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)
class TestVanillaLSTM(ZooTestCase):
def setup_method(self, method):
# super().setup_method(method)
train_data = pd.DataFrame(data=np.random.randn(64, 4))
val_data = pd.DataFrame(data=np.random.randn(16, 4))
test_data = pd.DataFrame(data=np.random.randn(16, 4))
future_seq_len = 1
past_seq_len = 6
# use roll method in time_sequence
tsft = TimeSequenceFeatureTransformer()
self.x_train, self.y_train = tsft._roll_train(
train_data,
past_seq_len=past_seq_len,
future_seq_len=future_seq_len)
self.x_val, self.y_val = tsft._roll_train(
val_data, past_seq_len=past_seq_len, future_seq_len=future_seq_len)
self.x_test = tsft._roll_test(test_data, past_seq_len=past_seq_len)
self.config = {
'epochs': 1,
"lr": 0.001,
"lstm_1_units": 16,
"dropout_1": 0.2,
"lstm_2_units": 10,
"dropout_2": 0.2,
"batch_size": 32,
}
self.model = VanillaLSTM(check_optional_config=False,
future_seq_len=future_seq_len)
def teardown_method(self, method):
pass
def test_fit_eval(self):
print("fit_eval:",
self.model.fit_eval(self.x_train, self.y_train, **self.config))
def test_fit_eval_mc(self):
print(
"fit_eval:",
self.model.fit_eval(self.x_train,
self.y_train,
mc=True,
**self.config))
def test_evaluate(self):
self.model.fit_eval(self.x_train, self.y_train, **self.config)
mse, rs = self.model.evaluate(self.x_val,
self.y_val,
metric=['mse', 'r2'])
print("Mean squared error is:", mse)
print("R square is:", rs)
def test_predict(self):
self.model.fit_eval(self.x_train, self.y_train, **self.config)
self.y_pred = self.model.predict(self.x_test)
assert self.y_pred.shape == (self.x_test.shape[0], 1)
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_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)