title: sklearn One Hot Encoder without fitting
body: <p>I have a sequence of data that I already know the categories. Is there anyway to use sklearn's <code>OneHotEncoder</code> without fitting?</p>
  <p>Specifically, I have already initialized the Encoder:</p>
  <pre><code>my_cat = ['a', 'b', 'c']
  enc = OneHotEncoder(categories=[my_cat])
  </code></pre>
  <p>However, I am unable to use the encoder, unless I fit it with some dummy values:</p>
  <pre><code>enc.fit([['a']])
  enc.transform([['a'], ['c']]).toarray()
  &gt;&gt;&gt;
  [[1 0 0]
   [0 0 1]]
  </code></pre>
  <p>If I do not fit the encoder first,</p>
  <pre><code># Missing enc.fit
  enc.transform([['a'], ['c']]).toarray()
  </code></pre>
  <p>there will be the following error,</p>
  <pre><code>sklearn.exceptions.NotFittedError: This OneHotEncoder instance is not fitted yet. Call 'fit' with appropriate arguments before using this estimator.
  </code></pre>
  <p>despite that I have already explicitly passed the categories to the encoder. This error is thrown by the method <code>check_is_fitted()</code>.</p>
  <p>Am I using <code>OneHotEncoder</code> for the wrong thing? What are some other methods to perform a dumb mapping, or should I just write my own?</p>