You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
44 lines
1.6 KiB
44 lines
1.6 KiB
# MIPLearn: A Machine-Learning Framework for Mixed-Integer Optimization
|
|
# Copyright (C) 2019-2020 Argonne National Laboratory. All rights reserved.
|
|
# Written by Alinson S. Xavier <axavier@anl.gov>
|
|
|
|
import numpy as np
|
|
from sklearn.pipeline import make_pipeline
|
|
from sklearn.linear_model import LogisticRegression
|
|
from sklearn.preprocessing import StandardScaler
|
|
|
|
|
|
class WarmStartPredictor:
|
|
def __init__(self,
|
|
thr_fix_zero=0.05,
|
|
thr_fix_one=0.95,
|
|
thr_predict=0.95):
|
|
self.model = None
|
|
self.thr_predict = thr_predict
|
|
self.thr_fix_zero = thr_fix_zero
|
|
self.thr_fix_one = thr_fix_one
|
|
|
|
def fit(self, x_train, y_train):
|
|
assert isinstance(x_train, np.ndarray)
|
|
assert isinstance(y_train, np.ndarray)
|
|
assert y_train.shape[1] == 2
|
|
assert y_train.shape[0] == x_train.shape[0]
|
|
y_hat = np.average(y_train[:, 1])
|
|
if y_hat < self.thr_fix_zero or y_hat > self.thr_fix_one:
|
|
self.model = int(y_hat)
|
|
else:
|
|
self.model = make_pipeline(StandardScaler(), LogisticRegression())
|
|
self.model.fit(x_train, y_train[:, 1].astype(int))
|
|
|
|
def predict(self, x_test):
|
|
assert isinstance(x_test, np.ndarray)
|
|
if isinstance(self.model, int):
|
|
p_test = np.array([[1 - self.model, self.model]
|
|
for _ in range(x_test.shape[0])])
|
|
else:
|
|
p_test = self.model.predict_proba(x_test)
|
|
p_test[p_test < self.thr_predict] = 0
|
|
p_test[p_test > 0] = 1
|
|
p_test = p_test.astype(int)
|
|
return p_test
|