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Merged
merged 7 commits into from
Jan 4, 2021
Merged

TST/CLN: deduplicate troublesome rank values #38894

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b6e52de
WIP
mzeitlin11 Dec 31, 2020
c43318c
Merge remote-tracking branch 'upstream/master' into ref/rank_fixture
mzeitlin11 Jan 2, 2021
863e4d6
TST/CLN: deduplicate troublesome rank values
mzeitlin11 Jan 2, 2021
da4c76b
Remove unneeded imports
mzeitlin11 Jan 2, 2021
b816586
Merge remote-tracking branch 'upstream/master' into ref/rank_fixture
mzeitlin11 Jan 3, 2021
c77a84f
Address comments
mzeitlin11 Jan 3, 2021
4961e38
Use frame_or_series instead
mzeitlin11 Jan 3, 2021
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87 changes: 87 additions & 0 deletions pandas/conftest.py
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Original file line number Diff line number Diff line change
Expand Up @@ -31,6 +31,8 @@
import pytest
from pytz import FixedOffset, utc

from pandas._libs import iNaT
from pandas._libs.algos import Infinity, NegInfinity
import pandas.util._test_decorators as td

from pandas.core.dtypes.dtypes import DatetimeTZDtype, IntervalDtype
Expand Down Expand Up @@ -591,6 +593,91 @@ def narrow_series(request):
return _narrow_series[request.param].copy()


_dtype_nuisance_arr_map = {
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can you add a comment here about where this is used

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Done

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put rank in the name

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Done

"float64": [
-np.inf,
-50,
-1,
-1e-20,
-1e-25,
-1e-50,
0,
1e-40,
1e-20,
1e-10,
2,
40,
np.inf,
],
"float32": [
-np.inf,
-50,
-1,
-1e-20,
-1e-25,
-1e-45,
0,
1e-40,
1e-20,
1e-10,
2,
40,
np.inf,
],
"uint8": [np.iinfo(np.uint8).min, 1, 2, 100, np.iinfo(np.uint8).max],
"int64": [
np.iinfo(np.int64).min,
-100,
0,
1,
9999,
100000,
1e10,
np.iinfo(np.int64).max,
],
"object": [NegInfinity(), "1", "A", "BA", "Ba", "C", Infinity()],
}

_dtype_na_map = {
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can you put this inside the fixture, this is generally a special case

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Done

"float64": np.nan,
"float32": np.nan,
"int64": iNaT,
"object": None,
}


@pytest.fixture(params=_dtype_nuisance_arr_map.keys())
def nuisance_rank_series_and_expected(request):
"""
Fixture for Series with troublesome values for rank
algorithms
"""
dtype = request.param
if dtype == "int64":
mark = pytest.mark.xfail(
reason="iNaT is equivalent to minimum value of dtype"
"int64 pending issue GH#16674"
)
request.node.add_marker(mark)
data = _dtype_nuisance_arr_map[dtype]
values = np.array(data, dtype=dtype)
exp_order = np.array(range(len(values)), dtype="float64") + 1.0
# Insert nans at random positions if underlying dtype has missing
# value. Then adjust the expected order by adding nans accordingly
# This is for testing whether rank calculation is affected
# when values are interwined with nan values.
if dtype in _dtype_na_map:
na_value = _dtype_na_map[dtype]
nan_indices = np.random.choice(range(len(values)), 5)
values = np.insert(values, nan_indices, na_value)
exp_order = np.insert(exp_order, nan_indices, np.nan)
# shuffle the testing array and expected results in the same way
random_order = np.random.permutation(len(values))
iseries = Series(values[random_order])
exp = Series(exp_order[random_order], dtype="float64")
return iseries, exp


_index_or_series_objs = {**indices_dict, **_series, **_narrow_series}


Expand Down
88 changes: 4 additions & 84 deletions pandas/tests/frame/methods/test_rank.py
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Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -3,8 +3,6 @@
import numpy as np
import pytest

from pandas._libs import iNaT
from pandas._libs.algos import Infinity, NegInfinity
import pandas.util._test_decorators as td

from pandas import DataFrame, Series
Expand Down Expand Up @@ -332,88 +330,10 @@ def test_pct_max_many_rows(self):
result = df.rank(pct=True).max()
assert (result == 1).all()

@pytest.mark.parametrize(
"contents,dtype",
[
(
[
-np.inf,
-50,
-1,
-1e-20,
-1e-25,
-1e-50,
0,
1e-40,
1e-20,
1e-10,
2,
40,
np.inf,
],
"float64",
),
(
[
-np.inf,
-50,
-1,
-1e-20,
-1e-25,
-1e-45,
0,
1e-40,
1e-20,
1e-10,
2,
40,
np.inf,
],
"float32",
),
([np.iinfo(np.uint8).min, 1, 2, 100, np.iinfo(np.uint8).max], "uint8"),
pytest.param(
[
np.iinfo(np.int64).min,
-100,
0,
1,
9999,
100000,
1e10,
np.iinfo(np.int64).max,
],
"int64",
marks=pytest.mark.xfail(
reason="iNaT is equivalent to minimum value of dtype"
"int64 pending issue GH#16674"
),
),
([NegInfinity(), "1", "A", "BA", "Ba", "C", Infinity()], "object"),
],
)
def test_rank_inf_and_nan(self, contents, dtype):
dtype_na_map = {
"float64": np.nan,
"float32": np.nan,
"int64": iNaT,
"object": None,
}
# Insert nans at random positions if underlying dtype has missing
# value. Then adjust the expected order by adding nans accordingly
# This is for testing whether rank calculation is affected
# when values are interwined with nan values.
values = np.array(contents, dtype=dtype)
exp_order = np.array(range(len(values)), dtype="float64") + 1.0
if dtype in dtype_na_map:
na_value = dtype_na_map[dtype]
nan_indices = np.random.choice(range(len(values)), 5)
values = np.insert(values, nan_indices, na_value)
exp_order = np.insert(exp_order, nan_indices, np.nan)
# shuffle the testing array and expected results in the same way
random_order = np.random.permutation(len(values))
df = DataFrame({"a": values[random_order]})
expected = DataFrame({"a": exp_order[random_order]}, dtype="float64")
def test_rank_inf_and_nan(self, nuisance_rank_series_and_expected):
series, expected = nuisance_rank_series_and_expected
df = DataFrame({"a": series})
expected = DataFrame({"a": expected})
result = df.rank()
tm.assert_frame_equal(result, expected)

Expand Down
89 changes: 4 additions & 85 deletions pandas/tests/series/methods/test_rank.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -3,7 +3,6 @@
import numpy as np
import pytest

from pandas._libs import iNaT
from pandas._libs.algos import Infinity, NegInfinity
import pandas.util._test_decorators as td

Expand Down Expand Up @@ -206,90 +205,10 @@ def test_rank_signature(self):
with pytest.raises(ValueError, match=msg):
s.rank("average")

@pytest.mark.parametrize(
"contents,dtype",
[
(
[
-np.inf,
-50,
-1,
-1e-20,
-1e-25,
-1e-50,
0,
1e-40,
1e-20,
1e-10,
2,
40,
np.inf,
],
"float64",
),
(
[
-np.inf,
-50,
-1,
-1e-20,
-1e-25,
-1e-45,
0,
1e-40,
1e-20,
1e-10,
2,
40,
np.inf,
],
"float32",
),
([np.iinfo(np.uint8).min, 1, 2, 100, np.iinfo(np.uint8).max], "uint8"),
pytest.param(
[
np.iinfo(np.int64).min,
-100,
0,
1,
9999,
100000,
1e10,
np.iinfo(np.int64).max,
],
"int64",
marks=pytest.mark.xfail(
reason="iNaT is equivalent to minimum value of dtype"
"int64 pending issue GH#16674"
),
),
([NegInfinity(), "1", "A", "BA", "Ba", "C", Infinity()], "object"),
],
)
def test_rank_inf(self, contents, dtype):
dtype_na_map = {
"float64": np.nan,
"float32": np.nan,
"int64": iNaT,
"object": None,
}
# Insert nans at random positions if underlying dtype has missing
# value. Then adjust the expected order by adding nans accordingly
# This is for testing whether rank calculation is affected
# when values are interwined with nan values.
values = np.array(contents, dtype=dtype)
exp_order = np.array(range(len(values)), dtype="float64") + 1.0
if dtype in dtype_na_map:
na_value = dtype_na_map[dtype]
nan_indices = np.random.choice(range(len(values)), 5)
values = np.insert(values, nan_indices, na_value)
exp_order = np.insert(exp_order, nan_indices, np.nan)
# shuffle the testing array and expected results in the same way
random_order = np.random.permutation(len(values))
iseries = Series(values[random_order])
exp = Series(exp_order[random_order], dtype="float64")
iranks = iseries.rank()
tm.assert_series_equal(iranks, exp)
def test_rank_inf(self, nuisance_rank_series_and_expected):
series, expected = nuisance_rank_series_and_expected
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actually an alternative here is simply to use frame_or_series fixture and leave the fixture in tests/frame/methods (e.g. this is what we do for things for both series & frame), i think in this case it makes more sense.

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yah i think this makes a lot more sense than a relatively-complicated fixture in conftest

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Thanks that is much nicer, good pattern to know

result = series.rank()
tm.assert_series_equal(result, expected)

def test_rank_tie_methods(self):
s = self.s
Expand Down