def test_predict(self): reg = KNeighborsRegressor(n_neighbors=1) reg.fit([[1, 2], [6, 7], [8, 9]], [[1, 0], [20, 5], [8, 6]]) neigh_idx = reg.regressor.predict([[1, 2], [8, 9], [6, 7]]) assert (neigh_idx[0] == [1, 0]).all() assert (neigh_idx[1] == [8, 6]).all() assert (neigh_idx[2] == [20, 5]).all()
def test_kneighbors(self): reg = KNeighborsRegressor(n_neighbors=2) reg.fit([[1, 2], [6, 7], [8, 9]], [[1, 0], [20, 5], [8, 6]]) neigh_idx = reg.regressor.kneighbors([[6, 6]], return_distance=False)[0] assert neigh_idx[0] == 1 assert neigh_idx[1] == 2 assert len(neigh_idx) == 2
def test_wrong2(self): # wrong number of values with warnings.catch_warnings(): warnings.filterwarnings("ignore", category=np.VisibleDeprecationWarning) with self.assertRaises(Exception): reg = KNeighborsRegressor() reg.fit([[1, 2], [ 6, ], [8, 9]], [[20, 5], [8, 6]])
def test_fit_biparam_bifunc(self): reg = KNeighborsRegressor() reg.fit([[1, 2], [6, 7], [8, 9]], [[1, 0], [20, 5], [8, 6]]) assert reg.regressor.n_samples_fit_ == 3
def test_fit_biparam_scalarfunc(self): reg = KNeighborsRegressor() reg.fit([[1, 2], [6, 7], [8, 9]], [1, 5, 6]) assert reg.regressor.n_samples_fit_ == 3
def test_fit_scalarparam_scalarfunc(self): reg = KNeighborsRegressor() reg.fit([1, 2, 5, 7, 2], [2, 5, 7, 83, 3]) assert reg.regressor.n_samples_fit_ == 5
def test_fit_onescalarparam_scalarfunc(self): reg = KNeighborsRegressor() reg.fit([1], [20]) assert reg.regressor.n_samples_fit_ == 1