在应用sklearn.com.pose.columntransformer上，如何保留列订单

Question

我想使用Pipeline和columntransformer来自Sklearn库的模块在Numpy数组上应用缩放。 Scaleer应用于某些列。而且，我想具有相同的输入列顺序输出。

示例：

import numpy as np
from sklearn.compose import ColumnTransformer 
from sklearn.preprocessing import  MinMaxScaler


X = np.array ( [(25, 1, 2, 0),
                (30, 1, 5, 0),
                (25, 10, 2, 1),
                (25, 1, 2, 0),
                (np.nan, 10, 4, 1),
                (40, 1, 2, 1) ] )



column_trans = ColumnTransformer(
    [ ('scaler', MinMaxScaler(), [0,2]) ], 
     remainder='passthrough') 
      
X_scaled = column_trans.fit_transform(X)

问题是columnTransFormer更改列的顺序。如何保留列的原始顺序？

我知道这 post 。但是，它是用于熊猫数据框架的。由于某些原因，我无法使用数据框，并且必须在代码中使用Numpy数组。

谢谢。

Answer 1

这是一个解决方案，通过添加一个变压器，该变压器将在列变换后应用逆列置换：

from sklearn.base import BaseEstimator, TransformerMixin
import re


class ReorderColumnTransformer(BaseEstimator, TransformerMixin):
    index_pattern = re.compile(r'\d+
它依赖于解析
column_trans.get_feature_names_out()
# = array(['scaler__x1', 'scaler__x3', 'remainder__x0', 'remainder__x2'],
#      dtype=object)

来读取后缀号码的初始列顺序。然后计算和应用逆置换。
用作：
import numpy as np
from sklearn.compose import ColumnTransformer 
from sklearn.preprocessing import  MinMaxScaler
from sklearn.pipeline import make_pipeline

X = np.array ( [(25, 1, 2, 0),
                (30, 1, 5, 0),
                (25, 10, 2, 1),
                (25, 1, 2, 0),
                (np.nan, 10, 4, 1),
                (40, 1, 2, 1) ] )



column_trans = ColumnTransformer(
    [ ('scaler', MinMaxScaler(), [0,2]) ], 
     remainder='passthrough') 

pipeline = make_pipeline( column_trans, ReorderColumnTransformer(column_transformer=column_trans))
X_scaled = pipeline.fit_transform(X)
#X_scaled has same column order as X


替代解决方案不依赖字符串解析，而是读取列变压器的列切片：
from sklearn.base import BaseEstimator, TransformerMixin


class ReorderColumnTransformer(BaseEstimator, TransformerMixin):
    
    def __init__(self, column_transformer):
        self.column_transformer = column_transformer
        
    def fit(self, X, y=None):
        return self

    def transform(self, X, y=None):
        slices = self.column_transformer.output_indices_.values()
        n_cols = self.column_transformer.n_features_in_
        order_after_column_transform = [value for slice_ in slices for value in range(n_cols)[slice_]]
        
        order_inverse = np.zeros(n_cols, dtype=int)
        order_inverse[order_after_column_transform] = np.arange(n_cols)
        return X[:, order_inverse]

)
    
    def __init__(self, column_transformer):
        self.column_transformer = column_transformer
        
    def fit(self, X, y=None):
        return self

    def transform(self, X, y=None):
        order_after_column_transform = [int( self.index_pattern.search(col).group()) for col in self.column_transformer.get_feature_names_out()]
        order_inverse = np.zeros(len(order_after_column_transform), dtype=int)
        order_inverse[order_after_column_transform] = np.arange(len(order_after_column_transform))
        return X[:, order_inverse]

它依赖于解析

来读取后缀号码的初始列顺序。然后计算和应用逆置换。

用作：

---

替代解决方案不依赖字符串解析，而是读取列变压器的列切片：

Answer 2

columnTransFormer可用于重新排序列，但是您希望通过以所需的顺序传递列索引。配对columnTransFormer与Identity functionTransFormer将其无能为力，只能重新排序列。 （您可以通过在初始化func时通过不分配funcontransform来创建身份functionTransformer function> functionTransFormer ，在这种情况下，数据将通过而不会转换）。

import numpy as np
from sklearn.compose import make_column_transformer
from sklearn.preprocessing import FunctionTransformer

X = np.array ( [[30, 20, 10]] )
new_column_order = [2, 1, 0]
column_reorder_transformer = make_column_transformer((FunctionTransformer(), new_column_order))
Xt = column_reorder_transformer.fit_transform(X)
print(f"Xt = {Xt}")
# arr = [[10 20 30]]