def test_predict(self): X = np.array([[1, 1], [0, 0]]) y = np.array([1, 2]) regression = NearestNeighborsRegression() self.assertRaises(RuntimeError, regression.predict, X) regression.fit(X, y) y_pred = regression.predict(X) self.assertEqual(y_pred.shape[0], X.shape[0])
def test_predict(self): X = np.array([[1,1], [0,0]]) y = np.array([1,2]) regression = NearestNeighborsRegression() self.assertRaises(RuntimeError, regression.predict, X) regression.fit(X, y) y_pred = regression.predict(X) self.assertEqual(y_pred.shape[0], X.shape[0])
def test_partial_fit(self): X = np.array([[1,1], [0,0]]) y = np.array([1,2]) regression = NearestNeighborsRegression() self.assertTrue(regression.clf_ is None) self.assertEqual(regression.fitted_, False) regression.partial_fit(X, y) self.assertTrue(regression.clf_ is not None) self.assertEqual(regression.fitted_, True) regression.stop()
def test_get_params(self): params = { 'method': 'lsh', 'nearest_neighbor_num': 10, 'hash_num': 512, 'n_iter': 5, 'shuffle': True, 'embedded': True, 'seed': 42 } regression = NearestNeighborsRegression(**params) self.assertDictEqual(params, regression.get_params())
def test_class_params(self): regression = NearestNeighborsRegression() params = [ 'method', 'nearest_neighbor_num', 'hash_num', 'n_iter', 'shuffle', 'embedded', 'seed' ] for param in params: self.assertTrue(param in regression.__dict__) self.assertTrue('invalid_param' not in regression.__dict__)
def test_set_params(self): params = { 'method': 'lsh', 'nearest_neighbor_num': 10, 'hash_num': 512, 'n_iter': 5, 'shuffle': True, 'embedded': True, 'seed': 42 } regression = NearestNeighborsRegression() regression.set_params(**params) self.assertEqual(regression.method, params['method']) self.assertEqual(regression.nearest_neighbor_num, params['nearest_neighbor_num']) self.assertEqual(regression.hash_num, params['hash_num']) self.assertEqual(regression.n_iter, params['n_iter']) self.assertEqual(regression.shuffle, params['shuffle']) self.assertEqual(regression.embedded, params['embedded']) self.assertEqual(regression.seed, params['seed'])
def test_partial_fit(self): X = np.array([[1, 1], [0, 0]]) y = np.array([1, 2]) regression = NearestNeighborsRegression() self.assertTrue(regression.clf_ is None) self.assertEqual(regression.fitted_, False) regression.partial_fit(X, y) self.assertTrue(regression.clf_ is not None) self.assertEqual(regression.fitted_, True) regression.stop()
def test_save(self): name = 'test' regression = NearestNeighborsRegression() regression.save(name)
def launch_regression(method): regression = NearestNeighborsRegression(method=method) regression._launch_regression()
def test_simple(self): regression = NearestNeighborsRegression() regression.stop()
def test_embedded(self): regression = NearestNeighborsRegression(embedded=True)