DeepClustering_5.py

# Eval.
q = model.predict(x, verbose=0)
p = target_distribution(q)  # update the auxiliary target distribution p

# evaluate the clustering performance
y_pred = q.argmax(1)
if y is not None:
    acc = np.round(accu(y, y_pred), 5)
    nmi = np.round(nmis(y, y_pred), 5)
    ari = np.round(aris(y, y_pred), 5)
    loss = np.round(loss, 5)
    print('Acc = %.5f, nmi = %.5f, ari = %.5f' % (acc, nmi, ari), ' ; loss=', loss)
#%%
import seaborn as sns
import sklearn.metrics
import matplotlib.pyplot as plt
sns.set(font_scale=3)
confusion_matrix = sklearn.metrics.confusion_matrix(y, y_pred)

plt.figure(figsize=(16, 14))
sns.heatmap(confusion_matrix, annot=True, fmt="d", annot_kws={"size": 20});
plt.title("Confusion matrix", fontsize=30)
plt.ylabel('True label', fontsize=25)
plt.xlabel('Clustering label', fontsize=25)
plt.show()
plt.savefig("Result.png",dpi=400)
#%%
from sklearn.utils.linear_assignment_ import linear_assignment

y_true = y.astype(np.int64)
D = max(y_pred.max(), y_true.max()) + 1
w = np.zeros((D, D), dtype=np.int64)
# Confusion matrix.
for i in range(y_pred.size):
    w[y_pred[i], y_true[i]] += 1
ind = linear_assignment(-w)

sum([w[i, j] for i, j in ind]) * 1.0 / y_pred.size