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67 lines
2.4 KiB
67 lines
2.4 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 typing import Any, Dict
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from sklearn.linear_model import LogisticRegression
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from sklearn.neighbors import KNeighborsClassifier
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from sklearn.pipeline import make_pipeline
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from sklearn.preprocessing import StandardScaler
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from miplearn.classifiers import Classifier
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from miplearn.classifiers.counting import CountingClassifier
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from miplearn.classifiers.evaluator import ClassifierEvaluator
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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 its cross-validation score on a particular training data set.
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"""
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def __init__(
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self,
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candidates: Dict[str, Any] = None,
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evaluator: ClassifierEvaluator = ClassifierEvaluator(),
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) -> None:
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if candidates is None:
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candidates = {
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"knn(100)": {
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"classifier": KNeighborsClassifier(n_neighbors=100),
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"min samples": 100,
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},
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"logistic": {
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"classifier": make_pipeline(StandardScaler(), LogisticRegression()),
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"min samples": 30,
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},
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"counting": {
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"classifier": CountingClassifier(),
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"min samples": 0,
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},
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}
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self.candidates = candidates
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self.evaluator = evaluator
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self.classifier = None
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def fit(self, x_train, y_train):
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best_name, best_clf, best_score = None, None, -float("inf")
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n_samples = x_train.shape[0]
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for (name, clf_dict) in self.candidates.items():
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if n_samples < clf_dict["min samples"]:
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continue
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clf = deepcopy(clf_dict["classifier"])
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clf.fit(x_train, y_train)
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score = self.evaluator.evaluate(clf, x_train, y_train)
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if score > best_score:
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best_name, best_clf, best_score = name, clf, score
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logger.debug("Best classifier: %s (score=%.3f)" % (best_name, best_score))
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self.classifier = best_clf
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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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