Mordicus.Core.BasicAlgorithms.ScikitLearnRegressor module¶
- Mordicus.Core.BasicAlgorithms.ScikitLearnRegressor.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)
- Mordicus.Core.BasicAlgorithms.ScikitLearnRegressor.GridSearchCVRegression(regressor, paramGrid, X, y)[source]¶
Optimizes a scikit learn regressor using gridSearchCV, using training data and target values
- Parameters
regressor (objects satisfying the scikit-learn regressors API) – input regressor to be fitted and optimized
paramGrid (float) – the truncation tolerence, determining the number of keps eigenvalues
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