get_sklearn_wrapper¶
-
hcrystalball.wrappers.
get_sklearn_wrapper
(model_cls, **model_params)[source]¶ Factory function returning the model specific SklearnWrapper with provided
model_cls
parameters.This function is required for sklearn compatibility since our SklearnWrapper need to have all parameters of
model_cls
set already during SklearnWrapper definition time. This factory function is not needed in case of other wrappers since the regressor is already part of the wrapper.- Parameters
model_cls (class of sklearn compatible regressor) – i.e. LinearRegressor, GradientBoostingRegressor
model_params –
model_cls
specific parameters (e.g. max_depth) and/or SklearnWrapper specific parameters (e.g. clip_predictions_lower)
Example
>>> from hcrystalball.wrappers._sklearn import _get_sklearn_wrapper >>> from sklearn.ensemble import RandomForestRegressor >>> est = get_sklearn_wrapper(RandomForestRegressor, max_depth=6, clip_predictions_lower=0.) >>> est SklearnWrapper(bootstrap=True, ccp_alpha=0.0, clip_predictions_lower=0.0, clip_predictions_upper=None, criterion='mse', fit_params=None, lags=3, max_depth=6, max_features='auto', max_leaf_nodes=None, max_samples=None, min_impurity_decrease=0.0, min_impurity_split=None, min_samples_leaf=1, min_samples_split=2, min_weight_fraction_leaf=0.0, n_estimators=100, n_jobs=None, name='sklearn', oob_score=False, optimize_for_horizon=False, random_state=None, verbose=0, warm_start=False)
- Returns
- Return type
SklearnWrapper