def test_predict_with_uncertainty(self, ):
x_train_2, y_train_2 = self.feat._roll_train(
self.train_data,
past_seq_len=self.past_seq_len,
future_seq_len=self.future_seq_len_2)
x_test_2 = self.feat._roll_test(self.test_data,
past_seq_len=self.past_seq_len)
self.model_2.fit_eval(x_train_2, y_train_2, mc=True, **self.config)
prediction, uncertainty = self.model_2.predict_with_uncertainty(
x_test_2, n_iter=2)
assert prediction.shape == (x_test_2.shape[0], self.future_seq_len_2)
assert uncertainty.shape == (x_test_2.shape[0], self.future_seq_len_2)
assert np.any(uncertainty)
new_model_2 = LSTMSeq2Seq(check_optional_config=False)
dirname = tempfile.mkdtemp(prefix="automl_test_feature")
try:
save(dirname, model=self.model_2)
restore(dirname, model=new_model_2, config=self.config)
prediction, uncertainty = new_model_2.predict_with_uncertainty(
x_test_2, n_iter=2)
assert prediction.shape == (x_test_2.shape[0],
self.future_seq_len_2)
assert uncertainty.shape == (x_test_2.shape[0],
self.future_seq_len_2)
assert np.any(uncertainty)
finally:
shutil.rmtree(dirname)
def test_save_restore_2(self):
x_train_2, y_train_2 = self.feat._roll_train(
self.train_data,
past_seq_len=self.past_seq_len,
future_seq_len=self.future_seq_len_2)
x_test_2 = self.feat._roll_test(self.test_data,
past_seq_len=self.past_seq_len)
self.model_2.fit_eval(x_train_2, y_train_2, **self.config)
predict_2_before = self.model_2.predict(x_test_2)
new_model_2 = LSTMSeq2Seq(check_optional_config=False)
dirname = tempfile.mkdtemp(prefix="automl_test_feature")
try:
save(dirname, model=self.model_2)
restore(dirname, model=new_model_2, config=self.config)
predict_2_after = new_model_2.predict(x_test_2)
assert_array_almost_equal(predict_2_before, predict_2_after, decimal=2), \
"Prediction values are not the same after restore: " \
"predict before is {}, and predict after is {}".format(predict_2_before,
predict_2_after)
new_config = {'epochs': 2}
new_model_2.fit_eval(x_train_2, y_train_2, **new_config)
finally:
shutil.rmtree(dirname)
def test_save_restore(self):
new_model = VanillaLSTM(check_optional_config=False)
self.model.fit_eval(self.x_train, self.y_train, **self.config)
predict_before = self.model.predict(self.x_test)
dirname = tempfile.mkdtemp(prefix="automl_test_vanilla")
try:
save(dirname, model=self.model)
restore(dirname, model=new_model, config=self.config)
predict_after = new_model.predict(self.x_test)
assert_array_almost_equal(predict_before, predict_after, decimal=2)
new_config = {'epochs': 2}
new_model.fit_eval(self.x_train, self.y_train, **new_config)
finally:
shutil.rmtree(dirname)
def test_save_restore(self):
self.model.fit_eval(data=(self.x_train, self.y_train),
validation_data=(self.x_val, self.y_val),
**self.config)
y_pred = self.model.predict(self.x_test)
assert y_pred.shape == (self.x_test.shape[0], self.y_train.shape[1])
dirname = "tmp"
restored_model = MTNetKeras()
try:
save(dirname, model=self.model)
restore(dirname, model=restored_model, config=self.config)
predict_after = restored_model.predict(self.x_test)
assert_array_almost_equal(y_pred, predict_after, decimal=2), \
"Prediction values are not the same after restore: " \
"predict before is {}, and predict after is {}".format(y_pred, predict_after)
restored_model.fit_eval((self.x_train, self.y_train), epochs=1)
restored_model.evaluate(self.x_val, self.y_val)
finally:
shutil.rmtree("tmp")
def test_predict_with_uncertainty(self, ):
self.model.fit_eval(self.x_train, self.y_train, mc=True, **self.config)
prediction, uncertainty = self.model.predict_with_uncertainty(
self.x_test, n_iter=10)
assert prediction.shape == (self.x_test.shape[0], 1)
assert uncertainty.shape == (self.x_test.shape[0], 1)
assert np.any(uncertainty)
new_model = VanillaLSTM(check_optional_config=False)
dirname = tempfile.mkdtemp(prefix="automl_test_feature")
try:
save(dirname, model=self.model)
restore(dirname, model=new_model, config=self.config)
prediction, uncertainty = new_model.predict_with_uncertainty(
self.x_test, n_iter=2)
assert prediction.shape == (self.x_test.shape[0], 1)
assert uncertainty.shape == (self.x_test.shape[0], 1)
assert np.any(uncertainty)
finally:
shutil.rmtree(dirname)
