# -*- coding: utf-8 -*-
"""
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-- Author: Jhosimar George Arias Figueroa
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Metrics used to evaluate our model
"""
import numpy as np
from scipy.optimize import linear_sum_assignment
from sklearn.metrics.cluster import normalized_mutual_info_score
class Metrics:
# Code taken from the work
# VaDE (Variational Deep Embedding:A Generative Approach to Clustering)
def cluster_acc(self, Y_pred, Y):
Y_pred, Y = np.array(Y_pred), np.array(Y)
assert Y_pred.size == Y.size
D = max(Y_pred.max(), Y.max())+1
w = np.zeros((D,D), dtype=np.int64)
for i in range(Y_pred.size):
w[Y_pred[i], Y[i]] += 1
row, col = linear_sum_assignment(w.max()-w)
return sum([w[row[i],col[i]] for i in range(row.shape[0])]) * 1.0/Y_pred.size
def nmi(self, Y_pred, Y):
Y_pred, Y = np.array(Y_pred), np.array(Y)
assert Y_pred.size == Y.size
return normalized_mutual_info_score(Y_pred, Y, average_method='arithmetic')
