Plots the confusion matrix as a seaborn heatmap

plot_cm.py

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.metrics import confusion_matrix

def plot_cm(y_true, y_pred, figsize=(10,10),
            class_labels=['class1', 'class2'],
            suffix_label=None):
    cm = confusion_matrix(y_true, y_pred, labels=np.unique(y_true))
    cm_sum = np.sum(cm, axis=1, keepdims=True)
    cm_perc = cm / cm_sum.astype(float) * 100
    annot = np.empty_like(cm).astype(str)
    nrows, ncols = cm.shape
    for i in range(nrows):
        for j in range(ncols):
            c = cm[i, j]
            p = cm_perc[i, j]
            if i == j:
                s = cm_sum[i]
                annot[i, j] = '%.1f%%\n%d/%d' % (p, c, s)
            elif c == 0:
                annot[i, j] = ''
            else:
                annot[i, j] = '%.1f%%\n%d' % (p, c)
    cm = pd.DataFrame(cm, index=np.unique(y_true), columns=np.unique(y_true))
    cm.index.name = 'Actual'
    cm.columns.name = 'Predicted'
    sns.heatmap(cm, cmap=sns.light_palette("navy", 12),
                annot=annot, fmt='', cbar=False,
                xticklabels=class_labels,
                yticklabels=class_labels,
                annot_kws={"fontsize":14})
    if suffix_label:
        plt.title(f"Confusion Matrix - {suffix_label}", size=20, weight='bold')
    else:
        plt.title(f"Confusion Matrix", size=20, weight='bold')
    
    plt.ylabel(cm.index.name, size=16, weight='bold')
    plt.xlabel(cm.columns.name, size=16, weight='bold')
    plt.xticks(size=14)
    plt.yticks(size=14)