skactiveml.base.ProbabilisticRegressor#

class skactiveml.base.ProbabilisticRegressor(missing_label=nan, random_state=None)[source]#

Bases: SkactivemlRegressor

Base class for scikit-activeml probabilistic regressors.

Methods

fit(X, y[, sample_weight])

Fit the model using X as training data and y as numerical labels.

get_metadata_routing()

Get metadata routing of this object.

get_params([deep])

Get parameters for this estimator.

predict(X[, return_std, return_entropy])

Returns the mean, std (optional) and differential entropy (optional) of the predicted target distribution conditioned on the test samples X.

predict_target_distribution(X)

Returns the predicted target distribution conditioned on the test samples X.

sample_y(X[, n_samples, random_state])

Returns random samples from the predicted target distribution conditioned on the test samples X.

score(X, y[, sample_weight])

Return the coefficient of determination of the prediction.

set_fit_request(*[, sample_weight])

Request metadata passed to the fit method.

set_params(**params)

Set the parameters of this estimator.

set_predict_request(*[, return_entropy, ...])

Request metadata passed to the predict method.

set_score_request(*[, sample_weight])

Request metadata passed to the score method.
abstract fit(X, y, sample_weight=None)#

Fit the model using X as training data and y as numerical labels.

Parameters
Xmatrix-like, shape (n_samples, n_features)

The sample matrix X is the feature matrix representing the samples.

yarray-like, shape (n_samples) or (n_samples, n_targets)

It contains the labels of the training samples. The number of numerical labels may be variable for the samples, where missing labels are represented the attribute ‘missing_label’.

sample_weightarray-like, shape (n_samples)

It contains the weights of the training samples’ values.

Returns
self: skactiveml.base.SkactivemlRegressor,

The skactiveml.base.SkactivemlRegressor object fitted on the training data.

get_metadata_routing()#

Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.

Returns
routingMetadataRequest

A MetadataRequest encapsulating routing information.

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, return_std=False, return_entropy=False)[source]#

Returns the mean, std (optional) and differential entropy (optional) of the predicted target distribution conditioned on the test samples X.

Parameters
Xarray-like, shape (n_samples, n_features)

Input samples.

return_stdbool, optional (default=False)

Whether to return the standard deviation.

return_entropybool, optional (default=False)

Whether to return the differential entropy.

Returns
munumpy.ndarray, shape (n_samples)

Predicted mean conditioned on X.

stdnumpy.ndarray, shape (n_samples), optional

Predicted standard deviation conditioned on X.

entropynumpy..ndarray, optional

Predicted differential entropy conditioned on X.

abstract predict_target_distribution(X)[source]#

Returns the predicted target distribution conditioned on the test samples X.

Parameters
Xarray-like, shape (n_samples, n_features)

Input samples.

Returns
distscipy.stats._distn_infrastructure.rv_frozen

The distribution of the targets at the test samples.

sample_y(X, n_samples=1, random_state=None)[source]#

Returns random samples from the predicted target distribution conditioned on the test samples X.

Parameters
Xarray-like, shape (n_samples_X, n_features)

Input samples, where the target values are drawn from.

n_samples: int, optional (default=1)

Number of random samples to be drawn.

random_stateint, RandomState instance or None, optional
(default=None)

Determines random number generation to randomly draw samples. Pass an int for reproducible results across multiple method calls.

Returns
y_samplesnumpy.ndarray, shape (n_samples_X, n_samples)

Drawn random target samples.

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).

set_fit_request(*, sample_weight: Union[bool, None, str] = '$UNCHANGED$') → ProbabilisticRegressor#

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
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.

set_predict_request(*, return_entropy: Union[bool, None, str] = '$UNCHANGED$', return_std: Union[bool, None, str] = '$UNCHANGED$') → ProbabilisticRegressor#

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

Metadata routing for return_entropy parameter in predict.

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

Metadata routing for return_std parameter in predict.

Returns
selfobject

The updated object.

set_score_request(*, sample_weight: Union[bool, None, str] = '$UNCHANGED$') → ProbabilisticRegressor#

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.

Examples using skactiveml.base.ProbabilisticRegressor#

Greedy Sampling on the Feature Space (GSx)

Greedy Sampling on the Feature Space (GSx)

Greedy Sampling on the Target Space (GSy)

Greedy Sampling on the Target Space (GSy)

Improved Greedy Sampling (GSi)

Improved Greedy Sampling (GSi)

Expected Model Variance Reduction

Expected Model Variance Reduction

Expected Model Change

Expected Model Change

Expected Model Output Change

Expected Model Output Change

Regression based Kullback Leibler Divergence Maximization

Regression based Kullback Leibler Divergence Maximization