def predict_seasonal_components(self, df):
"""Predict seasonality components, holidays, and added regressors.
Parameters
----------
df: Prediction dataframe.
Returns
-------
Dataframe with seasonal components.
"""
seasonal_features, _ = self.make_all_seasonality_features(df)
lower_p = 100 * (1.0 - self.interval_width) / 2
upper_p = 100 * (1.0 + self.interval_width) / 2
components = pd.DataFrame({
'col': np.arange(seasonal_features.shape[1]),
'component': [x.split('_delim_')[0] for x in seasonal_features.columns],
})
# Add total for all regression components
components = components.append(pd.DataFrame({
'col': np.arange(seasonal_features.shape[1]),
'component': 'seasonal',
}))
# Add totals for seasonality, holiday, and extra regressors
components = self.add_group_component(
components, 'seasonalities', self.seasonalities.keys())
if self.holidays is not None:
components = self.add_group_component(
components, 'holidays', self.holidays['holiday'].unique())
components = self.add_group_component(
components, 'extra_regressors', self.extra_regressors.keys())
# Remove the placeholder
components = components[components['component'] != 'zeros']
X = seasonal_features.as_matrix()
data = {}
for component, features in components.groupby('component'):
cols = features.col.tolist()
comp_beta = self.params['beta'][:, cols]
comp_features = X[:, cols]
comp = (
np.matmul(comp_features, comp_beta.transpose())
* self.y_scale # noqa W503
)
data[component] = np.nanmean(comp, axis=1)
data[component + '_lower'] = np.nanpercentile(comp, lower_p,
axis=1)
data[component + '_upper'] = np.nanpercentile(comp, upper_p,
axis=1)
return pd.DataFrame(data)
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