def test_lmnn(self):
lmnn = LMNN(n_neighbors=5, learn_rate=1e-6, verbose=False)
lmnn.fit(self.X, self.y)
res_1 = lmnn.transform(self.X)
lmnn = LMNN(n_neighbors=5, learn_rate=1e-6, verbose=False)
res_2 = lmnn.fit_transform(self.X, self.y)
assert_array_almost_equal(res_1, res_2)
def test_lmnn(self):
lmnn = LMNN(k=5, learn_rate=1e-6, verbose=False)
lmnn.fit(self.X, self.y)
res_1 = lmnn.transform(self.X)
lmnn = LMNN(k=5, learn_rate=1e-6, verbose=False)
res_2 = lmnn.fit_transform(self.X, self.y)
assert_array_almost_equal(res_1, res_2)
def lmnn_apply(x_input, y_input, k_input):
y_classified = classify_y(y_input)
lmnn = LMNN(k=k_input, learn_rate=1e-6)
x_output = lmnn.fit_transform(x_input, y_classified)
return x_output
def exeLMNN(self, knum=5):
lmnn = LMNN(k=knum)
X_new = lmnn.fit_transform(self.X, self.y)
return X_new
total_train_labels):
train_features = total_train_features[train_index]
train_labels = total_train_labels[train_index]
valid_features = total_train_features[valid_index]
valid_labels = total_train_labels[valid_index]
fold_cnt += 1
print("k:", knn_k)
print("fold:", fold_cnt)
print("train features shape:", train_features.shape)
print("train labels shape:", train_labels.shape)
print("valid features shape:", valid_features.shape)
print("valid labels shape:", valid_labels.shape)
lmnn = LMNN(k=5)
transformed_features = lmnn.fit_transform(train_features, train_labels)
neigh = KNeighborsClassifier(n_neighbors=knn_k)
neigh.fit(transformed_features, train_labels)
neigh_orig = KNeighborsClassifier(n_neighbors=knn_k)
neigh_orig.fit(train_features, train_labels)
predict = neigh.predict(lmnn.transform(valid_features))
predict_orig = neigh_orig.predict(valid_features)
accuracy = metrics.accuracy_score(valid_labels, predict)
accuracy_orig = metrics.accuracy_score(valid_labels, predict_orig)
print("accuracy after metric learning:{}".format(accuracy))
print("accuracy before metric learning:{}".format(accuracy_orig))
ac_list.append(accuracy)
ac_list_orig.append(accuracy_orig)
final_train_accuracy = np.mean(ac_list)
print(final_train_accuracy)
