title: Vectorizer object toArray(), Array is Too Large Error
body: <p>I have created pre-processed data. Now, I would like to vectorize it and write it on a text file. While transforming vectorizer object to array, I get this error. What could be possible solutions?  </p>
  
  <pre><code>from sklearn.feature_extraction.text import CountVectorizer
      import numpy as np
      vectorizer = CountVectorizer(analyzer = "word",   \
                                   tokenizer = None,    \
                                   preprocessor = None, \
                                   stop_words = None,   \
                                   max_features = 1000)
      newTestFile = open("testfile.txt", 'r', encoding='latin-1')
      featureVector=vectorizer.fit_transform(newTestFile)
      train_data_features = featureVector.toarray()
      np.savetxt('plotFeatureVector.txt', train_data_features, fmt="%10s %10.3f")
  
  The error:
  
      Traceback (most recent call last):
        File "C:/Users/NuMA/Desktop/Lecture Stuff/EE 485/Project/Deneme/bagOfWords.py", line 12, in &lt;module&gt;
          train_data_features = featureVector.toarray()
        File "C:\Users\NuMA\AppData\Local\Programs\Python\Python35-32\lib\site-packages\scipy\sparse\compressed.py", line 964, in toarray
          return self.tocoo(copy=False).toarray(order=order, out=out)
        File "C:\Users\NuMA\AppData\Local\Programs\Python\Python35-32\lib\site-packages\scipy\sparse\coo.py", line 252, in toarray
          B = self._process_toarray_args(order, out)
        File "C:\Users\NuMA\AppData\Local\Programs\Python\Python35-32\lib\site-packages\scipy\sparse\base.py", line 1039, in _process_toarray_args
          return np.zeros(self.shape, dtype=self.dtype, order=order)
      ValueError: array is too big; `arr.size * arr.dtype.itemsize` is larger than the maximum possible size.
  </code></pre>