numpy - 为什么 LGBM 的预测函数返回值错误？

问题描述

我正在尝试建立一个交叉验证模型，但我似乎无法弄清楚为什么预测功能不起作用。这是我的代码：

results = {}

kf = KFold(3, shuffle=True) 
c=0
for train_index, test_index in kf.split(X, y):
    c+=1;print(c)
    mdl =  lgb.LGBMClassifier(objective="binary",  n_estimators=50, importance_type="gain")
    X_train, X_test = X.iloc[train_index], X.iloc[test_index]
    y_train, y_test = y[train_index], y[test_index]
    mdl.fit(X=X_train, y=y_train, eval_metric=roc_auc_score, )
    #import pdb;pdb.set_trace()
    y_proba = mdl.predict_proba(X_test, y_test)
    results["estimator"] =mdl
    results["score"] = roc_auc_score(y_test, y_proba[:, 1])
    results["feature_importances"] = [mdl.feature_names, mdl.feature_importances_]

这是错误堆栈：

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-24-d5ef259cd27f> in <module>
     10     mdl.fit(X=X_train, y=y_train, eval_metric=roc_auc_score, )
     11     #import pdb;pdb.set_trace()
---> 12     y_proba = mdl.predict_proba(X_test, y_test)
     13     results["estimator"] =mdl
     14     results["score"] = roc_auc_score(y_test, y_proba[:, 1])

C:\ProgramData\Anaconda3\lib\site-packages\lightgbm\sklearn.py in predict_proba(self, X, raw_score, start_iteration, num_iteration, pred_leaf, pred_contrib, **kwargs)
    918                       pred_leaf=False, pred_contrib=False, **kwargs):
    919         """Docstring is set after definition, using a template."""
--> 920         result = super().predict(X, raw_score, start_iteration, num_iteration, pred_leaf, pred_contrib, **kwargs)
    921         if callable(self._objective) and not (raw_score or pred_leaf or pred_contrib):
    922             _log_warning("Cannot compute class probabilities or labels "

C:\ProgramData\Anaconda3\lib\site-packages\lightgbm\sklearn.py in predict(self, X, raw_score, start_iteration, num_iteration, pred_leaf, pred_contrib, **kwargs)
    724                              "input n_features is %s "
    725                              % (self._n_features, n_features))
--> 726         return self._Booster.predict(X, raw_score=raw_score, start_iteration=start_iteration, num_iteration=num_iteration,
    727                                      pred_leaf=pred_leaf, pred_contrib=pred_contrib, **kwargs)
    728 

C:\ProgramData\Anaconda3\lib\site-packages\lightgbm\basic.py in predict(self, data, start_iteration, num_iteration, raw_score, pred_leaf, pred_contrib, data_has_header, is_reshape, **kwargs)
   3140             else:
   3141                 num_iteration = -1
-> 3142         return predictor.predict(data, start_iteration, num_iteration,
   3143                                  raw_score, pred_leaf, pred_contrib,
   3144                                  data_has_header, is_reshape)

C:\ProgramData\Anaconda3\lib\site-packages\lightgbm\basic.py in predict(self, data, start_iteration, num_iteration, raw_score, pred_leaf, pred_contrib, data_has_header, is_reshape)
    698         data = _data_from_pandas(data, None, None, self.pandas_categorical)[0]
    699         predict_type = C_API_PREDICT_NORMAL
--> 700         if raw_score:
    701             predict_type = C_API_PREDICT_RAW_SCORE
    702         if pred_leaf:

C:\ProgramData\Anaconda3\lib\site-packages\pandas\core\generic.py in __nonzero__(self)
   1440     @final
   1441     def __nonzero__(self):
-> 1442         raise ValueError(
   1443             f"The truth value of a {type(self).__name__} is ambiguous. "
   1444             "Use a.empty, a.bool(), a.item(), a.any() or a.all()."

ValueError: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().

我想不出为什么它会在这种情况下尝试测试数组的真实性。

标签： numpyscikit-learnlightgbm

numpy - 为什么 LGBM 的预测函数返回值错误？

问题描述

解决方案

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