def test_load(self):
x = np.array([[1, 0, 0], [0, 1, 1], [1, 1, 1], [0, 1, 1]])
y = {"default": np.array([1, 0, 2, 0])}
knn = KNN()
knn.fit(EncodedData(sparse.csr_matrix(x), y), "default")
path = EnvironmentSettings.root_path / "test/tmp/knn2/"
PathBuilder.build(path)
pickle_file_path = path / "knn.pickle"
with pickle_file_path.open("wb") as file:
pickle.dump(knn.get_model(), file)
knn2 = KNN()
knn2.load(path)
self.assertTrue(isinstance(knn2.get_model(), KNeighborsClassifier))
shutil.rmtree(path)
def test_load(self):
x = np.array([[1, 0, 0], [0, 1, 1], [1, 1, 1], [0, 1, 1]])
y = {"default": np.array([1, 0, 2, 0])}
knn = KNN()
knn.fit(EncodedData(examples=sparse.csr_matrix(x), labels=y), Label("default"))
path = EnvironmentSettings.root_path / "test/tmp/loadtestsklearn/"
PathBuilder.build(path)
with open(path / "knn.pickle", "wb") as file:
pickle.dump(knn.model, file)
config = MLMethodConfiguration()
config.labels_with_values = {"default": [0, 1, 2]}
config.store(path / "config.json")
knn2 = KNN()
knn2.load(path)
self.assertTrue(isinstance(knn2.model, KNeighborsClassifier))
shutil.rmtree(path)