confusion_matrix.py

#confusionmatrix
from sklearn.metrics import confusion_matrix
import numpy as np

def print_confusion_matrix(confusion_matrix, class_names, figsize = (10,7), fontsize=14, normalize=False):
  
    if normalize:
        confusion_matrix = confusion_matrix.astype('float') / confusion_matrix.sum(axis=1)[:, np.newaxis]
        fmt = '.2f'
        title = 'Normalized Confusion Matrix'
    else:
        fmt = 'd'
        title = 'Confusion Matrix'
        
    df_cm = pd.DataFrame(confusion_matrix, index=class_names, columns=class_names)
    fig = plt.figure(figsize=figsize)
    heatmap = sns.heatmap(df_cm, annot=True, fmt=fmt)

    heatmap.yaxis.set_ticklabels(heatmap.yaxis.get_ticklabels(), rotation=0, ha='right', fontsize=fontsize)
    heatmap.xaxis.set_ticklabels(heatmap.yaxis.get_ticklabels(), rotation=45, ha='right', fontsize=fontsize)
    heatmap.set_ylabel('True label')
    heatmap.set_xlabel('Predicted label')
    heatmap.set_title(title)
    return fig

conf_mat = confusion_matrix(y_test, rf.predict(x_test))

# readable labels
labels = [cat_map[encoder_mapping[label]] for label in sorted(encoder_mapping.keys())]
ax = print_confusion_matrix(conf_mat, labels, normalize=True)
plt.show()