lale.lib.autogen.lasso_lars_ic module

class lale.lib.autogen.lasso_lars_ic.LassoLarsIC(*, criterion='aic', fit_intercept=True, verbose=False, precompute='auto', max_iter=500, eps=2.220446049250313e-16, copy_X=True, positive=False)

Bases: PlannedIndividualOp

Combined schema for expected data and hyperparameters.

This documentation is auto-generated from JSON schemas.

Parameters

• criterion (‘bic’ or ‘aic’, default ‘aic’) – The type of criterion to use.

• 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

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

  Whether to use a precomputed Gram matrix to speed up calculations

  • array, not for optimizer of items : Any

  • or boolean

  • or ‘auto’

  See also constraint-2, constraint-3.

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

• 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.

• positive (boolean, default False) – Restrict coefficients to be >= 0

Notes

constraint-1 : any type

constraint-2 : union type

X cannot be None if Gram is not NoneUse lars_path_gram to avoid passing X and y.)

• any type

• or precompute : None

constraint-3 : union type

From /linear_model/_least_angle.py:None:_lars_path_solver, Exception: raise ValueError(‘X and Gram cannot both be unspecified.’)

• precompute : negated type of None or False

• or 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

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

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