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 test_predict_save_restore(self):
config = {"batch_size": 128}
self.model.fit_eval(self.train_data[0], self.train_data[1],
self.val_data, **config)
pred = self.model.predict(self.test_data[0])
assert pred.shape == self.test_data[1].shape
with tempfile.TemporaryDirectory() as tmp_dir_name:
ckpt_name = os.path.join(tmp_dir_name, "ckpt")
self.model.save(ckpt_name)
model_1 = VanillaLSTM()
model_1.restore(ckpt_name)
pred_1 = model_1.predict(self.test_data[0])
assert np.allclose(pred, pred_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 _build(self):
"""
Build LSTM Model in tf.keras
"""
# build model with TF/Keras
self.internal = LSTMKerasModel(
check_optional_config=self.check_optional_config,
future_seq_len=self.target_dim)
return self.internal._build(mc=self.uncertainty, **self.model_config)
class LSTMForecaster(TFParkForecaster):
"""
Vanilla LSTM Forecaster
"""
def __init__(self,
target_dim=1,
feature_dim=1,
lstm_1_units=16,
dropout_1=0.2,
lstm_2_units=8,
dropout_2=0.2,
metric="mean_squared_error",
lr=0.001,
loss="mse",
uncertainty: bool = False):
"""
Build a LSTM Forecast Model.
:param target_dim: dimension of model output
:param feature_dim: dimension of input feature
:param lstm_1_units: num of units for the 1st LSTM layer
:param dropout_1: p for the 1st dropout layer
:param lstm_2_units: num of units for the 2nd LSTM layer
:param dropout_2: p for the 2nd dropout layer
:param metric: the metric for validation and evaluation
:param lr: learning rate
:param uncertainty: whether to return uncertainty
:param loss: the target function you want to optimize on
"""
#
self.target_dim = target_dim
self.check_optional_config = False
self.uncertainty = uncertainty
self.model_config = {
"lr": lr,
"lstm_1_units": lstm_1_units,
"dropout_1": dropout_1,
"lstm_2_units": lstm_2_units,
"dropout_2": dropout_2,
"metric": metric,
"feature_num": feature_dim,
"loss": loss
}
self.internal = None
super().__init__()
def _build(self):
"""
Build LSTM Model in tf.keras
"""
# build model with TF/Keras
self.internal = LSTMKerasModel(
check_optional_config=self.check_optional_config,
future_seq_len=self.target_dim)
return self.internal._build(mc=self.uncertainty, **self.model_config)
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 LSTMForecaster(Forecaster):
"""
Vanilla LSTM Forecaster
"""
def __init__(self,
horizon=1,
feature_dim=1,
lstm_1_units=16,
dropout_1=0.2,
lstm_2_units=8,
dropout_2=0.2,
metric="mean_squared_error",
lr=0.001,
uncertainty: bool = False):
"""
Build a LSTM Forecast Model.
@param horizon: steps to look forward
@param feature_dim: dimension of input feature
@param lstm_1_units: num of units for the 1st LSTM layer
@param dropout_1: p for the 1st dropout layer
@param lstm_2_units: num of units for the 2nd LSTM layer
@param dropout_2: p for the 2nd dropout layer
@param metric: the metric for validation and evaluation
@param lr: learning rate
@param uncertainty: whether to return uncertainty
"""
#
self.horizon = horizon
self.check_optional_config = False
self.uncertainty = uncertainty
self.model_config = {
"lr": lr,
"lstm_1_units": lstm_1_units,
"dropout_1": dropout_1,
"lstm_2_units": lstm_2_units,
"dropout_2": dropout_2,
"metric": metric,
"feature_num": feature_dim
}
self.internal = None
super().__init__()
def _build(self):
"""
Build LSTM Model in tf.keras
"""
# build model with TF/Keras
self.internal = LSTMKerasModel(
check_optional_config=self.check_optional_config,
future_seq_len=self.horizon)
return self.internal._build(mc=self.uncertainty, **self.model_config)
class TestVanillaLSTM(TestCase):
train_data, val_data, test_data = create_data()
model = VanillaLSTM()
def test_fit_evaluate(self):
config = {"batch_size": 128}
self.model.fit_eval(self.train_data[0], self.train_data[1],
self.val_data, **config)
mse, smape = self.model.evaluate(self.val_data[0],
self.val_data[1],
metrics=["mse", "smape"])
assert len(mse) == self.val_data[1].shape[-1]
assert len(smape) == self.val_data[1].shape[-1]
def test_config(self):
config = {"lstm_units": [128] * 2, "dropouts": [0.2] * 2}
self.model.fit_eval(self.train_data[0], self.train_data[1],
self.val_data, **config)
config = {"lstm_units": 128, "dropouts": 0.2}
self.model.fit_eval(self.train_data[0], self.train_data[1],
self.val_data, **config)
with pytest.raises(ValueError):
config = {"lstm_units": 0.1, "dropouts": 0.2}
self.model.fit_eval(self.train_data[0], self.train_data[1],
self.val_data, **config)
with pytest.raises(ValueError):
config = {"lstm_units": [128] * 2, "dropouts": [0.2] * 3}
self.model.fit_eval(self.train_data[0], self.train_data[1],
self.val_data, **config)
with pytest.raises(ValueError):
config = {"lstm_units": 128, "dropouts": [0.2] * 2}
self.model.fit_eval(self.train_data[0], self.train_data[1],
self.val_data, **config)
def test_predict_save_restore(self):
model = VanillaLSTM()
config = {
"lstm_units": [128] * 2,
"dropouts": [0.2] * 2,
"batch_size": 128
}
model.fit_eval(self.train_data[0], self.train_data[1], self.val_data,
**config)
pred = model.predict(self.test_data[0])
assert pred.shape == self.test_data[1].shape
with tempfile.TemporaryDirectory() as tmp_dir_name:
ckpt_name = os.path.join(tmp_dir_name, "ckpt")
model.save(ckpt_name)
model_1 = VanillaLSTM()
model_1.restore(ckpt_name)
pred_1 = model_1.predict(self.test_data[0])
assert np.allclose(pred, pred_1)
def test_predict_with_uncertainty(self):
config = {"batch_size": 128}
self.model.fit_eval(self.train_data[0], self.train_data[1],
self.val_data, **config)
prediction, uncertainty = self.model.predict_with_uncertainty(
self.test_data[0], n_iter=100)
assert prediction.shape == self.test_data[1].shape
assert uncertainty.shape == self.test_data[1].shape
assert np.any(uncertainty)
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)
