Question

我目前正在研究ts数据，我正在尝试做的是为模型评估生成交叉验证集。这就是我的做法：

# Splitting train & validation
df['date_posted'] = pd.to_datetime(df['date_posted'])
df_train = df[(df['date_posted'].dt.year > 2009) & (df['date_posted'].dt.year < 2014)].copy()
df_test = df[df['date_posted'].dt.year >= 2014].copy()

from sklearn.model_selection import GroupShuffleSplit

groups = df_train.groupby(df_train['date_posted'].dt.year).groups

X = df_train.drop(['short_description', 'is_exciting'], axis=1).copy()
y = df_train['is_exciting']

cv = GroupShuffleSplit().split(X, y, groups)

# Baseline model 

from sklearn.model_selection import KFold

clf_lgbm = lgbm.LGBMClassifier(is_unbalance=True, random_state=0, n_jobs=-1)
# cv = KFold(n_splits=10, random_state=0)

results = cross_val_score(clf_lgbm, X, y, cv=cv)

print("Accuracy: %.3f%% (%.3f%%)" % (results.mean()*100.0, results.std()*100.0))

这是错误追溯：

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-24-7091271a325a> in <module>()
      6 # cv = KFold(n_splits=10, random_state=0)
      7 
----> 8 results = cross_val_score(clf_lgbm, X, y, cv=cv)
      9 
     10 print("Accuracy: %.3f%% (%.3f%%)" % (results.mean()*100.0, results.std()*100.0))

~\AppData\Local\Continuum\Anaconda3\lib\site-packages\sklearn\model_selection\_validation.py in cross_val_score(estimator, X, y, groups, scoring, cv, n_jobs, verbose, fit_params, pre_dispatch)
    340                                 n_jobs=n_jobs, verbose=verbose,
    341                                 fit_params=fit_params,
--> 342                                 pre_dispatch=pre_dispatch)
    343     return cv_results['test_score']
    344 

~\AppData\Local\Continuum\Anaconda3\lib\site-packages\sklearn\model_selection\_validation.py in cross_validate(estimator, X, y, groups, scoring, cv, n_jobs, verbose, fit_params, pre_dispatch, return_train_score)
    192     X, y, groups = indexable(X, y, groups)
    193 
--> 194     cv = check_cv(cv, y, classifier=is_classifier(estimator))
    195     scorers, _ = _check_multimetric_scoring(estimator, scoring=scoring)
    196 

~\AppData\Local\Continuum\Anaconda3\lib\site-packages\sklearn\model_selection\_split.py in check_cv(cv, y, classifier)
   1913                              "object (from sklearn.model_selection) "
   1914                              "or an iterable. Got %s." % cv)
-> 1915         return _CVIterableWrapper(cv)
   1916 
   1917     return cv  # New style cv objects are passed without any modification

~\AppData\Local\Continuum\Anaconda3\lib\site-packages\sklearn\model_selection\_split.py in __init__(self, cv)
   1815     """Wrapper class for old style cv objects and iterables."""
   1816     def __init__(self, cv):
-> 1817         self.cv = list(cv)
   1818 
   1819     def get_n_splits(self, X=None, y=None, groups=None):

~\AppData\Local\Continuum\Anaconda3\lib\site-packages\sklearn\model_selection\_split.py in split(self, X, y, groups)
   1201         to an integer.
   1202         """
-> 1203         X, y, groups = indexable(X, y, groups)
   1204         for train, test in self._iter_indices(X, y, groups):
   1205             yield train, test

~\AppData\Local\Continuum\Anaconda3\lib\site-packages\sklearn\utils\validation.py in indexable(*iterables)
    227         else:
    228             result.append(np.array(X))
--> 229     check_consistent_length(*result)
    230     return result
    231 

~\AppData\Local\Continuum\Anaconda3\lib\site-packages\sklearn\utils\validation.py in check_consistent_length(*arrays)
    202     if len(uniques) > 1:
    203         raise ValueError("Found input variables with inconsistent numbers of"
--> 204                          " samples: %r" % [int(l) for l in lengths])
    205 
    206 

ValueError: Found input variables with inconsistent numbers of samples: [439599, 439599, 4]

X形状是（439599,51），y形状是（439599，）。

任何帮助将不胜感激。

Answer 1

您可以在the documentation of GroupShuffleSplit：

查看groups param的要求

groups : array-like, with shape (n_samples,), optional

    Group labels for the samples used while splitting 
    the dataset into train/test set.

组的长度应该等于样本数，每个值表示组标签的一部分。

此行的输出：

groups = df_train.groupby(df_train['date_posted'].dt.year).groups

是一个dict，其中key是组标签，value是属于该列的行的索引。但这不是scikit-learn所期望的。

例如，对于此数据：

Index   A  B  #Columns
  0     0  2  
  1     3  1  
  2     0  0  
  3     0  0 
  4     0  2

如果您希望前三行属于group1，最后两列属于group2，则需要将groups传递为：

 groups=['group1','group1','group1','group2','group2']

或

groups=[0, 0, 0, 1, 1]

在此观察，总值为5，对应于我的数据中的行，每个值表示该索引所属的行所属的组。

因此，在您的情况下，您可以使用以下代码将返回的dict转换为列表：

import numpy as np

groups_proper = np.zeros(len(df_train))          
for val in groups.iteritems():
    for index in val[1].tolist():
        groups_proper[index]=val[0]

然后通过它：

cv = GroupShuffleSplit().split(X, y, groups=groups_proper)

CV拆分时间序列数据

1 个答案: