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Reformat source code with Black; add pre-commit hooks and CI checks

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Alinson S. Xavier
2020-12-05 10:59:33 -06:00
parent 3823931382
commit d99600f101
49 changed files with 1291 additions and 972 deletions
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36
miplearn/classifiers/tests/test_cv.py
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@@ -13,34 +13,36 @@ E = 0.1
def test_cv():
# Training set: label is true if point is inside a 2D circle
x_train = np.array([[x1, x2]
for x1 in range(-10, 11)
for x2 in range(-10, 11)])
x_train = np.array([[x1, x2] for x1 in range(-10, 11) for x2 in range(-10, 11)])
x_train = StandardScaler().fit_transform(x_train)
n_samples = x_train.shape[0]
y_train = np.array([1.0 if x1*x1 + x2*x2 <= 100 else 0.0
for x1 in range(-10, 11)
for x2 in range(-10, 11)])
y_train = np.array(
[
1.0 if x1 * x1 + x2 * x2 <= 100 else 0.0
for x1 in range(-10, 11)
for x2 in range(-10, 11)
]
)
# Support vector machines with linear kernels do not perform well on this
# data set, so predictor should return the given constant.
clf = CrossValidatedClassifier(classifier=SVC(probability=True,
random_state=42),
threshold=0.90,
constant=0.0,
cv=30)
clf = CrossValidatedClassifier(
classifier=SVC(probability=True, random_state=42),
threshold=0.90,
constant=0.0,
cv=30,
)
clf.fit(x_train, y_train)
assert norm(np.zeros(n_samples) - clf.predict(x_train)) < E
# Support vector machines with quadratic kernels perform almost perfectly
# on this data set, so predictor should return their prediction.
clf = CrossValidatedClassifier(classifier=SVC(probability=True,
kernel='poly',
degree=2,
random_state=42),
threshold=0.90,
cv=30)
clf = CrossValidatedClassifier(
classifier=SVC(probability=True, kernel="poly", degree=2, random_state=42),
threshold=0.90,
cv=30,
)
clf.fit(x_train, y_train)
print(y_train - clf.predict(x_train))
assert norm(y_train - clf.predict(x_train)) < E