I'm using PCA to reduce my feature vector dimension. I'm saving its model and transformed output like this:

from sklearn.externals import joblib    
from sklearn.decomposition import PCA

pca_transformed_fit = pca.fit(data)
pca_transformed = np.around(pca.fit_transform(data), 10)
np.savetxt('reducedFeatures.txt', pca_transformed, delimiter=',', newline='\n', fmt='%1.10f')
joblib.dump(pca_transformed_fit, 'pca.model')

data is the output of VGG16. As I want to have only 10 decimal places, I rounded the transformed output. So I want to use saved model to process feature vectors again with the code below:

from sklearn.externals import joblib

pca = joblib.load('pca.model')
pca_transformed = np.around(pca.transform(samples), 10)
np.savetxt('reducedFeatures_again.txt', pca_transformed, delimiter=',', newline='\n', fmt='%1.10f')

BUT the problem is the outputs are not the same for a given feature vector.

enter image description here

As long as these transformed data are very meaningful, what can I do to have the exact output??
If you need more clarification, just ask for it. Thank you!


1 Answer 1


You're fitting the PCA twice in the first code snippet. First time here: pca_transformed_fit = pca.fit(data), second time here: pca_transformed = np.around(pca.fit_transform(data), 10). I guess there are some small computational errors that result in a slightly different decomposition upon every repeated call to fit() or fit_transform(). Replace fit_transform() with transform() and it should work (at least it worked for me when I tested it).

  • $\begingroup$ Sir, first of all THANK YOU! I don't know why I did this mess!!! It's obvious from docs that fit_transform produce different results than fit then transform. Thank you for your help. :) $\endgroup$ Aug 3, 2019 at 9:32
  • $\begingroup$ you're welcome! $\endgroup$ Aug 4, 2019 at 15:54

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