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.
88 lines
2.2 KiB
88 lines
2.2 KiB
# MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
|
|
# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
|
|
# Released under the modified BSD license. See COPYING.md for more details.
|
|
|
|
from miplearn 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() |