BrushRegressor

class pybrush.BrushEstimator.BrushRegressor(**kwargs)[source]

Brush with c++ engine for regression.

Parameters are defined and documented in EstimatorInterface

This class inherits from BrushEstimator. A full documentation of the methods and attributes can be found there.

Examples

>>> import pandas as pd
>>> df = pd.read_csv('docs/examples/datasets/d_enc.csv')
>>> X = df.drop(columns='label')
>>> y = df['label']
>>> from pybrush import BrushRegressor
>>> est = BrushRegressor()
>>> est.fit(X,y)
>>> # print('score:', est.score(X,y))
set_partial_fit_request(*, keep_current_weights: bool | None | str = '$UNCHANGED$', keep_leaves_unlocked: bool | None | str = '$UNCHANGED$', lock_nodes_depth: bool | None | str = '$UNCHANGED$') → BrushRegressor[source]

Configure whether metadata should be requested to be passed to the partial_fit method.

Note that this method is only relevant when this estimator is used as a sub-estimator within a meta-estimator and metadata routing is enabled with enable_metadata_routing=True (see sklearn.set_config()). Please check the User Guide on how the routing mechanism works.

The options for each parameter are:

  • True: metadata is requested, and passed to partial_fit if provided. The request is ignored if metadata is not provided.

  • False: metadata is not requested and the meta-estimator will not pass it to partial_fit.

  • None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.

  • str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

Added in version 1.3.

Parameters

keep_current_weightsstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

Metadata routing for keep_current_weights parameter in partial_fit.

keep_leaves_unlockedstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

Metadata routing for keep_leaves_unlocked parameter in partial_fit.

lock_nodes_depthstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

Metadata routing for lock_nodes_depth parameter in partial_fit.

Returns

selfobject

The updated object.

set_score_request(*, sample_weight: bool | None | str = '$UNCHANGED$') → BrushRegressor[source]

Configure whether metadata should be requested to be passed to the score method.

Note that this method is only relevant when this estimator is used as a sub-estimator within a meta-estimator and metadata routing is enabled with enable_metadata_routing=True (see sklearn.set_config()). Please check the User Guide on how the routing mechanism works.

The options for each parameter are:

  • True: metadata is requested, and passed to score if provided. The request is ignored if metadata is not provided.

  • False: metadata is not requested and the meta-estimator will not pass it to score.

  • None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.

  • str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

Added in version 1.3.

Parameters

sample_weightstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

Metadata routing for sample_weight parameter in score.

Returns

selfobject

The updated object.