lale.lib.autogen.bayesian_ridge module

class lale.lib.autogen.bayesian_ridge.BayesianRidge(*, n_iter='deprecated', tol=0.001, alpha_1=1e-06, alpha_2=1e-06, lambda_1=1e-06, lambda_2=1e-06, compute_score=False, fit_intercept=True, copy_X=True, verbose=False, max_iter=None)

Bases: PlannedIndividualOp

Combined schema for expected data and hyperparameters.

This documentation is auto-generated from JSON schemas.

Parameters

  • n_iter (union type, default 'deprecated') –

    Deprecated. Use max_iter instead.

    • integer, >=5 for optimizer, <=1000 for optimizer, uniform distribution, default 300

      Maximum number of iterations

    • or ‘deprecated’

  • tol (float, >=1e-08 for optimizer, <=0.01 for optimizer, default 0.001) – Stop the algorithm if w has converged

  • alpha_1 (float, not for optimizer, default 1e-06) – Hyper-parameter : shape parameter for the Gamma distribution prior over the alpha parameter

  • alpha_2 (float, not for optimizer, default 1e-06) – Hyper-parameter : inverse scale parameter (rate parameter) for the Gamma distribution prior over the alpha parameter

  • lambda_1 (float, not for optimizer, default 1e-06) – Hyper-parameter : shape parameter for the Gamma distribution prior over the lambda parameter

  • lambda_2 (float, not for optimizer, default 1e-06) – Hyper-parameter : inverse scale parameter (rate parameter) for the Gamma distribution prior over the lambda parameter

  • compute_score (boolean, default False) – If True, compute the objective function at each step of the model

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

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

  • verbose (boolean, not for optimizer, default False) – Verbose mode when fitting the model.

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

    Maximum number of iterations

    • integer, >=5 for optimizer, <=1000 for optimizer, uniform distribution

    • or None

      Corresponds to 300

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

  • sample_weight (array, optional of items : float) – Individual weights for each sample

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 (array of items : array of items : float) – Samples.

  • return_std (union type, optional, default None) –

    Whether to return the standard deviation of posterior prediction.

    • boolean

    • or None

Returns

result – Predict using the linear model.

Return type

Any