how to plot confusion matrix for logistic regression code example

Example 1: print labels on confusion_matrix

import pandas as pd
cmtx = pd.DataFrame(
    confusion_matrix(y_true, y_pred, labels=['yes', 'no']), 
    index=['true:yes', 'true:no'], 
    columns=['pred:yes', 'pred:no']
)
print(cmtx)
# Output:
#           pred:yes  pred:no
# true:yes         1        2
# true:no          0        3

Example 2: plot confusion matrix function deep learning

def plot_confusion_matrix(cm, classes,
                        normalize=False,
                        title='Confusion matrix',
                        cmap=plt.cm.Blues):
    """
    This function prints and plots the confusion matrix.
    Normalization can be applied by setting `normalize=True`.
    """
    plt.imshow(cm, interpolation='nearest', cmap=cmap)
    plt.title(title)
    plt.colorbar()
    tick_marks = np.arange(len(classes))
    plt.xticks(tick_marks, classes, rotation=45)
    plt.yticks(tick_marks, classes)

    if normalize:
        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]
        print("Normalized confusion matrix")
    else:
        print('Confusion matrix, without normalization')

    print(cm)

    thresh = cm.max() / 2.
    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):
        plt.text(j, i, cm[i, j],
            horizontalalignment="center",
            color="white" if cm[i, j] > thresh else "black")

    plt.tight_layout()
    plt.ylabel('True label')
    plt.xlabel('Predicted label')