title: Making ROC curve using python for multiclassification
body: <p>Following up from here: <a href="https://stackoverflow.com/questions/25125893/converting-a-1d-array-into-a-2d-class-based-matrix-in-python/25126089#25126089">Converting a 1D array into a 2D class-based matrix in python</a></p>
  
  <p>I want to draw ROC curves for each of my 46 classes. I have 300 test samples for which I've run my classifier to make a prediction.</p>
  
  <p><code>y_test</code> is the true classes, and <code>y_pred</code> is what my classifier predicted.</p>
  
  <p>Here's my code:</p>
  
  <pre><code>    from sklearn.metrics import confusion_matrix, roc_curve, auc
      from sklearn.preprocessing import label_binarize
      import numpy as np
  
      y_test_bi = label_binarize(y_test, classes=[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18, 19,20,21,2,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,3,40,41,42,43,44,45])
      y_pred_bi = label_binarize(y_pred, classes=[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18, 19,20,21,2,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,3,40,41,42,43,44,45])
      # Compute ROC curve and ROC area for each class
      fpr = dict()
      tpr = dict()
      roc_auc = dict()
      for i in range(2):
          fpr[i], tpr[i], _ = roc_curve(y_test_bi, y_pred_bi)
          roc_auc[i] = auc(fpr[i], tpr[i])
  </code></pre>
  
  <p>However, now I'm getting the following error:</p>
  
  <pre><code>Traceback (most recent call last):
    File "C:\Users\app\Documents\Python Scripts\gbc_classifier_test.py", line 152, in &lt;module&gt;
      fpr[i], tpr[i], _ = roc_curve(y_test_bi, y_pred_bi)
    File "C:\Users\app\Anaconda\lib\site-packages\sklearn\metrics\metrics.py", line 672, in roc_curve
      fps, tps, thresholds = _binary_clf_curve(y_true, y_score, pos_label)
    File "C:\Users\app\Anaconda\lib\site-packages\sklearn\metrics\metrics.py", line 505, in _binary_clf_curve
      y_true = column_or_1d(y_true)
    File "C:\Users\app\Anaconda\lib\site-packages\sklearn\utils\validation.py", line 265, in column_or_1d
      raise ValueError("bad input shape {0}".format(shape))
  ValueError: bad input shape (300L, 46L)
  </code></pre>