Mordicus.BasicAlgorithms.ScikitLearnRegressor module

ComputeRegressionApproximation(model, scalerX, scalery, XTest)[source]

Computes the prediction of the Regressor,taking into account prelearned scalers for input and output

Parameters:

  • model (sklearn.model_selection._search.GridSearchCV) – trained and optimized scikit learn regressor

  • scalerX (sklearn.preprocessing._data.StandardScaler) – scaler trained on input data

  • scalery (sklearn.preprocessing._data.StandardScaler) – scaler trained on output data

  • XTest (np.ndarray) – testing data

Returns:

  • np.ndarray – kept eigenvalues, of size (numberOfEigenvalues)

  • np.ndarray – kept eigenvectors, of size (numberOfEigenvalues, numberOfSnapshots)

GridSearchCVRegression(regressor, paramGrid, X, y)[source]

Optimizes a scikit learn regressor using gridSearchCV, using training data and target values

Parameters:

  • regressor (object satisfying the scikit-learn regressors API) – input regressor to be fitted and optimized

  • paramGrid (dict) – of lists (of floats) containing hyperparameter values of the considered regressor

  • X (np.ndarray) – training data

  • y (np.ndarray) – target values

Returns:

  • sklearn.model_selection._search.GridSearchCV – trained and optimized scikit learn regressor

  • sklearn.preprocessing._data.StandardScaler – scaler trained on input data

  • sklearn.preprocessing._data.StandardScaler – scaler trained on output data

class MyGPR(kernel)[source]

Bases: GaussianProcessRegressor

Customization of scikit-learn’s GaussianProcessRegressor

set_predict_request(*, return_cov: bool | None | str = '$UNCHANGED$', return_std: bool | None | str = '$UNCHANGED$') → MyGPR

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.

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:

  • return_cov (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for return_cov parameter in predict.

  • return_std (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for return_std parameter in predict.

Returns:

self – The updated object.

Return type:

object

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

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