def test_rank_with_groups(self):
data_rank = rank(self.x, groups=self.groups)
df = pd.DataFrame(self.x, index=self.groups)
expected_rank = df.groupby(
level=0).apply(lambda x: x.values.argsort(axis=0).argsort(axis=0))
print(expected_rank)
def test_rank(self):
data_rank = rank(self.x)
sorted_array = np.zeros_like(self.x)
for i in range(self.x.shape[0]):
for j in range(self.x.shape[1]):
sorted_array[int(data_rank[i, j]), j] = self.x[i, j]
arr_diff = np.diff(sorted_array, axis=0)
np.testing.assert_array_less(0, arr_diff)
def test_rank_with_groups(self):
data = pd.DataFrame(data={'raw': self.x.tolist()}, index=self.groups)
data['rank'] = rank(data['raw'].values, groups=data.index)
groups = dict(list(data['rank'].groupby(level=0)))
ret = []
for index in range(10):
ret.append(groups[index].values)
ret = np.concatenate(ret).reshape(-1, 1)
expected_rank = data['raw'].groupby(
level=0).apply(lambda x: x.values.argsort(axis=0).argsort(axis=0))
expected_rank = np.concatenate(expected_rank).reshape(-1, 1)
np.testing.assert_array_equal(ret, expected_rank)