在PCA之后解释OLS权重（在Python中）

Question

我想解释输入数据已经用PCA预处理的模型中的回归模型权重。实际上，我有100个高度相关的输入维度，所以我知道PCA很有用。但是，为了便于说明，我将使用Iris数据集。

下面的sklearn代码说明了我的问题：

import numpy as np
import sklearn.datasets, sklearn.decomposition
from sklearn.linear_model import LinearRegression

# load data
X = sklearn.datasets.load_iris().data
w = np.array([0.3, 10, -0.1, -0.01])
Y = np.dot(X, w)

# set number of components to keep from PCA
n_components = 4

# reconstruct w
reg = LinearRegression().fit(X, Y)
w_hat = reg.coef_
print(w_hat)

# apply PCA
pca = sklearn.decomposition.PCA(n_components=n_components)
pca.fit(X)
X_trans = pca.transform(X)

# reconstruct w
reg_trans = LinearRegression().fit(X_trans, Y)
w_trans_hat = np.dot(reg_trans.coef_, pca.components_)
print(w_trans_hat)

运行此代码，可以看到权重被正确再现。

但是，如果我将组件的数量设置为3（即n_components = 3），那么打印出的权重将基本上偏离真实的权重。

我是否误解了如何改变这些重量？或者是因为PCA的信息丢失从4个变为3个？

Answer 1

我认为这很好，只是我在看w_trans_hat而不是重建的Y：

import numpy as np
import sklearn.datasets, sklearn.decomposition
from sklearn.linear_model import LinearRegression

# load data
X = sklearn.datasets.load_iris().data
# create fake loadings
w = np.array([0.3, 10, -0.1, -0.01])
# centre X
X = np.subtract(X, np.mean(X, 0))
# calculate Y
Y = np.dot(X, w)

# set number of components to keep from PCA
n_components = 3

# reconstruct w using linear regression
reg = LinearRegression().fit(X, Y)
w_hat = reg.coef_
print(w_hat)

# apply PCA
pca = sklearn.decomposition.PCA(n_components=n_components)
pca.fit(X)
X_trans = pca.transform(X)

# regress Y on principal components
reg_trans = LinearRegression().fit(X_trans, Y)
# reconstruct Y using regressed weights and transformed X
Y_trans = np.dot(X_trans, reg_trans.coef_)
# show MSE to original Y
print(np.mean((Y - Y_trans) ** 2))

# show w implied by reduced model in original space
w_trans_hat = np.dot(reg_trans.coef_, pca.components_)
print(w_trans_hat)