def eval_perplexity(self):
"""
Knowledge evaluation on dataset, perplexity
:param dataset:
:return:
"""
print("Knowledge evaluating ...")
dataset = self.dataset
self.forward_init(1)
perpls = []
knw_size = len(self.knw_list)
hidden = torch.zeros(1, 1, knw_size)
datab = []
for data in dataset:
datavec = np.zeros(self.lsize)
datavec[data] = 1
datab.append(datavec)
datab = np.array(datab)
databpt = torch.from_numpy(datab)
databpt = databpt.type(torch.FloatTensor)
for nn in range(len(databpt) - 1):
x = databpt[nn]
y = databpt[nn + 1]
prd, hidden = self.forward(x.view(1, 1, self.lsize),
hidden,
plogits=self.plogits)
prd = torch.exp(prd) / torch.sum(np.exp(prd))
perp = cal_kldiv(y, prd.view(-1))
perpls.append(perp)
avperp = np.mean(np.array(perpls))
print("Calculated knowledge perplexity:", np.exp(avperp))
return np.exp(avperp)
def cal_knwU(self):
tmp = self.prior.copy()
tmp[self.data[-1]] = 0
KnwU = cal_kldiv(
tmp, self.prior) * (self.prior[self.data[0]] / np.sum(self.prior))
return KnwU