def test_prophet(daily_data):
"""Tests prophet estimator."""
model = OneByOneEstimator(estimator="ProphetEstimator",
forecast_horizon=3,
estimator_map=[1, 2],
coverage=None)
train_df = daily_data["train_df"]
model.fit(train_df, time_col=cst.TIME_COL, value_col=cst.VALUE_COL)
def test_forecast_one_by_one_not_activated(daily_data):
"""Tests forecast one-by-one is not activated when no parameter
depends on forecast horizon.
"""
model = OneByOneEstimator(estimator="SimpleSilverkiteEstimator",
forecast_horizon=3,
estimator_map=[1, 2],
estimator_params={
"autoreg_dict": None,
"yearly_seasonality": 2,
"feature_sets_enabled": False
})
train_df = daily_data["train_df"]
model.fit(train_df, time_col=cst.TIME_COL, value_col=cst.VALUE_COL)
assert len(model.estimators) == 1
assert model.estimator_map_list == [3]
assert model.pred_indices is None
def __apply_forecast_one_by_one_to_pipeline_parameters(self):
"""If forecast_one_by_one is activated,
1. replaces the estimator with ``OneByOneEstimator`` in pipeline.
2. Adds one by one estimator's parameters to ``hyperparameter_grid``.
"""
if self.config.forecast_one_by_one not in (None, False):
pipeline = get_basic_pipeline(
estimator=OneByOneEstimator(
estimator=self.template.estimator.__class__.__name__,
forecast_horizon=self.config.forecast_horizon),
score_func=self.template.score_func,
score_func_greater_is_better=self.template.
score_func_greater_is_better,
agg_periods=self.template.config.evaluation_metric_param.
agg_periods,
agg_func=self.template.config.evaluation_metric_param.agg_func,
relative_error_tolerance=self.template.config.
evaluation_metric_param.relative_error_tolerance,
coverage=self.template.config.coverage,
null_model_params=self.template.config.evaluation_metric_param.
null_model_params,
regressor_cols=self.template.regressor_cols)
self.pipeline_params["pipeline"] = pipeline
if isinstance(self.pipeline_params["hyperparameter_grid"], list):
for i in range(len(
self.pipeline_params["hyperparameter_grid"])):
self.pipeline_params["hyperparameter_grid"][i][
"estimator__forecast_horizon"] = [
self.config.forecast_horizon
]
self.pipeline_params["hyperparameter_grid"][i][
"estimator__estimator_map"] = [
self.config.forecast_one_by_one
]
else:
self.pipeline_params["hyperparameter_grid"][
"estimator__forecast_horizon"] = [
self.config.forecast_horizon
]
self.pipeline_params["hyperparameter_grid"][
"estimator__estimator_map"] = [
self.config.forecast_one_by_one
]