def test_fit(self):
x = np.array([[1, 0, 0], [0, 1, 1], [1, 1, 1], [0, 1, 1]])
y = {"test": np.array([1, 0, 2, 0])}
knn = KNN()
knn.fit(EncodedData(examples=sparse.csr_matrix(x), labels=y),
Label("test"))
def test_fit_by_cross_validation(self):
x = EncodedData(np.array([[1, 0, 0], [0, 1, 1], [1, 1, 1], [0, 1, 1],
[1, 0, 0], [0, 1, 1], [1, 1, 1], [0, 1, 1]]),
labels={
"test1": [1, 0, 2, 0, 1, 0, 2, 0],
"test2": [1, 0, 2, 0, 1, 0, 2, 0]
})
knn = KNN(parameters={"n_neighbors": 2})
knn.fit_by_cross_validation(x, number_of_splits=2, label_name="test1")
def test_predict(self):
x = np.array([[1, 0, 0], [0, 1, 1], [1, 1, 1], [0, 1, 1]])
y = {"test1": [1, 0, 2, 0], "test2": [1, 0, 2, 0]}
knn = KNN(parameters={"n_neighbors": 2})
knn.fit(EncodedData(sparse.csr_matrix(x), labels=y), "test2")
test_x = np.array([[0, 1, 0], [1, 0, 0]])
y = knn.predict(EncodedData(sparse.csr_matrix(test_x)), "test2")
self.assertTrue(len(y["test2"]) == 2)
self.assertTrue(y["test2"][1] in [0, 1, 2])
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)
def test_store(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/knn/"
knn.store(path, ["f1", "f2", "f3"])
pickle_file_path = path / "knn.pickle"
self.assertTrue(os.path.isfile(str(pickle_file_path)))
with pickle_file_path.open("rb") as file:
knn2 = pickle.load(file)
self.assertTrue(isinstance(knn2, KNeighborsClassifier))
shutil.rmtree(path)