Old engine for Continuous Time Bayesian Networks. Superseded by reCTBN. 🐍
https://github.com/madlabunimib/PyCTBN
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93 lines
2.3 KiB
93 lines
2.3 KiB
4 years ago
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import numpy as np
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import pytest
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from pandas import Categorical, Series
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import pandas._testing as tm
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from pandas.core.construction import create_series_with_explicit_dtype
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def test_nunique():
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# basics.rst doc example
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series = Series(np.random.randn(500))
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series[20:500] = np.nan
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series[10:20] = 5000
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result = series.nunique()
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assert result == 11
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# GH 18051
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s = Series(Categorical([]))
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assert s.nunique() == 0
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s = Series(Categorical([np.nan]))
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assert s.nunique() == 0
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def test_unique():
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# GH714 also, dtype=float
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s = Series([1.2345] * 100)
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s[::2] = np.nan
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result = s.unique()
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assert len(result) == 2
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s = Series([1.2345] * 100, dtype="f4")
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s[::2] = np.nan
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result = s.unique()
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assert len(result) == 2
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# NAs in object arrays #714
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s = Series(["foo"] * 100, dtype="O")
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s[::2] = np.nan
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result = s.unique()
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assert len(result) == 2
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# decision about None
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s = Series([1, 2, 3, None, None, None], dtype=object)
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result = s.unique()
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expected = np.array([1, 2, 3, None], dtype=object)
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tm.assert_numpy_array_equal(result, expected)
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# GH 18051
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s = Series(Categorical([]))
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tm.assert_categorical_equal(s.unique(), Categorical([]))
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s = Series(Categorical([np.nan]))
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tm.assert_categorical_equal(s.unique(), Categorical([np.nan]))
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def test_unique_data_ownership():
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# it works! #1807
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Series(Series(["a", "c", "b"]).unique()).sort_values()
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@pytest.mark.parametrize(
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"data, expected",
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[
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(np.random.randint(0, 10, size=1000), False),
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(np.arange(1000), True),
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([], True),
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([np.nan], True),
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(["foo", "bar", np.nan], True),
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(["foo", "foo", np.nan], False),
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(["foo", "bar", np.nan, np.nan], False),
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],
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)
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def test_is_unique(data, expected):
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# GH11946 / GH25180
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s = create_series_with_explicit_dtype(data, dtype_if_empty=object)
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assert s.is_unique is expected
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def test_is_unique_class_ne(capsys):
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# GH 20661
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class Foo:
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def __init__(self, val):
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self._value = val
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def __ne__(self, other):
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raise Exception("NEQ not supported")
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with capsys.disabled():
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li = [Foo(i) for i in range(5)]
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s = Series(li, index=list(range(5)))
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s.is_unique
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captured = capsys.readouterr()
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assert len(captured.err) == 0
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