Sklearn 中 make_scorer 函数中的 "Too many indices for array" 错误

Question

目标：使用 brier score loss 使用 GridSearchCV 训练随机森林算法

问题：使用 make_scorer 时，目标 "y" 的概率预测是错误的维度。

在查看之后，我正在使用其建议的代理函数来使用经过 brier 分数损失训练的 GridSearchCV。下面是一个设置示例：

from sklearn.model_selection import GridSearchCV
from sklearn.metrics import brier_score_loss,make_scorer
from sklearn.ensemble import RandomForestClassifier
import numpy as np

def ProbaScoreProxy(y_true, y_probs, class_idx, proxied_func, **kwargs):
    return proxied_func(y_true, y_probs[:, class_idx], **kwargs)

brier_scorer = make_scorer(ProbaScoreProxy, greater_is_better=False, \
                           needs_proba=True, class_idx=1, proxied_func=brier_score_loss)

X = np.random.randn(100,2)
y = (X[:,0]>0).astype(int)

random_forest = RandomForestClassifier(n_estimators=10)

random_forest.fit(X,y)

probs = random_forest.predict_proba(X)

现在将 probs 和 y 直接传递给 brier_score_loss 或 ProbaScoreProxy 将不会导致错误：

ProbaScoreProxy(y,probs,1,brier_score_loss)

输出：

0.0006

现在通过 brier_scorer:

brier_scorer(random_forest,X,y)

输出：

---------------------------------------------------------------------------
IndexError                                Traceback (most recent call last)
<ipython-input-28-1474bb08e572> in <module>()
----> 1 brier_scorer(random_forest,X,y)

~/anaconda3/lib/python3.6/site-packages/sklearn/metrics/_scorer.py in __call__(self, estimator, X, y_true, sample_weight)
    167                           stacklevel=2)
    168         return self._score(partial(_cached_call, None), estimator, X, y_true,
--> 169                            sample_weight=sample_weight)
    170 
    171     def _factory_args(self):

~/anaconda3/lib/python3.6/site-packages/sklearn/metrics/_scorer.py in _score(self, method_caller, clf, X, y, sample_weight)
    258                                                  **self._kwargs)
    259         else:
--> 260             return self._sign * self._score_func(y, y_pred, **self._kwargs)
    261 
    262     def _factory_args(self):

<ipython-input-25-5321477444e1> in ProbaScoreProxy(y_true, y_probs, class_idx, proxied_func, **kwargs)
      5 
      6 def ProbaScoreProxy(y_true, y_probs, class_idx, proxied_func, **kwargs):
----> 7     return proxied_func(y_true, y_probs[:, class_idx], **kwargs)
      8 
      9 brier_scorer = make_scorer(ProbaScoreProxy, greater_is_better=False,                            needs_proba=True, class_idx=1, proxied_func=brier_score_loss)

IndexError: too many indices for array

所以似乎 make_scorer 中发生了一些事情来改变它的概率输入的维度，但我似乎看不出问题是什么。

版本： - sklearn: '0.22.2.post1' - numpy: '1.18.1'

请注意，这里的 y 是正确的维度 (1-d)，您会通过摆弄发现它是传递给 ProbaScoreProxy 的 y_probs 的维度导致问题。

这只是最后一个问题中写得不好的代码吗？ 最终有什么方法可以让 make_score 对象接受 GridSearchCV 之类的东西来训练 RF？

Answer 1

Goal: use brier score loss to train a random forest algorithm using GridSearchCV

为了这个目标，可以直接使用字符串值'neg_brier_score' in GridSearchCV scoring参数。

例如：

gc = GridSearchCV(random_forest,
                  param_grid={"n_estimators":[5, 10]},
                  scoring="neg_brier_score")

gc.fit(X, y)
print(gc.scorer_) 
# make_scorer(brier_score_loss, greater_is_better=False, needs_proba=True)

Sklearn 中 make_scorer 函数中的 "Too many indices for array" 错误

"Too many indices for array" error in make_scorer function in Sklearn

python

machine-learning

probability

scikit-learn