NoCalibration¶
-
class
pycalib.calibration_methods.NoCalibration(logits=False)[source]¶ Bases:
pycalib.calibration_methods.CalibrationMethodA class that performs no calibration.
This class can be used as a baseline for benchmarking.
- logitsbool, default=False
Are the inputs for calibration logits (e.g. from a neural network)?
Methods Summary
fit(X, y)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.
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)[source]¶ 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.
- Returns
self – Returns an instance of self.
- Return type
-
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
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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,)
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predict_proba(X)[source]¶ 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)
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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.