避免在sci-kit中学习StandsardScaler中的二进制列

Question

我正在sci-kit学习中构建线性回归模型,并将输入扩展为sci-kit学习管道中的预处理步骤.有什么办法可以避免缩放二进制列吗？发生的事情是这些列与其他列一起缩放,导致值以0为中心,而不是0或1,所以我得到的值如[-0.6,0.3],这会导致输入值为0影响我的线性模型中的预测.

基本代码说明:

>>> import numpy as np
>>> from sklearn.pipeline import Pipeline
>>> from sklearn.preprocessing import StandardScaler
>>> from sklearn.linear_model import Ridge
>>> X = np.hstack( (np.random.random((1000, 2)),
                np.random.randint(2, size=(1000, 2))) )
>>> X
array([[ 0.30314072,  0.22981496,  1.        ,  1.        ],
       [ 0.08373292,  0.66170678,  1.        ,  0.        ],
       [ 0.76279599,  0.36658793,  1.        ,  0.        ],
       ...,
       [ 0.81517519,  0.40227095,  0.        ,  0.        ],
       [ 0.21244587,  0.34141014,  0.        ,  0.        ],
       [ 0.2328417 ,  0.14119217,  0.        ,  0.        ]])
>>> scaler = StandardScaler()
>>> scaler.fit_transform(X)
array([[-0.67768374, -0.95108883,  1.00803226,  1.03667198],
       [-1.43378124,  0.53576375,  1.00803226, -0.96462528],
       [ 0.90632643, -0.48022732,  1.00803226, -0.96462528],
       ...,
       [ 1.08682952, -0.35738315, -0.99203175, -0.96462528],
       [-0.99022572, -0.56690563, -0.99203175, -0.96462528],
       [-0.91994001, -1.25618613, -0.99203175, -0.96462528]])

我喜欢最后一行的输出:

>>> scaler.fit_transform(X, dont_scale_binary_or_something=True)
array([[-0.67768374, -0.95108883,  1.        ,  1.        ],
       [-1.43378124,  0.53576375,  1.        ,  0.        ],
       [ 0.90632643, -0.48022732,  1.        ,  0.        ],
       ...,
       [ 1.08682952, -0.35738315,  0.        ,  0.        ],
       [-0.99022572, -0.56690563,  0.        ,  0.        ],
       [-0.91994001, -1.25618613,  0.        ,  0.        ]])

我能以任何方式完成这项任务吗 我想我可以选择不是二进制的列,只转换那些,然后将转换后的值替换回数组,但我希望它与sci-kit学习管道工作流程很好地配合,所以我可以做类似的事情:

clf = Pipeline([('scaler', StandardScaler()), ('ridge', Ridge())])
clf.set_params(scaler__dont_scale_binary_features=True, ridge__alpha=0.04).fit(X, y)

Answer 1

我发布的代码是我改编自@ miindlek的回复,以防它对其他人有帮助.我没有包含BaseEstimator时遇到错误.再次感谢@miindlek.下面,bin_vars_index是二进制变量的列索引数组,cont_vars_index对于要扩展的连续变量是相同的.

from sklearn.preprocessing import StandardScaler
from sklearn.base import BaseEstimator, TransformerMixin
import numpy as np

class CustomScaler(BaseEstimator,TransformerMixin): 
    # note: returns the feature matrix with the binary columns ordered first  
    def __init__(self,bin_vars_index,cont_vars_index,copy=True,with_mean=True,with_std=True):
        self.scaler = StandardScaler(copy,with_mean,with_std)
        self.bin_vars_index = bin_vars_index
        self.cont_vars_index = cont_vars_index

    def fit(self, X, y=None):
        self.scaler.fit(X[:,self.cont_vars_index], y)
        return self

    def transform(self, X, y=None, copy=None):
        X_tail = self.scaler.transform(X[:,self.cont_vars_index],y,copy)
        return np.concatenate((X[:,self.bin_vars_index],X_tail), axis=1)

Answer 2

您应该创建一个自定义缩放器,在缩放时忽略最后两列.

from sklearn.base import TransformerMixin
import numpy as np

class CustomScaler(TransformerMixin): 
    def __init__(self):
        self.scaler = StandardScaler()

    def fit(self, X, y):
        self.scaler.fit(X[:, :-2], y)
        return self

    def transform(self, X):
        X_head = self.scaler.transform(X[:, :-2])
        return np.concatenate(X_head, X[:, -2:], axis=1)

Answer 3

我已经对 @J_C 代码进行了一些调整，以便与 pandas 数据框架一起使用。您可以传递要缩放的列名称，并获得具有初始列顺序的结果。

enter code here
from sklearn.preprocessing import StandardScaler
from sklearn.base import BaseEstimator, TransformerMixin
import pandas as pd

class CustomScaler(BaseEstimator,TransformerMixin): 
    def __init__(self,columns,copy=True,with_mean=True,with_std=True):
        self.scaler = StandardScaler(copy,with_mean,with_std)
        self.columns = columns

    def fit(self, X, y=None):
        self.scaler.fit(X[self.columns], y)
        return self

    def transform(self, X, y=None, copy=None):
        init_col_order = X.columns
        X_scaled = pd.DataFrame(self.scaler.transform(X[self.columns]), columns=self.columns)
        X_not_scaled = X.ix[:,~X.columns.isin(self.columns)]
        return pd.concat([X_not_scaled, X_scaled], axis=1)[init_col_order]

用法：

scale = CustomScaler(columns=['duration', 'num_operations'])
scaled = scale.fit_transform(churn_d)

Answer 4

您的管道应更改为：

from sklearn.preprocessing import StandardScaler,FunctionTransformer
from sklearn.pipeline import Pipeline,FeatureUnion


pipeline=Pipeline(steps= [
    ('feature_processing', FeatureUnion(transformer_list = [
            ('categorical', FunctionTransformer(lambda data: data[:, cat_indices])),

            #numeric
            ('numeric', Pipeline(steps = [
                ('select', FunctionTransformer(lambda data: data[:, num_indices])),
                ('scale', StandardScaler())
                        ]))
        ])),
    ('clf', Ridge())
    ]
)