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50 lines
1.5 KiB
50 lines
1.5 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 abc import abstractmethod, ABC
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import numpy as np
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from sklearn.metrics._ranking import _binary_clf_curve
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class DynamicThreshold(ABC):
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@abstractmethod
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def find(self, clf, x_train, y_train):
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"""
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Given a trained binary classifier `clf` and a training data set,
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returns the numerical threshold (float) satisfying some criterea.
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"""
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pass
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class MinPrecisionThreshold(DynamicThreshold):
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"""
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The smallest possible threshold satisfying a minimum acceptable true
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positive rate (also known as precision).
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"""
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def __init__(self, min_precision):
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self.min_precision = min_precision
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def find(self, clf, x_train, y_train):
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proba = clf.predict_proba(x_train)
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assert isinstance(proba, np.ndarray), "classifier should return numpy array"
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assert proba.shape == (
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x_train.shape[0],
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2,
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), "classifier should return (%d,%d)-shaped array, not %s" % (
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x_train.shape[0],
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2,
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str(proba.shape),
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)
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fps, tps, thresholds = _binary_clf_curve(y_train, proba[:, 1])
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precision = tps / (tps + fps)
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for k in reversed(range(len(precision))):
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if precision[k] >= self.min_precision:
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return thresholds[k]
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return 2.0
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