lale.lib.autogen.lars module

class lale.lib.autogen.lars.Lars(*, fit_intercept=True, verbose=False, precompute='auto', n_nonzero_coefs=500, eps=2.220446049250313e-16, copy_X=True, fit_path=True, jitter=None, random_state=None)

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

  • 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’

  • n_nonzero_coefs (integer, >=500 for optimizer, <=501 for optimizer, uniform distribution, default 500) – Target number of non-zero coefficients

  • 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.

  • fit_path (boolean, default True) – If True the full path is stored in the coef_path_ attribute

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

    Upper bound on a uniform noise parameter to be added to the y values, to satisfy the model’s assumption of one-at-a-time computations

    • float

    • or None

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

    The seed of the pseudo random number generator to use when shuffling the data

    • integer

    • or numpy.random.RandomState

    • or None

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 (union type) –

    Target values.

    • array of items : float

    • or array of items : array of items : float

  • Xy (Any, optional) – Xy = np.dot(X.T, y) that can be precomputed

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