spe.bagging.ParametricBaggingRegressor#

class spe.bagging.ParametricBaggingRegressor(estimator=None, n_estimators=10, *, max_samples=1.0, max_features=1.0, bootstrap=True, bootstrap_features=False, oob_score=False, warm_start=False, n_jobs=None, random_state=None, verbose=0)#

A Bagging regressor.

A Bagging regressor is an ensemble meta-estimator that fits base regressors each on random subsets of the original dataset and then aggregate their individual predictions (either by voting or by averaging) to form a final prediction. Such a meta-estimator can typically be used as a way to reduce the variance of a black-box estimator (e.g., a decision tree), by introducing randomization into its construction procedure and then making an ensemble out of it.

This algorithm encompasses several works from the literature. When random subsets of the dataset are drawn as random subsets of the samples, then this algorithm is known as Pasting [1]. If samples are drawn with replacement, then the method is known as Bagging [2]. When random subsets of the dataset are drawn as random subsets of the features, then the method is known as Random Subspaces [3]. Finally, when base estimators are built on subsets of both samples and features, then the method is known as Random Patches [4].

Additionally, can fit with Gaussian parametric bootstraps, and is a subclass of AdaptiveLinearSmoother.

Documentation is heavily lifted from BaggingRegressor class, which this class inherits from.

Parameters:

estimatorobject, default=None

The base estimator to fit on random subsets of the dataset. If None, then the base estimator is a DecisionTreeRegressor.

n_estimatorsint, default=10

The number of base estimators in the ensemble.

max_samplesint or float, default=1.0

The number of samples to draw from X to train each base estimator (with replacement by default, see bootstrap for more details).

If int, then draw max_samples samples.
If float, then draw max_samples * X.shape[0] samples.

max_featuresint or float, default=1.0

The number of features to draw from X to train each base estimator ( without replacement by default, see bootstrap_features for more details).

If int, then draw max_features features.
If float, then draw max(1, int(max_features * n_features_in_)) features.

bootstrapbool, default=True

Whether samples are drawn with replacement. If False, sampling without replacement is performed.

bootstrap_featuresbool, default=False

Whether features are drawn with replacement.

oob_scorebool, default=False

Whether to use out-of-bag samples to estimate the generalization error. Only available if bootstrap=True.

warm_startbool, default=False

When set to True, reuse the solution of the previous call to fit and add more estimators to the ensemble, otherwise, just fit a whole new ensemble.

n_jobsint, default=None

The number of jobs to run in parallel for both fit() and predict(). None means 1 unless in a joblib.parallel_backend context. -1 means using all processors.

random_stateint, RandomState instance or None, default=None

Controls the random resampling of the original dataset (sample wise and feature wise). If the base estimator accepts a random_state attribute, a different seed is generated for each instance in the ensemble. Pass an int for reproducible output across multiple function calls.

verboseint, default=0

Controls the verbosity when fitting and predicting.

See also

BaggingClassifier: A Bagging classifier.

References

[1]

L. Breiman, “Pasting small votes for classification in large databases and on-line”, Machine Learning, 36(1), 85-103, 1999.

[2]

L. Breiman, “Bagging predictors”, Machine Learning, 24(2), 123-140, 1996.

[3]

T. Ho, “The random subspace method for constructing decision forests”, Pattern Analysis and Machine Intelligence, 20(8), 832-844, 1998.

[4]

G. Louppe and P. Geurts, “Ensembles on Random Patches”, Machine Learning and Knowledge Discovery in Databases, 346-361, 2012.

Examples

>>> from sklearn.svm import SVR
>>> from spe.bagging import BaggingRegressor
>>> from sklearn.datasets import make_regression
>>> X, y = make_regression(n_samples=100, n_features=4,
...                        n_informative=2, n_targets=1,
...                        random_state=0, shuffle=False)
>>> regr = ParametricBaggingRegressor(estimator=SVR(),
...                         n_estimators=10, random_state=0).fit(X, y)
>>> regr.predict([[0, 0, 0, 0]])
array([-2.8720...])

Attributes:

estimator_estimator: The base estimator from which the ensemble is grown.
n_features_in_int: Number of features seen during fit.
feature_names_in_ndarray of shape (n_features_in_,): Names of features seen during fit. Defined only when X has feature names that are all strings.
estimators_list of estimators: The collection of fitted sub-estimators.
estimators_samples_list of arrays: The subset of drawn samples for each base estimator.
estimators_features_list of arrays: The subset of drawn features for each base estimator.
oob_score_float: Score of the training dataset obtained using an out-of-bag estimate. This attribute exists only when oob_score is True.
oob_prediction_ndarray of shape (n_samples,): Prediction computed with out-of-bag estimate on the training set. If n_estimators is small it might be possible that a data point was never left out during the bootstrap. In this case, oob_prediction_ might contain NaN. This attribute exists only when oob_score is True.

Methods

`__init__`([estimator, n_estimators, ...])
`fit`(X, y[, max_samples, max_depth, ...])	Build a Bagging ensemble of estimators from the training
`get_linear_smoother`(X, tr_idx, ts_idx[, ...])	Get fitted adaptive linear smoother matrix $S(W)$.
`get_metadata_routing`()	Raise NotImplementedError.
`get_params`([deep])	Get parameters for this estimator.
`predict`(X[, tr_idx, ts_idx, y_refit])	Compute $S(W)y_{refit}$ as predictions.
`score`(X, y[, sample_weight])	Return the coefficient of determination of the prediction.
`set_fit_request`(*[, check_input, chol_eps, ...])	Request metadata passed to the `fit` method.
`set_params`(**params)	Set the parameters of this estimator.
`set_predict_request`(*[, tr_idx, ts_idx, y_refit])	Request metadata passed to the `predict` method.
`set_score_request`(*[, sample_weight])	Request metadata passed to the `score` method.

Attributes

estimators_samples_

The subset of drawn samples for each base estimator.

__init__(estimator=None, n_estimators=10, *, max_samples=1.0, max_features=1.0, bootstrap=True, bootstrap_features=False, oob_score=False, warm_start=False, n_jobs=None, random_state=None, verbose=0)#

fit(X, y, max_samples=None, max_depth=None, sample_weight=None, check_input=True, chol_eps=None, do_param_boot=False)#

Build a Bagging ensemble of estimators from the training: set (X, y).

Parameters:

X{array-like, sparse matrix} of shape (n_samples, n_features): The training input samples. Sparse matrices are accepted only if they are supported by the base estimator.
yarray-like of shape (n_samples,): The target values (class labels in classification, real numbers in regression).
max_samplesint or float, default=None: Argument to use instead of self.max_samples.
max_depthint, default=None: Override value used when constructing base estimator. Only supported if the base estimator has a max_depth parameter.
sample_weightarray-like of shape (n_samples,), default=None: Sample weights. If None, then samples are equally weighted. Note that this is supported only if the base estimator supports sample weighting.
check_inputbool, default=True: Override value used when fitting base estimator. Only supported if the base estimator has a check_input parameter for fit function.
chol_epsarray-like of shape (n_samples,n_samples), optional: Cholesky of parametric bootstrap covariance matrix. In the case of do_param_boot is False, chol_eps is ignored. If chol_eps is None and do_param_boot is True, then chol_eps is np.eye(n_samples). Default is None.
do_param_bootbool, optional: If True performs parametric bootstrap sampling. Default is False.

Returns:

selfobject: Fitted estimator.

get_linear_smoother(X, tr_idx, ts_idx, ret_full_P=False)#: Get fitted adaptive linear smoother matrix $S(W)$.

get_metadata_routing()#

Raise NotImplementedError.

This estimator does not support metadata routing yet.

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.

predict(X, tr_idx=None, ts_idx=None, y_refit=None)#

Compute $S(W)y_{refit}$ as predictions.

Computes predictions as adaptive linear smoothing where $S(W)$ is the output of instance’s AdaptiveLinearSmoother().

score(X, y, sample_weight=None)#

Return the coefficient of determination of the prediction.

The coefficient of determination $R^2$ is defined as $(1 - \frac{u}{v})$, where $u$ is the residual sum of squares ((y_true - y_pred)** 2).sum() and $v$ is the total sum of squares ((y_true - y_true.mean()) ** 2).sum(). The best possible score is 1.0 and it can be negative (because the model can be arbitrarily worse). A constant model that always predicts the expected value of y, disregarding the input features, would get a $R^2$ score of 0.0.

Parameters:

Xarray-like of shape (n_samples, n_features): Test samples. For some estimators this may be a precomputed kernel matrix or a list of generic objects instead with shape (n_samples, n_samples_fitted), where n_samples_fitted is the number of samples used in the fitting for the estimator.
yarray-like of shape (n_samples,) or (n_samples, n_outputs): True values for X.
sample_weightarray-like of shape (n_samples,), default=None: Sample weights.

Returns:

scorefloat: $R^2$ of self.predict(X) w.r.t. y.

Notes

The $R^2$ score used when calling score on a regressor uses multioutput='uniform_average' from version 0.23 to keep consistent with default value of r2_score(). This influences the score method of all the multioutput regressors (except for MultiOutputRegressor).

Request metadata passed to the 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 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 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.

New 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:

check_inputstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED: Metadata routing for check_input parameter in fit.
chol_epsstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED: Metadata routing for chol_eps parameter in fit.
do_param_bootstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED: Metadata routing for do_param_boot parameter in fit.
max_depthstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED: Metadata routing for max_depth parameter in fit.
max_samplesstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED: Metadata routing for max_samples parameter in fit.
sample_weightstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED: Metadata routing for sample_weight parameter in fit.

Returns:

selfobject: The updated object.

set_params(**params)#

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Parameters:

**paramsdict: Estimator parameters.

Returns:

selfestimator instance: Estimator instance.

Request metadata passed to the predict 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 predict 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 predict.
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.

New 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:

tr_idxstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED: Metadata routing for tr_idx parameter in predict.
ts_idxstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED: Metadata routing for ts_idx parameter in predict.
y_refitstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED: Metadata routing for y_refit parameter in predict.

Returns:

selfobject: The updated object.

set_score_request(*, sample_weight: bool | None | str = '$UNCHANGED$') → ParametricBaggingRegressor#

Request metadata passed to the score 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 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.

New 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:

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.

estimators_samples_#

The subset of drawn samples for each base estimator.

Returns a dynamically generated list of indices identifying the samples used for fitting each member of the ensemble, i.e., the in-bag samples.

Note: the list is re-created at each call to the property in order to reduce the object memory footprint by not storing the sampling data. Thus fetching the property may be slower than expected.