qolmat.imputations.preprocessing.MixteHGBM

class qolmat.imputations.preprocessing.MixteHGBM[source]

MixteHGBM class.

This is a custom scikit-learn estimator implementing a mixed model using HistGradientBoostingClassifier for string target data and HistGradientBoostingRegressor for numeric target data.

__init__()[source]
fit(X: ndarray[tuple[int, ...], dtype[_ScalarType_co]], y: ndarray[tuple[int, ...], dtype[_ScalarType_co]]) → MixteHGBM[source]

Fit the model according to the given training data.

Parameters
X{array-like, sparse matrix}, shape (n_samples, n_features)

Training vectors.

yarray-like, shape (n_samples,)

Target values.

Returns
selfobject

Returns self.

predict(X: ndarray[tuple[int, ...], dtype[_ScalarType_co]]) → ndarray[tuple[int, ...], dtype[_ScalarType_co]][source]

Predict using the fitted model.

Parameters
X{array-like, sparse matrix}, shape (n_samples, n_features)

Samples.

Returns
y_predarray-like, shape (n_samples,)

Predicted target values.

set_model_parameters(**args_model)[source]

Set the arguments of the underlying model.

Parameters
**args_modeldict

Additional keyword arguments to be passed to the underlying models.

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

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

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.