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107 lines
3.7 KiB
107 lines
3.7 KiB
# MIPLearn, an extensible framework for Learning-Enhanced Mixed-Integer Optimization
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# Copyright (C) 2019-2020 Argonne National Laboratory. All rights reserved.
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# Written by Alinson S. Xavier <axavier@anl.gov>
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from abc import ABC, abstractmethod
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import numpy as np
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from sklearn.pipeline import make_pipeline
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from sklearn.linear_model import LogisticRegression
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from sklearn.preprocessing import StandardScaler
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from sklearn.model_selection import cross_val_score
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from sklearn.neighbors import KNeighborsClassifier
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class WarmStartPredictor(ABC):
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def __init__(self, thr_clip=[0.50, 0.50]):
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self.models = [None, None]
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self.thr_clip = thr_clip
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def fit(self, x_train, y_train):
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assert isinstance(x_train, np.ndarray)
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assert isinstance(y_train, np.ndarray)
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assert y_train.shape[0] == x_train.shape[0]
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assert y_train.shape[1] == 2
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for i in [0,1]:
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self.models[i] = self._fit(x_train, y_train[:, i], i)
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def predict(self, x_test):
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assert isinstance(x_test, np.ndarray)
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y_pred = np.zeros((x_test.shape[0], 2))
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for i in [0,1]:
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if isinstance(self.models[i], int):
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y_pred[:, i] = self.models[i]
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else:
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y = self.models[i].predict_proba(x_test)[:,1]
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y[y < self.thr_clip[i]] = 0.
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y[y > 0.] = 1.
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y_pred[:, i] = y
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return y_pred.astype(int)
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@abstractmethod
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def _fit(self, x_train, y_train, label):
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pass
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class LogisticWarmStartPredictor(WarmStartPredictor):
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def __init__(self,
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min_samples=100,
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thr_fix=[0.99, 0.99],
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thr_balance=[0.95, 0.95],
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thr_score=[0.95, 0.95]):
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super().__init__()
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self.min_samples = min_samples
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self.thr_fix = thr_fix
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self.thr_balance = thr_balance
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self.thr_score = thr_score
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def _fit(self, x_train, y_train, label):
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y_train_avg = np.average(y_train)
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# If number of samples is too small, don't predict anything.
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if x_train.shape[0] < self.min_samples:
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return 0
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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[label]:
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return 1
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# If dataset is not balanced enough, don't predict anything.
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if y_train_avg < (1 - self.thr_balance[label]) or y_train_avg > self.thr_balance[label]:
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return 0
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reg = make_pipeline(StandardScaler(), LogisticRegression())
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reg_score = np.mean(cross_val_score(reg, x_train, y_train, cv=5))
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# If cross-validation score is too low, don't predict anything.
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if reg_score < self.thr_score[label]:
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return 0
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reg.fit(x_train, y_train.astype(int))
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return reg
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class KnnWarmStartPredictor(WarmStartPredictor):
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def __init__(self, k=50,
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thr_clip=[0.90, 0.90],
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thr_fix=[0.99, 0.99]):
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super().__init__(thr_clip=thr_clip)
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self.k = k
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self.thr_fix = thr_fix
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def _fit(self, x_train, y_train, label):
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y_train_avg = np.average(y_train)
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# If number of training samples is too small, don't predict anything.
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if x_train.shape[0] < self.k:
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return 0
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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[label]:
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return 1
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# If vast majority of observations are false, always return false.
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if y_train_avg < (1 - self.thr_fix[label]):
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return 0
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knn = KNeighborsClassifier(n_neighbors=self.k)
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knn.fit(x_train, y_train)
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return knn |