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River2SKLRegressor

Compatibility layer from River to scikit-learn for regression.

Parameters

Methods

fit

Fits to an entire dataset contained in memory.

Parameters

  • X
  • y

Returns

self

get_metadata_routing

Get metadata routing of this object.

Please check :ref:User Guide <metadata_routing> on how the routing mechanism works.

Returns

MetadataRequest

get_params

Get parameters for this estimator.

Parameters

  • deep — defaults to True

Returns

dict

partial_fit

Fits incrementally on a portion of a dataset.

Parameters

  • X
  • y

Returns

self

predict

Predicts the target of an entire dataset contained in memory.

Parameters

  • X

Returns

np.ndarray: Predicted target values for each row of X.

score

Return the coefficient of determination of the prediction.

The coefficient of determination :math:R^2 is defined as :math:(1 - \frac{u}{v}), where :math:u is the residual sum of squares ((y_true - y_pred)** 2).sum() and :math: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 :math:R^2 score of 0.0.

Parameters

  • X
  • y
  • sample_weight — defaults to None

Returns

float

set_params

Set the parameters of this estimator.

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

Parameters

  • params

Returns

estimator instance

set_score_request

Request metadata passed to the score method.

Note that this method is only relevant if enable_metadata_routing=True (see :func:sklearn.set_config). Please see :ref:User Guide <metadata_routing> 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. .. versionadded:: 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 :class:pipeline.Pipeline. Otherwise it has no effect.

Parameters

  • sample_weight — Union[bool, NoneType, str] — defaults to $UNCHANGED$

Returns

River2SKLRegressor: object