title: Unexpected keyword argument python
body: <p>I am passing the correct parameters, but I am still getting the following error:</p>
  
  <pre class="lang-none prettyprint-override"><code>Starting classification 
  
   Classification running ... 
  
  Traceback (most recent call last):
    File "C:/Classify/classifier.py", line 95, in &lt;module&gt;
  
      train_avg, test_avg, cms = train_model(X, y, "ceps", plot=True)
    File "C:/Classify/classifier.py", line 25, in train_model
  
      cv = ShuffleSplit(n=len(X), n_iterations=1, test_fraction=0.3, indices=True, random_state=0)
  
  TypeError: __init__() got an unexpected keyword argument 'test_fraction'
  
  Process finished with exit code 1
  </code></pre>
  
  <p>My code is:</p>
  
  <pre><code>def train_model(X, Y, name, plot=False):
  
      labels = np.unique(Y)
  
      cv = ShuffleSplit(n=len(X), n_iterations=1, test_fraction=0.3, indices=True, random_state=0)
  
      train_errors = []
      test_errors = []
  
      scores = []
      pr_scores = defaultdict(list)
      precisions, recalls, thresholds = defaultdict(list), defaultdict(list), defaultdict(list)
  
      roc_scores = defaultdict(list)
      tprs = defaultdict(list)
      fprs = defaultdict(list)
  
      clfs = []  # for the median
  
      cms = []
  
      for train, test in cv:
          X_train, y_train = X[train], Y[train]
          X_test, y_test = X[test], Y[test]
  
          clf = LogisticRegression()
          clf.fit(X_train, y_train)
          clfs.append(clf)
  
          train_score = clf.score(X_train, y_train)
          test_score = clf.score(X_test, y_test)
          scores.append(test_score)
  
          train_errors.append(1 - train_score)
          test_errors.append(1 - test_score)
  
          y_pred = clf.predict(X_test)
          cm = confusion_matrix(y_test, y_pred)
          cms.append(cm)
  
          for label in labels:
              y_label_test = np.asarray(y_test == label, dtype=int)
              proba = clf.predict_proba(X_test)
              proba_label = proba[:, label]
  
              fpr, tpr, roc_thresholds = roc_curve(y_label_test, proba_label)
              roc_scores[label].append(auc(fpr, tpr))
              tprs[label].append(tpr)
              fprs[label].append(fpr)
  
      if plot:
          for label in labels:
              scores_to_sort = roc_scores[label]
              median = np.argsort(scores_to_sort)[len(scores_to_sort) / 2]
              desc = "%s %s" % (name, genre_list[label])
              plot_roc_curves(roc_scores[label][median], desc, tprs[label][median],fprs[label][median], label='%s vs rest' % genre_list[label])
  
      all_pr_scores = np.asarray(pr_scores.values()).flatten()
      summary = (np.mean(scores), np.std(scores), np.mean(all_pr_scores), np.std(all_pr_scores))
  
      #print("%.3f\t%.3f\t%.3f\t%.3f\t" % summary)
  
      #save the trained model to disk
      joblib.dump(clf, 'saved_model/model_ceps.pkl')
  
      return np.mean(train_errors), np.mean(test_errors), np.asarray(cms)
  </code></pre>