Python scikit learn pca.explained_variance_ratio_ cutoff

Question

When choosing the number of principal components (k), we choose k to be the smallest value so that for example, 99% of variance, is retained.

However, in the Python Scikit learn, I am not 100% sure pca.explained_variance_ratio_ = 0.99 is equal to "99% of variance is retained"? Could anyone enlighten? Thanks.

• The Python Scikit learn PCA manual is here

http://scikit-learn.org/stable/modules/generated/sklearn.decomposition.PCA.html#sklearn.decomposition.PCA

Answer 1

Yes, you are nearly right. The pca.explained_variance_ratio_ parameter returns a vector of the variance explained by each dimension. Thus pca.explained_variance_ratio_[i] gives the variance explained solely by the i+1st dimension.

You probably want to do pca.explained_variance_ratio_.cumsum(). That will return a vector x such that x[i] returns the cumulative variance explained by the first i+1 dimensions.

import numpy as np from sklearn.decomposition import PCA  np.random.seed(0) my_matrix = np.random.randn(20, 5)  my_model = PCA(n_components=5) my_model.fit_transform(my_matrix)  print my_model.explained_variance_ print my_model.explained_variance_ratio_ print my_model.explained_variance_ratio_.cumsum() 

---

[ 1.50756565  1.29374452  0.97042041  0.61712667  0.31529082] [ 0.32047581  0.27502207  0.20629036  0.13118776  0.067024  ] [ 0.32047581  0.59549787  0.80178824  0.932976    1.        ] 

So in my random toy data, if I picked k=4 I would retain 93.3% of the variance.

Answer 2

Although this question is older than 2 years i want to provide an update on this. I wanted to do the same and it looks like sklearn now provides this feature out of the box.

As stated in the docs

if 0 < n_components < 1 and svd_solver == ‘full’, select the number of components such that the amount of variance that needs to be explained is greater than the percentage specified by n_components

So the code required is now

my_model = PCA(n_components=0.99, svd_solver='full') my_model.fit_transform(my_matrix)