StackingEnsemble¶
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class
hcrystalball.ensemble.StackingEnsemble(base_learners, meta_model, name='stacking_ensemble', train_n_splits=1, train_horizon=10, horizons_as_features=True, weekdays_as_features=True, fit_meta_model_always=False, clip_predictions_lower=None, clip_predictions_upper=None)[source]¶ Bases:
sklearn.base.BaseEstimatorStackingEnsemble model, which takes a list of any hcrystalball model wrapper instance(s) as base learners.
During fitting the base learners are fitted and prediction(s) will be made for the requested horizon, using possibly more than one splits. The predictions for each model in all splits are concatenated and serve as the feature matrix for the meta model, with the prediction of each model over all splits being a distinct feature. Finally the meta model, which is just a regular regressor, will then be fitted to the data to determine the relative weights of each base learner in the prediction of the ensemble.
As a default behaviour the meta model is fitted only the first time the fit() method is called, then in each subsequent calls of the fit() method (of a given StackingEnsemble instance) omits the fitting of the meta model and fits only the base learners. This behaviour can, however be changed using the fit_meta_model_always parameter to force the meta model to be refitted every time the fit method is called. Note, however, that this latter behaviour can be computationally expensive, as fitting the meta model requires fitting the base learners train_n_splits times.
- Parameters
name (str) – Unique name / identifier of the model instance
base_learners (list) – List of fully instantiated hcrystalball model wrappers
meta_model (sklearn.base.BaseEstimator) – Scikit-learn compatible regressor
train_n_splits (int) – Number of splits used for fitting the meta model
train_horizon (int) – Max. number of steps ahead to be predicted. Ideally this value should not be identical to the forecasting horizon in prediction.
horizons_as_features (bool) – Adds horizon feature for meta model
weekdays_as_features (bool) – Adds weekdays feature for meta model
fit_meta_model_always (bool) – If True the meta model will always be re-fitted, each time the fit() method is called, if False the meta model will only be fitted the first time the fit() method is called and in subsequent calls of the fit() method only the base learners will be re-fitted.
Methods Summary
fit(X, y)Fit the stacking ensemble model
get_params([deep])Get parameters for this estimator.
predict(X)Calculate the prediction of the ensemble for a given set of date / time
set_params(**params)Set the parameters of this estimator.
Methods Documentation
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fit(X, y)[source]¶ Fit the stacking ensemble model
- Parameters
X (pandas.DataFrame) – Input features.
y (numpy.ndarray) – Target vector.
- Returns
A fitted StackingEnsemble instance
- Return type
-
get_params(deep=True)¶ Get parameters for this estimator.
- Parameters
deep (bool, default=True) – If True, will return the parameters for this estimator and contained subobjects that are estimators.
- Returns
params – Parameter names mapped to their values.
- Return type
mapping of string to any
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predict(X)[source]¶ Calculate the prediction of the ensemble for a given set of date / time
- Parameters
X (pandas.DataFrame) – Input features.
- Returns
A DataFrame container with the index being the input (date)time vector. The single column in the DataFrame contains the prediction and the column name is the name of the model (i.e. the
nameparameter passed to the constructor)- Return type
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set_params(**params)¶ Set the parameters of this estimator.
The method works on simple estimators as well as on nested objects (such as pipelines). The latter have parameters of the form
<component>__<parameter>so that it’s possible to update each component of a nested object.