title: module 'sklearn.metrics' has no attribute 'plot_roc_curve'
body: <p>I am trying to plot ROC curve for stratifiedKfold validation. Heres the code-</p>
  <pre class="lang-py prettyprint-override"><code>from sklearn import metrics
  
  # Run classifier with crossvalidation and plot ROC curves
  cv = StratifiedKFold(n_splits=10)
  tprs = []
  aucs = []
  mean_fpr = np.linspace(0, 1, 100)
  
  fig, ax = plt.subplots()
  for i, (train, test) in enumerate(cv.split(X, y)):
      logisticRegr.fit(X[train], y[train])
      viz = metrics.plot_roc_curve(logisticRegr, X[test], y[test],
                           name='ROC fold {}'.format(i),
                           alpha=0.3, lw=1, ax=ax)
      interp_tpr = np.interp(mean_fpr, viz.fpr, viz.tpr)
      interp_tpr[0] = 0.0
      tprs.append(interp_tpr)
      aucs.append(viz.roc_auc)
  
  ax.plot([0, 1], [0, 1], linestyle='--', lw=2, color='r',
          label='Chance', alpha=.8)
  
  mean_tpr = np.mean(tprs, axis=0)
  mean_tpr[-1] = 1.0
  mean_auc = auc(mean_fpr, mean_tpr)
  std_auc = np.std(aucs)
  ax.plot(mean_fpr, mean_tpr, color='b',
          label=r'Mean ROC (AUC = %0.2f $\pm$ %0.2f)' % (mean_auc, std_auc),
          lw=2, alpha=.8)
  
  std_tpr = np.std(tprs, axis=0)
  tprs_upper = np.minimum(mean_tpr + std_tpr, 1)
  tprs_lower = np.maximum(mean_tpr - std_tpr, 0)
  ax.fill_between(mean_fpr, tprs_lower, tprs_upper, color='grey', alpha=.2,
                  label=r'$\pm$ 1 std. dev.')
  
  ax.set(xlim=[-0.05, 1.05], ylim=[-0.05, 1.05],
         title=&quot;Receiver operating characteristic example&quot;)
  ax.legend(loc=&quot;lower right&quot;)
  plt.show()
  </code></pre>
  <p>I am getting this error-</p>
  <pre><code>AttributeError: module 'sklearn.metrics' has no attribute 'plot_roc_curve'
  </code></pre>
  <p>The version is 0.21.3.</p>