is it possible Apply PCA on any Text Classification?

Question

I'm trying a classification with python. I'm using Naive Bayes MultinomialNB classifier for the web pages (Retrieving data form web to text , later I classify this text: web classification).

Now, I'm trying to apply PCA on this data, but python is giving some errors.

My code for classification with Naive Bayes :

from sklearn import PCA
from sklearn import RandomizedPCA
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.naive_bayes import MultinomialNB
vectorizer = CountVectorizer()
classifer = MultinomialNB(alpha=.01)

x_train = vectorizer.fit_transform(temizdata)
classifer.fit(x_train, y_train)

This naive bayes classification gives that output :

>>> x_train
<43x4429 sparse matrix of type '<class 'numpy.int64'>'
    with 6302 stored elements in Compressed Sparse Row format>

>>> print(x_train)
(0, 2966)   1
(0, 1974)   1
(0, 3296)   1
..
..
(42, 1629)  1
(42, 2833)  1
(42, 876)   1

Than I try to apply PCA on my data (temizdata) :

>>> v_temizdata = vectorizer.fit_transform(temizdata)
>>> pca_t = PCA.fit_transform(v_temizdata)
>>> pca_t = PCA().fit_transform(v_temizdata)

but this raise following erros:

raise TypeError('A sparse matrix was passed, but dense ' TypeError: A sparse matrix was passed, but dense data is required. Use X.toarray() to convert to a dense numpy array.

I convert matrix to densematrix or numpy array. Then I tried to classfy new densematrix , but I have error.

My main aim is that test PCA effect on Classification on text.

Convert to dense array :

v_temizdatatodense = v_temizdata.todense()
pca_t = PCA().fit_transform(v_temizdatatodense)

Finally try classfy :

classifer.fit(pca_t,y_train)

error for final classfy :

raise ValueError("Input X must be non-negative") ValueError: Input X must be non-negative

On one side my data (temizdata) is put in Naive Bayes only, on the other side temizdata firstly put in PCA (for reduce inputs) than classify. __

Answer 1

Rather than converting a sparse matrix to dense (which is discouraged), I would use scikits-learn's TruncatedSVD, which is a PCA-like dimmensionality reduction algorithm (using by default Randomized SVD) which works on sparse data:

svd = TruncatedSVD(n_components=5, random_state=42)
data = svd.fit_transform(data) 

And, citing from the TruncatedSVD documentation:

In particular, truncated SVD works on term count/tf-idf matrices as returned by the vectorizers in sklearn.feature_extraction.text. In that context, it is known as latent semantic analysis (LSA).

which is exactly your use case.