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)