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Merge pull request #7 from Final-Project-fairpyx/main
fix the OC algo
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,279 @@ | ||
| """ | ||
| 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 | ||
| import cvxpy as cp | ||
| import logging | ||
| from fairpyx.explanations import ConsoleExplanationLogger, FilesExplanationLogger, StringsExplanationLogger | ||
| console_explanation_logger = ConsoleExplanationLogger(level=logging.DEBUG) | ||
|
|
||
| max_value = 1000 | ||
| normalized_sum_of_values = 1000 | ||
| TIME_LIMIT = 300 | ||
|
|
||
| algorithms_with_specific_solver = [ | ||
| crs.TTC_O_function, | ||
| crs.SP_O_function, | ||
| crs.OC_function, | ||
| ] | ||
|
|
||
| algorithm_only_oc = [ | ||
| crs.OC_function, | ||
| ] | ||
|
|
||
| algorithms_with_none_solver = [ | ||
| crs.iterated_maximum_matching_unadjusted, | ||
| crs.iterated_maximum_matching_adjusted, | ||
| crs.TTC_function, | ||
| crs.SP_function, | ||
| ] | ||
|
|
||
| def evaluate_algorithm_on_instance(algorithm, instance, solver): | ||
| if algorithm in algorithms_with_none_solver: | ||
| allocation = divide(algorithm, instance) | ||
| else: | ||
| allocation = divide(algorithm, instance, solver=solver, explanation_logger=console_explanation_logger) | ||
| 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), | ||
| "solver": solver, | ||
| } | ||
|
|
||
| ######### 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, solver): | ||
| 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, solver) | ||
|
|
||
| def run_uniform_experiment(): | ||
| experiment = experiments_csv.Experiment("results/", "with_solver_algo_for_course_allocation_uniform.csv", backup_folder="results/backup/") | ||
| input_ranges_none_solver = { | ||
| "num_of_agents": [100,200,300], | ||
| "num_of_items": [25], | ||
| "value_noise_ratio": [0, 0.2, 0.5, 0.8, 1], | ||
| "algorithm": algorithms_with_none_solver, | ||
| "random_seed": range(5), | ||
| "solver": [None], | ||
| } | ||
| # experiment.run_with_time_limit(course_allocation_with_random_instance_uniform, input_ranges_none_solver, time_limit=TIME_LIMIT) | ||
|
|
||
| input_ranges_specific_solver = { | ||
| "num_of_agents": [100], | ||
| "num_of_items": [25], | ||
| "value_noise_ratio": [0, 0.2, 0.5, 0.8, 1], | ||
| "algorithm": algorithms_with_specific_solver, | ||
| "random_seed": range(5), | ||
| "solver": [None, cp.CBC, cp.MOSEK, cp.SCIP, cp.XPRESS], #, cp.COPT, cp.CPLEX, cp.GUROBI | ||
| } | ||
| # experiment.run_with_time_limit(course_allocation_with_random_instance_uniform, input_ranges_specific_solver, time_limit=TIME_LIMIT) | ||
|
|
||
| input_ranges_specific_solver = { | ||
| "num_of_agents": [200, 300], | ||
| "num_of_items": [25], | ||
| "value_noise_ratio": [0, 0.2, 0.5, 0.8, 1], | ||
| "algorithm": algorithms_with_specific_solver, | ||
| "random_seed": range(5), | ||
| "solver": [None, cp.CBC, cp.MOSEK, cp.SCIP], #, cp.XPRESS , cp.COPT, cp.CPLEX, cp.GUROBI | ||
| } | ||
| # experiment.run_with_time_limit(course_allocation_with_random_instance_uniform, input_ranges_specific_solver, time_limit=TIME_LIMIT) | ||
|
|
||
| input_ranges_specific_solver = { | ||
| "num_of_agents": [100, 200, 300], | ||
| "num_of_items": [25], | ||
| "value_noise_ratio": [0, 0.2, 0.5, 0.8, 1], | ||
| "algorithm": algorithms_with_specific_solver, | ||
| "random_seed": range(5), | ||
| "solver": [cp.SCIPY] | ||
| } | ||
| experiment.run_with_time_limit(course_allocation_with_random_instance_uniform, input_ranges_specific_solver, 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, solver): | ||
| 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, solver) | ||
|
|
||
| def run_szws_experiment(): | ||
| experiment = experiments_csv.Experiment("results/", "with_solver_algo_for_course_allocation_szws.csv", backup_folder="results/backup/") | ||
| input_ranges_none_solver = { | ||
| "num_of_agents": [100,200,300], | ||
| "num_of_items": [25], | ||
| "agent_capacity": [5], | ||
| "supply_ratio": [1.1, 1.25, 1.5], | ||
| "num_of_popular_items": [6, 9], | ||
| "mean_num_of_favorite_items": [2.6, 3.85], | ||
| "favorite_item_value_bounds": [(50,100)], | ||
| "nonfavorite_item_value_bounds": [(0,50)], | ||
| "algorithm": algorithms_with_none_solver, | ||
| "random_seed": range(5), | ||
| "solver": [None], | ||
| } | ||
| # experiment.run_with_time_limit(course_allocation_with_random_instance_szws, input_ranges_none_solver, time_limit=TIME_LIMIT) | ||
|
|
||
| input_ranges_specific_solver = { | ||
| "num_of_agents": [100], | ||
| "num_of_items": [25], | ||
| "agent_capacity": [5], | ||
| "supply_ratio": [1.1, 1.25, 1.5], | ||
| "num_of_popular_items": [6, 9], | ||
| "mean_num_of_favorite_items": [2.6, 3.85], | ||
| "favorite_item_value_bounds": [(50,100)], | ||
| "nonfavorite_item_value_bounds": [(0,50)], | ||
| "algorithm": algorithms_with_specific_solver, | ||
| "random_seed": range(5), | ||
| "solver": [None, cp.CBC, cp.MOSEK, cp.SCIP, cp.XPRESS], #, cp.COPT, cp.CPLEX, cp.GUROBI | ||
| } | ||
| # experiment.run_with_time_limit(course_allocation_with_random_instance_szws, input_ranges_specific_solver, time_limit=TIME_LIMIT) | ||
|
|
||
| input_ranges_specific_solver = { | ||
| "num_of_agents": [200, 300], | ||
| "num_of_items": [25], | ||
| "agent_capacity": [5], | ||
| "supply_ratio": [1.1, 1.25, 1.5], | ||
| "num_of_popular_items": [6, 9], | ||
| "mean_num_of_favorite_items": [2.6, 3.85], | ||
| "favorite_item_value_bounds": [(50, 100)], | ||
| "nonfavorite_item_value_bounds": [(0, 50)], | ||
| "algorithm": algorithms_with_specific_solver, | ||
| "random_seed": range(5), | ||
| "solver": [None, cp.CBC, cp.MOSEK, cp.SCIP], #, cp.XPRESS , cp.COPT, cp.CPLEX, cp.GUROBI | ||
| } | ||
| # experiment.run_with_time_limit(course_allocation_with_random_instance_szws, input_ranges_specific_solver, time_limit=TIME_LIMIT) | ||
|
|
||
| input_ranges_specific_solver = { | ||
| "num_of_agents": [100, 200, 300], | ||
| "num_of_items": [25], | ||
| "agent_capacity": [5], | ||
| "supply_ratio": [1.1, 1.25, 1.5], | ||
| "num_of_popular_items": [6, 9], | ||
| "mean_num_of_favorite_items": [2.6, 3.85], | ||
| "favorite_item_value_bounds": [(50, 100)], | ||
| "nonfavorite_item_value_bounds": [(0, 50)], | ||
| "algorithm": algorithms_with_specific_solver, | ||
| "random_seed": range(5), | ||
| "solver": [cp.SCIPY] | ||
| } | ||
| experiment.run_with_time_limit(course_allocation_with_random_instance_szws, input_ranges_specific_solver, 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, solver): | ||
| 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, solver) | ||
|
|
||
| def run_ariel_experiment(): | ||
| experiment = experiments_csv.Experiment("results/", "with_solver_algo_for_course_allocation_ariel.csv", backup_folder="results/backup/") | ||
| input_ranges_none_solver = { | ||
| "max_total_agent_capacity": [1000, 1115, 1500, 2000], | ||
| "algorithm": algorithms_with_none_solver, | ||
| "random_seed": range(10), | ||
| "solver": [cp.CBC, cp.MOSEK, cp.SCIP, cp.SCIPY] | ||
| } | ||
| # experiment.run_with_time_limit(course_allocation_with_random_instance_sample, input_ranges_none_solver, time_limit=TIME_LIMIT) | ||
|
|
||
| input_ranges_specific_solver = { | ||
| "max_total_agent_capacity": [1000, 1115, 1500, 2000], | ||
| "algorithm": algorithms_with_specific_solver, | ||
| "random_seed": range(10), | ||
| "solver": [cp.XPRESS] | ||
| } | ||
| experiment.run_with_time_limit(course_allocation_with_random_instance_sample, input_ranges_specific_solver, time_limit=TIME_LIMIT) | ||
|
|
||
| #######TEST FOR OC########## | ||
| def run_uniform_oc_experiment(): | ||
| experiment = experiments_csv.Experiment("results/", "test_for_oc_course_allocation_uniform.csv", backup_folder="results/backup/") | ||
| input_ranges_none_solver = { | ||
| "num_of_agents": [300], | ||
| "num_of_items": [25], | ||
| "value_noise_ratio": [0.5], | ||
| "algorithm": algorithm_only_oc, | ||
| "random_seed": range(1,2), | ||
| "solver": [cp.SCIPY], | ||
| } | ||
| experiment.run_with_time_limit(course_allocation_with_random_instance_uniform, input_ranges_none_solver, time_limit=TIME_LIMIT) | ||
|
|
||
| ######### MAIN PROGRAM ########## | ||
|
|
||
| if __name__ == "__main__": | ||
| import logging, experiments_csv | ||
| experiments_csv.logger.setLevel(logging.DEBUG) | ||
|
|
||
| from fairpyx.algorithms.Optimization_based_Mechanisms.TTC import logger as TTC_logger | ||
|
|
||
| # TTC_logger.setLevel(logging.INFO) | ||
| # TTC_logger.addHandler(logging.StreamHandler()) | ||
| #run_uniform_experiment() #done | ||
| #run_szws_experiment() #done | ||
| # run_ariel_experiment() #done | ||
| run_uniform_oc_experiment() |
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