diff --git a/experiments/compare_cache_in_tabu_search.py b/experiments/compare_cache_in_tabu_search.py new file mode 100644 index 0000000..5884bda --- /dev/null +++ b/experiments/compare_cache_in_tabu_search.py @@ -0,0 +1,185 @@ +""" +Compare the performance of algorithms for fair course allocation. + +Programmer: Erel Segal-Halevi +Since: 2023-07 +""" +import os +######### COMMON VARIABLES AND ROUTINES ########## + +import random + +from fairpyx import Instance, AgentBundleValueMatrix, divide +from typing import * +import numpy as np +import matplotlib.pyplot as plt +import pandas as pd +import ast +import seaborn as sns +import fairpyx.algorithms as crs + +max_value = 1000 +normalized_sum_of_values = 1000 +TIME_LIMIT = 100 + + +def create_initial_budgets(num_of_agents: int, beta: float = 100) -> dict: + # Create initial budgets for each agent, uniformly distributed in the range [1, 1 + beta] + initial_budgets = np.random.uniform(1, 1 + beta, num_of_agents) + return {f's{agent + 1}': initial_budgets[agent] for agent in range(num_of_agents)} + + +def evaluate_algorithm_on_instance(algorithm, instance, **kwargs): + beta = kwargs.get("beta", 100) + initial_budgets = create_initial_budgets(instance.num_of_agents, beta) + allocation = divide(algorithm, instance=instance, initial_budgets=initial_budgets, **kwargs) + + matrix = AgentBundleValueMatrix(instance, allocation) + matrix.use_normalized_values() + return { + "utilitarian_value": matrix.utilitarian_value(), + "egalitarian_value": matrix.egalitarian_value(), + "max_envy": matrix.max_envy(), + "mean_envy": matrix.mean_envy(), + "max_deficit": matrix.max_deficit(), + "mean_deficit": matrix.mean_deficit(), + "num_with_top_1": matrix.count_agents_with_top_rank(1), + "num_with_top_2": matrix.count_agents_with_top_rank(2), + "num_with_top_3": matrix.count_agents_with_top_rank(3), + } + + +######### EXPERIMENT WITH UNIFORMLY-RANDOM DATA ########## + +def course_allocation_with_random_instance_uniform( + num_of_agents: int, num_of_items: int, + value_noise_ratio: float, + algorithm: Callable, + random_seed: int, **kwargs): + agent_capacity_bounds = [6, 6] + item_capacity_bounds = [40, 40] + np.random.seed(random_seed) + instance = Instance.random_uniform( + num_of_agents=num_of_agents, num_of_items=num_of_items, + normalized_sum_of_values=normalized_sum_of_values, + agent_capacity_bounds=agent_capacity_bounds, + item_capacity_bounds=item_capacity_bounds, + item_base_value_bounds=[1, max_value], + item_subjective_ratio_bounds=[1 - value_noise_ratio, 1 + value_noise_ratio] + ) + return evaluate_algorithm_on_instance(algorithm, instance, **kwargs) + + +######### COMPARING USING CACHE - TABU SEARCH ########## + +RESULTS_CACHE_TABU_SEARCH = "results/compering_using_cache_tabu_search.csv" + +def run_cache_experiment_tabu_search(): + # Run on uniformly-random data with beta and delta parameters: + experiment = experiments_csv.Experiment("results/", "compering_using_cache_tabu_search.csv", + backup_folder="results/backup/") + input_ranges = { + "num_of_agents": [10, 20, 30], + "num_of_items": [5, 10, 15], + "value_noise_ratio": [0, 0.2], + # "value_noise_ratio": [0, 0.2, 0.4, 0.8, 1], + "beta": [3], + "delta": [{0.5}], + # "delta": [{0.001}, {0.1}, {0.3}, {0.5}, {0.9}], + "use_cache": [False, True], + "algorithm": [crs.tabu_search], + "random_seed": range(5), + } + experiment.run_with_time_limit(course_allocation_with_random_instance_uniform, input_ranges, time_limit=TIME_LIMIT) + + + +def analyze_experiment_results_cache(): + # Load the results from the CSV file + df = pd.read_csv(RESULTS_CACHE_TABU_SEARCH) + + best_row = df.loc[df['runtime'].idxmin()] + + # Extract relevant columns or parameters + best_use_cache_value = best_row['use_cache'] + best_runtime = best_row['runtime'] + + print(f"Best use_cache: {best_use_cache_value}") + print(f"Corresponding Runtime: {best_runtime} seconds") + + return df + + +##### PLOT ####### + +def plot_speed_vs_param(df, param, algorithm_name): + avg_runtime = df.groupby(param)['runtime'].mean().reset_index() + + plt.figure(figsize=(10, 6)) + sns.lineplot(data=avg_runtime, x=param, y='runtime', marker='o', err_style=None) + plt.title(f'Algorithm Speed vs. {param.capitalize()} for {algorithm_name}') + plt.xlabel(param.capitalize()) + plt.ylabel('Average Runtime (seconds)') + plt.grid(True) + plt.tight_layout() + plt.show() + + +def plot_speed_vs_params(df, param1, param2, algorithm_name): + fig = plt.figure(figsize=(10, 6)) + ax = fig.add_subplot(111, projection='3d') + + param1_values = df[param1] + param2_values = df[param2].apply( + lambda x: float(list(ast.literal_eval(x))[0]) if isinstance(x, str) else x) # Convert if necessary + runtime_values = df['runtime'] + + ax.scatter(param1_values, param2_values, runtime_values, c='b', marker='o') + ax.set_title(f'Algorithm Speed vs. {param1.capitalize()} and {param2.capitalize()} for {algorithm_name}') + ax.set_xlabel(param1.capitalize()) + ax.set_ylabel(param2.capitalize()) + ax.set_zlabel('Runtime (seconds)') + + plt.tight_layout() + plt.show() + + +def plot_runtime_vs_cache(df, algorithm_name): + plt.figure(figsize=(12, 8)) + + # Plotting runtime vs. num_of_agents for each use_cache value + sns.lineplot(data=df[df['use_cache'] == True], x='num_of_agents', y='runtime', marker='o', + label='Use Cache = True') + sns.lineplot(data=df[df['use_cache'] == False], x='num_of_agents', y='runtime', marker='o', + label='Use Cache = False') + + plt.title(f'Runtime vs. Number of Agents for {algorithm_name}') + plt.xlabel('Number of Agents') + plt.ylabel('Runtime (seconds)') + plt.grid(True) + + # Customizing the legend + legend_labels = { + 'Use Cache = True': 'blue', # Blue line and markers for Use Cache = True + 'Use Cache = False': 'orange' # Orange line and markers for Use Cache = False + } + + handles = [plt.Line2D([0, 0], [0, 0], color=color, marker='o', linestyle='') for color in legend_labels.values()] + labels = legend_labels.keys() + plt.legend(handles, labels, title='Legend') + + plt.tight_layout() + plt.show() + + + +########################### + +if __name__ == "__main__": + import logging, experiments_csv + + experiments_csv.logger.setLevel(logging.INFO) + + run_cache_experiment_tabu_search() + df = analyze_experiment_results_cache() + plot_runtime_vs_cache(df, 'Tabu Search') \ No newline at end of file diff --git a/experiments/compare_course_allocation_algorithms.py b/experiments/compare_course_allocation_algorithms.py index 80499da..764d363 100644 --- a/experiments/compare_course_allocation_algorithms.py +++ b/experiments/compare_course_allocation_algorithms.py @@ -166,7 +166,7 @@ def run_ariel_experiment(): import logging, experiments_csv experiments_csv.logger.setLevel(logging.INFO) run_uniform_experiment() - run_szws_experiment() - run_ariel_experiment() - + # run_szws_experiment() + # run_ariel_experiment() + # diff --git a/experiments/compare_course_allocation_algorithms_ACEEI_Tabu_Search.py b/experiments/compare_course_allocation_algorithms_ACEEI_Tabu_Search.py new file mode 100644 index 0000000..b10e67e --- /dev/null +++ b/experiments/compare_course_allocation_algorithms_ACEEI_Tabu_Search.py @@ -0,0 +1,221 @@ +""" +Compare the performance of algorithms for fair course allocation. + +Programmer: Erel Segal-Halevi +Since: 2023-07 +""" + + +######### COMMON VARIABLES AND ROUTINES ########## + +from fairpyx import divide, AgentBundleValueMatrix, Instance +import fairpyx.algorithms as crs +from typing import * +import numpy as np + +max_value = 1000 +normalized_sum_of_values = 1000 +TIME_LIMIT = 100 + +from fairpyx.algorithms.ACEEI.ACEEI import ACEEI_without_EFTB, ACEEI_with_EFTB, ACEEI_with_contested_EFTB +from fairpyx.algorithms.ACEEI.tabu_search import run_tabu_search + +algorithms_to_check = [ + ACEEI_without_EFTB, + ACEEI_with_EFTB, + ACEEI_with_contested_EFTB, + run_tabu_search, + # crs.iterated_maximum_matching_adjusted, + # crs.bidirectional_round_robin, +] + +def evaluate_algorithm_on_instance(algorithm, instance): + allocation = divide(algorithm, instance) + matrix = AgentBundleValueMatrix(instance, allocation) + matrix.use_normalized_values() + return { + "utilitarian_value": matrix.utilitarian_value(), + "egalitarian_value": matrix.egalitarian_value(), + "max_envy": matrix.max_envy(), + "mean_envy": matrix.mean_envy(), + "max_deficit": matrix.max_deficit(), + "mean_deficit": matrix.mean_deficit(), + "num_with_top_1": matrix.count_agents_with_top_rank(1), + "num_with_top_2": matrix.count_agents_with_top_rank(2), + "num_with_top_3": matrix.count_agents_with_top_rank(3), + } + + + +######### EXPERIMENT WITH UNIFORMLY-RANDOM DATA ########## + +def course_allocation_with_random_instance_uniform( + num_of_agents:int, num_of_items:int, + value_noise_ratio:float, + algorithm:Callable, + random_seed: int,): + agent_capacity_bounds = [6,6] + item_capacity_bounds = [40,40] + np.random.seed(random_seed) + instance = Instance.random_uniform( + num_of_agents=num_of_agents, num_of_items=num_of_items, + normalized_sum_of_values=normalized_sum_of_values, + agent_capacity_bounds=agent_capacity_bounds, + item_capacity_bounds=item_capacity_bounds, + item_base_value_bounds=[1,max_value], + item_subjective_ratio_bounds=[1-value_noise_ratio, 1+value_noise_ratio] + ) + return evaluate_algorithm_on_instance(algorithm, instance) + +def run_uniform_experiment(): + # Run on uniformly-random data: + experiment = experiments_csv.Experiment("results/", "course_allocation_uniform.csv", backup_folder="results/backup/") + input_ranges = { + "num_of_agents": [5, 8, 10], + "num_of_items": [4, 6], + "value_noise_ratio": [0.2], + "algorithm": algorithms_to_check, + "random_seed": range(5), + } + experiment.run_with_time_limit(course_allocation_with_random_instance_uniform, input_ranges, time_limit=TIME_LIMIT) + + + +######### EXPERIMENT WITH DATA GENERATED ACCORDING TO THE SZWS MODEL ########## + +def course_allocation_with_random_instance_szws( + num_of_agents:int, num_of_items:int, + agent_capacity:int, + supply_ratio:float, + num_of_popular_items:int, + mean_num_of_favorite_items:float, + favorite_item_value_bounds:tuple[int,int], + nonfavorite_item_value_bounds:tuple[int,int], + algorithm:Callable, + random_seed: int,): + np.random.seed(random_seed) + instance = Instance.random_szws( + num_of_agents=num_of_agents, num_of_items=num_of_items, normalized_sum_of_values=normalized_sum_of_values, + agent_capacity=agent_capacity, + supply_ratio=supply_ratio, + num_of_popular_items=num_of_popular_items, + mean_num_of_favorite_items=mean_num_of_favorite_items, + favorite_item_value_bounds=favorite_item_value_bounds, + nonfavorite_item_value_bounds=nonfavorite_item_value_bounds, + ) + return evaluate_algorithm_on_instance(algorithm, instance) + +def run_szws_experiment(): + # Run on SZWS simulated data: + experiment = experiments_csv.Experiment("results/", "course_allocation_szws.csv", backup_folder="results/backup/") + input_ranges = { + "num_of_agents": [5,10], + "num_of_items": [6], # in SZWS: 25 + "agent_capacity": [5], # as in SZWS + "supply_ratio": [1.1, 1.25, 1.5], # as in SZWS + "num_of_popular_items": [6, 9], # as in SZWS + "mean_num_of_favorite_items": [2.6, 3.85], # as in SZWS code https://github.com/marketdesignresearch/Course-Match-Preference-Simulator/blob/main/preference_generator_demo.ipynb + "favorite_item_value_bounds": [(50,100)], # as in SZWS code https://github.com/marketdesignresearch/Course-Match-Preference-Simulator/blob/main/preference_generator.py + "nonfavorite_item_value_bounds": [(0,50)], # as in SZWS code https://github.com/marketdesignresearch/Course-Match-Preference-Simulator/blob/main/preference_generator.py + "algorithm": algorithms_to_check, + "random_seed": range(5), + } + experiment.run_with_time_limit(course_allocation_with_random_instance_szws, input_ranges, time_limit=TIME_LIMIT) + + + +######### EXPERIMENT WITH DATA SAMPLED FROM ARIEL 5783 DATA ########## + +import json +filename = "data/ariel_5783_input.json" +with open(filename, "r", encoding="utf-8") as file: + ariel_5783_input = json.load(file) + +def course_allocation_with_random_instance_sample( + max_total_agent_capacity:int, + algorithm:Callable, + random_seed: int,): + np.random.seed(random_seed) + + (valuations, agent_capacities, item_capacities, agent_conflicts, item_conflicts) = \ + (ariel_5783_input["valuations"], ariel_5783_input["agent_capacities"], ariel_5783_input["item_capacities"], ariel_5783_input["agent_conflicts"], ariel_5783_input["item_conflicts"]) + instance = Instance.random_sample( + max_num_of_agents = max_total_agent_capacity, + max_total_agent_capacity = max_total_agent_capacity, + prototype_agent_conflicts=agent_conflicts, + prototype_agent_capacities=agent_capacities, + prototype_valuations=valuations, + item_capacities=item_capacities, + item_conflicts=item_conflicts) + return evaluate_algorithm_on_instance(algorithm, instance) + +def run_ariel_experiment(): + # Run on Ariel sample data: + experiment = experiments_csv.Experiment("results/", "course_allocation_ariel.csv", backup_folder="results/backup/") + input_ranges = { + "max_total_agent_capacity": [1000, 1115, 1500, 2000], # in reality: 1115 + "algorithm": algorithms_to_check, + "random_seed": range(10), + } + experiment.run_with_time_limit(course_allocation_with_random_instance_sample, input_ranges, time_limit=TIME_LIMIT) + + + +######### MAIN PROGRAM ########## + +if __name__ == "__main__": + import logging, experiments_csv + experiments_csv.logger.setLevel(logging.INFO) + run_uniform_experiment() + run_szws_experiment() + # run_ariel_experiment() + # + + import pandas as pd + import matplotlib.pyplot as plt + + + # Function to load experiment results from CSV + def load_experiment_results(filename): + df = pd.read_csv(filename) + return df + + + # Function to plot runtime vs. number of students + def plot_runtime_vs_students(df, algorithm_name): + num_of_agents = df['num_of_agents'] + runtime = df['runtime'] + + plt.plot(num_of_agents, runtime, marker='o', label=algorithm_name) + plt.xlabel('Number of Students') + plt.ylabel('Runtime (seconds)') + plt.title(f'Runtime vs. Number of Students ({algorithm_name})') + plt.legend() + plt.grid(True) + + + # Load and plot data for run_uniform_experiment() + uniform_results = load_experiment_results('results/course_allocation_uniform.csv') + plt.figure(figsize=(10, 6)) # Adjust figure size if needed + + for algorithm in algorithms_to_check: + algorithm_name = algorithm.__name__ + algorithm_data = uniform_results[uniform_results['algorithm'] == algorithm_name] + plot_runtime_vs_students(algorithm_data, algorithm_name) + + plt.tight_layout() + plt.show() + + # Load and plot data for run_szws_experiment() + szws_results = load_experiment_results('results/course_allocation_szws.csv') + plt.figure(figsize=(10, 6)) # Adjust figure size if needed + + for algorithm in algorithms_to_check: + algorithm_name = algorithm.__name__ + algorithm_data = szws_results[szws_results['algorithm'] == algorithm_name] + plot_runtime_vs_students(algorithm_data, algorithm_name) + + plt.tight_layout() + plt.show() + + diff --git a/experiments/results/comparing_cache_Tabu_Search.csv b/experiments/results/comparing_cache_Tabu_Search.csv new file mode 100644 index 0000000..191f412 --- /dev/null +++ b/experiments/results/comparing_cache_Tabu_Search.csv @@ -0,0 +1,573 @@ +num_of_agents,num_of_items,value_noise_ratio,beta,delta,use_cache,algorithm,random_seed,utilitarian_value,egalitarian_value,max_envy,mean_envy,max_deficit,mean_deficit,num_with_top_1,num_with_top_2,num_with_top_3,runtime +30,8,0.0,0.001,{0.34},False,tabu_search,0,100.0,100.0,0.0,0.0,0,0.0,30,30,30,0.1716009000083431 +30,8,0.0,0.001,{0.34},False,tabu_search,1,100.0,100.0,0.0,0.0,0,0.0,30,30,30,0.1852064999984577 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+29,8,0.4,0.001,{0.34},False,tabu_search,0,100.0,100.0,0.0,0.0,0,0.0,29,29,29,0.16398040000058245 +29,8,0.4,0.001,{0.34},False,tabu_search,1,100.0,100.0,0.0,0.0,0,0.0,29,29,29,0.16519939999852795 +29,8,0.4,0.001,{0.34},False,tabu_search,2,100.0,100.0,0.0,0.0,0,0.0,29,29,29,0.16473170000244863 +29,8,0.4,0.001,{0.34},False,tabu_search,3,100.0,100.0,0.0,0.0,0,0.0,29,29,29,0.1737898999999743 +29,8,0.4,0.001,{0.34},False,tabu_search,4,100.0,100.0,0.0,0.0,0,0.0,29,29,29,0.16346640000119805 +29,8,0.4,0.001,{0.34},True,tabu_search,0,100.0,100.0,0.0,0.0,0,0.0,29,29,29,0.20859979999659117 +29,8,0.4,0.001,{0.34},True,tabu_search,1,100.0,100.0,0.0,0.0,0,0.0,29,29,29,0.171634999991511 +29,8,0.4,0.001,{0.34},True,tabu_search,2,100.0,100.0,0.0,0.0,0,0.0,29,29,29,0.1669077999977162 +29,8,0.4,0.001,{0.34},True,tabu_search,3,100.0,100.0,0.0,0.0,0,0.0,29,29,29,0.1673218999931123 +29,8,0.4,0.001,{0.34},True,tabu_search,4,100.0,100.0,0.0,0.0,0,0.0,29,29,29,0.16504939999140333 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a/experiments/results/course_allocation_szws.csv b/experiments/results/course_allocation_szws.csv index 88c4cca..ae91e2f 100644 --- a/experiments/results/course_allocation_szws.csv +++ b/experiments/results/course_allocation_szws.csv @@ -300,7 +300,7 @@ num_of_agents,num_of_items,agent_capacity,supply_ratio,num_of_popular_items,mean 100,25,5,1.1,6,3.85,"(50, 100)","(0, 50)",round_robin,1,81.15875593098043,62.903225806451616,9.018567639257284,1.4819889460591669,0,0.0,100,100,100,0.0259018000215291 100,25,5,1.1,6,3.85,"(50, 100)","(0, 50)",round_robin,2,80.5884142363113,67.32456140350878,12.28070175438596,1.3672394516850626,0,0.0,100,100,100,0.0263872999930754 100,25,5,1.1,6,3.85,"(50, 100)","(0, 50)",round_robin,3,80.52874551298791,65.48856548856548,9.725685785536156,1.4164281512181025,0,0.0,100,100,100,0.0274657999980263 -100,25,5,1.1,6,3.85,"(50, 100)","(0, 50)",round_robin,4,81.37008280797014,61.839530332681015,12.98076923076924,1.8137712478905157,0,0.0,100,100,100,0.0267427000217139 +100,25,5,1.1,6,3.85,"(50, 100)","(0, 50)",round_robin,4,81.37008280797014,61.839530332681015,12.98076923076924,1.813771247890516,0,0.0,100,100,100,0.0267427000217139 100,25,5,1.1,6,3.85,"(50, 100)","(0, 50)",bidirectional_round_robin,0,81.6175381676916,60.526315789473685,9.19540229885058,1.3701307010812116,0,0.0,100,100,100,0.0284417999791912 100,25,5,1.1,6,3.85,"(50, 100)","(0, 50)",bidirectional_round_robin,1,80.99853094654647,64.02116402116403,11.264367816091962,1.6146096287937644,0,0.0,100,100,100,0.026127399993129 100,25,5,1.1,6,3.85,"(50, 100)","(0, 50)",bidirectional_round_robin,2,80.57962302937065,67.32456140350878,10.381355932203393,1.6888885464609336,0,0.0,100,100,100,0.0274687000201083 @@ -356,20 +356,20 @@ num_of_agents,num_of_items,agent_capacity,supply_ratio,num_of_popular_items,mean 100,25,5,1.1,9,2.6,"(50, 100)","(0, 50)",almost_egalitarian_with_donation,2,95.3533087053234,83.65122615803816,7.629427792915521,0.1699124011249716,0,0.0,98,100,100,4.004766399972141 100,25,5,1.1,9,2.6,"(50, 100)","(0, 50)",almost_egalitarian_with_donation,3,94.86775229815848,85.61946902654867,5.735660847880297,0.237312101113917,0,0.0,97,100,100,3.9203052000375465 100,25,5,1.1,9,2.6,"(50, 100)","(0, 50)",almost_egalitarian_with_donation,4,94.49649212223233,84.27518427518427,7.526881720430111,0.1543475860607361,0,0.0,97,100,100,4.1142606000066735 -100,25,5,1.1,9,3.85,"(50, 100)","(0, 50)",utilitarian_matching,0,88.37596254548055,72.58064516129032,16.17647058823529,1.8326020476733291,0,0.0,96,98,98,0.1152390000061132 +100,25,5,1.1,9,3.85,"(50, 100)","(0, 50)",utilitarian_matching,0,88.37596254548055,72.58064516129032,16.17647058823529,1.832602047673329,0,0.0,96,98,98,0.1152390000061132 100,25,5,1.1,9,3.85,"(50, 100)","(0, 50)",utilitarian_matching,1,88.68336105076305,69.12568306010928,15.123456790123456,1.4426847821961448,0,0.0,95,96,96,0.1083541000261902 -100,25,5,1.1,9,3.85,"(50, 100)","(0, 50)",utilitarian_matching,2,89.03995729658382,64.43298969072166,20.103092783505147,1.8304046030144476,0,0.0,94,95,95,0.120577800029423 +100,25,5,1.1,9,3.85,"(50, 100)","(0, 50)",utilitarian_matching,2,89.03995729658382,64.43298969072166,20.103092783505147,1.830404603014448,0,0.0,94,95,95,0.120577800029423 100,25,5,1.1,9,3.85,"(50, 100)","(0, 50)",utilitarian_matching,3,88.26355412684512,69.73684210526315,18.684210526315795,2.1870704161696755,0,0.0,93,95,95,0.1149949000100605 100,25,5,1.1,9,3.85,"(50, 100)","(0, 50)",utilitarian_matching,4,88.57399016322364,72.7536231884058,18.23708206686929,1.5667564173500523,0,0.0,93,95,96,0.1273005999973975 100,25,5,1.1,9,3.85,"(50, 100)","(0, 50)",iterated_maximum_matching_unadjusted,0,87.64277030305146,75.50607287449392,7.356321839080465,0.0735632183908046,0,0.0,100,100,100,0.1978709000395611 100,25,5,1.1,9,3.85,"(50, 100)","(0, 50)",iterated_maximum_matching_unadjusted,1,87.90293903857197,74.29193899782135,0.0,0.0,0,0.0,100,100,100,0.2018243999918922 100,25,5,1.1,9,3.85,"(50, 100)","(0, 50)",iterated_maximum_matching_unadjusted,2,88.3625579190454,76.90677966101694,2.083333333333343,0.0208333333333334,0,0.0,100,100,100,0.2097865000250749 -100,25,5,1.1,9,3.85,"(50, 100)","(0, 50)",iterated_maximum_matching_unadjusted,3,87.41903379484373,77.40492170022371,1.9607843137254972,0.0196078431372549,0,0.0,100,100,100,0.2035478000179864 +100,25,5,1.1,9,3.85,"(50, 100)","(0, 50)",iterated_maximum_matching_unadjusted,3,87.41903379484373,77.40492170022371,1.9607843137254968,0.0196078431372549,0,0.0,100,100,100,0.2035478000179864 100,25,5,1.1,9,3.85,"(50, 100)","(0, 50)",iterated_maximum_matching_unadjusted,4,87.87196875304755,75.93360995850622,1.1976047904191631,0.0119760479041916,0,0.0,100,100,100,0.2032434999709949 100,25,5,1.1,9,3.85,"(50, 100)","(0, 50)",iterated_maximum_matching_adjusted,0,87.64277030305146,75.50607287449392,7.356321839080465,0.0735632183908046,0,0.0,100,100,100,0.215407200041227 100,25,5,1.1,9,3.85,"(50, 100)","(0, 50)",iterated_maximum_matching_adjusted,1,87.90293903857197,74.29193899782135,0.0,0.0,0,0.0,100,100,100,0.2006317999912426 100,25,5,1.1,9,3.85,"(50, 100)","(0, 50)",iterated_maximum_matching_adjusted,2,88.3625579190454,76.90677966101694,2.083333333333343,0.0208333333333334,0,0.0,100,100,100,0.2103275000117719 -100,25,5,1.1,9,3.85,"(50, 100)","(0, 50)",iterated_maximum_matching_adjusted,3,87.41903379484373,77.40492170022371,1.9607843137254972,0.0196078431372549,0,0.0,100,100,100,0.2065717999939806 +100,25,5,1.1,9,3.85,"(50, 100)","(0, 50)",iterated_maximum_matching_adjusted,3,87.41903379484373,77.40492170022371,1.9607843137254968,0.0196078431372549,0,0.0,100,100,100,0.2065717999939806 100,25,5,1.1,9,3.85,"(50, 100)","(0, 50)",iterated_maximum_matching_adjusted,4,87.87196875304755,75.93360995850622,1.1976047904191631,0.0119760479041916,0,0.0,100,100,100,0.2056125000235624 100,25,5,1.1,9,3.85,"(50, 100)","(0, 50)",serial_dictatorship,0,79.56579972693108,42.29249011857708,56.07287449392713,15.054014368958097,0,0.0,48,51,53,0.0264589999569579 100,25,5,1.1,9,3.85,"(50, 100)","(0, 50)",serial_dictatorship,1,78.67627590616003,42.462311557788944,53.01507537688443,16.262146618229657,0,0.0,48,51,52,0.0276594000170007 @@ -520,7 +520,7 @@ num_of_agents,num_of_items,agent_capacity,supply_ratio,num_of_popular_items,mean 100,25,5,1.25,9,3.85,"(50, 100)","(0, 50)",utilitarian_matching,1,91.21942620878484,72.48520710059172,12.721893491124264,1.0923811642760408,0,0.0,96,98,98,0.1037443000241182 100,25,5,1.25,9,3.85,"(50, 100)","(0, 50)",utilitarian_matching,2,91.50419079514504,74.71910112359551,17.69662921348315,1.3034337116040944,0,0.0,98,99,99,0.110038099985104 100,25,5,1.25,9,3.85,"(50, 100)","(0, 50)",utilitarian_matching,3,90.7269941006346,69.73684210526315,18.684210526315795,2.0948057305473724,0,0.0,94,95,95,0.1108839999651536 -100,25,5,1.25,9,3.85,"(50, 100)","(0, 50)",utilitarian_matching,4,90.9697749796041,75.98784194528876,20.638820638820636,1.7792587968252862,0,0.0,96,97,97,0.1049093999899923 +100,25,5,1.25,9,3.85,"(50, 100)","(0, 50)",utilitarian_matching,4,90.9697749796041,75.98784194528876,20.63882063882064,1.7792587968252862,0,0.0,96,97,97,0.1049093999899923 100,25,5,1.25,9,3.85,"(50, 100)","(0, 50)",iterated_maximum_matching_unadjusted,0,90.42709582590028,77.34204793028321,5.977011494252892,0.2406262864329051,0,0.0,100,100,100,0.2087314000236801 100,25,5,1.25,9,3.85,"(50, 100)","(0, 50)",iterated_maximum_matching_unadjusted,1,90.71497117087132,80.07590132827325,6.93069306930694,0.1164601851765853,0,0.0,100,100,100,0.2121500999783165 100,25,5,1.25,9,3.85,"(50, 100)","(0, 50)",iterated_maximum_matching_unadjusted,2,91.1634670468718,80.73394495412845,5.581395348837219,0.0849424638668826,0,0.0,100,100,100,0.2125273999990895 @@ -613,8 +613,8 @@ num_of_agents,num_of_items,agent_capacity,supply_ratio,num_of_popular_items,mean 100,25,5,1.5,6,3.85,"(50, 100)","(0, 50)",iterated_maximum_matching_adjusted,4,87.66739407907521,73.84615384615385,5.599999999999994,0.2110422707023697,0,0.0,100,100,100,0.2121457000030204 100,25,5,1.5,6,3.85,"(50, 100)","(0, 50)",serial_dictatorship,0,79.92581810373112,44.534412955465584,55.06072874493927,18.183652343792154,0,0.0,45,47,47,0.027460399956908 100,25,5,1.5,6,3.85,"(50, 100)","(0, 50)",serial_dictatorship,1,80.64277408816763,48.49699398797595,49.09819639278557,17.344076401247534,0,0.0,47,49,50,0.0277321999892592 -100,25,5,1.5,6,3.85,"(50, 100)","(0, 50)",serial_dictatorship,2,79.01526838881641,50.50301810865191,46.55963302752294,18.997858064290917,0,0.0,44,44,45,0.0268275000271387 -100,25,5,1.5,6,3.85,"(50, 100)","(0, 50)",serial_dictatorship,3,78.90221255399143,46.98544698544699,53.01455301455301,19.039251245869156,0,0.0,44,46,48,0.0276876999996602 +100,25,5,1.5,6,3.85,"(50, 100)","(0, 50)",serial_dictatorship,2,79.01526838881641,50.50301810865191,46.55963302752294,18.99785806429092,0,0.0,44,44,45,0.0268275000271387 +100,25,5,1.5,6,3.85,"(50, 100)","(0, 50)",serial_dictatorship,3,78.90221255399143,46.98544698544699,53.01455301455301,19.03925124586916,0,0.0,44,46,48,0.0276876999996602 100,25,5,1.5,6,3.85,"(50, 100)","(0, 50)",serial_dictatorship,4,80.16380013137217,42.85714285714285,55.77299412915851,17.952787048819832,0,0.0,43,44,47,0.0274860999779775 100,25,5,1.5,6,3.85,"(50, 100)","(0, 50)",round_robin,0,86.91928384637573,63.73390557939914,11.007025761124112,0.8226823816665797,0,0.0,100,100,100,0.0325757000246085 100,25,5,1.5,6,3.85,"(50, 100)","(0, 50)",round_robin,1,86.54738633454582,62.903225806451616,10.250569476082006,1.1340815662924364,0,0.0,100,100,100,0.02809639996849 @@ -632,7 +632,7 @@ num_of_agents,num_of_items,agent_capacity,supply_ratio,num_of_popular_items,mean 100,25,5,1.5,6,3.85,"(50, 100)","(0, 50)",almost_egalitarian_without_donation,3,87.11560300460359,73.73271889400922,16.919191919191903,1.686631122013193,0,0.0,98,100,100,4.3673173999995925 100,25,5,1.5,6,3.85,"(50, 100)","(0, 50)",almost_egalitarian_without_donation,4,87.79557155584202,75.35885167464114,12.799999999999995,2.050717042314828,0,0.0,97,98,98,4.143235799972899 100,25,5,1.5,6,3.85,"(50, 100)","(0, 50)",almost_egalitarian_with_donation,0,88.15292895596394,76.73860911270984,10.79136690647482,1.2876792533472212,0,0.0,97,98,98,4.110859500011429 -100,25,5,1.5,6,3.85,"(50, 100)","(0, 50)",almost_egalitarian_with_donation,1,87.6550433900682,75.31172069825436,14.651162790697668,1.2596326339146944,0,0.0,96,99,99,3.8652698999503623 +100,25,5,1.5,6,3.85,"(50, 100)","(0, 50)",almost_egalitarian_with_donation,1,87.6550433900682,75.31172069825436,14.651162790697668,1.2596326339146944,0,0.0,96,99,99,3.8652698999503614 100,25,5,1.5,6,3.85,"(50, 100)","(0, 50)",almost_egalitarian_with_donation,2,87.22311405034259,77.11864406779661,8.980582524271853,1.0909489390197449,0,0.0,96,98,98,4.286960299999919 100,25,5,1.5,6,3.85,"(50, 100)","(0, 50)",almost_egalitarian_with_donation,3,87.14826986567948,73.73271889400922,16.919191919191903,2.1190640906421514,0,0.0,98,100,100,4.11028849997092 100,25,5,1.5,6,3.85,"(50, 100)","(0, 50)",almost_egalitarian_with_donation,4,87.83867197427772,75.98152424942263,10.539845758354744,1.871014230693143,0,0.0,98,98,98,4.099437700002454 @@ -682,12 +682,12 @@ num_of_agents,num_of_items,agent_capacity,supply_ratio,num_of_popular_items,mean 100,25,5,1.5,9,3.85,"(50, 100)","(0, 50)",utilitarian_matching,3,94.23404300812784,80.0,8.16901408450704,0.7659305682020799,0,0.0,98,99,99,0.0983281999942846 100,25,5,1.5,9,3.85,"(50, 100)","(0, 50)",utilitarian_matching,4,94.40838997103434,75.98784194528876,24.01215805471124,1.1275087333743858,0,0.0,97,98,98,0.1065357000334188 100,25,5,1.5,9,3.85,"(50, 100)","(0, 50)",iterated_maximum_matching_unadjusted,0,94.0121625807498,86.61971830985915,4.427083333333314,0.1323590288162297,0,0.0,100,100,100,0.2036526000010781 -100,25,5,1.5,9,3.85,"(50, 100)","(0, 50)",iterated_maximum_matching_unadjusted,1,94.32369911457435,86.71023965141612,1.8372703412073437,0.0366921824187976,0,0.0,100,100,100,0.2007581000216305 +100,25,5,1.5,9,3.85,"(50, 100)","(0, 50)",iterated_maximum_matching_unadjusted,1,94.32369911457435,86.71023965141612,1.837270341207344,0.0366921824187976,0,0.0,100,100,100,0.2007581000216305 100,25,5,1.5,9,3.85,"(50, 100)","(0, 50)",iterated_maximum_matching_unadjusted,2,94.51803935944886,87.67123287671232,4.511278195488728,0.0880140994039673,0,0.0,100,100,100,0.1999445999972522 100,25,5,1.5,9,3.85,"(50, 100)","(0, 50)",iterated_maximum_matching_unadjusted,3,93.5541659795278,82.58928571428571,2.743142144638398,0.0656228822412282,0,0.0,100,100,100,0.2074239000212401 100,25,5,1.5,9,3.85,"(50, 100)","(0, 50)",iterated_maximum_matching_unadjusted,4,94.1873047513372,85.48387096774194,9.701492537313428,0.278339103067892,0,0.0,100,100,100,0.2151623999816365 100,25,5,1.5,9,3.85,"(50, 100)","(0, 50)",iterated_maximum_matching_adjusted,0,94.0121625807498,86.61971830985915,4.427083333333314,0.1323590288162297,0,0.0,100,100,100,0.2213703999877907 -100,25,5,1.5,9,3.85,"(50, 100)","(0, 50)",iterated_maximum_matching_adjusted,1,94.32369911457435,86.71023965141612,1.8372703412073437,0.0366921824187976,0,0.0,100,100,100,0.2179495000164024 +100,25,5,1.5,9,3.85,"(50, 100)","(0, 50)",iterated_maximum_matching_adjusted,1,94.32369911457435,86.71023965141612,1.837270341207344,0.0366921824187976,0,0.0,100,100,100,0.2179495000164024 100,25,5,1.5,9,3.85,"(50, 100)","(0, 50)",iterated_maximum_matching_adjusted,2,94.51803935944886,87.67123287671232,4.511278195488728,0.0880140994039673,0,0.0,100,100,100,0.206146499956958 100,25,5,1.5,9,3.85,"(50, 100)","(0, 50)",iterated_maximum_matching_adjusted,3,93.5541659795278,82.58928571428571,2.743142144638398,0.0656228822412282,0,0.0,100,100,100,0.2148712999769486 100,25,5,1.5,9,3.85,"(50, 100)","(0, 50)",iterated_maximum_matching_adjusted,4,94.1873047513372,85.48387096774194,9.701492537313428,0.278339103067892,0,0.0,100,100,100,0.2138637999887578 @@ -802,12 +802,12 @@ num_of_agents,num_of_items,agent_capacity,supply_ratio,num_of_popular_items,mean 200,25,5,1.1,9,2.6,"(50, 100)","(0, 50)",utilitarian_matching,3,95.19253756329546,75.59681697612733,17.664670658682624,0.7021058210034048,0,0.0,188,193,197,0.2701405999832786 200,25,5,1.1,9,2.6,"(50, 100)","(0, 50)",utilitarian_matching,4,95.6285194632036,82.63473053892216,9.281437125748496,0.2549762720170945,0,0.0,198,198,199,0.2781168999499641 200,25,5,1.1,9,2.6,"(50, 100)","(0, 50)",iterated_maximum_matching_unadjusted,0,94.72262829612411,83.11965811965813,5.035971223021576,0.0734992275525728,0,0.0,200,200,200,0.4969777999795042 -200,25,5,1.1,9,2.6,"(50, 100)","(0, 50)",iterated_maximum_matching_unadjusted,1,94.88484272963932,82.16704288939052,3.9267015706806343,0.0329695304983183,0,0.0,200,200,200,0.478354099963326 +200,25,5,1.1,9,2.6,"(50, 100)","(0, 50)",iterated_maximum_matching_unadjusted,1,94.88484272963932,82.16704288939052,3.9267015706806334,0.0329695304983183,0,0.0,200,200,200,0.478354099963326 200,25,5,1.1,9,2.6,"(50, 100)","(0, 50)",iterated_maximum_matching_unadjusted,2,94.9364128318822,79.90074441687345,0.0,0.0,0,0.0,200,200,200,0.4710555999772623 200,25,5,1.1,9,2.6,"(50, 100)","(0, 50)",iterated_maximum_matching_unadjusted,3,94.68378798704094,80.16877637130801,3.982300884955748,0.0604938865879083,0,0.0,200,200,200,0.4859166999813169 200,25,5,1.1,9,2.6,"(50, 100)","(0, 50)",iterated_maximum_matching_unadjusted,4,95.1132457818247,82.4742268041237,8.851674641148335,0.1505958128338,0,0.0,200,200,200,0.7355843000113964 200,25,5,1.1,9,2.6,"(50, 100)","(0, 50)",iterated_maximum_matching_adjusted,0,94.72262829612411,83.11965811965813,5.035971223021576,0.0734992275525728,0,0.0,200,200,200,0.505691200029105 -200,25,5,1.1,9,2.6,"(50, 100)","(0, 50)",iterated_maximum_matching_adjusted,1,94.88484272963932,82.16704288939052,3.9267015706806343,0.0329695304983183,0,0.0,200,200,200,0.5118290999671444 +200,25,5,1.1,9,2.6,"(50, 100)","(0, 50)",iterated_maximum_matching_adjusted,1,94.88484272963932,82.16704288939052,3.9267015706806334,0.0329695304983183,0,0.0,200,200,200,0.5118290999671444 200,25,5,1.1,9,2.6,"(50, 100)","(0, 50)",iterated_maximum_matching_adjusted,2,94.9364128318822,79.90074441687345,0.0,0.0,0,0.0,200,200,200,0.50116360001266 200,25,5,1.1,9,2.6,"(50, 100)","(0, 50)",iterated_maximum_matching_adjusted,3,94.68378798704094,80.16877637130801,3.982300884955748,0.0604938865879083,0,0.0,200,200,200,0.4843290999997407 200,25,5,1.1,9,2.6,"(50, 100)","(0, 50)",iterated_maximum_matching_adjusted,4,95.1132457818247,82.4742268041237,8.851674641148335,0.1505958128338,0,0.0,200,200,200,0.5086156000033952 @@ -918,7 +918,7 @@ num_of_agents,num_of_items,agent_capacity,supply_ratio,num_of_popular_items,mean 200,25,5,1.25,6,2.6,"(50, 100)","(0, 50)",almost_egalitarian_with_donation,4,92.19066036015025,79.64824120603015,11.420612813370468,0.954143969423088,0,0.0,191,193,200,8.17790840001544 200,25,5,1.25,6,3.85,"(50, 100)","(0, 50)",utilitarian_matching,0,85.33890311365296,61.458333333333336,26.30208333333332,5.573683754752485,0,0.0,172,176,176,0.264820700045675 200,25,5,1.25,6,3.85,"(50, 100)","(0, 50)",utilitarian_matching,1,84.8336170235696,60.31331592689296,28.35820895522388,7.131983841795546,0,0.0,167,172,172,0.2627806999953463 -200,25,5,1.25,6,3.85,"(50, 100)","(0, 50)",utilitarian_matching,2,83.93086150892121,64.84375,30.024813895781637,8.129491093317801,0,0.0,170,173,174,0.2510506000253372 +200,25,5,1.25,6,3.85,"(50, 100)","(0, 50)",utilitarian_matching,2,83.93086150892121,64.84375,30.02481389578164,8.129491093317801,0,0.0,170,173,174,0.2510506000253372 200,25,5,1.25,6,3.85,"(50, 100)","(0, 50)",utilitarian_matching,3,84.5010887728544,62.77173913043478,33.5195530726257,6.056199678517271,0,0.0,179,179,179,0.270503000007011 200,25,5,1.25,6,3.85,"(50, 100)","(0, 50)",utilitarian_matching,4,85.06841893935434,62.30366492146597,28.97727272727272,6.0191189849506,0,0.0,172,172,172,0.2586257000220939 200,25,5,1.25,6,3.85,"(50, 100)","(0, 50)",iterated_maximum_matching_unadjusted,0,85.13344794209947,70.31630170316302,10.165484633569733,1.3437634639384286,0,0.0,200,200,200,0.4857674000086263 @@ -932,7 +932,7 @@ num_of_agents,num_of_items,agent_capacity,supply_ratio,num_of_popular_items,mean 200,25,5,1.25,6,3.85,"(50, 100)","(0, 50)",iterated_maximum_matching_adjusted,3,84.45880922459408,70.09345794392523,10.098522167487673,1.3397104096482726,0,0.0,200,200,200,0.4950892999768257 200,25,5,1.25,6,3.85,"(50, 100)","(0, 50)",iterated_maximum_matching_adjusted,4,84.91092201287267,71.46067415730337,11.111111111111114,1.2377180176227316,0,0.0,200,200,200,0.5287086999742314 200,25,5,1.25,6,3.85,"(50, 100)","(0, 50)",serial_dictatorship,0,77.41032763933252,48.31223628691983,47.85992217898832,20.74332486516641,0,0.0,76,78,80,0.079756700026337 -200,25,5,1.25,6,3.85,"(50, 100)","(0, 50)",serial_dictatorship,1,77.68033404625723,48.61751152073733,50.5952380952381,20.956791382941876,0,0.0,77,79,80,0.0937631000415422 +200,25,5,1.25,6,3.85,"(50, 100)","(0, 50)",serial_dictatorship,1,77.68033404625723,48.61751152073733,50.5952380952381,20.95679138294188,0,0.0,77,79,80,0.0937631000415422 200,25,5,1.25,6,3.85,"(50, 100)","(0, 50)",serial_dictatorship,2,76.25668161216753,36.234458259325045,63.76554174067496,22.078378385694744,0,0.0,74,75,76,0.098666499950923 200,25,5,1.25,6,3.85,"(50, 100)","(0, 50)",serial_dictatorship,3,76.48282580819028,43.43629343629344,55.01930501930502,21.937650730885736,0,0.0,75,77,80,0.1071438000071793 200,25,5,1.25,6,3.85,"(50, 100)","(0, 50)",serial_dictatorship,4,77.06126091303686,42.85714285714285,55.77299412915851,21.45208216783536,0,0.0,75,76,76,0.082916599989403 @@ -946,7 +946,7 @@ num_of_agents,num_of_items,agent_capacity,supply_ratio,num_of_popular_items,mean 200,25,5,1.25,6,3.85,"(50, 100)","(0, 50)",bidirectional_round_robin,2,82.66546698784745,67.32456140350878,13.348946135831383,2.2271861693144,0,0.0,200,200,200,0.0799269000417552 200,25,5,1.25,6,3.85,"(50, 100)","(0, 50)",bidirectional_round_robin,3,83.43217616223791,63.1578947368421,12.293144208037816,2.0392687419266933,0,0.0,200,200,200,0.0808474999503232 200,25,5,1.25,6,3.85,"(50, 100)","(0, 50)",bidirectional_round_robin,4,83.50143933544926,61.839530332681015,14.468085106382986,2.270750801748296,0,0.0,200,200,200,0.080473699956201 -200,25,5,1.25,6,3.85,"(50, 100)","(0, 50)",almost_egalitarian_without_donation,0,84.89778603906156,71.84684684684684,18.090452261306535,2.1840106033808917,0,0.0,185,189,189,8.625106999999844 +200,25,5,1.25,6,3.85,"(50, 100)","(0, 50)",almost_egalitarian_without_donation,0,84.89778603906156,71.84684684684684,18.09045226130653,2.184010603380892,0,0.0,185,189,189,8.625106999999844 200,25,5,1.25,6,3.85,"(50, 100)","(0, 50)",almost_egalitarian_without_donation,1,84.47041194746024,70.13333333333334,20.53333333333332,2.621260732981193,0,0.0,186,189,189,8.341009799973108 200,25,5,1.25,6,3.85,"(50, 100)","(0, 50)",almost_egalitarian_without_donation,2,83.76935587348797,69.55503512880561,23.382045929018787,2.962306316442057,0,0.0,187,191,191,7.934184400015511 200,25,5,1.25,6,3.85,"(50, 100)","(0, 50)",almost_egalitarian_without_donation,3,84.1408919021938,70.74829931972789,22.777777777777786,2.4056978962989746,0,0.0,190,193,193,8.424328699999023 @@ -1114,7 +1114,7 @@ num_of_agents,num_of_items,agent_capacity,supply_ratio,num_of_popular_items,mean 200,25,5,1.5,6,3.85,"(50, 100)","(0, 50)",almost_egalitarian_with_donation,0,88.240302330689,76.55502392344498,19.61722488038278,2.492805019225708,0,0.0,193,195,195,8.360235699976329 200,25,5,1.5,6,3.85,"(50, 100)","(0, 50)",almost_egalitarian_with_donation,1,87.72482279890839,75.1111111111111,13.054830287206272,1.905244012861544,0,0.0,195,196,196,8.627697699994314 200,25,5,1.5,6,3.85,"(50, 100)","(0, 50)",almost_egalitarian_with_donation,2,87.05593418378048,75.78125,13.380281690140848,2.337702126061905,0,0.0,195,198,198,7.993366399954539 -200,25,5,1.5,6,3.85,"(50, 100)","(0, 50)",almost_egalitarian_with_donation,3,87.45962379984006,75.36585365853658,19.55307262569832,1.9827060225478916,0,0.0,196,197,197,8.279081199958455 +200,25,5,1.5,6,3.85,"(50, 100)","(0, 50)",almost_egalitarian_with_donation,3,87.45962379984006,75.36585365853658,19.55307262569832,1.982706022547892,0,0.0,196,197,197,8.279081199958455 200,25,5,1.5,6,3.85,"(50, 100)","(0, 50)",almost_egalitarian_with_donation,4,88.04116767531528,75.98152424942263,13.51981351981351,2.2901947819680912,0,0.0,200,200,200,8.23431069997605 200,25,5,1.5,9,2.6,"(50, 100)","(0, 50)",utilitarian_matching,0,99.05897259153896,92.5207756232687,2.506963788300837,0.0373363463761182,0,0.0,200,200,200,0.2373196000116877 200,25,5,1.5,9,2.6,"(50, 100)","(0, 50)",utilitarian_matching,1,98.96468286043702,91.64882226980728,5.217391304347828,0.1408042608464455,0,0.0,199,200,200,0.2320596000063233 @@ -1268,7 +1268,7 @@ num_of_agents,num_of_items,agent_capacity,supply_ratio,num_of_popular_items,mean 300,25,5,1.1,6,3.85,"(50, 100)","(0, 50)",bidirectional_round_robin,4,81.56025916697213,61.839530332681015,14.421252371916507,2.7530905193220727,0,0.0,300,300,300,0.160976400016807 300,25,5,1.1,6,3.85,"(50, 100)","(0, 50)",almost_egalitarian_without_donation,0,82.32607530713096,68.48958333333334,27.34375,3.2118381227613524,0,0.0,272,279,279,12.807422500045504 300,25,5,1.1,6,3.85,"(50, 100)","(0, 50)",almost_egalitarian_without_donation,1,82.46353291930463,66.95095948827291,22.9381443298969,3.8060913086026487,0,0.0,270,277,278,12.906547699996736 -300,25,5,1.1,6,3.85,"(50, 100)","(0, 50)",almost_egalitarian_without_donation,2,82.0594588329246,67.13947990543736,23.209876543209877,3.995023834935862,0,0.0,272,275,277,13.003186799993273 +300,25,5,1.1,6,3.85,"(50, 100)","(0, 50)",almost_egalitarian_without_donation,2,82.0594588329246,67.13947990543736,23.20987654320988,3.995023834935862,0,0.0,272,275,277,13.003186799993273 300,25,5,1.1,6,3.85,"(50, 100)","(0, 50)",almost_egalitarian_without_donation,3,82.27436324479855,66.40471512770138,21.1031175059952,3.165325579883347,0,0.0,277,282,282,12.545877199969254 300,25,5,1.1,6,3.85,"(50, 100)","(0, 50)",almost_egalitarian_without_donation,4,82.52302450165756,67.5603217158177,20.7977207977208,3.447997088799698,0,0.0,272,279,279,13.056192499992903 300,25,5,1.1,6,3.85,"(50, 100)","(0, 50)",almost_egalitarian_with_donation,0,82.38144759294828,69.14660831509846,20.7977207977208,3.240208676263699,0,0.0,273,276,276,12.48413170001004 @@ -1429,7 +1429,7 @@ num_of_agents,num_of_items,agent_capacity,supply_ratio,num_of_popular_items,mean 300,25,5,1.25,6,3.85,"(50, 100)","(0, 50)",almost_egalitarian_without_donation,0,84.52230807691095,70.31630170316302,20.044543429844097,3.161737361691262,0,0.0,280,284,284,13.377319000021089 300,25,5,1.25,6,3.85,"(50, 100)","(0, 50)",almost_egalitarian_without_donation,1,84.54109254934386,70.13333333333334,23.09582309582308,2.9702978139206606,0,0.0,286,289,290,13.1640964000253 300,25,5,1.25,6,3.85,"(50, 100)","(0, 50)",almost_egalitarian_without_donation,2,84.28821050126908,71.0762331838565,22.22222222222221,3.0776694252573584,0,0.0,286,288,288,12.946058200031985 -300,25,5,1.25,6,3.85,"(50, 100)","(0, 50)",almost_egalitarian_without_donation,3,84.39390691449232,70.33707865168539,20.620842572062077,2.953456299724706,0,0.0,291,292,292,12.858996899973135 +300,25,5,1.25,6,3.85,"(50, 100)","(0, 50)",almost_egalitarian_without_donation,3,84.39390691449232,70.33707865168539,20.62084257206208,2.953456299724706,0,0.0,291,292,292,12.858996899973135 300,25,5,1.25,6,3.85,"(50, 100)","(0, 50)",almost_egalitarian_without_donation,4,84.63360500893116,70.37861915367483,19.51219512195121,2.9819599236055083,0,0.0,288,291,291,12.88878029998159 300,25,5,1.25,6,3.85,"(50, 100)","(0, 50)",almost_egalitarian_with_donation,0,84.54810082695397,70.31630170316302,20.044543429844097,3.003001379154697,0,0.0,278,284,284,12.893019200011622 300,25,5,1.25,6,3.85,"(50, 100)","(0, 50)",almost_egalitarian_with_donation,1,84.54346883108616,70.90517241379311,21.333333333333343,2.8001834303311783,0,0.0,285,289,290,12.36405350000132 @@ -1480,7 +1480,7 @@ num_of_agents,num_of_items,agent_capacity,supply_ratio,num_of_popular_items,mean 300,25,5,1.25,9,3.85,"(50, 100)","(0, 50)",utilitarian_matching,1,91.25915737629052,69.62750716332378,23.34293948126802,2.346187786039772,0,0.0,291,292,292,0.4549317000200972 300,25,5,1.25,9,3.85,"(50, 100)","(0, 50)",utilitarian_matching,2,90.9946556531222,73.71007371007371,18.37837837837837,2.6286545069921288,0,0.0,291,295,295,0.4512803999823518 300,25,5,1.25,9,3.85,"(50, 100)","(0, 50)",utilitarian_matching,3,91.13969819136702,72.8125,19.43661971830987,2.759831960110701,0,0.0,290,293,293,0.4470023000030778 -300,25,5,1.25,9,3.85,"(50, 100)","(0, 50)",utilitarian_matching,4,91.66044243092756,73.71428571428571,18.28571428571429,1.8973170981999092,0,0.0,297,297,297,0.4255536000127904 +300,25,5,1.25,9,3.85,"(50, 100)","(0, 50)",utilitarian_matching,4,91.66044243092756,73.71428571428571,18.28571428571429,1.8973170981999088,0,0.0,297,297,297,0.4255536000127904 300,25,5,1.25,9,3.85,"(50, 100)","(0, 50)",iterated_maximum_matching_unadjusted,0,90.30258587738804,79.91071428571429,12.222222222222214,0.6095551147542653,0,0.0,300,300,300,0.840479200007394 300,25,5,1.25,9,3.85,"(50, 100)","(0, 50)",iterated_maximum_matching_unadjusted,1,90.8921537591387,80.95238095238095,6.93069306930694,0.5582997534011516,0,0.0,300,300,300,1.2798259999835864 300,25,5,1.25,9,3.85,"(50, 100)","(0, 50)",iterated_maximum_matching_unadjusted,2,90.6618079863019,80.73394495412845,8.92857142857143,0.5108100492958745,0,0.0,300,300,300,0.8572887000045739 @@ -1583,7 +1583,7 @@ num_of_agents,num_of_items,agent_capacity,supply_ratio,num_of_popular_items,mean 300,25,5,1.5,6,3.85,"(50, 100)","(0, 50)",bidirectional_round_robin,3,86.8564868682753,63.1578947368421,12.783505154639172,1.2482666299827916,0,0.0,300,300,300,0.1590215000323951 300,25,5,1.5,6,3.85,"(50, 100)","(0, 50)",bidirectional_round_robin,4,86.76858878682538,62.97872340425532,14.988290398126452,1.30301847745981,0,0.0,300,300,300,0.1528918999829329 300,25,5,1.5,6,3.85,"(50, 100)","(0, 50)",almost_egalitarian_without_donation,0,87.79002384374746,74.8283752860412,21.689497716894977,2.9876988261126343,0,0.0,299,299,299,12.907390200009104 -300,25,5,1.5,6,3.85,"(50, 100)","(0, 50)",almost_egalitarian_without_donation,1,87.79535055747195,76.53061224489795,17.164179104477626,2.9958001846539397,0,0.0,293,295,296,12.82577290001791 +300,25,5,1.5,6,3.85,"(50, 100)","(0, 50)",almost_egalitarian_without_donation,1,87.79535055747195,76.53061224489795,17.164179104477626,2.99580018465394,0,0.0,293,295,296,12.82577290001791 300,25,5,1.5,6,3.85,"(50, 100)","(0, 50)",almost_egalitarian_without_donation,2,87.58289248696057,75.57603686635944,15.43778801843318,2.886913127145628,0,0.0,290,294,294,13.258243699965533 300,25,5,1.5,6,3.85,"(50, 100)","(0, 50)",almost_egalitarian_without_donation,3,87.67796499095391,75.87064676616916,17.1875,2.6062324573664744,0,0.0,298,300,300,12.944945299997926 300,25,5,1.5,6,3.85,"(50, 100)","(0, 50)",almost_egalitarian_without_donation,4,87.83427744946994,75.35885167464114,18.010752688172047,2.9564458292871034,0,0.0,297,298,298,12.941251999989618 @@ -1658,517 +1658,517 @@ num_of_agents,num_of_items,agent_capacity,supply_ratio,num_of_popular_items,mean 300,25,5,1.5,9,3.85,"(50, 100)","(0, 50)",almost_egalitarian_without_donation,3,94.44629399940044,83.48214285714286,8.962264150943398,0.898915205613832,0,0.0,298,300,300,12.439004899992142 300,25,5,1.5,9,3.85,"(50, 100)","(0, 50)",almost_egalitarian_without_donation,4,94.75820378502348,85.49222797927462,11.200000000000005,0.8980019442455517,0,0.0,300,300,300,12.999182800005656 100,25,5,1.1,6,2.6,"(50, 100)","(0, 50)",utilitarian_matching,0,91.01465052092666,75.58441558441558,19.61325966850829,1.6565634869489474,0,0.0,85,87,95,0.1378458999679424 -100,25,5,1.1,6,2.6,"(50, 100)","(0, 50)",utilitarian_matching,1,90.46266823624113,72.77777777777777,13.63636363636364,1.5098292614546187,0,0.0,83,84,91,0.11513519997242838 -100,25,5,1.1,6,2.6,"(50, 100)","(0, 50)",utilitarian_matching,2,90.66139355620881,74.71264367816092,15.34772182254197,1.1566266260879317,0,0.0,85,86,95,0.1112132000271231 -100,25,5,1.1,6,2.6,"(50, 100)","(0, 50)",utilitarian_matching,3,90.54056073413525,73.48066298342542,23.180592991913755,1.534518839391132,0,0.0,88,88,93,0.10552850004751235 -100,25,5,1.1,6,2.6,"(50, 100)","(0, 50)",utilitarian_matching,4,90.90706955039724,77.92553191489363,17.72486772486772,1.2599697463498045,0,0.0,86,86,93,0.11221430002478883 -100,25,5,1.1,6,2.6,"(50, 100)","(0, 50)",iterated_maximum_matching_unadjusted,0,90.69800944892519,73.91304347826086,4.926108374384242,0.2530781252975517,0,0.0,100,100,100,0.24069830001099035 -100,25,5,1.1,6,2.6,"(50, 100)","(0, 50)",iterated_maximum_matching_unadjusted,1,90.05898169298018,75.75057736720554,7.6530612244897895,0.28109584936391385,0,0.0,98,100,100,0.20564780000131577 -100,25,5,1.1,6,2.6,"(50, 100)","(0, 50)",iterated_maximum_matching_unadjusted,2,90.31369025023268,76.63755458515283,4.092071611253203,0.24592381006821384,0,0.0,100,100,100,0.25678930000867695 -100,25,5,1.1,6,2.6,"(50, 100)","(0, 50)",iterated_maximum_matching_unadjusted,3,90.17477726567944,76.06837606837607,6.43564356435644,0.27102276919757345,0,0.0,100,100,100,0.21140159998321906 +100,25,5,1.1,6,2.6,"(50, 100)","(0, 50)",utilitarian_matching,1,90.46266823624111,72.77777777777777,13.63636363636364,1.5098292614546187,0,0.0,83,84,91,0.1151351999724283 +100,25,5,1.1,6,2.6,"(50, 100)","(0, 50)",utilitarian_matching,2,90.6613935562088,74.71264367816092,15.34772182254197,1.1566266260879317,0,0.0,85,86,95,0.1112132000271231 +100,25,5,1.1,6,2.6,"(50, 100)","(0, 50)",utilitarian_matching,3,90.54056073413524,73.48066298342542,23.180592991913755,1.534518839391132,0,0.0,88,88,93,0.1055285000475123 +100,25,5,1.1,6,2.6,"(50, 100)","(0, 50)",utilitarian_matching,4,90.90706955039724,77.92553191489363,17.72486772486772,1.2599697463498043,0,0.0,86,86,93,0.1122143000247888 +100,25,5,1.1,6,2.6,"(50, 100)","(0, 50)",iterated_maximum_matching_unadjusted,0,90.6980094489252,73.91304347826086,4.926108374384242,0.2530781252975517,0,0.0,100,100,100,0.2406983000109903 +100,25,5,1.1,6,2.6,"(50, 100)","(0, 50)",iterated_maximum_matching_unadjusted,1,90.05898169298018,75.75057736720554,7.653061224489789,0.2810958493639138,0,0.0,98,100,100,0.2056478000013157 +100,25,5,1.1,6,2.6,"(50, 100)","(0, 50)",iterated_maximum_matching_unadjusted,2,90.31369025023268,76.63755458515283,4.092071611253203,0.2459238100682138,0,0.0,100,100,100,0.2567893000086769 +100,25,5,1.1,6,2.6,"(50, 100)","(0, 50)",iterated_maximum_matching_unadjusted,3,90.17477726567944,76.06837606837607,6.43564356435644,0.2710227691975734,0,0.0,100,100,100,0.211401599983219 100,25,5,1.1,6,2.6,"(50, 100)","(0, 50)",iterated_maximum_matching_unadjusted,4,90.52673942284926,78.47222222222221,11.420612813370468,0.5994824364690353,0,0.0,100,100,100,0.2172184999799356 -100,25,5,1.1,6,2.6,"(50, 100)","(0, 50)",iterated_maximum_matching_adjusted,0,90.69800944892519,73.91304347826086,4.926108374384242,0.2530781252975517,0,0.0,100,100,100,0.25654829997802153 -100,25,5,1.1,6,2.6,"(50, 100)","(0, 50)",iterated_maximum_matching_adjusted,1,90.05898169298018,75.75057736720554,7.6530612244897895,0.28109584936391385,0,0.0,98,100,100,0.21431210002629086 -100,25,5,1.1,6,2.6,"(50, 100)","(0, 50)",iterated_maximum_matching_adjusted,2,90.31369025023268,76.63755458515283,4.092071611253203,0.24592381006821384,0,0.0,100,100,100,0.25392360001569614 -100,25,5,1.1,6,2.6,"(50, 100)","(0, 50)",iterated_maximum_matching_adjusted,3,90.17477726567944,76.06837606837607,6.43564356435644,0.27102276919757345,0,0.0,100,100,100,0.21624510001856834 -100,25,5,1.1,6,2.6,"(50, 100)","(0, 50)",iterated_maximum_matching_adjusted,4,90.52673942284926,78.47222222222221,11.420612813370468,0.5994824364690353,0,0.0,100,100,100,0.21266000001924112 -100,25,5,1.1,6,2.6,"(50, 100)","(0, 50)",serial_dictatorship,0,83.93973256454247,53.65853658536586,42.350332594235034,10.391810756021624,0,0.0,45,49,65,0.027946599991992116 -100,25,5,1.1,6,2.6,"(50, 100)","(0, 50)",serial_dictatorship,1,83.85994067253297,52.23880597014925,40.79601990049752,10.47909766545374,0,0.0,48,51,66,0.026801500003784895 -100,25,5,1.1,6,2.6,"(50, 100)","(0, 50)",serial_dictatorship,2,82.76285699533251,55.1578947368421,39.05882352941177,11.119402076666312,0,0.0,42,45,68,0.026120300055481493 -100,25,5,1.1,6,2.6,"(50, 100)","(0, 50)",serial_dictatorship,3,82.96733961454473,53.81165919282511,36.986301369863014,10.957029429368244,0,0.0,43,49,58,0.02737880003405735 -100,25,5,1.1,6,2.6,"(50, 100)","(0, 50)",serial_dictatorship,4,83.81649370081732,47.18875502008032,47.79116465863454,10.507997183487241,0,0.0,44,47,68,0.02595019998261705 -100,25,5,1.1,6,2.6,"(50, 100)","(0, 50)",round_robin,0,89.14502498564894,70.28824833702882,13.525498891352555,0.8188242749435879,0,0.0,100,100,100,0.02545219997409731 -100,25,5,1.1,6,2.6,"(50, 100)","(0, 50)",round_robin,1,88.9569501802385,71.14427860696517,10.629067245119316,0.9373711451525982,0,0.0,97,100,100,0.026164600043557584 -100,25,5,1.1,6,2.6,"(50, 100)","(0, 50)",round_robin,2,89.30765437914384,75.52941176470588,13.17647058823529,0.6264500383101366,0,0.0,100,100,100,0.02552949998062104 -100,25,5,1.1,6,2.6,"(50, 100)","(0, 50)",round_robin,3,88.54225579603148,72.16699801192843,9.214092140921409,0.7784956180631252,0,0.0,100,100,100,0.02816089999396354 -100,25,5,1.1,6,2.6,"(50, 100)","(0, 50)",round_robin,4,89.01348388841065,68.07228915662651,10.886075949367083,1.0044038757047764,0,0.0,100,100,100,0.027487899991683662 -100,25,5,1.1,6,2.6,"(50, 100)","(0, 50)",bidirectional_round_robin,0,89.25537789336595,72.5609756097561,12.732095490716176,0.6628459124395357,0,0.0,100,100,100,0.025176500028464943 -100,25,5,1.1,6,2.6,"(50, 100)","(0, 50)",bidirectional_round_robin,1,88.47098486972988,71.08655616942909,8.62944162436547,0.449421380473662,0,0.0,97,100,100,0.05759470001794398 -100,25,5,1.1,6,2.6,"(50, 100)","(0, 50)",bidirectional_round_robin,2,89.20892948493197,71.96467991169978,10.070257611241217,0.5846588306373961,0,0.0,100,100,100,0.025046099966857582 -100,25,5,1.1,6,2.6,"(50, 100)","(0, 50)",bidirectional_round_robin,3,88.83135433066383,71.17117117117117,13.8888888888889,0.6720168592752749,0,0.0,100,100,100,0.02670460002264008 -100,25,5,1.1,6,2.6,"(50, 100)","(0, 50)",bidirectional_round_robin,4,88.92636174958639,71.54989384288747,17.15686274509804,1.162246145877168,0,0.0,100,100,100,0.026761599991004914 -100,25,5,1.1,6,2.6,"(50, 100)","(0, 50)",almost_egalitarian_without_donation,0,90.54216632239007,73.00884955752213,16.577540106951872,1.1590061364473878,0,0.0,88,92,98,3.7175331999897026 +100,25,5,1.1,6,2.6,"(50, 100)","(0, 50)",iterated_maximum_matching_adjusted,0,90.6980094489252,73.91304347826086,4.926108374384242,0.2530781252975517,0,0.0,100,100,100,0.2565482999780215 +100,25,5,1.1,6,2.6,"(50, 100)","(0, 50)",iterated_maximum_matching_adjusted,1,90.05898169298018,75.75057736720554,7.653061224489789,0.2810958493639138,0,0.0,98,100,100,0.2143121000262908 +100,25,5,1.1,6,2.6,"(50, 100)","(0, 50)",iterated_maximum_matching_adjusted,2,90.31369025023268,76.63755458515283,4.092071611253203,0.2459238100682138,0,0.0,100,100,100,0.2539236000156961 +100,25,5,1.1,6,2.6,"(50, 100)","(0, 50)",iterated_maximum_matching_adjusted,3,90.17477726567944,76.06837606837607,6.43564356435644,0.2710227691975734,0,0.0,100,100,100,0.2162451000185683 +100,25,5,1.1,6,2.6,"(50, 100)","(0, 50)",iterated_maximum_matching_adjusted,4,90.52673942284926,78.47222222222221,11.420612813370468,0.5994824364690353,0,0.0,100,100,100,0.2126600000192411 +100,25,5,1.1,6,2.6,"(50, 100)","(0, 50)",serial_dictatorship,0,83.93973256454247,53.65853658536586,42.35033259423504,10.391810756021624,0,0.0,45,49,65,0.0279465999919921 +100,25,5,1.1,6,2.6,"(50, 100)","(0, 50)",serial_dictatorship,1,83.85994067253297,52.23880597014925,40.79601990049752,10.47909766545374,0,0.0,48,51,66,0.0268015000037848 +100,25,5,1.1,6,2.6,"(50, 100)","(0, 50)",serial_dictatorship,2,82.76285699533251,55.1578947368421,39.05882352941177,11.119402076666312,0,0.0,42,45,68,0.0261203000554814 +100,25,5,1.1,6,2.6,"(50, 100)","(0, 50)",serial_dictatorship,3,82.96733961454473,53.81165919282511,36.98630136986301,10.957029429368244,0,0.0,43,49,58,0.0273788000340573 +100,25,5,1.1,6,2.6,"(50, 100)","(0, 50)",serial_dictatorship,4,83.81649370081732,47.18875502008032,47.79116465863454,10.50799718348724,0,0.0,44,47,68,0.025950199982617 +100,25,5,1.1,6,2.6,"(50, 100)","(0, 50)",round_robin,0,89.14502498564894,70.28824833702882,13.525498891352555,0.8188242749435879,0,0.0,100,100,100,0.0254521999740973 +100,25,5,1.1,6,2.6,"(50, 100)","(0, 50)",round_robin,1,88.9569501802385,71.14427860696517,10.629067245119316,0.9373711451525982,0,0.0,97,100,100,0.0261646000435575 +100,25,5,1.1,6,2.6,"(50, 100)","(0, 50)",round_robin,2,89.30765437914384,75.52941176470588,13.17647058823529,0.6264500383101366,0,0.0,100,100,100,0.025529499980621 +100,25,5,1.1,6,2.6,"(50, 100)","(0, 50)",round_robin,3,88.54225579603148,72.16699801192843,9.214092140921409,0.7784956180631252,0,0.0,100,100,100,0.0281608999939635 +100,25,5,1.1,6,2.6,"(50, 100)","(0, 50)",round_robin,4,89.01348388841065,68.07228915662651,10.886075949367084,1.0044038757047764,0,0.0,100,100,100,0.0274878999916836 +100,25,5,1.1,6,2.6,"(50, 100)","(0, 50)",bidirectional_round_robin,0,89.25537789336595,72.5609756097561,12.732095490716176,0.6628459124395357,0,0.0,100,100,100,0.0251765000284649 +100,25,5,1.1,6,2.6,"(50, 100)","(0, 50)",bidirectional_round_robin,1,88.47098486972988,71.08655616942909,8.62944162436547,0.449421380473662,0,0.0,97,100,100,0.0575947000179439 +100,25,5,1.1,6,2.6,"(50, 100)","(0, 50)",bidirectional_round_robin,2,89.20892948493197,71.96467991169978,10.070257611241216,0.5846588306373961,0,0.0,100,100,100,0.0250460999668575 +100,25,5,1.1,6,2.6,"(50, 100)","(0, 50)",bidirectional_round_robin,3,88.83135433066383,71.17117117117117,13.8888888888889,0.6720168592752749,0,0.0,100,100,100,0.02670460002264 +100,25,5,1.1,6,2.6,"(50, 100)","(0, 50)",bidirectional_round_robin,4,88.92636174958639,71.54989384288747,17.15686274509804,1.162246145877168,0,0.0,100,100,100,0.0267615999910049 +100,25,5,1.1,6,2.6,"(50, 100)","(0, 50)",almost_egalitarian_without_donation,0,90.54216632239007,73.00884955752213,16.577540106951872,1.1590061364473878,0,0.0,88,92,98,3.717533199989703 100,25,5,1.1,6,2.6,"(50, 100)","(0, 50)",almost_egalitarian_without_donation,1,89.86473318137267,73.67021276595744,11.403508771929822,0.6740777679349628,0,0.0,90,93,97,3.840356700005941 100,25,5,1.1,6,2.6,"(50, 100)","(0, 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(A-CEEI)" + by ERIC BUDISH, RUIQUAN GAO, ABRAHAM OTHMAN, AVIAD RUBINSTEIN, QIANFAN ZHANG. (2023) + link to the article: https://arxiv.org/pdf/2305.11406 + ALGORITHM 1: find an A-CEEI with (contested) EF-TB property + + +Programmers: Erga Bar-Ilan, Ofir Shitrit and Renana Turgeman. +Since: 2024-01 +""" +from enum import Enum +import logging +import numpy as np + +from fairpyx import Instance, AllocationBuilder +from fairpyx.algorithms.ACEEI import linear_program as lp +from fairpyx.algorithms.ACEEI.calculate_combinations import get_combinations_courses_sorted + + +class EFTBStatus(Enum): + NO_EF_TB = 0 + EF_TB = 1 + CONTESTED_EF_TB = 2 + + +logger = logging.getLogger(__name__) + + +# ---------------------The main function--------------------- + +def find_ACEEI_with_EFTB(alloc: AllocationBuilder, **kwargs): + """ + ALGORITHM 1: find an A-CEEI with (contested) EF-TB property + + :param instance: a fair-course-allocation instance + :param initial_budgets: Students' initial budgets + :param delta: The step size + :param epsilon: maximum budget perturbation + :param t: type 𝑑 of the EF-TB constraint, + 0 for no EF-TB constraint, + 1 for EF-TB constraint, + 2 for contested EF-TB + :return final courses prices, final budgets, final distribution + + >>> from fairpyx.adaptors import divide + + >>> from fairpyx.utils.test_utils import stringify + + >>> instance = Instance( + ... valuations={"avi":{"x":1, "y":2, "z":4}, "beni":{"x":2, "y":3, "z":1}}, + ... agent_capacities=2, + ... item_capacities={"x":1, "y":1, "z":2}) + >>> initial_budgets = {"avi":2, "beni":3} + >>> delta = 0.5 + >>> epsilon = 0.5 + >>> t = EFTBStatus.NO_EF_TB + >>> stringify(divide(find_ACEEI_with_EFTB, instance=instance, initial_budgets=initial_budgets, + ... delta=delta, epsilon=epsilon, t=t)) + "{avi:['x', 'z'], beni:['y', 'z']}" + + >>> instance = Instance( + ... valuations={"avi":{"x":5, "y":2, "z":1}, "beni":{"x":4, "y":1, "z":3}}, + ... agent_capacities=2, + ... item_capacities={"x":1, "y":1, "z":2}) + >>> initial_budgets = {"avi":3, "beni":4} + >>> delta = 0.5 + >>> epsilon = 1 + >>> t = EFTBStatus.EF_TB + >>> stringify(divide(find_ACEEI_with_EFTB, instance=instance, initial_budgets=initial_budgets, + ... delta=delta, epsilon=epsilon, t=t)) + "{avi:['y', 'z'], beni:['x', 'z']}" + + >>> instance = Instance( + ... valuations={"avi":{"x":5, "y":5, "z":1}, "beni":{"x":4, "y":6, "z":4}}, + ... agent_capacities=2, + ... item_capacities={"x":1, "y":2, "z":2}) + >>> initial_budgets = {"avi":5, "beni":4} + >>> delta = 0.5 + >>> epsilon = 2 + >>> t = EFTBStatus.EF_TB + >>> stringify(divide(find_ACEEI_with_EFTB, instance=instance, initial_budgets=initial_budgets, + ... delta=delta, epsilon=epsilon, t=t)) + "{avi:['x', 'y'], beni:['y', 'z']}" + + >>> instance = Instance( + ... valuations={"avi":{"x":10, "y":20}, "beni":{"x":10, "y":20}}, + ... agent_capacities=1, + ... item_capacities = {"x":1, "y":1}) + >>> initial_budgets = {"avi":1.1, "beni":1} + >>> delta = 0.1 + >>> epsilon = 0.2 + >>> t = EFTBStatus.EF_TB + >>> stringify(divide(find_ACEEI_with_EFTB, instance=instance, initial_budgets=initial_budgets, + ... delta=delta, epsilon=epsilon, t=t)) + "{avi:['y'], beni:['x']}" + + >>> instance = Instance( + ... valuations={"avi":{"x":2}, "beni":{"x":3}}, + ... agent_capacities=1, + ... item_capacities = {"x":1}) + >>> initial_budgets = {"avi":1.1, "beni":1} + >>> delta = 0.1 + >>> epsilon = 0.2 + >>> t = EFTBStatus.EF_TB + >>> stringify(divide(find_ACEEI_with_EFTB, instance=instance, + ... initial_budgets=initial_budgets,delta=delta, epsilon=epsilon, t=t)) + "{avi:['x'], beni:[]}" + + >>> instance = Instance(valuations={"avi":{"x":5, "y":4, "z":1}, + ... "beni":{"x":4, "y":6, "z":3}}, + ... agent_capacities=2, + ... item_capacities={"x":1, "y":1, "z":2}) + >>> initial_budgets = {"avi":5, "beni":4} + >>> delta = 0.5 + >>> epsilon = 2 + >>> t = EFTBStatus.CONTESTED_EF_TB + >>> stringify(divide(find_ACEEI_with_EFTB, instance=instance, initial_budgets=initial_budgets, + ... delta=delta, epsilon=epsilon, t=t)) + "{avi:['x', 'z'], beni:['y', 'z']}" + """ + # allocation = [[0 for _ in range(instance.num_of_agents)] for _ in range(instance.num_of_items)] + # 1) init prices vector to be 0 + + initial_budgets = kwargs.get('initial_budgets') + delta = kwargs.get('delta') + epsilon = kwargs.get('epsilon') + t = kwargs.get('t') + + logger.info("ACEEI algorithm with initial budgets = %s, delta = %s, epsilon = %s, t = %s", initial_budgets, delta, + epsilon, t) + + prices = {key: 0 for key in alloc.remaining_items()} + clearing_error = 1 + new_budgets = {} + combinations_courses_sorted = get_combinations_courses_sorted(alloc.instance) + + while clearing_error: + # 2) πœ–-budget perturbation + new_budgets, clearing_error, allocation, excess_demand_per_course = find_budget_perturbation( + initial_budgets, epsilon, prices, alloc.instance, t, combinations_courses_sorted) + + if clearing_error is None: + raise ValueError("Clearing error is None") + # 3) If βˆ₯π’›Λœ(𝒖,𝒄, 𝒑, 𝒃) βˆ₯2 = 0, terminate with 𝒑* = 𝒑, 𝒃* = 𝒃 + logger.info("Clearing error is %s", clearing_error) + if np.allclose(clearing_error, 0): + break + # 4) update 𝒑 ← 𝒑 + π›Ώπ’›Λœ(𝒖,𝒄, 𝒑, 𝒃), then go back to step 2. + for key in prices: + prices[key] += delta * excess_demand_per_course[key] + logger.info("Update prices to %s\n", prices) + + logger.info("Clearing error 0!") + for student, (price, bundle) in new_budgets.items(): + logger.info(f"Giving {bundle} to {student}") + alloc.give_bundle(student, bundle) + + # print the final budget (b* = new_budgets) for each student + final_budget = "" + for key, value in new_budgets.items(): + final_budget += f"{key}: {value[0]}, " + + # Remove the trailing comma and space + final_budget = final_budget.rstrip(", ") + logger.info(f"\nfinal budget b* = {final_budget}") + # print the final price (p* = prices) for each course + logger.info(f"\nfinal prices p* = {prices}") + + +# ---------------------helper functions:--------------------- +def student_best_bundle_per_budget(prices: dict, instance: Instance, epsilon: any, initial_budgets: dict, + combinations_courses_sorted: dict): + """ + Return a dict that says for each budget what is the bundle with the maximum utility that a student can take + + :param different_budgets: different budgets that will give to every student different bundles + :param prices: courses prices + :param instance: a fair-course-allocation instance + + :return matrix_a: that says for each budget what is the bundle with the maximum utility that a student can take + + Example run 3 iteration 7 + >>> instance = Instance( + ... valuations={"Alice":{"x":5, "y":5, "z":1}, "Bob":{"x":4, "y":6, "z":4}}, + ... agent_capacities=2, + ... item_capacities={"x":1, "y":2, "z":2}) + >>> initial_budgets = {"Alice": 5, "Bob": 4} + >>> epsilon = 2 + >>> prices = {"x": 2.5, "y": 0, "z": 0} + >>> combinations_courses_sorted = {'Alice': [('x', 'y'), ('x', 'z'), ('y', 'z'), ('x',), ('y',), ('z',)], 'Bob': [('x', 'y'), ('y', 'z'), ('x', 'z'), ('y',), ('x',), ('z',)]} + >>> student_best_bundle_per_budget(prices, instance, epsilon,initial_budgets, combinations_courses_sorted) + {'Alice': {3: ('x', 'y')}, 'Bob': {2.5: ('x', 'y'), 2: ('y', 'z')}} + + # Alice: 3-7: (10, [x,y] , p=2.5) (6, [x,z] p=2.5) (6, [y,z] p=0) + # BOB: 2-6: (10, [x,y] p=2,5), (10, [y,z] p=0) (8, [x,z] p=2.5) + + Example run 6 iteration 5 + >>> instance = Instance( + ... valuations={"Alice":{"x":5, "y":4, "z":1}, "Bob":{"x":4, "y":6, "z":3}}, + ... agent_capacities=2, + ... item_capacities={"x":1, "y":1, "z":2}) + >>> initial_budgets = {"Alice": 5, "Bob": 4} + >>> epsilon = 2 + >>> prices = {"x": 1.5, "y": 2, "z": 0} + >>> combinations_courses_sorted = {'Alice': [('x', 'y'), ('x', 'z'), ('y', 'z'), ('x',), ('y',), ('z',)], 'Bob': [('x', 'y'), ('y', 'z'), ('x', 'z'), ('y',), ('x',), ('z',)]} + >>> student_best_bundle_per_budget(prices, instance, epsilon,initial_budgets, combinations_courses_sorted) + {'Alice': {3.5: ('x', 'y'), 3: ('x', 'z')}, 'Bob': {3.5: ('x', 'y'), 2: ('y', 'z')}} + + # Alice: 3-7 -> (9, [x,y], p=3.5) (6, [x,z], p=1.5) (5, [y,z], p=2) (5 , x , p=1.5) (4, y, p=2) (1, z, p=0) + # Bob: 2-6 -> (10, [x,y]. p=3.5) , (9, [y,z], p=2) , (7, [x.z] , p=1.5) , (6, [y] p=1.5) , (4, [x]. p= 1.5), (3, [z]), p=0) + + + >>> instance = Instance( + ... valuations={"Alice":{"x":1, "y":1, "z":3}}, + ... agent_capacities=2, + ... item_capacities={"x":1, "y":1, "z":2}) + >>> initial_budgets = {"Alice": 5} + >>> epsilon = 0.1 + >>> prices = {"x": 2, "y": 2, "z": 5} + >>> combinations_courses_sorted = {'Alice': [('x', 'z'), ('y', 'z'), ('x', 'y'), ('z',), ('x',), ('y',)]} + >>> student_best_bundle_per_budget(prices, instance, epsilon,initial_budgets, combinations_courses_sorted) + {'Alice': {4.9: ('x', 'y')}} + + Example with a student with no bundle + >>> instance = Instance( + ... valuations={"avi":{"x":5}, "beni":{"x":5}}, + ... agent_capacities=1, + ... item_capacities={"x":1}) + >>> initial_budgets = {"avi": 1.1, "beni": 1} + >>> epsilon = 0.2 + >>> prices = {"x": 1.3} + >>> combinations_courses_sorted = {'avi': [('x',)], 'beni': [('x',)]} + >>> student_best_bundle_per_budget(prices, instance, epsilon,initial_budgets, combinations_courses_sorted) + {'avi': {1.3: ('x',)}, 'beni': {0: ()}} + """ + + logger.debug(" student_best_bundle_per_budget for initial budgets = %s, prices = %s, epsilon = %s", + initial_budgets, prices, epsilon) + best_bundle_per_budget = {student: {} for student in instance.agents} + + # logger.info("START combinations") + for student in instance.agents: + # Setting the min and max budget according to the definition + min_budget = initial_budgets[student] - epsilon + max_budget = initial_budgets[student] + epsilon + + # Sort the combinations of the courses in descending order according to utility. We went through the + # budgets in descending order, for each budget we looked for the combination with the maximum value that + # could be taken in that budget. + min_price = float('inf') + + for combination in combinations_courses_sorted[student]: + price_of_combination = sum(prices[course] for course in combination) + + if price_of_combination > max_budget: + continue # bundle is too expensive for student - irrelevant + + if price_of_combination <= min_budget: + best_bundle_per_budget[student][min_budget] = combination + break + + if price_of_combination < min_price: + min_price = price_of_combination + best_bundle_per_budget[student][price_of_combination] = combination + if best_bundle_per_budget[student] == {}: + best_bundle_per_budget[student][0] = () + logger.debug(" for student %s, the best bundles are %s", student, best_bundle_per_budget[student]) + + return best_bundle_per_budget + + +def find_budget_perturbation(initial_budgets: dict, epsilon: float, prices: dict, instance: Instance, t: Enum, + combinations_courses_sorted: dict): + logger.debug(" find_budget_perturbation for initial budgets = %s, prices = %s, epsilon = %s", initial_budgets, + prices, epsilon) + map_student_to_best_bundle_per_budget = student_best_bundle_per_budget(prices, instance, epsilon, initial_budgets, + combinations_courses_sorted) + new_budgets, clearing_error, excess_demand_per_course = lp.optimize_model( + map_student_to_best_bundle_per_budget, instance, prices, t, initial_budgets) + logger.debug( + " Budget perturbation with lowest clearing error: new_budgets = %s, clearing_error = %s, excess_demand_per_course = %s", + new_budgets, clearing_error, excess_demand_per_course) + if clearing_error is None: + raise ValueError("Clearing error is None") + return new_budgets, clearing_error, map_student_to_best_bundle_per_budget, excess_demand_per_course + + +def ACEEI_without_EFTB(alloc: AllocationBuilder, **kwargs): + initial_budgets = random_initial_budgets(alloc.instance.num_of_agents) + return find_ACEEI_with_EFTB(alloc, initial_budgets=initial_budgets, delta=0.5, epsilon=3.0, t=EFTBStatus.NO_EF_TB, + **kwargs) + + +def ACEEI_with_EFTB(alloc: AllocationBuilder, **kwargs): + initial_budgets = random_initial_budgets(alloc.instance.num_of_agents) + return find_ACEEI_with_EFTB(alloc, initial_budgets=initial_budgets, delta=0.5, epsilon=3.0, t=EFTBStatus.EF_TB, + **kwargs) + + +def ACEEI_with_contested_EFTB(alloc: AllocationBuilder, **kwargs): + initial_budgets = random_initial_budgets(alloc.instance.num_of_agents) + return find_ACEEI_with_EFTB(alloc, initial_budgets=initial_budgets, delta=0.5, epsilon=3.0, + t=EFTBStatus.CONTESTED_EF_TB, **kwargs) + + +def random_initial_budgets(num_of_agents: int) -> dict: + # Create initial budgets for each agent, uniformly distributed in the range [0, 1) + initial_budgets = np.random.rand(num_of_agents) + return {f's{agent + 1}': initial_budgets[agent] for agent in range(num_of_agents)} + + + +if __name__ == "__main__": + import doctest + + print("\n", doctest.testmod(), "\n") + # sys.exit(0) + + from fairpyx.adaptors import divide + + logger.setLevel(logging.INFO) + lp.logger.setLevel(logging.WARNING) + + import coloredlogs + level_styles = { + 'debug': {'color': 'green'}, + 'info': {'color': 'cyan'}, + 'warning': {'color': 'yellow'}, + 'error': {'color': 'red', 'bold': True}, + 'critical': {'color': 'red', 'bold': True, 'background': 'white'} + } + coloredlogs.install(level='DEBUG', logger=logger, fmt='%(message)s', level_styles=level_styles) + + instance = Instance( + valuations={"alice": {"CS161": 5, "ECON101": 3, "IR": 6}, + "bob": {"CS161": 3, "ECON101": 2, "IR": 0}, + "eve-1": {"CS161": 0, "ECON101": 10, "IR": 1}, + "eve-2": {"CS161": 0, "ECON101": 10, "IR": 1}, + "eve-3": {"CS161": 0, "ECON101": 10, "IR": 1}, + "eve-4": {"CS161": 0, "ECON101": 10, "IR": 1}, + "eve-5": {"CS161": 0, "ECON101": 10, "IR": 1}, + "eve-6": {"CS161": 0, "ECON101": 10, "IR": 1}, + "eve-7": {"CS161": 0, "ECON101": 10, "IR": 1}, + "eve-8": {"CS161": 0, "ECON101": 10, "IR": 1}, + "eve-9": {"CS161": 0, "ECON101": 10, "IR": 1}, + "eve-10": {"CS161": 0, "ECON101": 10, "IR": 1}}, + agent_capacities=2, + item_capacities={"CS161": 1, "ECON101": 10, "IR": 100}) + initial_budgets = {"alice": 4.7, "bob": 4.4, "eve-1": 6, "eve-2": 1, "eve-3": 1, "eve-4": 1, "eve-5": 1, "eve-6": 1, + "eve-7": 1, "eve-8": 1, "eve-9": 1, "eve-10": 1} + delta = 0.5 + epsilon = 0.5 + t = EFTBStatus.EF_TB + + print(divide(find_ACEEI_with_EFTB, instance=instance, initial_budgets=initial_budgets, delta=delta, epsilon=epsilon, + t=t)) + + # instance = Instance( + # valuations={"alice": {"CS161": 5, "ECON101": 3, "IR": 6}, "bob": {"CS161": 3, "ECON101": 5, "IR": 0}, + # "eve": {"CS161": 1, "ECON101": 10, "IR": 0}}, + # agent_capacities=2, + # item_capacities={"CS161": 1, "ECON101": 1, "IR": 100000}) + # initial_budgets = {"alice": 2, "bob": 1, "eve": 4} + # delta = 0.5 + # epsilon = 0.5 + # t = EFTBStatus.EF_TB + # print(divide(find_ACEEI_with_EFTB, instance=instance, initial_budgets=initial_budgets, delta=delta, epsilon=epsilon, + # t=t)) diff --git a/fairpyx/algorithms/ACEEI/__init__.py b/fairpyx/algorithms/ACEEI/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/fairpyx/algorithms/ACEEI/calculate_combinations.py b/fairpyx/algorithms/ACEEI/calculate_combinations.py new file mode 100644 index 0000000..c39ddc9 --- /dev/null +++ b/fairpyx/algorithms/ACEEI/calculate_combinations.py @@ -0,0 +1,92 @@ +from itertools import combinations +from fairpyx import Instance + + +def get_combinations_courses_sorted(instance: Instance): + """ + >>> instance = Instance( + ... valuations={"Alice":{"x":3, "y":4, "z":2}, "Bob":{"x":4, "y":3, "z":2}, "Eve":{"x":2, "y":4, "z":3}}, + ... agent_capacities=2, + ... item_capacities={"x":2, "y":1, "z":3}) + >>> get_combinations_courses_sorted(instance) + {'Alice': [('x', 'y'), ('y', 'z'), ('x', 'z'), ('y',), ('x',), ('z',)], 'Bob': [('x', 'y'), ('x', 'z'), ('y', 'z'), ('x',), ('y',), ('z',)], 'Eve': [('y', 'z'), ('x', 'y'), ('x', 'z'), ('y',), ('z',), ('x',)]} + + + >>> instance = Instance( + ... valuations={"Alice":{"x":5, "y":4, "z":3, "w":2}, "Bob":{"x":5, "y":2, "z":4, "w":3}}, + ... agent_capacities=3, + ... item_capacities={"x":1, "y":2, "z":1, "w":2}) + >>> get_combinations_courses_sorted(instance) + {'Alice': [('x', 'y', 'z'), ('x', 'y', 'w'), ('x', 'z', 'w'), ('y', 'z', 'w'), ('x', 'y'), ('x', 'z'), ('x', 'w'), ('y', 'z'), ('y', 'w'), ('x',), ('z', 'w'), ('y',), ('z',), ('w',)], 'Bob': [('x', 'z', 'w'), ('x', 'y', 'z'), ('x', 'y', 'w'), ('y', 'z', 'w'), ('x', 'z'), ('x', 'w'), ('x', 'y'), ('z', 'w'), ('y', 'z'), ('x',), ('y', 'w'), ('z',), ('w',), ('y',)]} + + >>> instance = Instance( + ... valuations={"Alice":{"x":3, "y":3, "z":3, "w":3}, "Bob":{"x":3, "y":3, "z":3, "w":3}, "Eve":{"x":4, "y":4, "z":4, "w":4}}, + ... agent_capacities=2, + ... item_capacities={"x":1, "y":2, "z":2, "w":1}) + >>> get_combinations_courses_sorted(instance) + {'Alice': [('x', 'y'), ('x', 'z'), ('x', 'w'), ('y', 'z'), ('y', 'w'), ('z', 'w'), ('x',), ('y',), ('z',), ('w',)], 'Bob': [('x', 'y'), ('x', 'z'), ('x', 'w'), ('y', 'z'), ('y', 'w'), ('z', 'w'), ('x',), ('y',), ('z',), ('w',)], 'Eve': [('x', 'y'), ('x', 'z'), ('x', 'w'), ('y', 'z'), ('y', 'w'), ('z', 'w'), ('x',), ('y',), ('z',), ('w',)]} + + >>> instance = Instance( + ... valuations={"Alice":{"x":5, "y":5, "z":1}, "Bob":{"x":4, "y":6, "z":4}}, + ... agent_capacities=2, + ... item_capacities={"x":1, "y":2, "z":2}) + >>> get_combinations_courses_sorted(instance) + {'Alice': [('x', 'y'), ('x', 'z'), ('y', 'z'), ('x',), ('y',), ('z',)], 'Bob': [('x', 'y'), ('y', 'z'), ('x', 'z'), ('y',), ('x',), ('z',)]} + + >>> instance = Instance( + ... valuations={"Alice":{"x":5, "y":4, "z":1}, "Bob":{"x":4, "y":6, "z":3}}, + ... agent_capacities=2, + ... item_capacities={"x":1, "y":1, "z":2}) + >>> get_combinations_courses_sorted(instance) + {'Alice': [('x', 'y'), ('x', 'z'), ('y', 'z'), ('x',), ('y',), ('z',)], 'Bob': [('x', 'y'), ('y', 'z'), ('x', 'z'), ('y',), ('x',), ('z',)]} + + >>> instance = Instance( + ... valuations={"Alice":{"x":1, "y":1, "z":3}}, + ... agent_capacities=2, + ... item_capacities={"x":1, "y":1, "z":2}) + >>> get_combinations_courses_sorted(instance) + {'Alice': [('x', 'z'), ('y', 'z'), ('x', 'y'), ('z',), ('x',), ('y',)]} + + >>> instance = Instance( + ... valuations={"avi":{"x":5}, "beni":{"x":5}}, + ... agent_capacities=1, + ... item_capacities={"x":1}) + >>> get_combinations_courses_sorted(instance) + {'avi': [('x',)], 'beni': [('x',)]} + + >>> instance = Instance( + ... valuations={"ami":{"x":3, "y":4, "z":2}, "tami":{"x":4, "y":3, "z":2}, "tzumi":{"x":2, "y":4, "z":3}}, + ... agent_capacities=2, + ... item_capacities={"x":2, "y":1, "z":3}) + >>> get_combinations_courses_sorted(instance) + {'ami': [('x', 'y'), ('y', 'z'), ('x', 'z'), ('y',), ('x',), ('z',)], 'tami': [('x', 'y'), ('x', 'z'), ('y', 'z'), ('x',), ('y',), ('z',)], 'tzumi': [('y', 'z'), ('x', 'y'), ('x', 'z'), ('y',), ('z',), ('x',)]} + + + """ + combinations_courses = {student: [] for student in instance.agents} + for student in instance.agents: + combinations_for_student = [] + # Creating a list of combinations of courses up to the size of the student's capacity + capacity = instance.agent_capacity(student) + for r in range(1, capacity + 1): + combinations_for_student.extend(combinations(instance.items, r)) + # logger.info(f"FINISH combinations for {student}") + + # We would like to meet the requirement of the number of courses a student needs, therefore if + # the current combination meets the requirement we will give it more weight + large_num = instance.agent_maximum_value(student) + + # Define a lambda function that calculates the valuation of a combination + valuation_function = lambda combination: instance.agent_bundle_value(student, combination) + ( + large_num if len(combination) == instance.agent_capacity(student) else 0) + + # Sort the combinations_set based on their valuations in descending order + combinations_for_student_sorted = sorted(combinations_for_student, key=valuation_function, reverse=True) + combinations_courses[student] = combinations_for_student_sorted + + return combinations_courses + + +if __name__ == "__main__": + import doctest, sys + print("\n", doctest.testmod(), "\n") diff --git a/fairpyx/algorithms/ACEEI/find_profitable_manipulation.py b/fairpyx/algorithms/ACEEI/find_profitable_manipulation.py new file mode 100644 index 0000000..4b76407 --- /dev/null +++ b/fairpyx/algorithms/ACEEI/find_profitable_manipulation.py @@ -0,0 +1,402 @@ +""" +"Practical algorithms and experimentally validated incentives for equilibrium-based fair division (A-CEEI)" + by ERIC BUDISH, RUIQUAN GAO, ABRAHAM OTHMAN, AVIAD RUBINSTEIN, QIANFAN ZHANG. (2023) + link to the article: https://arxiv.org/pdf/2305.11406 + ALGORITHM 2: Find a profitable manipulation for a student + +Programmers: Erga Bar-Ilan, Ofir Shitrit and Renana Turgeman. +Since: 2024-05 +""" +import logging +from enum import Enum + +import numpy as np + +from fairpyx import Instance, AllocationBuilder +from fairpyx.adaptors import divide +from fairpyx.algorithms.ACEEI.ACEEI import find_ACEEI_with_EFTB + + +class criteria_for_profitable_manipulation(Enum): + randomness = 0 + population = 1 + + +logger = logging.getLogger(__name__) +NUMBER_OF_ITERATIONS = 10 + +# ---------------------The main function--------------------- + +def find_profitable_manipulation(mechanism: callable, student: str, true_student_utility: dict, + criteria:criteria_for_profitable_manipulation, + eta: float, instance: Instance, initial_budgets: dict, beta: float, **kwargs): + """ + ALGORITHM 2: Find a profitable manipulation for a student + + :param mechanism: A randomized mechanism M for course-allocation + :param student: The student who is being tested to see if he can manipulate + :param true_student_utility: The student's utility + :param criteria: The type of criteria for profitable manipulation + 0 for resampled randomness + 1 for population + :param eta: a local update coefficient neu + :param alloc: a fair-course-allocation instance + :param initial_budgets: Students' initial budgets + :param kwargs: additional keyword arguments sent to the mechanism. + :param beta: a parameter that determines the distribution of the initial budgets + + return: The profitable manipulation + + >>> from fairpyx.algorithms.ACEEI.ACEEI import find_ACEEI_with_EFTB + >>> from fairpyx.algorithms import ACEEI, tabu_search + + + Example run 1 + >>> mechanism = find_ACEEI_with_EFTB + >>> student = "moti" + >>> true_student_utility = {"x":1, "y":2, "z":4} + >>> criteria = criteria_for_profitable_manipulation.randomness + >>> eta = 2 + >>> instance = Instance( + ... valuations={"avi":{"x":3, "y":5, "z":1}, "beni":{"x":2, "y":3, "z":1}, "moti": {"x":1, "y":2, "z":4}}, + ... agent_capacities=2, + ... item_capacities={"x":1, "y":2, "z":3}) + >>> beta = 2 + >>> initial_budgets = random_initial_budgets(instance, beta) + >>> delta = 0.5 + >>> epsilon = 0.5 + >>> t = ACEEI.ACEEI.EFTBStatus.NO_EF_TB + >>> find_profitable_manipulation(mechanism, student, true_student_utility, criteria, eta, instance, initial_budgets, beta, delta=delta, epsilon=epsilon, t=t) + {'x': 1, 'y': 2, 'z': 4} + + Example run 2 + >>> mechanism = find_ACEEI_with_EFTB + >>> student = "moti" + >>> true_student_utility = {"x":1, "y":2, "z":4} + >>> criteria = criteria_for_profitable_manipulation.randomness + >>> eta = 2 + >>> instance = Instance( + ... valuations={"avi":{"x":3, "y":5, "z":1}, "beni":{"x":2, "y":3, "z":1}, "moti": {"x":1, "y":2, "z":4}}, + ... agent_capacities=2, + ... item_capacities={"x":1, "y":2, "z":3}) + >>> beta = 2 + >>> initial_budgets = random_initial_budgets(instance, beta) + >>> delta = 0.5 + >>> epsilon = 0.5 + >>> t = ACEEI.ACEEI.EFTBStatus.EF_TB + >>> find_profitable_manipulation(mechanism, student, true_student_utility, criteria, eta, instance, initial_budgets, beta, delta=delta, epsilon=epsilon, t=t) + {'x': 1, 'y': 2, 'z': 4} + + + Example run 4 + >>> mechanism = find_ACEEI_with_EFTB + >>> student = "moti" + >>> true_student_utility = {"x":6, "y":2} + >>> criteria = criteria_for_profitable_manipulation.randomness + >>> eta = 2 + >>> instance = Instance( + ... valuations={"avi":{"x":5, "y":3}, "moti": {"x":6, "y":2}}, + ... agent_capacities=2, + ... item_capacities={"x":1, "y":2}) + >>> beta = 2 + >>> initial_budgets = random_initial_budgets(instance, beta) + >>> delta = 0.5 + >>> epsilon = 0.5 + >>> t = ACEEI.ACEEI.EFTBStatus.NO_EF_TB + >>> find_profitable_manipulation(mechanism, student, true_student_utility, criteria, eta, instance, initial_budgets, beta, delta=delta, epsilon=epsilon, t=t) + {'x': 6, 'y': 2} + + # Example run 5 + >>> mechanism = find_ACEEI_with_EFTB + >>> student = "moti" + >>> true_student_utility = {"x":1, "y":2, "z":5} + >>> criteria = criteria_for_profitable_manipulation.population + >>> eta = 2 + >>> instance = Instance(valuations={"avi":{"x":5, "y":4, "z":1}, "beni":{"x":4, "y":6, "z":3}, "moti":{"x":1, "y":2, "z":5}}, + ... agent_capacities=2, + ... item_capacities={"x":1, "y":2, "z":3}) + >>> beta = 2 + >>> initial_budgets = random_initial_budgets(instance, beta) + >>> delta = 0.5 + >>> epsilon = 0.5 + >>> t = ACEEI.ACEEI.EFTBStatus.NO_EF_TB + >>> find_profitable_manipulation(mechanism, student, true_student_utility, criteria, eta, instance, initial_budgets, beta, delta=delta, epsilon=epsilon, t=t) + {'x': 1, 'y': 2, 'z': 5} + + """ + # (1) Let 𝑣0 ←𝑒( or the best manipulation found in previous iterations with different πœ‚). + current_best_manipulation = true_student_utility # v_0 <- u + + initial_budgets = random_initial_budgets(instance, beta) + + while True: + # (2) Try to increase or decrease the weight 𝑀𝑗 for each course 𝑗 in 𝑣0 to obtain new misreports + # 𝑉 = {𝑣𝑗,Β±1}π‘—βˆˆ[π‘š]} + misreports = create_misreports(current_best_manipulation, eta) + + # (3) Let π‘£βˆ— = argmaxπ‘£βˆˆπ‘‰βˆͺ{𝑣0} Eπ’“βˆΌR[𝑒𝑖(𝑴𝑖([𝑣𝑗, π’–βˆ’π‘–], 𝒄, 𝒓))] resampled randomness, + # argmaxπ‘£βˆˆπ‘‰βˆͺ{𝑣0} Eπ’–βˆ’π‘–βˆΌUβˆ’π‘–, π’“βˆΌR[𝑒𝑖(𝑴𝑖([𝑣𝑗, π’–βˆ’π‘–], 𝒄, 𝒓))] resampled population. + + if criteria == criteria_for_profitable_manipulation.population: + new_best_manipulation = criteria_population(mechanism, student, true_student_utility, instance, beta, misreports, **kwargs) + else: # criteria == criteria_for_profitable_manipulation.randomness + new_best_manipulation = criteria_randomness(mechanism, student, true_student_utility, instance, beta, misreports, initial_budgets, **kwargs) + if current_best_manipulation == new_best_manipulation: + break + else: + current_best_manipulation = new_best_manipulation + + # (4) If π‘£βˆ— = 𝑣0, terminate with 𝑣0 as the best manipulation found when 𝑣0 β‰  𝑒, otherwise return failed. + logger.info(f"current_best_manipulation = {current_best_manipulation}") + logger.info("NO MENIPULATION" if true_student_utility==current_best_manipulation else "MENIPULATION!!!!!") + return current_best_manipulation + +# ---------------------helper functions:--------------------- +def random_initial_budgets(instance: Instance, beta: float): + + """ + Create random initial budgets for each student + :param instance: a fair-course-allocation instance + :param beta: a parameter that determines the distribution of the initial budgets + + >>> instance = Instance( + ... valuations={"Alice":{"x":5, "y":4, "z":1}, "Bob":{"x":4, "y":6, "z":3}}, + ... agent_capacities=2, + ... item_capacities={"x":1, "y":1, "z":2}) + >>> beta = 0.5 + >>> budgets = random_initial_budgets(instance, beta) + >>> isinstance(budgets, dict) + True + >>> all(agent in budgets.keys() for agent in instance.agents) + True + """ + return {agent: np.random.uniform(1 + (beta / 4), 1 + ((3 * beta) / 4)) for agent in instance.agents} + + +def create_misreports(original, neu): + """ + Creates misreports for our student according to the neu parameter. + :param original: the original student's utility + :param neu: a local update coefficient + + >>> original = {"x": 1, "y": 2, "z": 4} + >>> neu = 2 + >>> create_misreports(original, neu) + [{'x': 0.5, 'y': 2, 'z': 4}, {'x': 2, 'y': 2, 'z': 4}, {'x': 1, 'y': 1.0, 'z': 4}, {'x': 1, 'y': 4, 'z': 4}, {'x': 1, 'y': 2, 'z': 2.0}, {'x': 1, 'y': 2, 'z': 8}] + + >>> original = {"x": 3, "y": 6, "z": 9} + >>> neu = 3 + >>> create_misreports(original, neu) + [{'x': 1.0, 'y': 6, 'z': 9}, {'x': 9, 'y': 6, 'z': 9}, {'x': 3, 'y': 2.0, 'z': 9}, {'x': 3, 'y': 18, 'z': 9}, {'x': 3, 'y': 6, 'z': 3.0}, {'x': 3, 'y': 6, 'z': 27}] + """ + transformed_dicts = [] + + for course in original: + # Create a copy of the original dictionary for division + divided_dict = original.copy() + divided_dict[course] = original[course] / neu + transformed_dicts.append(divided_dict) + + # Create a copy of the original dictionary for multiplication + multiplied_dict = original.copy() + multiplied_dict[course] = original[course] * neu + transformed_dicts.append(multiplied_dict) + + return transformed_dicts + + +def get_random_utilities(instance: Instance): + """ + Create random utilities for each student + :param instance: a fair-course-allocation instance + + >>> instance = Instance( + ... valuations={"Alice":{"x":5, "y":4, "z":1}, "Bob":{"x":4, "y":6, "z":3}}, + ... agent_capacities=2, + ... item_capacities={"x":1, "y":1, "z":2}) + >>> utilities = get_random_utilities(instance) + >>> isinstance(utilities, dict) + True + >>> all(course in utilities.keys() for course in instance.items) + True + """ + return {course: np.random.uniform(1, 100) for course in instance.items} + + +def expected_value_of_specific_report_for_population(random_utilities: list[dict], random_budgets: list[dict], mechanism: callable, + instance: Instance, student: str, report: dict, **kwargs): + """ + Calculate the expected value of a student given a random utilities. + + :param random_utilities: a dictionary of random utilities + :param random_budgets: a dictionary of random budgets + :param mechanism: A randomized mechanism M for course-allocation + :param instance: a fair-course-allocation instance + :param student: The student who is being tested to see if he can manipulate + :param report: our student's utility + """ + sum_utilities = 0 + for budgets, valuations in zip(random_budgets, random_utilities): + # todo: ask erel how to test it + utilities = {agent: (report if agent == student else utility) for agent,utility in valuations.items()} + + new_instance = Instance(valuations=utilities, agent_capacities=instance.agent_capacity, item_capacities=instance.item_capacity) + allocation = divide(mechanism, instance=new_instance, initial_budgets=budgets, **kwargs) # todo: change to kwargs + current_utility_found = instance.agent_bundle_value(student, allocation[student]) + sum_utilities += current_utility_found + return sum_utilities / NUMBER_OF_ITERATIONS + + + + +def expected_value_of_specific_report_for_randomness(random_utilities: dict, random_budgets: list[dict], mechanism: callable, + instance: Instance, student: str, report: dict, **kwargs): + """ + Calculate the expected value of a student given a random utilities. + + :param random_utilities: a dictionary of random utilities + :param random_budgets: a dictionary of random budgets + :param mechanism: A randomized mechanism M for course-allocation + :param instance: a fair-course-allocation instance + :param student: The student who is being tested to see if he can manipulate + :param delta: The step size + :param epsilon: maximum budget perturbation + :param t: type 𝑑 of the EF-TB constraint, + 0 for no EF-TB constraint, + 1 for EF-TB constraint, + 2 for contested EF-TB + :param report: our student's utility + + >>> from fairpyx.algorithms.ACEEI.ACEEI import find_ACEEI_with_EFTB, EFTBStatus + >>> random_utilities = {"avi":{"x":5, "y":5, "z":5},"beni":{"x":4, "y":6, "z":3}} + >>> random_budgets = [{"avi": 5, "beni":2},{"avi": 5, "beni":2},{"avi": 5, "beni":2},{"avi": 5, "beni":2},{"avi": 5, "beni":2},{"avi": 5, "beni":2},{"avi": 5, "beni":2},{"avi": 5, "beni":2},{"avi": 5, "beni":2},{"avi": 5, "beni":2}] + >>> mechanism = find_ACEEI_with_EFTB + >>> instance = Instance(valuations={"avi":{"x":5, "y":5, "z":5}, + ... "beni":{"x":4, "y":6, "z":3}}, + ... agent_capacities=2, + ... item_capacities={"x":1, "y":1, "z":2}) + >>> student = "avi" + >>> delta = 0.5 + >>> epsilon = 0.5 + >>> t = EFTBStatus.NO_EF_TB + >>> report = {"x":5, "y":5, "z":5} + >>> expected_value_of_specific_report_for_randomness(random_utilities, random_budgets, mechanism, instance, student, report, delta=delta, epsilon=epsilon, t=t) + 2.0 + + """ + + sum_utilities = 0 + for utility, iteration in zip(random_utilities, range(NUMBER_OF_ITERATIONS)): + utilities = {agent: (report if agent == student else utility) for agent, utility in random_utilities.items()} + + new_instance = Instance(valuations=utilities, agent_capacities=instance.agent_capacity, item_capacities=instance.item_capacity) + allocation = divide(mechanism, instance=new_instance, initial_budgets=random_budgets[iteration], **kwargs) + # print(allocation) + current_utility_found = instance.agent_bundle_value(student, allocation[student]) + sum_utilities += current_utility_found + return sum_utilities / NUMBER_OF_ITERATIONS + +def criteria_population(mechanism: callable, student: str, current_best_manipulation: dict, instance: Instance, + beta: float, misreports: list, **kwargs): + """ + Run algorithm 1 when initial budgets and other students utilities are Unknown. + + :param mechanism: A randomized mechanism M for course-allocation + :param student: The student who is being tested to see if he can manipulate + :param current_best_manipulation: The student's utility + :param instance: a fair-course-allocation instance + :param initial_budgets: Students' initial budgets + :param misreports: List of misreports for the students + + :return best manipulation that found for our student - the report that gives him the most benefit + """ + best_manipulation_found = current_best_manipulation + + random_utilities = [{agent: get_random_utilities(instance) for agent in instance.agents} for _ in range(NUMBER_OF_ITERATIONS)] + random_budgets = [random_initial_budgets(instance, beta) for _ in range(NUMBER_OF_ITERATIONS)] + + # run for original utility + max_expected_value = expected_value_of_specific_report_for_population(random_utilities, random_budgets, mechanism, + instance, student, current_best_manipulation, **kwargs) + + for misreport in misreports: + current_expected_value = expected_value_of_specific_report_for_population(random_utilities, random_budgets, mechanism, + instance, student, misreport, **kwargs) + if current_expected_value > max_expected_value: + max_expected_value = current_expected_value + best_manipulation_found = misreport + #todo: change to arg max + + return best_manipulation_found + + +def criteria_randomness(mechanism: callable, student: str, utility: dict, instance: Instance, beta: float, misreports: list, initial_budgets: dict, **kwargs): + """ + Run algorithm 1 when initial budgets are Unknown. + + :param mechanism: A randomized mechanism M for course-allocation + :param student: The student who is being tested to see if he can manipulate + :param utility: The student's utility + :param instance: a fair-course-allocation instance + :param initial_budgets: Students' initial budgets + :param misreports: List of misreports for the students + + :return best manipulation that found for our student - the report that gives him the most benefit + """ + best_manipulation_found = utility + + random_budgets = [random_initial_budgets(instance, beta) for _ in range(NUMBER_OF_ITERATIONS)] + + # run for original utility + max_expected_value = expected_value_of_specific_report_for_randomness(instance._valuations, random_budgets, mechanism, + instance, student, utility, **kwargs) + + + for misreport in misreports: + current_expected_value = expected_value_of_specific_report_for_randomness(instance._valuations, random_budgets, mechanism, + instance, student, misreport, **kwargs) + if current_expected_value > max_expected_value: + max_expected_value = current_expected_value + best_manipulation_found = misreport + + return best_manipulation_found + + + + +if __name__ == '__main__': + import doctest + print(doctest.testmod()) + logger.addHandler(logging.StreamHandler()) + logger.setLevel(logging.INFO) + # from fairpyx.algorithms import ACEEI + # mechanism = find_ACEEI_with_EFTB + # student = "moti" + # utility = {"x": 1, "y": 2, "z": 4} + # criteria = criteria_for_profitable_manipulation.randomness + # neu = 2 + # instance = Instance(valuations = {"avi": {"x": 3, "y": 5, "z": 1}, "beni": {"x": 2, "y": 3, "z": 1}, "moti": {"x": 1, "y": 2, "z": 4}}, + # agent_capacities = 2, + # item_capacities = {"x": 1, "y": 2, "z": 3}) + # beta = 2 + # initial_budgets = random_initial_budgets(instance, beta) + # delta = 0.5 + # epsilon = 0.5 + # t = ACEEI.ACEEI.EFTBStatus.NO_EF_TB + # find_profitable_manipulation(mechanism, student, utility, criteria, neu, instance, initial_budgets, beta, delta=delta, epsilon=epsilon, t=t) + + # + # mechanism = find_ACEEI_with_EFTB + # student = "moti" + # utility = {"x":1, "y":2, "z":5} + # criteria = criteria_for_profitable_manipulation.population + # neu = 2 + # instance = Instance(valuations={"avi":{"x":5, "y":4, "z":1}, "beni":{"x":4, "y":6, "z":3}, "moti":{"x":1, "y":2, "z":5}}, + # agent_capacities=2, + # item_capacities={"x":1, "y":2, "z":3}) + # beta = 2 + # initial_budgets = random_initial_budgets(instance, beta) + # delta = 0.5 + # epsilon = 0.5 + # t = ACEEI.ACEEI.EFTBStatus.NO_EF_TB + # find_profitable_manipulation(mechanism, student, utility, criteria, neu, instance, initial_budgets, beta, delta=delta, epsilon=epsilon, t=t) diff --git a/fairpyx/algorithms/ACEEI/linear_program.py b/fairpyx/algorithms/ACEEI/linear_program.py new file mode 100644 index 0000000..2a31863 --- /dev/null +++ b/fairpyx/algorithms/ACEEI/linear_program.py @@ -0,0 +1,343 @@ +""" +"Practical algorithms and experimentally validated incentives for equilibrium-based fair division (A-CEEI)" + by ERIC BUDISH, RUIQUAN GAO, ABRAHAM OTHMAN, AVIAD RUBINSTEIN, QIANFAN ZHANG. (2023) + link to the article: https://arxiv.org/pdf/2305.11406 + Linear programming that used in algorithm 1 + +Programmers: Erga Bar-Ilan, Ofir Shitrit and Renana Turgeman. +Since: 2024-01 +""" +from mip import * +import sys +import logging +from contextlib import redirect_stdout +import os + +from fairpyx import Instance +from fairpyx.algorithms import ACEEI + +logger = logging.getLogger(__name__) + + +# ---------------------The main function--------------------- +def optimize_model(map_student_to_best_bundle_per_budget: dict, instance: Instance, prices: dict, t: Enum, initial_budgets: dict): + """ + This function calculates a linear program whose goal is to find the distribution + and budgets that meet the constraints. + It is used in Algorithm 1. + + :param map_student_to_best_bundle_per_budget: a dictionary that maps each student to his best bundle per budget. + :param instance: a fair-course-allocation instance + :param prices: the prices of courses + :param t: type 𝑑 of the EF-TB constraint, + 0 for no EF-TB constraint, + 1 for EF-TB constraint, + 2 for contested EF-TB + :param initial_budgets: Students' initial budgets + + :return final courses prices, final budgets, final allocation + + >>> from fairpyx import Instance + >>> from fairpyx.algorithms import ACEEI + + Example run 6 iteration 5 + >>> instance = Instance( + ... valuations={"Alice":{"x":5, "y":4, "z":1}, "Bob":{"x":4, "y":6, "z":3}}, + ... agent_capacities=2, + ... item_capacities={"x":1, "y":1, "z":2}) + >>> map_student_to_best_bundle_per_budget = {'Alice': {3.5: ('x', 'y'), 3: ('x', 'z')}, 'Bob': {3.5: ('x', 'y'), 2: ('y', 'z')}} + >>> initial_budgets = {"Alice": 5, "Bob": 4} + >>> prices = {"x": 1.5, "y": 2, "z": 0} + >>> t = ACEEI.ACEEI.EFTBStatus.EF_TB + >>> optimize_model(map_student_to_best_bundle_per_budget,instance,prices,t,initial_budgets) + ({'Alice': (3, ('x', 'z')), 'Bob': (2, ('y', 'z'))}, 0.0, {'x': 0.0, 'y': 0.0, 'z': 0.0}) + + Example with a student with no bundle + >>> instance = Instance( + ... valuations={"avi":{"x":5}, "beni":{"x":5}}, + ... agent_capacities=1, + ... item_capacities={"x":1}) + >>> map_student_to_best_bundle_per_budget = {'avi': {1.3: ('x',)}, 'beni': {0: ()}} + >>> initial_budgets = {"avi": 1.1, "beni": 1} + >>> prices = {"x": 1.3} + >>> t = ACEEI.ACEEI.EFTBStatus.EF_TB + >>> optimize_model(map_student_to_best_bundle_per_budget,instance,prices,t,initial_budgets) + ({'avi': (1.3, ('x',)), 'beni': (0, ())}, 0.0, {'x': 0.0}) + + """ + + logger.info("\n----START LINEAR_PROGRAM: a = %s, p = %s, t = %s, initial_budgets = %s", map_student_to_best_bundle_per_budget, prices, t, initial_budgets) + + model = Model("allocations") + courses_names = list(instance.items) + students_names = list(instance.agents) + + # Decision variables + x = {(student, bundle): model.add_var(var_type=BINARY) for student in students_names for bundle in + map_student_to_best_bundle_per_budget[student].values()} + + z = {course: model.add_var(var_type=CONTINUOUS, lb=-instance.item_capacity(course)) for course in courses_names} + y = {course: model.add_var(var_type=CONTINUOUS) for course in courses_names} + + # Define binary variables Ξ΄ + delta = {course: model.add_var(var_type=BINARY) for course in courses_names} + # Big-M value, should be large enough to cover the range of z + M = 1e6 + + # Objective function + objective_expr = xsum(y[course] for course in courses_names) + model.objective = minimize(objective_expr) + + for course in courses_names: + # Add constraints to define y based on the value of z + model += y[course] <= z[course] + M * (1 - delta[course]) + model += y[course] >= z[course] + model += y[course] <= M * delta[course] + model += z[course] <= M * delta[course] + model += z[course] >= 0 - M * (1 - delta[course]) + + # Course allocation constraints + for course in courses_names: + # constraint 1: βˆ‘ βˆ‘(π‘₯_𝑖ℓ Β· π‘Ž_𝑖ℓ𝑗) = 𝑐_𝑗 + 𝑧_𝑗 βˆ€π‘— ∈ [π‘š], 𝑝_𝑗 > 0 + # π‘–βˆˆ[𝑛] β„“ ∈ [π‘˜_𝑖] + if prices[course] > 0: + model += xsum( + x[student, bundle] * (1 if course in bundle else 0) for student in students_names for bundle in + map_student_to_best_bundle_per_budget[student].values()) == instance.item_capacity(course) + z[course] + + # constraint 2: βˆ‘ βˆ‘(π‘₯_𝑖ℓ Β· π‘Ž_𝑖ℓ𝑗) ≀ 𝑐𝑗 + 𝑧𝑗 βˆ€π‘— ∈ [π‘š], 𝑝𝑗 = 0 + # π‘–βˆˆ[𝑛] β„“βˆˆ[π‘˜_𝑖] + else: + model += xsum( + x[student, bundle] * (1 if course in bundle else 0) for student in students_names for bundle in + map_student_to_best_bundle_per_budget[student].values()) <= instance.item_capacity(course) + z[course] + + # constraint 3: βˆ‘π‘₯_𝑖ℓ = 1 βˆ€π‘– ∈ [𝑛] + # β„“βˆˆ[π‘˜_𝑖] + for student in students_names: + model += xsum(x[student, bundle] for bundle in map_student_to_best_bundle_per_budget[student].values()) == 1 + + # Add EF-TB constraints based on parameter t + if t == ACEEI.ACEEI.EFTBStatus.NO_EF_TB: + pass # No EF-TB constraints, no need to anything + + elif t == ACEEI.ACEEI.EFTBStatus.EF_TB or t == ACEEI.ACEEI.EFTBStatus.CONTESTED_EF_TB: + # Add EF-TB constraints here + envy_constraints = get_envy_constraints(instance, initial_budgets, map_student_to_best_bundle_per_budget, t, prices) + for constraint in envy_constraints: + model += x[constraint[0]] + x[constraint[1]] <= 1 + + # Redirect solver output to null device + model.verbose = 0 + + # Optimize the model + with open(os.devnull, 'w') as devnull: + with redirect_stdout(devnull): + model.optimize() + + if model.num_solutions: + excess_demand_per_course = {course: y[course].x for course in courses_names} + logger.debug("model.optimize found %s solutions. excess_demand_per_course=%s", model.num_solutions, excess_demand_per_course) + else: + excess_demand_per_course = model.status + logger.debug("model.optimize found no solutions.") + + new_budgets = {} + for (student, bundle), var in x.items(): + if var.x == 1: # Check if the decision variable is set to 1 + price = list(map_student_to_best_bundle_per_budget[student].keys())[ + list(map_student_to_best_bundle_per_budget[student].values()).index(bundle)] # Extract the price from dictionary a + new_budgets[student] = (price, bundle) + + # print("New budgets:", new_budgets) + # print("Objective Value:", model.objective_value) + # print("Excess Demand:", excess_demand) + logging.info("\nNew budgets: %s\nObjective Value: %s\nExcess Demand: %s", new_budgets, model.objective_value, + excess_demand_per_course) + logger.info("FINISH LINEAR_PROGRAM\n") + + return new_budgets, model.objective_value, excess_demand_per_course + + +# ---------------------helper functions:--------------------- + +def check_envy(instance: Instance, student: str, other_student: str, a: dict, t: Enum, prices: dict): + """ + The function accepts a pair of students, and returns pairs of courses for which envy exists. + + :param instance: a fair-course-allocation instance + :param student: The student with the highest initial budget + :param other_student: The student with the lowest initial budget + :param a: dict that says for each budget what is the bundle with the maximum utility that a student can take + :param t: type 𝑑 of the EF-TB constraint, + 0 for no EF-TB constraint, + 1 for EF-TB constraint, + 2 for contested EF-TB + :param prices: courses prices + + Example run 6 iteration 5 + >>> from fairpyx import Instance + >>> from fairpyx.algorithms import ACEEI + + >>> instance = Instance( + ... valuations={"Alice":{"x":5, "y":4, "z":1}, "Bob":{"x":4, "y":6, "z":3}}, + ... agent_capacities=2, + ... item_capacities={"x":1, "y":1, "z":2}) + >>> student = "Alice" + >>> other_student = "Bob" + >>> a = {'Alice': {3.5: ('x', 'y'), 3: ('x', 'z')}, 'Bob': {3.5: ('x', 'y'), 2: ('y', 'z')}} + >>> t = ACEEI.ACEEI.EFTBStatus.EF_TB + >>> prices = {"x": 1.5, "y": 2, "z": 0} + >>> check_envy(instance, student, other_student, a, t, prices) + [(('x', 'z'), ('x', 'y'))] + + >>> instance = Instance( + ... valuations={"Alice":{"x":10, "y":20}, "Bob":{"x":10, "y":20}}, + ... agent_capacities=1, + ... item_capacities={"x":1, "y":1}) + >>> student = "Alice" + >>> other_student = "Bob" + >>> a = {'Alice': {0: (), 1.1: ('y')}, 'Bob': {1.1: ('y'), 1: ('x')}} + >>> t = ACEEI.ACEEI.EFTBStatus.EF_TB + >>> prices = {"x": 1, "y": 1.1} + >>> check_envy(instance, student, other_student, a, t, prices) + [((), 'y'), ((), 'x')] + + new example + >>> instance = Instance( + ... valuations={"Alice": {"x": 5, "y": 4, "z": 1, "w": 6}, "Bob": {"x": 4, "y": 6, "z": 3, "w": 1}}, + ... agent_capacities=2, + ... item_capacities={"x":1, "y":1, "z":2, "w":1}) + >>> student = "Alice" + >>> other_student = "Bob" + >>> a = {'Alice': {3.5: ('x', 'y')}, 'Bob': {3.5: ('x'), 2: ('y', 'z')}} + >>> t = ACEEI.ACEEI.EFTBStatus.CONTESTED_EF_TB + >>> prices = {"x": 1, "y": 0.1, "z": 0, "w": 0} + >>> check_envy(instance, student, other_student, a, t, prices) + [(('x', 'y'), 'x'), (('x', 'y'), ('y', 'z'))] + + """ + result = [] + # check if student envies in other_student + for bundle_i in a[student].values(): + for bundle_j in a[other_student].values(): + original_bundle_j = bundle_j + if t == ACEEI.ACEEI.EFTBStatus.CONTESTED_EF_TB: + bundle_j = list(bundle_j) # Convert bundle_j to a list + + # Iterate through keys in prices + for key, value in prices.items(): + # Check if value is 0 and key is not already in bundle_j + if value == 0 and key not in bundle_j: + # Add key to bundle_j + bundle_j.append(key) + + # logger.info(f"----------{t}---------") + # logger.info(f"bundle_j of {other_student} = {bundle_j}") + + sorted_bundle_j = sorted(bundle_j, key=lambda course: instance.agent_item_value(student, course), + reverse=True) + # logger.info(f"sorted_bundle_j by {student} valuation = {sorted_bundle_j}") + + sorted_bundle_j = sorted_bundle_j[:instance.agent_capacity(student)] + # logger.info(f"instance.agent_capacity = {instance.agent_capacity(student)}") + # logger.info(f"sorted_bundle_j of {student} = {sorted_bundle_j}") + + bundle_j = tuple(sorted_bundle_j) + # logger.info(f"finish update bundle_j of {student} = {bundle_j}") + + if instance.agent_bundle_value(student, bundle_j) > instance.agent_bundle_value(student, bundle_i): + result.append((bundle_i, original_bundle_j)) + + return result + + +def get_envy_constraints(instance: Instance, initial_budgets: dict, a: dict, t: Enum, prices: dict): + """ + This function checks for every two students if there is envy between them, + in case there is a constraint required for the model. + + :param instance: a fair-course-allocation instance + :param initial_budgets: the initial budgets of the students + :param a: dict that says for each budget what is the bundle with the maximum utility that a student can take + :param t: type 𝑑 of the EF-TB constraint, + 0 for no EF-TB constraint, + 1 for EF-TB constraint, + 2 for contested EF-TB + :param prices: courses prices + + + Example run 6 iteration 5 + >>> from fairpyx import Instance + >>> from fairpyx.algorithms import ACEEI + >>> instance = Instance( + ... valuations={"Alice":{"x":5, "y":4, "z":1}, "Bob":{"x":4, "y":6, "z":3}}, + ... agent_capacities=2, + ... item_capacities={"x":1, "y":1, "z":2}) + >>> initial_budgets = {"Alice": 5, "Bob": 4} + >>> a = {'Alice': {3.5: ('x', 'y'), 3: ('x', 'z')}, 'Bob': {3.5: ('x', 'y'), 2: ('y', 'z')}} + >>> t = ACEEI.ACEEI.EFTBStatus.EF_TB + >>> prices = {"x": 1, "y": 1.1} + >>> get_envy_constraints(instance, initial_budgets, a, t, prices) + [(('Alice', ('x', 'z')), ('Bob', ('x', 'y')))] + + >>> instance = Instance( + ... valuations={"Alice":{"x":10, "y":20}, "Bob":{"x":10, "y":20}}, + ... agent_capacities=1, + ... item_capacities={"x":1, "y":1}) + >>> initial_budgets = {"Alice": 1.1, "Bob": 1} + >>> a = {'Alice': {0: (), 1.1: ('y')}, 'Bob': {1.1: ('y'), 1: ('x')}} + >>> t = ACEEI.ACEEI.EFTBStatus.EF_TB + >>> prices = {"x": 1, "y": 1.1} + >>> get_envy_constraints(instance, initial_budgets, a, t, prices) + [(('Alice', ()), ('Bob', 'y')), (('Alice', ()), ('Bob', 'x'))] + """ + + students_names = instance.agents + envy_constraints = [] + + for student in students_names: + for other_student in students_names: + if student is not other_student: + if initial_budgets[student] > initial_budgets[other_student]: # check envy + # result contain the index of the bundles that student envious other_student + result = check_envy(instance, student, other_student, a, t, prices) + + if result: + for pair in result: + i, j = pair + # logger.info(f"bundle {i} , bundle {j}") + # envy_constraints.append((x[student, i], x[other_student, j])) + envy_constraints.append(((student, i), (other_student, j))) + # logger.info(f"student {student} bundle {i} envy student {other_student} bundle {j}") + return envy_constraints + + +if __name__ == "__main__": + import doctest + print(doctest.testmod()) + + + # instance = Instance( + # valuations={"Alice": {"x": 5, "y": 4, "z": 1, "w": 6}, "Bob": {"x": 4, "y": 6, "z": 3, "w": 1}}, + # agent_capacities=2, + # item_capacities={"x": 1, "y": 1, "z": 2} + # ) + # + # a = {'Alice': {3.5: ('x', 'y')}, 'Bob': {3.5: ('x'), 2: ('y', 'z')}} + # initial_budgets = {"Alice": 1.1, "Bob": 1} + # prices = {"x": 1, "y": 0.1, "z": 0, "w": 0} + # t = EFTBStatus.CONTESTED_EF_TB + # + # print(check_envy(instance, "Alice", "Bob", a, t, prices)) + + # optimize_model(a, instance, prices, t, initial_budgets) + # result = [(('x', 'y'), ('x')), (('x', 'y'), ('y', 'z'))] + + # instance = Instance(valuations = {"Alice": {"x": 5, "y": 4, "z": 1, "w": 6}, "Bob": {"x": 4, "y": 6, "z": 3, "w": 1}}, agent_capacities = 2, item_capacities = {"x": 1, "y": 1, "z": 2, "w": 1}) + # student = "Alice" + # other_student = "Bob" + # a = {'Alice': {3.5: ('x', 'y')}, 'Bob': {3.5: ('x'), 2: ('y', 'z')}} + # t = EFTBStatus.CONTESTED_EF_TB + # prices = {"x": 1, "y": 0.1, "z": 0, "w": 0} + # print(check_envy(instance, student, other_student, a, t, prices)) diff --git a/fairpyx/algorithms/ACEEI/tabu_search.py b/fairpyx/algorithms/ACEEI/tabu_search.py new file mode 100644 index 0000000..bcd18cb --- /dev/null +++ b/fairpyx/algorithms/ACEEI/tabu_search.py @@ -0,0 +1,806 @@ +""" +"Practical algorithms and experimentally validated incentives for equilibrium-based fair division (A-CEEI)" + by ERIC BUDISH, RUIQUAN GAO, ABRAHAM OTHMAN, AVIAD RUBINSTEIN, QIANFAN ZHANG. (2023) + link to the article: https://arxiv.org/pdf/2305.11406 + ALGORITHM 3: Tabu search + +Programmers: Erga Bar-Ilan, Ofir Shitrit and Renana Turgeman. +Since: 2024-01 +""" +import logging +import random +from itertools import combinations, product + +import numpy as np + +from fairpyx import Instance, AllocationBuilder +from fairpyx.algorithms.ACEEI.calculate_combinations import get_combinations_courses_sorted +from functools import lru_cache + +# Setup logger and colored logs +logger = logging.getLogger(__name__) + + +# ---------------------The main function--------------------- +def tabu_search(alloc: AllocationBuilder, **kwargs): + """ + ALGORITHM 3: Tabu search + + :param alloc: a fair-course-allocation instance + :param initial_budgets: Students' initial budgets, b_0∈[1,1+Ξ²]^n + :param beta: creates the range of initial_budgets + + :return final courses prices, final distribution + + >>> from fairpyx.adaptors import divide + >>> from fairpyx.utils.test_utils import stringify + >>> from fairpyx import Instance + + >>> random.seed(9865) + >>> instance = Instance( + ... valuations={"ami":{"x":3, "y":4, "z":2}, "tami":{"x":4, "y":3, "z":2}, "tzumi":{"x":2, "y":4, "z":3}}, + ... agent_capacities=2, + ... item_capacities={"x":2, "y":1, "z":3}) + >>> initial_budgets={"ami":5, "tami":4, "tzumi":3} + >>> beta = 4 + >>> stringify(divide(tabu_search, instance=instance, initial_budgets=initial_budgets,beta=beta, delta={0.1, 0.8})) + "{ami:['y', 'z'], tami:['x', 'z'], tzumi:['x', 'z']}" + + Example run 2 + >>> random.seed(4675) + >>> instance = Instance( + ... valuations={"ami":{"x":5, "y":4, "z":3, "w":2}, "tami":{"x":5, "y":2, "z":4, "w":3}}, + ... agent_capacities=3, + ... item_capacities={"x":1, "y":2, "z":1, "w":2}) + >>> initial_budgets={"ami":8, "tami":6} + >>> beta = 9 + >>> stringify(divide(tabu_search, instance=instance, initial_budgets=initial_budgets,beta=beta, delta={0.2})) + "{ami:['w', 'x', 'y'], tami:['w', 'y', 'z']}" + + >>> random.seed(1805) + >>> instance = Instance( + ... valuations={"ami":{"x":4, "y":3, "z":2}, "tami":{"x":5, "y":1, "z":2}}, + ... agent_capacities=2, + ... item_capacities={"x":1, "y":2, "z":3}) + >>> initial_budgets={"ami":6, "tami":4} + >>> beta = 6 + >>> stringify(divide(tabu_search, instance=instance, initial_budgets=initial_budgets,beta=beta, delta={0.72})) + "{ami:['x', 'y'], tami:['y', 'z']}" + + >>> random.seed(100) + >>> instance = Instance( + ... valuations={"ami":{"x":4, "y":3, "z":2}, "tami":{"x":5, "y":1, "z":2}}, + ... agent_capacities=2, + ... item_capacities={"x":1, "y":1, "z":1}) + >>> initial_budgets={"ami":5, "tami":3} + >>> beta = 6 + >>> stringify(divide(tabu_search, instance=instance, initial_budgets=initial_budgets,beta=beta, delta={0.91})) + "{ami:['x', 'y'], tami:['z']}" + + Example run 3 + >>> random.seed(4341) + >>> instance = Instance( + ... valuations={"ami":{"x":3, "y":3, "z":3}, "tami":{"x":3, "y":3, "z":3}, "tzumi":{"x":4, "y":4, "z":4}}, + ... agent_capacities=2, + ... item_capacities={"x":1, "y":2, "z":2}) + >>> initial_budgets={"ami":4, "tami":5, "tzumi":2} + >>> beta = 5 + >>> stringify(divide(tabu_search, instance=instance, initial_budgets=initial_budgets,beta=beta, delta={0.34})) + "{ami:['x', 'y'], tami:['y', 'z'], tzumi:['z']}" + """ + initial_budgets = kwargs.get('initial_budgets') + beta = kwargs.get('beta') + delta = kwargs.get('delta') + logger.info("Tabu search: initial budgets = %s, beta = %s, delta = %s", initial_budgets, beta, delta) + + prices = {course: random.uniform(1, 1 + beta) for course in alloc.instance.items} + logger.info("1) Let 𝒑 ← uniform(1, 1 + 𝛽)^π‘š, H ← βˆ…: p = %s", prices) + history = [] + combinations_courses_sorted = get_combinations_courses_sorted(alloc.instance) + + logger.info("2) If βˆ₯𝒛(𝒖,𝒄, 𝒑, 𝒃0)βˆ₯2 = 0, terminate with π’‘βˆ— = 𝒑.") + + while True: + max_utilities_allocations = student_best_bundles(prices.copy(), alloc.instance, initial_budgets, + combinations_courses_sorted) + allocation, excess_demand_vector, norma = min_excess_demand_for_allocation(alloc.instance, prices, + max_utilities_allocations) + logger.info("\nprices=%s, excess demand=%s, best bundle=%s, norma=%s", prices, excess_demand_vector, allocation, + norma) + best_allocation, best_prices, best_norma = allocation, prices, norma + if np.allclose(norma, 0): + logger.info("2) βˆ₯𝒛(𝒖,𝒄, 𝒑, 𝒃0)βˆ₯2 = 0: terminate with π’‘βˆ— = 𝒑.") + break + + logger.info("3) Include all equivalent prices of 𝒑 into the history: H ← H + {𝒑′ : 𝒑′ βˆΌπ‘ 𝒑}") + equivalent_prices = find_all_equivalent_prices(alloc.instance, initial_budgets, allocation) + history.append(equivalent_prices) + neighbors = find_all_neighbors(alloc.instance, history, prices, delta, excess_demand_vector, + initial_budgets, + allocation, combinations_courses_sorted) + logger.info("Found %d neighbors", len(neighbors)) + if len(neighbors) == 0: + logger.info("--- No new neighbors to price-vector - no optimal solution") + break + + logger.info(" update 𝒑 ← arg minπ’‘β€²βˆˆN (𝒑)βˆ’H βˆ₯𝒛(𝒖,𝒄, 𝒑', 𝒃0)βˆ₯2") + allocation, excess_demand_vector, norma, prices = find_min_error_prices(alloc.instance, neighbors, + initial_budgets, + combinations_courses_sorted) + + if norma < best_norma: + logger.info(" Found a better norma") + best_allocation, best_prices, best_norma = allocation, prices, norma + + logger.info(f"\nfinal prices p* = {best_prices}") + logger.info(f"allocation is: {best_allocation}") + for student, bundle in best_allocation.items(): + for item in bundle: + if item in alloc.remaining_item_capacities: + alloc.give(student, item, logger) + + return best_allocation + + +# ---------------------helper functions:--------------------- +def min_excess_demand_for_allocation(instance: Instance, prices: dict, max_utilities_allocations: list[dict]): + """ + Goes through all allocations with the highest utilities of the students, and returns the allocation with the + lowest norm + + :param instance: fair-course-allocation instance + :param prices: dictionary with courses prices + :param max_utilities_allocations: A list of dictionaries that tells for each student all the package options he + can take with the maximum utility + + :return: Allocation, and its excess demand vector, which gives the lowest norm + + Example run 2 iteration 1 + >>> instance = Instance( + ... valuations={"Alice":{"x":5, "y":4, "z":3, "w":2}, "Bob":{"x":5, "y":2, "z":4, "w":3}}, + ... agent_capacities=3, + ... item_capacities={"x":1, "y":2, "z":1, "w":2}) + >>> prices = {"x": 1, "y": 2, "z": 3, "w":4} + >>> max_utilities_allocations = [{'Alice': ('x', 'y', 'z'), 'Bob': ('x', 'y', 'z')}] + >>> min_excess_demand_for_allocation(instance, prices, max_utilities_allocations) + ({'Alice': ('x', 'y', 'z'), 'Bob': ('x', 'y', 'z')}, {'x': 1, 'y': 0, 'z': 1, 'w': -2}, 2.449489742783178) + + + Example run 3 iteration 1 + >>> instance = Instance( + ... valuations={"Alice":{"x":3, "y":3, "z":3, "w":3}, "Bob":{"x":3, "y":3, "z":3, "w":3}, "Eve":{"x":4, "y":4, "z":4, "w":4}}, + ... agent_capacities=2, + ... item_capacities={"x":1, "y":2, "z":2, "w":1}) + >>> prices = {'x': 2.6124658024539347, 'y': 0, 'z': 1.1604071365185367, 'w': 5.930224022321449} + >>> max_utilities_allocations = [{'Alice': ('x', 'y'), 'Bob': ('x', 'y'), 'Eve': ('y', 'z')}, {'Alice': ('x', 'y'), 'Bob': ('x', 'z'), 'Eve': ('y', 'z')}, {'Alice': ('x', 'y'), 'Bob': ('y', 'z'), 'Eve': ('y', 'z')}, {'Alice': ('x', 'z'), 'Bob': ('x', 'y'), 'Eve': ('y', 'z')}, {'Alice': ('x', 'z'), 'Bob': ('x', 'z'), 'Eve': ('y', 'z')}, {'Alice': ('x', 'z'), 'Bob': ('y', 'z'), 'Eve': ('y', 'z')}, {'Alice': ('y', 'z'), 'Bob': ('x', 'y'), 'Eve': ('y', 'z')}, {'Alice': ('y', 'z'), 'Bob': ('x', 'z'), 'Eve': ('y', 'z')}, {'Alice': ('y', 'z'), 'Bob': ('y', 'z'), 'Eve': ('y', 'z')}] + >>> min_excess_demand_for_allocation(instance, prices, max_utilities_allocations) + ({'Alice': ('x', 'y'), 'Bob': ('x', 'z'), 'Eve': ('y', 'z')}, {'x': 1, 'y': 0, 'z': 0, 'w': -1}, 1.4142135623730951) + """ + min_norma = float("inf") + min_excess_demand = {} + best_alloc = {} + for alloc in max_utilities_allocations: + excess_demand_vector = clipped_excess_demand(instance, prices, alloc) + values = np.array(list(excess_demand_vector.values())) + curr_norma = np.linalg.norm(values) + + if curr_norma < min_norma: + min_norma = curr_norma + min_excess_demand = excess_demand_vector + best_alloc = alloc + + return best_alloc, min_excess_demand, min_norma + + +def excess_demand(instance: Instance, allocation: dict): + """ + Calculate for every course its excess demand + 𝑧𝑗 (𝒖,𝒄, 𝒑, 𝒃) = βˆ‘οΈ π‘Žπ‘–π‘— (𝒖, 𝒑, 𝒃) βˆ’ 𝑐𝑗 + 𝑖=1 to n + + :param instance: fair-course-allocation instance + :param allocation: a dictionary that maps each student to his bundle + + :return: a dictionary that maps each course to its excess demand + + >>> instance = Instance( + ... valuations={"ami":{"x":3, "y":4, "z":2}, "tami":{"x":4, "y":3, "z":2}}, + ... agent_capacities=2, + ... item_capacities={"x":2, "y":1, "z":3}) + >>> allocation = {"ami":('x','y'), "tami":('x','y')} + >>> excess_demand(instance, allocation) + {'x': 0, 'y': 1, 'z': -3} + """ + z = {} # Initialize z as a dictionary + for course in instance.items: + sum_allocation = 0 + for student, bundle in allocation.items(): + if course in bundle: + sum_allocation += 1 + z[course] = sum_allocation - instance.item_capacity(course) + return z + + +def clipped_excess_demand(instance: Instance, prices: dict, allocation: dict): + """ + Calculate for every course its clipped excess demand + π‘§Λœπ‘— (𝒖,𝒄, 𝒑, 𝒃) = 𝑧𝑗 (𝒖,𝒄, 𝒑, 𝒃) if 𝑝𝑗 > 0, + max{0, 𝑧𝑗 (𝒖,𝒄, 𝒑, 𝒃)} if 𝑝𝑗 = 0 + + + :param instance: fair-course-allocation instance + :param allocation: a dictionary that maps each student to his bundle + + :return: a dictionary that maps each course to its clipped excess demand + + >>> instance = Instance( + ... valuations={"ami":{"x":3, "y":4, "z":2}, "tami":{"x":4, "y":3, "z":2}}, + ... agent_capacities=2, + ... item_capacities={"x":3, "y":1, "z":3}) + >>> allocation = {"ami":('x','y'), "tami":('x','y')} + >>> prices = {"x":0, "y":2, "z":0} + >>> clipped_excess_demand(instance ,prices, allocation) + {'x': 0, 'y': 1, 'z': 0} + """ + z = excess_demand(instance, allocation) + clipped_z = {course: max(0, z[course]) if prices[course] == 0 else z[course] for course in z} + # logger.debug(f"excess demand: {clipped_z}") + return clipped_z + + +def student_best_bundles(prices: dict, instance: Instance, initial_budgets: dict, combinations_courses_sorted: dict): + """ + Return a list of dictionaries that tells for each student all the bundle options he can take with the maximum benefit. + + :param prices: dictionary with courses prices + :param instance: fair-course-allocation instance + :param initial_budgets: students' initial budgets + :param combinations_courses_sorted: sorted list of course combinations for each student + + :return: a list of dictionaries that maps each student to its best bundle. + + Example run 1 iteration 1 + >>> instance = Instance( + ... valuations={"Alice":{"x":3, "y":4, "z":2}, "Bob":{"x":4, "y":3, "z":2}, "Eve":{"x":2, "y":4, "z":3}}, + ... agent_capacities=2, + ... item_capacities={"x":2, "y":1, "z":3}) + >>> initial_budgets = {"Alice": 5, "Bob": 4, "Eve": 3} + >>> prices = {"x": 1, "y": 2, "z": 1} + >>> combinations_courses_sorted = {'Alice': [('x', 'y'), ('y', 'z'), ('x', 'z'), ('y',), ('x',), ('z',)], 'Bob': [('x', 'y'), ('x', 'z'), ('y', 'z'), ('x',), ('y',), ('z',)], 'Eve': [('y', 'z'), ('x', 'y'), ('x', 'z'), ('y',), ('z',), ('x',)]} + >>> student_best_bundles(prices, instance, initial_budgets, combinations_courses_sorted) + [{'Alice': ('x', 'y'), 'Bob': ('x', 'y'), 'Eve': ('y', 'z')}] + + Example run 2 iteration 1 + >>> instance = Instance( + ... valuations={"Alice":{"x":5, "y":4, "z":3, "w":2}, "Bob":{"x":5, "y":2, "z":4, "w":3}}, + ... agent_capacities=3, + ... item_capacities={"x":1, "y":2, "z":1, "w":2}) + >>> initial_budgets = {"Alice": 8, "Bob": 6} + >>> prices = {"x": 1, "y": 2, "z": 3, "w":4} + >>> combinations_courses_sorted = {'Alice': [('x', 'y', 'z'), ('x', 'y', 'w'), ('x', 'z', 'w'), ('y', 'z', 'w'), ('x', 'y'), ('x', 'z'), ('x', 'w'), ('y', 'z'), ('y', 'w'), ('x',), ('z', 'w'), ('y',), ('z',), ('w',)], 'Bob': [('x', 'z', 'w'), ('x', 'y', 'z'), ('x', 'y', 'w'), ('y', 'z', 'w'), ('x', 'z'), ('x', 'w'), ('x', 'y'), ('z', 'w'), ('y', 'z'), ('x',), ('y', 'w'), ('z',), ('w',), ('y',)]} + >>> student_best_bundles(prices, instance, initial_budgets, combinations_courses_sorted) + [{'Alice': ('x', 'y', 'z'), 'Bob': ('x', 'y', 'z')}] + + + Example run 3 iteration 1 + >>> instance = Instance( + ... valuations={"Alice":{"x":3, "y":3, "z":3, "w":3}, "Bob":{"x":3, "y":3, "z":3, "w":3}, "Eve":{"x":4, "y":4, "z":4, "w":4}}, + ... agent_capacities=2, + ... item_capacities={"x":1, "y":2, "z":2, "w":1}) + >>> initial_budgets = {"Alice": 4, "Bob": 5, "Eve": 2} + >>> prices = {'x': 2.6124658024539347, 'y': 0, 'z': 1.1604071365185367, 'w': 5.930224022321449} + >>> combinations_courses_sorted = {'Alice': [('x', 'y'), ('x', 'z'), ('x', 'w'), ('y', 'z'), ('y', 'w'), ('z', 'w'), ('x',), ('y',), ('z',), ('w',)], 'Bob': [('x', 'y'), ('x', 'z'), ('x', 'w'), ('y', 'z'), ('y', 'w'), ('z', 'w'), ('x',), ('y',), ('z',), ('w',)], 'Eve': [('x', 'y'), ('x', 'z'), ('x', 'w'), ('y', 'z'), ('y', 'w'), ('z', 'w'), ('x',), ('y',), ('z',), ('w',)]} + >>> student_best_bundles(prices, instance, initial_budgets, combinations_courses_sorted) + [{'Alice': ('x', 'y'), 'Bob': ('x', 'y'), 'Eve': ('y', 'z')}, {'Alice': ('x', 'y'), 'Bob': ('x', 'z'), 'Eve': ('y', 'z')}, {'Alice': ('x', 'y'), 'Bob': ('y', 'z'), 'Eve': ('y', 'z')}, {'Alice': ('x', 'z'), 'Bob': ('x', 'y'), 'Eve': ('y', 'z')}, {'Alice': ('x', 'z'), 'Bob': ('x', 'z'), 'Eve': ('y', 'z')}, {'Alice': ('x', 'z'), 'Bob': ('y', 'z'), 'Eve': ('y', 'z')}, {'Alice': ('y', 'z'), 'Bob': ('x', 'y'), 'Eve': ('y', 'z')}, {'Alice': ('y', 'z'), 'Bob': ('x', 'z'), 'Eve': ('y', 'z')}, {'Alice': ('y', 'z'), 'Bob': ('y', 'z'), 'Eve': ('y', 'z')}] + """ + + @lru_cache(maxsize=None) + def bundle_valuation(student, combination): + return instance.agent_bundle_value(student, combination) + + all_combinations = {student: [] for student in instance.agents} + for student in instance.agents: + max_valuation = -1 + for combination in combinations_courses_sorted[student]: + price_combination = sum(prices[course] for course in combination) + if price_combination <= initial_budgets[student]: + current_valuation = bundle_valuation(student, combination) + if current_valuation >= max_valuation: + if current_valuation > max_valuation: + all_combinations[student] = [] + max_valuation = current_valuation + all_combinations[student].append(combination) + + if not all_combinations[student]: + all_combinations[student].append(()) + + all_combinations_list = list(product(*all_combinations.values())) + + valid_allocations = [] + for allocation in all_combinations_list: + valid_allocation = {} + for student, bundle in zip(instance.agents, allocation): + if sum(prices[item] for item in bundle) <= initial_budgets[student]: + valid_allocation[student] = bundle + if len(valid_allocation) == len(instance.agents): + valid_allocations.append(valid_allocation) + + return valid_allocations + + +def find_all_equivalent_prices(instance: Instance, initial_budgets: dict, allocation: dict): + """ + find all equivalent prices list of all equivalent prices of 𝒑 (the history). + + :param instance: fair-course-allocation + :param initial_budgets: students' initial budgets + :param allocation: a dictionary that maps each student to his bundle + + Example run 1 + >>> instance = Instance(valuations={"A":{"x":3, "y":4, "z":2}, + ... "B":{"x":4, "y":3, "z":2}, "C":{"x":2, "y":4, "z":3}}, + ... agent_capacities=2, + ... item_capacities={"x":2, "y":1, "z":3}) + >>> initial_budgets = {"A": 5, "B":4, "C":3} + >>> allocation = {"A": {'x', 'y'}, "B":{'x', 'y'}, "C":{'y', 'z'}} + >>> equivalent_prices = find_all_equivalent_prices(instance, initial_budgets, allocation) + >>> p = {"x":1, "y":2, "z":1} + >>> all([f(p) for f in equivalent_prices]) + True + >>> p = {"x":5, "y":5, "z":5} + >>> all([f(p) for f in equivalent_prices]) + False + + # [(['x', 'y'], '<=', 5), (['x', 'y'], '<=', 4), (['y', 'z'], '<=', 3)] + + Example run 1 + >>> instance = Instance(valuations={"A":{"x":3, "y":4, "z":2}, + ... "B":{"x":4, "y":3, "z":2}, "C":{"x":2, "y":4, "z":3}}, + ... agent_capacities=2, + ... item_capacities={"x":2, "y":1, "z":3}) + >>> initial_budgets = {"A": 5, "B":4, "C":3} + >>> allocation = {"A": {'x', 'y'}, "B":{'x', 'z'}, "C":{'x', 'z'}} + >>> equivalent_prices = find_all_equivalent_prices(instance, initial_budgets, allocation) + >>> p = {"x":1, "y":5, "z":1} + >>> equivalent_prices[0](p) + False + >>> all([equivalent_prices[0](p)]) + False + >>> all([f(p) for f in equivalent_prices]) + False + + + >>> p = {"x":0, "y":0, "z":0} + >>> all([f(p) for f in equivalent_prices]) + False + + >>> p = {"x":1, "y":5, "z":1} + >>> all([f(p) for f in equivalent_prices]) + False + + Example run 2 iteration 1 + >>> instance = Instance(valuations={"A":{"x":5, "y":4, "z":3, "w":2},"B":{"x":5, "y":2, "z":4, "w":3}}, + ... agent_capacities=3, + ... item_capacities={"x":1, "y":2, "z":1, "w":2}) + >>> initial_budgets = {"A": 8, "B":6} + >>> allocation = {"A": {'x', 'y','z'}, "B":{'x','y' ,'z'}} + >>> equivalent_prices = find_all_equivalent_prices(instance, initial_budgets, allocation) + >>> p = {"x":2, "y":2, "z":4, "w":2} + >>> all([f(p) for f in equivalent_prices]) + False + + >>> p = {"x":2, "y":4, "z":3,"w":0} + >>> all([f(p) for f in equivalent_prices]) + False + + # p(x) + p(y) +p (z) <=8, p(x) + p(y) +p (z) <=6 + # p(x) + p(z) + p (w) > 6 + + + Example run 3 + >>> instance = Instance( + ... valuations={"ami": {"x": 3, "y": 3, "z": 3, "w":3}, "tami": {"x": 3, "y": 3, "z": 3, "w":3}, + ... "tzumi": {"x": 4, "y": 4, "z": 4, "w":4}}, + ... agent_capacities=2, + ... item_capacities={"x": 1, "y": 2, "z": 2, "w":1}) + >>> initial_budgets = {"ami": 4, "tami": 5, "tzumi": 2} + >>> allocation = {'ami': ('x', 'z'), 'tami': ('x', 'z'), 'tzumi': 'z'} + >>> equivalent_prices = find_all_equivalent_prices(instance, initial_budgets, allocation) + >>> p = {'x': 2.6124658024539347, 'y': 0, 'z': 1.1604071365185367, 'w': 5.930224022321449} + >>> all([f(p) for f in equivalent_prices]) + False + + """ + equivalent_prices = [] + # The constraints that the bundles they get in allocation meet their budgets + for student in instance.agents: + func = lambda p, agent=student, keys=allocation[student]: ( + sum(p[key] for key in keys) <= initial_budgets[agent]) + equivalent_prices.append(func) + + # Constraints that will ensure that this is the allocation that will be accepted + for student in instance.agents: + # Creating a list of combinations of courses up to the size of the student's capacity + combinations_courses_list = [] + capacity = instance.agent_capacity(student) + for r in range(1, capacity + 1): + combinations_courses_list.extend(combinations(instance.items, r)) + + original_utility = instance.agent_bundle_value(student, allocation[student]) + current_alloc = False + + for combination in combinations_courses_list: + current_utility = instance.agent_bundle_value(student, combination) + sorted_combination = sorted(combination) # Sort the combination + sorted_alloc_student = sorted(allocation[student]) + + if sorted_combination == sorted_alloc_student: + current_alloc = True + continue + + if current_alloc and len(sorted_combination) == len(sorted_alloc_student): + continue + + if current_utility >= original_utility: + # Create a copy of sorted_combination for the lambda function + combination_copy = sorted_combination.copy() # todo - we dont use it + + func = lambda p, agent=student, keys=allocation[student]: ( + sum(p[key] for key in keys) > initial_budgets[agent]) + equivalent_prices.append(func) + + return list(equivalent_prices) + + +def find_gradient_neighbors(prices: dict, delta: set, excess_demand_vector: dict, history: list[list]): + """ + Add the gradient neighbors to the neighbors list + N_gradient(𝒑, Ξ”) = {𝒑 + 𝛿 Β· 𝒛(𝒖,𝒄, 𝒑, 𝒃) : 𝛿 ∈ Ξ”} + + :param history: all equivalent prices of 𝒑 + :param prices: dictionary with courses prices + :param delta: The step size + :param excess_demand_vector: excess demand of the courses + :return: None + + Example run 1 iteration 1 + >>> prices = {"x": 1, "y": 2, "z": 1} + >>> delta = {1} + >>> excess_demand_vector = {"x":0,"y":2,"z":-2} + >>> history = [] + >>> find_gradient_neighbors(prices,delta,excess_demand_vector, history) + [{'x': 1, 'y': 4, 'z': 0}] + + Example run 1 iteration 2 + >>> prices = {"x": 1, "y": 4, "z": 0} + >>> delta = {1} + >>> excess_demand_vector = {"x":1,"y":0,"z":0} + >>> history = [] + >>> find_gradient_neighbors(prices,delta,excess_demand_vector, history) + [{'x': 2, 'y': 4, 'z': 0}] + + Example run 1 iteration 2 + >>> prices = {'x': 1, 'y': 4, 'z': 0} + >>> delta = {1} + >>> excess_demand_vector = {'x':1,'y':0,'z':0} + >>> history = [[lambda p: p['x']+p['y']<=6, lambda p: p['x']+p['y']<=8, lambda p: p['y']+p['z']<=5]] + >>> find_gradient_neighbors(prices,delta,excess_demand_vector, history) + [] + + >>> prices = {"x": 1, "y": 4, "z": 0} + >>> delta = {0.5, 1} + >>> excess_demand_vector = {"x":1,"y":0,"z":2} + >>> history = [] + >>> find_gradient_neighbors(prices,delta,excess_demand_vector, history) + [{'x': 1.5, 'y': 4.0, 'z': 1.0}, {'x': 2, 'y': 4, 'z': 2}] + """ + new_neighbors = [] + updated_prices = {} + for d in delta: + for course, price in prices.items(): + updated_prices[course] = max(0, price + d * excess_demand_vector.get(course, 0)) + + if not any(all(f(updated_prices) for f in sublist) for sublist in history): + new_neighbors.append(updated_prices.copy()) # Using copy() to append a new dictionary to the list + + return new_neighbors + + +def differ_in_one_value(original_allocation: dict, new_allocation: dict, course: str) -> bool: + """ + Check if two dictionaries differ with each other in exactly one value. + + :param original_allocation: First dictionary + :param new_allocation: Second dictionary + :return: True if the dictionaries differ in exactly one value, False otherwise + + >>> allocation1 = {"ami":('x','y'),"tami":('x','z'),"tzumi":('x','z')} + >>> allocation2 = {"ami":('x','y'),"tami":('x','z'),"tzumi":('x','t')} + >>> course ="z" + >>> differ_in_one_value(allocation1, allocation2, course) + True + + >>> allocation1 = {"ami":('x','y'),"tami":('x','z'),"tzumi":('x','z')} + >>> allocation2 = {"ami":('x','y'),"tami":('h','z'),"tzumi":('x','t')} + >>> course = "x" + >>> differ_in_one_value(allocation1, allocation2, course) + False + + >>> allocation1 = {"ami":('x','y'),"tami":('x','z'),"tzumi":('x','z')} + >>> allocation2 = {"ami":('x','y'),"tami":('x','z'),"tzumi":('x','z')} + >>> course = "z" + >>> differ_in_one_value(allocation1, allocation2, course) + False + + >>> allocation1 = {"ami":('x','y'),"tami":('x','z'),"tzumi":('x','z')} + >>> allocation2 = {"ami":('y','z'),"tami":('x','z'),"tzumi":('x','z')} + >>> course = "x" + >>> differ_in_one_value(allocation1, allocation2 , course) + True + """ + # Count the number of differing values + diff_count = 0 + diff_course = None + for key in original_allocation: + if key in new_allocation and original_allocation[key] != new_allocation[key]: + diff_course = key + diff_count += 1 + # If more than one value differs, return False immediately + if diff_count > 1: + return False + # Return True if exactly one value differs + return diff_count == 1 and course in original_allocation[diff_course] and course not in new_allocation[diff_course] + + +def find_individual_price_adjustment_neighbors(instance: Instance, history: list[list], prices: dict, + excess_demand_vector: dict, initial_budgets: dict, allocation: dict, + combinations_courses_sorted: dict = {}): + """ + Add the individual price adjustment neighbors N(p) to the neighbors list + + :param instance: fair-course-allocation + :param history: all equivalent prices of 𝒑 + :param prices: dictionary with courses prices + :param excess_demand_vector: excess demand of the courses + :param initial_budgets: students' initial budgets + :param allocation: a dictionary that maps each student to his bundle + :return: None + + Example run 1 iteration 1 + >>> instance = Instance( + ... valuations={"ami":{"x":3, "y":4, "z":2}, "tami":{"x":4, "y":3, "z":2}, "tzumi":{"x":2, "y":4, "z":3}}, + ... agent_capacities=2, + ... item_capacities={"x":2, "y":1, "z":3}) + >>> history = [[lambda p: p['x']+p['y']<=5, lambda p: p['x']+p['y']<=4, lambda p: p['y']+p['z']<=3]] + >>> prices = {"x": 1, "y": 2, "z": 1} + >>> excess_demand_vector = {"x":0,"y":2,"z":-2} + >>> initial_budgets = {"ami":5,"tami":4,"tzumi":3} + >>> allocation = {"ami":('x','y'),"tami":('x','y'),"tzumi":('y','z')} + >>> combinations_courses_sorted = {'ami': [('x', 'y'), ('y', 'z'), ('x', 'z'), ('y',), ('x',), ('z',)], 'tami': [('x', 'y'), ('x', 'z'), ('y', 'z'), ('x',), ('y',), ('z',)], 'tzumi': [('y', 'z'), ('x', 'y'), ('x', 'z'), ('y',), ('z',), ('x',)]} + >>> find_individual_price_adjustment_neighbors(instance, history, prices, excess_demand_vector, initial_budgets, allocation, combinations_courses_sorted) + [{'x': 1, 'y': 2.7071067811865475, 'z': 1}] + + + Example run 1 iteration 2 + >>> instance = Instance( + ... valuations={"ami":{"x":3, "y":4, "z":2}, "tami":{"x":4, "y":3, "z":2}, "tzumi":{"x":2, "y":4, "z":3}}, + ... agent_capacities=2, + ... item_capacities={"x":2, "y":1, "z":3}) + >>> history = [[lambda p: p['x']+p['y']<=5, lambda p: p['x']+p['y']<=4, lambda p: p['y']+p['z']<=3, + ... lambda p: p['x']+p['z']<=4, lambda p: p['x']+p['z']<=3, lambda p: p['y']+p['z']>=3, lambda p: p['x']+p['y']>=4]] + >>> prices = {"x": 1, "y": 4, "z": 0} + >>> excess_demand_vector = {"x":1,"y":0,"z":0} + >>> initial_budgets = {"ami":5,"tami":4,"tzumi":3} + >>> allocation = {"ami":('x','y'),"tami":('x','z'),"tzumi":('x','z')} + >>> combinations_courses_sorted = {'ami': [('x', 'y'), ('y', 'z'), ('x', 'z'), ('y',), ('x',), ('z',)], 'tami': [('x', 'y'), ('x', 'z'), ('y', 'z'), ('x',), ('y',), ('z',)], 'tzumi': [('y', 'z'), ('x', 'y'), ('x', 'z'), ('y',), ('z',), ('x',)]} + >>> find_individual_price_adjustment_neighbors(instance, history, prices, excess_demand_vector, initial_budgets, allocation, combinations_courses_sorted) + [{'x': 1.7071067811865475, 'y': 4, 'z': 0}, {'x': 2.414213562373095, 'y': 4, 'z': 0}] + + + Example run 3 iteration 1 + >>> instance = Instance( + ... valuations={"ami": {"x": 3, "y": 3, "z": 3, "w":3}, "tami": {"x": 3, "y": 3, "z": 3, "w":3}, + ... "tzumi": {"x": 4, "y": 4, "z": 4, "w":4}}, + ... agent_capacities=2, + ... item_capacities={"x": 1, "y": 2, "z": 2, "w":1}) + >>> history = [[lambda p: p['x']+p['z']<=4, lambda p: p['x']+p['z']<=5, lambda p: p['z']<=2, + ... lambda p: p['x']+p['y']>4, lambda p: p['x']+p['y']>5, lambda p: p['x']+p['y']>2, + ... lambda p: p['x']+p['z']>2, lambda p: p['x']+p['w']>2, lambda p: p['y']+p['z']>2, + ... lambda p: p['y']+p['w']>2, lambda p: p['z']+p['w']>2, lambda p: p['x']>2, + ... lambda p: p['y']>2]] + >>> prices = {'x': 2.6124658024539347, 'y': 4.138416343413373, 'z': 1.1604071365185367, 'w': 5.930224022321449} + >>> excess_demand_vector = {'x': 1, 'y': -2, 'z': 1, 'w': -1} + >>> initial_budgets = {"ami": 4, "tami": 5, "tzumi": 2} + >>> allocation = {'ami': ('x', 'z'), 'tami': ('x', 'z'), 'tzumi': 'z'} + >>> combinations_courses_sorted = {'ami': [('x', 'y'), ('x', 'z'), ('x', 'w'), ('y', 'z'), ('y', 'w'), ('z', 'w'), ('x',), ('y',), ('z',), ('w',)], 'tami': [('x', 'y'), ('x', 'z'), ('x', 'w'), ('y', 'z'), ('y', 'w'), ('z', 'w'), ('x',), ('y',), ('z',), ('w',)], 'tzumi': [('x', 'y'), ('x', 'z'), ('x', 'w'), ('y', 'z'), ('y', 'w'), ('z', 'w'), ('x',), ('y',), ('z',), ('w',)]} + >>> find_individual_price_adjustment_neighbors(instance, history, prices, excess_demand_vector, initial_budgets, allocation, combinations_courses_sorted) + [{'x': 2.6124658024539347, 'y': 0, 'z': 1.1604071365185367, 'w': 5.930224022321449}, {'x': 2.6124658024539347, 'y': 4.138416343413373, 'z': 1.1604071365185367, 'w': 0}] + """ + new_neighbors = [] + for course, excess_demand in excess_demand_vector.items(): + if len(new_neighbors) >= 35: + break + if excess_demand == 0: + continue + updated_prices = prices.copy() + if excess_demand > 0: + for _ in range(10): + updated_prices[course] += np.sqrt(0.5) + if any(all(f(updated_prices) for f in sublist) for sublist in history): + continue + # get the new demand of the course + new_allocations = student_best_bundles(updated_prices.copy(), instance, initial_budgets, + combinations_courses_sorted) + + for new_allocation in new_allocations: + if differ_in_one_value(allocation, new_allocation, course): + new_neighbors.append(updated_prices.copy()) + + elif excess_demand < 0: + updated_prices[course] = 0 + if not any(all(f(updated_prices) for f in sublist) for sublist in history): + new_neighbors.append(updated_prices) + + return new_neighbors + + +def find_all_neighbors(instance: Instance, history: list, prices: dict, delta: set, + excess_demand_vector: dict, initial_budgets: dict, allocation: dict, + combinations_courses_sorted: dict = {}): + """ + Update neighbors N (𝒑) - list of Gradient neighbors and Individual price adjustment neighbors. + + :param instance: fair-course-allocation + :param history: all equivalent prices of 𝒑 + :param prices: dictionary with courses prices + :param delta: The step size + """ + + gradient_neighbors = find_gradient_neighbors(prices, delta, excess_demand_vector, history) + individual_price_adjustment_neighbors = find_individual_price_adjustment_neighbors(instance, history, + prices, + excess_demand_vector, + initial_budgets, allocation, + combinations_courses_sorted) + logger.debug(f"neighbors: \ngradient_neighbors = {gradient_neighbors}") + logger.debug(f"individual_price = {individual_price_adjustment_neighbors}") + + return gradient_neighbors + individual_price_adjustment_neighbors + + +def find_min_error_prices(instance: Instance, neighbors: list, initial_budgets: dict, + combinations_courses_sorted: dict = {}): + """ + Return the update prices that minimize the market clearing error. + + :param instance: fair-course-allocation + :param prices: dictionary with course prices + :param neighbors: list of Gradient neighbors and Individual price adjustment neighbors. + :param initial_budgets: students' initial budgets + + :return: allocation, excess_demand, norm and update price with the minimum norm + + Example run 1 iteration 1 + >>> instance = Instance( + ... valuations={"ami":{"x":3, "y":4, "z":2}, "tami":{"x":4, "y":3, "z":2}, "tzumi":{"x":2, "y":4, "z":3}}, + ... agent_capacities=2, + ... item_capacities={"x":2, "y":1, "z":3}) + >>> neighbors = [{"x":1, "y":4, "z":0}, {"x":1, "y":3, "z":1}] + >>> initial_budgets={"ami":5, "tami":4, "tzumi":3} + >>> combinations_courses_sorted = {'ami': [('x', 'y'), ('y', 'z'), ('x', 'z'), ('y',), ('x',), ('z',)], 'tami': [('x', 'y'), ('x', 'z'), ('y', 'z'), ('x',), ('y',), ('z',)], 'tzumi': [('y', 'z'), ('x', 'y'), ('x', 'z'), ('y',), ('z',), ('x',)]} + >>> find_min_error_prices(instance, neighbors, initial_budgets, combinations_courses_sorted) + ({'ami': ('x', 'y'), 'tami': ('x', 'z'), 'tzumi': ('x', 'z')}, {'x': 1, 'y': 0, 'z': 0}, 1.0, {'x': 1, 'y': 4, 'z': 0}) + + Example run 1 iteration 2 + >>> instance = Instance( + ... valuations={"ami":{"x":3, "y":4, "z":2}, "tami":{"x":4, "y":3, "z":2}, "tzumi":{"x":2, "y":4, "z":3}}, + ... agent_capacities=2, + ... item_capacities={"x":2, "y":1, "z":3}) + >>> neighbors = [{"x":2, "y":4, "z":0}, {"x":3, "y":4, "z":0}] + >>> initial_budgets={"ami":5, "tami":4, "tzumi":3} + >>> combinations_courses_sorted = {'ami': [('x', 'y'), ('y', 'z'), ('x', 'z'), ('y',), ('x',), ('z',)], 'tami': [('x', 'y'), ('x', 'z'), ('y', 'z'), ('x',), ('y',), ('z',)], 'tzumi': [('y', 'z'), ('x', 'y'), ('x', 'z'), ('y',), ('z',), ('x',)]} + >>> find_min_error_prices(instance, neighbors, initial_budgets, combinations_courses_sorted) + ({'ami': ('y', 'z'), 'tami': ('x', 'z'), 'tzumi': ('x', 'z')}, {'x': 0, 'y': 0, 'z': 0}, 0.0, {'x': 2, 'y': 4, 'z': 0}) + """ + errors = [] # tuple of (allocation, excess_demand, norm, price) + min_error_prices = [] + logger.debug("\nChecking the neighbors:") + for neighbor in neighbors: + logger.debug(f"neighbor: {neighbor}") + allocations = student_best_bundles(neighbor.copy(), instance, initial_budgets, combinations_courses_sorted) + allocation, excess_demand_vector, norma = min_excess_demand_for_allocation(instance, neighbor, allocations) + logger.debug(f"excess demand: {excess_demand_vector}") + logger.debug(f"norma = {norma}") + errors.append((allocation, excess_demand_vector, norma, neighbor)) + logger.debug("") + + min_error_tuple = min(errors, key=lambda x: x[2]) + return min_error_tuple + + +def run_tabu_search(alloc: AllocationBuilder, **kwargs): + beta = 0.001 + initial_budgets = random_initial_budgets(alloc.instance.num_of_agents, beta) + return tabu_search(alloc, initial_budgets=initial_budgets, beta=beta, delta={0.34}, **kwargs) + +def random_initial_budgets(num_of_agents: int, beta: float = 100) -> dict: + # Create initial budgets for each agent, uniformly distributed in the range [1, 1 + beta] + initial_budgets = np.random.uniform(1, 1 + beta, num_of_agents) + return {f's{agent + 1}': initial_budgets[agent] for agent in range(num_of_agents)} + + +if __name__ == "__main__": + import doctest, sys + + print("\n", doctest.testmod(), "\n") + # sys.exit(0) + + logger.addHandler(logging.StreamHandler()) + logger.setLevel(logging.INFO) + + import coloredlogs + level_styles = { + 'debug': {'color': 'green'}, + 'info': {'color': 'cyan'}, + 'warning': {'color': 'yellow'}, + 'error': {'color': 'red', 'bold': True}, + 'critical': {'color': 'red', 'bold': True, 'background': 'white'} + } + coloredlogs.install(level='DEBUG', logger=logger, fmt='%(message)s', level_styles=level_styles) + + from fairpyx.adaptors import divide + + random_delta = {random.uniform(0.1, 1)} + random_beta = random.uniform(1, 100) + + + def random_initial_budgets(num): + return {f"s{key}": random.uniform(1, 1 + random_beta) for key in range(1, num + 1)} + + num_of_agents = 3 + utilities = {f"s{i}": {f"c{num_of_agents + 1 - j}": j for j in range(num_of_agents, 0, -1)} for i in + range(1, num_of_agents + 1)} + instance = Instance(valuations=utilities, agent_capacities=1, item_capacities=1) + initial_budgets = {f"s{key}": (num_of_agents + 1 - key) for key in range(1, num_of_agents + 1)} + logger.error(f"initial_budgets = {initial_budgets}") + logger.error(f"random_beta = {random_beta}") + # initial_budgets = {f"s{key}": (random_beta + key) for key in range(1, num_of_agents + 1)} + allocation = divide(tabu_search, instance=instance, + initial_budgets=initial_budgets, + beta=random_beta, delta=random_delta) + # for i in range(1, num_of_agents + 1): + # assert (f"c{i}" in allocation[f"s{i}"]) + + # seed = random.randint(1, 10000) + # # seed = 2006 + # # random.seed(seed) + # logger.debug(f"seed is {seed}") + # + # # Write the seed to a new file + # with open('seed.txt', 'a') as file: + # file.write(f"seed is {seed}\n") + # + # # instance = Instance(valuations = {"ami": {"x": 4, "y": 3, "z": 2}, "tami": {"x": 5, "y": 1, "z": 2}}, agent_capacities = 2, + # # item_capacities = {"x": 1, "y": 2, "z": 3}) + # # initial_budgets = {"ami": 6, "tami": 4} + # # beta = 6 + # # divide(tabu_search, instance=instance, initial_budgets=initial_budgets, beta=beta, delta={0.72}) + # # # "{ami:['x', 'y'], tami:['y', 'z']}" + # + # # instance = Instance(valuations = {"ami": {"x": 4, "y": 3, "z": 2}, "tami": {"x": 5, "y": 1, "z": 2}}, + # # agent_capacities = 2, item_capacities = {"x": 1, "y": 1, "z": 1}) + # # initial_budgets = {"ami": 5, "tami": 3} + # # beta = 6 + # # divide(tabu_search, instance=instance, initial_budgets=initial_budgets, beta=beta, delta={0.91}) + # # # "{ami:['x', 'y'], tami:['z']}" + # # + # # Example run 3 + # # random.seed(1000) + # instance = Instance(valuations = {"ami": {"x": 3, "y": 3, "z": 3}, "tami": {"x": 3, "y": 3, "z": 3}, "tzumi": {"x": 4, "y": 4, "z": 4}}, + # agent_capacities = 2, item_capacities = {"x": 1, "y": 2, "z": 2}) + # initial_budgets = {"ami": 4, "tami": 5, "tzumi": 2} + # beta = 5 + # divide(tabu_search, instance=instance, initial_budgets=initial_budgets, beta=beta, delta={0.34}) + # # "{ami:['y', 'z'], tami:['x', 'y'], tzumi:['z']}" + # + # diff --git a/fairpyx/algorithms/__init__.py b/fairpyx/algorithms/__init__.py index 32a3ed6..261466d 100644 --- a/fairpyx/algorithms/__init__.py +++ b/fairpyx/algorithms/__init__.py @@ -2,6 +2,8 @@ from fairpyx.algorithms.iterated_maximum_matching import iterated_maximum_matching, iterated_maximum_matching_adjusted, iterated_maximum_matching_unadjusted from fairpyx.algorithms.picking_sequence import round_robin, bidirectional_round_robin, serial_dictatorship from fairpyx.algorithms.utilitarian_matching import utilitarian_matching +from fairpyx.algorithms.ACEEI.tabu_search import tabu_search +from fairpyx.algorithms.ACEEI.ACEEI import find_ACEEI_with_EFTB from fairpyx.algorithms.Optimization_based_Mechanisms.OC import OC_function from fairpyx.algorithms.Optimization_based_Mechanisms.SP_O import SP_O_function from fairpyx.algorithms.Optimization_based_Mechanisms.SP import SP_function diff --git a/requirements.txt b/requirements.txt index 24c4104..c2d0093 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,6 +1,6 @@ numpy>=1.21.3,<2.0.0 scipy>=1.6.1 -networkz +networkz~=1.0.6 cvxpy_base>=1.1.17 prtpy # cmake @@ -10,4 +10,4 @@ prtpy # cvxpy_leximin>=0.4.4 # prtpy>=0.7.0 # more_itertools -# scs +# scs \ No newline at end of file diff --git a/tests/test_ACEEI.py b/tests/test_ACEEI.py new file mode 100644 index 0000000..a81932f --- /dev/null +++ b/tests/test_ACEEI.py @@ -0,0 +1,130 @@ +""" +"Practical algorithms and experimentally validated incentives +for equilibrium-based fair division (A-CEEI)" +tests for algorithm 1 - ACEEI + +Programmers: Erga Bar-Ilan, Ofir Shitrit and Renana Turgeman. +Since: 2024-01 +""" + +import random +import pytest +import logging +import fairpyx +from fairpyx import Instance, divide +# from fairpyx.algorithms import ACEEI +# from fairpyx.algorithms.linear_program import optimize_model +from fairpyx.algorithms.ACEEI.ACEEI import EFTBStatus, logger, find_ACEEI_with_EFTB +import numpy as np + + +def random_initial_budgets(num): + return {f"s{key}": random.randint(10, 20) for key in range(1, num + 1)} + + +random_value = random.uniform(0.1, 2) +random_t = random.choice(list(EFTBStatus)) + + +# Each student will get all the courses +def test_case_1(): + instance = Instance.random_uniform(num_of_agents=200, num_of_items=6, agent_capacity_bounds=(6, 6), + item_capacity_bounds=(400, 400), item_base_value_bounds=(1, 5), + item_subjective_ratio_bounds=(1, 1.5), + normalized_sum_of_values=1000) + allocation = divide(find_ACEEI_with_EFTB, instance=instance, initial_budgets=random_initial_budgets(instance.num_of_agents), + delta=random_value, epsilon=random_value, t=random_t) + for agent in instance.agents: + for item in instance.items: + assert (item in allocation[agent]) + + +# Each student i will get course i +def test_case_2(): + utilities = {f"s{i}": {f"c{j}": 1 if j == i else 0 for j in range(1, 101)} for i in range(1, 101)} + instance = Instance(valuations=utilities, agent_capacities=1, item_capacities=1) + allocation = divide(find_ACEEI_with_EFTB, instance=instance, initial_budgets=random_initial_budgets(100), + delta=random_value, epsilon=random_value, t=random_t) + for i in range(1, 101): + assert (f"c{i}" in allocation[f"s{i}"]) + + +# Each student i will get course i, because student i have the highest i budget. +# def test_case_3(): +# utilities = {f"s{i}": {f"c{101 - j}": j for j in range(100, 0, -1)} for i in range(1, 101)} +# instance = Instance(valuations=utilities, agent_capacities=1, item_capacities=1) +# initial_budgets = {f"s{key}": (101 - key) for key in range(1, 101)} +# allocation = divide(find_ACEEI_with_EFTB, instance=instance, initial_budgets=initial_budgets, +# delta=0.5, epsilon=0.5, t=EFTBStatus.NO_EF_TB) +# for i in range(1, 101): +# assert (f"c{i}" in allocation[f"s{i}"]) + + +def test_case__3_mini(): + utilities = {f"s{i}": {f"c{44 - j}": j for j in range(43, 0, -1)} for i in range(1, 44)} + instance = Instance(valuations=utilities, agent_capacities=1, item_capacities=1) + initial_budgets = {f"s{key}": (44 - key) for key in range(1, 44)} + allocation = divide(find_ACEEI_with_EFTB, instance=instance, initial_budgets=initial_budgets, + delta=0.5, epsilon=0.5, t=EFTBStatus.EF_TB) + for i in range(1, 44): + assert (f"c{i}" in allocation[f"s{i}"]) + + +# Each student will get his 3 favorite courses +# +def test_case_4(): + instance = Instance.random_uniform(num_of_agents=200, num_of_items=6, agent_capacity_bounds=(3, 3), + item_capacity_bounds=(200, 200), item_base_value_bounds=(1, 5), + item_subjective_ratio_bounds=(0.5, 1.5), + normalized_sum_of_values=1000) + allocation = divide(find_ACEEI_with_EFTB, instance=instance, initial_budgets=random_initial_budgets(instance.num_of_agents), + delta=random_value, epsilon=random_value, t=random_t) + + # Checking if each student receives the 3 courses with the highest valuation + for student, allocated_courses in allocation.items(): + # Getting the agent's ranking of items and selecting the top 3 + top_3_courses = list(instance.agent_ranking(student))[:3] + # Asserting if the allocated courses for the student are within their top 3 favorite courses + assert all(course in top_3_courses for course in allocated_courses) + + +# def test_case_4_mini(): +# instance = Instance.random_uniform(num_of_agents=10, num_of_items=30, agent_capacity_bounds=(3, 3), +# item_capacity_bounds=(20, 20), item_base_value_bounds=(1, 5), +# item_subjective_ratio_bounds=(0.5, 1.5), +# normalized_sum_of_values=1000) +# allocation = divide(find_ACEEI_with_EFTB, instance=instance, initial_budgets=random_initial_budgets, +# delta=random_value, epsilon=random_value, t=random_t) +# # Checking if each student receives the 3 courses with the highest valuation +# for student, allocated_courses in allocation.items(): +# # Getting the agent's ranking of items and selecting the top 3 +# top_3_courses = list(instance.agent_ranking(student))[:3] +# # Asserting if the allocated courses for the student are within their top 3 favorite courses +# assert all(course in top_3_courses for course in allocated_courses) + + +# Checking if the values that the function returns are correct +def test_case_5(): + instance = Instance.random_uniform(num_of_agents=100, num_of_items=6, agent_capacity_bounds=(3, 3), + item_capacity_bounds=(200, 200), item_base_value_bounds=(1, 5), + item_subjective_ratio_bounds=(0.5, 1.5), + normalized_sum_of_values=1000) + allocation = divide(find_ACEEI_with_EFTB, instance=instance, initial_budgets=random_initial_budgets(instance.num_of_agents), + delta=random_value, epsilon=random_value, t=random_t) + fairpyx.validate_allocation(instance, allocation, title="validate Algorithm 1") + + +# def test_case_5_mini(): +# instance = Instance.random_uniform(num_of_agents=10, num_of_items=30, agent_capacity_bounds=(3, 3), +# item_capacity_bounds=(20, 20), item_base_value_bounds=(1, 5), +# item_subjective_ratio_bounds=(0.5, 1.5), +# normalized_sum_of_values=1000) +# allocation = divide(find_ACEEI_with_EFTB, instance=instance, initial_budgets=random_initial_budgets, +# delta=random_value, epsilon=random_value, t=random_t) +# fairpyx.validate_allocation(instance, allocation, title="validate Algorithm 1") + + +if __name__ == "__main__": + # logger.addHandler(logging.StreamHandler()) + # logger.setLevel(logging.INFO) + pytest.main(["-v", __file__]) diff --git a/tests/test_tabu_search.py b/tests/test_tabu_search.py new file mode 100644 index 0000000..006306c --- /dev/null +++ b/tests/test_tabu_search.py @@ -0,0 +1,116 @@ +""" +"Practical algorithms and experimentally validated incentives +for equilibrium-based fair division (A-CEEI)" +tests for algorithm 3 - Tabu Search + +Programmers: Erga Bar-Ilan, Ofir Shitrit and Renana Turgeman. +Since: 2024-01 +""" + +import random + +import pytest + +import fairpyx +from fairpyx import Instance, divide +from fairpyx.algorithms.ACEEI.tabu_search import tabu_search + +random_delta = {random.uniform(0.1, 1)} +random_beta = random.uniform(1, 100) + + +def random_initial_budgets(num): + return {f"s{key}": random.uniform(1, 1 + random_beta) for key in range(1, num + 1)} + + +# Each student will get all the courses +def test_case1(): + instance = Instance.random_uniform(num_of_agents=200, num_of_items=6, agent_capacity_bounds=(6, 6), + item_capacity_bounds=(400, 400), item_base_value_bounds=(1, 5), + item_subjective_ratio_bounds=(1, 1.5), + normalized_sum_of_values=1000) + allocation = divide(tabu_search, instance=instance, + initial_budgets=random_initial_budgets(instance.num_of_agents), + beta=random_beta, delta=random_delta) + for agent in instance.agents: + for item in instance.items: + assert (item in allocation[agent]) + + +# TODO: not working: 3 +# Each student i will get course i +def test_case2(): + num_of_agents = 100 + utilities = {f"s{i}": {f"c{j}": 1 if j == i else 0 for j in range(1, num_of_agents)} for i in + range(1, num_of_agents)} + initial_budgets = {f"s{key}": (random_beta + 1) for key in range(1, num_of_agents)} + instance = Instance(valuations=utilities, agent_capacities=1, item_capacities=1) + allocation = divide(tabu_search, instance=instance, + initial_budgets=initial_budgets, + beta=random_beta, delta=random_delta) + for i in range(1, num_of_agents): + assert (f"c{i}" in allocation[f"s{i}"]) + + +# Each student i will get course i, because student i have the highest i budget. +def test_case3(): + num_of_agents = 20 + utilities = {f"s{i}": {f"c{num_of_agents + 1 - j}": j for j in range(num_of_agents, 0, -1)} for i in + range(1, num_of_agents + 1)} + instance = Instance(valuations=utilities, agent_capacities=1, item_capacities=1) + initial_budgets = {f"s{key}": (num_of_agents + 1 - key) for key in range(1, num_of_agents + 1)} + # logger.error(f"initial_budgets = {initial_budgets}") + # logger.error(f"random_beta = {random_beta}") + # initial_budgets = {f"s{key}": (random_beta + key) for key in range(1, num_of_agents + 1)} + allocation = divide(tabu_search, instance=instance, + initial_budgets=initial_budgets, + beta=0.1, delta={0.9}) + for i in range(1, num_of_agents + 1): + assert (f"c{i}" in allocation[f"s{i}"]) + + +# def test_case__3_mini(): +# utilities = {f"s{i}": {f"c{44 - j}": j for j in range(43, 0, -1)} for i in range(1, 44)} +# instance = Instance(valuations=utilities, agent_capacities=1, item_capacities=1) +# initial_budgets = {f"s{key}": (44 - key) for key in range(1, 44)} +# allocation = divide(tabu_search, instance=instance, +# initial_budgets=random_initial_budgets(instance.num_of_agents), +# beta=random_beta, delta=random_delta) +# for i in range(1, 44): +# assert (f"c{i}" in allocation[f"s{i}"]) + + +# Each student will get his 3 favorite courses +def test_case4(): + instance = Instance.random_uniform(num_of_agents=200, num_of_items=6, agent_capacity_bounds=(3, 3), + item_capacity_bounds=(200, 200), item_base_value_bounds=(1, 5), + item_subjective_ratio_bounds=(0.5, 1.5), + normalized_sum_of_values=1000) + allocation = divide(tabu_search, instance=instance, + initial_budgets=random_initial_budgets(instance.num_of_agents), + beta=random_beta, delta=random_delta) + + # Checking if each student receives the 3 courses with the highest valuation + for student, allocated_courses in allocation.items(): + # Getting the agent's ranking of items and selecting the top 3 + top_3_courses = list(instance.agent_ranking(student))[:3] + # Asserting if the allocated courses for the student are within their top 3 favorite courses + assert all(course in top_3_courses for course in allocated_courses) + + +# Checking if the values that the function returns are correct +def test_case5(): + instance = Instance.random_uniform(num_of_agents=100, num_of_items=6, agent_capacity_bounds=(3, 3), + item_capacity_bounds=(200, 200), item_base_value_bounds=(1, 5), + item_subjective_ratio_bounds=(0.5, 1.5), + normalized_sum_of_values=1000) + allocation = divide(tabu_search, instance=instance, + initial_budgets=random_initial_budgets(instance.num_of_agents), + beta=random_beta, delta=random_delta) + fairpyx.validate_allocation(instance, allocation, title="validate Algorithm 3") + + +if __name__ == "__main__": + # logger.addHandler(logging.StreamHandler()) + # logger.setLevel(logging.INFO) + pytest.main(["-v", __file__])