lale.lib.autogen.elastic_net module

class lale.lib.autogen.elastic_net.ElasticNet(*, alpha=1.0, l1_ratio=0.5, fit_intercept=True, precompute=False, max_iter=1000, copy_X=True, tol=0.0001, warm_start=False, positive=False, random_state=None, selection='cyclic')

Bases: PlannedIndividualOp

Combined schema for expected data and hyperparameters.

This documentation is auto-generated from JSON schemas.

Parameters

  • alpha (float, >=1e-10 for optimizer, <=1.0 for optimizer, loguniform distribution, default 1.0) – Constant that multiplies the penalty terms

  • l1_ratio (float, not for optimizer, default 0.5) – The ElasticNet mixing parameter, with 0 <= l1_ratio <= 1

  • fit_intercept (boolean, default True) – Whether the intercept should be estimated or not

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

    Whether to use a precomputed Gram matrix to speed up calculations

    • array of items : Any

    • or boolean

  • max_iter (integer, >=10 for optimizer, <=1000 for optimizer, uniform distribution, default 1000) – The maximum number of iterations

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

  • tol (float, >=1e-08 for optimizer, <=0.01 for optimizer, default 0.0001) – The tolerance for the optimization: if the updates are smaller than tol, the optimization code checks the dual gap for optimality and continues until it is smaller than tol.

  • warm_start (boolean, not for optimizer, default False) – When set to True, reuse the solution of the previous call to fit as initialization, otherwise, just erase the previous solution

  • positive (boolean, default False) – When set to True, forces the coefficients to be positive.

  • random_state (union type, not for optimizer, default None) –

    The seed of the pseudo random number generator that selects a random feature to update

    • integer

    • or numpy.random.RandomState

    • or None

  • selection (‘random’ or ‘cyclic’, default ‘cyclic’) – If set to ‘random’, a random coefficient is updated every iteration rather than looping over features sequentially by default

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 (Any) – Data

  • y (Any) – Target

  • check_input (boolean, optional, default True) – Allow to bypass several input checking

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