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406 lines
14 KiB
406 lines
14 KiB
import warnings
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import numpy as np
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import pytest
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from pandas import (
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DataFrame,
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Index,
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MultiIndex,
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Series,
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date_range,
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)
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import pandas._testing as tm
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from pandas.core.algorithms import safe_sort
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def test_rolling_cov(series):
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A = series
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B = A + np.random.randn(len(A))
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result = A.rolling(window=50, min_periods=25).cov(B)
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tm.assert_almost_equal(result[-1], np.cov(A[-50:], B[-50:])[0, 1])
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def test_rolling_corr(series):
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A = series
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B = A + np.random.randn(len(A))
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result = A.rolling(window=50, min_periods=25).corr(B)
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tm.assert_almost_equal(result[-1], np.corrcoef(A[-50:], B[-50:])[0, 1])
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# test for correct bias correction
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a = tm.makeTimeSeries()
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b = tm.makeTimeSeries()
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a[:5] = np.nan
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b[:10] = np.nan
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result = a.rolling(window=len(a), min_periods=1).corr(b)
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tm.assert_almost_equal(result[-1], a.corr(b))
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@pytest.mark.parametrize("func", ["cov", "corr"])
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def test_rolling_pairwise_cov_corr(func, frame):
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result = getattr(frame.rolling(window=10, min_periods=5), func)()
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result = result.loc[(slice(None), 1), 5]
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result.index = result.index.droplevel(1)
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expected = getattr(frame[1].rolling(window=10, min_periods=5), func)(frame[5])
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tm.assert_series_equal(result, expected, check_names=False)
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@pytest.mark.parametrize("method", ["corr", "cov"])
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def test_flex_binary_frame(method, frame):
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series = frame[1]
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res = getattr(series.rolling(window=10), method)(frame)
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res2 = getattr(frame.rolling(window=10), method)(series)
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exp = frame.apply(lambda x: getattr(series.rolling(window=10), method)(x))
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tm.assert_frame_equal(res, exp)
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tm.assert_frame_equal(res2, exp)
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frame2 = frame.copy()
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frame2.values[:] = np.random.randn(*frame2.shape)
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res3 = getattr(frame.rolling(window=10), method)(frame2)
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exp = DataFrame(
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{k: getattr(frame[k].rolling(window=10), method)(frame2[k]) for k in frame}
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)
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tm.assert_frame_equal(res3, exp)
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@pytest.mark.parametrize("window", range(7))
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def test_rolling_corr_with_zero_variance(window):
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# GH 18430
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s = Series(np.zeros(20))
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other = Series(np.arange(20))
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assert s.rolling(window=window).corr(other=other).isna().all()
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def test_corr_sanity():
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# GH 3155
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df = DataFrame(
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np.array(
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[
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[0.87024726, 0.18505595],
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[0.64355431, 0.3091617],
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[0.92372966, 0.50552513],
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[0.00203756, 0.04520709],
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[0.84780328, 0.33394331],
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[0.78369152, 0.63919667],
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]
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)
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)
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res = df[0].rolling(5, center=True).corr(df[1])
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assert all(np.abs(np.nan_to_num(x)) <= 1 for x in res)
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df = DataFrame(np.random.rand(30, 2))
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res = df[0].rolling(5, center=True).corr(df[1])
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assert all(np.abs(np.nan_to_num(x)) <= 1 for x in res)
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def test_rolling_cov_diff_length():
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# GH 7512
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s1 = Series([1, 2, 3], index=[0, 1, 2])
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s2 = Series([1, 3], index=[0, 2])
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result = s1.rolling(window=3, min_periods=2).cov(s2)
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expected = Series([None, None, 2.0])
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tm.assert_series_equal(result, expected)
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s2a = Series([1, None, 3], index=[0, 1, 2])
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result = s1.rolling(window=3, min_periods=2).cov(s2a)
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tm.assert_series_equal(result, expected)
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def test_rolling_corr_diff_length():
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# GH 7512
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s1 = Series([1, 2, 3], index=[0, 1, 2])
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s2 = Series([1, 3], index=[0, 2])
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result = s1.rolling(window=3, min_periods=2).corr(s2)
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expected = Series([None, None, 1.0])
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tm.assert_series_equal(result, expected)
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s2a = Series([1, None, 3], index=[0, 1, 2])
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result = s1.rolling(window=3, min_periods=2).corr(s2a)
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tm.assert_series_equal(result, expected)
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@pytest.mark.parametrize(
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"f",
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[
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lambda x: (x.rolling(window=10, min_periods=5).cov(x, pairwise=True)),
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lambda x: (x.rolling(window=10, min_periods=5).corr(x, pairwise=True)),
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],
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)
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def test_rolling_functions_window_non_shrinkage_binary(f):
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# corr/cov return a MI DataFrame
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df = DataFrame(
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[[1, 5], [3, 2], [3, 9], [-1, 0]],
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columns=Index(["A", "B"], name="foo"),
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index=Index(range(4), name="bar"),
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)
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df_expected = DataFrame(
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columns=Index(["A", "B"], name="foo"),
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index=MultiIndex.from_product([df.index, df.columns], names=["bar", "foo"]),
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dtype="float64",
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)
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df_result = f(df)
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tm.assert_frame_equal(df_result, df_expected)
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@pytest.mark.parametrize(
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"f",
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[
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lambda x: (x.rolling(window=10, min_periods=5).cov(x, pairwise=True)),
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lambda x: (x.rolling(window=10, min_periods=5).corr(x, pairwise=True)),
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],
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)
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def test_moment_functions_zero_length_pairwise(f):
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df1 = DataFrame()
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df2 = DataFrame(columns=Index(["a"], name="foo"), index=Index([], name="bar"))
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df2["a"] = df2["a"].astype("float64")
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df1_expected = DataFrame(
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index=MultiIndex.from_product([df1.index, df1.columns]), columns=Index([])
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)
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df2_expected = DataFrame(
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index=MultiIndex.from_product([df2.index, df2.columns], names=["bar", "foo"]),
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columns=Index(["a"], name="foo"),
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dtype="float64",
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)
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df1_result = f(df1)
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tm.assert_frame_equal(df1_result, df1_expected)
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df2_result = f(df2)
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tm.assert_frame_equal(df2_result, df2_expected)
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class TestPairwise:
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# GH 7738
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@pytest.mark.parametrize("f", [lambda x: x.cov(), lambda x: x.corr()])
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def test_no_flex(self, pairwise_frames, pairwise_target_frame, f):
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# DataFrame methods (which do not call flex_binary_moment())
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result = f(pairwise_frames)
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tm.assert_index_equal(result.index, pairwise_frames.columns)
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tm.assert_index_equal(result.columns, pairwise_frames.columns)
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expected = f(pairwise_target_frame)
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# since we have sorted the results
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# we can only compare non-nans
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result = result.dropna().values
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expected = expected.dropna().values
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tm.assert_numpy_array_equal(result, expected, check_dtype=False)
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@pytest.mark.parametrize(
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"f",
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[
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lambda x: x.expanding().cov(pairwise=True),
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lambda x: x.expanding().corr(pairwise=True),
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lambda x: x.rolling(window=3).cov(pairwise=True),
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lambda x: x.rolling(window=3).corr(pairwise=True),
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lambda x: x.ewm(com=3).cov(pairwise=True),
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lambda x: x.ewm(com=3).corr(pairwise=True),
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],
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)
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def test_pairwise_with_self(self, pairwise_frames, pairwise_target_frame, f):
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# DataFrame with itself, pairwise=True
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# note that we may construct the 1st level of the MI
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# in a non-monotonic way, so compare accordingly
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result = f(pairwise_frames)
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tm.assert_index_equal(
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result.index.levels[0], pairwise_frames.index, check_names=False
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)
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tm.assert_numpy_array_equal(
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safe_sort(result.index.levels[1]),
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safe_sort(pairwise_frames.columns.unique()),
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)
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tm.assert_index_equal(result.columns, pairwise_frames.columns)
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expected = f(pairwise_target_frame)
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# since we have sorted the results
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# we can only compare non-nans
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result = result.dropna().values
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expected = expected.dropna().values
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tm.assert_numpy_array_equal(result, expected, check_dtype=False)
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@pytest.mark.parametrize(
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"f",
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[
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lambda x: x.expanding().cov(pairwise=False),
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lambda x: x.expanding().corr(pairwise=False),
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lambda x: x.rolling(window=3).cov(pairwise=False),
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lambda x: x.rolling(window=3).corr(pairwise=False),
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lambda x: x.ewm(com=3).cov(pairwise=False),
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lambda x: x.ewm(com=3).corr(pairwise=False),
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],
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)
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def test_no_pairwise_with_self(self, pairwise_frames, pairwise_target_frame, f):
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# DataFrame with itself, pairwise=False
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result = f(pairwise_frames)
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tm.assert_index_equal(result.index, pairwise_frames.index)
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tm.assert_index_equal(result.columns, pairwise_frames.columns)
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expected = f(pairwise_target_frame)
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# since we have sorted the results
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# we can only compare non-nans
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result = result.dropna().values
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expected = expected.dropna().values
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tm.assert_numpy_array_equal(result, expected, check_dtype=False)
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@pytest.mark.parametrize(
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"f",
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[
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lambda x, y: x.expanding().cov(y, pairwise=True),
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lambda x, y: x.expanding().corr(y, pairwise=True),
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lambda x, y: x.rolling(window=3).cov(y, pairwise=True),
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lambda x, y: x.rolling(window=3).corr(y, pairwise=True),
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lambda x, y: x.ewm(com=3).cov(y, pairwise=True),
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lambda x, y: x.ewm(com=3).corr(y, pairwise=True),
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],
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)
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def test_pairwise_with_other(
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self, pairwise_frames, pairwise_target_frame, pairwise_other_frame, f
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):
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# DataFrame with another DataFrame, pairwise=True
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result = f(pairwise_frames, pairwise_other_frame)
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tm.assert_index_equal(
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result.index.levels[0], pairwise_frames.index, check_names=False
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)
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tm.assert_numpy_array_equal(
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safe_sort(result.index.levels[1]),
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safe_sort(pairwise_other_frame.columns.unique()),
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)
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expected = f(pairwise_target_frame, pairwise_other_frame)
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# since we have sorted the results
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# we can only compare non-nans
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result = result.dropna().values
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expected = expected.dropna().values
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tm.assert_numpy_array_equal(result, expected, check_dtype=False)
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@pytest.mark.parametrize(
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"f",
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[
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lambda x, y: x.expanding().cov(y, pairwise=False),
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lambda x, y: x.expanding().corr(y, pairwise=False),
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lambda x, y: x.rolling(window=3).cov(y, pairwise=False),
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lambda x, y: x.rolling(window=3).corr(y, pairwise=False),
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lambda x, y: x.ewm(com=3).cov(y, pairwise=False),
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lambda x, y: x.ewm(com=3).corr(y, pairwise=False),
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],
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)
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def test_no_pairwise_with_other(self, pairwise_frames, pairwise_other_frame, f):
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# DataFrame with another DataFrame, pairwise=False
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result = (
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f(pairwise_frames, pairwise_other_frame)
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if pairwise_frames.columns.is_unique
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else None
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)
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if result is not None:
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with warnings.catch_warnings(record=True):
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warnings.simplefilter("ignore", RuntimeWarning)
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# we can have int and str columns
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expected_index = pairwise_frames.index.union(pairwise_other_frame.index)
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expected_columns = pairwise_frames.columns.union(
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pairwise_other_frame.columns
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)
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tm.assert_index_equal(result.index, expected_index)
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tm.assert_index_equal(result.columns, expected_columns)
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else:
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with pytest.raises(ValueError, match="'arg1' columns are not unique"):
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f(pairwise_frames, pairwise_other_frame)
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with pytest.raises(ValueError, match="'arg2' columns are not unique"):
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f(pairwise_other_frame, pairwise_frames)
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@pytest.mark.parametrize(
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"f",
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[
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lambda x, y: x.expanding().cov(y),
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lambda x, y: x.expanding().corr(y),
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lambda x, y: x.rolling(window=3).cov(y),
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lambda x, y: x.rolling(window=3).corr(y),
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lambda x, y: x.ewm(com=3).cov(y),
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lambda x, y: x.ewm(com=3).corr(y),
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],
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)
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def test_pairwise_with_series(self, pairwise_frames, pairwise_target_frame, f):
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# DataFrame with a Series
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result = f(pairwise_frames, Series([1, 1, 3, 8]))
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tm.assert_index_equal(result.index, pairwise_frames.index)
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tm.assert_index_equal(result.columns, pairwise_frames.columns)
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expected = f(pairwise_target_frame, Series([1, 1, 3, 8]))
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# since we have sorted the results
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# we can only compare non-nans
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result = result.dropna().values
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expected = expected.dropna().values
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tm.assert_numpy_array_equal(result, expected, check_dtype=False)
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result = f(Series([1, 1, 3, 8]), pairwise_frames)
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tm.assert_index_equal(result.index, pairwise_frames.index)
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tm.assert_index_equal(result.columns, pairwise_frames.columns)
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expected = f(Series([1, 1, 3, 8]), pairwise_target_frame)
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# since we have sorted the results
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# we can only compare non-nans
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result = result.dropna().values
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expected = expected.dropna().values
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tm.assert_numpy_array_equal(result, expected, check_dtype=False)
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def test_corr_freq_memory_error(self):
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# GH 31789
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s = Series(range(5), index=date_range("2020", periods=5))
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result = s.rolling("12H").corr(s)
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expected = Series([np.nan] * 5, index=date_range("2020", periods=5))
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tm.assert_series_equal(result, expected)
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def test_cov_mulittindex(self):
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# GH 34440
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columns = MultiIndex.from_product([list("ab"), list("xy"), list("AB")])
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index = range(3)
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df = DataFrame(np.arange(24).reshape(3, 8), index=index, columns=columns)
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result = df.ewm(alpha=0.1).cov()
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index = MultiIndex.from_product([range(3), list("ab"), list("xy"), list("AB")])
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columns = MultiIndex.from_product([list("ab"), list("xy"), list("AB")])
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expected = DataFrame(
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np.vstack(
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(
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np.full((8, 8), np.NaN),
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np.full((8, 8), 32.000000),
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np.full((8, 8), 63.881919),
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)
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),
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index=index,
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columns=columns,
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)
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tm.assert_frame_equal(result, expected)
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def test_multindex_columns_pairwise_func(self):
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# GH 21157
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columns = MultiIndex.from_arrays([["M", "N"], ["P", "Q"]], names=["a", "b"])
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df = DataFrame(np.ones((5, 2)), columns=columns)
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result = df.rolling(3).corr()
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expected = DataFrame(
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np.nan,
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index=MultiIndex.from_arrays(
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[np.repeat(np.arange(5), 2), ["M", "N"] * 5, ["P", "Q"] * 5],
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names=[None, "a", "b"],
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),
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columns=columns,
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)
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tm.assert_frame_equal(result, expected)
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