from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import GridSearchCV, cross_val_predict
from sklearn.base import clone
import quapy as qp
from Retrieval.commons import *
from Retrieval.methods import *
from method.non_aggregative import MaximumLikelihoodPrevalenceEstimation as Naive
from quapy.method.aggregative import ClassifyAndCount, EMQ, ACC, PCC, PACC, KDEyML
from quapy.data.base import LabelledCollection
from os.path import join
from tqdm import tqdm
from result_table.src.table import Table
"""
In this sixth experiment, we have a collection C of >6M documents.
We split C in two equally-sized pools TrPool, TePool
I have randomly split the collection in 50% train and 50% split. In each split we have approx. 3.25 million documents.
We have 5 categories we can evaluate over: Continent, Years_Category, Num_Site_Links, Relative Pageviews and Gender.
From the training set I have created smaller subsets for each category:
100K, 500K, 1M and FULL (3.25M)
For each category and subset, I have created a training set called: "classifier_training.json". This is the "base" training set for the classifier. In this set we have 500 documents per group in a category. (For example: Male 500, Female 500, Unknown 500). Let me know if you think we need more.
To "bias" the quantifier towards a query, I have executed the queries (97) on the different training sets and retrieved the 200 most relevant documents per group.
For example: (Male 200, Female 200, Unknown 200)
Sometimes this is infeasible, we should probably discuss this at some point.
You can find the results for every query in a file named:
"training_Query-[QID]Sample-200SPLIT.json"
Test:
To evaluate our approach, I have executed the queries on the test split. You can find the results for all 97 queries up till k=1000 in this file.
testRanking_Results.json
"""
def methods(classifier, class_name=None, binarize=False):
kde_param = {
'continent': 0.01,
'gender': 0.03,
'years_category':0.03
}
yield ('NaiveQuery', Naive())
yield ('CC', ClassifyAndCount(classifier))
yield ('PACC', PACC(classifier, val_split=5, n_jobs=-1))
yield ('KDEy-ML', KDEyML(classifier, val_split=5, n_jobs=-1, bandwidth=kde_param.get(class_name, 0.01)))
if binarize:
yield ('M3b', M3rND_ModelB(classifier))
yield ('M3b+', M3rND_ModelB(classifier))
yield ('M3d', M3rND_ModelD(classifier))
yield ('M3d+', M3rND_ModelD(classifier))
def train_classifier_fn(train_path):
"""
Trains a classifier. To do so, it loads the training set, transforms it into a tfidf representation.
The classifier is Logistic Regression, with hyperparameters C (range [0.001, 0.01, ..., 1000]) and
class_weight (range {'balanced', None}) optimized via 5FCV.
:return: the tfidf-vectorizer and the classifier trained
"""
texts, labels = load_sample(train_path, class_name=class_name)
if BINARIZE:
labels = binarize_labels(labels, positive_class=protected_group[class_name])
tfidf = TfidfVectorizer(sublinear_tf=True, min_df=3)
Xtr = tfidf.fit_transform(texts)
print(f'Xtr shape={Xtr.shape}')
print('training classifier...', end='')
classifier = LogisticRegression(max_iter=5000)
modsel = GridSearchCV(
classifier,
param_grid={'C': np.logspace(-4, 4, 9), 'class_weight': ['balanced', None]},
n_jobs=-1,
cv=5
)
modsel.fit(Xtr, labels)
classifier = modsel.best_estimator_
classifier_acc = modsel.best_score_
best_params = modsel.best_params_
print(f'[done] best-params={best_params} got {classifier_acc:.4f} score')
print('generating cross-val predictions for M3')
predictions = cross_val_predict(clone(classifier), Xtr, labels, cv=10, n_jobs=-1, verbose=10)
conf_matrix = confusion_matrix(labels, predictions, labels=classifier.classes_)
training = LabelledCollection(Xtr, labels)
print('training classes:', training.classes_)
print('training prevalence:', training.prevalence())
return tfidf, classifier, conf_matrix
def reduceAtK(data: LabelledCollection, k):
# if k > len(data):
# print(f'[warning] {k=}>{len(data)=}')
X, y = data.Xy
X = X[:k]
y = y[:k]
return LabelledCollection(X, y, classes=data.classes_)
def benchmark_name(class_name, k=None):
scape_class_name = class_name.replace('_', '\_')
if k is None:
return scape_class_name
else:
return f'{scape_class_name}@{k}'
def run_experiment():
results = {
'mae': {k: [] for k in Ks},
'mrae': {k: [] for k in Ks},
'rKL_error': [],
'rND_error': []
}
pbar = tqdm(experiment_prot(), total=experiment_prot.total())
for train, test, q_rel_prevs in pbar:
Xtr, ytr, score_tr = train
Xte, yte, score_te = test
train_col = LabelledCollection(Xtr, ytr, classes=classifier.classes_)
if not method_name.startswith('Naive') and not method_name.startswith('M3'):
method.fit(train_col, val_split=train_col, fit_classifier=False)
elif method_name == 'Naive':
method.fit(train_col)
test_col = LabelledCollection(Xte, yte, classes=classifier.classes_)
rKL_estim, rKL_true = [], []
rND_estim, rND_true = [], []
for k in Ks:
test_k = reduceAtK(test_col, k)
if method_name == 'NaiveQuery':
train_k = reduceAtK(train_col, k)
method.fit(train_k)
estim_prev = method.quantify(test_k.instances)
# epsilon value for prevalence smoothing
eps=(1. / (2. * k))
# error metrics
test_k_prev = test_k.prevalence()
mae = qp.error.mae(test_k_prev, estim_prev)
mrae = qp.error.mrae(test_k_prev, estim_prev, eps=eps)
rKL_at_k_estim = qp.error.kld(estim_prev, q_rel_prevs, eps=eps)
rKL_at_k_true = qp.error.kld(test_k_prev, q_rel_prevs, eps=eps)
if BINARIZE:
# [1] is the index of the minority or historically disadvantaged group
rND_at_k_estim = np.abs(estim_prev[1] - q_rel_prevs[1])
rND_at_k_true = np.abs(test_k_prev[1] - q_rel_prevs[1])
# collect results
results['mae'][k].append(mae)
results['mrae'][k].append(mrae)
rKL_estim.append(rKL_at_k_estim)
rKL_true.append(rKL_at_k_true)
if BINARIZE:
rND_estim.append(rND_at_k_estim)
rND_true.append(rND_at_k_true)
# aggregate fairness metrics
def aggregate(rMs, Ks, Z=1):
return (1 / Z) * sum((1. / np.log2(k)) * v for v, k in zip(rMs, Ks))
Z = sum((1. / np.log2(k)) for k in Ks)
rKL_estim = aggregate(rKL_estim, Ks, Z)
rKL_true = aggregate(rKL_true, Ks, Z)
rKL_error = np.abs(rKL_true-rKL_estim)
results['rKL_error'].append(rKL_error)
if BINARIZE:
rND_estim = aggregate(rND_estim, Ks, Z)
rND_true = aggregate(rND_true, Ks, Z)
if isinstance(method, AbstractM3rND):
if method_name.endswith('+'):
# learns the correction parameters from the query-specific training data
conf_matrix_ = method.get_confusion_matrix(*train_col.Xy)
else:
# learns the correction parameters from the training data used to train the classifier
conf_matrix_ = conf_matrix.copy()
rND_estim = method.fair_measure_correction(rND_estim, conf_matrix_)
rND_error = np.abs(rND_true - rND_estim)
results['rND_error'].append(rND_error)
pbar.set_description(f'{method_name}')
return results
data_home = 'data'
if __name__ == '__main__':
# final tables only contain the information for the data size 10K, each row is a class name and each colum
# the corresponding rND (for binary) or rKL (for multiclass) score
tables_RND, tables_DKL = [], []
tables_final = []
for class_mode in ['multiclass', 'binary']:
BINARIZE = (class_mode=='binary')
method_names = [name for name, *other in methods(None, binarize=BINARIZE)]
table_final = Table(name=f'rND' if BINARIZE else f'rKL', benchmarks=[benchmark_name(c) for c in CLASS_NAMES], methods=method_names)
table_final.format.mean_macro = False
tables_final.append(table_final)
for class_name in CLASS_NAMES:
tables_mae, tables_mrae = [], []
benchmarks_size =[benchmark_name(class_name, s) for s in DATA_SIZES]
table_DKL = Table(name=f'rKL-{class_name}', benchmarks=benchmarks_size, methods=method_names)
table_RND = Table(name=f'rND-{class_name}', benchmarks=benchmarks_size, methods=method_names)
for data_size in DATA_SIZES:
print(class_name, class_mode, data_size)
benchmarks_k = [benchmark_name(class_name, k) for k in Ks]
# table_mae = Table(name=f'{class_name}-{data_size}-mae', benchmarks=benchmarks_k, methods=method_names)
table_mrae = Table(name=f'{class_name}-{data_size}-mrae', benchmarks=benchmarks_k, methods=method_names)
# tables_mae.append(table_mae)
tables_mrae.append(table_mrae)
# sets all paths
class_home = join(data_home, class_name, data_size)
train_data_path = join(data_home, class_name, 'FULL', 'classifier_training.json') # <----- fixed classifier
classifier_path = join('classifiers', 'FULL', f'classifier_{class_name}_{class_mode}.pkl')
test_rankings_path = join(data_home, 'testRanking_Results.json')
test_query_prevs_path = join(data_home, 'prevelance_vectors_judged_docs.json')
results_home = join('results', class_name, class_mode, data_size)
positive_class = protected_group[class_name] if BINARIZE else None
# instantiates the classifier (trains it the first time, loads it in the subsequent executions)
tfidf, classifier, conf_matrix \
= qp.util.pickled_resource(classifier_path, train_classifier_fn, train_data_path)
experiment_prot = RetrievedSamples(
class_home,
test_rankings_path,
test_query_prevs_path,
vectorizer=tfidf,
class_name=class_name,
positive_class=positive_class,
classes=classifier.classes_
)
for method_name, method in methods(classifier, class_name, BINARIZE):
results_path = join(results_home, method_name + '.pkl')
results = qp.util.pickled_resource(results_path, run_experiment)
# compose the tables
for k in Ks:
# table_mae.add(benchmark=benchmark_name(class_name, k), method=method_name, v=results['mae'][k])
table_mrae.add(benchmark=benchmark_name(class_name, k), method=method_name, v=results['mrae'][k])
table_DKL.add(benchmark=benchmark_name(class_name, data_size), method=method_name, v=results['rKL_error'])
if BINARIZE:
table_RND.add(benchmark=benchmark_name(class_name, data_size), method=method_name, v=results['rND_error'])
if data_size=='10K':
value = results['rND_error'] if BINARIZE else results['rKL_error']
table_final.add(benchmark=benchmark_name(class_name), method=method_name, v=value)
tables = ([table_RND] + tables_mrae) if BINARIZE else ([table_DKL] + tables_mrae)
Table.LatexPDF(f'./latex/{class_mode}/{class_name}.pdf', tables=tables)
if BINARIZE:
tables_RND.append(table_RND)
else:
tables_DKL.append(table_DKL)
Table.LatexPDF(f'./latex/global/main.pdf', tables=tables_RND+tables_DKL, dedicated_pages=False)
Table.LatexPDF(f'./latex/final/main.pdf', tables=tables_final, dedicated_pages=False)