我有一组数据，每个样本都有不同的权重。在我的应用中，这些权重在模型估计和比较不同模型时非常重要。

我使用 sklearn 来估计模型并比较不同的超参数选择。但这个单元测试显示 GridSearchCV 在估计分数时并未应用 sample_weights。

有没有办法让 sklearn 使用 sample_weight 来评分模型？

单元测试：

from __future__ import divisionimport numpy as npfrom sklearn.datasets import load_irisfrom sklearn.ensemble import RandomForestClassifierfrom sklearn.metrics import log_lossfrom sklearn.model_selection import GridSearchCV, RepeatedKFolddef grid_cv(X_in, y_in, w_in, cv, max_features_grid, use_weighting):  out_results = dict()  for k in max_features_grid:    clf = RandomForestClassifier(n_estimators=256,                                 criterion="entropy",                                 warm_start=False,                                 n_jobs=-1,                                 random_state=RANDOM_STATE,                                 max_features=k)    for train_ndx, test_ndx in cv.split(X=X_in, y=y_in):      X_train = X_in[train_ndx, :]      y_train = y_in[train_ndx]      w_train = w_in[train_ndx]      y_test = y[test_ndx]      clf.fit(X=X_train, y=y_train, sample_weight=w_train)      y_hat = clf.predict_proba(X=X_in[test_ndx, :])      if use_weighting:        w_test = w_in[test_ndx]        w_i_sum = w_test.sum()        score = w_i_sum / w_in.sum() * log_loss(y_true=y_test, y_pred=y_hat, sample_weight=w_test)      else:        score = log_loss(y_true=y_test, y_pred=y_hat)      results = out_results.get(k, [])      results.append(score)      out_results.update({k: results})  for k, v in out_results.items():    if use_weighting:      mean_score = sum(v)    else:      mean_score = np.mean(v)    out_results.update({k: mean_score})  best_score = min(out_results.values())  best_param = min(out_results, key=out_results.get)  return best_score, best_paramif __name__ == "__main__":  RANDOM_STATE = 1337  X, y = load_iris(return_X_y=True)  sample_weight = np.array([1 + 100 * (i % 25) for i in range(len(X))])  # sample_weight = np.array([1 for _ in range(len(X))])  inner_cv = RepeatedKFold(n_splits=3, n_repeats=1, random_state=RANDOM_STATE)  outer_cv = RepeatedKFold(n_splits=3, n_repeats=1, random_state=RANDOM_STATE)  rfc = RandomForestClassifier(n_estimators=256,                               criterion="entropy",                               warm_start=False,                               n_jobs=-1,                               random_state=RANDOM_STATE)  search_params = {"max_features": [1, 2, 3, 4]}  fit_params = {"sample_weight": sample_weight}  my_scorer = make_scorer(log_loss,                greater_is_better=False,                needs_proba=True,                needs_threshold=False)  grid_clf = GridSearchCV(estimator=rfc,                          scoring=my_scorer,                          cv=inner_cv,                          param_grid=search_params,                          refit=True,                          return_train_score=False,                          iid=False)  # in this usage, the results are the same for `iid=True` and `iid=False`  grid_clf.fit(X, y, **fit_params)  print("This is the best out-of-sample score using GridSearchCV: %.6f." % -grid_clf.best_score_)  msg = """This is the best out-of-sample score %s weighting using grid_cv: %.6f."""  score_with_weights, param_with_weights = grid_cv(X_in=X,                                                   y_in=y,                                                   w_in=sample_weight,                                                   cv=inner_cv,                                                   max_features_grid=search_params.get(                                                     "max_features"),                                                   use_weighting=True)  print(msg % ("WITH", score_with_weights))  score_without_weights, param_without_weights = grid_cv(X_in=X,                                                         y_in=y,                                                         w_in=sample_weight,                                                         cv=inner_cv,                                                         max_features_grid=search_params.get(                                                           "max_features"),                                                         use_weighting=False)  print(msg % ("WITHOUT", score_without_weights))

这会产生以下输出：

This is the best out-of-sample score using GridSearchCV: 0.135692.This is the best out-of-sample score WITH weighting using grid_cv: 0.099367.This is the best out-of-sample score WITHOUT weighting using grid_cv: 0.135692.

解释：由于手动计算未加权的损失与 GridSearchCV 的评分结果相同，我们可以确定样本权重并未被使用。

回答：

sklearn 版本 >= 1.4 和 1.4 夜间发布版

从 sklearn 1.4 版本开始（预计发布日期在 2023 年 10 月左右），以及从 2023 年 9 月起可用的夜间发布版（您可以按照此处的指南进行安装），您可以使用新的元数据路由机制。

您可以简单地决定哪些对象应该或不应该接收元数据，例如 sample_weight，如下面的脚本所示：

...（代码保持不变）