def test_exogenous_input(self):
"""Test that no errors are raised when using exogenous features."""
dtype = dtypes.float64
times = [1, 2, 3, 4, 5, 6]
values = [[0.01], [5.10], [5.21], [0.30], [5.41], [0.50]]
feature_a = [["off"], ["on"], ["on"], ["off"], ["on"], ["off"]]
sparse_column_a = feature_column.sparse_column_with_keys(
column_name="feature_a", keys=["on", "off"])
one_hot_a = layers.one_hot_column(sparse_id_column=sparse_column_a)
regressor = estimators.StructuralEnsembleRegressor(
periodicities=[],
num_features=1,
moving_average_order=0,
exogenous_feature_columns=[one_hot_a],
dtype=dtype)
features = {
TrainEvalFeatures.TIMES: times,
TrainEvalFeatures.VALUES: values,
"feature_a": feature_a
}
train_input_fn = input_pipeline.RandomWindowInputFn(
input_pipeline.NumpyReader(features), window_size=6, batch_size=1)
regressor.train(input_fn=train_input_fn, steps=1)
eval_input_fn = input_pipeline.WholeDatasetInputFn(
input_pipeline.NumpyReader(features))
evaluation = regressor.evaluate(input_fn=eval_input_fn, steps=1)
predict_input_fn = input_pipeline.predict_continuation_input_fn(
evaluation,
times=[[7, 8, 9]],
exogenous_features={"feature_a": [[["on"], ["off"], ["on"]]]})
regressor.predict(input_fn=predict_input_fn)
def _structural_ensemble_regressor(
model_dir, head_type, exogenous_feature_columns):
return ts_estimators.StructuralEnsembleRegressor(
periodicities=None,
num_features=5,
exogenous_feature_columns=exogenous_feature_columns,
head_type=head_type,
model_dir=model_dir)
def _estimator_fn(model_dir, exogenous_feature_columns):
return estimators.StructuralEnsembleRegressor(
num_features=1,
periodicities=10,
model_dir=model_dir,
dtype=dtype,
config=_SeedRunConfig(),
exogenous_feature_columns=exogenous_feature_columns)
def test_structural_ensemble_numpy_input(self):
numpy_data = {
"times": numpy.arange(50),
"values": numpy.random.normal(size=[50])
}
estimators.StructuralEnsembleRegressor(
num_features=1,
periodicities=[],
model_dir=self.get_temp_dir(),
config=_SeedRunConfig()).train(input_pipeline.WholeDatasetInputFn(
input_pipeline.NumpyReader(numpy_data)),
steps=1)
def test_no_periodicity(self):
"""Test that no errors are raised when periodicites is None."""
dtype = dtypes.float64
times = [1, 2, 3, 4, 5, 6]
values = [[0.01], [5.10], [5.21], [0.30], [5.41], [0.50]]
regressor = estimators.StructuralEnsembleRegressor(
periodicities=None,
num_features=1,
moving_average_order=0,
dtype=dtype)
features = {
TrainEvalFeatures.TIMES: times,
TrainEvalFeatures.VALUES: values
}
train_input_fn = input_pipeline.RandomWindowInputFn(
input_pipeline.NumpyReader(features), window_size=6, batch_size=1)
regressor.train(input_fn=train_input_fn, steps=1)
eval_input_fn = input_pipeline.WholeDatasetInputFn(
input_pipeline.NumpyReader(features))
evaluation = regressor.evaluate(input_fn=eval_input_fn, steps=1)
predict_input_fn = input_pipeline.predict_continuation_input_fn(
evaluation, times=[[7, 8, 9]])
regressor.predict(input_fn=predict_input_fn)