text: import matplotlib.pyplot as plt
  import numpy as np
  from sklearn.metrics import accuracy_score, f1_score, precision_score, recall_score
  
  
  <<<<<<< HEAD
  def build_comparison_graph(labels, values, values_labels, width):
  =======
  def build_comparison_graph(labels, values, values_labels, width, graph_title, y_label='Scores'):
  >>>>>>> 5b791facd23c54532cf29cd110517ed8143d30ac
      x = np.arange(len(labels))  # the label locations
  
      fig, ax = plt.subplots()
      rects = [ax.bar((x - width / 2) + width * i, value, width, label=values_labels[i]) for i, value in
               enumerate(values)]
  
      # Add some text for labels, title and custom x-axis tick labels, etc.
      ax.set_ylabel(y_label)
      ax.set_xticks(x)
      ax.set_xticklabels(labels)
      ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))
      fig.tight_layout()
  
      plt.title(graph_title)
      plt.show()
  
  
  def get_scores(y_test, y_pred):
      f1 = f1_score(y_test, y_pred, average="macro")
      acc = accuracy_score(y_test, y_pred)
      precision = precision_score(y_test, y_pred, average="macro")
      recall = recall_score(y_test, y_pred, average="macro")
      return [f1, acc, precision, recall]
  