TF-IDF implementation

tfidf.py

from sklearn.feature_extraction.text import TfidfTransformer
import numpy as np

counts = [[3, 0, 1],
          [2, 0, 0],
          [3, 0, 0],
          [4, 0, 0],
          [3, 2, 0],
          [3, 0, 2]]

counts = np.array(counts)

temp = counts*(np.log(len(counts) / (counts>0).sum(0))+1).reshape(1,-1)
tfidf = temp/np.linalg.norm(temp,axis=1).reshape(-1,1)

transformer = TfidfTransformer(smooth_idf=False)
tfidf_skl = transformer.fit_transform(counts)
tfidf_skl = tfidf_skl.toarray()

print(np.allclose(tfidf, tfidf_skl))

temp = counts*(np.log((len(counts)+1) / ((counts>0).sum(0)+1))+1).reshape(1,-1)
tfidf_smooth = temp/np.linalg.norm(temp,axis=1).reshape(-1,1)


transformer = TfidfTransformer()
tfidf_skl_smooth = transformer.fit_transform(counts)
tfidf_skl_smooth = tfidf_skl_smooth.toarray()

print(np.allclose(tfidf_smooth, tfidf_skl_smooth))