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TST: Move some window/moments tests to test_ewm #44128

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Merged
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Oct 24, 2021
Merged

TST: Move some window/moments tests to test_ewm #44128

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da891de
TST: Move some window/moments tests to test_ewm
mroeschke Oct 21, 2021
d3fadb0
Merge remote-tracking branch 'upstream/master' into test/move_ewm
mroeschke Oct 22, 2021
c197ef6
Merge remote-tracking branch 'upstream/master' into test/move_ewm
mroeschke Oct 23, 2021
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52 changes: 0 additions & 52 deletions pandas/tests/window/moments/test_moments_consistency_ewm.py
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Original file line number Diff line number Diff line change
Expand Up @@ -18,58 +18,6 @@ def test_ewm_pairwise_cov_corr(func, frame):
tm.assert_series_equal(result, expected, check_names=False)


@pytest.mark.parametrize("name", ["cov", "corr"])
def test_ewm_corr_cov(name):
A = Series(np.random.randn(50), index=np.arange(50))
B = A[2:] + np.random.randn(48)

A[:10] = np.NaN
B[-10:] = np.NaN

result = getattr(A.ewm(com=20, min_periods=5), name)(B)
assert np.isnan(result.values[:14]).all()
assert not np.isnan(result.values[14:]).any()


@pytest.mark.parametrize("min_periods", [0, 1, 2])
@pytest.mark.parametrize("name", ["cov", "corr"])
def test_ewm_corr_cov_min_periods(name, min_periods):
# GH 7898
A = Series(np.random.randn(50), index=np.arange(50))
B = A[2:] + np.random.randn(48)

A[:10] = np.NaN
B[-10:] = np.NaN

result = getattr(A.ewm(com=20, min_periods=min_periods), name)(B)
# binary functions (ewmcov, ewmcorr) with bias=False require at
# least two values
assert np.isnan(result.values[:11]).all()
assert not np.isnan(result.values[11:]).any()

# check series of length 0
empty = Series([], dtype=np.float64)
result = getattr(empty.ewm(com=50, min_periods=min_periods), name)(empty)
tm.assert_series_equal(result, empty)

# check series of length 1
result = getattr(Series([1.0]).ewm(com=50, min_periods=min_periods), name)(
Series([1.0])
)
tm.assert_series_equal(result, Series([np.NaN]))


@pytest.mark.parametrize("name", ["cov", "corr"])
def test_different_input_array_raise_exception(name):
A = Series(np.random.randn(50), index=np.arange(50))
A[:10] = np.NaN

msg = "other must be a DataFrame or Series"
# exception raised is Exception
with pytest.raises(ValueError, match=msg):
getattr(A.ewm(com=20, min_periods=5), name)(np.random.randn(50))


def create_mock_weights(obj, com, adjust, ignore_na):
if isinstance(obj, DataFrame):
if not len(obj.columns):
Expand Down
291 changes: 0 additions & 291 deletions pandas/tests/window/moments/test_moments_ewm.py
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Original file line number Diff line number Diff line change
@@ -1,4 +1,3 @@
import numpy as np
import pytest

from pandas import (
Expand All @@ -20,187 +19,6 @@ def test_ewma_frame(frame, name):
assert isinstance(frame_result, DataFrame)


def test_ewma_adjust():
vals = Series(np.zeros(1000))
vals[5] = 1
result = vals.ewm(span=100, adjust=False).mean().sum()
assert np.abs(result - 1) < 1e-2


@pytest.mark.parametrize("adjust", [True, False])
@pytest.mark.parametrize("ignore_na", [True, False])
def test_ewma_cases(adjust, ignore_na):
# try adjust/ignore_na args matrix

s = Series([1.0, 2.0, 4.0, 8.0])

if adjust:
expected = Series([1.0, 1.6, 2.736842, 4.923077])
else:
expected = Series([1.0, 1.333333, 2.222222, 4.148148])

result = s.ewm(com=2.0, adjust=adjust, ignore_na=ignore_na).mean()
tm.assert_series_equal(result, expected)


def test_ewma_nan_handling():
s = Series([1.0] + [np.nan] * 5 + [1.0])
result = s.ewm(com=5).mean()
tm.assert_series_equal(result, Series([1.0] * len(s)))

s = Series([np.nan] * 2 + [1.0] + [np.nan] * 2 + [1.0])
result = s.ewm(com=5).mean()
tm.assert_series_equal(result, Series([np.nan] * 2 + [1.0] * 4))


@pytest.mark.parametrize(
"s, adjust, ignore_na, w",
[
(
Series([np.nan, 1.0, 101.0]),
True,
False,
[np.nan, (1.0 - (1.0 / (1.0 + 2.0))), 1.0],
),
(
Series([np.nan, 1.0, 101.0]),
True,
True,
[np.nan, (1.0 - (1.0 / (1.0 + 2.0))), 1.0],
),
(
Series([np.nan, 1.0, 101.0]),
False,
False,
[np.nan, (1.0 - (1.0 / (1.0 + 2.0))), (1.0 / (1.0 + 2.0))],
),
(
Series([np.nan, 1.0, 101.0]),
False,
True,
[np.nan, (1.0 - (1.0 / (1.0 + 2.0))), (1.0 / (1.0 + 2.0))],
),
(
Series([1.0, np.nan, 101.0]),
True,
False,
[(1.0 - (1.0 / (1.0 + 2.0))) ** 2, np.nan, 1.0],
),
(
Series([1.0, np.nan, 101.0]),
True,
True,
[(1.0 - (1.0 / (1.0 + 2.0))), np.nan, 1.0],
),
(
Series([1.0, np.nan, 101.0]),
False,
False,
[(1.0 - (1.0 / (1.0 + 2.0))) ** 2, np.nan, (1.0 / (1.0 + 2.0))],
),
(
Series([1.0, np.nan, 101.0]),
False,
True,
[(1.0 - (1.0 / (1.0 + 2.0))), np.nan, (1.0 / (1.0 + 2.0))],
),
(
Series([np.nan, 1.0, np.nan, np.nan, 101.0, np.nan]),
True,
False,
[np.nan, (1.0 - (1.0 / (1.0 + 2.0))) ** 3, np.nan, np.nan, 1.0, np.nan],
),
(
Series([np.nan, 1.0, np.nan, np.nan, 101.0, np.nan]),
True,
True,
[np.nan, (1.0 - (1.0 / (1.0 + 2.0))), np.nan, np.nan, 1.0, np.nan],
),
(
Series([np.nan, 1.0, np.nan, np.nan, 101.0, np.nan]),
False,
False,
[
np.nan,
(1.0 - (1.0 / (1.0 + 2.0))) ** 3,
np.nan,
np.nan,
(1.0 / (1.0 + 2.0)),
np.nan,
],
),
(
Series([np.nan, 1.0, np.nan, np.nan, 101.0, np.nan]),
False,
True,
[
np.nan,
(1.0 - (1.0 / (1.0 + 2.0))),
np.nan,
np.nan,
(1.0 / (1.0 + 2.0)),
np.nan,
],
),
(
Series([1.0, np.nan, 101.0, 50.0]),
True,
False,
[
(1.0 - (1.0 / (1.0 + 2.0))) ** 3,
np.nan,
(1.0 - (1.0 / (1.0 + 2.0))),
1.0,
],
),
(
Series([1.0, np.nan, 101.0, 50.0]),
True,
True,
[
(1.0 - (1.0 / (1.0 + 2.0))) ** 2,
np.nan,
(1.0 - (1.0 / (1.0 + 2.0))),
1.0,
],
),
(
Series([1.0, np.nan, 101.0, 50.0]),
False,
False,
[
(1.0 - (1.0 / (1.0 + 2.0))) ** 3,
np.nan,
(1.0 - (1.0 / (1.0 + 2.0))) * (1.0 / (1.0 + 2.0)),
(1.0 / (1.0 + 2.0))
* ((1.0 - (1.0 / (1.0 + 2.0))) ** 2 + (1.0 / (1.0 + 2.0))),
],
),
(
Series([1.0, np.nan, 101.0, 50.0]),
False,
True,
[
(1.0 - (1.0 / (1.0 + 2.0))) ** 2,
np.nan,
(1.0 - (1.0 / (1.0 + 2.0))) * (1.0 / (1.0 + 2.0)),
(1.0 / (1.0 + 2.0)),
],
),
],
)
def test_ewma_nan_handling_cases(s, adjust, ignore_na, w):
# GH 7603
expected = (s.multiply(w).cumsum() / Series(w).cumsum()).fillna(method="ffill")
result = s.ewm(com=2.0, adjust=adjust, ignore_na=ignore_na).mean()

tm.assert_series_equal(result, expected)
if ignore_na is False:
# check that ignore_na defaults to False
result = s.ewm(com=2.0, adjust=adjust).mean()
tm.assert_series_equal(result, expected)


def test_ewma_span_com_args(series):
A = series.ewm(com=9.5).mean()
B = series.ewm(span=20).mean()
Expand Down Expand Up @@ -230,22 +48,6 @@ def test_ewma_halflife_arg(series):
series.ewm()


def test_ewm_alpha():
# GH 10789
arr = np.random.randn(100)
locs = np.arange(20, 40)
arr[locs] = np.NaN

s = Series(arr)
a = s.ewm(alpha=0.61722699889169674).mean()
b = s.ewm(com=0.62014947789973052).mean()
c = s.ewm(span=2.240298955799461).mean()
d = s.ewm(halflife=0.721792864318).mean()
tm.assert_series_equal(a, b)
tm.assert_series_equal(a, c)
tm.assert_series_equal(a, d)


def test_ewm_alpha_arg(series):
# GH 10789
s = series
Expand All @@ -260,96 +62,3 @@ def test_ewm_alpha_arg(series):
s.ewm(span=10.0, alpha=0.5)
with pytest.raises(ValueError, match=msg):
s.ewm(halflife=10.0, alpha=0.5)


def test_ewm_domain_checks():
# GH 12492
arr = np.random.randn(100)
locs = np.arange(20, 40)
arr[locs] = np.NaN

s = Series(arr)
msg = "comass must satisfy: comass >= 0"
with pytest.raises(ValueError, match=msg):
s.ewm(com=-0.1)
s.ewm(com=0.0)
s.ewm(com=0.1)

msg = "span must satisfy: span >= 1"
with pytest.raises(ValueError, match=msg):
s.ewm(span=-0.1)
with pytest.raises(ValueError, match=msg):
s.ewm(span=0.0)
with pytest.raises(ValueError, match=msg):
s.ewm(span=0.9)
s.ewm(span=1.0)
s.ewm(span=1.1)

msg = "halflife must satisfy: halflife > 0"
with pytest.raises(ValueError, match=msg):
s.ewm(halflife=-0.1)
with pytest.raises(ValueError, match=msg):
s.ewm(halflife=0.0)
s.ewm(halflife=0.1)

msg = "alpha must satisfy: 0 < alpha <= 1"
with pytest.raises(ValueError, match=msg):
s.ewm(alpha=-0.1)
with pytest.raises(ValueError, match=msg):
s.ewm(alpha=0.0)
s.ewm(alpha=0.1)
s.ewm(alpha=1.0)
with pytest.raises(ValueError, match=msg):
s.ewm(alpha=1.1)


@pytest.mark.parametrize("method", ["mean", "std", "var"])
def test_ew_empty_series(method):
vals = Series([], dtype=np.float64)

ewm = vals.ewm(3)
result = getattr(ewm, method)()
tm.assert_almost_equal(result, vals)


@pytest.mark.parametrize("min_periods", [0, 1])
@pytest.mark.parametrize("name", ["mean", "var", "std"])
def test_ew_min_periods(min_periods, name):
# excluding NaNs correctly
arr = np.random.randn(50)
arr[:10] = np.NaN
arr[-10:] = np.NaN
s = Series(arr)

# check min_periods
# GH 7898
result = getattr(s.ewm(com=50, min_periods=2), name)()
assert result[:11].isna().all()
assert not result[11:].isna().any()

result = getattr(s.ewm(com=50, min_periods=min_periods), name)()
if name == "mean":
assert result[:10].isna().all()
assert not result[10:].isna().any()
else:
# ewm.std, ewm.var (with bias=False) require at least
# two values
assert result[:11].isna().all()
assert not result[11:].isna().any()

# check series of length 0
result = getattr(Series(dtype=object).ewm(com=50, min_periods=min_periods), name)()
tm.assert_series_equal(result, Series(dtype="float64"))

# check series of length 1
result = getattr(Series([1.0]).ewm(50, min_periods=min_periods), name)()
if name == "mean":
tm.assert_series_equal(result, Series([1.0]))
else:
# ewm.std, ewm.var with bias=False require at least
# two values
tm.assert_series_equal(result, Series([np.NaN]))

# pass in ints
result2 = getattr(Series(np.arange(50)).ewm(span=10), name)()
assert result2.dtype == np.float_
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