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62 lines
2.2 KiB
62 lines
2.2 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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import logging
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from copy import deepcopy
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from miplearn.classifiers import Classifier
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from miplearn.classifiers.counting import CountingClassifier
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from sklearn.linear_model import LogisticRegression
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from sklearn.pipeline import make_pipeline
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from sklearn.preprocessing import StandardScaler
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logger = logging.getLogger(__name__)
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class AdaptiveClassifier(Classifier):
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"""
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A meta-classifier which dynamically selects what actual classifier to use
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based on the number of samples in the training data.
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By default, uses CountingClassifier for less than 30 samples and
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LogisticRegression (with standard scaling) for 30 or more samples.
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"""
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def __init__(self, classifiers=None):
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"""
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Initializes the classifier.
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The `classifiers` argument must be a list of tuples where the second element
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of the tuple is the classifier and the first element is the number of
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samples required. For example, if `classifiers` is set to
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```
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[(100, ClassifierA()),
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(50, ClassifierB()),
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(0, ClassifierC())]
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``` then ClassifierA will be used if n_samples >= 100, ClassifierB will
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be used if 100 > n_samples >= 50 and ClassifierC will be used if
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50 > n_samples. The list must be ordered in (strictly) decreasing order.
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"""
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if classifiers is None:
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classifiers = [
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(30, make_pipeline(StandardScaler(), LogisticRegression())),
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(0, CountingClassifier())
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]
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self.available_classifiers = classifiers
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self.classifier = None
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def fit(self, x_train, y_train):
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n_samples = x_train.shape[0]
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for (min_samples, clf_prototype) in self.available_classifiers:
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if n_samples >= min_samples:
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self.classifier = deepcopy(clf_prototype)
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self.classifier.fit(x_train, y_train)
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break
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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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