216 lines
10 KiB
Python
216 lines
10 KiB
Python
from copy import deepcopy
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from abstention.calibration import NoBiasVectorScaling, TempScaling, VectorScaling
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from sklearn.base import BaseEstimator, clone
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from sklearn.model_selection import cross_val_predict, train_test_split
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import numpy as np
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# Wrappers of calibration defined by Alexandari et al. in paper <http://proceedings.mlr.press/v119/alexandari20a.html>
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# requires "pip install abstension"
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# see https://github.com/kundajelab/abstention
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class RecalibratedProbabilisticClassifier:
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"""
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Abstract class for (re)calibration method from `abstention.calibration`, as defined in
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`Alexandari, A., Kundaje, A., & Shrikumar, A. (2020, November). Maximum likelihood with bias-corrected calibration
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is hard-to-beat at label shift adaptation. In International Conference on Machine Learning (pp. 222-232). PMLR.
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<http://proceedings.mlr.press/v119/alexandari20a.html>`_:
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"""
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pass
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class RecalibratedProbabilisticClassifierBase(BaseEstimator, RecalibratedProbabilisticClassifier):
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"""
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Applies a (re)calibration method from `abstention.calibration`, as defined in
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`Alexandari et al. paper <http://proceedings.mlr.press/v119/alexandari20a.html>`_:
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:param classifier: a scikit-learn probabilistic classifier
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:param calibrator: the calibration object (an instance of abstention.calibration.CalibratorFactory)
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:param val_split: indicate an integer k for performing kFCV to obtain the posterior probabilities, or a float p
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in (0,1) to indicate that the posteriors are obtained in a stratified validation split containing p% of the
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training instances (the rest is used for training). In any case, the classifier is retrained in the whole
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training set afterwards. Default value is 5.
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:param n_jobs: indicate the number of parallel workers (only when val_split is an integer); default=None
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:param verbose: whether or not to display information in the standard output
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"""
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def __init__(self, classifier, calibrator, val_split=5, n_jobs=None, verbose=False):
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self.classifier = classifier
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self.calibrator = calibrator
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self.val_split = val_split
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self.n_jobs = n_jobs
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self.verbose = verbose
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def fit(self, X, y):
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"""
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Fits the calibration for the probabilistic classifier.
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:param X: array-like of shape `(n_samples, n_features)` with the data instances
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:param y: array-like of shape `(n_samples,)` with the class labels
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:return: self
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"""
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k = self.val_split
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if isinstance(k, int):
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if k < 2:
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raise ValueError('wrong value for val_split: the number of folds must be > 2')
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return self.fit_cv(X, y)
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elif isinstance(k, float):
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if not (0 < k < 1):
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raise ValueError('wrong value for val_split: the proportion of validation documents must be in (0,1)')
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return self.fit_cv(X, y)
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def fit_cv(self, X, y):
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"""
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Fits the calibration in a cross-validation manner, i.e., it generates posterior probabilities for all
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training instances via cross-validation, and then retrains the classifier on all training instances.
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The posterior probabilities thus generated are used for calibrating the outputs of the classifier.
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:param X: array-like of shape `(n_samples, n_features)` with the data instances
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:param y: array-like of shape `(n_samples,)` with the class labels
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:return: self
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"""
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posteriors = cross_val_predict(
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self.classifier, X, y, cv=self.val_split, n_jobs=self.n_jobs, verbose=self.verbose, method='predict_proba'
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)
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self.classifier.fit(X, y)
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nclasses = len(np.unique(y))
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self.calibration_function = self.calibrator(posteriors, np.eye(nclasses)[y], posterior_supplied=True)
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return self
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def fit_tr_val(self, X, y):
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"""
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Fits the calibration in a train/val-split manner, i.e.t, it partitions the training instances into a
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training and a validation set, and then uses the training samples to learn classifier which is then used
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to generate posterior probabilities for the held-out validation data. These posteriors are used to calibrate
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the classifier. The classifier is not retrained on the whole dataset.
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:param X: array-like of shape `(n_samples, n_features)` with the data instances
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:param y: array-like of shape `(n_samples,)` with the class labels
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:return: self
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"""
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Xtr, Xva, ytr, yva = train_test_split(X, y, test_size=self.val_split, stratify=y)
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self.classifier.fit(Xtr, ytr)
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posteriors = self.classifier.predict_proba(Xva)
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nclasses = len(np.unique(yva))
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self.calibrator = self.calibrator(posteriors, np.eye(nclasses)[yva], posterior_supplied=True)
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return self
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def predict(self, X):
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"""
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Predicts class labels for the data instances in `X`
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:param X: array-like of shape `(n_samples, n_features)` with the data instances
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:return: array-like of shape `(n_samples,)` with the class label predictions
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"""
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return self.classifier.predict(X)
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def predict_proba(self, X):
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"""
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Generates posterior probabilities for the data instances in `X`
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:param X: array-like of shape `(n_samples, n_features)` with the data instances
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:return: array-like of shape `(n_samples, n_classes)` with posterior probabilities
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"""
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posteriors = self.classifier.predict_proba(X)
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return self.calibration_function(posteriors)
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@property
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def classes_(self):
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"""
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Returns the classes on which the classifier has been trained on
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:return: array-like of shape `(n_classes)`
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"""
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return self.classifier.classes_
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class NBVSCalibration(RecalibratedProbabilisticClassifierBase):
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"""
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Applies the No-Bias Vector Scaling (NBVS) calibration method from `abstention.calibration`, as defined in
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`Alexandari et al. paper <http://proceedings.mlr.press/v119/alexandari20a.html>`_:
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:param classifier: a scikit-learn probabilistic classifier
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:param val_split: indicate an integer k for performing kFCV to obtain the posterior prevalences, or a float p
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in (0,1) to indicate that the posteriors are obtained in a stratified validation split containing p% of the
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training instances (the rest is used for training). In any case, the classifier is retrained in the whole
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training set afterwards. Default value is 5.
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:param n_jobs: indicate the number of parallel workers (only when val_split is an integer)
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:param verbose: whether or not to display information in the standard output
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"""
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def __init__(self, classifier, val_split=5, n_jobs=None, verbose=False):
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self.classifier = classifier
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self.calibrator = NoBiasVectorScaling(verbose=verbose)
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self.val_split = val_split
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self.n_jobs = n_jobs
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self.verbose = verbose
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class BCTSCalibration(RecalibratedProbabilisticClassifierBase):
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"""
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Applies the Bias-Corrected Temperature Scaling (BCTS) calibration method from `abstention.calibration`, as defined in
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`Alexandari et al. paper <http://proceedings.mlr.press/v119/alexandari20a.html>`_:
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:param classifier: a scikit-learn probabilistic classifier
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:param val_split: indicate an integer k for performing kFCV to obtain the posterior prevalences, or a float p
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in (0,1) to indicate that the posteriors are obtained in a stratified validation split containing p% of the
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training instances (the rest is used for training). In any case, the classifier is retrained in the whole
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training set afterwards. Default value is 5.
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:param n_jobs: indicate the number of parallel workers (only when val_split is an integer)
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:param verbose: whether or not to display information in the standard output
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"""
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def __init__(self, classifier, val_split=5, n_jobs=None, verbose=False):
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self.classifier = classifier
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self.calibrator = TempScaling(verbose=verbose, bias_positions='all')
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self.val_split = val_split
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self.n_jobs = n_jobs
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self.verbose = verbose
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class TSCalibration(RecalibratedProbabilisticClassifierBase):
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"""
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Applies the Temperature Scaling (TS) calibration method from `abstention.calibration`, as defined in
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`Alexandari et al. paper <http://proceedings.mlr.press/v119/alexandari20a.html>`_:
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:param classifier: a scikit-learn probabilistic classifier
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:param val_split: indicate an integer k for performing kFCV to obtain the posterior prevalences, or a float p
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in (0,1) to indicate that the posteriors are obtained in a stratified validation split containing p% of the
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training instances (the rest is used for training). In any case, the classifier is retrained in the whole
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training set afterwards. Default value is 5.
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:param n_jobs: indicate the number of parallel workers (only when val_split is an integer)
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:param verbose: whether or not to display information in the standard output
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"""
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def __init__(self, classifier, val_split=5, n_jobs=None, verbose=False):
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self.classifier = classifier
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self.calibrator = TempScaling(verbose=verbose)
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self.val_split = val_split
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self.n_jobs = n_jobs
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self.verbose = verbose
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class VSCalibration(RecalibratedProbabilisticClassifierBase):
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"""
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Applies the Vector Scaling (VS) calibration method from `abstention.calibration`, as defined in
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`Alexandari et al. paper <http://proceedings.mlr.press/v119/alexandari20a.html>`_:
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:param classifier: a scikit-learn probabilistic classifier
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:param val_split: indicate an integer k for performing kFCV to obtain the posterior prevalences, or a float p
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in (0,1) to indicate that the posteriors are obtained in a stratified validation split containing p% of the
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training instances (the rest is used for training). In any case, the classifier is retrained in the whole
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training set afterwards. Default value is 5.
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:param n_jobs: indicate the number of parallel workers (only when val_split is an integer)
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:param verbose: whether or not to display information in the standard output
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"""
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def __init__(self, classifier, val_split=5, n_jobs=None, verbose=False):
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self.classifier = classifier
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self.calibrator = VectorScaling(verbose=verbose)
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self.val_split = val_split
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self.n_jobs = n_jobs
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self.verbose = verbose
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