original_docstring: 
      The default Confusion Matrix image creator.
      Code adapted from
      `Scikit learn <https://scikit-learn.org/stable/modules/generated/sklearn.metrics.plot_confusion_matrix.html>`_ # noqa
  
      :param confusion_matrix_tensor: The tensor describing the confusion matrix.
          This can be easily obtained through Scikit-learn `confusion_matrix`
          utility.
      :param display_labels: Target names used for plotting. By default, `labels`
          will be used if it is defined, otherwise the values will be inferred by
          the matrix tensor.
      :param include_values: Includes values in confusion matrix. Defaults to
          `False`.
      :param xticks_rotation: Rotation of xtick labels. Valid values are
          float point value. Defaults to 0.
      :param yticks_rotation: Rotation of ytick labels. Valid values are
          float point value. Defaults to 0.
      :param values_format: Format specification for values in confusion matrix.
          Defaults to `None`, which means that the format specification is
          'd' or '.2g', whichever is shorter.
      :param cmap: Must be a str or a Colormap recognized by matplotlib.
          Defaults to 'viridis'.
      :param image_title: The title of the image. Defaults to an empty string.
      :return: The Confusion Matrix as a PIL Image.
      
docstring: The default Confusion Matrix image creator.
  Code adapted from
  `Scikit learn `_ # noqa
docstring_tokens: The default Confusion Matrix image creator . Code adapted from ` Scikit learn ` _ # noqa
code: def default_cm_image_creator(confusion_matrix_tensor: Tensor,
                               display_labels: Sequence = None,
                               include_values=False,
                               xticks_rotation=0,
                               yticks_rotation=0,
                               values_format=None,
                               cmap='viridis',
                               image_title=''):
      """
      The default Confusion Matrix image creator.
      Code adapted from
      `Scikit learn <https://scikit-learn.org/stable/modules/generated/sklearn.metrics.plot_confusion_matrix.html>`_ # noqa
  
      :param confusion_matrix_tensor: The tensor describing the confusion matrix.
          This can be easily obtained through Scikit-learn `confusion_matrix`
          utility.
      :param display_labels: Target names used for plotting. By default, `labels`
          will be used if it is defined, otherwise the values will be inferred by
          the matrix tensor.
      :param include_values: Includes values in confusion matrix. Defaults to
          `False`.
      :param xticks_rotation: Rotation of xtick labels. Valid values are
          float point value. Defaults to 0.
      :param yticks_rotation: Rotation of ytick labels. Valid values are
          float point value. Defaults to 0.
      :param values_format: Format specification for values in confusion matrix.
          Defaults to `None`, which means that the format specification is
          'd' or '.2g', whichever is shorter.
      :param cmap: Must be a str or a Colormap recognized by matplotlib.
          Defaults to 'viridis'.
      :param image_title: The title of the image. Defaults to an empty string.
      :return: The Confusion Matrix as a PIL Image.
      """
  
      fig, ax = plt.subplots()
  
      cm = confusion_matrix_tensor.numpy()
      n_classes = cm.shape[0]
      im_ = ax.imshow(cm, interpolation='nearest', cmap=cmap)
      cmap_min, cmap_max = im_.cmap(0), im_.cmap(256)
  
      if include_values:
          text_ = np.empty_like(cm, dtype=object)
  
          # print text with appropriate color depending on background
          thresh = (cm.max() + cm.min()) / 2.0
  
          for i in range(n_classes):
              for j in range(n_classes):
                  color = cmap_max if cm[i, j] < thresh else cmap_min
  
                  if values_format is None:
                      text_cm = format(cm[i, j], '.2g')
                      if cm.dtype.kind != 'f':
                          text_d = format(cm[i, j], 'd')
                          if len(text_d) < len(text_cm):
                              text_cm = text_d
                  else:
                      text_cm = format(cm[i, j], values_format)
  
                  text_[i, j] = ax.text(
                      j, i, text_cm,
                      ha="center", va="center",
                      color=color)
  
      if display_labels is None:
          display_labels = np.arange(n_classes)
  
      fig.colorbar(im_, ax=ax)
  
      ax.set(xticks=np.arange(n_classes),
             yticks=np.arange(n_classes),
             xticklabels=display_labels,
             yticklabels=display_labels,
             ylabel="True label",
             xlabel="Predicted label")
  
      if image_title != '':
          ax.set_title(image_title)
  
      ax.set_ylim((n_classes - 0.5, -0.5))
      plt.setp(ax.get_xticklabels(), rotation=xticks_rotation)
      plt.setp(ax.get_yticklabels(), rotation=yticks_rotation)
  
      fig.tight_layout()
      return fig
code_tokens: def default_cm_image_creator ( confusion_matrix_tensor : Tensor , display_labels : Sequence = None , include_values = False , xticks_rotation = 0 , yticks_rotation = 0 , values_format = None , cmap = 'viridis' , image_title = '' ) : fig , ax = plt . subplots ( ) cm = confusion_matrix_tensor . numpy ( ) n_classes = cm . shape [ 0 ] im_ = ax . imshow ( cm , interpolation = 'nearest' , cmap = cmap ) cmap_min , cmap_max = im_ . cmap ( 0 ) , im_ . cmap ( 256 ) if include_values : text_ = np . empty_like ( cm , dtype = object ) thresh = ( cm . max ( ) + cm . min ( ) ) / 2.0 for i in range ( n_classes ) : for j in range ( n_classes ) : color = cmap_max if cm [ i , j ] < thresh else cmap_min if values_format is None : text_cm = format ( cm [ i , j ] , '.2g' ) if cm . dtype . kind != 'f' : text_d = format ( cm [ i , j ] , 'd' ) if len ( text_d ) < len ( text_cm ) : text_cm = text_d else : text_cm = format ( cm [ i , j ] , values_format ) text_ [ i , j ] = ax . text ( j , i , text_cm , ha = "center" , va = "center" , color = color ) if display_labels is None : display_labels = np . arange ( n_classes ) fig . colorbar ( im_ , ax = ax ) ax . set ( xticks = np . arange ( n_classes ) , yticks = np . arange ( n_classes ) , xticklabels = display_labels , yticklabels = display_labels , ylabel = "True label" , xlabel = "Predicted label" ) if image_title != '' : ax . set_title ( image_title ) ax . set_ylim ( ( n_classes - 0.5 , - 0.5 ) ) plt . setp ( ax . get_xticklabels ( ) , rotation = xticks_rotation ) plt . setp ( ax . get_yticklabels ( ) , rotation = yticks_rotation ) fig . tight_layout ( ) return fig
short_docstring: The default Confusion Matrix image creator.
short_docstring_tokens: The default Confusion Matrix image creator .
comment: """
      The default Confusion Matrix image creator.
      Code adapted from
      `Scikit learn <https://scikit-learn.org/stable/modules/generated/sklearn.metrics.plot_confusion_matrix.html>`_ # noqa
  
      :param confusion_matrix_tensor: The tensor describing the confusion matrix.
          This can be easily obtained through Scikit-learn `confusion_matrix`
          utility.
      :param display_labels: Target names used for plotting. By default, `labels`
          will be used if it is defined, otherwise the values will be inferred by
          the matrix tensor.
      :param include_values: Includes values in confusion matrix. Defaults to
          `False`.
      :param xticks_rotation: Rotation of xtick labels. Valid values are
          float point value. Defaults to 0.
      :param yticks_rotation: Rotation of ytick labels. Valid values are
          float point value. Defaults to 0.
      :param values_format: Format specification for values in confusion matrix.
          Defaults to `None`, which means that the format specification is
          'd' or '.2g', whichever is shorter.
      :param cmap: Must be a str or a Colormap recognized by matplotlib.
          Defaults to 'viridis'.
      :param image_title: The title of the image. Defaults to an empty string.
      :return: The Confusion Matrix as a PIL Image.
      """$SEP$# print text with appropriate color depending on background