title: How to plot ROC curve with scikit learn for the multiclass case?
body: <p>I would like to plot the ROC curve for the multiclass case for my own dataset. By the <a href="http://scikit-learn.org/stable/auto_examples/model_selection/plot_roc.html#example-model-selection-plot-roc-py" rel="noreferrer">documentation</a> I read that the labels must been binary(I have 5 labels from 1 to 5), so I followed the example provided in the documentation:</p>
  
  <pre><code>print(__doc__)
  
  import numpy as np
  import matplotlib.pyplot as plt
  from sklearn import svm, datasets
  from sklearn.metrics import roc_curve, auc
  from sklearn.cross_validation import train_test_split
  from sklearn.preprocessing import label_binarize
  from sklearn.svm import SVC
  from sklearn.multiclass import OneVsRestClassifier
  
  
  
  from sklearn.feature_extraction.text import TfidfVectorizer
  import numpy as np
  tfidf_vect= TfidfVectorizer(use_idf=True, smooth_idf=True, sublinear_tf=False, ngram_range=(2,2))
  from sklearn.cross_validation import train_test_split, cross_val_score
  
  import pandas as pd
  
  df = pd.read_csv('path/file.csv',
                       header=0, sep=',', names=['id', 'content', 'label'])
  
  
  X = tfidf_vect.fit_transform(df['content'].values)
  y = df['label'].values
  
  
  
  
  # Binarize the output
  y = label_binarize(y, classes=[1,2,3,4,5])
  n_classes = y.shape[1]
  
  # Add noisy features to make the problem harder
  random_state = np.random.RandomState(0)
  n_samples, n_features = X.shape
  X = np.c_[X, random_state.randn(n_samples, 200 * n_features)]
  
  # shuffle and split training and test sets
  X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33
                                                      ,random_state=0)
  
  # Learn to predict each class against the other
  classifier = OneVsRestClassifier(svm.SVC(kernel='linear', probability=True,
                                   random_state=random_state))
  y_score = classifier.fit(X_train, y_train).decision_function(X_test)
  
  # Compute ROC curve and ROC area for each class
  fpr = dict()
  tpr = dict()
  roc_auc = dict()
  for i in range(n_classes):
      fpr[i], tpr[i], _ = roc_curve(y_test[:, i], y_score[:, i])
      roc_auc[i] = auc(fpr[i], tpr[i])
  
  # Compute micro-average ROC curve and ROC area
  fpr["micro"], tpr["micro"], _ = roc_curve(y_test.ravel(), y_score.ravel())
  roc_auc["micro"] = auc(fpr["micro"], tpr["micro"])
  
  # Plot of a ROC curve for a specific class
  plt.figure()
  plt.plot(fpr[2], tpr[2], label='ROC curve (area = %0.2f)' % roc_auc[2])
  plt.plot([0, 1], [0, 1], 'k--')
  plt.xlim([0.0, 1.0])
  plt.ylim([0.0, 1.05])
  plt.xlabel('False Positive Rate')
  plt.ylabel('True Positive Rate')
  plt.title('Receiver operating characteristic example')
  plt.legend(loc="lower right")
  plt.show()
  
  # Plot ROC curve
  plt.figure()
  plt.plot(fpr["micro"], tpr["micro"],
           label='micro-average ROC curve (area = {0:0.2f})'
                 ''.format(roc_auc["micro"]))
  for i in range(n_classes):
      plt.plot(fpr[i], tpr[i], label='ROC curve of class {0} (area = {1:0.2f})'
                                     ''.format(i, roc_auc[i]))
  
  plt.plot([0, 1], [0, 1], 'k--')
  plt.xlim([0.0, 1.0])
  plt.ylim([0.0, 1.05])
  plt.xlabel('False Positive Rate')
  plt.ylabel('True Positive Rate')
  plt.title('Some extension of Receiver operating characteristic to multi-class')
  plt.legend(loc="lower right")
  plt.show()
  </code></pre>
  
  <p>The problem with this is that this aproach never finish. Any idea of how to plot this ROC curve for this <a href="http://pastebin.com/KWALx8PM" rel="noreferrer">dataset</a>?.</p>