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

Author: mroeschke

pandas/tests/window/moments/test_moments_consistency_ewm.py

@@ -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):

pandas/tests/window/moments/test_moments_ewm.py

@@ -1,4 +1,3 @@
-import numpy as np
 import pytest
 
 from pandas import (
@@ -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()
@@ -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
@@ -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_