title: AttributeError: 'Pipeline' object has no attribute 'partial_fit'
body: <p>I am trying to train my binary classifier over a huge data. Previously, I could accomplish training via using fit method of sklearn. But now, I have more data and I cannot cope with them. I am trying to fitting them partially but couldn't get rid of errors. How can I train my huge data incrementally? With applying my previous approach, I get an error about pipeline object. I have gone through the examples from <a href="http://dask-ml.readthedocs.io/en/latest/incremental.html" rel="nofollow noreferrer">Incremental Learning</a> but still running these code samples gives error. I will appreciate any help.</p>
  
  <pre><code>X,y = transform_to_dataset(training_data)
  
  clf = Pipeline([
      ('vectorizer', DictVectorizer()),
      ('classifier', LogisticRegression())])
  
  length=len(X)/2
  
  clf.partial_fit(X[:length],y[:length],classes=np.array([0,1]))
  
  clf.partial_fit(X[length:],y[length:],classes=np.array([0,1]))
  </code></pre>
  
  <p><b>ERROR</b></p>
  
  <pre><code>AttributeError: 'Pipeline' object has no attribute 'partial_fit'
  </code></pre>
  
  <p><b>TRYING GIVEN CODE SAMPLES:</b></p>
  
  <pre><code>clf=SGDClassifier(alpha=.0001, loss='log', penalty='l2', n_jobs=-1,
                        #shuffle=True, n_iter=10, 
                        verbose=1)
  length=len(X)/2
  
  clf.partial_fit(X[:length],y[:length],classes=np.array([0,1]))
  
  clf.partial_fit(X[length:],y[length:],classes=np.array([0,1]))
  </code></pre>
  
  <p><b>ERROR</b></p>
  
  <pre><code>File "/home/kntgu/anaconda2/lib/python2.7/site-packages/sklearn/utils/validation.py", line 573, in check_X_y
  ensure_min_features, warn_on_dtype, estimator)
  File "/home/kntgu/anaconda2/lib/python2.7/site-packages/sklearn/utils/validation.py", line 433, in check_array
  array = np.array(array, dtype=dtype, order=order, copy=copy)
  TypeError: float() argument must be a string or a number
  </code></pre>
  
  <p>My dataset consists of some sentences with their part of speech tags and dependency relations. </p>
  
  <pre><code>Thanks  NN  0   root
  to  IN  3   case
  all DT  1   nmod
  who WP  5   nsubj
  volunteered VBD 3   acl:relcl
  .   .   1   punct
  
  You PRP 3   nsubj
  will    MD  3   aux
  remain  VB  0   root
  as  IN  5   case
  alternates  NNS 3   obl
  .   .   3   punct
  </code></pre>