BrushEstimator

class pybrush.BrushEstimator.BrushEstimator(**kwargs)

This is the base class for Brush estimators using the c++ engine.

Parameters are defined and documented in EstimatorInterface

Attributes:

best_estimator_pybrush.Program

The final model picked from training. Used in subsequent calls to predict().

archive_list[deap_api.DeapIndividual]

The final population from training.

data_pybrush.Dataset

The complete data in Brush format.

train_pybrush.Dataset

Partition of data_ containing `(1-validation_size)`% of the data, in Brush format.

validation_pybrush.Dataset

Partition of data_ containing `(validation_size)`% of the data, in Brush format.

search_space_a Brush SearchSpace object.

Holds the operators and terminals and sampling utilities to update programs.

> NOTE: as for now, when serializing the model with pickle, the objects of type `Dataset` and `SearchSpace` are not serialized.

Methods

fit(X, y)	Fit an estimator to X,y.
get_metadata_routing()	Get metadata routing of this object.
get_params([deep])	Get parameters for this estimator.
partial_fit(X, y[, lock_nodes_depth, ...])	Fit an estimator to X,y, without reseting the estimator.
predict(X)	Predict using the best estimator in the archive.
predict_archive(X)	Returns a list of dictionary predictions for all models.
set_params(**params)	Set the parameters of this estimator.
set_partial_fit_request(*[, ...])	Request metadata passed to the partial_fit method.

fit(X, y)

Fit an estimator to X,y.

Parameters:

Xnp.ndarray

2-d array of input data.

ynp.ndarray

1-d array of (boolean) target values.

get_params(deep=True)

Get parameters for this estimator.

Parameters:

deepbool, default=True

If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns:

paramsdict

Parameter names mapped to their values.

partial_fit(X, y, lock_nodes_depth=0, skip_leaves=True)

Fit an estimator to X,y, without reseting the estimator.

Parameters:

Xnp.ndarray

2-d array of input data.

ynp.ndarray

1-d array of (boolean) target values.

lock_nodes_depthint, optional

The depth of the tree to lock. Default is 0.

skip_leavesbool, optional

Whether to skip leaves when locking nodes. Default is True.

predict(X)

Predict using the best estimator in the archive.

predict_archive(X)

Returns a list of dictionary predictions for all models.

set_partial_fit_request(*, lock_nodes_depth: bool | None | str = '$UNCHANGED$', skip_leaves: bool | None | str = '$UNCHANGED$') → BrushEstimator

Request metadata passed to the partial_fit method.

Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config()). Please see 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.

Note

This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a Pipeline. Otherwise it has no effect.

Parameters:

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

Metadata routing for lock_nodes_depth parameter in partial_fit.

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

Metadata routing for skip_leaves parameter in partial_fit.

Returns:

selfobject

The updated object.