mirror of
https://github.com/PiBrewing/craftbeerpi4.git
synced 2024-11-15 11:38:12 +01:00
220 lines
8.5 KiB
Python
220 lines
8.5 KiB
Python
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import warnings
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import numpy as np
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import pytest
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from pandas import DataFrame, MultiIndex, Series, date_range
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import pandas._testing as tm
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from pandas.core.algorithms import safe_sort
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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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