title: OneHotEncoder categories argument
body: <p>With  sklearn 0.22 the categorical_features argument will be removed, thus the following code is not executable anymore:</p>
  
  <pre><code>import numpy as np
  from sklearn.preprocessing import OneHotEncoder
  
  X = np.array([[1, 1], [2, 2], [1, 3]])
  encoder = OneHotEncoder(categorical_features=[1], sparse=False)
  
  print(encoder.fit_transform(X))
  </code></pre>
  
  <p>The question is, how do I achieve the same behavior as in the code above using the categories argument, since <code>OneHotEncoder(categories=[[1, 2], [1, 2, 3]], sparse=False)</code> would also encode the first column and <code>OneHotEncoder(categories=[[1, 2, 3]], sparse=False)</code> throws an Error</p>