OneVsRestCalibrator¶

class pycalib.calibration_methods.OneVsRestCalibrator(calibrator, n_jobs=None)[source]¶

Bases: sklearn.base.BaseEstimator

One-vs-the-rest (OvR) multiclass strategy Also known as one-vs-all, this strategy consists in fitting one calibrator per class. The probabilities to be calibrated of the other classes are summed. For each calibrator, the class is fitted against all the other classes.

Parameters

calibrator (CalibrationMethod object) – A CalibrationMethod object implementing fit and predict_proba.
n_jobs (int or None, optional (default=None)) – The number of jobs to use for the computation. None means 1 unless in a joblib.parallel_backend context. -1 means using all processors. for more details.

calibrators_¶

Estimators used for predictions.

Type: list of n_classes estimators

classes_¶

Class labels.

Type: array, shape = [n_classes]

label_binarizer_¶

Object used to transform multiclass labels to binary labels and vice-versa.

Type: LabelBinarizer object

Attributes Summary

n_classes_

Methods Summary

`fit`(X, y)	Fit underlying estimators.
`get_params`([deep])	Get parameters for this estimator.
`predict_proba`(X)	Probability estimates.
`set_params`(**params)	Set the parameters of this estimator.

Attributes Documentation

n_classes_¶

Methods Documentation

fit(X, y)[source]¶

Fit underlying estimators. :param X: Calibration data. :type X: (sparse) array-like, shape = [n_samples, n_features] :param y: Multi-class labels. :type y: (sparse) array-like, shape = [n_samples, ]

Returns
Return type: self

get_params(deep=True)¶

Get parameters for this estimator.

Parameters: deep (bool, default=True) – If True, will return the parameters for this estimator and contained subobjects that are estimators.
Returns: params – Parameter names mapped to their values.
Return type: mapping of string to any

predict_proba(X)[source]¶

Probability estimates.

The returned estimates for all classes are ordered by label of classes.

Parameters: X (array-like, shape = [n_samples, n_features]) –
Returns: T – Returns the probability of the sample for each class in the model, where classes are ordered as they are in self.classes_.
Return type: (sparse) array-like, shape = [n_samples, n_classes]

set_params(**params)¶

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as pipelines). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Parameters: **params (dict) – Estimator parameters.
Returns: self – Estimator instance.
Return type: object