Old engine for Continuous Time Bayesian Networks. Superseded by reCTBN. 🐍
https://github.com/madlabunimib/PyCTBN
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445 lines
15 KiB
445 lines
15 KiB
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, Series, concat
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import pandas._testing as tm
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from pandas.core.base import DataError
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def test_rank_apply():
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lev1 = tm.rands_array(10, 100)
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lev2 = tm.rands_array(10, 130)
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lab1 = np.random.randint(0, 100, size=500)
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lab2 = np.random.randint(0, 130, size=500)
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df = DataFrame(
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{
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"value": np.random.randn(500),
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"key1": lev1.take(lab1),
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"key2": lev2.take(lab2),
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}
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)
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result = df.groupby(["key1", "key2"]).value.rank()
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expected = [piece.value.rank() for key, piece in df.groupby(["key1", "key2"])]
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expected = concat(expected, axis=0)
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expected = expected.reindex(result.index)
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tm.assert_series_equal(result, expected)
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result = df.groupby(["key1", "key2"]).value.rank(pct=True)
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expected = [
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piece.value.rank(pct=True) for key, piece in df.groupby(["key1", "key2"])
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]
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expected = concat(expected, axis=0)
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expected = expected.reindex(result.index)
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tm.assert_series_equal(result, expected)
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@pytest.mark.parametrize("grps", [["qux"], ["qux", "quux"]])
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@pytest.mark.parametrize(
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"vals",
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[
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[2, 2, 8, 2, 6],
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[
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pd.Timestamp("2018-01-02"),
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pd.Timestamp("2018-01-02"),
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pd.Timestamp("2018-01-08"),
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pd.Timestamp("2018-01-02"),
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pd.Timestamp("2018-01-06"),
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],
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],
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)
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@pytest.mark.parametrize(
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"ties_method,ascending,pct,exp",
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[
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("average", True, False, [2.0, 2.0, 5.0, 2.0, 4.0]),
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("average", True, True, [0.4, 0.4, 1.0, 0.4, 0.8]),
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("average", False, False, [4.0, 4.0, 1.0, 4.0, 2.0]),
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("average", False, True, [0.8, 0.8, 0.2, 0.8, 0.4]),
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("min", True, False, [1.0, 1.0, 5.0, 1.0, 4.0]),
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("min", True, True, [0.2, 0.2, 1.0, 0.2, 0.8]),
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("min", False, False, [3.0, 3.0, 1.0, 3.0, 2.0]),
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("min", False, True, [0.6, 0.6, 0.2, 0.6, 0.4]),
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("max", True, False, [3.0, 3.0, 5.0, 3.0, 4.0]),
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("max", True, True, [0.6, 0.6, 1.0, 0.6, 0.8]),
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("max", False, False, [5.0, 5.0, 1.0, 5.0, 2.0]),
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("max", False, True, [1.0, 1.0, 0.2, 1.0, 0.4]),
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("first", True, False, [1.0, 2.0, 5.0, 3.0, 4.0]),
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("first", True, True, [0.2, 0.4, 1.0, 0.6, 0.8]),
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("first", False, False, [3.0, 4.0, 1.0, 5.0, 2.0]),
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("first", False, True, [0.6, 0.8, 0.2, 1.0, 0.4]),
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("dense", True, False, [1.0, 1.0, 3.0, 1.0, 2.0]),
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("dense", True, True, [1.0 / 3.0, 1.0 / 3.0, 3.0 / 3.0, 1.0 / 3.0, 2.0 / 3.0]),
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("dense", False, False, [3.0, 3.0, 1.0, 3.0, 2.0]),
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("dense", False, True, [3.0 / 3.0, 3.0 / 3.0, 1.0 / 3.0, 3.0 / 3.0, 2.0 / 3.0]),
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],
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)
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def test_rank_args(grps, vals, ties_method, ascending, pct, exp):
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key = np.repeat(grps, len(vals))
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vals = vals * len(grps)
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df = DataFrame({"key": key, "val": vals})
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result = df.groupby("key").rank(method=ties_method, ascending=ascending, pct=pct)
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exp_df = DataFrame(exp * len(grps), columns=["val"])
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tm.assert_frame_equal(result, exp_df)
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@pytest.mark.parametrize("grps", [["qux"], ["qux", "quux"]])
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@pytest.mark.parametrize(
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"vals", [[-np.inf, -np.inf, np.nan, 1.0, np.nan, np.inf, np.inf]]
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)
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@pytest.mark.parametrize(
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"ties_method,ascending,na_option,exp",
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[
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("average", True, "keep", [1.5, 1.5, np.nan, 3, np.nan, 4.5, 4.5]),
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("average", True, "top", [3.5, 3.5, 1.5, 5.0, 1.5, 6.5, 6.5]),
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("average", True, "bottom", [1.5, 1.5, 6.5, 3.0, 6.5, 4.5, 4.5]),
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("average", False, "keep", [4.5, 4.5, np.nan, 3, np.nan, 1.5, 1.5]),
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("average", False, "top", [6.5, 6.5, 1.5, 5.0, 1.5, 3.5, 3.5]),
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("average", False, "bottom", [4.5, 4.5, 6.5, 3.0, 6.5, 1.5, 1.5]),
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("min", True, "keep", [1.0, 1.0, np.nan, 3.0, np.nan, 4.0, 4.0]),
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("min", True, "top", [3.0, 3.0, 1.0, 5.0, 1.0, 6.0, 6.0]),
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("min", True, "bottom", [1.0, 1.0, 6.0, 3.0, 6.0, 4.0, 4.0]),
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("min", False, "keep", [4.0, 4.0, np.nan, 3.0, np.nan, 1.0, 1.0]),
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("min", False, "top", [6.0, 6.0, 1.0, 5.0, 1.0, 3.0, 3.0]),
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("min", False, "bottom", [4.0, 4.0, 6.0, 3.0, 6.0, 1.0, 1.0]),
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("max", True, "keep", [2.0, 2.0, np.nan, 3.0, np.nan, 5.0, 5.0]),
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("max", True, "top", [4.0, 4.0, 2.0, 5.0, 2.0, 7.0, 7.0]),
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("max", True, "bottom", [2.0, 2.0, 7.0, 3.0, 7.0, 5.0, 5.0]),
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("max", False, "keep", [5.0, 5.0, np.nan, 3.0, np.nan, 2.0, 2.0]),
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("max", False, "top", [7.0, 7.0, 2.0, 5.0, 2.0, 4.0, 4.0]),
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("max", False, "bottom", [5.0, 5.0, 7.0, 3.0, 7.0, 2.0, 2.0]),
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("first", True, "keep", [1.0, 2.0, np.nan, 3.0, np.nan, 4.0, 5.0]),
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("first", True, "top", [3.0, 4.0, 1.0, 5.0, 2.0, 6.0, 7.0]),
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("first", True, "bottom", [1.0, 2.0, 6.0, 3.0, 7.0, 4.0, 5.0]),
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("first", False, "keep", [4.0, 5.0, np.nan, 3.0, np.nan, 1.0, 2.0]),
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("first", False, "top", [6.0, 7.0, 1.0, 5.0, 2.0, 3.0, 4.0]),
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("first", False, "bottom", [4.0, 5.0, 6.0, 3.0, 7.0, 1.0, 2.0]),
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("dense", True, "keep", [1.0, 1.0, np.nan, 2.0, np.nan, 3.0, 3.0]),
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("dense", True, "top", [2.0, 2.0, 1.0, 3.0, 1.0, 4.0, 4.0]),
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("dense", True, "bottom", [1.0, 1.0, 4.0, 2.0, 4.0, 3.0, 3.0]),
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("dense", False, "keep", [3.0, 3.0, np.nan, 2.0, np.nan, 1.0, 1.0]),
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("dense", False, "top", [4.0, 4.0, 1.0, 3.0, 1.0, 2.0, 2.0]),
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("dense", False, "bottom", [3.0, 3.0, 4.0, 2.0, 4.0, 1.0, 1.0]),
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],
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)
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def test_infs_n_nans(grps, vals, ties_method, ascending, na_option, exp):
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# GH 20561
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key = np.repeat(grps, len(vals))
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vals = vals * len(grps)
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df = DataFrame({"key": key, "val": vals})
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result = df.groupby("key").rank(
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method=ties_method, ascending=ascending, na_option=na_option
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)
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exp_df = DataFrame(exp * len(grps), columns=["val"])
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tm.assert_frame_equal(result, exp_df)
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@pytest.mark.parametrize("grps", [["qux"], ["qux", "quux"]])
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@pytest.mark.parametrize(
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"vals",
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[
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[2, 2, np.nan, 8, 2, 6, np.nan, np.nan],
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[
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pd.Timestamp("2018-01-02"),
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pd.Timestamp("2018-01-02"),
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np.nan,
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pd.Timestamp("2018-01-08"),
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pd.Timestamp("2018-01-02"),
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pd.Timestamp("2018-01-06"),
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np.nan,
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np.nan,
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],
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],
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)
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@pytest.mark.parametrize(
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"ties_method,ascending,na_option,pct,exp",
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[
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(
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"average",
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True,
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"keep",
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False,
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[2.0, 2.0, np.nan, 5.0, 2.0, 4.0, np.nan, np.nan],
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),
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(
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"average",
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True,
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"keep",
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True,
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[0.4, 0.4, np.nan, 1.0, 0.4, 0.8, np.nan, np.nan],
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),
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(
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"average",
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False,
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"keep",
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False,
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[4.0, 4.0, np.nan, 1.0, 4.0, 2.0, np.nan, np.nan],
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),
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(
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"average",
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False,
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"keep",
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True,
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[0.8, 0.8, np.nan, 0.2, 0.8, 0.4, np.nan, np.nan],
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),
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("min", True, "keep", False, [1.0, 1.0, np.nan, 5.0, 1.0, 4.0, np.nan, np.nan]),
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("min", True, "keep", True, [0.2, 0.2, np.nan, 1.0, 0.2, 0.8, np.nan, np.nan]),
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(
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"min",
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False,
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"keep",
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False,
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[3.0, 3.0, np.nan, 1.0, 3.0, 2.0, np.nan, np.nan],
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),
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("min", False, "keep", True, [0.6, 0.6, np.nan, 0.2, 0.6, 0.4, np.nan, np.nan]),
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("max", True, "keep", False, [3.0, 3.0, np.nan, 5.0, 3.0, 4.0, np.nan, np.nan]),
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("max", True, "keep", True, [0.6, 0.6, np.nan, 1.0, 0.6, 0.8, np.nan, np.nan]),
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(
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"max",
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False,
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"keep",
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False,
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[5.0, 5.0, np.nan, 1.0, 5.0, 2.0, np.nan, np.nan],
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),
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("max", False, "keep", True, [1.0, 1.0, np.nan, 0.2, 1.0, 0.4, np.nan, np.nan]),
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(
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"first",
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True,
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"keep",
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False,
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[1.0, 2.0, np.nan, 5.0, 3.0, 4.0, np.nan, np.nan],
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),
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(
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"first",
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True,
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"keep",
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True,
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[0.2, 0.4, np.nan, 1.0, 0.6, 0.8, np.nan, np.nan],
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),
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(
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"first",
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False,
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"keep",
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False,
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[3.0, 4.0, np.nan, 1.0, 5.0, 2.0, np.nan, np.nan],
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),
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(
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"first",
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False,
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"keep",
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True,
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[0.6, 0.8, np.nan, 0.2, 1.0, 0.4, np.nan, np.nan],
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),
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(
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"dense",
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True,
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"keep",
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False,
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[1.0, 1.0, np.nan, 3.0, 1.0, 2.0, np.nan, np.nan],
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),
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(
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"dense",
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True,
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"keep",
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True,
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[
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1.0 / 3.0,
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1.0 / 3.0,
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np.nan,
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3.0 / 3.0,
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1.0 / 3.0,
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2.0 / 3.0,
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np.nan,
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np.nan,
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],
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),
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(
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"dense",
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False,
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"keep",
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False,
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[3.0, 3.0, np.nan, 1.0, 3.0, 2.0, np.nan, np.nan],
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),
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(
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"dense",
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False,
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"keep",
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True,
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[
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3.0 / 3.0,
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3.0 / 3.0,
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np.nan,
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1.0 / 3.0,
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3.0 / 3.0,
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2.0 / 3.0,
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np.nan,
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np.nan,
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],
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),
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("average", True, "bottom", False, [2.0, 2.0, 7.0, 5.0, 2.0, 4.0, 7.0, 7.0]),
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(
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"average",
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True,
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"bottom",
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True,
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[0.25, 0.25, 0.875, 0.625, 0.25, 0.5, 0.875, 0.875],
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),
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("average", False, "bottom", False, [4.0, 4.0, 7.0, 1.0, 4.0, 2.0, 7.0, 7.0]),
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(
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"average",
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False,
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"bottom",
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True,
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[0.5, 0.5, 0.875, 0.125, 0.5, 0.25, 0.875, 0.875],
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),
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("min", True, "bottom", False, [1.0, 1.0, 6.0, 5.0, 1.0, 4.0, 6.0, 6.0]),
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(
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"min",
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True,
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"bottom",
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True,
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[0.125, 0.125, 0.75, 0.625, 0.125, 0.5, 0.75, 0.75],
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),
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("min", False, "bottom", False, [3.0, 3.0, 6.0, 1.0, 3.0, 2.0, 6.0, 6.0]),
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(
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"min",
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False,
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"bottom",
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True,
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[0.375, 0.375, 0.75, 0.125, 0.375, 0.25, 0.75, 0.75],
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),
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("max", True, "bottom", False, [3.0, 3.0, 8.0, 5.0, 3.0, 4.0, 8.0, 8.0]),
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("max", True, "bottom", True, [0.375, 0.375, 1.0, 0.625, 0.375, 0.5, 1.0, 1.0]),
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("max", False, "bottom", False, [5.0, 5.0, 8.0, 1.0, 5.0, 2.0, 8.0, 8.0]),
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(
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"max",
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False,
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"bottom",
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True,
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[0.625, 0.625, 1.0, 0.125, 0.625, 0.25, 1.0, 1.0],
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),
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("first", True, "bottom", False, [1.0, 2.0, 6.0, 5.0, 3.0, 4.0, 7.0, 8.0]),
|
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(
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"first",
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True,
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"bottom",
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True,
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[0.125, 0.25, 0.75, 0.625, 0.375, 0.5, 0.875, 1.0],
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),
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("first", False, "bottom", False, [3.0, 4.0, 6.0, 1.0, 5.0, 2.0, 7.0, 8.0]),
|
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(
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"first",
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False,
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"bottom",
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True,
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[0.375, 0.5, 0.75, 0.125, 0.625, 0.25, 0.875, 1.0],
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),
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("dense", True, "bottom", False, [1.0, 1.0, 4.0, 3.0, 1.0, 2.0, 4.0, 4.0]),
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("dense", True, "bottom", True, [0.25, 0.25, 1.0, 0.75, 0.25, 0.5, 1.0, 1.0]),
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("dense", False, "bottom", False, [3.0, 3.0, 4.0, 1.0, 3.0, 2.0, 4.0, 4.0]),
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("dense", False, "bottom", True, [0.75, 0.75, 1.0, 0.25, 0.75, 0.5, 1.0, 1.0]),
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],
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)
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def test_rank_args_missing(grps, vals, ties_method, ascending, na_option, pct, exp):
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key = np.repeat(grps, len(vals))
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vals = vals * len(grps)
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df = DataFrame({"key": key, "val": vals})
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result = df.groupby("key").rank(
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method=ties_method, ascending=ascending, na_option=na_option, pct=pct
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)
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exp_df = DataFrame(exp * len(grps), columns=["val"])
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tm.assert_frame_equal(result, exp_df)
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@pytest.mark.parametrize(
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"pct,exp", [(False, [3.0, 3.0, 3.0, 3.0, 3.0]), (True, [0.6, 0.6, 0.6, 0.6, 0.6])]
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)
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def test_rank_resets_each_group(pct, exp):
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df = DataFrame(
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{"key": ["a", "a", "a", "a", "a", "b", "b", "b", "b", "b"], "val": [1] * 10}
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)
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result = df.groupby("key").rank(pct=pct)
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exp_df = DataFrame(exp * 2, columns=["val"])
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tm.assert_frame_equal(result, exp_df)
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def test_rank_avg_even_vals():
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df = DataFrame({"key": ["a"] * 4, "val": [1] * 4})
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result = df.groupby("key").rank()
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exp_df = DataFrame([2.5, 2.5, 2.5, 2.5], columns=["val"])
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tm.assert_frame_equal(result, exp_df)
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@pytest.mark.parametrize("ties_method", ["average", "min", "max", "first", "dense"])
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@pytest.mark.parametrize("ascending", [True, False])
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@pytest.mark.parametrize("na_option", ["keep", "top", "bottom"])
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@pytest.mark.parametrize("pct", [True, False])
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@pytest.mark.parametrize(
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"vals", [["bar", "bar", "foo", "bar", "baz"], ["bar", np.nan, "foo", np.nan, "baz"]]
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)
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def test_rank_object_raises(ties_method, ascending, na_option, pct, vals):
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df = DataFrame({"key": ["foo"] * 5, "val": vals})
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|
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|
with pytest.raises(DataError, match="No numeric types to aggregate"):
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|
df.groupby("key").rank(
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|
method=ties_method, ascending=ascending, na_option=na_option, pct=pct
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|
)
|
|
|
|
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|
@pytest.mark.parametrize("na_option", [True, "bad", 1])
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@pytest.mark.parametrize("ties_method", ["average", "min", "max", "first", "dense"])
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|
@pytest.mark.parametrize("ascending", [True, False])
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|
@pytest.mark.parametrize("pct", [True, False])
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|
@pytest.mark.parametrize(
|
|
"vals",
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|
[
|
|
["bar", "bar", "foo", "bar", "baz"],
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|
["bar", np.nan, "foo", np.nan, "baz"],
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|
[1, np.nan, 2, np.nan, 3],
|
|
],
|
|
)
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|
def test_rank_naoption_raises(ties_method, ascending, na_option, pct, vals):
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|
df = DataFrame({"key": ["foo"] * 5, "val": vals})
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|
msg = "na_option must be one of 'keep', 'top', or 'bottom'"
|
|
|
|
with pytest.raises(ValueError, match=msg):
|
|
df.groupby("key").rank(
|
|
method=ties_method, ascending=ascending, na_option=na_option, pct=pct
|
|
)
|
|
|
|
|
|
def test_rank_empty_group():
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|
# see gh-22519
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|
column = "A"
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|
df = DataFrame({"A": [0, 1, 0], "B": [1.0, np.nan, 2.0]})
|
|
|
|
result = df.groupby(column).B.rank(pct=True)
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|
expected = Series([0.5, np.nan, 1.0], name="B")
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|
tm.assert_series_equal(result, expected)
|
|
|
|
result = df.groupby(column).rank(pct=True)
|
|
expected = DataFrame({"B": [0.5, np.nan, 1.0]})
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"input_key,input_value,output_value",
|
|
[
|
|
([1, 2], [1, 1], [1.0, 1.0]),
|
|
([1, 1, 2, 2], [1, 2, 1, 2], [0.5, 1.0, 0.5, 1.0]),
|
|
([1, 1, 2, 2], [1, 2, 1, np.nan], [0.5, 1.0, 1.0, np.nan]),
|
|
([1, 1, 2], [1, 2, np.nan], [0.5, 1.0, np.nan]),
|
|
],
|
|
)
|
|
def test_rank_zero_div(input_key, input_value, output_value):
|
|
# GH 23666
|
|
df = DataFrame({"A": input_key, "B": input_value})
|
|
|
|
result = df.groupby("A").rank(method="dense", pct=True)
|
|
expected = DataFrame({"B": output_value})
|
|
tm.assert_frame_equal(result, expected)
|
|
|