text: import matplotlib.pyplot as plt
  from sklearn import metrics
  from sklearn.linear_model import LogisticRegression
  from sklearn.pipeline import Pipeline
  from sklearn.preprocessing import RobustScaler
  
  
  def plot_roc_curve(df, column, y):
      pipe = Pipeline(steps=[('scaler', RobustScaler()), ('clf', LogisticRegression())])
      y_score = pipe.fit(df.loc[:, column].values.reshape(-1, 1), y).decision_function(
          df.loc[:, column].values.reshape(-1, 1))
      fpr, tpr, _ = metrics.roc_curve(y, y_score)
      roc_auc = metrics.auc(fpr, tpr)
      plt.figure()
      lw = 2
      plt.plot(fpr, tpr, color='darkorange', lw=lw, label='AUC({}) = {:.2f}'.format(column, roc_auc))
      plt.plot([0, 1], [0, 1], color='navy', lw=lw, linestyle='--')
      plt.xlim([0.0, 1.0])
      plt.ylim([0.0, 1.05])
      plt.xlabel('False Positive Rate')
      plt.ylabel('True Positive Rate')
      plt.legend(loc="lower right")
      plt.tight_layout()
      plt.show()
      return fpr, tpr, roc_auc
  
  
  def plot_auc_vs_wavelet(df_auc, feat2flip, rownames, colnames):
      arr_foo = df_auc.loc[feat2flip, 'AUC'].values.reshape(9, 154).T
      fig, ax = plt.subplots(figsize=(30, 30))
      plt.imshow(arr_foo)
      plt.yticks(range(154), rownames)
      plt.xticks(range(9), colnames, rotation='vertical')
      ax.xaxis.tick_top()
      ax.set_xlabel('Wavelet transformation')
      ax.set_ylabel('Feature')
      plt.colorbar()
      plt.show()
  