def test_load(self):
x, y, encoded_data = self._prepare_data()
cls = TCRdistClassifier(0.75)
cls.fit(encoded_data, label_name="test", cores_for_training=4)
path = PathBuilder.build(EnvironmentSettings.root_path / "test/tmp/tcrdist_classifier_load/")
with open(path / "tcrdist_classifier.pickle", "wb") as file:
dill.dump(cls.get_model(), file)
cls2 = TCRdistClassifier(percentage=1.)
cls2.load(path)
self.assertTrue(isinstance(cls2.get_model(), KNeighborsClassifier))
self.assertTrue(isinstance(cls2, TCRdistClassifier))
self.assertEqual(3, cls2.model.n_neighbors)
shutil.rmtree(path)
def test_fit(self):
x, y, encoded_data = self._prepare_data()
knn = TCRdistClassifier(percentage=0.75)
knn.fit(encoded_data, "test", cores_for_training=4)
predictions = knn.predict(encoded_data, 'test')
self.assertTrue(np.array_equal(y["test"], predictions["test"]))
encoded_data.examples = np.array([[1.1, 0.1, 0.9, 1.9]])
predictions = knn.predict(encoded_data, 'test')
self.assertTrue(np.array_equal([0], predictions["test"]))
def test_store(self):
x, y, encoded_data = self._prepare_data()
cls = TCRdistClassifier(0.75)
cls.fit(encoded_data, label_name="test", cores_for_training=4)
path = EnvironmentSettings.root_path / "test/tmp/tcrdist_classifier/"
cls.store(path)
self.assertTrue(os.path.isfile(path / "tcrdist_classifier.pickle"))
with open(path / "tcrdist_classifier.pickle", "rb") as file:
cls2 = pickle.load(file)
self.assertTrue(isinstance(cls2, KNeighborsClassifier))
shutil.rmtree(path)