Old engine for Continuous Time Bayesian Networks. Superseded by reCTBN. 🐍
https://github.com/madlabunimib/PyCTBN
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217 lines
8.2 KiB
217 lines
8.2 KiB
4 years ago
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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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df1s = [
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DataFrame([[2, 4], [1, 2], [5, 2], [8, 1]], columns=[0, 1]),
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DataFrame([[2, 4], [1, 2], [5, 2], [8, 1]], columns=[1, 0]),
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DataFrame([[2, 4], [1, 2], [5, 2], [8, 1]], columns=[1, 1]),
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DataFrame([[2, 4], [1, 2], [5, 2], [8, 1]], columns=["C", "C"]),
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DataFrame([[2, 4], [1, 2], [5, 2], [8, 1]], columns=[1.0, 0]),
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DataFrame([[2, 4], [1, 2], [5, 2], [8, 1]], columns=[0.0, 1]),
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DataFrame([[2, 4], [1, 2], [5, 2], [8, 1]], columns=["C", 1]),
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DataFrame([[2.0, 4.0], [1.0, 2.0], [5.0, 2.0], [8.0, 1.0]], columns=[1, 0.0]),
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DataFrame([[2, 4.0], [1, 2.0], [5, 2.0], [8, 1.0]], columns=[0, 1.0]),
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DataFrame([[2, 4], [1, 2], [5, 2], [8, 1.0]], columns=[1.0, "X"]),
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]
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df2 = DataFrame(
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[[None, 1, 1], [None, 1, 2], [None, 3, 2], [None, 8, 1]],
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columns=["Y", "Z", "X"],
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)
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s = Series([1, 1, 3, 8])
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def compare(self, result, expected):
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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("f", [lambda x: x.cov(), lambda x: x.corr()])
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def test_no_flex(self, f):
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# DataFrame methods (which do not call _flex_binary_moment())
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results = [f(df) for df in self.df1s]
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for (df, result) in zip(self.df1s, results):
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tm.assert_index_equal(result.index, df.columns)
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tm.assert_index_equal(result.columns, df.columns)
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for i, result in enumerate(results):
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if i > 0:
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self.compare(result, results[0])
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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, 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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results = []
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for i, df in enumerate(self.df1s):
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result = f(df)
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tm.assert_index_equal(result.index.levels[0], df.index, check_names=False)
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tm.assert_numpy_array_equal(
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safe_sort(result.index.levels[1]), safe_sort(df.columns.unique())
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)
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tm.assert_index_equal(result.columns, df.columns)
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results.append(df)
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for i, result in enumerate(results):
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if i > 0:
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self.compare(result, results[0])
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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, f):
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# DataFrame with itself, pairwise=False
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results = [f(df) for df in self.df1s]
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for (df, result) in zip(self.df1s, results):
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tm.assert_index_equal(result.index, df.index)
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tm.assert_index_equal(result.columns, df.columns)
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for i, result in enumerate(results):
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if i > 0:
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self.compare(result, results[0])
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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(self, f):
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# DataFrame with another DataFrame, pairwise=True
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results = [f(df, self.df2) for df in self.df1s]
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for (df, result) in zip(self.df1s, results):
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tm.assert_index_equal(result.index.levels[0], df.index, check_names=False)
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tm.assert_numpy_array_equal(
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safe_sort(result.index.levels[1]), safe_sort(self.df2.columns.unique())
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)
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for i, result in enumerate(results):
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if i > 0:
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self.compare(result, results[0])
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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, f):
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# DataFrame with another DataFrame, pairwise=False
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results = [
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f(df, self.df2) if df.columns.is_unique else None for df in self.df1s
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]
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for (df, result) in zip(self.df1s, results):
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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 = df.index.union(self.df2.index)
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expected_columns = df.columns.union(self.df2.columns)
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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(df, self.df2)
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with pytest.raises(ValueError, match="'arg2' columns are not unique"):
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f(self.df2, df)
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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, f):
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# DataFrame with a Series
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results = [f(df, self.s) for df in self.df1s] + [
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f(self.s, df) for df in self.df1s
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]
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for (df, result) in zip(self.df1s, results):
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tm.assert_index_equal(result.index, df.index)
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tm.assert_index_equal(result.columns, df.columns)
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for i, result in enumerate(results):
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if i > 0:
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self.compare(result, results[0])
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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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