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# 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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import numpy as np
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from miplearn.classifiers import Classifier
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from sklearn.model_selection import cross_val_score
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logger = logging.getLogger(__name__)
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class AdaptiveClassifier(Classifier):
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"""
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A classifier that automatically switches strategies based on the number of
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samples and cross-validation scores.
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"""
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def __init__(self,
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predictor=None,
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min_samples_predict=1,
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min_samples_cv=100,
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thr_fix=0.999,
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thr_alpha=0.50,
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thr_balance=0.95,
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):
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self.min_samples_predict = min_samples_predict
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self.min_samples_cv = min_samples_cv
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self.thr_fix = thr_fix
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self.thr_alpha = thr_alpha
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self.thr_balance = thr_balance
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self.predictor_factory = predictor
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self.predictor = 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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# If number of samples is too small, don't predict anything.
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if n_samples < self.min_samples_predict:
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logger.debug(" Too few samples (%d); always predicting false" % n_samples)
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self.predictor = 0
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return
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# If vast majority of observations are false, always return false.
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y_train_avg = np.average(y_train)
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if y_train_avg <= 1.0 - self.thr_fix:
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logger.debug(" Most samples are negative (%.3f); always returning false" % y_train_avg)
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self.predictor = 0
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return
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# If vast majority of observations are true, always return true.
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if y_train_avg >= self.thr_fix:
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logger.debug(" Most samples are positive (%.3f); always returning true" % y_train_avg)
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self.predictor = 1
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return
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# If classes are too unbalanced, don't predict anything.
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if y_train_avg < (1 - self.thr_balance) or y_train_avg > self.thr_balance:
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logger.debug(" Classes are too unbalanced (%.3f); always returning false" % y_train_avg)
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self.predictor = 0
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return
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# Select ML model if none is provided
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if self.predictor_factory is None:
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if n_samples < 30:
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from sklearn.neighbors import KNeighborsClassifier
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self.predictor_factory = KNeighborsClassifier(n_neighbors=n_samples)
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else:
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from sklearn.pipeline import make_pipeline
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from sklearn.preprocessing import StandardScaler
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from sklearn.linear_model import LogisticRegression
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self.predictor_factory = make_pipeline(StandardScaler(), LogisticRegression())
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# Create predictor
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if callable(self.predictor_factory):
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pred = self.predictor_factory()
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else:
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pred = deepcopy(self.predictor_factory)
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# Skip cross-validation if number of samples is too small
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if n_samples < self.min_samples_cv:
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logger.debug(" Too few samples (%d); skipping cross validation" % n_samples)
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self.predictor = pred
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self.predictor.fit(x_train, y_train)
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return
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# Calculate cross-validation score
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cv_score = np.mean(cross_val_score(pred, x_train, y_train, cv=5))
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dummy_score = max(y_train_avg, 1 - y_train_avg)
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cv_thr = 1. * self.thr_alpha + dummy_score * (1 - self.thr_alpha)
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# If cross-validation score is too low, don't predict anything.
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if cv_score < cv_thr:
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logger.debug(" Score is too low (%.3f < %.3f); always returning false" % (cv_score, cv_thr))
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self.predictor = 0
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else:
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logger.debug(" Score is acceptable (%.3f > %.3f); training classifier" % (cv_score, cv_thr))
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self.predictor = pred
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self.predictor.fit(x_train, y_train)
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def predict_proba(self, x_test):
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if isinstance(self.predictor, int):
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y_pred = np.zeros((x_test.shape[0], 2))
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y_pred[:, self.predictor] = 1.0
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return y_pred
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else:
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return self.predictor.predict_proba(x_test)
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@ -0,0 +1,25 @@
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# 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 ABC, abstractmethod
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class Classifier(ABC):
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@abstractmethod
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def fit(self, x_train, y_train):
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pass
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@abstractmethod
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def predict_proba(self, x_test):
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pass
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class Regressor(ABC):
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@abstractmethod
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def fit(self, x_train, y_train):
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pass
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@abstractmethod
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def predict(self):
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pass
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