def test_same_lmnn_parallel():
X, y = datasets.make_classification(n_samples=30,
n_features=5,
n_redundant=0,
random_state=0)
X_train, X_test, y_train, y_test = train_test_split(X, y)
lmnn = LargeMarginNearestNeighbor(n_neighbors=3)
lmnn.fit(X_train, y_train)
components = lmnn.components_
lmnn.set_params(n_jobs=3)
lmnn.fit(X_train, y_train)
components_parallel = lmnn.components_
assert_array_almost_equal(components, components_parallel)
def test_neighbors_iris():
# Sanity checks on the iris dataset
# Puts three points of each label in the plane and performs a
# nearest neighbor query on points near the decision boundary.
lmnn = LargeMarginNearestNeighbor(n_neighbors=1)
lmnn.fit(iris_data, iris_target)
knn = KNeighborsClassifier(n_neighbors=lmnn.n_neighbors_)
LX = lmnn.transform(iris_data)
knn.fit(LX, iris_target)
y_pred = knn.predict(LX)
assert_array_equal(y_pred, iris_target)
lmnn.set_params(n_neighbors=9)
lmnn.fit(iris_data, iris_target)
knn = KNeighborsClassifier(n_neighbors=lmnn.n_neighbors_)
knn.fit(LX, iris_target)
assert (knn.score(LX, iris_target) > 0.95)
