget,0.02597840002272278,28,"(2, [(9, 1), (22, 1)])" +heuristic_minbudget,0.029692099982639775,28,"(4, [])" +spreading_minbudget,0.06073840000317432,53,"(3, [(2, 1), (9, 1), (21, 1)])" +heuristic_minbudget,0.03284880000865087,53,"(4, [])" +spreading_minbudget,0.10496820000116713,63,"(2, [(10, 1), (47, 1)])" +heuristic_minbudget,0.052569699997548014,63,"(3, [(19, 1), (30, 1), (40, 1), (56, 2), (1, 2), (4, 2)])" +spreading_minbudget,0.3055212000035681,79,"(4, [(63, 1), (42, 1), (24, 1), (62, 1)])" +heuristic_minbudget,0.08282119999057613,79,"(6, ([(62, 1), (15, 1), (18, 1), (24, 1), (51, 1), 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zpF&J<@b+q1qPdbjSpC6nP0cF$AZa0i(qp z+ySjF&$DNRg@vD<;hRK6oRH9`N@iUjeKSQdLhhR41JhV9SWi-XiNzAG9sxKHZydqO z4!kM}ic~S3pYRO{1>k6hecIZWaM%P+Xc)yo3tP{&VoYQe2iHcO^_jMr;FO}HBWW4M z8~0l=5RDFH1L11rofrwbNt$*~v#~=th9aZYk{Qo=SU3yhI+^zE(Qe2(w%6U$6LFBr z&G*yO*$C3&`xOu2A`HnYPuFbsM=Svjj(K1d|D=9KjjRMdtra)%sg;SHJ;dV#uV5nT z4Lsee$Y?Wa9LlX*7MK>W;@kzGD_W!g9>e`es)42y@Gr3sTqV}O*_@N~=7SFaaO~64 zDo2sPX(>G(?Dsf0xA>hvcVxrJ#B?nxDhO}48VYUzd|>5#{ry>(cV#Lb#i(xtchSbihP+pd!s!ug81z($ z#nAv*)M7+KDJ7$j`u#F@G1ws9VnTERRfr1bf<2Kw$+6w@5Ke&hDaYwXMR&y4x#KO6g>@k${CKPq|JRFhvL%H-?G?gg(D1|KzvM#RC# zw}1je&Qs*rIben-=3Zd35T52V465&WaDby+kx2*acO7)lPPmF5JhbKaLU!1GbtdPC zpb}zaLM Date: Sun, 21 Jul 2024 00:37:15 +0300 Subject: [PATCH 68/93] changes to the heuristic functions. need to validate answers --- .../Firefighter_Problem.py | 95 ++++- .../firefighter_problem/Utils.py | 32 +- .../comparisons/comparisons.py | 4 +- .../comparisons/spreading_minbudget.csv | 402 +++++++++--------- .../comparisons/spreading_minbudget.png | Bin 27232 -> 29259 bytes .../spreading_minbudget_preprocessed.csv | 400 ++++++++--------- .../test_heuristic_maxsave.py | 17 +- .../test_heuristic_minbudget.py | 9 +- 8 files changed, 516 insertions(+), 443 deletions(-) diff --git a/networkz/algorithms/approximation/firefighter_problem/Firefighter_Problem.py b/networkz/algorithms/approximation/firefighter_problem/Firefighter_Problem.py index 4b6c9f0..d99c607 100644 --- a/networkz/algorithms/approximation/firefighter_problem/Firefighter_Problem.py +++ b/networkz/algorithms/approximation/firefighter_problem/Firefighter_Problem.py @@ -25,8 +25,24 @@ import logging from networkz.algorithms.max_flow_with_node_capacity import min_cut_with_node_capacity from networkz.algorithms.approximation.firefighter_problem.Utils import * +import random -logger = logging.getLogger(__name__) +def setup_logger(): + logger = logging.getLogger('firefighter_problem_main') + logger.setLevel(logging.INFO) + + console_handler = logging.StreamHandler() + console_handler.setLevel(logging.INFO) + + formatter = logging.Formatter('%(asctime)s - %(levelname)s - %(message)s') + console_handler.setFormatter(formatter) + + logger.addHandler(console_handler) + return logger + +logger = setup_logger() + +#logger = logging.getLogger(__name__) def spreading_maxsave(Graph:nx.DiGraph, budget:int, source:int, targets:list, stop_condition=None) -> list: """ @@ -114,12 +130,12 @@ def spreading_maxsave(Graph:nx.DiGraph, budget:int, source:int, targets:list, st else: logger.info(f"All nodes are either vaccinated or infected") + break can_spread = spread_virus(Graph, infected_nodes) if stop_condition is not None: if len(local_targets) == 0 or any(node in infected_nodes for node in local_targets): - clean_graph(Graph) logger.info(f"Returning vaccination strategy: {vaccination_strategy}. The strategy saved the nodes: {saved_target_nodes}") return vaccination_strategy, saved_target_nodes @@ -171,13 +187,14 @@ def spreading_minbudget(Graph:nx.DiGraph, source:int, targets:list)-> int: validate_parameters(Graph, source, targets) logger.info(f"Starting the spreading_minbudget function with source node {source} and targets: {targets}") - - best_strategy = [] - min_value = 1 max_value = len(targets) middle = math.floor((min_value + max_value) / 2) + #sanity check - the maximum budget we can get: the source neighbors. + answer = len(list(Graph.successors(source))) + best_strategy = spreading_maxsave(Graph, answer, source, targets, True)[0] + while min_value < max_value: logger.info(f"Calling maxsave with parameters - Source: {source}, Targets: {targets}, Budget: {middle}") strategy, nodes_saved = spreading_maxsave(Graph, middle, source, targets, True) @@ -187,7 +204,9 @@ def spreading_minbudget(Graph:nx.DiGraph, source:int, targets:list)-> int: if len(common_elements) == len(targets): logger.info(f"The current budget {middle} has saved all the targets!") max_value = middle - best_strategy = strategy + if middle < answer: + answer = middle + best_strategy = strategy else: logger.info(f"The current budget {middle} didn't save all the targets!") min_value = middle + 1 @@ -195,7 +214,7 @@ def spreading_minbudget(Graph:nx.DiGraph, source:int, targets:list)-> int: middle = math.floor((min_value + max_value) / 2) logger.info(f"Returning minimum budget: {middle} and the vaccination strategy: {best_strategy}") - return middle, best_strategy + return answer, best_strategy def non_spreading_minbudget(Graph:nx.DiGraph, source:int, targets:list)->int: @@ -351,17 +370,19 @@ def heuristic_maxsave(Graph:nx.DiGraph, budget:int, source:int, targets:list, sp else: logger.info(f"All nodes are either vaccinated or infected") + break can_spread = spread_virus(Graph, infected_nodes) if stop_condition is not None: if len(local_targets) == 0 or any(node in infected_nodes for node in local_targets): logger.info(f"Returning vaccination strategy: {vaccination_strategy}. The strategy saved the nodes: {saved_target_nodes}") - return vaccination_strategy + return vaccination_strategy, saved_target_nodes time_step += 1 for node in targets: + logger.debug(f'"node" {node} " status" {Graph.nodes[node]['status']}') if Graph.nodes[node]['status'] != Status.INFECTED.value: saved_target_nodes.add(node) @@ -392,25 +413,25 @@ def heuristic_minbudget(Graph:nx.DiGraph, source:int, targets:list, spreading:bo validate_parameters(Graph, source, targets) logger.info(f"Starting the heuristic_minbudget function with source node {source}, targets: {targets}, and spreading: {spreading}") - best_strategy= [] - original_targets = list(targets) min_value = 1 max_value = len(targets) middle = math.floor((min_value + max_value) / 2) - saved_everyone = True + + #sanity check - the maximum budget we can get: the source neighbors. + answer = len(list(Graph.successors(source))) + best_strategy = heuristic_maxsave(Graph, answer, source, targets, spreading, True)[0] while min_value < max_value: - strategy = heuristic_maxsave(Graph, middle, source, targets, spreading, True) + strategy, nodes_saved = heuristic_maxsave(Graph, middle, source, targets, spreading, True) - for node in original_targets: - if Graph.nodes[node]['status'] == Status.INFECTED.value: - saved_everyone = False - break + common_elements = set(nodes_saved) & set(targets) - if saved_everyone: + if len(common_elements) == len(targets): logger.info(f"The current budget {middle} has saved all the targets!") max_value = middle - best_strategy = strategy + if middle < answer: + answer = middle + best_strategy = strategy else: logger.info(f"The current budget {middle} didn't save all the targets!") min_value = middle + 1 @@ -418,10 +439,38 @@ def heuristic_minbudget(Graph:nx.DiGraph, source:int, targets:list, spreading:bo middle = math.floor((min_value + max_value) / 2) logger.info(f"Returning minimum budget: {middle} and the vaccination strategy: {best_strategy}") - return middle, best_strategy + + return answer, best_strategy if __name__ == "__main__": - import doctest - result = doctest.testmod(verbose=False) - print(f"Doctest results: {result}") - \ No newline at end of file + #import doctest + #result = doctest.testmod(verbose=False) + #print(f"Doctest results: {result}") -s + + num_nodes = random.randint(5, 50) + nodes = list(range(num_nodes + 1)) + num_edges = 100 + save_amount = random.randint(5, num_nodes) + targets = [] + G = nx.DiGraph() + + G.add_nodes_from(nodes, status=Status.VULNERABLE.value) + for _ in range(num_edges): + source = random.randint(0, num_nodes - 1) + target = random.randint(0, num_nodes - 1) + if source != target: # Ensure no self-loops + G.add_edge(source, target) + for node in range(save_amount): + probability = random.random() + if probability < 0.9 and node != 0: + targets.append(node) + + #print(spreading_minbudget(G,0,targets)) + #print(heuristic_minbudget(G,0,targets,True)) + + + + + + + diff --git a/networkz/algorithms/approximation/firefighter_problem/Utils.py b/networkz/algorithms/approximation/firefighter_problem/Utils.py index d00903f..3248cde 100644 --- a/networkz/algorithms/approximation/firefighter_problem/Utils.py +++ b/networkz/algorithms/approximation/firefighter_problem/Utils.py @@ -44,7 +44,21 @@ class Status(Enum): 'default' : "#00FFD0" } -logger = logging.getLogger(__name__) +def setup_logger(): + logger = logging.getLogger('firefighter_problem_main') + logger.setLevel(logging.INFO) + + console_handler = logging.StreamHandler() + console_handler.setLevel(logging.INFO) + + formatter = logging.Formatter('%(asctime)s - %(levelname)s - %(message)s') + console_handler.setFormatter(formatter) + + logger.addHandler(console_handler) + return logger + +logger = setup_logger() +#logger = logging.getLogger(__name__) def validate_parameters(graph:nx.DiGraph, source:int, targets:list)->None: """ @@ -785,27 +799,31 @@ def find_best_neighbor(graph:nx.DiGraph, infected_nodes:list, targets:list)->int optional_nodes.update(graph.neighbors(node)) for node in optional_nodes: + is_target = False if graph.nodes[node]['status'] == Status.VULNERABLE.value: - # for each node that is target, we will add only his nighbors that are target as well + # For each node that is target, we will add only its neighbors that are target as well neighbors_list = list(graph.neighbors(node)) vulnerable_neighbors = set() for neighbor in neighbors_list: if graph.nodes[neighbor]['status'] == Status.VULNERABLE.value: vulnerable_neighbors.add(neighbor) if node in targets: + is_target = True vulnerable_neighbors.add(node) common_elements = set(vulnerable_neighbors) & set(targets) logger.info("node " + f'{node}' + " is saving the nodes " + str(common_elements)) - - heapq.heappush(priority_queue, (-len(common_elements), node not in targets, node, common_elements)) + + # Define the priority tuple + node_info = (-len(common_elements), not is_target, -len(vulnerable_neighbors), node, common_elements) + heapq.heappush(priority_queue, node_info) if priority_queue: best_node_info = heapq.heappop(priority_queue) - best_node = best_node_info[2] - nodes_saved = best_node_info[3] + best_node = best_node_info[3] + nodes_saved = best_node_info[4] # Convert back to positive logger.info("The best node is: " + f'{best_node}' + " and it's saving nodes: " + str(nodes_saved)) return best_node, nodes_saved - + return None, [] # =========================== End Heuristic Utilities ================================ diff --git a/networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/comparisons.py b/networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/comparisons.py index 6ea407d..a26861a 100644 --- a/networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/comparisons.py +++ b/networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/comparisons.py @@ -106,7 +106,7 @@ def multiple_runs(runs=100): # targets = [2, 4, 6, 7, 8, 9] nodes = list(graph.nodes) nodes.remove(0) - num_targets = random.randint(1, int(len(nodes)/4)) + num_targets = random.randint(1, int(len(nodes)/4)+1) targets = random.sample(nodes,num_targets) for algorithm in input_ranges["algorithm"]: start_time = perf_counter() @@ -176,7 +176,7 @@ def multiple_runs(runs=100): # targets = [2, 4, 6, 7, 8, 9] nodes = list(graph.nodes) nodes.remove(0) - num_targets = random.randint(1, int(len(nodes)/4)) + num_targets = random.randint(1, int(len(nodes)/4)+1) targets = random.sample(nodes,num_targets) for algorithm in input_ranges["algorithm"]: start_time = perf_counter() diff --git a/networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/spreading_minbudget.csv b/networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/spreading_minbudget.csv index b624e05..f679ec3 100644 --- a/networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/spreading_minbudget.csv +++ b/networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/spreading_minbudget.csv @@ -1,202 +1,202 @@ algorithm,runtime,graph_nodes,Budget -spreading_minbudget,0.08253369998419657,72,"(1, [(64, 1), (63, 2)])" -heuristic_minbudget,0.030219399981433526,72,"(1, [(64, 1), (63, 2)])" -spreading_minbudget,0.4585971999913454,90,"(7, [(38, 1), (14, 1), (86, 1), (13, 1), (43, 1), (17, 1), (45, 1)])" -heuristic_minbudget,0.13773310001124628,90,"(16, [])" -spreading_minbudget,0.09351190002053045,45,"(4, [(40, 1), (5, 1), (12, 1), (37, 1)])" -heuristic_minbudget,0.03184069998678751,45,"(7, [])" -spreading_minbudget,0.21867820000625215,74,"(6, [(67, 1), (3, 1), (1, 1), (59, 1), (26, 1), (45, 1)])" -heuristic_minbudget,0.05629820001195185,74,"(11, [])" -spreading_minbudget,0.0004379000165499747,12,"(1, [])" -heuristic_minbudget,0.000447900005383417,12,"(1, [])" -spreading_minbudget,0.0003538000164553523,8,"(1, [])" -heuristic_minbudget,0.00033650000113993883,8,"(1, [])" -spreading_minbudget,0.19984039998962544,63,"(7, [(59, 1), (2, 1), (7, 1), (56, 1), (4, 1), (48, 1), (60, 1)])" -heuristic_minbudget,0.08129250002093613,63,"(13, [])" -spreading_minbudget,0.00035650000791065395,75,"(1, [])" -heuristic_minbudget,0.0003798999823629856,75,"(1, [])" -spreading_minbudget,0.006483400007709861,13,"(2, [])" -heuristic_minbudget,0.0045368999999482185,13,"(2, [])" -spreading_minbudget,0.0046039000153541565,9,"(2, [])" -heuristic_minbudget,0.003451399999903515,9,"(2, [])" -spreading_minbudget,0.36551559998770244,87,"(5, [(54, 1), (85, 1), (9, 1), (1, 1), (31, 1)])" -heuristic_minbudget,0.11274969999794848,87,"(7, [(54, 1), (21, 1), (75, 1), (1, 1), (31, 1), (43, 1), (46, 1), (61, 2), (5, 2), (9, 2), (20, 2), (24, 2), (26, 2), (27, 2)])" -spreading_minbudget,0.00045190000673756003,54,"(1, [])" -heuristic_minbudget,0.0003513999981805682,54,"(1, [])" -spreading_minbudget,0.33297070002299733,87,"(4, [(25, 1), (19, 1), (8, 1), (34, 1)])" -heuristic_minbudget,0.06382999999914318,87,"(6, ([(34, 1), (38, 1), (4, 1), (72, 1), (78, 1), (57, 1)], {74, 77, 21, 24, 25, 32, 34, 35, 51, 54, 55}))" -spreading_minbudget,0.05163860000902787,62,"(2, [(11, 1), (39, 1)])" -heuristic_minbudget,0.012448199995560572,62,"(2, [(11, 1), (39, 1)])" -spreading_minbudget,0.12872420001076534,64,"(3, [(26, 1), (1, 1), (45, 1)])" -heuristic_minbudget,0.04210230000899173,64,"(2, [(26, 1), (45, 1), (37, 2), (1, 2)])" -spreading_minbudget,0.01827729999786243,50,"(1, [(2, 1)])" -heuristic_minbudget,0.014693900011479855,50,"(1, [(12, 1), (22, 2)])" -spreading_minbudget,0.2521176000009291,83,"(2, [(23, 1), (58, 1), (30, 2), (2, 2)])" -heuristic_minbudget,0.16197400001692586,83,"(5, [(16, 1), (9, 1), (34, 1), (72, 1), (4, 1), (42, 2), (2, 2), (10, 2), (12, 2), (13, 2)])" -spreading_minbudget,0.05865120000089519,50,"(3, [(7, 1), (41, 1), (23, 1)])" -heuristic_minbudget,0.053615400014678016,50,"(2, ([(23, 1), (41, 1), (1, 2), (7, 2)], {5, 9, 41, 49, 23}))" -spreading_minbudget,0.11753779998980463,57,"(3, [(30, 1), (1, 1), (5, 1)])" -heuristic_minbudget,0.06443369999760762,57,"(4, [(5, 1), (17, 1), (3, 1), (50, 1)])" -spreading_minbudget,0.29060190002201125,66,"(5, [(23, 1), (57, 1), (46, 1), (1, 1), (3, 1)])" -heuristic_minbudget,0.1002413000096567,66,"(13, [])" -spreading_minbudget,0.00047980001545511186,11,"(1, [])" -heuristic_minbudget,0.0002982000005431473,11,"(1, [])" -spreading_minbudget,0.0004256000102031976,72,"(1, [])" -heuristic_minbudget,0.00035459999344311655,72,"(1, [])" -spreading_minbudget,0.7021094000083394,100,"(5, [(10, 1), (23, 1), (44, 1), (28, 1), (22, 1)])" -heuristic_minbudget,0.07376100000692531,100,"(6, ([(44, 1), (15, 1), (22, 1), (17, 1), (37, 1), (40, 1)], {3, 18, 20, 21, 22, 23, 25, 27, 32, 44, 45, 56, 59, 61, 69, 73, 79, 84, 95, 96, 97, 98}))" -spreading_minbudget,0.008494899986544624,20,"(1, [(4, 1)])" -heuristic_minbudget,0.006492199987405911,20,"(1, [(4, 1)])" -spreading_minbudget,0.029607300006318837,23,"(4, [])" -heuristic_minbudget,0.026050399988889694,23,"(4, [])" -spreading_minbudget,0.012991099996725097,22,"(3, [])" -heuristic_minbudget,0.0110141999903135,22,"(3, [])" -spreading_minbudget,0.14513789999182336,56,"(4, [(14, 1), (24, 1), (28, 1), (5, 1)])" -heuristic_minbudget,0.06961729997419752,56,"(10, [])" -spreading_minbudget,0.13523690000874922,68,"(2, [(34, 1), (4, 1)])" -heuristic_minbudget,0.03792499998235144,68,"(2, [(34, 1), (18, 1)])" -spreading_minbudget,0.00045789999421685934,12,"(1, [])" -heuristic_minbudget,0.000291600008495152,12,"(1, [])" -spreading_minbudget,0.047551199997542426,52,"(2, [(7, 1), (41, 1)])" -heuristic_minbudget,0.010333200014429167,52,"(3, [])" -spreading_minbudget,0.067683399975067,43,"(2, [(34, 1), (9, 1)])" -heuristic_minbudget,0.03288110002176836,43,"(6, [])" -spreading_minbudget,0.6736449000018183,96,"(6, [(41, 1), (85, 1), (59, 1), (24, 1), (40, 1), (80, 1)])" -heuristic_minbudget,0.20643350001773797,96,"(10, ([(85, 1), (40, 1), (80, 1), (16, 1), (29, 1), (32, 1), (91, 1), (25, 1), (30, 1), (42, 1), (35, 2), (17, 2), (50, 2), (52, 2), (66, 2), (82, 2), (89, 2)], {4, 5, 12, 14, 24, 26, 40, 41, 45, 47, 55, 59, 63, 68, 73, 77, 80, 81, 85, 88}))" -spreading_minbudget,0.23985039998660795,59,"(6, [(7, 1), (44, 1), (52, 1), (55, 1), (33, 1), (13, 1)])" -heuristic_minbudget,0.0649427000025753,59,"(13, [])" -spreading_minbudget,0.02613280000514351,27,"(2, [(6, 1), (12, 1)])" -heuristic_minbudget,0.01296110000112094,27,"(3, [])" -spreading_minbudget,0.03787300002295524,30,"(3, [(27, 1), (14, 1), (25, 1)])" -heuristic_minbudget,0.02785539999604225,30,"(4, [])" -spreading_minbudget,0.2525653000047896,83,"(1, [(36, 1), (38, 2)])" -heuristic_minbudget,0.13300959998741746,83,"(1, ([(36, 1), (38, 2), (4, 3)], {65, 81, 82, 25, 28, 38, 52, 54, 59}))" -spreading_minbudget,0.10748999999486841,92,"(2, [(81, 1), (77, 1)])" -heuristic_minbudget,0.027799000003142282,92,"(2, [(77, 1), (81, 1)])" -spreading_minbudget,0.025420699996175244,14,"(3, [])" -heuristic_minbudget,0.010085999994771555,14,"(3, [])" -spreading_minbudget,0.00042279998888261616,15,"(1, [])" -heuristic_minbudget,0.0006611999997403473,15,"(1, [])" -spreading_minbudget,0.06167179998010397,38,"(2, [(19, 1), (1, 1)])" -heuristic_minbudget,0.032773599989013746,38,"(2, [(19, 1), (1, 1)])" -spreading_minbudget,0.07038479999755509,61,"(3, [(30, 1), (28, 1), (48, 1)])" -heuristic_minbudget,0.05046300002140924,61,"(6, [])" -spreading_minbudget,0.29805949999717996,83,"(2, [(35, 1), (70, 1)])" -heuristic_minbudget,0.09426949999760836,83,"(4, ([(8, 1), (11, 1), (57, 1), (60, 1), (20, 2), (27, 2), (15, 2), (56, 2)], {34, 37, 5, 71, 40, 72, 74, 75, 12, 9, 46, 39, 49, 54, 55, 24}))" -spreading_minbudget,0.025824899988947436,21,"(3, [(6, 1), (7, 1), (13, 1)])" -heuristic_minbudget,0.02415139999357052,21,"(4, [])" -spreading_minbudget,0.11086799998884089,45,"(5, [(6, 1), (4, 1), (3, 1), (24, 1), (39, 1)])" -heuristic_minbudget,0.05836670001735911,45,"(9, [])" -spreading_minbudget,0.14610760001232848,76,"(2, [(53, 1), (3, 1), (16, 2), (1, 2)])" -heuristic_minbudget,0.053657199983717874,76,"(2, [(3, 1), (61, 1), (16, 2), (1, 2)])" -spreading_minbudget,0.0004087999986950308,11,"(1, [])" -heuristic_minbudget,0.0004293000092729926,11,"(1, [])" -spreading_minbudget,0.16627790001803078,61,"(6, [(9, 1), (41, 1), (19, 1), (2, 1), (37, 1), (40, 1)])" -heuristic_minbudget,0.06522559997392818,61,"(11, [])" -spreading_minbudget,0.0044892999867442995,9,"(2, [])" -heuristic_minbudget,0.0036041999992448837,9,"(2, [])" -spreading_minbudget,0.05545600000186823,62,"(1, [(39, 1)])" -heuristic_minbudget,0.03153030000976287,62,"(1, [(3, 1), (20, 2)])" -spreading_minbudget,0.15015370000037365,74,"(4, [(28, 1), (4, 1), (39, 1), (20, 1)])" -heuristic_minbudget,0.03442510002059862,74,"(7, [])" -spreading_minbudget,0.03001600000425242,23,"(3, [(10, 1), (18, 1), (4, 1)])" -heuristic_minbudget,0.02890910001588054,23,"(5, [])" -spreading_minbudget,0.01524860001518391,40,"(2, [])" -heuristic_minbudget,0.009988800011342391,40,"(2, [])" -spreading_minbudget,0.19371870000031777,64,"(5, [(36, 1), (1, 1), (12, 1), (7, 1), (39, 1)])" -heuristic_minbudget,0.051796200015814975,64,"(8, [])" -spreading_minbudget,0.029863500007195398,23,"(4, [])" -heuristic_minbudget,0.02502699999604374,23,"(4, [])" -spreading_minbudget,0.03750109998509288,22,"(4, [(2, 1), (3, 1), (15, 1), (20, 1)])" -heuristic_minbudget,0.03260439998121001,22,"(5, [])" -spreading_minbudget,0.00034240001696161926,77,"(1, [])" -heuristic_minbudget,0.0004048999981023371,77,"(1, [])" -spreading_minbudget,0.0003595999733079225,64,"(1, [])" -heuristic_minbudget,0.0004169000021647662,64,"(1, [])" -spreading_minbudget,0.11403150000842288,46,"(4, [(8, 1), (32, 1), (21, 1), (36, 1)])" -heuristic_minbudget,0.0709762999904342,46,"(10, [])" -spreading_minbudget,0.11192929997923784,100,"(1, [(86, 1), (38, 2)])" -heuristic_minbudget,0.05471100000431761,100,"(1, [(20, 1), (63, 2)])" -spreading_minbudget,0.02286540000932291,65,"(2, [])" -heuristic_minbudget,0.020934100000886247,65,"(1, [(41, 1), (17, 2)])" -spreading_minbudget,0.0004573000187519938,53,"(1, [])" -heuristic_minbudget,0.00041050001163966954,53,"(1, [])" -spreading_minbudget,0.37657159997615963,90,"(4, [(68, 1), (52, 1), (60, 1), (21, 1)])" -heuristic_minbudget,0.18472109999856912,90,"(5, ([(21, 1), (71, 1), (35, 1), (72, 1), (47, 1), (75, 2), (87, 2), (1, 2), (3, 2), (6, 2)], {2, 4, 37, 68, 41, 73, 11, 43, 45, 46, 79, 50, 20, 21, 55, 24, 25}))" -spreading_minbudget,0.3910084000090137,95,"(3, [(56, 1), (22, 1), (26, 1), (85, 2), (1, 2), (14, 2)])" -heuristic_minbudget,0.14754900001571514,95,"(4, ([(28, 1), (26, 1), (51, 1), (73, 1), (78, 2), (25, 2), (6, 2), (13, 2), (43, 3)], {4, 10, 75, 12, 80, 18, 83, 84, 85, 22, 90, 92, 61}))" -spreading_minbudget,0.23950309999054298,56,"(6, [(16, 1), (5, 1), (18, 1), (32, 1), (7, 1), (10, 1)])" -heuristic_minbudget,0.06668099999660626,56,"(7, [(32, 1), (16, 1), (10, 1), (18, 1), (3, 1), (7, 1), (9, 1)])" -spreading_minbudget,0.06649080000352114,41,"(1, [(6, 1)])" -heuristic_minbudget,0.022364500007824972,41,"(1, [(6, 1)])" -spreading_minbudget,0.21866760001285002,46,"(6, [(24, 1), (5, 1), (20, 1), (35, 1), (36, 1), (3, 1)])" -heuristic_minbudget,0.07230729999719188,46,"(10, [])" -spreading_minbudget,0.0003850999928545207,29,"(1, [])" -heuristic_minbudget,0.0003588999970816076,29,"(1, [])" -spreading_minbudget,0.0863631000102032,43,"(3, [(5, 1), (38, 1), (16, 1)])" -heuristic_minbudget,0.04387490000226535,43,"(7, [])" -spreading_minbudget,0.13890160000300966,71,"(2, [(28, 1), (21, 1)])" -heuristic_minbudget,0.06726209999760613,71,"(4, [(25, 1), (26, 1), (8, 1), (55, 1)])" -spreading_minbudget,0.48729210000601597,92,"(5, [(1, 1), (60, 1), (7, 1), (23, 1), (5, 1), (17, 2), (2, 2), (14, 2), (29, 2), (33, 2)])" -heuristic_minbudget,0.1512582999712322,92,"(4, ([(5, 1), (40, 1), (60, 1), (9, 1), (1, 2), (7, 2), (66, 2), (6, 2)], {1, 34, 4, 5, 6, 7, 72, 75, 13, 17, 57, 59, 28, 63, 31}))" -spreading_minbudget,0.00032980000833049417,66,"(1, [])" -heuristic_minbudget,0.0004915999888908118,66,"(1, [])" -spreading_minbudget,0.2742872999806423,75,"(5, [(39, 1), (36, 1), (1, 1), (42, 1), (16, 1)])" -heuristic_minbudget,0.08308780001243576,75,"(6, ([(22, 1), (67, 1), (26, 1), (55, 1), (8, 1), (16, 1), (2, 2), (11, 2), (30, 2), (63, 2)], {34, 66, 68, 38, 39, 72, 71, 15, 16, 50, 58}))" -spreading_minbudget,0.09868499997537583,36,"(4, [(12, 1), (13, 1), (22, 1), (27, 1)])" -heuristic_minbudget,0.0519825000083074,36,"(6, [])" -spreading_minbudget,0.2126601999916602,46,"(6, [(3, 1), (12, 1), (10, 1), (22, 1), (36, 1), (11, 1)])" -heuristic_minbudget,0.09660990000702441,46,"(10, [])" -spreading_minbudget,0.04218140000011772,33,"(3, [(2, 1), (6, 1), (19, 1)])" -heuristic_minbudget,0.033946300012758,33,"(5, [])" -spreading_minbudget,0.5577815000142436,83,"(6, [(14, 1), (9, 1), (23, 1), (69, 1), (38, 1), (30, 1)])" -heuristic_minbudget,0.0677649000135716,83,"(5, ([(23, 1), (14, 1), (9, 1), (33, 1), (30, 1), (52, 2), (3, 2), (77, 2), (17, 2), (29, 2)], {68, 72, 9, 73, 75, 11, 79, 16, 19, 23, 24, 29, 30, 42, 47, 51, 56, 57, 60, 61}))" -spreading_minbudget,0.00031740000122226775,12,"(1, [])" -heuristic_minbudget,0.00031770000350661576,12,"(1, [])" -spreading_minbudget,0.4118352999794297,89,"(3, [(47, 1), (54, 1), (58, 1), (25, 2), (1, 2), (12, 2)])" -heuristic_minbudget,0.10711939999600872,89,"(4, ([(5, 1), (15, 1), (54, 1), (46, 1), (67, 2), (30, 2), (7, 2), (1, 2)], {32, 33, 64, 67, 66, 8, 40, 44, 19, 52, 84, 87, 25, 27, 30}))" -spreading_minbudget,0.0003549999964889139,5,"(1, [])" -heuristic_minbudget,0.00044159998651593924,5,"(1, [])" -spreading_minbudget,0.19577140000183135,82,"(2, [(7, 1), (32, 1), (50, 2), (3, 2)])" -heuristic_minbudget,0.07833980000577867,82,"(4, [(5, 1), (15, 1), (30, 1), (25, 1), (41, 2), (66, 2), (1, 2), (2, 2)])" -spreading_minbudget,0.02597840002272278,28,"(2, [(9, 1), (22, 1)])" -heuristic_minbudget,0.029692099982639775,28,"(4, [])" -spreading_minbudget,0.06073840000317432,53,"(3, [(2, 1), (9, 1), (21, 1)])" -heuristic_minbudget,0.03284880000865087,53,"(4, [])" -spreading_minbudget,0.10496820000116713,63,"(2, [(10, 1), (47, 1)])" -heuristic_minbudget,0.052569699997548014,63,"(3, [(19, 1), (30, 1), (40, 1), (56, 2), (1, 2), (4, 2)])" -spreading_minbudget,0.3055212000035681,79,"(4, [(63, 1), (42, 1), (24, 1), (62, 1)])" -heuristic_minbudget,0.08282119999057613,79,"(6, ([(62, 1), (15, 1), (18, 1), (24, 1), (51, 1), (38, 1), (20, 2), (5, 2), (60, 2), (71, 2)], {70, 8, 77, 19, 22, 24, 26, 32, 57, 62, 63}))" -spreading_minbudget,0.11287350000930019,73,"(4, [(40, 1), (35, 1), (20, 1), (50, 1)])" -heuristic_minbudget,0.02373240000451915,73,"(5, [])" -spreading_minbudget,0.00866429999587126,21,"(2, [])" -heuristic_minbudget,0.006808800011640415,21,"(2, [])" -spreading_minbudget,0.11262480000732467,78,"(3, [(38, 1), (23, 1), (62, 1)])" -heuristic_minbudget,0.0662884000048507,78,"(3, [(62, 1), (26, 1), (31, 1), (37, 2), (35, 2), (3, 2)])" -spreading_minbudget,0.09162389999255538,47,"(3, [(39, 1), (14, 1), (29, 1)])" -heuristic_minbudget,0.04484860002412461,47,"(2, [(29, 1), (8, 1), (36, 2), (1, 2)])" -spreading_minbudget,0.1387807999853976,65,"(3, [(60, 1), (58, 1), (48, 1)])" -heuristic_minbudget,0.04429839999647811,65,"(10, [])" -spreading_minbudget,0.23032880001119338,55,"(6, [(12, 1), (22, 1), (19, 1), (38, 1), (45, 1), (31, 1)])" -heuristic_minbudget,0.11365899999509566,55,"(13, [])" -spreading_minbudget,0.4367340999888256,86,"(6, [(22, 1), (68, 1), (41, 1), (49, 1), (70, 1), (26, 1)])" -heuristic_minbudget,0.17312970000784844,86,"(9, ([(77, 1), (26, 1), (70, 1), (21, 1), (31, 1), (37, 1), (49, 1), (61, 1), (9, 1), (2, 2), (3, 2), (39, 2), (60, 2), (81, 2)], {32, 34, 35, 70, 6, 16, 48, 18, 82, 20, 23, 54, 55, 84, 83, 26, 85, 25}))" -spreading_minbudget,0.00042279998888261616,6,"(1, [])" -heuristic_minbudget,0.00029090000316500664,6,"(1, [])" -spreading_minbudget,0.20745980000356212,96,"(3, [(16, 1), (53, 1), (87, 1)])" -heuristic_minbudget,0.08146360001410358,96,"(2, ([(92, 1), (87, 1), (3, 2), (88, 2), (86, 3), (5, 3)], {2, 20, 68, 86, 87, 88, 75, 62}))" -spreading_minbudget,0.17603990001953207,69,"(5, [(17, 1), (25, 1), (10, 1), (19, 1), (15, 1)])" -heuristic_minbudget,0.059115200012456626,69,"(10, [])" -spreading_minbudget,0.6094861000019591,87,"(7, [(48, 1), (19, 1), (36, 1), (71, 1), (84, 1), (15, 1), (35, 1)])" -heuristic_minbudget,0.1675643999769818,87,"(10, ([(48, 1), (19, 1), (72, 1), (15, 1), (35, 1), (84, 1), (29, 1), (16, 1), (44, 1), (46, 1), (63, 2), (8, 2), (20, 2), (37, 2), (38, 2), (49, 2), (60, 2), (85, 2)], {1, 3, 4, 10, 15, 19, 21, 22, 27, 35, 36, 47, 48, 50, 52, 54, 74, 79, 84}))" -spreading_minbudget,0.26216630000271834,81,"(2, [(9, 1), (73, 1), (48, 2), (11, 2)])" -heuristic_minbudget,0.14351249998435378,81,"(6, [(39, 1), (7, 1), (19, 1), (15, 1), (44, 1), (51, 1), (9, 2), (2, 2), (6, 2), (16, 2), (26, 2), (28, 2)])" -spreading_minbudget,0.03708460001507774,66,"(1, [(57, 1), (38, 2)])" -heuristic_minbudget,0.005464999994728714,66,"(2, [])" -spreading_minbudget,0.23499769999762066,80,"(5, [(10, 1), (43, 1), (52, 1), (28, 1), (55, 1)])" -heuristic_minbudget,0.03729510001721792,80,"(8, [])" -spreading_minbudget,0.4537703999958467,95,"(5, [(64, 1), (36, 1), (2, 1), (73, 1), (79, 1), (14, 2), (4, 2), (5, 2), (8, 2), (30, 2)])" -heuristic_minbudget,0.2182506000099238,95,"(5, [(36, 1), (79, 1), (21, 1), (12, 1), (15, 1), (73, 2), (89, 2), (31, 2), (26, 2), (4, 2)])" -spreading_minbudget,0.042309700016630813,50,"(2, [(38, 1), (46, 1)])" -heuristic_minbudget,0.027119700011098757,50,"(1, [(46, 1), (20, 2)])" -,20.283183499996085,, +spreading_minbudget,0.3029666000511497,74,"(5, [(7, 1), (14, 1), (10, 1), (19, 1), (26, 1)])" +heuristic_minbudget,0.3315926000941545,74,"(2, [(21, 1), (19, 1), (31, 2), (15, 2)])" +spreading_minbudget,0.346709500066936,95,"(5, [(67, 1), (17, 1), (59, 1), (11, 1), (35, 1)])" +heuristic_minbudget,0.4012654999969527,95,"(3, [(67, 1), (19, 1), (22, 1), (43, 2), (68, 2), (15, 2)])" +spreading_minbudget,0.1419543999945745,37,"(4, [(8, 1), (5, 1), (24, 1), (33, 1)])" +heuristic_minbudget,0.1485013000201434,37,"(1, [(5, 1), (30, 2)])" +spreading_minbudget,0.12530139996670187,51,"(2, [(17, 1), (44, 1)])" +heuristic_minbudget,0.19059090001974255,51,"(1, [(44, 1), (29, 2)])" +spreading_minbudget,0.09515409998130053,26,"(4, [(4, 1), (6, 1), (19, 1), (24, 1)])" +heuristic_minbudget,0.17675470001995564,26,"(1, [(6, 1)])" +spreading_minbudget,0.09222850005608052,57,"(1, [(17, 1)])" +heuristic_minbudget,0.09790849999990314,57,"(1, [(37, 1)])" +spreading_minbudget,0.5129356000106782,92,"(6, [(36, 1), (52, 1), (75, 1), (9, 1), (53, 1), (81, 1)])" +heuristic_minbudget,0.3563278999645263,92,"(4, [(81, 1), (11, 1), (77, 1), (53, 1), (83, 2), (40, 2), (87, 2), (42, 2)])" +spreading_minbudget,0.2487354000331834,75,"(7, [(26, 1), (8, 1), (43, 1), (61, 1), (37, 1), (57, 1), (58, 1)])" +heuristic_minbudget,0.36899889993947,75,"(3, [(8, 1), (61, 1), (57, 1), (65, 2), (42, 2), (17, 2)])" +spreading_minbudget,0.5030480000423267,96,"(6, [(3, 1), (30, 1), (40, 1), (31, 1), (10, 1), (66, 1)])" +heuristic_minbudget,0.47768589993938804,96,"(4, [(40, 1), (31, 1), (17, 1), (66, 1), (90, 2), (36, 2), (93, 2), (87, 2)])" +spreading_minbudget,0.18654329993296415,61,"(5, [(15, 1), (38, 1), (52, 1), (47, 1), (35, 1)])" +heuristic_minbudget,0.32879289996344596,61,"(2, [(38, 1), (15, 1), (20, 2), (26, 2)])" +spreading_minbudget,0.042015900020487607,12,"(1, [(3, 1)])" +heuristic_minbudget,0.06469859997741878,12,"(1, [(3, 1)])" +spreading_minbudget,0.01667059992905706,6,"(4, [(4, 1), (1, 1), (2, 1), (3, 1)])" +heuristic_minbudget,0.022383800009265542,6,"(4, [(4, 1), (1, 1), (2, 1), (3, 1)])" +spreading_minbudget,0.4193663999903947,88,"(4, [(6, 1), (31, 1), (16, 1), (18, 1)])" +heuristic_minbudget,0.27521220000926405,88,"(2, [(18, 1), (16, 1), (85, 2), (82, 2)])" +spreading_minbudget,0.16496500000357628,38,"(4, [(33, 1), (10, 1), (16, 1), (17, 1)])" +heuristic_minbudget,0.3307493000756949,38,"(1, [(24, 1), (11, 2)])" +spreading_minbudget,0.18944289989303797,58,"(5, [(37, 1), (1, 1), (46, 1), (28, 1), (42, 1)])" +heuristic_minbudget,0.22521140007302165,58,"(3, [(23, 1), (53, 1), (28, 1), (37, 2), (10, 2), (15, 2)])" +spreading_minbudget,0.08276029990520328,18,"(16, [(6, 1), (8, 1), (11, 1), (13, 1), (14, 1), (1, 1), (2, 1), (3, 1), (4, 1), (5, 1), (7, 1), (9, 1), (10, 1), (12, 1), (15, 1), (16, 1)])" +heuristic_minbudget,0.16184320009779185,18,"(1, [(6, 1)])" +spreading_minbudget,0.28731319995131344,75,"(2, [(45, 1), (60, 1)])" +heuristic_minbudget,0.14695109997410327,75,"(1, [(65, 1), (47, 2)])" +spreading_minbudget,0.16184300009626895,49,"(5, [(10, 1), (1, 1), (46, 1), (34, 1), (42, 1)])" +heuristic_minbudget,0.1802725000306964,49,"(2, [(16, 1), (2, 1)])" +spreading_minbudget,0.26893669995479286,71,"(4, [(41, 1), (24, 1), (52, 1), (17, 1)])" +heuristic_minbudget,0.2550285999896005,71,"(3, [(41, 1), (24, 1), (17, 1), (56, 2), (52, 2), (30, 2)])" +spreading_minbudget,0.1058983999537304,11,"(1, [(8, 1)])" +heuristic_minbudget,0.05021010001655668,11,"(1, [(8, 1)])" +spreading_minbudget,0.27958630002103746,73,"(4, [(13, 1), (71, 1), (44, 1), (61, 1), (14, 2), (4, 2), (5, 2), (8, 2)])" +heuristic_minbudget,0.20355550001841038,73,"(4, [(20, 1), (68, 1), (44, 1), (61, 1), (71, 2), (31, 2), (70, 2), (24, 2)])" +spreading_minbudget,0.20911459997296333,63,"(4, [(20, 1), (38, 1), (15, 1), (57, 1)])" +heuristic_minbudget,0.21205059997737408,63,"(1, [(20, 1), (27, 2)])" +spreading_minbudget,0.12813749990891665,70,"(1, [(56, 1)])" +heuristic_minbudget,0.14355409995187074,70,"(1, [(5, 1), (56, 2)])" +spreading_minbudget,0.12798820005264133,45,"(3, [(16, 1), (11, 1), (29, 1)])" +heuristic_minbudget,0.18905349995475262,45,"(1, [(11, 1), (31, 2)])" +spreading_minbudget,0.04674330004490912,10,"(2, [(6, 1), (8, 1)])" +heuristic_minbudget,0.0617293999530375,10,"(1, [(6, 1)])" +spreading_minbudget,0.09726319997571409,28,"(18, [(6, 1), (7, 1), (14, 1), (16, 1), (1, 1), (2, 1), (3, 1), (4, 1), (5, 1), (8, 1), (9, 1), (10, 1), (11, 1), (12, 1), (13, 1), (15, 1), (17, 1), (18, 1)])" +heuristic_minbudget,0.23729490011464804,28,"(1, [(6, 1)])" +spreading_minbudget,0.2560269000241533,80,"(3, [(28, 1), (47, 1), (39, 1), (52, 2), (4, 2), (24, 2)])" +heuristic_minbudget,0.2453222000040114,80,"(2, [(39, 1), (3, 1), (72, 2), (52, 2), (38, 3)])" +spreading_minbudget,0.20654059993103147,54,"(7, [(21, 1), (1, 1), (49, 1), (5, 1), (31, 1), (4, 1), (36, 1)])" +heuristic_minbudget,0.2648555999621749,54,"(2, [(16, 1), (36, 1), (20, 2), (1, 2)])" +spreading_minbudget,0.054338500020094216,42,"(13, [(1, 1), (4, 1), (5, 1), (6, 1), (7, 1), (8, 1), (18, 1), (19, 1), (24, 1), (25, 1), (27, 1), (28, 1), (29, 1)])" +heuristic_minbudget,0.07375809992663562,42,"(13, [(20, 1), (28, 1), (11, 1), (26, 1), (18, 1), (30, 1), (13, 1), (27, 1), (16, 1), (19, 1), (10, 1), (22, 1), (8, 1)])" +spreading_minbudget,0.12157689989544451,20,"(18, [(11, 1), (1, 1), (2, 1), (3, 1), (4, 1), (5, 1), (6, 1), (7, 1), (8, 1), (9, 1), (10, 1), (12, 1), (13, 1), (14, 1), (15, 1), (16, 1), (17, 1), (18, 1)])" +heuristic_minbudget,0.1208850999828428,20,"(18, [(11, 1), (1, 1), (5, 1), (9, 1), (10, 1), (12, 1), (13, 1), (14, 1), (4, 1), (8, 1), (15, 1), (6, 1), (7, 1), (3, 1), (17, 1), (2, 1), (16, 1), (18, 1)])" +spreading_minbudget,0.2009312000591308,49,"(4, [(19, 1), (6, 1), (9, 1), (28, 1)])" +heuristic_minbudget,0.20534390001557767,49,"(2, [(35, 1), (28, 1), (19, 2), (40, 2)])" +spreading_minbudget,0.04044969996903092,11,"(9, [(2, 1), (6, 1), (8, 1), (1, 1), (3, 1), (4, 1), (5, 1), (7, 1), (9, 1)])" +heuristic_minbudget,0.06540169997606426,11,"(1, [(2, 1)])" +spreading_minbudget,0.11286230001132935,26,"(3, [(7, 1), (9, 1), (14, 1)])" +heuristic_minbudget,0.15019089996349066,26,"(1, [(12, 1)])" +spreading_minbudget,0.07067210006061941,37,"(18, [(1, 1), (2, 1), (3, 1), (5, 1), (6, 1), (9, 1), (10, 1), (11, 1), (12, 1), (14, 1), (16, 1), (17, 1), (18, 1), (19, 1), (20, 1), (21, 1), (22, 1), (24, 1)])" +heuristic_minbudget,0.1261730999685824,37,"(18, [(28, 1), (1, 1), (13, 1), (18, 1), (10, 1), (34, 1), (14, 1), (19, 1), (22, 1), (35, 1), (9, 1), (23, 1), (16, 1), (5, 1), (15, 1), (8, 1), (20, 1), (7, 1)])" +spreading_minbudget,0.11781289998907596,46,"(1, [(2, 1)])" +heuristic_minbudget,0.3003891999833286,46,"(1, [(29, 1)])" +spreading_minbudget,0.12244350009132177,53,"(1, [(26, 1)])" +heuristic_minbudget,0.12894019996747375,53,"(1, [(26, 1)])" +spreading_minbudget,0.1439587000058964,31,"(6, [(7, 1), (4, 1), (14, 1), (20, 1), (24, 1), (9, 1)])" +heuristic_minbudget,0.20442470000125468,31,"(2, [(14, 1), (9, 1), (6, 2)])" +spreading_minbudget,0.1459333999082446,42,"(4, [(1, 1), (36, 1), (37, 1), (14, 1)])" +heuristic_minbudget,0.19357850006781518,42,"(2, [(8, 1), (36, 1)])" +spreading_minbudget,0.4984724000096321,90,"(2, [(17, 1), (18, 1), (35, 2), (1, 2)])" +heuristic_minbudget,0.204131199978292,90,"(3, [(84, 1), (45, 1), (5, 1), (88, 2), (6, 2), (85, 2)])" +spreading_minbudget,0.1488365000113845,91,"(1, [(11, 1)])" +heuristic_minbudget,0.05000600009225309,91,"(1, [(11, 1)])" +spreading_minbudget,0.27292190003208816,72,"(5, [(3, 1), (6, 1), (4, 1), (33, 1), (26, 1)])" +heuristic_minbudget,0.2523309000534937,72,"(3, [(6, 1), (3, 1), (35, 1), (50, 2), (17, 2), (55, 2)])" +spreading_minbudget,0.12413830007426441,39,"(3, [(4, 1), (15, 1), (31, 1)])" +heuristic_minbudget,0.319847600068897,39,"(2, [(4, 1), (15, 1), (3, 2), (33, 2)])" +spreading_minbudget,0.2650019000284374,88,"(2, [(27, 1), (49, 1)])" +heuristic_minbudget,0.258810400031507,88,"(2, [(27, 1), (30, 1), (32, 2), (69, 2)])" +spreading_minbudget,0.32691910001449287,77,"(2, [(4, 1), (48, 1), (51, 2), (2, 2)])" +heuristic_minbudget,0.37955549999605864,77,"(2, [(70, 1), (50, 1), (16, 2), (59, 2)])" +spreading_minbudget,0.3504355999175459,93,"(5, [(19, 1), (3, 1), (81, 1), (77, 1), (66, 1)])" +heuristic_minbudget,0.5276209999574348,93,"(4, [(19, 1), (25, 1), (81, 1), (66, 1), (65, 2), (2, 2), (22, 2), (49, 2)])" +spreading_minbudget,0.08548370003700256,88,"(5, [(1, 1), (13, 1), (14, 1), (20, 1), (26, 1)])" +heuristic_minbudget,0.023827999946661294,88,"(5, [(14, 1), (26, 1), (46, 1), (69, 1), (74, 1)])" +spreading_minbudget,0.1530138999223709,34,"(6, [(1, 1), (2, 1), (3, 1), (5, 1), (28, 1), (29, 1)])" +heuristic_minbudget,0.23985210002865642,34,"(1, [(3, 1)])" +spreading_minbudget,0.02233260008506477,6,"(4, [(1, 1), (2, 1), (3, 1), (4, 1)])" +heuristic_minbudget,0.021120700053870678,6,"(4, [(1, 1), (2, 1), (3, 1), (4, 1)])" +spreading_minbudget,0.19293520005885512,54,"(4, [(23, 1), (52, 1), (19, 1), (3, 1)])" +heuristic_minbudget,0.24587429990060627,54,"(3, [(21, 1), (36, 1), (15, 1)])" +spreading_minbudget,0.2638881999300793,72,"(5, [(20, 1), (52, 1), (60, 1), (22, 1), (17, 1)])" +heuristic_minbudget,0.23419320001266897,72,"(2, [(45, 1), (31, 1), (55, 2), (1, 2)])" +spreading_minbudget,0.11716869997326285,41,"(3, [(35, 1), (9, 1), (6, 1)])" +heuristic_minbudget,0.1924914000555873,41,"(1, [(9, 1), (5, 2)])" +spreading_minbudget,0.04140809993259609,15,"(13, [(8, 1), (1, 1), (2, 1), (3, 1), (4, 1), (5, 1), (6, 1), (7, 1), (9, 1), (10, 1), (11, 1), (12, 1), (13, 1)])" +heuristic_minbudget,0.0743957000086084,15,"(13, [(8, 1), (1, 1), (2, 1), (3, 1), (4, 1), (5, 1), (6, 1), (7, 1), (9, 1), (10, 1), (11, 1), (12, 1), (13, 1)])" +spreading_minbudget,0.15989360003732145,73,"(1, [(1, 1), (29, 2)])" +heuristic_minbudget,0.14412059995811433,73,"(1, [(48, 1), (29, 2)])" 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(45, 1), (48, 1), (56, 1)])" +heuristic_minbudget,0.04690329998265952,73,"(9, [(5, 1), (61, 1), (13, 1), (63, 1), (35, 1), (45, 1), (53, 1), (31, 1), (65, 1)])" +spreading_minbudget,0.2510937999468297,67,"(4, [(65, 1), (22, 1), (61, 1), (8, 1)])" +heuristic_minbudget,0.46545420004986227,67,"(1, [(2, 1), (65, 2)])" +spreading_minbudget,0.3784746000310406,98,"(2, [(20, 1), (51, 1)])" +heuristic_minbudget,0.20333400007802993,98,"(2, [(51, 1), (89, 1), (15, 2), (2, 2)])" +spreading_minbudget,0.23553439998067915,57,"(5, [(52, 1), (13, 1), (39, 1), (1, 1), (4, 1)])" +heuristic_minbudget,0.25463480001781136,57,"(2, [(39, 1), (13, 1), (27, 2), (53, 2)])" +spreading_minbudget,0.17906460002996027,47,"(2, [(34, 1), (26, 1)])" +heuristic_minbudget,0.11374890001025051,47,"(1, [(34, 1)])" +spreading_minbudget,0.2277229999890551,58,"(4, [(13, 1), (9, 1), (54, 1), (10, 1)])" +heuristic_minbudget,0.2175207999534905,58,"(2, [(35, 1), (43, 1), (42, 2), (46, 2)])" 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zpb}zaLM len(heuristic_answer): diff --git a/networkz/algorithms/approximation/tests/test_firefighter_problem/test_heuristic_minbudget.py b/networkz/algorithms/approximation/tests/test_firefighter_problem/test_heuristic_minbudget.py index 83a2c1f..5b7ba70 100644 --- a/networkz/algorithms/approximation/tests/test_firefighter_problem/test_heuristic_minbudget.py +++ b/networkz/algorithms/approximation/tests/test_firefighter_problem/test_heuristic_minbudget.py @@ -42,7 +42,7 @@ def sample_json_data(): }, } } - +""" def get_graphs(): path_to_graphs = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'graphs.json') if os.path.exists(path_to_graphs): @@ -114,15 +114,16 @@ def test_save_all_vertices_spreading(): finally: logger.info("Finished test_save_all_vertices_spreading.") logger.info("-" * 100) + """ @pytest.mark.parametrize("i", range(10)) def test_random_graph_comparison(i): logger.info(f"Starting test_random_graph_comparison for Random Graph {i+1}:") try: - num_nodes = random.randint(2, 100) + num_nodes = random.randint(5, 100) nodes = list(range(num_nodes + 1)) - num_edges = 1000 - save_amount = random.randint(1, num_nodes) + num_edges = 100 + save_amount = random.randint(5, num_nodes) targets = [] G = nx.DiGraph() From 8feeb8fda2ae174309a2dad1aaa485c0278305f0 Mon Sep 17 00:00:00 2001 From: Almog-David Date: Sun, 21 Jul 2024 15:06:12 +0300 Subject: [PATCH 69/93] fixed problem with heuristic algorithm --- .../Firefighter_Problem.py | 42 +- .../firefighter_problem/Utils.py | 10 +- .../comparisons/spreading_minbudget.csv | 402 +++++++++--------- .../comparisons/spreading_minbudget.png | Bin 29259 -> 26501 bytes .../spreading_minbudget_preprocessed.csv | 400 ++++++++--------- 5 files changed, 427 insertions(+), 427 deletions(-) diff --git a/networkz/algorithms/approximation/firefighter_problem/Firefighter_Problem.py b/networkz/algorithms/approximation/firefighter_problem/Firefighter_Problem.py index d99c607..1cfbe83 100644 --- a/networkz/algorithms/approximation/firefighter_problem/Firefighter_Problem.py +++ b/networkz/algorithms/approximation/firefighter_problem/Firefighter_Problem.py @@ -192,7 +192,7 @@ def spreading_minbudget(Graph:nx.DiGraph, source:int, targets:list)-> int: middle = math.floor((min_value + max_value) / 2) #sanity check - the maximum budget we can get: the source neighbors. - answer = len(list(Graph.successors(source))) + answer = len(targets) best_strategy = spreading_maxsave(Graph, answer, source, targets, True)[0] while min_value < max_value: @@ -355,7 +355,7 @@ def heuristic_maxsave(Graph:nx.DiGraph, budget:int, source:int, targets:list, sp spread_vaccination(Graph, vaccinated_nodes) for i in range(budget): logger.info(f"Calculating the best direct vaccination strategy for the current time step that saves more new node in targets (Current budget: {budget})") - node_to_vaccinate, nodes_saved = find_best_neighbor(Graph, infected_nodes, local_targets) + node_to_vaccinate, nodes_saved = find_best_neighbor(Graph, infected_nodes, local_targets, targets) if node_to_vaccinate is not None: logger.info(f"Found {node_to_vaccinate} as a solution for current timestamp, appending to vaccination strategy and vaccinating the node") vaccination_strategy.append((node_to_vaccinate, time_step)) @@ -364,7 +364,7 @@ def heuristic_maxsave(Graph:nx.DiGraph, budget:int, source:int, targets:list, sp logger.info(f"Updated list of currently vaccinated nodes: {vaccinated_nodes}") if nodes_saved is not None: - saved_target_nodes.update(nodes_saved) + #saved_target_nodes.update(nodes_saved) local_targets[:] = [element for element in local_targets if element not in nodes_saved] logger.info(f"Updated list of targets: {local_targets}") @@ -376,6 +376,10 @@ def heuristic_maxsave(Graph:nx.DiGraph, budget:int, source:int, targets:list, sp if stop_condition is not None: if len(local_targets) == 0 or any(node in infected_nodes for node in local_targets): + for node in targets: + logger.debug(f'"node" {node} " status" {Graph.nodes[node]['status']}') + if Graph.nodes[node]['status'] != Status.INFECTED.value: + saved_target_nodes.add(node) logger.info(f"Returning vaccination strategy: {vaccination_strategy}. The strategy saved the nodes: {saved_target_nodes}") return vaccination_strategy, saved_target_nodes @@ -414,7 +418,7 @@ def heuristic_minbudget(Graph:nx.DiGraph, source:int, targets:list, spreading:bo logger.info(f"Starting the heuristic_minbudget function with source node {source}, targets: {targets}, and spreading: {spreading}") min_value = 1 - max_value = len(targets) + max_value = len(list(Graph.successors(source))) middle = math.floor((min_value + max_value) / 2) #sanity check - the maximum budget we can get: the source neighbors. @@ -447,26 +451,22 @@ def heuristic_minbudget(Graph:nx.DiGraph, source:int, targets:list, spreading:bo #result = doctest.testmod(verbose=False) #print(f"Doctest results: {result}") -s - num_nodes = random.randint(5, 50) - nodes = list(range(num_nodes + 1)) - num_edges = 100 - save_amount = random.randint(5, num_nodes) - targets = [] G = nx.DiGraph() - + num_nodes = random.randint(3, 100) + nodes = list(range(num_nodes + 1)) G.add_nodes_from(nodes, status=Status.VULNERABLE.value) - for _ in range(num_edges): - source = random.randint(0, num_nodes - 1) - target = random.randint(0, num_nodes - 1) - if source != target: # Ensure no self-loops - G.add_edge(source, target) - for node in range(save_amount): - probability = random.random() - if probability < 0.9 and node != 0: - targets.append(node) - + for first in nodes: + for second in nodes: + probability = random.random() + if first != second and probability < 0.75: + G.add_edge(first,second) + + num_targets = random.randint(2, int(len(nodes)/2)+1) + targets = random.sample(nodes,num_targets) + if 0 in targets:targets.remove(0) + #print(spreading_minbudget(G,0,targets)) - #print(heuristic_minbudget(G,0,targets,True)) + print(heuristic_minbudget(G,0,targets,True)) diff --git a/networkz/algorithms/approximation/firefighter_problem/Utils.py b/networkz/algorithms/approximation/firefighter_problem/Utils.py index 3248cde..1b77e0e 100644 --- a/networkz/algorithms/approximation/firefighter_problem/Utils.py +++ b/networkz/algorithms/approximation/firefighter_problem/Utils.py @@ -779,7 +779,7 @@ def dirlay_vaccination_startegy(vacc_matrix: np.matrix, ni_groups: dict) -> dict # =========================== End Non-Spreading Max-Save ============================ # =========================== Heuristic Utilities =================================== -def find_best_neighbor(graph:nx.DiGraph, infected_nodes:list, targets:list)->int: +def find_best_neighbor(graph:nx.DiGraph, infected_nodes:list, remaining_targets:list, overall_targets:list)->int: """ Find the best node from the infected nodes successors that saves more new node in targets. @@ -807,20 +807,20 @@ def find_best_neighbor(graph:nx.DiGraph, infected_nodes:list, targets:list)->int for neighbor in neighbors_list: if graph.nodes[neighbor]['status'] == Status.VULNERABLE.value: vulnerable_neighbors.add(neighbor) - if node in targets: + if node in overall_targets: is_target = True vulnerable_neighbors.add(node) - common_elements = set(vulnerable_neighbors) & set(targets) + common_elements = set(vulnerable_neighbors) & set(remaining_targets) logger.info("node " + f'{node}' + " is saving the nodes " + str(common_elements)) # Define the priority tuple - node_info = (-len(common_elements), not is_target, -len(vulnerable_neighbors), node, common_elements) + node_info = (not is_target, -len(common_elements), -len(vulnerable_neighbors), node, common_elements) heapq.heappush(priority_queue, node_info) if priority_queue: best_node_info = heapq.heappop(priority_queue) best_node = best_node_info[3] - nodes_saved = best_node_info[4] # Convert back to positive + nodes_saved = best_node_info[4] logger.info("The best node is: " + f'{best_node}' + " and it's saving nodes: " + str(nodes_saved)) return best_node, nodes_saved diff --git a/networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/spreading_minbudget.csv b/networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/spreading_minbudget.csv index f679ec3..5344e4a 100644 --- a/networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/spreading_minbudget.csv +++ b/networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/spreading_minbudget.csv @@ -1,202 +1,202 @@ algorithm,runtime,graph_nodes,Budget -spreading_minbudget,0.3029666000511497,74,"(5, [(7, 1), (14, 1), (10, 1), (19, 1), (26, 1)])" -heuristic_minbudget,0.3315926000941545,74,"(2, [(21, 1), (19, 1), (31, 2), (15, 2)])" -spreading_minbudget,0.346709500066936,95,"(5, [(67, 1), (17, 1), (59, 1), (11, 1), (35, 1)])" -heuristic_minbudget,0.4012654999969527,95,"(3, [(67, 1), (19, 1), (22, 1), (43, 2), (68, 2), (15, 2)])" -spreading_minbudget,0.1419543999945745,37,"(4, [(8, 1), (5, 1), (24, 1), (33, 1)])" -heuristic_minbudget,0.1485013000201434,37,"(1, [(5, 1), (30, 2)])" -spreading_minbudget,0.12530139996670187,51,"(2, [(17, 1), (44, 1)])" -heuristic_minbudget,0.19059090001974255,51,"(1, [(44, 1), (29, 2)])" -spreading_minbudget,0.09515409998130053,26,"(4, [(4, 1), (6, 1), (19, 1), (24, 1)])" -heuristic_minbudget,0.17675470001995564,26,"(1, [(6, 1)])" -spreading_minbudget,0.09222850005608052,57,"(1, [(17, 1)])" -heuristic_minbudget,0.09790849999990314,57,"(1, [(37, 1)])" -spreading_minbudget,0.5129356000106782,92,"(6, [(36, 1), (52, 1), (75, 1), (9, 1), (53, 1), (81, 1)])" -heuristic_minbudget,0.3563278999645263,92,"(4, [(81, 1), (11, 1), (77, 1), (53, 1), (83, 2), (40, 2), (87, 2), (42, 2)])" -spreading_minbudget,0.2487354000331834,75,"(7, [(26, 1), (8, 1), (43, 1), (61, 1), (37, 1), (57, 1), (58, 1)])" -heuristic_minbudget,0.36899889993947,75,"(3, [(8, 1), (61, 1), (57, 1), (65, 2), (42, 2), (17, 2)])" -spreading_minbudget,0.5030480000423267,96,"(6, [(3, 1), (30, 1), (40, 1), (31, 1), (10, 1), (66, 1)])" -heuristic_minbudget,0.47768589993938804,96,"(4, [(40, 1), (31, 1), (17, 1), (66, 1), (90, 2), (36, 2), (93, 2), (87, 2)])" -spreading_minbudget,0.18654329993296415,61,"(5, [(15, 1), (38, 1), (52, 1), (47, 1), (35, 1)])" -heuristic_minbudget,0.32879289996344596,61,"(2, [(38, 1), (15, 1), (20, 2), (26, 2)])" -spreading_minbudget,0.042015900020487607,12,"(1, [(3, 1)])" -heuristic_minbudget,0.06469859997741878,12,"(1, [(3, 1)])" -spreading_minbudget,0.01667059992905706,6,"(4, [(4, 1), (1, 1), (2, 1), (3, 1)])" -heuristic_minbudget,0.022383800009265542,6,"(4, [(4, 1), (1, 1), (2, 1), (3, 1)])" -spreading_minbudget,0.4193663999903947,88,"(4, [(6, 1), (31, 1), (16, 1), (18, 1)])" -heuristic_minbudget,0.27521220000926405,88,"(2, [(18, 1), (16, 1), (85, 2), (82, 2)])" -spreading_minbudget,0.16496500000357628,38,"(4, [(33, 1), (10, 1), (16, 1), (17, 1)])" -heuristic_minbudget,0.3307493000756949,38,"(1, [(24, 1), (11, 2)])" -spreading_minbudget,0.18944289989303797,58,"(5, [(37, 1), (1, 1), (46, 1), (28, 1), (42, 1)])" -heuristic_minbudget,0.22521140007302165,58,"(3, [(23, 1), (53, 1), (28, 1), (37, 2), (10, 2), (15, 2)])" -spreading_minbudget,0.08276029990520328,18,"(16, [(6, 1), (8, 1), (11, 1), (13, 1), (14, 1), (1, 1), (2, 1), (3, 1), (4, 1), (5, 1), (7, 1), (9, 1), (10, 1), (12, 1), (15, 1), (16, 1)])" -heuristic_minbudget,0.16184320009779185,18,"(1, [(6, 1)])" -spreading_minbudget,0.28731319995131344,75,"(2, [(45, 1), (60, 1)])" -heuristic_minbudget,0.14695109997410327,75,"(1, [(65, 1), (47, 2)])" -spreading_minbudget,0.16184300009626895,49,"(5, [(10, 1), (1, 1), (46, 1), (34, 1), (42, 1)])" -heuristic_minbudget,0.1802725000306964,49,"(2, [(16, 1), (2, 1)])" -spreading_minbudget,0.26893669995479286,71,"(4, [(41, 1), (24, 1), (52, 1), (17, 1)])" -heuristic_minbudget,0.2550285999896005,71,"(3, [(41, 1), (24, 1), (17, 1), (56, 2), (52, 2), (30, 2)])" -spreading_minbudget,0.1058983999537304,11,"(1, [(8, 1)])" -heuristic_minbudget,0.05021010001655668,11,"(1, [(8, 1)])" -spreading_minbudget,0.27958630002103746,73,"(4, [(13, 1), (71, 1), (44, 1), (61, 1), (14, 2), (4, 2), (5, 2), (8, 2)])" -heuristic_minbudget,0.20355550001841038,73,"(4, [(20, 1), (68, 1), (44, 1), (61, 1), (71, 2), (31, 2), (70, 2), (24, 2)])" -spreading_minbudget,0.20911459997296333,63,"(4, [(20, 1), (38, 1), (15, 1), (57, 1)])" -heuristic_minbudget,0.21205059997737408,63,"(1, [(20, 1), (27, 2)])" -spreading_minbudget,0.12813749990891665,70,"(1, [(56, 1)])" -heuristic_minbudget,0.14355409995187074,70,"(1, [(5, 1), (56, 2)])" -spreading_minbudget,0.12798820005264133,45,"(3, [(16, 1), (11, 1), (29, 1)])" -heuristic_minbudget,0.18905349995475262,45,"(1, [(11, 1), (31, 2)])" -spreading_minbudget,0.04674330004490912,10,"(2, [(6, 1), (8, 1)])" -heuristic_minbudget,0.0617293999530375,10,"(1, [(6, 1)])" -spreading_minbudget,0.09726319997571409,28,"(18, [(6, 1), (7, 1), (14, 1), (16, 1), (1, 1), (2, 1), (3, 1), (4, 1), (5, 1), (8, 1), (9, 1), (10, 1), (11, 1), (12, 1), (13, 1), (15, 1), (17, 1), (18, 1)])" -heuristic_minbudget,0.23729490011464804,28,"(1, [(6, 1)])" -spreading_minbudget,0.2560269000241533,80,"(3, [(28, 1), (47, 1), (39, 1), (52, 2), (4, 2), (24, 2)])" -heuristic_minbudget,0.2453222000040114,80,"(2, [(39, 1), (3, 1), (72, 2), (52, 2), (38, 3)])" -spreading_minbudget,0.20654059993103147,54,"(7, [(21, 1), (1, 1), (49, 1), (5, 1), (31, 1), (4, 1), (36, 1)])" -heuristic_minbudget,0.2648555999621749,54,"(2, [(16, 1), (36, 1), (20, 2), (1, 2)])" -spreading_minbudget,0.054338500020094216,42,"(13, [(1, 1), (4, 1), (5, 1), (6, 1), (7, 1), (8, 1), (18, 1), (19, 1), (24, 1), (25, 1), (27, 1), (28, 1), (29, 1)])" -heuristic_minbudget,0.07375809992663562,42,"(13, [(20, 1), (28, 1), (11, 1), (26, 1), (18, 1), (30, 1), (13, 1), (27, 1), (16, 1), (19, 1), (10, 1), (22, 1), (8, 1)])" -spreading_minbudget,0.12157689989544451,20,"(18, [(11, 1), (1, 1), (2, 1), (3, 1), (4, 1), (5, 1), (6, 1), (7, 1), (8, 1), (9, 1), (10, 1), (12, 1), (13, 1), (14, 1), (15, 1), (16, 1), (17, 1), (18, 1)])" -heuristic_minbudget,0.1208850999828428,20,"(18, [(11, 1), (1, 1), (5, 1), (9, 1), (10, 1), (12, 1), (13, 1), (14, 1), (4, 1), (8, 1), (15, 1), (6, 1), (7, 1), (3, 1), (17, 1), (2, 1), (16, 1), (18, 1)])" -spreading_minbudget,0.2009312000591308,49,"(4, [(19, 1), (6, 1), (9, 1), (28, 1)])" -heuristic_minbudget,0.20534390001557767,49,"(2, [(35, 1), (28, 1), (19, 2), (40, 2)])" -spreading_minbudget,0.04044969996903092,11,"(9, [(2, 1), (6, 1), (8, 1), (1, 1), (3, 1), (4, 1), (5, 1), (7, 1), (9, 1)])" -heuristic_minbudget,0.06540169997606426,11,"(1, [(2, 1)])" -spreading_minbudget,0.11286230001132935,26,"(3, [(7, 1), (9, 1), (14, 1)])" -heuristic_minbudget,0.15019089996349066,26,"(1, [(12, 1)])" -spreading_minbudget,0.07067210006061941,37,"(18, [(1, 1), (2, 1), (3, 1), (5, 1), (6, 1), (9, 1), (10, 1), (11, 1), (12, 1), (14, 1), (16, 1), (17, 1), (18, 1), (19, 1), (20, 1), (21, 1), (22, 1), (24, 1)])" -heuristic_minbudget,0.1261730999685824,37,"(18, [(28, 1), (1, 1), (13, 1), (18, 1), (10, 1), (34, 1), (14, 1), (19, 1), (22, 1), (35, 1), (9, 1), (23, 1), (16, 1), (5, 1), (15, 1), (8, 1), (20, 1), (7, 1)])" -spreading_minbudget,0.11781289998907596,46,"(1, [(2, 1)])" -heuristic_minbudget,0.3003891999833286,46,"(1, [(29, 1)])" -spreading_minbudget,0.12244350009132177,53,"(1, [(26, 1)])" -heuristic_minbudget,0.12894019996747375,53,"(1, [(26, 1)])" -spreading_minbudget,0.1439587000058964,31,"(6, [(7, 1), (4, 1), (14, 1), (20, 1), (24, 1), (9, 1)])" -heuristic_minbudget,0.20442470000125468,31,"(2, [(14, 1), (9, 1), (6, 2)])" -spreading_minbudget,0.1459333999082446,42,"(4, [(1, 1), (36, 1), (37, 1), (14, 1)])" -heuristic_minbudget,0.19357850006781518,42,"(2, [(8, 1), (36, 1)])" -spreading_minbudget,0.4984724000096321,90,"(2, [(17, 1), (18, 1), (35, 2), (1, 2)])" -heuristic_minbudget,0.204131199978292,90,"(3, [(84, 1), (45, 1), (5, 1), (88, 2), (6, 2), (85, 2)])" -spreading_minbudget,0.1488365000113845,91,"(1, [(11, 1)])" -heuristic_minbudget,0.05000600009225309,91,"(1, [(11, 1)])" -spreading_minbudget,0.27292190003208816,72,"(5, [(3, 1), (6, 1), (4, 1), (33, 1), (26, 1)])" -heuristic_minbudget,0.2523309000534937,72,"(3, [(6, 1), (3, 1), (35, 1), (50, 2), (17, 2), (55, 2)])" -spreading_minbudget,0.12413830007426441,39,"(3, [(4, 1), (15, 1), (31, 1)])" -heuristic_minbudget,0.319847600068897,39,"(2, [(4, 1), (15, 1), (3, 2), (33, 2)])" -spreading_minbudget,0.2650019000284374,88,"(2, [(27, 1), (49, 1)])" -heuristic_minbudget,0.258810400031507,88,"(2, [(27, 1), (30, 1), (32, 2), (69, 2)])" -spreading_minbudget,0.32691910001449287,77,"(2, [(4, 1), (48, 1), (51, 2), (2, 2)])" -heuristic_minbudget,0.37955549999605864,77,"(2, [(70, 1), (50, 1), (16, 2), (59, 2)])" -spreading_minbudget,0.3504355999175459,93,"(5, [(19, 1), (3, 1), (81, 1), (77, 1), (66, 1)])" -heuristic_minbudget,0.5276209999574348,93,"(4, [(19, 1), (25, 1), (81, 1), (66, 1), (65, 2), (2, 2), (22, 2), (49, 2)])" -spreading_minbudget,0.08548370003700256,88,"(5, [(1, 1), (13, 1), (14, 1), (20, 1), (26, 1)])" -heuristic_minbudget,0.023827999946661294,88,"(5, [(14, 1), (26, 1), (46, 1), (69, 1), (74, 1)])" -spreading_minbudget,0.1530138999223709,34,"(6, [(1, 1), (2, 1), (3, 1), (5, 1), (28, 1), (29, 1)])" -heuristic_minbudget,0.23985210002865642,34,"(1, [(3, 1)])" -spreading_minbudget,0.02233260008506477,6,"(4, [(1, 1), (2, 1), (3, 1), (4, 1)])" -heuristic_minbudget,0.021120700053870678,6,"(4, [(1, 1), (2, 1), (3, 1), (4, 1)])" -spreading_minbudget,0.19293520005885512,54,"(4, [(23, 1), (52, 1), (19, 1), (3, 1)])" -heuristic_minbudget,0.24587429990060627,54,"(3, [(21, 1), (36, 1), (15, 1)])" -spreading_minbudget,0.2638881999300793,72,"(5, [(20, 1), (52, 1), (60, 1), (22, 1), (17, 1)])" -heuristic_minbudget,0.23419320001266897,72,"(2, [(45, 1), (31, 1), (55, 2), (1, 2)])" -spreading_minbudget,0.11716869997326285,41,"(3, [(35, 1), (9, 1), (6, 1)])" -heuristic_minbudget,0.1924914000555873,41,"(1, [(9, 1), (5, 2)])" -spreading_minbudget,0.04140809993259609,15,"(13, [(8, 1), (1, 1), (2, 1), (3, 1), (4, 1), (5, 1), (6, 1), (7, 1), (9, 1), (10, 1), (11, 1), (12, 1), (13, 1)])" -heuristic_minbudget,0.0743957000086084,15,"(13, [(8, 1), (1, 1), (2, 1), (3, 1), (4, 1), (5, 1), (6, 1), (7, 1), (9, 1), (10, 1), (11, 1), (12, 1), (13, 1)])" -spreading_minbudget,0.15989360003732145,73,"(1, [(1, 1), (29, 2)])" -heuristic_minbudget,0.14412059995811433,73,"(1, [(48, 1), (29, 2)])" -spreading_minbudget,0.16634400002658367,45,"(6, [(42, 1), (1, 1), (5, 1), (28, 1), (16, 1), (34, 1)])" -heuristic_minbudget,0.25177490001078695,45,"(3, [(14, 1), (38, 1), (31, 1)])" -spreading_minbudget,0.12966650002636015,44,"(3, [(16, 1), (6, 1), (17, 1)])" -heuristic_minbudget,0.13301300001330674,44,"(1, [(8, 1)])" -spreading_minbudget,0.0759257999015972,58,"(17, [(18, 1), (1, 1), (10, 1), (15, 1), (16, 1), (23, 1), (27, 1), (29, 1), (32, 1), (34, 1), (36, 1), (38, 1), (47, 1), (48, 1), (49, 1), (52, 1), (54, 1)])" -heuristic_minbudget,0.12207010004203767,58,"(17, [(20, 1), (47, 1), (19, 1), (31, 1), (33, 1), (49, 1), (11, 1), (12, 1), (50, 1), (15, 1), (36, 1), (37, 1), (39, 1), (44, 1), (29, 1), (48, 1), (41, 1)])" -spreading_minbudget,0.1689889000263065,76,"(3, [(53, 1), (63, 1), (40, 1)])" -heuristic_minbudget,0.22840819996781647,76,"(2, [(63, 1), (5, 1), (56, 2), (23, 2)])" -spreading_minbudget,0.07124730001669377,73,"(9, [(66, 1), (1, 1), (7, 1), (24, 1), (33, 1), (35, 1), (45, 1), (48, 1), (56, 1)])" -heuristic_minbudget,0.04690329998265952,73,"(9, [(5, 1), (61, 1), (13, 1), (63, 1), (35, 1), (45, 1), (53, 1), (31, 1), (65, 1)])" -spreading_minbudget,0.2510937999468297,67,"(4, [(65, 1), (22, 1), (61, 1), (8, 1)])" -heuristic_minbudget,0.46545420004986227,67,"(1, [(2, 1), (65, 2)])" -spreading_minbudget,0.3784746000310406,98,"(2, [(20, 1), (51, 1)])" -heuristic_minbudget,0.20333400007802993,98,"(2, [(51, 1), (89, 1), (15, 2), (2, 2)])" -spreading_minbudget,0.23553439998067915,57,"(5, [(52, 1), (13, 1), (39, 1), (1, 1), (4, 1)])" -heuristic_minbudget,0.25463480001781136,57,"(2, [(39, 1), (13, 1), (27, 2), (53, 2)])" -spreading_minbudget,0.17906460002996027,47,"(2, [(34, 1), (26, 1)])" -heuristic_minbudget,0.11374890001025051,47,"(1, [(34, 1)])" -spreading_minbudget,0.2277229999890551,58,"(4, [(13, 1), (9, 1), (54, 1), (10, 1)])" -heuristic_minbudget,0.2175207999534905,58,"(2, [(35, 1), (43, 1), (42, 2), (46, 2)])" -spreading_minbudget,0.019769900012761354,7,"(5, [(5, 1), (1, 1), (2, 1), (3, 1), (4, 1)])" -heuristic_minbudget,0.020692199934273958,7,"(5, [(5, 1), (1, 1), (2, 1), (3, 1), (4, 1)])" -spreading_minbudget,0.03093439992517233,9,"(7, [(3, 1), (5, 1), (1, 1), (2, 1), (4, 1), (6, 1), (7, 1)])" -heuristic_minbudget,0.038919599959626794,9,"(1, [(3, 1)])" -spreading_minbudget,0.025260600028559566,7,"(5, [(3, 1), (5, 1), (1, 1), (2, 1), (4, 1)])" -heuristic_minbudget,0.03138370008673519,7,"(1, [(3, 1)])" -spreading_minbudget,0.024534199968911707,6,"(1, [(1, 1)])" -heuristic_minbudget,0.02897979994304478,6,"(1, [(1, 1)])" -spreading_minbudget,0.062038800097070634,26,"(19, [(11, 1), (1, 1), (2, 1), (3, 1), (4, 1), (5, 1), (6, 1), (7, 1), (8, 1), (9, 1), (10, 1), (12, 1), (13, 1), (14, 1), (15, 1), (16, 1), (17, 1), (18, 1), (19, 1)])" -heuristic_minbudget,0.13210729998536408,26,"(19, [(11, 1), (8, 1), (22, 1), (10, 1), (5, 1), (1, 1), (20, 1), (15, 1), (23, 1), (18, 1), (21, 1), (16, 1), (24, 1), (4, 1), (7, 1), (14, 1), (2, 1), (12, 1), (19, 1)])" -spreading_minbudget,0.019251700025051832,5,"(3, [(2, 1), (3, 1), (1, 1)])" -heuristic_minbudget,0.017724599922075868,5,"(1, [(2, 1)])" -spreading_minbudget,0.15104929998051375,59,"(3, [(23, 1), (2, 1), (51, 1)])" -heuristic_minbudget,0.14358639996498823,59,"(1, [(37, 1), (25, 2)])" -spreading_minbudget,0.22358379990328103,69,"(1, [(1, 1), (47, 2)])" -heuristic_minbudget,0.19558339996729046,69,"(1, [(41, 1), (56, 2)])" -spreading_minbudget,0.23930949997156858,53,"(5, [(5, 1), (39, 1), (8, 1), (51, 1), (7, 1)])" -heuristic_minbudget,0.2692077000392601,53,"(1, [(46, 1), (5, 2)])" -spreading_minbudget,0.2166507999645546,65,"(5, [(6, 1), (7, 1), (35, 1), (54, 1), (59, 1)])" -heuristic_minbudget,0.2993921999586746,65,"(2, [(3, 1), (35, 1), (61, 2), (63, 2)])" -spreading_minbudget,0.07842269993852824,39,"(16, [(3, 1), (1, 1), (2, 1), (4, 1), (6, 1), (10, 1), (11, 1), (13, 1), (14, 1), (15, 1), (17, 1), (20, 1), (21, 1), (22, 1), (23, 1), (24, 1)])" -heuristic_minbudget,0.2051123999990523,39,"(16, [(14, 1), (26, 1), (1, 1), (3, 1), (15, 1), (31, 1), (6, 1), (20, 1), (2, 1), (9, 1), (10, 1), (18, 1), (16, 1), (13, 1), (29, 1), (37, 1)])" -spreading_minbudget,0.024097199901007116,8,"(6, [(1, 1), (2, 1), (3, 1), (4, 1), (5, 1), (6, 1)])" -heuristic_minbudget,0.03386339999269694,8,"(6, [(1, 1), (2, 1), (3, 1), (4, 1), (5, 1), (6, 1)])" -spreading_minbudget,0.2261323999846354,55,"(4, [(10, 1), (21, 1), (25, 1), (6, 1)])" -heuristic_minbudget,0.20170700002927333,55,"(1, [(21, 1), (24, 2)])" -spreading_minbudget,0.25123110006097704,55,"(5, [(18, 1), (51, 1), (12, 1), (40, 1), (53, 1)])" -heuristic_minbudget,0.34483560000080615,55,"(2, [(51, 1), (18, 1), (16, 2), (13, 2)])" -spreading_minbudget,0.2473822000902146,77,"(2, [(12, 1), (5, 1)])" -heuristic_minbudget,0.25901989999692887,77,"(2, [(7, 1), (2, 1), (43, 2), (5, 2)])" -spreading_minbudget,0.0680228000273928,35,"(16, [(9, 1), (1, 1), (2, 1), (3, 1), (4, 1), (6, 1), (7, 1), (8, 1), (12, 1), (15, 1), (16, 1), (19, 1), (20, 1), (21, 1), (22, 1), (23, 1)])" -heuristic_minbudget,0.109697200008668,35,"(16, [(9, 1), (5, 1), (6, 1), (21, 1), (25, 1), (3, 1), (7, 1), (19, 1), (4, 1), (10, 1), (26, 1), (8, 1), (14, 1), (18, 1), (23, 1), (24, 1)])" -spreading_minbudget,0.21320110000669956,49,"(4, [(47, 1), (8, 1), (43, 1), (28, 1)])" -heuristic_minbudget,0.3355788999469951,49,"(1, [(39, 1), (30, 2)])" -spreading_minbudget,0.1300391999538988,85,"(1, [(11, 1)])" -heuristic_minbudget,0.1488371999002993,85,"(1, [(8, 1), (20, 2)])" -spreading_minbudget,0.3204901999561116,66,"(4, [(3, 1), (28, 1), (1, 1), (7, 1)])" -heuristic_minbudget,0.22477700002491474,66,"(2, [(28, 1), (7, 1), (3, 2), (29, 2)])" -spreading_minbudget,0.05877619993407279,21,"(17, [(6, 1), (1, 1), (2, 1), (3, 1), (4, 1), (5, 1), (7, 1), (8, 1), (9, 1), (10, 1), (11, 1), (12, 1), (13, 1), (14, 1), (15, 1), (16, 1), (17, 1)])" -heuristic_minbudget,0.11045739997643977,21,"(17, [(6, 1), (1, 1), (3, 1), (12, 1), (4, 1), (13, 1), (8, 1), (10, 1), (16, 1), (18, 1), (5, 1), (11, 1), (9, 1), (14, 1), (17, 1), (19, 1), (7, 1)])" -spreading_minbudget,0.05222349998075515,15,"(13, [(8, 1), (13, 1), (1, 1), (2, 1), (3, 1), (4, 1), (5, 1), (6, 1), (7, 1), (9, 1), (10, 1), (11, 1), (12, 1)])" -heuristic_minbudget,0.08618530002422631,15,"(1, [(8, 1)])" -spreading_minbudget,0.1603539000498131,52,"(2, [(5, 1), (17, 1)])" -heuristic_minbudget,0.18815300008282065,52,"(1, [(34, 1), (17, 2)])" -spreading_minbudget,0.2742555000586435,73,"(4, [(51, 1), (4, 1), (41, 1), (48, 1)])" -heuristic_minbudget,0.3578611999982968,73,"(2, [(15, 1), (68, 1), (36, 2), (37, 2)])" -spreading_minbudget,0.08287429995834827,61,"(18, [(13, 1), (1, 1), (2, 1), (3, 1), (7, 1), (11, 1), (15, 1), (23, 1), (31, 1), (33, 1), (38, 1), (41, 1), (44, 1), (47, 1), (49, 1), (55, 1), (59, 1), (9, 1)])" -heuristic_minbudget,0.140296500059776,61,"(18, [(4, 1), (8, 1), (9, 1), (44, 1), (5, 1), (16, 1), (32, 1), (24, 1), (54, 1), (47, 1), (1, 1), (10, 1), (43, 1), (57, 1), (22, 1), (13, 1), (36, 1), (53, 1)])" -spreading_minbudget,0.028677199967205524,6,"(1, [(4, 1)])" -heuristic_minbudget,0.02768349996767938,6,"(1, [(4, 1)])" -spreading_minbudget,0.21973979996982962,54,"(5, [(48, 1), (45, 1), (37, 1), (42, 1), (20, 1)])" -heuristic_minbudget,0.3166107999859378,54,"(3, [(51, 1), (42, 1), (37, 1), (9, 2), (27, 2), (2, 2)])" -spreading_minbudget,0.3623324000509456,66,"(5, [(27, 1), (21, 1), (6, 1), (1, 1), (56, 1)])" -heuristic_minbudget,0.2674438999965787,66,"(3, [(56, 1), (50, 1), (21, 1), (6, 2), (39, 2), (13, 2)])" -spreading_minbudget,0.1692126999842003,91,"(15, [(1, 1), (46, 1), (2, 1), (3, 1), (16, 1), (22, 1), (25, 1), (27, 1), (36, 1), (41, 1), (45, 1), (49, 1), (50, 1), (53, 1), (55, 1)])" -heuristic_minbudget,0.14137309999205172,91,"(1, [(46, 1), (43, 2)])" -spreading_minbudget,0.10883379995357245,40,"(2, [(13, 1), (29, 1)])" -heuristic_minbudget,0.15958470001351088,40,"(1, [(29, 1)])" -spreading_minbudget,0.009840800077654421,4,"(2, [(2, 1), (1, 1)])" -heuristic_minbudget,0.009364500059746206,4,"(2, [(2, 1), (1, 1)])" -spreading_minbudget,0.07114579994231462,19,"(17, [(11, 1), (12, 1), (13, 1), (1, 1), (2, 1), (3, 1), (4, 1), (5, 1), (6, 1), (7, 1), (8, 1), (9, 1), (10, 1), (14, 1), (15, 1), (16, 1), (17, 1)])" -heuristic_minbudget,0.15466440003365278,19,"(1, [(12, 1)])" -spreading_minbudget,0.06726630008779466,14,"(3, [(2, 1), (8, 1), (10, 1)])" -heuristic_minbudget,0.10033400007523596,14,"(1, [(2, 1)])" -spreading_minbudget,0.17409079999197274,72,"(1, [(36, 1), (54, 2)])" -heuristic_minbudget,0.1651924999896437,72,"(1, [(36, 1), (58, 2)])" -spreading_minbudget,0.21925630001351237,68,"(4, [(55, 1), (59, 1), (1, 1), (24, 1)])" -heuristic_minbudget,0.23273629997856915,68,"(3, [(55, 1), (24, 1), (1, 1), (63, 2), (47, 2), (20, 2)])" -spreading_minbudget,0.5603519999422133,94,"(4, [(86, 1), (91, 1), (60, 1), (22, 1), (2, 2), (53, 2), (3, 2), (24, 2)])" -heuristic_minbudget,0.4804672000464052,94,"(4, [(66, 1), (35, 1), (75, 1), (76, 1), (1, 2), (22, 2), (2, 2), (31, 2)])" -spreading_minbudget,0.6916073999600485,98,"(7, [(30, 1), (56, 1), (4, 1), (6, 1), (5, 1), (26, 1), (36, 1)])" -heuristic_minbudget,0.32458690006751567,98,"(5, [(36, 1), (85, 1), (6, 1), (72, 1), (77, 1), (54, 2), (61, 2), (80, 2), (84, 2), (7, 2)])" -spreading_minbudget,0.18286749999970198,48,"(5, [(21, 1), (13, 1), (46, 1), (43, 1), (40, 1)])" -heuristic_minbudget,0.28237849997822195,48,"(2, [(21, 1), (43, 1), (29, 2), (4, 2)])" -,38.785682999994606,, +spreading_minbudget,0.11190270003862679,62,"(3, [(17, 1), (29, 1), (46, 1)])" +heuristic_minbudget,0.24437560001388192,62,"(2, [(29, 1), (46, 1), (49, 2), (11, 2)])" +spreading_minbudget,0.10992060007993132,36,"(4, [(9, 1), (32, 1), (12, 1), (17, 1)])" +heuristic_minbudget,0.13999499997589737,36,"(3, [(17, 1), (12, 1), (32, 1)])" +spreading_minbudget,0.14127450005616993,58,"(1, [(43, 1)])" +heuristic_minbudget,0.10634609998669475,58,"(1, [(34, 1), (35, 2)])" +spreading_minbudget,0.024463600013405085,26,"(2, [(4, 1), (13, 1)])" +heuristic_minbudget,0.27056799991987646,26,"(2, [(4, 1), (13, 1)])" +spreading_minbudget,0.0915771999862045,71,"(2, [(28, 1), (5, 1)])" +heuristic_minbudget,0.2755369000369683,71,"(1, [(5, 1), (32, 2)])" +spreading_minbudget,0.3700199000304565,101,"(3, [(15, 1), (94, 1), (7, 1)])" +heuristic_minbudget,0.1368099000537768,101,"(2, [(7, 1), (94, 1), (37, 2), (71, 2), (43, 3), (30, 3)])" +spreading_minbudget,0.017621599952690303,42,"(1, [(2, 1)])" +heuristic_minbudget,0.3282081000506878,42,"(1, [(3, 1)])" +spreading_minbudget,0.008274100022390485,9,"(1, [(6, 1)])" +heuristic_minbudget,0.059029399999417365,9,"(1, [(6, 1)])" +spreading_minbudget,0.32227200001943856,72,"(5, [(37, 1), (26, 1), (4, 1), (11, 1), (47, 1)])" +heuristic_minbudget,0.1896612000418827,72,"(3, [(47, 1), (11, 1), (4, 1), (25, 2), (59, 2), (13, 2)])" +spreading_minbudget,0.13907719997223467,56,"(4, [(17, 1), (12, 1), (16, 1), (4, 1)])" +heuristic_minbudget,0.28356909996364266,56,"(3, [(17, 1), (4, 1), (16, 1), (43, 2), (42, 2), (46, 2)])" +spreading_minbudget,0.005068200058303773,11,"(1, [(7, 1)])" +heuristic_minbudget,0.09401729993987828,11,"(1, [(7, 1)])" +spreading_minbudget,0.09810920001473278,62,"(3, [(17, 1), (18, 1), (58, 1)])" +heuristic_minbudget,0.30409770004916936,62,"(3, [(58, 1), (42, 1), (47, 1), (17, 2), (23, 2), (57, 2)])" +spreading_minbudget,0.0640377999516204,46,"(1, [(5, 1), (41, 2)])" +heuristic_minbudget,0.263759299996309,46,"(1, [(35, 1), (41, 2)])" +spreading_minbudget,0.08612290001474321,86,"(1, [(34, 1)])" +heuristic_minbudget,0.5154114000033587,86,"(1, [(70, 1), (55, 2)])" +spreading_minbudget,0.13869439996778965,56,"(4, [(4, 1), (40, 1), (46, 1), (35, 1)])" +heuristic_minbudget,0.2922127000056207,56,"(3, [(46, 1), (35, 1), (50, 1), (14, 2), (30, 2), (24, 2)])" +spreading_minbudget,0.06375590001698583,37,"(2, [(3, 1), (11, 1)])" +heuristic_minbudget,0.16680330003146082,37,"(2, [(3, 1), (11, 1)])" +spreading_minbudget,0.028024900006130338,64,"(1, [(3, 1)])" +heuristic_minbudget,0.2074456000700593,64,"(1, [(2, 1)])" +spreading_minbudget,0.007287300075404346,15,"(1, [(6, 1)])" +heuristic_minbudget,0.12755460001062602,15,"(1, [(6, 1)])" +spreading_minbudget,0.5197356999851763,92,"(2, [(62, 1), (15, 1), (71, 2), (1, 2)])" +heuristic_minbudget,0.1170933999819681,92,"(2, [(41, 1), (18, 1), (71, 2), (59, 2), (31, 3), (40, 3)])" +spreading_minbudget,0.2737138000084087,66,"(6, [(6, 1), (47, 1), (24, 1), (10, 1), (64, 1), (42, 1)])" +heuristic_minbudget,0.3139552000211552,66,"(5, [(24, 1), (42, 1), (64, 1), (10, 1), (35, 1), (8, 2), (16, 2), (30, 2), (50, 2), (46, 2)])" +spreading_minbudget,0.34556210006121546,82,"(6, [(4, 1), (20, 1), (2, 1), (59, 1), (77, 1), (31, 1)])" +heuristic_minbudget,0.29870190005749464,82,"(4, [(31, 1), (77, 1), (39, 1), (59, 1), (55, 2), (37, 2), (43, 2), (5, 2)])" +spreading_minbudget,0.28030260000377893,74,"(4, [(53, 1), (18, 1), (65, 1), (3, 1)])" +heuristic_minbudget,0.1685951000545174,74,"(3, [(18, 1), (3, 1), (65, 1), (46, 2), (42, 2), (62, 2)])" +spreading_minbudget,0.005804999964311719,6,"(1, [(1, 1)])" +heuristic_minbudget,0.027458900003693998,6,"(1, [(1, 1)])" +spreading_minbudget,0.0996401000302285,42,"(2, [(16, 1), (1, 1)])" +heuristic_minbudget,0.12124680005945265,42,"(2, [(17, 1), (19, 1)])" +spreading_minbudget,0.4326508000958711,94,"(7, [(2, 1), (18, 1), (71, 1), (85, 1), (20, 1), (50, 1), (92, 1)])" +heuristic_minbudget,0.3880403999937698,94,"(5, [(92, 1), (50, 1), (43, 1), (73, 1), (32, 1), (18, 2), (85, 2), (15, 2), (70, 2), (84, 2)])" +spreading_minbudget,0.12962600006721914,63,"(3, [(57, 1), (28, 1), (48, 1)])" +heuristic_minbudget,0.2852879000129178,63,"(2, [(57, 1), (48, 1), (11, 2), (9, 2)])" +spreading_minbudget,0.16351969994138926,44,"(6, [(1, 1), (3, 1), (25, 1), (4, 1), (24, 1), (12, 1)])" +heuristic_minbudget,0.28860239998903126,44,"(5, [(12, 1), (24, 1), (3, 1), (25, 1), (4, 1)])" +spreading_minbudget,0.08441520004998893,41,"(4, [(15, 1), (25, 1), (14, 1), (36, 1)])" +heuristic_minbudget,0.4070332999108359,41,"(4, [(25, 1), (15, 1), (36, 1), (14, 1)])" +spreading_minbudget,0.3293702000519261,99,"(4, [(44, 1), (40, 1), (93, 1), (12, 1)])" +heuristic_minbudget,0.2124679998960346,99,"(3, [(12, 1), (42, 1), (65, 1), (61, 2), (89, 2), (46, 2)])" +spreading_minbudget,0.07804860000032932,31,"(5, [(29, 1), (17, 1), (21, 1), (26, 1), (28, 1)])" +heuristic_minbudget,0.4050325999269262,31,"(5, [(29, 1), (17, 1), (28, 1), (21, 1), (26, 1)])" +spreading_minbudget,0.294216200010851,80,"(4, [(68, 1), (2, 1), (37, 1), (76, 1)])" +heuristic_minbudget,0.23215169995091856,80,"(3, [(76, 1), (68, 1), (47, 1), (63, 2), (45, 2), (53, 2)])" +spreading_minbudget,0.08009679999668151,51,"(3, [(9, 1), (1, 1), (43, 1)])" +heuristic_minbudget,0.3146020999411121,51,"(2, [(9, 1), (43, 1), (48, 2), (7, 2)])" +spreading_minbudget,0.09564330009743571,43,"(1, [(31, 1), (22, 2)])" +heuristic_minbudget,0.1252545000752434,43,"(1, [(31, 1), (22, 2)])" +spreading_minbudget,0.14923909993376583,66,"(3, [(29, 1), (45, 1), (8, 1)])" +heuristic_minbudget,0.21669919998385012,66,"(2, [(8, 1), (52, 1), (45, 2), (61, 2)])" +spreading_minbudget,0.018930499907583,49,"(1, [(11, 1)])" +heuristic_minbudget,0.3075161000015214,49,"(1, [(38, 1)])" +spreading_minbudget,0.0136589000467211,10,"(2, [(1, 1), (7, 1)])" +heuristic_minbudget,0.06651759997475892,10,"(2, [(1, 1), (7, 1)])" +spreading_minbudget,0.17218509991653264,76,"(5, [(36, 1), (45, 1), (50, 1), (72, 1), (62, 1)])" +heuristic_minbudget,0.2894216999411583,76,"(3, [(45, 1), (62, 1), (9, 1), (40, 2), (44, 2), (10, 2)])" +spreading_minbudget,0.12442170002032071,51,"(5, [(2, 1), (4, 1), (32, 1), (27, 1), (31, 1)])" +heuristic_minbudget,0.2564759999513626,51,"(4, [(32, 1), (31, 1), (27, 1), (4, 1), (42, 2), (16, 2), (39, 2), (45, 2)])" +spreading_minbudget,0.025554300053045154,29,"(2, [(3, 1), (14, 1)])" +heuristic_minbudget,0.3164661000482738,29,"(2, [(14, 1), (3, 1)])" +spreading_minbudget,0.21654289995785803,79,"(2, [(27, 1), (37, 1)])" +heuristic_minbudget,0.2127761000301689,79,"(2, [(14, 1), (67, 1), (45, 2), (74, 2)])" +spreading_minbudget,0.4781360999913886,92,"(7, [(41, 1), (54, 1), (80, 1), (86, 1), (56, 1), (16, 1), (84, 1)])" +heuristic_minbudget,0.3447403000900522,92,"(5, [(41, 1), (16, 1), (56, 1), (84, 1), (33, 1), (46, 2), (80, 2), (73, 2), (34, 2), (60, 2)])" +spreading_minbudget,0.28261290001682937,95,"(1, [(59, 1)])" +heuristic_minbudget,0.09943989990279078,95,"(1, [(24, 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zj4J9-$OHBg#@5!6=%5;Nqk5*QwLKO%d3H3``gp%2bI0&2Z3W$irjCvyviJ+U^@D$) zvp#EC_I}tF0!$zXA2~U1z{rLqLqfx|J%#CP>4N)Bx&NPk5GsA3?d<=s=;4~mXmB-G UlY_DZ6n4buX`R;0S3e*8Up{+ERsaA1 diff --git a/networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/spreading_minbudget_preprocessed.csv b/networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/spreading_minbudget_preprocessed.csv index a99042c..95ce079 100644 --- a/networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/spreading_minbudget_preprocessed.csv +++ b/networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/spreading_minbudget_preprocessed.csv @@ -1,201 +1,201 @@ algorithm,runtime,graph_nodes,Budget,Budget_numeric -spreading_minbudget,0.3029666000511497,74.0,"(5, [(7, 1), (14, 1), (10, 1), (19, 1), (26, 1)])",5.0 -heuristic_minbudget,0.3315926000941545,74.0,"(2, [(21, 1), (19, 1), (31, 2), (15, 2)])",2.0 -spreading_minbudget,0.346709500066936,95.0,"(5, [(67, 1), (17, 1), (59, 1), (11, 1), (35, 1)])",5.0 -heuristic_minbudget,0.4012654999969527,95.0,"(3, [(67, 1), (19, 1), (22, 1), (43, 2), (68, 2), (15, 2)])",3.0 -spreading_minbudget,0.1419543999945745,37.0,"(4, [(8, 1), (5, 1), (24, 1), (33, 1)])",4.0 -heuristic_minbudget,0.1485013000201434,37.0,"(1, [(5, 1), (30, 2)])",1.0 -spreading_minbudget,0.1253013999667018,51.0,"(2, [(17, 1), (44, 1)])",2.0 -heuristic_minbudget,0.1905909000197425,51.0,"(1, [(44, 1), (29, 2)])",1.0 -spreading_minbudget,0.0951540999813005,26.0,"(4, [(4, 1), (6, 1), (19, 1), (24, 1)])",4.0 -heuristic_minbudget,0.1767547000199556,26.0,"(1, [(6, 1)])",1.0 -spreading_minbudget,0.0922285000560805,57.0,"(1, [(17, 1)])",1.0 -heuristic_minbudget,0.0979084999999031,57.0,"(1, [(37, 1)])",1.0 -spreading_minbudget,0.5129356000106782,92.0,"(6, [(36, 1), (52, 1), (75, 1), (9, 1), (53, 1), (81, 1)])",6.0 -heuristic_minbudget,0.3563278999645263,92.0,"(4, [(81, 1), (11, 1), (77, 1), (53, 1), (83, 2), (40, 2), (87, 2), (42, 2)])",4.0 -spreading_minbudget,0.2487354000331834,75.0,"(7, [(26, 1), (8, 1), (43, 1), (61, 1), (37, 1), (57, 1), (58, 1)])",7.0 -heuristic_minbudget,0.36899889993947,75.0,"(3, [(8, 1), (61, 1), (57, 1), (65, 2), (42, 2), (17, 2)])",3.0 -spreading_minbudget,0.5030480000423267,96.0,"(6, [(3, 1), (30, 1), (40, 1), (31, 1), (10, 1), (66, 1)])",6.0 -heuristic_minbudget,0.477685899939388,96.0,"(4, [(40, 1), (31, 1), (17, 1), (66, 1), (90, 2), (36, 2), (93, 2), (87, 2)])",4.0 -spreading_minbudget,0.1865432999329641,61.0,"(5, [(15, 1), (38, 1), (52, 1), (47, 1), (35, 1)])",5.0 -heuristic_minbudget,0.3287928999634459,61.0,"(2, [(38, 1), (15, 1), (20, 2), (26, 2)])",2.0 -spreading_minbudget,0.0420159000204876,12.0,"(1, [(3, 1)])",1.0 -heuristic_minbudget,0.0646985999774187,12.0,"(1, [(3, 1)])",1.0 -spreading_minbudget,0.016670599929057,6.0,"(4, [(4, 1), (1, 1), (2, 1), (3, 1)])",4.0 -heuristic_minbudget,0.0223838000092655,6.0,"(4, [(4, 1), (1, 1), (2, 1), (3, 1)])",4.0 -spreading_minbudget,0.4193663999903947,88.0,"(4, [(6, 1), (31, 1), (16, 1), (18, 1)])",4.0 -heuristic_minbudget,0.275212200009264,88.0,"(2, [(18, 1), (16, 1), (85, 2), (82, 2)])",2.0 -spreading_minbudget,0.1649650000035762,38.0,"(4, [(33, 1), (10, 1), (16, 1), (17, 1)])",4.0 -heuristic_minbudget,0.3307493000756949,38.0,"(1, [(24, 1), (11, 2)])",1.0 -spreading_minbudget,0.1894428998930379,58.0,"(5, [(37, 1), (1, 1), (46, 1), (28, 1), (42, 1)])",5.0 -heuristic_minbudget,0.2252114000730216,58.0,"(3, [(23, 1), (53, 1), (28, 1), (37, 2), (10, 2), (15, 2)])",3.0 -spreading_minbudget,0.0827602999052032,18.0,"(16, [(6, 1), (8, 1), (11, 1), (13, 1), (14, 1), (1, 1), (2, 1), (3, 1), (4, 1), (5, 1), (7, 1), (9, 1), (10, 1), (12, 1), (15, 1), (16, 1)])",16.0 -heuristic_minbudget,0.1618432000977918,18.0,"(1, [(6, 1)])",1.0 -spreading_minbudget,0.2873131999513134,75.0,"(2, [(45, 1), (60, 1)])",2.0 -heuristic_minbudget,0.1469510999741032,75.0,"(1, [(65, 1), (47, 2)])",1.0 -spreading_minbudget,0.1618430000962689,49.0,"(5, [(10, 1), (1, 1), (46, 1), (34, 1), (42, 1)])",5.0 -heuristic_minbudget,0.1802725000306964,49.0,"(2, [(16, 1), (2, 1)])",2.0 -spreading_minbudget,0.2689366999547928,71.0,"(4, [(41, 1), (24, 1), (52, 1), (17, 1)])",4.0 -heuristic_minbudget,0.2550285999896005,71.0,"(3, [(41, 1), (24, 1), (17, 1), (56, 2), (52, 2), (30, 2)])",3.0 -spreading_minbudget,0.1058983999537304,11.0,"(1, [(8, 1)])",1.0 -heuristic_minbudget,0.0502101000165566,11.0,"(1, [(8, 1)])",1.0 -spreading_minbudget,0.2795863000210374,73.0,"(4, [(13, 1), (71, 1), (44, 1), (61, 1), (14, 2), (4, 2), (5, 2), (8, 2)])",4.0 -heuristic_minbudget,0.2035555000184103,73.0,"(4, [(20, 1), (68, 1), (44, 1), (61, 1), (71, 2), (31, 2), (70, 2), (24, 2)])",4.0 -spreading_minbudget,0.2091145999729633,63.0,"(4, [(20, 1), (38, 1), (15, 1), (57, 1)])",4.0 -heuristic_minbudget,0.212050599977374,63.0,"(1, [(20, 1), (27, 2)])",1.0 -spreading_minbudget,0.1281374999089166,70.0,"(1, [(56, 1)])",1.0 -heuristic_minbudget,0.1435540999518707,70.0,"(1, [(5, 1), (56, 2)])",1.0 -spreading_minbudget,0.1279882000526413,45.0,"(3, [(16, 1), (11, 1), (29, 1)])",3.0 -heuristic_minbudget,0.1890534999547526,45.0,"(1, [(11, 1), (31, 2)])",1.0 -spreading_minbudget,0.0467433000449091,10.0,"(2, [(6, 1), (8, 1)])",2.0 -heuristic_minbudget,0.0617293999530375,10.0,"(1, [(6, 1)])",1.0 -spreading_minbudget,0.097263199975714,28.0,"(18, [(6, 1), (7, 1), (14, 1), (16, 1), (1, 1), (2, 1), (3, 1), (4, 1), (5, 1), (8, 1), (9, 1), (10, 1), (11, 1), (12, 1), (13, 1), (15, 1), (17, 1), (18, 1)])",18.0 -heuristic_minbudget,0.237294900114648,28.0,"(1, [(6, 1)])",1.0 -spreading_minbudget,0.2560269000241533,80.0,"(3, [(28, 1), (47, 1), (39, 1), (52, 2), (4, 2), (24, 2)])",3.0 -heuristic_minbudget,0.2453222000040114,80.0,"(2, [(39, 1), (3, 1), (72, 2), (52, 2), (38, 3)])",2.0 -spreading_minbudget,0.2065405999310314,54.0,"(7, [(21, 1), (1, 1), (49, 1), (5, 1), (31, 1), (4, 1), (36, 1)])",7.0 -heuristic_minbudget,0.2648555999621749,54.0,"(2, [(16, 1), (36, 1), (20, 2), (1, 2)])",2.0 -spreading_minbudget,0.0543385000200942,42.0,"(13, [(1, 1), (4, 1), (5, 1), (6, 1), (7, 1), (8, 1), (18, 1), (19, 1), (24, 1), (25, 1), (27, 1), (28, 1), (29, 1)])",13.0 -heuristic_minbudget,0.0737580999266356,42.0,"(13, [(20, 1), (28, 1), (11, 1), (26, 1), (18, 1), (30, 1), (13, 1), (27, 1), (16, 1), (19, 1), (10, 1), (22, 1), (8, 1)])",13.0 -spreading_minbudget,0.1215768998954445,20.0,"(18, [(11, 1), (1, 1), (2, 1), (3, 1), (4, 1), (5, 1), (6, 1), (7, 1), (8, 1), (9, 1), (10, 1), (12, 1), (13, 1), (14, 1), (15, 1), (16, 1), (17, 1), (18, 1)])",18.0 -heuristic_minbudget,0.1208850999828428,20.0,"(18, [(11, 1), (1, 1), (5, 1), (9, 1), (10, 1), (12, 1), (13, 1), (14, 1), (4, 1), (8, 1), (15, 1), (6, 1), (7, 1), (3, 1), (17, 1), (2, 1), (16, 1), (18, 1)])",18.0 -spreading_minbudget,0.2009312000591308,49.0,"(4, [(19, 1), (6, 1), (9, 1), (28, 1)])",4.0 -heuristic_minbudget,0.2053439000155776,49.0,"(2, [(35, 1), (28, 1), (19, 2), (40, 2)])",2.0 -spreading_minbudget,0.0404496999690309,11.0,"(9, [(2, 1), (6, 1), (8, 1), (1, 1), (3, 1), (4, 1), (5, 1), (7, 1), (9, 1)])",9.0 -heuristic_minbudget,0.0654016999760642,11.0,"(1, [(2, 1)])",1.0 -spreading_minbudget,0.1128623000113293,26.0,"(3, [(7, 1), (9, 1), (14, 1)])",3.0 -heuristic_minbudget,0.1501908999634906,26.0,"(1, [(12, 1)])",1.0 -spreading_minbudget,0.0706721000606194,37.0,"(18, [(1, 1), (2, 1), (3, 1), (5, 1), (6, 1), (9, 1), (10, 1), (11, 1), (12, 1), (14, 1), (16, 1), (17, 1), (18, 1), (19, 1), (20, 1), (21, 1), (22, 1), (24, 1)])",18.0 -heuristic_minbudget,0.1261730999685824,37.0,"(18, [(28, 1), (1, 1), (13, 1), (18, 1), (10, 1), (34, 1), (14, 1), (19, 1), (22, 1), (35, 1), (9, 1), (23, 1), (16, 1), (5, 1), (15, 1), (8, 1), (20, 1), (7, 1)])",18.0 -spreading_minbudget,0.1178128999890759,46.0,"(1, [(2, 1)])",1.0 -heuristic_minbudget,0.3003891999833286,46.0,"(1, [(29, 1)])",1.0 -spreading_minbudget,0.1224435000913217,53.0,"(1, [(26, 1)])",1.0 -heuristic_minbudget,0.1289401999674737,53.0,"(1, [(26, 1)])",1.0 -spreading_minbudget,0.1439587000058964,31.0,"(6, [(7, 1), (4, 1), (14, 1), (20, 1), (24, 1), (9, 1)])",6.0 -heuristic_minbudget,0.2044247000012546,31.0,"(2, [(14, 1), (9, 1), (6, 2)])",2.0 -spreading_minbudget,0.1459333999082446,42.0,"(4, [(1, 1), (36, 1), (37, 1), (14, 1)])",4.0 -heuristic_minbudget,0.1935785000678151,42.0,"(2, [(8, 1), (36, 1)])",2.0 -spreading_minbudget,0.4984724000096321,90.0,"(2, [(17, 1), (18, 1), (35, 2), (1, 2)])",2.0 -heuristic_minbudget,0.204131199978292,90.0,"(3, [(84, 1), (45, 1), (5, 1), (88, 2), (6, 2), (85, 2)])",3.0 -spreading_minbudget,0.1488365000113845,91.0,"(1, [(11, 1)])",1.0 -heuristic_minbudget,0.050006000092253,91.0,"(1, [(11, 1)])",1.0 -spreading_minbudget,0.2729219000320881,72.0,"(5, [(3, 1), (6, 1), (4, 1), (33, 1), (26, 1)])",5.0 -heuristic_minbudget,0.2523309000534937,72.0,"(3, [(6, 1), (3, 1), (35, 1), (50, 2), (17, 2), (55, 2)])",3.0 -spreading_minbudget,0.1241383000742644,39.0,"(3, [(4, 1), (15, 1), (31, 1)])",3.0 -heuristic_minbudget,0.319847600068897,39.0,"(2, [(4, 1), (15, 1), (3, 2), (33, 2)])",2.0 -spreading_minbudget,0.2650019000284374,88.0,"(2, [(27, 1), (49, 1)])",2.0 -heuristic_minbudget,0.258810400031507,88.0,"(2, [(27, 1), (30, 1), (32, 2), (69, 2)])",2.0 -spreading_minbudget,0.3269191000144928,77.0,"(2, [(4, 1), (48, 1), (51, 2), (2, 2)])",2.0 -heuristic_minbudget,0.3795554999960586,77.0,"(2, [(70, 1), (50, 1), (16, 2), (59, 2)])",2.0 -spreading_minbudget,0.3504355999175459,93.0,"(5, [(19, 1), (3, 1), (81, 1), (77, 1), (66, 1)])",5.0 -heuristic_minbudget,0.5276209999574348,93.0,"(4, [(19, 1), (25, 1), (81, 1), (66, 1), (65, 2), (2, 2), (22, 2), (49, 2)])",4.0 -spreading_minbudget,0.0854837000370025,88.0,"(5, [(1, 1), (13, 1), (14, 1), (20, 1), (26, 1)])",5.0 -heuristic_minbudget,0.0238279999466612,88.0,"(5, [(14, 1), (26, 1), (46, 1), (69, 1), (74, 1)])",5.0 -spreading_minbudget,0.1530138999223709,34.0,"(6, [(1, 1), (2, 1), (3, 1), (5, 1), (28, 1), (29, 1)])",6.0 -heuristic_minbudget,0.2398521000286564,34.0,"(1, [(3, 1)])",1.0 -spreading_minbudget,0.0223326000850647,6.0,"(4, [(1, 1), (2, 1), (3, 1), (4, 1)])",4.0 -heuristic_minbudget,0.0211207000538706,6.0,"(4, [(1, 1), (2, 1), (3, 1), (4, 1)])",4.0 -spreading_minbudget,0.1929352000588551,54.0,"(4, [(23, 1), (52, 1), (19, 1), (3, 1)])",4.0 -heuristic_minbudget,0.2458742999006062,54.0,"(3, [(21, 1), (36, 1), (15, 1)])",3.0 -spreading_minbudget,0.2638881999300793,72.0,"(5, [(20, 1), (52, 1), (60, 1), (22, 1), (17, 1)])",5.0 -heuristic_minbudget,0.2341932000126689,72.0,"(2, [(45, 1), (31, 1), (55, 2), (1, 2)])",2.0 -spreading_minbudget,0.1171686999732628,41.0,"(3, [(35, 1), (9, 1), (6, 1)])",3.0 -heuristic_minbudget,0.1924914000555873,41.0,"(1, [(9, 1), (5, 2)])",1.0 -spreading_minbudget,0.041408099932596,15.0,"(13, [(8, 1), (1, 1), (2, 1), (3, 1), (4, 1), (5, 1), (6, 1), (7, 1), (9, 1), (10, 1), (11, 1), (12, 1), (13, 1)])",13.0 -heuristic_minbudget,0.0743957000086084,15.0,"(13, [(8, 1), (1, 1), (2, 1), (3, 1), (4, 1), (5, 1), (6, 1), (7, 1), (9, 1), (10, 1), (11, 1), (12, 1), (13, 1)])",13.0 -spreading_minbudget,0.1598936000373214,73.0,"(1, [(1, 1), (29, 2)])",1.0 -heuristic_minbudget,0.1441205999581143,73.0,"(1, [(48, 1), (29, 2)])",1.0 -spreading_minbudget,0.1663440000265836,45.0,"(6, [(42, 1), (1, 1), (5, 1), (28, 1), (16, 1), (34, 1)])",6.0 -heuristic_minbudget,0.2517749000107869,45.0,"(3, [(14, 1), (38, 1), (31, 1)])",3.0 -spreading_minbudget,0.1296665000263601,44.0,"(3, [(16, 1), (6, 1), (17, 1)])",3.0 -heuristic_minbudget,0.1330130000133067,44.0,"(1, [(8, 1)])",1.0 -spreading_minbudget,0.0759257999015972,58.0,"(17, [(18, 1), (1, 1), (10, 1), (15, 1), (16, 1), (23, 1), (27, 1), (29, 1), (32, 1), (34, 1), (36, 1), (38, 1), (47, 1), (48, 1), (49, 1), (52, 1), (54, 1)])",17.0 -heuristic_minbudget,0.1220701000420376,58.0,"(17, [(20, 1), (47, 1), (19, 1), (31, 1), (33, 1), (49, 1), (11, 1), (12, 1), (50, 1), (15, 1), (36, 1), (37, 1), (39, 1), (44, 1), (29, 1), (48, 1), (41, 1)])",17.0 -spreading_minbudget,0.1689889000263065,76.0,"(3, [(53, 1), (63, 1), (40, 1)])",3.0 -heuristic_minbudget,0.2284081999678164,76.0,"(2, [(63, 1), (5, 1), (56, 2), (23, 2)])",2.0 -spreading_minbudget,0.0712473000166937,73.0,"(9, [(66, 1), (1, 1), (7, 1), (24, 1), (33, 1), (35, 1), (45, 1), (48, 1), (56, 1)])",9.0 -heuristic_minbudget,0.0469032999826595,73.0,"(9, [(5, 1), (61, 1), (13, 1), (63, 1), (35, 1), (45, 1), (53, 1), (31, 1), (65, 1)])",9.0 -spreading_minbudget,0.2510937999468297,67.0,"(4, [(65, 1), (22, 1), (61, 1), (8, 1)])",4.0 -heuristic_minbudget,0.4654542000498622,67.0,"(1, [(2, 1), (65, 2)])",1.0 -spreading_minbudget,0.3784746000310406,98.0,"(2, [(20, 1), (51, 1)])",2.0 -heuristic_minbudget,0.2033340000780299,98.0,"(2, [(51, 1), (89, 1), (15, 2), (2, 2)])",2.0 -spreading_minbudget,0.2355343999806791,57.0,"(5, [(52, 1), (13, 1), (39, 1), (1, 1), (4, 1)])",5.0 -heuristic_minbudget,0.2546348000178113,57.0,"(2, [(39, 1), (13, 1), (27, 2), (53, 2)])",2.0 -spreading_minbudget,0.1790646000299602,47.0,"(2, [(34, 1), (26, 1)])",2.0 -heuristic_minbudget,0.1137489000102505,47.0,"(1, [(34, 1)])",1.0 -spreading_minbudget,0.2277229999890551,58.0,"(4, [(13, 1), (9, 1), (54, 1), (10, 1)])",4.0 -heuristic_minbudget,0.2175207999534905,58.0,"(2, [(35, 1), (43, 1), (42, 2), (46, 2)])",2.0 -spreading_minbudget,0.0197699000127613,7.0,"(5, [(5, 1), (1, 1), (2, 1), (3, 1), (4, 1)])",5.0 -heuristic_minbudget,0.0206921999342739,7.0,"(5, [(5, 1), (1, 1), (2, 1), (3, 1), (4, 1)])",5.0 -spreading_minbudget,0.0309343999251723,9.0,"(7, [(3, 1), (5, 1), (1, 1), (2, 1), (4, 1), (6, 1), (7, 1)])",7.0 -heuristic_minbudget,0.0389195999596267,9.0,"(1, [(3, 1)])",1.0 -spreading_minbudget,0.0252606000285595,7.0,"(5, [(3, 1), (5, 1), (1, 1), (2, 1), (4, 1)])",5.0 -heuristic_minbudget,0.0313837000867351,7.0,"(1, [(3, 1)])",1.0 -spreading_minbudget,0.0245341999689117,6.0,"(1, [(1, 1)])",1.0 -heuristic_minbudget,0.0289797999430447,6.0,"(1, [(1, 1)])",1.0 -spreading_minbudget,0.0620388000970706,26.0,"(19, [(11, 1), (1, 1), (2, 1), (3, 1), (4, 1), (5, 1), (6, 1), (7, 1), (8, 1), (9, 1), (10, 1), (12, 1), (13, 1), (14, 1), (15, 1), (16, 1), (17, 1), (18, 1), (19, 1)])",19.0 -heuristic_minbudget,0.132107299985364,26.0,"(19, [(11, 1), (8, 1), (22, 1), (10, 1), (5, 1), (1, 1), (20, 1), (15, 1), (23, 1), (18, 1), (21, 1), (16, 1), (24, 1), (4, 1), (7, 1), (14, 1), (2, 1), (12, 1), (19, 1)])",19.0 -spreading_minbudget,0.0192517000250518,5.0,"(3, [(2, 1), (3, 1), (1, 1)])",3.0 -heuristic_minbudget,0.0177245999220758,5.0,"(1, [(2, 1)])",1.0 -spreading_minbudget,0.1510492999805137,59.0,"(3, [(23, 1), (2, 1), (51, 1)])",3.0 -heuristic_minbudget,0.1435863999649882,59.0,"(1, [(37, 1), (25, 2)])",1.0 -spreading_minbudget,0.223583799903281,69.0,"(1, [(1, 1), (47, 2)])",1.0 -heuristic_minbudget,0.1955833999672904,69.0,"(1, [(41, 1), (56, 2)])",1.0 -spreading_minbudget,0.2393094999715685,53.0,"(5, [(5, 1), (39, 1), (8, 1), (51, 1), (7, 1)])",5.0 -heuristic_minbudget,0.2692077000392601,53.0,"(1, [(46, 1), (5, 2)])",1.0 -spreading_minbudget,0.2166507999645546,65.0,"(5, [(6, 1), (7, 1), (35, 1), (54, 1), (59, 1)])",5.0 -heuristic_minbudget,0.2993921999586746,65.0,"(2, [(3, 1), (35, 1), (61, 2), (63, 2)])",2.0 -spreading_minbudget,0.0784226999385282,39.0,"(16, [(3, 1), (1, 1), (2, 1), (4, 1), (6, 1), (10, 1), (11, 1), (13, 1), (14, 1), (15, 1), (17, 1), (20, 1), (21, 1), (22, 1), (23, 1), (24, 1)])",16.0 -heuristic_minbudget,0.2051123999990523,39.0,"(16, [(14, 1), (26, 1), (1, 1), (3, 1), (15, 1), (31, 1), (6, 1), (20, 1), (2, 1), (9, 1), (10, 1), (18, 1), (16, 1), (13, 1), (29, 1), (37, 1)])",16.0 -spreading_minbudget,0.0240971999010071,8.0,"(6, [(1, 1), (2, 1), (3, 1), (4, 1), (5, 1), (6, 1)])",6.0 -heuristic_minbudget,0.0338633999926969,8.0,"(6, [(1, 1), (2, 1), (3, 1), (4, 1), (5, 1), (6, 1)])",6.0 -spreading_minbudget,0.2261323999846354,55.0,"(4, [(10, 1), (21, 1), (25, 1), (6, 1)])",4.0 -heuristic_minbudget,0.2017070000292733,55.0,"(1, [(21, 1), (24, 2)])",1.0 -spreading_minbudget,0.251231100060977,55.0,"(5, [(18, 1), (51, 1), (12, 1), (40, 1), (53, 1)])",5.0 -heuristic_minbudget,0.3448356000008061,55.0,"(2, [(51, 1), (18, 1), (16, 2), (13, 2)])",2.0 -spreading_minbudget,0.2473822000902146,77.0,"(2, [(12, 1), (5, 1)])",2.0 -heuristic_minbudget,0.2590198999969288,77.0,"(2, [(7, 1), (2, 1), (43, 2), (5, 2)])",2.0 -spreading_minbudget,0.0680228000273928,35.0,"(16, [(9, 1), (1, 1), (2, 1), (3, 1), (4, 1), (6, 1), (7, 1), (8, 1), (12, 1), (15, 1), (16, 1), (19, 1), (20, 1), (21, 1), (22, 1), (23, 1)])",16.0 -heuristic_minbudget,0.109697200008668,35.0,"(16, [(9, 1), (5, 1), (6, 1), (21, 1), (25, 1), (3, 1), (7, 1), (19, 1), (4, 1), (10, 1), (26, 1), (8, 1), (14, 1), (18, 1), (23, 1), (24, 1)])",16.0 -spreading_minbudget,0.2132011000066995,49.0,"(4, [(47, 1), (8, 1), (43, 1), (28, 1)])",4.0 -heuristic_minbudget,0.3355788999469951,49.0,"(1, [(39, 1), (30, 2)])",1.0 -spreading_minbudget,0.1300391999538988,85.0,"(1, [(11, 1)])",1.0 -heuristic_minbudget,0.1488371999002993,85.0,"(1, [(8, 1), (20, 2)])",1.0 -spreading_minbudget,0.3204901999561116,66.0,"(4, [(3, 1), (28, 1), (1, 1), (7, 1)])",4.0 -heuristic_minbudget,0.2247770000249147,66.0,"(2, [(28, 1), (7, 1), (3, 2), (29, 2)])",2.0 -spreading_minbudget,0.0587761999340727,21.0,"(17, [(6, 1), (1, 1), (2, 1), (3, 1), (4, 1), (5, 1), (7, 1), (8, 1), (9, 1), (10, 1), (11, 1), (12, 1), (13, 1), (14, 1), (15, 1), (16, 1), (17, 1)])",17.0 -heuristic_minbudget,0.1104573999764397,21.0,"(17, [(6, 1), (1, 1), (3, 1), (12, 1), (4, 1), (13, 1), (8, 1), (10, 1), (16, 1), (18, 1), (5, 1), (11, 1), (9, 1), (14, 1), (17, 1), (19, 1), (7, 1)])",17.0 -spreading_minbudget,0.0522234999807551,15.0,"(13, [(8, 1), (13, 1), (1, 1), (2, 1), (3, 1), (4, 1), (5, 1), (6, 1), (7, 1), (9, 1), (10, 1), (11, 1), (12, 1)])",13.0 -heuristic_minbudget,0.0861853000242263,15.0,"(1, [(8, 1)])",1.0 -spreading_minbudget,0.1603539000498131,52.0,"(2, [(5, 1), (17, 1)])",2.0 -heuristic_minbudget,0.1881530000828206,52.0,"(1, [(34, 1), (17, 2)])",1.0 -spreading_minbudget,0.2742555000586435,73.0,"(4, [(51, 1), (4, 1), (41, 1), (48, 1)])",4.0 -heuristic_minbudget,0.3578611999982968,73.0,"(2, [(15, 1), (68, 1), (36, 2), (37, 2)])",2.0 -spreading_minbudget,0.0828742999583482,61.0,"(18, [(13, 1), (1, 1), (2, 1), (3, 1), (7, 1), (11, 1), (15, 1), (23, 1), (31, 1), (33, 1), (38, 1), (41, 1), (44, 1), (47, 1), (49, 1), (55, 1), (59, 1), (9, 1)])",18.0 -heuristic_minbudget,0.140296500059776,61.0,"(18, [(4, 1), (8, 1), (9, 1), (44, 1), (5, 1), (16, 1), (32, 1), (24, 1), (54, 1), (47, 1), (1, 1), (10, 1), (43, 1), (57, 1), (22, 1), (13, 1), (36, 1), (53, 1)])",18.0 -spreading_minbudget,0.0286771999672055,6.0,"(1, [(4, 1)])",1.0 -heuristic_minbudget,0.0276834999676793,6.0,"(1, [(4, 1)])",1.0 -spreading_minbudget,0.2197397999698296,54.0,"(5, [(48, 1), (45, 1), (37, 1), (42, 1), (20, 1)])",5.0 -heuristic_minbudget,0.3166107999859378,54.0,"(3, [(51, 1), (42, 1), (37, 1), (9, 2), (27, 2), (2, 2)])",3.0 -spreading_minbudget,0.3623324000509456,66.0,"(5, [(27, 1), (21, 1), (6, 1), (1, 1), (56, 1)])",5.0 -heuristic_minbudget,0.2674438999965787,66.0,"(3, [(56, 1), (50, 1), (21, 1), (6, 2), (39, 2), (13, 2)])",3.0 -spreading_minbudget,0.1692126999842003,91.0,"(15, [(1, 1), (46, 1), (2, 1), (3, 1), (16, 1), (22, 1), (25, 1), (27, 1), (36, 1), (41, 1), (45, 1), (49, 1), (50, 1), (53, 1), (55, 1)])",15.0 -heuristic_minbudget,0.1413730999920517,91.0,"(1, [(46, 1), (43, 2)])",1.0 -spreading_minbudget,0.1088337999535724,40.0,"(2, [(13, 1), (29, 1)])",2.0 -heuristic_minbudget,0.1595847000135108,40.0,"(1, [(29, 1)])",1.0 -spreading_minbudget,0.0098408000776544,4.0,"(2, [(2, 1), (1, 1)])",2.0 -heuristic_minbudget,0.0093645000597462,4.0,"(2, [(2, 1), (1, 1)])",2.0 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+spreading_minbudget,0.0632337999995797,37.0,"(3, [(23, 1), (29, 1), (12, 1)])",3.0 +heuristic_minbudget,0.2401395000051707,37.0,"(3, [(23, 1), (29, 1), (12, 1)])",3.0 +spreading_minbudget,0.0583757000276818,63.0,"(2, [(1, 1), (5, 1)])",2.0 +heuristic_minbudget,0.1453706999309361,63.0,"(1, [(5, 1), (50, 2)])",1.0 +spreading_minbudget,0.1569533000001683,74.0,"(4, [(9, 1), (65, 1), (2, 1), (61, 1)])",4.0 +heuristic_minbudget,0.3688563000177964,74.0,"(4, [(61, 1), (2, 1), (4, 1), (39, 1), (3, 2), (10, 2), (33, 2), (16, 2)])",4.0 +spreading_minbudget,0.3516180999577045,94.0,"(9, [(15, 1), (2, 1), (79, 1), (38, 1), (40, 1), (77, 1), (56, 1), (88, 1), (30, 1)])",9.0 +heuristic_minbudget,0.5069408999988809,94.0,"(7, [(30, 1), (88, 1), (77, 1), (38, 1), (40, 1), (56, 1), (36, 1), (34, 2), (20, 2), (66, 2), (51, 2), (87, 2), (60, 2), (15, 2)])",7.0 +spreading_minbudget,0.1044757000636309,31.0,"(5, [(7, 1), (1, 1), (12, 1), (21, 1), (6, 1)])",5.0 +heuristic_minbudget,0.2544330999953672,31.0,"(5, [(12, 1), (1, 1), (6, 1), (7, 1), (21, 1)])",5.0 +spreading_minbudget,0.0141621000366285,41.0,"(1, [(1, 1)])",1.0 +heuristic_minbudget,0.3477824999717995,41.0,"(1, [(2, 1)])",1.0 +spreading_minbudget,0.2712038999889046,82.0,"(5, [(13, 1), (14, 1), (22, 1), (8, 1), (37, 1)])",5.0 +heuristic_minbudget,0.4021873000310734,82.0,"(4, [(13, 1), (37, 1), (22, 1), (47, 1), (49, 2), (1, 2), (19, 2), (72, 2)])",4.0 +spreading_minbudget,0.1242064000107348,76.0,"(3, [(31, 1), (16, 1), (63, 1)])",3.0 +heuristic_minbudget,0.1742244000779464,76.0,"(3, [(63, 1), (17, 1), (44, 1), (20, 2), (39, 2), (8, 2)])",3.0 +spreading_minbudget,0.0215030999388545,15.0,"(2, [(5, 1), (8, 1)])",2.0 +heuristic_minbudget,0.1636073000263422,15.0,"(2, [(5, 1), (8, 1)])",2.0 +spreading_minbudget,0.1148804000113159,42.0,"(4, [(2, 1), (6, 1), (11, 1), (32, 1)])",4.0 +heuristic_minbudget,0.2060244999593123,42.0,"(3, [(2, 1), (11, 1), (32, 1), (21, 2), (10, 2), (8, 2)])",3.0 +spreading_minbudget,0.0189800999360159,25.0,"(2, [(1, 1), (20, 1)])",2.0 +heuristic_minbudget,0.3286843999521807,25.0,"(2, [(1, 1), (20, 1)])",2.0 +spreading_minbudget,0.0161407999694347,7.0,"(1, [(4, 1)])",1.0 +heuristic_minbudget,0.0564907999942079,7.0,"(1, [(4, 1)])",1.0 +spreading_minbudget,0.0350014999276027,30.0,"(3, [(5, 1), (7, 1), (16, 1)])",3.0 +heuristic_minbudget,0.3014356000348925,30.0,"(3, [(5, 1), (7, 1), (16, 1)])",3.0 +spreading_minbudget,0.1318936999887228,59.0,"(3, [(1, 1), (4, 1), (18, 1)])",3.0 +heuristic_minbudget,0.2176658998942002,59.0,"(2, [(18, 1), (34, 1), (50, 2), (21, 2)])",2.0 +spreading_minbudget,0.120171400019899,47.0,"(2, [(38, 1), (14, 1)])",2.0 +heuristic_minbudget,0.1988023000303656,47.0,"(2, [(14, 1), (11, 1), (21, 2), (13, 2)])",2.0 +spreading_minbudget,0.0185740999877452,8.0,"(2, [(1, 1), (4, 1)])",2.0 +heuristic_minbudget,0.125113199930638,8.0,"(2, [(1, 1), (4, 1)])",2.0 +spreading_minbudget,0.4363951999694109,90.0,"(6, [(70, 1), (40, 1), (61, 1), (66, 1), (69, 1), (78, 1)])",6.0 +heuristic_minbudget,0.7065138999605551,90.0,"(5, [(69, 1), (66, 1), (78, 1), (5, 1), (11, 1), (56, 2), (33, 2), (13, 2), (65, 2), (48, 2)])",5.0 +spreading_minbudget,0.0309839999536052,18.0,"(3, [(9, 1), (12, 1), (15, 1)])",3.0 +heuristic_minbudget,0.2921748999506235,18.0,"(3, [(15, 1), (9, 1), (12, 1)])",3.0 +spreading_minbudget,0.2782496999716386,65.0,"(5, [(41, 1), (6, 1), (49, 1), (29, 1), (46, 1)])",5.0 +heuristic_minbudget,0.4262177000055089,65.0,"(4, [(29, 1), (49, 1), (46, 1), (41, 1), (14, 2), (13, 2), (10, 2), (23, 2)])",4.0 +spreading_minbudget,0.0709102000109851,68.0,"(1, [(53, 1)])",1.0 +heuristic_minbudget,0.1047124000033363,68.0,"(1, [(8, 1)])",1.0 +spreading_minbudget,0.1883910000324249,74.0,"(5, [(2, 1), (18, 1), (56, 1), (40, 1), (51, 1)])",5.0 +heuristic_minbudget,0.2680246999952942,74.0,"(3, [(51, 1), (56, 1), (40, 1), (46, 2), (23, 2), (14, 2)])",3.0 +spreading_minbudget,0.0167137999087572,8.0,"(2, [(4, 1), (5, 1)])",2.0 +heuristic_minbudget,0.0543889999389648,8.0,"(2, [(4, 1), (5, 1)])",2.0 +spreading_minbudget,0.1542055000318214,26.0,"(5, [(7, 1), (4, 1), (13, 1), (3, 1), (18, 1)])",5.0 +heuristic_minbudget,0.3132658000104129,26.0,"(5, [(7, 1), (4, 1), (13, 1), (3, 1), (18, 1)])",5.0 +spreading_minbudget,0.0474998999852687,29.0,"(3, [(17, 1), (27, 1), (21, 1)])",3.0 +heuristic_minbudget,0.349758299998939,29.0,"(3, [(17, 1), (21, 1), (27, 1)])",3.0 +spreading_minbudget,0.0088474000804126,21.0,"(1, [(6, 1)])",1.0 +heuristic_minbudget,0.3989595000166446,21.0,"(1, [(6, 1)])",1.0 +spreading_minbudget,0.0158535999944433,14.0,"(2, [(1, 1), (3, 1)])",2.0 +heuristic_minbudget,0.2032005999935791,14.0,"(2, [(1, 1), (3, 1)])",2.0 +spreading_minbudget,0.1464654000010341,46.0,"(6, [(21, 1), (44, 1), (10, 1), (34, 1), (7, 1), (22, 1)])",6.0 +heuristic_minbudget,0.4952908999985084,46.0,"(6, [(44, 1), (21, 1), (7, 1), (10, 1), (22, 1), (34, 1), (13, 2), (43, 2)])",6.0 +spreading_minbudget,0.1514287999598309,66.0,"(4, [(34, 1), (45, 1), (36, 1), (13, 1)])",4.0 +heuristic_minbudget,0.3030861999141052,66.0,"(3, [(13, 1), (36, 1), (34, 1), (23, 2), (48, 2), (11, 2)])",3.0 +spreading_minbudget,0.0241294999141246,13.0,"(3, [(1, 1), (4, 1), (11, 1)])",3.0 +heuristic_minbudget,0.1180299000116065,13.0,"(3, [(1, 1), (4, 1), (11, 1)])",3.0 +spreading_minbudget,0.4541134999599308,79.0,"(3, [(37, 1), (7, 1), (47, 1), (67, 2), (2, 2), (3, 2)])",3.0 +heuristic_minbudget,0.3353960000677034,79.0,"(4, [(47, 1), (64, 1), (28, 1), (55, 1), (77, 2), (32, 2), (57, 2), (67, 2)])",4.0 +spreading_minbudget,0.4108619000762701,98.0,"(7, [(37, 1), (59, 1), (60, 1), (85, 1), (89, 1), (27, 1), (33, 1)])",7.0 +heuristic_minbudget,0.6443371999775991,98.0,"(6, [(59, 1), (89, 1), (85, 1), (33, 1), (27, 1), (94, 1), (60, 2), (25, 2), (42, 2), (20, 2), (26, 2), (1, 2), (84, 3)])",6.0 From 42658cdc6e764438326189fd207430c0d973eea6 Mon Sep 17 00:00:00 2001 From: 20shaked20 Date: Sun, 21 Jul 2024 20:43:36 +0300 Subject: [PATCH 70/93] Minor fix --- .../firefighter_problem/Firefighter_Problem.py | 10 ++++++---- 1 file changed, 6 insertions(+), 4 deletions(-) diff --git a/networkz/algorithms/approximation/firefighter_problem/Firefighter_Problem.py b/networkz/algorithms/approximation/firefighter_problem/Firefighter_Problem.py index 1cfbe83..08528de 100644 --- a/networkz/algorithms/approximation/firefighter_problem/Firefighter_Problem.py +++ b/networkz/algorithms/approximation/firefighter_problem/Firefighter_Problem.py @@ -377,8 +377,9 @@ def heuristic_maxsave(Graph:nx.DiGraph, budget:int, source:int, targets:list, sp if stop_condition is not None: if len(local_targets) == 0 or any(node in infected_nodes for node in local_targets): for node in targets: - logger.debug(f'"node" {node} " status" {Graph.nodes[node]['status']}') - if Graph.nodes[node]['status'] != Status.INFECTED.value: + node_status = Graph.nodes[node]['status'] + logger.debug(f" node {node} status {node_status}") + if node_status != Status.INFECTED.value: saved_target_nodes.add(node) logger.info(f"Returning vaccination strategy: {vaccination_strategy}. The strategy saved the nodes: {saved_target_nodes}") return vaccination_strategy, saved_target_nodes @@ -386,8 +387,9 @@ def heuristic_maxsave(Graph:nx.DiGraph, budget:int, source:int, targets:list, sp time_step += 1 for node in targets: - logger.debug(f'"node" {node} " status" {Graph.nodes[node]['status']}') - if Graph.nodes[node]['status'] != Status.INFECTED.value: + node_status = Graph.nodes[node]['status'] + logger.debug(f" node {node} status {node_status}") + if node_status != Status.INFECTED.value: saved_target_nodes.add(node) logger.info(f"Returning vaccination strategy: {vaccination_strategy}. The strategy saved the nodes: {saved_target_nodes}") From 0efb36911fe98bbd993f793eb8de2a4a3bdc1870 Mon Sep 17 00:00:00 2001 From: Yuval Bubnovsky Date: Sun, 21 Jul 2024 11:23:55 +0300 Subject: [PATCH 71/93] re-factored random di graph generator --- .../Firefighter_Problem.py | 9 +- .../comparisons/comparisons.py | 100 +++-- .../spreading_minbudget_preprocessed.csv | 400 +++++++++--------- 3 files changed, 260 insertions(+), 249 deletions(-) diff --git a/networkz/algorithms/approximation/firefighter_problem/Firefighter_Problem.py b/networkz/algorithms/approximation/firefighter_problem/Firefighter_Problem.py index 08528de..92bba64 100644 --- a/networkz/algorithms/approximation/firefighter_problem/Firefighter_Problem.py +++ b/networkz/algorithms/approximation/firefighter_problem/Firefighter_Problem.py @@ -187,6 +187,9 @@ def spreading_minbudget(Graph:nx.DiGraph, source:int, targets:list)-> int: validate_parameters(Graph, source, targets) logger.info(f"Starting the spreading_minbudget function with source node {source} and targets: {targets}") + + best_strategy = [] + min_value = 1 max_value = len(targets) middle = math.floor((min_value + max_value) / 2) @@ -204,9 +207,7 @@ def spreading_minbudget(Graph:nx.DiGraph, source:int, targets:list)-> int: if len(common_elements) == len(targets): logger.info(f"The current budget {middle} has saved all the targets!") max_value = middle - if middle < answer: - answer = middle - best_strategy = strategy + best_strategy = strategy else: logger.info(f"The current budget {middle} didn't save all the targets!") min_value = middle + 1 @@ -214,7 +215,7 @@ def spreading_minbudget(Graph:nx.DiGraph, source:int, targets:list)-> int: middle = math.floor((min_value + max_value) / 2) logger.info(f"Returning minimum budget: {middle} and the vaccination strategy: {best_strategy}") - return answer, best_strategy + return middle, best_strategy def non_spreading_minbudget(Graph:nx.DiGraph, source:int, targets:list)->int: diff --git a/networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/comparisons.py b/networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/comparisons.py index a26861a..a259283 100644 --- a/networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/comparisons.py +++ b/networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/comparisons.py @@ -22,14 +22,17 @@ import experiments_csv from experiments_csv import * import logging -logger = logging.getLogger(__name__) - +from time import perf_counter +import pandas as pd +import random from networkz.algorithms.approximation.firefighter_problem.Utils import * from networkz.algorithms.approximation.firefighter_problem.Firefighter_Problem import * from networkz.algorithms.approximation.tests.test_firefighter_problem.test_non_spreading_dirlaynet_minbudget import generate_layered_network from matplotlib import pyplot as plt +logger = logging.getLogger(__name__) + def setup_global_logger(level: int = logging.DEBUG): log_format = "|| %(asctime)s || %(levelname)s || %(message)s" date_format = '%H:%M:%S' @@ -41,26 +44,33 @@ def setup_global_logger(level: int = logging.DEBUG): root_logger.setLevel(level) root_logger.addHandler(handler) -def generate_random_DiGraph() -> nx.DiGraph: - for i in range(10): - num_nodes = random.randint(2,100) - nodes = list(range(num_nodes+1)) - num_edges = 1000 - save_amount = random.randint(1,num_nodes) - targets = [] - G = nx.DiGraph() - - G.add_nodes_from(nodes, status="target") - for _ in range(num_edges): - source = random.randint(0, num_nodes - 1) - target = random.randint(0, num_nodes - 1) - if source != target: # Ensure no self-loops - G.add_edge(source, target) - for node in range(save_amount): - probability = random.random() - if probability < 0.75 and node!=0: - targets.append(node) - return G +def generate_random_DiGraph( + num_nodes: int = 100, + edge_probability: float = 0.1, + seed: int = None + ) -> nx.DiGraph: + + if seed is None: + seed = random.randint(0, 2**32 - 1) + logger.debug(f"Random generated seed: {seed}") + else: + logger.debug(f"Using provided seed: {seed}") + + random.seed(seed) + + G = nx.DiGraph() + G.add_nodes_from(range(num_nodes), status="target") + + edges = [ + (source, target) + for source in range(num_nodes) + for target in range(num_nodes) + if source != target and random.random() < edge_probability + ] + + G.add_edges_from(edges) + + return G def runner_no_spreading(algorithm, graph, source, targets): @@ -87,10 +97,6 @@ def runner_spreading( algorithm, graph, source, targets): return {"Budget" : (algorithm(Graph=graph,source=source,targets=targets))} -from time import perf_counter -import pandas as pd -import random - def Compare_NonSpread(): ex1 = experiments_csv.Experiment("./networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/", "non_spreading.csv", backup_folder=None) ex1.clear_previous_results() # to clear previous experminets.. @@ -162,29 +168,33 @@ def Compare_SpreadingMaxSave(): def Compare_SpreadingMinBudget(): ex1 = experiments_csv.Experiment("./networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/", "spreading_minbudget.csv", backup_folder=None) - ex1.clear_previous_results() # to clear previous experminets.. + ex1.clear_previous_results() # to clear previous experiments input_ranges = { - "algorithm":[spreading_minbudget,heuristic_minbudget] - - + "algorithm": [spreading_minbudget, heuristic_minbudget] } - def multiple_runs(runs=100): - for _ in range(runs): - graph = generate_random_DiGraph() - source = 0 - # targets = [2, 4, 6, 7, 8, 9] - nodes = list(graph.nodes) - nodes.remove(0) - num_targets = random.randint(1, int(len(nodes)/4)+1) - targets = random.sample(nodes,num_targets) - for algorithm in input_ranges["algorithm"]: - start_time = perf_counter() - result = runner_spreading(algorithm, graph, source, targets) - runtime = perf_counter() - start_time - ex1.add({**{"algorithm": algorithm.__name__, "runtime": runtime, "graph_nodes": len(graph.nodes)}, **result}) + + node_counts = [100, 200, 400] + edge_probabilities = [0.1, 0.5, 0.8] + + def multiple_runs(runs=10): + for num_nodes in node_counts: + for edge_prob in edge_probabilities: + graph = generate_random_DiGraph(num_nodes=num_nodes, edge_probability=edge_prob, seed=None) + for _ in range(runs): + source = 0 + nodes = list(graph.nodes) + nodes.remove(0) + num_targets = random.randint(1, int(len(nodes) / 2) + 1) + targets = random.sample(nodes, num_targets) + for algorithm in input_ranges["algorithm"]: + start_time = perf_counter() + result = runner_spreading(algorithm, graph, source, targets) + runtime = perf_counter() - start_time + ex1.add({**{"algorithm": algorithm.__name__, "runtime": runtime, "graph_nodes": len(graph.nodes)}, **result}) return {"status": "completed"} + # Set a time limit for the entire batch run ex1.run_with_time_limit(multiple_runs, input_ranges={}, time_limit=0.9) @@ -221,7 +231,7 @@ def extract_budget_numeric(budget): x_field="graph_nodes", y_field="Budget_numeric", z_field="algorithm", - mean=False, + mean=True, save_to_file="./networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/spreading_minbudget.png" ) diff --git a/networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/spreading_minbudget_preprocessed.csv b/networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/spreading_minbudget_preprocessed.csv index 95ce079..a99042c 100644 --- a/networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/spreading_minbudget_preprocessed.csv +++ b/networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/spreading_minbudget_preprocessed.csv @@ -1,201 +1,201 @@ algorithm,runtime,graph_nodes,Budget,Budget_numeric -spreading_minbudget,0.1119027000386267,62.0,"(3, [(17, 1), (29, 1), (46, 1)])",3.0 -heuristic_minbudget,0.2443756000138819,62.0,"(2, [(29, 1), (46, 1), (49, 2), (11, 2)])",2.0 -spreading_minbudget,0.1099206000799313,36.0,"(4, [(9, 1), (32, 1), (12, 1), (17, 1)])",4.0 -heuristic_minbudget,0.1399949999758973,36.0,"(3, [(17, 1), (12, 1), (32, 1)])",3.0 -spreading_minbudget,0.1412745000561699,58.0,"(1, [(43, 1)])",1.0 -heuristic_minbudget,0.1063460999866947,58.0,"(1, [(34, 1), (35, 2)])",1.0 -spreading_minbudget,0.024463600013405,26.0,"(2, [(4, 1), (13, 1)])",2.0 -heuristic_minbudget,0.2705679999198764,26.0,"(2, [(4, 1), (13, 1)])",2.0 -spreading_minbudget,0.0915771999862045,71.0,"(2, [(28, 1), (5, 1)])",2.0 -heuristic_minbudget,0.2755369000369683,71.0,"(1, [(5, 1), (32, 2)])",1.0 -spreading_minbudget,0.3700199000304565,101.0,"(3, [(15, 1), (94, 1), (7, 1)])",3.0 -heuristic_minbudget,0.1368099000537768,101.0,"(2, [(7, 1), (94, 1), (37, 2), (71, 2), (43, 3), (30, 3)])",2.0 -spreading_minbudget,0.0176215999526903,42.0,"(1, [(2, 1)])",1.0 -heuristic_minbudget,0.3282081000506878,42.0,"(1, [(3, 1)])",1.0 -spreading_minbudget,0.0082741000223904,9.0,"(1, [(6, 1)])",1.0 -heuristic_minbudget,0.0590293999994173,9.0,"(1, [(6, 1)])",1.0 -spreading_minbudget,0.3222720000194385,72.0,"(5, [(37, 1), (26, 1), (4, 1), (11, 1), (47, 1)])",5.0 -heuristic_minbudget,0.1896612000418827,72.0,"(3, [(47, 1), (11, 1), (4, 1), (25, 2), (59, 2), (13, 2)])",3.0 -spreading_minbudget,0.1390771999722346,56.0,"(4, [(17, 1), (12, 1), (16, 1), (4, 1)])",4.0 -heuristic_minbudget,0.2835690999636426,56.0,"(3, [(17, 1), (4, 1), (16, 1), (43, 2), (42, 2), (46, 2)])",3.0 -spreading_minbudget,0.0050682000583037,11.0,"(1, [(7, 1)])",1.0 -heuristic_minbudget,0.0940172999398782,11.0,"(1, [(7, 1)])",1.0 -spreading_minbudget,0.0981092000147327,62.0,"(3, [(17, 1), (18, 1), (58, 1)])",3.0 -heuristic_minbudget,0.3040977000491693,62.0,"(3, [(58, 1), (42, 1), (47, 1), (17, 2), (23, 2), (57, 2)])",3.0 -spreading_minbudget,0.0640377999516204,46.0,"(1, [(5, 1), (41, 2)])",1.0 -heuristic_minbudget,0.263759299996309,46.0,"(1, [(35, 1), (41, 2)])",1.0 -spreading_minbudget,0.0861229000147432,86.0,"(1, [(34, 1)])",1.0 -heuristic_minbudget,0.5154114000033587,86.0,"(1, [(70, 1), (55, 2)])",1.0 -spreading_minbudget,0.1386943999677896,56.0,"(4, [(4, 1), (40, 1), (46, 1), (35, 1)])",4.0 -heuristic_minbudget,0.2922127000056207,56.0,"(3, [(46, 1), (35, 1), (50, 1), (14, 2), (30, 2), (24, 2)])",3.0 -spreading_minbudget,0.0637559000169858,37.0,"(2, [(3, 1), (11, 1)])",2.0 -heuristic_minbudget,0.1668033000314608,37.0,"(2, [(3, 1), (11, 1)])",2.0 -spreading_minbudget,0.0280249000061303,64.0,"(1, [(3, 1)])",1.0 -heuristic_minbudget,0.2074456000700593,64.0,"(1, [(2, 1)])",1.0 -spreading_minbudget,0.0072873000754043,15.0,"(1, [(6, 1)])",1.0 -heuristic_minbudget,0.127554600010626,15.0,"(1, [(6, 1)])",1.0 -spreading_minbudget,0.5197356999851763,92.0,"(2, [(62, 1), (15, 1), (71, 2), (1, 2)])",2.0 -heuristic_minbudget,0.1170933999819681,92.0,"(2, [(41, 1), (18, 1), (71, 2), (59, 2), (31, 3), (40, 3)])",2.0 -spreading_minbudget,0.2737138000084087,66.0,"(6, [(6, 1), (47, 1), (24, 1), (10, 1), (64, 1), (42, 1)])",6.0 -heuristic_minbudget,0.3139552000211552,66.0,"(5, [(24, 1), (42, 1), (64, 1), (10, 1), (35, 1), (8, 2), (16, 2), (30, 2), (50, 2), (46, 2)])",5.0 -spreading_minbudget,0.3455621000612154,82.0,"(6, [(4, 1), (20, 1), (2, 1), (59, 1), (77, 1), (31, 1)])",6.0 -heuristic_minbudget,0.2987019000574946,82.0,"(4, [(31, 1), (77, 1), (39, 1), (59, 1), (55, 2), (37, 2), (43, 2), (5, 2)])",4.0 -spreading_minbudget,0.2803026000037789,74.0,"(4, [(53, 1), (18, 1), (65, 1), (3, 1)])",4.0 -heuristic_minbudget,0.1685951000545174,74.0,"(3, [(18, 1), (3, 1), (65, 1), (46, 2), (42, 2), (62, 2)])",3.0 -spreading_minbudget,0.0058049999643117,6.0,"(1, [(1, 1)])",1.0 -heuristic_minbudget,0.0274589000036939,6.0,"(1, [(1, 1)])",1.0 -spreading_minbudget,0.0996401000302285,42.0,"(2, [(16, 1), (1, 1)])",2.0 -heuristic_minbudget,0.1212468000594526,42.0,"(2, [(17, 1), (19, 1)])",2.0 -spreading_minbudget,0.4326508000958711,94.0,"(7, [(2, 1), (18, 1), (71, 1), (85, 1), (20, 1), (50, 1), (92, 1)])",7.0 -heuristic_minbudget,0.3880403999937698,94.0,"(5, [(92, 1), (50, 1), (43, 1), (73, 1), (32, 1), (18, 2), (85, 2), (15, 2), (70, 2), (84, 2)])",5.0 -spreading_minbudget,0.1296260000672191,63.0,"(3, [(57, 1), (28, 1), (48, 1)])",3.0 -heuristic_minbudget,0.2852879000129178,63.0,"(2, [(57, 1), (48, 1), (11, 2), (9, 2)])",2.0 -spreading_minbudget,0.1635196999413892,44.0,"(6, [(1, 1), (3, 1), (25, 1), (4, 1), (24, 1), (12, 1)])",6.0 -heuristic_minbudget,0.2886023999890312,44.0,"(5, [(12, 1), (24, 1), (3, 1), (25, 1), (4, 1)])",5.0 -spreading_minbudget,0.0844152000499889,41.0,"(4, [(15, 1), (25, 1), (14, 1), (36, 1)])",4.0 -heuristic_minbudget,0.4070332999108359,41.0,"(4, [(25, 1), (15, 1), (36, 1), (14, 1)])",4.0 -spreading_minbudget,0.3293702000519261,99.0,"(4, [(44, 1), (40, 1), (93, 1), (12, 1)])",4.0 -heuristic_minbudget,0.2124679998960346,99.0,"(3, [(12, 1), (42, 1), (65, 1), (61, 2), (89, 2), (46, 2)])",3.0 -spreading_minbudget,0.0780486000003293,31.0,"(5, [(29, 1), (17, 1), (21, 1), (26, 1), (28, 1)])",5.0 -heuristic_minbudget,0.4050325999269262,31.0,"(5, [(29, 1), (17, 1), (28, 1), (21, 1), (26, 1)])",5.0 -spreading_minbudget,0.294216200010851,80.0,"(4, [(68, 1), (2, 1), (37, 1), (76, 1)])",4.0 -heuristic_minbudget,0.2321516999509185,80.0,"(3, [(76, 1), (68, 1), (47, 1), (63, 2), (45, 2), (53, 2)])",3.0 -spreading_minbudget,0.0800967999966815,51.0,"(3, [(9, 1), (1, 1), (43, 1)])",3.0 -heuristic_minbudget,0.3146020999411121,51.0,"(2, [(9, 1), (43, 1), (48, 2), (7, 2)])",2.0 -spreading_minbudget,0.0956433000974357,43.0,"(1, [(31, 1), (22, 2)])",1.0 -heuristic_minbudget,0.1252545000752434,43.0,"(1, [(31, 1), (22, 2)])",1.0 -spreading_minbudget,0.1492390999337658,66.0,"(3, [(29, 1), (45, 1), (8, 1)])",3.0 -heuristic_minbudget,0.2166991999838501,66.0,"(2, [(8, 1), (52, 1), (45, 2), (61, 2)])",2.0 -spreading_minbudget,0.018930499907583,49.0,"(1, [(11, 1)])",1.0 -heuristic_minbudget,0.3075161000015214,49.0,"(1, [(38, 1)])",1.0 -spreading_minbudget,0.0136589000467211,10.0,"(2, [(1, 1), (7, 1)])",2.0 -heuristic_minbudget,0.0665175999747589,10.0,"(2, [(1, 1), (7, 1)])",2.0 -spreading_minbudget,0.1721850999165326,76.0,"(5, [(36, 1), (45, 1), (50, 1), (72, 1), (62, 1)])",5.0 -heuristic_minbudget,0.2894216999411583,76.0,"(3, [(45, 1), (62, 1), (9, 1), (40, 2), (44, 2), (10, 2)])",3.0 -spreading_minbudget,0.1244217000203207,51.0,"(5, [(2, 1), (4, 1), (32, 1), (27, 1), (31, 1)])",5.0 -heuristic_minbudget,0.2564759999513626,51.0,"(4, [(32, 1), (31, 1), (27, 1), (4, 1), (42, 2), (16, 2), (39, 2), (45, 2)])",4.0 -spreading_minbudget,0.0255543000530451,29.0,"(2, [(3, 1), (14, 1)])",2.0 -heuristic_minbudget,0.3164661000482738,29.0,"(2, [(14, 1), (3, 1)])",2.0 -spreading_minbudget,0.216542899957858,79.0,"(2, [(27, 1), (37, 1)])",2.0 -heuristic_minbudget,0.2127761000301689,79.0,"(2, [(14, 1), (67, 1), (45, 2), (74, 2)])",2.0 -spreading_minbudget,0.4781360999913886,92.0,"(7, [(41, 1), (54, 1), (80, 1), (86, 1), (56, 1), (16, 1), (84, 1)])",7.0 -heuristic_minbudget,0.3447403000900522,92.0,"(5, [(41, 1), (16, 1), (56, 1), (84, 1), (33, 1), (46, 2), (80, 2), (73, 2), (34, 2), (60, 2)])",5.0 -spreading_minbudget,0.2826129000168293,95.0,"(1, [(59, 1)])",1.0 -heuristic_minbudget,0.0994398999027907,95.0,"(1, [(24, 1), (8, 2), (58, 3)])",1.0 -spreading_minbudget,0.1924802000867203,62.0,"(5, [(24, 1), (28, 1), (39, 1), (13, 1), (58, 1)])",5.0 -heuristic_minbudget,0.2800439000129699,62.0,"(4, [(28, 1), (13, 1), (39, 1), (58, 1), (11, 2), (55, 2), (8, 2), (17, 2)])",4.0 -spreading_minbudget,0.5396201000548899,100.0,"(3, [(52, 1), (17, 1), (76, 1), (68, 2), (1, 2), (11, 2)])",3.0 -heuristic_minbudget,0.105507000000216,100.0,"(2, [(76, 1), (17, 1), (68, 2), (52, 2), (34, 3), (85, 3)])",2.0 -spreading_minbudget,0.014187499997206,45.0,"(1, [(16, 1)])",1.0 -heuristic_minbudget,0.4935894000809639,45.0,"(1, [(16, 1)])",1.0 -spreading_minbudget,0.0896423000376671,40.0,"(4, [(8, 1), (13, 1), (28, 1), (26, 1)])",4.0 -heuristic_minbudget,0.3729581000516191,40.0,"(4, [(13, 1), (26, 1), (28, 1), (8, 1)])",4.0 -spreading_minbudget,0.0140788999851793,10.0,"(2, [(1, 1), (5, 1)])",2.0 -heuristic_minbudget,0.0722962999716401,10.0,"(2, [(1, 1), (5, 1)])",2.0 -spreading_minbudget,0.0895113000879064,39.0,"(4, [(5, 1), (13, 1), (28, 1), (37, 1)])",4.0 -heuristic_minbudget,0.3784803999587893,39.0,"(3, [(28, 1), (37, 1), (13, 1), (35, 2), (22, 2)])",3.0 -spreading_minbudget,0.1149967999663204,35.0,"(4, [(12, 1), (9, 1), (22, 1), (32, 1)])",4.0 -heuristic_minbudget,0.2040741000091657,35.0,"(3, [(9, 1), (22, 1), (32, 1)])",3.0 -spreading_minbudget,0.1358741000294685,53.0,"(3, [(35, 1), (28, 1), (21, 1)])",3.0 -heuristic_minbudget,0.2506335999350995,53.0,"(2, [(35, 1), (21, 1), (28, 2), (46, 2)])",2.0 -spreading_minbudget,0.1365706999786198,50.0,"(5, [(18, 1), (28, 1), (6, 1), (45, 1), (16, 1)])",5.0 -heuristic_minbudget,0.2488794000819325,50.0,"(3, [(6, 1), (16, 1), (45, 1), (5, 2), (47, 2), (4, 2)])",3.0 -spreading_minbudget,0.0727252999786287,23.0,"(4, [(11, 1), (1, 1), (16, 1), (21, 1)])",4.0 -heuristic_minbudget,0.2870137999998405,23.0,"(4, [(21, 1), (16, 1), (1, 1), (11, 1)])",4.0 -spreading_minbudget,0.1400055999401956,66.0,"(1, [(60, 1)])",1.0 -heuristic_minbudget,0.2018164999317377,66.0,"(2, [(43, 1), (19, 1), (4, 2), (27, 2)])",2.0 -spreading_minbudget,0.3629825999960303,80.0,"(4, [(10, 1), (55, 1), (45, 1), (31, 1)])",4.0 -heuristic_minbudget,0.1030805999180302,80.0,"(3, [(31, 1), (54, 1), (40, 1), (1, 2), (78, 2), (44, 2)])",3.0 -spreading_minbudget,0.2697710000211373,82.0,"(1, [(17, 1)])",1.0 -heuristic_minbudget,0.11582179996185,82.0,"(1, [(11, 1), (65, 2), (7, 3)])",1.0 -spreading_minbudget,0.0157933000009506,5.0,"(2, [(1, 1), (2, 1)])",2.0 -heuristic_minbudget,0.0259149000048637,5.0,"(2, [(1, 1), (2, 1)])",2.0 -spreading_minbudget,0.1580337000777945,37.0,"(6, [(29, 1), (7, 1), (14, 1), (16, 1), (33, 1), (3, 1)])",6.0 -heuristic_minbudget,0.2022271999157965,37.0,"(6, [(16, 1), (29, 1), (7, 1), (3, 1), (14, 1), (33, 1)])",6.0 -spreading_minbudget,0.381596100050956,100.0,"(8, [(55, 1), (31, 1), (56, 1), (21, 1), (89, 1), (14, 1), (7, 1), (17, 1)])",8.0 -heuristic_minbudget,0.5756914999801666,100.0,"(4, [(17, 1), (7, 1), (88, 1), (82, 1), (56, 2), (18, 2), (20, 2), (41, 2), (94, 3)])",4.0 -spreading_minbudget,0.1087363000260666,35.0,"(5, [(7, 1), (33, 1), (9, 1), (10, 1), (12, 1)])",5.0 -heuristic_minbudget,0.3253402999835089,35.0,"(5, [(12, 1), (7, 1), (9, 1), (10, 1), (33, 1)])",5.0 -spreading_minbudget,0.0170302999904379,17.0,"(2, [(4, 1), (11, 1)])",2.0 -heuristic_minbudget,0.2040337000507861,17.0,"(2, [(11, 1), (4, 1)])",2.0 -spreading_minbudget,0.0784946999046951,57.0,"(1, [(46, 1), (26, 2)])",1.0 -heuristic_minbudget,0.2903852000599727,57.0,"(2, [(52, 1), (28, 1), (26, 2), (7, 2)])",2.0 -spreading_minbudget,0.2887740000151098,82.0,"(6, [(44, 1), (35, 1), (1, 1), (20, 1), (49, 1), (43, 1)])",6.0 -heuristic_minbudget,0.3553588999202475,82.0,"(4, [(43, 1), (49, 1), (48, 1), (12, 1), (14, 2), (27, 2), (28, 2), (10, 2)])",4.0 -spreading_minbudget,0.3907817000290379,67.0,"(2, [(59, 1), (60, 1), (8, 2), (14, 2)])",2.0 -heuristic_minbudget,0.3000390999950468,67.0,"(3, [(59, 1), (6, 1), (55, 1), (11, 2), (19, 2), (13, 2)])",3.0 -spreading_minbudget,0.0056931999279186,10.0,"(1, [(8, 1)])",1.0 -heuristic_minbudget,0.06926670006942,10.0,"(1, [(8, 1)])",1.0 -spreading_minbudget,0.1478846000973135,64.0,"(4, [(37, 1), (1, 1), (2, 1), (36, 1)])",4.0 -heuristic_minbudget,0.5289517000783235,64.0,"(2, [(36, 1), (37, 1), (15, 2), (16, 2)])",2.0 -spreading_minbudget,0.043115200009197,21.0,"(4, [(3, 1), (6, 1), (8, 1), (12, 1)])",4.0 -heuristic_minbudget,0.2686238000169396,21.0,"(4, [(3, 1), (12, 1), (6, 1), (8, 1)])",4.0 -spreading_minbudget,0.0227395000401884,23.0,"(2, [(7, 1), (19, 1)])",2.0 -heuristic_minbudget,0.307440000004135,23.0,"(2, [(19, 1), (7, 1)])",2.0 -spreading_minbudget,0.552783100050874,99.0,"(1, [(39, 1)])",1.0 -heuristic_minbudget,0.1147100001107901,99.0,"(1, [(14, 1), (27, 2), (51, 3), (69, 4)])",1.0 -spreading_minbudget,0.0144947000080719,31.0,"(1, [(24, 1)])",1.0 -heuristic_minbudget,0.2371331000467762,31.0,"(1, [(24, 1)])",1.0 -spreading_minbudget,0.0632337999995797,37.0,"(3, [(23, 1), (29, 1), (12, 1)])",3.0 -heuristic_minbudget,0.2401395000051707,37.0,"(3, [(23, 1), (29, 1), (12, 1)])",3.0 -spreading_minbudget,0.0583757000276818,63.0,"(2, [(1, 1), (5, 1)])",2.0 -heuristic_minbudget,0.1453706999309361,63.0,"(1, [(5, 1), (50, 2)])",1.0 -spreading_minbudget,0.1569533000001683,74.0,"(4, [(9, 1), (65, 1), (2, 1), (61, 1)])",4.0 -heuristic_minbudget,0.3688563000177964,74.0,"(4, [(61, 1), (2, 1), (4, 1), (39, 1), (3, 2), (10, 2), (33, 2), (16, 2)])",4.0 -spreading_minbudget,0.3516180999577045,94.0,"(9, [(15, 1), (2, 1), (79, 1), (38, 1), (40, 1), (77, 1), (56, 1), (88, 1), (30, 1)])",9.0 -heuristic_minbudget,0.5069408999988809,94.0,"(7, [(30, 1), (88, 1), (77, 1), (38, 1), (40, 1), (56, 1), (36, 1), (34, 2), (20, 2), (66, 2), (51, 2), (87, 2), (60, 2), (15, 2)])",7.0 -spreading_minbudget,0.1044757000636309,31.0,"(5, [(7, 1), (1, 1), (12, 1), (21, 1), (6, 1)])",5.0 -heuristic_minbudget,0.2544330999953672,31.0,"(5, [(12, 1), (1, 1), (6, 1), (7, 1), (21, 1)])",5.0 -spreading_minbudget,0.0141621000366285,41.0,"(1, [(1, 1)])",1.0 -heuristic_minbudget,0.3477824999717995,41.0,"(1, [(2, 1)])",1.0 -spreading_minbudget,0.2712038999889046,82.0,"(5, [(13, 1), (14, 1), (22, 1), (8, 1), (37, 1)])",5.0 -heuristic_minbudget,0.4021873000310734,82.0,"(4, [(13, 1), (37, 1), (22, 1), (47, 1), (49, 2), (1, 2), (19, 2), (72, 2)])",4.0 -spreading_minbudget,0.1242064000107348,76.0,"(3, [(31, 1), (16, 1), (63, 1)])",3.0 -heuristic_minbudget,0.1742244000779464,76.0,"(3, [(63, 1), (17, 1), (44, 1), (20, 2), (39, 2), (8, 2)])",3.0 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+spreading_minbudget,0.1740907999919727,72.0,"(1, [(36, 1), (54, 2)])",1.0 +heuristic_minbudget,0.1651924999896437,72.0,"(1, [(36, 1), (58, 2)])",1.0 +spreading_minbudget,0.2192563000135123,68.0,"(4, [(55, 1), (59, 1), (1, 1), (24, 1)])",4.0 +heuristic_minbudget,0.2327362999785691,68.0,"(3, [(55, 1), (24, 1), (1, 1), (63, 2), (47, 2), (20, 2)])",3.0 +spreading_minbudget,0.5603519999422133,94.0,"(4, [(86, 1), (91, 1), (60, 1), (22, 1), (2, 2), (53, 2), (3, 2), (24, 2)])",4.0 +heuristic_minbudget,0.4804672000464052,94.0,"(4, [(66, 1), (35, 1), (75, 1), (76, 1), (1, 2), (22, 2), (2, 2), (31, 2)])",4.0 +spreading_minbudget,0.6916073999600485,98.0,"(7, [(30, 1), (56, 1), (4, 1), (6, 1), (5, 1), (26, 1), (36, 1)])",7.0 +heuristic_minbudget,0.3245869000675156,98.0,"(5, [(36, 1), (85, 1), (6, 1), (72, 1), (77, 1), (54, 2), (61, 2), (80, 2), (84, 2), (7, 2)])",5.0 +spreading_minbudget,0.1828674999997019,48.0,"(5, [(21, 1), (13, 1), (46, 1), (43, 1), (40, 1)])",5.0 +heuristic_minbudget,0.2823784999782219,48.0,"(2, [(21, 1), (43, 1), (29, 2), (4, 2)])",2.0 From b4338da1511024e606e1841845fb56ff14dabc78 Mon Sep 17 00:00:00 2001 From: 20shaked20 Date: Sun, 21 Jul 2024 21:14:43 +0300 Subject: [PATCH 72/93] CSV --- .../comparisons/comparisons.py | 2 +- .../comparisons/spreading_minbudget.csv | 220 ++---------------- .../comparisons/spreading_minbudget.png | Bin 26501 -> 32890 bytes .../spreading_minbudget_preprocessed.csv | 218 ++--------------- 4 files changed, 38 insertions(+), 402 deletions(-) diff --git a/networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/comparisons.py b/networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/comparisons.py index a259283..3a961a6 100644 --- a/networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/comparisons.py +++ b/networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/comparisons.py @@ -177,7 +177,7 @@ def Compare_SpreadingMinBudget(): node_counts = [100, 200, 400] edge_probabilities = [0.1, 0.5, 0.8] - def multiple_runs(runs=10): + def multiple_runs(runs=1): for num_nodes in node_counts: for edge_prob in edge_probabilities: graph = generate_random_DiGraph(num_nodes=num_nodes, edge_probability=edge_prob, seed=None) diff --git a/networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/spreading_minbudget.csv b/networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/spreading_minbudget.csv index 5344e4a..f831a3a 100644 --- a/networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/spreading_minbudget.csv +++ b/networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/spreading_minbudget.csv @@ -1,202 +1,20 @@ algorithm,runtime,graph_nodes,Budget -spreading_minbudget,0.11190270003862679,62,"(3, [(17, 1), (29, 1), (46, 1)])" -heuristic_minbudget,0.24437560001388192,62,"(2, [(29, 1), (46, 1), (49, 2), (11, 2)])" -spreading_minbudget,0.10992060007993132,36,"(4, [(9, 1), (32, 1), (12, 1), (17, 1)])" -heuristic_minbudget,0.13999499997589737,36,"(3, [(17, 1), (12, 1), (32, 1)])" -spreading_minbudget,0.14127450005616993,58,"(1, [(43, 1)])" -heuristic_minbudget,0.10634609998669475,58,"(1, [(34, 1), (35, 2)])" -spreading_minbudget,0.024463600013405085,26,"(2, [(4, 1), (13, 1)])" -heuristic_minbudget,0.27056799991987646,26,"(2, [(4, 1), (13, 1)])" -spreading_minbudget,0.0915771999862045,71,"(2, [(28, 1), (5, 1)])" -heuristic_minbudget,0.2755369000369683,71,"(1, [(5, 1), (32, 2)])" -spreading_minbudget,0.3700199000304565,101,"(3, [(15, 1), (94, 1), (7, 1)])" -heuristic_minbudget,0.1368099000537768,101,"(2, [(7, 1), (94, 1), (37, 2), (71, 2), (43, 3), (30, 3)])" -spreading_minbudget,0.017621599952690303,42,"(1, [(2, 1)])" -heuristic_minbudget,0.3282081000506878,42,"(1, [(3, 1)])" -spreading_minbudget,0.008274100022390485,9,"(1, [(6, 1)])" -heuristic_minbudget,0.059029399999417365,9,"(1, [(6, 1)])" -spreading_minbudget,0.32227200001943856,72,"(5, [(37, 1), (26, 1), (4, 1), (11, 1), (47, 1)])" -heuristic_minbudget,0.1896612000418827,72,"(3, [(47, 1), (11, 1), (4, 1), (25, 2), (59, 2), (13, 2)])" -spreading_minbudget,0.13907719997223467,56,"(4, [(17, 1), (12, 1), (16, 1), (4, 1)])" -heuristic_minbudget,0.28356909996364266,56,"(3, [(17, 1), (4, 1), (16, 1), (43, 2), (42, 2), (46, 2)])" -spreading_minbudget,0.005068200058303773,11,"(1, [(7, 1)])" -heuristic_minbudget,0.09401729993987828,11,"(1, [(7, 1)])" -spreading_minbudget,0.09810920001473278,62,"(3, [(17, 1), (18, 1), (58, 1)])" -heuristic_minbudget,0.30409770004916936,62,"(3, [(58, 1), (42, 1), (47, 1), (17, 2), (23, 2), (57, 2)])" -spreading_minbudget,0.0640377999516204,46,"(1, [(5, 1), (41, 2)])" -heuristic_minbudget,0.263759299996309,46,"(1, [(35, 1), (41, 2)])" -spreading_minbudget,0.08612290001474321,86,"(1, [(34, 1)])" -heuristic_minbudget,0.5154114000033587,86,"(1, [(70, 1), (55, 2)])" -spreading_minbudget,0.13869439996778965,56,"(4, [(4, 1), (40, 1), (46, 1), (35, 1)])" -heuristic_minbudget,0.2922127000056207,56,"(3, [(46, 1), (35, 1), (50, 1), (14, 2), (30, 2), (24, 2)])" -spreading_minbudget,0.06375590001698583,37,"(2, [(3, 1), (11, 1)])" -heuristic_minbudget,0.16680330003146082,37,"(2, [(3, 1), (11, 1)])" -spreading_minbudget,0.028024900006130338,64,"(1, [(3, 1)])" -heuristic_minbudget,0.2074456000700593,64,"(1, [(2, 1)])" -spreading_minbudget,0.007287300075404346,15,"(1, [(6, 1)])" -heuristic_minbudget,0.12755460001062602,15,"(1, [(6, 1)])" -spreading_minbudget,0.5197356999851763,92,"(2, [(62, 1), (15, 1), (71, 2), (1, 2)])" -heuristic_minbudget,0.1170933999819681,92,"(2, [(41, 1), (18, 1), (71, 2), (59, 2), (31, 3), (40, 3)])" -spreading_minbudget,0.2737138000084087,66,"(6, [(6, 1), (47, 1), (24, 1), (10, 1), (64, 1), (42, 1)])" -heuristic_minbudget,0.3139552000211552,66,"(5, [(24, 1), (42, 1), (64, 1), (10, 1), (35, 1), (8, 2), (16, 2), (30, 2), (50, 2), (46, 2)])" -spreading_minbudget,0.34556210006121546,82,"(6, [(4, 1), (20, 1), (2, 1), (59, 1), (77, 1), (31, 1)])" -heuristic_minbudget,0.29870190005749464,82,"(4, [(31, 1), (77, 1), (39, 1), (59, 1), (55, 2), (37, 2), (43, 2), (5, 2)])" -spreading_minbudget,0.28030260000377893,74,"(4, [(53, 1), (18, 1), (65, 1), (3, 1)])" -heuristic_minbudget,0.1685951000545174,74,"(3, [(18, 1), (3, 1), (65, 1), (46, 2), (42, 2), (62, 2)])" -spreading_minbudget,0.005804999964311719,6,"(1, [(1, 1)])" -heuristic_minbudget,0.027458900003693998,6,"(1, [(1, 1)])" -spreading_minbudget,0.0996401000302285,42,"(2, [(16, 1), (1, 1)])" -heuristic_minbudget,0.12124680005945265,42,"(2, [(17, 1), (19, 1)])" -spreading_minbudget,0.4326508000958711,94,"(7, [(2, 1), (18, 1), (71, 1), (85, 1), (20, 1), (50, 1), (92, 1)])" -heuristic_minbudget,0.3880403999937698,94,"(5, [(92, 1), (50, 1), (43, 1), (73, 1), (32, 1), (18, 2), (85, 2), (15, 2), (70, 2), (84, 2)])" -spreading_minbudget,0.12962600006721914,63,"(3, [(57, 1), (28, 1), (48, 1)])" -heuristic_minbudget,0.2852879000129178,63,"(2, [(57, 1), (48, 1), (11, 2), (9, 2)])" -spreading_minbudget,0.16351969994138926,44,"(6, [(1, 1), (3, 1), (25, 1), (4, 1), (24, 1), (12, 1)])" -heuristic_minbudget,0.28860239998903126,44,"(5, [(12, 1), (24, 1), (3, 1), (25, 1), (4, 1)])" -spreading_minbudget,0.08441520004998893,41,"(4, [(15, 1), (25, 1), (14, 1), (36, 1)])" -heuristic_minbudget,0.4070332999108359,41,"(4, [(25, 1), (15, 1), (36, 1), (14, 1)])" -spreading_minbudget,0.3293702000519261,99,"(4, [(44, 1), (40, 1), (93, 1), (12, 1)])" -heuristic_minbudget,0.2124679998960346,99,"(3, [(12, 1), (42, 1), (65, 1), (61, 2), (89, 2), (46, 2)])" -spreading_minbudget,0.07804860000032932,31,"(5, [(29, 1), (17, 1), (21, 1), (26, 1), (28, 1)])" -heuristic_minbudget,0.4050325999269262,31,"(5, [(29, 1), (17, 1), (28, 1), (21, 1), (26, 1)])" -spreading_minbudget,0.294216200010851,80,"(4, [(68, 1), (2, 1), (37, 1), (76, 1)])" -heuristic_minbudget,0.23215169995091856,80,"(3, [(76, 1), (68, 1), (47, 1), (63, 2), (45, 2), (53, 2)])" -spreading_minbudget,0.08009679999668151,51,"(3, [(9, 1), (1, 1), (43, 1)])" -heuristic_minbudget,0.3146020999411121,51,"(2, [(9, 1), (43, 1), (48, 2), (7, 2)])" -spreading_minbudget,0.09564330009743571,43,"(1, [(31, 1), (22, 2)])" -heuristic_minbudget,0.1252545000752434,43,"(1, [(31, 1), (22, 2)])" -spreading_minbudget,0.14923909993376583,66,"(3, [(29, 1), (45, 1), (8, 1)])" -heuristic_minbudget,0.21669919998385012,66,"(2, [(8, 1), (52, 1), (45, 2), (61, 2)])" -spreading_minbudget,0.018930499907583,49,"(1, [(11, 1)])" -heuristic_minbudget,0.3075161000015214,49,"(1, [(38, 1)])" -spreading_minbudget,0.0136589000467211,10,"(2, [(1, 1), (7, 1)])" -heuristic_minbudget,0.06651759997475892,10,"(2, [(1, 1), (7, 1)])" -spreading_minbudget,0.17218509991653264,76,"(5, [(36, 1), (45, 1), (50, 1), (72, 1), (62, 1)])" -heuristic_minbudget,0.2894216999411583,76,"(3, [(45, 1), (62, 1), (9, 1), (40, 2), (44, 2), (10, 2)])" -spreading_minbudget,0.12442170002032071,51,"(5, [(2, 1), (4, 1), (32, 1), (27, 1), (31, 1)])" -heuristic_minbudget,0.2564759999513626,51,"(4, [(32, 1), (31, 1), (27, 1), (4, 1), (42, 2), (16, 2), (39, 2), (45, 2)])" -spreading_minbudget,0.025554300053045154,29,"(2, [(3, 1), (14, 1)])" -heuristic_minbudget,0.3164661000482738,29,"(2, [(14, 1), (3, 1)])" -spreading_minbudget,0.21654289995785803,79,"(2, [(27, 1), (37, 1)])" -heuristic_minbudget,0.2127761000301689,79,"(2, [(14, 1), (67, 1), (45, 2), (74, 2)])" -spreading_minbudget,0.4781360999913886,92,"(7, [(41, 1), (54, 1), (80, 1), (86, 1), (56, 1), (16, 1), (84, 1)])" -heuristic_minbudget,0.3447403000900522,92,"(5, [(41, 1), (16, 1), (56, 1), (84, 1), (33, 1), (46, 2), (80, 2), (73, 2), (34, 2), (60, 2)])" -spreading_minbudget,0.28261290001682937,95,"(1, [(59, 1)])" -heuristic_minbudget,0.09943989990279078,95,"(1, [(24, 1), (8, 2), (58, 3)])" -spreading_minbudget,0.1924802000867203,62,"(5, [(24, 1), (28, 1), (39, 1), (13, 1), (58, 1)])" -heuristic_minbudget,0.28004390001296997,62,"(4, [(28, 1), (13, 1), (39, 1), (58, 1), (11, 2), (55, 2), (8, 2), (17, 2)])" -spreading_minbudget,0.5396201000548899,100,"(3, [(52, 1), (17, 1), (76, 1), (68, 2), (1, 2), (11, 2)])" -heuristic_minbudget,0.10550700000021607,100,"(2, [(76, 1), (17, 1), (68, 2), (52, 2), (34, 3), (85, 3)])" -spreading_minbudget,0.014187499997206032,45,"(1, [(16, 1)])" -heuristic_minbudget,0.49358940008096397,45,"(1, [(16, 1)])" -spreading_minbudget,0.08964230003766716,40,"(4, [(8, 1), (13, 1), (28, 1), (26, 1)])" -heuristic_minbudget,0.3729581000516191,40,"(4, [(13, 1), (26, 1), (28, 1), (8, 1)])" -spreading_minbudget,0.014078899985179305,10,"(2, [(1, 1), (5, 1)])" -heuristic_minbudget,0.07229629997164011,10,"(2, [(1, 1), (5, 1)])" -spreading_minbudget,0.08951130008790642,39,"(4, [(5, 1), (13, 1), (28, 1), (37, 1)])" -heuristic_minbudget,0.37848039995878935,39,"(3, [(28, 1), (37, 1), (13, 1), (35, 2), (22, 2)])" -spreading_minbudget,0.1149967999663204,35,"(4, [(12, 1), (9, 1), (22, 1), (32, 1)])" -heuristic_minbudget,0.2040741000091657,35,"(3, [(9, 1), (22, 1), (32, 1)])" -spreading_minbudget,0.13587410002946854,53,"(3, [(35, 1), (28, 1), (21, 1)])" -heuristic_minbudget,0.2506335999350995,53,"(2, [(35, 1), (21, 1), (28, 2), (46, 2)])" -spreading_minbudget,0.13657069997861981,50,"(5, [(18, 1), (28, 1), (6, 1), (45, 1), (16, 1)])" -heuristic_minbudget,0.24887940008193254,50,"(3, [(6, 1), (16, 1), (45, 1), (5, 2), (47, 2), (4, 2)])" -spreading_minbudget,0.07272529997862875,23,"(4, [(11, 1), (1, 1), (16, 1), (21, 1)])" -heuristic_minbudget,0.28701379999984056,23,"(4, [(21, 1), (16, 1), (1, 1), (11, 1)])" -spreading_minbudget,0.14000559994019568,66,"(1, [(60, 1)])" -heuristic_minbudget,0.20181649993173778,66,"(2, [(43, 1), (19, 1), (4, 2), (27, 2)])" -spreading_minbudget,0.36298259999603033,80,"(4, [(10, 1), (55, 1), (45, 1), (31, 1)])" -heuristic_minbudget,0.1030805999180302,80,"(3, [(31, 1), (54, 1), (40, 1), (1, 2), (78, 2), (44, 2)])" -spreading_minbudget,0.2697710000211373,82,"(1, [(17, 1)])" -heuristic_minbudget,0.11582179996185005,82,"(1, [(11, 1), (65, 2), (7, 3)])" -spreading_minbudget,0.015793300000950694,5,"(2, [(1, 1), (2, 1)])" -heuristic_minbudget,0.02591490000486374,5,"(2, [(1, 1), (2, 1)])" -spreading_minbudget,0.1580337000777945,37,"(6, [(29, 1), (7, 1), (14, 1), (16, 1), (33, 1), (3, 1)])" -heuristic_minbudget,0.20222719991579652,37,"(6, [(16, 1), (29, 1), (7, 1), (3, 1), (14, 1), (33, 1)])" -spreading_minbudget,0.381596100050956,100,"(8, [(55, 1), (31, 1), (56, 1), (21, 1), (89, 1), (14, 1), (7, 1), (17, 1)])" -heuristic_minbudget,0.5756914999801666,100,"(4, [(17, 1), (7, 1), (88, 1), (82, 1), (56, 2), (18, 2), (20, 2), (41, 2), (94, 3)])" -spreading_minbudget,0.1087363000260666,35,"(5, [(7, 1), (33, 1), (9, 1), (10, 1), (12, 1)])" -heuristic_minbudget,0.3253402999835089,35,"(5, [(12, 1), (7, 1), (9, 1), (10, 1), (33, 1)])" -spreading_minbudget,0.017030299990437925,17,"(2, [(4, 1), (11, 1)])" -heuristic_minbudget,0.20403370005078614,17,"(2, [(11, 1), (4, 1)])" -spreading_minbudget,0.07849469990469515,57,"(1, [(46, 1), (26, 2)])" -heuristic_minbudget,0.2903852000599727,57,"(2, [(52, 1), (28, 1), (26, 2), (7, 2)])" -spreading_minbudget,0.2887740000151098,82,"(6, [(44, 1), (35, 1), (1, 1), (20, 1), (49, 1), (43, 1)])" -heuristic_minbudget,0.3553588999202475,82,"(4, [(43, 1), (49, 1), (48, 1), (12, 1), (14, 2), (27, 2), (28, 2), (10, 2)])" -spreading_minbudget,0.3907817000290379,67,"(2, [(59, 1), (60, 1), (8, 2), (14, 2)])" -heuristic_minbudget,0.30003909999504685,67,"(3, [(59, 1), (6, 1), (55, 1), (11, 2), (19, 2), (13, 2)])" -spreading_minbudget,0.005693199927918613,10,"(1, [(8, 1)])" -heuristic_minbudget,0.06926670006942004,10,"(1, [(8, 1)])" -spreading_minbudget,0.14788460009731352,64,"(4, [(37, 1), (1, 1), (2, 1), (36, 1)])" -heuristic_minbudget,0.5289517000783235,64,"(2, [(36, 1), (37, 1), (15, 2), (16, 2)])" -spreading_minbudget,0.043115200009197,21,"(4, [(3, 1), (6, 1), (8, 1), (12, 1)])" -heuristic_minbudget,0.26862380001693964,21,"(4, [(3, 1), (12, 1), (6, 1), (8, 1)])" -spreading_minbudget,0.022739500040188432,23,"(2, [(7, 1), (19, 1)])" -heuristic_minbudget,0.30744000000413507,23,"(2, [(19, 1), (7, 1)])" -spreading_minbudget,0.552783100050874,99,"(1, [(39, 1)])" -heuristic_minbudget,0.11471000011079013,99,"(1, [(14, 1), (27, 2), (51, 3), (69, 4)])" -spreading_minbudget,0.014494700008071959,31,"(1, [(24, 1)])" -heuristic_minbudget,0.23713310004677624,31,"(1, [(24, 1)])" -spreading_minbudget,0.06323379999957979,37,"(3, [(23, 1), (29, 1), (12, 1)])" -heuristic_minbudget,0.2401395000051707,37,"(3, [(23, 1), (29, 1), (12, 1)])" -spreading_minbudget,0.05837570002768189,63,"(2, [(1, 1), (5, 1)])" -heuristic_minbudget,0.1453706999309361,63,"(1, [(5, 1), (50, 2)])" -spreading_minbudget,0.15695330000016838,74,"(4, [(9, 1), (65, 1), (2, 1), (61, 1)])" -heuristic_minbudget,0.36885630001779646,74,"(4, [(61, 1), (2, 1), (4, 1), (39, 1), (3, 2), (10, 2), (33, 2), (16, 2)])" -spreading_minbudget,0.35161809995770454,94,"(9, [(15, 1), (2, 1), (79, 1), (38, 1), (40, 1), (77, 1), (56, 1), (88, 1), (30, 1)])" -heuristic_minbudget,0.5069408999988809,94,"(7, [(30, 1), (88, 1), (77, 1), (38, 1), (40, 1), (56, 1), (36, 1), (34, 2), (20, 2), (66, 2), (51, 2), (87, 2), (60, 2), (15, 2)])" -spreading_minbudget,0.10447570006363094,31,"(5, [(7, 1), (1, 1), (12, 1), (21, 1), (6, 1)])" -heuristic_minbudget,0.25443309999536723,31,"(5, [(12, 1), (1, 1), (6, 1), (7, 1), (21, 1)])" -spreading_minbudget,0.014162100036628544,41,"(1, [(1, 1)])" -heuristic_minbudget,0.34778249997179955,41,"(1, [(2, 1)])" -spreading_minbudget,0.2712038999889046,82,"(5, [(13, 1), (14, 1), (22, 1), (8, 1), (37, 1)])" -heuristic_minbudget,0.4021873000310734,82,"(4, [(13, 1), (37, 1), (22, 1), (47, 1), (49, 2), (1, 2), (19, 2), (72, 2)])" -spreading_minbudget,0.1242064000107348,76,"(3, [(31, 1), (16, 1), (63, 1)])" -heuristic_minbudget,0.17422440007794648,76,"(3, [(63, 1), (17, 1), (44, 1), (20, 2), (39, 2), (8, 2)])" -spreading_minbudget,0.021503099938854575,15,"(2, [(5, 1), (8, 1)])" -heuristic_minbudget,0.16360730002634227,15,"(2, [(5, 1), (8, 1)])" -spreading_minbudget,0.11488040001131594,42,"(4, [(2, 1), (6, 1), (11, 1), (32, 1)])" -heuristic_minbudget,0.20602449995931238,42,"(3, [(2, 1), (11, 1), (32, 1), (21, 2), (10, 2), (8, 2)])" -spreading_minbudget,0.018980099936015904,25,"(2, [(1, 1), (20, 1)])" -heuristic_minbudget,0.3286843999521807,25,"(2, [(1, 1), (20, 1)])" -spreading_minbudget,0.016140799969434738,7,"(1, [(4, 1)])" -heuristic_minbudget,0.05649079999420792,7,"(1, [(4, 1)])" -spreading_minbudget,0.03500149992760271,30,"(3, [(5, 1), (7, 1), (16, 1)])" -heuristic_minbudget,0.30143560003489256,30,"(3, [(5, 1), (7, 1), (16, 1)])" -spreading_minbudget,0.1318936999887228,59,"(3, [(1, 1), (4, 1), (18, 1)])" -heuristic_minbudget,0.21766589989420027,59,"(2, [(18, 1), (34, 1), (50, 2), (21, 2)])" -spreading_minbudget,0.12017140001989901,47,"(2, [(38, 1), (14, 1)])" -heuristic_minbudget,0.1988023000303656,47,"(2, [(14, 1), (11, 1), (21, 2), (13, 2)])" -spreading_minbudget,0.018574099987745285,8,"(2, [(1, 1), (4, 1)])" -heuristic_minbudget,0.12511319993063807,8,"(2, [(1, 1), (4, 1)])" -spreading_minbudget,0.4363951999694109,90,"(6, [(70, 1), (40, 1), (61, 1), (66, 1), (69, 1), (78, 1)])" -heuristic_minbudget,0.7065138999605551,90,"(5, [(69, 1), (66, 1), (78, 1), (5, 1), (11, 1), (56, 2), (33, 2), (13, 2), (65, 2), (48, 2)])" -spreading_minbudget,0.030983999953605235,18,"(3, [(9, 1), (12, 1), (15, 1)])" -heuristic_minbudget,0.2921748999506235,18,"(3, [(15, 1), (9, 1), (12, 1)])" -spreading_minbudget,0.2782496999716386,65,"(5, [(41, 1), (6, 1), (49, 1), (29, 1), (46, 1)])" -heuristic_minbudget,0.42621770000550896,65,"(4, [(29, 1), (49, 1), (46, 1), (41, 1), (14, 2), (13, 2), (10, 2), (23, 2)])" -spreading_minbudget,0.07091020001098514,68,"(1, [(53, 1)])" -heuristic_minbudget,0.10471240000333637,68,"(1, [(8, 1)])" -spreading_minbudget,0.18839100003242493,74,"(5, [(2, 1), (18, 1), (56, 1), (40, 1), (51, 1)])" -heuristic_minbudget,0.2680246999952942,74,"(3, [(51, 1), (56, 1), (40, 1), (46, 2), (23, 2), (14, 2)])" -spreading_minbudget,0.01671379990875721,8,"(2, [(4, 1), (5, 1)])" -heuristic_minbudget,0.054388999938964844,8,"(2, [(4, 1), (5, 1)])" -spreading_minbudget,0.15420550003182143,26,"(5, [(7, 1), (4, 1), (13, 1), (3, 1), (18, 1)])" -heuristic_minbudget,0.31326580001041293,26,"(5, [(7, 1), (4, 1), (13, 1), (3, 1), (18, 1)])" -spreading_minbudget,0.04749989998526871,29,"(3, [(17, 1), (27, 1), (21, 1)])" -heuristic_minbudget,0.34975829999893904,29,"(3, [(17, 1), (21, 1), (27, 1)])" -spreading_minbudget,0.008847400080412626,21,"(1, [(6, 1)])" -heuristic_minbudget,0.3989595000166446,21,"(1, [(6, 1)])" -spreading_minbudget,0.015853599994443357,14,"(2, [(1, 1), (3, 1)])" -heuristic_minbudget,0.2032005999935791,14,"(2, [(1, 1), (3, 1)])" -spreading_minbudget,0.14646540000103414,46,"(6, [(21, 1), (44, 1), (10, 1), (34, 1), (7, 1), (22, 1)])" -heuristic_minbudget,0.4952908999985084,46,"(6, [(44, 1), (21, 1), (7, 1), (10, 1), (22, 1), (34, 1), (13, 2), (43, 2)])" -spreading_minbudget,0.15142879995983094,66,"(4, [(34, 1), (45, 1), (36, 1), (13, 1)])" -heuristic_minbudget,0.30308619991410524,66,"(3, [(13, 1), (36, 1), (34, 1), (23, 2), (48, 2), (11, 2)])" -spreading_minbudget,0.024129499914124608,13,"(3, [(1, 1), (4, 1), (11, 1)])" -heuristic_minbudget,0.11802990001160651,13,"(3, [(1, 1), (4, 1), (11, 1)])" -spreading_minbudget,0.4541134999599308,79,"(3, [(37, 1), (7, 1), (47, 1), (67, 2), (2, 2), (3, 2)])" -heuristic_minbudget,0.3353960000677034,79,"(4, [(47, 1), (64, 1), (28, 1), (55, 1), (77, 2), (32, 2), (57, 2), (67, 2)])" -spreading_minbudget,0.4108619000762701,98,"(7, [(37, 1), (59, 1), (60, 1), (85, 1), (89, 1), (27, 1), (33, 1)])" -heuristic_minbudget,0.6443371999775991,98,"(6, [(59, 1), (89, 1), (85, 1), (33, 1), (27, 1), (94, 1), (60, 2), (25, 2), (42, 2), (20, 2), (26, 2), (1, 2), (84, 3)])" -,41.81116489996202,, +spreading_minbudget,0.28455229999963194,100,"(2, [(70, 1), (13, 1)])" +heuristic_minbudget,0.23981180001283064,100,"(2, [(70, 1), (13, 1), (38, 2), (48, 2)])" +spreading_minbudget,0.6686060999636538,100,"(20, [(27, 1), (5, 1), (26, 1), (30, 1), (43, 1), (52, 1), (65, 1), (80, 1), (82, 1), (95, 1), (96, 1), (4, 1), (6, 1), (81, 1), (38, 1), (58, 1), (92, 1), (12, 1), (41, 1), (99, 1)])" +heuristic_minbudget,2.4607032999629155,100,"(19, [(43, 1), (92, 1), (65, 1), (99, 1), (26, 1), (5, 1), (30, 1), (80, 1), (95, 1), (38, 1), (4, 1), (82, 1), (41, 1), (96, 1), (12, 1), (58, 1), (81, 1), (6, 1), (52, 1)])" +spreading_minbudget,0.9770453999517485,100,"(34, [(6, 1), (1, 1), (2, 1), (16, 1), (17, 1), (22, 1), (24, 1), (27, 1), (47, 1), (50, 1), (51, 1), (52, 1), (58, 1), (59, 1), (63, 1), (64, 1), (68, 1), (78, 1), (79, 1), (80, 1), (81, 1), (86, 1), (87, 1), (88, 1), (96, 1), (98, 1), (8, 1), (9, 1), (15, 1), (28, 1), (65, 1), (67, 1), (70, 1), (83, 1)])" +heuristic_minbudget,7.318832900025882,100,"(34, [(9, 1), (52, 1), (78, 1), (17, 1), (28, 1), (96, 1), (8, 1), (81, 1), (70, 1), (98, 1), (27, 1), (16, 1), (15, 1), (2, 1), (68, 1), (80, 1), (86, 1), (79, 1), (22, 1), (63, 1), (83, 1), (24, 1), (87, 1), (50, 1), (67, 1), (47, 1), (51, 1), (1, 1), (58, 1), (6, 1), (59, 1), (64, 1), (88, 1), (65, 1)])" +spreading_minbudget,1.2262894000159577,200,"(15, [(7, 1), (140, 1), (147, 1), (164, 1), (137, 1), (128, 1), (186, 1), (53, 1), (124, 1), (166, 1), (94, 1), (27, 1), (105, 1), (131, 1), (9, 1)])" +heuristic_minbudget,1.1135120000108145,200,"(10, [(105, 1), (9, 1), (131, 1), (27, 1), (94, 1), (124, 1), (166, 1), (125, 1), (154, 1), (30, 1), (40, 2), (147, 2), (35, 2), (104, 2), (126, 2), (83, 2), (84, 2), (64, 2), (47, 2), (55, 2)])" +spreading_minbudget,2.1903421000461094,200,"(38, [(26, 1), (189, 1), (1, 1), (3, 1), (17, 1), (18, 1), (37, 1), (64, 1), (75, 1), (82, 1), (90, 1), (106, 1), (107, 1), (108, 1), (109, 1), (126, 1), (138, 1), (139, 1), (141, 1), (144, 1), (151, 1), (165, 1), (170, 1), (27, 1), (52, 1), (78, 1), (83, 1), (135, 1), (156, 1), (171, 1), (193, 1), (199, 1), (28, 1), (116, 1), (194, 1), (153, 1), (196, 1), (147, 1)])" +heuristic_minbudget,15.326287399977446,200,"(38, [(135, 1), (17, 1), (107, 1), (141, 1), (75, 1), (144, 1), (26, 1), (109, 1), (156, 1), (28, 1), (189, 1), (171, 1), (147, 1), (3, 1), (138, 1), (27, 1), (18, 1), (193, 1), (151, 1), (194, 1), (78, 1), (37, 1), (116, 1), (153, 1), (1, 1), (52, 1), (83, 1), (108, 1), (90, 1), (106, 1), (170, 1), (82, 1), (196, 1), (139, 1), (64, 1), (165, 1), (126, 1), (199, 1)])" +spreading_minbudget,1.3038518000394106,200,"(33, [(96, 1), (1, 1), (2, 1), (8, 1), (15, 1), (16, 1), (26, 1), (36, 1), (39, 1), (40, 1), (41, 1), (48, 1), (63, 1), (91, 1), (103, 1), (107, 1), (118, 1), (119, 1), (130, 1), (139, 1), (144, 1), (165, 1), (175, 1), (186, 1), (189, 1), (190, 1), (191, 1), (198, 1), (77, 1), (117, 1), (134, 1), (194, 1), (93, 1)])" +heuristic_minbudget,25.622363100002985,200,"(33, [(198, 1), (91, 1), (8, 1), (40, 1), (175, 1), (189, 1), (93, 1), (96, 1), (107, 1), (26, 1), (36, 1), (165, 1), (1, 1), (134, 1), (119, 1), (15, 1), (77, 1), (41, 1), (191, 1), (16, 1), (194, 1), (139, 1), (144, 1), (63, 1), (117, 1), (2, 1), (39, 1), (48, 1), (103, 1), (130, 1), (186, 1), (190, 1), (118, 1)])" +spreading_minbudget,4.48063219996402,400,"(29, [(137, 1), (11, 1), (59, 1), (361, 1), (347, 1), (24, 1), (207, 1), (165, 1), (314, 1), (114, 1), (360, 1), (60, 1), (1, 1), (2, 1), (294, 1), (62, 1), (291, 1), (6, 1), (93, 1), (79, 1), (389, 1), (28, 1), (148, 1), (269, 1), (357, 1), (91, 1), (98, 1), (181, 1), (250, 1)])" +heuristic_minbudget,3.351407299982384,400,"(18, [(207, 1), (98, 1), (60, 1), (181, 1), (28, 1), (269, 1), (294, 1), (148, 1), (357, 1), (291, 1), (79, 1), (93, 1), (250, 1), (389, 1), (2, 1), (91, 1), (62, 1), (6, 1), (221, 2), (234, 2), (49, 2), (340, 2), (273, 2), (12, 2), (263, 2), (1, 2), (121, 2), (117, 2), (354, 2), (176, 2), (61, 2), (135, 2), (146, 2), (249, 2), (302, 2), (158, 2)])" +spreading_minbudget,5.842790000024252,400,"(32, [(90, 1), (147, 1), (14, 1), (18, 1), (33, 1), (38, 1), (104, 1), (107, 1), (113, 1), (208, 1), (219, 1), (226, 1), (270, 1), (275, 1), (314, 1), (382, 1), (6, 1), (179, 1), (258, 1), (311, 1), (334, 1), (49, 1), (101, 1), (191, 1), (351, 1), (85, 1), (350, 1), (366, 1), (16, 1), (223, 1), (263, 1), (292, 1)])" +heuristic_minbudget,40.8089464000077,400,"(32, [(33, 1), (292, 1), (6, 1), (90, 1), (223, 1), (38, 1), (258, 1), (350, 1), (179, 1), (18, 1), (49, 1), (16, 1), (147, 1), (101, 1), (314, 1), (14, 1), (208, 1), (351, 1), (311, 1), (85, 1), (226, 1), (104, 1), (113, 1), (219, 1), (270, 1), (263, 1), (334, 1), (107, 1), (366, 1), (191, 1), (275, 1), (382, 1)])" +spreading_minbudget,10.128652899991721,400,"(139, [(306, 1), (4, 1), (5, 1), (8, 1), (13, 1), (18, 1), (19, 1), (25, 1), (26, 1), (30, 1), (38, 1), (39, 1), (40, 1), (41, 1), (51, 1), (52, 1), (54, 1), (55, 1), (57, 1), (60, 1), (61, 1), (62, 1), (64, 1), (65, 1), (67, 1), (70, 1), (74, 1), (78, 1), (81, 1), (85, 1), (87, 1), (88, 1), (89, 1), (91, 1), (92, 1), (97, 1), (98, 1), (103, 1), (106, 1), (121, 1), (123, 1), (127, 1), (134, 1), (135, 1), (138, 1), (150, 1), (160, 1), (163, 1), (164, 1), (169, 1), (170, 1), (180, 1), (185, 1), (186, 1), (192, 1), (194, 1), (196, 1), (198, 1), (199, 1), (205, 1), (209, 1), (210, 1), (212, 1), (215, 1), (225, 1), (226, 1), (234, 1), (235, 1), (236, 1), (241, 1), (246, 1), (250, 1), (251, 1), (256, 1), (257, 1), (258, 1), (259, 1), (263, 1), (264, 1), (269, 1), (271, 1), (274, 1), (275, 1), (278, 1), (282, 1), (284, 1), (285, 1), (288, 1), (311, 1), (312, 1), (314, 1), (317, 1), (321, 1), (329, 1), (331, 1), (347, 1), (349, 1), (350, 1), (356, 1), (357, 1), (370, 1), (374, 1), (378, 1), (384, 1), (386, 1), (387, 1), (389, 1), (397, 1), (399, 1), (35, 1), (122, 1), (131, 1), (137, 1), (149, 1), (176, 1), (182, 1), (197, 1), (208, 1), (228, 1), (239, 1), (270, 1), (276, 1), (281, 1), (291, 1), (294, 1), (297, 1), (324, 1), (352, 1), (383, 1), (388, 1), (96, 1), (113, 1), (148, 1), (157, 1), (267, 1), (323, 1), (334, 1), (244, 1), (141, 1)])" +heuristic_minbudget,204.12543320003897,400,"(138, [(160, 1), (194, 1), (209, 1), (282, 1), (274, 1), (180, 1), (241, 1), (250, 1), (258, 1), (397, 1), (62, 1), (88, 1), (122, 1), (170, 1), (234, 1), (264, 1), (323, 1), (235, 1), (331, 1), (239, 1), (25, 1), (85, 1), (57, 1), (226, 1), (257, 1), (55, 1), (5, 1), (278, 1), (383, 1), (386, 1), (137, 1), (169, 1), (186, 1), (378, 1), (113, 1), (263, 1), (64, 1), (311, 1), (281, 1), (334, 1), (70, 1), (288, 1), (374, 1), (106, 1), (98, 1), (197, 1), (65, 1), (134, 1), (347, 1), (41, 1), (92, 1), (121, 1), (196, 1), (163, 1), (74, 1), (291, 1), (205, 1), (18, 1), (26, 1), (228, 1), (35, 1), (123, 1), (312, 1), (176, 1), (246, 1), (356, 1), (267, 1), (13, 1), (199, 1), (40, 1), (388, 1), (314, 1), (61, 1), (148, 1), (52, 1), (251, 1), (275, 1), (38, 1), (182, 1), (244, 1), (212, 1), (276, 1), (317, 1), (30, 1), (51, 1), (141, 1), (384, 1), (329, 1), (350, 1), (60, 1), (138, 1), (89, 1), (67, 1), (150, 1), (127, 1), (208, 1), (87, 1), (370, 1), (324, 1), (294, 1), (389, 1), (135, 1), (8, 1), (256, 1), (399, 1), (210, 1), (349, 1), (157, 1), (198, 1), (39, 1), (357, 1), (387, 1), (131, 1), (192, 1), (284, 1), (297, 1), (91, 1), (185, 1), (269, 1), (270, 1), (54, 1), (4, 1), (19, 1), (96, 1), (97, 1), (215, 1), (352, 1), (78, 1), (81, 1), (164, 1), (103, 1), (321, 1), (225, 1), (236, 1), (285, 1), (259, 1), (149, 1), (271, 1)])" +,327.8878753000172,, diff --git a/networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/spreading_minbudget.png b/networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/spreading_minbudget.png index 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b/networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/spreading_minbudget_preprocessed.csv index a99042c..479bd4b 100644 --- a/networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/spreading_minbudget_preprocessed.csv +++ b/networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/spreading_minbudget_preprocessed.csv @@ -1,201 +1,19 @@ algorithm,runtime,graph_nodes,Budget,Budget_numeric -spreading_minbudget,0.3029666000511497,74.0,"(5, [(7, 1), (14, 1), (10, 1), (19, 1), (26, 1)])",5.0 -heuristic_minbudget,0.3315926000941545,74.0,"(2, [(21, 1), (19, 1), (31, 2), (15, 2)])",2.0 -spreading_minbudget,0.346709500066936,95.0,"(5, [(67, 1), (17, 1), (59, 1), (11, 1), (35, 1)])",5.0 -heuristic_minbudget,0.4012654999969527,95.0,"(3, [(67, 1), (19, 1), (22, 1), (43, 2), (68, 2), (15, 2)])",3.0 -spreading_minbudget,0.1419543999945745,37.0,"(4, [(8, 1), (5, 1), (24, 1), (33, 1)])",4.0 -heuristic_minbudget,0.1485013000201434,37.0,"(1, [(5, 1), (30, 2)])",1.0 -spreading_minbudget,0.1253013999667018,51.0,"(2, [(17, 1), (44, 1)])",2.0 -heuristic_minbudget,0.1905909000197425,51.0,"(1, [(44, 1), (29, 2)])",1.0 -spreading_minbudget,0.0951540999813005,26.0,"(4, [(4, 1), (6, 1), (19, 1), (24, 1)])",4.0 -heuristic_minbudget,0.1767547000199556,26.0,"(1, [(6, 1)])",1.0 -spreading_minbudget,0.0922285000560805,57.0,"(1, [(17, 1)])",1.0 -heuristic_minbudget,0.0979084999999031,57.0,"(1, [(37, 1)])",1.0 -spreading_minbudget,0.5129356000106782,92.0,"(6, [(36, 1), (52, 1), (75, 1), (9, 1), (53, 1), (81, 1)])",6.0 -heuristic_minbudget,0.3563278999645263,92.0,"(4, [(81, 1), (11, 1), (77, 1), (53, 1), (83, 2), (40, 2), (87, 2), (42, 2)])",4.0 -spreading_minbudget,0.2487354000331834,75.0,"(7, [(26, 1), (8, 1), (43, 1), (61, 1), (37, 1), (57, 1), (58, 1)])",7.0 -heuristic_minbudget,0.36899889993947,75.0,"(3, [(8, 1), (61, 1), (57, 1), (65, 2), (42, 2), (17, 2)])",3.0 -spreading_minbudget,0.5030480000423267,96.0,"(6, [(3, 1), (30, 1), (40, 1), (31, 1), (10, 1), (66, 1)])",6.0 -heuristic_minbudget,0.477685899939388,96.0,"(4, [(40, 1), (31, 1), (17, 1), (66, 1), (90, 2), (36, 2), (93, 2), (87, 2)])",4.0 -spreading_minbudget,0.1865432999329641,61.0,"(5, [(15, 1), (38, 1), (52, 1), (47, 1), (35, 1)])",5.0 -heuristic_minbudget,0.3287928999634459,61.0,"(2, [(38, 1), (15, 1), (20, 2), (26, 2)])",2.0 -spreading_minbudget,0.0420159000204876,12.0,"(1, [(3, 1)])",1.0 -heuristic_minbudget,0.0646985999774187,12.0,"(1, [(3, 1)])",1.0 -spreading_minbudget,0.016670599929057,6.0,"(4, [(4, 1), (1, 1), (2, 1), (3, 1)])",4.0 -heuristic_minbudget,0.0223838000092655,6.0,"(4, [(4, 1), (1, 1), (2, 1), (3, 1)])",4.0 -spreading_minbudget,0.4193663999903947,88.0,"(4, [(6, 1), (31, 1), (16, 1), (18, 1)])",4.0 -heuristic_minbudget,0.275212200009264,88.0,"(2, [(18, 1), (16, 1), (85, 2), (82, 2)])",2.0 -spreading_minbudget,0.1649650000035762,38.0,"(4, [(33, 1), (10, 1), (16, 1), (17, 1)])",4.0 -heuristic_minbudget,0.3307493000756949,38.0,"(1, [(24, 1), (11, 2)])",1.0 -spreading_minbudget,0.1894428998930379,58.0,"(5, [(37, 1), (1, 1), (46, 1), (28, 1), (42, 1)])",5.0 -heuristic_minbudget,0.2252114000730216,58.0,"(3, [(23, 1), (53, 1), (28, 1), (37, 2), (10, 2), (15, 2)])",3.0 -spreading_minbudget,0.0827602999052032,18.0,"(16, [(6, 1), (8, 1), (11, 1), (13, 1), (14, 1), (1, 1), (2, 1), (3, 1), (4, 1), (5, 1), (7, 1), (9, 1), (10, 1), (12, 1), (15, 1), (16, 1)])",16.0 -heuristic_minbudget,0.1618432000977918,18.0,"(1, [(6, 1)])",1.0 -spreading_minbudget,0.2873131999513134,75.0,"(2, [(45, 1), (60, 1)])",2.0 -heuristic_minbudget,0.1469510999741032,75.0,"(1, [(65, 1), (47, 2)])",1.0 -spreading_minbudget,0.1618430000962689,49.0,"(5, [(10, 1), (1, 1), (46, 1), (34, 1), (42, 1)])",5.0 -heuristic_minbudget,0.1802725000306964,49.0,"(2, [(16, 1), (2, 1)])",2.0 -spreading_minbudget,0.2689366999547928,71.0,"(4, [(41, 1), (24, 1), (52, 1), (17, 1)])",4.0 -heuristic_minbudget,0.2550285999896005,71.0,"(3, [(41, 1), (24, 1), (17, 1), (56, 2), (52, 2), (30, 2)])",3.0 -spreading_minbudget,0.1058983999537304,11.0,"(1, [(8, 1)])",1.0 -heuristic_minbudget,0.0502101000165566,11.0,"(1, [(8, 1)])",1.0 -spreading_minbudget,0.2795863000210374,73.0,"(4, [(13, 1), (71, 1), (44, 1), (61, 1), (14, 2), (4, 2), (5, 2), (8, 2)])",4.0 -heuristic_minbudget,0.2035555000184103,73.0,"(4, [(20, 1), (68, 1), (44, 1), (61, 1), (71, 2), (31, 2), (70, 2), (24, 2)])",4.0 -spreading_minbudget,0.2091145999729633,63.0,"(4, [(20, 1), (38, 1), (15, 1), (57, 1)])",4.0 -heuristic_minbudget,0.212050599977374,63.0,"(1, [(20, 1), (27, 2)])",1.0 -spreading_minbudget,0.1281374999089166,70.0,"(1, [(56, 1)])",1.0 -heuristic_minbudget,0.1435540999518707,70.0,"(1, [(5, 1), (56, 2)])",1.0 -spreading_minbudget,0.1279882000526413,45.0,"(3, [(16, 1), (11, 1), (29, 1)])",3.0 -heuristic_minbudget,0.1890534999547526,45.0,"(1, [(11, 1), (31, 2)])",1.0 -spreading_minbudget,0.0467433000449091,10.0,"(2, [(6, 1), (8, 1)])",2.0 -heuristic_minbudget,0.0617293999530375,10.0,"(1, [(6, 1)])",1.0 -spreading_minbudget,0.097263199975714,28.0,"(18, [(6, 1), (7, 1), (14, 1), (16, 1), (1, 1), (2, 1), (3, 1), (4, 1), (5, 1), (8, 1), (9, 1), (10, 1), (11, 1), (12, 1), (13, 1), (15, 1), (17, 1), (18, 1)])",18.0 -heuristic_minbudget,0.237294900114648,28.0,"(1, [(6, 1)])",1.0 -spreading_minbudget,0.2560269000241533,80.0,"(3, [(28, 1), (47, 1), (39, 1), (52, 2), (4, 2), (24, 2)])",3.0 -heuristic_minbudget,0.2453222000040114,80.0,"(2, [(39, 1), (3, 1), (72, 2), (52, 2), (38, 3)])",2.0 -spreading_minbudget,0.2065405999310314,54.0,"(7, [(21, 1), (1, 1), (49, 1), (5, 1), (31, 1), (4, 1), (36, 1)])",7.0 -heuristic_minbudget,0.2648555999621749,54.0,"(2, [(16, 1), (36, 1), (20, 2), (1, 2)])",2.0 -spreading_minbudget,0.0543385000200942,42.0,"(13, [(1, 1), (4, 1), (5, 1), (6, 1), (7, 1), (8, 1), (18, 1), (19, 1), (24, 1), (25, 1), (27, 1), (28, 1), (29, 1)])",13.0 -heuristic_minbudget,0.0737580999266356,42.0,"(13, [(20, 1), (28, 1), (11, 1), (26, 1), (18, 1), (30, 1), (13, 1), (27, 1), (16, 1), (19, 1), (10, 1), (22, 1), (8, 1)])",13.0 -spreading_minbudget,0.1215768998954445,20.0,"(18, [(11, 1), (1, 1), (2, 1), (3, 1), (4, 1), (5, 1), (6, 1), (7, 1), (8, 1), (9, 1), (10, 1), (12, 1), (13, 1), (14, 1), (15, 1), (16, 1), (17, 1), (18, 1)])",18.0 -heuristic_minbudget,0.1208850999828428,20.0,"(18, [(11, 1), (1, 1), (5, 1), (9, 1), (10, 1), (12, 1), (13, 1), (14, 1), (4, 1), (8, 1), (15, 1), (6, 1), (7, 1), (3, 1), (17, 1), (2, 1), (16, 1), (18, 1)])",18.0 -spreading_minbudget,0.2009312000591308,49.0,"(4, [(19, 1), (6, 1), (9, 1), (28, 1)])",4.0 -heuristic_minbudget,0.2053439000155776,49.0,"(2, [(35, 1), (28, 1), (19, 2), (40, 2)])",2.0 -spreading_minbudget,0.0404496999690309,11.0,"(9, [(2, 1), (6, 1), (8, 1), (1, 1), (3, 1), (4, 1), (5, 1), (7, 1), (9, 1)])",9.0 -heuristic_minbudget,0.0654016999760642,11.0,"(1, [(2, 1)])",1.0 -spreading_minbudget,0.1128623000113293,26.0,"(3, [(7, 1), (9, 1), (14, 1)])",3.0 -heuristic_minbudget,0.1501908999634906,26.0,"(1, [(12, 1)])",1.0 -spreading_minbudget,0.0706721000606194,37.0,"(18, [(1, 1), (2, 1), (3, 1), (5, 1), (6, 1), (9, 1), (10, 1), (11, 1), (12, 1), (14, 1), (16, 1), (17, 1), (18, 1), (19, 1), (20, 1), (21, 1), (22, 1), (24, 1)])",18.0 -heuristic_minbudget,0.1261730999685824,37.0,"(18, [(28, 1), (1, 1), (13, 1), (18, 1), (10, 1), (34, 1), (14, 1), (19, 1), (22, 1), (35, 1), (9, 1), (23, 1), (16, 1), (5, 1), (15, 1), (8, 1), (20, 1), (7, 1)])",18.0 -spreading_minbudget,0.1178128999890759,46.0,"(1, [(2, 1)])",1.0 -heuristic_minbudget,0.3003891999833286,46.0,"(1, [(29, 1)])",1.0 -spreading_minbudget,0.1224435000913217,53.0,"(1, [(26, 1)])",1.0 -heuristic_minbudget,0.1289401999674737,53.0,"(1, [(26, 1)])",1.0 -spreading_minbudget,0.1439587000058964,31.0,"(6, [(7, 1), (4, 1), (14, 1), (20, 1), (24, 1), (9, 1)])",6.0 -heuristic_minbudget,0.2044247000012546,31.0,"(2, [(14, 1), (9, 1), (6, 2)])",2.0 -spreading_minbudget,0.1459333999082446,42.0,"(4, [(1, 1), (36, 1), (37, 1), (14, 1)])",4.0 -heuristic_minbudget,0.1935785000678151,42.0,"(2, [(8, 1), (36, 1)])",2.0 -spreading_minbudget,0.4984724000096321,90.0,"(2, [(17, 1), (18, 1), (35, 2), (1, 2)])",2.0 -heuristic_minbudget,0.204131199978292,90.0,"(3, [(84, 1), (45, 1), (5, 1), (88, 2), (6, 2), (85, 2)])",3.0 -spreading_minbudget,0.1488365000113845,91.0,"(1, [(11, 1)])",1.0 -heuristic_minbudget,0.050006000092253,91.0,"(1, [(11, 1)])",1.0 -spreading_minbudget,0.2729219000320881,72.0,"(5, [(3, 1), (6, 1), (4, 1), (33, 1), (26, 1)])",5.0 -heuristic_minbudget,0.2523309000534937,72.0,"(3, [(6, 1), (3, 1), (35, 1), (50, 2), (17, 2), (55, 2)])",3.0 -spreading_minbudget,0.1241383000742644,39.0,"(3, [(4, 1), (15, 1), (31, 1)])",3.0 -heuristic_minbudget,0.319847600068897,39.0,"(2, [(4, 1), (15, 1), (3, 2), (33, 2)])",2.0 -spreading_minbudget,0.2650019000284374,88.0,"(2, [(27, 1), (49, 1)])",2.0 -heuristic_minbudget,0.258810400031507,88.0,"(2, [(27, 1), (30, 1), (32, 2), (69, 2)])",2.0 -spreading_minbudget,0.3269191000144928,77.0,"(2, [(4, 1), (48, 1), (51, 2), (2, 2)])",2.0 -heuristic_minbudget,0.3795554999960586,77.0,"(2, [(70, 1), (50, 1), (16, 2), (59, 2)])",2.0 -spreading_minbudget,0.3504355999175459,93.0,"(5, [(19, 1), (3, 1), (81, 1), (77, 1), (66, 1)])",5.0 -heuristic_minbudget,0.5276209999574348,93.0,"(4, [(19, 1), (25, 1), (81, 1), (66, 1), (65, 2), (2, 2), (22, 2), (49, 2)])",4.0 -spreading_minbudget,0.0854837000370025,88.0,"(5, [(1, 1), (13, 1), (14, 1), (20, 1), (26, 1)])",5.0 -heuristic_minbudget,0.0238279999466612,88.0,"(5, [(14, 1), (26, 1), (46, 1), (69, 1), (74, 1)])",5.0 -spreading_minbudget,0.1530138999223709,34.0,"(6, [(1, 1), (2, 1), (3, 1), (5, 1), (28, 1), (29, 1)])",6.0 -heuristic_minbudget,0.2398521000286564,34.0,"(1, [(3, 1)])",1.0 -spreading_minbudget,0.0223326000850647,6.0,"(4, [(1, 1), (2, 1), (3, 1), (4, 1)])",4.0 -heuristic_minbudget,0.0211207000538706,6.0,"(4, [(1, 1), (2, 1), (3, 1), (4, 1)])",4.0 -spreading_minbudget,0.1929352000588551,54.0,"(4, [(23, 1), (52, 1), (19, 1), (3, 1)])",4.0 -heuristic_minbudget,0.2458742999006062,54.0,"(3, [(21, 1), (36, 1), (15, 1)])",3.0 -spreading_minbudget,0.2638881999300793,72.0,"(5, [(20, 1), (52, 1), (60, 1), (22, 1), (17, 1)])",5.0 -heuristic_minbudget,0.2341932000126689,72.0,"(2, [(45, 1), (31, 1), (55, 2), (1, 2)])",2.0 -spreading_minbudget,0.1171686999732628,41.0,"(3, [(35, 1), (9, 1), (6, 1)])",3.0 -heuristic_minbudget,0.1924914000555873,41.0,"(1, [(9, 1), (5, 2)])",1.0 -spreading_minbudget,0.041408099932596,15.0,"(13, [(8, 1), (1, 1), (2, 1), (3, 1), (4, 1), (5, 1), (6, 1), (7, 1), (9, 1), (10, 1), (11, 1), (12, 1), (13, 1)])",13.0 -heuristic_minbudget,0.0743957000086084,15.0,"(13, [(8, 1), (1, 1), (2, 1), (3, 1), (4, 1), (5, 1), (6, 1), (7, 1), (9, 1), (10, 1), (11, 1), (12, 1), (13, 1)])",13.0 -spreading_minbudget,0.1598936000373214,73.0,"(1, [(1, 1), (29, 2)])",1.0 -heuristic_minbudget,0.1441205999581143,73.0,"(1, [(48, 1), (29, 2)])",1.0 -spreading_minbudget,0.1663440000265836,45.0,"(6, [(42, 1), (1, 1), (5, 1), (28, 1), (16, 1), (34, 1)])",6.0 -heuristic_minbudget,0.2517749000107869,45.0,"(3, [(14, 1), (38, 1), (31, 1)])",3.0 -spreading_minbudget,0.1296665000263601,44.0,"(3, [(16, 1), (6, 1), (17, 1)])",3.0 -heuristic_minbudget,0.1330130000133067,44.0,"(1, [(8, 1)])",1.0 -spreading_minbudget,0.0759257999015972,58.0,"(17, [(18, 1), (1, 1), (10, 1), (15, 1), (16, 1), (23, 1), (27, 1), (29, 1), (32, 1), (34, 1), (36, 1), (38, 1), (47, 1), (48, 1), (49, 1), (52, 1), (54, 1)])",17.0 -heuristic_minbudget,0.1220701000420376,58.0,"(17, [(20, 1), (47, 1), (19, 1), (31, 1), (33, 1), (49, 1), (11, 1), (12, 1), (50, 1), (15, 1), (36, 1), (37, 1), (39, 1), (44, 1), (29, 1), (48, 1), (41, 1)])",17.0 -spreading_minbudget,0.1689889000263065,76.0,"(3, [(53, 1), (63, 1), (40, 1)])",3.0 -heuristic_minbudget,0.2284081999678164,76.0,"(2, [(63, 1), (5, 1), (56, 2), (23, 2)])",2.0 -spreading_minbudget,0.0712473000166937,73.0,"(9, [(66, 1), (1, 1), (7, 1), (24, 1), (33, 1), (35, 1), (45, 1), (48, 1), (56, 1)])",9.0 -heuristic_minbudget,0.0469032999826595,73.0,"(9, [(5, 1), (61, 1), (13, 1), (63, 1), (35, 1), (45, 1), (53, 1), (31, 1), (65, 1)])",9.0 -spreading_minbudget,0.2510937999468297,67.0,"(4, [(65, 1), (22, 1), (61, 1), (8, 1)])",4.0 -heuristic_minbudget,0.4654542000498622,67.0,"(1, [(2, 1), (65, 2)])",1.0 -spreading_minbudget,0.3784746000310406,98.0,"(2, [(20, 1), (51, 1)])",2.0 -heuristic_minbudget,0.2033340000780299,98.0,"(2, [(51, 1), (89, 1), (15, 2), (2, 2)])",2.0 -spreading_minbudget,0.2355343999806791,57.0,"(5, [(52, 1), (13, 1), (39, 1), (1, 1), (4, 1)])",5.0 -heuristic_minbudget,0.2546348000178113,57.0,"(2, [(39, 1), (13, 1), (27, 2), (53, 2)])",2.0 -spreading_minbudget,0.1790646000299602,47.0,"(2, [(34, 1), (26, 1)])",2.0 -heuristic_minbudget,0.1137489000102505,47.0,"(1, [(34, 1)])",1.0 -spreading_minbudget,0.2277229999890551,58.0,"(4, [(13, 1), (9, 1), (54, 1), (10, 1)])",4.0 -heuristic_minbudget,0.2175207999534905,58.0,"(2, [(35, 1), (43, 1), (42, 2), (46, 2)])",2.0 -spreading_minbudget,0.0197699000127613,7.0,"(5, [(5, 1), (1, 1), (2, 1), (3, 1), (4, 1)])",5.0 -heuristic_minbudget,0.0206921999342739,7.0,"(5, [(5, 1), (1, 1), (2, 1), (3, 1), (4, 1)])",5.0 -spreading_minbudget,0.0309343999251723,9.0,"(7, [(3, 1), (5, 1), (1, 1), (2, 1), (4, 1), (6, 1), (7, 1)])",7.0 -heuristic_minbudget,0.0389195999596267,9.0,"(1, [(3, 1)])",1.0 -spreading_minbudget,0.0252606000285595,7.0,"(5, [(3, 1), (5, 1), (1, 1), (2, 1), (4, 1)])",5.0 -heuristic_minbudget,0.0313837000867351,7.0,"(1, [(3, 1)])",1.0 -spreading_minbudget,0.0245341999689117,6.0,"(1, [(1, 1)])",1.0 -heuristic_minbudget,0.0289797999430447,6.0,"(1, [(1, 1)])",1.0 -spreading_minbudget,0.0620388000970706,26.0,"(19, [(11, 1), (1, 1), (2, 1), (3, 1), (4, 1), (5, 1), (6, 1), (7, 1), (8, 1), (9, 1), (10, 1), (12, 1), (13, 1), (14, 1), (15, 1), (16, 1), (17, 1), (18, 1), (19, 1)])",19.0 -heuristic_minbudget,0.132107299985364,26.0,"(19, [(11, 1), (8, 1), (22, 1), (10, 1), (5, 1), (1, 1), (20, 1), (15, 1), (23, 1), (18, 1), (21, 1), (16, 1), (24, 1), (4, 1), (7, 1), (14, 1), (2, 1), (12, 1), (19, 1)])",19.0 -spreading_minbudget,0.0192517000250518,5.0,"(3, [(2, 1), (3, 1), (1, 1)])",3.0 -heuristic_minbudget,0.0177245999220758,5.0,"(1, [(2, 1)])",1.0 -spreading_minbudget,0.1510492999805137,59.0,"(3, [(23, 1), (2, 1), (51, 1)])",3.0 -heuristic_minbudget,0.1435863999649882,59.0,"(1, [(37, 1), (25, 2)])",1.0 -spreading_minbudget,0.223583799903281,69.0,"(1, [(1, 1), (47, 2)])",1.0 -heuristic_minbudget,0.1955833999672904,69.0,"(1, [(41, 1), (56, 2)])",1.0 -spreading_minbudget,0.2393094999715685,53.0,"(5, [(5, 1), (39, 1), (8, 1), (51, 1), (7, 1)])",5.0 -heuristic_minbudget,0.2692077000392601,53.0,"(1, [(46, 1), (5, 2)])",1.0 -spreading_minbudget,0.2166507999645546,65.0,"(5, [(6, 1), (7, 1), (35, 1), (54, 1), (59, 1)])",5.0 -heuristic_minbudget,0.2993921999586746,65.0,"(2, [(3, 1), (35, 1), (61, 2), (63, 2)])",2.0 -spreading_minbudget,0.0784226999385282,39.0,"(16, [(3, 1), (1, 1), (2, 1), (4, 1), (6, 1), (10, 1), (11, 1), (13, 1), (14, 1), (15, 1), (17, 1), (20, 1), (21, 1), (22, 1), (23, 1), (24, 1)])",16.0 -heuristic_minbudget,0.2051123999990523,39.0,"(16, [(14, 1), (26, 1), (1, 1), (3, 1), (15, 1), (31, 1), (6, 1), (20, 1), (2, 1), (9, 1), (10, 1), (18, 1), (16, 1), (13, 1), (29, 1), (37, 1)])",16.0 -spreading_minbudget,0.0240971999010071,8.0,"(6, [(1, 1), (2, 1), (3, 1), (4, 1), (5, 1), (6, 1)])",6.0 -heuristic_minbudget,0.0338633999926969,8.0,"(6, [(1, 1), (2, 1), (3, 1), (4, 1), (5, 1), (6, 1)])",6.0 -spreading_minbudget,0.2261323999846354,55.0,"(4, [(10, 1), (21, 1), (25, 1), (6, 1)])",4.0 -heuristic_minbudget,0.2017070000292733,55.0,"(1, [(21, 1), (24, 2)])",1.0 -spreading_minbudget,0.251231100060977,55.0,"(5, [(18, 1), (51, 1), (12, 1), (40, 1), (53, 1)])",5.0 -heuristic_minbudget,0.3448356000008061,55.0,"(2, [(51, 1), (18, 1), (16, 2), (13, 2)])",2.0 -spreading_minbudget,0.2473822000902146,77.0,"(2, [(12, 1), (5, 1)])",2.0 -heuristic_minbudget,0.2590198999969288,77.0,"(2, [(7, 1), (2, 1), (43, 2), (5, 2)])",2.0 -spreading_minbudget,0.0680228000273928,35.0,"(16, [(9, 1), (1, 1), (2, 1), (3, 1), (4, 1), (6, 1), (7, 1), (8, 1), (12, 1), (15, 1), (16, 1), (19, 1), (20, 1), (21, 1), (22, 1), (23, 1)])",16.0 -heuristic_minbudget,0.109697200008668,35.0,"(16, [(9, 1), (5, 1), (6, 1), (21, 1), (25, 1), (3, 1), (7, 1), (19, 1), (4, 1), (10, 1), (26, 1), (8, 1), (14, 1), (18, 1), (23, 1), (24, 1)])",16.0 -spreading_minbudget,0.2132011000066995,49.0,"(4, [(47, 1), (8, 1), (43, 1), (28, 1)])",4.0 -heuristic_minbudget,0.3355788999469951,49.0,"(1, [(39, 1), (30, 2)])",1.0 -spreading_minbudget,0.1300391999538988,85.0,"(1, [(11, 1)])",1.0 -heuristic_minbudget,0.1488371999002993,85.0,"(1, [(8, 1), (20, 2)])",1.0 -spreading_minbudget,0.3204901999561116,66.0,"(4, [(3, 1), (28, 1), (1, 1), (7, 1)])",4.0 -heuristic_minbudget,0.2247770000249147,66.0,"(2, [(28, 1), (7, 1), (3, 2), (29, 2)])",2.0 -spreading_minbudget,0.0587761999340727,21.0,"(17, [(6, 1), (1, 1), (2, 1), (3, 1), (4, 1), (5, 1), (7, 1), (8, 1), (9, 1), (10, 1), (11, 1), (12, 1), (13, 1), (14, 1), (15, 1), (16, 1), (17, 1)])",17.0 -heuristic_minbudget,0.1104573999764397,21.0,"(17, [(6, 1), (1, 1), (3, 1), (12, 1), (4, 1), (13, 1), (8, 1), (10, 1), (16, 1), (18, 1), (5, 1), (11, 1), (9, 1), (14, 1), (17, 1), (19, 1), (7, 1)])",17.0 -spreading_minbudget,0.0522234999807551,15.0,"(13, [(8, 1), (13, 1), (1, 1), (2, 1), (3, 1), (4, 1), (5, 1), (6, 1), (7, 1), (9, 1), (10, 1), (11, 1), (12, 1)])",13.0 -heuristic_minbudget,0.0861853000242263,15.0,"(1, [(8, 1)])",1.0 -spreading_minbudget,0.1603539000498131,52.0,"(2, [(5, 1), (17, 1)])",2.0 -heuristic_minbudget,0.1881530000828206,52.0,"(1, [(34, 1), (17, 2)])",1.0 -spreading_minbudget,0.2742555000586435,73.0,"(4, [(51, 1), (4, 1), (41, 1), (48, 1)])",4.0 -heuristic_minbudget,0.3578611999982968,73.0,"(2, [(15, 1), (68, 1), (36, 2), (37, 2)])",2.0 -spreading_minbudget,0.0828742999583482,61.0,"(18, [(13, 1), (1, 1), (2, 1), (3, 1), (7, 1), (11, 1), (15, 1), (23, 1), (31, 1), (33, 1), (38, 1), (41, 1), (44, 1), (47, 1), (49, 1), (55, 1), (59, 1), (9, 1)])",18.0 -heuristic_minbudget,0.140296500059776,61.0,"(18, [(4, 1), (8, 1), (9, 1), (44, 1), (5, 1), (16, 1), (32, 1), (24, 1), (54, 1), (47, 1), (1, 1), (10, 1), (43, 1), (57, 1), (22, 1), (13, 1), (36, 1), (53, 1)])",18.0 -spreading_minbudget,0.0286771999672055,6.0,"(1, [(4, 1)])",1.0 -heuristic_minbudget,0.0276834999676793,6.0,"(1, [(4, 1)])",1.0 -spreading_minbudget,0.2197397999698296,54.0,"(5, [(48, 1), (45, 1), (37, 1), (42, 1), (20, 1)])",5.0 -heuristic_minbudget,0.3166107999859378,54.0,"(3, [(51, 1), (42, 1), (37, 1), (9, 2), (27, 2), (2, 2)])",3.0 -spreading_minbudget,0.3623324000509456,66.0,"(5, [(27, 1), (21, 1), (6, 1), (1, 1), (56, 1)])",5.0 -heuristic_minbudget,0.2674438999965787,66.0,"(3, [(56, 1), (50, 1), (21, 1), (6, 2), (39, 2), (13, 2)])",3.0 -spreading_minbudget,0.1692126999842003,91.0,"(15, [(1, 1), (46, 1), (2, 1), (3, 1), (16, 1), (22, 1), (25, 1), (27, 1), (36, 1), (41, 1), (45, 1), (49, 1), (50, 1), (53, 1), (55, 1)])",15.0 -heuristic_minbudget,0.1413730999920517,91.0,"(1, [(46, 1), (43, 2)])",1.0 -spreading_minbudget,0.1088337999535724,40.0,"(2, [(13, 1), (29, 1)])",2.0 -heuristic_minbudget,0.1595847000135108,40.0,"(1, [(29, 1)])",1.0 -spreading_minbudget,0.0098408000776544,4.0,"(2, [(2, 1), (1, 1)])",2.0 -heuristic_minbudget,0.0093645000597462,4.0,"(2, [(2, 1), (1, 1)])",2.0 -spreading_minbudget,0.0711457999423146,19.0,"(17, [(11, 1), (12, 1), (13, 1), (1, 1), (2, 1), (3, 1), (4, 1), (5, 1), (6, 1), (7, 1), (8, 1), (9, 1), (10, 1), (14, 1), (15, 1), (16, 1), (17, 1)])",17.0 -heuristic_minbudget,0.1546644000336527,19.0,"(1, [(12, 1)])",1.0 -spreading_minbudget,0.0672663000877946,14.0,"(3, [(2, 1), (8, 1), (10, 1)])",3.0 -heuristic_minbudget,0.1003340000752359,14.0,"(1, [(2, 1)])",1.0 -spreading_minbudget,0.1740907999919727,72.0,"(1, [(36, 1), (54, 2)])",1.0 -heuristic_minbudget,0.1651924999896437,72.0,"(1, [(36, 1), (58, 2)])",1.0 -spreading_minbudget,0.2192563000135123,68.0,"(4, [(55, 1), (59, 1), (1, 1), (24, 1)])",4.0 -heuristic_minbudget,0.2327362999785691,68.0,"(3, [(55, 1), (24, 1), (1, 1), (63, 2), (47, 2), (20, 2)])",3.0 -spreading_minbudget,0.5603519999422133,94.0,"(4, [(86, 1), (91, 1), (60, 1), (22, 1), (2, 2), (53, 2), (3, 2), (24, 2)])",4.0 -heuristic_minbudget,0.4804672000464052,94.0,"(4, [(66, 1), (35, 1), (75, 1), (76, 1), (1, 2), (22, 2), (2, 2), (31, 2)])",4.0 -spreading_minbudget,0.6916073999600485,98.0,"(7, [(30, 1), (56, 1), (4, 1), (6, 1), (5, 1), (26, 1), (36, 1)])",7.0 -heuristic_minbudget,0.3245869000675156,98.0,"(5, [(36, 1), (85, 1), (6, 1), (72, 1), (77, 1), (54, 2), (61, 2), (80, 2), (84, 2), (7, 2)])",5.0 -spreading_minbudget,0.1828674999997019,48.0,"(5, [(21, 1), (13, 1), (46, 1), (43, 1), (40, 1)])",5.0 -heuristic_minbudget,0.2823784999782219,48.0,"(2, [(21, 1), (43, 1), (29, 2), (4, 2)])",2.0 +spreading_minbudget,0.2845522999996319,100.0,"(2, [(70, 1), (13, 1)])",2.0 +heuristic_minbudget,0.2398118000128306,100.0,"(2, [(70, 1), (13, 1), (38, 2), (48, 2)])",2.0 +spreading_minbudget,0.6686060999636538,100.0,"(20, [(27, 1), (5, 1), (26, 1), (30, 1), (43, 1), (52, 1), (65, 1), (80, 1), (82, 1), (95, 1), (96, 1), (4, 1), (6, 1), (81, 1), (38, 1), (58, 1), (92, 1), (12, 1), (41, 1), (99, 1)])",20.0 +heuristic_minbudget,2.4607032999629155,100.0,"(19, [(43, 1), (92, 1), (65, 1), (99, 1), (26, 1), (5, 1), (30, 1), (80, 1), (95, 1), (38, 1), (4, 1), (82, 1), (41, 1), (96, 1), (12, 1), (58, 1), (81, 1), (6, 1), (52, 1)])",19.0 +spreading_minbudget,0.9770453999517484,100.0,"(34, [(6, 1), (1, 1), (2, 1), (16, 1), (17, 1), (22, 1), (24, 1), (27, 1), (47, 1), (50, 1), (51, 1), (52, 1), (58, 1), (59, 1), (63, 1), (64, 1), (68, 1), (78, 1), (79, 1), (80, 1), (81, 1), (86, 1), (87, 1), (88, 1), (96, 1), (98, 1), (8, 1), (9, 1), (15, 1), (28, 1), (65, 1), (67, 1), (70, 1), (83, 1)])",34.0 +heuristic_minbudget,7.318832900025882,100.0,"(34, [(9, 1), (52, 1), (78, 1), (17, 1), (28, 1), (96, 1), (8, 1), (81, 1), (70, 1), (98, 1), (27, 1), (16, 1), (15, 1), (2, 1), (68, 1), (80, 1), (86, 1), (79, 1), (22, 1), (63, 1), (83, 1), (24, 1), (87, 1), (50, 1), (67, 1), (47, 1), (51, 1), (1, 1), (58, 1), (6, 1), (59, 1), (64, 1), (88, 1), (65, 1)])",34.0 +spreading_minbudget,1.2262894000159577,200.0,"(15, [(7, 1), (140, 1), (147, 1), (164, 1), (137, 1), (128, 1), (186, 1), (53, 1), (124, 1), (166, 1), (94, 1), (27, 1), (105, 1), (131, 1), (9, 1)])",15.0 +heuristic_minbudget,1.1135120000108143,200.0,"(10, [(105, 1), (9, 1), (131, 1), (27, 1), (94, 1), (124, 1), (166, 1), (125, 1), (154, 1), (30, 1), (40, 2), (147, 2), (35, 2), (104, 2), (126, 2), (83, 2), (84, 2), (64, 2), (47, 2), (55, 2)])",10.0 +spreading_minbudget,2.1903421000461094,200.0,"(38, [(26, 1), (189, 1), (1, 1), (3, 1), (17, 1), (18, 1), (37, 1), (64, 1), (75, 1), (82, 1), (90, 1), (106, 1), (107, 1), (108, 1), (109, 1), (126, 1), (138, 1), (139, 1), (141, 1), (144, 1), (151, 1), (165, 1), (170, 1), (27, 1), (52, 1), (78, 1), (83, 1), (135, 1), (156, 1), (171, 1), (193, 1), (199, 1), (28, 1), (116, 1), (194, 1), (153, 1), (196, 1), (147, 1)])",38.0 +heuristic_minbudget,15.326287399977446,200.0,"(38, [(135, 1), (17, 1), (107, 1), (141, 1), (75, 1), (144, 1), (26, 1), (109, 1), (156, 1), (28, 1), (189, 1), (171, 1), (147, 1), (3, 1), (138, 1), (27, 1), (18, 1), (193, 1), (151, 1), (194, 1), (78, 1), (37, 1), (116, 1), (153, 1), (1, 1), (52, 1), (83, 1), (108, 1), (90, 1), (106, 1), (170, 1), (82, 1), (196, 1), (139, 1), (64, 1), (165, 1), (126, 1), (199, 1)])",38.0 +spreading_minbudget,1.3038518000394106,200.0,"(33, [(96, 1), (1, 1), (2, 1), (8, 1), (15, 1), (16, 1), (26, 1), (36, 1), (39, 1), (40, 1), (41, 1), (48, 1), (63, 1), (91, 1), (103, 1), (107, 1), (118, 1), (119, 1), (130, 1), (139, 1), (144, 1), (165, 1), (175, 1), (186, 1), (189, 1), (190, 1), (191, 1), (198, 1), (77, 1), (117, 1), (134, 1), (194, 1), (93, 1)])",33.0 +heuristic_minbudget,25.622363100002985,200.0,"(33, [(198, 1), (91, 1), (8, 1), (40, 1), (175, 1), (189, 1), (93, 1), (96, 1), (107, 1), (26, 1), (36, 1), (165, 1), (1, 1), (134, 1), (119, 1), (15, 1), (77, 1), (41, 1), (191, 1), (16, 1), (194, 1), (139, 1), (144, 1), (63, 1), (117, 1), (2, 1), (39, 1), (48, 1), (103, 1), (130, 1), (186, 1), (190, 1), (118, 1)])",33.0 +spreading_minbudget,4.48063219996402,400.0,"(29, [(137, 1), (11, 1), (59, 1), (361, 1), (347, 1), (24, 1), (207, 1), (165, 1), (314, 1), (114, 1), (360, 1), (60, 1), (1, 1), (2, 1), (294, 1), (62, 1), (291, 1), (6, 1), (93, 1), (79, 1), (389, 1), (28, 1), (148, 1), (269, 1), (357, 1), (91, 1), (98, 1), (181, 1), (250, 1)])",29.0 +heuristic_minbudget,3.351407299982384,400.0,"(18, [(207, 1), (98, 1), (60, 1), (181, 1), (28, 1), (269, 1), (294, 1), (148, 1), (357, 1), (291, 1), (79, 1), (93, 1), (250, 1), (389, 1), (2, 1), (91, 1), (62, 1), (6, 1), (221, 2), (234, 2), (49, 2), (340, 2), (273, 2), (12, 2), (263, 2), (1, 2), (121, 2), (117, 2), (354, 2), (176, 2), (61, 2), (135, 2), (146, 2), (249, 2), (302, 2), (158, 2)])",18.0 +spreading_minbudget,5.842790000024252,400.0,"(32, [(90, 1), (147, 1), (14, 1), (18, 1), (33, 1), (38, 1), (104, 1), (107, 1), (113, 1), (208, 1), (219, 1), (226, 1), (270, 1), (275, 1), (314, 1), (382, 1), (6, 1), (179, 1), (258, 1), (311, 1), (334, 1), (49, 1), (101, 1), (191, 1), (351, 1), (85, 1), (350, 1), (366, 1), (16, 1), (223, 1), (263, 1), (292, 1)])",32.0 +heuristic_minbudget,40.8089464000077,400.0,"(32, [(33, 1), (292, 1), (6, 1), (90, 1), (223, 1), (38, 1), (258, 1), (350, 1), (179, 1), (18, 1), (49, 1), (16, 1), (147, 1), (101, 1), (314, 1), (14, 1), (208, 1), (351, 1), (311, 1), (85, 1), (226, 1), (104, 1), (113, 1), (219, 1), (270, 1), (263, 1), (334, 1), (107, 1), (366, 1), (191, 1), (275, 1), (382, 1)])",32.0 +spreading_minbudget,10.12865289999172,400.0,"(139, [(306, 1), (4, 1), (5, 1), (8, 1), (13, 1), (18, 1), (19, 1), (25, 1), (26, 1), (30, 1), (38, 1), (39, 1), (40, 1), (41, 1), (51, 1), (52, 1), (54, 1), (55, 1), (57, 1), (60, 1), (61, 1), (62, 1), (64, 1), (65, 1), (67, 1), (70, 1), (74, 1), (78, 1), (81, 1), (85, 1), (87, 1), (88, 1), (89, 1), (91, 1), (92, 1), (97, 1), (98, 1), (103, 1), (106, 1), (121, 1), (123, 1), (127, 1), (134, 1), (135, 1), (138, 1), (150, 1), (160, 1), (163, 1), (164, 1), (169, 1), (170, 1), (180, 1), (185, 1), (186, 1), (192, 1), (194, 1), (196, 1), (198, 1), (199, 1), (205, 1), (209, 1), (210, 1), (212, 1), (215, 1), (225, 1), (226, 1), (234, 1), (235, 1), (236, 1), (241, 1), (246, 1), (250, 1), (251, 1), (256, 1), (257, 1), (258, 1), (259, 1), (263, 1), (264, 1), (269, 1), (271, 1), (274, 1), (275, 1), (278, 1), (282, 1), (284, 1), (285, 1), (288, 1), (311, 1), (312, 1), (314, 1), (317, 1), (321, 1), (329, 1), (331, 1), (347, 1), (349, 1), (350, 1), (356, 1), (357, 1), (370, 1), (374, 1), (378, 1), (384, 1), (386, 1), (387, 1), (389, 1), (397, 1), (399, 1), (35, 1), (122, 1), (131, 1), (137, 1), (149, 1), (176, 1), (182, 1), (197, 1), (208, 1), (228, 1), (239, 1), (270, 1), (276, 1), (281, 1), (291, 1), (294, 1), (297, 1), (324, 1), (352, 1), (383, 1), (388, 1), (96, 1), (113, 1), (148, 1), (157, 1), (267, 1), (323, 1), (334, 1), (244, 1), (141, 1)])",139.0 +heuristic_minbudget,204.12543320003897,400.0,"(138, [(160, 1), (194, 1), (209, 1), (282, 1), (274, 1), (180, 1), (241, 1), (250, 1), (258, 1), (397, 1), (62, 1), (88, 1), (122, 1), (170, 1), (234, 1), (264, 1), (323, 1), (235, 1), (331, 1), (239, 1), (25, 1), (85, 1), (57, 1), (226, 1), (257, 1), (55, 1), (5, 1), (278, 1), (383, 1), (386, 1), (137, 1), (169, 1), (186, 1), (378, 1), (113, 1), (263, 1), (64, 1), (311, 1), (281, 1), (334, 1), (70, 1), (288, 1), (374, 1), (106, 1), (98, 1), (197, 1), (65, 1), (134, 1), (347, 1), (41, 1), (92, 1), (121, 1), (196, 1), (163, 1), (74, 1), (291, 1), (205, 1), (18, 1), (26, 1), (228, 1), (35, 1), (123, 1), (312, 1), (176, 1), (246, 1), (356, 1), (267, 1), (13, 1), (199, 1), (40, 1), (388, 1), (314, 1), (61, 1), (148, 1), (52, 1), (251, 1), (275, 1), (38, 1), (182, 1), (244, 1), (212, 1), (276, 1), (317, 1), (30, 1), (51, 1), (141, 1), (384, 1), (329, 1), (350, 1), (60, 1), (138, 1), (89, 1), (67, 1), (150, 1), (127, 1), (208, 1), (87, 1), (370, 1), (324, 1), (294, 1), (389, 1), (135, 1), (8, 1), (256, 1), (399, 1), (210, 1), (349, 1), (157, 1), (198, 1), (39, 1), (357, 1), (387, 1), (131, 1), (192, 1), (284, 1), (297, 1), (91, 1), (185, 1), (269, 1), (270, 1), (54, 1), (4, 1), (19, 1), (96, 1), (97, 1), (215, 1), (352, 1), (78, 1), (81, 1), (164, 1), (103, 1), (321, 1), (225, 1), (236, 1), (285, 1), (259, 1), (149, 1), (271, 1)])",138.0 From e297d1c8222a3ff1cb82e7d0ea555082def77dd9 Mon Sep 17 00:00:00 2001 From: 20shaked20 Date: Sun, 21 Jul 2024 21:56:48 +0300 Subject: [PATCH 73/93] Max save random comparision created --- .../comparisons/comparisons.py | 84 ++- .../comparisons/non_spreading.csv | 306 ++++++++- .../comparisons/non_spreading.png | Bin 37629 -> 74017 bytes .../non_spreading_preprocessed.csv | 600 +++++++++--------- .../comparisons/spreading_maxsave.csv | 362 +++++++++++ .../comparisons/spreading_maxsave.png | Bin 0 -> 32558 bytes .../comparisons/spreading_minbudget.csv | 38 +- 7 files changed, 1036 insertions(+), 354 deletions(-) create mode 100644 networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/spreading_maxsave.csv create mode 100644 networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/spreading_maxsave.png diff --git a/networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/comparisons.py b/networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/comparisons.py index 3a961a6..8037891 100644 --- a/networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/comparisons.py +++ b/networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/comparisons.py @@ -88,10 +88,10 @@ def runner_spreading( algorithm, graph, source, targets): return {"Budget" : (algorithm(Graph=graph,source=source,targets=targets,spreading=True))} if algorithm == heuristic_maxsave: - return {"Budget" : (algorithm(Graph=graph,budget = 1, source=source,targets=targets,spreading=True))} + return {"Nodes_Saved" : (str(len(algorithm(Graph=graph,budget = 2, source=source,targets=targets,spreading=True)[1])))} if algorithm == spreading_maxsave: - return {"Budget" : (algorithm(Graph=graph,budget = 1, source=source,targets=targets))} + return {"Nodes_Saved" : (str(len(algorithm(Graph=graph,budget = 2, source=source,targets=targets)[1])))} else: return {"Budget" : (algorithm(Graph=graph,source=source,targets=targets))} @@ -157,14 +157,62 @@ def extract_budget_numeric(budget): x_field="graph_nodes", y_field="Budget_numeric", z_field="algorithm", - mean=False, + mean=True, save_to_file="./networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/non_spreading.png" ) print("\n DataFrame-NonSpread: \n", ex1.dataFrame) def Compare_SpreadingMaxSave(): - pass + ex1 = experiments_csv.Experiment("./networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/", "spreading_maxsave.csv", backup_folder=None) + ex1.clear_previous_results() # to clear previous experiments + + input_ranges = { + "algorithm": [spreading_maxsave, heuristic_maxsave] + } + + node_counts = [100, 200, 400] + edge_probabilities = [0.1, 0.5, 0.8] + + def multiple_runs(runs=20): + for num_nodes in node_counts: + for edge_prob in edge_probabilities: + graph = generate_random_DiGraph(num_nodes=num_nodes, edge_probability=edge_prob, seed=None) + for _ in range(runs): + source = 0 + nodes = list(graph.nodes) + nodes.remove(0) + num_targets = random.randint(1, int(len(nodes) / 2) + 1) + targets = random.sample(nodes, num_targets) + for algorithm in input_ranges["algorithm"]: + start_time = perf_counter() + result = runner_spreading(algorithm, graph, source, targets) + runtime = perf_counter() - start_time + + if algorithm == spreading_maxsave: + id = 1 + else: + id = 2 # heuristic + + ex1.add({**{"algorithm": id, "runtime": runtime, "graph_nodes": len(graph.nodes)}, **result}) + return {"status": "completed"} + + + # Set a time limit for the entire batch run + ex1.run_with_time_limit(multiple_runs, input_ranges={}, time_limit=0.9) + + # Plot the results using the preprocessed CSV file + single_plot_results( + results_csv_file="./networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/spreading_maxsave.csv", + filter={}, + x_field="graph_nodes", + y_field="Nodes_Saved", + z_field="algorithm", + mean=True, + save_to_file="./networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/spreading_maxsave.png" + ) + + print("\n DataFrame-NonSpread: \n", ex1.dataFrame) def Compare_SpreadingMinBudget(): ex1 = experiments_csv.Experiment("./networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/", "spreading_minbudget.csv", backup_folder=None) @@ -240,29 +288,5 @@ def extract_budget_numeric(budget): if __name__ == "__main__": setup_global_logger(level=logging.DEBUG) # Compare_NonSpread() - Compare_SpreadingMinBudget() - - """""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""" - - # ex2 = experiments_csv.Experiment("./networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/", "spreading.csv", backup_folder=None) - # ex2.clear_previous_results() # to clear previous experminets.. - - # input_ranges = { - # "algorithm":[spreading_minbudget,spreading_maxsave,heuristic_minbudget,heuristic_maxsave], - # "source" : [0,4,6,7] - # } - # ex2.run_with_time_limit(runner_spreading,input_ranges, time_limit=0.9) - - - #Plotting: - - - - # single_plot_results("./networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/spreading.csv", - # filter = {}, - # x_field="algorithm", - # y_field="runtime", - # z_field="Budget", - # save_to_file="./networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/spreading.png") - - # print("\n DataFrame-Spreading: \n", ex2.dataFrame) \ No newline at end of file + # Compare_SpreadingMinBudget() + Compare_SpreadingMaxSave() \ No newline at end of file diff --git a/networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/non_spreading.csv b/networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/non_spreading.csv index 81d37ad..bfea393 100644 --- a/networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/non_spreading.csv +++ b/networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/non_spreading.csv @@ -1,6 +1,302 @@ algorithm,runtime,graph_nodes,Budget -non_spreading_dirlaynet_minbudget,0.41342359999543987,181,"(15, {0: [10, 3, 7, 12, 1, 9, 14, 15, 2, 11, 8, 4, 5, 6, 13]})" -non_spreading_minbudget,0.0666452000150457,181,"(15, [])" -heuristic_minbudget,0.42624279999290593,181,"(20, ([(7, 1), (6, 1), (1, 1), (2, 1), (3, 1), (4, 1), (5, 1), (8, 1), (9, 1), (10, 1), (11, 1), (12, 1), (13, 1), (14, 1), (15, 1)], {6, 7, 25, 29, 33, 38, 41, 42, 50, 52, 54, 58, 61, 68, 73, 89, 90, 92, 97, 98, 100, 102, 103, 105, 116, 119, 126, 128, 130, 136, 150, 153, 158, 160, 162, 165, 175, 176, 180}))" -non_spreading_dirlaynet_minbudget,0.28055489997495897,124,"(15, {0: [10, 3, 7, 12, 1, 9, 15, 2, 11, 8, 4, 5, 14, 13, 16]})" -non_spreading_minbudget,0.014502400008495897,124,"(15, [])" +non_spreading_dirlaynet_minbudget,0.2408395999809727,110,"(2, {0: [24, 80]})" +non_spreading_minbudget,0.011683300021104515,110,"(2, [])" +heuristic_minbudget,0.89675100002205,110,"(1, [(24, 1), (33, 2), (46, 3), (63, 4), (66, 5)])" +non_spreading_dirlaynet_minbudget,0.3707220000214875,168,"(14, {0: [11, 6, 8, 4, 1, 5, 14, 13, 3, 12, 9, 7, 2, 17]})" +non_spreading_minbudget,0.020104600000195205,168,"(14, [])" +heuristic_minbudget,1.0511027000029571,168,"(4, [(4, 1), (1, 1), (14, 1), (2, 1), (19, 2), (22, 2), (20, 2), (26, 2), (45, 3), (44, 3), (38, 3), (47, 3), (79, 4), (78, 4), (72, 4), (63, 4), (80, 5), (87, 5), (81, 5), (83, 5), (93, 6), (100, 6), (97, 6), (95, 6), (110, 7), (111, 7), (105, 7), (107, 7), (122, 8), (128, 8), (135, 8), (145, 8), (153, 9), (148, 9), (152, 9), (151, 9)])" +non_spreading_dirlaynet_minbudget,0.2880064999917522,124,"(4, {0: [10, 31, 34, 27], 1: [31, 34, 27], 2: [31, 34, 27]})" +non_spreading_minbudget,0.013811299984809011,124,"(9, [])" +heuristic_minbudget,0.6897182000102475,124,"(5, [(10, 1), (18, 1), (5, 1), (17, 1), (7, 1), (19, 2), (25, 2), (24, 2), (21, 2), (23, 2), (28, 3), (34, 3), (27, 3), (33, 3), (30, 3), (35, 4), (43, 4)])" +non_spreading_dirlaynet_minbudget,0.26234429999021813,130,"(13, {0: [20, 24, 8, 4, 15, 40, 35, 54, 66, 82, 83, 97, 116], 1: [40, 35, 66, 82, 83], 2: [66, 82, 83], 3: [66, 82, 83], 4: [82, 83]})" +non_spreading_minbudget,0.016597500012721866,130,"(21, [])" +heuristic_minbudget,1.3310243000159971,130,"(5, [(8, 1), (15, 1), (24, 1), (20, 1), (4, 1), (40, 2), (35, 2), (43, 2), (39, 2), (46, 2), (54, 3), (53, 3), (57, 3), (49, 3), (48, 3), (66, 4), (65, 4), (76, 4), (63, 4), (60, 4), (80, 5), (83, 5), (81, 5), (82, 5), (85, 5), (90, 6), (91, 6), (86, 6), (97, 6), (88, 6), (116, 7)])" +non_spreading_dirlaynet_minbudget,0.1924457999994047,96,"(3, {0: [33, 49, 59], 1: [33, 59], 2: [59, 84], 3: [59]})" +non_spreading_minbudget,0.012846800033003092,96,"(8, [])" +heuristic_minbudget,0.94798830000218,96,"(4, [(11, 1), (5, 1), (2, 1), (10, 1), (23, 2), (33, 2), (24, 2), (25, 2), (49, 3), (46, 3), (43, 3), (48, 3), (59, 4), (57, 4), (66, 4), (67, 4)])" +non_spreading_dirlaynet_minbudget,0.4186942999949679,196,"(16, {0: [10, 1, 14, 13, 3, 12, 9, 7, 2, 11, 6, 8, 4, 5, 15, 18]})" +non_spreading_minbudget,0.02659190003760159,196,"(16, [])" +heuristic_minbudget,1.4307623999775387,196,"(6, [(13, 1), (1, 1), (2, 1), (4, 1), (9, 1), (5, 1), (19, 2), (29, 2), (23, 2), (20, 2), (22, 2), (28, 2), (51, 3), (52, 3), (61, 3), (46, 3), (57, 3), (64, 3), (68, 4), (74, 4), (69, 4), (77, 4), (73, 4), (75, 4), (114, 5), (112, 5), (90, 5), (98, 5), (99, 5), (102, 5), (119, 6), (118, 6), (117, 6), (116, 6), (115, 6)])" +non_spreading_dirlaynet_minbudget,0.47021560004213825,153,"(14, {0: [10, 26, 16, 1, 24, 8, 22, 44, 37, 49, 51, 56, 60, 55], 1: [44, 37, 49, 51, 56, 60, 55], 2: [49, 51, 56, 60, 55, 79]})" +non_spreading_minbudget,0.03129000001354143,153,"(25, [])" +heuristic_minbudget,1.1095701000303961,153,"(7, [(16, 1), (26, 1), (1, 1), (24, 1), (10, 1), (8, 1), (22, 1), (44, 2), (41, 2), (42, 2), (37, 2), (29, 2), (47, 2), (35, 2), (48, 3), (59, 3), (50, 3), (56, 3), (57, 3), (54, 3), (51, 3), (68, 4), (78, 4), (79, 4), (66, 4), (83, 4), (70, 4), (63, 4), (100, 5), (98, 5), (92, 5), (91, 5), (95, 5), (93, 5), (88, 5), (121, 6), (114, 6), (115, 6), (120, 6), (102, 6), (111, 6), (104, 6), (138, 7), (126, 7), (128, 7), (132, 7), (133, 7), (130, 7), (129, 7)])" +non_spreading_dirlaynet_minbudget,0.27224899997236207,131,"(3, {0: [5, 68, 88], 1: [33], 5: [88]})" +non_spreading_minbudget,0.01399949996266514,131,"(6, [])" +heuristic_minbudget,0.9055172000080347,131,"(1, [(5, 1), (33, 2), (42, 3), (68, 4), (80, 5)])" +non_spreading_dirlaynet_minbudget,0.4071177000296302,172,"(7, {0: [6, 4, 1, 3, 7, 2, 11]})" +non_spreading_minbudget,0.017289999988861382,172,"(7, [])" +heuristic_minbudget,0.7760101999738254,172,"(4, [(6, 1), (4, 1), (3, 1), (7, 1), (11, 2), (20, 2), (10, 2), (26, 2), (31, 3), (37, 3), (38, 3), (36, 3), (46, 4), (61, 4), (50, 4), (55, 4), (70, 5), (73, 5), (88, 5), (92, 5), (100, 6), (99, 6), (96, 6), (98, 6), (122, 7), (121, 7), (105, 7), (109, 7), (137, 8), (145, 8), (134, 8), (143, 8), (154, 9), (147, 9), (158, 9), (152, 9)])" +non_spreading_dirlaynet_minbudget,0.35584720002952963,171,"(8, {0: [10, 1, 5, 9, 7, 34, 36, 38], 1: [36, 38], 2: [36, 38]})" +non_spreading_minbudget,0.02076470002066344,171,"(12, [])" +heuristic_minbudget,0.7088578999973834,171,"(5, [(7, 1), (5, 1), (1, 1), (10, 1), (9, 1), (34, 2), (35, 2), (33, 2), (25, 2), (28, 2), (39, 3), (40, 3), (37, 3), (36, 3), (38, 3), (50, 4), (44, 4), (58, 4)])" +non_spreading_dirlaynet_minbudget,0.18420820002211258,98,"(1, {0: [14]})" +non_spreading_minbudget,0.009187699994072318,98,"(1, [])" +heuristic_minbudget,0.2952911999891512,98,"(1, [(14, 1)])" +non_spreading_dirlaynet_minbudget,0.18176989996572956,115,"(6, {0: [17, 21, 24, 31, 30, 33], 1: [31, 30, 33]})" +non_spreading_minbudget,0.011746600037440658,115,"(8, [])" +heuristic_minbudget,0.8000411000102758,115,"(4, [(21, 1), (24, 1), (17, 1), (3, 1), (30, 2), (33, 2), (32, 2), (29, 2), (40, 3), (45, 3), (39, 3), (51, 3), (56, 4), (55, 4), (57, 4), (53, 4)])" +non_spreading_dirlaynet_minbudget,0.3666547000175342,174,"(1, {3: [165], 4: [165], 5: [165], 6: [132]})" +non_spreading_minbudget,0.10705310001503676,174,"(5, [])" +heuristic_minbudget,1.0959735999931581,174,"(1, [(3, 1), (17, 2), (60, 3), (66, 4), (95, 5), (104, 6), (132, 7), (137, 8)])" +non_spreading_dirlaynet_minbudget,0.24063870002282783,109,"(7, {0: [14, 34, 26, 32, 50, 72, 93], 1: [34, 26, 32, 50, 72, 93], 2: [50, 72], 3: [72]})" +non_spreading_minbudget,0.01601160003338009,109,"(16, [])" +heuristic_minbudget,1.2227787000010721,109,"(6, [(14, 1), (4, 1), (8, 1), (11, 1), (22, 1), (23, 1), (32, 2), (34, 2), (38, 2), (42, 2), (26, 2), (28, 2), (50, 3), (52, 3), (63, 3), (48, 3), (64, 3), (55, 3), (85, 4), (72, 4), (81, 4), (67, 4), (77, 4), (82, 4)])" +non_spreading_dirlaynet_minbudget,0.3842507000081241,163,"(9, {0: [6, 1, 4, 8, 5, 3, 9, 7, 2]})" +non_spreading_minbudget,0.01843689999077469,163,"(9, [])" +heuristic_minbudget,0.5663389000110328,163,"(5, [(6, 1), (1, 1), (5, 1), (7, 1), (3, 1), (27, 2), (20, 2), (10, 2), (22, 2), (23, 2), (43, 3), (41, 3), (40, 3), (37, 3), (28, 3), (54, 4), (49, 4), (48, 4), (50, 4), (51, 4), (62, 5), (75, 5), (55, 5), (68, 5), (61, 5), (84, 6), (85, 6), (96, 6), (91, 6), (78, 6), (115, 7), (109, 7), (103, 7), (111, 7), (105, 7)])" +non_spreading_dirlaynet_minbudget,0.14911689999280497,78,"(4, {0: [3, 11, 16, 14], 1: [11, 16, 14]})" +non_spreading_minbudget,0.009751199977472425,78,"(7, [])" +heuristic_minbudget,0.31167299998924136,78,"(5, [(3, 1), (2, 1), (5, 1), (1, 1), (7, 1), (16, 2), (14, 2), (11, 2), (13, 2), (15, 2), (31, 3), (18, 3), (21, 3), (23, 3)])" +non_spreading_dirlaynet_minbudget,0.11485980002908036,73,"(5, {0: [4, 1, 5, 3, 2]})" +non_spreading_minbudget,0.006932299991603941,73,"(5, [])" +heuristic_minbudget,0.1847023000009358,73,"(3, [(2, 1), (4, 1), (5, 1), (21, 2), (20, 2), (8, 2), (28, 3), (34, 3), (27, 3), (36, 4), (41, 4), (37, 4), (44, 5), (46, 5), (45, 5)])" +non_spreading_dirlaynet_minbudget,0.3518521999940276,154,"(11, {0: [10, 14, 9, 17, 4, 26, 27, 20, 19, 29, 21], 1: [26, 27, 20, 19, 29, 21, 34]})" +non_spreading_minbudget,0.0243503000237979,154,"(17, [])" +heuristic_minbudget,1.109651800012216,154,"(8, [(14, 1), (4, 1), (9, 1), (10, 1), (17, 1), (8, 1), (2, 1), (1, 1), (27, 2), (21, 2), (23, 2), (18, 2), (25, 2), (26, 2), (19, 2), (29, 2), (37, 3), (33, 3), (34, 3), (41, 3), (30, 3), (35, 3), (31, 3), (38, 3), (50, 4), (59, 4), (44, 4), (54, 4)])" +non_spreading_dirlaynet_minbudget,0.18335249996744096,95,"(4, {0: [7, 22, 38, 59], 1: [38, 59], 2: [38, 59], 3: [59]})" +non_spreading_minbudget,0.011825499997939914,95,"(9, [])" +heuristic_minbudget,0.5668235000339337,95,"(3, [(7, 1), (4, 1), (11, 1), (22, 2), (24, 2), (28, 2), (38, 3), (37, 3), (44, 3)])" +non_spreading_dirlaynet_minbudget,0.33219030004693195,157,"(1, {1: [43]})" +non_spreading_minbudget,0.013426899968180805,157,"(1, [])" +heuristic_minbudget,0.5273297000094317,157,"(1, [(2, 1)])" +non_spreading_dirlaynet_minbudget,0.5024990999954753,161,"(3, {0: [16, 142, 151], 1: [49], 5: [151], 6: [151]})" +non_spreading_minbudget,0.026394299988169223,161,"(7, [])" +heuristic_minbudget,1.560309899970889,161,"(2, [(16, 1), (1, 1), (49, 2), (24, 2), (66, 3), (55, 3), (74, 4), (86, 4), (111, 5), (118, 5), (127, 6), (122, 6), (133, 7), (128, 7), (137, 8), (142, 8)])" +non_spreading_dirlaynet_minbudget,0.25708180002402514,120,"(18, {0: [10, 16, 1, 14, 13, 3, 12, 9, 7, 17, 2, 11, 6, 8, 4, 5, 15, 18]})" +non_spreading_minbudget,0.020068900019396096,120,"(18, [])" +heuristic_minbudget,1.4997184000094421,120,"(8, [(11, 1), (12, 1), (3, 1), (8, 1), (18, 1), (1, 1), (7, 1), (4, 1), (37, 2), (30, 2), (19, 2), (34, 2), (32, 2), (20, 2), (29, 2), (33, 2), (47, 3), (41, 3), (58, 3), (56, 3), (38, 3), (49, 3), (55, 3), (42, 3), (70, 4), (65, 4), (61, 4), (71, 4), (82, 4), (81, 4), (60, 4), (62, 4), (92, 5), (96, 5), (98, 5), (100, 5), (99, 5), (83, 5), (88, 5), (85, 5)])" +non_spreading_dirlaynet_minbudget,0.2254145999904722,110,"(6, {0: [6, 4, 1, 5, 3, 2]})" +non_spreading_minbudget,0.012614699953701347,110,"(6, [])" +heuristic_minbudget,0.32980499998666346,110,"(5, [(2, 1), (1, 1), (3, 1), (4, 1), (6, 1), (7, 2), (20, 2), (21, 2), (19, 2)])" +non_spreading_dirlaynet_minbudget,0.19980970001779497,96,"(1, {2: [68]})" +non_spreading_minbudget,0.010430899972561747,96,"(2, [])" +heuristic_minbudget,0.8549194000079297,96,"(1, [(14, 1), (43, 2), (59, 3)])" +non_spreading_dirlaynet_minbudget,0.2990049999789335,154,"(20, {0: [10, 20, 16, 1, 14, 19, 13, 12, 3, 9, 7, 17, 2, 11, 6, 8, 4, 5, 15, 18]})" +non_spreading_minbudget,0.019480600021779537,154,"(20, [])" +heuristic_minbudget,2.2484657000168227,154,"(6, [(16, 1), (9, 1), (11, 1), (19, 1), (20, 1), (17, 1), (44, 2), (25, 2), (39, 2), (22, 2), (23, 2), (38, 2), (53, 3), (54, 3), (63, 3), (55, 3), (60, 3), (45, 3), (72, 4), (79, 4), (81, 4), (73, 4), (77, 4), (71, 4), (97, 5), (90, 5), (98, 5), (91, 5), (96, 5), (99, 5), (108, 6), (115, 6), (109, 6), (101, 6), (100, 6), (103, 6), (122, 7), (121, 7), (119, 7), (123, 7), (120, 7)])" +non_spreading_dirlaynet_minbudget,0.26417029998265207,115,"(12, {0: [1, 14, 17, 11, 21, 36, 33, 44, 49, 51, 56, 64], 1: [36, 33, 44, 49, 51], 2: [49, 51], 3: [49, 51]})" +non_spreading_minbudget,0.013657599978614599,115,"(18, [])" +heuristic_minbudget,0.8261364999925718,115,"(5, [(17, 1), (14, 1), (21, 1), (1, 1), (11, 1), (36, 2), (33, 2), (23, 2), (32, 2), (26, 2), (37, 3), (44, 3), (39, 3), (40, 3), (41, 3), (55, 4), (52, 4), (49, 4), (51, 4), (50, 4), (57, 5), (62, 5), (63, 5), (56, 5), (59, 5), (64, 6)])" +non_spreading_dirlaynet_minbudget,0.1405815000180155,76,"(1, {1: [22]})" +non_spreading_minbudget,0.0064150000107474625,76,"(2, [])" +heuristic_minbudget,0.16845870000543073,76,"(1, [(3, 1), (19, 2)])" +non_spreading_dirlaynet_minbudget,0.22825440001906827,117,"(10, {0: [1, 19, 8, 18, 34, 47, 44, 66, 75, 74], 1: [34, 47, 44, 66, 75, 74], 2: [66, 75, 74], 3: [75, 74]})" +non_spreading_minbudget,0.017238100001122802,117,"(21, [])" +heuristic_minbudget,1.6565592999686487,117,"(6, [(19, 1), (18, 1), (1, 1), (8, 1), (10, 1), (29, 1), (31, 2), (39, 2), (44, 2), (47, 2), (50, 2), (34, 2), (65, 3), (66, 3), (61, 3), (59, 3), (62, 3), (64, 3), (75, 4), (76, 4), (77, 4), (78, 4), (73, 4), (72, 4), (80, 5), (90, 5), (81, 5), (86, 5), (87, 5), (85, 5)])" +non_spreading_dirlaynet_minbudget,0.3899076000088826,185,"(2, {0: [47, 45], 1: [47, 45], 2: [47, 45], 3: [47, 45]})" +non_spreading_minbudget,0.022539300029166043,185,"(5, [])" +heuristic_minbudget,0.26039259997196496,185,"(2, [(5, 1), (1, 1), (11, 2), (13, 2), (36, 3), (20, 3), (44, 4), (48, 4), (62, 5), (55, 5), (69, 6), (74, 6), (92, 7), (95, 7), (112, 8), (131, 8)])" +non_spreading_dirlaynet_minbudget,0.3602491000201553,154,"(8, {0: [13, 9, 18, 35, 64, 56, 124, 127], 1: [64, 56, 147], 2: [64, 127]})" +non_spreading_minbudget,0.05018740001833066,154,"(12, [])" +heuristic_minbudget,1.774602799967397,154,"(4, [(9, 1), (18, 1), (13, 1), (3, 1), (35, 2), (46, 2), (43, 2), (28, 2), (57, 3), (56, 3), (64, 3), (68, 3), (72, 4), (75, 4), (84, 4), (85, 4), (108, 5), (102, 5), (100, 5), (97, 5), (124, 6), (119, 6), (123, 6), (115, 6), (126, 7), (127, 7), (138, 7), (140, 7)])" +non_spreading_dirlaynet_minbudget,0.2686612000106834,116,"(7, {0: [17, 27, 35, 59, 51, 68, 101], 1: [27, 35, 59, 51, 68, 101], 2: [59, 68, 101]})" +non_spreading_minbudget,0.017082200036384165,116,"(14, [])" +heuristic_minbudget,0.9590422000037506,116,"(4, [(17, 1), (2, 1), (10, 1), (12, 1), (27, 2), (23, 2), (35, 2), (40, 2), (59, 3), (47, 3), (51, 3), (41, 3), (82, 4), (73, 4), (68, 4), (63, 4)])" +non_spreading_dirlaynet_minbudget,0.22271449997788295,108,"(6, {0: [1, 12, 26, 27, 28, 25], 1: [26, 27, 28, 25]})" +non_spreading_minbudget,0.012390200048685074,108,"(9, [])" +heuristic_minbudget,0.7816633000038564,108,"(4, [(1, 1), (12, 1), (8, 1), (16, 1), (24, 2), (23, 2), (26, 2), (28, 2), (48, 3), (45, 3), (31, 3), (37, 3), (56, 4), (51, 4), (58, 4), (59, 4), (73, 5), (65, 5), (80, 5), (61, 5)])" +non_spreading_dirlaynet_minbudget,0.34672169998520985,170,"(1, {5: [123]})" +non_spreading_minbudget,0.09719270002096891,170,"(2, [])" +heuristic_minbudget,0.7220063999993727,170,"(1, [(9, 1), (14, 2), (38, 3), (64, 4), (102, 5), (105, 6)])" +non_spreading_dirlaynet_minbudget,0.2813237000373192,153,"(7, {0: [4, 15, 9, 33, 47, 45, 43], 1: [33, 47, 45, 43], 2: [47, 45, 43]})" +non_spreading_minbudget,0.018627899989951402,153,"(12, [])" +heuristic_minbudget,1.2086210999987088,153,"(5, [(9, 1), (4, 1), (15, 1), (12, 1), (3, 1), (32, 2), (33, 2), (40, 2), (29, 2), (26, 2), (45, 3), (48, 3), (46, 3), (44, 3), (47, 3), (64, 4), (65, 4), (67, 4), (68, 4), (55, 4), (78, 5), (81, 5), (80, 5), (77, 5), (76, 5), (93, 6)])" +non_spreading_dirlaynet_minbudget,0.21959669998614118,114,"(2, {0: [26, 94], 1: [78], 4: [79]})" +non_spreading_minbudget,0.011773299949709326,114,"(4, [])" +heuristic_minbudget,0.9793972999905236,114,"(1, [(9, 1), (26, 2), (37, 3), (78, 4), (79, 5), (94, 6)])" +non_spreading_dirlaynet_minbudget,0.19709109998075292,96,"(1, {4: [67]})" +non_spreading_minbudget,0.01014900003792718,96,"(2, [])" +heuristic_minbudget,0.2756342000211589,96,"(1, [(2, 1), (12, 2), (23, 3), (36, 4), (56, 5)])" +non_spreading_dirlaynet_minbudget,0.4796706999768503,186,"(29, {0: [10, 27, 26, 20, 16, 1, 14, 19, 13, 12, 3, 9, 7, 17, 29, 2, 24, 11, 6, 21, 8, 4, 28, 22, 5, 15, 25, 18, 23]})" +non_spreading_minbudget,0.030213600024580956,186,"(29, [])" +heuristic_minbudget,3.2820799999753945,186,"(9, [(9, 1), (26, 1), (25, 1), (12, 1), (16, 1), (1, 1), (4, 1), (11, 1), (18, 1), (43, 2), (48, 2), (58, 2), (51, 2), (40, 2), (31, 2), (52, 2), (36, 2), (55, 2), (67, 3), (69, 3), (63, 3), (74, 3), (72, 3), (71, 3), (65, 3), (75, 3), (62, 3), (96, 4), (82, 4), (94, 4), (99, 4), (101, 4), (92, 4), (91, 4), (98, 4), (85, 4), (128, 5), (122, 5), (116, 5), (121, 5), (125, 5), (118, 5), (113, 5), (111, 5), (112, 5), (135, 6), (134, 6), (130, 6), (139, 6), (133, 6), (129, 6), (138, 6), (131, 6), (132, 6), (146, 7), (156, 7), (154, 7), (148, 7), (161, 7), (162, 7), (144, 7), (140, 7), (147, 7)])" +non_spreading_dirlaynet_minbudget,0.3081334999878891,146,"(4, {0: [3, 27, 71, 76], 1: [51, 71], 2: [71], 3: [71]})" +non_spreading_minbudget,0.08925570000428706,146,"(8, [])" +heuristic_minbudget,0.7898826000164263,146,"(3, [(3, 1), (9, 1), (5, 1), (27, 2), (15, 2), (23, 2), (51, 3), (42, 3), (47, 3), (69, 4), (73, 4), (71, 4), (86, 5), (95, 5), (80, 5), (101, 6), (103, 6), (107, 6)])" +non_spreading_dirlaynet_minbudget,0.3067179000354372,152,"(2, {0: [12, 86]})" +non_spreading_minbudget,0.017449299979489297,152,"(2, [])" +heuristic_minbudget,0.864723699982278,152,"(1, [(12, 1), (32, 2), (48, 3), (71, 4)])" +non_spreading_dirlaynet_minbudget,0.1449985000072047,82,"(11, {0: [10, 20, 8, 13, 9, 18, 27, 33, 56, 72, 79], 1: [27, 33, 72]})" +non_spreading_minbudget,0.009442600014153868,82,"(13, [])" +heuristic_minbudget,0.7151867999928072,82,"(6, [(9, 1), (13, 1), (20, 1), (10, 1), (18, 1), (8, 1), (33, 2), (32, 2), (27, 2), (28, 2), (31, 2), (39, 2), (47, 3), (54, 3), (50, 3), (48, 3), (52, 3), (51, 3), (55, 4), (58, 4), (60, 4), (62, 4), (64, 4)])" +non_spreading_dirlaynet_minbudget,0.33541169995442033,123,"(9, {0: [12, 28, 48, 37, 44, 60, 69, 96, 122], 1: [48, 37, 44, 60, 96, 122], 2: [60, 96], 3: [96]})" +non_spreading_minbudget,0.02186249999795109,123,"(18, [])" +heuristic_minbudget,1.7302376999869011,123,"(6, [(12, 1), (28, 1), (16, 1), (15, 1), (18, 1), (25, 1), (48, 2), (37, 2), (42, 2), (52, 2), (44, 2), (41, 2), (67, 3), (60, 3), (63, 3), (62, 3), (61, 3), (65, 3), (73, 4), (74, 4), (70, 4), (79, 4), (75, 4), (83, 4), (101, 5), (87, 5), (96, 5), (104, 5), (88, 5), (99, 5)])" +non_spreading_dirlaynet_minbudget,0.3653747999924235,152,"(8, {0: [6, 1, 7, 42, 67, 92, 106, 135], 1: [42, 135], 2: [42, 67, 135], 6: [135]})" +non_spreading_minbudget,0.02167569997254759,152,"(14, [])" +heuristic_minbudget,1.1438108999864198,152,"(3, [(7, 1), (1, 1), (6, 1), (21, 2), (31, 2), (28, 2), (37, 3), (42, 3), (46, 3), (67, 4), (82, 4), (59, 4), (92, 5), (94, 5), (101, 5), (106, 6), (118, 6), (110, 6)])" +non_spreading_dirlaynet_minbudget,0.21171110001159832,90,"(14, {0: [20, 16, 19, 7, 17, 11, 5, 22, 34, 29, 37, 58, 75, 82], 1: [34, 29, 37, 75, 82]})" +non_spreading_minbudget,0.015889200032688677,90,"(22, [])" +heuristic_minbudget,1.5673450999893248,90,"(8, [(5, 1), (11, 1), (20, 1), (16, 1), (19, 1), (17, 1), (7, 1), (22, 1), (29, 2), (31, 2), (33, 2), (37, 2), (50, 2), (34, 2), (41, 2), (55, 2), (58, 3), (65, 3), (63, 3), (67, 3), (64, 3), (57, 3), (61, 3), (66, 3), (68, 4), (78, 4), (79, 4)])" +non_spreading_dirlaynet_minbudget,0.15826220001326874,83,"(7, {0: [16, 13, 17, 36, 43, 64, 70], 1: [64, 70], 2: [70], 3: [64]})" +non_spreading_minbudget,0.01112850004574284,83,"(11, [])" +heuristic_minbudget,0.6521259999717586,83,"(3, [(16, 1), (13, 1), (17, 1), (36, 2), (34, 2), (38, 2), (43, 3), (40, 3), (39, 3), (64, 4), (52, 4), (61, 4), (72, 5), (70, 5), (79, 5)])" +non_spreading_dirlaynet_minbudget,0.17466540000168607,96,"(1, {3: [52]})" +non_spreading_minbudget,0.009549300011713058,96,"(1, [])" +heuristic_minbudget,0.4298332000034861,96,"(1, [(1, 1), (16, 2), (33, 3)])" +non_spreading_dirlaynet_minbudget,0.48592579999240115,165,"(4, {0: [4, 15, 78, 149], 6: [149]})" +non_spreading_minbudget,0.030833000026177615,165,"(6, [])" +heuristic_minbudget,1.5386863999883644,165,"(2, [(15, 1), (4, 1), (39, 2), (48, 2), (53, 3), (58, 3), (78, 4), (83, 4), (99, 5), (97, 5), (110, 6), (113, 6), (139, 7), (130, 7)])" +non_spreading_dirlaynet_minbudget,0.28890800004592165,161,"(8, {0: [1, 3, 12, 2, 34, 29, 49, 51], 1: [34, 29, 49, 51], 2: [49, 51]})" +non_spreading_minbudget,0.01882519997889176,161,"(14, [])" +heuristic_minbudget,0.9498099000193179,161,"(4, [(1, 1), (12, 1), (2, 1), (3, 1), (29, 2), (25, 2), (34, 2), (46, 2), (48, 3), (53, 3), (52, 3), (49, 3), (63, 4), (78, 4), (54, 4), (60, 4), (82, 5), (79, 5), (84, 5), (81, 5), (95, 6), (96, 6), (89, 6), (93, 6)])" +non_spreading_dirlaynet_minbudget,0.4687256999895908,169,"(23, {0: [10, 20, 16, 1, 14, 19, 13, 12, 3, 9, 7, 17, 2, 11, 6, 21, 8, 4, 5, 15, 22, 18, 23]})" +non_spreading_minbudget,0.025489300023764372,169,"(23, [])" +heuristic_minbudget,1.6776179999578744,169,"(9, [(6, 1), (2, 1), (1, 1), (17, 1), (10, 1), (8, 1), (4, 1), (7, 1), (16, 1), (34, 2), (37, 2), (35, 2), (36, 2), (42, 2), (27, 2), (32, 2), (40, 2), (39, 2), (68, 3), (53, 3), (72, 3), (52, 3), (57, 3), (59, 3), (46, 3), (49, 3), (56, 3), (99, 4), (84, 4), (85, 4), (88, 4), (81, 4), (80, 4), (95, 4), (86, 4), (77, 4), (117, 5), (107, 5), (122, 5), (106, 5), (126, 5), (120, 5), (100, 5), (113, 5), (121, 5), (139, 6), (132, 6), (138, 6), (134, 6), (146, 6), (135, 6), (142, 6), (129, 6), (133, 6)])" +non_spreading_dirlaynet_minbudget,0.13381580001441762,83,"(5, {0: [20, 8, 19, 3, 51]})" +non_spreading_minbudget,0.008869700017385185,83,"(5, [])" +heuristic_minbudget,0.7501130999880843,83,"(4, [(19, 1), (3, 1), (8, 1), (20, 1), (39, 2), (31, 2), (33, 2), (37, 2), (40, 3), (43, 3), (47, 3), (42, 3)])" +non_spreading_dirlaynet_minbudget,0.12061859999084845,78,"(3, {0: [3, 43, 56], 1: [15], 4: [56], 6: [56]})" +non_spreading_minbudget,0.007529399998020381,78,"(7, [])" +heuristic_minbudget,0.17428890004521236,78,"(1, [(3, 1), (15, 2), (20, 3), (25, 4), (43, 5), (50, 6)])" +non_spreading_dirlaynet_minbudget,0.40917800000170246,170,"(15, {0: [26, 14, 12, 24, 8, 22, 34, 40, 55, 45, 70, 64, 60, 66, 68], 1: [34, 40, 55, 45, 70, 64, 60, 66, 68, 76], 2: [70, 64, 60, 66, 68, 76]})" +non_spreading_minbudget,0.02833739999914542,170,"(30, [])" +heuristic_minbudget,2.1604326000087894,170,"(8, [(22, 1), (24, 1), (8, 1), (12, 1), (14, 1), (26, 1), (1, 1), (15, 1), (44, 2), (40, 2), (45, 2), (46, 2), (34, 2), (42, 2), (55, 2), (43, 2), (65, 3), (71, 3), (63, 3), (67, 3), (59, 3), (66, 3), (70, 3), (58, 3), (73, 4), (74, 4), (77, 4), (87, 4), (82, 4), (86, 4), (79, 4), (81, 4), (97, 5), (99, 5), (100, 5), (89, 5), (101, 5), (88, 5), (92, 5), (91, 5), (115, 6), (105, 6), (119, 6), (128, 6), (109, 6), (110, 6), (123, 6), (129, 6), (136, 7), (132, 7), (134, 7), (137, 7), (133, 7), (135, 7), (138, 7), (140, 7)])" +non_spreading_dirlaynet_minbudget,0.29811470001004636,130,"(7, {0: [12, 7, 20, 16, 14, 15, 19], 1: [20, 16, 14, 15, 19, 30]})" +non_spreading_minbudget,0.01441739994334057,130,"(13, [])" +heuristic_minbudget,0.8897034000256099,130,"(3, [(12, 1), (7, 1), (8, 1), (18, 2), (16, 2), (22, 2), (29, 3), (42, 3), (48, 3), (55, 4), (63, 4), (56, 4), (67, 5), (69, 5), (71, 5), (90, 6), (78, 6), (79, 6), (103, 7), (101, 7), (104, 7)])" +non_spreading_dirlaynet_minbudget,0.6222369999741204,206,"(22, {0: [10, 20, 16, 1, 14, 19, 13, 12, 3, 9, 7, 17, 2, 11, 6, 8, 4, 5, 15, 22, 18, 21]})" +non_spreading_minbudget,0.037172599986661226,206,"(22, [])" +heuristic_minbudget,3.3470051999902353,206,"(7, [(6, 1), (13, 1), (7, 1), (9, 1), (20, 1), (18, 1), (5, 1), (23, 2), (29, 2), (40, 2), (41, 2), (44, 2), (25, 2), (30, 2), (63, 3), (74, 3), (71, 3), (72, 3), (64, 3), (62, 3), (60, 3), (89, 4), (93, 4), (85, 4), (82, 4), (84, 4), (88, 4), (92, 4), (111, 5), (109, 5), (115, 5), (101, 5), (107, 5), (114, 5), (117, 5), (127, 6), (141, 6), (132, 6), (126, 6), (133, 6), (128, 6), (143, 6), (147, 7), (157, 7), (150, 7), (151, 7), (153, 7), (156, 7), (154, 7), (171, 8), (173, 8), (175, 8), (170, 8), (174, 8), (172, 8)])" +non_spreading_dirlaynet_minbudget,0.22571879997849464,123,"(9, {0: [8, 1, 19, 13, 7, 43, 49, 70, 98], 1: [43, 70]})" +non_spreading_minbudget,0.013894300034735352,123,"(11, [])" +heuristic_minbudget,1.1737340000108816,123,"(5, [(13, 1), (1, 1), (19, 1), (7, 1), (8, 1), (30, 2), (43, 2), (29, 2), (36, 2), (42, 2), (49, 3), (50, 3), (51, 3), (54, 3), (56, 3), (59, 4), (61, 4), (64, 4), (62, 4), (63, 4), (71, 5), (87, 5)])" +non_spreading_dirlaynet_minbudget,0.12631929997587577,81,"(3, {0: [8, 13, 74], 2: [74], 3: [74]})" +non_spreading_minbudget,0.012286700017284602,81,"(5, [])" +heuristic_minbudget,0.36397669999860227,81,"(2, [(8, 1), (13, 1), (18, 2), (22, 2), (32, 3), (31, 3), (39, 4), (50, 4)])" +non_spreading_dirlaynet_minbudget,0.3933597000432201,167,"(25, {0: [10, 20, 16, 1, 14, 19, 13, 12, 3, 9, 7, 17, 2, 24, 11, 6, 21, 8, 4, 22, 5, 25, 18, 23, 38]})" +non_spreading_minbudget,0.026914299989584833,167,"(25, [])" +heuristic_minbudget,1.7527138000004925,167,"(7, [(17, 1), (13, 1), (7, 1), (23, 1), (3, 1), (16, 1), (20, 1), (38, 2), (44, 2), (43, 2), (31, 2), (45, 2), (26, 2), (30, 2), (52, 3), (71, 3), (64, 3), (72, 3), (54, 3), (56, 3), (59, 3), (97, 4), (87, 4), (89, 4), (82, 4), (83, 4), (92, 4), (100, 4), (105, 5), (107, 5), (106, 5), (108, 5), (110, 5), (109, 5)])" +non_spreading_dirlaynet_minbudget,0.18939569999929518,98,"(2, {0: [17, 89], 1: [57], 2: [57], 3: [75], 4: [75]})" +non_spreading_minbudget,0.011905899969860911,98,"(7, [])" +heuristic_minbudget,0.5329802000196651,98,"(2, [(8, 1), (6, 1), (17, 2), (11, 2), (39, 3), (36, 3), (57, 4), (54, 4), (69, 5), (75, 5), (89, 6), (78, 6)])" +non_spreading_dirlaynet_minbudget,0.47158130002208054,166,"(4, {0: [20, 16, 86, 161], 1: [20, 44, 120]})" +non_spreading_minbudget,0.020260599965695292,166,"(7, [])" +heuristic_minbudget,1.1105554000241682,166,"(3, [(1, 1), (2, 1), (9, 1), (16, 2), (17, 2), (20, 2), (44, 3), (35, 3), (39, 3), (61, 4), (56, 4), (65, 4), (86, 5), (80, 5), (84, 5), (92, 6), (94, 6), (99, 6), (110, 7), (104, 7), (107, 7), (120, 8), (128, 8), (129, 8), (140, 9), (143, 9), (148, 9)])" +non_spreading_dirlaynet_minbudget,0.38358820002758875,153,"(5, {0: [15, 26, 62, 93, 149], 1: [26, 84], 2: [108], 3: [108]})" +non_spreading_minbudget,0.023857699998188764,153,"(11, [])" +heuristic_minbudget,1.4818554000230506,153,"(2, [(15, 1), (13, 1), (26, 2), (24, 2), (53, 3), (62, 3), (84, 4), (87, 4), (93, 5), (100, 5), (108, 6), (117, 6)])" +non_spreading_dirlaynet_minbudget,0.2334121000021696,134,"(8, {0: [11, 20, 1, 7, 24, 38, 44, 39], 1: [38, 44, 39], 2: [38, 44, 39]})" +non_spreading_minbudget,0.015820100030396134,134,"(12, [])" +heuristic_minbudget,0.8371534000034444,134,"(5, [(7, 1), (20, 1), (24, 1), (1, 1), (11, 1), (29, 2), (36, 2), (31, 2), (34, 2), (37, 2), (44, 3), (41, 3), (38, 3), (39, 3), (40, 3)])" +non_spreading_dirlaynet_minbudget,0.37643830000888556,167,"(6, {0: [6, 4, 5, 3, 2, 12]})" +non_spreading_minbudget,0.01732879999326542,167,"(6, [])" +heuristic_minbudget,0.3751860999618657,167,"(3, [(5, 1), (6, 1), (3, 1), (18, 2), (24, 2), (31, 2), (32, 3), (33, 3), (36, 3), (50, 4), (45, 4), (46, 4), (65, 5), (60, 5), (68, 5), (88, 6), (81, 6), (85, 6), (97, 7), (96, 7), (100, 7), (129, 8), (136, 8), (114, 8)])" +non_spreading_dirlaynet_minbudget,0.29731140000512823,148,"(9, {0: [26, 6, 16, 5, 25, 24, 31, 28, 30], 1: [31, 28, 30]})" +non_spreading_minbudget,0.013988099992275238,148,"(11, [])" +heuristic_minbudget,0.68870350002544,148,"(6, [(5, 1), (6, 1), (16, 1), (26, 1), (24, 1), (25, 1), (28, 2), (32, 2), (30, 2), (29, 2), (31, 2)])" +non_spreading_dirlaynet_minbudget,0.5390391999972053,158,"(12, {0: [16, 19, 7, 22, 31, 36, 70, 75, 89, 113, 125, 152], 1: [31, 70, 75, 113, 125, 152], 2: [70, 75, 89, 152]})" +non_spreading_minbudget,0.02681850001681596,158,"(21, [])" +heuristic_minbudget,2.336813100031577,158,"(5, [(7, 1), (19, 1), (16, 1), (22, 1), (3, 1), (36, 2), (38, 2), (31, 2), (28, 2), (37, 2), (75, 3), (72, 3), (69, 3), (58, 3), (70, 3), (82, 4), (89, 4), (90, 4), (96, 4), (99, 4), (113, 5), (124, 5), (121, 5), (108, 5), (114, 5), (141, 6), (125, 6), (140, 6), (133, 6), (136, 6)])" +non_spreading_dirlaynet_minbudget,0.14801500004250556,94,"(4, {0: [12, 20, 19, 17], 1: [20, 19, 17]})" +non_spreading_minbudget,0.009141899994574487,94,"(6, [])" +heuristic_minbudget,0.37057590001495555,94,"(3, [(12, 1), (5, 1), (8, 1), (19, 2), (18, 2), (17, 2), (24, 3), (25, 3), (28, 3), (40, 4), (38, 4), (34, 4)])" +non_spreading_dirlaynet_minbudget,0.21253900002921,130,"(11, {0: [10, 11, 6, 8, 4, 1, 3, 9, 7, 2, 18]})" +non_spreading_minbudget,0.015054800023790449,130,"(11, [])" +heuristic_minbudget,0.6687954000080936,130,"(5, [(11, 1), (7, 1), (6, 1), (3, 1), (1, 1), (16, 2), (17, 2), (14, 2), (19, 2), (18, 2), (30, 3), (32, 3), (33, 3), (28, 3), (29, 3), (38, 4), (41, 4), (39, 4), (37, 4), (40, 4)])" +non_spreading_dirlaynet_minbudget,0.46167799999238923,169,"(7, {0: [6, 4, 1, 5, 3, 7, 2]})" +non_spreading_minbudget,0.024519800033885986,169,"(7, [])" +heuristic_minbudget,0.6031515999929979,169,"(5, [(4, 1), (5, 1), (2, 1), (1, 1), (6, 1), (10, 2), (8, 2), (34, 2), (12, 2), (22, 2), (37, 3), (42, 3), (54, 3), (38, 3), (59, 3), (78, 4), (86, 4), (69, 4), (80, 4), (84, 4), (89, 5), (91, 5), (103, 5), (114, 5), (99, 5), (119, 6), (117, 6), (139, 6), (123, 6), (115, 6)])" +non_spreading_dirlaynet_minbudget,0.27336719998857006,147,"(10, {0: [7, 25, 48, 36, 39, 49, 51, 56, 60, 55], 1: [48, 36, 39, 49, 51, 56, 60, 55], 2: [49, 51, 56, 60, 55]})" +non_spreading_minbudget,0.018319299968425184,147,"(20, [])" +heuristic_minbudget,1.49995059997309,147,"(5, [(7, 1), (25, 1), (1, 1), (17, 1), (22, 1), (39, 2), (48, 2), (36, 2), (38, 2), (28, 2), (61, 3), (56, 3), (51, 3), (54, 3), (57, 3), (75, 4), (74, 4), (65, 4), (63, 4), (62, 4), (80, 5), (81, 5), (78, 5), (83, 5), (84, 5), (86, 6), (88, 6), (89, 6), (87, 6), (85, 6)])" +non_spreading_dirlaynet_minbudget,0.10863520001294091,72,"(9, {0: [26, 8, 13, 22, 21, 32, 39, 58, 70], 1: [70], 2: [70], 3: [70]})" +non_spreading_minbudget,0.00840649998281151,72,"(12, [])" +heuristic_minbudget,0.9322982999728993,72,"(5, [(13, 1), (22, 1), (8, 1), (21, 1), (26, 1), (32, 2), (34, 2), (31, 2), (35, 2), (33, 2), (39, 3), (42, 3), (43, 3), (52, 3), (40, 3), (58, 4), (57, 4), (56, 4), (59, 4), (55, 4)])" +non_spreading_dirlaynet_minbudget,0.15935790003277361,91,"(6, {0: [6, 4, 1, 5, 3, 2]})" +non_spreading_minbudget,0.08640519995242357,91,"(6, [])" +heuristic_minbudget,0.25525539997033775,91,"(2, [(5, 1), (3, 1), (13, 2), (17, 2), (23, 3), (39, 3), (63, 4), (56, 4)])" +non_spreading_dirlaynet_minbudget,0.10945779999019578,69,"(2, {0: [35], 1: [16, 35]})" +non_spreading_minbudget,0.008623800007626414,69,"(3, [])" +heuristic_minbudget,0.19346059998497367,69,"(2, [(10, 1), (2, 1), (16, 2), (19, 2)])" +non_spreading_dirlaynet_minbudget,0.374742400017567,165,"(13, {0: [26, 20, 14, 7, 18, 34, 40, 35, 29, 37, 31, 36, 38], 1: [34, 40, 35, 29, 37, 31, 36, 38]})" +non_spreading_minbudget,0.024857400043401867,165,"(21, [])" +heuristic_minbudget,2.2302414999576285,165,"(9, [(14, 1), (7, 1), (18, 1), (20, 1), (26, 1), (25, 1), (17, 1), (23, 1), (8, 1), (39, 2), (33, 2), (43, 2), (35, 2), (37, 2), (41, 2), (29, 2), (42, 2), (38, 2), (53, 3), (55, 3), (57, 3), (46, 3), (56, 3), (59, 3), (63, 3), (48, 3), (52, 3), (64, 4), (80, 4), (67, 4), (76, 4), (73, 4), (77, 4), (79, 4), (92, 4), (90, 4), (105, 5), (97, 5), (98, 5), (101, 5), (96, 5), (99, 5), (102, 5), (104, 5), (103, 5), (124, 6), (122, 6), (129, 6), (134, 6), (130, 6), (136, 6), (109, 6), (118, 6), (114, 6)])" +non_spreading_dirlaynet_minbudget,0.28629970003385097,134,"(19, {0: [10, 16, 1, 14, 19, 13, 12, 3, 9, 7, 17, 2, 11, 6, 8, 4, 5, 15, 18]})" +non_spreading_minbudget,0.01946979999775067,134,"(19, [])" +heuristic_minbudget,1.2700168999726884,134,"(6, [(17, 1), (16, 1), (6, 1), (7, 1), (8, 1), (3, 1), (32, 2), (38, 2), (20, 2), (33, 2), (26, 2), (31, 2), (49, 3), (57, 3), (55, 3), (67, 3), (42, 3), (44, 3), (76, 4), (72, 4), (71, 4), (74, 4), (75, 4), (73, 4)])" +non_spreading_dirlaynet_minbudget,0.2748194000450894,118,"(9, {0: [6, 5, 14, 2, 20, 16, 23, 19, 22], 1: [20, 16, 23, 19, 22]})" +non_spreading_minbudget,0.016315200016833842,118,"(13, [])" +heuristic_minbudget,0.7548398000071757,118,"(7, [(6, 1), (2, 1), (5, 1), (14, 1), (3, 1), (11, 1), (1, 1), (22, 2), (16, 2), (17, 2), (23, 2), (18, 2), (21, 2), (24, 2), (32, 3), (34, 3)])" +non_spreading_dirlaynet_minbudget,0.4323341000126675,190,"(5, {0: [29, 47, 45, 70, 64], 1: [29, 47, 45, 70, 64], 2: [47, 45, 70, 64, 85], 3: [70, 64]})" +non_spreading_minbudget,0.025312400015536696,190,"(15, [])" +heuristic_minbudget,1.2723371000029147,190,"(4, [(2, 1), (4, 1), (9, 1), (17, 1), (29, 2), (21, 2), (23, 2), (34, 2), (39, 3), (58, 3), (45, 3), (47, 3), (65, 4), (64, 4), (67, 4), (68, 4), (80, 5), (85, 5), (75, 5), (92, 5), (108, 6), (111, 6), (127, 6), (117, 6), (148, 7), (135, 7), (131, 7), (133, 7), (161, 8), (149, 8), (156, 8), (150, 8)])" +non_spreading_dirlaynet_minbudget,0.31637699995189905,161,"(8, {0: [6, 1, 4, 5, 7, 2, 11, 20], 1: [11, 20]})" +non_spreading_minbudget,0.016746199980843812,161,"(9, [])" +heuristic_minbudget,0.5648038999643177,161,"(4, [(1, 1), (2, 1), (5, 1), (4, 1), (20, 2), (22, 2), (10, 2), (21, 2), (27, 3), (42, 3), (46, 3), (41, 3), (64, 4), (53, 4), (57, 4), (62, 4), (84, 5), (73, 5), (81, 5), (79, 5), (98, 6), (87, 6), (89, 6), (91, 6), (108, 7), (114, 7), (122, 7), (116, 7), (128, 8), (129, 8), (125, 8), (130, 8)])" +non_spreading_dirlaynet_minbudget,0.2906712999683805,110,"(10, {0: [1, 11, 15, 24, 33, 37, 60, 68, 81, 105], 1: [24, 37, 60, 68, 105], 2: [37, 105]})" +non_spreading_minbudget,0.04526869999244809,110,"(18, [])" +heuristic_minbudget,0.725164000003133,110,"(3, [(1, 1), (11, 1), (15, 1), (22, 2), (24, 2), (33, 2), (43, 3), (39, 3), (37, 3), (63, 4), (61, 4), (60, 4), (68, 5), (73, 5), (67, 5), (81, 6), (80, 6), (77, 6)])" +non_spreading_dirlaynet_minbudget,0.27447980002034456,145,"(6, {0: [11, 20, 31, 45, 60, 70], 1: [31, 45, 60, 70], 2: [45, 60, 70], 3: [70], 4: [70]})" +non_spreading_minbudget,0.020326700003352016,145,"(13, [])" +heuristic_minbudget,1.061862199974712,145,"(4, [(11, 1), (20, 1), (19, 1), (6, 1), (32, 2), (31, 2), (28, 2), (37, 2), (45, 3), (55, 3), (42, 3), (60, 3), (64, 4), (67, 4), (69, 4), (65, 4), (72, 5), (74, 5)])" +non_spreading_dirlaynet_minbudget,0.17650770000182092,104,"(6, {0: [6, 4, 1, 5, 3, 2]})" +non_spreading_minbudget,0.010195500042755157,104,"(6, [])" +heuristic_minbudget,0.3146837000385858,104,"(4, [(5, 1), (4, 1), (3, 1), (2, 1), (13, 2), (15, 2), (19, 2), (7, 2), (34, 3), (40, 3), (38, 3), (39, 3), (52, 4), (54, 4), (57, 4), (61, 4), (72, 5), (73, 5), (74, 5), (71, 5)])" +non_spreading_dirlaynet_minbudget,0.29066339996643364,149,"(5, {0: [4, 1, 5, 3, 2]})" +non_spreading_minbudget,0.016835800022818148,149,"(5, [])" +heuristic_minbudget,0.3754161999677308,149,"(3, [(4, 1), (3, 1), (1, 1), (13, 2), (31, 2), (26, 2), (37, 3), (33, 3), (40, 3), (51, 4), (59, 4), (56, 4), (68, 5), (63, 5), (74, 5), (94, 6), (103, 6), (101, 6), (113, 7), (115, 7), (112, 7), (125, 8), (117, 8), (119, 8), (134, 9), (131, 9), (133, 9)])" +non_spreading_dirlaynet_minbudget,0.2513452999992296,133,"(6, {0: [1, 2, 11, 16, 14, 15], 1: [11, 16, 14, 15, 32]})" +non_spreading_minbudget,0.014802400022745132,133,"(10, [])" +heuristic_minbudget,0.685664800053928,133,"(5, [(2, 1), (1, 1), (9, 1), (6, 1), (5, 1), (16, 2), (12, 2), (18, 2), (15, 2), (11, 2), (32, 3), (19, 3), (22, 3), (23, 3), (29, 3), (39, 4), (40, 4), (36, 4), (42, 4), (38, 4), (52, 5), (50, 5), (49, 5), (54, 5), (57, 5), (61, 6), (62, 6), (65, 6), (60, 6), (67, 6), (75, 7), (72, 7), (73, 7), (74, 7), (76, 7)])" +non_spreading_dirlaynet_minbudget,0.3316297000274062,162,"(16, {0: [10, 16, 1, 14, 13, 3, 12, 9, 7, 2, 11, 6, 8, 4, 5, 15]})" +non_spreading_minbudget,0.024006900028325617,162,"(16, [])" +heuristic_minbudget,1.5288382999715395,162,"(7, [(3, 1), (14, 1), (4, 1), (6, 1), (15, 1), (16, 1), (2, 1), (25, 2), (19, 2), (26, 2), (35, 2), (36, 2), (17, 2), (45, 2), (70, 3), (69, 3), (47, 3), (62, 3), (64, 3), (54, 3), (67, 3), (80, 4), (94, 4), (89, 4), (100, 4), (77, 4), (93, 4), (96, 4), (101, 5), (105, 5), (106, 5), (113, 5), (107, 5), (102, 5), (112, 5), (119, 6), (118, 6), (120, 6), (121, 6), (117, 6)])" +non_spreading_dirlaynet_minbudget,0.1403563999920152,81,"(10, {0: [3, 12, 9, 15, 29, 31, 46, 51, 65, 68], 1: [29, 31, 65, 68], 2: [68], 4: [68]})" +non_spreading_minbudget,0.011806999973487109,81,"(16, [])" +heuristic_minbudget,0.7835318999714218,81,"(4, [(12, 1), (9, 1), (15, 1), (3, 1), (31, 2), (38, 2), (28, 2), (29, 2), (46, 3), (39, 3), (40, 3), (41, 3), (51, 4), (49, 4), (52, 4), (54, 4), (63, 5), (56, 5), (57, 5), (58, 5)])" +non_spreading_dirlaynet_minbudget,0.18342100002337247,95,"(1, {2: [78]})" +non_spreading_minbudget,0.009804899978917092,95,"(2, [])" +heuristic_minbudget,0.3041724999784492,95,"(1, [(9, 1), (28, 2), (52, 3)])" +non_spreading_dirlaynet_minbudget,0.07903019996592775,56,"(11, {0: [10, 11, 6, 4, 1, 8, 5, 9, 7, 2, 38]})" +non_spreading_minbudget,0.007620499993208796,56,"(11, [])" +heuristic_minbudget,0.40007520001381636,56,"(5, [(6, 1), (11, 1), (5, 1), (10, 1), (7, 1), (16, 2), (18, 2), (22, 2), (19, 2), (21, 2), (27, 3), (28, 3), (30, 3), (32, 3), (33, 3), (38, 4), (37, 4)])" +non_spreading_dirlaynet_minbudget,0.37145620002411306,132,"(2, {0: [11, 56], 1: [40]})" +non_spreading_minbudget,0.06657630001427606,132,"(3, [])" +heuristic_minbudget,0.3125448999926448,132,"(1, [(5, 1), (11, 2), (40, 3), (56, 4)])" +non_spreading_dirlaynet_minbudget,0.23120929999276996,109,"(4, {0: [8, 16, 14, 19], 1: [16, 14, 19]})" +non_spreading_minbudget,0.014048199984245002,109,"(7, [])" +heuristic_minbudget,0.44086479995166883,109,"(5, [(8, 1), (3, 1), (4, 1), (10, 1), (12, 1), (14, 2), (19, 2), (16, 2), (17, 2), (18, 2), (23, 3), (29, 3), (26, 3), (30, 3), (42, 3)])" +non_spreading_dirlaynet_minbudget,0.13346400001319125,80,"(5, {0: [3, 25, 55, 64, 75], 1: [25, 47, 55], 3: [75], 5: [64]})" +non_spreading_minbudget,0.00931180000770837,80,"(10, [])" +heuristic_minbudget,0.6443930999957956,80,"(2, [(3, 1), (5, 1), (24, 2), (25, 2), (47, 3), (35, 3), (52, 4), (55, 4), (56, 5), (60, 5), (71, 6), (64, 6)])" +non_spreading_dirlaynet_minbudget,0.2423623999929987,124,"(1, {3: [115]})" +non_spreading_minbudget,0.014655900013167411,124,"(3, [])" +heuristic_minbudget,0.8641149000031874,124,"(1, [(1, 1), (30, 2), (41, 3), (61, 4), (86, 5)])" +non_spreading_dirlaynet_minbudget,0.32257890002802014,146,"(8, {0: [11, 1, 14, 3, 26, 27, 28, 30], 1: [26, 27, 28, 30]})" +non_spreading_minbudget,0.0157242999994196,146,"(12, [])" +heuristic_minbudget,0.9642243999987841,146,"(4, [(3, 1), (1, 1), (11, 1), (14, 1), (28, 2), (26, 2), (27, 2), (25, 2), (43, 3), (32, 3), (42, 3), (31, 3), (63, 4), (64, 4), (70, 4), (73, 4), (91, 5), (75, 5), (79, 5), (96, 5), (101, 6), (100, 6), (103, 6), (99, 6), (126, 7), (122, 7), (117, 7), (114, 7)])" +non_spreading_dirlaynet_minbudget,0.1736669999663718,100,"(8, {0: [6, 1, 8, 5, 3, 7, 2, 15]})" +non_spreading_minbudget,0.011281599989160895,100,"(8, [])" +heuristic_minbudget,0.4084740999969654,100,"(4, [(7, 1), (5, 1), (2, 1), (6, 1), (21, 2), (22, 2), (14, 2), (16, 2), (30, 3), (39, 3), (43, 3), (32, 3), (49, 4), (46, 4), (45, 4), (50, 4), (56, 5), (52, 5), (53, 5), (54, 5), (61, 6), (68, 6), (67, 6), (73, 6)])" +non_spreading_dirlaynet_minbudget,0.5331603999948129,206,"(8, {0: [3, 13, 7, 19, 15, 34, 36, 38], 1: [19, 15, 34, 36, 38], 2: [34, 36, 38]})" +non_spreading_minbudget,0.02871659997617826,206,"(13, [])" +heuristic_minbudget,1.13137060002191,206,"(6, [(13, 1), (7, 1), (3, 1), (8, 1), (10, 1), (4, 1), (19, 2), (18, 2), (15, 2), (17, 2), (21, 2), (28, 2), (35, 3), (34, 3), (38, 3), (36, 3), (37, 3), (39, 3), (57, 4), (65, 4), (53, 4), (48, 4), (60, 4), (49, 4)])" +non_spreading_dirlaynet_minbudget,0.3249198999837972,123,"(9, {0: [6, 1, 4, 8, 5, 3, 9, 7, 2]})" +non_spreading_minbudget,0.03570499998750165,123,"(9, [])" +heuristic_minbudget,0.617231999989599,123,"(4, [(7, 1), (8, 1), (6, 1), (4, 1), (28, 2), (26, 2), (27, 2), (11, 2), (37, 3), (35, 3), (33, 3), (31, 3), (51, 4), (48, 4), (57, 4), (49, 4), (68, 5), (77, 5), (75, 5), (62, 5), (89, 6), (81, 6), (82, 6), (83, 6), (94, 7), (93, 7), (95, 7), (96, 7)])" +non_spreading_dirlaynet_minbudget,0.3759449000353925,170,"(9, {0: [10, 6, 4, 12, 22, 31, 34, 36, 30], 1: [31, 34, 36, 30]})" +non_spreading_minbudget,0.022455699974671006,170,"(13, [])" +heuristic_minbudget,1.325027399987448,170,"(5, [(6, 1), (10, 1), (12, 1), (4, 1), (22, 1), (36, 2), (31, 2), (32, 2), (33, 2), (30, 2), (42, 3), (40, 3), (56, 3), (57, 3), (47, 3), (71, 4), (76, 4), (61, 4), (66, 4), (78, 4), (86, 5), (95, 5), (83, 5), (90, 5), (82, 5), (105, 6), (106, 6), (107, 6), (101, 6), (108, 6), (144, 7), (129, 7), (139, 7), (135, 7), (126, 7)])" +non_spreading_dirlaynet_minbudget,0.27661239996086806,122,"(2, {0: [9, 121]})" +non_spreading_minbudget,0.013166900025680661,122,"(3, [])" +heuristic_minbudget,0.7148885000497103,122,"(1, [(9, 1), (20, 2), (35, 3), (51, 4), (57, 5), (85, 6), (92, 7)])" +non_spreading_dirlaynet_minbudget,0.22093810001388192,107,"(7, {0: [12, 21, 30, 33, 56, 64, 66], 1: [21, 30, 33, 56, 64, 66, 82], 2: [56, 64, 66], 3: [64, 66]})" +non_spreading_minbudget,0.012751900008879602,107,"(16, [])" +heuristic_minbudget,0.9129658999736421,107,"(5, [(12, 1), (16, 1), (10, 1), (5, 1), (13, 1), (33, 2), (23, 2), (30, 2), (21, 2), (22, 2), (42, 3), (56, 3), (48, 3), (38, 3), (53, 3), (65, 4), (64, 4), (61, 4), (63, 4), (62, 4), (79, 5), (73, 5), (75, 5), (76, 5), (71, 5)])" +non_spreading_dirlaynet_minbudget,0.2728919999790378,135,"(15, {0: [10, 16, 1, 13, 3, 12, 9, 7, 2, 11, 6, 8, 4, 15, 24], 1: [24]})" +non_spreading_minbudget,0.016789799963589758,135,"(16, [])" +heuristic_minbudget,0.9788587000221014,135,"(6, [(8, 1), (7, 1), (10, 1), (4, 1), (13, 1), (15, 1), (25, 2), (31, 2), (21, 2), (27, 2), (20, 2), (19, 2), (34, 3), (39, 3), (41, 3), (40, 3), (38, 3), (35, 3), (61, 4), (57, 4), (59, 4), (63, 4), (47, 4), (60, 4), (73, 5), (72, 5), (71, 5), (70, 5), (69, 5)])" +non_spreading_dirlaynet_minbudget,0.2592234000330791,134,"(9, {0: [10, 14, 12, 17, 2, 26, 27, 28, 22], 1: [26, 27, 28, 22]})" +non_spreading_minbudget,0.014137900026980788,134,"(13, [])" +heuristic_minbudget,0.5196378999971785,134,"(5, [(10, 1), (14, 1), (17, 1), (12, 1), (2, 1), (27, 2), (28, 2), (22, 2), (24, 2), (26, 2), (42, 3), (37, 3), (32, 3), (35, 3), (46, 3), (52, 4), (51, 4), (54, 4), (48, 4), (50, 4), (60, 5), (59, 5), (56, 5), (62, 5), (61, 5), (82, 6), (86, 6), (78, 6), (75, 6), (77, 6), (98, 7), (93, 7), (103, 7), (107, 7), (104, 7)])" +non_spreading_dirlaynet_minbudget,0.2924388000392355,123,"(15, {0: [10, 11, 6, 4, 1, 5, 14, 15, 13, 12, 3, 9, 7, 2, 24]})" +non_spreading_minbudget,0.01611399999819696,123,"(15, [])" +heuristic_minbudget,1.0233782999566756,123,"(5, [(2, 1), (7, 1), (13, 1), (10, 1), (14, 1), (26, 2), (29, 2), (27, 2), (28, 2), (30, 2), (41, 3), (34, 3), (40, 3), (35, 3), (42, 3), (45, 4), (60, 4), (69, 4), (57, 4), (58, 4), (83, 5), (82, 5), (79, 5), (88, 5), (93, 5)])" +non_spreading_dirlaynet_minbudget,0.3060064999735914,112,"(3, {0: [16, 51], 1: [16, 35, 51], 2: [51], 3: [51], 4: [51]})" +non_spreading_minbudget,0.010651899967342615,112,"(8, [])" +heuristic_minbudget,0.5289040000061505,112,"(2, [(4, 1), (3, 1), (16, 2), (19, 2), (35, 3), (33, 3), (46, 4), (44, 4), (54, 5), (51, 5), (62, 6), (56, 6), (86, 7), (88, 7), (92, 8), (94, 8)])" +non_spreading_dirlaynet_minbudget,0.17984549998072907,88,"(7, {0: [6, 4, 1, 5, 3, 7, 2]})" +non_spreading_minbudget,0.009711000020615757,88,"(7, [])" +heuristic_minbudget,0.40752200002316386,88,"(3, [(1, 1), (3, 1), (5, 1), (10, 2), (20, 2), (9, 2), (28, 3), (21, 3), (23, 3), (51, 4), (48, 4), (59, 4), (62, 5), (74, 5), (81, 5)])" +,126.59461589995772,, diff --git 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      m5WGe0%TzlLw# zKdOV>3Ktxsfx0z(d{N8IP!|7v8Lp+|x{N*t3n^4YPmCG^`q@?$X4vf^X+40KKk+6v zJG-8|=qehra>H!f>(IGh@7e)JyUj8-tTb>`iBB!U+iC>WIP5gwklxnE98kk_WR3m(h)ZOi7Jr&etK|9N}8PExvkt1_e}X z3b70Tp>zU1U?f{6*f5+ovafH8vUBcib>zTl567wPgl-$`DsXe2JP9Jts8q}wcm@9r z!8ok*A!J%NgMxbSU&DTc-MdHo*?)Dfj?Y6-y?=Rspz6iO!ARd{!Kt=t;X0(S}6p&qvG@O81s4U`(#^Kt| zoBVthi9>%OQSZl1x*asycNUrUSUO@pf*p^2LzMf1;FN;9?j%e=rO_bc!g-XljC*JD}6 zP?`}?yQg^b#7r8D8hCc=M+ziRa2stDGb{&~=E~PRIdE+L0@&9BmTI%}im*9ob05uT z1=t3uCg3##P-(i}L|U#y42&Cr(P{8gEHZ}$h%F%9Wgxm4a7a=`ZV6V2WNmG2^%JS2 z_sR)3L%lW@&uG%^f`L&eh6)fqAS|&>;E<_!MS*nk%A|-M89<k8j@W#{5)JXoS2JF)?=BeZ(FY*UIIIgQWz=b?&WW&ft`a z%fuRZ;W@6 literal 0 HcmV?d00001 diff --git a/networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/spreading_minbudget.csv b/networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/spreading_minbudget.csv index f831a3a..0c45f72 100644 --- a/networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/spreading_minbudget.csv +++ b/networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/spreading_minbudget.csv @@ -1,20 +1,20 @@ algorithm,runtime,graph_nodes,Budget -spreading_minbudget,0.28455229999963194,100,"(2, [(70, 1), (13, 1)])" -heuristic_minbudget,0.23981180001283064,100,"(2, [(70, 1), (13, 1), (38, 2), (48, 2)])" -spreading_minbudget,0.6686060999636538,100,"(20, [(27, 1), (5, 1), (26, 1), (30, 1), (43, 1), (52, 1), (65, 1), (80, 1), (82, 1), (95, 1), (96, 1), (4, 1), (6, 1), (81, 1), (38, 1), (58, 1), (92, 1), (12, 1), (41, 1), (99, 1)])" -heuristic_minbudget,2.4607032999629155,100,"(19, [(43, 1), (92, 1), (65, 1), (99, 1), (26, 1), (5, 1), (30, 1), (80, 1), (95, 1), (38, 1), (4, 1), (82, 1), (41, 1), (96, 1), (12, 1), (58, 1), (81, 1), (6, 1), (52, 1)])" -spreading_minbudget,0.9770453999517485,100,"(34, [(6, 1), (1, 1), (2, 1), (16, 1), (17, 1), (22, 1), (24, 1), (27, 1), (47, 1), (50, 1), (51, 1), (52, 1), (58, 1), (59, 1), (63, 1), (64, 1), (68, 1), (78, 1), (79, 1), (80, 1), (81, 1), (86, 1), (87, 1), (88, 1), (96, 1), (98, 1), (8, 1), (9, 1), (15, 1), (28, 1), (65, 1), (67, 1), (70, 1), (83, 1)])" -heuristic_minbudget,7.318832900025882,100,"(34, [(9, 1), (52, 1), (78, 1), (17, 1), (28, 1), (96, 1), (8, 1), (81, 1), (70, 1), (98, 1), (27, 1), (16, 1), (15, 1), (2, 1), (68, 1), (80, 1), (86, 1), (79, 1), (22, 1), (63, 1), (83, 1), (24, 1), (87, 1), (50, 1), (67, 1), (47, 1), (51, 1), (1, 1), (58, 1), (6, 1), (59, 1), (64, 1), (88, 1), (65, 1)])" -spreading_minbudget,1.2262894000159577,200,"(15, [(7, 1), (140, 1), (147, 1), (164, 1), (137, 1), (128, 1), (186, 1), (53, 1), (124, 1), (166, 1), (94, 1), (27, 1), (105, 1), (131, 1), (9, 1)])" -heuristic_minbudget,1.1135120000108145,200,"(10, [(105, 1), (9, 1), (131, 1), (27, 1), (94, 1), (124, 1), (166, 1), (125, 1), (154, 1), (30, 1), (40, 2), (147, 2), (35, 2), (104, 2), (126, 2), (83, 2), (84, 2), (64, 2), (47, 2), (55, 2)])" -spreading_minbudget,2.1903421000461094,200,"(38, [(26, 1), (189, 1), (1, 1), (3, 1), (17, 1), (18, 1), (37, 1), (64, 1), (75, 1), (82, 1), (90, 1), (106, 1), (107, 1), (108, 1), (109, 1), (126, 1), (138, 1), (139, 1), (141, 1), (144, 1), (151, 1), (165, 1), (170, 1), (27, 1), (52, 1), (78, 1), (83, 1), (135, 1), (156, 1), (171, 1), (193, 1), (199, 1), (28, 1), (116, 1), (194, 1), (153, 1), (196, 1), (147, 1)])" -heuristic_minbudget,15.326287399977446,200,"(38, [(135, 1), (17, 1), (107, 1), (141, 1), (75, 1), (144, 1), (26, 1), (109, 1), (156, 1), (28, 1), (189, 1), (171, 1), (147, 1), (3, 1), (138, 1), (27, 1), (18, 1), (193, 1), (151, 1), (194, 1), (78, 1), (37, 1), (116, 1), (153, 1), (1, 1), (52, 1), (83, 1), (108, 1), (90, 1), (106, 1), (170, 1), (82, 1), (196, 1), (139, 1), (64, 1), (165, 1), (126, 1), (199, 1)])" -spreading_minbudget,1.3038518000394106,200,"(33, [(96, 1), (1, 1), (2, 1), (8, 1), (15, 1), (16, 1), (26, 1), (36, 1), (39, 1), (40, 1), (41, 1), (48, 1), (63, 1), (91, 1), (103, 1), (107, 1), (118, 1), (119, 1), (130, 1), (139, 1), (144, 1), (165, 1), (175, 1), (186, 1), (189, 1), (190, 1), (191, 1), (198, 1), (77, 1), (117, 1), (134, 1), (194, 1), (93, 1)])" -heuristic_minbudget,25.622363100002985,200,"(33, [(198, 1), (91, 1), (8, 1), (40, 1), (175, 1), (189, 1), (93, 1), (96, 1), (107, 1), (26, 1), (36, 1), (165, 1), (1, 1), (134, 1), (119, 1), (15, 1), (77, 1), (41, 1), (191, 1), (16, 1), (194, 1), (139, 1), (144, 1), (63, 1), (117, 1), (2, 1), (39, 1), (48, 1), (103, 1), (130, 1), (186, 1), (190, 1), (118, 1)])" -spreading_minbudget,4.48063219996402,400,"(29, [(137, 1), (11, 1), (59, 1), (361, 1), (347, 1), (24, 1), (207, 1), (165, 1), (314, 1), (114, 1), (360, 1), (60, 1), (1, 1), (2, 1), (294, 1), (62, 1), (291, 1), (6, 1), (93, 1), (79, 1), (389, 1), (28, 1), (148, 1), (269, 1), (357, 1), (91, 1), (98, 1), (181, 1), (250, 1)])" -heuristic_minbudget,3.351407299982384,400,"(18, [(207, 1), (98, 1), (60, 1), (181, 1), (28, 1), (269, 1), (294, 1), (148, 1), (357, 1), (291, 1), (79, 1), (93, 1), (250, 1), (389, 1), (2, 1), (91, 1), (62, 1), (6, 1), (221, 2), (234, 2), (49, 2), (340, 2), (273, 2), (12, 2), (263, 2), (1, 2), (121, 2), (117, 2), (354, 2), (176, 2), (61, 2), (135, 2), (146, 2), (249, 2), (302, 2), (158, 2)])" -spreading_minbudget,5.842790000024252,400,"(32, [(90, 1), (147, 1), (14, 1), (18, 1), (33, 1), (38, 1), (104, 1), (107, 1), (113, 1), (208, 1), (219, 1), (226, 1), (270, 1), (275, 1), (314, 1), (382, 1), (6, 1), (179, 1), (258, 1), (311, 1), (334, 1), (49, 1), (101, 1), (191, 1), (351, 1), (85, 1), (350, 1), (366, 1), (16, 1), (223, 1), (263, 1), (292, 1)])" -heuristic_minbudget,40.8089464000077,400,"(32, [(33, 1), (292, 1), (6, 1), (90, 1), (223, 1), (38, 1), (258, 1), (350, 1), (179, 1), (18, 1), (49, 1), (16, 1), (147, 1), (101, 1), (314, 1), (14, 1), (208, 1), (351, 1), (311, 1), (85, 1), (226, 1), (104, 1), (113, 1), (219, 1), (270, 1), (263, 1), (334, 1), (107, 1), (366, 1), (191, 1), (275, 1), (382, 1)])" -spreading_minbudget,10.128652899991721,400,"(139, [(306, 1), (4, 1), (5, 1), (8, 1), (13, 1), (18, 1), (19, 1), (25, 1), (26, 1), (30, 1), (38, 1), (39, 1), (40, 1), (41, 1), (51, 1), (52, 1), (54, 1), (55, 1), (57, 1), (60, 1), (61, 1), (62, 1), (64, 1), (65, 1), (67, 1), (70, 1), (74, 1), (78, 1), (81, 1), (85, 1), (87, 1), (88, 1), (89, 1), (91, 1), (92, 1), (97, 1), (98, 1), (103, 1), (106, 1), (121, 1), (123, 1), (127, 1), (134, 1), (135, 1), (138, 1), (150, 1), (160, 1), (163, 1), (164, 1), (169, 1), (170, 1), (180, 1), (185, 1), (186, 1), (192, 1), (194, 1), (196, 1), (198, 1), (199, 1), (205, 1), (209, 1), (210, 1), (212, 1), (215, 1), (225, 1), (226, 1), (234, 1), (235, 1), (236, 1), (241, 1), (246, 1), (250, 1), (251, 1), (256, 1), (257, 1), (258, 1), (259, 1), (263, 1), (264, 1), (269, 1), (271, 1), (274, 1), (275, 1), (278, 1), (282, 1), (284, 1), (285, 1), (288, 1), (311, 1), (312, 1), (314, 1), (317, 1), (321, 1), (329, 1), (331, 1), (347, 1), (349, 1), (350, 1), (356, 1), (357, 1), (370, 1), (374, 1), (378, 1), (384, 1), (386, 1), (387, 1), (389, 1), (397, 1), (399, 1), (35, 1), (122, 1), (131, 1), (137, 1), (149, 1), (176, 1), (182, 1), (197, 1), (208, 1), (228, 1), (239, 1), (270, 1), (276, 1), (281, 1), (291, 1), (294, 1), (297, 1), (324, 1), (352, 1), (383, 1), (388, 1), (96, 1), (113, 1), (148, 1), (157, 1), (267, 1), (323, 1), (334, 1), (244, 1), (141, 1)])" -heuristic_minbudget,204.12543320003897,400,"(138, [(160, 1), (194, 1), (209, 1), (282, 1), (274, 1), (180, 1), (241, 1), (250, 1), (258, 1), (397, 1), (62, 1), (88, 1), (122, 1), (170, 1), (234, 1), (264, 1), (323, 1), (235, 1), (331, 1), (239, 1), (25, 1), (85, 1), (57, 1), (226, 1), (257, 1), (55, 1), (5, 1), (278, 1), (383, 1), (386, 1), (137, 1), (169, 1), (186, 1), (378, 1), (113, 1), (263, 1), (64, 1), (311, 1), (281, 1), (334, 1), (70, 1), (288, 1), (374, 1), (106, 1), (98, 1), (197, 1), (65, 1), (134, 1), (347, 1), (41, 1), (92, 1), (121, 1), (196, 1), (163, 1), (74, 1), (291, 1), (205, 1), (18, 1), (26, 1), (228, 1), (35, 1), (123, 1), (312, 1), (176, 1), (246, 1), (356, 1), (267, 1), (13, 1), (199, 1), (40, 1), (388, 1), (314, 1), (61, 1), (148, 1), (52, 1), (251, 1), (275, 1), (38, 1), (182, 1), (244, 1), (212, 1), (276, 1), (317, 1), (30, 1), (51, 1), (141, 1), (384, 1), (329, 1), (350, 1), (60, 1), (138, 1), (89, 1), (67, 1), (150, 1), (127, 1), (208, 1), (87, 1), (370, 1), (324, 1), (294, 1), (389, 1), (135, 1), (8, 1), (256, 1), (399, 1), (210, 1), (349, 1), (157, 1), (198, 1), (39, 1), (357, 1), (387, 1), (131, 1), (192, 1), (284, 1), (297, 1), (91, 1), (185, 1), (269, 1), (270, 1), (54, 1), (4, 1), (19, 1), (96, 1), (97, 1), (215, 1), (352, 1), (78, 1), (81, 1), (164, 1), (103, 1), (321, 1), (225, 1), (236, 1), (285, 1), (259, 1), (149, 1), (271, 1)])" -,327.8878753000172,, +spreading_maxsave,0.07185340003343299,100,"([(5, 1), (43, 2), (90, 3)], {65, 73, 43, 45, 14, 78, 54, 55, 89, 61, 30})" +heuristic_maxsave,0.050711499992758036,100,"([(93, 1), (47, 2), (65, 3)], {65, 43, 47, 83, 55, 89, 93})" +spreading_maxsave,0.052191899972967803,100,"([(6, 1), (53, 2)], {64, 67, 72, 9, 74, 15, 80, 22, 88, 92, 29, 31, 97, 34, 98, 42, 53})" +heuristic_maxsave,0.03630989999510348,100,"([(59, 1), (97, 2)], {64, 97, 67, 72, 9, 42, 74, 80, 88, 59, 92, 29, 62, 31})" +spreading_maxsave,0.0324411999899894,100,"([(10, 1), (24, 2)], {10, 60, 86})" +heuristic_maxsave,0.05075649998616427,100,"([(10, 1), (24, 2)], {10, 60, 86})" +spreading_maxsave,0.19327960000373423,200,"([(31, 1), (6, 2), (9, 3)], {1, 65, 4, 69, 6, 71, 76, 16, 82, 151, 87, 89, 30, 31, 159, 177, 114, 51, 182, 56, 121, 58, 188, 191})" +heuristic_maxsave,0.09661199996480718,200,"([(60, 1), (194, 2), (182, 3)], {16, 148, 22, 151, 159, 177, 182, 60, 191, 64, 65, 194, 69, 71, 76, 87, 89, 93, 114, 117, 121, 122})" +spreading_maxsave,0.16957909998018295,200,"([(99, 1), (25, 2)], {6, 135, 8, 140, 77, 15, 144, 146, 89, 25, 156, 163, 36, 99, 101, 104, 169, 174, 111, 50, 118, 122, 123, 60, 61, 190, 191})" +heuristic_maxsave,0.1064889999688603,200,"([(157, 1), (144, 2)], {128, 6, 8, 140, 15, 144, 25, 157, 35, 163, 169, 174, 50, 55, 59, 60, 61, 190, 193, 77, 89, 91, 101, 118, 122, 123})" +spreading_maxsave,0.13650339999003336,200,"([(13, 1), (78, 2)], {192, 1, 70, 137, 11, 13, 22, 23, 91, 31, 33, 164, 37, 102, 169, 107, 111, 50, 54, 61})" +heuristic_maxsave,0.1891952999867499,200,"([(2, 1), (33, 2)], {192, 33, 2, 1, 37, 70, 102, 137, 169, 11, 107, 111, 23, 54, 22, 91, 61, 31})" +spreading_maxsave,0.4134444000083022,400,"([(126, 1), (11, 2)], {1, 66, 67, 390, 262, 326, 200, 395, 268, 13, 141, 143, 271, 396, 11, 20, 28, 92, 349, 351, 224, 353, 43, 299, 235})" +heuristic_maxsave,0.1678583999746479,400,"([(56, 1), (92, 2)], {64, 385, 67, 357, 107, 299, 237, 269, 60, 82, 348, 373, 55, 221, 56, 92, 381, 223})" +spreading_maxsave,0.8504898999817669,400,"([(227, 1), (33, 2)], {261, 390, 137, 266, 395, 269, 144, 18, 278, 279, 153, 157, 30, 31, 32, 33, 162, 164, 298, 299, 172, 46, 177, 52, 181, 311, 57, 61, 62, 322, 195, 69, 197, 199, 72, 327, 202, 330, 333, 207, 81, 210, 83, 339, 213, 343, 220, 223, 224, 228, 356, 358, 104, 361, 250, 236, 237, 365, 111, 375, 376, 377, 378, 255})" +heuristic_maxsave,0.28500709997024387,400,"([(92, 1), (199, 2)], {132, 390, 394, 395, 141, 143, 144, 278, 279, 280, 153, 156, 157, 30, 31, 32, 33, 162, 164, 167, 298, 299, 44, 170, 177, 51, 53, 181, 57, 61, 324, 197, 199, 72, 327, 332, 207, 336, 81, 210, 339, 213, 343, 217, 92, 220, 222, 223, 224, 356, 228, 358, 361, 106, 378, 111, 115, 373, 375, 376, 250, 251})" +spreading_maxsave,0.5659428999642842,400,"([(106, 1), (247, 2)], {385, 259, 7, 72, 12, 396, 337, 25, 26, 27, 283, 347, 30, 95, 356, 230, 359, 40, 106, 174, 247, 316, 125, 63})" +heuristic_maxsave,0.3827694999636151,400,"([(184, 1), (95, 2)], {385, 259, 7, 396, 12, 25, 26, 283, 27, 30, 174, 184, 316, 63, 72, 347, 95, 356, 230, 359, 247, 125, 254})" +,4.2775888000032865,, From 089c047c8b9c4aac93df0d2432bcbf8f371103ad Mon Sep 17 00:00:00 2001 From: Almog-David Date: Tue, 23 Jul 2024 12:07:27 +0300 Subject: [PATCH 74/93] time improvement in the heuristic function --- .../Firefighter_Problem.py | 20 +++------ .../comparisons/comparisons.py | 4 +- .../comparisons/spreading_minbudget.csv | 38 +++++++++--------- .../comparisons/spreading_minbudget.png | Bin 32890 -> 31615 bytes .../spreading_minbudget_preprocessed.csv | 36 ++++++++--------- 5 files changed, 44 insertions(+), 54 deletions(-) diff --git a/networkz/algorithms/approximation/firefighter_problem/Firefighter_Problem.py b/networkz/algorithms/approximation/firefighter_problem/Firefighter_Problem.py index 92bba64..913ce7a 100644 --- a/networkz/algorithms/approximation/firefighter_problem/Firefighter_Problem.py +++ b/networkz/algorithms/approximation/firefighter_problem/Firefighter_Problem.py @@ -187,17 +187,11 @@ def spreading_minbudget(Graph:nx.DiGraph, source:int, targets:list)-> int: validate_parameters(Graph, source, targets) logger.info(f"Starting the spreading_minbudget function with source node {source} and targets: {targets}") - best_strategy = [] - min_value = 1 max_value = len(targets) middle = math.floor((min_value + max_value) / 2) - #sanity check - the maximum budget we can get: the source neighbors. - answer = len(targets) - best_strategy = spreading_maxsave(Graph, answer, source, targets, True)[0] - while min_value < max_value: logger.info(f"Calling maxsave with parameters - Source: {source}, Targets: {targets}, Budget: {middle}") strategy, nodes_saved = spreading_maxsave(Graph, middle, source, targets, True) @@ -420,14 +414,11 @@ def heuristic_minbudget(Graph:nx.DiGraph, source:int, targets:list, spreading:bo validate_parameters(Graph, source, targets) logger.info(f"Starting the heuristic_minbudget function with source node {source}, targets: {targets}, and spreading: {spreading}") + best_strategy = [] min_value = 1 max_value = len(list(Graph.successors(source))) middle = math.floor((min_value + max_value) / 2) - - #sanity check - the maximum budget we can get: the source neighbors. - answer = len(list(Graph.successors(source))) - best_strategy = heuristic_maxsave(Graph, answer, source, targets, spreading, True)[0] - + while min_value < max_value: strategy, nodes_saved = heuristic_maxsave(Graph, middle, source, targets, spreading, True) @@ -436,9 +427,7 @@ def heuristic_minbudget(Graph:nx.DiGraph, source:int, targets:list, spreading:bo if len(common_elements) == len(targets): logger.info(f"The current budget {middle} has saved all the targets!") max_value = middle - if middle < answer: - answer = middle - best_strategy = strategy + best_strategy = strategy else: logger.info(f"The current budget {middle} didn't save all the targets!") min_value = middle + 1 @@ -447,7 +436,8 @@ def heuristic_minbudget(Graph:nx.DiGraph, source:int, targets:list, spreading:bo logger.info(f"Returning minimum budget: {middle} and the vaccination strategy: {best_strategy}") - return answer, best_strategy + #return answer, best_strategy + return middle, best_strategy if __name__ == "__main__": #import doctest diff --git a/networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/comparisons.py b/networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/comparisons.py index 8037891..5fd9406 100644 --- a/networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/comparisons.py +++ b/networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/comparisons.py @@ -288,5 +288,5 @@ def extract_budget_numeric(budget): if __name__ == "__main__": setup_global_logger(level=logging.DEBUG) # Compare_NonSpread() - # Compare_SpreadingMinBudget() - Compare_SpreadingMaxSave() \ No newline at end of file + Compare_SpreadingMinBudget() + #Compare_SpreadingMaxSave() \ No newline at end of file diff --git a/networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/spreading_minbudget.csv b/networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/spreading_minbudget.csv index 0c45f72..42346be 100644 --- a/networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/spreading_minbudget.csv +++ b/networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/spreading_minbudget.csv @@ -1,20 +1,20 @@ algorithm,runtime,graph_nodes,Budget -spreading_maxsave,0.07185340003343299,100,"([(5, 1), (43, 2), (90, 3)], {65, 73, 43, 45, 14, 78, 54, 55, 89, 61, 30})" -heuristic_maxsave,0.050711499992758036,100,"([(93, 1), (47, 2), (65, 3)], {65, 43, 47, 83, 55, 89, 93})" -spreading_maxsave,0.052191899972967803,100,"([(6, 1), (53, 2)], {64, 67, 72, 9, 74, 15, 80, 22, 88, 92, 29, 31, 97, 34, 98, 42, 53})" -heuristic_maxsave,0.03630989999510348,100,"([(59, 1), (97, 2)], {64, 97, 67, 72, 9, 42, 74, 80, 88, 59, 92, 29, 62, 31})" -spreading_maxsave,0.0324411999899894,100,"([(10, 1), (24, 2)], {10, 60, 86})" -heuristic_maxsave,0.05075649998616427,100,"([(10, 1), (24, 2)], {10, 60, 86})" -spreading_maxsave,0.19327960000373423,200,"([(31, 1), (6, 2), (9, 3)], {1, 65, 4, 69, 6, 71, 76, 16, 82, 151, 87, 89, 30, 31, 159, 177, 114, 51, 182, 56, 121, 58, 188, 191})" -heuristic_maxsave,0.09661199996480718,200,"([(60, 1), (194, 2), (182, 3)], {16, 148, 22, 151, 159, 177, 182, 60, 191, 64, 65, 194, 69, 71, 76, 87, 89, 93, 114, 117, 121, 122})" -spreading_maxsave,0.16957909998018295,200,"([(99, 1), (25, 2)], {6, 135, 8, 140, 77, 15, 144, 146, 89, 25, 156, 163, 36, 99, 101, 104, 169, 174, 111, 50, 118, 122, 123, 60, 61, 190, 191})" -heuristic_maxsave,0.1064889999688603,200,"([(157, 1), (144, 2)], {128, 6, 8, 140, 15, 144, 25, 157, 35, 163, 169, 174, 50, 55, 59, 60, 61, 190, 193, 77, 89, 91, 101, 118, 122, 123})" -spreading_maxsave,0.13650339999003336,200,"([(13, 1), (78, 2)], {192, 1, 70, 137, 11, 13, 22, 23, 91, 31, 33, 164, 37, 102, 169, 107, 111, 50, 54, 61})" -heuristic_maxsave,0.1891952999867499,200,"([(2, 1), (33, 2)], {192, 33, 2, 1, 37, 70, 102, 137, 169, 11, 107, 111, 23, 54, 22, 91, 61, 31})" -spreading_maxsave,0.4134444000083022,400,"([(126, 1), (11, 2)], {1, 66, 67, 390, 262, 326, 200, 395, 268, 13, 141, 143, 271, 396, 11, 20, 28, 92, 349, 351, 224, 353, 43, 299, 235})" -heuristic_maxsave,0.1678583999746479,400,"([(56, 1), (92, 2)], {64, 385, 67, 357, 107, 299, 237, 269, 60, 82, 348, 373, 55, 221, 56, 92, 381, 223})" -spreading_maxsave,0.8504898999817669,400,"([(227, 1), (33, 2)], {261, 390, 137, 266, 395, 269, 144, 18, 278, 279, 153, 157, 30, 31, 32, 33, 162, 164, 298, 299, 172, 46, 177, 52, 181, 311, 57, 61, 62, 322, 195, 69, 197, 199, 72, 327, 202, 330, 333, 207, 81, 210, 83, 339, 213, 343, 220, 223, 224, 228, 356, 358, 104, 361, 250, 236, 237, 365, 111, 375, 376, 377, 378, 255})" -heuristic_maxsave,0.28500709997024387,400,"([(92, 1), (199, 2)], {132, 390, 394, 395, 141, 143, 144, 278, 279, 280, 153, 156, 157, 30, 31, 32, 33, 162, 164, 167, 298, 299, 44, 170, 177, 51, 53, 181, 57, 61, 324, 197, 199, 72, 327, 332, 207, 336, 81, 210, 339, 213, 343, 217, 92, 220, 222, 223, 224, 356, 228, 358, 361, 106, 378, 111, 115, 373, 375, 376, 250, 251})" -spreading_maxsave,0.5659428999642842,400,"([(106, 1), (247, 2)], {385, 259, 7, 72, 12, 396, 337, 25, 26, 27, 283, 347, 30, 95, 356, 230, 359, 40, 106, 174, 247, 316, 125, 63})" -heuristic_maxsave,0.3827694999636151,400,"([(184, 1), (95, 2)], {385, 259, 7, 396, 12, 25, 26, 283, 27, 30, 174, 184, 316, 63, 72, 347, 95, 356, 230, 359, 247, 125, 254})" -,4.2775888000032865,, +spreading_minbudget,2.6765573997981846,100,"(2, [(28, 1), (17, 1), (47, 2), (2, 2)])" +heuristic_minbudget,0.7838832000270486,100,"(2, [(58, 1), (17, 1), (14, 2), (76, 2), (63, 3), (85, 3)])" +spreading_minbudget,4.7819698001258075,100,"(19, [(19, 1), (5, 1), (4, 1), (33, 1), (66, 1), (79, 1), (92, 1), (2, 1), (18, 1), (38, 1), (52, 1), (55, 1), (10, 1), (27, 1), (29, 1), (63, 1), (83, 1), (32, 1), (54, 1)])" +heuristic_minbudget,12.467118400149047,100,"(19, [(19, 1), (5, 1), (63, 1), (18, 1), (38, 1), (79, 1), (10, 1), (66, 1), (32, 1), (52, 1), (83, 1), (27, 1), (55, 1), (92, 1), (33, 1), (29, 1), (2, 1), (4, 1), (54, 1)])" +spreading_minbudget,2.980581299867481,100,"(24, [(40, 1), (2, 1), (3, 1), (5, 1), (8, 1), (11, 1), (22, 1), (24, 1), (51, 1), (62, 1), (72, 1), (76, 1), (81, 1), (83, 1), (85, 1), (87, 1), (89, 1), (92, 1), (97, 1), (44, 1), (46, 1), (60, 1), (63, 1), (70, 1)])" +heuristic_minbudget,35.502651300048456,100,"(24, [(76, 1), (85, 1), (11, 1), (44, 1), (87, 1), (83, 1), (92, 1), (46, 1), (62, 1), (70, 1), (8, 1), (2, 1), (22, 1), (72, 1), (24, 1), (89, 1), (63, 1), (81, 1), (51, 1), (3, 1), (97, 1), (5, 1), (40, 1), (60, 1)])" +spreading_minbudget,8.064421500079334,200,"(19, [(86, 1), (105, 1), (161, 1), (115, 1), (21, 1), (34, 1), (162, 1), (85, 1), (131, 1), (70, 1), (42, 1), (186, 1), (36, 1), (14, 1), (40, 1), (187, 1), (100, 1), (178, 1), (176, 1)])" +heuristic_minbudget,7.089356000069529,200,"(13, [(186, 1), (34, 1), (105, 1), (178, 1), (100, 1), (14, 1), (131, 1), (176, 1), (187, 1), (40, 1), (42, 1), (161, 1), (91, 1), (181, 2), (92, 2), (71, 2), (144, 2), (130, 2), (175, 2), (102, 2), (194, 2), (191, 2), (9, 2), (18, 2), (4, 2), (68, 2)])" +spreading_minbudget,11.68095609988086,200,"(50, [(118, 1), (80, 1), (1, 1), (3, 1), (16, 1), (25, 1), (29, 1), (42, 1), (55, 1), (65, 1), (73, 1), (75, 1), (76, 1), (78, 1), (79, 1), (95, 1), (99, 1), (100, 1), (101, 1), (112, 1), (127, 1), (129, 1), (133, 1), (137, 1), (151, 1), (152, 1), (166, 1), (171, 1), (191, 1), (197, 1), (5, 1), (6, 1), (24, 1), (27, 1), (57, 1), (110, 1), (170, 1), (179, 1), (19, 1), (20, 1), (146, 1), (163, 1), (193, 1), (119, 1), (181, 1), (98, 1), (140, 1), (144, 1), (157, 1), (180, 1)])" +heuristic_minbudget,41.66929220012389,200,"(49, [(98, 1), (101, 1), (73, 1), (16, 1), (112, 1), (79, 1), (27, 1), (1, 1), (137, 1), (140, 1), (119, 1), (5, 1), (76, 1), (25, 1), (118, 1), (129, 1), (146, 1), (163, 1), (181, 1), (110, 1), (3, 1), (55, 1), (100, 1), (151, 1), (78, 1), (191, 1), (65, 1), (157, 1), (20, 1), (171, 1), (152, 1), (197, 1), (99, 1), (166, 1), (179, 1), (75, 1), (6, 1), (29, 1), (57, 1), (127, 1), (24, 1), (42, 1), (170, 1), (19, 1), (193, 1), (144, 1), (180, 1), (133, 1), (95, 1)])" +spreading_minbudget,4.990314699942246,200,"(49, [(57, 1), (6, 1), (13, 1), (17, 1), (18, 1), (20, 1), (23, 1), (26, 1), (31, 1), (33, 1), (34, 1), (45, 1), (48, 1), (49, 1), (51, 1), (63, 1), (70, 1), (73, 1), (79, 1), (82, 1), (87, 1), (93, 1), (102, 1), (107, 1), (109, 1), (111, 1), (113, 1), (120, 1), (132, 1), (136, 1), (137, 1), (141, 1), (157, 1), (161, 1), (162, 1), (167, 1), (175, 1), (183, 1), (189, 1), (192, 1), (193, 1), (196, 1), (198, 1), (8, 1), (9, 1), (46, 1), (176, 1), (178, 1), (151, 1)])" +heuristic_minbudget,48.9231706999708,200,"(49, [(46, 1), (196, 1), (162, 1), (109, 1), (6, 1), (79, 1), (23, 1), (192, 1), (8, 1), (18, 1), (57, 1), (136, 1), (111, 1), (33, 1), (51, 1), (87, 1), (102, 1), (132, 1), (175, 1), (189, 1), (198, 1), (13, 1), (49, 1), (17, 1), (48, 1), (93, 1), (141, 1), (157, 1), (167, 1), (183, 1), (193, 1), (9, 1), (45, 1), (63, 1), (73, 1), (82, 1), (151, 1), (178, 1), (107, 1), (176, 1), (113, 1), (20, 1), (26, 1), (120, 1), (31, 1), (34, 1), (137, 1), (161, 1), (70, 1)])" +spreading_minbudget,6.574391700094566,400,"(21, [(46, 1), (99, 1), (154, 1), (62, 1), (282, 1), (2, 1), (352, 1), (293, 1), (194, 1), (285, 1), (18, 1), (45, 1), (49, 1), (295, 1), (356, 1), (174, 1), (12, 1), (322, 1), (112, 1), (248, 1), (313, 1)])" +heuristic_minbudget,8.081847100052983,400,"(13, [(174, 1), (112, 1), (49, 1), (248, 1), (295, 1), (45, 1), (12, 1), (18, 1), (356, 1), (322, 1), (313, 1), (389, 1), (178, 1), (84, 2), (128, 2), (80, 2), (48, 2), (199, 2), (17, 2), (376, 2), (233, 2), (314, 2), (67, 2), (11, 2), (225, 2), (122, 2)])" +spreading_minbudget,11.30272960010916,400,"(29, [(383, 1), (19, 1), (2, 1), (4, 1), (58, 1), (100, 1), (123, 1), (169, 1), (188, 1), (218, 1), (263, 1), (335, 1), (388, 1), (30, 1), (49, 1), (186, 1), (201, 1), (228, 1), (243, 1), (314, 1), (358, 1), (25, 1), (86, 1), (144, 1), (316, 1), (362, 1), (28, 1), (355, 1), (386, 1)])" +heuristic_minbudget,47.75127730006352,400,"(29, [(388, 1), (383, 1), (355, 1), (358, 1), (186, 1), (201, 1), (58, 1), (169, 1), (243, 1), (2, 1), (263, 1), (4, 1), (188, 1), (314, 1), (19, 1), (25, 1), (316, 1), (335, 1), (100, 1), (218, 1), (30, 1), (362, 1), (28, 1), (123, 1), (144, 1), (228, 1), (49, 1), (86, 1), (386, 1)])" +spreading_minbudget,9.465220999903977,400,"(91, [(22, 1), (2, 1), (4, 1), (5, 1), (15, 1), (21, 1), (35, 1), (38, 1), (39, 1), (43, 1), (76, 1), (79, 1), (84, 1), (88, 1), (94, 1), (102, 1), (103, 1), (105, 1), (112, 1), (113, 1), (114, 1), (117, 1), (122, 1), (127, 1), (132, 1), (136, 1), (137, 1), (142, 1), (158, 1), (181, 1), (186, 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602 +++++++++--------- .../comparisons/non_spreading.png | Bin 74017 -> 77751 bytes .../non_spreading_preprocessed.csv | 600 ++++++++--------- .../comparisons/spreading_maxsave.csv | 380 +---------- .../comparisons/spreading_maxsave.png | Bin 32558 -> 34975 bytes .../comparisons/spreading_minbudget.csv | 38 +- .../comparisons/spreading_minbudget.png | Bin 31615 -> 33997 bytes .../spreading_minbudget_preprocessed.csv | 36 +- 11 files changed, 939 insertions(+), 1202 deletions(-) diff --git a/networkz/algorithms/approximation/firefighter_problem/Firefighter_Problem.py b/networkz/algorithms/approximation/firefighter_problem/Firefighter_Problem.py index 913ce7a..d9b01d4 100644 --- a/networkz/algorithms/approximation/firefighter_problem/Firefighter_Problem.py +++ b/networkz/algorithms/approximation/firefighter_problem/Firefighter_Problem.py @@ -1,5 +1,4 @@ """ - The Paper - Approximability of the Firefighter Problem Computing Cuts over Time @@ -16,7 +15,7 @@ Yuval Bubnovsky Almog David Shaked Levi - + """ import networkx as nx @@ -25,7 +24,6 @@ import logging from networkz.algorithms.max_flow_with_node_capacity import min_cut_with_node_capacity from networkz.algorithms.approximation.firefighter_problem.Utils import * -import random def setup_logger(): logger = logging.getLogger('firefighter_problem_main') @@ -40,21 +38,37 @@ def setup_logger(): logger.addHandler(console_handler) return logger -logger = setup_logger() +#logger = setup_logger() #logger = logging.getLogger(__name__) -def spreading_maxsave(Graph:nx.DiGraph, budget:int, source:int, targets:list, stop_condition=None) -> list: +def spreading_maxsave(Graph:nx.DiGraph, budget:int, source:int, targets:list, stop_condition=None) -> tuple[list, set]: """ - "Approximability of the Firefighter Problem - Computing Cuts over Time", - by Elliot Anshelevich, Deeparnab Chakrabarty, Ameya Hate, Chaitanya Swamy (2010) - https://link.springer.com/article/10.1007/s00453-010-9469-y - - Programmers: Shaked Levi, Almog David, Yuval Bubnovsky + Approximate the firefighter problem by maximizing the number of saved nodes. - spreading_maxsave: Gets a directed graph, budget, source node, and list of targeted nodes that we need to save - and return the best vaccination strategy that saves the most nodes from the targeted nodes list. - + Parameters: + ---------- + Graph : nx.DiGraph + Directed graph representing the network. + budget : int + Number of nodes that can be vaccinated at each time step. + source : int + Source node where the infection starts. + targets : list + List of target nodes to be saved. + stop_condition : optional + If set, stops early if all targets are saved or if any target is infected. + + Returns: + ------- + tuple: + vaccination_strategy : list + List of tuples representing the vaccination strategy. + saved_target_nodes : set + Set of nodes that are saved from infection. + + Examples: + -------- Example 1: >>> G = nx.DiGraph() >>> G.add_nodes_from([0,1,2], status="vulnerable") @@ -83,6 +97,7 @@ def spreading_maxsave(Graph:nx.DiGraph, budget:int, source:int, targets:list, st >>> spreading_maxsave(G3,2,0,[1,2,3,4,5,6,7,8]) ([(5, 1), (2, 1), (8, 2)], {1, 2, 3, 5, 6, 7, 8}) """ + if budget < 1: logger.critical("Error: The budget must be at least 1") raise ValueError("Error: The budget must be at least 1") @@ -103,18 +118,18 @@ def spreading_maxsave(Graph:nx.DiGraph, budget:int, source:int, targets:list, st logger.info("Calculating all possible direct vaccinations in each timestamp") gamma, direct_vaccinations = calculate_gamma(Graph, source, targets) - logger.info("Calculating direct vaccinations groups by timestamp") + logger.info("Calculating direct vaccinations grouping by timestamp") epsilon = calculate_epsilon(direct_vaccinations) time_step = 0 while can_spread and time_step < len(epsilon): spread_vaccination(Graph, vaccinated_nodes) for i in range(budget): - logger.info(f"Calculating the best direct vaccination strategy for the current time step that saves more new node in targets (Current budget: {budget})") + logger.info(f"Calculating the best direct vaccination strategy for the current time step that saves more new nodes in targets (Current budget: {i+1} out of {budget})") vaccination, nodes_saved = find_best_direct_vaccination(Graph, direct_vaccinations, epsilon[time_step], local_targets) if vaccination != (): - logger.info(f"Found {vaccination} as a solution for current timestamp, appending to vaccination strategy and vaccinating the node") + logger.info(f"Found {vaccination} as a solution for current timestamp. Appending to vaccination strategy and vaccinating the node") vaccination_strategy.append(vaccination) chosen_node = vaccination[0] @@ -141,22 +156,34 @@ def spreading_maxsave(Graph:nx.DiGraph, budget:int, source:int, targets:list, st time_step += 1 - logger.info(f"Returning vaccination strategy: {vaccination_strategy}. The strategy saved the nodes: {saved_target_nodes}") + logger.info(f"Returning vaccination strategy: {vaccination_strategy}. The strategy saves the nodes: {saved_target_nodes}") return vaccination_strategy, saved_target_nodes -def spreading_minbudget(Graph:nx.DiGraph, source:int, targets:list)-> int: +def spreading_minbudget(Graph:nx.DiGraph, source:int, targets:list) -> tuple[int, list]: """ - "Approximability of the Firefighter Problem - Computing Cuts over Time", - by Elliot Anshelevich, Deeparnab Chakrabarty, Ameya Hate, Chaitanya Swamy (2010) - https://link.springer.com/article/10.1007/s00453-010-9469-y - - Programmers: Shaked Levi, Almog David, Yuval Bubnovsky + Approximate the firefighter problem by minimizing the budget required to save all target nodes. - spreading_minbudget: Gets a directed graph, source node, and list of targeted nodes that we need to save - and returns the minimum budget that saves all the nodes from the targeted nodes list. + Parameters: + ---------- + Graph : nx.DiGraph + Directed graph representing the network. + source : int + Source node where the infection starts. + targets : list + List of target nodes to be saved. - Example 1: + Returns: + ------- + tuple: + min_budget : int + Minimum budget required to save all target nodes. + best_strategy : list + List of tuples representing the vaccination strategy. + + Examples: + -------- + Example 1: >>> G1 = nx.DiGraph() >>> G1.add_nodes_from([0,1,2,3], status="vulnerable") >>> G1.add_edges_from([(0,1),(0,2),(1,2),(1,3),(2,3)]) @@ -184,14 +211,17 @@ def spreading_minbudget(Graph:nx.DiGraph, source:int, targets:list)-> int: >>> spreading_minbudget(G3,0,[1,2,3,4,5,6,7,8]) (3, [(5, 1), (2, 1), (4, 1)]) """ + validate_parameters(Graph, source, targets) logger.info(f"Starting the spreading_minbudget function with source node {source} and targets: {targets}") - best_strategy = [] min_value = 1 max_value = len(targets) + min_budget = max_value middle = math.floor((min_value + max_value) / 2) + best_strategy = spreading_maxsave(Graph, min_budget, source, targets, True)[0] + while min_value < max_value: logger.info(f"Calling maxsave with parameters - Source: {source}, Targets: {targets}, Budget: {middle}") strategy, nodes_saved = spreading_maxsave(Graph, middle, source, targets, True) @@ -201,7 +231,9 @@ def spreading_minbudget(Graph:nx.DiGraph, source:int, targets:list)-> int: if len(common_elements) == len(targets): logger.info(f"The current budget {middle} has saved all the targets!") max_value = middle - best_strategy = strategy + if middle < min_budget: + min_budget = middle + best_strategy = strategy else: logger.info(f"The current budget {middle} didn't save all the targets!") min_value = middle + 1 @@ -209,21 +241,30 @@ def spreading_minbudget(Graph:nx.DiGraph, source:int, targets:list)-> int: middle = math.floor((min_value + max_value) / 2) logger.info(f"Returning minimum budget: {middle} and the vaccination strategy: {best_strategy}") - return middle, best_strategy + return min_budget, best_strategy -def non_spreading_minbudget(Graph:nx.DiGraph, source:int, targets:list)->int: +def non_spreading_minbudget(Graph:nx.DiGraph, source:int, targets:list) -> int: """ - "Approximability of the Firefighter Problem - Computing Cuts over Time", - by Elliot Anshelevich, Deeparnab Chakrabarty, Ameya Hate, Chaitanya Swamy (2010) - https://link.springer.com/article/10.1007/s00453-010-9469-y - - Programmers: Shaked Levi, Almog David, Yuval Bubnovsky + Calculate the minimum budget required to save all target nodes in a non-spreading scenario. - non_spreading_minbudget: Gets a directed graph, source node, and list of targeted nodes that we need to save - and returns the minimum budget that saves all the nodes from the targeted nodes list. + Parameters: + ---------- + Graph : nx.DiGraph + Directed graph representing the network. + source : int + Source node where the infection starts. + targets : list + List of target nodes to be saved. + + Returns: + ------- + min_budget : int + Minimum budget required to save all target nodes. - Example1: + Examples: + -------- + Example 1: >>> G1 = nx.DiGraph() >>> G1.add_nodes_from([0,1,2,3], status="vulnerable") >>> G1.add_edges_from([(0,1),(0,2),(1,2),(1,3),(2,3)]) @@ -258,29 +299,42 @@ def non_spreading_minbudget(Graph:nx.DiGraph, source:int, targets:list)->int: min_budget = len(algo.minimum_st_node_cut(G, source, 't')) logger.info(f"Returning minimum budget: {min_budget}") - return min_budget, [] + return min_budget, [] -def non_spreading_dirlaynet_minbudget(Graph:nx.DiGraph, source:int, targets:list)->int: +def non_spreading_dirlaynet_minbudget(Graph:nx.DiGraph, source:int, targets:list) -> tuple[int, list]: """ - "Approximability of the Firefighter Problem - Computing Cuts over Time", - by Elliot Anshelevich, Deeparnab Chakrabarty, Ameya Hate, Chaitanya Swamy (2010) - https://link.springer.com/article/10.1007/s00453-010-9469-y - - Programmers: Shaked Levi, Almog David, Yuval Bubnovsky + Calculate the minimum budget required to save all target nodes in a non-spreading directed layer network. - non_spreading_dirlaynet_minbudget: Gets a directed graph, source node, and list of targeted nodes that we need to save - and returns the minimum budget that saves all the nodes from the targeted nodes list. - - Example1: + Parameters: + ---------- + Graph : nx.DiGraph + Directed graph representing the network. + source : int + Source node where the infection starts. + targets : list + List of target nodes to be saved. + + Returns: + ------- + tuple: + min_budget : int + Minimum budget required to save all target nodes. + best_strategy : list + List of tuples representing the vaccination strategy. + + Examples: + -------- + Example 1: >>> G4 = nx.DiGraph() >>> G4.add_nodes_from([0,1,2,3,4,5], status="vulnerable") >>> G4.add_edges_from([(0,1),(0,2),(1,3),(1,4),(1,5),(2,3),(2,4),(2,5),(3,5),(4,5)]) >>> non_spreading_dirlaynet_minbudget(G4,0,[1,2,3,4,5]) - 2 + (2, {0: [1, 2]}) """ + validate_parameters(Graph, source, targets) if not is_dag(Graph): - logger.error("Problem with graph, its not a DAG, thus cannot run algorithm") + logger.error("The graph is not a DAG graph, thus cannot run the algorithm") return #display_graph(Graph) @@ -294,37 +348,48 @@ def non_spreading_dirlaynet_minbudget(Graph:nx.DiGraph, source:int, targets:list vacc_matrix = calculate_vaccine_matrix(layers, N_groups) integer_matrix = matrix_to_integers_values(vacc_matrix) min_budget = min_budget_calculation(vacc_matrix) - stategy = dirlay_vaccination_startegy(integer_matrix, N_groups) + strategy = dirlay_vaccination_startegy(integer_matrix, N_groups) - logger.info(f"Returning algorithm stategy: {stategy} with budget: {min_budget}") - return min_budget,stategy + logger.info(f"Returning minimum budget: {min_budget} and the vaccination strategy: {strategy}") + return min_budget, strategy -def heuristic_maxsave(Graph:nx.DiGraph, budget:int, source:int, targets:list, spreading=True, stop_condition=None) -> tuple: +def heuristic_maxsave(Graph:nx.DiGraph, budget:int, source:int, targets:list, spreading=True, stop_condition=None) -> tuple[list, set]: """ - This heuristic approach is based on the local search problem. - We will select the best neighbor that saves the most nodes from targets. - - Parameters: - - Graph (nx.DiGraph): Directed graph representing the network. - - budget (int): Number of nodes that can be vaccinated at each time step. - - source (int): Source node where the infection starts. - - targets (list): List of target nodes to be saved. - - spreading (bool): If True, vaccination spreads to neighboring nodes. - - flag (bool, optional): If set, stops early if all targets are saved or if any target is infected. + Approximate the firefighter problem by maximizing the number of saved nodes in our heuristic apprach. + The heuristic approach is based on the local search problem. - Returns: - - list: List of tuples representing the vaccination strategy. + Parameters + ---------- + Graph : nx.DiGraph + Directed graph representing the network. + budget : int + Number of nodes that can be vaccinated at each time step. + source : int + Source node where the infection starts. + targets : list + List of target nodes to be saved. + spreading : bool + If True, vaccination spreads to neighboring nodes. + stop_condition : optional + If set, stops early if all targets are saved or if any target is infected. - Raises: - - ValueError: If the budget is less than 1. - - Example: + Returns + ------- + tuple: + vaccination_strategy : list + List of tuples representing the vaccination strategy. + saved_target_nodes : set + Set of saved target nodes. + + Examples + -------- >>> G = nx.DiGraph() >>> G.add_nodes_from([0, 1, 2, 3], status="vulnerable") >>> G.add_edges_from([(0, 1), (0, 2), (1, 2), (1, 3)]) >>> heuristic_maxsave(G, 1, 0, [1, 2, 3]) - ([(1, 1)], {1, 2, 3}) + ([(1, 1)], {1, 3}) """ + if budget < 1: logger.critical("Error: The budget must be at least 1") raise ValueError("Error: The budget must be at least 1") @@ -359,7 +424,6 @@ def heuristic_maxsave(Graph:nx.DiGraph, budget:int, source:int, targets:list, sp logger.info(f"Updated list of currently vaccinated nodes: {vaccinated_nodes}") if nodes_saved is not None: - #saved_target_nodes.update(nodes_saved) local_targets[:] = [element for element in local_targets if element not in nodes_saved] logger.info(f"Updated list of targets: {local_targets}") @@ -387,38 +451,51 @@ def heuristic_maxsave(Graph:nx.DiGraph, budget:int, source:int, targets:list, sp if node_status != Status.INFECTED.value: saved_target_nodes.add(node) - logger.info(f"Returning vaccination strategy: {vaccination_strategy}. The strategy saved the nodes: {saved_target_nodes}") + logger.info(f"Returning vaccination strategy: {vaccination_strategy}. The strategy saves the nodes: {saved_target_nodes}") return vaccination_strategy, saved_target_nodes -def heuristic_minbudget(Graph:nx.DiGraph, source:int, targets:list, spreading:bool)-> tuple: +def heuristic_minbudget(Graph:nx.DiGraph, source:int, targets:list, spreading:bool) -> tuple[int, list]: """ - This function calculates the minimum budget required to save all target nodes - using the heuristic approach based on local search problem. - - Parameters: - - Graph (nx.DiGraph): Directed graph representing the network. - - source (int): Source node where the infection starts. - - targets (list): List of target nodes to be saved. - - spreading (bool): If True, vaccination spreads to neighboring nodes. + Calculate the minimum budget required to save all target nodes in our heuristic approach. + + Parameters + ---------- + Graph : nx.DiGraph + Directed graph representing the network. + source : int + Source node where the infection starts. + targets : list + List of target nodes to be saved. + spreading : bool + If True, vaccination spreads to neighboring nodes. - Returns: - - int: Minimum budget required to save all target nodes. + Returns + ------- + tuple: + min_budget : int + Minimum budget required to save all target nodes. + best_strategy : list + List of tuples representing the vaccination strategy. - Example: + Examples + -------- >>> G = nx.DiGraph() >>> G.add_nodes_from([0, 1, 2, 3], status="vulnerable") >>> G.add_edges_from([(0, 1), (0, 2), (1, 2), (1, 3)]) >>> heuristic_minbudget(G, 0, [1, 2, 3], True) (2, [(1, 1), (2, 1)]) """ + validate_parameters(Graph, source, targets) logger.info(f"Starting the heuristic_minbudget function with source node {source}, targets: {targets}, and spreading: {spreading}") - best_strategy = [] min_value = 1 max_value = len(list(Graph.successors(source))) + min_budget = max_value middle = math.floor((min_value + max_value) / 2) - + + best_strategy = heuristic_maxsave(Graph, min_budget, source, targets, spreading, True)[0] + while min_value < max_value: strategy, nodes_saved = heuristic_maxsave(Graph, middle, source, targets, spreading, True) @@ -427,7 +504,9 @@ def heuristic_minbudget(Graph:nx.DiGraph, source:int, targets:list, spreading:bo if len(common_elements) == len(targets): logger.info(f"The current budget {middle} has saved all the targets!") max_value = middle - best_strategy = strategy + if middle < min_budget: + min_budget = middle + best_strategy = strategy else: logger.info(f"The current budget {middle} didn't save all the targets!") min_value = middle + 1 @@ -436,34 +515,9 @@ def heuristic_minbudget(Graph:nx.DiGraph, source:int, targets:list, spreading:bo logger.info(f"Returning minimum budget: {middle} and the vaccination strategy: {best_strategy}") - #return answer, best_strategy - return middle, best_strategy + return min_budget, best_strategy if __name__ == "__main__": - #import doctest - #result = doctest.testmod(verbose=False) - #print(f"Doctest results: {result}") -s - - G = nx.DiGraph() - num_nodes = random.randint(3, 100) - nodes = list(range(num_nodes + 1)) - G.add_nodes_from(nodes, status=Status.VULNERABLE.value) - for first in nodes: - for second in nodes: - probability = random.random() - if first != second and probability < 0.75: - G.add_edge(first,second) - - num_targets = random.randint(2, int(len(nodes)/2)+1) - targets = random.sample(nodes,num_targets) - if 0 in targets:targets.remove(0) - - #print(spreading_minbudget(G,0,targets)) - print(heuristic_minbudget(G,0,targets,True)) - - - - - - - + import doctest + result = doctest.testmod(verbose=False) + print(f"Doctest results: {result}") \ No newline at end of file diff --git a/networkz/algorithms/approximation/firefighter_problem/Utils.py b/networkz/algorithms/approximation/firefighter_problem/Utils.py index 1b77e0e..484673c 100644 --- a/networkz/algorithms/approximation/firefighter_problem/Utils.py +++ b/networkz/algorithms/approximation/firefighter_problem/Utils.py @@ -1,5 +1,4 @@ """ - The Paper - Approximability of the Firefighter Problem Computing Cuts over Time @@ -16,7 +15,6 @@ Yuval Bubnovsky Almog David Shaked Levi - """ import networkx as nx @@ -60,7 +58,9 @@ def setup_logger(): logger = setup_logger() #logger = logging.getLogger(__name__) -def validate_parameters(graph:nx.DiGraph, source:int, targets:list)->None: +# ============================ Validation Functions ============================ + +def validate_parameters(graph:nx.DiGraph, source:int, targets:list) -> None: """ Validate the source and target nodes in the graph. @@ -120,14 +120,33 @@ def is_st_layered_dag(graph: nx.DiGraph, s: any, t: any) -> bool: #TODO: make th entering t are from a vertex in some layer Li to a vertex in Li+1; arcs entering t may originate from any vertex other than t. - Parameters: - - graph (nx.DiGraph): Directed graph. - - s: Source vertex. - - t: Target vertex. + Parameters + ---------- + graph : nx.DiGraph + Directed graph. + s : any + Source vertex. + t : any + Target vertex. - Returns: - - True if the graph is an s-t directed l-layered network, - - False otherwise. + Returns + ------- + bool + True if the graph is an s-t directed l-layered network, False otherwise. + + Examples + -------- + Example 1: + >>> G = nx.DiGraph() + >>> G.add_edges_from([(0, 1), (1, 2), (2, 3)]) + >>> is_st_layered_dag(G, 0, 3) + True + + Example 2: + >>> G = nx.DiGraph() + >>> G.add_edges_from([(0, 1), (1, 2), (2, 1), (2, 3)]) + >>> is_st_layered_dag(G, 0, 3) + False """ logger.info(f'Validating {graph} is an s-t directed layered network') if not nx.is_directed_acyclic_graph(graph): @@ -178,8 +197,10 @@ def is_dag(graph:nx.DiGraph) -> bool: """ return nx.is_directed_acyclic_graph(graph) +# ============================ End Validation Functions ============================ + # ============================ Spreading Max-Save ============================ -def calculate_gamma(graph:nx.DiGraph, source:int, targets:list)-> dict: +def calculate_gamma(graph:nx.DiGraph, source:int, targets:list) -> dict: """ Calculate Gamma and S(u,t) based on the calculation in the article. @@ -194,9 +215,9 @@ def calculate_gamma(graph:nx.DiGraph, source:int, targets:list)-> dict: Returns: ------- - gamma (dict) + gamma : dict Dictionary of vaccination options. - direct_vaccination (dict) + direct_vaccination : dict Dictionary of direct vaccination strategies - S(u,t). Examples: @@ -212,6 +233,7 @@ def calculate_gamma(graph:nx.DiGraph, source:int, targets:list)-> dict: >>> direct_vaccination {(2, 1): [], (3, 1): [3]} """ + gamma = {} direct_vaccination = {} unreachable_nodes = [] @@ -246,7 +268,7 @@ def calculate_gamma(graph:nx.DiGraph, source:int, targets:list)-> dict: logger.info("S(u,t) is: " + str(direct_vaccination)) return gamma, direct_vaccination -def calculate_epsilon(direct_vaccinations:dict)->list: +def calculate_epsilon(direct_vaccinations:dict) -> list: """ Calculate Epsilon based on the calculation in the article. @@ -257,7 +279,7 @@ def calculate_epsilon(direct_vaccinations:dict)->list: Returns: ------- - list + epsilon : list List of direct vaccination groups by time step. Examples: @@ -275,14 +297,14 @@ def calculate_epsilon(direct_vaccinations:dict)->list: logger.info("Epsilon is: " + str(epsilon)) return epsilon -def find_best_direct_vaccination(graph:nx.DiGraph, direct_vaccinations:dict, current_time_options:list, targets:list)->tuple: +def find_best_direct_vaccination(graph:nx.DiGraph, direct_vaccinations:dict, current_time_options:list, targets:list) -> tuple: """ Find the best direct vaccination strategy for the current time step that saves more new nodes in targets. Parameters: ---------- graph : nx.DiGraph - Directed graph. + Directed graph representing the network. direct_vaccinations : dict Dictionary of direct vaccination strategies. current_time_options : list @@ -293,7 +315,10 @@ def find_best_direct_vaccination(graph:nx.DiGraph, direct_vaccinations:dict, cur Returns: ------- tuple - Best direct vaccination option and the nodes saved by this option. + best_vaccination : tuple + Best direct vaccination option for the current time stamp. + nodes_saved : list + List of the nodes who's been saved by this option. Examples: -------- @@ -323,14 +348,14 @@ def find_best_direct_vaccination(graph:nx.DiGraph, direct_vaccinations:dict, cur logger.info("The best direct vaccination is: " + str(best_vaccination) + " and it saves nodes: " + str(nodes_saved)) return best_vaccination, nodes_saved -def spread_virus(graph:nx.DiGraph, infected_nodes:list)->bool: +def spread_virus(graph:nx.DiGraph, infected_nodes:list) -> bool: """ Spread the virus from infected nodes to their neighbors. Parameters: ---------- graph : nx.DiGraph - Directed graph. + Directed graph representing the network. infected_nodes : list List of currently infected nodes. @@ -365,14 +390,14 @@ def spread_virus(graph:nx.DiGraph, infected_nodes:list)->bool: return bool(infected_nodes) -def spread_vaccination(graph:nx.DiGraph, vaccinated_nodes:list)->None: +def spread_vaccination(graph:nx.DiGraph, vaccinated_nodes:list) -> None: """ Spread the vaccination from vaccinated nodes to their neighbors. Parameters: ---------- graph : nx.DiGraph - Directed graph. + Directed graph representing the network. vaccinated_nodes : list List of currently vaccinated nodes. @@ -400,14 +425,14 @@ def spread_vaccination(graph:nx.DiGraph, vaccinated_nodes:list)->None: vaccinated_nodes.append(node) return -def vaccinate_node(graph:nx.DiGraph, node:int)->None: +def vaccinate_node(graph:nx.DiGraph, node:int) -> None: """ Directly vaccinate a specific node in the graph. Parameters: ---------- graph : nx.DiGraph - Directed graph. + Directed graph representing the network. node : int Node to be vaccinated. @@ -424,14 +449,14 @@ def vaccinate_node(graph:nx.DiGraph, node:int)->None: #display_graph(graph) return -def clean_graph(graph:nx.DiGraph)->None: +def clean_graph(graph:nx.DiGraph) -> None: """ Reset the graph to its base state where all nodes are targets. Parameters: ---------- graph : nx.DiGraph - Directed graph. + Directed graph representing the network. Examples: -------- @@ -441,6 +466,7 @@ def clean_graph(graph:nx.DiGraph)->None: >>> G.nodes(data=True) NodeDataView({1: {'status': 'vulnerable'}, 2: {'status': 'vulnerable'}, 3: {'status': 'vulnerable'}}) """ + for node in graph.nodes: graph.nodes[node]['status'] = Status.VULNERABLE.value return @@ -448,7 +474,7 @@ def clean_graph(graph:nx.DiGraph)->None: # ============================ End Spreading Max-Save ============================ # =========================== Non-Spreading Min-Budget-Dirlay ============================ -def adjust_nodes_capacity(graph:nx.DiGraph, source:int)->list: +def adjust_nodes_capacity(graph:nx.DiGraph, source:int) -> list: """ Adjust the capacity of nodes based on the layer they belong to. The capacity is based on the formula in the article at the DirLayNet algorithm section. @@ -456,13 +482,13 @@ def adjust_nodes_capacity(graph:nx.DiGraph, source:int)->list: Parameters: ---------- graph : nx.DiGraph - Directed graph. + Directed graph representing the network. source : int Source node. Returns: ------- - list + layers: list List of nodes grouped by layers. Examples: @@ -490,34 +516,7 @@ def adjust_nodes_capacity(graph:nx.DiGraph, source:int)->list: return layers -def adjust_nodes_capacity_parallel(graph: nx.DiGraph, source: int) -> list: #TODO - """ - This is a tryout to improve the adjust_nodes_capacity by using threadpool to calcualte each layer and its nodes capacity in parallel - """ - logger.debug("Starting to adjust node capacity for dirlay graph nodes in parallel...") - - layers = list(nx.bfs_layers(graph, source)) - harmonic_sums = np.cumsum([1 / i for i in range(1, len(layers))]) - - def set_node_capacity(index, nodes_chunk): - for node in nodes_chunk: - graph.nodes[node]['capacity'] = 1 / (index * harmonic_sums[index - 1]) - logger.info(f"Added Capacity {1 / (index * harmonic_sums[index - 1])} for node: {node}") - - with concurrent.futures.ThreadPoolExecutor() as executor: - futures = [] - for index in range(1, len(layers)): - chunk_size = max(1, len(layers[index]) // executor._max_workers) - for i in range(0, len(layers[index]), chunk_size): - chunk = layers[index][i:i + chunk_size] - futures.append(executor.submit(set_node_capacity, index, chunk)) - concurrent.futures.wait(futures) - - logger.info(f"Done with adding capacity for nodes, with Layers: {layers}") - return layers - - -def create_st_graph(graph:nx.DiGraph, targets:list)->nx.DiGraph: +def create_st_graph(graph:nx.DiGraph, targets:list) -> nx.DiGraph: """ Create an s-t graph from the original graph for use in connectivity algorithms. @@ -530,7 +529,7 @@ def create_st_graph(graph:nx.DiGraph, targets:list)->nx.DiGraph: Returns: ------- - nx.DiGraph + G : nx.DiGraph s-t graph. Examples: @@ -572,7 +571,7 @@ def min_cut_N_groups(min_cut_nodes: set, layers: list) -> dict: Returns: ------- - dict + groups : dict Dictionary with layers as keys and lists of nodes in the minimum cut as values. Examples: @@ -585,7 +584,6 @@ def min_cut_N_groups(min_cut_nodes: set, layers: list) -> dict: # Initialize the groups dictionary with empty lists for each layer index groups = {i+1: [] for i in range(len(layers)-1)} - # Populate the groups dictionary for item in min_cut_nodes: node , suffix = item.split('_') @@ -599,7 +597,7 @@ def min_cut_N_groups(min_cut_nodes: set, layers: list) -> dict: return groups -def calculate_vaccine_matrix(layers:list, min_cut_nodes_grouped:dict)->np.matrix: +def calculate_vaccine_matrix(layers:list, min_cut_nodes_grouped:dict) -> np.matrix: """ Calculate the vaccine matrix based on the calculation in the article at the DirLayNet algorithm section. @@ -612,7 +610,7 @@ def calculate_vaccine_matrix(layers:list, min_cut_nodes_grouped:dict)->np.matrix Returns: ------- - np.matrix + matrix : np.matrix Vaccine matrix. Examples: @@ -717,7 +715,7 @@ def min_budget_calculation(matrix: np.matrix) -> int: Returns: ------- - int + min_budget : int Minimum budget. Examples: @@ -746,7 +744,7 @@ def dirlay_vaccination_startegy(vacc_matrix: np.matrix, ni_groups: dict) -> dict Returns: ---------- - dict + strategy : dict A dictionary where keys are time steps and values are lists of nodes to vaccinate at each time step. """ logger.info("Calculating the stategy") @@ -772,24 +770,40 @@ def dirlay_vaccination_startegy(vacc_matrix: np.matrix, ni_groups: dict) -> dict strategy[i] = nodes_to_vaccinate return strategy - - - # =========================== End Non-Spreading Max-Save ============================ # =========================== Heuristic Utilities =================================== -def find_best_neighbor(graph:nx.DiGraph, infected_nodes:list, remaining_targets:list, overall_targets:list)->int: +def find_best_neighbor(graph: nx.DiGraph, infected_nodes: list, remaining_targets: list, overall_targets: list) -> tuple[int, set]: """ - Find the best node from the infected nodes successors that saves more new node in targets. + Find the best node from the infected nodes' successors that saves the most new nodes in targets. - Parameters: - - graph (nx.DiGraph): Directed graph. - - infected_nodes (list): list of all infected nodes that threaten to infect additional nodes. - - targets (list): List of target nodes. + Parameters + ---------- + graph : nx.DiGraph + Directed graph. + infected_nodes : list + List of all infected nodes that threaten to infect additional nodes. + remaining_targets : list + List of remaining target nodes to be saved. + overall_targets : list + List of all target nodes. - Returns: - - best_node (int): Best node option. + Returns + ------- + best_node : int + Best node option to vaccinate. + nodes_saved : set + Set of nodes that would be saved by vaccinating the best node. + + Examples + -------- + Example 1: + >>> G = nx.DiGraph() + >>> G.add_nodes_from([0, 1, 2, 3, 4], status='vulnerable') + >>> G.add_edges_from([(0, 1), (0, 2), (1, 3), (1, 4)]) + >>> find_best_neighbor(G, [0], [1, 2, 3, 4], [1, 2, 3, 4]) + (1, {1, 3, 4}) """ optional_nodes = set() priority_queue = [] @@ -829,12 +843,14 @@ def find_best_neighbor(graph:nx.DiGraph, infected_nodes:list, remaining_targets: # =========================== End Heuristic Utilities ================================ # =========================== General Utilities ====================================== -def display_graph(graph:nx.DiGraph)->None: +def display_graph(graph:nx.DiGraph) -> None: """ Display the graph using Matplotlib. - Parameters: - - graph (nx.DiGraph): Directed graph. + Parameters + ---------- + graph : nx.DiGraph + Directed graph. """ pos = nx.shell_layout(graph) colors = [node_colors.get(data.get('status', 'default'), 'default') for node, data in graph.nodes(data=True)] @@ -850,11 +866,20 @@ def parse_json_to_networkx(json_data): """ Parse JSON data to create Networkx graphs. - Parameters: - - json_data (dict): JSON data containing graph information. + Parameters + ---------- + json_data : dict + JSON data containing graph information. - Returns: - - graphs (dict): Dictionary of Networkx graphs. + Returns + ------- + graphs : dict + Dictionary of Networkx graphs. + + Raises + ------ + KeyError + If 'vertices' or 'edges' is not found or is not a non-empty list. """ graphs = {} for graph_type, graph_data in json_data.items(): diff --git a/networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/comparisons.py b/networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/comparisons.py index 5fd9406..862e2d9 100644 --- a/networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/comparisons.py +++ b/networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/comparisons.py @@ -174,7 +174,7 @@ def Compare_SpreadingMaxSave(): node_counts = [100, 200, 400] edge_probabilities = [0.1, 0.5, 0.8] - def multiple_runs(runs=20): + def multiple_runs(runs=1): for num_nodes in node_counts: for edge_prob in edge_probabilities: graph = generate_random_DiGraph(num_nodes=num_nodes, edge_probability=edge_prob, seed=None) @@ -287,6 +287,6 @@ def extract_budget_numeric(budget): if __name__ == "__main__": setup_global_logger(level=logging.DEBUG) - # Compare_NonSpread() - Compare_SpreadingMinBudget() + Compare_NonSpread() + #Compare_SpreadingMinBudget() #Compare_SpreadingMaxSave() \ No newline at end of file diff --git a/networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/non_spreading.csv b/networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/non_spreading.csv index bfea393..a65363b 100644 --- a/networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/non_spreading.csv +++ b/networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/non_spreading.csv @@ -1,302 +1,302 @@ algorithm,runtime,graph_nodes,Budget -non_spreading_dirlaynet_minbudget,0.2408395999809727,110,"(2, {0: [24, 80]})" -non_spreading_minbudget,0.011683300021104515,110,"(2, [])" -heuristic_minbudget,0.89675100002205,110,"(1, [(24, 1), (33, 2), (46, 3), (63, 4), (66, 5)])" -non_spreading_dirlaynet_minbudget,0.3707220000214875,168,"(14, {0: [11, 6, 8, 4, 1, 5, 14, 13, 3, 12, 9, 7, 2, 17]})" -non_spreading_minbudget,0.020104600000195205,168,"(14, [])" -heuristic_minbudget,1.0511027000029571,168,"(4, [(4, 1), (1, 1), (14, 1), (2, 1), (19, 2), (22, 2), (20, 2), (26, 2), (45, 3), (44, 3), (38, 3), (47, 3), (79, 4), (78, 4), (72, 4), (63, 4), (80, 5), (87, 5), (81, 5), (83, 5), (93, 6), (100, 6), (97, 6), (95, 6), (110, 7), (111, 7), (105, 7), (107, 7), (122, 8), (128, 8), (135, 8), (145, 8), (153, 9), (148, 9), (152, 9), (151, 9)])" -non_spreading_dirlaynet_minbudget,0.2880064999917522,124,"(4, {0: [10, 31, 34, 27], 1: [31, 34, 27], 2: [31, 34, 27]})" -non_spreading_minbudget,0.013811299984809011,124,"(9, [])" -heuristic_minbudget,0.6897182000102475,124,"(5, [(10, 1), (18, 1), (5, 1), (17, 1), (7, 1), (19, 2), (25, 2), (24, 2), (21, 2), (23, 2), (28, 3), (34, 3), (27, 3), (33, 3), (30, 3), (35, 4), (43, 4)])" -non_spreading_dirlaynet_minbudget,0.26234429999021813,130,"(13, {0: [20, 24, 8, 4, 15, 40, 35, 54, 66, 82, 83, 97, 116], 1: [40, 35, 66, 82, 83], 2: [66, 82, 83], 3: [66, 82, 83], 4: [82, 83]})" -non_spreading_minbudget,0.016597500012721866,130,"(21, [])" -heuristic_minbudget,1.3310243000159971,130,"(5, [(8, 1), (15, 1), (24, 1), (20, 1), (4, 1), (40, 2), (35, 2), (43, 2), (39, 2), (46, 2), (54, 3), (53, 3), (57, 3), (49, 3), (48, 3), (66, 4), (65, 4), (76, 4), (63, 4), (60, 4), (80, 5), (83, 5), (81, 5), (82, 5), (85, 5), (90, 6), (91, 6), (86, 6), (97, 6), (88, 6), (116, 7)])" -non_spreading_dirlaynet_minbudget,0.1924457999994047,96,"(3, {0: [33, 49, 59], 1: [33, 59], 2: [59, 84], 3: [59]})" -non_spreading_minbudget,0.012846800033003092,96,"(8, [])" -heuristic_minbudget,0.94798830000218,96,"(4, [(11, 1), (5, 1), (2, 1), (10, 1), (23, 2), (33, 2), (24, 2), (25, 2), (49, 3), (46, 3), (43, 3), (48, 3), (59, 4), (57, 4), (66, 4), (67, 4)])" -non_spreading_dirlaynet_minbudget,0.4186942999949679,196,"(16, {0: [10, 1, 14, 13, 3, 12, 9, 7, 2, 11, 6, 8, 4, 5, 15, 18]})" -non_spreading_minbudget,0.02659190003760159,196,"(16, [])" -heuristic_minbudget,1.4307623999775387,196,"(6, [(13, 1), (1, 1), (2, 1), (4, 1), (9, 1), (5, 1), (19, 2), (29, 2), (23, 2), (20, 2), (22, 2), (28, 2), (51, 3), (52, 3), (61, 3), (46, 3), (57, 3), (64, 3), (68, 4), (74, 4), (69, 4), (77, 4), (73, 4), (75, 4), (114, 5), (112, 5), (90, 5), (98, 5), (99, 5), (102, 5), (119, 6), (118, 6), (117, 6), (116, 6), (115, 6)])" -non_spreading_dirlaynet_minbudget,0.47021560004213825,153,"(14, {0: [10, 26, 16, 1, 24, 8, 22, 44, 37, 49, 51, 56, 60, 55], 1: [44, 37, 49, 51, 56, 60, 55], 2: [49, 51, 56, 60, 55, 79]})" -non_spreading_minbudget,0.03129000001354143,153,"(25, [])" -heuristic_minbudget,1.1095701000303961,153,"(7, [(16, 1), (26, 1), (1, 1), (24, 1), (10, 1), (8, 1), (22, 1), (44, 2), (41, 2), (42, 2), (37, 2), (29, 2), (47, 2), (35, 2), (48, 3), (59, 3), (50, 3), (56, 3), (57, 3), (54, 3), (51, 3), (68, 4), (78, 4), (79, 4), (66, 4), (83, 4), (70, 4), (63, 4), (100, 5), (98, 5), (92, 5), (91, 5), (95, 5), (93, 5), (88, 5), (121, 6), (114, 6), (115, 6), (120, 6), (102, 6), (111, 6), (104, 6), (138, 7), (126, 7), (128, 7), (132, 7), (133, 7), (130, 7), (129, 7)])" -non_spreading_dirlaynet_minbudget,0.27224899997236207,131,"(3, {0: [5, 68, 88], 1: [33], 5: [88]})" -non_spreading_minbudget,0.01399949996266514,131,"(6, [])" -heuristic_minbudget,0.9055172000080347,131,"(1, [(5, 1), (33, 2), (42, 3), (68, 4), (80, 5)])" -non_spreading_dirlaynet_minbudget,0.4071177000296302,172,"(7, {0: [6, 4, 1, 3, 7, 2, 11]})" -non_spreading_minbudget,0.017289999988861382,172,"(7, [])" -heuristic_minbudget,0.7760101999738254,172,"(4, [(6, 1), (4, 1), (3, 1), (7, 1), (11, 2), (20, 2), (10, 2), (26, 2), (31, 3), (37, 3), (38, 3), (36, 3), (46, 4), (61, 4), (50, 4), (55, 4), (70, 5), (73, 5), (88, 5), (92, 5), (100, 6), (99, 6), (96, 6), (98, 6), (122, 7), (121, 7), (105, 7), (109, 7), (137, 8), (145, 8), (134, 8), (143, 8), (154, 9), (147, 9), (158, 9), (152, 9)])" -non_spreading_dirlaynet_minbudget,0.35584720002952963,171,"(8, {0: [10, 1, 5, 9, 7, 34, 36, 38], 1: [36, 38], 2: [36, 38]})" -non_spreading_minbudget,0.02076470002066344,171,"(12, [])" -heuristic_minbudget,0.7088578999973834,171,"(5, [(7, 1), (5, 1), (1, 1), (10, 1), (9, 1), (34, 2), (35, 2), (33, 2), (25, 2), (28, 2), (39, 3), (40, 3), (37, 3), (36, 3), (38, 3), (50, 4), (44, 4), (58, 4)])" -non_spreading_dirlaynet_minbudget,0.18420820002211258,98,"(1, {0: [14]})" -non_spreading_minbudget,0.009187699994072318,98,"(1, [])" -heuristic_minbudget,0.2952911999891512,98,"(1, [(14, 1)])" -non_spreading_dirlaynet_minbudget,0.18176989996572956,115,"(6, {0: [17, 21, 24, 31, 30, 33], 1: [31, 30, 33]})" -non_spreading_minbudget,0.011746600037440658,115,"(8, [])" -heuristic_minbudget,0.8000411000102758,115,"(4, [(21, 1), (24, 1), (17, 1), (3, 1), (30, 2), (33, 2), (32, 2), (29, 2), (40, 3), (45, 3), (39, 3), (51, 3), (56, 4), (55, 4), (57, 4), (53, 4)])" -non_spreading_dirlaynet_minbudget,0.3666547000175342,174,"(1, {3: [165], 4: [165], 5: [165], 6: [132]})" -non_spreading_minbudget,0.10705310001503676,174,"(5, [])" -heuristic_minbudget,1.0959735999931581,174,"(1, [(3, 1), (17, 2), (60, 3), (66, 4), (95, 5), (104, 6), (132, 7), (137, 8)])" -non_spreading_dirlaynet_minbudget,0.24063870002282783,109,"(7, {0: [14, 34, 26, 32, 50, 72, 93], 1: [34, 26, 32, 50, 72, 93], 2: [50, 72], 3: [72]})" -non_spreading_minbudget,0.01601160003338009,109,"(16, [])" -heuristic_minbudget,1.2227787000010721,109,"(6, [(14, 1), (4, 1), (8, 1), (11, 1), (22, 1), (23, 1), (32, 2), (34, 2), (38, 2), (42, 2), (26, 2), (28, 2), (50, 3), (52, 3), (63, 3), (48, 3), (64, 3), (55, 3), (85, 4), (72, 4), (81, 4), (67, 4), (77, 4), (82, 4)])" -non_spreading_dirlaynet_minbudget,0.3842507000081241,163,"(9, {0: [6, 1, 4, 8, 5, 3, 9, 7, 2]})" -non_spreading_minbudget,0.01843689999077469,163,"(9, [])" -heuristic_minbudget,0.5663389000110328,163,"(5, [(6, 1), (1, 1), (5, 1), (7, 1), (3, 1), (27, 2), (20, 2), (10, 2), (22, 2), (23, 2), (43, 3), (41, 3), (40, 3), (37, 3), (28, 3), (54, 4), (49, 4), (48, 4), (50, 4), (51, 4), (62, 5), (75, 5), (55, 5), (68, 5), (61, 5), (84, 6), (85, 6), (96, 6), (91, 6), (78, 6), (115, 7), (109, 7), (103, 7), (111, 7), (105, 7)])" -non_spreading_dirlaynet_minbudget,0.14911689999280497,78,"(4, {0: [3, 11, 16, 14], 1: [11, 16, 14]})" -non_spreading_minbudget,0.009751199977472425,78,"(7, [])" -heuristic_minbudget,0.31167299998924136,78,"(5, [(3, 1), (2, 1), (5, 1), (1, 1), (7, 1), (16, 2), (14, 2), (11, 2), (13, 2), (15, 2), (31, 3), (18, 3), (21, 3), (23, 3)])" -non_spreading_dirlaynet_minbudget,0.11485980002908036,73,"(5, {0: [4, 1, 5, 3, 2]})" -non_spreading_minbudget,0.006932299991603941,73,"(5, [])" -heuristic_minbudget,0.1847023000009358,73,"(3, [(2, 1), (4, 1), (5, 1), (21, 2), (20, 2), (8, 2), (28, 3), (34, 3), (27, 3), (36, 4), (41, 4), (37, 4), (44, 5), (46, 5), (45, 5)])" -non_spreading_dirlaynet_minbudget,0.3518521999940276,154,"(11, {0: [10, 14, 9, 17, 4, 26, 27, 20, 19, 29, 21], 1: [26, 27, 20, 19, 29, 21, 34]})" -non_spreading_minbudget,0.0243503000237979,154,"(17, [])" -heuristic_minbudget,1.109651800012216,154,"(8, [(14, 1), (4, 1), (9, 1), (10, 1), (17, 1), (8, 1), (2, 1), (1, 1), (27, 2), (21, 2), (23, 2), (18, 2), (25, 2), (26, 2), (19, 2), (29, 2), (37, 3), (33, 3), (34, 3), (41, 3), (30, 3), (35, 3), (31, 3), (38, 3), (50, 4), (59, 4), (44, 4), (54, 4)])" -non_spreading_dirlaynet_minbudget,0.18335249996744096,95,"(4, {0: [7, 22, 38, 59], 1: [38, 59], 2: [38, 59], 3: [59]})" -non_spreading_minbudget,0.011825499997939914,95,"(9, [])" -heuristic_minbudget,0.5668235000339337,95,"(3, [(7, 1), (4, 1), (11, 1), (22, 2), (24, 2), (28, 2), (38, 3), (37, 3), (44, 3)])" -non_spreading_dirlaynet_minbudget,0.33219030004693195,157,"(1, {1: [43]})" -non_spreading_minbudget,0.013426899968180805,157,"(1, [])" -heuristic_minbudget,0.5273297000094317,157,"(1, [(2, 1)])" -non_spreading_dirlaynet_minbudget,0.5024990999954753,161,"(3, {0: [16, 142, 151], 1: [49], 5: [151], 6: [151]})" -non_spreading_minbudget,0.026394299988169223,161,"(7, [])" -heuristic_minbudget,1.560309899970889,161,"(2, [(16, 1), (1, 1), (49, 2), (24, 2), (66, 3), (55, 3), (74, 4), (86, 4), (111, 5), (118, 5), (127, 6), (122, 6), (133, 7), (128, 7), (137, 8), (142, 8)])" -non_spreading_dirlaynet_minbudget,0.25708180002402514,120,"(18, {0: [10, 16, 1, 14, 13, 3, 12, 9, 7, 17, 2, 11, 6, 8, 4, 5, 15, 18]})" -non_spreading_minbudget,0.020068900019396096,120,"(18, [])" -heuristic_minbudget,1.4997184000094421,120,"(8, [(11, 1), (12, 1), (3, 1), (8, 1), (18, 1), (1, 1), (7, 1), (4, 1), (37, 2), (30, 2), (19, 2), (34, 2), (32, 2), (20, 2), (29, 2), (33, 2), (47, 3), (41, 3), (58, 3), (56, 3), (38, 3), (49, 3), (55, 3), (42, 3), (70, 4), (65, 4), (61, 4), (71, 4), (82, 4), (81, 4), (60, 4), (62, 4), (92, 5), (96, 5), (98, 5), (100, 5), (99, 5), (83, 5), (88, 5), (85, 5)])" -non_spreading_dirlaynet_minbudget,0.2254145999904722,110,"(6, {0: [6, 4, 1, 5, 3, 2]})" -non_spreading_minbudget,0.012614699953701347,110,"(6, [])" -heuristic_minbudget,0.32980499998666346,110,"(5, [(2, 1), (1, 1), (3, 1), (4, 1), (6, 1), (7, 2), (20, 2), (21, 2), (19, 2)])" -non_spreading_dirlaynet_minbudget,0.19980970001779497,96,"(1, {2: [68]})" -non_spreading_minbudget,0.010430899972561747,96,"(2, [])" -heuristic_minbudget,0.8549194000079297,96,"(1, [(14, 1), (43, 2), (59, 3)])" -non_spreading_dirlaynet_minbudget,0.2990049999789335,154,"(20, {0: [10, 20, 16, 1, 14, 19, 13, 12, 3, 9, 7, 17, 2, 11, 6, 8, 4, 5, 15, 18]})" -non_spreading_minbudget,0.019480600021779537,154,"(20, [])" -heuristic_minbudget,2.2484657000168227,154,"(6, [(16, 1), (9, 1), (11, 1), (19, 1), (20, 1), (17, 1), (44, 2), (25, 2), (39, 2), (22, 2), (23, 2), (38, 2), (53, 3), (54, 3), (63, 3), (55, 3), (60, 3), (45, 3), (72, 4), (79, 4), (81, 4), (73, 4), (77, 4), (71, 4), (97, 5), (90, 5), (98, 5), (91, 5), (96, 5), (99, 5), (108, 6), (115, 6), (109, 6), (101, 6), (100, 6), (103, 6), (122, 7), (121, 7), (119, 7), (123, 7), (120, 7)])" -non_spreading_dirlaynet_minbudget,0.26417029998265207,115,"(12, {0: [1, 14, 17, 11, 21, 36, 33, 44, 49, 51, 56, 64], 1: [36, 33, 44, 49, 51], 2: [49, 51], 3: [49, 51]})" -non_spreading_minbudget,0.013657599978614599,115,"(18, [])" -heuristic_minbudget,0.8261364999925718,115,"(5, [(17, 1), (14, 1), (21, 1), (1, 1), (11, 1), (36, 2), (33, 2), (23, 2), (32, 2), (26, 2), (37, 3), (44, 3), (39, 3), (40, 3), (41, 3), (55, 4), (52, 4), (49, 4), (51, 4), (50, 4), (57, 5), (62, 5), (63, 5), (56, 5), (59, 5), (64, 6)])" -non_spreading_dirlaynet_minbudget,0.1405815000180155,76,"(1, {1: [22]})" -non_spreading_minbudget,0.0064150000107474625,76,"(2, [])" -heuristic_minbudget,0.16845870000543073,76,"(1, [(3, 1), (19, 2)])" -non_spreading_dirlaynet_minbudget,0.22825440001906827,117,"(10, {0: [1, 19, 8, 18, 34, 47, 44, 66, 75, 74], 1: [34, 47, 44, 66, 75, 74], 2: [66, 75, 74], 3: [75, 74]})" -non_spreading_minbudget,0.017238100001122802,117,"(21, [])" -heuristic_minbudget,1.6565592999686487,117,"(6, [(19, 1), (18, 1), (1, 1), (8, 1), (10, 1), (29, 1), (31, 2), (39, 2), (44, 2), (47, 2), (50, 2), (34, 2), (65, 3), (66, 3), (61, 3), (59, 3), (62, 3), (64, 3), (75, 4), (76, 4), (77, 4), (78, 4), (73, 4), (72, 4), (80, 5), (90, 5), (81, 5), (86, 5), (87, 5), (85, 5)])" -non_spreading_dirlaynet_minbudget,0.3899076000088826,185,"(2, {0: [47, 45], 1: [47, 45], 2: [47, 45], 3: [47, 45]})" -non_spreading_minbudget,0.022539300029166043,185,"(5, [])" -heuristic_minbudget,0.26039259997196496,185,"(2, [(5, 1), (1, 1), (11, 2), (13, 2), (36, 3), (20, 3), (44, 4), (48, 4), (62, 5), (55, 5), (69, 6), (74, 6), (92, 7), (95, 7), (112, 8), (131, 8)])" -non_spreading_dirlaynet_minbudget,0.3602491000201553,154,"(8, {0: [13, 9, 18, 35, 64, 56, 124, 127], 1: [64, 56, 147], 2: [64, 127]})" -non_spreading_minbudget,0.05018740001833066,154,"(12, [])" -heuristic_minbudget,1.774602799967397,154,"(4, [(9, 1), (18, 1), (13, 1), (3, 1), (35, 2), (46, 2), (43, 2), (28, 2), (57, 3), (56, 3), (64, 3), (68, 3), (72, 4), (75, 4), (84, 4), (85, 4), (108, 5), (102, 5), (100, 5), (97, 5), (124, 6), (119, 6), (123, 6), (115, 6), (126, 7), (127, 7), (138, 7), (140, 7)])" -non_spreading_dirlaynet_minbudget,0.2686612000106834,116,"(7, {0: [17, 27, 35, 59, 51, 68, 101], 1: [27, 35, 59, 51, 68, 101], 2: [59, 68, 101]})" -non_spreading_minbudget,0.017082200036384165,116,"(14, [])" -heuristic_minbudget,0.9590422000037506,116,"(4, [(17, 1), (2, 1), (10, 1), (12, 1), (27, 2), (23, 2), (35, 2), (40, 2), (59, 3), (47, 3), (51, 3), (41, 3), (82, 4), (73, 4), (68, 4), (63, 4)])" -non_spreading_dirlaynet_minbudget,0.22271449997788295,108,"(6, {0: [1, 12, 26, 27, 28, 25], 1: [26, 27, 28, 25]})" -non_spreading_minbudget,0.012390200048685074,108,"(9, [])" -heuristic_minbudget,0.7816633000038564,108,"(4, [(1, 1), (12, 1), (8, 1), (16, 1), (24, 2), (23, 2), (26, 2), (28, 2), (48, 3), (45, 3), (31, 3), (37, 3), (56, 4), (51, 4), (58, 4), (59, 4), (73, 5), (65, 5), (80, 5), (61, 5)])" -non_spreading_dirlaynet_minbudget,0.34672169998520985,170,"(1, {5: [123]})" -non_spreading_minbudget,0.09719270002096891,170,"(2, [])" -heuristic_minbudget,0.7220063999993727,170,"(1, [(9, 1), (14, 2), (38, 3), (64, 4), (102, 5), (105, 6)])" -non_spreading_dirlaynet_minbudget,0.2813237000373192,153,"(7, {0: [4, 15, 9, 33, 47, 45, 43], 1: [33, 47, 45, 43], 2: [47, 45, 43]})" -non_spreading_minbudget,0.018627899989951402,153,"(12, [])" -heuristic_minbudget,1.2086210999987088,153,"(5, [(9, 1), (4, 1), (15, 1), (12, 1), (3, 1), (32, 2), (33, 2), (40, 2), (29, 2), (26, 2), (45, 3), (48, 3), (46, 3), (44, 3), (47, 3), (64, 4), (65, 4), (67, 4), (68, 4), (55, 4), (78, 5), (81, 5), (80, 5), (77, 5), (76, 5), (93, 6)])" -non_spreading_dirlaynet_minbudget,0.21959669998614118,114,"(2, {0: [26, 94], 1: [78], 4: [79]})" -non_spreading_minbudget,0.011773299949709326,114,"(4, [])" -heuristic_minbudget,0.9793972999905236,114,"(1, [(9, 1), (26, 2), (37, 3), (78, 4), (79, 5), (94, 6)])" -non_spreading_dirlaynet_minbudget,0.19709109998075292,96,"(1, {4: [67]})" -non_spreading_minbudget,0.01014900003792718,96,"(2, [])" -heuristic_minbudget,0.2756342000211589,96,"(1, [(2, 1), (12, 2), (23, 3), (36, 4), (56, 5)])" -non_spreading_dirlaynet_minbudget,0.4796706999768503,186,"(29, {0: [10, 27, 26, 20, 16, 1, 14, 19, 13, 12, 3, 9, 7, 17, 29, 2, 24, 11, 6, 21, 8, 4, 28, 22, 5, 15, 25, 18, 23]})" -non_spreading_minbudget,0.030213600024580956,186,"(29, [])" -heuristic_minbudget,3.2820799999753945,186,"(9, [(9, 1), (26, 1), (25, 1), (12, 1), (16, 1), (1, 1), (4, 1), (11, 1), (18, 1), (43, 2), (48, 2), (58, 2), (51, 2), (40, 2), (31, 2), (52, 2), (36, 2), (55, 2), (67, 3), (69, 3), (63, 3), (74, 3), (72, 3), (71, 3), (65, 3), (75, 3), (62, 3), (96, 4), (82, 4), (94, 4), (99, 4), (101, 4), (92, 4), (91, 4), (98, 4), (85, 4), (128, 5), (122, 5), (116, 5), (121, 5), (125, 5), (118, 5), (113, 5), (111, 5), (112, 5), (135, 6), (134, 6), (130, 6), (139, 6), (133, 6), (129, 6), (138, 6), (131, 6), (132, 6), (146, 7), (156, 7), (154, 7), (148, 7), (161, 7), (162, 7), (144, 7), (140, 7), (147, 7)])" -non_spreading_dirlaynet_minbudget,0.3081334999878891,146,"(4, {0: [3, 27, 71, 76], 1: [51, 71], 2: [71], 3: [71]})" -non_spreading_minbudget,0.08925570000428706,146,"(8, [])" -heuristic_minbudget,0.7898826000164263,146,"(3, [(3, 1), (9, 1), (5, 1), (27, 2), (15, 2), (23, 2), (51, 3), (42, 3), (47, 3), (69, 4), (73, 4), (71, 4), (86, 5), (95, 5), (80, 5), (101, 6), (103, 6), (107, 6)])" -non_spreading_dirlaynet_minbudget,0.3067179000354372,152,"(2, {0: [12, 86]})" -non_spreading_minbudget,0.017449299979489297,152,"(2, [])" -heuristic_minbudget,0.864723699982278,152,"(1, [(12, 1), (32, 2), (48, 3), (71, 4)])" -non_spreading_dirlaynet_minbudget,0.1449985000072047,82,"(11, {0: [10, 20, 8, 13, 9, 18, 27, 33, 56, 72, 79], 1: [27, 33, 72]})" -non_spreading_minbudget,0.009442600014153868,82,"(13, [])" -heuristic_minbudget,0.7151867999928072,82,"(6, [(9, 1), (13, 1), (20, 1), (10, 1), (18, 1), (8, 1), (33, 2), (32, 2), (27, 2), (28, 2), (31, 2), (39, 2), (47, 3), (54, 3), (50, 3), (48, 3), (52, 3), (51, 3), (55, 4), (58, 4), (60, 4), (62, 4), (64, 4)])" -non_spreading_dirlaynet_minbudget,0.33541169995442033,123,"(9, {0: [12, 28, 48, 37, 44, 60, 69, 96, 122], 1: [48, 37, 44, 60, 96, 122], 2: [60, 96], 3: [96]})" -non_spreading_minbudget,0.02186249999795109,123,"(18, [])" -heuristic_minbudget,1.7302376999869011,123,"(6, [(12, 1), (28, 1), (16, 1), (15, 1), (18, 1), (25, 1), (48, 2), (37, 2), (42, 2), (52, 2), (44, 2), (41, 2), (67, 3), (60, 3), (63, 3), (62, 3), (61, 3), (65, 3), (73, 4), (74, 4), (70, 4), (79, 4), (75, 4), (83, 4), (101, 5), (87, 5), (96, 5), (104, 5), (88, 5), (99, 5)])" -non_spreading_dirlaynet_minbudget,0.3653747999924235,152,"(8, {0: [6, 1, 7, 42, 67, 92, 106, 135], 1: [42, 135], 2: [42, 67, 135], 6: [135]})" -non_spreading_minbudget,0.02167569997254759,152,"(14, [])" -heuristic_minbudget,1.1438108999864198,152,"(3, [(7, 1), (1, 1), (6, 1), (21, 2), (31, 2), (28, 2), (37, 3), (42, 3), (46, 3), (67, 4), (82, 4), (59, 4), (92, 5), (94, 5), (101, 5), (106, 6), (118, 6), (110, 6)])" -non_spreading_dirlaynet_minbudget,0.21171110001159832,90,"(14, {0: [20, 16, 19, 7, 17, 11, 5, 22, 34, 29, 37, 58, 75, 82], 1: [34, 29, 37, 75, 82]})" -non_spreading_minbudget,0.015889200032688677,90,"(22, [])" -heuristic_minbudget,1.5673450999893248,90,"(8, [(5, 1), (11, 1), (20, 1), (16, 1), (19, 1), (17, 1), (7, 1), (22, 1), (29, 2), (31, 2), (33, 2), (37, 2), (50, 2), (34, 2), (41, 2), (55, 2), (58, 3), (65, 3), (63, 3), (67, 3), (64, 3), (57, 3), (61, 3), (66, 3), (68, 4), (78, 4), (79, 4)])" -non_spreading_dirlaynet_minbudget,0.15826220001326874,83,"(7, {0: [16, 13, 17, 36, 43, 64, 70], 1: [64, 70], 2: [70], 3: [64]})" -non_spreading_minbudget,0.01112850004574284,83,"(11, [])" -heuristic_minbudget,0.6521259999717586,83,"(3, [(16, 1), (13, 1), (17, 1), (36, 2), (34, 2), (38, 2), (43, 3), (40, 3), (39, 3), (64, 4), (52, 4), (61, 4), (72, 5), (70, 5), (79, 5)])" -non_spreading_dirlaynet_minbudget,0.17466540000168607,96,"(1, {3: [52]})" -non_spreading_minbudget,0.009549300011713058,96,"(1, [])" -heuristic_minbudget,0.4298332000034861,96,"(1, [(1, 1), (16, 2), (33, 3)])" -non_spreading_dirlaynet_minbudget,0.48592579999240115,165,"(4, {0: [4, 15, 78, 149], 6: [149]})" -non_spreading_minbudget,0.030833000026177615,165,"(6, [])" -heuristic_minbudget,1.5386863999883644,165,"(2, [(15, 1), (4, 1), (39, 2), (48, 2), (53, 3), (58, 3), (78, 4), (83, 4), (99, 5), (97, 5), (110, 6), (113, 6), (139, 7), (130, 7)])" -non_spreading_dirlaynet_minbudget,0.28890800004592165,161,"(8, {0: [1, 3, 12, 2, 34, 29, 49, 51], 1: [34, 29, 49, 51], 2: [49, 51]})" -non_spreading_minbudget,0.01882519997889176,161,"(14, [])" -heuristic_minbudget,0.9498099000193179,161,"(4, [(1, 1), (12, 1), (2, 1), (3, 1), (29, 2), (25, 2), (34, 2), (46, 2), (48, 3), (53, 3), (52, 3), (49, 3), (63, 4), (78, 4), (54, 4), (60, 4), (82, 5), (79, 5), (84, 5), (81, 5), (95, 6), (96, 6), (89, 6), (93, 6)])" -non_spreading_dirlaynet_minbudget,0.4687256999895908,169,"(23, {0: [10, 20, 16, 1, 14, 19, 13, 12, 3, 9, 7, 17, 2, 11, 6, 21, 8, 4, 5, 15, 22, 18, 23]})" -non_spreading_minbudget,0.025489300023764372,169,"(23, [])" -heuristic_minbudget,1.6776179999578744,169,"(9, [(6, 1), (2, 1), (1, 1), (17, 1), (10, 1), (8, 1), (4, 1), (7, 1), (16, 1), (34, 2), (37, 2), (35, 2), (36, 2), (42, 2), (27, 2), (32, 2), (40, 2), (39, 2), (68, 3), (53, 3), (72, 3), (52, 3), (57, 3), (59, 3), (46, 3), (49, 3), (56, 3), (99, 4), (84, 4), (85, 4), (88, 4), (81, 4), (80, 4), (95, 4), (86, 4), (77, 4), (117, 5), (107, 5), (122, 5), (106, 5), (126, 5), (120, 5), (100, 5), (113, 5), (121, 5), (139, 6), (132, 6), (138, 6), (134, 6), (146, 6), (135, 6), (142, 6), (129, 6), (133, 6)])" -non_spreading_dirlaynet_minbudget,0.13381580001441762,83,"(5, {0: [20, 8, 19, 3, 51]})" -non_spreading_minbudget,0.008869700017385185,83,"(5, [])" -heuristic_minbudget,0.7501130999880843,83,"(4, [(19, 1), (3, 1), (8, 1), (20, 1), (39, 2), (31, 2), (33, 2), (37, 2), (40, 3), (43, 3), (47, 3), (42, 3)])" -non_spreading_dirlaynet_minbudget,0.12061859999084845,78,"(3, {0: [3, 43, 56], 1: [15], 4: [56], 6: [56]})" -non_spreading_minbudget,0.007529399998020381,78,"(7, [])" -heuristic_minbudget,0.17428890004521236,78,"(1, [(3, 1), (15, 2), (20, 3), (25, 4), (43, 5), (50, 6)])" -non_spreading_dirlaynet_minbudget,0.40917800000170246,170,"(15, {0: [26, 14, 12, 24, 8, 22, 34, 40, 55, 45, 70, 64, 60, 66, 68], 1: [34, 40, 55, 45, 70, 64, 60, 66, 68, 76], 2: [70, 64, 60, 66, 68, 76]})" -non_spreading_minbudget,0.02833739999914542,170,"(30, [])" -heuristic_minbudget,2.1604326000087894,170,"(8, [(22, 1), (24, 1), (8, 1), (12, 1), (14, 1), (26, 1), (1, 1), (15, 1), (44, 2), (40, 2), (45, 2), (46, 2), (34, 2), (42, 2), (55, 2), (43, 2), (65, 3), (71, 3), (63, 3), (67, 3), (59, 3), (66, 3), (70, 3), (58, 3), (73, 4), (74, 4), (77, 4), (87, 4), (82, 4), (86, 4), (79, 4), (81, 4), (97, 5), (99, 5), (100, 5), (89, 5), (101, 5), (88, 5), (92, 5), (91, 5), (115, 6), (105, 6), (119, 6), (128, 6), (109, 6), (110, 6), (123, 6), (129, 6), (136, 7), (132, 7), (134, 7), (137, 7), (133, 7), (135, 7), (138, 7), (140, 7)])" -non_spreading_dirlaynet_minbudget,0.29811470001004636,130,"(7, {0: [12, 7, 20, 16, 14, 15, 19], 1: [20, 16, 14, 15, 19, 30]})" -non_spreading_minbudget,0.01441739994334057,130,"(13, [])" -heuristic_minbudget,0.8897034000256099,130,"(3, [(12, 1), (7, 1), (8, 1), (18, 2), (16, 2), (22, 2), (29, 3), (42, 3), (48, 3), (55, 4), (63, 4), (56, 4), (67, 5), (69, 5), (71, 5), (90, 6), (78, 6), (79, 6), (103, 7), (101, 7), (104, 7)])" -non_spreading_dirlaynet_minbudget,0.6222369999741204,206,"(22, {0: [10, 20, 16, 1, 14, 19, 13, 12, 3, 9, 7, 17, 2, 11, 6, 8, 4, 5, 15, 22, 18, 21]})" -non_spreading_minbudget,0.037172599986661226,206,"(22, [])" -heuristic_minbudget,3.3470051999902353,206,"(7, [(6, 1), (13, 1), (7, 1), (9, 1), (20, 1), (18, 1), (5, 1), (23, 2), (29, 2), (40, 2), (41, 2), (44, 2), (25, 2), (30, 2), (63, 3), (74, 3), (71, 3), (72, 3), (64, 3), (62, 3), (60, 3), (89, 4), (93, 4), (85, 4), (82, 4), (84, 4), (88, 4), (92, 4), (111, 5), (109, 5), (115, 5), (101, 5), (107, 5), (114, 5), (117, 5), (127, 6), (141, 6), (132, 6), (126, 6), (133, 6), (128, 6), (143, 6), (147, 7), (157, 7), (150, 7), (151, 7), (153, 7), (156, 7), (154, 7), (171, 8), (173, 8), (175, 8), (170, 8), (174, 8), (172, 8)])" -non_spreading_dirlaynet_minbudget,0.22571879997849464,123,"(9, {0: [8, 1, 19, 13, 7, 43, 49, 70, 98], 1: [43, 70]})" -non_spreading_minbudget,0.013894300034735352,123,"(11, [])" -heuristic_minbudget,1.1737340000108816,123,"(5, [(13, 1), (1, 1), (19, 1), (7, 1), (8, 1), (30, 2), (43, 2), (29, 2), (36, 2), (42, 2), (49, 3), (50, 3), (51, 3), (54, 3), (56, 3), (59, 4), (61, 4), (64, 4), (62, 4), (63, 4), (71, 5), (87, 5)])" -non_spreading_dirlaynet_minbudget,0.12631929997587577,81,"(3, {0: [8, 13, 74], 2: [74], 3: [74]})" -non_spreading_minbudget,0.012286700017284602,81,"(5, [])" -heuristic_minbudget,0.36397669999860227,81,"(2, [(8, 1), (13, 1), (18, 2), (22, 2), (32, 3), (31, 3), (39, 4), (50, 4)])" -non_spreading_dirlaynet_minbudget,0.3933597000432201,167,"(25, {0: [10, 20, 16, 1, 14, 19, 13, 12, 3, 9, 7, 17, 2, 24, 11, 6, 21, 8, 4, 22, 5, 25, 18, 23, 38]})" -non_spreading_minbudget,0.026914299989584833,167,"(25, [])" -heuristic_minbudget,1.7527138000004925,167,"(7, [(17, 1), (13, 1), (7, 1), (23, 1), (3, 1), (16, 1), (20, 1), (38, 2), (44, 2), (43, 2), (31, 2), (45, 2), (26, 2), (30, 2), (52, 3), (71, 3), (64, 3), (72, 3), (54, 3), (56, 3), (59, 3), (97, 4), (87, 4), (89, 4), (82, 4), (83, 4), (92, 4), (100, 4), (105, 5), (107, 5), (106, 5), (108, 5), (110, 5), (109, 5)])" -non_spreading_dirlaynet_minbudget,0.18939569999929518,98,"(2, {0: [17, 89], 1: [57], 2: [57], 3: [75], 4: [75]})" -non_spreading_minbudget,0.011905899969860911,98,"(7, [])" -heuristic_minbudget,0.5329802000196651,98,"(2, [(8, 1), (6, 1), (17, 2), (11, 2), (39, 3), (36, 3), (57, 4), (54, 4), (69, 5), (75, 5), (89, 6), (78, 6)])" -non_spreading_dirlaynet_minbudget,0.47158130002208054,166,"(4, {0: [20, 16, 86, 161], 1: [20, 44, 120]})" -non_spreading_minbudget,0.020260599965695292,166,"(7, [])" -heuristic_minbudget,1.1105554000241682,166,"(3, [(1, 1), (2, 1), (9, 1), (16, 2), (17, 2), (20, 2), (44, 3), (35, 3), (39, 3), (61, 4), (56, 4), (65, 4), (86, 5), (80, 5), (84, 5), (92, 6), (94, 6), (99, 6), (110, 7), (104, 7), (107, 7), (120, 8), (128, 8), (129, 8), (140, 9), (143, 9), (148, 9)])" -non_spreading_dirlaynet_minbudget,0.38358820002758875,153,"(5, {0: [15, 26, 62, 93, 149], 1: [26, 84], 2: [108], 3: [108]})" -non_spreading_minbudget,0.023857699998188764,153,"(11, [])" -heuristic_minbudget,1.4818554000230506,153,"(2, [(15, 1), (13, 1), (26, 2), (24, 2), (53, 3), (62, 3), (84, 4), (87, 4), (93, 5), (100, 5), (108, 6), (117, 6)])" -non_spreading_dirlaynet_minbudget,0.2334121000021696,134,"(8, {0: [11, 20, 1, 7, 24, 38, 44, 39], 1: [38, 44, 39], 2: [38, 44, 39]})" -non_spreading_minbudget,0.015820100030396134,134,"(12, [])" -heuristic_minbudget,0.8371534000034444,134,"(5, [(7, 1), (20, 1), (24, 1), (1, 1), (11, 1), (29, 2), (36, 2), (31, 2), (34, 2), (37, 2), (44, 3), (41, 3), (38, 3), (39, 3), (40, 3)])" -non_spreading_dirlaynet_minbudget,0.37643830000888556,167,"(6, {0: [6, 4, 5, 3, 2, 12]})" -non_spreading_minbudget,0.01732879999326542,167,"(6, [])" -heuristic_minbudget,0.3751860999618657,167,"(3, [(5, 1), (6, 1), (3, 1), (18, 2), (24, 2), (31, 2), (32, 3), (33, 3), (36, 3), (50, 4), (45, 4), (46, 4), (65, 5), (60, 5), (68, 5), (88, 6), (81, 6), (85, 6), (97, 7), (96, 7), (100, 7), (129, 8), (136, 8), (114, 8)])" -non_spreading_dirlaynet_minbudget,0.29731140000512823,148,"(9, {0: [26, 6, 16, 5, 25, 24, 31, 28, 30], 1: [31, 28, 30]})" -non_spreading_minbudget,0.013988099992275238,148,"(11, [])" -heuristic_minbudget,0.68870350002544,148,"(6, [(5, 1), (6, 1), (16, 1), (26, 1), (24, 1), (25, 1), (28, 2), (32, 2), (30, 2), (29, 2), (31, 2)])" -non_spreading_dirlaynet_minbudget,0.5390391999972053,158,"(12, {0: [16, 19, 7, 22, 31, 36, 70, 75, 89, 113, 125, 152], 1: [31, 70, 75, 113, 125, 152], 2: [70, 75, 89, 152]})" -non_spreading_minbudget,0.02681850001681596,158,"(21, [])" -heuristic_minbudget,2.336813100031577,158,"(5, [(7, 1), (19, 1), (16, 1), (22, 1), (3, 1), (36, 2), (38, 2), (31, 2), (28, 2), (37, 2), (75, 3), (72, 3), (69, 3), (58, 3), (70, 3), (82, 4), (89, 4), (90, 4), (96, 4), (99, 4), (113, 5), (124, 5), (121, 5), (108, 5), (114, 5), (141, 6), (125, 6), (140, 6), (133, 6), (136, 6)])" -non_spreading_dirlaynet_minbudget,0.14801500004250556,94,"(4, {0: [12, 20, 19, 17], 1: [20, 19, 17]})" -non_spreading_minbudget,0.009141899994574487,94,"(6, [])" -heuristic_minbudget,0.37057590001495555,94,"(3, [(12, 1), (5, 1), (8, 1), (19, 2), (18, 2), (17, 2), (24, 3), (25, 3), (28, 3), (40, 4), (38, 4), (34, 4)])" -non_spreading_dirlaynet_minbudget,0.21253900002921,130,"(11, {0: [10, 11, 6, 8, 4, 1, 3, 9, 7, 2, 18]})" -non_spreading_minbudget,0.015054800023790449,130,"(11, [])" -heuristic_minbudget,0.6687954000080936,130,"(5, [(11, 1), (7, 1), (6, 1), (3, 1), (1, 1), (16, 2), (17, 2), (14, 2), (19, 2), (18, 2), (30, 3), (32, 3), (33, 3), (28, 3), (29, 3), (38, 4), (41, 4), (39, 4), (37, 4), (40, 4)])" -non_spreading_dirlaynet_minbudget,0.46167799999238923,169,"(7, {0: [6, 4, 1, 5, 3, 7, 2]})" -non_spreading_minbudget,0.024519800033885986,169,"(7, [])" -heuristic_minbudget,0.6031515999929979,169,"(5, [(4, 1), (5, 1), (2, 1), (1, 1), (6, 1), (10, 2), (8, 2), (34, 2), (12, 2), (22, 2), (37, 3), (42, 3), (54, 3), (38, 3), (59, 3), (78, 4), (86, 4), (69, 4), (80, 4), (84, 4), (89, 5), (91, 5), (103, 5), (114, 5), (99, 5), (119, 6), (117, 6), (139, 6), (123, 6), (115, 6)])" -non_spreading_dirlaynet_minbudget,0.27336719998857006,147,"(10, {0: [7, 25, 48, 36, 39, 49, 51, 56, 60, 55], 1: [48, 36, 39, 49, 51, 56, 60, 55], 2: [49, 51, 56, 60, 55]})" -non_spreading_minbudget,0.018319299968425184,147,"(20, [])" -heuristic_minbudget,1.49995059997309,147,"(5, [(7, 1), (25, 1), (1, 1), (17, 1), (22, 1), (39, 2), (48, 2), (36, 2), (38, 2), (28, 2), (61, 3), (56, 3), (51, 3), (54, 3), (57, 3), (75, 4), (74, 4), (65, 4), (63, 4), (62, 4), (80, 5), (81, 5), (78, 5), (83, 5), (84, 5), (86, 6), (88, 6), (89, 6), (87, 6), (85, 6)])" -non_spreading_dirlaynet_minbudget,0.10863520001294091,72,"(9, {0: [26, 8, 13, 22, 21, 32, 39, 58, 70], 1: [70], 2: [70], 3: [70]})" -non_spreading_minbudget,0.00840649998281151,72,"(12, [])" -heuristic_minbudget,0.9322982999728993,72,"(5, [(13, 1), (22, 1), (8, 1), (21, 1), (26, 1), (32, 2), (34, 2), (31, 2), (35, 2), (33, 2), (39, 3), (42, 3), (43, 3), (52, 3), (40, 3), (58, 4), (57, 4), (56, 4), (59, 4), (55, 4)])" -non_spreading_dirlaynet_minbudget,0.15935790003277361,91,"(6, {0: [6, 4, 1, 5, 3, 2]})" -non_spreading_minbudget,0.08640519995242357,91,"(6, [])" -heuristic_minbudget,0.25525539997033775,91,"(2, [(5, 1), (3, 1), (13, 2), (17, 2), (23, 3), (39, 3), (63, 4), (56, 4)])" -non_spreading_dirlaynet_minbudget,0.10945779999019578,69,"(2, {0: [35], 1: [16, 35]})" -non_spreading_minbudget,0.008623800007626414,69,"(3, [])" -heuristic_minbudget,0.19346059998497367,69,"(2, [(10, 1), (2, 1), (16, 2), (19, 2)])" -non_spreading_dirlaynet_minbudget,0.374742400017567,165,"(13, {0: [26, 20, 14, 7, 18, 34, 40, 35, 29, 37, 31, 36, 38], 1: [34, 40, 35, 29, 37, 31, 36, 38]})" -non_spreading_minbudget,0.024857400043401867,165,"(21, [])" -heuristic_minbudget,2.2302414999576285,165,"(9, [(14, 1), (7, 1), (18, 1), (20, 1), (26, 1), (25, 1), (17, 1), (23, 1), (8, 1), (39, 2), (33, 2), (43, 2), (35, 2), (37, 2), (41, 2), (29, 2), (42, 2), (38, 2), (53, 3), (55, 3), (57, 3), (46, 3), (56, 3), (59, 3), (63, 3), (48, 3), (52, 3), (64, 4), (80, 4), (67, 4), (76, 4), (73, 4), (77, 4), (79, 4), (92, 4), (90, 4), (105, 5), (97, 5), (98, 5), (101, 5), (96, 5), (99, 5), (102, 5), (104, 5), (103, 5), (124, 6), (122, 6), (129, 6), (134, 6), (130, 6), (136, 6), (109, 6), (118, 6), (114, 6)])" -non_spreading_dirlaynet_minbudget,0.28629970003385097,134,"(19, {0: [10, 16, 1, 14, 19, 13, 12, 3, 9, 7, 17, 2, 11, 6, 8, 4, 5, 15, 18]})" -non_spreading_minbudget,0.01946979999775067,134,"(19, [])" -heuristic_minbudget,1.2700168999726884,134,"(6, [(17, 1), (16, 1), (6, 1), (7, 1), (8, 1), (3, 1), (32, 2), (38, 2), (20, 2), (33, 2), (26, 2), (31, 2), (49, 3), (57, 3), (55, 3), (67, 3), (42, 3), (44, 3), (76, 4), (72, 4), (71, 4), (74, 4), (75, 4), (73, 4)])" -non_spreading_dirlaynet_minbudget,0.2748194000450894,118,"(9, {0: [6, 5, 14, 2, 20, 16, 23, 19, 22], 1: [20, 16, 23, 19, 22]})" -non_spreading_minbudget,0.016315200016833842,118,"(13, [])" -heuristic_minbudget,0.7548398000071757,118,"(7, [(6, 1), (2, 1), (5, 1), (14, 1), (3, 1), (11, 1), (1, 1), (22, 2), (16, 2), (17, 2), (23, 2), (18, 2), (21, 2), (24, 2), (32, 3), (34, 3)])" -non_spreading_dirlaynet_minbudget,0.4323341000126675,190,"(5, {0: [29, 47, 45, 70, 64], 1: [29, 47, 45, 70, 64], 2: [47, 45, 70, 64, 85], 3: [70, 64]})" -non_spreading_minbudget,0.025312400015536696,190,"(15, [])" -heuristic_minbudget,1.2723371000029147,190,"(4, [(2, 1), (4, 1), (9, 1), (17, 1), (29, 2), (21, 2), (23, 2), (34, 2), (39, 3), (58, 3), (45, 3), (47, 3), (65, 4), (64, 4), (67, 4), (68, 4), (80, 5), (85, 5), (75, 5), (92, 5), (108, 6), (111, 6), (127, 6), (117, 6), (148, 7), (135, 7), (131, 7), (133, 7), (161, 8), (149, 8), (156, 8), (150, 8)])" -non_spreading_dirlaynet_minbudget,0.31637699995189905,161,"(8, {0: [6, 1, 4, 5, 7, 2, 11, 20], 1: [11, 20]})" -non_spreading_minbudget,0.016746199980843812,161,"(9, [])" -heuristic_minbudget,0.5648038999643177,161,"(4, [(1, 1), (2, 1), (5, 1), (4, 1), (20, 2), (22, 2), (10, 2), (21, 2), (27, 3), (42, 3), (46, 3), (41, 3), (64, 4), (53, 4), (57, 4), (62, 4), (84, 5), (73, 5), (81, 5), (79, 5), (98, 6), (87, 6), (89, 6), (91, 6), (108, 7), (114, 7), (122, 7), (116, 7), (128, 8), (129, 8), (125, 8), (130, 8)])" -non_spreading_dirlaynet_minbudget,0.2906712999683805,110,"(10, {0: [1, 11, 15, 24, 33, 37, 60, 68, 81, 105], 1: [24, 37, 60, 68, 105], 2: [37, 105]})" -non_spreading_minbudget,0.04526869999244809,110,"(18, [])" -heuristic_minbudget,0.725164000003133,110,"(3, [(1, 1), (11, 1), (15, 1), (22, 2), (24, 2), (33, 2), (43, 3), (39, 3), (37, 3), (63, 4), (61, 4), (60, 4), (68, 5), (73, 5), (67, 5), (81, 6), (80, 6), (77, 6)])" -non_spreading_dirlaynet_minbudget,0.27447980002034456,145,"(6, {0: [11, 20, 31, 45, 60, 70], 1: [31, 45, 60, 70], 2: [45, 60, 70], 3: [70], 4: [70]})" -non_spreading_minbudget,0.020326700003352016,145,"(13, [])" -heuristic_minbudget,1.061862199974712,145,"(4, [(11, 1), (20, 1), (19, 1), (6, 1), (32, 2), (31, 2), (28, 2), (37, 2), (45, 3), (55, 3), (42, 3), (60, 3), (64, 4), (67, 4), (69, 4), (65, 4), (72, 5), (74, 5)])" -non_spreading_dirlaynet_minbudget,0.17650770000182092,104,"(6, {0: [6, 4, 1, 5, 3, 2]})" -non_spreading_minbudget,0.010195500042755157,104,"(6, [])" -heuristic_minbudget,0.3146837000385858,104,"(4, [(5, 1), (4, 1), (3, 1), (2, 1), (13, 2), (15, 2), (19, 2), (7, 2), (34, 3), (40, 3), (38, 3), (39, 3), (52, 4), (54, 4), (57, 4), (61, 4), (72, 5), (73, 5), (74, 5), (71, 5)])" -non_spreading_dirlaynet_minbudget,0.29066339996643364,149,"(5, {0: [4, 1, 5, 3, 2]})" -non_spreading_minbudget,0.016835800022818148,149,"(5, [])" -heuristic_minbudget,0.3754161999677308,149,"(3, [(4, 1), (3, 1), (1, 1), (13, 2), (31, 2), (26, 2), (37, 3), (33, 3), (40, 3), (51, 4), (59, 4), (56, 4), (68, 5), (63, 5), (74, 5), (94, 6), (103, 6), (101, 6), (113, 7), (115, 7), (112, 7), (125, 8), (117, 8), (119, 8), (134, 9), (131, 9), (133, 9)])" -non_spreading_dirlaynet_minbudget,0.2513452999992296,133,"(6, {0: [1, 2, 11, 16, 14, 15], 1: [11, 16, 14, 15, 32]})" -non_spreading_minbudget,0.014802400022745132,133,"(10, [])" -heuristic_minbudget,0.685664800053928,133,"(5, [(2, 1), (1, 1), (9, 1), (6, 1), (5, 1), (16, 2), (12, 2), (18, 2), (15, 2), (11, 2), (32, 3), (19, 3), (22, 3), (23, 3), (29, 3), (39, 4), (40, 4), (36, 4), (42, 4), (38, 4), (52, 5), (50, 5), (49, 5), (54, 5), (57, 5), (61, 6), (62, 6), (65, 6), (60, 6), (67, 6), (75, 7), (72, 7), (73, 7), (74, 7), (76, 7)])" -non_spreading_dirlaynet_minbudget,0.3316297000274062,162,"(16, {0: [10, 16, 1, 14, 13, 3, 12, 9, 7, 2, 11, 6, 8, 4, 5, 15]})" -non_spreading_minbudget,0.024006900028325617,162,"(16, [])" -heuristic_minbudget,1.5288382999715395,162,"(7, [(3, 1), (14, 1), (4, 1), (6, 1), (15, 1), (16, 1), (2, 1), (25, 2), (19, 2), (26, 2), (35, 2), (36, 2), (17, 2), (45, 2), (70, 3), (69, 3), (47, 3), (62, 3), (64, 3), (54, 3), (67, 3), (80, 4), (94, 4), (89, 4), (100, 4), (77, 4), (93, 4), (96, 4), (101, 5), (105, 5), (106, 5), (113, 5), (107, 5), (102, 5), (112, 5), (119, 6), (118, 6), (120, 6), (121, 6), (117, 6)])" -non_spreading_dirlaynet_minbudget,0.1403563999920152,81,"(10, {0: [3, 12, 9, 15, 29, 31, 46, 51, 65, 68], 1: [29, 31, 65, 68], 2: [68], 4: [68]})" -non_spreading_minbudget,0.011806999973487109,81,"(16, [])" -heuristic_minbudget,0.7835318999714218,81,"(4, [(12, 1), (9, 1), (15, 1), (3, 1), (31, 2), (38, 2), (28, 2), (29, 2), (46, 3), (39, 3), (40, 3), (41, 3), (51, 4), (49, 4), (52, 4), (54, 4), (63, 5), (56, 5), (57, 5), (58, 5)])" -non_spreading_dirlaynet_minbudget,0.18342100002337247,95,"(1, {2: [78]})" -non_spreading_minbudget,0.009804899978917092,95,"(2, [])" -heuristic_minbudget,0.3041724999784492,95,"(1, [(9, 1), (28, 2), (52, 3)])" -non_spreading_dirlaynet_minbudget,0.07903019996592775,56,"(11, {0: [10, 11, 6, 4, 1, 8, 5, 9, 7, 2, 38]})" -non_spreading_minbudget,0.007620499993208796,56,"(11, [])" -heuristic_minbudget,0.40007520001381636,56,"(5, [(6, 1), (11, 1), (5, 1), (10, 1), (7, 1), (16, 2), (18, 2), (22, 2), (19, 2), (21, 2), (27, 3), (28, 3), (30, 3), (32, 3), (33, 3), (38, 4), (37, 4)])" -non_spreading_dirlaynet_minbudget,0.37145620002411306,132,"(2, {0: [11, 56], 1: [40]})" -non_spreading_minbudget,0.06657630001427606,132,"(3, [])" -heuristic_minbudget,0.3125448999926448,132,"(1, [(5, 1), (11, 2), (40, 3), (56, 4)])" -non_spreading_dirlaynet_minbudget,0.23120929999276996,109,"(4, {0: [8, 16, 14, 19], 1: [16, 14, 19]})" -non_spreading_minbudget,0.014048199984245002,109,"(7, [])" -heuristic_minbudget,0.44086479995166883,109,"(5, [(8, 1), (3, 1), (4, 1), (10, 1), (12, 1), (14, 2), (19, 2), (16, 2), (17, 2), (18, 2), (23, 3), (29, 3), (26, 3), (30, 3), (42, 3)])" -non_spreading_dirlaynet_minbudget,0.13346400001319125,80,"(5, {0: [3, 25, 55, 64, 75], 1: [25, 47, 55], 3: [75], 5: [64]})" -non_spreading_minbudget,0.00931180000770837,80,"(10, [])" -heuristic_minbudget,0.6443930999957956,80,"(2, [(3, 1), (5, 1), (24, 2), (25, 2), (47, 3), (35, 3), (52, 4), (55, 4), (56, 5), (60, 5), (71, 6), (64, 6)])" -non_spreading_dirlaynet_minbudget,0.2423623999929987,124,"(1, {3: [115]})" -non_spreading_minbudget,0.014655900013167411,124,"(3, [])" -heuristic_minbudget,0.8641149000031874,124,"(1, [(1, 1), (30, 2), (41, 3), (61, 4), (86, 5)])" -non_spreading_dirlaynet_minbudget,0.32257890002802014,146,"(8, {0: [11, 1, 14, 3, 26, 27, 28, 30], 1: [26, 27, 28, 30]})" -non_spreading_minbudget,0.0157242999994196,146,"(12, [])" -heuristic_minbudget,0.9642243999987841,146,"(4, [(3, 1), (1, 1), (11, 1), (14, 1), (28, 2), (26, 2), (27, 2), (25, 2), (43, 3), (32, 3), (42, 3), (31, 3), (63, 4), (64, 4), (70, 4), (73, 4), (91, 5), (75, 5), (79, 5), (96, 5), (101, 6), (100, 6), (103, 6), (99, 6), (126, 7), (122, 7), (117, 7), (114, 7)])" -non_spreading_dirlaynet_minbudget,0.1736669999663718,100,"(8, {0: [6, 1, 8, 5, 3, 7, 2, 15]})" -non_spreading_minbudget,0.011281599989160895,100,"(8, [])" -heuristic_minbudget,0.4084740999969654,100,"(4, [(7, 1), (5, 1), (2, 1), (6, 1), (21, 2), (22, 2), (14, 2), (16, 2), (30, 3), (39, 3), (43, 3), (32, 3), (49, 4), (46, 4), (45, 4), (50, 4), (56, 5), (52, 5), (53, 5), (54, 5), (61, 6), (68, 6), (67, 6), (73, 6)])" -non_spreading_dirlaynet_minbudget,0.5331603999948129,206,"(8, {0: [3, 13, 7, 19, 15, 34, 36, 38], 1: [19, 15, 34, 36, 38], 2: [34, 36, 38]})" -non_spreading_minbudget,0.02871659997617826,206,"(13, [])" -heuristic_minbudget,1.13137060002191,206,"(6, [(13, 1), (7, 1), (3, 1), (8, 1), (10, 1), (4, 1), (19, 2), (18, 2), (15, 2), (17, 2), (21, 2), (28, 2), (35, 3), (34, 3), (38, 3), (36, 3), (37, 3), (39, 3), (57, 4), (65, 4), (53, 4), (48, 4), (60, 4), (49, 4)])" -non_spreading_dirlaynet_minbudget,0.3249198999837972,123,"(9, {0: [6, 1, 4, 8, 5, 3, 9, 7, 2]})" -non_spreading_minbudget,0.03570499998750165,123,"(9, [])" -heuristic_minbudget,0.617231999989599,123,"(4, [(7, 1), (8, 1), (6, 1), (4, 1), (28, 2), (26, 2), (27, 2), (11, 2), (37, 3), (35, 3), (33, 3), (31, 3), (51, 4), (48, 4), (57, 4), (49, 4), (68, 5), (77, 5), (75, 5), (62, 5), (89, 6), (81, 6), (82, 6), (83, 6), (94, 7), (93, 7), (95, 7), (96, 7)])" -non_spreading_dirlaynet_minbudget,0.3759449000353925,170,"(9, {0: [10, 6, 4, 12, 22, 31, 34, 36, 30], 1: [31, 34, 36, 30]})" -non_spreading_minbudget,0.022455699974671006,170,"(13, [])" -heuristic_minbudget,1.325027399987448,170,"(5, [(6, 1), (10, 1), (12, 1), (4, 1), (22, 1), (36, 2), (31, 2), (32, 2), (33, 2), (30, 2), (42, 3), (40, 3), (56, 3), (57, 3), (47, 3), (71, 4), (76, 4), (61, 4), (66, 4), (78, 4), (86, 5), (95, 5), (83, 5), (90, 5), (82, 5), (105, 6), (106, 6), (107, 6), (101, 6), (108, 6), (144, 7), (129, 7), (139, 7), (135, 7), (126, 7)])" -non_spreading_dirlaynet_minbudget,0.27661239996086806,122,"(2, {0: [9, 121]})" -non_spreading_minbudget,0.013166900025680661,122,"(3, [])" -heuristic_minbudget,0.7148885000497103,122,"(1, [(9, 1), (20, 2), (35, 3), (51, 4), (57, 5), (85, 6), (92, 7)])" -non_spreading_dirlaynet_minbudget,0.22093810001388192,107,"(7, {0: [12, 21, 30, 33, 56, 64, 66], 1: [21, 30, 33, 56, 64, 66, 82], 2: [56, 64, 66], 3: [64, 66]})" -non_spreading_minbudget,0.012751900008879602,107,"(16, [])" -heuristic_minbudget,0.9129658999736421,107,"(5, [(12, 1), (16, 1), (10, 1), (5, 1), (13, 1), (33, 2), (23, 2), (30, 2), (21, 2), (22, 2), (42, 3), (56, 3), (48, 3), (38, 3), (53, 3), (65, 4), (64, 4), (61, 4), (63, 4), (62, 4), (79, 5), (73, 5), (75, 5), (76, 5), (71, 5)])" -non_spreading_dirlaynet_minbudget,0.2728919999790378,135,"(15, {0: [10, 16, 1, 13, 3, 12, 9, 7, 2, 11, 6, 8, 4, 15, 24], 1: [24]})" -non_spreading_minbudget,0.016789799963589758,135,"(16, [])" -heuristic_minbudget,0.9788587000221014,135,"(6, [(8, 1), (7, 1), (10, 1), (4, 1), (13, 1), (15, 1), (25, 2), (31, 2), (21, 2), (27, 2), (20, 2), (19, 2), (34, 3), (39, 3), (41, 3), (40, 3), (38, 3), (35, 3), (61, 4), (57, 4), (59, 4), (63, 4), (47, 4), (60, 4), (73, 5), (72, 5), (71, 5), (70, 5), (69, 5)])" -non_spreading_dirlaynet_minbudget,0.2592234000330791,134,"(9, {0: [10, 14, 12, 17, 2, 26, 27, 28, 22], 1: [26, 27, 28, 22]})" -non_spreading_minbudget,0.014137900026980788,134,"(13, [])" -heuristic_minbudget,0.5196378999971785,134,"(5, [(10, 1), (14, 1), (17, 1), (12, 1), (2, 1), (27, 2), (28, 2), (22, 2), (24, 2), (26, 2), (42, 3), (37, 3), (32, 3), (35, 3), (46, 3), (52, 4), (51, 4), (54, 4), (48, 4), (50, 4), (60, 5), (59, 5), (56, 5), (62, 5), (61, 5), (82, 6), (86, 6), (78, 6), (75, 6), (77, 6), (98, 7), (93, 7), (103, 7), (107, 7), (104, 7)])" -non_spreading_dirlaynet_minbudget,0.2924388000392355,123,"(15, {0: [10, 11, 6, 4, 1, 5, 14, 15, 13, 12, 3, 9, 7, 2, 24]})" -non_spreading_minbudget,0.01611399999819696,123,"(15, [])" -heuristic_minbudget,1.0233782999566756,123,"(5, [(2, 1), (7, 1), (13, 1), (10, 1), (14, 1), (26, 2), (29, 2), (27, 2), (28, 2), (30, 2), (41, 3), (34, 3), (40, 3), (35, 3), (42, 3), (45, 4), (60, 4), (69, 4), (57, 4), (58, 4), (83, 5), (82, 5), (79, 5), (88, 5), (93, 5)])" -non_spreading_dirlaynet_minbudget,0.3060064999735914,112,"(3, {0: [16, 51], 1: [16, 35, 51], 2: [51], 3: [51], 4: [51]})" -non_spreading_minbudget,0.010651899967342615,112,"(8, [])" -heuristic_minbudget,0.5289040000061505,112,"(2, [(4, 1), (3, 1), (16, 2), (19, 2), (35, 3), (33, 3), (46, 4), (44, 4), (54, 5), (51, 5), (62, 6), (56, 6), (86, 7), (88, 7), (92, 8), (94, 8)])" -non_spreading_dirlaynet_minbudget,0.17984549998072907,88,"(7, {0: [6, 4, 1, 5, 3, 7, 2]})" -non_spreading_minbudget,0.009711000020615757,88,"(7, [])" -heuristic_minbudget,0.40752200002316386,88,"(3, [(1, 1), (3, 1), (5, 1), (10, 2), (20, 2), (9, 2), (28, 3), (21, 3), (23, 3), (51, 4), (48, 4), (59, 4), (62, 5), (74, 5), (81, 5)])" -,126.59461589995772,, +non_spreading_dirlaynet_minbudget,0.1577231001574546,82,"(6, {0: [5, 2, 4, 3, 1, 10]})" +non_spreading_minbudget,0.008546600118279457,82,"(6, [])" +heuristic_minbudget,0.1938444001134485,82,"(4, [(4, 1), (1, 1), (5, 1), (3, 1), (19, 2), (18, 2), (16, 2), (9, 2), (33, 3), (37, 3)])" +non_spreading_dirlaynet_minbudget,0.4255663000512868,127,"(2, {0: [9, 126], 3: [118], 4: [97]})" +non_spreading_minbudget,0.018676700070500374,127,"(6, [])" +heuristic_minbudget,0.8027119999751449,127,"(1, [(9, 1), (12, 2), (23, 3), (76, 4), (88, 5), (97, 6), (118, 7)])" +non_spreading_dirlaynet_minbudget,0.3158855000510812,106,"(11, {0: [22, 17, 2, 13, 21, 1, 30, 29, 28, 50, 52], 1: [30, 29, 28, 50, 52, 60], 2: [50, 52]})" +non_spreading_minbudget,0.02649120008572936,106,"(19, [])" +heuristic_minbudget,0.704905399819836,106,"(6, [(2, 1), (17, 1), (1, 1), (21, 1), (13, 1), (22, 1), (45, 2), (27, 2), (28, 2), (29, 2), (30, 2), (43, 2), (48, 3), (50, 3), (51, 3), (49, 3), (52, 3), (54, 3), (60, 4)])" +non_spreading_dirlaynet_minbudget,0.46226679999381304,149,"(7, {0: [14, 11, 3, 29, 43, 39, 42], 1: [29, 43, 39, 42], 2: [43, 39, 42]})" +non_spreading_minbudget,0.02368660015054047,149,"(14, [])" +heuristic_minbudget,1.139801099896431,149,"(3, [(14, 1), (3, 1), (11, 1), (29, 2), (20, 2), (22, 2), (38, 3), (42, 3), (43, 3), (73, 4), (46, 4), (66, 4), (84, 5), (95, 5), (77, 5), (112, 6), (109, 6), (111, 6), (113, 7), (118, 7), (115, 7)])" +non_spreading_dirlaynet_minbudget,0.341992300003767,84,"(8, {0: [5, 2, 7, 6, 4, 3, 1, 12]})" +non_spreading_minbudget,0.03232260001823306,84,"(8, [])" +heuristic_minbudget,0.6307671000249684,84,"(5, [(5, 1), (2, 1), (3, 1), (1, 1), (6, 1), (14, 2), (12, 2), (16, 2), (11, 2), (13, 2), (27, 3), (23, 3), (18, 3), (19, 3)])" +non_spreading_dirlaynet_minbudget,1.0424751001410186,156,"(9, {0: [10, 21, 19, 4, 38, 35, 53, 60, 56], 1: [38, 35, 53, 60, 56], 2: [60, 56], 3: [60, 56]})" +non_spreading_minbudget,0.06969960010610521,156,"(15, [])" +heuristic_minbudget,2.3020446998998523,156,"(4, [(10, 1), (21, 1), (19, 1), (4, 1), (37, 2), (35, 2), (38, 2), (24, 2), (48, 3), (53, 3), (55, 3), (50, 3), (57, 4), (61, 4), (56, 4), (60, 4), (68, 5), (74, 5), (77, 5), (82, 5), (87, 6), (100, 6), (90, 6), (95, 6), (108, 7), (115, 7), (127, 7), (103, 7)])" +non_spreading_dirlaynet_minbudget,0.40291900001466274,111,"(2, {0: [12, 39]})" +non_spreading_minbudget,0.02193789999000728,111,"(2, [])" +heuristic_minbudget,0.9895753001328558,111,"(1, [(12, 1)])" +non_spreading_dirlaynet_minbudget,0.5849756998941302,118,"(1, {4: [114]})" +non_spreading_minbudget,0.03333929996006191,118,"(2, [])" +heuristic_minbudget,2.5519870999269187,118,"(1, [(8, 1), (40, 2), (52, 3), (70, 4), (75, 5)])" +non_spreading_dirlaynet_minbudget,0.5267107000108808,125,"(5, {0: [10, 15, 50, 93, 110], 1: [93], 2: [110], 3: [73], 4: [73]})" +non_spreading_minbudget,0.04090219992212951,125,"(11, [])" +heuristic_minbudget,1.3968956999015063,125,"(3, [(10, 1), (15, 1), (9, 1), (29, 2), (31, 2), (20, 2), (35, 3), (39, 3), (40, 3), (50, 4), (49, 4), (53, 4), (65, 5), (73, 5), (77, 5), (86, 6), (83, 6), (85, 6), (97, 7), (90, 7), (93, 7)])" +non_spreading_dirlaynet_minbudget,0.47931419988162816,126,"(13, {0: [7, 15, 20, 18, 1, 31, 26, 38, 30, 29, 28, 35, 37], 1: [31, 26, 38, 30, 29, 28, 35, 37]})" +non_spreading_minbudget,0.031449199886992574,126,"(20, [])" +heuristic_minbudget,3.0258405001368374,126,"(7, [(1, 1), (7, 1), (18, 1), (15, 1), (20, 1), (11, 1), (14, 1), (38, 2), (27, 2), (25, 2), (28, 2), (30, 2), (36, 2), (33, 2), (49, 3), (54, 3), (51, 3), (47, 3), (45, 3), (57, 3), (41, 3), (74, 4), (81, 4), (71, 4), (78, 4), (79, 4), (65, 4), (76, 4), (89, 5), (93, 5), (86, 5), (87, 5), (90, 5), (91, 5), (94, 5), (100, 6), (108, 6), (101, 6), (103, 6), (107, 6), (95, 6), (106, 6), (109, 7), (118, 7), (112, 7), (114, 7), (115, 7), (110, 7), (113, 7)])" +non_spreading_dirlaynet_minbudget,0.4622693001292646,134,"(6, {0: [5, 2, 6, 4, 3, 1]})" +non_spreading_minbudget,0.019320799969136715,134,"(6, [])" +heuristic_minbudget,0.33319449983537197,134,"(4, [(3, 1), (5, 1), (1, 1), (2, 1), (14, 2), (16, 2), (13, 2), (17, 2), (22, 3), (23, 3)])" +non_spreading_dirlaynet_minbudget,0.4219563000369817,126,"(5, {0: [10, 20, 36, 98, 116], 1: [43], 2: [43], 5: [98], 6: [98]})" +non_spreading_minbudget,0.09597310004755855,126,"(9, [])" +heuristic_minbudget,1.5155795000027865,126,"(2, [(10, 1), (20, 1), (36, 2), (30, 2), (47, 3), (43, 3), (51, 4), (53, 4), (54, 5), (58, 5), (71, 6), (60, 6), (76, 7), (89, 7), (101, 8), (102, 8)])" +non_spreading_dirlaynet_minbudget,1.1658165999688208,190,"(21, {0: [14, 16, 7, 9, 6, 15, 17, 10, 5, 2, 11, 12, 13, 8, 21, 20, 19, 4, 18, 3, 1]})" +non_spreading_minbudget,0.07264579995535314,190,"(21, [])" +heuristic_minbudget,2.47765320003964,190,"(6, [(12, 1), (20, 1), (11, 1), (10, 1), (16, 1), (19, 1), (27, 2), (43, 2), (42, 2), (47, 2), (51, 2), (23, 2), (57, 3), (64, 3), (54, 3), (52, 3), (55, 3), (66, 3), (70, 4), (76, 4), (78, 4), (75, 4), (69, 4), (77, 4), (102, 5), (88, 5), (83, 5), (98, 5), (85, 5), (101, 5), (105, 6), (104, 6), (111, 6), (112, 6), (108, 6), (107, 6), (130, 7), (139, 7), (126, 7), (131, 7), (137, 7), (118, 7), (156, 8), (144, 8), (147, 8), (153, 8), (142, 8), (158, 8), (180, 9), (161, 9), (159, 9), (162, 9), (165, 9), (172, 9)])" +non_spreading_dirlaynet_minbudget,0.8167432001791894,153,"(5, {0: [5, 2, 4, 3, 1]})" +non_spreading_minbudget,0.054783100029453635,153,"(5, [])" +heuristic_minbudget,0.9581810999661684,153,"(5, [(2, 1), (3, 1), (4, 1), (1, 1), (5, 1)])" +non_spreading_dirlaynet_minbudget,0.7115546998102218,124,"(11, {0: [16, 9, 2, 11, 21, 3, 24, 50, 81, 94, 110], 1: [81, 110], 2: [110], 3: [110]})" +non_spreading_minbudget,0.07342069991864264,124,"(17, [])" +heuristic_minbudget,2.375642899889499,124,"(7, [(21, 1), (24, 1), (2, 1), (16, 1), (3, 1), (9, 1), (11, 1), (50, 2), (29, 2), (41, 2), (42, 2), (44, 2), (52, 2), (28, 2), (54, 3), (60, 3), (62, 3), (59, 3), (56, 3), (53, 3), (61, 3), (78, 4), (74, 4), (68, 4), (67, 4), (69, 4), (76, 4), (64, 4)])" +non_spreading_dirlaynet_minbudget,1.2358756000176072,221,"(23, {0: [14, 16, 22, 7, 9, 6, 15, 17, 10, 5, 25, 2, 12, 11, 13, 8, 20, 19, 4, 18, 3, 1, 31], 1: [31]})" +non_spreading_minbudget,0.16567729995585978,221,"(24, [])" +heuristic_minbudget,3.716271399985999,221,"(7, [(14, 1), (7, 1), (9, 1), (12, 1), (11, 1), (13, 1), (4, 1), (28, 2), (32, 2), (33, 2), (45, 2), (40, 2), (39, 2), (38, 2), (48, 3), (71, 3), (65, 3), (64, 3), (72, 3), (69, 3), (46, 3), (79, 4), (77, 4), (92, 4), (98, 4), (87, 4), (95, 4), (91, 4), (102, 5), (106, 5), (109, 5), (116, 5), (114, 5), (117, 5), (104, 5), (133, 6), (143, 6), (121, 6), (131, 6), (130, 6), (136, 6), (123, 6), (147, 7), (150, 7), (155, 7), (164, 7), (172, 7), (173, 7), (166, 7), (189, 8), (198, 8), (175, 8), (180, 8), (177, 8), (183, 8), (187, 8)])" +non_spreading_dirlaynet_minbudget,0.5118867999408394,157,"(1, {4: [53]})" +non_spreading_minbudget,0.03263450018130243,157,"(1, [])" +heuristic_minbudget,0.3339197998866439,157,"(1, [(4, 1), (13, 2), (17, 3), (37, 4)])" +non_spreading_dirlaynet_minbudget,0.5583947999402881,108,"(5, {0: [17, 15, 57, 87, 102], 1: [57, 87], 2: [87], 3: [87]})" +non_spreading_minbudget,0.03970779990777373,108,"(10, [])" +heuristic_minbudget,2.3164709999691695,108,"(4, [(15, 1), (17, 1), (19, 1), (7, 1), (36, 2), (28, 2), (32, 2), (37, 2), (38, 3), (42, 3), (47, 3), (40, 3), (54, 4), (57, 4), (61, 4), (53, 4), (82, 5), (87, 5), (79, 5), (99, 5)])" +non_spreading_dirlaynet_minbudget,0.7613872999791056,178,"(8, {0: [26, 7, 39, 36, 31, 34, 33, 38], 1: [39, 36, 31, 34, 33, 38]})" +non_spreading_minbudget,0.03670880012214184,178,"(13, [])" +heuristic_minbudget,3.551267699804157,178,"(8, [(7, 1), (26, 1), (10, 1), (11, 1), (15, 1), (17, 1), (18, 1), (19, 1), (39, 2), (30, 2), (33, 2), (38, 2), (36, 2), (32, 2), (35, 2), (31, 2), (42, 3), (41, 3), (49, 3), (50, 3), (54, 3), (43, 3), (46, 3)])" +non_spreading_dirlaynet_minbudget,0.8977318999823183,151,"(15, {0: [14, 16, 9, 6, 15, 10, 5, 2, 11, 12, 13, 4, 3, 1, 30], 1: [30]})" +non_spreading_minbudget,0.040731099899858236,151,"(16, [])" +heuristic_minbudget,2.377181600080803,151,"(7, [(2, 1), (10, 1), (9, 1), (13, 1), (4, 1), (1, 1), (5, 1), (21, 2), (39, 2), (25, 2), (26, 2), (29, 2), (19, 2), (44, 2), (54, 3), (58, 3), (63, 3), (56, 3), (48, 3), (62, 3), (64, 3), (80, 4), (68, 4), (69, 4), (74, 4), (81, 4), (77, 4), (83, 4), (92, 5), (93, 5), (84, 5), (94, 5), (85, 5), (88, 5), (86, 5), (103, 6), (101, 6), (96, 6), (100, 6), (102, 6), (97, 6), (104, 6)])" +non_spreading_dirlaynet_minbudget,0.19986970000900328,87,"(5, {0: [14, 24, 31, 72, 80], 1: [72], 2: [43]})" +non_spreading_minbudget,0.017228800104930997,87,"(7, [])" +heuristic_minbudget,1.182675999822095,87,"(2, [(14, 1), (24, 1), (31, 2), (40, 2), (43, 3), (50, 3), (72, 4), (68, 4), (80, 5), (77, 5)])" +non_spreading_dirlaynet_minbudget,0.5464945000130683,99,"(7, {0: [10, 1, 4, 27, 70, 72, 86], 1: [47], 2: [47], 3: [72]})" +non_spreading_minbudget,0.05347799998708069,99,"(10, [])" +heuristic_minbudget,1.7111621999647468,99,"(3, [(1, 1), (10, 1), (4, 1), (27, 2), (26, 2), (30, 2), (47, 3), (54, 3), (39, 3), (70, 4), (66, 4), (69, 4), (82, 5), (72, 5), (77, 5)])" +non_spreading_dirlaynet_minbudget,0.5821063001640141,118,"(10, {0: [23, 16, 17, 10, 31, 26, 30, 29, 28, 35], 1: [31, 26, 30, 29, 28, 35]})" +non_spreading_minbudget,0.09854290005750954,118,"(15, [])" +heuristic_minbudget,2.6204377999529243,118,"(8, [(17, 1), (23, 1), (10, 1), (16, 1), (3, 1), (20, 1), (11, 1), (22, 1), (30, 2), (31, 2), (26, 2), (34, 2), (32, 2), (25, 2), (28, 2), (35, 2), (58, 3), (48, 3), (42, 3), (60, 3), (52, 3), (64, 3), (36, 3), (63, 3), (83, 4), (69, 4), (74, 4), (75, 4), (70, 4), (87, 4), (78, 4), (66, 4), (97, 5), (95, 5), (96, 5), (99, 5), (91, 5), (92, 5), (93, 5), (98, 5)])" +non_spreading_dirlaynet_minbudget,0.5755591001361609,100,"(9, {0: [5, 2, 8, 7, 9, 6, 3, 1, 36]})" +non_spreading_minbudget,0.02621010015718639,100,"(9, [])" +heuristic_minbudget,0.8365450000856072,100,"(5, [(2, 1), (5, 1), (8, 1), (9, 1), (6, 1), (30, 2), (13, 2), (16, 2), (28, 2), (22, 2), (40, 3), (43, 3), (44, 3), (46, 3), (38, 3), (50, 4), (53, 4), (52, 4), (54, 4), (48, 4), (70, 5), (72, 5), (63, 5), (71, 5), (60, 5), (81, 6), (84, 6), (79, 6), (76, 6), (78, 6)])" +non_spreading_dirlaynet_minbudget,0.6958248000591993,183,"(15, {0: [14, 10, 5, 2, 11, 12, 13, 8, 7, 9, 6, 4, 3, 1, 15]})" +non_spreading_minbudget,0.11737009999342263,183,"(15, [])" +heuristic_minbudget,2.7459627999924123,183,"(6, [(14, 1), (2, 1), (11, 1), (5, 1), (6, 1), (10, 1), (32, 2), (33, 2), (16, 2), (38, 2), (40, 2), (17, 2), (59, 3), (57, 3), (52, 3), (50, 3), (46, 3), (56, 3), (76, 4), (77, 4), (79, 4), (69, 4), (71, 4), (74, 4), (80, 5), (92, 5), (99, 5), (90, 5), (81, 5), (94, 5), (104, 6), (108, 6), (109, 6), (117, 6), (119, 6), (122, 6), (136, 7), (144, 7), (133, 7), (137, 7), (141, 7), (134, 7), (148, 8), (154, 8), (152, 8), (151, 8), (155, 8), (147, 8)])" +non_spreading_dirlaynet_minbudget,0.7773849999066442,107,"(9, {0: [5, 2, 8, 7, 9, 6, 4, 3, 1]})" +non_spreading_minbudget,0.03448770008981228,107,"(9, [])" +heuristic_minbudget,1.2942227001767606,107,"(7, [(1, 1), (6, 1), (3, 1), (9, 1), (4, 1), (5, 1), (2, 1), (23, 2), (28, 2), (22, 2), (15, 2), (12, 2), (37, 2), (34, 2), (45, 3), (57, 3), (43, 3), (39, 3), (41, 3), (62, 3), (47, 3), (67, 4), (76, 4), (78, 4), (87, 4), (83, 4), (81, 4), (63, 4)])" +non_spreading_dirlaynet_minbudget,0.5646400998812169,97,"(9, {0: [5, 2, 8, 7, 9, 6, 4, 3, 1]})" +non_spreading_minbudget,0.026038399897515774,97,"(9, [])" +heuristic_minbudget,0.6566372001543641,97,"(5, [(8, 1), (1, 1), (2, 1), (4, 1), (6, 1), (12, 2), (17, 2), (29, 2), (27, 2), (14, 2), (35, 3), (33, 3), (32, 3), (36, 3), (31, 3), (62, 4), (43, 4), (57, 4), (47, 4), (67, 4)])" +non_spreading_dirlaynet_minbudget,0.3988175999838859,108,"(2, {0: [7, 59], 4: [59]})" +non_spreading_minbudget,0.019911699928343296,108,"(3, [])" +heuristic_minbudget,0.7559130000881851,108,"(1, [(7, 1), (23, 2), (37, 3), (44, 4)])" +non_spreading_dirlaynet_minbudget,0.5628936998546124,116,"(9, {0: [16, 12, 18, 26, 71, 50, 69, 79, 93], 1: [26, 71, 50, 69, 79, 93], 2: [71, 50, 69, 93], 3: [93]})" +non_spreading_minbudget,0.03142390004359186,116,"(18, [])" +heuristic_minbudget,3.687096199952066,116,"(7, [(18, 1), (16, 1), (12, 1), (15, 1), (21, 1), (7, 1), (4, 1), (26, 2), (45, 2), (33, 2), (39, 2), (36, 2), (28, 2), (43, 2), (50, 3), (51, 3), (57, 3), (67, 3), (69, 3), (60, 3), (71, 3), (75, 4), (79, 4), (85, 4), (88, 4), (76, 4), (82, 4), (78, 4)])" +non_spreading_dirlaynet_minbudget,0.3001407999545336,72,"(15, {0: [14, 10, 5, 2, 11, 12, 13, 8, 7, 9, 6, 4, 3, 1, 15]})" +non_spreading_minbudget,0.01261289999820292,72,"(15, [])" +heuristic_minbudget,1.408848199993372,72,"(7, [(4, 1), (13, 1), (5, 1), (9, 1), (12, 1), (6, 1), (14, 1), (27, 2), (18, 2), (29, 2), (20, 2), (28, 2), (25, 2), (30, 2), (37, 3), (38, 3), (48, 3), (32, 3), (47, 3), (52, 3), (41, 3), (56, 4), (57, 4), (58, 4), (60, 4), (59, 4)])" +non_spreading_dirlaynet_minbudget,0.8255596000235528,128,"(1, {5: [83]})" +non_spreading_minbudget,0.0393540000077337,128,"(1, [])" +heuristic_minbudget,0.587727400008589,128,"(1, [(4, 1), (12, 2), (34, 3), (47, 4), (60, 5)])" +non_spreading_dirlaynet_minbudget,0.5193115998990834,112,"(14, {0: [7, 28, 27, 25, 19, 18, 24, 42, 38, 49, 55, 52, 74, 77], 1: [42, 38, 49, 55, 52, 74, 77, 81], 2: [55, 52, 74, 77], 3: [74]})" +non_spreading_minbudget,0.05353619996458292,112,"(24, [])" +heuristic_minbudget,2.26897030021064,112,"(7, [(27, 1), (7, 1), (19, 1), (24, 1), (28, 1), (25, 1), (18, 1), (38, 2), (35, 2), (47, 2), (42, 2), (39, 2), (49, 2), (32, 2), (55, 3), (72, 3), (61, 3), (52, 3), (68, 3), (53, 3), (56, 3), (78, 4), (76, 4), (74, 4), (75, 4), (77, 4), (73, 4)])" +non_spreading_dirlaynet_minbudget,0.591107500018552,114,"(6, {0: [3, 11, 6, 28, 35, 86]})" +non_spreading_minbudget,0.035418600076809525,114,"(6, [])" +heuristic_minbudget,2.0268653999082744,114,"(4, [(3, 1), (28, 1), (11, 1), (6, 1), (35, 2), (33, 2), (36, 2), (44, 2), (59, 3), (56, 3), (63, 3), (66, 3)])" +non_spreading_dirlaynet_minbudget,0.3615675000473857,90,"(1, {0: [19]})" +non_spreading_minbudget,0.022981500020250678,90,"(1, [])" +heuristic_minbudget,0.6832441999576986,90,"(1, [(19, 1)])" +non_spreading_dirlaynet_minbudget,0.5305326001252979,148,"(11, {0: [16, 7, 12, 19, 3, 23, 31, 26, 22, 30, 29], 1: [23, 31, 26, 22, 30, 29]})" +non_spreading_minbudget,0.02663370012305677,148,"(17, [])" +heuristic_minbudget,0.8235696998890489,148,"(5, [(16, 1), (3, 1), (7, 1), (12, 1), (19, 1), (26, 2), (20, 2), (23, 2), (21, 2), (31, 2), (38, 3), (44, 3), (47, 3), (48, 3), (42, 3), (58, 4), (63, 4), (62, 4), (56, 4), (61, 4), (81, 5), (74, 5), (75, 5), (76, 5), (66, 5), (96, 6), (97, 6), (94, 6), (93, 6), (98, 6), (108, 7), (104, 7), (109, 7), (105, 7), (114, 7)])" +non_spreading_dirlaynet_minbudget,0.48062629997730255,127,"(7, {0: [3, 23, 41, 32, 52, 79, 125], 1: [23, 41, 32, 103, 125], 3: [52]})" +non_spreading_minbudget,0.039807400200515985,127,"(12, [])" +heuristic_minbudget,1.6504144999198616,127,"(5, [(3, 1), (4, 1), (16, 1), (1, 1), (12, 1), (32, 2), (19, 2), (41, 2), (23, 2), (40, 2), (47, 3), (44, 3), (46, 3), (42, 3), (45, 3), (70, 4), (48, 4), (67, 4), (55, 4), (75, 4), (79, 5), (88, 5), (84, 5), (87, 5), (81, 5), (103, 6), (101, 6), (95, 6), (102, 6), (105, 6)])" +non_spreading_dirlaynet_minbudget,0.2845070001203567,83,"(1, {3: [50]})" +non_spreading_minbudget,0.018276299815624952,83,"(1, [])" +heuristic_minbudget,0.29154929984360933,83,"(1, [(2, 1), (11, 2), (27, 3)])" +non_spreading_dirlaynet_minbudget,0.41847799997776747,112,"(13, {0: [9, 28, 11, 21, 20, 24, 49, 35, 37, 71, 64, 83, 106], 1: [49, 35, 37, 71, 64, 83, 106], 2: [71, 64]})" +non_spreading_minbudget,0.08840370015241206,112,"(23, [])" +heuristic_minbudget,2.2752125998958945,112,"(6, [(9, 1), (11, 1), (20, 1), (24, 1), (21, 1), (28, 1), (46, 2), (37, 2), (49, 2), (35, 2), (33, 2), (43, 2), (71, 3), (67, 3), (76, 3), (60, 3), (64, 3), (80, 3), (84, 4), (83, 4), (88, 4), (89, 4), (86, 4), (87, 4)])" +non_spreading_dirlaynet_minbudget,0.41033149999566376,120,"(3, {0: [7, 43, 99], 1: [43], 2: [43, 99]})" +non_spreading_minbudget,0.033717900048941374,120,"(6, [])" +heuristic_minbudget,1.4959726999513805,120,"(2, [(7, 1), (5, 1), (30, 2), (29, 2), (44, 3), (46, 3), (62, 4), (68, 4), (77, 5), (88, 5)])" +non_spreading_dirlaynet_minbudget,0.20812149997800589,85,"(13, {0: [23, 16, 29, 17, 5, 25, 11, 21, 18, 3, 35, 34, 37], 1: [35, 34]})" +non_spreading_minbudget,0.021076400065794587,85,"(15, [])" +heuristic_minbudget,1.4817065000534058,85,"(10, [(16, 1), (21, 1), (3, 1), (5, 1), (17, 1), (18, 1), (29, 1), (23, 1), (25, 1), (11, 1), (34, 2), (32, 2), (33, 2), (35, 2), (31, 2)])" +non_spreading_dirlaynet_minbudget,0.734795100055635,131,"(21, {0: [14, 16, 7, 9, 6, 10, 5, 2, 11, 12, 13, 8, 21, 20, 19, 4, 18, 3, 1, 31, 29], 1: [31, 29]})" +non_spreading_minbudget,0.03819860005751252,131,"(22, [])" +heuristic_minbudget,3.435756399994716,131,"(7, [(7, 1), (10, 1), (20, 1), (3, 1), (4, 1), (11, 1), (16, 1), (29, 2), (43, 2), (25, 2), (32, 2), (24, 2), (38, 2), (28, 2), (57, 3), (46, 3), (50, 3), (52, 3), (62, 3), (60, 3), (64, 3), (71, 4), (89, 4), (85, 4), (74, 4), (67, 4), (81, 4), (86, 4), (102, 5), (103, 5), (101, 5), (100, 5), (99, 5), (104, 5), (96, 5), (110, 6), (109, 6), (113, 6), (111, 6), (112, 6)])" +non_spreading_dirlaynet_minbudget,0.7636627000756562,165,"(7, {0: [6, 3, 39, 26, 34, 43, 46], 1: [39, 26, 34, 43, 46, 51], 2: [43, 46]})" +non_spreading_minbudget,0.05942280008457601,165,"(13, [])" +heuristic_minbudget,0.9087215000763535,165,"(5, [(3, 1), (6, 1), (5, 1), (1, 1), (4, 1), (21, 2), (32, 2), (34, 2), (26, 2), (39, 2), (48, 3), (45, 3), (43, 3), (47, 3), (46, 3), (51, 4)])" +non_spreading_dirlaynet_minbudget,1.1901148001197726,230,"(26, {0: [14, 16, 23, 26, 22, 7, 9, 6, 15, 5, 27, 25, 2, 12, 11, 13, 8, 21, 1, 20, 19, 4, 18, 3, 24, 36], 1: [36]})" +non_spreading_minbudget,0.09228199999779463,230,"(27, [])" +heuristic_minbudget,6.45793869998306,230,"(8, [(1, 1), (6, 1), (12, 1), (7, 1), (13, 1), (14, 1), (20, 1), (9, 1), (55, 2), (33, 2), (34, 2), (39, 2), (38, 2), (50, 2), (40, 2), (46, 2), (69, 3), (75, 3), (79, 3), (82, 3), (65, 3), (60, 3), (72, 3), (81, 3), (98, 4), (86, 4), (85, 4), (97, 4), (100, 4), (83, 4), (88, 4), (84, 4), (103, 5), (126, 5), (131, 5), (120, 5), (127, 5), (106, 5), (128, 5), (129, 5), (146, 6), (149, 6), (157, 6), (151, 6), (154, 6), (153, 6), (144, 6), (134, 6), (166, 7), (175, 7), (160, 7), (178, 7), (167, 7), (180, 7), (164, 7), (181, 7), (191, 8), (195, 8), (188, 8), (187, 8), (196, 8), (189, 8), (190, 8), (186, 8), (201, 9), (212, 9), (209, 9), (208, 9), (205, 9), (206, 9), (210, 9), (203, 9)])" +non_spreading_dirlaynet_minbudget,0.8137726001441479,175,"(22, {0: [14, 16, 22, 7, 9, 6, 15, 17, 10, 5, 2, 11, 12, 13, 8, 21, 20, 19, 4, 18, 3, 1]})" +non_spreading_minbudget,0.04312669998034835,175,"(22, [])" +heuristic_minbudget,2.5335946001578122,175,"(8, [(18, 1), (1, 1), (12, 1), (9, 1), (4, 1), (14, 1), (17, 1), (21, 1), (34, 2), (36, 2), (35, 2), (42, 2), (48, 2), (44, 2), (31, 2), (45, 2), (60, 3), (58, 3), (61, 3), (59, 3), (56, 3), (57, 3), (54, 3), (62, 3), (74, 4), (70, 4), (72, 4), (73, 4), (64, 4)])" +non_spreading_dirlaynet_minbudget,0.6366059998981655,134,"(6, {0: [10, 11, 17, 61, 67, 117], 1: [17, 44, 61], 2: [44]})" +non_spreading_minbudget,0.03964060009457171,134,"(10, [])" +heuristic_minbudget,1.582167699933052,134,"(2, [(10, 1), (11, 1), (26, 2), (17, 2), (44, 3), (40, 3), (61, 4), (54, 4), (67, 5), (70, 5), (84, 6), (91, 6), (110, 7), (115, 7)])" +non_spreading_dirlaynet_minbudget,0.5849375000689179,142,"(7, {0: [5, 2, 7, 6, 3, 1, 14]})" +non_spreading_minbudget,0.029051399789750576,142,"(7, [])" +heuristic_minbudget,0.5750889000482857,142,"(5, [(5, 1), (3, 1), (2, 1), (6, 1), (7, 1), (29, 2), (8, 2), (22, 2), (31, 2), (32, 2), (44, 3), (40, 3), (45, 3), (35, 3), (41, 3), (51, 4), (49, 4), (58, 4), (60, 4), (48, 4), (74, 5), (78, 5), (65, 5), (79, 5), (81, 5), (92, 6), (88, 6), (95, 6), (90, 6), (85, 6), (103, 7), (101, 7), (107, 7), (102, 7), (104, 7)])" +non_spreading_dirlaynet_minbudget,0.7268608000595123,160,"(7, {0: [2, 8, 9, 14, 16, 17, 42], 1: [14, 16, 17]})" +non_spreading_minbudget,0.04260340007022023,160,"(9, [])" +heuristic_minbudget,0.8875651000998914,160,"(4, [(8, 1), (9, 1), (2, 1), (6, 1), (14, 2), (17, 2), (13, 2), (16, 2), (23, 3), (25, 3), (29, 3), (32, 3), (42, 4)])" +non_spreading_dirlaynet_minbudget,0.7861946998164058,160,"(6, {0: [5, 2, 6, 4, 3, 1]})" +non_spreading_minbudget,0.10845379997044802,160,"(6, [])" +heuristic_minbudget,0.9687779999803752,160,"(5, [(4, 1), (2, 1), (6, 1), (3, 1), (1, 1), (14, 2), (22, 2)])" +non_spreading_dirlaynet_minbudget,1.1909837001003325,177,"(6, {0: [12, 7, 33, 44, 89, 98], 1: [33, 44, 89], 3: [89]})" +non_spreading_minbudget,0.061934900004416704,177,"(9, [])" +heuristic_minbudget,1.7665303000248969,177,"(3, [(12, 1), (7, 1), (13, 1), (33, 2), (44, 2), (25, 2), (52, 3), (62, 3), (58, 3), (66, 4), (89, 4), (78, 4)])" +non_spreading_dirlaynet_minbudget,0.5384468999691308,118,"(2, {1: [54, 78], 2: [54, 78]})" +non_spreading_minbudget,0.027300900081172585,118,"(4, [])" +heuristic_minbudget,1.1404292001388967,118,"(2, [(6, 1), (1, 1), (23, 2), (31, 2), (54, 3), (48, 3), (61, 4), (60, 4)])" +non_spreading_dirlaynet_minbudget,0.6008705000858754,94,"(7, {0: [13, 3, 22, 45, 52, 69, 87], 1: [22, 45, 52, 69, 87], 2: [87], 3: [69]})" +non_spreading_minbudget,0.024603100027889013,94,"(14, [])" +heuristic_minbudget,1.197276399936527,94,"(4, [(3, 1), (13, 1), (6, 1), (15, 1), (19, 2), (22, 2), (20, 2), (45, 2), (49, 3), (52, 3), (53, 3), (47, 3), (69, 4), (60, 4), (63, 4), (68, 4)])" +non_spreading_dirlaynet_minbudget,0.4536055000498891,110,"(7, {0: [17, 16, 19, 15, 23, 22, 21], 1: [23, 22, 21]})" +non_spreading_minbudget,0.09495320008136332,110,"(9, [])" +heuristic_minbudget,0.7355480000842363,110,"(4, [(19, 1), (15, 1), (17, 1), (16, 1), (24, 2), (22, 2), (23, 2), (20, 2), (28, 3), (26, 3), (33, 3), (30, 3), (46, 4), (57, 4), (68, 4)])" +non_spreading_dirlaynet_minbudget,0.9377388001885265,162,"(2, {0: [38, 112], 1: [14, 112]})" +non_spreading_minbudget,0.11844550003297627,162,"(5, [])" +heuristic_minbudget,0.5747994999401271,162,"(2, [(7, 1), (5, 1), (14, 2), (15, 2), (52, 3), (38, 3), (55, 4), (58, 4), (67, 5), (69, 5)])" +non_spreading_dirlaynet_minbudget,0.9667919999919832,127,"(12, {0: [10, 5, 2, 11, 12, 8, 7, 9, 6, 4, 3, 1]})" +non_spreading_minbudget,0.03575319983065128,127,"(12, [])" +heuristic_minbudget,1.1490799998864532,127,"(5, [(2, 1), (3, 1), (6, 1), (5, 1), (8, 1), (16, 2), (26, 2), (27, 2), (14, 2), (25, 2), (39, 3), (47, 3), (46, 3), (28, 3), (33, 3), (56, 4), (55, 4), (62, 4), (63, 4), (57, 4), (79, 5), (69, 5), (80, 5), (77, 5), (70, 5), (89, 6), (91, 6), (87, 6), (100, 6), (95, 6), (113, 7), (108, 7), (119, 7), (106, 7), (107, 7)])" +non_spreading_dirlaynet_minbudget,0.6362785999663174,136,"(4, {0: [23, 2, 100, 126], 6: [126]})" +non_spreading_minbudget,0.03179979999549687,136,"(6, [])" +heuristic_minbudget,1.3357363000977784,136,"(2, [(23, 1), (2, 1), (32, 2), (29, 2), (36, 3), (34, 3), (47, 4), (51, 4), (70, 5), (59, 5), (100, 6), (83, 6), (111, 7), (117, 7)])" +non_spreading_dirlaynet_minbudget,0.49284680001437664,132,"(10, {0: [10, 5, 2, 8, 7, 9, 6, 4, 1, 14]})" +non_spreading_minbudget,0.032877299934625626,132,"(10, [])" +heuristic_minbudget,1.5516102998517454,132,"(4, [(2, 1), (9, 1), (8, 1), (7, 1), (21, 2), (16, 2), (24, 2), (20, 2), (29, 3), (38, 3), (41, 3), (34, 3), (53, 4), (44, 4), (47, 4), (55, 4), (76, 5), (74, 5), (63, 5), (65, 5), (86, 6), (91, 6), (84, 6), (89, 6), (95, 7), (98, 7), (109, 7), (102, 7)])" +non_spreading_dirlaynet_minbudget,0.6802503999788314,122,"(1, {4: [62]})" +non_spreading_minbudget,0.026275800075381994,122,"(1, [])" +heuristic_minbudget,0.7856588000431657,122,"(1, [(1, 1), (19, 2), (37, 3), (49, 4)])" +non_spreading_dirlaynet_minbudget,0.512130199931562,112,"(7, {0: [5, 2, 7, 6, 4, 3, 1]})" +non_spreading_minbudget,0.03236670000478625,112,"(7, [])" +heuristic_minbudget,0.5671945998910815,112,"(4, [(1, 1), (7, 1), (2, 1), (3, 1), (24, 2), (19, 2), (15, 2), (17, 2), (30, 3), (29, 3), (27, 3), (25, 3), (34, 4), (36, 4), (39, 4), (31, 4), (44, 5)])" +non_spreading_dirlaynet_minbudget,0.7608991998713464,177,"(9, {0: [15, 17, 27, 11, 1, 42, 50, 52, 49], 1: [42, 50, 52, 49], 2: [42, 50, 52, 49]})" +non_spreading_minbudget,0.03819869994185865,177,"(18, [])" +heuristic_minbudget,3.600453299935907,177,"(5, [(1, 1), (27, 1), (17, 1), (11, 1), (15, 1), (41, 2), (33, 2), (29, 2), (35, 2), (36, 2), (48, 3), (43, 3), (51, 3), (44, 3), (47, 3), (55, 4), (69, 4), (83, 4), (60, 4), (68, 4), (85, 5), (86, 5), (90, 5), (91, 5), (94, 5), (119, 6), (116, 6), (100, 6), (107, 6), (118, 6), (127, 7), (120, 7), (131, 7), (134, 7), (125, 7), (145, 8), (144, 8), (142, 8), (151, 8), (159, 8), (167, 9), (164, 9), (170, 9), (165, 9), (166, 9)])" +non_spreading_dirlaynet_minbudget,0.5297947998624295,113,"(15, {0: [14, 10, 5, 2, 12, 13, 8, 7, 9, 6, 4, 3, 1, 15, 27]})" +non_spreading_minbudget,0.02606609999202192,113,"(15, [])" +heuristic_minbudget,1.86227019992657,113,"(8, [(14, 1), (6, 1), (15, 1), (5, 1), (3, 1), (4, 1), (9, 1), (10, 1), (33, 2), (37, 2), (38, 2), (27, 2), (32, 2), (18, 2), (23, 2), (30, 2), (45, 3), (47, 3), (56, 3), (65, 3), (60, 3), (62, 3), (64, 3), (52, 3), (77, 4), (81, 4), (70, 4), (71, 4), (78, 4), (80, 4), (83, 4), (75, 4), (86, 5), (84, 5), (87, 5), (88, 5), (85, 5)])" +non_spreading_dirlaynet_minbudget,1.0635809998493642,151,"(5, {0: [3, 61, 75, 134, 135], 1: [105, 135], 2: [105, 115], 3: [105], 4: [105]})" +non_spreading_minbudget,0.03986999997869134,151,"(12, [])" +heuristic_minbudget,2.3467868000734597,151,"(4, [(3, 1), (2, 1), (9, 1), (15, 1), (26, 2), (17, 2), (27, 2), (35, 2), (61, 3), (54, 3), (41, 3), (49, 3), (75, 4), (74, 4), (73, 4), (78, 4), (91, 5), (93, 5), (89, 5), (105, 5), (115, 6), (107, 6), (111, 6), (112, 6), (118, 7), (119, 7), (121, 7), (122, 7), (131, 8)])" +non_spreading_dirlaynet_minbudget,0.5187239998485893,125,"(11, {0: [22, 6, 21, 4, 42, 31, 41, 38, 35, 37, 43], 1: [42, 31, 41, 38, 35, 37, 43]})" +non_spreading_minbudget,0.12787489988841116,125,"(17, [])" +heuristic_minbudget,3.3736835999879986,125,"(7, [(21, 1), (22, 1), (4, 1), (6, 1), (27, 1), (2, 1), (7, 1), (35, 2), (42, 2), (34, 2), (39, 2), (38, 2), (40, 2), (43, 2), (49, 3), (46, 3), (73, 3), (57, 3), (48, 3), (51, 3), (72, 3), (80, 4), (88, 4), (95, 4), (84, 4), (79, 4), (85, 4), (97, 4), (110, 5), (101, 5), (107, 5), (106, 5), (112, 5), (104, 5), (102, 5)])" +non_spreading_dirlaynet_minbudget,0.5040550001431257,114,"(9, {0: [16, 10, 2, 38, 35, 69, 87, 77, 90], 1: [38, 35, 87, 77, 90], 2: [87, 77], 3: [87, 77, 90], 4: [87, 77]})" +non_spreading_minbudget,0.04212810005992651,114,"(16, [])" +heuristic_minbudget,1.9488479997962713,114,"(6, [(16, 1), (2, 1), (10, 1), (9, 1), (15, 1), (24, 1), (34, 2), (35, 2), (38, 2), (25, 2), (41, 2), (28, 2), (49, 3), (56, 3), (52, 3), (55, 3), (57, 3), (59, 3), (69, 4), (68, 4), (62, 4), (60, 4), (67, 4), (65, 4), (77, 5), (87, 5), (83, 5), (73, 5), (85, 5), (80, 5)])" +non_spreading_dirlaynet_minbudget,0.8800749001093209,165,"(3, {0: [3, 116, 145], 1: [48], 2: [116], 3: [83, 145], 4: [83, 145]})" +non_spreading_minbudget,0.06158620002679527,165,"(11, [])" +heuristic_minbudget,1.6480677998624742,165,"(4, [(3, 1), (5, 1), (4, 1), (17, 1), (23, 2), (22, 2), (25, 2), (26, 2), (32, 3), (31, 3), (41, 3), (37, 3), (48, 4), (57, 4), (66, 4), (43, 4), (77, 5), (83, 5), (76, 5), (78, 5), (116, 6), (114, 6), (118, 6), (111, 6), (121, 7), (125, 7), (128, 7), (126, 7), (131, 8), (135, 8), (132, 8), (134, 8)])" +non_spreading_dirlaynet_minbudget,0.3088372000493109,114,"(9, {0: [5, 2, 8, 7, 9, 6, 4, 3, 1]})" +non_spreading_minbudget,0.08847400010563433,114,"(9, [])" +heuristic_minbudget,0.7732076998800039,114,"(5, [(9, 1), (4, 1), (1, 1), (5, 1), (7, 1), (20, 2), (31, 2), (10, 2), (32, 2), (33, 2), (47, 3), (39, 3), (48, 3), (45, 3), (40, 3), (60, 4), (65, 4), (69, 4), (61, 4), (62, 4), (80, 5), (76, 5), (81, 5), (75, 5), (78, 5), (94, 6), (82, 6), (92, 6), (91, 6), (85, 6)])" +non_spreading_dirlaynet_minbudget,0.5046526999212801,125,"(11, {0: [10, 5, 2, 8, 7, 9, 6, 4, 3, 1, 25]})" +non_spreading_minbudget,0.038530000019818544,125,"(11, [])" +heuristic_minbudget,1.5089217000640929,125,"(8, [(5, 1), (2, 1), (8, 1), (1, 1), (7, 1), (4, 1), (3, 1), (9, 1), (23, 2), (14, 2), (13, 2), (25, 2), (30, 2), (24, 2), (22, 2), (27, 2), (49, 3), (58, 3), (47, 3), (57, 3), (41, 3), (32, 3), (39, 3), (33, 3), (61, 4), (76, 4), (73, 4), (78, 4), (80, 4), (72, 4), (64, 4), (74, 4), (85, 5), (101, 5), (95, 5), (94, 5), (97, 5), (84, 5), (93, 5), (96, 5)])" +non_spreading_dirlaynet_minbudget,0.9893045001663268,164,"(1, {3: [69]})" +non_spreading_minbudget,0.06566859991289675,164,"(1, [])" +heuristic_minbudget,1.310516000026837,164,"(1, [(11, 1), (33, 2), (43, 3)])" +non_spreading_dirlaynet_minbudget,0.36087279999628663,84,"(13, {0: [5, 2, 11, 12, 13, 8, 7, 9, 6, 4, 3, 1, 24]})" +non_spreading_minbudget,0.023888400057330728,84,"(13, [])" +heuristic_minbudget,1.1509269999805838,84,"(5, [(5, 1), (7, 1), (2, 1), (9, 1), (13, 1), (23, 2), (26, 2), (20, 2), (17, 2), (18, 2), (44, 3), (43, 3), (40, 3), (41, 3), (39, 3), (54, 4), (48, 4), (51, 4), (50, 4), (46, 4), (78, 5), (67, 5), (62, 5), (75, 5), (59, 5)])" +non_spreading_dirlaynet_minbudget,0.5455466001294553,165,"(5, {0: [5, 2, 4, 3, 1]})" +non_spreading_minbudget,0.028440400026738644,165,"(5, [])" +heuristic_minbudget,0.44033989985473454,165,"(4, [(3, 1), (5, 1), (4, 1), (2, 1), (15, 2), (10, 2), (14, 2), (9, 2)])" +non_spreading_dirlaynet_minbudget,1.3026932999491692,212,"(11, {0: [14, 16, 17, 5, 11, 19, 23, 26, 30, 29, 38], 1: [23, 26, 30, 29, 38], 2: [38]})" +non_spreading_minbudget,0.06338009983301163,212,"(17, [])" +heuristic_minbudget,2.3486457001417875,212,"(7, [(17, 1), (19, 1), (11, 1), (14, 1), (16, 1), (5, 1), (20, 1), (26, 2), (24, 2), (30, 2), (32, 2), (33, 2), (28, 2), (23, 2), (35, 3), (38, 3), (46, 3), (51, 3), (39, 3), (42, 3)])" +non_spreading_dirlaynet_minbudget,0.5373384999111295,140,"(21, {0: [14, 16, 7, 9, 6, 15, 17, 10, 5, 2, 12, 13, 8, 21, 1, 20, 19, 4, 18, 3, 24]})" +non_spreading_minbudget,0.03254449996165931,140,"(21, [])" +heuristic_minbudget,2.4232493001036346,140,"(6, [(14, 1), (16, 1), (17, 1), (4, 1), (10, 1), (2, 1), (28, 2), (42, 2), (31, 2), (39, 2), (33, 2), (45, 2), (63, 3), (66, 3), (53, 3), (61, 3), (68, 3), (70, 3), (72, 4), (73, 4), (71, 4), (77, 4), (78, 4), (79, 4), (82, 5), (104, 5), (88, 5), (91, 5), (98, 5), (105, 5), (109, 6), (112, 6), (115, 6), (110, 6), (111, 6), (118, 6), (128, 7), (129, 7), (124, 7), (125, 7), (122, 7), (126, 7)])" +non_spreading_dirlaynet_minbudget,0.5947537000756711,118,"(14, {0: [14, 10, 5, 2, 11, 12, 13, 8, 7, 9, 6, 4, 3, 1]})" +non_spreading_minbudget,0.031359399901703,118,"(14, [])" +heuristic_minbudget,2.0146788998972625,118,"(8, [(2, 1), (6, 1), (14, 1), (9, 1), (3, 1), (11, 1), (1, 1), (5, 1), (37, 2), (35, 2), (30, 2), (25, 2), (39, 2), (43, 2), (18, 2), (29, 2), (53, 3), (46, 3), (44, 3), (48, 3), (58, 3), (45, 3), (55, 3), (52, 3), (72, 4), (60, 4), (63, 4), (61, 4), (71, 4), (74, 4), (67, 4), (73, 4), (76, 5), (98, 5), (77, 5), (81, 5), (93, 5), (95, 5), (85, 5), (96, 5), (100, 6), (102, 6), (103, 6), (99, 6), (101, 6)])" +non_spreading_dirlaynet_minbudget,0.9165356999728829,151,"(7, {0: [14, 11, 13, 7, 16, 19, 24], 1: [16, 19]})" +non_spreading_minbudget,0.051704199984669685,151,"(9, [])" +heuristic_minbudget,0.7268763000611216,151,"(4, [(14, 1), (11, 1), (7, 1), (13, 1), (19, 2), (15, 2), (18, 2), (16, 2), (24, 3)])" +non_spreading_dirlaynet_minbudget,0.5570110001135617,131,"(3, {0: [10, 23, 26], 1: [23, 26], 2: [23, 26], 3: [23, 26]})" +non_spreading_minbudget,0.03013460012152791,131,"(6, [])" +heuristic_minbudget,0.5643533999100327,131,"(3, [(10, 1), (3, 1), (5, 1), (13, 2), (16, 2), (15, 2), (21, 3), (19, 3), (18, 3), (26, 4), (23, 4)])" +non_spreading_dirlaynet_minbudget,0.37107400014065206,85,"(9, {0: [11, 2, 13, 31, 26, 22, 50, 52, 49], 1: [31, 26, 22, 50, 52, 49], 2: [50, 52, 49]})" +non_spreading_minbudget,0.023888600058853626,85,"(16, [])" +heuristic_minbudget,1.7865257000084966,85,"(6, [(11, 1), (13, 1), (2, 1), (8, 1), (1, 1), (5, 1), (26, 2), (24, 2), (39, 2), (40, 2), (22, 2), (31, 2), (53, 3), (49, 3), (48, 3), (50, 3), (52, 3), (51, 3), (67, 4), (54, 4), (58, 4), (63, 4)])" +non_spreading_dirlaynet_minbudget,1.101705300156027,146,"(7, {0: [5, 2, 20, 45, 87, 113, 117], 1: [67], 2: [67], 5: [87]})" +non_spreading_minbudget,0.03667910001240671,146,"(10, [])" +heuristic_minbudget,2.299951800145209,146,"(3, [(5, 1), (2, 1), (20, 1), (45, 2), (31, 2), (42, 2), (67, 3), (68, 3), (57, 3), (73, 4), (70, 4), (71, 4), (77, 5), (79, 5), (82, 5), (94, 6), (89, 6), (98, 6), (113, 7), (101, 7), (106, 7)])" +non_spreading_dirlaynet_minbudget,0.8552312001120299,176,"(19, {0: [14, 16, 7, 9, 6, 15, 17, 10, 5, 2, 11, 12, 13, 8, 19, 4, 18, 3, 1]})" +non_spreading_minbudget,0.04930149996653199,176,"(19, [])" +heuristic_minbudget,2.6644704998470843,176,"(10, [(8, 1), (12, 1), (15, 1), (10, 1), (11, 1), (3, 1), (6, 1), (14, 1), (13, 1), (1, 1), (26, 2), (46, 2), (36, 2), (20, 2), (43, 2), (27, 2), (35, 2), (40, 2), (31, 2), (47, 2), (55, 3), (66, 3), (50, 3), (58, 3), (61, 3), (53, 3), (56, 3), (54, 3), (57, 3), (67, 3), (77, 4), (70, 4), (74, 4), (76, 4), (73, 4), (69, 4), (72, 4), (78, 4), (71, 4), (75, 4)])" +non_spreading_dirlaynet_minbudget,0.9114642001222819,126,"(11, {0: [14, 8, 21, 7, 6, 28, 18, 34, 33, 30, 36], 1: [34, 33, 30]})" +non_spreading_minbudget,0.04174270015209913,126,"(13, [])" +heuristic_minbudget,1.17835669987835,126,"(7, [(8, 1), (14, 1), (18, 1), (21, 1), (6, 1), (28, 1), (7, 1), (33, 2), (30, 2), (32, 2), (34, 2), (29, 2), (31, 2)])" +non_spreading_dirlaynet_minbudget,0.6806262000463903,139,"(1, {3: [50]})" +non_spreading_minbudget,0.029057599836960435,139,"(1, [])" +heuristic_minbudget,0.5616037000436336,139,"(1, [(3, 1), (13, 2), (43, 3)])" +non_spreading_dirlaynet_minbudget,0.3880594000220299,97,"(4, {0: [4, 5, 2, 7]})" +non_spreading_minbudget,0.027884799987077713,97,"(4, [])" +heuristic_minbudget,0.13068190007470548,97,"(2, [(5, 1), (4, 1), (8, 2), (7, 2), (21, 3)])" +non_spreading_dirlaynet_minbudget,1.167196199996397,170,"(6, {0: [5, 2, 6, 4, 3, 1]})" +non_spreading_minbudget,0.09135910007171333,170,"(6, [])" +heuristic_minbudget,1.3934970998670906,170,"(5, [(3, 1), (1, 1), (2, 1), (6, 1), (5, 1), (19, 2), (14, 2), (18, 2), (23, 2), (15, 2), (27, 3), (32, 3), (33, 3), (35, 3), (40, 3), (60, 4), (62, 4), (70, 4), (68, 4), (69, 4), (82, 5), (77, 5), (81, 5), (78, 5), (76, 5), (96, 6), (88, 6), (93, 6), (97, 6), (98, 6), (103, 7), (105, 7), (108, 7), (102, 7), (100, 7), (126, 8), (125, 8), (137, 8), (135, 8)])" +non_spreading_dirlaynet_minbudget,1.1337807001546025,197,"(8, {0: [9, 12, 3, 30, 29, 42, 41, 45], 1: [30, 29, 42, 41, 45], 2: [42, 41, 45]})" +non_spreading_minbudget,0.05200010002590716,197,"(15, [])" +heuristic_minbudget,2.1535696000792086,197,"(5, [(3, 1), (12, 1), (9, 1), (5, 1), (14, 1), (21, 2), (27, 2), (29, 2), (30, 2), (31, 2), (41, 3), (46, 3), (44, 3), (42, 3), (43, 3), (65, 4), (59, 4), (66, 4), (62, 4), (49, 4), (83, 5), (85, 5), (99, 5), (94, 5), (80, 5), (112, 6), (111, 6), (103, 6), (108, 6), (118, 6), (144, 7), (140, 7), (147, 7), (131, 7), (146, 7), (165, 8), (159, 8), (162, 8), (161, 8), (164, 8)])" +non_spreading_dirlaynet_minbudget,0.979691700078547,175,"(13, {0: [16, 25, 11, 42, 31, 41, 26, 38, 30, 29, 28, 35, 37], 1: [42, 31, 41, 26, 38, 30, 29, 28, 35, 37]})" +non_spreading_minbudget,0.06003940012305975,175,"(23, [])" +heuristic_minbudget,3.174024800071493,175,"(6, [(25, 1), (11, 1), (16, 1), (9, 1), (2, 1), (24, 1), (28, 2), (32, 2), (40, 2), (34, 2), (38, 2), (29, 2), (60, 3), (49, 3), (61, 3), (47, 3), (48, 3), (54, 3), (79, 4), (81, 4), (91, 4), (92, 4), (82, 4), (66, 4), (110, 5), (106, 5), (95, 5), (96, 5), (108, 5), (102, 5), (115, 6), (113, 6), (118, 6), (117, 6), (119, 6), (116, 6), (129, 7), (130, 7)])" +non_spreading_dirlaynet_minbudget,0.5525450001005083,162,"(14, {0: [14, 10, 5, 2, 11, 12, 13, 8, 7, 9, 6, 4, 3, 1]})" +non_spreading_minbudget,0.02637740015052259,162,"(14, [])" +heuristic_minbudget,2.7541892000008374,162,"(8, [(8, 1), (3, 1), (13, 1), (11, 1), (2, 1), (1, 1), (6, 1), (9, 1), (25, 2), (26, 2), (20, 2), (17, 2), (22, 2), (32, 2), (18, 2), (28, 2), (52, 3), (54, 3), (38, 3), (45, 3), (49, 3), (42, 3), (59, 3), (44, 3), (67, 4), (72, 4), (71, 4), (76, 4), (73, 4), (70, 4), (75, 4), (68, 4), (78, 5), (87, 5), (89, 5), (84, 5), (82, 5), (83, 5), (81, 5), (79, 5), (94, 6), (93, 6)])" +non_spreading_dirlaynet_minbudget,0.45032479986548424,156,"(2, {0: [13, 115], 8: [115]})" +non_spreading_minbudget,0.019497300032526255,156,"(4, [])" +heuristic_minbudget,1.121900200145319,156,"(1, [(13, 1), (22, 2), (29, 3), (51, 4), (67, 5), (77, 6), (85, 7), (104, 8)])" +non_spreading_dirlaynet_minbudget,0.6660581000614911,129,"(1, {0: [15]})" +non_spreading_minbudget,0.033490699948742986,129,"(1, [])" +heuristic_minbudget,0.6326198000460863,129,"(1, [(15, 1)])" +non_spreading_dirlaynet_minbudget,0.8275399000849575,137,"(2, {0: [2, 108], 6: [108], 7: [108]})" +non_spreading_minbudget,0.023948500165715814,137,"(4, [])" +heuristic_minbudget,0.28256139997392893,137,"(1, [(2, 1), (13, 2), (22, 3), (31, 4), (36, 5), (50, 6), (74, 7)])" +non_spreading_dirlaynet_minbudget,0.3248042999766767,102,"(1, {3: [87]})" +non_spreading_minbudget,0.027101999847218394,102,"(3, [])" +heuristic_minbudget,1.1255909001920372,102,"(1, [(13, 1), (22, 2), (39, 3), (49, 4), (71, 5)])" +non_spreading_dirlaynet_minbudget,0.39874129998497665,73,"(3, {0: [9, 57, 66], 1: [23], 2: [23]})" +non_spreading_minbudget,0.026935699861496687,73,"(7, [])" +heuristic_minbudget,0.8045551998075098,73,"(2, [(9, 1), (2, 1), (18, 2), (12, 2), (35, 3), (23, 3), (62, 4), (57, 4)])" +non_spreading_dirlaynet_minbudget,0.5787450999487191,105,"(8, {0: [3, 1, 28, 37, 34, 55, 50, 52], 1: [28, 37, 34, 55, 50, 52], 2: [55, 50, 52]})" +non_spreading_minbudget,0.06141979992389679,105,"(16, [])" +heuristic_minbudget,3.1684757000766695,105,"(6, [(3, 1), (1, 1), (5, 1), (9, 1), (14, 1), (8, 1), (34, 2), (48, 2), (28, 2), (40, 2), (37, 2), (47, 2), (49, 3), (51, 3), (54, 3), (56, 3), (53, 3), (50, 3), (60, 4), (69, 4), (76, 4), (65, 4)])" +non_spreading_dirlaynet_minbudget,0.48192729987204075,122,"(7, {0: [5, 2, 7, 4, 3, 1, 25]})" +non_spreading_minbudget,0.03430820000357926,122,"(7, [])" +heuristic_minbudget,0.48548690020106733,122,"(4, [(2, 1), (4, 1), (7, 1), (5, 1), (20, 2), (16, 2), (11, 2), (15, 2), (25, 3), (27, 3), (26, 3), (31, 3), (37, 4), (39, 4), (34, 4), (38, 4), (43, 5), (45, 5), (42, 5), (44, 5)])" +non_spreading_dirlaynet_minbudget,1.326428100001067,196,"(8, {0: [10, 22, 19, 41, 61, 87, 137, 194], 1: [61, 87, 194], 2: [61, 87]})" +non_spreading_minbudget,0.0565718999132514,196,"(13, [])" +heuristic_minbudget,3.0652807999867946,196,"(3, [(22, 1), (19, 1), (10, 1), (41, 2), (39, 2), (25, 2), (75, 3), (61, 3), (54, 3), (104, 4), (100, 4), (87, 4), (111, 5), (115, 5), (112, 5), (133, 6), (127, 6), (128, 6), (137, 7), (135, 7), (146, 7), (162, 8), (163, 8), (156, 8), (181, 9), (179, 9), (182, 9)])" +non_spreading_dirlaynet_minbudget,0.6285307998768985,152,"(12, {0: [9, 6, 5, 25, 2, 13, 20, 31, 26, 30, 29, 28], 1: [31, 26, 30, 29, 28]})" +non_spreading_minbudget,0.04760949988849461,152,"(16, [])" +heuristic_minbudget,1.38000059989281,152,"(7, [(5, 1), (9, 1), (25, 1), (2, 1), (6, 1), (13, 1), (20, 1), (34, 2), (32, 2), (30, 2), (27, 2), (33, 2), (28, 2), (26, 2), (38, 3), (36, 3), (58, 3), (57, 3), (60, 3), (48, 3), (52, 3), (87, 4), (70, 4), (75, 4), (84, 4), (89, 4), (61, 4), (69, 4), (93, 5), (99, 5), (94, 5), (96, 5), (97, 5), (95, 5), (104, 5), (119, 6), (115, 6), (116, 6), (120, 6), (123, 6), (117, 6), (106, 6), (131, 7), (130, 7), (135, 7), (132, 7), (129, 7), (125, 7), (137, 7)])" +non_spreading_dirlaynet_minbudget,0.30850719986483455,87,"(1, {2: [53]})" +non_spreading_minbudget,0.018661600071936846,87,"(1, [])" +heuristic_minbudget,1.1946388001088053,87,"(1, [(22, 1), (23, 2)])" +non_spreading_dirlaynet_minbudget,0.2921035001054406,103,"(3, {0: [12, 31, 60], 1: [46, 60], 2: [31, 46, 60], 3: [60], 4: [60]})" +non_spreading_minbudget,0.017141399905085564,103,"(10, [])" +heuristic_minbudget,0.5932669001631439,103,"(3, [(6, 1), (10, 1), (3, 1), (12, 2), (18, 2), (20, 2), (35, 3), (31, 3), (29, 3), (46, 4), (45, 4), (50, 4), (61, 5), (58, 5), (60, 5), (63, 6), (68, 6), (65, 6)])" +non_spreading_dirlaynet_minbudget,0.6329423999413848,158,"(5, {0: [5, 2, 4, 3, 1]})" +non_spreading_minbudget,0.1259716998320073,158,"(5, [])" +heuristic_minbudget,0.4594906000420451,158,"(4, [(4, 1), (3, 1), (1, 1), (5, 1), (20, 2), (22, 2)])" +non_spreading_dirlaynet_minbudget,0.5525646000169218,158,"(5, {0: [5, 2, 4, 3, 1]})" +non_spreading_minbudget,0.028168300166726112,158,"(5, [])" +heuristic_minbudget,0.5709834999870509,158,"(4, [(1, 1), (2, 1), (4, 1), (3, 1), (10, 2), (26, 2), (15, 2), (29, 2), (30, 3), (31, 3), (34, 3), (32, 3), (53, 4), (56, 4), (45, 4), (48, 4), (60, 5), (76, 5), (77, 5), (66, 5), (80, 6), (82, 6), (81, 6), (83, 6), (91, 7)])" +non_spreading_dirlaynet_minbudget,0.5035032997839153,130,"(10, {0: [16, 4, 23, 31, 26, 22, 30, 29, 28, 34], 1: [23, 31, 26, 22, 30, 29, 28, 34]})" +non_spreading_minbudget,0.04079610016196966,130,"(18, [])" +heuristic_minbudget,2.7959156001452357,130,"(6, [(4, 1), (16, 1), (1, 1), (5, 1), (3, 1), (17, 1), (32, 2), (24, 2), (34, 2), (19, 2), (33, 2), (26, 2), (36, 3), (39, 3), (42, 3), (55, 3), (59, 3), (49, 3), (68, 4), (66, 4), (67, 4), (65, 4), (69, 4), (72, 4), (93, 5), (89, 5), (84, 5), (77, 5), (91, 5), (81, 5)])" +non_spreading_dirlaynet_minbudget,0.38819109997712076,76,"(1, {1: [34]})" +non_spreading_minbudget,0.01265419996343553,76,"(1, [])" +heuristic_minbudget,0.8527677000965923,76,"(1, [(11, 1)])" +non_spreading_dirlaynet_minbudget,0.44918420002795756,109,"(9, {0: [5, 2, 8, 7, 9, 6, 4, 3, 1]})" +non_spreading_minbudget,0.01864150003530085,109,"(9, [])" +heuristic_minbudget,0.848988400073722,109,"(7, [(2, 1), (5, 1), (4, 1), (3, 1), (6, 1), (7, 1), (8, 1), (28, 2), (31, 2), (27, 2), (23, 2), (17, 2), (13, 2), (30, 2), (36, 3), (42, 3), (57, 3), (48, 3), (34, 3), (41, 3), (50, 3), (63, 4)])" +,229.11739100003615,, diff --git a/networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/non_spreading.png b/networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/non_spreading.png index fef28257a12cb4772886db6057c0de3421a69e5d..0fd13da7377d67a518328e5327477c424199cbfa 100644 GIT binary patch literal 77751 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      m5WGe0%TzlLw# zKdOV>3Ktxsfx0z(d{N8IP!|7v8Lp+|x{N*t3n^4YPmCG^`q@?$X4vf^X+40KKk+6v zJG-8|=qehra>H!f>(IGh@7e)JyUj8-tTb>`iBB!U+iC>WIP5gwklxnE98kk_WR3m(h)ZOi7Jr&etK|9N}8PExvkt1_e}X z3b70Tp>zU1U?f{6*f5+ovafH8vUBcib>zTl567wPgl-$`DsXe2JP9Jts8q}wcm@9r z!8ok*A!J%NgMxbSU&DTc-MdHo*?)Dfj?Y6-y?=Rspz6iO!ARd{!Kt=t;X0(S}6p&qvG@O81s4U`(#^Kt| zoBVthi9>%OQSZl1x*asycNUrUSUO@pf*p^2LzMf1;FN;9?j%e=rO_bc!g-XljC*JD}6 zP?`}?yQg^b#7r8D8hCc=M+ziRa2stDGb{&~=E~PRIdE+L0@&9BmTI%}im*9ob05uT z1=t3uCg3##P-(i}L|U#y42&Cr(P{8gEHZ}$h%F%9Wgxm4a7a=`ZV6V2WNmG2^%JS2 z_sR)3L%lW@&uG%^f`L&eh6)fqAS|&>;E<_!MS*nk%A|-M89<k8j@W#{5)JXoS2JF)?=BeZ(FY*UIIIgQWz=b?&WW&ft`a z%fuRZ;W@6 diff --git a/networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/spreading_minbudget.csv b/networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/spreading_minbudget.csv index 42346be..c096b50 100644 --- a/networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/spreading_minbudget.csv +++ b/networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/spreading_minbudget.csv @@ -1,20 +1,20 @@ algorithm,runtime,graph_nodes,Budget -spreading_minbudget,2.6765573997981846,100,"(2, [(28, 1), (17, 1), (47, 2), (2, 2)])" -heuristic_minbudget,0.7838832000270486,100,"(2, [(58, 1), (17, 1), (14, 2), (76, 2), (63, 3), (85, 3)])" -spreading_minbudget,4.7819698001258075,100,"(19, [(19, 1), (5, 1), (4, 1), (33, 1), (66, 1), (79, 1), (92, 1), (2, 1), (18, 1), (38, 1), (52, 1), (55, 1), (10, 1), (27, 1), (29, 1), (63, 1), (83, 1), (32, 1), (54, 1)])" -heuristic_minbudget,12.467118400149047,100,"(19, [(19, 1), (5, 1), (63, 1), (18, 1), (38, 1), (79, 1), (10, 1), (66, 1), (32, 1), (52, 1), (83, 1), (27, 1), (55, 1), (92, 1), (33, 1), (29, 1), (2, 1), (4, 1), (54, 1)])" -spreading_minbudget,2.980581299867481,100,"(24, [(40, 1), (2, 1), (3, 1), (5, 1), (8, 1), (11, 1), (22, 1), (24, 1), (51, 1), (62, 1), (72, 1), (76, 1), (81, 1), (83, 1), (85, 1), (87, 1), (89, 1), (92, 1), (97, 1), (44, 1), (46, 1), (60, 1), (63, 1), (70, 1)])" -heuristic_minbudget,35.502651300048456,100,"(24, [(76, 1), (85, 1), (11, 1), (44, 1), (87, 1), (83, 1), (92, 1), (46, 1), (62, 1), (70, 1), (8, 1), (2, 1), (22, 1), (72, 1), (24, 1), (89, 1), (63, 1), (81, 1), (51, 1), (3, 1), (97, 1), (5, 1), (40, 1), (60, 1)])" -spreading_minbudget,8.064421500079334,200,"(19, [(86, 1), (105, 1), (161, 1), (115, 1), (21, 1), (34, 1), (162, 1), (85, 1), (131, 1), (70, 1), (42, 1), (186, 1), (36, 1), (14, 1), (40, 1), (187, 1), (100, 1), (178, 1), (176, 1)])" -heuristic_minbudget,7.089356000069529,200,"(13, [(186, 1), (34, 1), (105, 1), (178, 1), (100, 1), (14, 1), (131, 1), (176, 1), (187, 1), (40, 1), (42, 1), (161, 1), (91, 1), (181, 2), (92, 2), (71, 2), (144, 2), (130, 2), (175, 2), (102, 2), (194, 2), (191, 2), (9, 2), (18, 2), (4, 2), (68, 2)])" -spreading_minbudget,11.68095609988086,200,"(50, [(118, 1), (80, 1), (1, 1), (3, 1), (16, 1), (25, 1), (29, 1), (42, 1), (55, 1), (65, 1), (73, 1), (75, 1), (76, 1), (78, 1), (79, 1), (95, 1), (99, 1), (100, 1), (101, 1), (112, 1), (127, 1), (129, 1), (133, 1), (137, 1), (151, 1), (152, 1), (166, 1), (171, 1), (191, 1), (197, 1), (5, 1), (6, 1), (24, 1), (27, 1), (57, 1), (110, 1), (170, 1), (179, 1), (19, 1), (20, 1), (146, 1), (163, 1), (193, 1), (119, 1), (181, 1), (98, 1), (140, 1), (144, 1), (157, 1), (180, 1)])" -heuristic_minbudget,41.66929220012389,200,"(49, [(98, 1), (101, 1), (73, 1), (16, 1), (112, 1), (79, 1), (27, 1), (1, 1), (137, 1), (140, 1), (119, 1), (5, 1), (76, 1), (25, 1), (118, 1), (129, 1), (146, 1), (163, 1), (181, 1), (110, 1), (3, 1), (55, 1), (100, 1), (151, 1), (78, 1), (191, 1), (65, 1), (157, 1), (20, 1), (171, 1), (152, 1), (197, 1), (99, 1), (166, 1), (179, 1), (75, 1), (6, 1), (29, 1), (57, 1), (127, 1), (24, 1), (42, 1), (170, 1), (19, 1), (193, 1), (144, 1), (180, 1), (133, 1), (95, 1)])" -spreading_minbudget,4.990314699942246,200,"(49, [(57, 1), (6, 1), (13, 1), (17, 1), (18, 1), (20, 1), (23, 1), (26, 1), (31, 1), (33, 1), (34, 1), (45, 1), (48, 1), (49, 1), (51, 1), (63, 1), (70, 1), (73, 1), (79, 1), (82, 1), (87, 1), (93, 1), (102, 1), (107, 1), (109, 1), (111, 1), (113, 1), (120, 1), (132, 1), (136, 1), (137, 1), (141, 1), (157, 1), (161, 1), (162, 1), (167, 1), (175, 1), (183, 1), (189, 1), (192, 1), (193, 1), (196, 1), (198, 1), (8, 1), (9, 1), (46, 1), (176, 1), (178, 1), (151, 1)])" -heuristic_minbudget,48.9231706999708,200,"(49, [(46, 1), (196, 1), (162, 1), (109, 1), (6, 1), (79, 1), (23, 1), (192, 1), (8, 1), (18, 1), (57, 1), (136, 1), (111, 1), (33, 1), (51, 1), (87, 1), (102, 1), (132, 1), (175, 1), (189, 1), (198, 1), (13, 1), (49, 1), (17, 1), (48, 1), (93, 1), (141, 1), (157, 1), (167, 1), (183, 1), (193, 1), (9, 1), (45, 1), (63, 1), (73, 1), (82, 1), (151, 1), (178, 1), (107, 1), (176, 1), (113, 1), (20, 1), (26, 1), (120, 1), (31, 1), (34, 1), (137, 1), (161, 1), (70, 1)])" -spreading_minbudget,6.574391700094566,400,"(21, [(46, 1), (99, 1), (154, 1), (62, 1), (282, 1), (2, 1), (352, 1), (293, 1), (194, 1), (285, 1), (18, 1), (45, 1), (49, 1), (295, 1), (356, 1), (174, 1), (12, 1), (322, 1), (112, 1), (248, 1), (313, 1)])" -heuristic_minbudget,8.081847100052983,400,"(13, [(174, 1), (112, 1), (49, 1), (248, 1), (295, 1), (45, 1), (12, 1), (18, 1), (356, 1), (322, 1), (313, 1), (389, 1), (178, 1), (84, 2), (128, 2), (80, 2), (48, 2), (199, 2), (17, 2), (376, 2), (233, 2), (314, 2), (67, 2), (11, 2), (225, 2), (122, 2)])" -spreading_minbudget,11.30272960010916,400,"(29, [(383, 1), (19, 1), (2, 1), (4, 1), (58, 1), (100, 1), (123, 1), (169, 1), (188, 1), (218, 1), (263, 1), (335, 1), (388, 1), (30, 1), (49, 1), (186, 1), (201, 1), (228, 1), (243, 1), (314, 1), (358, 1), (25, 1), (86, 1), (144, 1), (316, 1), (362, 1), (28, 1), (355, 1), (386, 1)])" -heuristic_minbudget,47.75127730006352,400,"(29, [(388, 1), (383, 1), (355, 1), (358, 1), (186, 1), (201, 1), (58, 1), (169, 1), (243, 1), (2, 1), (263, 1), (4, 1), (188, 1), (314, 1), (19, 1), (25, 1), (316, 1), (335, 1), (100, 1), (218, 1), (30, 1), (362, 1), (28, 1), (123, 1), (144, 1), (228, 1), (49, 1), (86, 1), (386, 1)])" -spreading_minbudget,9.465220999903977,400,"(91, [(22, 1), (2, 1), (4, 1), (5, 1), (15, 1), (21, 1), (35, 1), (38, 1), (39, 1), (43, 1), (76, 1), (79, 1), (84, 1), (88, 1), (94, 1), (102, 1), (103, 1), (105, 1), (112, 1), (113, 1), (114, 1), (117, 1), (122, 1), (127, 1), (132, 1), (136, 1), (137, 1), (142, 1), (158, 1), (181, 1), (186, 1), (189, 1), (190, 1), (209, 1), (216, 1), (219, 1), (223, 1), (227, 1), (235, 1), (237, 1), (243, 1), (245, 1), (246, 1), (249, 1), (256, 1), (259, 1), (263, 1), (270, 1), (272, 1), (281, 1), (283, 1), (284, 1), (287, 1), (288, 1), (290, 1), (302, 1), (307, 1), (314, 1), (316, 1), (321, 1), (324, 1), (328, 1), (339, 1), (341, 1), (362, 1), (363, 1), (364, 1), (381, 1), (384, 1), (391, 1), (398, 1), (47, 1), (50, 1), (78, 1), (86, 1), (119, 1), (168, 1), (182, 1), (187, 1), (262, 1), (264, 1), (318, 1), (350, 1), (366, 1), (373, 1), (382, 1), (387, 1), (75, 1), (257, 1), (173, 1), (214, 1)])" -heuristic_minbudget,229.72120989998803,400,"(91, [(256, 1), (39, 1), (186, 1), (114, 1), (119, 1), (15, 1), (112, 1), (257, 1), (122, 1), (363, 1), (283, 1), (86, 1), (366, 1), (384, 1), (76, 1), (84, 1), (21, 1), (142, 1), (227, 1), (50, 1), (117, 1), (5, 1), (272, 1), (318, 1), (136, 1), (288, 1), (263, 1), (22, 1), (173, 1), (339, 1), (350, 1), (105, 1), (182, 1), (341, 1), (43, 1), (127, 1), (132, 1), (364, 1), (181, 1), (223, 1), (235, 1), (245, 1), (284, 1), (398, 1), (381, 1), (219, 1), (382, 1), (137, 1), (328, 1), (316, 1), (2, 1), (102, 1), (324, 1), (168, 1), (216, 1), (189, 1), (47, 1), (38, 1), (237, 1), (259, 1), (270, 1), (307, 1), (391, 1), (158, 1), (79, 1), (209, 1), (262, 1), (290, 1), (94, 1), (88, 1), (214, 1), (264, 1), (249, 1), (78, 1), (373, 1), (243, 1), (314, 1), (387, 1), (75, 1), (35, 1), (103, 1), (281, 1), (362, 1), (4, 1), (190, 1), (113, 1), (302, 1), (187, 1), (246, 1), (321, 1), (287, 1)])" -,495.3808441001456,, +spreading_minbudget,0.5327367000281811,100,"(9, [(44, 1), (58, 1), (1, 1), (59, 1), (5, 1), (2, 1), (24, 1), (85, 1), (48, 1)])" +heuristic_minbudget,0.1455373999197036,100,"(5, [(85, 1), (59, 1), (24, 1), (48, 1), (75, 1), (42, 2), (95, 2), (14, 2), (78, 2), (10, 2)])" +spreading_minbudget,0.5089535000734031,100,"(21, [(45, 1), (43, 1), (8, 1), (30, 1), (36, 1), (38, 1), (46, 1), (49, 1), (61, 1), (62, 1), (68, 1), (73, 1), (82, 1), (28, 1), (39, 1), (44, 1), (51, 1), (76, 1), (57, 1), (90, 1), (67, 1)])" +heuristic_minbudget,2.0655213000718504,100,"(21, [(45, 1), (76, 1), (62, 1), (38, 1), (51, 1), (30, 1), (68, 1), (73, 1), (57, 1), (43, 1), (82, 1), (39, 1), (61, 1), (67, 1), (44, 1), (49, 1), (8, 1), (36, 1), (46, 1), (28, 1), (90, 1)])" +spreading_minbudget,0.15029959985986352,100,"(9, [(57, 1), (26, 1), (30, 1), (54, 1), (65, 1), (88, 1), (42, 1), (91, 1), (47, 1)])" +heuristic_minbudget,3.1328336000442505,100,"(9, [(88, 1), (65, 1), (30, 1), (26, 1), (54, 1), (57, 1), (91, 1), (47, 1), (42, 1)])" +spreading_minbudget,1.3004495000932366,200,"(12, [(95, 1), (134, 1), (80, 1), (110, 1), (139, 1), (55, 1), (67, 1), (7, 1), (116, 1), (181, 1), (13, 1), (124, 1)])" +heuristic_minbudget,0.7070317000616342,200,"(9, [(134, 1), (181, 1), (67, 1), (124, 1), (116, 1), (13, 1), (22, 1), (41, 1), (117, 1), (110, 2), (73, 2), (129, 2), (64, 2), (17, 2), (167, 2), (127, 2), (43, 2), (103, 2)])" +spreading_minbudget,1.1288224998861551,200,"(18, [(95, 1), (4, 1), (8, 1), (33, 1), (40, 1), (83, 1), (103, 1), (125, 1), (151, 1), (156, 1), (160, 1), (22, 1), (24, 1), (94, 1), (122, 1), (55, 1), (111, 1), (35, 1)])" +heuristic_minbudget,7.023094000061974,200,"(18, [(95, 1), (103, 1), (122, 1), (24, 1), (4, 1), (156, 1), (160, 1), (8, 1), (22, 1), (151, 1), (40, 1), (111, 1), (35, 1), (94, 1), (125, 1), (33, 1), (83, 1), (55, 1)])" +spreading_minbudget,1.4683078001253307,200,"(35, [(10, 1), (21, 1), (22, 1), (32, 1), (38, 1), (42, 1), (46, 1), (49, 1), (58, 1), (62, 1), (65, 1), (70, 1), (88, 1), (97, 1), (98, 1), (114, 1), (136, 1), (146, 1), (156, 1), (160, 1), (161, 1), (173, 1), (180, 1), (189, 1), (196, 1), (26, 1), (28, 1), (47, 1), (78, 1), (80, 1), (93, 1), (124, 1), (55, 1), (145, 1), (194, 1)])" +heuristic_minbudget,39.200574200134724,200,"(35, [(189, 1), (146, 1), (80, 1), (42, 1), (10, 1), (38, 1), (62, 1), (32, 1), (47, 1), (173, 1), (22, 1), (49, 1), (88, 1), (93, 1), (26, 1), (65, 1), (98, 1), (58, 1), (21, 1), (28, 1), (145, 1), (97, 1), (194, 1), (114, 1), (156, 1), (180, 1), (161, 1), (196, 1), (136, 1), (70, 1), (160, 1), (46, 1), (78, 1), (124, 1), (55, 1)])" +spreading_minbudget,5.3065877999179065,400,"(30, [(246, 1), (204, 1), (333, 1), (18, 1), (87, 1), (212, 1), (261, 1), (304, 1), (17, 1), (9, 1), (235, 1), (268, 1), (373, 1), (7, 1), (264, 1), (57, 1), (238, 1), (16, 1), (237, 1), (303, 1), (134, 1), (315, 1), (292, 1), (331, 1), (15, 1), (55, 1), (173, 1), (221, 1), (258, 1), (199, 1)])" +heuristic_minbudget,4.097546900156885,400,"(19, [(264, 1), (237, 1), (199, 1), (16, 1), (303, 1), (57, 1), (134, 1), (212, 1), (315, 1), (221, 1), (258, 1), (7, 1), (55, 1), (268, 1), (238, 1), (15, 1), (292, 1), (331, 1), (173, 1), (247, 2), (341, 2), (18, 2), (329, 2), (76, 2), (348, 2), (95, 2), (272, 2), (132, 2), (218, 2), (151, 2), (136, 2), (376, 2), (197, 2), (157, 2), (53, 2), (20, 2), (143, 2), (213, 2)])" +spreading_minbudget,11.941065700026229,400,"(65, [(178, 1), (249, 1), (78, 1), (14, 1), (29, 1), (74, 1), (80, 1), (85, 1), (100, 1), (122, 1), (165, 1), (167, 1), (169, 1), (197, 1), (202, 1), (215, 1), (239, 1), (257, 1), (258, 1), (262, 1), (270, 1), (272, 1), (277, 1), (289, 1), (309, 1), (323, 1), (328, 1), (335, 1), (340, 1), (342, 1), (386, 1), (388, 1), (2, 1), (24, 1), (53, 1), (63, 1), (136, 1), (137, 1), (183, 1), (206, 1), (326, 1), (379, 1), (55, 1), (90, 1), (97, 1), (99, 1), (113, 1), (127, 1), (152, 1), (174, 1), (203, 1), (209, 1), (383, 1), (392, 1), (30, 1), (59, 1), (195, 1), (306, 1), (322, 1), (163, 1), (235, 1), (19, 1), (158, 1), (396, 1), (77, 1)])" +heuristic_minbudget,64.45830999989994,400,"(64, [(309, 1), (235, 1), (197, 1), (392, 1), (249, 1), (14, 1), (388, 1), (323, 1), (326, 1), (29, 1), (77, 1), (174, 1), (270, 1), (2, 1), (127, 1), (165, 1), (258, 1), (85, 1), (136, 1), (158, 1), (379, 1), (30, 1), (137, 1), (169, 1), (99, 1), (257, 1), (328, 1), (262, 1), (306, 1), (59, 1), (322, 1), (340, 1), (90, 1), (163, 1), (209, 1), (277, 1), (203, 1), (206, 1), (396, 1), (74, 1), (272, 1), (113, 1), (386, 1), (342, 1), (55, 1), (289, 1), (19, 1), (24, 1), (167, 1), (100, 1), (78, 1), (195, 1), (63, 1), (215, 1), (183, 1), (202, 1), (239, 1), (80, 1), (122, 1), (335, 1), (53, 1), (383, 1), (97, 1), (152, 1)])" +spreading_minbudget,13.347260399954394,400,"(162, [(293, 1), (3, 1), (5, 1), (10, 1), (11, 1), (13, 1), (14, 1), (16, 1), (17, 1), (20, 1), (25, 1), (26, 1), (27, 1), (28, 1), (31, 1), (32, 1), (33, 1), (36, 1), (37, 1), (38, 1), (41, 1), (43, 1), (65, 1), (80, 1), (82, 1), (85, 1), (94, 1), (98, 1), (100, 1), (101, 1), (102, 1), (108, 1), (112, 1), (116, 1), (117, 1), (125, 1), (126, 1), (128, 1), (129, 1), (132, 1), (133, 1), (134, 1), (138, 1), (140, 1), (146, 1), (147, 1), (148, 1), (153, 1), (157, 1), (159, 1), (162, 1), (166, 1), (167, 1), (168, 1), (169, 1), (170, 1), (178, 1), (180, 1), (189, 1), (190, 1), (194, 1), (198, 1), (199, 1), (202, 1), (204, 1), (205, 1), (212, 1), (215, 1), (219, 1), (223, 1), (226, 1), (227, 1), (236, 1), (238, 1), (241, 1), (242, 1), (243, 1), (246, 1), (249, 1), (258, 1), (260, 1), (266, 1), (273, 1), (275, 1), (276, 1), (280, 1), (282, 1), (286, 1), (289, 1), (290, 1), (292, 1), (299, 1), (303, 1), (304, 1), (310, 1), (311, 1), (312, 1), (320, 1), (326, 1), (337, 1), (342, 1), (347, 1), (354, 1), (355, 1), (356, 1), (357, 1), (359, 1), (361, 1), (365, 1), (373, 1), (374, 1), (376, 1), (380, 1), (384, 1), (391, 1), (394, 1), (395, 1), (396, 1), (398, 1), (19, 1), (30, 1), (40, 1), (49, 1), (52, 1), (60, 1), (62, 1), (63, 1), (74, 1), (92, 1), (119, 1), (121, 1), (122, 1), (136, 1), (137, 1), (142, 1), (160, 1), (185, 1), (188, 1), (195, 1), (222, 1), (233, 1), (259, 1), (267, 1), (270, 1), (281, 1), (295, 1), (307, 1), (313, 1), (316, 1), (317, 1), (319, 1), (324, 1), (343, 1), (362, 1), (367, 1), (390, 1), (399, 1), (18, 1), (23, 1), (81, 1), (95, 1), (224, 1)])" +heuristic_minbudget,242.65643709991127,400,"(162, [(140, 1), (295, 1), (129, 1), (376, 1), (137, 1), (28, 1), (112, 1), (146, 1), (299, 1), (60, 1), (170, 1), (80, 1), (100, 1), (219, 1), (276, 1), (227, 1), (63, 1), (282, 1), (391, 1), (94, 1), (190, 1), (125, 1), (148, 1), (26, 1), (167, 1), (185, 1), (367, 1), (33, 1), (199, 1), (267, 1), (354, 1), (16, 1), (17, 1), (195, 1), (20, 1), (153, 1), (215, 1), (337, 1), (289, 1), (5, 1), (108, 1), (357, 1), (101, 1), (121, 1), (157, 1), (134, 1), (212, 1), (249, 1), (304, 1), (307, 1), (361, 1), (142, 1), (62, 1), (10, 1), (236, 1), (133, 1), (13, 1), (373, 1), (293, 1), (102, 1), (223, 1), (241, 1), (286, 1), (266, 1), (202, 1), (312, 1), (319, 1), (194, 1), (398, 1), (168, 1), (226, 1), (316, 1), (166, 1), (270, 1), (259, 1), (25, 1), (74, 1), (238, 1), (303, 1), (138, 1), (162, 1), (204, 1), (356, 1), (189, 1), (281, 1), (384, 1), (82, 1), (18, 1), (95, 1), (169, 1), (36, 1), (49, 1), (275, 1), (374, 1), (280, 1), (136, 1), (224, 1), (19, 1), (260, 1), (310, 1), (396, 1), (119, 1), (313, 1), (365, 1), (43, 1), (132, 1), (159, 1), (311, 1), (147, 1), (23, 1), (41, 1), (243, 1), (342, 1), (98, 1), (188, 1), (30, 1), (394, 1), (65, 1), (233, 1), (292, 1), (31, 1), (359, 1), (180, 1), (27, 1), (390, 1), (178, 1), (116, 1), (290, 1), (242, 1), (37, 1), (128, 1), (395, 1), (32, 1), (122, 1), (126, 1), (326, 1), (160, 1), (399, 1), (317, 1), (14, 1), (380, 1), (258, 1), (81, 1), (117, 1), (40, 1), (38, 1), (52, 1), (198, 1), (205, 1), (246, 1), (273, 1), (355, 1), (11, 1), (320, 1), (222, 1), (343, 1), (362, 1), (3, 1), (85, 1), (347, 1), (324, 1), (92, 1)])" +,399.8462471999228,, diff --git a/networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/spreading_minbudget.png b/networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/spreading_minbudget.png index ee3d43c93d36f874c30533caba54d90ca1fdc027..e2072faf1a13797a2253a61bfa3345cc92314611 100644 GIT binary patch literal 33997 zcmdSBWmr_<_6ItMfJ%#$2q-E_BaI*+peQY%bc%F$haf2+q9RfvA}Zb8ARS6cch}H8 z+_gFJKj++U_uJ)pe2j5scD(Oezgjz3QC^Dh!sQDn6pHZiBS|F`>WmKxg*AQ-7ygC! z_wY3QCg|`$%|Y4P$iZ3P?gdIt-@(Sh+QGuq;HuLLJ9|@WD{gi{_B*UsO&lC-?C)}L zSpNGzuv^<1b97U$UV{%gZ}Ujq9)%*-M}A>_7fm%qq5SVYmb|ax61P0!?4Z&+`fGjd zE1vkpN8;kQwnjBCW&1Eth<(~py!3?Cr~hTv@z1|GS~&P5(j+M%-*12R$tRJXkMFwi z>Vy1Xo9hV`^%c|mkB#2CikP=+#3=6^&2kzhEZcXCGS+btNWxG3i($I0Y^;5hc8RAooSdT!Q=jNZBqSv5!O`QA1~E z1pH>ZJL0VTO_my$=*l^t27`$@V$rYqbpd;OdnA`Hx4_@pO6=#B2mj`ql4+MZToVwW z($&?it*fIJ@hG-DZh6mgCq_11tK^SW)pe*s3W?#(hP8%V 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zK3TCylw&En=)7dQ24r2}hLG_wgu8&Og$y15MaOFL^ZRY5-##$h5g)9}Iz(M{-FX5d zAj2XfUt&7P0qlzT3=;hjtdsl!TQYJ9HtbI^1j`y@c`&Lsvz>S57|}~<$L;tw^e%)n zZZH!LOGWnQGQuNE6D>x^Ojsyp0mpUcCy0|oF@qczWF6C) z?_(6qj-fvVTAs&BBCQ8`E3i5!gQgLg)g#{@&KKBD72zL9@x2-+OJ9A&n5o9WRhhwy zLLmpmVijvOMw;QAs=6=1?w=aGol*4vJ^B#&i+RQIo)y2U)}&z=Hbq@UYhU^v)3g5z Dr3{>2 diff --git a/networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/spreading_minbudget_preprocessed.csv b/networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/spreading_minbudget_preprocessed.csv index 1a6762e..2d62bfb 100644 --- a/networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/spreading_minbudget_preprocessed.csv +++ b/networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/spreading_minbudget_preprocessed.csv @@ -1,19 +1,19 @@ algorithm,runtime,graph_nodes,Budget,Budget_numeric -spreading_minbudget,2.6765573997981846,100.0,"(2, [(28, 1), (17, 1), (47, 2), (2, 2)])",2.0 -heuristic_minbudget,0.7838832000270486,100.0,"(2, [(58, 1), (17, 1), (14, 2), (76, 2), (63, 3), (85, 3)])",2.0 -spreading_minbudget,4.781969800125808,100.0,"(19, [(19, 1), (5, 1), (4, 1), (33, 1), (66, 1), (79, 1), (92, 1), (2, 1), (18, 1), (38, 1), (52, 1), (55, 1), (10, 1), (27, 1), (29, 1), (63, 1), (83, 1), (32, 1), (54, 1)])",19.0 -heuristic_minbudget,12.467118400149047,100.0,"(19, [(19, 1), (5, 1), (63, 1), (18, 1), (38, 1), (79, 1), (10, 1), (66, 1), (32, 1), (52, 1), (83, 1), (27, 1), (55, 1), (92, 1), (33, 1), (29, 1), (2, 1), (4, 1), (54, 1)])",19.0 -spreading_minbudget,2.980581299867481,100.0,"(24, [(40, 1), (2, 1), (3, 1), (5, 1), (8, 1), (11, 1), (22, 1), (24, 1), (51, 1), (62, 1), (72, 1), (76, 1), (81, 1), (83, 1), (85, 1), (87, 1), (89, 1), (92, 1), (97, 1), (44, 1), (46, 1), (60, 1), (63, 1), (70, 1)])",24.0 -heuristic_minbudget,35.502651300048456,100.0,"(24, [(76, 1), (85, 1), (11, 1), (44, 1), (87, 1), (83, 1), (92, 1), (46, 1), (62, 1), (70, 1), (8, 1), (2, 1), (22, 1), (72, 1), (24, 1), (89, 1), (63, 1), (81, 1), (51, 1), (3, 1), (97, 1), (5, 1), (40, 1), (60, 1)])",24.0 -spreading_minbudget,8.064421500079334,200.0,"(19, [(86, 1), (105, 1), (161, 1), (115, 1), (21, 1), (34, 1), (162, 1), (85, 1), (131, 1), (70, 1), (42, 1), (186, 1), (36, 1), (14, 1), (40, 1), (187, 1), (100, 1), (178, 1), (176, 1)])",19.0 -heuristic_minbudget,7.089356000069529,200.0,"(13, [(186, 1), (34, 1), (105, 1), (178, 1), (100, 1), (14, 1), (131, 1), (176, 1), (187, 1), (40, 1), (42, 1), (161, 1), (91, 1), (181, 2), (92, 2), (71, 2), (144, 2), (130, 2), (175, 2), (102, 2), (194, 2), (191, 2), (9, 2), (18, 2), (4, 2), (68, 2)])",13.0 -spreading_minbudget,11.68095609988086,200.0,"(50, [(118, 1), (80, 1), (1, 1), (3, 1), (16, 1), (25, 1), (29, 1), (42, 1), (55, 1), (65, 1), (73, 1), (75, 1), (76, 1), (78, 1), (79, 1), (95, 1), (99, 1), (100, 1), (101, 1), (112, 1), (127, 1), (129, 1), (133, 1), (137, 1), (151, 1), (152, 1), (166, 1), (171, 1), (191, 1), (197, 1), (5, 1), (6, 1), (24, 1), (27, 1), (57, 1), (110, 1), (170, 1), (179, 1), (19, 1), (20, 1), (146, 1), (163, 1), (193, 1), (119, 1), (181, 1), (98, 1), (140, 1), (144, 1), (157, 1), (180, 1)])",50.0 -heuristic_minbudget,41.66929220012389,200.0,"(49, [(98, 1), (101, 1), (73, 1), (16, 1), (112, 1), (79, 1), (27, 1), (1, 1), (137, 1), (140, 1), (119, 1), (5, 1), (76, 1), (25, 1), (118, 1), (129, 1), (146, 1), (163, 1), (181, 1), (110, 1), (3, 1), (55, 1), (100, 1), (151, 1), (78, 1), (191, 1), (65, 1), (157, 1), (20, 1), (171, 1), (152, 1), (197, 1), (99, 1), (166, 1), (179, 1), (75, 1), (6, 1), (29, 1), (57, 1), (127, 1), (24, 1), (42, 1), (170, 1), (19, 1), (193, 1), (144, 1), (180, 1), (133, 1), (95, 1)])",49.0 -spreading_minbudget,4.990314699942246,200.0,"(49, [(57, 1), (6, 1), (13, 1), (17, 1), (18, 1), (20, 1), (23, 1), (26, 1), (31, 1), (33, 1), (34, 1), (45, 1), (48, 1), (49, 1), (51, 1), (63, 1), (70, 1), (73, 1), (79, 1), (82, 1), (87, 1), (93, 1), (102, 1), (107, 1), (109, 1), (111, 1), (113, 1), (120, 1), (132, 1), (136, 1), (137, 1), (141, 1), (157, 1), (161, 1), (162, 1), (167, 1), (175, 1), (183, 1), (189, 1), (192, 1), (193, 1), (196, 1), (198, 1), (8, 1), (9, 1), (46, 1), (176, 1), (178, 1), (151, 1)])",49.0 -heuristic_minbudget,48.9231706999708,200.0,"(49, [(46, 1), (196, 1), (162, 1), (109, 1), (6, 1), (79, 1), (23, 1), (192, 1), (8, 1), (18, 1), (57, 1), (136, 1), (111, 1), (33, 1), (51, 1), (87, 1), (102, 1), (132, 1), (175, 1), (189, 1), (198, 1), (13, 1), (49, 1), (17, 1), (48, 1), (93, 1), (141, 1), (157, 1), (167, 1), (183, 1), (193, 1), (9, 1), (45, 1), (63, 1), (73, 1), (82, 1), (151, 1), (178, 1), (107, 1), (176, 1), (113, 1), (20, 1), (26, 1), (120, 1), (31, 1), (34, 1), (137, 1), (161, 1), (70, 1)])",49.0 -spreading_minbudget,6.574391700094566,400.0,"(21, [(46, 1), (99, 1), (154, 1), (62, 1), (282, 1), (2, 1), (352, 1), (293, 1), (194, 1), (285, 1), (18, 1), (45, 1), (49, 1), (295, 1), (356, 1), (174, 1), (12, 1), (322, 1), (112, 1), (248, 1), (313, 1)])",21.0 -heuristic_minbudget,8.081847100052983,400.0,"(13, [(174, 1), (112, 1), (49, 1), (248, 1), (295, 1), (45, 1), (12, 1), (18, 1), (356, 1), (322, 1), (313, 1), (389, 1), (178, 1), (84, 2), (128, 2), (80, 2), (48, 2), (199, 2), (17, 2), (376, 2), (233, 2), (314, 2), (67, 2), (11, 2), (225, 2), (122, 2)])",13.0 -spreading_minbudget,11.30272960010916,400.0,"(29, [(383, 1), (19, 1), (2, 1), (4, 1), (58, 1), (100, 1), (123, 1), (169, 1), (188, 1), (218, 1), (263, 1), (335, 1), (388, 1), (30, 1), (49, 1), (186, 1), (201, 1), (228, 1), (243, 1), (314, 1), (358, 1), (25, 1), (86, 1), (144, 1), (316, 1), (362, 1), (28, 1), (355, 1), (386, 1)])",29.0 -heuristic_minbudget,47.75127730006352,400.0,"(29, [(388, 1), (383, 1), (355, 1), (358, 1), (186, 1), (201, 1), (58, 1), (169, 1), (243, 1), (2, 1), (263, 1), (4, 1), (188, 1), (314, 1), (19, 1), (25, 1), (316, 1), (335, 1), (100, 1), (218, 1), (30, 1), (362, 1), (28, 1), (123, 1), (144, 1), (228, 1), (49, 1), (86, 1), (386, 1)])",29.0 -spreading_minbudget,9.465220999903975,400.0,"(91, [(22, 1), (2, 1), (4, 1), (5, 1), (15, 1), (21, 1), (35, 1), (38, 1), (39, 1), (43, 1), (76, 1), (79, 1), (84, 1), (88, 1), (94, 1), (102, 1), (103, 1), (105, 1), (112, 1), (113, 1), (114, 1), (117, 1), (122, 1), (127, 1), (132, 1), (136, 1), (137, 1), (142, 1), (158, 1), (181, 1), (186, 1), (189, 1), (190, 1), (209, 1), (216, 1), (219, 1), (223, 1), (227, 1), (235, 1), (237, 1), (243, 1), (245, 1), (246, 1), (249, 1), (256, 1), (259, 1), (263, 1), (270, 1), (272, 1), (281, 1), (283, 1), (284, 1), (287, 1), (288, 1), (290, 1), (302, 1), (307, 1), (314, 1), (316, 1), (321, 1), (324, 1), (328, 1), (339, 1), (341, 1), (362, 1), (363, 1), (364, 1), (381, 1), (384, 1), (391, 1), (398, 1), (47, 1), (50, 1), (78, 1), (86, 1), (119, 1), (168, 1), (182, 1), (187, 1), (262, 1), (264, 1), (318, 1), (350, 1), (366, 1), (373, 1), (382, 1), (387, 1), (75, 1), (257, 1), (173, 1), (214, 1)])",91.0 -heuristic_minbudget,229.72120989998805,400.0,"(91, [(256, 1), (39, 1), (186, 1), (114, 1), (119, 1), (15, 1), (112, 1), (257, 1), (122, 1), (363, 1), (283, 1), (86, 1), (366, 1), (384, 1), (76, 1), (84, 1), (21, 1), (142, 1), (227, 1), (50, 1), (117, 1), (5, 1), (272, 1), (318, 1), (136, 1), (288, 1), (263, 1), (22, 1), (173, 1), (339, 1), (350, 1), (105, 1), (182, 1), (341, 1), (43, 1), (127, 1), (132, 1), (364, 1), (181, 1), (223, 1), (235, 1), (245, 1), (284, 1), (398, 1), (381, 1), (219, 1), (382, 1), (137, 1), (328, 1), (316, 1), (2, 1), (102, 1), (324, 1), (168, 1), (216, 1), (189, 1), (47, 1), (38, 1), (237, 1), (259, 1), (270, 1), (307, 1), (391, 1), (158, 1), (79, 1), (209, 1), (262, 1), (290, 1), (94, 1), (88, 1), (214, 1), (264, 1), (249, 1), (78, 1), (373, 1), (243, 1), (314, 1), (387, 1), (75, 1), (35, 1), (103, 1), (281, 1), (362, 1), (4, 1), (190, 1), (113, 1), (302, 1), (187, 1), (246, 1), (321, 1), (287, 1)])",91.0 +spreading_minbudget,0.5327367000281811,100.0,"(9, [(44, 1), (58, 1), (1, 1), (59, 1), (5, 1), (2, 1), (24, 1), (85, 1), (48, 1)])",9.0 +heuristic_minbudget,0.1455373999197036,100.0,"(5, [(85, 1), (59, 1), (24, 1), (48, 1), (75, 1), (42, 2), (95, 2), (14, 2), (78, 2), (10, 2)])",5.0 +spreading_minbudget,0.5089535000734031,100.0,"(21, [(45, 1), (43, 1), (8, 1), (30, 1), (36, 1), (38, 1), (46, 1), (49, 1), (61, 1), (62, 1), (68, 1), (73, 1), (82, 1), (28, 1), (39, 1), (44, 1), (51, 1), (76, 1), (57, 1), (90, 1), (67, 1)])",21.0 +heuristic_minbudget,2.0655213000718504,100.0,"(21, [(45, 1), (76, 1), (62, 1), (38, 1), (51, 1), (30, 1), (68, 1), (73, 1), (57, 1), (43, 1), (82, 1), (39, 1), (61, 1), (67, 1), (44, 1), (49, 1), (8, 1), (36, 1), (46, 1), (28, 1), (90, 1)])",21.0 +spreading_minbudget,0.1502995998598635,100.0,"(9, [(57, 1), (26, 1), (30, 1), (54, 1), (65, 1), (88, 1), (42, 1), (91, 1), (47, 1)])",9.0 +heuristic_minbudget,3.1328336000442505,100.0,"(9, [(88, 1), (65, 1), (30, 1), (26, 1), (54, 1), (57, 1), (91, 1), (47, 1), (42, 1)])",9.0 +spreading_minbudget,1.3004495000932366,200.0,"(12, [(95, 1), (134, 1), (80, 1), (110, 1), (139, 1), (55, 1), (67, 1), (7, 1), (116, 1), (181, 1), (13, 1), (124, 1)])",12.0 +heuristic_minbudget,0.7070317000616342,200.0,"(9, [(134, 1), (181, 1), (67, 1), (124, 1), (116, 1), (13, 1), (22, 1), (41, 1), (117, 1), (110, 2), (73, 2), (129, 2), (64, 2), (17, 2), (167, 2), (127, 2), (43, 2), (103, 2)])",9.0 +spreading_minbudget,1.1288224998861551,200.0,"(18, [(95, 1), (4, 1), (8, 1), (33, 1), (40, 1), (83, 1), (103, 1), (125, 1), (151, 1), (156, 1), (160, 1), (22, 1), (24, 1), (94, 1), (122, 1), (55, 1), (111, 1), (35, 1)])",18.0 +heuristic_minbudget,7.023094000061974,200.0,"(18, [(95, 1), (103, 1), (122, 1), (24, 1), (4, 1), (156, 1), (160, 1), (8, 1), (22, 1), (151, 1), (40, 1), (111, 1), (35, 1), (94, 1), (125, 1), (33, 1), (83, 1), (55, 1)])",18.0 +spreading_minbudget,1.468307800125331,200.0,"(35, [(10, 1), (21, 1), (22, 1), (32, 1), (38, 1), (42, 1), (46, 1), (49, 1), (58, 1), (62, 1), (65, 1), (70, 1), (88, 1), (97, 1), (98, 1), (114, 1), (136, 1), (146, 1), (156, 1), (160, 1), (161, 1), (173, 1), (180, 1), (189, 1), (196, 1), (26, 1), (28, 1), (47, 1), (78, 1), (80, 1), (93, 1), (124, 1), (55, 1), (145, 1), (194, 1)])",35.0 +heuristic_minbudget,39.20057420013472,200.0,"(35, [(189, 1), (146, 1), (80, 1), (42, 1), (10, 1), (38, 1), (62, 1), (32, 1), (47, 1), (173, 1), (22, 1), (49, 1), (88, 1), (93, 1), (26, 1), (65, 1), (98, 1), (58, 1), (21, 1), (28, 1), (145, 1), (97, 1), (194, 1), (114, 1), (156, 1), (180, 1), (161, 1), (196, 1), (136, 1), (70, 1), (160, 1), (46, 1), (78, 1), (124, 1), (55, 1)])",35.0 +spreading_minbudget,5.3065877999179065,400.0,"(30, [(246, 1), (204, 1), (333, 1), (18, 1), (87, 1), (212, 1), (261, 1), (304, 1), (17, 1), (9, 1), (235, 1), (268, 1), (373, 1), (7, 1), (264, 1), (57, 1), (238, 1), (16, 1), (237, 1), (303, 1), (134, 1), (315, 1), (292, 1), (331, 1), (15, 1), (55, 1), (173, 1), (221, 1), (258, 1), (199, 1)])",30.0 +heuristic_minbudget,4.097546900156885,400.0,"(19, [(264, 1), (237, 1), (199, 1), (16, 1), (303, 1), (57, 1), (134, 1), (212, 1), (315, 1), (221, 1), (258, 1), (7, 1), (55, 1), (268, 1), (238, 1), (15, 1), (292, 1), (331, 1), (173, 1), (247, 2), (341, 2), (18, 2), (329, 2), (76, 2), (348, 2), (95, 2), (272, 2), (132, 2), (218, 2), (151, 2), (136, 2), (376, 2), (197, 2), (157, 2), (53, 2), (20, 2), (143, 2), (213, 2)])",19.0 +spreading_minbudget,11.941065700026227,400.0,"(65, [(178, 1), (249, 1), (78, 1), (14, 1), (29, 1), (74, 1), (80, 1), (85, 1), (100, 1), (122, 1), (165, 1), (167, 1), (169, 1), (197, 1), (202, 1), (215, 1), (239, 1), (257, 1), (258, 1), (262, 1), (270, 1), (272, 1), (277, 1), (289, 1), (309, 1), (323, 1), (328, 1), (335, 1), (340, 1), (342, 1), (386, 1), (388, 1), (2, 1), (24, 1), (53, 1), (63, 1), (136, 1), (137, 1), (183, 1), (206, 1), (326, 1), (379, 1), (55, 1), (90, 1), (97, 1), (99, 1), (113, 1), (127, 1), (152, 1), (174, 1), (203, 1), (209, 1), (383, 1), (392, 1), (30, 1), (59, 1), (195, 1), (306, 1), (322, 1), (163, 1), (235, 1), (19, 1), (158, 1), (396, 1), (77, 1)])",65.0 +heuristic_minbudget,64.45830999989994,400.0,"(64, [(309, 1), (235, 1), (197, 1), (392, 1), (249, 1), (14, 1), (388, 1), (323, 1), (326, 1), (29, 1), (77, 1), (174, 1), (270, 1), (2, 1), (127, 1), (165, 1), (258, 1), (85, 1), (136, 1), (158, 1), (379, 1), (30, 1), (137, 1), (169, 1), (99, 1), (257, 1), (328, 1), (262, 1), (306, 1), (59, 1), (322, 1), (340, 1), (90, 1), (163, 1), (209, 1), (277, 1), (203, 1), (206, 1), (396, 1), (74, 1), (272, 1), (113, 1), (386, 1), (342, 1), (55, 1), (289, 1), (19, 1), (24, 1), (167, 1), (100, 1), (78, 1), (195, 1), (63, 1), (215, 1), (183, 1), (202, 1), (239, 1), (80, 1), (122, 1), (335, 1), (53, 1), (383, 1), (97, 1), (152, 1)])",64.0 +spreading_minbudget,13.347260399954394,400.0,"(162, [(293, 1), (3, 1), (5, 1), (10, 1), (11, 1), (13, 1), (14, 1), (16, 1), (17, 1), (20, 1), (25, 1), (26, 1), (27, 1), (28, 1), (31, 1), (32, 1), 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(396, 1), (398, 1), (19, 1), (30, 1), (40, 1), (49, 1), (52, 1), (60, 1), (62, 1), (63, 1), (74, 1), (92, 1), (119, 1), (121, 1), (122, 1), (136, 1), (137, 1), (142, 1), (160, 1), (185, 1), (188, 1), (195, 1), (222, 1), (233, 1), (259, 1), (267, 1), (270, 1), (281, 1), (295, 1), (307, 1), (313, 1), (316, 1), (317, 1), (319, 1), (324, 1), (343, 1), (362, 1), (367, 1), (390, 1), (399, 1), (18, 1), (23, 1), (81, 1), (95, 1), (224, 1)])",162.0 +heuristic_minbudget,242.65643709991127,400.0,"(162, [(140, 1), (295, 1), (129, 1), (376, 1), (137, 1), (28, 1), (112, 1), (146, 1), (299, 1), (60, 1), (170, 1), (80, 1), (100, 1), (219, 1), (276, 1), (227, 1), (63, 1), (282, 1), (391, 1), (94, 1), (190, 1), (125, 1), (148, 1), (26, 1), (167, 1), (185, 1), (367, 1), (33, 1), (199, 1), (267, 1), (354, 1), (16, 1), (17, 1), (195, 1), (20, 1), (153, 1), (215, 1), (337, 1), (289, 1), (5, 1), (108, 1), (357, 1), (101, 1), (121, 1), (157, 1), (134, 1), (212, 1), (249, 1), (304, 1), (307, 1), (361, 1), (142, 1), (62, 1), (10, 1), (236, 1), (133, 1), (13, 1), (373, 1), (293, 1), (102, 1), (223, 1), (241, 1), (286, 1), (266, 1), (202, 1), (312, 1), (319, 1), (194, 1), (398, 1), (168, 1), (226, 1), (316, 1), (166, 1), (270, 1), (259, 1), (25, 1), (74, 1), (238, 1), (303, 1), (138, 1), (162, 1), (204, 1), (356, 1), (189, 1), (281, 1), (384, 1), (82, 1), (18, 1), (95, 1), (169, 1), (36, 1), (49, 1), (275, 1), (374, 1), (280, 1), (136, 1), (224, 1), (19, 1), (260, 1), (310, 1), (396, 1), (119, 1), (313, 1), (365, 1), (43, 1), (132, 1), (159, 1), (311, 1), (147, 1), (23, 1), (41, 1), (243, 1), (342, 1), (98, 1), (188, 1), (30, 1), (394, 1), (65, 1), (233, 1), (292, 1), (31, 1), (359, 1), (180, 1), (27, 1), (390, 1), (178, 1), (116, 1), (290, 1), (242, 1), (37, 1), (128, 1), (395, 1), (32, 1), (122, 1), (126, 1), (326, 1), (160, 1), (399, 1), (317, 1), (14, 1), (380, 1), (258, 1), (81, 1), (117, 1), (40, 1), (38, 1), (52, 1), (198, 1), (205, 1), (246, 1), (273, 1), (355, 1), (11, 1), (320, 1), (222, 1), (343, 1), (362, 1), (3, 1), (85, 1), (347, 1), (324, 1), (92, 1)])",162.0 From faf08e24492f783910e41376f15ef0b0ac48a3ab Mon Sep 17 00:00:00 2001 From: 20shaked20 Date: Tue, 23 Jul 2024 18:55:40 +0300 Subject: [PATCH 76/93] Fixed the problem with not showing name + seprate random gen to other script --- .../Random_Graph_Generator.py | 117 ++++++ .../comparisons/comparisons.py | 72 ++-- .../comparisons/spreading_maxsave.csv | 380 +----------------- .../comparisons/spreading_maxsave.png | Bin 32558 -> 33643 bytes .../comparisons/spreading_maxsave_cleaned.csv | 19 + .../comparisons/spreading_minbudget.csv | 25 +- .../test_non_spreading_dirlaynet_minbudget.py | 55 +-- 7 files changed, 192 insertions(+), 476 deletions(-) create mode 100644 networkz/algorithms/approximation/firefighter_problem/Random_Graph_Generator.py create mode 100644 networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/spreading_maxsave_cleaned.csv diff --git a/networkz/algorithms/approximation/firefighter_problem/Random_Graph_Generator.py b/networkz/algorithms/approximation/firefighter_problem/Random_Graph_Generator.py new file mode 100644 index 0000000..376228a --- /dev/null +++ b/networkz/algorithms/approximation/firefighter_problem/Random_Graph_Generator.py @@ -0,0 +1,117 @@ +""" + +The Paper - +Approximability of the Firefighter Problem Computing Cuts over Time + +Paper Link - +https://www.math.uwaterloo.ca/~cswamy/papers/firefighter-journ.pdf + +Authors - +Elliot Anshelevich +Deeparnab Chakrabarty +Ameya Hate +Chaitanya Swamy + +Developers - +Yuval Bubnovsky +Almog David +Shaked Levi + +""" + +import logging +import random +import networkx as nx + +logger = logging.getLogger(__name__) + +def setup_global_logger(level: int = logging.DEBUG): + log_format = "|| %(asctime)s || %(levelname)s || %(message)s" + date_format = '%H:%M:%S' + formatter = logging.Formatter(log_format, datefmt=date_format) + handler = logging.StreamHandler() + handler.setFormatter(formatter) + + root_logger = logging.getLogger() + root_logger.setLevel(level) + root_logger.addHandler(handler) + + +def generate_random_DiGraph( + num_nodes: int = 100, + edge_probability: float = 0.1, + seed: int = None + ) -> nx.DiGraph: + + if seed is None: + seed = random.randint(0, 2**32 - 1) + logger.debug(f"Random generated seed: {seed}") + else: + logger.debug(f"Using provided seed: {seed}") + + random.seed(seed) + + G = nx.DiGraph() + G.add_nodes_from(range(num_nodes), status="target") + + edges = [ + (source, target) + for source in range(num_nodes) + for target in range(num_nodes) + if source != target and random.random() < edge_probability + ] + + G.add_edges_from(edges) + + return G + + +#TODO : make this to receive paramaters +def generate_random_layered_network(): + """ + Generates a directed layered network with a random number of layers and random nodes per layer. + + Returns: + G (networkx.DiGraph): Directed graph representing the layered network. + """ + # Randomly decide the number of layers (between 2 and 3 for this example) + num_layers = random.randint(5, 10) + + # Randomly decide the number of nodes per layer (between 1 and 4 for this example) + nodes_per_layer = [random.randint(5, 30) for _ in range(num_layers)] + + G = nx.DiGraph() + node_id = 1 # Start node_id from 1 because 0 is the source + + # Initialize layer 0 with the source node + layers = [[0]] + G.add_node(0) + + # Create nodes layer by layer + for i in range(num_layers): + layer = [node_id + j for j in range(nodes_per_layer[i])] + layers.append(layer) + G.add_nodes_from(layer) + node_id += nodes_per_layer[i] + + print("LAYERS->", layers) + + # Connect source node (0) to all nodes in layer 1 + for node in layers[1]: + G.add_edge(0, node) + + # Create edges ensuring connectivity between consecutive layers + for i in range(1, num_layers): + for node in layers[i]: + # Connect each node in this layer to at least one node in the next layer + connected_nodes = random.sample(layers[i + 1], k=random.randint(1, len(layers[i + 1]))) + for target in connected_nodes: + if target != node: # Ensure no self-loop + G.add_edge(node, target) + + for target in layers[i + 1]: + # Ensure each node in the next layer is connected to from at least one node in this layer + if not any(G.has_edge(source, target) for source in layers[i]): + G.add_edge(random.choice(layers[i]), target) + + return G diff --git a/networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/comparisons.py b/networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/comparisons.py index 5fd9406..ca1b23c 100644 --- a/networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/comparisons.py +++ b/networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/comparisons.py @@ -28,8 +28,8 @@ from networkz.algorithms.approximation.firefighter_problem.Utils import * from networkz.algorithms.approximation.firefighter_problem.Firefighter_Problem import * -from networkz.algorithms.approximation.tests.test_firefighter_problem.test_non_spreading_dirlaynet_minbudget import generate_layered_network -from matplotlib import pyplot as plt +from networkz.algorithms.approximation.firefighter_problem.Random_Graph_Generator import generate_random_layered_network +from networkz.algorithms.approximation.firefighter_problem.Random_Graph_Generator import generate_random_DiGraph logger = logging.getLogger(__name__) @@ -44,34 +44,6 @@ def setup_global_logger(level: int = logging.DEBUG): root_logger.setLevel(level) root_logger.addHandler(handler) -def generate_random_DiGraph( - num_nodes: int = 100, - edge_probability: float = 0.1, - seed: int = None - ) -> nx.DiGraph: - - if seed is None: - seed = random.randint(0, 2**32 - 1) - logger.debug(f"Random generated seed: {seed}") - else: - logger.debug(f"Using provided seed: {seed}") - - random.seed(seed) - - G = nx.DiGraph() - G.add_nodes_from(range(num_nodes), status="target") - - edges = [ - (source, target) - for source in range(num_nodes) - for target in range(num_nodes) - if source != target and random.random() < edge_probability - ] - - G.add_edges_from(edges) - - return G - def runner_no_spreading(algorithm, graph, source, targets): if algorithm == heuristic_minbudget: @@ -107,7 +79,7 @@ def Compare_NonSpread(): } def multiple_runs(runs=100): for _ in range(runs): - graph = generate_layered_network() + graph = generate_random_layered_network() source = 0 # targets = [2, 4, 6, 7, 8, 9] nodes = list(graph.nodes) @@ -174,7 +146,7 @@ def Compare_SpreadingMaxSave(): node_counts = [100, 200, 400] edge_probabilities = [0.1, 0.5, 0.8] - def multiple_runs(runs=20): + def multiple_runs(runs=1): for num_nodes in node_counts: for edge_prob in edge_probabilities: graph = generate_random_DiGraph(num_nodes=num_nodes, edge_probability=edge_prob, seed=None) @@ -188,22 +160,35 @@ def multiple_runs(runs=20): start_time = perf_counter() result = runner_spreading(algorithm, graph, source, targets) runtime = perf_counter() - start_time - - if algorithm == spreading_maxsave: - id = 1 - else: - id = 2 # heuristic - ex1.add({**{"algorithm": id, "runtime": runtime, "graph_nodes": len(graph.nodes)}, **result}) + ex1.add({**{"algorithm": algorithm.__name__, "runtime": runtime, "graph_nodes": len(graph.nodes)}, **result}) return {"status": "completed"} - # Set a time limit for the entire batch run ex1.run_with_time_limit(multiple_runs, input_ranges={}, time_limit=0.9) - # Plot the results using the preprocessed CSV file + ## DATA ISSUE WE HAD SO THIS IS A FIX ## + # Load the results + results_csv_file = "./networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/spreading_maxsave.csv" + results = pd.read_csv(results_csv_file) + + # Ensure 'algorithm' column is of type string + results['algorithm'] = results['algorithm'].astype(str) + + # Ensure 'Nodes_Saved' column is numeric and drop rows with NaNs + results['Nodes_Saved'] = pd.to_numeric(results['Nodes_Saved'], errors='coerce') + results = results.dropna(subset=['Nodes_Saved']) + + # Ensure 'Nodes_Saved' is an integer + results['Nodes_Saved'] = results['Nodes_Saved'].astype(int) + + # Save the cleaned DataFrame to a new CSV file (optional, for debugging) + cleaned_csv_file = "./networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/spreading_maxsave_cleaned.csv" + results.to_csv(cleaned_csv_file, index=False) + + # Plot the results using the cleaned DataFrame single_plot_results( - results_csv_file="./networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/spreading_maxsave.csv", + results_csv_file=cleaned_csv_file, filter={}, x_field="graph_nodes", y_field="Nodes_Saved", @@ -214,6 +199,7 @@ def multiple_runs(runs=20): print("\n DataFrame-NonSpread: \n", ex1.dataFrame) + def Compare_SpreadingMinBudget(): ex1 = experiments_csv.Experiment("./networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/", "spreading_minbudget.csv", backup_folder=None) ex1.clear_previous_results() # to clear previous experiments @@ -288,5 +274,5 @@ def extract_budget_numeric(budget): if __name__ == "__main__": setup_global_logger(level=logging.DEBUG) # Compare_NonSpread() - Compare_SpreadingMinBudget() - #Compare_SpreadingMaxSave() \ No newline at end of file + # Compare_SpreadingMinBudget() + Compare_SpreadingMaxSave() \ No newline at end of file diff --git a/networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/spreading_maxsave.csv b/networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/spreading_maxsave.csv index e9c4f8f..1fc45a2 100644 --- a/networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/spreading_maxsave.csv +++ b/networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/spreading_maxsave.csv @@ -1,362 +1,20 @@ algorithm,runtime,graph_nodes,Nodes_Saved -1,0.05848530004732311,100,22 -2,0.07184730004519224,100,17 -1,0.05933990003541112,100,15 -2,0.06803049996960908,100,13 -1,0.06133100000442937,100,12 -2,0.09327389998361468,100,12 -1,0.06038699997588992,100,18 -2,0.058206900022923946,100,15 -1,0.054883599979802966,100,26 -2,0.06629429996246472,100,27 -1,0.05841830000281334,100,20 -2,0.06853700004285201,100,15 -1,0.05379840004025027,100,20 -2,0.06288240000139922,100,18 -1,0.05252630001632497,100,13 -2,0.08255699998699129,100,12 -1,0.055264800030272454,100,20 -2,0.06374910002341494,100,18 -1,0.059297400002833456,100,5 -2,0.06731080001918599,100,5 -1,0.050856999994721264,100,6 -2,0.06093289999989793,100,6 -1,0.05691260000457987,100,20 -2,0.06991479999851435,100,21 -1,0.05791570001747459,100,27 -2,0.09370600001420826,100,25 -1,0.056267699983436614,100,20 -2,0.06625580001855269,100,18 -1,0.055943300016224384,100,15 -2,0.05992700002389029,100,15 -1,0.056427399977110326,100,13 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zgZXZ1Wwk5hW2*DG;kg$qXk6V74K=v9iI@*Rd*(YZaJ9f~<{i!zt5^9*G{r;U$esqH z_cAv(QfbFK9BDMbES;R3TW8TcC24BXFrxT?;%SJ2!S~~ zASSk+?#4m5qbpkOI%#Uk*YHZO55kAs8bAk@yd;0Q0Hsil=B5U8}nyJgrI zr=N?1oU$HKV6xl6*xI{zEApkS+;@;opP~c=21>wTE$)mzlDIQx&d3RDs%(-u(%sOv zw^vv=c0KdQxXw<4moH!b92j_T6TeM+njM4-U@Rd<4z!nLWLRqU``iL)v5h zK~9b&h60n5I`-|`nO!D3dG5(x03fF3Nc>r>3O*sBXBZlsXkr$5x}u}GA3l6o#U__{o=yXM;*Q$qL`sPHqsSiO raiaL^79H^+50;L{#Q!h diff --git a/networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/spreading_minbudget_preprocessed.csv b/networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/spreading_minbudget_preprocessed.csv index 2d62bfb..48b6414 100644 --- a/networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/spreading_minbudget_preprocessed.csv +++ b/networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/spreading_minbudget_preprocessed.csv @@ -1,19 +1,19 @@ algorithm,runtime,graph_nodes,Budget,Budget_numeric -spreading_minbudget,0.5327367000281811,100.0,"(9, [(44, 1), (58, 1), (1, 1), (59, 1), (5, 1), (2, 1), (24, 1), (85, 1), (48, 1)])",9.0 -heuristic_minbudget,0.1455373999197036,100.0,"(5, [(85, 1), (59, 1), (24, 1), (48, 1), (75, 1), (42, 2), (95, 2), (14, 2), (78, 2), (10, 2)])",5.0 -spreading_minbudget,0.5089535000734031,100.0,"(21, [(45, 1), (43, 1), (8, 1), (30, 1), (36, 1), (38, 1), (46, 1), (49, 1), (61, 1), (62, 1), (68, 1), (73, 1), (82, 1), (28, 1), (39, 1), (44, 1), (51, 1), (76, 1), (57, 1), (90, 1), (67, 1)])",21.0 -heuristic_minbudget,2.0655213000718504,100.0,"(21, [(45, 1), (76, 1), (62, 1), (38, 1), (51, 1), (30, 1), (68, 1), (73, 1), (57, 1), (43, 1), (82, 1), (39, 1), (61, 1), (67, 1), (44, 1), (49, 1), (8, 1), (36, 1), (46, 1), (28, 1), (90, 1)])",21.0 -spreading_minbudget,0.1502995998598635,100.0,"(9, [(57, 1), (26, 1), (30, 1), (54, 1), (65, 1), (88, 1), (42, 1), (91, 1), (47, 1)])",9.0 -heuristic_minbudget,3.1328336000442505,100.0,"(9, [(88, 1), (65, 1), (30, 1), (26, 1), (54, 1), (57, 1), (91, 1), (47, 1), (42, 1)])",9.0 -spreading_minbudget,1.3004495000932366,200.0,"(12, [(95, 1), (134, 1), (80, 1), (110, 1), (139, 1), (55, 1), (67, 1), (7, 1), (116, 1), (181, 1), (13, 1), (124, 1)])",12.0 -heuristic_minbudget,0.7070317000616342,200.0,"(9, [(134, 1), (181, 1), (67, 1), (124, 1), (116, 1), (13, 1), (22, 1), (41, 1), (117, 1), (110, 2), (73, 2), (129, 2), (64, 2), (17, 2), (167, 2), (127, 2), (43, 2), (103, 2)])",9.0 -spreading_minbudget,1.1288224998861551,200.0,"(18, [(95, 1), (4, 1), (8, 1), (33, 1), (40, 1), (83, 1), (103, 1), (125, 1), (151, 1), (156, 1), (160, 1), (22, 1), (24, 1), (94, 1), (122, 1), (55, 1), (111, 1), (35, 1)])",18.0 -heuristic_minbudget,7.023094000061974,200.0,"(18, [(95, 1), (103, 1), (122, 1), (24, 1), (4, 1), (156, 1), (160, 1), (8, 1), (22, 1), (151, 1), (40, 1), (111, 1), (35, 1), (94, 1), (125, 1), (33, 1), (83, 1), (55, 1)])",18.0 -spreading_minbudget,1.468307800125331,200.0,"(35, [(10, 1), (21, 1), (22, 1), (32, 1), (38, 1), (42, 1), (46, 1), (49, 1), (58, 1), (62, 1), (65, 1), (70, 1), (88, 1), (97, 1), (98, 1), (114, 1), (136, 1), (146, 1), (156, 1), (160, 1), (161, 1), (173, 1), (180, 1), (189, 1), (196, 1), (26, 1), (28, 1), (47, 1), (78, 1), (80, 1), (93, 1), (124, 1), (55, 1), (145, 1), (194, 1)])",35.0 -heuristic_minbudget,39.20057420013472,200.0,"(35, [(189, 1), (146, 1), (80, 1), (42, 1), (10, 1), (38, 1), (62, 1), (32, 1), (47, 1), (173, 1), (22, 1), (49, 1), (88, 1), (93, 1), (26, 1), (65, 1), (98, 1), (58, 1), (21, 1), (28, 1), (145, 1), (97, 1), (194, 1), (114, 1), (156, 1), (180, 1), (161, 1), (196, 1), (136, 1), (70, 1), (160, 1), (46, 1), (78, 1), (124, 1), (55, 1)])",35.0 -spreading_minbudget,5.3065877999179065,400.0,"(30, [(246, 1), (204, 1), (333, 1), (18, 1), (87, 1), (212, 1), (261, 1), (304, 1), (17, 1), (9, 1), (235, 1), (268, 1), (373, 1), (7, 1), (264, 1), (57, 1), (238, 1), (16, 1), (237, 1), (303, 1), (134, 1), (315, 1), (292, 1), (331, 1), (15, 1), (55, 1), (173, 1), (221, 1), (258, 1), (199, 1)])",30.0 -heuristic_minbudget,4.097546900156885,400.0,"(19, [(264, 1), (237, 1), (199, 1), (16, 1), (303, 1), (57, 1), (134, 1), (212, 1), (315, 1), (221, 1), (258, 1), (7, 1), (55, 1), (268, 1), (238, 1), (15, 1), (292, 1), (331, 1), (173, 1), (247, 2), (341, 2), (18, 2), (329, 2), (76, 2), (348, 2), (95, 2), (272, 2), (132, 2), (218, 2), (151, 2), (136, 2), (376, 2), (197, 2), (157, 2), (53, 2), (20, 2), (143, 2), (213, 2)])",19.0 -spreading_minbudget,11.941065700026227,400.0,"(65, [(178, 1), (249, 1), (78, 1), (14, 1), (29, 1), (74, 1), (80, 1), (85, 1), (100, 1), (122, 1), (165, 1), (167, 1), (169, 1), (197, 1), (202, 1), (215, 1), (239, 1), (257, 1), (258, 1), (262, 1), (270, 1), (272, 1), (277, 1), (289, 1), (309, 1), (323, 1), (328, 1), (335, 1), (340, 1), (342, 1), (386, 1), (388, 1), (2, 1), (24, 1), (53, 1), (63, 1), (136, 1), (137, 1), (183, 1), (206, 1), (326, 1), (379, 1), (55, 1), (90, 1), (97, 1), (99, 1), (113, 1), (127, 1), (152, 1), (174, 1), (203, 1), (209, 1), (383, 1), (392, 1), (30, 1), (59, 1), (195, 1), (306, 1), (322, 1), (163, 1), (235, 1), (19, 1), (158, 1), (396, 1), (77, 1)])",65.0 -heuristic_minbudget,64.45830999989994,400.0,"(64, [(309, 1), (235, 1), (197, 1), (392, 1), (249, 1), (14, 1), (388, 1), (323, 1), (326, 1), (29, 1), (77, 1), (174, 1), (270, 1), (2, 1), (127, 1), (165, 1), (258, 1), (85, 1), (136, 1), (158, 1), (379, 1), (30, 1), (137, 1), (169, 1), (99, 1), (257, 1), (328, 1), (262, 1), (306, 1), (59, 1), (322, 1), (340, 1), (90, 1), (163, 1), (209, 1), (277, 1), (203, 1), (206, 1), (396, 1), (74, 1), (272, 1), (113, 1), (386, 1), (342, 1), (55, 1), (289, 1), (19, 1), (24, 1), (167, 1), (100, 1), (78, 1), (195, 1), (63, 1), (215, 1), (183, 1), (202, 1), (239, 1), (80, 1), (122, 1), (335, 1), (53, 1), (383, 1), (97, 1), (152, 1)])",64.0 -spreading_minbudget,13.347260399954394,400.0,"(162, [(293, 1), (3, 1), (5, 1), (10, 1), (11, 1), (13, 1), (14, 1), (16, 1), (17, 1), (20, 1), (25, 1), (26, 1), (27, 1), (28, 1), (31, 1), (32, 1), (33, 1), (36, 1), (37, 1), (38, 1), (41, 1), (43, 1), (65, 1), (80, 1), (82, 1), (85, 1), (94, 1), (98, 1), (100, 1), (101, 1), (102, 1), (108, 1), (112, 1), (116, 1), (117, 1), (125, 1), (126, 1), (128, 1), (129, 1), (132, 1), (133, 1), (134, 1), (138, 1), (140, 1), (146, 1), (147, 1), (148, 1), (153, 1), (157, 1), (159, 1), (162, 1), (166, 1), (167, 1), (168, 1), (169, 1), (170, 1), (178, 1), (180, 1), (189, 1), (190, 1), (194, 1), (198, 1), (199, 1), (202, 1), (204, 1), (205, 1), (212, 1), (215, 1), (219, 1), (223, 1), (226, 1), (227, 1), (236, 1), (238, 1), (241, 1), (242, 1), (243, 1), (246, 1), (249, 1), (258, 1), (260, 1), (266, 1), (273, 1), (275, 1), (276, 1), (280, 1), (282, 1), (286, 1), (289, 1), (290, 1), (292, 1), (299, 1), (303, 1), (304, 1), (310, 1), (311, 1), (312, 1), (320, 1), (326, 1), (337, 1), (342, 1), (347, 1), (354, 1), (355, 1), (356, 1), (357, 1), (359, 1), (361, 1), (365, 1), (373, 1), (374, 1), (376, 1), (380, 1), (384, 1), (391, 1), (394, 1), (395, 1), (396, 1), (398, 1), (19, 1), (30, 1), (40, 1), (49, 1), (52, 1), (60, 1), (62, 1), (63, 1), (74, 1), (92, 1), (119, 1), (121, 1), (122, 1), (136, 1), (137, 1), (142, 1), (160, 1), (185, 1), (188, 1), (195, 1), (222, 1), (233, 1), (259, 1), (267, 1), (270, 1), (281, 1), (295, 1), (307, 1), (313, 1), (316, 1), (317, 1), (319, 1), (324, 1), (343, 1), (362, 1), (367, 1), (390, 1), (399, 1), (18, 1), (23, 1), (81, 1), (95, 1), (224, 1)])",162.0 -heuristic_minbudget,242.65643709991127,400.0,"(162, [(140, 1), (295, 1), (129, 1), (376, 1), (137, 1), (28, 1), (112, 1), (146, 1), (299, 1), (60, 1), (170, 1), (80, 1), (100, 1), (219, 1), (276, 1), (227, 1), (63, 1), (282, 1), (391, 1), (94, 1), (190, 1), (125, 1), (148, 1), (26, 1), (167, 1), (185, 1), (367, 1), (33, 1), (199, 1), (267, 1), (354, 1), (16, 1), (17, 1), (195, 1), (20, 1), (153, 1), (215, 1), (337, 1), (289, 1), (5, 1), (108, 1), (357, 1), (101, 1), (121, 1), (157, 1), (134, 1), (212, 1), (249, 1), (304, 1), (307, 1), (361, 1), (142, 1), (62, 1), (10, 1), (236, 1), (133, 1), (13, 1), (373, 1), (293, 1), (102, 1), (223, 1), (241, 1), (286, 1), (266, 1), (202, 1), (312, 1), (319, 1), (194, 1), (398, 1), (168, 1), (226, 1), (316, 1), (166, 1), (270, 1), (259, 1), (25, 1), (74, 1), (238, 1), (303, 1), (138, 1), (162, 1), (204, 1), (356, 1), (189, 1), (281, 1), (384, 1), (82, 1), (18, 1), (95, 1), (169, 1), (36, 1), (49, 1), (275, 1), (374, 1), (280, 1), (136, 1), (224, 1), (19, 1), (260, 1), (310, 1), (396, 1), (119, 1), (313, 1), (365, 1), (43, 1), (132, 1), (159, 1), (311, 1), (147, 1), (23, 1), (41, 1), (243, 1), (342, 1), (98, 1), (188, 1), (30, 1), (394, 1), (65, 1), (233, 1), (292, 1), (31, 1), (359, 1), (180, 1), (27, 1), (390, 1), (178, 1), (116, 1), (290, 1), (242, 1), (37, 1), (128, 1), (395, 1), (32, 1), (122, 1), (126, 1), (326, 1), (160, 1), (399, 1), (317, 1), (14, 1), (380, 1), (258, 1), (81, 1), (117, 1), (40, 1), (38, 1), (52, 1), (198, 1), (205, 1), (246, 1), (273, 1), (355, 1), (11, 1), (320, 1), (222, 1), (343, 1), (362, 1), (3, 1), (85, 1), (347, 1), (324, 1), (92, 1)])",162.0 +spreading_minbudget,0.4963123999768868,100.0,"(4, [(89, 1), (47, 1), (84, 1), (30, 1), (38, 2), (50, 2), (3, 2), (26, 2)])",4.0 +heuristic_minbudget,0.1241345999296754,100.0,"(4, [(47, 1), (92, 1), (44, 1), (17, 1), (83, 2), (62, 2), (50, 2), (26, 2), (69, 3), (29, 3)])",4.0 +spreading_minbudget,0.3931754000950604,100.0,"(11, [(3, 1), (15, 1), (28, 1), (54, 1), (71, 1), (96, 1), (97, 1), (17, 1), (35, 1), (45, 1), (22, 1)])",11.0 +heuristic_minbudget,1.0460371000226587,100.0,"(10, [(22, 1), (35, 1), (28, 1), (54, 1), (96, 1), (17, 1), (97, 1), (15, 1), (45, 1), (71, 1)])",10.0 +spreading_minbudget,0.2668848999310285,100.0,"(14, [(14, 1), (20, 1), (30, 1), (40, 1), (48, 1), (57, 1), (71, 1), (80, 1), (86, 1), (91, 1), (92, 1), (96, 1), (5, 1), (89, 1)])",14.0 +heuristic_minbudget,3.63022309995722,100.0,"(14, [(14, 1), (40, 1), (48, 1), (86, 1), (96, 1), (20, 1), (91, 1), (80, 1), (92, 1), (71, 1), (30, 1), (5, 1), (57, 1), (89, 1)])",14.0 +spreading_minbudget,1.212209300021641,200.0,"(22, [(70, 1), (128, 1), (53, 1), (55, 1), (125, 1), (152, 1), (28, 1), (104, 1), (17, 1), (20, 1), (174, 1), (5, 1), (31, 1), (35, 1), (44, 1), (120, 1), (151, 1), (172, 1), (175, 1), (190, 1), (146, 1), (168, 1)])",22.0 +heuristic_minbudget,1.0129838000284508,200.0,"(14, [(174, 1), (151, 1), (146, 1), (168, 1), (17, 1), (172, 1), (152, 1), (190, 1), (35, 1), (5, 1), (44, 1), (175, 1), (120, 1), (52, 1), (128, 2), (109, 2), (148, 2), (16, 2), (76, 2), (32, 2), (114, 2), (8, 2), (95, 2), (39, 2), (48, 2), (138, 2), (116, 2), (157, 2)])",14.0 +spreading_minbudget,0.5931069999933243,200.0,"(5, [(5, 1), (44, 1), (193, 1), (170, 1), (24, 1)])",5.0 +heuristic_minbudget,5.592074999934994,200.0,"(4, [(44, 1), (24, 1), (193, 1), (170, 1)])",4.0 +spreading_minbudget,1.2939483999507502,200.0,"(40, [(89, 1), (3, 1), (4, 1), (13, 1), (17, 1), (19, 1), (27, 1), (45, 1), (50, 1), (62, 1), (74, 1), (84, 1), (92, 1), (95, 1), (97, 1), (118, 1), (119, 1), (123, 1), (124, 1), (128, 1), (138, 1), (142, 1), (144, 1), (146, 1), (152, 1), (153, 1), (180, 1), (191, 1), (198, 1), (11, 1), (29, 1), (93, 1), (100, 1), (141, 1), (164, 1), (167, 1), (173, 1), (184, 1), (102, 1), (86, 1)])",40.0 +heuristic_minbudget,20.179923600051552,200.0,"(40, [(4, 1), (146, 1), (144, 1), (153, 1), (152, 1), (62, 1), (173, 1), (119, 1), (13, 1), (89, 1), (142, 1), (164, 1), (27, 1), (45, 1), (102, 1), (95, 1), (141, 1), (128, 1), (29, 1), (167, 1), (86, 1), (191, 1), (74, 1), (100, 1), (11, 1), (84, 1), (3, 1), (118, 1), (93, 1), (180, 1), (92, 1), (50, 1), (138, 1), (17, 1), (184, 1), (19, 1), (124, 1), (123, 1), (198, 1), (97, 1)])",40.0 +spreading_minbudget,3.5869039999088272,400.0,"(21, [(69, 1), (116, 1), (239, 1), (159, 1), (250, 1), (37, 1), (192, 1), (144, 1), (387, 1), (29, 1), (287, 1), (7, 1), (63, 1), (300, 1), (305, 1), (93, 1), (280, 1), (112, 1), (149, 1), (11, 1), (230, 1)])",21.0 +heuristic_minbudget,2.819514799979516,400.0,"(15, [(287, 1), (305, 1), (300, 1), (149, 1), (63, 1), (11, 1), (93, 1), (280, 1), (112, 1), (230, 1), (29, 1), (343, 1), (103, 1), (364, 1), (339, 1), (190, 2), (110, 2), (121, 2), (393, 2), (193, 2), (200, 2), (354, 2), (1, 2), (147, 2), (159, 2), (367, 2), (267, 2), (204, 2), (50, 2), (373, 2)])",15.0 +spreading_minbudget,6.544073999975808,400.0,"(63, [(103, 1), (333, 1), (88, 1), (2, 1), (17, 1), (27, 1), (29, 1), (66, 1), (72, 1), (76, 1), (89, 1), (135, 1), (143, 1), (148, 1), (150, 1), (152, 1), (165, 1), (166, 1), (190, 1), (222, 1), (250, 1), (257, 1), (275, 1), (289, 1), (291, 1), (292, 1), (293, 1), (306, 1), (315, 1), (326, 1), (327, 1), (363, 1), (373, 1), (380, 1), (386, 1), (40, 1), (109, 1), (112, 1), (140, 1), (182, 1), (229, 1), (238, 1), (239, 1), (325, 1), (390, 1), (9, 1), (90, 1), (171, 1), (184, 1), (359, 1), (385, 1), (393, 1), (11, 1), (51, 1), (71, 1), (212, 1), (364, 1), (230, 1), (368, 1), (391, 1), (52, 1), (353, 1), (276, 1)])",63.0 +heuristic_minbudget,45.2434607000323,400.0,"(62, [(327, 1), (150, 1), (364, 1), (166, 1), (291, 1), (143, 1), (229, 1), (315, 1), (17, 1), (109, 1), (212, 1), (27, 1), (2, 1), (11, 1), (257, 1), (306, 1), (148, 1), (239, 1), (390, 1), (363, 1), (71, 1), (76, 1), (275, 1), (276, 1), (72, 1), (184, 1), (325, 1), (353, 1), (51, 1), (135, 1), (112, 1), (165, 1), (238, 1), (293, 1), (289, 1), (385, 1), (9, 1), (140, 1), (222, 1), (250, 1), (386, 1), (359, 1), (29, 1), (373, 1), (292, 1), (391, 1), (326, 1), (40, 1), (380, 1), (171, 1), (230, 1), (393, 1), (182, 1), (368, 1), (52, 1), (66, 1), (152, 1), (88, 1), (89, 1), (333, 1), (190, 1), (90, 1)])",62.0 +spreading_minbudget,4.017668400076218,400.0,"(43, [(44, 1), (56, 1), (71, 1), (104, 1), (105, 1), (110, 1), (130, 1), (142, 1), (151, 1), (157, 1), (171, 1), (179, 1), (189, 1), (214, 1), (222, 1), (229, 1), (232, 1), (271, 1), (281, 1), (296, 1), (297, 1), (308, 1), (309, 1), (335, 1), (339, 1), (344, 1), (354, 1), (355, 1), (359, 1), (371, 1), (373, 1), (382, 1), (385, 1), (68, 1), (89, 1), (98, 1), (150, 1), (253, 1), (254, 1), (307, 1), (369, 1), (378, 1), (84, 1)])",43.0 +heuristic_minbudget,96.92596789996604,400.0,"(43, [(56, 1), (84, 1), (222, 1), (214, 1), (307, 1), (335, 1), (369, 1), (371, 1), (309, 1), (339, 1), (104, 1), (189, 1), (229, 1), (232, 1), (382, 1), (296, 1), (130, 1), (151, 1), (171, 1), (110, 1), (179, 1), (271, 1), (71, 1), (344, 1), (354, 1), (378, 1), (254, 1), (385, 1), (68, 1), (157, 1), (281, 1), (308, 1), (44, 1), (373, 1), (105, 1), (359, 1), (142, 1), (253, 1), (297, 1), (150, 1), (355, 1), (89, 1), (98, 1)])",43.0 From 947988c3e8354553997f8b33e90ad95f8f3acaf1 Mon Sep 17 00:00:00 2001 From: Almog-David Date: Tue, 23 Jul 2024 21:06:01 +0300 Subject: [PATCH 78/93] minor fixes due to changes in the main functions of non_spreading --- .../test_non_spreading_dirlaynet_minbudget.py | 2 +- .../test_non_spreading_minbudget.py | 28 +++++++++---------- 2 files changed, 15 insertions(+), 15 deletions(-) diff --git a/networkz/algorithms/approximation/tests/test_firefighter_problem/test_non_spreading_dirlaynet_minbudget.py b/networkz/algorithms/approximation/tests/test_firefighter_problem/test_non_spreading_dirlaynet_minbudget.py index 6ed6cd6..16cca24 100644 --- a/networkz/algorithms/approximation/tests/test_firefighter_problem/test_non_spreading_dirlaynet_minbudget.py +++ b/networkz/algorithms/approximation/tests/test_firefighter_problem/test_non_spreading_dirlaynet_minbudget.py @@ -378,7 +378,7 @@ def test_non_spreading_dirlaynet_minbudget(): num_targets = random.randint(1, len(nodes) - 1) # Ensure at least one target node targets = random.sample(nodes[1:], num_targets) # Randomly choose nodes to save, excluding the source node - min_budget = non_spreading_dirlaynet_minbudget(G, source, targets) + min_budget = non_spreading_dirlaynet_minbudget(G, source, targets)[0] assert min_budget > 0, "Minimum budget should be non-negative" if __name__ == "__main__": diff --git a/networkz/algorithms/approximation/tests/test_firefighter_problem/test_non_spreading_minbudget.py b/networkz/algorithms/approximation/tests/test_firefighter_problem/test_non_spreading_minbudget.py index 6968228..14cdd67 100644 --- a/networkz/algorithms/approximation/tests/test_firefighter_problem/test_non_spreading_minbudget.py +++ b/networkz/algorithms/approximation/tests/test_firefighter_problem/test_non_spreading_minbudget.py @@ -108,22 +108,22 @@ def test_source_is_target(): non_spreading_minbudget(graphs["RegularGraph_Graph-8"], 0, [13,10,8,6,5,4,3,0,1,2]) def test_save_all_vertices(): - assert 2 == non_spreading_minbudget(graphs["RegularGraph_Graph-1"], 0, [1,2,3,4,5,6]) #answer is 2 - assert 2 == non_spreading_minbudget(graphs["RegularGraph_Graph-2"], 0, [1,2,3,4,5,6,7]) #answer is 2 - assert 3 != non_spreading_minbudget(graphs["RegularGraph_Graph-3"], 0, [1,2,3,4,5]) #answer is 2 - assert 3 > non_spreading_minbudget(graphs["RegularGraph_Graph-4"], 0, [1,2,3,4,5,6,7]) #answer is 2 - assert 1 == non_spreading_minbudget(graphs["RegularGraph_Graph-6"], 1, [0,2,3,4,5,6,7,8,9]) #answer is 1 - assert 3 == non_spreading_minbudget(graphs["RegularGraph_Graph-7"], 1, [0,2,3,4,5,6]) #answer is 3 - assert 2 != non_spreading_minbudget(graphs["RegularGraph_Graph-8"], 0, [1,2,3,4,5,6,7,8,9,10,11,12,13,14]) #answer is 3 + assert 2 == non_spreading_minbudget(graphs["RegularGraph_Graph-1"], 0, [1,2,3,4,5,6])[0] #answer is 2 + assert 2 == non_spreading_minbudget(graphs["RegularGraph_Graph-2"], 0, [1,2,3,4,5,6,7])[0] #answer is 2 + assert 3 != non_spreading_minbudget(graphs["RegularGraph_Graph-3"], 0, [1,2,3,4,5])[0] #answer is 2 + assert 3 > non_spreading_minbudget(graphs["RegularGraph_Graph-4"], 0, [1,2,3,4,5,6,7])[0] #answer is 2 + assert 1 == non_spreading_minbudget(graphs["RegularGraph_Graph-6"], 1, [0,2,3,4,5,6,7,8,9])[0] #answer is 1 + assert 3 == non_spreading_minbudget(graphs["RegularGraph_Graph-7"], 1, [0,2,3,4,5,6])[0] #answer is 3 + assert 2 != non_spreading_minbudget(graphs["RegularGraph_Graph-8"], 0, [1,2,3,4,5,6,7,8,9,10,11,12,13,14])[0] #answer is 3 def test_save_subgroup_vertices(): - assert 1 != non_spreading_minbudget(graphs["RegularGraph_Graph-1"], 0, [1,5,6]) #answer is 2 - assert 1 == non_spreading_minbudget(graphs["RegularGraph_Graph-2"], 0, [1,3,4,5,6]) #answer is 1 - assert 3 > non_spreading_minbudget(graphs["RegularGraph_Graph-3"], 0, [1,2]) #answer is 2 - assert 1 < non_spreading_minbudget(graphs["RegularGraph_Graph-4"], 0, [2,3,5,7]) #answer is 2 - assert 4 > non_spreading_minbudget(graphs["RegularGraph_Graph-6"], 1, [0,3,5,6,8,9]) #answer is 1 - assert 2 == non_spreading_minbudget(graphs["RegularGraph_Graph-7"], 1, [0,2,5,6]) #answer is 2 - assert 3 == non_spreading_minbudget(graphs["RegularGraph_Graph-8"], 0, [1,3,4,5,6,9,10,12,14]) #answer is 3 + assert 1 != non_spreading_minbudget(graphs["RegularGraph_Graph-1"], 0, [1,5,6])[0] #answer is 2 + assert 1 == non_spreading_minbudget(graphs["RegularGraph_Graph-2"], 0, [1,3,4,5,6])[0] #answer is 1 + assert 3 > non_spreading_minbudget(graphs["RegularGraph_Graph-3"], 0, [1,2])[0] #answer is 2 + assert 1 < non_spreading_minbudget(graphs["RegularGraph_Graph-4"], 0, [2,3,5,7])[0] #answer is 2 + assert 4 > non_spreading_minbudget(graphs["RegularGraph_Graph-6"], 1, [0,3,5,6,8,9])[0] #answer is 1 + assert 2 == non_spreading_minbudget(graphs["RegularGraph_Graph-7"], 1, [0,2,5,6])[0] #answer is 2 + assert 3 == non_spreading_minbudget(graphs["RegularGraph_Graph-8"], 0, [1,3,4,5,6,9,10,12,14])[0] #answer is 3 if __name__ == "__main__": From 5991624e41e0319b9ea35b07720df8cde4a009bf Mon Sep 17 00:00:00 2001 From: 20shaked20 Date: Fri, 26 Jul 2024 14:12:41 +0300 Subject: [PATCH 79/93] Fixed some testings + commentings --- .../firefighter_problem/Utils.py | 1 - .../test_heuristic_maxsave.py | 19 +++-- .../test_heuristic_minbudget.py | 77 ++++++++----------- .../test_non_spreading_minbudget.py | 1 - .../test_spreading_minbudget.py | 31 +++----- 5 files changed, 49 insertions(+), 80 deletions(-) diff --git a/networkz/algorithms/approximation/firefighter_problem/Utils.py b/networkz/algorithms/approximation/firefighter_problem/Utils.py index 484673c..57105ab 100644 --- a/networkz/algorithms/approximation/firefighter_problem/Utils.py +++ b/networkz/algorithms/approximation/firefighter_problem/Utils.py @@ -25,7 +25,6 @@ import copy import logging import heapq -import concurrent.futures # this is used for improving the dirlay function - adjust_nodes_capacity class Status(Enum): VULNERABLE = "vulnerable" diff --git a/networkz/algorithms/approximation/tests/test_firefighter_problem/test_heuristic_maxsave.py b/networkz/algorithms/approximation/tests/test_firefighter_problem/test_heuristic_maxsave.py index 82015a5..6e79cb2 100644 --- a/networkz/algorithms/approximation/tests/test_firefighter_problem/test_heuristic_maxsave.py +++ b/networkz/algorithms/approximation/tests/test_firefighter_problem/test_heuristic_maxsave.py @@ -24,10 +24,9 @@ import logging import os import time -from datetime import datetime from networkz.algorithms.approximation.firefighter_problem.Firefighter_Problem import heuristic_maxsave, spreading_maxsave -from networkz.algorithms.approximation.firefighter_problem.Utils import find_best_neighbor, parse_json_to_networkx, Status +from networkz.algorithms.approximation.firefighter_problem.Utils import parse_json_to_networkx, Status def setup_logger(): logger = logging.getLogger('firefighter_problem_tests') @@ -52,7 +51,7 @@ def setup_logger(): raise FileNotFoundError(f"{path_to_graphs} does not exist.") graphs = parse_json_to_networkx(json_data) -""" + @pytest.mark.parametrize("graph_key, budget, source, targets", [ ("RegularGraph_Graph-1", 1, -2, [1, 2, 3, 4, 5, 6]), ("RegularGraph_Graph-4", 1, 8, [1, 2, 4, 6, 7]), @@ -112,19 +111,19 @@ def test_save_all_vertices(graph_key, budget, source, targets, expected_strategy assert calculated_strategy == expected_strategy logger.info(f"Save all vertices test passed for {graph_key}") -@pytest.mark.parametrize("graph_key, budget, source, targets, expected_strategy", [ - ("RegularGraph_Graph-6", 2, 1, [3, 9, 0, 5, 6], [(2, 1)]), - ("RegularGraph_Graph-4", 1, 0, [2, 6, 4], [(1, 1)]), +@pytest.mark.parametrize("graph_key, budget, source, targets, expected_save_amount", [ + ("RegularGraph_Graph-6", 2, 1, [3, 9, 0, 5, 6], 5), + ("RegularGraph_Graph-4", 1, 0, [2, 6, 4], 3), ]) -def test_save_subgroup_vertices(graph_key, budget, source, targets, expected_strategy): +def test_save_subgroup_vertices(graph_key, budget, source, targets, expected_save_amount): logger.info(f"Testing save subgroup vertices for {graph_key}") graph = graphs[graph_key] - calculated_strategy = heuristic_maxsave(graph, budget, source, targets)[0] + calculated_strategy = len(heuristic_maxsave(graph, budget, source, targets)[1]) logger.info(f"Calculated strategy: {calculated_strategy}") - assert calculated_strategy == expected_strategy + assert calculated_strategy >= expected_save_amount logger.info(f"Save subgroup vertices test passed for {graph_key}") - """ + def test_random_graph_comparison(): logger.info("Starting test_random_graph_comparison:") diff --git a/networkz/algorithms/approximation/tests/test_firefighter_problem/test_heuristic_minbudget.py b/networkz/algorithms/approximation/tests/test_firefighter_problem/test_heuristic_minbudget.py index 5b7ba70..353f27e 100644 --- a/networkz/algorithms/approximation/tests/test_firefighter_problem/test_heuristic_minbudget.py +++ b/networkz/algorithms/approximation/tests/test_firefighter_problem/test_heuristic_minbudget.py @@ -1,15 +1,35 @@ +""" + +The Paper - +Approximability of the Firefighter Problem Computing Cuts over Time + +Paper Link - +https://www.math.uwaterloo.ca/~cswamy/papers/firefighter-journ.pdf + +Authors - +Elliot Anshelevich +Deeparnab Chakrabarty +Ameya Hate +Chaitanya Swamy + +Developers - +Yuval Bubnovsky +Almog David +Shaked Levi + +""" import pytest -import networkx as nx -import json import random import time import logging import os -from datetime import datetime +import json from networkz.algorithms.approximation.firefighter_problem.Firefighter_Problem import spreading_minbudget, heuristic_minbudget +from networkz.algorithms.approximation.firefighter_problem.Random_Graph_Generator import generate_random_DiGraph from networkz.algorithms.approximation.firefighter_problem.Utils import parse_json_to_networkx + def setup_logger(): logger = logging.getLogger('firefighter_problem_tests') logger.setLevel(logging.INFO) @@ -42,7 +62,7 @@ def sample_json_data(): }, } } -""" + def get_graphs(): path_to_graphs = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'graphs.json') if os.path.exists(path_to_graphs): @@ -89,54 +109,17 @@ def test_source_is_target(G, source, targets, directed): with pytest.raises(ValueError, match="Error: The source node can't be a part of the targets list, since the virus is spreading from the source"): heuristic_minbudget(G, source, targets, directed) -def test_save_all_vertices_spreading(): - logger.info("Starting test_save_all_vertices_spreading:") - try: - for graph_name, G in graphs.items(): - source = 0 - targets = [n for n in G.nodes if n != source] - - start_time = time.time() - spreading_result = spreading_minbudget(G, source, targets)[0] - spreading_time = time.time() - start_time - - start_time = time.time() - heuristic_result = heuristic_minbudget(G, source, targets, True)[0] - heuristic_time = time.time() - start_time - - logger.info(f"{graph_name} - Spreading Result: {spreading_result}, Heuristic Result: {heuristic_result}") - logger.info(f"{graph_name} - Spreading Time: {spreading_time:.6f}s, Heuristic Time: {heuristic_time:.6f}s") - - if spreading_result < heuristic_result: - warning_message = f"Warning: Heuristic result ({heuristic_result}) is greater than spreading result ({spreading_result}) for {graph_name}" - logger.warning(warning_message) - - finally: - logger.info("Finished test_save_all_vertices_spreading.") - logger.info("-" * 100) - """ @pytest.mark.parametrize("i", range(10)) def test_random_graph_comparison(i): logger.info(f"Starting test_random_graph_comparison for Random Graph {i+1}:") try: - num_nodes = random.randint(5, 100) - nodes = list(range(num_nodes + 1)) - num_edges = 100 - save_amount = random.randint(5, num_nodes) - targets = [] - G = nx.DiGraph() - - G.add_nodes_from(nodes, status="target") - for _ in range(num_edges): - source = random.randint(0, num_nodes - 1) - target = random.randint(0, num_nodes - 1) - if source != target: # Ensure no self-loops - G.add_edge(source, target) - for node in range(save_amount): - probability = random.random() - if probability < 0.75 and node != 0: - targets.append(node) + G = generate_random_DiGraph(num_nodes=100, edge_probability=0.5) + + num_targets = random.randint(1, int(len(G.nodes) / 2) + 1) + nodes_to_sample = list(G.nodes) + nodes_to_sample.remove(0) + targets = random.sample(list(nodes_to_sample), num_targets) start_time = time.time() spreading_answer = spreading_minbudget(G, 0, targets)[0] diff --git a/networkz/algorithms/approximation/tests/test_firefighter_problem/test_non_spreading_minbudget.py b/networkz/algorithms/approximation/tests/test_firefighter_problem/test_non_spreading_minbudget.py index 14cdd67..16c230a 100644 --- a/networkz/algorithms/approximation/tests/test_firefighter_problem/test_non_spreading_minbudget.py +++ b/networkz/algorithms/approximation/tests/test_firefighter_problem/test_non_spreading_minbudget.py @@ -20,7 +20,6 @@ """ import pytest -import networkx as nx import json import os diff --git a/networkz/algorithms/approximation/tests/test_firefighter_problem/test_spreading_minbudget.py b/networkz/algorithms/approximation/tests/test_firefighter_problem/test_spreading_minbudget.py index b232077..29c9865 100644 --- a/networkz/algorithms/approximation/tests/test_firefighter_problem/test_spreading_minbudget.py +++ b/networkz/algorithms/approximation/tests/test_firefighter_problem/test_spreading_minbudget.py @@ -20,13 +20,14 @@ """ import pytest -import networkx as nx import json import random import os from networkz.algorithms.approximation.firefighter_problem.Firefighter_Problem import spreading_minbudget from networkz.algorithms.approximation.firefighter_problem.Utils import parse_json_to_networkx, calculate_gamma, calculate_epsilon, find_best_direct_vaccination +from networkz.algorithms.approximation.firefighter_problem.Random_Graph_Generator import generate_random_DiGraph + @pytest.fixture def sample_json_data(): @@ -269,31 +270,19 @@ def test_save_subgroup_vertices(): assert 2 == spreading_minbudget(graphs["RegularGraph_Graph-7"], 1, [4,2,5,6])[0] #answer is 2 assert spreading_minbudget(graphs["RegularGraph_Graph-8"], 0, [1,2,3,4,5,6,7,8,9,10,11,12,13,14])[0] != spreading_minbudget(graphs["RegularGraph_Graph-8"], 0, [1,3,4,5,6,9,10,12,14])[0] #answer is 3 + def test_random_graph(): for i in range(10): - num_nodes = random.randint(2,100) - nodes = list(range(num_nodes+1)) - num_edges = 1000 - save_amount = random.randint(1,num_nodes) - targets = [] - G = nx.DiGraph() - - G.add_nodes_from(nodes, status="target") - for _ in range(num_edges): - source = random.randint(0, num_nodes - 1) - target = random.randint(0, num_nodes - 1) - if source != target: # Ensure no self-loops - G.add_edge(source, target) - for node in range(save_amount): - probability = random.random() - if probability < 0.75 and node!=0: - targets.append(node) + G = generate_random_DiGraph(num_nodes=100, edge_probability=0.5) + + num_targets = random.randint(1, int(len(G.nodes) / 2) + 1) + nodes_to_sample = list(G.nodes) + nodes_to_sample.remove(0) + targets = random.sample(nodes_to_sample, num_targets) target_length = len(targets) ans = spreading_minbudget(G,0,targets)[0] - print(ans) - print(target_length) - + assert ans <= target_length print("All tests have passed!") From a3f3d9fd3d9349fe099deab0f25d2c35da1cb122 Mon Sep 17 00:00:00 2001 From: Almog-David Date: Fri, 26 Jul 2024 15:16:42 +0300 Subject: [PATCH 80/93] loggs minor changes --- .../firefighter_problem/Firefighter_Problem.py | 8 +++----- .../Random_Graph_Generator.py | 12 ------------ .../approximation/firefighter_problem/Utils.py | 17 +---------------- 3 files changed, 4 insertions(+), 33 deletions(-) diff --git a/networkz/algorithms/approximation/firefighter_problem/Firefighter_Problem.py b/networkz/algorithms/approximation/firefighter_problem/Firefighter_Problem.py index d9b01d4..84cc3cb 100644 --- a/networkz/algorithms/approximation/firefighter_problem/Firefighter_Problem.py +++ b/networkz/algorithms/approximation/firefighter_problem/Firefighter_Problem.py @@ -38,9 +38,7 @@ def setup_logger(): logger.addHandler(console_handler) return logger -#logger = setup_logger() - -#logger = logging.getLogger(__name__) +logger = setup_logger() def spreading_maxsave(Graph:nx.DiGraph, budget:int, source:int, targets:list, stop_condition=None) -> tuple[list, set]: """ @@ -296,6 +294,7 @@ def non_spreading_minbudget(Graph:nx.DiGraph, source:int, targets:list) -> int: logger.info(f"Starting the non_spreading_minbudget function with source node {source} and targets: {targets}") G = create_st_graph(Graph, targets) + logger.info(f"Calculating the minimum budget using edmonds karp algorithm") min_budget = len(algo.minimum_st_node_cut(G, source, 't')) logger.info(f"Returning minimum budget: {min_budget}") @@ -437,7 +436,6 @@ def heuristic_maxsave(Graph:nx.DiGraph, budget:int, source:int, targets:list, sp if len(local_targets) == 0 or any(node in infected_nodes for node in local_targets): for node in targets: node_status = Graph.nodes[node]['status'] - logger.debug(f" node {node} status {node_status}") if node_status != Status.INFECTED.value: saved_target_nodes.add(node) logger.info(f"Returning vaccination strategy: {vaccination_strategy}. The strategy saved the nodes: {saved_target_nodes}") @@ -447,7 +445,6 @@ def heuristic_maxsave(Graph:nx.DiGraph, budget:int, source:int, targets:list, sp for node in targets: node_status = Graph.nodes[node]['status'] - logger.debug(f" node {node} status {node_status}") if node_status != Status.INFECTED.value: saved_target_nodes.add(node) @@ -497,6 +494,7 @@ def heuristic_minbudget(Graph:nx.DiGraph, source:int, targets:list, spreading:bo best_strategy = heuristic_maxsave(Graph, min_budget, source, targets, spreading, True)[0] while min_value < max_value: + logger.info(f"Calling heuristic_maxsave with parameters - Source: {source}, Targets: {targets}, Budget: {middle}") strategy, nodes_saved = heuristic_maxsave(Graph, middle, source, targets, spreading, True) common_elements = set(nodes_saved) & set(targets) diff --git a/networkz/algorithms/approximation/firefighter_problem/Random_Graph_Generator.py b/networkz/algorithms/approximation/firefighter_problem/Random_Graph_Generator.py index 376228a..309614f 100644 --- a/networkz/algorithms/approximation/firefighter_problem/Random_Graph_Generator.py +++ b/networkz/algorithms/approximation/firefighter_problem/Random_Graph_Generator.py @@ -25,18 +25,6 @@ logger = logging.getLogger(__name__) -def setup_global_logger(level: int = logging.DEBUG): - log_format = "|| %(asctime)s || %(levelname)s || %(message)s" - date_format = '%H:%M:%S' - formatter = logging.Formatter(log_format, datefmt=date_format) - handler = logging.StreamHandler() - handler.setFormatter(formatter) - - root_logger = logging.getLogger() - root_logger.setLevel(level) - root_logger.addHandler(handler) - - def generate_random_DiGraph( num_nodes: int = 100, edge_probability: float = 0.1, diff --git a/networkz/algorithms/approximation/firefighter_problem/Utils.py b/networkz/algorithms/approximation/firefighter_problem/Utils.py index 57105ab..4f05e1b 100644 --- a/networkz/algorithms/approximation/firefighter_problem/Utils.py +++ b/networkz/algorithms/approximation/firefighter_problem/Utils.py @@ -40,22 +40,7 @@ class Status(Enum): 'directly vaccinated': 'green', 'default' : "#00FFD0" } - -def setup_logger(): - logger = logging.getLogger('firefighter_problem_main') - logger.setLevel(logging.INFO) - - console_handler = logging.StreamHandler() - console_handler.setLevel(logging.INFO) - - formatter = logging.Formatter('%(asctime)s - %(levelname)s - %(message)s') - console_handler.setFormatter(formatter) - - logger.addHandler(console_handler) - return logger - -logger = setup_logger() -#logger = logging.getLogger(__name__) +logger = logging.getLogger(__name__) # ============================ Validation Functions ============================ From 33abffd834de3e51c4ec82fb85b78739a6588b9e Mon Sep 17 00:00:00 2001 From: Erel Segal-Halevi Date: Wed, 31 Jul 2024 10:37:55 +0300 Subject: [PATCH 81/93] setup logger to main --- .../firefighter_problem/Firefighter_Problem.py | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git a/networkz/algorithms/approximation/firefighter_problem/Firefighter_Problem.py b/networkz/algorithms/approximation/firefighter_problem/Firefighter_Problem.py index 84cc3cb..be0d810 100644 --- a/networkz/algorithms/approximation/firefighter_problem/Firefighter_Problem.py +++ b/networkz/algorithms/approximation/firefighter_problem/Firefighter_Problem.py @@ -25,8 +25,7 @@ from networkz.algorithms.max_flow_with_node_capacity import min_cut_with_node_capacity from networkz.algorithms.approximation.firefighter_problem.Utils import * -def setup_logger(): - logger = logging.getLogger('firefighter_problem_main') +def setup_logger(logger): logger.setLevel(logging.INFO) console_handler = logging.StreamHandler() @@ -38,7 +37,8 @@ def setup_logger(): logger.addHandler(console_handler) return logger -logger = setup_logger() +logger = logging.getLogger('firefighter_problem_main') + def spreading_maxsave(Graph:nx.DiGraph, budget:int, source:int, targets:list, stop_condition=None) -> tuple[list, set]: """ @@ -516,6 +516,7 @@ def heuristic_minbudget(Graph:nx.DiGraph, source:int, targets:list, spreading:bo return min_budget, best_strategy if __name__ == "__main__": + import doctest result = doctest.testmod(verbose=False) print(f"Doctest results: {result}") \ No newline at end of file From 4ea6d73006204bed52cc42cd9674d3de8733b326 Mon Sep 17 00:00:00 2001 From: Erel Segal-Halevi Date: Wed, 31 Jul 2024 10:46:19 +0300 Subject: [PATCH 82/93] minbudget using mincut --- .../firefighter_problem/Firefighter_Problem.py | 11 ++++++----- .../approximation/firefighter_problem/Utils.py | 6 +++--- 2 files changed, 9 insertions(+), 8 deletions(-) diff --git a/networkz/algorithms/approximation/firefighter_problem/Firefighter_Problem.py b/networkz/algorithms/approximation/firefighter_problem/Firefighter_Problem.py index be0d810..81ac065 100644 --- a/networkz/algorithms/approximation/firefighter_problem/Firefighter_Problem.py +++ b/networkz/algorithms/approximation/firefighter_problem/Firefighter_Problem.py @@ -259,7 +259,7 @@ def non_spreading_minbudget(Graph:nx.DiGraph, source:int, targets:list) -> int: ------- min_budget : int Minimum budget required to save all target nodes. - + Examples: -------- Example 1: @@ -293,12 +293,13 @@ def non_spreading_minbudget(Graph:nx.DiGraph, source:int, targets:list) -> int: validate_parameters(Graph, source, targets) logger.info(f"Starting the non_spreading_minbudget function with source node {source} and targets: {targets}") - G = create_st_graph(Graph, targets) - logger.info(f"Calculating the minimum budget using edmonds karp algorithm") - min_budget = len(algo.minimum_st_node_cut(G, source, 't')) + G = create_st_graph(Graph, targets, 't') + min_cut = algo.minimum_st_node_cut(G, source, 't') + logger.info(f"Minimum s-t node cut: {min_cut}") + min_budget = len(min_cut) logger.info(f"Returning minimum budget: {min_budget}") - return min_budget, [] + return min_budget, [] # TODO: vaccinate all nodes in min_cut in the first step. def non_spreading_dirlaynet_minbudget(Graph:nx.DiGraph, source:int, targets:list) -> tuple[int, list]: """ diff --git a/networkz/algorithms/approximation/firefighter_problem/Utils.py b/networkz/algorithms/approximation/firefighter_problem/Utils.py index 4f05e1b..e4f4565 100644 --- a/networkz/algorithms/approximation/firefighter_problem/Utils.py +++ b/networkz/algorithms/approximation/firefighter_problem/Utils.py @@ -500,7 +500,7 @@ def adjust_nodes_capacity(graph:nx.DiGraph, source:int) -> list: return layers -def create_st_graph(graph:nx.DiGraph, targets:list) -> nx.DiGraph: +def create_st_graph(graph:nx.DiGraph, targets:list, new_target:str) -> nx.DiGraph: """ Create an s-t graph from the original graph for use in connectivity algorithms. @@ -532,9 +532,9 @@ def create_st_graph(graph:nx.DiGraph, targets:list) -> nx.DiGraph: logger.info(f"Creating a s-t graph to connect nodes to save") G = copy.deepcopy(graph) - G.add_node('t', status = Status.VULNERABLE.value) + G.add_node(new_target, status = Status.VULNERABLE.value) for node in targets: - G.add_edge(node,'t') + G.add_edge(node, new_target) #display_graph(G) logger.info(f"Done creating a s-t graph") From 9a43bc70e5c6381148a721c9e33d1e4ae7731c87 Mon Sep 17 00:00:00 2001 From: Erel Segal-Halevi Date: Wed, 31 Jul 2024 11:05:46 +0300 Subject: [PATCH 83/93] png file name --- ...on_spreading.png => non_spreading_minbudget.png} | Bin 1 file changed, 0 insertions(+), 0 deletions(-) rename networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/{non_spreading.png => non_spreading_minbudget.png} (100%) diff --git a/networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/non_spreading.png b/networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/non_spreading_minbudget.png similarity index 100% rename from networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/non_spreading.png rename to networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/non_spreading_minbudget.png From 04501b25e45452769e6e7981fc456d9a8d43a1fc Mon Sep 17 00:00:00 2001 From: Almog-David Date: Wed, 31 Jul 2024 15:17:36 +0300 Subject: [PATCH 84/93] nonspreading minbudget returns the vaccination strategy --- .../firefighter_problem/Firefighter_Problem.py | 15 ++++++++------- .../test_non_spreading_dirlaynet_minbudget.py | 8 ++++---- 2 files changed, 12 insertions(+), 11 deletions(-) diff --git a/networkz/algorithms/approximation/firefighter_problem/Firefighter_Problem.py b/networkz/algorithms/approximation/firefighter_problem/Firefighter_Problem.py index 81ac065..204f0c6 100644 --- a/networkz/algorithms/approximation/firefighter_problem/Firefighter_Problem.py +++ b/networkz/algorithms/approximation/firefighter_problem/Firefighter_Problem.py @@ -267,28 +267,28 @@ def non_spreading_minbudget(Graph:nx.DiGraph, source:int, targets:list) -> int: >>> G1.add_nodes_from([0,1,2,3], status="vulnerable") >>> G1.add_edges_from([(0,1),(0,2),(1,2),(1,3),(2,3)]) >>> non_spreading_minbudget(G1,0,[1,3]) - 2 + (2, [(1, 1), (3, 1)]) Example 2: >>> G1 = nx.DiGraph() >>> G1.add_nodes_from([0,1,2,3], status="vulnerable") >>> G1.add_edges_from([(0,1),(0,2),(1,2),(1,3),(2,3)]) >>> non_spreading_minbudget(G1,0,[1,2,3]) - 2 + (2, [(1, 1), (2, 1)]) Example 3: >>> G2 = nx.DiGraph() >>> G2.add_nodes_from([0,1,2,3,4,5,6], status="vulnerable") >>> G2.add_edges_from([(0,1),(0,2),(1,2),(1,4),(2,3),(2,6),(3,5)]) >>> non_spreading_minbudget(G2,0,[1,2,3,4,5,6]) - 2 + (2, [(1, 1), (2, 1)]) Example 4: >>> G3 = nx.DiGraph() >>> G3.add_nodes_from([0,1,2,3,4,5,6,7,8], status="vulnerable") >>> G3.add_edges_from([(0,2),(0,4),(0,5),(2,1),(2,3),(4,1),(4,6),(5,3),(5,6),(5,7),(6,7),(6,8),(7,8)]) >>> non_spreading_minbudget(G3,0,[2,6,1,8]) - 3 + (3, [(2, 1), (4, 1), (5, 1)]) """ validate_parameters(Graph, source, targets) logger.info(f"Starting the non_spreading_minbudget function with source node {source} and targets: {targets}") @@ -297,9 +297,10 @@ def non_spreading_minbudget(Graph:nx.DiGraph, source:int, targets:list) -> int: min_cut = algo.minimum_st_node_cut(G, source, 't') logger.info(f"Minimum s-t node cut: {min_cut}") min_budget = len(min_cut) + strategy = [(item, 1) for item in min_cut] logger.info(f"Returning minimum budget: {min_budget}") - return min_budget, [] # TODO: vaccinate all nodes in min_cut in the first step. + return min_budget, strategy def non_spreading_dirlaynet_minbudget(Graph:nx.DiGraph, source:int, targets:list) -> tuple[int, list]: """ @@ -329,7 +330,7 @@ def non_spreading_dirlaynet_minbudget(Graph:nx.DiGraph, source:int, targets:list >>> G4.add_nodes_from([0,1,2,3,4,5], status="vulnerable") >>> G4.add_edges_from([(0,1),(0,2),(1,3),(1,4),(1,5),(2,3),(2,4),(2,5),(3,5),(4,5)]) >>> non_spreading_dirlaynet_minbudget(G4,0,[1,2,3,4,5]) - (2, {0: [1, 2]}) + (2, {0: [2, 1]}) """ validate_parameters(Graph, source, targets) @@ -341,7 +342,7 @@ def non_spreading_dirlaynet_minbudget(Graph:nx.DiGraph, source:int, targets:list logger.info(f"Starting the non_spreading_dirlaynet_minbudget function with source node {source} and targets: {targets}") layers = adjust_nodes_capacity(Graph, source) - G = create_st_graph(Graph, targets) + G = create_st_graph(Graph, targets, 't') #display_graph(G) G_reduction_min_cut = min_cut_with_node_capacity(G, source=source, target='t') N_groups = min_cut_N_groups(G_reduction_min_cut,layers) diff --git a/networkz/algorithms/approximation/tests/test_firefighter_problem/test_non_spreading_dirlaynet_minbudget.py b/networkz/algorithms/approximation/tests/test_firefighter_problem/test_non_spreading_dirlaynet_minbudget.py index 16cca24..15a2347 100644 --- a/networkz/algorithms/approximation/tests/test_firefighter_problem/test_non_spreading_dirlaynet_minbudget.py +++ b/networkz/algorithms/approximation/tests/test_firefighter_problem/test_non_spreading_dirlaynet_minbudget.py @@ -82,7 +82,7 @@ def test_source_is_target(graph_key, source, targets): graph_1 = graphs["Dirlay_Graph-1"] layers_1 = adjust_nodes_capacity(graph_1, 0) # src is 0 targets_1 = [1, 2, 3] # saving 1,2,3 -G1 = create_st_graph(graph_1, targets_1) +G1 = create_st_graph(graph_1, targets_1,'t') reduction_G1_min_cut = min_cut_with_node_capacity(G1, 0) N_1_groups = min_cut_N_groups(reduction_G1_min_cut,layers_1) matrix_1 = calculate_vaccine_matrix(layers_1, N_1_groups) @@ -94,7 +94,7 @@ def test_source_is_target(graph_key, source, targets): graph_2 = graphs["Dirlay_Graph-2"] layers_2 = adjust_nodes_capacity(graph_2, 0) # src is 0 targets_2 = [2, 4] # saving 2,4 -G2 = create_st_graph(graph_2, targets_2) +G2 = create_st_graph(graph_2, targets_2,'t') reduction_G2_min_cut = min_cut_with_node_capacity(G2, 0) N_2_groups = min_cut_N_groups(reduction_G2_min_cut, layers_2) matrix_2 = calculate_vaccine_matrix(layers_2, N_2_groups) @@ -106,7 +106,7 @@ def test_source_is_target(graph_key, source, targets): graph_3 = graphs["Dirlay_Graph-3"] layers_3 = adjust_nodes_capacity(graph_3, 0) # src is 0 targets_3 = [1, 5, 7] # saving 1,5,7 -G3 = create_st_graph(graph_3, targets_3) +G3 = create_st_graph(graph_3, targets_3,'t') reduction_G3_min_cut = min_cut_with_node_capacity(G3, 0) N_3_groups = min_cut_N_groups(reduction_G3_min_cut, layers_3) matrix_3 = calculate_vaccine_matrix(layers_3, N_3_groups) @@ -118,7 +118,7 @@ def test_source_is_target(graph_key, source, targets): graph_4 = graphs["Dirlay_Graph-4"] layers_4 = adjust_nodes_capacity(graph_4, 0) # src is 0 targets_4 = [4, 5, 6, 8] # saving 4,5,6,8 -G4 = create_st_graph(graph_4, targets_4) +G4 = create_st_graph(graph_4, targets_4,'t') reduction_G4_min_cut = min_cut_with_node_capacity(G4, 0) N_4_groups = min_cut_N_groups(reduction_G4_min_cut, layers_4) matrix_4 = calculate_vaccine_matrix(layers_4, N_4_groups) From 738fd26326d8403c3f7ad979ddd7d0789ec96abc Mon Sep 17 00:00:00 2001 From: Almog-David Date: Wed, 31 Jul 2024 18:55:30 +0300 Subject: [PATCH 85/93] minor changes in comparison --- .../comparisons/comparisons.py | 69 +- .../comparisons/spreading_maxsave.csv | 1102 ++++++++++++++++- .../comparisons/spreading_maxsave.png | Bin 30056 -> 0 bytes .../comparisons/spreading_maxsave_budget.png | Bin 0 -> 45795 bytes .../comparisons/spreading_maxsave_cleaned.csv | 19 - .../spreading_maxsave_preprocessed.csv | 1081 ++++++++++++++++ 6 files changed, 2213 insertions(+), 58 deletions(-) delete mode 100644 networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/spreading_maxsave.png create mode 100644 networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/spreading_maxsave_budget.png delete mode 100644 networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/spreading_maxsave_cleaned.csv create mode 100644 networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/spreading_maxsave_preprocessed.csv diff --git a/networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/comparisons.py b/networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/comparisons.py index 303e8c6..ed6e702 100644 --- a/networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/comparisons.py +++ b/networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/comparisons.py @@ -79,7 +79,7 @@ def runner_no_spreading(algorithm, graph, source, targets): result = algorithm(Graph=graph, source=source, targets=targets) return {"Budget": result} -def runner_spreading(algorithm, graph, source, targets): +def runner_spreading(algorithm, graph, budget, source, targets): """ Run the specified algorithm with spreading. @@ -103,10 +103,10 @@ def runner_spreading(algorithm, graph, source, targets): return {"Budget": (algorithm(Graph=graph, source=source, targets=targets, spreading=True))} if algorithm == heuristic_maxsave: - return {"Nodes_Saved": (str(len(algorithm(Graph=graph, budget=2, source=source, targets=targets, spreading=True)[1])))} + return {"Nodes_Saved": (str(len(algorithm(Graph=graph, budget=budget, source=source, targets=targets, spreading=True)[1])))} if algorithm == spreading_maxsave: - return {"Nodes_Saved": (str(len(algorithm(Graph=graph, budget=2, source=source, targets=targets)[1])))} + return {"Nodes_Saved": (str(len(algorithm(Graph=graph, budget=budget, source=source, targets=targets)[1])))} else: return {"Budget": (algorithm(Graph=graph, source=source, targets=targets))} @@ -144,7 +144,7 @@ def multiple_runs(runs=30): ex1.run_with_time_limit(multiple_runs, input_ranges={}, time_limit=0.9) # Preprocess the DataFrame to extract numeric budget values - results_csv_file = "./networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/non_spreading.csv" + results_csv_file = "./networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/non_spreading_minbudget.csv" results = pd.read_csv(results_csv_file) # Extract the numeric budget from the 'Budget' column @@ -164,7 +164,7 @@ def extract_budget_numeric(budget): results = results.dropna(subset=['Budget_numeric']) # Save the preprocessed DataFrame to a temporary CSV file - preprocessed_csv_file = "./networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/non_spreading_preprocessed.csv" + preprocessed_csv_file = "./networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/non_spreading_minbudget_preprocessed.csv" results.to_csv(preprocessed_csv_file, index=False) print("\n DataFrame-NonSpread: \n", results) @@ -198,8 +198,9 @@ def Compare_SpreadingMaxSave(): node_counts = [100, 200, 400] edge_probabilities = [0.1, 0.5, 0.8] + budget_range = [1,2,3,5,7,10] - def multiple_runs(runs=1): + def multiple_runs(runs=10): for num_nodes in node_counts: for edge_prob in edge_probabilities: graph = generate_random_DiGraph(num_nodes=num_nodes, edge_probability=edge_prob, seed=None) @@ -209,12 +210,13 @@ def multiple_runs(runs=1): nodes.remove(0) num_targets = random.randint(1, int(len(nodes) / 2) + 1) targets = random.sample(nodes, num_targets) - for algorithm in input_ranges["algorithm"]: - start_time = perf_counter() - result = runner_spreading(algorithm, graph, source, targets) - runtime = perf_counter() - start_time - - ex2.add({**{"algorithm": algorithm.__name__, "runtime": runtime, "graph_nodes": len(graph.nodes)}, **result}) + for budget in budget_range: + for algorithm in input_ranges["algorithm"]: + start_time = perf_counter() + result = runner_spreading(algorithm, graph, budget, source, targets) + runtime = perf_counter() - start_time + + ex2.add({**{"algorithm": algorithm.__name__, "runtime": runtime, "Budget": budget, "graph_nodes": num_nodes, "edge_probability": edge_prob}, **result}) return {"status": "completed"} # Set a time limit for the entire batch run @@ -228,6 +230,13 @@ def multiple_runs(runs=1): # Ensure 'algorithm' column is of type string results['algorithm'] = results['algorithm'].astype(str) + # Ensure 'Budget' column is numeric and drop rows with NaNs + results['Budget'] = pd.to_numeric(results['Budget'], errors='coerce') + results = results.dropna(subset=['Budget']) + + # Ensure 'Budget' is an integer + results['Budget'] = results['Budget'].astype(int) + # Ensure 'Nodes_Saved' column is numeric and drop rows with NaNs results['Nodes_Saved'] = pd.to_numeric(results['Nodes_Saved'], errors='coerce') results = results.dropna(subset=['Nodes_Saved']) @@ -236,20 +245,42 @@ def multiple_runs(runs=1): results['Nodes_Saved'] = results['Nodes_Saved'].astype(int) # Save the cleaned DataFrame to a new CSV file (optional, for debugging) - cleaned_csv_file = "./networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/spreading_maxsave_cleaned.csv" + cleaned_csv_file = "./networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/spreading_maxsave_preprocessed.csv" results.to_csv(cleaned_csv_file, index=False) # Plot the results using the cleaned DataFrame - single_plot_results( + """ + multi_plot_results( + results_csv_file=cleaned_csv_file, + filter={}, + subplot_rows=1, + subplot_cols=3, + x_field="Budget", + y_field="Nodes_Saved", + z_field="algorithm", + subplot_field="edge_probability", + sharex=True, + sharey=True, + mean=True, + save_to_file="./networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/spreading_maxsave_edge_prob.png" + )""" + + multi_plot_results( results_csv_file=cleaned_csv_file, filter={}, + subplot_rows=2, + subplot_cols=3, x_field="graph_nodes", y_field="Nodes_Saved", z_field="algorithm", + subplot_field="Budget", + sharex=True, + sharey=True, mean=True, - save_to_file="./networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/spreading_maxsave.png" + save_to_file="./networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/spreading_maxsave_budget.png" ) - + + print("\n DataFrame-NonSpread: \n", ex2.dataFrame) def Compare_SpreadingMinBudget(): @@ -269,7 +300,7 @@ def Compare_SpreadingMinBudget(): node_counts = [100, 200, 400] edge_probabilities = [0.1, 0.5, 0.8] - def multiple_runs(runs=1): + def multiple_runs(runs=30): for num_nodes in node_counts: for edge_prob in edge_probabilities: graph = generate_random_DiGraph(num_nodes=num_nodes, edge_probability=edge_prob, seed=None) @@ -333,6 +364,6 @@ def extract_budget_numeric(budget): setup_global_logger(level=logging.DEBUG) - Compare_NonSpread() + #Compare_NonSpread() # Compare_SpreadingMinBudget() - # Compare_SpreadingMaxSave() \ No newline at end of file + Compare_SpreadingMaxSave() \ No newline at end of file diff --git a/networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/spreading_maxsave.csv b/networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/spreading_maxsave.csv index 5db9d96..9d19a75 100644 --- 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-spreading_maxsave,0.0580811999971047,100.0,12 -heuristic_maxsave,0.1019012000178918,100.0,8 -spreading_maxsave,0.0444004999008029,100.0,25 -heuristic_maxsave,0.0470084999687969,100.0,26 -spreading_maxsave,0.0284190999809652,100.0,6 -heuristic_maxsave,0.0666626999154686,100.0,6 -spreading_maxsave,0.1086813999572768,200.0,26 -heuristic_maxsave,0.0978079999331384,200.0,19 -spreading_maxsave,0.1280060999561101,200.0,13 -heuristic_maxsave,0.1266256000380963,200.0,14 -spreading_maxsave,0.1202372000552713,200.0,10 -heuristic_maxsave,0.1299593000439927,200.0,10 -spreading_maxsave,0.4431845999788493,400.0,19 -heuristic_maxsave,0.1864832999417558,400.0,15 -spreading_maxsave,0.588445600005798,400.0,19 -heuristic_maxsave,0.2307504001073539,400.0,19 -spreading_maxsave,0.467531600035727,400.0,25 -heuristic_maxsave,0.4711522000143304,400.0,25 diff --git a/networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/spreading_maxsave_preprocessed.csv b/networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/spreading_maxsave_preprocessed.csv new file mode 100644 index 0000000..3da94d9 --- /dev/null +++ b/networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/spreading_maxsave_preprocessed.csv @@ -0,0 +1,1081 @@ +algorithm,runtime,Budget,graph_nodes,edge_probability,Nodes_Saved +spreading_maxsave,0.0926433999557048,1,100.0,0.1,20 +heuristic_maxsave,0.0402037999592721,1,100.0,0.1,17 +spreading_maxsave,0.1724396999925375,2,100.0,0.1,27 +heuristic_maxsave,0.0379484000150114,2,100.0,0.1,24 +spreading_maxsave,0.1425126999965869,3,100.0,0.1,32 +heuristic_maxsave,0.0404063000460155,3,100.0,0.1,30 +spreading_maxsave,0.1646657999954186,5,100.0,0.1,40 +heuristic_maxsave,0.0400539000402204,5,100.0,0.1,40 +spreading_maxsave,0.2110122999874875,7,100.0,0.1,41 +heuristic_maxsave,0.0286949999863281,7,100.0,0.1,41 +spreading_maxsave,0.2944994999561459,10,100.0,0.1,41 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+spreading_maxsave,1.073670099955052,7,400.0,0.8,35 +heuristic_maxsave,1.4587548000272363,7,400.0,0.8,35 +spreading_maxsave,1.8994180000154304,10,400.0,0.8,38 +heuristic_maxsave,1.9158565999823625,10,400.0,0.8,38 From b4a3202d7fbe1b690e11ccb58b49868f8b8451d2 Mon Sep 17 00:00:00 2001 From: Almog-David Date: Thu, 1 Aug 2024 11:34:31 +0300 Subject: [PATCH 86/93] added more graph plots for maxsave --- .../comparisons/comparisons.py | 48 +- .../comparisons/spreading_maxsave.csv | 2162 ++++++++--------- .../spreading_maxsave_100_edge_prob.png | Bin 0 -> 41908 bytes .../spreading_maxsave_200_edge_prob.png | Bin 0 -> 42657 bytes .../spreading_maxsave_400_edge_prob.png | Bin 0 -> 41135 bytes .../comparisons/spreading_maxsave_budget.png | Bin 45795 -> 44811 bytes .../spreading_maxsave_preprocessed.csv | 2160 ++++++++-------- 7 files changed, 2199 insertions(+), 2171 deletions(-) create mode 100644 networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/spreading_maxsave_100_edge_prob.png create mode 100644 networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/spreading_maxsave_200_edge_prob.png create mode 100644 networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/spreading_maxsave_400_edge_prob.png diff --git a/networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/comparisons.py b/networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/comparisons.py index ed6e702..44aeb60 100644 --- a/networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/comparisons.py +++ b/networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/comparisons.py @@ -249,12 +249,26 @@ def multiple_runs(runs=10): results.to_csv(cleaned_csv_file, index=False) # Plot the results using the cleaned DataFrame - """ multi_plot_results( results_csv_file=cleaned_csv_file, filter={}, - subplot_rows=1, + subplot_rows=2, subplot_cols=3, + x_field="graph_nodes", + y_field="Nodes_Saved", + z_field="algorithm", + subplot_field="Budget", + sharex=True, + sharey=True, + mean=True, + save_to_file="./networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/spreading_maxsave_budget.png" + ) + + multi_plot_results( + results_csv_file=cleaned_csv_file, + filter={"graph_nodes":100}, + subplot_rows=3, + subplot_cols=1, x_field="Budget", y_field="Nodes_Saved", z_field="algorithm", @@ -262,24 +276,38 @@ def multiple_runs(runs=10): sharex=True, sharey=True, mean=True, - save_to_file="./networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/spreading_maxsave_edge_prob.png" - )""" + save_to_file="./networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/spreading_maxsave_100_edge_prob.png" + ) multi_plot_results( results_csv_file=cleaned_csv_file, - filter={}, - subplot_rows=2, - subplot_cols=3, - x_field="graph_nodes", + filter={"graph_nodes":200}, + subplot_rows=3, + subplot_cols=1, + x_field="Budget", y_field="Nodes_Saved", z_field="algorithm", - subplot_field="Budget", + subplot_field="edge_probability", sharex=True, sharey=True, mean=True, - save_to_file="./networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/spreading_maxsave_budget.png" + save_to_file="./networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/spreading_maxsave_200_edge_prob.png" ) + multi_plot_results( + results_csv_file=cleaned_csv_file, + filter={"graph_nodes":400}, + subplot_rows=3, + subplot_cols=1, + x_field="Budget", + y_field="Nodes_Saved", + z_field="algorithm", + subplot_field="edge_probability", + sharex=True, + sharey=True, + mean=True, + save_to_file="./networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/spreading_maxsave_400_edge_prob.png" + ) print("\n DataFrame-NonSpread: \n", ex2.dataFrame) diff --git a/networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/spreading_maxsave.csv b/networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/spreading_maxsave.csv index 9d19a75..402ba53 100644 --- a/networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/spreading_maxsave.csv +++ b/networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/spreading_maxsave.csv @@ -1,1082 +1,1082 @@ algorithm,runtime,Budget,graph_nodes,edge_probability,Nodes_Saved -spreading_maxsave,0.09264339995570481,1,100,0.1,20 -heuristic_maxsave,0.040203799959272146,1,100,0.1,17 -spreading_maxsave,0.1724396999925375,2,100,0.1,27 -heuristic_maxsave,0.03794840001501143,2,100,0.1,24 -spreading_maxsave,0.1425126999965869,3,100,0.1,32 -heuristic_maxsave,0.04040630004601553,3,100,0.1,30 -spreading_maxsave,0.16466579999541864,5,100,0.1,40 -heuristic_maxsave,0.04005390004022047,5,100,0.1,40 -spreading_maxsave,0.2110122999874875,7,100,0.1,41 -heuristic_maxsave,0.028694999986328185,7,100,0.1,41 -spreading_maxsave,0.2944994999561459,10,100,0.1,41 -heuristic_maxsave,0.01727830001618713,10,100,0.1,41 -spreading_maxsave,0.07911990000866354,1,100,0.1,7 -heuristic_maxsave,0.0292259999550879,1,100,0.1,7 -spreading_maxsave,0.09824489994207397,2,100,0.1,9 -heuristic_maxsave,0.04382869997061789,2,100,0.1,9 -spreading_maxsave,0.1321790999500081,3,100,0.1,9 -heuristic_maxsave,0.044759999960660934,3,100,0.1,11 -spreading_maxsave,0.15628419996937737,5,100,0.1,11 -heuristic_maxsave,0.04101359995547682,5,100,0.1,11 -spreading_maxsave,0.24242660001618788,7,100,0.1,11 -heuristic_maxsave,0.041445599985308945,7,100,0.1,11 -spreading_maxsave,0.22384240000974387,10,100,0.1,11 -heuristic_maxsave,0.016689600015524775,10,100,0.1,11 -spreading_maxsave,0.08411930000875145,1,100,0.1,19 -heuristic_maxsave,0.028105299978051335,1,100,0.1,17 -spreading_maxsave,0.10869299998739734,2,100,0.1,27 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-1,1081 +1,1081 @@ algorithm,runtime,Budget,graph_nodes,edge_probability,Nodes_Saved -spreading_maxsave,0.0926433999557048,1,100.0,0.1,20 -heuristic_maxsave,0.0402037999592721,1,100.0,0.1,17 -spreading_maxsave,0.1724396999925375,2,100.0,0.1,27 -heuristic_maxsave,0.0379484000150114,2,100.0,0.1,24 -spreading_maxsave,0.1425126999965869,3,100.0,0.1,32 -heuristic_maxsave,0.0404063000460155,3,100.0,0.1,30 -spreading_maxsave,0.1646657999954186,5,100.0,0.1,40 -heuristic_maxsave,0.0400539000402204,5,100.0,0.1,40 -spreading_maxsave,0.2110122999874875,7,100.0,0.1,41 -heuristic_maxsave,0.0286949999863281,7,100.0,0.1,41 -spreading_maxsave,0.2944994999561459,10,100.0,0.1,41 -heuristic_maxsave,0.0172783000161871,10,100.0,0.1,41 -spreading_maxsave,0.0791199000086635,1,100.0,0.1,7 -heuristic_maxsave,0.0292259999550879,1,100.0,0.1,7 -spreading_maxsave,0.0982448999420739,2,100.0,0.1,9 -heuristic_maxsave,0.0438286999706178,2,100.0,0.1,9 -spreading_maxsave,0.1321790999500081,3,100.0,0.1,9 -heuristic_maxsave,0.0447599999606609,3,100.0,0.1,11 -spreading_maxsave,0.1562841999693773,5,100.0,0.1,11 -heuristic_maxsave,0.0410135999554768,5,100.0,0.1,11 -spreading_maxsave,0.2424266000161878,7,100.0,0.1,11 -heuristic_maxsave,0.0414455999853089,7,100.0,0.1,11 -spreading_maxsave,0.2238424000097438,10,100.0,0.1,11 -heuristic_maxsave,0.0166896000155247,10,100.0,0.1,11 -spreading_maxsave,0.0841193000087514,1,100.0,0.1,19 -heuristic_maxsave,0.0281052999780513,1,100.0,0.1,17 -spreading_maxsave,0.1086929999873973,2,100.0,0.1,27 -heuristic_maxsave,0.0382591999950818,2,100.0,0.1,28 -spreading_maxsave,0.1199418000178411,3,100.0,0.1,33 -heuristic_maxsave,0.1031213999958708,3,100.0,0.1,33 -spreading_maxsave,0.164954200037755,5,100.0,0.1,38 -heuristic_maxsave,0.0477116000256501,5,100.0,0.1,41 -spreading_maxsave,0.1913089000154286,7,100.0,0.1,40 -heuristic_maxsave,0.0386142000206746,7,100.0,0.1,41 -spreading_maxsave,0.2302183000138029,10,100.0,0.1,41 -heuristic_maxsave,0.0193757999804802,10,100.0,0.1,41 -spreading_maxsave,0.1896415000082925,1,100.0,0.1,21 -heuristic_maxsave,0.0328265000134706,1,100.0,0.1,18 -spreading_maxsave,0.121705400000792,2,100.0,0.1,31 -heuristic_maxsave,0.0427724999608472,2,100.0,0.1,30 -spreading_maxsave,0.1509460000088438,3,100.0,0.1,38 -heuristic_maxsave,0.0453863000147976,3,100.0,0.1,36 -spreading_maxsave,0.2030844999826513,5,100.0,0.1,45 -heuristic_maxsave,0.0575835999916307,5,100.0,0.1,43 -spreading_maxsave,0.2987244999967515,7,100.0,0.1,45 -heuristic_maxsave,0.0406402999651618,7,100.0,0.1,48 -spreading_maxsave,0.2516182999825105,10,100.0,0.1,48 -heuristic_maxsave,0.016990500036627,10,100.0,0.1,48 -spreading_maxsave,0.0784893999807536,1,100.0,0.1,22 -heuristic_maxsave,0.0339833999751135,1,100.0,0.1,18 -spreading_maxsave,0.1109743000124581,2,100.0,0.1,31 -heuristic_maxsave,0.0416412000195123,2,100.0,0.1,30 -spreading_maxsave,0.1381746999686584,3,100.0,0.1,37 -heuristic_maxsave,0.036210199992638,3,100.0,0.1,34 -spreading_maxsave,0.2528164000250399,5,100.0,0.1,45 -heuristic_maxsave,0.0431022000266239,5,100.0,0.1,45 -spreading_maxsave,0.1993989000329747,7,100.0,0.1,45 -heuristic_maxsave,0.0268423000234179,7,100.0,0.1,47 -spreading_maxsave,0.2412506999680772,10,100.0,0.1,47 -heuristic_maxsave,0.0202688999706879,10,100.0,0.1,47 -spreading_maxsave,0.0854721000068821,1,100.0,0.1,17 -heuristic_maxsave,0.0836390000185929,1,100.0,0.1,17 -spreading_maxsave,0.1281881000031717,2,100.0,0.1,23 -heuristic_maxsave,0.0375545000424608,2,100.0,0.1,21 -spreading_maxsave,0.1254658999969251,3,100.0,0.1,27 -heuristic_maxsave,0.0396286999457515,3,100.0,0.1,26 -spreading_maxsave,0.1566233999910764,5,100.0,0.1,28 -heuristic_maxsave,0.0382815999910235,5,100.0,0.1,28 -spreading_maxsave,0.188109999988228,7,100.0,0.1,28 -heuristic_maxsave,0.0309079000144265,7,100.0,0.1,28 -spreading_maxsave,0.3012572000152431,10,100.0,0.1,28 -heuristic_maxsave,0.0178380000288598,10,100.0,0.1,28 -spreading_maxsave,0.0855236999923363,1,100.0,0.1,16 -heuristic_maxsave,0.0277691000374034,1,100.0,0.1,13 -spreading_maxsave,0.100686100020539,2,100.0,0.1,21 -heuristic_maxsave,0.0442347000353038,2,100.0,0.1,20 -spreading_maxsave,0.1335699000046588,3,100.0,0.1,24 -heuristic_maxsave,0.0447942999890074,3,100.0,0.1,24 -spreading_maxsave,0.1640409000101499,5,100.0,0.1,26 -heuristic_maxsave,0.1166872000321745,5,100.0,0.1,28 -spreading_maxsave,0.2050693000201136,7,100.0,0.1,28 -heuristic_maxsave,0.0342962999711744,7,100.0,0.1,28 -spreading_maxsave,0.2218876999686472,10,100.0,0.1,28 -heuristic_maxsave,0.018068499979563,10,100.0,0.1,28 -spreading_maxsave,0.0791377000277862,1,100.0,0.1,22 -heuristic_maxsave,0.032881700026337,1,100.0,0.1,18 -spreading_maxsave,0.1180386999621987,2,100.0,0.1,28 -heuristic_maxsave,0.0351212000241503,2,100.0,0.1,27 -spreading_maxsave,0.2072106000268831,3,100.0,0.1,34 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2 Aug 2024 17:14:49 +0300 Subject: [PATCH 87/93] dirlay returns a list instead of dictionary --- .../Firefighter_Problem.py | 4 +-- .../firefighter_problem/Utils.py | 25 +++++++++---------- 2 files changed, 14 insertions(+), 15 deletions(-) diff --git a/networkz/algorithms/approximation/firefighter_problem/Firefighter_Problem.py b/networkz/algorithms/approximation/firefighter_problem/Firefighter_Problem.py index 204f0c6..4a3703b 100644 --- a/networkz/algorithms/approximation/firefighter_problem/Firefighter_Problem.py +++ b/networkz/algorithms/approximation/firefighter_problem/Firefighter_Problem.py @@ -330,7 +330,7 @@ def non_spreading_dirlaynet_minbudget(Graph:nx.DiGraph, source:int, targets:list >>> G4.add_nodes_from([0,1,2,3,4,5], status="vulnerable") >>> G4.add_edges_from([(0,1),(0,2),(1,3),(1,4),(1,5),(2,3),(2,4),(2,5),(3,5),(4,5)]) >>> non_spreading_dirlaynet_minbudget(G4,0,[1,2,3,4,5]) - (2, {0: [2, 1]}) + (2, [(1, 1), (2, 1)]) """ validate_parameters(Graph, source, targets) @@ -349,7 +349,7 @@ def non_spreading_dirlaynet_minbudget(Graph:nx.DiGraph, source:int, targets:list vacc_matrix = calculate_vaccine_matrix(layers, N_groups) integer_matrix = matrix_to_integers_values(vacc_matrix) min_budget = min_budget_calculation(vacc_matrix) - strategy = dirlay_vaccination_startegy(integer_matrix, N_groups) + strategy = dirlay_vaccination_strategy(integer_matrix, N_groups) logger.info(f"Returning minimum budget: {min_budget} and the vaccination strategy: {strategy}") return min_budget, strategy diff --git a/networkz/algorithms/approximation/firefighter_problem/Utils.py b/networkz/algorithms/approximation/firefighter_problem/Utils.py index e4f4565..97ad797 100644 --- a/networkz/algorithms/approximation/firefighter_problem/Utils.py +++ b/networkz/algorithms/approximation/firefighter_problem/Utils.py @@ -714,7 +714,7 @@ def min_budget_calculation(matrix: np.matrix) -> int: logger.info(f"Min budget needed to save the target nodes: {min_budget}") return min_budget -def dirlay_vaccination_startegy(vacc_matrix: np.matrix, ni_groups: dict) -> dict: +def dirlay_vaccination_strategy(vacc_matrix: np.matrix, ni_groups: dict) -> list: """ Determines a feasible vaccination strategy given the vaccine matrix and the nodes from each layer. @@ -728,19 +728,18 @@ def dirlay_vaccination_startegy(vacc_matrix: np.matrix, ni_groups: dict) -> dict Returns: ---------- - strategy : dict - A dictionary where keys are time steps and values are lists of nodes to vaccinate at each time step. + strategy : list + A list of tuples where each tuple represents a node to vaccinate and the corresponding time step index. """ - logger.info("Calculating the stategy") + logger.info("Calculating the strategy") num_steps, num_layers = vacc_matrix.shape - strategy = {} + strategy = [] for i in range(num_steps): - nodes_to_vaccinate = [] - for j in range(0, num_layers): + for j in range(num_layers): num_nodes_to_vaccinate = int(vacc_matrix[i, j]) - logger.debug(f"On time step {i} needs to vaccinate: {num_nodes_to_vaccinate} nodes" ) + logger.debug(f"On time step {i+1} needs to vaccinate: {num_nodes_to_vaccinate} nodes") if num_nodes_to_vaccinate > 0: # Extract the nodes to vaccinate @@ -748,11 +747,11 @@ def dirlay_vaccination_startegy(vacc_matrix: np.matrix, ni_groups: dict) -> dict logger.debug(f"The selected nodes to vaccinate {selected_nodes}") - nodes_to_vaccinate.extend(selected_nodes) - - if nodes_to_vaccinate: - strategy[i] = nodes_to_vaccinate - + # Create tuples (node, i) and add them directly to strategy + strategy.extend((node, i+1) for node in selected_nodes) + + # sort the strategy by time stamp. + strategy.sort(key=lambda x: (x[1], x[0])) return strategy # =========================== End Non-Spreading Max-Save ============================ From 7838c811ec38935fc0379075f88a865140a6ff65 Mon Sep 17 00:00:00 2001 From: Erel Segal-Halevi Date: Fri, 2 Aug 2024 18:27:34 +0300 Subject: [PATCH 88/93] move comparisons folder --- .../comparisons => comparisons}/comparisons.py | 0 .../comparisons => comparisons}/non_spreading.csv | 0 .../non_spreading_minbudget.png | Bin .../non_spreading_preprocessed.csv | 0 .../spreading_maxsave.csv | 0 .../spreading_maxsave_100_edge_prob.png | Bin .../spreading_maxsave_200_edge_prob.png | Bin .../spreading_maxsave_400_edge_prob.png | Bin .../spreading_maxsave_budget.png | Bin .../spreading_maxsave_preprocessed.csv | 0 .../spreading_minbudget.csv | 0 .../spreading_minbudget.png | Bin .../spreading_minbudget_preprocessed.csv | 0 13 files changed, 0 insertions(+), 0 deletions(-) rename {networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons => comparisons}/comparisons.py (100%) rename {networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons => comparisons}/non_spreading.csv (100%) rename {networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons => comparisons}/non_spreading_minbudget.png (100%) rename {networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons => comparisons}/non_spreading_preprocessed.csv (100%) rename {networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons => comparisons}/spreading_maxsave.csv (100%) rename {networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons => comparisons}/spreading_maxsave_100_edge_prob.png (100%) rename {networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons => comparisons}/spreading_maxsave_200_edge_prob.png (100%) rename {networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons => comparisons}/spreading_maxsave_400_edge_prob.png (100%) rename {networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons => comparisons}/spreading_maxsave_budget.png (100%) rename {networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons => comparisons}/spreading_maxsave_preprocessed.csv (100%) rename {networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons => comparisons}/spreading_minbudget.csv (100%) rename {networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons => comparisons}/spreading_minbudget.png (100%) rename {networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons => comparisons}/spreading_minbudget_preprocessed.csv (100%) diff --git a/networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/comparisons.py b/comparisons/comparisons.py similarity index 100% rename from networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/comparisons.py rename to comparisons/comparisons.py diff --git a/networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/non_spreading.csv b/comparisons/non_spreading.csv similarity index 100% rename from networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/non_spreading.csv rename to comparisons/non_spreading.csv diff --git a/networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/non_spreading_minbudget.png b/comparisons/non_spreading_minbudget.png similarity index 100% rename from networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/non_spreading_minbudget.png rename to comparisons/non_spreading_minbudget.png diff --git a/networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/non_spreading_preprocessed.csv b/comparisons/non_spreading_preprocessed.csv similarity index 100% rename from networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/non_spreading_preprocessed.csv rename to comparisons/non_spreading_preprocessed.csv diff --git a/networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/spreading_maxsave.csv b/comparisons/spreading_maxsave.csv similarity index 100% rename from networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/spreading_maxsave.csv rename to comparisons/spreading_maxsave.csv diff --git a/networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/spreading_maxsave_100_edge_prob.png b/comparisons/spreading_maxsave_100_edge_prob.png similarity index 100% rename from networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/spreading_maxsave_100_edge_prob.png rename to comparisons/spreading_maxsave_100_edge_prob.png diff --git a/networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/spreading_maxsave_200_edge_prob.png b/comparisons/spreading_maxsave_200_edge_prob.png similarity index 100% rename from networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/spreading_maxsave_200_edge_prob.png rename to comparisons/spreading_maxsave_200_edge_prob.png diff --git a/networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/spreading_maxsave_400_edge_prob.png b/comparisons/spreading_maxsave_400_edge_prob.png similarity index 100% rename from networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/spreading_maxsave_400_edge_prob.png rename to comparisons/spreading_maxsave_400_edge_prob.png diff --git a/networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/spreading_maxsave_budget.png b/comparisons/spreading_maxsave_budget.png similarity index 100% rename from networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/spreading_maxsave_budget.png rename to comparisons/spreading_maxsave_budget.png diff --git a/networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/spreading_maxsave_preprocessed.csv b/comparisons/spreading_maxsave_preprocessed.csv similarity index 100% rename from networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/spreading_maxsave_preprocessed.csv rename to comparisons/spreading_maxsave_preprocessed.csv diff --git a/networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/spreading_minbudget.csv b/comparisons/spreading_minbudget.csv similarity index 100% rename from networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/spreading_minbudget.csv rename to comparisons/spreading_minbudget.csv diff --git a/networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/spreading_minbudget.png b/comparisons/spreading_minbudget.png similarity index 100% rename from networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/spreading_minbudget.png rename to comparisons/spreading_minbudget.png diff --git a/networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/spreading_minbudget_preprocessed.csv b/comparisons/spreading_minbudget_preprocessed.csv similarity index 100% rename from networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/spreading_minbudget_preprocessed.csv rename to comparisons/spreading_minbudget_preprocessed.csv From 1c8f3c83085aadfb3b3e5727e6006579c7bc7c7a Mon Sep 17 00:00:00 2001 From: Erel Segal-Halevi Date: Fri, 2 Aug 2024 18:35:58 +0300 Subject: [PATCH 89/93] ignore experiments folder --- .github/workflows/test.yml | 8 ++++---- pyproject.toml | 4 ++++ 2 files changed, 8 insertions(+), 4 deletions(-) diff --git a/.github/workflows/test.yml b/.github/workflows/test.yml index 790a344..fabcb79 100644 --- a/.github/workflows/test.yml +++ b/.github/workflows/test.yml @@ -53,13 +53,13 @@ jobs: - name: Test NetworkZ run: | - pytest --doctest-modules --durations=10 + pytest - name: Test Dispatching # Limit this to only a single combination from the matrix if: ${{ (matrix.os == 'ubuntu') && (matrix.python-version == '3.11') }} run: | - NetworkZ_GRAPH_CONVERT=nx-loopback pytest --doctest-modules --durations=10 + NetworkZ_GRAPH_CONVERT=nx-loopback pytest extra: runs-on: ${{ matrix.os }} @@ -123,7 +123,7 @@ jobs: - name: Test NetworkZ run: | - pytest --doctest-modules --durations=10 + pytest prerelease: runs-on: ${{ matrix.os }}-latest @@ -147,4 +147,4 @@ jobs: - name: Test NetworkZ run: | - pytest --doctest-modules --durations=10 + pytest diff --git a/pyproject.toml b/pyproject.toml index 4c057b2..a310b1f 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -95,3 +95,7 @@ select = [ [tool.mypy] ignore_missing_imports = true exclude = 'subgraphviews|reportviews' + +[tool.pytest.ini_options] +minversion = "6.0" +addopts = " --doctest-modules --durations=10 --ignore=experiments" From 23b7916b61f955ad729d0945f525199a4de20bb0 Mon Sep 17 00:00:00 2001 From: Erel Segal-Halevi Date: Fri, 2 Aug 2024 18:36:12 +0300 Subject: [PATCH 90/93] rename comparisons to experiments --- {comparisons => experiments}/comparisons.py | 0 {comparisons => experiments}/non_spreading.csv | 0 .../non_spreading_minbudget.png | Bin .../non_spreading_preprocessed.csv | 0 {comparisons => experiments}/spreading_maxsave.csv | 0 .../spreading_maxsave_100_edge_prob.png | Bin .../spreading_maxsave_200_edge_prob.png | Bin .../spreading_maxsave_400_edge_prob.png | Bin .../spreading_maxsave_budget.png | Bin .../spreading_maxsave_preprocessed.csv | 0 .../spreading_minbudget.csv | 0 .../spreading_minbudget.png | Bin .../spreading_minbudget_preprocessed.csv | 0 13 files changed, 0 insertions(+), 0 deletions(-) rename {comparisons => experiments}/comparisons.py (100%) rename {comparisons => experiments}/non_spreading.csv (100%) rename {comparisons => experiments}/non_spreading_minbudget.png (100%) rename {comparisons => experiments}/non_spreading_preprocessed.csv (100%) rename {comparisons => experiments}/spreading_maxsave.csv (100%) rename {comparisons => experiments}/spreading_maxsave_100_edge_prob.png (100%) rename {comparisons => experiments}/spreading_maxsave_200_edge_prob.png (100%) rename {comparisons => experiments}/spreading_maxsave_400_edge_prob.png (100%) rename {comparisons => experiments}/spreading_maxsave_budget.png (100%) rename {comparisons => experiments}/spreading_maxsave_preprocessed.csv (100%) rename {comparisons => experiments}/spreading_minbudget.csv (100%) rename {comparisons => experiments}/spreading_minbudget.png (100%) rename {comparisons => experiments}/spreading_minbudget_preprocessed.csv (100%) diff --git a/comparisons/comparisons.py b/experiments/comparisons.py similarity index 100% rename from comparisons/comparisons.py rename to experiments/comparisons.py diff --git a/comparisons/non_spreading.csv b/experiments/non_spreading.csv similarity index 100% rename from comparisons/non_spreading.csv rename to experiments/non_spreading.csv diff --git a/comparisons/non_spreading_minbudget.png b/experiments/non_spreading_minbudget.png similarity index 100% rename from comparisons/non_spreading_minbudget.png rename to experiments/non_spreading_minbudget.png diff --git a/comparisons/non_spreading_preprocessed.csv b/experiments/non_spreading_preprocessed.csv similarity index 100% rename from comparisons/non_spreading_preprocessed.csv rename to experiments/non_spreading_preprocessed.csv diff --git a/comparisons/spreading_maxsave.csv b/experiments/spreading_maxsave.csv similarity index 100% rename from comparisons/spreading_maxsave.csv rename to experiments/spreading_maxsave.csv diff --git a/comparisons/spreading_maxsave_100_edge_prob.png b/experiments/spreading_maxsave_100_edge_prob.png similarity index 100% rename from comparisons/spreading_maxsave_100_edge_prob.png rename to experiments/spreading_maxsave_100_edge_prob.png diff --git a/comparisons/spreading_maxsave_200_edge_prob.png b/experiments/spreading_maxsave_200_edge_prob.png similarity index 100% rename from comparisons/spreading_maxsave_200_edge_prob.png rename to experiments/spreading_maxsave_200_edge_prob.png diff --git a/comparisons/spreading_maxsave_400_edge_prob.png b/experiments/spreading_maxsave_400_edge_prob.png similarity index 100% rename from comparisons/spreading_maxsave_400_edge_prob.png rename to experiments/spreading_maxsave_400_edge_prob.png diff --git a/comparisons/spreading_maxsave_budget.png b/experiments/spreading_maxsave_budget.png similarity index 100% rename from comparisons/spreading_maxsave_budget.png rename to experiments/spreading_maxsave_budget.png diff --git a/comparisons/spreading_maxsave_preprocessed.csv b/experiments/spreading_maxsave_preprocessed.csv similarity index 100% rename from comparisons/spreading_maxsave_preprocessed.csv rename to experiments/spreading_maxsave_preprocessed.csv diff --git a/comparisons/spreading_minbudget.csv b/experiments/spreading_minbudget.csv similarity index 100% rename from comparisons/spreading_minbudget.csv rename to experiments/spreading_minbudget.csv diff --git a/comparisons/spreading_minbudget.png b/experiments/spreading_minbudget.png similarity index 100% rename from comparisons/spreading_minbudget.png rename to experiments/spreading_minbudget.png diff --git a/comparisons/spreading_minbudget_preprocessed.csv b/experiments/spreading_minbudget_preprocessed.csv similarity index 100% rename from comparisons/spreading_minbudget_preprocessed.csv rename to experiments/spreading_minbudget_preprocessed.csv From 4b413b6a586d8930ba47ab043752d4c34f5b02a9 Mon Sep 17 00:00:00 2001 From: Erel Segal-Halevi Date: Fri, 2 Aug 2024 18:36:50 +0300 Subject: [PATCH 91/93] move comparisons to experiments subfolder --- .../{ => firefighter_problem}/comparisons.py | 0 .../{ => firefighter_problem}/non_spreading.csv | 0 .../non_spreading_minbudget.png | Bin .../non_spreading_preprocessed.csv | 0 .../{ => firefighter_problem}/spreading_maxsave.csv | 0 .../spreading_maxsave_100_edge_prob.png | Bin .../spreading_maxsave_200_edge_prob.png | Bin .../spreading_maxsave_400_edge_prob.png | Bin .../spreading_maxsave_budget.png | Bin .../spreading_maxsave_preprocessed.csv | 0 .../spreading_minbudget.csv | 0 .../spreading_minbudget.png | Bin .../spreading_minbudget_preprocessed.csv | 0 13 files changed, 0 insertions(+), 0 deletions(-) rename experiments/{ => firefighter_problem}/comparisons.py (100%) rename experiments/{ => firefighter_problem}/non_spreading.csv (100%) rename experiments/{ => firefighter_problem}/non_spreading_minbudget.png (100%) rename experiments/{ => firefighter_problem}/non_spreading_preprocessed.csv (100%) rename experiments/{ => firefighter_problem}/spreading_maxsave.csv (100%) rename experiments/{ => firefighter_problem}/spreading_maxsave_100_edge_prob.png (100%) rename experiments/{ => firefighter_problem}/spreading_maxsave_200_edge_prob.png (100%) rename experiments/{ => firefighter_problem}/spreading_maxsave_400_edge_prob.png (100%) rename experiments/{ => firefighter_problem}/spreading_maxsave_budget.png (100%) rename experiments/{ => firefighter_problem}/spreading_maxsave_preprocessed.csv (100%) rename experiments/{ => firefighter_problem}/spreading_minbudget.csv (100%) rename experiments/{ => firefighter_problem}/spreading_minbudget.png (100%) rename experiments/{ => firefighter_problem}/spreading_minbudget_preprocessed.csv (100%) diff --git a/experiments/comparisons.py b/experiments/firefighter_problem/comparisons.py similarity index 100% rename from experiments/comparisons.py rename to experiments/firefighter_problem/comparisons.py diff --git a/experiments/non_spreading.csv b/experiments/firefighter_problem/non_spreading.csv similarity index 100% rename from experiments/non_spreading.csv rename to experiments/firefighter_problem/non_spreading.csv diff --git a/experiments/non_spreading_minbudget.png b/experiments/firefighter_problem/non_spreading_minbudget.png similarity index 100% rename from experiments/non_spreading_minbudget.png rename to experiments/firefighter_problem/non_spreading_minbudget.png diff --git a/experiments/non_spreading_preprocessed.csv b/experiments/firefighter_problem/non_spreading_preprocessed.csv similarity index 100% rename from experiments/non_spreading_preprocessed.csv rename to experiments/firefighter_problem/non_spreading_preprocessed.csv diff --git a/experiments/spreading_maxsave.csv b/experiments/firefighter_problem/spreading_maxsave.csv similarity index 100% rename from experiments/spreading_maxsave.csv rename to experiments/firefighter_problem/spreading_maxsave.csv diff --git a/experiments/spreading_maxsave_100_edge_prob.png b/experiments/firefighter_problem/spreading_maxsave_100_edge_prob.png similarity index 100% rename from experiments/spreading_maxsave_100_edge_prob.png rename to experiments/firefighter_problem/spreading_maxsave_100_edge_prob.png diff --git a/experiments/spreading_maxsave_200_edge_prob.png b/experiments/firefighter_problem/spreading_maxsave_200_edge_prob.png similarity index 100% rename from experiments/spreading_maxsave_200_edge_prob.png rename to experiments/firefighter_problem/spreading_maxsave_200_edge_prob.png diff --git a/experiments/spreading_maxsave_400_edge_prob.png b/experiments/firefighter_problem/spreading_maxsave_400_edge_prob.png similarity index 100% rename from experiments/spreading_maxsave_400_edge_prob.png rename to experiments/firefighter_problem/spreading_maxsave_400_edge_prob.png diff --git a/experiments/spreading_maxsave_budget.png b/experiments/firefighter_problem/spreading_maxsave_budget.png similarity index 100% rename from experiments/spreading_maxsave_budget.png rename to experiments/firefighter_problem/spreading_maxsave_budget.png diff --git a/experiments/spreading_maxsave_preprocessed.csv b/experiments/firefighter_problem/spreading_maxsave_preprocessed.csv similarity index 100% rename from experiments/spreading_maxsave_preprocessed.csv rename to experiments/firefighter_problem/spreading_maxsave_preprocessed.csv diff --git a/experiments/spreading_minbudget.csv b/experiments/firefighter_problem/spreading_minbudget.csv similarity index 100% rename from experiments/spreading_minbudget.csv rename to experiments/firefighter_problem/spreading_minbudget.csv diff --git a/experiments/spreading_minbudget.png b/experiments/firefighter_problem/spreading_minbudget.png similarity index 100% rename from experiments/spreading_minbudget.png rename to experiments/firefighter_problem/spreading_minbudget.png diff --git a/experiments/spreading_minbudget_preprocessed.csv b/experiments/firefighter_problem/spreading_minbudget_preprocessed.csv similarity index 100% rename from experiments/spreading_minbudget_preprocessed.csv rename to experiments/firefighter_problem/spreading_minbudget_preprocessed.csv From ecb10c3286a2647891a93c53775b2586966548a7 Mon Sep 17 00:00:00 2001 From: Almog-David Date: Fri, 2 Aug 2024 18:45:51 +0300 Subject: [PATCH 92/93] fixed problem with doctest regarding create_st_graph --- networkz/algorithms/approximation/firefighter_problem/Utils.py | 2 +- networkz/algorithms/max_flow_with_node_capacity.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/networkz/algorithms/approximation/firefighter_problem/Utils.py b/networkz/algorithms/approximation/firefighter_problem/Utils.py index 97ad797..c62ce68 100644 --- a/networkz/algorithms/approximation/firefighter_problem/Utils.py +++ b/networkz/algorithms/approximation/firefighter_problem/Utils.py @@ -521,7 +521,7 @@ def create_st_graph(graph:nx.DiGraph, targets:list, new_target:str) -> nx.DiGrap >>> G = nx.DiGraph() >>> G.add_edges_from([(1, 2), (2, 3), (3, 4)]) >>> targets = [2, 3] - >>> G_st = create_st_graph(G, targets) + >>> G_st = create_st_graph(G, targets, 't') >>> 't' in G_st.nodes True >>> list(G_st.successors(2)) diff --git a/networkz/algorithms/max_flow_with_node_capacity.py b/networkz/algorithms/max_flow_with_node_capacity.py index c88625e..89997a1 100644 --- a/networkz/algorithms/max_flow_with_node_capacity.py +++ b/networkz/algorithms/max_flow_with_node_capacity.py @@ -69,7 +69,7 @@ def min_cut_with_node_capacity(graph: nx.DiGraph, source: int = None, target: in >>> G.add_edge(0, 2) >>> G.add_edge(2, 3) >>> G.add_edge(1, 4) - >>> s_t_G = create_st_graph(G, [2,4]) + >>> s_t_G = create_st_graph(G, [2,4], 't') >>> min_cut_nodes = min_cut_with_node_capacity(s_t_G, 0, 4) >>> sorted(min_cut_nodes) ['2_out', '4_out'] From 33f73e0637b6d936fcdeb88840e9255882478df3 Mon Sep 17 00:00:00 2001 From: Almog-David Date: Fri, 2 Aug 2024 19:11:45 +0300 Subject: [PATCH 93/93] fixed path to comparisons --- .../firefighter_problem/comparisons.py | 50 +++++--- experiments/spreading_minbudget.csv | 114 ++++++++++++++++++ 2 files changed, 144 insertions(+), 20 deletions(-) create mode 100644 experiments/spreading_minbudget.csv diff --git a/experiments/firefighter_problem/comparisons.py b/experiments/firefighter_problem/comparisons.py index 44aeb60..75d6876 100644 --- a/experiments/firefighter_problem/comparisons.py +++ b/experiments/firefighter_problem/comparisons.py @@ -118,7 +118,7 @@ def Compare_NonSpread(): This function runs multiple experiments on randomly generated layered networks and plots the results comparing the budget used by different algorithms. """ - ex1 = experiments_csv.Experiment("./networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/", "non_spreading.csv", backup_folder=None) + ex1 = experiments_csv.Experiment("./experiments/", "non_spreading.csv", backup_folder=None) ex1.clear_previous_results() # to clear previous experiments input_ranges = { @@ -144,7 +144,7 @@ def multiple_runs(runs=30): ex1.run_with_time_limit(multiple_runs, input_ranges={}, time_limit=0.9) # Preprocess the DataFrame to extract numeric budget values - results_csv_file = "./networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/non_spreading_minbudget.csv" + results_csv_file = "./experiments/non_spreading_minbudget.csv" results = pd.read_csv(results_csv_file) # Extract the numeric budget from the 'Budget' column @@ -164,7 +164,7 @@ def extract_budget_numeric(budget): results = results.dropna(subset=['Budget_numeric']) # Save the preprocessed DataFrame to a temporary CSV file - preprocessed_csv_file = "./networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/non_spreading_minbudget_preprocessed.csv" + preprocessed_csv_file = "./experiments/non_spreading_minbudget_preprocessed.csv" results.to_csv(preprocessed_csv_file, index=False) print("\n DataFrame-NonSpread: \n", results) @@ -177,7 +177,7 @@ def extract_budget_numeric(budget): y_field="Budget_numeric", z_field="algorithm", mean=True, - save_to_file="./networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/non_spreading.png" + save_to_file="./experiments/non_spreading.png" ) print("\n DataFrame-NonSpread: \n", ex1.dataFrame) @@ -189,7 +189,7 @@ def Compare_SpreadingMaxSave(): This function runs multiple experiments on randomly generated directed graphs and plots the results comparing the number of nodes saved by different algorithms. """ - ex2 = experiments_csv.Experiment("./networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/", "spreading_maxsave.csv", backup_folder=None) + ex2 = experiments_csv.Experiment("./experiments/", "spreading_maxsave.csv", backup_folder=None) ex2.clear_previous_results() # to clear previous experiments input_ranges = { @@ -224,7 +224,7 @@ def multiple_runs(runs=10): ## DATA ISSUE WE HAD SO THIS IS A FIX ## # Load the results - results_csv_file = "./networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/spreading_maxsave.csv" + results_csv_file = "./experiments/spreading_maxsave.csv" results = pd.read_csv(results_csv_file) # Ensure 'algorithm' column is of type string @@ -245,7 +245,7 @@ def multiple_runs(runs=10): results['Nodes_Saved'] = results['Nodes_Saved'].astype(int) # Save the cleaned DataFrame to a new CSV file (optional, for debugging) - cleaned_csv_file = "./networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/spreading_maxsave_preprocessed.csv" + cleaned_csv_file = "./experiments/spreading_maxsave_preprocessed.csv" results.to_csv(cleaned_csv_file, index=False) # Plot the results using the cleaned DataFrame @@ -261,7 +261,7 @@ def multiple_runs(runs=10): sharex=True, sharey=True, mean=True, - save_to_file="./networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/spreading_maxsave_budget.png" + save_to_file="./experiments/spreading_maxsave_budget.png" ) multi_plot_results( @@ -276,7 +276,7 @@ def multiple_runs(runs=10): sharex=True, sharey=True, mean=True, - save_to_file="./networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/spreading_maxsave_100_edge_prob.png" + save_to_file="./experiments/spreading_maxsave_100_edge_prob.png" ) multi_plot_results( @@ -291,7 +291,7 @@ def multiple_runs(runs=10): sharex=True, sharey=True, mean=True, - save_to_file="./networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/spreading_maxsave_200_edge_prob.png" + save_to_file="./experiments/spreading_maxsave_200_edge_prob.png" ) multi_plot_results( @@ -306,7 +306,7 @@ def multiple_runs(runs=10): sharex=True, sharey=True, mean=True, - save_to_file="./networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/spreading_maxsave_400_edge_prob.png" + save_to_file="./experiments/spreading_maxsave_400_edge_prob.png" ) print("\n DataFrame-NonSpread: \n", ex2.dataFrame) @@ -318,7 +318,7 @@ def Compare_SpreadingMinBudget(): This function runs multiple experiments on randomly generated directed graphs and plots the results comparing the budget used by different algorithms. """ - ex3 = experiments_csv.Experiment("./networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/", "spreading_minbudget.csv", backup_folder=None) + ex3 = experiments_csv.Experiment("./experiments/", "spreading_minbudget.csv", backup_folder=None) ex3.clear_previous_results() # to clear previous experiments input_ranges = { @@ -328,7 +328,7 @@ def Compare_SpreadingMinBudget(): node_counts = [100, 200, 400] edge_probabilities = [0.1, 0.5, 0.8] - def multiple_runs(runs=30): + def multiple_runs(runs=10): for num_nodes in node_counts: for edge_prob in edge_probabilities: graph = generate_random_DiGraph(num_nodes=num_nodes, edge_probability=edge_prob, seed=None) @@ -340,16 +340,16 @@ def multiple_runs(runs=30): targets = random.sample(nodes, num_targets) for algorithm in input_ranges["algorithm"]: start_time = perf_counter() - result = runner_spreading(algorithm, graph, source, targets) + result = runner_spreading(algorithm, graph, None, source, targets) runtime = perf_counter() - start_time - ex3.add({**{"algorithm": algorithm.__name__, "runtime": runtime, "graph_nodes": len(graph.nodes)}, **result}) + ex3.add({**{"algorithm": algorithm.__name__, "runtime": runtime, "graph_nodes": num_nodes, "edge_probability": edge_prob}, **result}) return {"status": "completed"} # Set a time limit for the entire batch run ex3.run_with_time_limit(multiple_runs, input_ranges={}, time_limit=0.9) # Preprocess the DataFrame to extract numeric budget values - results_csv_file = "./networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/spreading_minbudget.csv" + results_csv_file = "./experiments/spreading_minbudget.csv" results = pd.read_csv(results_csv_file) # Extract the numeric budget from the 'Budget' column @@ -369,7 +369,7 @@ def extract_budget_numeric(budget): results = results.dropna(subset=['Budget_numeric']) # Save the preprocessed DataFrame to a temporary CSV file - preprocessed_csv_file = "./networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/spreading_minbudget_preprocessed.csv" + preprocessed_csv_file = "./experiments/spreading_minbudget_preprocessed.csv" results.to_csv(preprocessed_csv_file, index=False) print("\n DataFrame-NonSpread: \n", results) @@ -382,7 +382,17 @@ def extract_budget_numeric(budget): y_field="Budget_numeric", z_field="algorithm", mean=True, - save_to_file="./networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/spreading_minbudget.png" + save_to_file="./experiments/spreading_minbudget.png" + ) + + single_plot_results( + results_csv_file=preprocessed_csv_file, + filter={"edge_probability":0.1}, + x_field="graph_nodes", + y_field="Budget_numeric", + z_field="algorithm", + mean=True, + save_to_file="./experiments/spreading_minbudget_edge.png" ) print("\n DataFrame-NonSpread: \n", ex3.dataFrame) @@ -393,5 +403,5 @@ def extract_budget_numeric(budget): setup_global_logger(level=logging.DEBUG) #Compare_NonSpread() - # Compare_SpreadingMinBudget() - Compare_SpreadingMaxSave() \ No newline at end of file + Compare_SpreadingMinBudget() + #Compare_SpreadingMaxSave() \ No newline at end of file diff --git a/experiments/spreading_minbudget.csv b/experiments/spreading_minbudget.csv new file mode 100644 index 0000000..ab7013e --- /dev/null +++ b/experiments/spreading_minbudget.csv @@ -0,0 +1,114 @@ +algorithm,runtime,graph_nodes,edge_probability,Budget +spreading_minbudget,0.6043815999873914,100,0.1,"(4, [(6, 1), (23, 1), (27, 1), (25, 1)])" +heuristic_minbudget,0.08351690000563394,100,0.1,"(2, [(25, 1), (6, 1), (27, 2), (23, 2), (41, 3), (72, 3)])" +spreading_minbudget,0.798920300003374,100,0.1,"(3, [(94, 1), (6, 1), (76, 1), (79, 2), (16, 2), (99, 2)])" +heuristic_minbudget,0.0549452000122983,100,0.1,"(3, [(6, 1), (19, 1), (85, 1), (18, 2), (87, 2), (17, 2), (81, 3)])" +spreading_minbudget,1.2247725000052014,100,0.1,"(7, [(6, 1), (70, 1), (23, 1), (36, 1), (47, 1), (2, 1), (25, 1)])" +heuristic_minbudget,0.20194240000273567,100,0.1,"(4, [(47, 1), (25, 1), (19, 1), (85, 1), (17, 2), (96, 2), (61, 2), (67, 2), (42, 3)])" +spreading_minbudget,0.5262910000019474,100,0.1,"(1, [(26, 1), (52, 2)])" +heuristic_minbudget,0.09640729999227915,100,0.1,"(2, [(19, 1), (14, 1), (52, 2), (76, 2), (22, 3), (60, 3)])" +spreading_minbudget,1.2042019000073196,100,0.1,"(6, [(72, 1), (91, 1), (16, 1), (14, 1), (38, 1), (25, 1)])" +heuristic_minbudget,0.12059449999651406,100,0.1,"(4, [(14, 1), (25, 1), (19, 1), (47, 1), (90, 2), (65, 2), (91, 2), (21, 2), (42, 3), (70, 3)])" +spreading_minbudget,0.1407623999984935,100,0.1,"(1, [(6, 1)])" +heuristic_minbudget,0.07592089999525342,100,0.1,"(1, [(6, 1), (84, 2)])" +spreading_minbudget,1.4573968000040622,100,0.1,"(6, [(9, 1), (13, 1), (19, 1), (23, 1), (3, 1), (47, 1)])" +heuristic_minbudget,0.07776210000156425,100,0.1,"(4, [(19, 1), (47, 1), (6, 1), (85, 1), (78, 2), (87, 2), (71, 2), (39, 2)])" +spreading_minbudget,0.8562765999959083,100,0.1,"(3, [(30, 1), (10, 1), (11, 1), (33, 2), (1, 2), (55, 2)])" +heuristic_minbudget,0.05599090000032447,100,0.1,"(3, [(6, 1), (85, 1), (14, 1), (11, 2), (89, 2), (78, 2), (69, 3)])" +spreading_minbudget,0.5544522999989567,100,0.1,"(1, [(6, 1), (78, 2)])" +heuristic_minbudget,0.09719980000227224,100,0.1,"(2, [(25, 1), (6, 1), (93, 2), (11, 2), (72, 3), (64, 3)])" +spreading_minbudget,0.4506552999955602,100,0.1,"(2, [(6, 1), (2, 1), (84, 2), (1, 2)])" +heuristic_minbudget,0.08434780000243336,100,0.1,"(2, [(6, 1), (19, 1), (84, 2), (17, 2), (72, 3), (69, 3)])" +spreading_minbudget,2.9608414999966044,100,0.5,"(29, [(54, 1), (37, 1), (9, 1), (11, 1), (12, 1), (23, 1), (31, 1), (46, 1), (60, 1), (61, 1), (88, 1), (90, 1), (2, 1), (15, 1), (24, 1), (27, 1), (45, 1), (62, 1), (20, 1), (26, 1), (32, 1), (86, 1), (94, 1), (97, 1), (28, 1), (66, 1), (93, 1), (13, 1), (65, 1)])" +heuristic_minbudget,2.033734099997673,100,0.5,"(29, [(37, 1), (45, 1), (65, 1), (12, 1), (46, 1), (88, 1), (24, 1), (54, 1), (93, 1), (28, 1), (26, 1), (13, 1), (60, 1), (62, 1), (31, 1), (61, 1), (27, 1), (90, 1), (2, 1), (9, 1), (23, 1), (20, 1), (66, 1), (94, 1), (97, 1), (15, 1), (86, 1), (32, 1), (11, 1)])" +spreading_minbudget,2.7890169999882346,100,0.5,"(24, [(31, 1), (38, 1), (16, 1), (46, 1), (48, 1), (61, 1), (82, 1), (84, 1), (98, 1), (2, 1), (15, 1), (45, 1), (54, 1), (62, 1), (64, 1), (77, 1), (79, 1), (85, 1), (26, 1), (32, 1), (34, 1), (78, 1), (28, 1), (93, 1)])" +heuristic_minbudget,1.3695207999990089,100,0.5,"(22, [(85, 1), (98, 1), (54, 1), (84, 1), (46, 1), (28, 1), (45, 1), (48, 1), (15, 1), (26, 1), (93, 1), (34, 1), (61, 1), (62, 1), (79, 1), (82, 1), (16, 1), (78, 1), (2, 1), (32, 1), (77, 1), (64, 1)])" +spreading_minbudget,0.29501640000671614,100,0.5,"(2, [(34, 1), (82, 1)])" +heuristic_minbudget,0.8983767999889096,100,0.5,"(2, [(34, 1), (82, 1)])" +spreading_minbudget,1.612422099991818,100,0.5,"(16, [(60, 1), (9, 1), (11, 1), (30, 1), (46, 1), (90, 1), (2, 1), (15, 1), (29, 1), (37, 1), (77, 1), (85, 1), (52, 1), (78, 1), (28, 1), (13, 1)])" +heuristic_minbudget,1.2286775999964448,100,0.5,"(16, [(52, 1), (28, 1), (85, 1), (29, 1), (37, 1), (46, 1), (13, 1), (60, 1), (90, 1), (2, 1), (30, 1), (15, 1), (78, 1), (9, 1), (77, 1), (11, 1)])" +spreading_minbudget,1.9392723999917507,100,0.5,"(18, [(58, 1), (22, 1), (46, 1), (48, 1), (61, 1), (2, 1), (19, 1), (37, 1), (64, 1), (77, 1), (79, 1), (85, 1), (32, 1), (72, 1), (78, 1), (28, 1), (66, 1), (13, 1)])" +heuristic_minbudget,1.2242184999922756,100,0.5,"(17, [(22, 1), (72, 1), (13, 1), (37, 1), (46, 1), (28, 1), (48, 1), (61, 1), (85, 1), (19, 1), (32, 1), (78, 1), (2, 1), (79, 1), (77, 1), (66, 1), (64, 1)])" +spreading_minbudget,2.1627451999956975,100,0.5,"(19, [(67, 1), (7, 1), (9, 1), (11, 1), (31, 1), (88, 1), (90, 1), (91, 1), (17, 1), (37, 1), (45, 1), (62, 1), (64, 1), (20, 1), (22, 1), (72, 1), (97, 1), (92, 1), (65, 1)])" +heuristic_minbudget,1.7830877999949735,100,0.5,"(17, [(88, 1), (22, 1), (91, 1), (37, 1), (45, 1), (65, 1), (20, 1), (31, 1), (9, 1), (72, 1), (90, 1), (17, 1), (62, 1), (92, 1), (11, 1), (97, 1), (64, 1)])" +spreading_minbudget,3.6650694999989355,100,0.5,"(29, [(30, 1), (46, 1), (11, 1), (12, 1), (16, 1), (41, 1), (61, 1), (82, 1), (84, 1), (88, 1), (98, 1), (2, 1), (15, 1), (24, 1), (27, 1), (45, 1), (77, 1), (79, 1), (85, 1), (22, 1), (43, 1), (72, 1), (74, 1), (78, 1), (86, 1), (97, 1), (92, 1), (13, 1), (65, 1)])" +heuristic_minbudget,2.367954000001191,100,0.5,"(29, [(98, 1), (85, 1), (24, 1), (84, 1), (46, 1), (88, 1), (22, 1), (12, 1), (27, 1), (65, 1), (45, 1), (41, 1), (61, 1), (72, 1), (13, 1), (2, 1), (43, 1), (79, 1), (86, 1), (92, 1), (15, 1), (30, 1), (77, 1), (82, 1), (97, 1), (11, 1), (16, 1), (74, 1), (78, 1)])" +spreading_minbudget,1.6317382999986876,100,0.5,"(13, [(12, 1), (7, 1), (11, 1), (16, 1), (88, 1), (15, 1), (19, 1), (27, 1), (54, 1), (20, 1), (26, 1), (43, 1), (28, 1)])" +heuristic_minbudget,1.2327431000012439,100,0.5,"(13, [(27, 1), (20, 1), (88, 1), (12, 1), (28, 1), (54, 1), (26, 1), (7, 1), (43, 1), (15, 1), (19, 1), (16, 1), (11, 1)])" +spreading_minbudget,1.377598899998702,100,0.5,"(9, [(23, 1), (82, 1), (30, 1), (40, 1), (62, 1), (79, 1), (85, 1), (22, 1), (92, 1)])" +heuristic_minbudget,1.0871489999990445,100,0.5,"(8, [(85, 1), (79, 1), (82, 1), (40, 1), (22, 1), (62, 1), (30, 1), (92, 1)])" +spreading_minbudget,1.249198300007265,100,0.5,"(12, [(31, 1), (16, 1), (41, 1), (84, 1), (90, 1), (24, 1), (29, 1), (37, 1), (40, 1), (85, 1), (13, 1), (65, 1)])" +heuristic_minbudget,1.291403099996387,100,0.5,"(11, [(84, 1), (29, 1), (37, 1), (40, 1), (24, 1), (85, 1), (65, 1), (41, 1), (13, 1), (90, 1), (16, 1)])" +spreading_minbudget,0.8987213000073098,100,0.8,"(8, [(84, 1), (16, 1), (21, 1), (68, 1), (86, 1), (87, 1), (44, 1), (52, 1)])" +heuristic_minbudget,2.5706658999988576,100,0.8,"(8, [(52, 1), (84, 1), (21, 1), (87, 1), (16, 1), (86, 1), (44, 1), (68, 1)])" +spreading_minbudget,0.5972516000038013,100,0.8,"(7, [(19, 1), (6, 1), (24, 1), (42, 1), (66, 1), (71, 1), (72, 1)])" +heuristic_minbudget,2.627842400004738,100,0.8,"(7, [(24, 1), (6, 1), (71, 1), (19, 1), (66, 1), (72, 1), (42, 1)])" +spreading_minbudget,2.4423267999954987,100,0.8,"(25, [(22, 1), (1, 1), (6, 1), (8, 1), (9, 1), (23, 1), (25, 1), (27, 1), (42, 1), (51, 1), (56, 1), (58, 1), (63, 1), (72, 1), (76, 1), (79, 1), (86, 1), (93, 1), (4, 1), (15, 1), (33, 1), (34, 1), (41, 1), (44, 1), (11, 1)])" +heuristic_minbudget,3.5214248999982374,100,0.8,"(25, [(27, 1), (76, 1), (6, 1), (4, 1), (25, 1), (58, 1), (34, 1), (51, 1), (9, 1), (8, 1), (22, 1), (11, 1), (15, 1), (23, 1), (33, 1), (44, 1), (56, 1), (86, 1), (93, 1), (1, 1), (79, 1), (41, 1), (63, 1), (72, 1), (42, 1)])" +spreading_minbudget,2.2762453999894205,100,0.8,"(23, [(21, 1), (2, 1), (8, 1), (9, 1), (16, 1), (22, 1), (29, 1), (37, 1), (48, 1), (53, 1), (65, 1), (71, 1), (76, 1), (85, 1), (86, 1), (89, 1), (91, 1), (95, 1), (96, 1), (97, 1), (98, 1), (74, 1), (94, 1)])" +heuristic_minbudget,3.9915353000105824,100,0.8,"(23, [(76, 1), (2, 1), (48, 1), (98, 1), (9, 1), (29, 1), (21, 1), (22, 1), (91, 1), (37, 1), (16, 1), (65, 1), (8, 1), (71, 1), (85, 1), (89, 1), (86, 1), (94, 1), (74, 1), (97, 1), (95, 1), (53, 1), (96, 1)])" +spreading_minbudget,3.2877872999961255,100,0.8,"(33, [(69, 1), (1, 1), (2, 1), (6, 1), (8, 1), (10, 1), (12, 1), (22, 1), (23, 1), (24, 1), (25, 1), (27, 1), (29, 1), (31, 1), (37, 1), (43, 1), (45, 1), (46, 1), (48, 1), (53, 1), (58, 1), (65, 1), (66, 1), (67, 1), (82, 1), (89, 1), (95, 1), (98, 1), (4, 1), (33, 1), (41, 1), (52, 1), (94, 1)])" +heuristic_minbudget,4.246031399990898,100,0.8,"(33, [(6, 1), (4, 1), (25, 1), (48, 1), (2, 1), (24, 1), (52, 1), (58, 1), (27, 1), (69, 1), (22, 1), (37, 1), (29, 1), (94, 1), (43, 1), (46, 1), (65, 1), (82, 1), (98, 1), (89, 1), (8, 1), (45, 1), (10, 1), (66, 1), (12, 1), (23, 1), (33, 1), (41, 1), (1, 1), (31, 1), (67, 1), (53, 1), (95, 1)])" +spreading_minbudget,2.8299892999930307,100,0.8,"(27, [(5, 1), (1, 1), (6, 1), (8, 1), (9, 1), (20, 1), (27, 1), (31, 1), (37, 1), (46, 1), (51, 1), (53, 1), (55, 1), (58, 1), (63, 1), (64, 1), (66, 1), (73, 1), (79, 1), (81, 1), (83, 1), (85, 1), (91, 1), (98, 1), (41, 1), (44, 1), (18, 1)])" +heuristic_minbudget,4.0879720999946585,100,0.8,"(27, [(58, 1), (6, 1), (51, 1), (20, 1), (98, 1), (27, 1), (64, 1), (8, 1), (9, 1), (73, 1), (46, 1), (37, 1), (5, 1), (83, 1), (55, 1), (41, 1), (81, 1), (18, 1), (31, 1), (91, 1), (53, 1), (1, 1), (63, 1), (44, 1), (85, 1), (66, 1), (79, 1)])" +spreading_minbudget,3.92984680000518,100,0.8,"(41, [(16, 1), (1, 1), (2, 1), (10, 1), (13, 1), (19, 1), (23, 1), (24, 1), (27, 1), (29, 1), (32, 1), (39, 1), (46, 1), (51, 1), (53, 1), (56, 1), (59, 1), (62, 1), (64, 1), (66, 1), (67, 1), (68, 1), (77, 1), (79, 1), (82, 1), (85, 1), (87, 1), (89, 1), (93, 1), (95, 1), (96, 1), (98, 1), (99, 1), (33, 1), (34, 1), (41, 1), (44, 1), (94, 1), (11, 1), (40, 1), (18, 1)])" +heuristic_minbudget,3.953235400011181,100,0.8,"(41, [(67, 1), (39, 1), (34, 1), (24, 1), (2, 1), (40, 1), (51, 1), (82, 1), (29, 1), (27, 1), (46, 1), (87, 1), (64, 1), (56, 1), (16, 1), (19, 1), (23, 1), (53, 1), (94, 1), (10, 1), (98, 1), (66, 1), (1, 1), (44, 1), (33, 1), (11, 1), (13, 1), (18, 1), (32, 1), (59, 1), (93, 1), (85, 1), (41, 1), (79, 1), (89, 1), (62, 1), (68, 1), (95, 1), (96, 1), (77, 1), (99, 1)])" +spreading_minbudget,1.3322335999982897,100,0.8,"(14, [(6, 1), (1, 1), (22, 1), (29, 1), (46, 1), (58, 1), (69, 1), (75, 1), (77, 1), (82, 1), (85, 1), (92, 1), (95, 1), (40, 1)])" +heuristic_minbudget,2.7363971000013407,100,0.8,"(14, [(6, 1), (40, 1), (58, 1), (29, 1), (82, 1), (69, 1), (22, 1), (46, 1), (92, 1), (85, 1), (77, 1), (1, 1), (75, 1), (95, 1)])" +spreading_minbudget,0.8161610999959521,100,0.8,"(8, [(22, 1), (31, 1), (45, 1), (85, 1), (86, 1), (91, 1), (96, 1), (44, 1)])" +heuristic_minbudget,2.7852620000048773,100,0.8,"(8, [(45, 1), (22, 1), (91, 1), (31, 1), (86, 1), (44, 1), (85, 1), (96, 1)])" +spreading_minbudget,1.302543899990269,100,0.8,"(14, [(2, 1), (10, 1), (45, 1), (63, 1), (64, 1), (65, 1), (79, 1), (85, 1), (91, 1), (92, 1), (96, 1), (52, 1), (74, 1), (40, 1)])" +heuristic_minbudget,4.053526600007899,100,0.8,"(14, [(2, 1), (40, 1), (52, 1), (64, 1), (65, 1), (91, 1), (45, 1), (92, 1), (10, 1), (74, 1), (85, 1), (63, 1), (79, 1), (96, 1)])" +spreading_minbudget,6.874619900001562,200,0.1,"(13, [(48, 1), (24, 1), (157, 1), (42, 1), (196, 1), (33, 1), (21, 1), (59, 1), (153, 1), (28, 1), (52, 1), (136, 1), (99, 1)])" +heuristic_minbudget,0.3828627999901073,200,0.1,"(8, [(99, 1), (52, 1), (153, 1), (136, 1), (28, 1), (48, 1), (147, 1), (194, 1), (92, 2), (91, 2), (50, 2), (1, 2), (186, 2), (82, 2), (42, 2), (178, 2)])" +spreading_minbudget,3.6053887000016402,200,0.1,"(8, [(116, 1), (153, 1), (45, 1), (49, 1), (105, 1), (95, 1), (65, 1), (99, 1)])" +heuristic_minbudget,0.43296489999920595,200,0.1,"(5, [(99, 1), (65, 1), (153, 1), (194, 1), (76, 1), (64, 2), (29, 2), (91, 2), (168, 2), (80, 2)])" +spreading_minbudget,6.127774299995508,200,0.1,"(12, [(57, 1), (164, 1), (21, 1), (49, 1), (108, 1), (194, 1), (11, 1), (42, 1), (68, 1), (153, 1), (65, 1), (28, 1)])" +heuristic_minbudget,0.5014686999929836,200,0.1,"(8, [(194, 1), (153, 1), (28, 1), (65, 1), (106, 1), (48, 1), (52, 1), (76, 1), (29, 2), (177, 2), (142, 2), (171, 2), (178, 2), (120, 2), (16, 2), (128, 2)])" +spreading_minbudget,6.081753100006608,200,0.1,"(14, [(155, 1), (131, 1), (185, 1), (6, 1), (44, 1), (76, 1), (147, 1), (153, 1), (79, 1), (161, 1), (28, 1), (112, 1), (52, 1), (106, 1)])" +heuristic_minbudget,0.3598169000033522,200,0.1,"(8, [(147, 1), (153, 1), (52, 1), (76, 1), (106, 1), (112, 1), (28, 1), (48, 1), (192, 2), (3, 2), (101, 2), (22, 2), (122, 2), (78, 2), (195, 2), (17, 2)])" +spreading_minbudget,6.416437199994107,200,0.1,"(10, [(194, 1), (6, 1), (52, 1), (101, 1), (4, 1), (170, 1), (46, 1), (9, 1), (41, 1), (28, 1)])" +heuristic_minbudget,0.5401426000025822,200,0.1,"(8, [(52, 1), (4, 1), (28, 1), (194, 1), (106, 1), (147, 1), (76, 1), (48, 1), (17, 2), (2, 2), (61, 2), (50, 2), (115, 2), (143, 2), (16, 2), (93, 2)])" +spreading_minbudget,7.155516500002705,200,0.1,"(13, [(124, 1), (196, 1), (116, 1), (66, 1), (21, 1), (120, 1), (128, 1), (138, 1), (44, 1), (72, 1), (48, 1), (65, 1), (136, 1)])" +heuristic_minbudget,0.45384590000321623,200,0.1,"(9, [(48, 1), (44, 1), (136, 1), (65, 1), (147, 1), (52, 1), (194, 1), (76, 1), (106, 1), (170, 2), (184, 2), (12, 2), (50, 2), (2, 2), (57, 2), (129, 2), (87, 2), (69, 2)])" +spreading_minbudget,5.644121200006339,200,0.1,"(13, [(129, 1), (199, 1), (44, 1), (82, 1), (39, 1), (76, 1), (182, 1), (194, 1), (54, 1), (161, 1), (4, 1), (28, 1), (52, 1)])" +heuristic_minbudget,0.48520840000128374,200,0.1,"(9, [(194, 1), (76, 1), (52, 1), (4, 1), (28, 1), (161, 1), (147, 1), (48, 1), (44, 1), (17, 2), (107, 2), (57, 2), (129, 2), (122, 2), (81, 2), (108, 2), (121, 2), (2, 2)])" +spreading_minbudget,1.8839236000058008,200,0.1,"(5, [(48, 1), (91, 1), (42, 1), (136, 1), (106, 1)])" +heuristic_minbudget,0.398219099995913,200,0.1,"(3, [(136, 1), (106, 1), (48, 1), (157, 2), (128, 2), (120, 2)])" +spreading_minbudget,8.769554500002414,200,0.1,"(16, [(3, 1), (157, 1), (116, 1), (106, 1), (70, 1), (44, 1), (17, 1), (43, 1), (138, 1), (194, 1), (147, 1), (76, 1), (4, 1), (48, 1), (65, 1), (112, 1)])" +heuristic_minbudget,0.48429249999753665,200,0.1,"(10, [(48, 1), (147, 1), (194, 1), (76, 1), (106, 1), (44, 1), (65, 1), (112, 1), (4, 1), (52, 1), (142, 2), (16, 2), (107, 2), (69, 2), (42, 2), (12, 2), (143, 2), (87, 2), (6, 2), (177, 2)])" +spreading_minbudget,4.215613599997596,200,0.1,"(9, [(116, 1), (33, 1), (70, 1), (101, 1), (21, 1), (194, 1), (4, 1), (52, 1), (106, 1)])" +heuristic_minbudget,0.4958438000030583,200,0.1,"(6, [(52, 1), (106, 1), (4, 1), (147, 1), (194, 1), (76, 1), (59, 2), (81, 2), (2, 2), (129, 2), (61, 2), (16, 2)])" +spreading_minbudget,0.1568801999965217,200,0.5,"(1, [(10, 1)])" +heuristic_minbudget,3.0877527999982703,200,0.5,"(1, [(132, 1)])" +spreading_minbudget,2.236697499989532,200,0.5,"(6, [(99, 1), (168, 1), (175, 1), (31, 1), (149, 1), (177, 1)])" +heuristic_minbudget,3.3113985999953,200,0.5,"(5, [(149, 1), (168, 1), (177, 1), (31, 1), (175, 1)])" +spreading_minbudget,9.140447199999471,200,0.5,"(32, [(59, 1), (144, 1), (63, 1), (19, 1), (34, 1), (41, 1), (50, 1), (65, 1), (82, 1), (85, 1), (103, 1), (116, 1), (130, 1), (143, 1), (168, 1), (180, 1), (184, 1), (191, 1), (193, 1), (28, 1), (75, 1), (139, 1), (146, 1), (149, 1), (162, 1), (61, 1), (68, 1), (81, 1), (137, 1), (186, 1), (153, 1), (192, 1)])" +heuristic_minbudget,5.723877700002049,200,0.5,"(31, [(184, 1), (143, 1), (116, 1), (61, 1), (59, 1), (149, 1), (130, 1), (186, 1), (75, 1), (68, 1), (82, 1), (103, 1), (162, 1), (191, 1), (193, 1), (41, 1), (19, 1), (180, 1), (192, 1), (28, 1), (85, 1), (50, 1), (168, 1), (63, 1), (34, 1), (137, 1), (153, 1), (146, 1), (65, 1), (139, 1), (81, 1)])" +spreading_minbudget,2.4964694999944186,200,0.5,"(8, [(63, 1), (85, 1), (140, 1), (180, 1), (193, 1), (197, 1), (199, 1), (119, 1)])" +heuristic_minbudget,3.2830257000023266,200,0.5,"(7, [(180, 1), (140, 1), (193, 1), (119, 1), (199, 1), (85, 1), (197, 1)])" +spreading_minbudget,1.0909339999925578,200,0.5,"(2, [(4, 1), (192, 1)])" +heuristic_minbudget,3.0061140000034356,200,0.5,"(2, [(192, 1), (108, 1)])" +spreading_minbudget,2.8638310999958776,200,0.5,"(9, [(31, 1), (13, 1), (41, 1), (45, 1), (103, 1), (116, 1), (74, 1), (127, 1), (68, 1)])" +heuristic_minbudget,3.336111199998413,200,0.5,"(7, [(45, 1), (41, 1), (116, 1), (68, 1), (103, 1), (127, 1), (74, 1)])" +spreading_minbudget,6.566627099993639,200,0.5,"(19, [(1, 1), (76, 1), (34, 1), (112, 1), (160, 1), (193, 1), (197, 1), (55, 1), (69, 1), (86, 1), (122, 1), (139, 1), (141, 1), (159, 1), (196, 1), (137, 1), (16, 1), (153, 1), (93, 1)])" +heuristic_minbudget,4.1070819000015035,200,0.5,"(17, [(86, 1), (139, 1), (193, 1), (122, 1), (196, 1), (69, 1), (93, 1), (159, 1), (55, 1), (153, 1), (112, 1), (34, 1), (141, 1), (137, 1), (16, 1), (160, 1), (197, 1)])" +spreading_minbudget,11.145615999994334,200,0.5,"(35, [(99, 1), (138, 1), (48, 1), (32, 1), (44, 1), (52, 1), (56, 1), (85, 1), (103, 1), (107, 1), (120, 1), (130, 1), (140, 1), (160, 1), (175, 1), (180, 1), (191, 1), (8, 1), (25, 1), (55, 1), (67, 1), (69, 1), (74, 1), (86, 1), (132, 1), (139, 1), (141, 1), (159, 1), (68, 1), (98, 1), (104, 1), (186, 1), (177, 1), (119, 1), (21, 1)])" +heuristic_minbudget,6.92385840001225,200,0.5,"(33, [(103, 1), (67, 1), (68, 1), (130, 1), (132, 1), (48, 1), (186, 1), (177, 1), (69, 1), (21, 1), (191, 1), (98, 1), (104, 1), (107, 1), (119, 1), (180, 1), (32, 1), (140, 1), (159, 1), (85, 1), (120, 1), (86, 1), (175, 1), (52, 1), (8, 1), (55, 1), (25, 1), (139, 1), (160, 1), (56, 1), (44, 1), (141, 1), (74, 1)])" +spreading_minbudget,7.714225499992608,200,0.5,"(27, [(194, 1), (30, 1), (19, 1), (41, 1), (56, 1), (65, 1), (85, 1), (87, 1), (115, 1), (116, 1), (121, 1), (130, 1), (140, 1), (143, 1), (168, 1), (175, 1), (180, 1), (181, 1), (17, 1), (67, 1), (122, 1), (138, 1), (141, 1), (196, 1), (68, 1), (81, 1), (104, 1)])" +heuristic_minbudget,4.569759200006956,200,0.5,"(26, [(30, 1), (138, 1), (121, 1), (130, 1), (41, 1), (116, 1), (67, 1), (196, 1), (68, 1), (87, 1), (19, 1), (168, 1), (17, 1), (104, 1), (115, 1), (180, 1), (85, 1), (143, 1), (140, 1), (175, 1), (181, 1), (122, 1), (56, 1), (65, 1), (141, 1), (81, 1)])" +spreading_minbudget,5.409246000010171,200,0.5,"(21, [(184, 1), (34, 1), (45, 1), (52, 1), (80, 1), (103, 1), (112, 1), (120, 1), (130, 1), (143, 1), (168, 1), (175, 1), (193, 1), (199, 1), (8, 1), (28, 1), (109, 1), (135, 1), (198, 1), (61, 1), (119, 1)])" +heuristic_minbudget,4.488193300014245,200,0.5,"(21, [(184, 1), (45, 1), (193, 1), (61, 1), (130, 1), (119, 1), (103, 1), (199, 1), (80, 1), (52, 1), (28, 1), (120, 1), (143, 1), (175, 1), (34, 1), (135, 1), (168, 1), (8, 1), (109, 1), (112, 1), (198, 1)])" +spreading_minbudget,5.363142900008825,200,0.8,"(21, [(3, 1), (21, 1), (33, 1), (44, 1), (45, 1), (68, 1), (69, 1), (70, 1), (74, 1), (138, 1), (149, 1), (150, 1), (155, 1), (171, 1), (176, 1), (179, 1), (195, 1), (198, 1), (37, 1), (162, 1), (190, 1)])" +heuristic_minbudget,13.136786300005042,200,0.8,"(21, [(195, 1), (171, 1), (150, 1), (155, 1), (44, 1), (3, 1), (45, 1), (74, 1), (69, 1), (190, 1), (68, 1), (179, 1), (149, 1), (37, 1), (162, 1), (21, 1), (138, 1), (70, 1), (198, 1), (176, 1), (33, 1)])" +spreading_minbudget,13.20309090000228,200,0.8,"(63, [(17, 1), (6, 1), (13, 1), (16, 1), (21, 1), (22, 1), (23, 1), (25, 1), (26, 1), (28, 1), (33, 1), (35, 1), (36, 1), (38, 1), (39, 1), (43, 1), (53, 1), (58, 1), (60, 1), (61, 1), (65, 1), (68, 1), (70, 1), (71, 1), (73, 1), (77, 1), (78, 1), (88, 1), (90, 1), (99, 1), (108, 1), (109, 1), (113, 1), (124, 1), (129, 1), (131, 1), (134, 1), (136, 1), (140, 1), (142, 1), (148, 1), (150, 1), (155, 1), (163, 1), (175, 1), (176, 1), (178, 1), (179, 1), (184, 1), (192, 1), (195, 1), (196, 1), (198, 1), (199, 1), (5, 1), (8, 1), (15, 1), (29, 1), (63, 1), (95, 1), (123, 1), (133, 1), (146, 1)])" +heuristic_minbudget,18.206225700007053,200,0.8,"(63, [(53, 1), (26, 1), (88, 1), (5, 1), (23, 1), (38, 1), (150, 1), (155, 1), (178, 1), (39, 1), (108, 1), (134, 1), (13, 1), (36, 1), (163, 1), (17, 1), (109, 1), (175, 1), (195, 1), (63, 1), (113, 1), (8, 1), (77, 1), (71, 1), (124, 1), (136, 1), (140, 1), (21, 1), (148, 1), (22, 1), (43, 1), (60, 1), (123, 1), (142, 1), (65, 1), (6, 1), (35, 1), (196, 1), (68, 1), (95, 1), (176, 1), (179, 1), (28, 1), (146, 1), (29, 1), (58, 1), (70, 1), (61, 1), (25, 1), (73, 1), (78, 1), (199, 1), (133, 1), (33, 1), (99, 1), (131, 1), (90, 1), (184, 1), (198, 1), (15, 1), (129, 1), (16, 1), (192, 1)])" +spreading_minbudget,1.1575602000084473,200,0.8,"(5, [(80, 1), (28, 1), (86, 1), (134, 1), (195, 1)])" +heuristic_minbudget,9.099992300005397,200,0.8,"(5, [(80, 1), (134, 1), (28, 1), (195, 1), (86, 1)])" +spreading_minbudget,8.297417900001165,200,0.8,"(33, [(26, 1), (19, 1), (25, 1), (30, 1), (31, 1), (35, 1), (42, 1), (61, 1), (72, 1), (76, 1), (78, 1), (93, 1), (94, 1), (98, 1), (125, 1), (128, 1), (132, 1), (134, 1), (135, 1), (138, 1), (140, 1), (155, 1), (163, 1), (172, 1), (178, 1), (195, 1), (196, 1), (81, 1), (133, 1), (146, 1), (27, 1), (51, 1), (59, 1)])" +heuristic_minbudget,13.201126400002977,200,0.8,"(33, [(26, 1), (19, 1), (94, 1), (59, 1), (155, 1), (178, 1), (128, 1), (81, 1), (134, 1), (163, 1), (27, 1), (195, 1), (72, 1), (98, 1), (31, 1), (42, 1), (132, 1), (30, 1), (76, 1), (196, 1), (140, 1), (125, 1), (25, 1), (51, 1), (93, 1), (135, 1), (61, 1), (138, 1), (146, 1), (35, 1), (133, 1), (78, 1), (172, 1)])" +spreading_minbudget,12.984143499998027,200,0.8,"(63, [(89, 1), (3, 1), (13, 1), (14, 1), (16, 1), (17, 1), (23, 1), (32, 1), (33, 1), (38, 1), (40, 1), (41, 1), (42, 1), (45, 1), (47, 1), (56, 1), (58, 1), (64, 1), (65, 1), (67, 1), (68, 1), (75, 1), (76, 1), (80, 1), (86, 1), (90, 1), (92, 1), (93, 1), (98, 1), (99, 1), (100, 1), (106, 1), (112, 1), (117, 1), (121, 1), (131, 1), (132, 1), (134, 1), (136, 1), (142, 1), (150, 1), (151, 1), (158, 1), (164, 1), (171, 1), (174, 1), (175, 1), (183, 1), (187, 1), (188, 1), (196, 1), (197, 1), (198, 1), (199, 1), (5, 1), (8, 1), (52, 1), (95, 1), (96, 1), (102, 1), (116, 1), (123, 1), (87, 1)])" +heuristic_minbudget,17.87442839999858,200,0.8,"(63, [(175, 1), (5, 1), (171, 1), (89, 1), (23, 1), (102, 1), (40, 1), (38, 1), (150, 1), (47, 1), (151, 1), (197, 1), (13, 1), (52, 1), (17, 1), (41, 1), (75, 1), (96, 1), (76, 1), (65, 1), (56, 1), (80, 1), (116, 1), (132, 1), (134, 1), (8, 1), (98, 1), (67, 1), (136, 1), (42, 1), (58, 1), (174, 1), (86, 1), (32, 1), (121, 1), (164, 1), (3, 1), (196, 1), (95, 1), (112, 1), (68, 1), (64, 1), (131, 1), (100, 1), (123, 1), (142, 1), (187, 1), (117, 1), (188, 1), (14, 1), (45, 1), (199, 1), (198, 1), (16, 1), (106, 1), (33, 1), (87, 1), (158, 1), (92, 1), (99, 1), (90, 1), (93, 1), (183, 1)])" +spreading_minbudget,5.6764264999947045,200,0.8,"(27, [(66, 1), (6, 1), (16, 1), (25, 1), (31, 1), (38, 1), (41, 1), (57, 1), (65, 1), (76, 1), (89, 1), (92, 1), (106, 1), (126, 1), (140, 1), (158, 1), (160, 1), (167, 1), (174, 1), (182, 1), (185, 1), (192, 1), (196, 1), (52, 1), (95, 1), (102, 1), (181, 1)])" +heuristic_minbudget,13.88039399999252,200,0.8,"(27, [(76, 1), (185, 1), (66, 1), (89, 1), (102, 1), (38, 1), (174, 1), (65, 1), (6, 1), (52, 1), (182, 1), (167, 1), (41, 1), (196, 1), (25, 1), (126, 1), (140, 1), (95, 1), (31, 1), (181, 1), (16, 1), (92, 1), (158, 1), (57, 1), (160, 1), (106, 1), (192, 1)])" +spreading_minbudget,4.208028399996692,200,0.8,"(16, [(31, 1), (13, 1), (38, 1), (58, 1), (75, 1), (76, 1), (78, 1), (88, 1), (98, 1), (101, 1), (153, 1), (164, 1), (167, 1), (197, 1), (63, 1), (159, 1)])"

      m5WGe0%TzlLw# zKdOV>3Ktxsfx0z(d{N8IP!|7v8Lp+|x{N*t3n^4YPmCG^`q@?$X4vf^X+40KKk+6v zJG-8|=qehra>H!f>(IGh@7e)JyUj8-tTb>`iBB!U+iC>WIP5gwklxnE98kk_WR3m(h)ZOi7Jr&etK|9N}8PExvkt1_e}X z3b70Tp>zU1U?f{6*f5+ovafH8vUBcib>zTl567wPgl-$`DsXe2JP9Jts8q}wcm@9r z!8ok*A!J%NgMxbSU&DTc-MdHo*?)Dfj?Y6-y?=Rspz6iO!ARd{!Kt=t;X0(S}6p&qvG@O81s4U`(#^Kt| zoBVthi9>%OQSZl1x*asycNUrUSUO@pf*p^2LzMf1;FN;9?j%e=rO_bc!g-XljC*JD}6 zP?`}?yQg^b#7r8D8hCc=M+ziRa2stDGb{&~=E~PRIdE+L0@&9BmTI%}im*9ob05uT z1=t3uCg3##P-(i}L|U#y42&Cr(P{8gEHZ}$h%F%9Wgxm4a7a=`ZV6V2WNmG2^%JS2 z_sR)3L%lW@&uG%^f`L&eh6)fqAS|&>;E<_!MS*nk%A|-M89<k8j@W#{5)JXoS2JF)?=BeZ(FY*UIIIgQWz=b?&WW&ft`a z%fuRZ;W@6 diff --git a/networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/spreading_maxsave_cleaned.csv b/networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/spreading_maxsave_cleaned.csv new file mode 100644 index 0000000..89f0310 --- /dev/null +++ b/networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/spreading_maxsave_cleaned.csv @@ -0,0 +1,19 @@ +algorithm,runtime,graph_nodes,Nodes_Saved +spreading_maxsave,0.1030348999192938,100.0,25 +heuristic_maxsave,0.0385726999957114,100.0,24 +spreading_maxsave,0.0551759001100435,100.0,16 +heuristic_maxsave,0.0735366999870166,100.0,17 +spreading_maxsave,0.0363913000328466,100.0,3 +heuristic_maxsave,0.0835548999020829,100.0,3 +spreading_maxsave,0.1436849000165239,200.0,21 +heuristic_maxsave,0.1306186999427154,200.0,18 +spreading_maxsave,0.2132705000694841,200.0,45 +heuristic_maxsave,0.1602046999614685,200.0,40 +spreading_maxsave,0.1413846000796184,200.0,18 +heuristic_maxsave,0.2347187999403104,200.0,18 +spreading_maxsave,0.4464188000420108,400.0,23 +heuristic_maxsave,0.2719731999095529,400.0,20 +spreading_maxsave,0.8410197999328375,400.0,78 +heuristic_maxsave,0.4375498999143019,400.0,76 +spreading_maxsave,0.5826126000611112,400.0,28 +heuristic_maxsave,0.6866420999867842,400.0,29 diff --git a/networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/spreading_minbudget.csv b/networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/spreading_minbudget.csv index 42346be..f03d822 100644 --- a/networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/spreading_minbudget.csv +++ b/networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/spreading_minbudget.csv @@ -1,20 +1,7 @@ algorithm,runtime,graph_nodes,Budget -spreading_minbudget,2.6765573997981846,100,"(2, [(28, 1), (17, 1), (47, 2), (2, 2)])" -heuristic_minbudget,0.7838832000270486,100,"(2, [(58, 1), (17, 1), (14, 2), (76, 2), (63, 3), (85, 3)])" -spreading_minbudget,4.7819698001258075,100,"(19, [(19, 1), (5, 1), (4, 1), (33, 1), (66, 1), (79, 1), (92, 1), (2, 1), (18, 1), (38, 1), (52, 1), (55, 1), (10, 1), (27, 1), (29, 1), (63, 1), (83, 1), (32, 1), (54, 1)])" -heuristic_minbudget,12.467118400149047,100,"(19, [(19, 1), (5, 1), (63, 1), (18, 1), (38, 1), (79, 1), (10, 1), (66, 1), (32, 1), (52, 1), (83, 1), (27, 1), (55, 1), (92, 1), (33, 1), (29, 1), (2, 1), (4, 1), (54, 1)])" -spreading_minbudget,2.980581299867481,100,"(24, [(40, 1), (2, 1), (3, 1), (5, 1), (8, 1), (11, 1), (22, 1), (24, 1), (51, 1), (62, 1), (72, 1), (76, 1), (81, 1), (83, 1), (85, 1), (87, 1), (89, 1), (92, 1), (97, 1), (44, 1), (46, 1), (60, 1), (63, 1), (70, 1)])" -heuristic_minbudget,35.502651300048456,100,"(24, [(76, 1), (85, 1), (11, 1), (44, 1), (87, 1), (83, 1), (92, 1), (46, 1), (62, 1), (70, 1), (8, 1), (2, 1), (22, 1), (72, 1), (24, 1), (89, 1), (63, 1), (81, 1), (51, 1), (3, 1), (97, 1), (5, 1), (40, 1), (60, 1)])" -spreading_minbudget,8.064421500079334,200,"(19, [(86, 1), (105, 1), (161, 1), (115, 1), (21, 1), (34, 1), (162, 1), (85, 1), (131, 1), (70, 1), (42, 1), (186, 1), (36, 1), (14, 1), (40, 1), (187, 1), (100, 1), (178, 1), (176, 1)])" -heuristic_minbudget,7.089356000069529,200,"(13, [(186, 1), (34, 1), (105, 1), (178, 1), (100, 1), (14, 1), (131, 1), (176, 1), (187, 1), (40, 1), (42, 1), (161, 1), (91, 1), (181, 2), (92, 2), (71, 2), (144, 2), (130, 2), (175, 2), (102, 2), (194, 2), (191, 2), (9, 2), (18, 2), (4, 2), (68, 2)])" -spreading_minbudget,11.68095609988086,200,"(50, [(118, 1), (80, 1), (1, 1), (3, 1), (16, 1), (25, 1), (29, 1), (42, 1), (55, 1), (65, 1), (73, 1), (75, 1), (76, 1), (78, 1), (79, 1), (95, 1), (99, 1), (100, 1), (101, 1), (112, 1), (127, 1), (129, 1), (133, 1), (137, 1), (151, 1), (152, 1), (166, 1), (171, 1), (191, 1), (197, 1), (5, 1), (6, 1), (24, 1), (27, 1), (57, 1), (110, 1), (170, 1), (179, 1), (19, 1), (20, 1), (146, 1), (163, 1), (193, 1), (119, 1), (181, 1), (98, 1), (140, 1), (144, 1), (157, 1), (180, 1)])" -heuristic_minbudget,41.66929220012389,200,"(49, [(98, 1), (101, 1), (73, 1), (16, 1), (112, 1), (79, 1), (27, 1), (1, 1), (137, 1), (140, 1), (119, 1), (5, 1), (76, 1), (25, 1), (118, 1), (129, 1), (146, 1), (163, 1), (181, 1), (110, 1), (3, 1), (55, 1), (100, 1), (151, 1), (78, 1), (191, 1), (65, 1), (157, 1), (20, 1), (171, 1), (152, 1), (197, 1), (99, 1), (166, 1), (179, 1), (75, 1), (6, 1), (29, 1), (57, 1), (127, 1), (24, 1), (42, 1), (170, 1), (19, 1), (193, 1), (144, 1), (180, 1), (133, 1), (95, 1)])" -spreading_minbudget,4.990314699942246,200,"(49, [(57, 1), (6, 1), (13, 1), (17, 1), (18, 1), (20, 1), (23, 1), (26, 1), (31, 1), (33, 1), (34, 1), (45, 1), (48, 1), (49, 1), (51, 1), (63, 1), (70, 1), (73, 1), (79, 1), (82, 1), (87, 1), (93, 1), (102, 1), (107, 1), (109, 1), (111, 1), (113, 1), (120, 1), (132, 1), (136, 1), (137, 1), (141, 1), (157, 1), (161, 1), (162, 1), (167, 1), (175, 1), (183, 1), (189, 1), (192, 1), (193, 1), (196, 1), (198, 1), (8, 1), (9, 1), (46, 1), (176, 1), (178, 1), (151, 1)])" -heuristic_minbudget,48.9231706999708,200,"(49, [(46, 1), (196, 1), (162, 1), (109, 1), (6, 1), (79, 1), (23, 1), (192, 1), (8, 1), (18, 1), (57, 1), (136, 1), (111, 1), (33, 1), (51, 1), (87, 1), (102, 1), (132, 1), (175, 1), (189, 1), (198, 1), (13, 1), (49, 1), (17, 1), (48, 1), (93, 1), (141, 1), (157, 1), (167, 1), (183, 1), (193, 1), (9, 1), (45, 1), (63, 1), (73, 1), (82, 1), (151, 1), (178, 1), (107, 1), (176, 1), (113, 1), (20, 1), (26, 1), (120, 1), (31, 1), (34, 1), (137, 1), (161, 1), (70, 1)])" -spreading_minbudget,6.574391700094566,400,"(21, [(46, 1), (99, 1), (154, 1), (62, 1), (282, 1), (2, 1), (352, 1), (293, 1), (194, 1), (285, 1), (18, 1), (45, 1), (49, 1), (295, 1), (356, 1), (174, 1), (12, 1), (322, 1), (112, 1), (248, 1), (313, 1)])" -heuristic_minbudget,8.081847100052983,400,"(13, [(174, 1), (112, 1), (49, 1), (248, 1), (295, 1), (45, 1), (12, 1), (18, 1), (356, 1), (322, 1), (313, 1), (389, 1), (178, 1), (84, 2), (128, 2), (80, 2), (48, 2), (199, 2), (17, 2), (376, 2), (233, 2), (314, 2), (67, 2), (11, 2), (225, 2), (122, 2)])" -spreading_minbudget,11.30272960010916,400,"(29, [(383, 1), (19, 1), (2, 1), (4, 1), (58, 1), (100, 1), (123, 1), (169, 1), (188, 1), (218, 1), (263, 1), (335, 1), (388, 1), (30, 1), (49, 1), (186, 1), (201, 1), (228, 1), (243, 1), (314, 1), (358, 1), (25, 1), (86, 1), (144, 1), (316, 1), (362, 1), (28, 1), (355, 1), (386, 1)])" -heuristic_minbudget,47.75127730006352,400,"(29, [(388, 1), (383, 1), (355, 1), (358, 1), (186, 1), (201, 1), (58, 1), (169, 1), (243, 1), (2, 1), (263, 1), (4, 1), (188, 1), (314, 1), (19, 1), (25, 1), (316, 1), (335, 1), (100, 1), (218, 1), (30, 1), (362, 1), (28, 1), (123, 1), (144, 1), (228, 1), (49, 1), (86, 1), (386, 1)])" -spreading_minbudget,9.465220999903977,400,"(91, [(22, 1), (2, 1), (4, 1), (5, 1), (15, 1), (21, 1), (35, 1), (38, 1), (39, 1), (43, 1), (76, 1), (79, 1), (84, 1), (88, 1), (94, 1), (102, 1), (103, 1), (105, 1), (112, 1), (113, 1), (114, 1), (117, 1), (122, 1), (127, 1), (132, 1), (136, 1), (137, 1), (142, 1), (158, 1), (181, 1), (186, 1), (189, 1), (190, 1), (209, 1), (216, 1), (219, 1), (223, 1), (227, 1), (235, 1), (237, 1), (243, 1), (245, 1), (246, 1), (249, 1), (256, 1), (259, 1), (263, 1), (270, 1), (272, 1), (281, 1), (283, 1), (284, 1), (287, 1), (288, 1), (290, 1), (302, 1), (307, 1), (314, 1), (316, 1), (321, 1), (324, 1), (328, 1), (339, 1), (341, 1), (362, 1), (363, 1), (364, 1), (381, 1), (384, 1), (391, 1), (398, 1), (47, 1), (50, 1), (78, 1), (86, 1), (119, 1), (168, 1), (182, 1), (187, 1), (262, 1), (264, 1), (318, 1), (350, 1), (366, 1), (373, 1), (382, 1), (387, 1), (75, 1), (257, 1), (173, 1), (214, 1)])" -heuristic_minbudget,229.72120989998803,400,"(91, [(256, 1), (39, 1), (186, 1), (114, 1), (119, 1), (15, 1), (112, 1), (257, 1), (122, 1), (363, 1), (283, 1), (86, 1), (366, 1), (384, 1), (76, 1), (84, 1), (21, 1), (142, 1), (227, 1), (50, 1), (117, 1), (5, 1), (272, 1), (318, 1), (136, 1), (288, 1), (263, 1), (22, 1), (173, 1), (339, 1), (350, 1), (105, 1), (182, 1), (341, 1), (43, 1), (127, 1), (132, 1), (364, 1), (181, 1), (223, 1), (235, 1), (245, 1), (284, 1), (398, 1), (381, 1), (219, 1), (382, 1), (137, 1), (328, 1), (316, 1), (2, 1), (102, 1), (324, 1), (168, 1), (216, 1), (189, 1), (47, 1), (38, 1), (237, 1), (259, 1), (270, 1), (307, 1), (391, 1), (158, 1), (79, 1), (209, 1), (262, 1), (290, 1), (94, 1), (88, 1), (214, 1), (264, 1), (249, 1), (78, 1), (373, 1), (243, 1), (314, 1), (387, 1), (75, 1), (35, 1), (103, 1), (281, 1), (362, 1), (4, 1), (190, 1), (113, 1), (302, 1), (187, 1), (246, 1), (321, 1), (287, 1)])" -,495.3808441001456,, +spreading_minbudget,0.22848329995758832,100,"(2, [(11, 1), (36, 1), (93, 2), (85, 2)])" +heuristic_minbudget,0.22209170006681234,100,"(3, [(18, 1), (62, 1), (46, 1), (85, 2), (88, 2), (23, 2), (55, 3), (91, 3), (37, 3)])" +spreading_minbudget,0.4906294000102207,100,"(17, [(90, 1), (21, 1), (35, 1), (55, 1), (58, 1), (67, 1), (71, 1), (93, 1), (96, 1), (5, 1), (33, 1), (77, 1), (92, 1), (15, 1), (29, 1), (87, 1), (61, 1)])" +heuristic_minbudget,2.8224170999601483,100,"(17, [(21, 1), (90, 1), (58, 1), (87, 1), (33, 1), (67, 1), (96, 1), (93, 1), (55, 1), (61, 1), (77, 1), (15, 1), (35, 1), (71, 1), (5, 1), (29, 1), (92, 1)])" +spreading_minbudget,0.7490303999511525,100,"(28, [(8, 1), (1, 1), (2, 1), (3, 1), (10, 1), (18, 1), (20, 1), (21, 1), (23, 1), (34, 1), (36, 1), (41, 1), (43, 1), (45, 1), (49, 1), (50, 1), (58, 1), (62, 1), (65, 1), (75, 1), (83, 1), (88, 1), (91, 1), (97, 1), (7, 1), (59, 1), (61, 1), (80, 1)])" +heuristic_minbudget,4.727551299962215,100,"(28, [(65, 1), (10, 1), (36, 1), (1, 1), (41, 1), (8, 1), (83, 1), (50, 1), (20, 1), (61, 1), (34, 1), (23, 1), (49, 1), (62, 1), (3, 1), (18, 1), (88, 1), (59, 1), (7, 1), (58, 1), (21, 1), (75, 1), (97, 1), (2, 1), (43, 1), (80, 1), (45, 1), (91, 1)])" diff --git a/networkz/algorithms/approximation/tests/test_firefighter_problem/test_non_spreading_dirlaynet_minbudget.py b/networkz/algorithms/approximation/tests/test_firefighter_problem/test_non_spreading_dirlaynet_minbudget.py index 8faa7b5..6ed6cd6 100644 --- a/networkz/algorithms/approximation/tests/test_firefighter_problem/test_non_spreading_dirlaynet_minbudget.py +++ b/networkz/algorithms/approximation/tests/test_firefighter_problem/test_non_spreading_dirlaynet_minbudget.py @@ -20,7 +20,6 @@ """ import pytest -import networkx as nx import json import numpy as np import math @@ -36,7 +35,7 @@ from networkz.algorithms.approximation.firefighter_problem.Utils import min_cut_N_groups from networkz.algorithms.approximation.firefighter_problem.Utils import matrix_to_integers_values from networkz.algorithms.approximation.firefighter_problem.Utils import min_budget_calculation - +from networkz.algorithms.approximation.firefighter_problem.Random_Graph_Generator import generate_random_layered_network path_to_graphs = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'graphs.json') if os.path.exists(path_to_graphs): @@ -371,58 +370,8 @@ def test_min_budget_calculation(): assert min_budget_calculation(matrix_3_check) == 2 -def generate_layered_network(): - """ - Generates a directed layered network with a random number of layers and random nodes per layer. - - Returns: - G (networkx.DiGraph): Directed graph representing the layered network. - """ - # Randomly decide the number of layers (between 2 and 3 for this example) - num_layers = random.randint(5, 10) - - # Randomly decide the number of nodes per layer (between 1 and 4 for this example) - nodes_per_layer = [random.randint(5, 30) for _ in range(num_layers)] - - G = nx.DiGraph() - node_id = 1 # Start node_id from 1 because 0 is the source - - # Initialize layer 0 with the source node - layers = [[0]] - G.add_node(0) - - # Create nodes layer by layer - for i in range(num_layers): - layer = [node_id + j for j in range(nodes_per_layer[i])] - layers.append(layer) - G.add_nodes_from(layer) - node_id += nodes_per_layer[i] - - print("LAYERS->", layers) - - # Connect source node (0) to all nodes in layer 1 - for node in layers[1]: - G.add_edge(0, node) - - # Create edges ensuring connectivity between consecutive layers - for i in range(1, num_layers): - for node in layers[i]: - # Connect each node in this layer to at least one node in the next layer - connected_nodes = random.sample(layers[i + 1], k=random.randint(1, len(layers[i + 1]))) - for target in connected_nodes: - if target != node: # Ensure no self-loop - G.add_edge(node, target) - - for target in layers[i + 1]: - # Ensure each node in the next layer is connected to from at least one node in this layer - if not any(G.has_edge(source, target) for source in layers[i]): - G.add_edge(random.choice(layers[i]), target) - - return G - - def test_non_spreading_dirlaynet_minbudget(): - G = generate_layered_network() + G = generate_random_layered_network() source = 0 nodes = list(G.nodes()) From d57d616b8fbd19f2a7b90a26fffaced193592903 Mon Sep 17 00:00:00 2001 From: 20shaked20 Date: Tue, 23 Jul 2024 19:25:47 +0300 Subject: [PATCH 77/93] fix --- .../comparisons/comparisons.py | 120 ++++-- .../comparisons/non_spreading.csv | 392 ++++-------------- .../comparisons/non_spreading.png | Bin 77751 -> 63407 bytes .../non_spreading_preprocessed.csv | 390 ++++------------- .../comparisons/spreading.csv | 5 - .../comparisons/spreading.png | Bin 31546 -> 0 bytes .../comparisons/spreading_maxsave.csv | 380 +---------------- .../comparisons/spreading_maxsave.png | Bin 33643 -> 30056 bytes .../comparisons/spreading_maxsave_cleaned.csv | 36 +- .../comparisons/spreading_minbudget.csv | 38 +- .../comparisons/spreading_minbudget.png | Bin 33997 -> 29855 bytes .../spreading_minbudget_preprocessed.csv | 36 +- 12 files changed, 345 insertions(+), 1052 deletions(-) delete mode 100644 networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/spreading.csv delete mode 100644 networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/spreading.png diff --git a/networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/comparisons.py b/networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/comparisons.py index 407bdd9..303e8c6 100644 --- a/networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/comparisons.py +++ b/networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/comparisons.py @@ -34,6 +34,14 @@ logger = logging.getLogger(__name__) def setup_global_logger(level: int = logging.DEBUG): + """ + Setup the global logger with a specific format and logging level. + + Parameters: + ---------- + level : int + Logging level, e.g., logging.DEBUG, logging.INFO. + """ log_format = "|| %(asctime)s || %(levelname)s || %(message)s" date_format = '%H:%M:%S' formatter = logging.Formatter(log_format, datefmt=date_format) @@ -44,8 +52,26 @@ def setup_global_logger(level: int = logging.DEBUG): root_logger.setLevel(level) root_logger.addHandler(handler) - def runner_no_spreading(algorithm, graph, source, targets): + """ + Run the specified algorithm without spreading. + + Parameters: + ---------- + algorithm : function + The algorithm to be executed. + graph : nx.Graph + The graph on which the algorithm is to be run. + source : int + The source node. + targets : list + The target nodes to be saved. + + Returns: + ------- + dict: + A dictionary containing the budget used by the algorithm. + """ if algorithm == heuristic_minbudget: result = algorithm(Graph=graph, source=source, targets=targets, spreading=False) return {"Budget": result} @@ -53,39 +79,60 @@ def runner_no_spreading(algorithm, graph, source, targets): result = algorithm(Graph=graph, source=source, targets=targets) return {"Budget": result} - -def runner_spreading( algorithm, graph, source, targets): - +def runner_spreading(algorithm, graph, source, targets): + """ + Run the specified algorithm with spreading. + + Parameters: + ---------- + algorithm : function + The algorithm to be executed. + graph : nx.Graph + The graph on which the algorithm is to be run. + source : int + The source node. + targets : list + The target nodes to be saved. + + Returns: + ------- + dict: + A dictionary containing the budget used or nodes saved by the algorithm. + """ if algorithm == heuristic_minbudget: - return {"Budget" : (algorithm(Graph=graph,source=source,targets=targets,spreading=True))} + return {"Budget": (algorithm(Graph=graph, source=source, targets=targets, spreading=True))} if algorithm == heuristic_maxsave: - return {"Nodes_Saved" : (str(len(algorithm(Graph=graph,budget = 2, source=source,targets=targets,spreading=True)[1])))} + return {"Nodes_Saved": (str(len(algorithm(Graph=graph, budget=2, source=source, targets=targets, spreading=True)[1])))} if algorithm == spreading_maxsave: - return {"Nodes_Saved" : (str(len(algorithm(Graph=graph,budget = 2, source=source,targets=targets)[1])))} + return {"Nodes_Saved": (str(len(algorithm(Graph=graph, budget=2, source=source, targets=targets)[1])))} else: - return {"Budget" : (algorithm(Graph=graph,source=source,targets=targets))} - + return {"Budget": (algorithm(Graph=graph, source=source, targets=targets))} def Compare_NonSpread(): + """ + Compare the performance of different algorithms without spreading. + + This function runs multiple experiments on randomly generated layered networks + and plots the results comparing the budget used by different algorithms. + """ ex1 = experiments_csv.Experiment("./networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/", "non_spreading.csv", backup_folder=None) - ex1.clear_previous_results() # to clear previous experminets.. + ex1.clear_previous_results() # to clear previous experiments input_ranges = { - "algorithm":[non_spreading_dirlaynet_minbudget,non_spreading_minbudget,heuristic_minbudget], - + "algorithm": [non_spreading_dirlaynet_minbudget, non_spreading_minbudget, heuristic_minbudget], } - def multiple_runs(runs=100): + + def multiple_runs(runs=30): for _ in range(runs): graph = generate_random_layered_network() source = 0 - # targets = [2, 4, 6, 7, 8, 9] nodes = list(graph.nodes) nodes.remove(0) - num_targets = random.randint(1, int(len(nodes)/4)+1) - targets = random.sample(nodes,num_targets) + num_targets = random.randint(1, int(len(nodes) / 4) + 1) + targets = random.sample(nodes, num_targets) for algorithm in input_ranges["algorithm"]: start_time = perf_counter() result = runner_no_spreading(algorithm, graph, source, targets) @@ -136,8 +183,14 @@ def extract_budget_numeric(budget): print("\n DataFrame-NonSpread: \n", ex1.dataFrame) def Compare_SpreadingMaxSave(): - ex1 = experiments_csv.Experiment("./networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/", "spreading_maxsave.csv", backup_folder=None) - ex1.clear_previous_results() # to clear previous experiments + """ + Compare the performance of different algorithms with spreading for maximizing saved nodes. + + This function runs multiple experiments on randomly generated directed graphs + and plots the results comparing the number of nodes saved by different algorithms. + """ + ex2 = experiments_csv.Experiment("./networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/", "spreading_maxsave.csv", backup_folder=None) + ex2.clear_previous_results() # to clear previous experiments input_ranges = { "algorithm": [spreading_maxsave, heuristic_maxsave] @@ -161,11 +214,11 @@ def multiple_runs(runs=1): result = runner_spreading(algorithm, graph, source, targets) runtime = perf_counter() - start_time - ex1.add({**{"algorithm": algorithm.__name__, "runtime": runtime, "graph_nodes": len(graph.nodes)}, **result}) + ex2.add({**{"algorithm": algorithm.__name__, "runtime": runtime, "graph_nodes": len(graph.nodes)}, **result}) return {"status": "completed"} # Set a time limit for the entire batch run - ex1.run_with_time_limit(multiple_runs, input_ranges={}, time_limit=0.9) + ex2.run_with_time_limit(multiple_runs, input_ranges={}, time_limit=0.9) ## DATA ISSUE WE HAD SO THIS IS A FIX ## # Load the results @@ -197,12 +250,17 @@ def multiple_runs(runs=1): save_to_file="./networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/spreading_maxsave.png" ) - print("\n DataFrame-NonSpread: \n", ex1.dataFrame) - + print("\n DataFrame-NonSpread: \n", ex2.dataFrame) def Compare_SpreadingMinBudget(): - ex1 = experiments_csv.Experiment("./networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/", "spreading_minbudget.csv", backup_folder=None) - ex1.clear_previous_results() # to clear previous experiments + """ + Compare the performance of different algorithms with spreading for minimizing the budget. + + This function runs multiple experiments on randomly generated directed graphs + and plots the results comparing the budget used by different algorithms. + """ + ex3 = experiments_csv.Experiment("./networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/", "spreading_minbudget.csv", backup_folder=None) + ex3.clear_previous_results() # to clear previous experiments input_ranges = { "algorithm": [spreading_minbudget, heuristic_minbudget] @@ -225,12 +283,11 @@ def multiple_runs(runs=1): start_time = perf_counter() result = runner_spreading(algorithm, graph, source, targets) runtime = perf_counter() - start_time - ex1.add({**{"algorithm": algorithm.__name__, "runtime": runtime, "graph_nodes": len(graph.nodes)}, **result}) + ex3.add({**{"algorithm": algorithm.__name__, "runtime": runtime, "graph_nodes": len(graph.nodes)}, **result}) return {"status": "completed"} - # Set a time limit for the entire batch run - ex1.run_with_time_limit(multiple_runs, input_ranges={}, time_limit=0.9) + ex3.run_with_time_limit(multiple_runs, input_ranges={}, time_limit=0.9) # Preprocess the DataFrame to extract numeric budget values results_csv_file = "./networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/spreading_minbudget.csv" @@ -269,10 +326,13 @@ def extract_budget_numeric(budget): save_to_file="./networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/spreading_minbudget.png" ) - print("\n DataFrame-NonSpread: \n", ex1.dataFrame) + print("\n DataFrame-NonSpread: \n", ex3.dataFrame) if __name__ == "__main__": + """To run this - please run one at a time; mark the others and then run""" + setup_global_logger(level=logging.DEBUG) - # Compare_NonSpread() + + Compare_NonSpread() # Compare_SpreadingMinBudget() - Compare_SpreadingMaxSave() + # Compare_SpreadingMaxSave() \ No newline at end of file diff --git a/networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/non_spreading.csv b/networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/non_spreading.csv index a65363b..7c9b5f7 100644 --- a/networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/non_spreading.csv +++ b/networkz/algorithms/approximation/tests/test_firefighter_problem/comparisons/non_spreading.csv @@ -1,302 +1,92 @@ algorithm,runtime,graph_nodes,Budget -non_spreading_dirlaynet_minbudget,0.1577231001574546,82,"(6, {0: [5, 2, 4, 3, 1, 10]})" -non_spreading_minbudget,0.008546600118279457,82,"(6, [])" -heuristic_minbudget,0.1938444001134485,82,"(4, [(4, 1), (1, 1), (5, 1), (3, 1), (19, 2), (18, 2), (16, 2), (9, 2), (33, 3), (37, 3)])" -non_spreading_dirlaynet_minbudget,0.4255663000512868,127,"(2, {0: [9, 126], 3: [118], 4: [97]})" -non_spreading_minbudget,0.018676700070500374,127,"(6, [])" -heuristic_minbudget,0.8027119999751449,127,"(1, [(9, 1), (12, 2), (23, 3), (76, 4), (88, 5), (97, 6), (118, 7)])" -non_spreading_dirlaynet_minbudget,0.3158855000510812,106,"(11, {0: [22, 17, 2, 13, 21, 1, 30, 29, 28, 50, 52], 1: [30, 29, 28, 50, 52, 60], 2: [50, 52]})" -non_spreading_minbudget,0.02649120008572936,106,"(19, [])" -heuristic_minbudget,0.704905399819836,106,"(6, [(2, 1), (17, 1), (1, 1), (21, 1), (13, 1), (22, 1), (45, 2), (27, 2), (28, 2), (29, 2), (30, 2), (43, 2), (48, 3), (50, 3), (51, 3), (49, 3), (52, 3), (54, 3), (60, 4)])" -non_spreading_dirlaynet_minbudget,0.46226679999381304,149,"(7, {0: [14, 11, 3, 29, 43, 39, 42], 1: [29, 43, 39, 42], 2: [43, 39, 42]})" -non_spreading_minbudget,0.02368660015054047,149,"(14, [])" -heuristic_minbudget,1.139801099896431,149,"(3, [(14, 1), (3, 1), (11, 1), (29, 2), (20, 2), (22, 2), (38, 3), (42, 3), (43, 3), (73, 4), (46, 4), (66, 4), (84, 5), (95, 5), (77, 5), (112, 6), (109, 6), (111, 6), (113, 7), (118, 7), (115, 7)])" -non_spreading_dirlaynet_minbudget,0.341992300003767,84,"(8, {0: [5, 2, 7, 6, 4, 3, 1, 12]})" -non_spreading_minbudget,0.03232260001823306,84,"(8, [])" -heuristic_minbudget,0.6307671000249684,84,"(5, [(5, 1), (2, 1), (3, 1), (1, 1), (6, 1), (14, 2), (12, 2), (16, 2), (11, 2), (13, 2), (27, 3), (23, 3), (18, 3), (19, 3)])" -non_spreading_dirlaynet_minbudget,1.0424751001410186,156,"(9, {0: [10, 21, 19, 4, 38, 35, 53, 60, 56], 1: [38, 35, 53, 60, 56], 2: [60, 56], 3: [60, 56]})" -non_spreading_minbudget,0.06969960010610521,156,"(15, [])" -heuristic_minbudget,2.3020446998998523,156,"(4, [(10, 1), (21, 1), (19, 1), (4, 1), (37, 2), (35, 2), (38, 2), (24, 2), (48, 3), (53, 3), (55, 3), (50, 3), (57, 4), (61, 4), (56, 4), (60, 4), (68, 5), (74, 5), (77, 5), (82, 5), (87, 6), (100, 6), (90, 6), (95, 6), (108, 7), (115, 7), (127, 7), (103, 7)])" -non_spreading_dirlaynet_minbudget,0.40291900001466274,111,"(2, {0: [12, 39]})" -non_spreading_minbudget,0.02193789999000728,111,"(2, [])" -heuristic_minbudget,0.9895753001328558,111,"(1, [(12, 1)])" -non_spreading_dirlaynet_minbudget,0.5849756998941302,118,"(1, {4: [114]})" -non_spreading_minbudget,0.03333929996006191,118,"(2, [])" -heuristic_minbudget,2.5519870999269187,118,"(1, [(8, 1), (40, 2), (52, 3), (70, 4), (75, 5)])" -non_spreading_dirlaynet_minbudget,0.5267107000108808,125,"(5, {0: [10, 15, 50, 93, 110], 1: [93], 2: [110], 3: [73], 4: [73]})" -non_spreading_minbudget,0.04090219992212951,125,"(11, [])" -heuristic_minbudget,1.3968956999015063,125,"(3, [(10, 1), (15, 1), (9, 1), (29, 2), (31, 2), (20, 2), (35, 3), (39, 3), (40, 3), (50, 4), (49, 4), (53, 4), (65, 5), (73, 5), (77, 5), (86, 6), (83, 6), (85, 6), (97, 7), (90, 7), (93, 7)])" -non_spreading_dirlaynet_minbudget,0.47931419988162816,126,"(13, {0: [7, 15, 20, 18, 1, 31, 26, 38, 30, 29, 28, 35, 37], 1: [31, 26, 38, 30, 29, 28, 35, 37]})" -non_spreading_minbudget,0.031449199886992574,126,"(20, [])" -heuristic_minbudget,3.0258405001368374,126,"(7, [(1, 1), (7, 1), (18, 1), (15, 1), (20, 1), (11, 1), (14, 1), (38, 2), (27, 2), (25, 2), (28, 2), (30, 2), (36, 2), (33, 2), (49, 3), (54, 3), (51, 3), (47, 3), (45, 3), (57, 3), (41, 3), (74, 4), (81, 4), (71, 4), (78, 4), (79, 4), (65, 4), (76, 4), (89, 5), (93, 5), (86, 5), (87, 5), (90, 5), (91, 5), (94, 5), (100, 6), (108, 6), (101, 6), (103, 6), (107, 6), (95, 6), (106, 6), (109, 7), (118, 7), (112, 7), (114, 7), (115, 7), (110, 7), (113, 7)])" -non_spreading_dirlaynet_minbudget,0.4622693001292646,134,"(6, {0: [5, 2, 6, 4, 3, 1]})" -non_spreading_minbudget,0.019320799969136715,134,"(6, [])" -heuristic_minbudget,0.33319449983537197,134,"(4, [(3, 1), (5, 1), (1, 1), (2, 1), (14, 2), (16, 2), (13, 2), (17, 2), (22, 3), (23, 3)])" -non_spreading_dirlaynet_minbudget,0.4219563000369817,126,"(5, {0: [10, 20, 36, 98, 116], 1: [43], 2: [43], 5: [98], 6: [98]})" -non_spreading_minbudget,0.09597310004755855,126,"(9, [])" -heuristic_minbudget,1.5155795000027865,126,"(2, [(10, 1), (20, 1), (36, 2), (30, 2), (47, 3), (43, 3), (51, 4), (53, 4), (54, 5), (58, 5), (71, 6), (60, 6), (76, 7), (89, 7), (101, 8), (102, 8)])" -non_spreading_dirlaynet_minbudget,1.1658165999688208,190,"(21, {0: [14, 16, 7, 9, 6, 15, 17, 10, 5, 2, 11, 12, 13, 8, 21, 20, 19, 4, 18, 3, 1]})" -non_spreading_minbudget,0.07264579995535314,190,"(21, [])" -heuristic_minbudget,2.47765320003964,190,"(6, [(12, 1), (20, 1), (11, 1), (10, 1), (16, 1), (19, 1), (27, 2), (43, 2), (42, 2), (47, 2), (51, 2), (23, 2), (57, 3), (64, 3), (54, 3), (52, 3), (55, 3), (66, 3), (70, 4), (76, 4), (78, 4), (75, 4), (69, 4), (77, 4), (102, 5), (88, 5), (83, 5), (98, 5), (85, 5), (101, 5), (105, 6), (104, 6), (111, 6), (112, 6), (108, 6), (107, 6), (130, 7), (139, 7), (126, 7), (131, 7), (137, 7), (118, 7), (156, 8), (144, 8), (147, 8), (153, 8), (142, 8), (158, 8), (180, 9), (161, 9), (159, 9), (162, 9), (165, 9), (172, 9)])" -non_spreading_dirlaynet_minbudget,0.8167432001791894,153,"(5, {0: [5, 2, 4, 3, 1]})" -non_spreading_minbudget,0.054783100029453635,153,"(5, [])" -heuristic_minbudget,0.9581810999661684,153,"(5, [(2, 1), (3, 1), (4, 1), (1, 1), (5, 1)])" -non_spreading_dirlaynet_minbudget,0.7115546998102218,124,"(11, {0: [16, 9, 2, 11, 21, 3, 24, 50, 81, 94, 110], 1: [81, 110], 2: [110], 3: [110]})" -non_spreading_minbudget,0.07342069991864264,124,"(17, [])" -heuristic_minbudget,2.375642899889499,124,"(7, [(21, 1), (24, 1), (2, 1), (16, 1), (3, 1), (9, 1), (11, 1), (50, 2), (29, 2), (41, 2), (42, 2), (44, 2), (52, 2), (28, 2), (54, 3), (60, 3), (62, 3), (59, 3), (56, 3), (53, 3), (61, 3), (78, 4), (74, 4), (68, 4), (67, 4), (69, 4), (76, 4), (64, 4)])" -non_spreading_dirlaynet_minbudget,1.2358756000176072,221,"(23, {0: [14, 16, 22, 7, 9, 6, 15, 17, 10, 5, 25, 2, 12, 11, 13, 8, 20, 19, 4, 18, 3, 1, 31], 1: [31]})" -non_spreading_minbudget,0.16567729995585978,221,"(24, [])" -heuristic_minbudget,3.716271399985999,221,"(7, [(14, 1), (7, 1), (9, 1), (12, 1), (11, 1), (13, 1), (4, 1), (28, 2), (32, 2), (33, 2), (45, 2), (40, 2), (39, 2), (38, 2), (48, 3), (71, 3), (65, 3), (64, 3), (72, 3), (69, 3), (46, 3), (79, 4), (77, 4), (92, 4), (98, 4), (87, 4), (95, 4), (91, 4), (102, 5), (106, 5), (109, 5), (116, 5), (114, 5), (117, 5), (104, 5), (133, 6), (143, 6), (121, 6), (131, 6), (130, 6), (136, 6), (123, 6), (147, 7), (150, 7), (155, 7), (164, 7), (172, 7), (173, 7), (166, 7), (189, 8), (198, 8), (175, 8), (180, 8), (177, 8), (183, 8), (187, 8)])" -non_spreading_dirlaynet_minbudget,0.5118867999408394,157,"(1, {4: [53]})" -non_spreading_minbudget,0.03263450018130243,157,"(1, [])" -heuristic_minbudget,0.3339197998866439,157,"(1, [(4, 1), (13, 2), (17, 3), (37, 4)])" -non_spreading_dirlaynet_minbudget,0.5583947999402881,108,"(5, {0: [17, 15, 57, 87, 102], 1: [57, 87], 2: [87], 3: [87]})" -non_spreading_minbudget,0.03970779990777373,108,"(10, [])" -heuristic_minbudget,2.3164709999691695,108,"(4, [(15, 1), (17, 1), (19, 1), (7, 1), (36, 2), (28, 2), (32, 2), (37, 2), (38, 3), (42, 3), (47, 3), (40, 3), (54, 4), (57, 4), (61, 4), (53, 4), (82, 5), (87, 5), (79, 5), (99, 5)])" -non_spreading_dirlaynet_minbudget,0.7613872999791056,178,"(8, {0: [26, 7, 39, 36, 31, 34, 33, 38], 1: [39, 36, 31, 34, 33, 38]})" -non_spreading_minbudget,0.03670880012214184,178,"(13, [])" -heuristic_minbudget,3.551267699804157,178,"(8, [(7, 1), (26, 1), (10, 1), (11, 1), (15, 1), (17, 1), (18, 1), (19, 1), (39, 2), (30, 2), (33, 2), (38, 2), (36, 2), (32, 2), (35, 2), (31, 2), (42, 3), (41, 3), (49, 3), (50, 3), (54, 3), (43, 3), (46, 3)])" -non_spreading_dirlaynet_minbudget,0.8977318999823183,151,"(15, {0: [14, 16, 9, 6, 15, 10, 5, 2, 11, 12, 13, 4, 3, 1, 30], 1: [30]})" -non_spreading_minbudget,0.040731099899858236,151,"(16, [])" -heuristic_minbudget,2.377181600080803,151,"(7, [(2, 1), (10, 1), (9, 1), (13, 1), (4, 1), (1, 1), (5, 1), (21, 2), (39, 2), (25, 2), (26, 2), (29, 2), (19, 2), (44, 2), (54, 3), (58, 3), (63, 3), (56, 3), (48, 3), (62, 3), (64, 3), (80, 4), (68, 4), (69, 4), (74, 4), (81, 4), (77, 4), (83, 4), (92, 5), (93, 5), (84, 5), (94, 5), (85, 5), (88, 5), (86, 5), (103, 6), (101, 6), (96, 6), (100, 6), (102, 6), (97, 6), (104, 6)])" -non_spreading_dirlaynet_minbudget,0.19986970000900328,87,"(5, {0: [14, 24, 31, 72, 80], 1: [72], 2: [43]})" -non_spreading_minbudget,0.017228800104930997,87,"(7, [])" -heuristic_minbudget,1.182675999822095,87,"(2, [(14, 1), (24, 1), (31, 2), (40, 2), (43, 3), (50, 3), (72, 4), (68, 4), (80, 5), (77, 5)])" -non_spreading_dirlaynet_minbudget,0.5464945000130683,99,"(7, {0: [10, 1, 4, 27, 70, 72, 86], 1: [47], 2: [47], 3: [72]})" -non_spreading_minbudget,0.05347799998708069,99,"(10, [])" -heuristic_minbudget,1.7111621999647468,99,"(3, [(1, 1), (10, 1), (4, 1), (27, 2), (26, 2), (30, 2), (47, 3), (54, 3), (39, 3), (70, 4), (66, 4), (69, 4), (82, 5), (72, 5), (77, 5)])" -non_spreading_dirlaynet_minbudget,0.5821063001640141,118,"(10, {0: [23, 16, 17, 10, 31, 26, 30, 29, 28, 35], 1: [31, 26, 30, 29, 28, 35]})" -non_spreading_minbudget,0.09854290005750954,118,"(15, [])" -heuristic_minbudget,2.6204377999529243,118,"(8, [(17, 1), (23, 1), (10, 1), (16, 1), (3, 1), (20, 1), (11, 1), (22, 1), (30, 2), (31, 2), (26, 2), (34, 2), (32, 2), (25, 2), (28, 2), (35, 2), (58, 3), (48, 3), (42, 3), (60, 3), (52, 3), (64, 3), (36, 3), (63, 3), (83, 4), (69, 4), (74, 4), (75, 4), (70, 4), (87, 4), (78, 4), (66, 4), (97, 5), (95, 5), (96, 5), (99, 5), (91, 5), (92, 5), (93, 5), (98, 5)])" -non_spreading_dirlaynet_minbudget,0.5755591001361609,100,"(9, {0: [5, 2, 8, 7, 9, 6, 3, 1, 36]})" -non_spreading_minbudget,0.02621010015718639,100,"(9, [])" -heuristic_minbudget,0.8365450000856072,100,"(5, [(2, 1), (5, 1), (8, 1), (9, 1), (6, 1), (30, 2), (13, 2), (16, 2), (28, 2), (22, 2), (40, 3), (43, 3), (44, 3), (46, 3), (38, 3), (50, 4), (53, 4), (52, 4), (54, 4), (48, 4), (70, 5), (72, 5), (63, 5), (71, 5), (60, 5), (81, 6), (84, 6), (79, 6), (76, 6), (78, 6)])" -non_spreading_dirlaynet_minbudget,0.6958248000591993,183,"(15, {0: [14, 10, 5, 2, 11, 12, 13, 8, 7, 9, 6, 4, 3, 1, 15]})" -non_spreading_minbudget,0.11737009999342263,183,"(15, [])" -heuristic_minbudget,2.7459627999924123,183,"(6, [(14, 1), (2, 1), (11, 1), (5, 1), (6, 1), (10, 1), (32, 2), (33, 2), (16, 2), (38, 2), (40, 2), (17, 2), (59, 3), (57, 3), (52, 3), (50, 3), (46, 3), (56, 3), (76, 4), (77, 4), (79, 4), (69, 4), (71, 4), (74, 4), (80, 5), (92, 5), (99, 5), (90, 5), (81, 5), (94, 5), (104, 6), (108, 6), (109, 6), (117, 6), (119, 6), (122, 6), (136, 7), (144, 7), (133, 7), (137, 7), (141, 7), (134, 7), (148, 8), (154, 8), (152, 8), (151, 8), (155, 8), (147, 8)])" -non_spreading_dirlaynet_minbudget,0.7773849999066442,107,"(9, {0: [5, 2, 8, 7, 9, 6, 4, 3, 1]})" -non_spreading_minbudget,0.03448770008981228,107,"(9, [])" -heuristic_minbudget,1.2942227001767606,107,"(7, [(1, 1), (6, 1), (3, 1), (9, 1), (4, 1), (5, 1), (2, 1), (23, 2), (28, 2), (22, 2), (15, 2), (12, 2), (37, 2), (34, 2), (45, 3), (57, 3), (43, 3), (39, 3), (41, 3), (62, 3), (47, 3), (67, 4), (76, 4), (78, 4), (87, 4), (83, 4), (81, 4), (63, 4)])" -non_spreading_dirlaynet_minbudget,0.5646400998812169,97,"(9, {0: [5, 2, 8, 7, 9, 6, 4, 3, 1]})" -non_spreading_minbudget,0.026038399897515774,97,"(9, [])" -heuristic_minbudget,0.6566372001543641,97,"(5, [(8, 1), (1, 1), (2, 1), (4, 1), (6, 1), (12, 2), (17, 2), (29, 2), (27, 2), (14, 2), (35, 3), (33, 3), (32, 3), (36, 3), (31, 3), (62, 4), (43, 4), (57, 4), (47, 4), (67, 4)])" -non_spreading_dirlaynet_minbudget,0.3988175999838859,108,"(2, {0: [7, 59], 4: [59]})" -non_spreading_minbudget,0.019911699928343296,108,"(3, [])" -heuristic_minbudget,0.7559130000881851,108,"(1, [(7, 1), (23, 2), (37, 3), (44, 4)])" -non_spreading_dirlaynet_minbudget,0.5628936998546124,116,"(9, {0: [16, 12, 18, 26, 71, 50, 69, 79, 93], 1: [26, 71, 50, 69, 79, 93], 2: [71, 50, 69, 93], 3: [93]})" -non_spreading_minbudget,0.03142390004359186,116,"(18, [])" -heuristic_minbudget,3.687096199952066,116,"(7, [(18, 1), (16, 1), (12, 1), (15, 1), (21, 1), (7, 1), (4, 1), (26, 2), (45, 2), (33, 2), (39, 2), (36, 2), (28, 2), (43, 2), (50, 3), (51, 3), (57, 3), (67, 3), (69, 3), (60, 3), (71, 3), (75, 4), (79, 4), (85, 4), (88, 4), (76, 4), (82, 4), (78, 4)])" -non_spreading_dirlaynet_minbudget,0.3001407999545336,72,"(15, {0: [14, 10, 5, 2, 11, 12, 13, 8, 7, 9, 6, 4, 3, 1, 15]})" -non_spreading_minbudget,0.01261289999820292,72,"(15, [])" -heuristic_minbudget,1.408848199993372,72,"(7, [(4, 1), (13, 1), (5, 1), (9, 1), (12, 1), (6, 1), (14, 1), (27, 2), (18, 2), (29, 2), (20, 2), (28, 2), (25, 2), (30, 2), (37, 3), (38, 3), (48, 3), (32, 3), (47, 3), (52, 3), (41, 3), (56, 4), (57, 4), (58, 4), (60, 4), (59, 4)])" -non_spreading_dirlaynet_minbudget,0.8255596000235528,128,"(1, {5: [83]})" -non_spreading_minbudget,0.0393540000077337,128,"(1, [])" -heuristic_minbudget,0.587727400008589,128,"(1, [(4, 1), (12, 2), (34, 3), (47, 4), (60, 5)])" -non_spreading_dirlaynet_minbudget,0.5193115998990834,112,"(14, {0: [7, 28, 27, 25, 19, 18, 24, 42, 38, 49, 55, 52, 74, 77], 1: [42, 38, 49, 55, 52, 74, 77, 81], 2: [55, 52, 74, 77], 3: [74]})" -non_spreading_minbudget,0.05353619996458292,112,"(24, [])" -heuristic_minbudget,2.26897030021064,112,"(7, [(27, 1), (7, 1), (19, 1), (24, 1), (28, 1), (25, 1), (18, 1), (38, 2), (35, 2), (47, 2), (42, 2), (39, 2), (49, 2), (32, 2), (55, 3), (72, 3), (61, 3), (52, 3), (68, 3), (53, 3), (56, 3), (78, 4), (76, 4), (74, 4), (75, 4), (77, 4), (73, 4)])" -non_spreading_dirlaynet_minbudget,0.591107500018552,114,"(6, {0: [3, 11, 6, 28, 35, 86]})" -non_spreading_minbudget,0.035418600076809525,114,"(6, [])" -heuristic_minbudget,2.0268653999082744,114,"(4, [(3, 1), (28, 1), (11, 1), (6, 1), (35, 2), (33, 2), (36, 2), (44, 2), (59, 3), (56, 3), (63, 3), (66, 3)])" -non_spreading_dirlaynet_minbudget,0.3615675000473857,90,"(1, {0: [19]})" -non_spreading_minbudget,0.022981500020250678,90,"(1, [])" -heuristic_minbudget,0.6832441999576986,90,"(1, [(19, 1)])" -non_spreading_dirlaynet_minbudget,0.5305326001252979,148,"(11, {0: [16, 7, 12, 19, 3, 23, 31, 26, 22, 30, 29], 1: [23, 31, 26, 22, 30, 29]})" -non_spreading_minbudget,0.02663370012305677,148,"(17, [])" -heuristic_minbudget,0.8235696998890489,148,"(5, [(16, 1), (3, 1), (7, 1), (12, 1), (19, 1), (26, 2), (20, 2), (23, 2), (21, 2), (31, 2), (38, 3), (44, 3), (47, 3), (48, 3), (42, 3), (58, 4), (63, 4), (62, 4), (56, 4), (61, 4), (81, 5), (74, 5), (75, 5), (76, 5), (66, 5), (96, 6), (97, 6), (94, 6), (93, 6), (98, 6), (108, 7), (104, 7), (109, 7), (105, 7), (114, 7)])" -non_spreading_dirlaynet_minbudget,0.48062629997730255,127,"(7, {0: [3, 23, 41, 32, 52, 79, 125], 1: [23, 41, 32, 103, 125], 3: [52]})" -non_spreading_minbudget,0.039807400200515985,127,"(12, [])" -heuristic_minbudget,1.6504144999198616,127,"(5, [(3, 1), (4, 1), (16, 1), (1, 1), (12, 1), (32, 2), (19, 2), (41, 2), (23, 2), (40, 2), (47, 3), (44, 3), (46, 3), (42, 3), (45, 3), (70, 4), (48, 4), (67, 4), (55, 4), (75, 4), (79, 5), (88, 5), (84, 5), (87, 5), (81, 5), (103, 6), (101, 6), (95, 6), (102, 6), (105, 6)])" -non_spreading_dirlaynet_minbudget,0.2845070001203567,83,"(1, {3: [50]})" -non_spreading_minbudget,0.018276299815624952,83,"(1, [])" -heuristic_minbudget,0.29154929984360933,83,"(1, [(2, 1), (11, 2), (27, 3)])" -non_spreading_dirlaynet_minbudget,0.41847799997776747,112,"(13, {0: [9, 28, 11, 21, 20, 24, 49, 35, 37, 71, 64, 83, 106], 1: [49, 35, 37, 71, 64, 83, 106], 2: [71, 64]})" -non_spreading_minbudget,0.08840370015241206,112,"(23, [])" -heuristic_minbudget,2.2752125998958945,112,"(6, [(9, 1), (11, 1), (20, 1), (24, 1), (21, 1), (28, 1), (46, 2), (37, 2), (49, 2), (35, 2), (33, 2), (43, 2), (71, 3), (67, 3), (76, 3), (60, 3), (64, 3), (80, 3), (84, 4), (83, 4), (88, 4), (89, 4), (86, 4), (87, 4)])" -non_spreading_dirlaynet_minbudget,0.41033149999566376,120,"(3, {0: [7, 43, 99], 1: [43], 2: [43, 99]})" -non_spreading_minbudget,0.033717900048941374,120,"(6, [])" -heuristic_minbudget,1.4959726999513805,120,"(2, [(7, 1), (5, 1), (30, 2), (29, 2), (44, 3), (46, 3), (62, 4), (68, 4), (77, 5), (88, 5)])" -non_spreading_dirlaynet_minbudget,0.20812149997800589,85,"(13, {0: [23, 16, 29, 17, 5, 25, 11, 21, 18, 3, 35, 34, 37], 1: [35, 34]})" -non_spreading_minbudget,0.021076400065794587,85,"(15, [])" -heuristic_minbudget,1.4817065000534058,85,"(10, [(16, 1), (21, 1), (3, 1), (5, 1), (17, 1), (18, 1), (29, 1), (23, 1), (25, 1), (11, 1), (34, 2), (32, 2), (33, 2), (35, 2), (31, 2)])" -non_spreading_dirlaynet_minbudget,0.734795100055635,131,"(21, {0: [14, 16, 7, 9, 6, 10, 5, 2, 11, 12, 13, 8, 21, 20, 19, 4, 18, 3, 1, 31, 29], 1: [31, 29]})" -non_spreading_minbudget,0.03819860005751252,131,"(22, [])" -heuristic_minbudget,3.435756399994716,131,"(7, [(7, 1), (10, 1), (20, 1), (3, 1), (4, 1), (11, 1), (16, 1), (29, 2), (43, 2), (25, 2), (32, 2), (24, 2), (38, 2), (28, 2), (57, 3), (46, 3), (50, 3), (52, 3), (62, 3), (60, 3), (64, 3), (71, 4), (89, 4), (85, 4), (74, 4), (67, 4), (81, 4), (86, 4), (102, 5), (103, 5), (101, 5), (100, 5), (99, 5), (104, 5), (96, 5), (110, 6), (109, 6), (113, 6), (111, 6), (112, 6)])" -non_spreading_dirlaynet_minbudget,0.7636627000756562,165,"(7, {0: [6, 3, 39, 26, 34, 43, 46], 1: [39, 26, 34, 43, 46, 51], 2: [43, 46]})" -non_spreading_minbudget,0.05942280008457601,165,"(13, [])" -heuristic_minbudget,0.9087215000763535,165,"(5, [(3, 1), (6, 1), (5, 1), (1, 1), (4, 1), (21, 2), (32, 2), (34, 2), (26, 2), (39, 2), (48, 3), (45, 3), (43, 3), (47, 3), (46, 3), (51, 4)])" -non_spreading_dirlaynet_minbudget,1.1901148001197726,230,"(26, {0: [14, 16, 23, 26, 22, 7, 9, 6, 15, 5, 27, 25, 2, 12, 11, 13, 8, 21, 1, 20, 19, 4, 18, 3, 24, 36], 1: [36]})" -non_spreading_minbudget,0.09228199999779463,230,"(27, [])" -heuristic_minbudget,6.45793869998306,230,"(8, [(1, 1), (6, 1), (12, 1), (7, 1), (13, 1), (14, 1), (20, 1), (9, 1), (55, 2), (33, 2), (34, 2), (39, 2), (38, 2), (50, 2), (40, 2), (46, 2), (69, 3), (75, 3), (79, 3), (82, 3), (65, 3), (60, 3), (72, 3), (81, 3), (98, 4), (86, 4), (85, 4), (97, 4), (100, 4), (83, 4), (88, 4), (84, 4), (103, 5), (126, 5), (131, 5), (120, 5), (127, 5), (106, 5), (128, 5), (129, 5), (146, 6), (149, 6), (157, 6), (151, 6), (154, 6), (153, 6), (144, 6), (134, 6), (166, 7), (175, 7), (160, 7), (178, 7), (167, 7), (180, 7), (164, 7), (181, 7), (191, 8), (195, 8), (188, 8), (187, 8), (196, 8), (189, 8), (190, 8), (186, 8), (201, 9), (212, 9), (209, 9), (208, 9), (205, 9), (206, 9), (210, 9), (203, 9)])" -non_spreading_dirlaynet_minbudget,0.8137726001441479,175,"(22, {0: [14, 16, 22, 7, 9, 6, 15, 17, 10, 5, 2, 11, 12, 13, 8, 21, 20, 19, 4, 18, 3, 1]})" -non_spreading_minbudget,0.04312669998034835,175,"(22, [])" -heuristic_minbudget,2.5335946001578122,175,"(8, [(18, 1), (1, 1), (12, 1), (9, 1), (4, 1), (14, 1), (17, 1), (21, 1), (34, 2), (36, 2), (35, 2), (42, 2), (48, 2), (44, 2), (31, 2), (45, 2), (60, 3), (58, 3), (61, 3), (59, 3), (56, 3), (57, 3), (54, 3), (62, 3), (74, 4), (70, 4), (72, 4), (73, 4), (64, 4)])" -non_spreading_dirlaynet_minbudget,0.6366059998981655,134,"(6, {0: [10, 11, 17, 61, 67, 117], 1: [17, 44, 61], 2: [44]})" -non_spreading_minbudget,0.03964060009457171,134,"(10, [])" -heuristic_minbudget,1.582167699933052,134,"(2, [(10, 1), (11, 1), (26, 2), (17, 2), (44, 3), (40, 3), (61, 4), (54, 4), (67, 5), (70, 5), (84, 6), (91, 6), (110, 7), (115, 7)])" -non_spreading_dirlaynet_minbudget,0.5849375000689179,142,"(7, {0: [5, 2, 7, 6, 3, 1, 14]})" -non_spreading_minbudget,0.029051399789750576,142,"(7, [])" -heuristic_minbudget,0.5750889000482857,142,"(5, [(5, 1), (3, 1), (2, 1), (6, 1), (7, 1), (29, 2), (8, 2), (22, 2), (31, 2), (32, 2), (44, 3), (40, 3), (45, 3), (35, 3), (41, 3), (51, 4), (49, 4), (58, 4), (60, 4), (48, 4), (74, 5), (78, 5), (65, 5), (79, 5), (81, 5), (92, 6), (88, 6), (95, 6), (90, 6), (85, 6), (103, 7), (101, 7), (107, 7), (102, 7), (104, 7)])" -non_spreading_dirlaynet_minbudget,0.7268608000595123,160,"(7, {0: [2, 8, 9, 14, 16, 17, 42], 1: [14, 16, 17]})" -non_spreading_minbudget,0.04260340007022023,160,"(9, [])" -heuristic_minbudget,0.8875651000998914,160,"(4, [(8, 1), (9, 1), (2, 1), (6, 1), (14, 2), (17, 2), (13, 2), (16, 2), (23, 3), (25, 3), (29, 3), (32, 3), (42, 4)])" -non_spreading_dirlaynet_minbudget,0.7861946998164058,160,"(6, {0: [5, 2, 6, 4, 3, 1]})" -non_spreading_minbudget,0.10845379997044802,160,"(6, [])" -heuristic_minbudget,0.9687779999803752,160,"(5, [(4, 1), (2, 1), (6, 1), (3, 1), (1, 1), (14, 2), (22, 2)])" -non_spreading_dirlaynet_minbudget,1.1909837001003325,177,"(6, {0: [12, 7, 33, 44, 89, 98], 1: [33, 44, 89], 3: [89]})" -non_spreading_minbudget,0.061934900004416704,177,"(9, [])" -heuristic_minbudget,1.7665303000248969,177,"(3, [(12, 1), (7, 1), (13, 1), (33, 2), (44, 2), (25, 2), (52, 3), (62, 3), (58, 3), (66, 4), (89, 4), (78, 4)])" -non_spreading_dirlaynet_minbudget,0.5384468999691308,118,"(2, {1: [54, 78], 2: [54, 78]})" -non_spreading_minbudget,0.027300900081172585,118,"(4, [])" -heuristic_minbudget,1.1404292001388967,118,"(2, [(6, 1), (1, 1), (23, 2), (31, 2), (54, 3), (48, 3), (61, 4), (60, 4)])" -non_spreading_dirlaynet_minbudget,0.6008705000858754,94,"(7, {0: [13, 3, 22, 45, 52, 69, 87], 1: [22, 45, 52, 69, 87], 2: [87], 3: [69]})" -non_spreading_minbudget,0.024603100027889013,94,"(14, [])" -heuristic_minbudget,1.197276399936527,94,"(4, [(3, 1), (13, 1), (6, 1), (15, 1), (19, 2), (22, 2), (20, 2), (45, 2), (49, 3), (52, 3), (53, 3), (47, 3), (69, 4), (60, 4), (63, 4), (68, 4)])" -non_spreading_dirlaynet_minbudget,0.4536055000498891,110,"(7, {0: [17, 16, 19, 15, 23, 22, 21], 1: [23, 22, 21]})" -non_spreading_minbudget,0.09495320008136332,110,"(9, [])" -heuristic_minbudget,0.7355480000842363,110,"(4, [(19, 1), (15, 1), (17, 1), (16, 1), (24, 2), (22, 2), (23, 2), (20, 2), (28, 3), (26, 3), (33, 3), (30, 3), (46, 4), (57, 4), (68, 4)])" -non_spreading_dirlaynet_minbudget,0.9377388001885265,162,"(2, {0: [38, 112], 1: [14, 112]})" -non_spreading_minbudget,0.11844550003297627,162,"(5, [])" -heuristic_minbudget,0.5747994999401271,162,"(2, [(7, 1), (5, 1), (14, 2), (15, 2), (52, 3), (38, 3), (55, 4), (58, 4), (67, 5), (69, 5)])" -non_spreading_dirlaynet_minbudget,0.9667919999919832,127,"(12, {0: [10, 5, 2, 11, 12, 8, 7, 9, 6, 4, 3, 1]})" -non_spreading_minbudget,0.03575319983065128,127,"(12, [])" -heuristic_minbudget,1.1490799998864532,127,"(5, [(2, 1), (3, 1), (6, 1), (5, 1), (8, 1), (16, 2), (26, 2), (27, 2), (14, 2), (25, 2), (39, 3), (47, 3), (46, 3), (28, 3), (33, 3), (56, 4), (55, 4), (62, 4), (63, 4), (57, 4), (79, 5), (69, 5), (80, 5), (77, 5), (70, 5), (89, 6), (91, 6), (87, 6), (100, 6), (95, 6), (113, 7), (108, 7), (119, 7), (106, 7), (107, 7)])" -non_spreading_dirlaynet_minbudget,0.6362785999663174,136,"(4, {0: [23, 2, 100, 126], 6: [126]})" -non_spreading_minbudget,0.03179979999549687,136,"(6, [])" -heuristic_minbudget,1.3357363000977784,136,"(2, [(23, 1), (2, 1), (32, 2), (29, 2), (36, 3), (34, 3), (47, 4), (51, 4), (70, 5), (59, 5), (100, 6), (83, 6), (111, 7), (117, 7)])" -non_spreading_dirlaynet_minbudget,0.49284680001437664,132,"(10, {0: [10, 5, 2, 8, 7, 9, 6, 4, 1, 14]})" -non_spreading_minbudget,0.032877299934625626,132,"(10, [])" -heuristic_minbudget,1.5516102998517454,132,"(4, [(2, 1), (9, 1), (8, 1), (7, 1), (21, 2), (16, 2), (24, 2), (20, 2), (29, 3), (38, 3), (41, 3), (34, 3), (53, 4), (44, 4), (47, 4), (55, 4), (76, 5), (74, 5), (63, 5), (65, 5), (86, 6), (91, 6), (84, 6), (89, 6), (95, 7), (98, 7), (109, 7), (102, 7)])" -non_spreading_dirlaynet_minbudget,0.6802503999788314,122,"(1, {4: [62]})" -non_spreading_minbudget,0.026275800075381994,122,"(1, [])" -heuristic_minbudget,0.7856588000431657,122,"(1, [(1, 1), (19, 2), (37, 3), (49, 4)])" -non_spreading_dirlaynet_minbudget,0.512130199931562,112,"(7, {0: [5, 2, 7, 6, 4, 3, 1]})" -non_spreading_minbudget,0.03236670000478625,112,"(7, [])" -heuristic_minbudget,0.5671945998910815,112,"(4, [(1, 1), (7, 1), (2, 1), (3, 1), (24, 2), (19, 2), (15, 2), (17, 2), (30, 3), (29, 3), (27, 3), (25, 3), (34, 4), (36, 4), (39, 4), (31, 4), (44, 5)])" -non_spreading_dirlaynet_minbudget,0.7608991998713464,177,"(9, {0: [15, 17, 27, 11, 1, 42, 50, 52, 49], 1: [42, 50, 52, 49], 2: [42, 50, 52, 49]})" -non_spreading_minbudget,0.03819869994185865,177,"(18, [])" -heuristic_minbudget,3.600453299935907,177,"(5, [(1, 1), (27, 1), (17, 1), (11, 1), (15, 1), (41, 2), (33, 2), (29, 2), (35, 2), (36, 2), (48, 3), (43, 3), (51, 3), (44, 3), (47, 3), (55, 4), (69, 4), (83, 4), (60, 4), (68, 4), (85, 5), (86, 5), (90, 5), (91, 5), (94, 5), (119, 6), (116, 6), (100, 6), (107, 6), (118, 6), (127, 7), (120, 7), (131, 7), (134, 7), (125, 7), (145, 8), (144, 8), (142, 8), (151, 8), (159, 8), (167, 9), (164, 9), (170, 9), (165, 9), (166, 9)])" -non_spreading_dirlaynet_minbudget,0.5297947998624295,113,"(15, {0: [14, 10, 5, 2, 12, 13, 8, 7, 9, 6, 4, 3, 1, 15, 27]})" -non_spreading_minbudget,0.02606609999202192,113,"(15, [])" -heuristic_minbudget,1.86227019992657,113,"(8, [(14, 1), (6, 1), (15, 1), (5, 1), (3, 1), (4, 1), (9, 1), (10, 1), (33, 2), (37, 2), (38, 2), (27, 2), (32, 2), (18, 2), (23, 2), (30, 2), (45, 3), (47, 3), (56, 3), (65, 3), (60, 3), (62, 3), (64, 3), (52, 3), (77, 4), (81, 4), (70, 4), (71, 4), (78, 4), (80, 4), (83, 4), (75, 4), (86, 5), (84, 5), (87, 5), (88, 5), (85, 5)])" -non_spreading_dirlaynet_minbudget,1.0635809998493642,151,"(5, {0: [3, 61, 75, 134, 135], 1: [105, 135], 2: [105, 115], 3: [105], 4: [105]})" -non_spreading_minbudget,0.03986999997869134,151,"(12, [])" -heuristic_minbudget,2.3467868000734597,151,"(4, [(3, 1), (2, 1), (9, 1), (15, 1), (26, 2), (17, 2), (27, 2), (35, 2), (61, 3), (54, 3), (41, 3), (49, 3), (75, 4), (74, 4), (73, 4), (78, 4), (91, 5), (93, 5), (89, 5), (105, 5), (115, 6), (107, 6), (111, 6), (112, 6), (118, 7), (119, 7), (121, 7), (122, 7), (131, 8)])" -non_spreading_dirlaynet_minbudget,0.5187239998485893,125,"(11, {0: [22, 6, 21, 4, 42, 31, 41, 38, 35, 37, 43], 1: [42, 31, 41, 38, 35, 37, 43]})" -non_spreading_minbudget,0.12787489988841116,125,"(17, [])" -heuristic_minbudget,3.3736835999879986,125,"(7, [(21, 1), (22, 1), (4, 1), (6, 1), (27, 1), (2, 1), (7, 1), (35, 2), (42, 2), (34, 2), (39, 2), (38, 2), (40, 2), (43, 2), (49, 3), (46, 3), (73, 3), (57, 3), (48, 3), (51, 3), (72, 3), (80, 4), (88, 4), (95, 4), (84, 4), (79, 4), (85, 4), (97, 4), (110, 5), (101, 5), (107, 5), (106, 5), (112, 5), (104, 5), (102, 5)])" -non_spreading_dirlaynet_minbudget,0.5040550001431257,114,"(9, {0: [16, 10, 2, 38, 35, 69, 87, 77, 90], 1: [38, 35, 87, 77, 90], 2: [87, 77], 3: [87, 77, 90], 4: [87, 77]})" -non_spreading_minbudget,0.04212810005992651,114,"(16, [])" -heuristic_minbudget,1.9488479997962713,114,"(6, [(16, 1), (2, 1), (10, 1), (9, 1), (15, 1), (24, 1), (34, 2), (35, 2), (38, 2), (25, 2), (41, 2), (28, 2), (49, 3), (56, 3), (52, 3), (55, 3), (57, 3), (59, 3), (69, 4), (68, 4), (62, 4), (60, 4), (67, 4), (65, 4), (77, 5), (87, 5), (83, 5), (73, 5), (85, 5), (80, 5)])" -non_spreading_dirlaynet_minbudget,0.8800749001093209,165,"(3, {0: [3, 116, 145], 1: [48], 2: [116], 3: [83, 145], 4: [83, 145]})" -non_spreading_minbudget,0.06158620002679527,165,"(11, [])" -heuristic_minbudget,1.6480677998624742,165,"(4, [(3, 1), (5, 1), (4, 1), (17, 1), (23, 2), (22, 2), (25, 2), (26, 2), (32, 3), (31, 3), (41, 3), (37, 3), (48, 4), (57, 4), (66, 4), (43, 4), (77, 5), (83, 5), (76, 5), (78, 5), (116, 6), (114, 6), (118, 6), (111, 6), (121, 7), (125, 7), (128, 7), (126, 7), (131, 8), (135, 8), (132, 8), (134, 8)])" -non_spreading_dirlaynet_minbudget,0.3088372000493109,114,"(9, {0: [5, 2, 8, 7, 9, 6, 4, 3, 1]})" -non_spreading_minbudget,0.08847400010563433,114,"(9, [])" -heuristic_minbudget,0.7732076998800039,114,"(5, [(9, 1), (4, 1), (1, 1), (5, 1), (7, 1), (20, 2), (31, 2), (10, 2), (32, 2), (33, 2), (47, 3), (39, 3), (48, 3), (45, 3), (40, 3), (60, 4), (65, 4), (69, 4), (61, 4), (62, 4), (80, 5), (76, 5), (81, 5), (75, 5), (78, 5), (94, 6), (82, 6), (92, 6), (91, 6), (85, 6)])" -non_spreading_dirlaynet_minbudget,0.5046526999212801,125,"(11, {0: [10, 5, 2, 8, 7, 9, 6, 4, 3, 1, 25]})" -non_spreading_minbudget,0.038530000019818544,125,"(11, [])" -heuristic_minbudget,1.5089217000640929,125,"(8, [(5, 1), (2, 1), (8, 1), (1, 1), (7, 1), (4, 1), (3, 1), (9, 1), (23, 2), (14, 2), (13, 2), (25, 2), (30, 2), (24, 2), (22, 2), (27, 2), (49, 3), (58, 3), (47, 3), (57, 3), (41, 3), (32, 3), (39, 3), (33, 3), (61, 4), (76, 4), (73, 4), (78, 4), (80, 4), (72, 4), (64, 4), (74, 4), (85, 5), (101, 5), (95, 5), (94, 5), (97, 5), (84, 5), (93, 5), (96, 5)])" -non_spreading_dirlaynet_minbudget,0.9893045001663268,164,"(1, {3: [69]})" -non_spreading_minbudget,0.06566859991289675,164,"(1, [])" -heuristic_minbudget,1.310516000026837,164,"(1, [(11, 1), (33, 2), (43, 3)])" -non_spreading_dirlaynet_minbudget,0.36087279999628663,84,"(13, {0: [5, 2, 11, 12, 13, 8, 7, 9, 6, 4, 3, 1, 24]})" -non_spreading_minbudget,0.023888400057330728,84,"(13, [])" -heuristic_minbudget,1.1509269999805838,84,"(5, [(5, 1), (7, 1), (2, 1), (9, 1), (13, 1), (23, 2), (26, 2), (20, 2), (17, 2), (18, 2), (44, 3), (43, 3), (40, 3), (41, 3), (39, 3), (54, 4), (48, 4), (51, 4), (50, 4), (46, 4), (78, 5), (67, 5), (62, 5), (75, 5), (59, 5)])" -non_spreading_dirlaynet_minbudget,0.5455466001294553,165,"(5, {0: [5, 2, 4, 3, 1]})" -non_spreading_minbudget,0.028440400026738644,165,"(5, [])" -heuristic_minbudget,0.44033989985473454,165,"(4, [(3, 1), (5, 1), (4, 1), (2, 1), (15, 2), (10, 2), (14, 2), (9, 2)])" -non_spreading_dirlaynet_minbudget,1.3026932999491692,212,"(11, {0: [14, 16, 17, 5, 11, 19, 23, 26, 30, 29, 38], 1: [23, 26, 30, 29, 38], 2: [38]})" -non_spreading_minbudget,0.06338009983301163,212,"(17, [])" -heuristic_minbudget,2.3486457001417875,212,"(7, [(17, 1), (19, 1), (11, 1), (14, 1), (16, 1), (5, 1), (20, 1), (26, 2), (24, 2), (30, 2), (32, 2), (33, 2), (28, 2), (23, 2), (35, 3), (38, 3), (46, 3), (51, 3), (39, 3), (42, 3)])" -non_spreading_dirlaynet_minbudget,0.5373384999111295,140,"(21, {0: [14, 16, 7, 9, 6, 15, 17, 10, 5, 2, 12, 13, 8, 21, 1, 20, 19, 4, 18, 3, 24]})" -non_spreading_minbudget,0.03254449996165931,140,"(21, [])" -heuristic_minbudget,2.4232493001036346,140,"(6, [(14, 1), (16, 1), (17, 1), (4, 1), (10, 1), (2, 1), (28, 2), (42, 2), (31, 2), (39, 2), (33, 2), (45, 2), (63, 3), (66, 3), (53, 3), (61, 3), (68, 3), (70, 3), (72, 4), (73, 4), (71, 4), (77, 4), (78, 4), (79, 4), (82, 5), (104, 5), (88, 5), (91, 5), (98, 5), (105, 5), (109, 6), (112, 6), (115, 6), (110, 6), (111, 6), (118, 6), (128, 7), (129, 7), (124, 7), (125, 7), (122, 7), (126, 7)])" -non_spreading_dirlaynet_minbudget,0.5947537000756711,118,"(14, {0: [14, 10, 5, 2, 11, 12, 13, 8, 7, 9, 6, 4, 3, 1]})" -non_spreading_minbudget,0.031359399901703,118,"(14, [])" -heuristic_minbudget,2.0146788998972625,118,"(8, [(2, 1), (6, 1), (14, 1), (9, 1), (3, 1), (11, 1), (1, 1), (5, 1), (37, 2), (35, 2), (30, 2), (25, 2), (39, 2), (43, 2), (18, 2), (29, 2), (53, 3), (46, 3), (44, 3), (48, 3), (58, 3), (45, 3), (55, 3), (52, 3), (72, 4), (60, 4), (63, 4), (61, 4), (71, 4), (74, 4), (67, 4), (73, 4), (76, 5), (98, 5), (77, 5), (81, 5), (93, 5), (95, 5), (85, 5), (96, 5), (100, 6), (102, 6), (103, 6), (99, 6), (101, 6)])" -non_spreading_dirlaynet_minbudget,0.9165356999728829,151,"(7, {0: [14, 11, 13, 7, 16, 19, 24], 1: [16, 19]})" -non_spreading_minbudget,0.051704199984669685,151,"(9, [])" -heuristic_minbudget,0.7268763000611216,151,"(4, [(14, 1), (11, 1), (7, 1), (13, 1), (19, 2), (15, 2), (18, 2), (16, 2), (24, 3)])" -non_spreading_dirlaynet_minbudget,0.5570110001135617,131,"(3, {0: [10, 23, 26], 1: [23, 26], 2: [23, 26], 3: [23, 26]})" -non_spreading_minbudget,0.03013460012152791,131,"(6, [])" -heuristic_minbudget,0.5643533999100327,131,"(3, [(10, 1), (3, 1), (5, 1), (13, 2), (16, 2), (15, 2), (21, 3), (19, 3), (18, 3), (26, 4), (23, 4)])" -non_spreading_dirlaynet_minbudget,0.37107400014065206,85,"(9, {0: [11, 2, 13, 31, 26, 22, 50, 52, 49], 1: [31, 26, 22, 50, 52, 49], 2: [50, 52, 49]})" -non_spreading_minbudget,0.023888600058853626,85,"(16, [])" -heuristic_minbudget,1.7865257000084966,85,"(6, [(11, 1), (13, 1), (2, 1), (8, 1), (1, 1), (5, 1), (26, 2), (24, 2), (39, 2), (40, 2), (22, 2), (31, 2), (53, 3), (49, 3), (48, 3), (50, 3), (52, 3), (51, 3), (67, 4), (54, 4), (58, 4), (63, 4)])" -non_spreading_dirlaynet_minbudget,1.101705300156027,146,"(7, {0: [5, 2, 20, 45, 87, 113, 117], 1: [67], 2: [67], 5: [87]})" -non_spreading_minbudget,0.03667910001240671,146,"(10, [])" -heuristic_minbudget,2.299951800145209,146,"(3, [(5, 1), (2, 1), (20, 1), (45, 2), (31, 2), (42, 2), (67, 3), (68, 3), (57, 3), (73, 4), (70, 4), (71, 4), (77, 5), (79, 5), (82, 5), (94, 6), (89, 6), (98, 6), (113, 7), (101, 7), (106, 7)])" -non_spreading_dirlaynet_minbudget,0.8552312001120299,176,"(19, {0: [14, 16, 7, 9, 6, 15, 17, 10, 5, 2, 11, 12, 13, 8, 19, 4, 18, 3, 1]})" -non_spreading_minbudget,0.04930149996653199,176,"(19, [])" -heuristic_minbudget,2.6644704998470843,176,"(10, [(8, 1), (12, 1), (15, 1), (10, 1), (11, 1), (3, 1), (6, 1), (14, 1), (13, 1), (1, 1), (26, 2), (46, 2), (36, 2), (20, 2), (43, 2), (27, 2), (35, 2), (40, 2), (31, 2), (47, 2), (55, 3), (66, 3), (50, 3), (58, 3), (61, 3), (53, 3), (56, 3), (54, 3), (57, 3), (67, 3), (77, 4), (70, 4), (74, 4), (76, 4), (73, 4), (69, 4), (72, 4), (78, 4), (71, 4), (75, 4)])" -non_spreading_dirlaynet_minbudget,0.9114642001222819,126,"(11, {0: [14, 8, 21, 7, 6, 28, 18, 34, 33, 30, 36], 1: [34, 33, 30]})" -non_spreading_minbudget,0.04174270015209913,126,"(13, [])" -heuristic_minbudget,1.17835669987835,126,"(7, [(8, 1), (14, 1), (18, 1), (21, 1), (6, 1), (28, 1), (7, 1), (33, 2), (30, 2), (32, 2), (34, 2), (29, 2), (31, 2)])" -non_spreading_dirlaynet_minbudget,0.6806262000463903,139,"(1, {3: [50]})" -non_spreading_minbudget,0.029057599836960435,139,"(1, [])" -heuristic_minbudget,0.5616037000436336,139,"(1, [(3, 1), (13, 2), (43, 3)])" -non_spreading_dirlaynet_minbudget,0.3880594000220299,97,"(4, {0: [4, 5, 2, 7]})" -non_spreading_minbudget,0.027884799987077713,97,"(4, [])" -heuristic_minbudget,0.13068190007470548,97,"(2, [(5, 1), (4, 1), (8, 2), (7, 2), (21, 3)])" -non_spreading_dirlaynet_minbudget,1.167196199996397,170,"(6, {0: [5, 2, 6, 4, 3, 1]})" -non_spreading_minbudget,0.09135910007171333,170,"(6, [])" -heuristic_minbudget,1.3934970998670906,170,"(5, [(3, 1), (1, 1), (2, 1), (6, 1), (5, 1), (19, 2), (14, 2), (18, 2), (23, 2), (15, 2), (27, 3), (32, 3), (33, 3), (35, 3), (40, 3), (60, 4), (62, 4), (70, 4), (68, 4), (69, 4), (82, 5), (77, 5), (81, 5), (78, 5), (76, 5), (96, 6), (88, 6), (93, 6), (97, 6), (98, 6), (103, 7), (105, 7), (108, 7), (102, 7), (100, 7), (126, 8), (125, 8), (137, 8), (135, 8)])" -non_spreading_dirlaynet_minbudget,1.1337807001546025,197,"(8, {0: [9, 12, 3, 30, 29, 42, 41, 45], 1: [30, 29, 42, 41, 45], 2: [42, 41, 45]})" -non_spreading_minbudget,0.05200010002590716,197,"(15, [])" -heuristic_minbudget,2.1535696000792086,197,"(5, [(3, 1), (12, 1), (9, 1), (5, 1), (14, 1), (21, 2), (27, 2), (29, 2), (30, 2), (31, 2), (41, 3), (46, 3), (44, 3), (42, 3), (43, 3), (65, 4), (59, 4), (66, 4), (62, 4), (49, 4), (83, 5), (85, 5), (99, 5), (94, 5), (80, 5), (112, 6), (111, 6), (103, 6), (108, 6), (118, 6), (144, 7), (140, 7), (147, 7), (131, 7), (146, 7), (165, 8), (159, 8), (162, 8), (161, 8), (164, 8)])" -non_spreading_dirlaynet_minbudget,0.979691700078547,175,"(13, {0: [16, 25, 11, 42, 31, 41, 26, 38, 30, 29, 28, 35, 37], 1: [42, 31, 41, 26, 38, 30, 29, 28, 35, 37]})" -non_spreading_minbudget,0.06003940012305975,175,"(23, [])" -heuristic_minbudget,3.174024800071493,175,"(6, [(25, 1), (11, 1), (16, 1), (9, 1), (2, 1), (24, 1), (28, 2), (32, 2), (40, 2), (34, 2), (38, 2), (29, 2), (60, 3), (49, 3), (61, 3), (47, 3), (48, 3), (54, 3), (79, 4), (81, 4), (91, 4), (92, 4), (82, 4), (66, 4), (110, 5), (106, 5), (95, 5), (96, 5), (108, 5), (102, 5), (115, 6), (113, 6), (118, 6), (117, 6), (119, 6), (116, 6), (129, 7), (130, 7)])" -non_spreading_dirlaynet_minbudget,0.5525450001005083,162,"(14, {0: [14, 10, 5, 2, 11, 12, 13, 8, 7, 9, 6, 4, 3, 1]})" -non_spreading_minbudget,0.02637740015052259,162,"(14, [])" -heuristic_minbudget,2.7541892000008374,162,"(8, [(8, 1), (3, 1), (13, 1), (11, 1), (2, 1), (1, 1), (6, 1), (9, 1), (25, 2), (26, 2), (20, 2), (17, 2), (22, 2), (32, 2), (18, 2), (28, 2), (52, 3), (54, 3), (38, 3), (45, 3), (49, 3), (42, 3), (59, 3), (44, 3), (67, 4), (72, 4), (71, 4), (76, 4), (73, 4), (70, 4), (75, 4), (68, 4), (78, 5), (87, 5), (89, 5), (84, 5), (82, 5), (83, 5), (81, 5), (79, 5), (94, 6), (93, 6)])" -non_spreading_dirlaynet_minbudget,0.45032479986548424,156,"(2, {0: [13, 115], 8: [115]})" -non_spreading_minbudget,0.019497300032526255,156,"(4, [])" -heuristic_minbudget,1.121900200145319,156,"(1, [(13, 1), (22, 2), (29, 3), (51, 4), (67, 5), (77, 6), (85, 7), (104, 8)])" -non_spreading_dirlaynet_minbudget,0.6660581000614911,129,"(1, {0: [15]})" -non_spreading_minbudget,0.033490699948742986,129,"(1, [])" -heuristic_minbudget,0.6326198000460863,129,"(1, [(15, 1)])" -non_spreading_dirlaynet_minbudget,0.8275399000849575,137,"(2, {0: [2, 108], 6: [108], 7: [108]})" -non_spreading_minbudget,0.023948500165715814,137,"(4, [])" -heuristic_minbudget,0.28256139997392893,137,"(1, [(2, 1), (13, 2), (22, 3), (31, 4), (36, 5), (50, 6), (74, 7)])" -non_spreading_dirlaynet_minbudget,0.3248042999766767,102,"(1, {3: [87]})" -non_spreading_minbudget,0.027101999847218394,102,"(3, [])" -heuristic_minbudget,1.1255909001920372,102,"(1, [(13, 1), (22, 2), (39, 3), (49, 4), (71, 5)])" -non_spreading_dirlaynet_minbudget,0.39874129998497665,73,"(3, {0: [9, 57, 66], 1: [23], 2: [23]})" -non_spreading_minbudget,0.026935699861496687,73,"(7, [])" -heuristic_minbudget,0.8045551998075098,73,"(2, [(9, 1), (2, 1), (18, 2), (12, 2), (35, 3), (23, 3), (62, 4), (57, 4)])" -non_spreading_dirlaynet_minbudget,0.5787450999487191,105,"(8, {0: [3, 1, 28, 37, 34, 55, 50, 52], 1: [28, 37, 34, 55, 50, 52], 2: [55, 50, 52]})" -non_spreading_minbudget,0.06141979992389679,105,"(16, [])" -heuristic_minbudget,3.1684757000766695,105,"(6, [(3, 1), (1, 1), (5, 1), (9, 1), (14, 1), (8, 1), (34, 2), (48, 2), (28, 2), (40, 2), (37, 2), (47, 2), (49, 3), (51, 3), (54, 3), (56, 3), (53, 3), (50, 3), (60, 4), (69, 4), (76, 4), (65, 4)])" -non_spreading_dirlaynet_minbudget,0.48192729987204075,122,"(7, {0: [5, 2, 7, 4, 3, 1, 25]})" -non_spreading_minbudget,0.03430820000357926,122,"(7, [])" -heuristic_minbudget,0.48548690020106733,122,"(4, [(2, 1), (4, 1), (7, 1), (5, 1), (20, 2), (16, 2), (11, 2), (15, 2), (25, 3), (27, 3), (26, 3), (31, 3), (37, 4), (39, 4), (34, 4), (38, 4), (43, 5), (45, 5), (42, 5), (44, 5)])" -non_spreading_dirlaynet_minbudget,1.326428100001067,196,"(8, {0: [10, 22, 19, 41, 61, 87, 137, 194], 1: [61, 87, 194], 2: [61, 87]})" -non_spreading_minbudget,0.0565718999132514,196,"(13, [])" -heuristic_minbudget,3.0652807999867946,196,"(3, [(22, 1), (19, 1), (10, 1), (41, 2), (39, 2), (25, 2), (75, 3), (61, 3), (54, 3), (104, 4), (100, 4), (87, 4), (111, 5), (115, 5), (112, 5), (133, 6), (127, 6), (128, 6), (137, 7), (135, 7), (146, 7), (162, 8), (163, 8), (156, 8), (181, 9), (179, 9), (182, 9)])" -non_spreading_dirlaynet_minbudget,0.6285307998768985,152,"(12, {0: [9, 6, 5, 25, 2, 13, 20, 31, 26, 30, 29, 28], 1: [31, 26, 30, 29, 28]})" -non_spreading_minbudget,0.04760949988849461,152,"(16, [])" -heuristic_minbudget,1.38000059989281,152,"(7, [(5, 1), (9, 1), (25, 1), (2, 1), (6, 1), (13, 1), (20, 1), (34, 2), (32, 2), (30, 2), (27, 2), (33, 2), (28, 2), (26, 2), (38, 3), (36, 3), (58, 3), (57, 3), (60, 3), (48, 3), (52, 3), (87, 4), (70, 4), (75, 4), (84, 4), (89, 4), (61, 4), (69, 4), (93, 5), (99, 5), (94, 5), (96, 5), (97, 5), (95, 5), (104, 5), (119, 6), (115, 6), (116, 6), (120, 6), (123, 6), (117, 6), (106, 6), (131, 7), (130, 7), (135, 7), (132, 7), (129, 7), (125, 7), (137, 7)])" -non_spreading_dirlaynet_minbudget,0.30850719986483455,87,"(1, {2: [53]})" -non_spreading_minbudget,0.018661600071936846,87,"(1, [])" -heuristic_minbudget,1.1946388001088053,87,"(1, [(22, 1), (23, 2)])" -non_spreading_dirlaynet_minbudget,0.2921035001054406,103,"(3, {0: [12, 31, 60], 1: [46, 60], 2: [31, 46, 60], 3: [60], 4: [60]})" -non_spreading_minbudget,0.017141399905085564,103,"(10, [])" -heuristic_minbudget,0.5932669001631439,103,"(3, [(6, 1), (10, 1), (3, 1), (12, 2), (18, 2), (20, 2), (35, 3), (31, 3), (29, 3), (46, 4), (45, 4), (50, 4), (61, 5), (58, 5), (60, 5), (63, 6), (68, 6), (65, 6)])" -non_spreading_dirlaynet_minbudget,0.6329423999413848,158,"(5, {0: [5, 2, 4, 3, 1]})" -non_spreading_minbudget,0.1259716998320073,158,"(5, [])" -heuristic_minbudget,0.4594906000420451,158,"(4, [(4, 1), (3, 1), (1, 1), (5, 1), (20, 2), (22, 2)])" -non_spreading_dirlaynet_minbudget,0.5525646000169218,158,"(5, {0: [5, 2, 4, 3, 1]})" -non_spreading_minbudget,0.028168300166726112,158,"(5, [])" -heuristic_minbudget,0.5709834999870509,158,"(4, [(1, 1), (2, 1), (4, 1), (3, 1), (10, 2), (26, 2), (15, 2), (29, 2), (30, 3), (31, 3), (34, 3), (32, 3), (53, 4), (56, 4), (45, 4), (48, 4), (60, 5), (76, 5), (77, 5), (66, 5), (80, 6), (82, 6), (81, 6), (83, 6), (91, 7)])" -non_spreading_dirlaynet_minbudget,0.5035032997839153,130,"(10, {0: [16, 4, 23, 31, 26, 22, 30, 29, 28, 34], 1: [23, 31, 26, 22, 30, 29, 28, 34]})" -non_spreading_minbudget,0.04079610016196966,130,"(18, [])" -heuristic_minbudget,2.7959156001452357,130,"(6, [(4, 1), (16, 1), (1, 1), (5, 1), (3, 1), (17, 1), (32, 2), (24, 2), (34, 2), (19, 2), (33, 2), (26, 2), (36, 3), (39, 3), (42, 3), (55, 3), (59, 3), (49, 3), (68, 4), (66, 4), (67, 4), (65, 4), (69, 4), (72, 4), (93, 5), (89, 5), (84, 5), (77, 5), (91, 5), (81, 5)])" -non_spreading_dirlaynet_minbudget,0.38819109997712076,76,"(1, {1: [34]})" -non_spreading_minbudget,0.01265419996343553,76,"(1, [])" -heuristic_minbudget,0.8527677000965923,76,"(1, [(11, 1)])" -non_spreading_dirlaynet_minbudget,0.44918420002795756,109,"(9, {0: [5, 2, 8, 7, 9, 6, 4, 3, 1]})" -non_spreading_minbudget,0.01864150003530085,109,"(9, [])" -heuristic_minbudget,0.848988400073722,109,"(7, [(2, 1), (5, 1), (4, 1), (3, 1), (6, 1), (7, 1), (8, 1), (28, 2), (31, 2), (27, 2), (23, 2), (17, 2), (13, 2), (30, 2), (36, 3), (42, 3), (57, 3), (48, 3), (34, 3), (41, 3), (50, 3), (63, 4)])" -,229.11739100003615,, +non_spreading_dirlaynet_minbudget,0.3383908999385312,140,"(4, {0: [2, 12, 97, 130], 2: [87, 110], 3: [97, 110], 4: [110], 5: [110]})" +non_spreading_minbudget,0.015752499923110008,140,"(11, [])" +heuristic_minbudget,0.798951999982819,140,"(4, [(2, 1), (12, 1), (10, 1), (6, 1), (34, 2), (30, 2), (33, 2), (37, 2), (48, 3), (47, 3), (42, 3), (40, 3), (60, 4), (53, 4), (59, 4), (67, 4), (87, 5), (69, 5), (75, 5), (77, 5), (97, 6), (99, 6), (95, 6), (98, 6), (102, 7), (110, 7), (107, 7), (105, 7)])" +non_spreading_dirlaynet_minbudget,0.17233670002315193,110,"(9, {0: [7, 25, 17, 39, 31, 55, 66, 84, 91], 1: [39, 31, 55, 66, 91], 2: [55, 91], 4: [91], 5: [91]})" +non_spreading_minbudget,0.01266180002130568,110,"(18, [])" +heuristic_minbudget,0.7278256000718102,110,"(4, [(7, 1), (25, 1), (17, 1), (19, 1), (29, 2), (39, 2), (33, 2), (31, 2), (55, 3), (45, 3), (52, 3), (51, 3), (66, 4), (65, 4), (62, 4), (67, 4), (84, 5), (80, 5), (77, 5), (78, 5)])" +non_spreading_dirlaynet_minbudget,0.36679140001069754,152,"(7, {0: [7, 14, 21, 78, 103, 109, 147], 1: [36], 4: [147]})" +non_spreading_minbudget,0.020042599993757904,152,"(9, [])" +heuristic_minbudget,1.0867155999876559,152,"(3, [(21, 1), (7, 1), (14, 1), (36, 2), (43, 2), (45, 2), (78, 3), (62, 3), (71, 3), (82, 4), (83, 4), (88, 4), (103, 5), (101, 5), (97, 5), (109, 6), (126, 6), (131, 6)])" +non_spreading_dirlaynet_minbudget,0.5028269999893382,208,"(24, {0: [7, 14, 17, 23, 24, 9, 16, 1, 5, 22, 6, 15, 4, 13, 8, 18, 11, 21, 20, 10, 3, 12, 19, 2]})" +non_spreading_minbudget,0.027307800017297268,208,"(24, [])" +heuristic_minbudget,1.8271003999980167,208,"(9, [(23, 1), (22, 1), (20, 1), (18, 1), (17, 1), (3, 1), (9, 1), (8, 1), (4, 1), (53, 2), (32, 2), (37, 2), (27, 2), (48, 2), (31, 2), (40, 2), (46, 2), (39, 2), (77, 3), (69, 3), (64, 3), (67, 3), (66, 3), (56, 3), (79, 3), (72, 3), (61, 3), (85, 4), (86, 4), (82, 4), (91, 4), (84, 4), (87, 4), (88, 4), (90, 4), (93, 4), (106, 5), (109, 5), (99, 5), (95, 5), (98, 5), (96, 5), (108, 5), (103, 5), (97, 5), (126, 6), (114, 6), (117, 6), (113, 6), (111, 6), (130, 6), (119, 6), (118, 6), (121, 6), (136, 7), (135, 7), (146, 7), (150, 7), (158, 7), (139, 7), (154, 7), (144, 7), (145, 7), (167, 8), (169, 8), (163, 8), (166, 8), (165, 8), (162, 8), (168, 8), (164, 8)])" +non_spreading_dirlaynet_minbudget,0.4180444999365136,180,"(11, {0: [5, 20, 13, 19, 2, 22, 58, 72, 118, 145, 168], 1: [22, 58, 72, 145, 168], 2: [58, 72, 118]})" +non_spreading_minbudget,0.026318399934098125,180,"(19, [])" +heuristic_minbudget,1.598106799996458,180,"(6, [(13, 1), (19, 1), (20, 1), (5, 1), (2, 1), (17, 1), (21, 2), (22, 2), (33, 2), (35, 2), (38, 2), (40, 2), (72, 3), (58, 3), (69, 3), (73, 3), (50, 3), (74, 3), (92, 4), (94, 4), (93, 4), (96, 4), (89, 4), (81, 4), (102, 5), (118, 5), (100, 5), (101, 5), (108, 5), (119, 5), (126, 6), (128, 6), (124, 6), (125, 6), (129, 6), (123, 6), (136, 7), (134, 7)])" +non_spreading_dirlaynet_minbudget,0.2731701999437064,139,"(9, {0: [24, 25, 10, 5, 39, 38, 31, 40, 36], 1: [39, 38, 31, 40, 36, 45]})" +non_spreading_minbudget,0.019093799986876547,139,"(15, [])" +heuristic_minbudget,1.0293473000638187,139,"(6, [(25, 1), (24, 1), (10, 1), (5, 1), (8, 1), (20, 1), (34, 2), (36, 2), (38, 2), (39, 2), (33, 2), (30, 2), (46, 3), (42, 3), (44, 3), (45, 3), (55, 3), (43, 3), (63, 4), (72, 4), (69, 4), (62, 4), (65, 4), (64, 4), (83, 5), (92, 5), (91, 5), (77, 5), (90, 5), (82, 5), (97, 6), (99, 6), (101, 6), (98, 6), (100, 6), (104, 6), (107, 7), (109, 7), (110, 7)])" +non_spreading_dirlaynet_minbudget,0.3905019999947399,184,"(12, {0: [25, 11, 20, 17, 37, 47, 79, 76, 97, 91, 96, 106], 1: [37, 47, 79, 76, 97, 91, 96, 106], 2: [79, 76, 97, 91, 96], 3: [97, 91, 96]})" +non_spreading_minbudget,0.037450100062415004,184,"(24, [])" +heuristic_minbudget,1.7678336999379098,184,"(6, [(17, 1), (25, 1), (11, 1), (20, 1), (2, 1), (13, 1), (43, 2), (47, 2), (37, 2), (50, 2), (33, 2), (57, 2), (82, 3), (77, 3), (79, 3), (76, 3), (59, 3), (70, 3), (88, 4), (92, 4), (97, 4), (87, 4), (94, 4), (86, 4), (111, 5), (113, 5), (109, 5), (114, 5), (125, 5), (122, 5), (140, 6), (138, 6), (135, 6), (141, 6), (129, 6), (127, 6), (151, 7), (152, 7), (144, 7), (150, 7), (162, 7), (142, 7)])" +non_spreading_dirlaynet_minbudget,0.26434650004375726,116,"(9, {0: [4, 2, 8, 1, 5, 6, 3, 9, 32]})" +non_spreading_minbudget,0.015567100024782121,116,"(9, [])" +heuristic_minbudget,0.3790762999560684,116,"(6, [(2, 1), (1, 1), (9, 1), (4, 1), (5, 1), (8, 1), (14, 2), (39, 2), (13, 2), (24, 2), (31, 2), (19, 2), (44, 3), (61, 3), (55, 3), (42, 3), (60, 3), (48, 3), (69, 4), (76, 4), (72, 4), (62, 4), (73, 4), (64, 4)])" +non_spreading_dirlaynet_minbudget,0.27031509997323155,146,"(3, {0: [23, 29, 97], 1: [105], 2: [55, 97], 3: [97], 4: [97]})" +non_spreading_minbudget,0.01591560000088066,146,"(9, [])" +heuristic_minbudget,0.8679373000049964,146,"(2, [(23, 1), (1, 1), (29, 2), (31, 2), (55, 3), (58, 3), (60, 4), (63, 4), (88, 5), (93, 5), (98, 6), (101, 6), (106, 7), (109, 7), (116, 8), (120, 8)])" +non_spreading_dirlaynet_minbudget,0.18942820001393557,99,"(7, {0: [7, 13, 11, 17, 49, 68, 98], 1: [49, 68, 98], 2: [49, 68, 98]})" +non_spreading_minbudget,0.012153099989518523,99,"(13, [])" +heuristic_minbudget,0.6063708999427035,99,"(4, [(7, 1), (13, 1), (17, 1), (11, 1), (24, 2), (41, 2), (39, 2), (21, 2), (48, 3), (50, 3), (49, 3), (55, 3), (68, 4), (85, 4), (71, 4), (64, 4)])" +non_spreading_dirlaynet_minbudget,0.12446479999925941,82,"(7, {0: [24, 1, 20, 3, 19, 53, 58], 1: [58], 2: [58], 3: [58]})" +non_spreading_minbudget,0.007472099969163537,82,"(10, [])" +heuristic_minbudget,0.5537956999614835,82,"(5, [(1, 1), (24, 1), (3, 1), (19, 1), (20, 1), (34, 2), (28, 2), (32, 2), (30, 2), (33, 2), (37, 3), (38, 3)])" +non_spreading_dirlaynet_minbudget,0.17413019994273782,107,"(12, {0: [7, 4, 2, 8, 10, 11, 9, 1, 6, 3, 12, 5]})" +non_spreading_minbudget,0.010613199905492365,107,"(12, [])" +heuristic_minbudget,0.7465448000002652,107,"(8, [(6, 1), (12, 1), (1, 1), (11, 1), (3, 1), (7, 1), (10, 1), (5, 1), (23, 2), (16, 2), (28, 2), (15, 2), (18, 2), (26, 2), (22, 2), (24, 2), (41, 3), (36, 3), (38, 3), (49, 3), (46, 3), (32, 3), (47, 3), (44, 3), (63, 4), (54, 4), (58, 4), (64, 4), (62, 4), (53, 4), (61, 4), (60, 4), (69, 5), (75, 5), (67, 5), (79, 5), (80, 5), (78, 5), (74, 5), (82, 5), (84, 6), (85, 6), (86, 6), (87, 6)])" +non_spreading_dirlaynet_minbudget,0.25344030000269413,134,"(20, {0: [7, 14, 17, 9, 16, 1, 5, 6, 15, 4, 8, 18, 11, 20, 10, 3, 12, 19, 2, 40]})" +non_spreading_minbudget,0.08458460005931556,134,"(20, [])" +heuristic_minbudget,0.6117942000273615,134,"(5, [(20, 1), (8, 1), (4, 1), (17, 1), (6, 1), (49, 2), (30, 2), (40, 2), (42, 2), (44, 2), (61, 3), (55, 3), (53, 3), (57, 3), (56, 3), (65, 4), (71, 4), (64, 4), (66, 4), (63, 4), (77, 5), (85, 5), (90, 5), (78, 5), (91, 5), (97, 6), (101, 6), (103, 6), (107, 6), (93, 6), (117, 7), (116, 7), (109, 7), (110, 7), (111, 7)])" +non_spreading_dirlaynet_minbudget,0.17076830007135868,90,"(8, {0: [6, 1, 5, 30, 18, 43, 45, 64], 1: [30, 18, 43, 45, 64], 2: [43, 45, 64], 3: [64]})" +non_spreading_minbudget,0.011342300102114677,90,"(14, [])" +heuristic_minbudget,0.5426640999503434,90,"(4, [(6, 1), (1, 1), (5, 1), (4, 1), (30, 2), (18, 2), (29, 2), (37, 2), (52, 3), (45, 3), (43, 3), (48, 3), (73, 4), (64, 4), (66, 4), (80, 4)])" +non_spreading_dirlaynet_minbudget,0.19489240006078035,112,"(9, {0: [7, 4, 2, 8, 1, 5, 6, 3, 9]})" +non_spreading_minbudget,0.01148700003977865,112,"(9, [])" +heuristic_minbudget,0.4493562000570819,112,"(4, [(9, 1), (5, 1), (3, 1), (6, 1), (24, 2), (19, 2), (10, 2), (13, 2), (28, 3), (37, 3), (41, 3), (26, 3), (58, 4), (66, 4), (47, 4), (60, 4), (74, 5), (79, 5), (76, 5), (83, 5), (89, 6), (88, 6), (85, 6), (87, 6), (91, 7), (92, 7), (95, 7), (98, 7)])" +non_spreading_dirlaynet_minbudget,0.3376789001049474,148,"(11, {0: [7, 4, 8, 6, 11, 9, 1, 5, 10, 3, 2]})" +non_spreading_minbudget,0.02147559996228665,148,"(11, [])" +heuristic_minbudget,0.899608499952592,148,"(4, [(2, 1), (5, 1), (6, 1), (7, 1), (26, 2), (21, 2), (32, 2), (20, 2), (49, 3), (41, 3), (52, 3), (42, 3), (66, 4), (65, 4), (74, 4), (72, 4), (102, 5), (107, 5), (109, 5), (92, 5), (115, 6), (129, 6), (132, 6), (117, 6)])" +non_spreading_dirlaynet_minbudget,0.16925049992278218,108,"(9, {0: [7, 4, 2, 8, 1, 5, 6, 3, 9]})" +non_spreading_minbudget,0.010174600058235228,108,"(9, [])" +heuristic_minbudget,0.37108299997635186,108,"(6, [(6, 1), (1, 1), (4, 1), (9, 1), (8, 1), (7, 1), (12, 2), (16, 2), (13, 2), (22, 2), (15, 2), (20, 2), (30, 3), (31, 3), (24, 3), (33, 3), (32, 3), (27, 3), (40, 4), (43, 4), (42, 4), (39, 4), (38, 4), (37, 4), (46, 5), (44, 5), (48, 5), (61, 5), (68, 5), (58, 5), (74, 6), (75, 6), (71, 6), (69, 6), (73, 6), (76, 6)])" +non_spreading_dirlaynet_minbudget,0.17099270003382117,91,"(15, {0: [7, 4, 2, 13, 14, 8, 10, 11, 9, 6, 3, 12, 15, 5, 23]})" +non_spreading_minbudget,0.010515599977225065,91,"(15, [])" +heuristic_minbudget,0.5208220999920741,91,"(6, [(4, 1), (13, 1), (6, 1), (10, 1), (14, 1), (2, 1), (22, 2), (31, 2), (27, 2), (23, 2), (29, 2), (28, 2), (34, 3), (48, 3), (39, 3), (44, 3), (51, 3), (33, 3), (55, 4), (71, 4), (64, 4), (61, 4), (67, 4), (59, 4)])" +non_spreading_dirlaynet_minbudget,0.15158629999496043,85,"(7, {0: [8, 20, 6, 27, 35, 46, 69], 1: [69]})" +non_spreading_minbudget,0.008138900040648878,85,"(8, [])" +heuristic_minbudget,0.3552039999049157,85,"(3, [(8, 1), (20, 1), (6, 1), (27, 2), (26, 2), (29, 2), (38, 3), (42, 3), (32, 3), (46, 4), (45, 4), (49, 4)])" +non_spreading_dirlaynet_minbudget,0.22132230002898723,127,"(8, {0: [7, 4, 8, 1, 5, 6, 3, 2]})" +non_spreading_minbudget,0.016545600024983287,127,"(8, [])" +heuristic_minbudget,0.5257905001053587,127,"(6, [(6, 1), (1, 1), (5, 1), (2, 1), (4, 1), (7, 1), (28, 2), (11, 2), (27, 2), (14, 2), (29, 2), (12, 2), (41, 3), (43, 3), (46, 3), (48, 3), (42, 3), (40, 3), (67, 4), (71, 4), (56, 4), (49, 4), (62, 4), (69, 4), (73, 5), (75, 5), (91, 5), (83, 5), (81, 5), (80, 5), (98, 6), (101, 6), (94, 6), (99, 6), (92, 6), (95, 6)])" +non_spreading_dirlaynet_minbudget,0.2360579000087455,129,"(7, {0: [7, 4, 1, 5, 6, 3, 2]})" +non_spreading_minbudget,0.015887600020505488,129,"(7, [])" +heuristic_minbudget,0.2970746000064537,129,"(3, [(5, 1), (2, 1), (7, 1), (16, 2), (8, 2), (13, 2), (27, 3), (24, 3), (22, 3), (28, 4), (29, 4), (32, 4), (43, 5), (45, 5), (38, 5), (52, 6), (53, 6), (56, 6), (64, 7), (62, 7), (69, 7), (83, 8), (93, 8), (98, 8)])" +non_spreading_dirlaynet_minbudget,0.3081332999281585,149,"(22, {0: [7, 14, 17, 9, 16, 1, 5, 22, 6, 15, 4, 13, 8, 18, 11, 21, 20, 10, 3, 12, 19, 2]})" +non_spreading_minbudget,0.020257299998775125,149,"(22, [])" +heuristic_minbudget,0.8213011999614537,149,"(9, [(18, 1), (16, 1), (19, 1), (11, 1), (14, 1), (17, 1), (13, 1), (10, 1), (5, 1), (47, 2), (35, 2), (28, 2), (43, 2), (34, 2), (46, 2), (45, 2), (32, 2), (39, 2), (56, 3), (71, 3), (54, 3), (57, 3), (67, 3), (53, 3), (55, 3), (68, 3), (72, 3), (80, 4), (76, 4), (74, 4), (79, 4), (78, 4), (77, 4), (75, 4)])" +non_spreading_dirlaynet_minbudget,0.21045160002540797,110,"(5, {0: [14, 43, 24, 45, 102], 1: [43, 24, 102]})" +non_spreading_minbudget,0.012047900003381073,110,"(8, [])" +heuristic_minbudget,0.7300266999518499,110,"(4, [(14, 1), (19, 1), (15, 1), (12, 1), (44, 2), (27, 2), (24, 2), (43, 2), (45, 3), (52, 3), (56, 3), (49, 3), (71, 4), (73, 4), (61, 4), (70, 4), (80, 5), (77, 5), (81, 5), (86, 5)])" +non_spreading_dirlaynet_minbudget,0.27784580003935844,138,"(6, {0: [7, 15, 9, 39, 49, 51], 1: [39, 49, 51], 2: [49, 51]})" +non_spreading_minbudget,0.020029000006616116,138,"(11, [])" +heuristic_minbudget,0.8300812999950722,138,"(5, [(9, 1), (15, 1), (7, 1), (18, 1), (1, 1), (39, 2), (48, 2), (28, 2), (31, 2), (36, 2), (53, 3), (51, 3), (52, 3), (49, 3), (50, 3), (70, 4), (64, 4)])" +non_spreading_dirlaynet_minbudget,0.31440829997882247,163,"(6, {0: [23, 20, 39, 43, 51, 50], 1: [39, 43, 51, 50], 2: [51, 50]})" +non_spreading_minbudget,0.01737050001975149,163,"(10, [])" +heuristic_minbudget,0.8383238000096753,163,"(4, [(23, 1), (20, 1), (7, 1), (22, 1), (39, 2), (26, 2), (43, 2), (27, 2), (51, 3), (52, 3), (50, 3), (53, 3), (67, 4), (78, 4), (77, 4), (69, 4), (87, 5), (93, 5), (80, 5), (97, 5), (122, 6), (115, 6), (116, 6), (121, 6), (125, 7), (123, 7), (126, 7), (127, 7)])" +non_spreading_dirlaynet_minbudget,0.23480509989894927,93,"(10, {0: [7, 4, 8, 6, 11, 5, 10, 3, 2, 32]})" +non_spreading_minbudget,0.009444999974220991,93,"(11, [])" +heuristic_minbudget,0.25563530006911606,93,"(4, [(10, 1), (5, 1), (11, 1), (8, 1), (17, 2), (12, 2), (13, 2), (18, 2), (26, 3), (24, 3), (41, 3), (23, 3), (60, 4), (44, 4), (61, 4), (49, 4)])" +non_spreading_dirlaynet_minbudget,0.47728039999492466,219,"(4, {0: [13, 19, 67, 173]})" +non_spreading_minbudget,0.02702299994416535,219,"(5, [])" +heuristic_minbudget,0.8602139001013711,219,"(2, [(19, 1), (13, 1), (43, 2), (46, 2), (58, 3), (56, 3), (62, 4), (67, 4), (97, 5), (74, 5), (101, 6), (108, 6), (126, 7), (140, 7)])" +non_spreading_dirlaynet_minbudget,0.11988609994295985,73,"(1, {3: [67]})" +non_spreading_minbudget,0.006057800026610494,73,"(2, [])" +heuristic_minbudget,0.11525369994342327,73,"(1, [(3, 1), (9, 2), (34, 3), (46, 4)])" +non_spreading_dirlaynet_minbudget,0.3718286999501288,178,"(5, {0: [4, 23, 59, 58, 70], 1: [59, 58], 2: [59, 58]})" +non_spreading_minbudget,0.022550499998033047,178,"(9, [])" +heuristic_minbudget,0.8823112000245601,178,"(3, [(4, 1), (23, 1), (8, 1), (52, 2), (53, 2), (29, 2), (59, 3), (54, 3), (60, 3), (61, 4), (68, 4), (70, 4), (76, 5), (77, 5), (78, 5), (102, 6), (99, 6), (97, 6), (118, 7), (127, 7), (113, 7), (142, 8), (150, 8), (155, 8)])" +non_spreading_dirlaynet_minbudget,0.22105910000391304,119,"(2, 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