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4 | 4 |
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5 | 5 | from typing import TYPE_CHECKING |
6 | 6 |
|
7 | | -import math |
8 | 7 | import numpy as np |
9 | 8 | import pandas as pd |
10 | 9 |
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32 | 31 | def _get_sparce_nanmean_columns( |
33 | 32 | data: NDArray[Any], indices: NDArray[np.int32], shape: tuple |
34 | 33 | ) -> NDArray[np.float64]: |
35 | | - sum_arr = np.zeros(shape[1], dtype = np.float64) |
36 | | - nans_arr = np.zeros(shape[1], dtype = np.float64) |
| 34 | + sum_arr = np.zeros(shape[1], dtype=np.float64) |
| 35 | + nans_arr = np.zeros(shape[1], dtype=np.float64) |
37 | 36 | np.add.at(sum_arr, indices, np.nan_to_num(data, nan=0.0)) |
38 | 37 | np.add.at(nans_arr, indices, np.isnan(data)) |
39 | | - nans_arr[nans_arr==shape[0]] = np.nan |
40 | | - return sum_arr/(shape[0] - nans_arr) |
| 38 | + nans_arr[nans_arr == shape[0]] = np.nan |
| 39 | + return sum_arr / (shape[0] - nans_arr) |
41 | 40 |
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42 | 41 |
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43 | 42 | def _get_sparce_nanmean_rows( |
44 | 43 | data: NDArray[Any], indptr: NDArray[np.int32], shape: tuple |
45 | 44 | ) -> NDArray[np.float64]: |
46 | | - sum_arr = np.add.reduceat(np.nan_to_num(data, nan=0.0), indptr[:-1], dtype=np.float64) |
| 45 | + sum_arr = np.add.reduceat( |
| 46 | + np.nan_to_num(data, nan=0.0), indptr[:-1], dtype=np.float64 |
| 47 | + ) |
47 | 48 | nans_arr = np.add.reduceat(np.isnan(data), indptr[:-1], dtype=np.float64) |
48 | | - return sum_arr/(shape[1] - nans_arr) |
| 49 | + return sum_arr / (shape[1] - nans_arr) |
49 | 50 |
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50 | 51 |
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51 | 52 | def _sparse_nanmean(X: CSBase, axis: Literal[0, 1]) -> NDArray[np.float64]: |
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