def test_cv_finertimesplit_split_pandas_container_data(ts_data, expected_error):
n_splits = 2
horizon = 3
fts = FinerTimeSplit(n_splits=n_splits, horizon=horizon)
if expected_error is None:
result = fts.split(ts_data)
assert isinstance(result, types.GeneratorType)
result = list(result)
assert len(result) == n_splits
for i, isplit in enumerate(result):
assert len(isplit) == 2
assert len(isplit[0]) == len(ts_data) - (n_splits - i) * horizon
assert len(isplit[1]) == horizon
assert np.array_equal(isplit[0], np.arange(len(ts_data) - (n_splits - i) * horizon))
assert np.array_equal(isplit[1], np.arange(horizon) + len(ts_data) - (n_splits - i) * horizon)
else:
with pytest.raises(expected_error):
_ = list(fts.split(ts_data))
def test_cv_finertimesplit_split_input_data_types(test_data, expected_error):
n_splits = 2
horizon = 3
fts = FinerTimeSplit(n_splits=n_splits, horizon=horizon)
if expected_error is None:
result = list(fts.split(test_data))
assert len(result) == n_splits
for i, isplit in enumerate(result):
assert len(isplit) == 2
assert len(isplit[0]) == len(test_data) - (n_splits - i) * horizon
assert len(isplit[1]) == horizon
assert np.array_equal(isplit[0], np.arange(len(test_data) - (n_splits - i) * horizon))
assert np.array_equal(
isplit[1],
np.arange(horizon) + len(test_data) - (n_splits - i) * horizon,
)
else:
with pytest.raises(expected_error):
_ = list(fts.split(test_data))
def fit(self, X, y=None):
"""Fit the stacking ensemble model
Parameters
----------
X: pandas.DataFrame
Input features.
y: numpy.ndarray
Target vector.
Returns
-------
StackingEnsemble
A fitted StackingEnsemble instance
"""
self._check_base_learners_names(self.base_learners)
# Fit the base learners and the meta_model
if (not self.fitted) or self.fit_meta_model_always:
splitter = FinerTimeSplit(horizon=self.train_horizon,
n_splits=self.train_n_splits)
n_train_meta = self.train_n_splits * self.train_horizon
X_meta = pd.DataFrame(
index=X.index[-n_train_meta:],
columns=[get_estimator_name(bl) for bl in self.base_learners],
)
y_meta = y[-n_train_meta:]
# Get base learners predictions
for ind_train, ind_pred in splitter.split(X):
X_train = X.iloc[ind_train, :]
X_pred = X.iloc[ind_pred, :]
y_train = y[ind_train]
self._fit_base_learners(X_train, y_train)
X_meta.loc[
X_pred.index, :] = self._predict_features_for_meta_models(
X_pred)
# Add dummy horizon variable for meta model
if self.horizons_as_features:
X_meta = pd.concat(
[
X_meta,
self._create_horizons_as_features(
cross_results_index=X_meta.index,
horizon=self.train_horizon,
n_splits=self.train_n_splits,
),
],
axis=1,
)
if self.weekdays_as_features:
X_meta = pd.concat(
[
X_meta,
self._create_weekdays_as_features(
cross_results_index=X_meta.index)
],
axis=1,
)
self._fit_columns = X_meta.columns
self.meta_model.fit(X_meta.values, y_meta)
# Fit the base learners on the whole training set
self._fit_base_learners(X, y)
self.fitted = True
return self