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