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minor fixes , extracted functions from instance.py
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@Abodi-Massarwa
Abodi-Massarwa committed Jul 2, 2024
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81 changes: 47 additions & 34 deletions fairpyx/algorithms/heterogeneous_matroid_constraints_algorithms.py
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Original file line number Diff line number Diff line change
Expand Up @@ -7,6 +7,8 @@
import math
import random
from itertools import cycle

import experiments_csv
from networkx import DiGraph
import fairpyx.algorithms
from fairpyx import Instance, AllocationBuilder
Expand Down Expand Up @@ -557,12 +559,12 @@ def helper_categorization_friendly_picking_sequence(alloc:AllocationBuilder, age
agent_order = [agent for agent in alloc.remaining_agents() if agent_category_capacities[agent][target_category] > 0]

remaining_category_agent_capacities = {agent: agent_category_capacities[agent][target_category] for agent in
agent_category_capacities.keys()} # all the agents with non zero capacities in our category
agent_category_capacities.keys()}
logger.info(f"agent_category_capacities-> {agent_category_capacities}")
remaining_category_items = [x for x in alloc.remaining_items() if x in items_to_allocate]
logger.info(f'remaining_category_items -> {remaining_category_items} & remaining agent capacities {remaining_category_agent_capacities}')
logger.info(f"Agent order is -> {agent_order}")
remaining_agents_with_capacities = {agent for agent,capacity in remaining_category_agent_capacities.items() if capacity>0}
remaining_agents_with_capacities = {agent for agent,capacity in remaining_category_agent_capacities.items() if capacity>0}# all the agents with non zero capacities in our category
for agent in cycle(agent_order):
logger.info("Looping agent %s, remaining capacity %s", agent, remaining_category_agent_capacities[agent])
if remaining_category_agent_capacities[agent] <= 0:
Expand All @@ -571,16 +573,18 @@ def helper_categorization_friendly_picking_sequence(alloc:AllocationBuilder, age
logger.info(f'No more agents with capacity')
break
continue
potential_items_for_agent = set(remaining_category_items).difference(alloc.bundles[agent]) # in case difference is empty means already has item / there is no items left
if len(potential_items_for_agent) == 0:

potential_items_for_agent = set(remaining_category_items).difference(alloc.bundles[agent]) # in case difference is empty means already has a duplicate of the item(legal) / there is no items left
if len(potential_items_for_agent) == 0: # still has capacity, but no items to aquire (maybe no items left maybe already has copy of item)
logger.info(f'No potential items for agent {agent}')
# either no items left / or agent already has items (conflicted)
if agent in remaining_category_agent_capacities: # need to remove agent from our loop
del remaining_category_agent_capacities[agent]
if len(remaining_category_agent_capacities) == 0:
if agent in remaining_agents_with_capacities: # need to remove agent from our loop ,even if he still has capacity !
#del remaining_category_agent_capacities[agent]
remaining_agents_with_capacities.discard(agent)
if len(remaining_agents_with_capacities) == 0:
logger.info(f'No more agents with capacity')
break
continue
continue # otherwise pick the next agent !
#experiments_csv.logger.info(f'remaining agents are ->{remaining_category_agent_capacities}')
# safe to assume agent has capacity & has the best item to pick
best_item_for_agent = max(potential_items_for_agent, key=lambda item: alloc.instance.agent_item_value(agent, item))
logger.info(f'picked best item for {agent} -> item -> {best_item_for_agent}')
Expand Down Expand Up @@ -1008,28 +1012,37 @@ def helper_create_agent_item_bipartite_graph(agents, items, valuation_func):


if __name__ == "__main__":
# import doctest, sys
# logger.setLevel(logging.DEBUG)
# logger.addHandler(logging.StreamHandler())
# print("\n", doctest.testmod(), "\n")
# # doctest.run_docstring_examples(iterated_priority_matching, globals())
#
# order=['Agent1','Agent2','Agent3','Agent4']
# items=['m1','m2','m3','m4']
# item_categories = {'c1': ['m1', 'm2','m3'],'c2':['m4']}
# agent_category_capacities = {'Agent1': {'c1':3,'c2':2}, 'Agent2': {'c1':3,'c2':2},'Agent3': {'c1':3,'c2':2},'Agent4': {'c1':3,'c2':2}} # in the papers its written capacity=size(catergory)
# valuations = {'Agent1':{'m1':2,'m2':1,'m3':1,'m4':10},'Agent2':{'m1':1,'m2':2,'m3':1,'m4':10},'Agent3':{'m1':1,'m2':1,'m3':2,'m4':10},'Agent4':{'m1':1,'m2':1,'m3':1,'m4':10}}
# sum_agent_category_capacities={agent:sum(cap.values()) for agent,cap in agent_category_capacities.items()}
# instance=Instance(valuations=valuations,items=items,agent_capacities=sum_agent_category_capacities)
# # divide(algorithm=per_category_round_robin,instance=instance,item_categories=item_categories,agent_category_capacities=agent_category_capacities,initial_agent_order=order)
# # divide(algorithm=capped_round_robin,instance=instance,item_categories=item_categories,agent_category_capacities=agent_category_capacities,initial_agent_order=order,target_category="c1")
# # divide(algorithm=two_categories_capped_round_robin,instance=instance,item_categories=item_categories,agent_category_capacities=agent_category_capacities,initial_agent_order=order,target_category_pair=("c1","c2"))
# # divide(algorithm=per_category_capped_round_robin,instance=instance,item_categories=item_categories,agent_category_capacities=agent_category_capacities,initial_agent_order=order)
#
# items=['m1','m2','m3']
# item_categories = {'c1': ['m1'],'c2':['m2','m3']}
# agent_category_capacities = {'Agent1': {'c1':2,'c2':2}, 'Agent2': {'c1':2,'c2':2},'Agent3': {'c1':2,'c2':2}}
# valuations = {'Agent1':{'m1':1,'m2':1,'m3':1},'Agent2':{'m1':1,'m2':1,'m3':0},'Agent3':{'m1':0,'m2':0,'m3':0}} # TODO change valuation in paper
# instance=Instance(valuations=valuations,items=items,agent_capacities=sum_agent_category_capacities)
# # divide(algorithm=iterated_priority_matching,instance=instance,item_categories=item_categories,agent_category_capacities=agent_category_capacities)
pass
#import doctest, sys
logger.setLevel(logging.DEBUG)
logger.addHandler(logging.StreamHandler())
#print("\n", doctest.testmod(), "\n")
# # # doctest.run_docstring_examples(iterated_priority_matching, globals())
# #
# # order=['Agent1','Agent2','Agent3','Agent4']
# # items=['m1','m2','m3','m4']
# # item_categories = {'c1': ['m1', 'm2','m3'],'c2':['m4']}
# # agent_category_capacities = {'Agent1': {'c1':3,'c2':2}, 'Agent2': {'c1':3,'c2':2},'Agent3': {'c1':3,'c2':2},'Agent4': {'c1':3,'c2':2}} # in the papers its written capacity=size(catergory)
# # valuations = {'Agent1':{'m1':2,'m2':1,'m3':1,'m4':10},'Agent2':{'m1':1,'m2':2,'m3':1,'m4':10},'Agent3':{'m1':1,'m2':1,'m3':2,'m4':10},'Agent4':{'m1':1,'m2':1,'m3':1,'m4':10}}
# # sum_agent_category_capacities={agent:sum(cap.values()) for agent,cap in agent_category_capacities.items()}
# # instance=Instance(valuations=valuations,items=items,agent_capacities=sum_agent_category_capacities)
# # # divide(algorithm=per_category_round_robin,instance=instance,item_categories=item_categories,agent_category_capacities=agent_category_capacities,initial_agent_order=order)
# # # divide(algorithm=capped_round_robin,instance=instance,item_categories=item_categories,agent_category_capacities=agent_category_capacities,initial_agent_order=order,target_category="c1")
# # # divide(algorithm=two_categories_capped_round_robin,instance=instance,item_categories=item_categories,agent_category_capacities=agent_category_capacities,initial_agent_order=order,target_category_pair=("c1","c2"))
# # # divide(algorithm=per_category_capped_round_robin,instance=instance,item_categories=item_categories,agent_category_capacities=agent_category_capacities,initial_agent_order=order)
# #
# # items=['m1','m2','m3']
# # item_categories = {'c1': ['m1'],'c2':['m2','m3']}
# # agent_category_capacities = {'Agent1': {'c1':2,'c2':2}, 'Agent2': {'c1':2,'c2':2},'Agent3': {'c1':2,'c2':2}}
# # valuations = {'Agent1':{'m1':1,'m2':1,'m3':1},'Agent2':{'m1':1,'m2':1,'m3':0},'Agent3':{'m1':0,'m2':0,'m3':0}} # TODO change valuation in paper
# # instance=Instance(valuations=valuations,items=items,agent_capacities=sum_agent_category_capacities)
# # # divide(algorithm=iterated_priority_matching,instance=instance,item_categories=item_categories,agent_category_capacities=agent_category_capacities)
#
order = ['Agent1', 'Agent2']
items = ['m1']
item_categories = {'c1': ['m1']}
agent_category_capacities = {'Agent1': {'c1': 0}, 'Agent2': {'c1': 1}}
valuations = {'Agent1': {'m1': 0}, 'Agent2': {'m1': 420}}
target_category = 'c1'
divide(algorithm=capped_round_robin, instance=Instance(valuations=valuations, items=items),
item_categories=item_categories, agent_category_capacities=agent_category_capacities,
initial_agent_order=order, target_category=target_category)
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