y = self.enc_.transform(y)
predicted_labels = -np.ones(y.size, dtype=int)
// TODO: Vectorize Oracle code.
for sample_index, x in enumerate(X):
for idx, clf in enumerate(self.pool_classifiers_):
// If one base classifier predicts the correct answer, consider
// as a correct prediction
x_feat = x[self.estimator_features_[idx]].reshape(1, -1)
predicted = clf.predict(x_feat)[0]
if predicted == y[sample_index]:
predicted = int(predicted)
predicted_labels[sample_index] = predicted
break
predicted_labels[sample_index] = predicted
return self.classes_.take(predicted_labels)
def predict_proba(self, X, y):
After Change
y = self.enc_.transform(y)
preds = [clf.predict(X[:, self.estimator_features_[idx]])
for idx, clf in enumerate(self.pool_classifiers_)]
preds = np.asarray(preds).T
hit_miss = np.asarray(preds) == y.reshape(-1, 1)
idx_sel_classifier = hit_miss.argmax(axis=1)
predicted_labels = preds[np.arange(preds.shape[0]), idx_sel_classifier]
return self.classes_.take(predicted_labels.astype(int))
