from nltk import word_tokenize
from nltk.stem import WordNetLemmatizer

class LemmaTokenizer(object):
  def __init__(self):
    self.wnl = WordNetLemmatizer()
    def __call__(self, doc):
      return [self.wnl.lemmatize(t) for t in word_tokenize(doc)]

from sklearn.feature_extraction.text import TfidfVectorizer

vectorizer = TfidfVectorizer( tokenizer = LemmaTokenizer(), analyzer = 'word', max_df = 0.7, min_df = 50, stop_words = 'english' )
vectorizer.fit(corpus)
corpus_tf_idf = vectorizer.transform(corpus)