lale.lib.autogen.calibrated_classifier_cv module

class lale.lib.autogen.calibrated_classifier_cv.CalibratedClassifierCV(*, method='sigmoid', cv=None, n_jobs=None, ensemble=True, estimator=None)

Bases: PlannedIndividualOp

Combined schema for expected data and hyperparameters.

This documentation is auto-generated from JSON schemas.

Parameters

• method (‘sigmoid’ or ‘isotonic’, default ‘sigmoid’) – The method to use for calibration

• cv (union type, default None) –

  Cross-validation as integer or as object that has a split function.

  The fit method performs cross validation on the input dataset for per trial, and uses the mean cross validation performance for optimization. This behavior is also impacted by handle_cv_failure flag. If integer: number of folds in sklearn.model_selection.StratifiedKFold. If object with split function: generator yielding (train, test) splits as arrays of indices. Can use any of the iterators from https://scikit-learn.org/stable/modules/cross_validation.html#cross-validation-iterators.

  • integer, >=1, >=3 for optimizer, <=4 for optimizer, uniform distribution, default 5

  • or Any, not for optimizer

  • or None

  • or ‘prefit’

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

  Number of jobs to run in parallel.

  • None

    1 unless in joblib.parallel_backend context.

  • or -1

    Use all processors.

  • or integer, >=1

    Number of jobs to run in parallel.

• ensemble (boolean, optional, not for optimizer, default True) – Determines how the calibrator is fitted when cv is not ‘prefit’. Ignored if cv=’prefit

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

  The base estimator to fit on random subsets of the dataset.

  • operator

  • or None

    LinearSVC

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 (union type, optional) –

  Sample weights

  • array of items : float

  • or None

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) – The samples.

Returns

result – The predicted class.

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) – The samples.

Returns

result – The predicted probas.

Return type

array of items : array of items : float