lale.lib.autogen.orthogonal_matching_pursuit_cv module

class lale.lib.autogen.orthogonal_matching_pursuit_cv.OrthogonalMatchingPursuitCV(*, copy=True, fit_intercept=True, max_iter=None, cv, n_jobs=1, verbose=False)

Bases: PlannedIndividualOp

Combined schema for expected data and hyperparameters.

This documentation is auto-generated from JSON schemas.

Parameters

• copy (boolean, default True) – Whether the design matrix X must be copied by the algorithm

• fit_intercept (boolean, default True) – whether to calculate the intercept for this model

• max_iter (union type, default None) –

  Maximum numbers of iterations to perform, therefore maximum features to include

  • integer, >=10 for optimizer, <=1000 for optimizer, uniform distribution

  • or None

• cv (union type) –

  Cross-validation as integer or as object that has a split function.

  The fit method performs cross validation on the input dataset for per trial, and uses the mean cross validation performance for optimization. This behavior is also impacted by handle_cv_failure flag. If integer: number of folds in sklearn.model_selection.StratifiedKFold. If object with split function: generator yielding (train, test) splits as arrays of indices. Can use any of the iterators from https://scikit-learn.org/stable/modules/cross_validation.html#cross-validation-iterators.

  • integer, >=1, >=3 for optimizer, <=4 for optimizer, uniform distribution, default 5

  • or Any, not for optimizer

• n_jobs (union type, not for optimizer, default 1) –

  Number of CPUs to use during the cross validation

  • integer

  • or None

• verbose (union type, not for optimizer, default False) –

  Sets the verbosity amount

  • boolean

  • or integer

Notes

constraint-1 : any type

fit(X, y=None, **fit_params)

Train the operator.

Note: The fit method is not available until this operator is trainable.

Once this method is available, it will have the following signature:

Parameters

• X (array of items : array of items : float) – Training data.

• y (array of items : float) – Target values

predict(X, **predict_params)

Make predictions.

Note: The predict method is not available until this operator is trained.

Once this method is available, it will have the following signature:

Parameters

X (union type) –

Samples.

• array of items : Any

• or array of items : array of items : float

Returns

result – Returns predicted values.

Return type

array of items : float