some new features

.venv/lib/python3.12/site-packages/sklearn/utils/stats.py

@@ -0,0 +1,69 @@
+import numpy as np
+
+from .extmath import stable_cumsum
+
+
+def _weighted_percentile(array, sample_weight, percentile=50):
+    """Compute weighted percentile
+
+    Computes lower weighted percentile. If `array` is a 2D array, the
+    `percentile` is computed along the axis 0.
+
+        .. versionchanged:: 0.24
+            Accepts 2D `array`.
+
+    Parameters
+    ----------
+    array : 1D or 2D array
+        Values to take the weighted percentile of.
+
+    sample_weight: 1D or 2D array
+        Weights for each value in `array`. Must be same shape as `array` or
+        of shape `(array.shape[0],)`.
+
+    percentile: int or float, default=50
+        Percentile to compute. Must be value between 0 and 100.
+
+    Returns
+    -------
+    percentile : int if `array` 1D, ndarray if `array` 2D
+        Weighted percentile.
+    """
+    n_dim = array.ndim
+    if n_dim == 0:
+        return array[()]
+    if array.ndim == 1:
+        array = array.reshape((-1, 1))
+    # When sample_weight 1D, repeat for each array.shape[1]
+    if array.shape != sample_weight.shape and array.shape[0] == sample_weight.shape[0]:
+        sample_weight = np.tile(sample_weight, (array.shape[1], 1)).T
+    sorted_idx = np.argsort(array, axis=0)
+    sorted_weights = np.take_along_axis(sample_weight, sorted_idx, axis=0)
+
+    # Find index of median prediction for each sample
+    weight_cdf = stable_cumsum(sorted_weights, axis=0)
+    adjusted_percentile = percentile / 100 * weight_cdf[-1]
+
+    # For percentile=0, ignore leading observations with sample_weight=0. GH20528
+    mask = adjusted_percentile == 0
+    adjusted_percentile[mask] = np.nextafter(
+        adjusted_percentile[mask], adjusted_percentile[mask] + 1
+    )
+
+    percentile_idx = np.array(
+        [
+            np.searchsorted(weight_cdf[:, i], adjusted_percentile[i])
+            for i in range(weight_cdf.shape[1])
+        ]
+    )
+    percentile_idx = np.array(percentile_idx)
+    # In rare cases, percentile_idx equals to sorted_idx.shape[0]
+    max_idx = sorted_idx.shape[0] - 1
+    percentile_idx = np.apply_along_axis(
+        lambda x: np.clip(x, 0, max_idx), axis=0, arr=percentile_idx
+    )
+
+    col_index = np.arange(array.shape[1])
+    percentile_in_sorted = sorted_idx[percentile_idx, col_index]
+    percentile = array[percentile_in_sorted, col_index]
+    return percentile[0] if n_dim == 1 else percentile