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88 lines
2.1 KiB
88 lines
2.1 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 miplearn import KnnWarmStartPredictor
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from sklearn.metrics import accuracy_score, precision_score
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import numpy as np
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def test_knn_with_consensus():
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x_train = np.array([
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[0.0, 0.0],
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[0.1, 0.0],
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[0.0, 0.1],
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[1.0, 1.0],
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])
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y_train = np.array([
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[0., 1.],
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[0., 1.],
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[0., 1.],
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[1., 0.],
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])
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ws = KnnWarmStartPredictor(k=3, thr_clip=[0.75, 0.75])
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ws.fit(x_train, y_train)
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x_test = np.array([[0.0, 0.0]])
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y_test = np.array([[0, 1]])
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assert (ws.predict(x_test) == y_test).all()
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def test_knn_without_consensus():
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x_train = np.array([
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[0.0, 0.0],
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[0.1, 0.1],
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[0.9, 0.9],
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[1.0, 1.0],
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])
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y_train = np.array([
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[0., 1.],
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[0., 1.],
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[1., 0.],
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[1., 0.],
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])
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ws = KnnWarmStartPredictor(k=4, thr_clip=[0.75, 0.75])
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ws.fit(x_train, y_train)
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x_test = np.array([[0.5, 0.5]])
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y_test = np.array([[0, 0]])
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assert (ws.predict(x_test) == y_test).all()
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def test_knn_always_true():
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x_train = np.array([
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[0.0, 0.0],
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[0.1, 0.1],
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[0.9, 0.9],
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[1.0, 1.0],
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])
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y_train = np.array([
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[1., 0.],
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[1., 0.],
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[1., 0.],
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[1., 0.],
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])
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ws = KnnWarmStartPredictor(k=4, thr_clip=[0.75, 0.75])
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ws.fit(x_train, y_train)
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x_test = np.array([[0.5, 0.5]])
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y_test = np.array([[1, 0]])
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assert (ws.predict(x_test) == y_test).all()
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def test_knn_always_false():
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x_train = np.array([
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[0.0, 0.0],
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[0.1, 0.1],
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[0.9, 0.9],
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[1.0, 1.0],
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])
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y_train = np.array([
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[0., 1.],
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[0., 1.],
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[0., 1.],
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[0., 1.],
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])
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ws = KnnWarmStartPredictor(k=4, thr_clip=[0.75, 0.75])
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ws.fit(x_train, y_train)
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x_test = np.array([[0.5, 0.5]])
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y_test = np.array([[0, 1]])
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assert (ws.predict(x_test) == y_test).all() |