5.1.1. doubleml.utils.DMLDummyRegressor#

class doubleml.utils.DMLDummyRegressor#

A dummy regressor that raises an AttributeError when attempting to access its fit, predict, or set_params methods.

Methods

`fit`()	Raises AttributeError: "Accessed fit method of DummyRegressor!"
`get_metadata_routing`()	Get metadata routing of this object.
`get_params`([deep])	Get parameters for this estimator.
`predict`()	Raises AttributeError: "Accessed predict method of DummyRegressor!"
`score`(X, y[, sample_weight])	Return the coefficient of determination of the prediction.
`set_params`()	Raises AttributeError: "Accessed set_params method of DummyRegressor!"
`set_score_request`(*[, sample_weight])	Request metadata passed to the `score` method.

DMLDummyRegressor.fit()#: Raises AttributeError: “Accessed fit method of DummyRegressor!”

DMLDummyRegressor.get_metadata_routing()#

Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.

Returns:: routing – A MetadataRequest encapsulating routing information.
Return type:: MetadataRequest

DMLDummyRegressor.get_params(deep=True)#

Get parameters for this estimator.

Parameters:: deep (bool, default=True) – If True, will return the parameters for this estimator and contained subobjects that are estimators.
Returns:: params – Parameter names mapped to their values.
Return type:: dict

DMLDummyRegressor.predict()#: Raises AttributeError: “Accessed predict method of DummyRegressor!”

DMLDummyRegressor.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:

X (array-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.
y (array-like of shape (n_samples,) or (n_samples, n_outputs)) – True values for X.
sample_weight (array-like of shape (n_samples,), default=None) – Sample weights.

Returns:

score – $R^2$ of self.predict(X) w.r.t. y.

Return type:

float

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).

DMLDummyRegressor.set_params()#: Raises AttributeError: “Accessed set_params method of DummyRegressor!”

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

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.

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:: sample_weight (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for sample_weight parameter in score.
Returns:: self – The updated object.
Return type:: object