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_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_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_save(self):
name = 'test'
regression = NearestNeighborsRegression()
regression.save(name)
def launch_regression(method):
regression = NearestNeighborsRegression(method=method)
regression._launch_regression()
def test_embedded(self):
regression = NearestNeighborsRegression(embedded=True)
def test_simple(self):
regression = NearestNeighborsRegression()
regression.stop()