title: "__init__() got multiple values for argument 'n_splits'" error with sklearn ShuffleSplit
body: <p>I am getting </p>
  
  <blockquote>
    <p><strong>init</strong>() got multiple values for argument 'n_splits'</p>
  </blockquote>
  
  <p>error for this line:</p>
  
  <blockquote>
    <p>cv = ShuffleSplit(n_splits = 10, test_size = 0.2, random_state = 0)</p>
  </blockquote>
  
  <p>in the following code: </p>
  
  <pre><code>import matplotlib.pyplot as pl
  import numpy as np
  import sklearn.model_selection as curves
  from sklearn.tree import DecisionTreeRegressor
  from sklearn.model_selection import ShuffleSplit, train_test_split, learning_curve
  
  def ModelLearning(X, y):
      """ Calculates the performance of several models with varying sizes of training data.
          The learning and testing scores for each model are then plotted. """
  
      # Create 10 cross-validation sets for training and testing
      cv = ShuffleSplit(n_splits = 10, test_size = 0.2, random_state = 0)
  
      # Generate the training set sizes increasing by 50
      train_sizes = np.rint(np.linspace(1, X.shape[0]*0.8 - 1, 9)).astype(int)
  
      # Create the figure window
      fig = pl.figure(figsize=(10,7))
  
      # Create three different models based on max_depth
      for k, depth in enumerate([1,3,6,10]):
  
          # Create a Decision tree regressor at max_depth = depth
          regressor = DecisionTreeRegressor(max_depth = depth)
  
          # Calculate the training and testing scores
          sizes, train_scores, test_scores = learning_curve(regressor, X, y, \
             train_sizes = train_sizes,  cv = cv, scoring = 'r2')
  
          # Find the mean and standard deviation for smoothing
          train_std = np.std(train_scores, axis = 1)
          train_mean = np.mean(train_scores, axis = 1)
          test_std = np.std(test_scores, axis = 1)
          test_mean = np.mean(test_scores, axis = 1)
  
          # Subplot the learning curve 
          ax = fig.add_subplot(2, 2, k+1)
          ax.plot(sizes, train_mean, 'o-', color = 'r', label = 'Training Score')
          ax.plot(sizes, test_mean, 'o-', color = 'g', label = 'Testing Score')
          ax.fill_between(sizes, train_mean - train_std, \
              train_mean + train_std, alpha = 0.15, color = 'r')
          ax.fill_between(sizes, test_mean - test_std, \
              test_mean + test_std, alpha = 0.15, color = 'g')
  
          # Labels
          ax.set_title('max_depth = %s'%(depth))
          ax.set_xlabel('Number of Training Points')
          ax.set_ylabel('Score')
          ax.set_xlim([0, X.shape[0]*0.8])
          ax.set_ylim([-0.05, 1.05])
  
      # Visual aesthetics
      ax.legend(bbox_to_anchor=(1.05, 2.05), loc='lower left', borderaxespad = 0.)
      fig.suptitle('Decision Tree Regressor Learning Performances', fontsize = 16, y = 1.03)
      fig.tight_layout()
      fig.show()
  </code></pre>
  
  <p>I am aware this error usually indicates incorrect parameter order, however this should be the correct one. This is the example in the sklearn documentation:</p>
  
  <blockquote>
    <p>rs = ShuffleSplit(n_splits=3, test_size=.25, random_state=0)</p>
  </blockquote>
  
  <p>I also tried removing the n_splits parameter since 10 is the default value anyway:</p>
  
  <blockquote>
    <p>cv = ShuffleSplit(test_size = 0.2, random_state = 0)</p>
  </blockquote>
  
  <p>This produces the same error. </p>
  
  <p>I am converting the code from python 2.7 to 3.5 and from an earlier version of sklearn to 0.18.1 so I might have missed something, but I have no idea what it could be. The parameters in the line that calls the ShuffleSplit seem to be in order too:</p>
  
  <blockquote>
    <p>sizes, train_scores, test_scores = learning_curve(regressor, X, y, \
               train_sizes = train_sizes,  cv = cv, scoring = 'r2')</p>
  </blockquote>
  
  <p>The X and y that the function is called with worked with python 2.7 so they should be fine too.</p>
  
  <p>Traceback:</p>
  
  <pre><code>TypeError                                 Traceback (most recent call last)
  &lt;ipython-input-33-191abc15bbd7&gt; in &lt;module&gt;()
        1 # Produce learning curves for varying training set sizes and maximum depths
  ----&gt; 2 vs.ModelLearning(features, prices)
  
  E:\Python\machine-learning-master\projects\boston_housing\visuals.py in ModelLearning(X, y)
       21 
       22     # Create 10 cross-validation sets for training and testing
  ---&gt; 23     cv = ShuffleSplit(n_splits = 10, test_size = 0.2, random_state = 0)
       24 
       25     # Generate the training set sizes increasing by 50
  
  TypeError: __init__() got multiple values for argument 'n_splits'
  </code></pre>