lale.lib.autogen.gaussian_process_classifier module

class lale.lib.autogen.gaussian_process_classifier.GaussianProcessClassifier(*, kernel=None, optimizer='fmin_l_bfgs_b', n_restarts_optimizer=0, max_iter_predict=100, warm_start=False, copy_X_train=True, random_state=None, multi_class='one_vs_rest', n_jobs=1)

Bases: PlannedIndividualOp

Combined schema for expected data and hyperparameters.

This documentation is auto-generated from JSON schemas.

Parameters

  • kernel (None, not for optimizer, default None) – The kernel specifying the covariance function of the GP

  • optimizer (union type, default 'fmin_l_bfgs_b') –

    Can either be one of the internally supported optimizers for optimizing the kernel’s parameters, specified by a string, or an externally defined optimizer passed as a callable

    • callable, not for optimizer

    • or ‘fmin_l_bfgs_b’

  • n_restarts_optimizer (integer, >=0 for optimizer, <=1 for optimizer, uniform distribution, default 0) – The number of restarts of the optimizer for finding the kernel’s parameters which maximize the log-marginal likelihood

  • max_iter_predict (integer, >=100 for optimizer, <=101 for optimizer, uniform distribution, default 100) – The maximum number of iterations in Newton’s method for approximating the posterior during predict

  • warm_start (boolean, not for optimizer, default False) – If warm-starts are enabled, the solution of the last Newton iteration on the Laplace approximation of the posterior mode is used as initialization for the next call of _posterior_mode()

  • copy_X_train (boolean, not for optimizer, default True) – If True, a persistent copy of the training data is stored in the object

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

    The generator used to initialize the centers

    • integer

    • or numpy.random.RandomState

    • or None

  • multi_class (‘one_vs_one’ or ‘one_vs_rest’, default ‘one_vs_rest’) – Specifies how multi-class classification problems are handled

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

    The number of jobs to use for the computation

    • integer

    • 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 (array of items : float) – Target values, must be binary

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) –

Returns

result – Predicted target values for X, values are from classes_

Return type

array of items : float

predict_proba(X)

Probability estimates for all classes.

Note: The predict_proba 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) –

Returns

result – Returns the probability of the samples for each class in the model

Return type

array of items : array of items : float