Question

我无法在sklearn.model_selection.learning_curve中自定义评分。我有一个SVR作为估计器，也有一个回归器，但是估计器应该是一个分类器，我需要实现如何将连续值转换为类。我遵循了以下文档：https://scikit-learn.org/stable/modules/model_evaluation.html和https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.learning_curve.html#sklearn.model_selection.learning_curve

我正在使用scikit-learn-0.22和python 3.7。

这是我的代码：

def scorer(y_true, y_pred):
    closest = [ y_true[i] if abs(y_true[i] - y_) <= 1.0 else y_true.flat[np.abs(y_true - y_).argmin()] for i, y_ in enumerate(y_pred)]
    return accuracy_score(y_true, closest)

 train_sizes, train_scores, test_scores, fit_times, _ = \
        learning_curve(estimator, X, y, cv=cv, n_jobs=n_jobs,
                       train_sizes=train_sizes,
                       return_times=True, scoring=make_scorer(scorer))

我遇到了以下错误： AttributeError：“系列”对象没有属性“扁平”

<ipython-input-10-bc8ce2a8f15e> in plot_learning_curve(estimator, title, X, y, scoring, axes, ylim, cv, n_jobs, train_sizes)
     90         learning_curve(estimator, X, y, cv=cv, n_jobs=n_jobs,
     91                        train_sizes=train_sizes,
---> 92                        return_times=True, scoring=scoring)
     93     train_scores_mean = np.mean(train_scores, axis=1)
     94     train_scores_std = np.std(train_scores, axis=1)

~/miniconda3/envs/dtscience/lib/python3.7/site-packages/sklearn/model_selection/_validation.py in learning_curve(estimator, X, y, groups, train_sizes, cv, scoring, exploit_incremental_learning, n_jobs, pre_dispatch, verbose, shuffle, random_state, error_score, return_times)
   1265             parameters=None, fit_params=None, return_train_score=True,
   1266             error_score=error_score, return_times=return_times)
-> 1267             for train, test in train_test_proportions)
   1268         out = np.array(out)
   1269         n_cv_folds = out.shape[0] // n_unique_ticks

~/miniconda3/envs/dtscience/lib/python3.7/site-packages/joblib/parallel.py in __call__(self, iterable)
   1015 
   1016             with self._backend.retrieval_context():
-> 1017                 self.retrieve()
   1018             # Make sure that we get a last message telling us we are done
   1019             elapsed_time = time.time() - self._start_time

~/miniconda3/envs/dtscience/lib/python3.7/site-packages/joblib/parallel.py in retrieve(self)
    907             try:
    908                 if getattr(self._backend, 'supports_timeout', False):
--> 909                     self._output.extend(job.get(timeout=self.timeout))
    910                 else:
    911                     self._output.extend(job.get())

~/miniconda3/envs/dtscience/lib/python3.7/site-packages/joblib/_parallel_backends.py in wrap_future_result(future, timeout)
    560         AsyncResults.get from multiprocessing."""
    561         try:
--> 562             return future.result(timeout=timeout)
    563         except LokyTimeoutError:
    564             raise TimeoutError()

~/miniconda3/envs/dtscience/lib/python3.7/concurrent/futures/_base.py in result(self, timeout)
    433                 raise CancelledError()
    434             elif self._state == FINISHED:
--> 435                 return self.__get_result()
    436             else:
    437                 raise TimeoutError()

~/miniconda3/envs/dtscience/lib/python3.7/concurrent/futures/_base.py in __get_result(self)
    382     def __get_result(self):
    383         if self._exception:
--> 384             raise self._exception
    385         else:
    386             return self._result

AttributeError: 'Series' object has no attribute 'flat'

Answer 1

所以我们在y中看不到plot_learning_curve的值，但是从y熊猫提供了DataFrame行的某个地方，或者它是一个独立的{ {1}}。在Series函数的代码中，您具有以下内容：

scorer

因为此y_true.flat[np.abs(y_true - y_).argmin()]必须是一个小人物y。由于ndarray是y，因此您需要执行以下操作：

Series

或者确保在其他脚本中按以下方式调用绘图学习曲线：

learning_curve(estimator, X, y.values, cv=cv, n_jobs=n_jobs,
                   train_sizes=train_sizes,
                   return_times=True, scoring=make_scorer(scorer))

学习曲线中的自定义评分

1 个答案: