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BUG: Fix window aggregations to skip over unused elements (pandas-dev…
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,111 @@ | ||
| from functools import partial | ||
| import sys | ||
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| import numpy as np | ||
| import pytest | ||
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| import pandas._libs.window.aggregations as window_aggregations | ||
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| from pandas import Series | ||
| import pandas._testing as tm | ||
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| def _get_rolling_aggregations(): | ||
| # list pairs of name and function | ||
| # each function has this signature: | ||
| # (const float64_t[:] values, ndarray[int64_t] start, | ||
| # ndarray[int64_t] end, int64_t minp) -> np.ndarray | ||
| named_roll_aggs = ( | ||
| [ | ||
| ("roll_sum", window_aggregations.roll_sum), | ||
| ("roll_mean", window_aggregations.roll_mean), | ||
| ] | ||
| + [ | ||
| (f"roll_var({ddof})", partial(window_aggregations.roll_var, ddof=ddof)) | ||
| for ddof in [0, 1] | ||
| ] | ||
| + [ | ||
| ("roll_skew", window_aggregations.roll_skew), | ||
| ("roll_kurt", window_aggregations.roll_kurt), | ||
| ("roll_median_c", window_aggregations.roll_median_c), | ||
| ("roll_max", window_aggregations.roll_max), | ||
| ("roll_min", window_aggregations.roll_min), | ||
| ] | ||
| + [ | ||
| ( | ||
| f"roll_quantile({quantile},{interpolation})", | ||
| partial( | ||
| window_aggregations.roll_quantile, | ||
| quantile=quantile, | ||
| interpolation=interpolation, | ||
| ), | ||
| ) | ||
| for quantile in [0.0001, 0.5, 0.9999] | ||
| for interpolation in window_aggregations.interpolation_types | ||
| ] | ||
| + [ | ||
| ( | ||
| f"roll_rank({percentile},{method},{ascending})", | ||
| partial( | ||
| window_aggregations.roll_rank, | ||
| percentile=percentile, | ||
| method=method, | ||
| ascending=ascending, | ||
| ), | ||
| ) | ||
| for percentile in [True, False] | ||
| for method in window_aggregations.rolling_rank_tiebreakers.keys() | ||
| for ascending in [True, False] | ||
| ] | ||
| ) | ||
| # unzip to a list of 2 tuples, names and functions | ||
| unzipped = list(zip(*named_roll_aggs)) | ||
| return {"ids": unzipped[0], "params": unzipped[1]} | ||
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| _rolling_aggregations = _get_rolling_aggregations() | ||
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| @pytest.fixture( | ||
| params=_rolling_aggregations["params"], ids=_rolling_aggregations["ids"] | ||
| ) | ||
| def rolling_aggregation(request): | ||
| """Make a rolling aggregation function as fixture.""" | ||
| return request.param | ||
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| def test_rolling_aggregation_boundary_consistency(rolling_aggregation): | ||
| # GH-45647 | ||
| minp, step, width, size, selection = 0, 1, 3, 11, [2, 7] | ||
| values = np.arange(1, 1 + size, dtype=np.float64) | ||
| end = np.arange(width, size, step, dtype=np.int64) | ||
| start = end - width | ||
| selarr = np.array(selection, dtype=np.int32) | ||
| result = Series(rolling_aggregation(values, start[selarr], end[selarr], minp)) | ||
| expected = Series(rolling_aggregation(values, start, end, minp)[selarr]) | ||
| tm.assert_equal(expected, result) | ||
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| def test_rolling_aggregation_with_unused_elements(rolling_aggregation): | ||
| # GH-45647 | ||
| minp, width = 0, 5 # width at least 4 for kurt | ||
| size = 2 * width + 5 | ||
| values = np.arange(1, size + 1, dtype=np.float64) | ||
| values[width : width + 2] = sys.float_info.min | ||
| values[width + 2] = np.nan | ||
| values[width + 3 : width + 5] = sys.float_info.max | ||
| start = np.array([0, size - width], dtype=np.int64) | ||
| end = np.array([width, size], dtype=np.int64) | ||
| loc = np.array( | ||
| [j for i in range(len(start)) for j in range(start[i], end[i])], | ||
| dtype=np.int32, | ||
| ) | ||
| result = Series(rolling_aggregation(values, start, end, minp)) | ||
| compact_values = np.array(values[loc], dtype=np.float64) | ||
| compact_start = np.arange(0, len(start) * width, width, dtype=np.int64) | ||
| compact_end = compact_start + width | ||
| expected = Series( | ||
| rolling_aggregation(compact_values, compact_start, compact_end, minp) | ||
| ) | ||
| assert np.isfinite(expected.values).all(), "Not all expected values are finite" | ||
| tm.assert_equal(expected, result) |