避免在 sci-kit 中缩放二进制列学习 StandsardScaler [英] Avoid scaling binary columns in sci-kit learn StandsardScaler
问题描述
我正在 sci-kit learn 中构建线性回归模型，并将输入缩放作为 sci-kit learn 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，所以我得到像 [-0.6, 0.3] 这样的值，这导致输入值为 0影响我的线性模型中的预测.
用于说明的基本代码:
<预><代码>>>>将 numpy 导入为 np>>>从 sklearn.pipeline 导入管道>>>从 sklearn.preprocessing 导入 StandardScaler>>>从 sklearn.linear_model 导入岭>>>X = np.hstack( (np.random.random((1000, 2)),np.random.randint(2, size=(1000, 2))) )>>>X数组([[ 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. ]])>>>缩放器 = StandardScaler()>>>scaler.fit_transform(X)数组([[-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]])我希望最后一行的输出是:
<预><代码>>>>scaler.fit_transform(X，dont_scale_binary_or_something=True)数组([[-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. ]])有什么办法可以做到这一点?我想我可以只选择不是二进制的列，只转换它们，然后将转换后的值替换回数组，但我希望它与 sci-kit learn Pipeline 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)我发布了根据@miindlek 的回复改编的代码，以防对其他人有帮助.我在不包含 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 与要缩放的连续变量相同.
from sklearn.preprocessing import StandardScaler从 sklearn.base 导入 BaseEstimator，TransformerMixin将 numpy 导入为 np类 CustomScaler(BaseEstimator,TransformerMixin):# 注意:返回二进制列先排序的特征矩阵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_indexself.cont_vars_index = cont_vars_indexdef fit(self, X, y=None):self.scaler.fit(X[:,self.cont_vars_index], y)回归自我def 变换(self, X, y=None, copy=None):X_tail = self.scaler.transform(X[:,self.cont_vars_index],y,copy)返回 np.concatenate((X[:,self.bin_vars_index],X_tail),axis=1)I'm building a linear regression model in sci-kit learn, and am scaling the inputs as a preprocessing step in a sci-kit learn Pipeline. Is there any way I can avoid scaling binary columns? What's happening is that these columns are being scaled with every other column, causing the values to be centered around 0, rather than being 0 or 1, so I'm getting values like [-0.6, 0.3], which cause input values of 0 to influence predictions in my linear model.
Basic code to illustrate:
>>> 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]])
I'd love for the output of the last line to be:
>>> 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. ]])
Any way I can accomplish this? I suppose I could just select the columns that aren't binary, only transform those, then replace the transformed values back into the array, but I'd like it to play nicely with the sci-kit learn Pipeline workflow, so I can just do something like:
clf = Pipeline([('scaler', StandardScaler()), ('ridge', Ridge())])
clf.set_params(scaler__dont_scale_binary_features=True, ridge__alpha=0.04).fit(X, y)
I'm posting code that I adapted from @miindlek's response just in case it is helpful to others. I encountered an error when I didn't include BaseEstimator. Thank you again @miindlek. Below, bin_vars_index is an array of column indexes for the binary variable and cont_vars_index is the same for the continuous variables that you want to scale.
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)
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