[FIXED] Equivalent of Matlab's PCA in Python sklearn

Issue

I'm a Matlab user and I'm learning Python with the sklearn library. I want to translate this Matlab code

[coeff,score] = pca(X)

For coeff I have tried this in Python:

from sklearn.decomposition import PCA
import numpy as np
pca = PCA()
pca.fit(X)
coeff = print(np.transpose(pca.components_))

I don't know whether or not it's right; for score I have no idea.

Could anyone enlight me about correctness of coeff and feasibility of score?

Solution

The PCA score in sklearn is different from matlab.

In sklearn, pca.score() or pca.score_samples() gives the log-likelihood of samples whereas matlab gives the principal components.

From sklearn Documentation:

Return the log-likelihood of each sample.

Parameters:
X : array, shape(n_samples, n_features) The data.
Returns:
ll : array, shape (n_samples,) Log-likelihood of each sample under the current model

From matlab documentation:

[coeff,score,latent] = pca(___) also returns the principal component scores in score and the principal component variances in latent. You can use any of the input arguments in the previous syntaxes.

Principal component scores are the representations of X in the principal component space. Rows of score correspond to observations, and columns correspond to components.

The principal component variances are the eigenvalues of the covariance matrix of X.

Now, the equivalent of matlab score in pca is fit_transform() or transform() :

>>> import numpy as np
>>> from sklearn.decomposition import PCA
>>> X = np.array([[-1, -1], [-2, -1], [-3, -2], [1, 1], [2, 1], [3, 2]])
>>> pca = PCA(n_components=2)
>>> matlab_equi_score = pca.fit_transform(X)

Answered By - Venkatachalam