在
GridSearchCV需要一个
scoring作为输入，其可以是可调用。您可以在此处查看有关如何更改评分功能以及如何传递自己的评分功能的详细信息。为了完整起见，以下是该页面中的相关代码：
编辑 ： fit_params
仅传递给fit函数，而不传递给score函数。如果有应该传递给的参数
scorer，则应将其传递给
make_scorer。但这仍然不能解决问题，因为那将意味着将整个
sample_weight参数传递给
log_loss，而只
y_test应传递与计算损失时对应的部分。
sklearn不支持这样的事情，但是您可以使用来破解
padas.Dataframe。好消息是，您可以
sklearn理解
Dataframe并保持这种方式。这意味着您可以利用的
index，
Dataframe如您在此处的代码中所见：
# more pre X, y = load_iris(return_X_y=True) index = ['r%d' % x for x in range(len(y))] y_frame = pd.Dataframe(y, index=index) sample_weight = np.array([1 + 100 * (i % 25) for i in range(len(X))]) sample_weight_frame = pd.Dataframe(sample_weight, index=index) # more pre def score_f(y_true, y_pred, sample_weight): return log_loss(y_true.values, y_pred,sample_weight=sample_weight.loc[y_true.index.values].values.reshape(-1),normalize=True) score_params = {"sample_weight": sample_weight_frame} my_scorer = make_scorer(score_f, greater_is_better=False, needs_proba=True, needs_threshold=False, **score_params) 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_frame) # more pre如您所见，
score_f使用的
indexof
y_true查找
sample_weight要使用的部分。为了完整起见，下面是整个代码：
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, RepeatedKFoldfrom sklearn.metrics import make_scorerimport pandas as pddef 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_in[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_param#if __name__ == "__main__":if True: RANDOM_STATE = 1337 X, y = load_iris(return_X_y=True) index = ['r%d' % x for x in range(len(y))] y_frame = pd.Dataframe(y, index=index) sample_weight = np.array([1 + 100 * (i % 25) for i in range(len(X))]) sample_weight_frame = pd.Dataframe(sample_weight, index=index) # 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]} def score_f(y_true, y_pred, sample_weight): return log_loss(y_true.values, y_pred,sample_weight=sample_weight.loc[y_true.index.values].values.reshape(-1),normalize=True) score_params = {"sample_weight": sample_weight_frame} my_scorer = make_scorer(score_f, greater_is_better=False, needs_proba=True, needs_threshold=False, **score_params) 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_frame) 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))代码的输出为：
This is the best out-of-sample score using GridSearchCV: 0.095439.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.
编辑2 ：正如下面的评论中所说：
使用此解决方案时，我的分数和sklearn分数之差源自我计算分数的加权平均值的方式。如果省略代码的加权平均部分，则两个输出将与机器精度匹配。
