IsotonicRegression

class pycalib.calibration_methods.IsotonicRegression(out_of_bounds='clip')

Bases: pycalib.calibration_methods.CalibrationMethod

Probability calibration using Isotonic Regression

Isotonic regression 1 2 is a non-parametric approach to mapping (non-probabilistic) classifier scores to probabilities. It assumes an isotonic (non-decreasing) relationship between classifier scores and probabilities.

Parameters

out_of_bounds (string, optional, default: "clip") – The out_of_bounds parameter handles how x-values outside of the training domain are handled. When set to “nan”, predicted y-values will be NaN. When set to “clip”, predicted y-values will be set to the value corresponding to the nearest train interval endpoint. When set to “raise”, allow interp1d to throw ValueError.

References

1

Transforming Classifier Scores into Accurate Multiclass Probability Estimates, B. Zadrozny & C. Elkan, (KDD 2002)

2

Predicting Good Probabilities with Supervised Learning, A. Niculescu-Mizil & R. Caruana, ICML 2005

Methods Summary

fit(X, y[, n_jobs])	Fit the calibration method based on the given uncalibrated class probabilities X and ground truth labels y.
get_params([deep])	Get parameters for this estimator.
plot(filename[, xlim])	Plot the calibration map.
predict(X)	Predict the class of new samples after scaling.
predict_proba(X)	Compute calibrated posterior probabilities for a given array of posterior probabilities from an arbitrary classifier.
set_params(**params)	Set the parameters of this estimator.

Methods Documentation

fit(X, y, n_jobs=None)

Fit the calibration method based on the given uncalibrated class probabilities X and ground truth labels y.

Parameters

• X (array-like, shape (n_samples, n_classes)) – Training data, i.e. predicted probabilities of the base classifier on the calibration set.

• y (array-like, shape (n_samples,)) – Target classes.

• 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.

Returns

self – Returns an instance of self.

Return type

object

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

plot(filename, xlim=[0, 1], **kwargs)

Plot the calibration map.

Parameters

• xlim (array-like) – Range of inputs of the calibration map to be plotted.

• **kwargs – Additional arguments passed on to matplotlib.plot().

predict(X)

Predict the class of new samples after scaling. Predictions are identical to the ones from the uncalibrated classifier.

Parameters

X (array-like, shape (n_samples, n_classes)) – The uncalibrated posterior probabilities.

Returns

C – The predicted classes.

Return type

array, shape (n_samples,)

predict_proba(X)

Compute calibrated posterior probabilities for a given array of posterior probabilities from an arbitrary classifier.

Parameters

X (array-like, shape (n_samples, n_classes)) – The uncalibrated posterior probabilities.

Returns

P – The predicted probabilities.

Return type

array, 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