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.
59 lines
1.8 KiB
59 lines
1.8 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.
|
|
|
|
import numpy as np
|
|
from numpy.linalg import norm
|
|
from sklearn.svm import SVC
|
|
|
|
from miplearn.classifiers import ScikitLearnClassifier
|
|
from miplearn.classifiers.cv import CrossValidatedClassifier
|
|
from tests.classifiers import _build_circle_training_data
|
|
|
|
E = 0.1
|
|
|
|
|
|
def test_cv() -> None:
|
|
x_train, y_train = _build_circle_training_data()
|
|
n_samples = x_train.shape[0]
|
|
|
|
# Support vector machines with linear kernels do not perform well on this
|
|
# data set, so predictor should return the given constant.
|
|
clf = CrossValidatedClassifier(
|
|
classifier=ScikitLearnClassifier(
|
|
SVC(
|
|
probability=True,
|
|
random_state=42,
|
|
)
|
|
),
|
|
threshold=0.90,
|
|
constant=[True, False],
|
|
cv=30,
|
|
)
|
|
clf.fit(x_train, y_train)
|
|
proba = clf.predict_proba(x_train)
|
|
assert isinstance(proba, np.ndarray)
|
|
assert proba.shape == (n_samples, 2)
|
|
|
|
y_pred = (proba[:, 1] > 0.5).astype(float)
|
|
assert norm(np.zeros(n_samples) - y_pred) < E
|
|
|
|
# Support vector machines with quadratic kernels perform almost perfectly
|
|
# on this data set, so predictor should return their prediction.
|
|
clf = CrossValidatedClassifier(
|
|
classifier=ScikitLearnClassifier(
|
|
SVC(
|
|
probability=True,
|
|
kernel="poly",
|
|
degree=2,
|
|
random_state=42,
|
|
)
|
|
),
|
|
threshold=0.90,
|
|
cv=30,
|
|
)
|
|
clf.fit(x_train, y_train)
|
|
proba = clf.predict_proba(x_train)
|
|
y_pred = (proba[:, 1] > 0.5).astype(float)
|
|
assert norm(y_train[:, 1] - y_pred) < E
|