def test_fit_by_cross_validation(self):
x = EncodedData(sparse.csr_matrix(
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={"t1": [1, 0, 2, 0, 1, 0, 2, 0], "t2": [1, 0, 2, 0, 1, 0, 2, 0]})
rfc = RandomForestClassifier()
rfc.fit_by_cross_validation(x, number_of_splits=2, label=Label("t2"))
def test_predict(self):
x = np.array([[1, 0, 0], [0, 1, 1], [1, 1, 1], [0, 1, 1]])
y = {"default": np.array([1, 0, 2, 0])}
rfc = RandomForestClassifier()
rfc.fit(EncodedData(x, y), Label("default"))
test_x = np.array([[0, 1, 0], [1, 0, 0]])
y = rfc.predict(EncodedData(test_x), Label("default"))["default"]
self.assertTrue(len(y) == 2)
self.assertTrue(y[0] in [0, 1, 2])
self.assertTrue(y[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])}
rfc = RandomForestClassifier()
rfc.fit(EncodedData(x, y), "default")
path = EnvironmentSettings.root_path / "test/tmp/rfc2/"
PathBuilder.build(path)
with open(path / "random_forest_classifier.pickle", "wb") as file:
pickle.dump(rfc.get_model(), file)
rfc2 = RandomForestClassifier()
rfc2.load(path)
self.assertTrue(isinstance(rfc2.get_model()["default"], RFC))
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])}
rfc = RandomForestClassifier()
rfc.fit(EncodedData(x, y), Label("default"))
path = EnvironmentSettings.root_path / "test/tmp/rfc/"
rfc.store(path)
self.assertTrue(os.path.isfile(path / "random_forest_classifier.pickle"))
with open(path / "random_forest_classifier.pickle", "rb") as file:
rfc2 = pickle.load(file)
self.assertTrue(isinstance(rfc2, RFC))
shutil.rmtree(path)
def test_fit(self):
x = np.array([[1, 0, 0], [0, 1, 1], [1, 1, 1], [0, 1, 1]])
y = {"default": np.array([1, 0, 2, 0])}
rfc = RandomForestClassifier()
rfc.fit(EncodedData(sparse.csr_matrix(x), y), Label("default"))