使用 scikit-learn 管道与手动操作时的不同分数

Posted 2023-03-12

【中文标题】使用 scikit-learn 管道与手动操作时的不同分数

【英文标题】：different scores when using scikit-learn pipeline vs. doing it manually

【发布时间】：2019-12-05 08:33:22

【问题描述】：

下面使用 minmaxscaler、polyl 特征和线性回归分类器的简单示例。

通过管道进行：

pipeLine = make_pipeline(MinMaxScaler(),PolynomialFeatures(), LinearRegression())

pipeLine.fit(X_train,Y_train)
print(pipeLine.score(X_test,Y_test))
print(pipeLine.steps[2][1].intercept_)
print(pipeLine.steps[2][1].coef_)

0.4433729905419167
3.4067909278765605
[ 0.         -7.60868833  5.87162697]

手动操作：

X_trainScaled = MinMaxScaler().fit_transform(X_train)
X_trainScaledandPoly = PolynomialFeatures().fit_transform(X_trainScaled)

X_testScaled = MinMaxScaler().fit_transform(X_test)
X_testScaledandPoly = PolynomialFeatures().fit_transform(X_testScaled)

reg = LinearRegression()
reg.fit(X_trainScaledandPoly,Y_train)
print(reg.score(X_testScaledandPoly,Y_test))
print(reg.intercept_)
print(reg.coef_)
print(reg.intercept_ == pipeLine.steps[2][1].intercept_)
print(reg.coef_ == pipeLine.steps[2][1].coef_)

0.44099256691782807
3.4067909278765605
[ 0.         -7.60868833  5.87162697]
True
[ True  True  True]

【问题讨论】：

X_test 和 X_train 有可能有不同的最小值/最大值吗？您可以使用定义的数据集尝试它并将其添加到您的问题中吗？

你不应该fit_transform 两次。你应该 fit 使用训练数据，然后只调用 transform 来获取测试数据。

谢谢大家 :) 我现在可以看到我的方法的错误了 :)

【参考方案1】：

问题在于您的手动步骤，您使用测试数据对 Scaler 进行改装，您需要将其拟合到训练数据上并在测试数据上使用拟合实例，请参阅此处了解详细信息：How to normalize the Train and Test data using MinMaxScaler sklearn 和 @987654322 @

from sklearn.datasets import make_classification, make_regression
from sklearn.preprocessing import MinMaxScaler, PolynomialFeatures
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.pipeline import make_pipeline

X, y = make_regression(n_features=3, n_samples=50, n_informative=1, noise=1)
X_train, X_test, Y_train, Y_test = train_test_split(X, y)

pipeLine = make_pipeline(MinMaxScaler(),PolynomialFeatures(), LinearRegression())

pipeLine.fit(X_train,Y_train)
print(pipeLine.score(X_test,Y_test))
print(pipeLine.steps[2][1].intercept_)
print(pipeLine.steps[2][1].coef_)

scaler = MinMaxScaler().fit(X_train)
X_trainScaled = scaler.transform(X_train)
X_trainScaledandPoly = PolynomialFeatures().fit_transform(X_trainScaled)


X_testScaled = scaler.transform(X_test)
X_testScaledandPoly = PolynomialFeatures().fit_transform(X_testScaled)

reg = LinearRegression()
reg.fit(X_trainScaledandPoly,Y_train)
print(reg.score(X_testScaledandPoly,Y_test))
print(reg.intercept_)
print(reg.coef_)
print(reg.intercept_ == pipeLine.steps[2][1].intercept_)
print(reg.coef_ == pipeLine.steps[2][1].coef_)