python - 如何将随机森林中选定的特征转换为新列表

Question

我正在研究回归问题。对于我的模型，我使用随机森林分类器进行降维。输出是一个以空格分隔的 bool 值字符串，它将好的功能突出显示为“True”。它看起来像这样:

[ True  True  True  True  True  True  True  True  True  True  True  True
  True  True  True False  True  True False  True  True  True False  True
  True  True  True  True  True  True  True False  True False False  True
  True False False False False False False False False False False  True
 False False  True False False False False False False  True False False
 False  True False False False False False False False False False False
 False False False False False False False False False False False False
 False False False False False False False False False False False False
 False False False False False False False False False False False False
 False False False False False False  True False False  True False False
 False  True False  True False False False False False False False False
 False False False False False False False False False False False False
 False  True False False False False False False False False  True False
 False False False False False  True False False False  True  True False
 False False False False False False False False False False False False
 False False False False False False  True False False False False False
 False False  True False False  True False  True False  True False False
  True False False False False False False False False False False False
 False False False  True False  True False  True False False False False
 False False False False False  True  True False False False False False
 False False False False  True False  True  True False  True False False
 False False False  True  True  True False False False False False False
 False False False False False False False False False False False False
 False False False False False False  True False False False False False
 False False False False False False False False  True False False False
 False  True False]

所以我所做的就是将其转换为逗号分隔的列表，如下所示:

[True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, False, True, True, False, True, True, True, False, True, True, True, True, True, True, True, True, False, True, False, False, True, True, False, False, False, False, False, False, False, False, False, False, True, False, False, True, False, False, False, False, False, False, True, False, False, False, True, False, False, False, False, False, False, False, False, True, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, True, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, True, False, False, True, False, False, False, True, False, True, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, True, False, False, False, False, False, False, False, False, False, False, True, False, False, False, False, True, False, False, False, True, True, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, True, False, False, False, False, False, False, False, True, False, False, True, False, True, False, True, False, False, True, False, False, False, False, False, False, False, False, False, False, False, False, False, False, True, False, True, False, True, False, False, False, False, False, False, False, False, False, True, True, False, False, False, False, False, False, False, False, False, True, False, True, True, False, True, False, False, False, False, False, True, True, True, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, True, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, True, False]

然后循环遍历每个元素并检索相应的测试列。这是此过程的完整代码:

sel = SelectFromModel(RandomForestClassifier(n_estimators = 100), threshold = '1.25*mean')
sel.fit(x_train, y_train)

selected = sel.get_support()
selected_list = list(selected)
columns_list = []

for i in range(len(selected_list)):
    if(selected_list[i] == 'True'):
        columns_list.append(test[i])

print(columns_list)

但现在我得到一个空列表，尽管我尝试将其附加到我的 columns_list 中。基本上，我的目标是在我的预测中使用降维的列。我正在使用线性回归来解决这个问题。

更新

当我将代码更改为以下建议时，出现以下错误:

Traceback (most recent call last):
  File "/opt/anaconda/envs/lib/python3.7/site-packages/pandas/core/indexes/base.py", line 2890, in get_loc
    return self._engine.get_loc(key)
  File "pandas/_libs/index.pyx", line 107, in pandas._libs.index.IndexEngine.get_loc
  File "pandas/_libs/index.pyx", line 131, in pandas._libs.index.IndexEngine.get_loc
  File "pandas/_libs/hashtable_class_helper.pxi", line 1607, in pandas._libs.hashtable.PyObjectHashTable.get_item
  File "pandas/_libs/hashtable_class_helper.pxi", line 1614, in pandas._libs.hashtable.PyObjectHashTable.get_item
KeyError: 0

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "/home/onur/Documents/Boston-Kaggle/Model.py", line 100, in <module>
    columns_list.append(test[i])
  File "/opt/anaconda/envs/lib/python3.7/site-packages/pandas/core/frame.py", line 2975, in __getitem__
    indexer = self.columns.get_loc(key)
  File "/opt/anaconda/envs/lib/python3.7/site-packages/pandas/core/indexes/base.py", line 2892, in get_loc
    return self._engine.get_loc(self._maybe_cast_indexer(key))
  File "pandas/_libs/index.pyx", line 107, in pandas._libs.index.IndexEngine.get_loc
  File "pandas/_libs/index.pyx", line 131, in pandas._libs.index.IndexEngine.get_loc
  File "pandas/_libs/hashtable_class_helper.pxi", line 1607, in pandas._libs.hashtable.PyObjectHashTable.get_item
  File "pandas/_libs/hashtable_class_helper.pxi", line 1614, in pandas._libs.hashtable.PyObjectHashTable.get_item
KeyError: 0

Answer 1

您的问题在这里:

if(selected_list[i] == 'True'):
    columns_list.append(test[i])

您正在将 bool 值与字符串值进行比较'True'而不是True

一个紧凑且Pythonic的解决方案是:

 if selected_list[i]:
    columns_list.append(test[i])

对于第二个错误，那是因为您正在使用 [] 访问数据帧 test。您需要使用方法.iloc

对于用法，这取决于测试包含的内容:

test.iloc[0] # first row of data frame- Note a Series data type output.
test.iloc[1] # second row of data frame 
test.iloc[-1] # last row of data frame 
# Columns:
test.iloc[:,0] # first column of data frame 
test.iloc[:,1] # second column of data frame 
test.iloc[:,-1] # last column of data frame

编辑，更明确的解决方案:

columns_selected = test.iloc[:, [i for i in range(len(selected_list)) if selected_list[i]]]