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Implement KnnWarmStartPredictor; make it the default method

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Alinson S. Xavier
2020-01-23 12:32:40 -06:00
parent 35218d4893
commit 480da41fa9
3 changed files with 128 additions and 7 deletions
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miplearn/tests/test_warmstart_knn.py Normal file
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# MIPLearn, an extensible framework for Learning-Enhanced Mixed-Integer Optimization
# Copyright (C) 2019-2020 Argonne National Laboratory. All rights reserved.
# Written by Alinson S. Xavier <axavier@anl.gov>
from miplearn.warmstart import KnnWarmStartPredictor
from sklearn.metrics import accuracy_score, precision_score
import numpy as np
def test_knn_with_consensus():
x_train = np.array([
[0.0, 0.0],
[0.1, 0.0],
[0.0, 0.1],
[1.0, 1.0],
])
y_train = np.array([
[0., 1.],
[0., 1.],
[0., 1.],
[1., 0.],
])
ws = KnnWarmStartPredictor(k=3, thr_clip=[0.75, 0.75])
ws.fit(x_train, y_train)
x_test = np.array([[0.0, 0.0]])
y_test = np.array([[0, 1]])
assert (ws.predict(x_test) == y_test).all()
def test_knn_without_consensus():
x_train = np.array([
[0.0, 0.0],
[0.1, 0.1],
[0.9, 0.9],
[1.0, 1.0],
])
y_train = np.array([
[0., 1.],
[0., 1.],
[1., 0.],
[1., 0.],
])
ws = KnnWarmStartPredictor(k=4, thr_clip=[0.75, 0.75])
ws.fit(x_train, y_train)
x_test = np.array([[0.5, 0.5]])
y_test = np.array([[0, 0]])
assert (ws.predict(x_test) == y_test).all()
def test_knn_always_true():
x_train = np.array([
[0.0, 0.0],
[0.1, 0.1],
[0.9, 0.9],
[1.0, 1.0],
])
y_train = np.array([
[1., 0.],
[1., 0.],
[1., 0.],
[1., 0.],
])
ws = KnnWarmStartPredictor(k=4, thr_clip=[0.75, 0.75])
ws.fit(x_train, y_train)
x_test = np.array([[0.5, 0.5]])
y_test = np.array([[1, 0]])
assert (ws.predict(x_test) == y_test).all()
def test_knn_always_false():
x_train = np.array([
[0.0, 0.0],
[0.1, 0.1],
[0.9, 0.9],
[1.0, 1.0],
])
y_train = np.array([
[0., 1.],
[0., 1.],
[0., 1.],
[0., 1.],
])
ws = KnnWarmStartPredictor(k=4, thr_clip=[0.75, 0.75])
ws.fit(x_train, y_train)
x_test = np.array([[0.5, 0.5]])
y_test = np.array([[0, 1]])
assert (ws.predict(x_test) == y_test).all()