trainable_em.py 文件源码

def evaluate(self):
    for scores in self.em.fit(self.X, self.cats, iterations=100, learning_rate=1.0):
      print np.mean(scores)

    for p in self.em.kernel.params:
      print p, p.get_value()
    auc = roc_auc_score(self.cats.reshape(-1), result)
    acc = accuracy_score(self.cats, result > 0.5)

    for i in xrange(result.shape[0]):
      print('%d: %.2e' % (self.cats.reshape(-1)[i], result[i]))

    print('Time %.2f millisec' % (t * 1000.0))
    print('AUC: %.3f' % auc)

    return acc, auc