Old engine for Continuous Time Bayesian Networks. Superseded by reCTBN. 🐍
https://github.com/madlabunimib/PyCTBN
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429 lines
14 KiB
429 lines
14 KiB
import numpy as np
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import pytest
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from pandas.errors import PerformanceWarning
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from pandas.core.dtypes.common import is_object_dtype
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import pandas as pd
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from pandas import SparseDtype
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import pandas._testing as tm
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from pandas.arrays import SparseArray
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from pandas.tests.extension import base
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def make_data(fill_value):
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if np.isnan(fill_value):
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data = np.random.uniform(size=100)
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else:
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data = np.random.randint(1, 100, size=100)
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if data[0] == data[1]:
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data[0] += 1
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data[2::3] = fill_value
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return data
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@pytest.fixture
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def dtype():
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return SparseDtype()
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@pytest.fixture(params=[0, np.nan])
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def data(request):
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"""Length-100 PeriodArray for semantics test."""
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res = SparseArray(make_data(request.param), fill_value=request.param)
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return res
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@pytest.fixture
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def data_for_twos(request):
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return SparseArray(np.ones(100) * 2)
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@pytest.fixture(params=[0, np.nan])
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def data_missing(request):
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"""Length 2 array with [NA, Valid]"""
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return SparseArray([np.nan, 1], fill_value=request.param)
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@pytest.fixture(params=[0, np.nan])
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def data_repeated(request):
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"""Return different versions of data for count times"""
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def gen(count):
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for _ in range(count):
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yield SparseArray(make_data(request.param), fill_value=request.param)
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yield gen
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@pytest.fixture(params=[0, np.nan])
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def data_for_sorting(request):
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return SparseArray([2, 3, 1], fill_value=request.param)
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@pytest.fixture(params=[0, np.nan])
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def data_missing_for_sorting(request):
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return SparseArray([2, np.nan, 1], fill_value=request.param)
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@pytest.fixture
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def na_value():
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return np.nan
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@pytest.fixture
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def na_cmp():
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return lambda left, right: pd.isna(left) and pd.isna(right)
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@pytest.fixture(params=[0, np.nan])
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def data_for_grouping(request):
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return SparseArray([1, 1, np.nan, np.nan, 2, 2, 1, 3], fill_value=request.param)
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class BaseSparseTests:
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def _check_unsupported(self, data):
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if data.dtype == SparseDtype(int, 0):
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pytest.skip("Can't store nan in int array.")
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@pytest.mark.xfail(reason="SparseArray does not support setitem")
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def test_ravel(self, data):
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super().test_ravel(data)
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class TestDtype(BaseSparseTests, base.BaseDtypeTests):
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def test_array_type_with_arg(self, data, dtype):
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assert dtype.construct_array_type() is SparseArray
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class TestInterface(BaseSparseTests, base.BaseInterfaceTests):
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def test_no_values_attribute(self, data):
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pytest.skip("We have values")
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def test_copy(self, data):
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# __setitem__ does not work, so we only have a smoke-test
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data.copy()
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def test_view(self, data):
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# __setitem__ does not work, so we only have a smoke-test
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data.view()
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class TestConstructors(BaseSparseTests, base.BaseConstructorsTests):
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pass
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class TestReshaping(BaseSparseTests, base.BaseReshapingTests):
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def test_concat_mixed_dtypes(self, data):
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# https://github.com/pandas-dev/pandas/issues/20762
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# This should be the same, aside from concat([sparse, float])
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df1 = pd.DataFrame({"A": data[:3]})
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df2 = pd.DataFrame({"A": [1, 2, 3]})
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df3 = pd.DataFrame({"A": ["a", "b", "c"]}).astype("category")
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dfs = [df1, df2, df3]
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# dataframes
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result = pd.concat(dfs)
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expected = pd.concat(
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[x.apply(lambda s: np.asarray(s).astype(object)) for x in dfs]
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)
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self.assert_frame_equal(result, expected)
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def test_concat_columns(self, data, na_value):
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self._check_unsupported(data)
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super().test_concat_columns(data, na_value)
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def test_concat_extension_arrays_copy_false(self, data, na_value):
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self._check_unsupported(data)
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super().test_concat_extension_arrays_copy_false(data, na_value)
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def test_align(self, data, na_value):
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self._check_unsupported(data)
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super().test_align(data, na_value)
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def test_align_frame(self, data, na_value):
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self._check_unsupported(data)
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super().test_align_frame(data, na_value)
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def test_align_series_frame(self, data, na_value):
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self._check_unsupported(data)
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super().test_align_series_frame(data, na_value)
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def test_merge(self, data, na_value):
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self._check_unsupported(data)
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super().test_merge(data, na_value)
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class TestGetitem(BaseSparseTests, base.BaseGetitemTests):
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def test_get(self, data):
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s = pd.Series(data, index=[2 * i for i in range(len(data))])
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if np.isnan(s.values.fill_value):
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assert np.isnan(s.get(4)) and np.isnan(s.iloc[2])
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else:
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assert s.get(4) == s.iloc[2]
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assert s.get(2) == s.iloc[1]
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def test_reindex(self, data, na_value):
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self._check_unsupported(data)
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super().test_reindex(data, na_value)
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# Skipping TestSetitem, since we don't implement it.
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class TestMissing(BaseSparseTests, base.BaseMissingTests):
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def test_isna(self, data_missing):
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expected_dtype = SparseDtype(bool, pd.isna(data_missing.dtype.fill_value))
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expected = SparseArray([True, False], dtype=expected_dtype)
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result = pd.isna(data_missing)
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self.assert_equal(result, expected)
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result = pd.Series(data_missing).isna()
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expected = pd.Series(expected)
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self.assert_series_equal(result, expected)
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# GH 21189
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result = pd.Series(data_missing).drop([0, 1]).isna()
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expected = pd.Series([], dtype=expected_dtype)
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self.assert_series_equal(result, expected)
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def test_fillna_limit_pad(self, data_missing):
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with tm.assert_produces_warning(PerformanceWarning):
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super().test_fillna_limit_pad(data_missing)
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def test_fillna_limit_backfill(self, data_missing):
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with tm.assert_produces_warning(PerformanceWarning):
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super().test_fillna_limit_backfill(data_missing)
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def test_fillna_series_method(self, data_missing):
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with tm.assert_produces_warning(PerformanceWarning):
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super().test_fillna_limit_backfill(data_missing)
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@pytest.mark.skip(reason="Unsupported")
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def test_fillna_series(self):
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# this one looks doable.
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pass
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def test_fillna_frame(self, data_missing):
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# Have to override to specify that fill_value will change.
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fill_value = data_missing[1]
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result = pd.DataFrame({"A": data_missing, "B": [1, 2]}).fillna(fill_value)
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if pd.isna(data_missing.fill_value):
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dtype = SparseDtype(data_missing.dtype, fill_value)
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else:
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dtype = data_missing.dtype
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expected = pd.DataFrame(
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{
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"A": data_missing._from_sequence([fill_value, fill_value], dtype=dtype),
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"B": [1, 2],
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}
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)
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self.assert_frame_equal(result, expected)
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class TestMethods(BaseSparseTests, base.BaseMethodsTests):
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def test_combine_le(self, data_repeated):
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# We return a Series[SparseArray].__le__ returns a
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# Series[Sparse[bool]]
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# rather than Series[bool]
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orig_data1, orig_data2 = data_repeated(2)
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s1 = pd.Series(orig_data1)
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s2 = pd.Series(orig_data2)
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result = s1.combine(s2, lambda x1, x2: x1 <= x2)
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expected = pd.Series(
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SparseArray(
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[a <= b for (a, b) in zip(list(orig_data1), list(orig_data2))],
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fill_value=False,
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)
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)
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self.assert_series_equal(result, expected)
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val = s1.iloc[0]
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result = s1.combine(val, lambda x1, x2: x1 <= x2)
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expected = pd.Series(
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SparseArray([a <= val for a in list(orig_data1)], fill_value=False)
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)
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self.assert_series_equal(result, expected)
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def test_fillna_copy_frame(self, data_missing):
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arr = data_missing.take([1, 1])
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df = pd.DataFrame({"A": arr})
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filled_val = df.iloc[0, 0]
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result = df.fillna(filled_val)
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assert df.values.base is not result.values.base
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assert df.A._values.to_dense() is arr.to_dense()
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def test_fillna_copy_series(self, data_missing):
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arr = data_missing.take([1, 1])
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ser = pd.Series(arr)
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filled_val = ser[0]
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result = ser.fillna(filled_val)
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assert ser._values is not result._values
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assert ser._values.to_dense() is arr.to_dense()
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@pytest.mark.skip(reason="Not Applicable")
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def test_fillna_length_mismatch(self, data_missing):
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pass
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def test_where_series(self, data, na_value):
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assert data[0] != data[1]
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cls = type(data)
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a, b = data[:2]
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ser = pd.Series(cls._from_sequence([a, a, b, b], dtype=data.dtype))
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cond = np.array([True, True, False, False])
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result = ser.where(cond)
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new_dtype = SparseDtype("float", 0.0)
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expected = pd.Series(
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cls._from_sequence([a, a, na_value, na_value], dtype=new_dtype)
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)
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self.assert_series_equal(result, expected)
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other = cls._from_sequence([a, b, a, b], dtype=data.dtype)
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cond = np.array([True, False, True, True])
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result = ser.where(cond, other)
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expected = pd.Series(cls._from_sequence([a, b, b, b], dtype=data.dtype))
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self.assert_series_equal(result, expected)
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def test_combine_first(self, data):
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if data.dtype.subtype == "int":
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# Right now this is upcasted to float, just like combine_first
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# for Series[int]
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pytest.skip("TODO(SparseArray.__setitem__ will preserve dtype.")
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super().test_combine_first(data)
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def test_searchsorted(self, data_for_sorting, as_series):
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with tm.assert_produces_warning(PerformanceWarning):
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super().test_searchsorted(data_for_sorting, as_series)
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def test_shift_0_periods(self, data):
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# GH#33856 shifting with periods=0 should return a copy, not same obj
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result = data.shift(0)
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data._sparse_values[0] = data._sparse_values[1]
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assert result._sparse_values[0] != result._sparse_values[1]
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@pytest.mark.parametrize(
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"method", ["argmax", "argmin"],
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)
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def test_argmin_argmax_all_na(self, method, data, na_value):
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# overriding because Sparse[int64, 0] cannot handle na_value
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self._check_unsupported(data)
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super().test_argmin_argmax_all_na(method, data, na_value)
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@pytest.mark.parametrize("box", [pd.array, pd.Series, pd.DataFrame])
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def test_equals(self, data, na_value, as_series, box):
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self._check_unsupported(data)
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super().test_equals(data, na_value, as_series, box)
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class TestCasting(BaseSparseTests, base.BaseCastingTests):
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def test_astype_object_series(self, all_data):
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# Unlike the base class, we do not expect the resulting Block
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# to be ObjectBlock
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ser = pd.Series(all_data, name="A")
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result = ser.astype(object)
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assert is_object_dtype(result._data.blocks[0].dtype)
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def test_astype_object_frame(self, all_data):
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# Unlike the base class, we do not expect the resulting Block
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# to be ObjectBlock
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df = pd.DataFrame({"A": all_data})
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result = df.astype(object)
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assert is_object_dtype(result._data.blocks[0].dtype)
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# FIXME: these currently fail; dont leave commented-out
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# check that we can compare the dtypes
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# comp = result.dtypes.equals(df.dtypes)
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# assert not comp.any()
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def test_astype_str(self, data):
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result = pd.Series(data[:5]).astype(str)
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expected_dtype = pd.SparseDtype(str, str(data.fill_value))
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expected = pd.Series([str(x) for x in data[:5]], dtype=expected_dtype)
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self.assert_series_equal(result, expected)
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@pytest.mark.xfail(raises=TypeError, reason="no sparse StringDtype")
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def test_astype_string(self, data):
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super().test_astype_string(data)
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class TestArithmeticOps(BaseSparseTests, base.BaseArithmeticOpsTests):
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series_scalar_exc = None
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frame_scalar_exc = None
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divmod_exc = None
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series_array_exc = None
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def _skip_if_different_combine(self, data):
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if data.fill_value == 0:
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# arith ops call on dtype.fill_value so that the sparsity
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# is maintained. Combine can't be called on a dtype in
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# general, so we can't make the expected. This is tested elsewhere
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raise pytest.skip("Incorrected expected from Series.combine")
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def test_error(self, data, all_arithmetic_operators):
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pass
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def test_arith_series_with_scalar(self, data, all_arithmetic_operators):
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self._skip_if_different_combine(data)
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super().test_arith_series_with_scalar(data, all_arithmetic_operators)
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def test_arith_series_with_array(self, data, all_arithmetic_operators):
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self._skip_if_different_combine(data)
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super().test_arith_series_with_array(data, all_arithmetic_operators)
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class TestComparisonOps(BaseSparseTests, base.BaseComparisonOpsTests):
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def _compare_other(self, s, data, op_name, other):
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op = self.get_op_from_name(op_name)
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# array
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result = pd.Series(op(data, other))
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# hard to test the fill value, since we don't know what expected
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# is in general.
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# Rely on tests in `tests/sparse` to validate that.
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assert isinstance(result.dtype, SparseDtype)
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assert result.dtype.subtype == np.dtype("bool")
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with np.errstate(all="ignore"):
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expected = pd.Series(
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SparseArray(
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op(np.asarray(data), np.asarray(other)),
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fill_value=result.values.fill_value,
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)
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)
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tm.assert_series_equal(result, expected)
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# series
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s = pd.Series(data)
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result = op(s, other)
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tm.assert_series_equal(result, expected)
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class TestPrinting(BaseSparseTests, base.BasePrintingTests):
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@pytest.mark.xfail(reason="Different repr", strict=True)
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def test_array_repr(self, data, size):
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super().test_array_repr(data, size)
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class TestParsing(BaseSparseTests, base.BaseParsingTests):
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@pytest.mark.parametrize("engine", ["c", "python"])
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def test_EA_types(self, engine, data):
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expected_msg = r".*must implement _from_sequence_of_strings.*"
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with pytest.raises(NotImplementedError, match=expected_msg):
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super().test_EA_types(engine, data)
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