ax.w_xaxis.set_ticklabels([])
ax.set_ylabel("2nd eigenvector")
ax.w_yaxis.set_ticklabels([])
ax.set_zlabel("3rd eigenvector")
ax.w_zaxis.set_ticklabels([])
plt.show()
elif set_of_exps == 2:
from load_data import load_binary_diabetes_uci
from sklearn import svm
from sklearn.metrics import accuracy_score
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from sklearn.decomposition import PCA
diabetes = load_binary_diabetes_uci()
# 50% for train
ntrain = len(diabetes.target) // 2
# Train an SVM using the training set
clf = svm.SVC(C=10.0)
# with C = 10 and C = 100 => same accuracy, different fairness!
# with C = 1000 => from 76% to 74% in accuracy but fair!
clf.fit(diabetes.data[:ntrain, :], diabetes.target[:ntrain])
# The dataset becomes the test set
diabetes.data = diabetes.data[ntrain:, :]
diabetes.target = diabetes.target[ntrain:]
# Accuracy
pred = clf.predict(diabetes.data)
# print(newdata.shape)
self.dataset = namedtuple('_', 'data, target')(newdata,
self.dataset.target)
self.model.fit(self.dataset.data, self.dataset.target)
#if hasattr(self.model, 'best_estimator_'):
# self.model = self.model.best_estimator_
#self.coef_ = self.model.coef_
#self.intercept_ = self.model.intercept_
if __name__ == "__main__":
experiment_number = 0
if experiment_number == 0:
dataset_train = load_binary_diabetes_uci()
dataset_test = load_binary_diabetes_uci()
sensible_feature = 1 # sex
elif experiment_number == 1:
dataset_train = load_heart_uci()
dataset_test = load_heart_uci()
sensible_feature = 1 # sex
elif experiment_number == 2:
dataset_train, dataset_test = load_adult(smaller=False)
sensible_feature = 9 # sex
print('Different values of the sensible feature', sensible_feature,
':', set(dataset_train.data[:, sensible_feature]))
elif experiment_number == 3:
dataset_train, dataset_test = load_adult_race(smaller=False)
sensible_feature = 8 # race
print('Different values of the sensible feature', sensible_feature,