Python | SKlearn | PCA

Question

Edit: Thanks for spotting the typo, it should be 60*50, i have corrected the same in the question.

I am stuck on the following problem, After performing PCA on a matrix with 60 observations and 50 variables when i checked the shape of pca component it comes out to be 50*50. Whereas i think it should be 60*50. Same i checked in R, it comes out to be, as per my understanding, 60*50. Please let me know if i am doing something wrong. PFB the code:

import numpy as np
arr=np.random.randn(20*3*50)
from numpy import *
arr = (arr - mean(arr, axis=0)) / std(arr, axis=0)
arr=arr.reshape(60,50)
arr.shape
#output: (60, 50)

arr[1:20, 2] = 1
arr[21:40, 1] = 2
arr[21:40, 2] = 2
arr[41:60, 1] = 1
arr.shape
#output: (60, 50)

from sklearn.decomposition import PCA
pca = PCA()
X_train_pca = pca.fit_transform(arr)
pca.components_.shape
#output: (50, 50)

Answer 1

Look at PCA class in scikit-learn. It tells us that:

...if n_components is not set all components are kept:

n_components == min(n_samples, n_features)

As far as pca.components_ returns array of shape (n_components, n_features) , there is no confusion.