code: def plot_roc(self):
      y_test = label_binarize(
          self.y_test, classes=list(set(self.y_test.flatten()))
      )
      n_classes = y_test.shape[1]
  
      # 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], self.y_pred[:, 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(), self.y_pred.ravel()
      )
      roc_auc["micro"] = auc(fpr["micro"], tpr["micro"])
  
      # Compute macro-average ROC curve and ROC area
  
      # First aggregate all false positive rates
      all_fpr = np.unique(
          np.concatenate([fpr[i] for i in range(n_classes)])
      )
  
      # Then interpolate all ROC curves at this points
      mean_tpr = np.zeros_like(all_fpr)
      for i in range(n_classes):
          mean_tpr += np.interp(all_fpr, fpr[i], tpr[i])
  
      # Finally average it and compute AUC
      mean_tpr /= n_classes
  
      fpr["macro"] = all_fpr
      tpr["macro"] = mean_tpr
      roc_auc["macro"] = auc(fpr["macro"], tpr["macro"])
  
      # Plot all ROC curves
      data = []
      trace1 = go.Scatter(
          x=fpr["micro"],
          y=tpr["micro"],
          mode="lines",
          line=dict(color="deeppink", width=2, dash="dot"),
          name="micro-average ROC curve (area = {0:0.2f})"
          "".format(roc_auc["micro"]),
      )
      data.append(trace1)
  
      trace2 = go.Scatter(
          x=fpr["macro"],
          y=tpr["macro"],
          mode="lines",
          line=dict(color="navy", width=2, dash="dot"),
          name="macro-average ROC curve (area = {0:0.2f})"
          "".format(roc_auc["macro"]),
      )
      data.append(trace2)
  
      colors = cycle(
          [
              "#636EFA",
              "#EF553B",
              "#00CC96",
              "#AB63FA",
              "#FFA15A",
              "#19D3F3",
              "#FF6692",
              "#B6E880",
              "#FF97FF",
              "#FECB52",
          ]
      )
  
      for i, color in zip(range(n_classes), colors):
          trace3 = go.Scatter(
              x=fpr[i],
              y=tpr[i],
              mode="lines",
              line=dict(color=color, width=2),
              name="ROC curve of class {0} (area = {1:0.2f})"
              "".format(i, roc_auc[i]),
          )
          data.append(trace3)
  
      trace4 = go.Scatter(
          x=[0, 1],
          y=[0, 1],
          mode="lines",
          line=dict(color="black", width=2, dash="dash"),
          showlegend=False,
      )
  
      layout = go.Layout(
          title="Receiver operating characteristic",
          xaxis=dict(title="False Positive Rate"),
          yaxis=dict(title="True Positive Rate"),
          margin=dict(pad=25),
      )
  
      fig = go.Figure(data=data, layout=layout)
  
      return fig
  
imports: import pandas
  import numpy
  import sklearn
  from sklearn import *
  from sklearn.ensemble import RandomForestClassifier
  from sklearn.model_selection import train_test_split
  from sklearn.preprocessing import label_binarize
  from sklearn.metrics import *
  from itertools import cycle
  import colorlover
  import plotly.figure_factory
  import plotly.graph_objects
label_desc: Use image node_id 4 for calling the performance_metrics obj's underlying member method code with example usage: obj.plot_roc() and returns: fig