[英]fit_transform PCA inconsistent results
I am trying to do PCA
from sklearn
with n_components = 5
. 我正在尝试使用
sklearn
和n_components = 5
进行PCA
。 I apply the dimensionality reduction on my data using fit_transform(data)
. 我使用
fit_transform(data)
在我的数据上应用fit_transform(data)
维。
Initially I tried to do the classical matrix multiplication between pca.components_
values and my x_features
data, but the results are different. 最初,我尝试在
pca.components_
值和我的x_features
数据之间进行经典矩阵乘法,但是结果有所不同。 So I am wether doing my multiplication incorrectly or I did not understand how fit_transform
work. 所以我不正确地进行乘法运算,或者我不明白
fit_transform
如何工作的。
Below is a mock-up to compare classic matrix multiplication and fit_transform
: 下面是一个比较经典矩阵乘法和
fit_transform
:
import numpy as np
from sklearn import decomposition
np.random.seed(0)
my_matrix = np.random.randn(100, 5)`
mdl = decomposition.PCA(n_components=5)
mdl_FitTrans = mdl.fit_transform(my_matrix)
pca_components = mdl.components_
mdl_FitTrans_manual = np.dot(pca_components, my_matrix.transpose())
mdl_FitTrans_manualT = mdl_FitTrans_manual.transpose()
I am expecting mdl_FitTrans == mdl_FitTrans_manual
but the result is False
. 我期望
mdl_FitTrans == mdl_FitTrans_manual
但结果为False
。
Check out, how the transform()
method is implemented in sklearn: https://github.com/scikit-learn/scikit-learn/blob/a5ab948/sklearn/decomposition/base.py#L101 看看如何在sklearn中实现
transform()
方法: https : //github.com/scikit-learn/scikit-learn/blob/a5ab948/sklearn/decomposition/base.py#L101
According to it, manual reduction is done as following: 据此,手动减少操作如下:
import numpy as np
from sklearn import decomposition
np.random.seed(0)
data = np.random.randn(100, 100)
mdl = decomposition.PCA(n_components=5)
mdl_fit = mdl.fit(data)
data_transformed = mdl_fit.transform(data)
data_transformed_manual = np.dot(data - mdl_fit.mean_, mdl.components_.T)
np.all(data_transformed == data_transformed_manual)
True
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