lale.lib.autogen.lars_cv module

class lale.lib.autogen.lars_cv.LarsCV(*, fit_intercept=True, verbose=False, max_iter=500, precompute='auto', cv=None, max_n_alphas=1000, n_jobs=1, eps=2.220446049250313e-16, copy_X=True)

Bases: PlannedIndividualOp

Combined schema for expected data and hyperparameters.

This documentation is auto-generated from JSON schemas.

Parameters

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

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

    Sets the verbosity amount

    • boolean

    • or integer

  • max_iter (integer, >=10 for optimizer, <=1000 for optimizer, uniform distribution, default 500) – Maximum number of iterations to perform.

  • precompute (union type, default 'auto') –

    Whether to use a precomputed Gram matrix to speed up calculations

    • array, not for optimizer of items : Any

    • or ‘auto’

  • cv (union type, default None) –

    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

    • or None

  • max_n_alphas (integer, >=1000 for optimizer, <=1001 for optimizer, uniform distribution, default 1000) – The maximum number of points on the path used to compute the residuals in the cross-validation

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

    Number of CPUs to use during the cross validation

    • integer

    • or None

  • eps (float, >=0.001 for optimizer, <=0.1 for optimizer, loguniform distribution, default 2.220446049250313e-16) – The machine-precision regularization in the computation of the Cholesky diagonal factors

  • copy_X (boolean, default True) – If True, X will be copied; else, it may be overwritten.

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