import seaborn as sb
from sklearn.metrics import confusion_matrix
import matplotlib.pyplot as plt
import numpy as np
def plot_confusion(title, true_labels, predicted_labels, normalized=True):
labels = list(set(true_labels) | set(predicted_labels))
if normalized:
cm = confusion_matrix(true_labels, predicted_labels, labels=labels)
cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]
else:
cm = confusion_matrix(true_labels, predicted_labels, labels=labels)
fig, ax = plt.subplots(figsize=(10, 10))
ax.imshow(cm, interpolation='nearest', cmap=plt.cm.Blues)
ax.set_title(title)
# plt.colorbar()
tick_marks = np.arange(len(labels))
ax.set_xticks(tick_marks)
ax.set_xticklabels(labels, rotation=90)
ax.set_yticks(tick_marks)
ax.set_yticklabels(labels)
ax.set_ylabel('True Label')
ax.set_xlabel('Predicted Label')
ax.grid(False)
return fig, ax
