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29 lines
997 B
29 lines
997 B
# 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 miplearn.classifiers import Classifier
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import numpy as np
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class CountingClassifier(Classifier):
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"""
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A classifier that generates constant predictions, based only on the
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frequency of the training labels. For example, if y_train is [1.0, 0.0, 0.0]
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this classifier always returns [0.66 0.33] for any x_test. It essentially
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counts how many times each label appeared, hence the name.
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"""
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def __init__(self):
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self.mean = None
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def fit(self, x_train, y_train):
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self.mean = np.mean(y_train)
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def predict_proba(self, x_test):
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return np.array([[1 - self.mean, self.mean]
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for _ in range(x_test.shape[0])])
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def __repr__(self):
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return "CountingClassifier(mean=%s)" % self.mean
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