Old engine for Continuous Time Bayesian Networks. Superseded by reCTBN. 🐍
https://github.com/madlabunimib/PyCTBN
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116 lines
3.8 KiB
116 lines
3.8 KiB
4 years ago
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import numpy as np
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import pytest
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import pandas as pd
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from pandas import DataFrame, Period, Series, period_range
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import pandas._testing as tm
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from pandas.core.arrays import PeriodArray
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class TestSeriesPeriod:
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def setup_method(self, method):
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self.series = Series(period_range("2000-01-01", periods=10, freq="D"))
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def test_auto_conversion(self):
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series = Series(list(period_range("2000-01-01", periods=10, freq="D")))
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assert series.dtype == "Period[D]"
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series = pd.Series(
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[pd.Period("2011-01-01", freq="D"), pd.Period("2011-02-01", freq="D")]
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)
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assert series.dtype == "Period[D]"
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def test_isna(self):
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# GH 13737
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s = Series([pd.Period("2011-01", freq="M"), pd.Period("NaT", freq="M")])
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tm.assert_series_equal(s.isna(), Series([False, True]))
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tm.assert_series_equal(s.notna(), Series([True, False]))
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def test_dropna(self):
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# GH 13737
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s = Series([pd.Period("2011-01", freq="M"), pd.Period("NaT", freq="M")])
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tm.assert_series_equal(s.dropna(), Series([pd.Period("2011-01", freq="M")]))
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# ---------------------------------------------------------------------
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# NaT support
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@pytest.mark.xfail(reason="PeriodDtype Series not supported yet")
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def test_NaT_scalar(self):
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series = Series([0, 1000, 2000, pd._libs.iNaT], dtype="period[D]")
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val = series[3]
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assert pd.isna(val)
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series[2] = val
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assert pd.isna(series[2])
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def test_NaT_cast(self):
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result = Series([np.nan]).astype("period[D]")
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expected = Series([pd.NaT], dtype="period[D]")
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tm.assert_series_equal(result, expected)
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def test_set_none(self):
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self.series[3] = None
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assert self.series[3] is pd.NaT
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self.series[3:5] = None
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assert self.series[4] is pd.NaT
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def test_set_nan(self):
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# Do we want to allow this?
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self.series[5] = np.nan
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assert self.series[5] is pd.NaT
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self.series[5:7] = np.nan
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assert self.series[6] is pd.NaT
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def test_intercept_astype_object(self):
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expected = self.series.astype("object")
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df = DataFrame({"a": self.series, "b": np.random.randn(len(self.series))})
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result = df.values.squeeze()
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assert (result[:, 0] == expected.values).all()
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df = DataFrame({"a": self.series, "b": ["foo"] * len(self.series)})
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result = df.values.squeeze()
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assert (result[:, 0] == expected.values).all()
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@pytest.mark.parametrize(
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"input_vals",
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[
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[Period("2016-01", freq="M"), Period("2016-02", freq="M")],
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[Period("2016-01-01", freq="D"), Period("2016-01-02", freq="D")],
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[
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Period("2016-01-01 00:00:00", freq="H"),
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Period("2016-01-01 01:00:00", freq="H"),
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],
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[
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Period("2016-01-01 00:00:00", freq="M"),
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Period("2016-01-01 00:01:00", freq="M"),
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],
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[
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Period("2016-01-01 00:00:00", freq="S"),
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Period("2016-01-01 00:00:01", freq="S"),
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],
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],
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)
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def test_end_time_timevalues(self, input_vals):
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# GH 17157
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# Check that the time part of the Period is adjusted by end_time
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# when using the dt accessor on a Series
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input_vals = PeriodArray._from_sequence(np.asarray(input_vals))
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s = Series(input_vals)
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result = s.dt.end_time
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expected = s.apply(lambda x: x.end_time)
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tm.assert_series_equal(result, expected)
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@pytest.mark.parametrize("input_vals", [("2001"), ("NaT")])
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def test_to_period(self, input_vals):
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# GH 21205
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expected = Series([input_vals], dtype="Period[D]")
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result = Series([input_vals], dtype="datetime64[ns]").dt.to_period("D")
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tm.assert_series_equal(result, expected)
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