(y_true, y_pred, classes,
                          normalize=False,
                          title=None,
                          cmap=plt.cm.Blues,
                          size=None)
return ax, cm
(Copied from sklearn website)
    This function prints and plots the confusion matrix.
    Normalization can be applied by setting `normalize=True`.
(Copied from sklearn website)
    This function prints and plots the confusion matrix.
    Normalization can be applied by setting `normalize=True`.
( Copied from sklearn website ) This function prints and plots the confusion matrix . Normalization can be applied by setting normalize = True .
def plot_confusion_matrix(y_true, y_pred, classes,
                          normalize=False,
                          title=None,
                          cmap=plt.cm.Blues,
                          size=None):
    """ (Copied from sklearn website)
    This function prints and plots the confusion matrix.
    Normalization can be applied by setting `normalize=True`.
    """
    if not title:
        if normalize:
            title = 'Normalized confusion matrix'
        else:
            title = 'Confusion matrix, without normalization'

    # Compute confusion matrix
    cm = confusion_matrix(y_true, y_pred)
    # Only use the labels that appear in the data
    classes = classes[unique_labels(y_true, y_pred)]
    if normalize:
        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]
        print("Display normalized confusion matrix ...")
    else:
        print('Display confusion matrix without normalization ...')

    # print(cm)

    fig, ax = plt.subplots()
    if size is None:
        size = (12, 8)
    fig.set_size_inches(size[0], size[1])

    im = ax.imshow(cm, interpolation='nearest', cmap=cmap)
    ax.figure.colorbar(im, ax=ax)
    # We want to show all ticks...
    ax.set(xticks=np.arange(cm.shape[1]),
           yticks=np.arange(cm.shape[0]),
           # ... and label them with the respective list entries
           xticklabels=classes, yticklabels=classes,
           title=title,
           ylabel='True label',
           xlabel='Predicted label')
    ax.set_ylim([-0.5, len(classes)-0.5])

    # Rotate the tick labels and set their alignment.
    plt.setp(ax.get_xticklabels(), rotation=45, ha="right",
             rotation_mode="anchor")

    # Loop over data dimensions and create text annotations.
    fmt = '.2f' if normalize else 'd'
    thresh = cm.max() / 2.
    for i in range(cm.shape[0]):
        for j in range(cm.shape[1]):
            ax.text(j, i, format(cm[i, j], fmt),
                    ha="center", va="center",
                    color="white" if cm[i, j] > thresh else "black")
    fig.tight_layout()
    return ax, cm
def plot_confusion_matrix ( y_true , y_pred , classes , normalize = False , title = None , cmap = plt . cm . Blues , size = None ) : if not title : if normalize : title = 'Normalized confusion matrix' else : title = 'Confusion matrix, without normalization' # Compute confusion matrix cm = confusion_matrix ( y_true , y_pred ) # Only use the labels that appear in the data classes = classes [ unique_labels ( y_true , y_pred ) ] if normalize : cm = cm . astype ( 'float' ) / cm . sum ( axis = 1 ) [ : , np . newaxis ] print ( "Display normalized confusion matrix ..." ) else : print ( 'Display confusion matrix without normalization ...' ) # print(cm) fig , ax = plt . subplots ( ) if size is None : size = ( 12 , 8 ) fig . set_size_inches ( size [ 0 ] , size [ 1 ] ) im = ax . imshow ( cm , interpolation = 'nearest' , cmap = cmap ) ax . figure . colorbar ( im , ax = ax ) # We want to show all ticks... ax . set ( xticks = np . arange ( cm . shape [ 1 ] ) , yticks = np . arange ( cm . shape [ 0 ] ) , # ... and label them with the respective list entries xticklabels = classes , yticklabels = classes , title = title , ylabel = 'True label' , xlabel = 'Predicted label' ) ax . set_ylim ( [ - 0.5 , len ( classes ) - 0.5 ] ) # Rotate the tick labels and set their alignment. plt . setp ( ax . get_xticklabels ( ) , rotation = 45 , ha = "right" , rotation_mode = "anchor" ) # Loop over data dimensions and create text annotations. fmt = '.2f' if normalize else 'd' thresh = cm . max ( ) / 2. for i in range ( cm . shape [ 0 ] ) : for j in range ( cm . shape [ 1 ] ) : ax . text ( j , i , format ( cm [ i , j ] , fmt ) , ha = "center" , va = "center" , color = "white" if cm [ i , j ] > thresh else "black" ) fig . tight_layout ( ) return ax , cm