def train_classifier(X_train, X_test, y_train, y_test, sa_index, p_Group,
dataset, mutex, mode, base_learners):
if mode == 0:
classifier = AdaFairNoCumul(n_estimators=base_learners,
saIndex=sa_index,
saValue=p_Group,
CSB="CSB2")
elif mode == 1:
classifier = AdaFair(n_estimators=base_learners,
saIndex=sa_index,
saValue=p_Group,
CSB="CSB2")
classifier.fit(X_train, y_train)
y_pred_probs = classifier.predict_proba(X_test)[:, 1]
y_pred_labels = classifier.predict(X_test)
mutex.acquire()
infile = open(dataset, 'rb')
dict_to_ram = pickle.load(infile)
infile.close()
dict_to_ram.performance.append(
calculate_performance(X_test, y_test, y_pred_labels, y_pred_probs,
sa_index, p_Group))
outfile = open(dataset, 'wb')
pickle.dump(dict_to_ram, outfile)
outfile.close()
mutex.release()
def predict(clf, X_test, y_test, sa_index, p_Group):
y_pred_probs = clf.predict_proba(X_test)[:, 1]
y_pred_labels = clf.predict(X_test)
return calculate_performance(X_test, y_test, y_pred_labels, y_pred_probs, sa_index, p_Group)