Old engine for Continuous Time Bayesian Networks. Superseded by reCTBN. 🐍
https://github.com/madlabunimib/PyCTBN
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60 lines
1.9 KiB
60 lines
1.9 KiB
import warnings
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import pytest
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import pandas as pd
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import pandas._testing as tm
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from .base import BaseExtensionTests
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class BaseReduceTests(BaseExtensionTests):
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"""
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Reduction specific tests. Generally these only
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make sense for numeric/boolean operations.
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"""
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def check_reduce(self, s, op_name, skipna):
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result = getattr(s, op_name)(skipna=skipna)
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expected = getattr(s.astype("float64"), op_name)(skipna=skipna)
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tm.assert_almost_equal(result, expected)
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class BaseNoReduceTests(BaseReduceTests):
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""" we don't define any reductions """
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@pytest.mark.parametrize("skipna", [True, False])
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def test_reduce_series_numeric(self, data, all_numeric_reductions, skipna):
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op_name = all_numeric_reductions
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s = pd.Series(data)
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with pytest.raises(TypeError):
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getattr(s, op_name)(skipna=skipna)
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@pytest.mark.parametrize("skipna", [True, False])
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def test_reduce_series_boolean(self, data, all_boolean_reductions, skipna):
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op_name = all_boolean_reductions
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s = pd.Series(data)
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with pytest.raises(TypeError):
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getattr(s, op_name)(skipna=skipna)
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class BaseNumericReduceTests(BaseReduceTests):
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@pytest.mark.parametrize("skipna", [True, False])
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def test_reduce_series(self, data, all_numeric_reductions, skipna):
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op_name = all_numeric_reductions
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s = pd.Series(data)
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# min/max with empty produce numpy warnings
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with warnings.catch_warnings():
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warnings.simplefilter("ignore", RuntimeWarning)
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self.check_reduce(s, op_name, skipna)
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class BaseBooleanReduceTests(BaseReduceTests):
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@pytest.mark.parametrize("skipna", [True, False])
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def test_reduce_series(self, data, all_boolean_reductions, skipna):
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op_name = all_boolean_reductions
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s = pd.Series(data)
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self.check_reduce(s, op_name, skipna)
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