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CDF_plots.py
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CDF_plots.py
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import matplotlib
import numpy
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import sys
from AdaFair import AdaFair
sys.path.insert(0, 'DataPreprocessing')
sys.path.insert(0, 'equalized_odds_and_calibration-master')
# import funcs_disp_mist as fdm
from Competitors.AdaCost import AdaCostClassifier
from load_kdd import load_kdd
# from load_german import load_german
from load_compas_data import load_compas
from load_adult import load_adult
from load_bank import load_bank
def run_eval(dataset):
if dataset == "compass-gender":
X, y, sa_index, p_Group, x_control = load_compas("sex")
elif dataset == "compass-race":
X, y, sa_index, p_Group, x_control = load_compas("race")
elif dataset == "adult-gender":
X, y, sa_index, p_Group, x_control = load_adult("sex")
elif dataset == "bank":
X, y, sa_index, p_Group, x_control = load_bank()
elif dataset == "kdd":
X, y, sa_index, p_Group, x_control = load_kdd()
else:
exit(1)
base_learners = 200
adaboost, adaboost_weights, init_weights = train_classifier(X, y, sa_index, p_Group, 0, base_learners )
csb1, csb1_weights, temp= train_classifier(X, y, sa_index, p_Group, 1, base_learners )
csb2, csb2_weights, temp = train_classifier(X, y, sa_index, p_Group, 2, base_learners )
adaboost *= y
csb1 *= y
csb2 *= y
csb1_positives = csb1[y==1]
csb1_negatives = csb1[y==-1]
csb2_positives = csb2[y==1]
csb2_negatives = csb2[y==-1]
adaboost_positives = adaboost[y==1]
adaboost_negatives = adaboost[y==-1]
num_bins = 50
fig, (ax1, ax2) = plt.subplots(nrows=1, ncols=2, figsize=(12,3))
# fig, (ax1, ax2, ax3) = plt.subplots(nrows=1, ncols=3, figsize=(14,3))
plt.rcParams.update({'font.size': 11})
ax1.set_title( "Positive CDF")
ax1.grid(True)
counts_ada_positives, bin_edges_ada_positives = numpy.histogram(adaboost_positives, bins=num_bins, normed=True)
cdf_ada_positives = numpy.cumsum(counts_ada_positives)
ax1.plot(bin_edges_ada_positives[1:], cdf_ada_positives/ cdf_ada_positives[-1], c='blue', label= 'AdaBoost')
counts_csb1_positives, bin_edges_csb1_positives = numpy.histogram(csb1_positives, bins=num_bins, normed=True)
cdf_csb1_positives = numpy.cumsum(counts_csb1_positives)
ax1.plot(bin_edges_csb1_positives[1:], cdf_csb1_positives / cdf_csb1_positives[-1], c='green', linestyle='-.',label='AdaFair NoConf')
counts_csb2_positives, bin_edges_csb2_positives = numpy.histogram(csb2_positives, bins=num_bins, normed=True)
cdf_csb2_positives = numpy.cumsum(counts_csb2_positives)
ax1.plot(bin_edges_csb2_positives[1:], cdf_csb2_positives / cdf_csb2_positives[-1], c='red', linestyle='--', label='AdaFair')
ax1.legend(loc='best')
ax1.set_xlabel("Margin")
ax1.set_ylabel("Cumulative Distribution")
ax1.axhline(0, color='black')
ax1.axvline(0, color='black')
ax2.grid(True)
ax2.axhline(0, color='black')
ax2.axvline(0, color='black')
ax2.set_title("Negative CDF")
counts_ada_negatives, bin_edges_ada_negatives = numpy.histogram(adaboost_negatives, bins=num_bins, normed=True)
cdf_ada_negatives = numpy.cumsum(counts_ada_negatives)
ax2.plot(bin_edges_ada_negatives[1:], cdf_ada_negatives / cdf_ada_negatives[-1], c='blue',
label='AdaBoost')
ax2.set_ylabel("Cumulative Distribution")
ax2.set_xlabel("Margin")
counts_csb1_negatives, bin_edges_csb1_negatives = numpy.histogram(csb1_negatives, bins=num_bins, normed=True)
cdf_csb1_negatives = numpy.cumsum(counts_csb1_negatives)
ax2.plot(bin_edges_csb1_negatives[1:], cdf_csb1_negatives/ cdf_csb1_negatives[-1], c='green', linestyle='-.',label='AdaFair NoConf')
counts_csb2_negatives, bin_edges_csb2_negatives = numpy.histogram(csb2_negatives, bins=num_bins, normed=True)
cdf_csb2_negatives= numpy.cumsum(counts_csb2_negatives)
ax2.plot(bin_edges_csb2_negatives[1:], cdf_csb2_negatives/ cdf_csb2_negatives[-1], c='red', linestyle='--', label='AdaFair')
ax2.legend(loc='best')
fig.tight_layout()
plt.show()
plt.legend(loc='best',fancybox=True, framealpha=0.2)
plt.savefig("Images/cdf_" +dataset + ".png")
def train_classifier(X_train, y_train, sa_index, p_Group, mode, base_learners):
if mode == 0:
classifier = AdaCostClassifier(saIndex=sa_index, saValue=p_Group, n_estimators=base_learners, CSB="CSB1", debug=True)
elif mode == 1:
classifier = AdaFair(n_estimators=base_learners, saIndex=sa_index, saValue=p_Group, CSB="CSB1", debug=True, c=1)
elif mode == 2:
classifier = AdaFair(n_estimators=base_learners, saIndex=sa_index, saValue=p_Group, CSB="CSB2", debug=True, c=1)
classifier.fit(X_train, y_train)
return classifier.conf_scores, classifier.get_weights_over_iterations(), classifier.get_initial_weights()
def main(dataset):
run_eval(dataset)
if __name__ == '__main__':
main("compass-gender")
# main("adult-gender")
# main("bank")
# main("kdd")