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87 lines
3.0 KiB
87 lines
3.0 KiB
# MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
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# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
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# Released under the modified BSD license. See COPYING.md for more details.
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from copy import deepcopy
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import numpy as np
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from miplearn.classifiers import Classifier
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from sklearn.dummy import DummyClassifier
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from sklearn.linear_model import LogisticRegression
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from sklearn.model_selection import cross_val_score
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import logging
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logger = logging.getLogger(__name__)
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class CrossValidatedClassifier(Classifier):
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"""
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A meta-classifier that, upon training, evaluates the performance of another
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classifier on the training data set using k-fold cross validation, then
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either adopts the other classifier it if the cv-score is high enough, or
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returns a constant label for every x_test otherwise.
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The threshold is specified in comparison to a dummy classifier trained
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on the same dataset. For example, a threshold of 0.0 indicates that any
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classifier as good as the dummy predictor is acceptable. A threshold of 1.0
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indicates that only classifier with a perfect cross-validation score are
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acceptable. Other numbers are a linear interpolation of these two extremes.
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"""
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def __init__(
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self,
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classifier=LogisticRegression(),
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threshold=0.75,
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constant=0.0,
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cv=5,
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scoring="accuracy",
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):
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self.classifier = None
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self.classifier_prototype = classifier
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self.constant = constant
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self.threshold = threshold
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self.cv = cv
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self.scoring = scoring
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def fit(self, x_train, y_train):
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# Calculate dummy score and absolute score threshold
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y_train_avg = np.average(y_train)
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dummy_score = max(y_train_avg, 1 - y_train_avg)
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absolute_threshold = 1.0 * self.threshold + dummy_score * (1 - self.threshold)
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# Calculate cross validation score and decide which classifier to use
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clf = deepcopy(self.classifier_prototype)
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cv_score = float(
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np.mean(
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cross_val_score(
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clf,
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x_train,
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y_train,
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cv=self.cv,
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scoring=self.scoring,
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)
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)
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)
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if cv_score >= absolute_threshold:
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logger.debug(
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"cv_score is above threshold (%.2f >= %.2f); keeping"
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% (cv_score, absolute_threshold)
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)
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self.classifier = clf
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else:
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logger.debug(
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"cv_score is below threshold (%.2f < %.2f); discarding"
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% (cv_score, absolute_threshold)
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)
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self.classifier = DummyClassifier(
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strategy="constant",
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constant=self.constant,
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)
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# Train chosen classifier
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self.classifier.fit(x_train, y_train)
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def predict_proba(self, x_test):
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return self.classifier.predict_proba(x_test)
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