pandas feature_names 必须是唯一的 - Xgboost
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feature_names must be unique - Xgboost
提问by user2728024
I am running the xgboost model for a very sparse matrix.
我正在为一个非常稀疏的矩阵运行 xgboost 模型。
I am getting this error. ValueError: feature_names must be unique
我收到此错误。ValueError：feature_names 必须是唯一的
How can I deal with this?
我该如何处理？
This is my code.
这是我的代码。
yprob = bst.predict(xgb.DMatrix(test_df))[:,1]
回答by andrew_reece
According the the xgboostsource code documentation, this error only occurs in one place- in a DMatrixinternal function. Here's the source code excerpt:
根据xgboost源代码文档，此错误仅发生在一个地方- 在DMatrix内部函数中。这是源代码摘录：
if len(feature_names) != len(set(feature_names)):
raise ValueError('feature_names must be unique')
So, the error text is pretty literal here; your test_dfhas at least one duplicate feature/column name.
因此，这里的错误文本非常简单；您test_df至少有一个重复的功能/列名称。
You've tagged pandason this post; that suggests test_dfis a Pandas DataFrame. In this case, DMatrixliterally runs df.columnsto extract feature_names. Check your test_dffor repeat column names, remove or rename them, and then try DMatrix()again.
您已pandas在此帖子上加了标签；这表明test_df是 Pandas DataFrame。在这种情况下，DMatrix字面上运行df.columns提取feature_names。检查您test_df的重复列名称，删除或重命名它们，然后重试DMatrix()。
回答by Arjan Groen
Assuming the problem is indeed that columns are duplicated, the following line should solve your problem:
假设问题确实是列重复，以下行应该可以解决您的问题：
test_df = test_df.loc[:,~test_df.columns.duplicated()]
Source: python pandas remove duplicate columns
This line should identify which columns are duplicated:
此行应标识哪些列是重复的：
duplicate_columns = test_df.columns[test_df.columns.duplicated()]
回答by Akshay
One way around this can be to use column names that are unique while preparing the data and then it should work out.
解决此问题的一种方法是在准备数据时使用唯一的列名，然后它应该可以解决。
