Old engine for Continuous Time Bayesian Networks. Superseded by reCTBN. 🐍
https://github.com/madlabunimib/PyCTBN
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172 lines
5.4 KiB
172 lines
5.4 KiB
"""
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Tests for Series cumulative operations.
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See also
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--------
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tests.frame.test_cumulative
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"""
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from itertools import product
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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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import pandas._testing as tm
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def _check_accum_op(name, series, check_dtype=True):
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func = getattr(np, name)
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tm.assert_numpy_array_equal(
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func(series).values, func(np.array(series)), check_dtype=check_dtype,
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)
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# with missing values
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ts = series.copy()
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ts[::2] = np.NaN
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result = func(ts)[1::2]
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expected = func(np.array(ts.dropna()))
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tm.assert_numpy_array_equal(result.values, expected, check_dtype=False)
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class TestSeriesCumulativeOps:
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def test_cumsum(self, datetime_series):
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_check_accum_op("cumsum", datetime_series)
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def test_cumprod(self, datetime_series):
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_check_accum_op("cumprod", datetime_series)
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def test_cummin(self, datetime_series):
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tm.assert_numpy_array_equal(
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datetime_series.cummin().values,
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np.minimum.accumulate(np.array(datetime_series)),
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)
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ts = datetime_series.copy()
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ts[::2] = np.NaN
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result = ts.cummin()[1::2]
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expected = np.minimum.accumulate(ts.dropna())
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result.index = result.index._with_freq(None)
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tm.assert_series_equal(result, expected)
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def test_cummax(self, datetime_series):
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tm.assert_numpy_array_equal(
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datetime_series.cummax().values,
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np.maximum.accumulate(np.array(datetime_series)),
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)
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ts = datetime_series.copy()
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ts[::2] = np.NaN
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result = ts.cummax()[1::2]
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expected = np.maximum.accumulate(ts.dropna())
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result.index = result.index._with_freq(None)
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tm.assert_series_equal(result, expected)
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@pytest.mark.parametrize("tz", [None, "US/Pacific"])
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def test_cummin_datetime64(self, tz):
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s = pd.Series(
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pd.to_datetime(
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["NaT", "2000-1-2", "NaT", "2000-1-1", "NaT", "2000-1-3"]
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).tz_localize(tz)
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)
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expected = pd.Series(
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pd.to_datetime(
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["NaT", "2000-1-2", "NaT", "2000-1-1", "NaT", "2000-1-1"]
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).tz_localize(tz)
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)
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result = s.cummin(skipna=True)
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tm.assert_series_equal(expected, result)
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expected = pd.Series(
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pd.to_datetime(
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["NaT", "2000-1-2", "2000-1-2", "2000-1-1", "2000-1-1", "2000-1-1"]
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).tz_localize(tz)
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)
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result = s.cummin(skipna=False)
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tm.assert_series_equal(expected, result)
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@pytest.mark.parametrize("tz", [None, "US/Pacific"])
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def test_cummax_datetime64(self, tz):
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s = pd.Series(
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pd.to_datetime(
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["NaT", "2000-1-2", "NaT", "2000-1-1", "NaT", "2000-1-3"]
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).tz_localize(tz)
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)
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expected = pd.Series(
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pd.to_datetime(
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["NaT", "2000-1-2", "NaT", "2000-1-2", "NaT", "2000-1-3"]
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).tz_localize(tz)
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)
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result = s.cummax(skipna=True)
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tm.assert_series_equal(expected, result)
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expected = pd.Series(
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pd.to_datetime(
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["NaT", "2000-1-2", "2000-1-2", "2000-1-2", "2000-1-2", "2000-1-3"]
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).tz_localize(tz)
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)
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result = s.cummax(skipna=False)
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tm.assert_series_equal(expected, result)
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def test_cummin_timedelta64(self):
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s = pd.Series(pd.to_timedelta(["NaT", "2 min", "NaT", "1 min", "NaT", "3 min"]))
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expected = pd.Series(
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pd.to_timedelta(["NaT", "2 min", "NaT", "1 min", "NaT", "1 min"])
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)
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result = s.cummin(skipna=True)
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tm.assert_series_equal(expected, result)
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expected = pd.Series(
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pd.to_timedelta(["NaT", "2 min", "2 min", "1 min", "1 min", "1 min"])
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)
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result = s.cummin(skipna=False)
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tm.assert_series_equal(expected, result)
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def test_cummax_timedelta64(self):
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s = pd.Series(pd.to_timedelta(["NaT", "2 min", "NaT", "1 min", "NaT", "3 min"]))
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expected = pd.Series(
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pd.to_timedelta(["NaT", "2 min", "NaT", "2 min", "NaT", "3 min"])
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)
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result = s.cummax(skipna=True)
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tm.assert_series_equal(expected, result)
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expected = pd.Series(
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pd.to_timedelta(["NaT", "2 min", "2 min", "2 min", "2 min", "3 min"])
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)
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result = s.cummax(skipna=False)
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tm.assert_series_equal(expected, result)
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def test_cummethods_bool(self):
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# GH#6270
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a = pd.Series([False, False, False, True, True, False, False])
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b = ~a
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c = pd.Series([False] * len(b))
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d = ~c
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methods = {
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"cumsum": np.cumsum,
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"cumprod": np.cumprod,
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"cummin": np.minimum.accumulate,
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"cummax": np.maximum.accumulate,
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}
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args = product((a, b, c, d), methods)
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for s, method in args:
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expected = pd.Series(methods[method](s.values))
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result = getattr(s, method)()
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tm.assert_series_equal(result, expected)
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e = pd.Series([False, True, np.nan, False])
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cse = pd.Series([0, 1, np.nan, 1], dtype=object)
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cpe = pd.Series([False, 0, np.nan, 0])
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cmin = pd.Series([False, False, np.nan, False])
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cmax = pd.Series([False, True, np.nan, True])
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expecteds = {"cumsum": cse, "cumprod": cpe, "cummin": cmin, "cummax": cmax}
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for method in methods:
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res = getattr(e, method)()
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tm.assert_series_equal(res, expecteds[method])
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