NadarayaWatsonRegressor#

class skactiveml.regressor.NadarayaWatsonRegressor(metric='rbf', metric_dict=None, missing_label=nan, random_state=None)[source]#

Bases: NICKernelRegressor

Nadaraya Watson Regressor

The Nadaraya Watson Regressor predicts the target value by taking a weighted average based on a kernel. It is implemented as a NICKernelRegressor with different prior values.

Parameters:

metricstr or callable, default=’rbf’: The metric must a be a valid kernel defined by the function sklearn.metrics.pairwise.pairwise_kernels.
metric_dictdict, default=None: Any further parameters are passed directly to the kernel function.
missing_labelscalar or string or np.nan or or None, default=np.nan: Value to represent a missing label.
random_stateint or RandomState instance or None, default=None: Determines random number for predict method. Pass an int for reproducible results across multiple method calls.

Methods

`fit`(X, y[, sample_weight])	Fit the model using X as training data and y as 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 estimated 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 coefficient of determination on test data.
`set_fit_request`(*[, sample_weight])	Configure whether metadata should be requested to be passed to the `fit` method.
`set_params`(**params)	Set the parameters of this estimator.
`set_predict_request`(*[, return_entropy, ...])	Configure whether metadata should be requested to be passed to the `predict` method.
`set_score_request`(*[, sample_weight])	Configure whether metadata should be requested to be passed to the `score` method.

Attributes

METRICS

METRICS = ['additive_chi2', 'chi2', 'cosine', 'linear', 'poly', 'polynomial', 'rbf', 'laplacian', 'sigmoid', 'precomputed']#

fit(X, y, sample_weight=None)#

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

Parameters:

Xmatrix-like of shape (n_samples, n_features): Training data set, usually complete, i.e., including the labeled and unlabeled samples.
yarray-like of shape (n_samples,) or (n_samples, n_targets): Labels of the training data set (possibly including unlabeled ones indicated by self.missing_label).
sample_weightarray-like of shape (n_samples,): It contains the weights of the training samples’ values.

Returns:

self: SkactivemlRegressor,: The SkactivemlRegressor is 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)#

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

Parameters:

Xarray-like of shape (n_samples, n_features): Input samples.
return_stdbool, default=False: Whether to return the standard deviation.
return_entropybool, 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.

predict_target_distribution(X)#

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

Parameters:

Xarray-like of 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)#

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

Parameters:

Xarray-like of shape (n_samples_X, n_features): Input samples, where the target values are drawn from.
n_samples: int, default=1: Number of random samples to be drawn.
random_stateint or RandomState instance or None, default=None: Determines random number generation to randomly draw samples. Pass an int for reproducible results across multiple method calls.

Returns:

y_samplesnumpy.ndarray of shape (n_samples_X, n_samples): Drawn random target samples.

score(X, y, sample_weight=None)#

Return coefficient of determination on test data.

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: bool | None | str = '$UNCHANGED$') → NadarayaWatsonRegressor#

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

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 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: bool | None | str = '$UNCHANGED$', return_std: bool | None | str = '$UNCHANGED$') → NadarayaWatsonRegressor#

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

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.

Added in version 1.3.

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: bool | None | str = '$UNCHANGED$') → NadarayaWatsonRegressor#

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

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.

NadarayaWatsonRegressor#

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