def test_rank_min_a(self) -> None:
a1 = rank_1d(np.array([0, 2, 3, 2]), 'min', start=1)
self.assertEqual(a1.tolist(), [1, 2, 4, 2])
a2 = rank_1d(np.array([0, 2, 3, 2]), 'min', start=0)
self.assertEqual(a2.tolist(), [0, 1, 3, 1])
a3 = rank_1d(np.array([0, 2, 3, 2]), 'min', ascending=False)
self.assertEqual(a3.tolist(), [3, 1, 0, 1])
def test_rank_average_a(self) -> None:
a1 = rank_1d(np.array([0, 2, 3, 2]), 'mean', ascending=True)
self.assertEqual(a1.tolist(), [0.0, 1.5, 3.0, 1.5])
a2 = rank_1d(np.array([0, 2, 3, 2]), 'mean', start=1)
self.assertEqual(a2.tolist(), [1.0, 2.5, 4.0, 2.5])
a3 = rank_1d(np.array([0, 2, 3, 2]), 'mean', start=1, ascending=False)
self.assertEqual(a3.tolist(), [4.0, 2.5, 1.0, 2.5])
def test_rank_max_a(self) -> None:
a1 = rank_1d(np.array([0, 2, 3, 2]), 'max', start=1)
# import ipdb; ipdb.set_trace()
self.assertEqual(a1.tolist(), [1, 3, 4, 3])
a2 = rank_1d(np.array([0, 2, 3, 2]), 'max', start=0)
self.assertEqual(a2.tolist(), [0, 2, 3, 2])
a2 = rank_1d(np.array([0, 2, 3, 2]), 'max', ascending=False)
self.assertEqual(a2.tolist(), [3, 2, 0, 2])
def test_rank_1d_ordinal(self, value: np.ndarray) -> None:
a1 = rankdata(value, method='ordinal')
a2 = rank_1d(value, method='ordinal', start=1)
self.assertEqual(a1.tolist(), a2.tolist())
if len(value):
a3 = rank_1d(value, method='ordinal')
self.assertEqual(a3.min(), 0)
a4 = rank_1d(value, method='ordinal', ascending=False)
self.assertEqual(a4.min(), 0)
def test_rank_1d_pair_a(self) -> None:
self.assertEqual(
rank_1d(np.array([0, 0]), 'mean').tolist(), [0.5, 0.5])
self.assertEqual(rank_1d(np.array([0, 0]), 'min').tolist(), [0, 0])
self.assertEqual(rank_1d(np.array([0, 0]), 'max').tolist(), [1, 1])
self.assertEqual(rank_1d(np.array([0, 0]), 'dense').tolist(), [0, 0])
self.assertEqual(rank_1d(np.array([0, 0]), 'ordinal').tolist(), [0, 1])
def test_rank_ordinal_a(self) -> None:
self.assertEqual(
rank_1d(np.array([3, 2, 6, 20]), RankMethod.ORDINAL).tolist(),
[1, 0, 2, 3])
self.assertEqual(
rank_1d(np.array([3, 2, 6, 20]), RankMethod.ORDINAL,
start=1).tolist(), [2, 1, 3, 4])
a2 = rank_1d(np.array([3, 2, 6, 20]),
RankMethod.ORDINAL,
ascending=False)
self.assertEqual(a2.tolist(), [2, 3, 1, 0])
def test_rank_1d_min(self, value: np.ndarray) -> None:
# cannot compare values with NaN as scipy uses quicksort
if np.isnan(value).any():
return
a1 = rankdata(value, method='min')
a2 = rank_1d(value, method='min', start=1)
self.assertEqual(a1.tolist(), a2.tolist())
if len(value):
a3 = rank_1d(value, method='min')
self.assertEqual(a3.min(), 0)
a4 = rank_1d(value, method='min', ascending=False)
self.assertEqual(a4.min(), 0)
def test_rank_1d_average(self, value: np.ndarray) -> None:
# cannot compare values with NaN as scipy uses quicksort
if np.isnan(value).any():
return
a1 = rankdata(value, method='average')
a2 = rank_1d(value, method='mean', start=1)
self.assertEqual(a1.tolist(), a2.tolist())
def test_rank_average_d(self) -> None:
a1 = rank_1d(np.array([17, 10, 3, -4, 9, 3, -12, 18, 3, 0, 17]),
'mean',
start=1)
self.assertEqual(
a1.tolist(),
[9.5, 8.0, 5.0, 2.0, 7.0, 5.0, 1.0, 11.0, 5.0, 3.0, 9.5])
def test_rank_average_b(self) -> None:
a1 = rank_1d(np.array([0, 2, 5, 2, 2, 2]), 'mean', ascending=True)
self.assertEqual(a1.tolist(), [0.0, 2.5, 5.0, 2.5, 2.5, 2.5])
a1 = rank_1d(np.array([0, 2, 5, 2, 2, 2]),
'mean',
ascending=True,
start=1)
self.assertEqual(a1.tolist(), [1.0, 3.5, 6.0, 3.5, 3.5, 3.5])
#scipy: [1.0, 3.5, 6.0, 3.5, 3.5, 3.5]
# import ipdb; ipdb.set_trace()
a2 = rank_1d(np.array([0, 2, 3, 2]), 'mean', start=1)
self.assertEqual(a2.tolist(), [1.0, 2.5, 4.0, 2.5])
a3 = rank_1d(np.array([0, 2, 3, 2]), 'mean', start=1, ascending=False)
self.assertEqual(a3.tolist(), [4.0, 2.5, 1.0, 2.5])
def test_rank_1d_pair_b(self) -> None:
self.assertEqual(
rank_1d(np.array([0, 0, 1]), 'mean').tolist(), [0.5, 0.5, 2])
self.assertEqual(rank_1d(np.array([0, 0, 1]), 'mean').dtype.kind, 'f')
self.assertEqual(
rank_1d(np.array([0, 0, 1]), 'min').tolist(), [0, 0, 2])
self.assertEqual(rank_1d(np.array([0, 0, 1]), 'min').dtype.kind, 'i')
self.assertEqual(
rank_1d(np.array([0, 0, 1]), 'max').tolist(), [1, 1, 2])
self.assertEqual(rank_1d(np.array([0, 0, 1]), 'max').dtype.kind, 'i')
self.assertEqual(
rank_1d(np.array([0, 0, 1]), 'dense').tolist(), [0, 0, 1])
self.assertEqual(rank_1d(np.array([0, 0, 1]), 'dense').dtype.kind, 'i')
self.assertEqual(
rank_1d(np.array([0, 0, 1]), 'ordinal').tolist(), [0, 1, 2])
self.assertEqual(
rank_1d(np.array([0, 0, 1]), 'ordinal').dtype.kind, 'i')
def test_rank_method_a(self) -> None:
with self.assertRaises(NotImplementedError):
rank_1d(np.array([3, 2, 6, 20]), None) #type: ignore
def test_rank_min_b(self) -> None:
a1 = rank_1d(np.array([8, 15, 7, 2, 20, 4, 20, 7, 15, 15]),
'min',
start=1)
self.assertEqual(a1.tolist(), [5, 6, 3, 1, 9, 2, 9, 3, 6, 6])
def test_rank_min_c(self) -> None:
a1 = rank_1d(np.array([17, 10, 3, -4, 9, 3, -12, 18, 3, 0, 17]),
'min',
start=1)
self.assertEqual(a1.tolist(), [9, 8, 4, 2, 7, 4, 1, 11, 4, 3, 9])
def test_rank_average_c(self) -> None:
a1 = rank_1d(np.array([8, 15, 7, 2, 20, 4, 20, 7, 15, 15]),
'mean',
start=1)
self.assertEqual(a1.tolist(),
[5.0, 7.0, 3.5, 1.0, 9.5, 2.0, 9.5, 3.5, 7.0, 7.0])
def test_rank_max_b(self) -> None:
a1 = rank_1d(np.array([8, 15, 7, 2, 20, 4, 20, 7, 15, 15]),
'max',
start=1)
self.assertEqual(a1.tolist(), [5, 8, 4, 1, 10, 2, 10, 4, 8, 8])
def test_rank_ordinal_d(self) -> None:
a1 = rank_1d(np.array([17, 10, 3, -4, 9, 3, -12, 18, 3, 0, 17]),
'ordinal',
start=1)
self.assertEqual(a1.tolist(), [9, 8, 4, 2, 7, 5, 1, 11, 6, 3, 10])
def test_rank_ordinal_c(self) -> None:
a1 = rank_1d(np.array([8, 15, 7, 2, 20, 4, 20, 7, 15, 15]),
'ordinal',
start=1)
self.assertEqual(a1.tolist(), [5, 6, 3, 1, 9, 2, 10, 4, 7, 8])
def test_rank_max_c(self) -> None:
a1 = rank_1d(np.array([17, 10, 3, -4, 9, 3, -12, 18, 3, 0, 17]),
'max',
start=1)
self.assertEqual(a1.tolist(), [10, 8, 6, 2, 7, 6, 1, 11, 6, 3, 10])
def test_rank_dense_c(self) -> None:
a1 = rank_1d(np.array([17, 10, 3, -4, 9, 3, -12, 18, 3, 0, 17]),
'dense',
start=1)
self.assertEqual(a1.tolist(), [7, 6, 4, 2, 5, 4, 1, 8, 4, 3, 7])
def test_rank_dense_b(self) -> None:
a1 = rank_1d(np.array([8, 15, 7, 2, 20, 4, 20, 7, 15, 15]),
'dense',
start=1)
self.assertEqual(a1.tolist(), [4, 5, 3, 1, 6, 2, 6, 3, 5, 5])
