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added gender as a sensitive attribute and att as argument #80
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@Hamedloghmani
Hamedloghmani committed Aug 30, 2023
1 parent c454324 commit e8d4b15
Showing 1 changed file with 47 additions and 15 deletions.
62 changes: 47 additions & 15 deletions src/main.py
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Original file line number Diff line number Diff line change
Expand Up @@ -14,8 +14,23 @@
from cmn.metric import *

class Reranking:

@staticmethod
def gender_process(output: str):
ig = pd.read_csv(f'{output}gender.csv')
ig.fillna('M', inplace=True)
index_female = ig.loc[ig['gender'] == 'F', 'Unnamed: 0'].tolist()
index_male = ig.loc[ig['gender'] == 'M', 'Unnamed: 0'].tolist()
ig.replace(to_replace='M', value=True, inplace=True)
ig.replace(to_replace='F', value=False, inplace=True)
gender_ratio = len(index_female) / (len(index_female) + len(index_male))
ig = ig.rename(columns={'Unnamed: 0': 'memberidx'})
ig.sort_values(by='memberidx', inplace=True)
return ig, gender_ratio


@staticmethod
def get_stats(teamsvecs, coefficient: float, output: str, eq_op: bool = False) -> tuple:
def get_stats(teamsvecs, coefficient: float, output: str, eq_op: bool = False, att='gender') -> tuple:
"""
Args:
teamsvecs_members: teamsvecs pickle file
Expand All @@ -35,11 +50,16 @@ def get_stats(teamsvecs, coefficient: float, output: str, eq_op: bool = False) -
stats['*avg_nteams_member'] = col_sums.mean()
threshold = coefficient * stats['*avg_nteams_member']

labels = [True if threshold <= nteam_member else False for nteam_member in col_sums.getA1() ] #rowid maps to columnid in teamvecs['member']
stats['np_ratio'] = labels.count(False) / stats['*nmembers']
with open(f'{output}stats.pkl', 'wb') as f: pickle.dump(stats, f)
pd.DataFrame(data=labels, columns=['popularity']).to_csv(f'{output}popularity.csv', index_label='memberidx')
popularity = pd.read_csv(f'{output}popularity.csv')
if att == 'popularity':
labels = [True if threshold <= nteam_member else False for nteam_member in col_sums.getA1() ] #rowid maps to columnid in teamvecs['member']
stats['np_ratio'] = labels.count(False) / stats['*nmembers']
with open(f'{output}stats.pkl', 'wb') as f: pickle.dump(stats, f)
pd.DataFrame(data=labels, columns=['popularity']).to_csv(f'{output}popularity.csv', index_label='memberidx')
sensitive_att = pd.read_csv(f'{output}popularity.csv')

elif att == 'gender':
sensitive_att, stats['np_ratio'] = Reranking.gender_process(output)
labels = sensitive_att['gender'].tolist()

if eq_op:
skill_member = skillvecs.transpose() @ teamsvecs_members
Expand All @@ -53,8 +73,15 @@ def get_stats(teamsvecs, coefficient: float, output: str, eq_op: bool = False) -
if len(intersect) == 0:
intersect = [randrange(0, teamsvecs_members.shape[1]) for i in range(5)]

labels_ = [popularity.loc[popularity['memberidx'] == member, 'popularity'].tolist()[0] for member in
intersect]
if att == 'popularity':
labels_ = [sensitive_att.loc[sensitive_att['memberidx'] == member, 'popularity'].tolist()[0] for member in
intersect]
elif att == 'gender':
labels_ = [sensitive_att.loc[sensitive_att['memberidx'] == member, 'gender'].tolist()[0] for member in
intersect]
else:
raise ValueError('chosen sensitive attribute is not valid')

ratios.append(labels_.count(False) / len(intersect))

with open('ratios.pkl', 'wb') as file:
Expand Down Expand Up @@ -281,7 +308,7 @@ def utility_average(utilityevals: list, metric: str) -> float:
return statistics.mean([df.loc[df['metric'] == metric, 'mean'].tolist()[0] for df in utilityevals])

@staticmethod
def run(fpred, output, fteamsvecs, fsplits, np_ratio, algorithm='det_cons', k_max=None, fairness_metrics={'ndkl', 'skew'}, utility_metrics={'map_cut_2,5,10', 'ndcg_cut_2,5,10'}, eq_op: bool = False, alpha: float = 0.1) -> None:
def run(fpred, output, fteamsvecs, fsplits, np_ratio, algorithm='det_cons', k_max=None, fairness_metrics={'ndkl', 'skew'}, utility_metrics={'map_cut_2,5,10', 'ndcg_cut_2,5,10'}, eq_op: bool = False, alpha: float = 0.1, att='popularity') -> None:
"""
Args:
fpred: address of the .pred file
Expand All @@ -308,11 +335,11 @@ def run(fpred, output, fteamsvecs, fsplits, np_ratio, algorithm='det_cons', k_ma
try:
print('Loading popularity labels ...')
with open(f'{output}stats.pkl', 'rb') as f: stats = pickle.load(f)
labels = pd.read_csv(f'{output}popularity.csv')['popularity'].to_list()
labels = pd.read_csv(f'{output}{att}.csv')[att].to_list()
with open(f'ratios.pkl', 'rb') as f: ratios = pickle.load(f)
except (FileNotFoundError, EOFError):
print(f'Loading popularity labels failed! Generating popularity labels at {output}stats.pkl ...')
stats, labels, ratios = Reranking.get_stats(teamsvecs, coefficient=1, output=output, eq_op=eq_op)
stats, labels, ratios = Reranking.get_stats(teamsvecs, coefficient=1, output=output, eq_op=eq_op, att=att)

#creating a static ratio in case eq_op is turned off
if not eq_op:
Expand All @@ -329,7 +356,7 @@ def run(fpred, output, fteamsvecs, fsplits, np_ratio, algorithm='det_cons', k_ma
df = pd.read_csv(f'{new_output}.{algorithm}.{k_max}.rerank.csv', converters={'reranked_idx': eval, 'reranked_probs': eval})
reranked_idx, probs = df['reranked_idx'].to_list(), df['reranked_probs'].to_list()
except FileNotFoundError:
print(f'Loading re-ranking results failed! Reranking the predictions based on {algorithm} for top-{k_max} ...')
print(f'Loading re-ranking results failed! Reranking the predictions based on {att} with {algorithm} for top-{k_max} ...')
reranked_idx, probs, elapsed_time = Reranking.rerank(preds, labels, new_output, ratios, algorithm, k_max, eq_op, alpha)
#not sure os handles file locking for append during parallel run ...
# with open(f'{new_output}.rerank.time', 'a') as file: file.write(f'{elapsed_time} {new_output} {algorithm} {k_max}\n')
Expand Down Expand Up @@ -371,6 +398,7 @@ def addargs(parser):
fairness.add_argument('-utility_metrics', '--utility_metrics', nargs='+', type=set, default={'map_cut_2,5,10', 'ndcg_cut_2,5,10'}, required=False, help='list of utility metric in the form of pytrec_eval; default: map_cut_2,5,10')
fairness.add_argument('-eq_op', '--eq_op', type=bool, default=False, required=False,help='eq_op: a flag to turn equality of opportunity criteria on or off; default: False')
fairness.add_argument('-alpha', '--alpha', type=float, default=0.05, required=False,help='alpha: the significance value for fa*ir algortihm. Default value is 0.1')
fairness.add_argument('-att', '--att', type=str, default='popularity', required=True,help='alpha: the significance value for fa*ir algortihm. Default value is 0.1')

mode = parser.add_argument_group('mode')
mode.add_argument('-mode', type=int, default=1, choices=[0, 1], help='0 for sequential run and 1 for parallel; default: 1')
Expand Down Expand Up @@ -411,7 +439,9 @@ def addargs(parser):
k_max=args.k_max,
fairness_metrics=args.fairness_metrics,
eq_op=args.eq_op,
utility_metrics=args.utility_metrics)
utility_metrics=args.utility_metrics,
alpha=args.alpha,
att=args.att)
exit(0)

if os.path.isdir(args.fpred):
Expand All @@ -438,7 +468,8 @@ def addargs(parser):
fairness_metrics=args.fairness_metrics,
eq_op=args.eq_op,
utility_metrics=args.utility_metrics,
alpha=args.alpha)
alpha=args.alpha,
att=args.att)
elif args.mode == 1: # parallel run
print(f'Parallel run started ...')
with multiprocessing.Pool(multiprocessing.cpu_count() if args.core < 0 else args.core) as executor:
Expand All @@ -451,4 +482,5 @@ def addargs(parser):
fairness_metrics=args.fairness_metrics,
utility_metrics=args.utility_metrics,
eq_op=args.eq_op,
alpha=args.alpha), pairs)
alpha=args.alpha,
att=args.att), pairs)

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