lale.lib.sklearn.passive_aggressive_classifier module

class lale.lib.sklearn.passive_aggressive_classifier.PassiveAggressiveClassifier(*, C=1.0, fit_intercept=False, max_iter=1000, tol=None, early_stopping=False, validation_fraction=0.1, n_iter_no_change=5, shuffle=True, verbose=0, loss='hinge', n_jobs=None, random_state=None, warm_start=False, class_weight=None, average=False)

Bases: PlannedIndividualOp

Passive aggressive classifier from scikit-learn.

This documentation is auto-generated from JSON schemas.

Parameters

  • C (float, >=1e-05 for optimizer, <=10 for optimizer, loguniform distribution, default 1.0) – Maximum step size (regularization). Defaults to 1.0.

  • fit_intercept (boolean, default False) – Whether the intercept should be estimated or not. If False, thethe data is assumed to be already centered.

  • max_iter (integer, >=5 for optimizer, <=1000 for optimizer, uniform distribution, default 1000) – The maximum number of passes over the training data (aka epochs).

  • tol (union type, default None) –

    The stopping criterion. If it is not None, the iterations will stop

    • float, >=1e-08 for optimizer, <=0.01 for optimizer

    • or None

  • early_stopping (boolean, default False) – Whether to use early stopping to terminate training when validation.

  • validation_fraction (float, >=0, <=1, optional, not for optimizer, default 0.1) – The proportion of training data to set aside as validation set for early stopping.

  • n_iter_no_change (integer, >=5 for optimizer, <=10 for optimizer, optional, not for optimizer, default 5) – Number of iterations with no improvement to wait before early stopping.

  • shuffle (boolean, default True) – Whether or not the training data should be shuffled after each epoch.

  • verbose (union type, optional, not for optimizer, default 0) –

    The verbosity level

    • integer

    • or None

  • loss (‘hinge’ or ‘squared_hinge’, default ‘hinge’) – The loss function to be used:

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

    The number of CPUs to use to do the OVA (One Versus All, for

    • integer

    • or None

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

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

    • integer

    • or numpy.random.RandomState

    • or None

  • warm_start (boolean, optional, 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.

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

    Preset for the class_weight fit parameter.

    • dict

    • or ‘balanced’ or None

  • average (union type, default False) –

    When set to True, computes the averaged SGD weights and stores the result in the coef_ attribute.

    • boolean

    • or integer, not for optimizer

decision_function(X)

Confidence scores for all classes.

Note: The decision_function 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 – Confidence scores for samples for each class in the model.

  • array of items : array of items : float

    In the multi-way case, score per (sample, class) combination.

  • or array of items : float

    In the binary case, score for self._classes[1].

Return type

union 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) –

  • y (union type) –

    • array of items : string

    • or array of items : float

    • or array of items : boolean

  • coef_init (array, optional of items : array of items : float) – The initial coefficients to warm-start the optimization.

  • intercept_init (array, optional of items : float) – The initial intercept to warm-start the optimization.

partial_fit(X, y=None, **fit_params)

Incremental fit to train train the operator on a batch of samples.

Note: The partial_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) –

  • y (union type) –

    • array of items : string

    • or array of items : float

    • or array of items : boolean

  • classes (union type, optional) –

    • array of items : string

    • or array of items : float

    • or array of items : boolean

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 –

  • array of items : string

  • or array of items : float

  • or array of items : boolean

Return type

union type