title: Suppress scientific notation in sklearn.metrics.plot_confusion_matrix
body: <p>I was trying to plot a confusion matrix nicely, so I followed <a href="https://scikit-learn.org/stable/modules/generated/sklearn.metrics.plot_confusion_matrix.html#sklearn.metrics.plot_confusion_matrix" rel="noreferrer">scikit-learn's newer version 0.22's in built plot confusion matrix function</a>. However, one value of my confusion matrix value is 153, but it appears as 1.5e+02 in the confusion matrix plot: 
  <a href="https://i.stack.imgur.com/57Io2.png" rel="noreferrer"><img src="https://i.stack.imgur.com/57Io2.png" alt="enter image description here"></a></p>
  
  <p>Following the <a href="https://scikit-learn.org/stable/modules/generated/sklearn.metrics.plot_confusion_matrix.html#sklearn.metrics.plot_confusion_matrix" rel="noreferrer">scikit-learn's documentation</a>, I spotted this parameter called <code>values_format</code>, but I do not know how to manipulate this parameter so that it can suppress the scientific notation. My code is as follows.</p>
  
  <pre><code>from sklearn import svm, datasets
  from sklearn.model_selection import train_test_split
  from sklearn.metrics import plot_confusion_matrix
  
  # import some data to play with
  
  X = pd.read_csv("datasets/X.csv")
  y = pd.read_csv("datasets/y.csv")
  
  class_names = ['Not Fraud (positive)', 'Fraud (negative)']
  
  # Split the data into a training set and a test set
  X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42)
  
  # Run classifier, using a model that is too regularized (C too low) to see
  # the impact on the results
  logreg = LogisticRegression()
  logreg.fit(X_train, y_train)
  
  
  np.set_printoptions(precision=2)
  
  # Plot non-normalized confusion matrix
  titles_options = [("Confusion matrix, without normalization", None),
                    ("Normalized confusion matrix", 'true')]
  for title, normalize in titles_options:
      disp = plot_confusion_matrix(logreg, X_test, y_test,
                                   display_labels=class_names,
                                   cmap=plt.cm.Greens,
                                   normalize=normalize, values_format = '{:.5f}'.format)
      disp.ax_.set_title(title)
  
      print(title)
      print(disp.confusion_matrix)
  
  plt.show()
  </code></pre>