def predictByGreedySearch(self,
inputSeq,
maxAnswerLength=32,
showAttention=False,
figsize=(12, 6)):
inputSeq = filter_sent(inputSeq)
inputSeq = [
w for w in jieba.lcut(inputSeq) if w in self.word2id.keys()
] #先做分词
X = seq2id(self.word2id, inputSeq)
XLens = torch.tensor([len(X) + 1], dtype=torch.int,
device=self.device) #处理输入，计算长度，加结束符
X = X + [eosToken]
X = torch.tensor([X], dtype=torch.long, device=self.device)
#定义相关的层，确定相应的encoder，确定隐层
d = int(self.encoderRNN.bidirectional) + 1
hidden = torch.zeros(
(d * self.encoderRNN.numLayers, 1, self.hiddenSize),
dtype=torch.float32,
device=self.device)
encoderOutput, hidden = self.encoderRNN(
X, XLens, hidden
) # output: batchSize × seq_len × hiddenSize; hn: numLayers*d × batchSize × hiddenSize
hidden = hidden[-d * self.decoderRNN.numLayers::2].contiguous(
) # TODO hn: encodernumLayers*d × batchSize × hiddenSize -> decodernumLayers × batchSize × hiddenSize
attentionArrs = []
Y = []
decoderInput = torch.tensor([[sosToken]],
dtype=torch.long,
device=self.device) #给定decoder的输入
while decoderInput.item() != eosToken and len(
Y) < maxAnswerLength: #确定输出的序列，同时使用attention计算权重，选取最优解
decoderOutput, hidden, decoderAttentionWeight = self.decoderRNN(
decoderInput, hidden,
encoderOutput) # decoderOutput: batchSize(1) × 1 × outputSize
topv, topi = decoderOutput.topk(1)
decoderInput = topi[:, :, 0] #感觉去了也行
attentionArrs.append(
decoderAttentionWeight.data.cpu().numpy().reshape(1, XLens))
Y.append(decoderInput.item())
outputSeq = id2seq(self.id2word, Y)
if showAttention: #是否可视化attention，
attentionArrs = np.vstack(attentionArrs)
fig = plt.figure(figsize=figsize)
ax = fig.add_subplot('111')
cax = ax.matshow(attentionArrs, cmap='bone')
fig.colorbar(cax)
ax.set_xticklabels(['', '<SOS>'] + inputSeq)
ax.set_yticklabels([''] + outputSeq)
ax.xaxis.set_major_locator(ticker.MultipleLocator(1))
ax.yaxis.set_major_locator(ticker.MultipleLocator(1))
plt.show()
return ''.join(outputSeq[:-1])
def predictByBeamSearch(self,
inputSeq,
beamWidth=10,
maxAnswerLength=32,
alpha=0.7,
isRandomChoose=False,
allRandomChoose=False,
improve=True,
showInfo=False):
outputSize = len(self.id2word)
inputSeq = filter_sent(inputSeq)
inputSeq = [
w for w in jieba.lcut(inputSeq) if w in self.word2id.keys()
] #分词
X = seq2id(self.word2id, inputSeq)
XLens = torch.tensor([len(X) + 1], dtype=torch.int,
device=self.device) #输入转tensor 同时加结束符
X = X + [eosToken]
X = torch.tensor([X], dtype=torch.long, device=self.device)
#使用双向gru encoder 和2层GRU decoder
d = int(self.encoderRNN.bidirectional) + 1
hidden = torch.zeros(
(d * self.encoderRNN.numLayers, 1, self.hiddenSize),
dtype=torch.float32,
device=self.device)
encoderOutput, hidden = self.encoderRNN(X, XLens, hidden)
hidden = hidden[-d * self.decoderRNN.numLayers::2].contiguous()
#把搜索宽度和最大回答长度做个数组
Y = np.ones([beamWidth, maxAnswerLength], dtype='int32') * eosToken
# prob: beamWidth × 1
prob = np.zeros([beamWidth, 1], dtype='float32')
decoderInput = torch.tensor([[sosToken]],
dtype=torch.long,
device=self.device)
# decoderOutput: 1 × 1 × outputSize; hidden: numLayers × 1 × hiddenSize
decoderOutput, hidden, decoderAttentionWeight = self.decoderRNN(
decoderInput, hidden, encoderOutput)
# topv: 1 × 1 × beamWidth; topi: 1 × 1 × beamWidth
topv, topi = decoderOutput.topk(beamWidth)
# decoderInput: beamWidth × 1
decoderInput = topi.view(beamWidth, 1)
for i in range(beamWidth):
Y[i, 0] = decoderInput[i].item()
Y_ = Y.copy()
prob += topv.view(beamWidth, 1).data.cpu().numpy()
prob_ = prob.copy()
# hidden: numLayers × beamWidth × hiddenSize
hidden = hidden.expand(-1, beamWidth, -1).contiguous()
localRestId = np.array([i for i in range(beamWidth)], dtype='int32')
encoderOutput = encoderOutput.expand(
beamWidth, -1, -1) # => beamWidth × 1 × hiddenSize
for i in range(1, maxAnswerLength):
# decoderOutput: beamWidth × 1 × outputSize; hidden: numLayers × beamWidth × hiddenSize; decoderAttentionWeight: beamWidth × 1 × XSeqLen
decoderOutput, hidden, decoderAttentionWeight = self.decoderRNN(
decoderInput, hidden, encoderOutput)
# topv: beamWidth × 1; topi: beamWidth × 1
if improve:
decoderOutput = decoderOutput.view(-1, 1)
if allRandomChoose:
topv, topi = self._random_pick_k_by_prob(decoderOutput,
k=beamWidth)
else:
topv, topi = decoderOutput.topk(beamWidth, dim=0)
else:
topv, topi = (torch.tensor(prob[localRestId],
dtype=torch.float32,
device=self.device).unsqueeze(2) +
decoderOutput).view(-1, 1).topk(beamWidth, dim=0)
# decoderInput: beamWidth × 1
decoderInput = topi % outputSize
#计算过程，主要算概率，算路径上的最大概率
idFrom = topi.cpu().view(-1).numpy() // outputSize
Y[localRestId, :i + 1] = np.hstack(
[Y[localRestId[idFrom], :i],
decoderInput.cpu().numpy()])
prob[localRestId] = prob[
localRestId[idFrom]] + topv.data.cpu().numpy()
hidden = hidden[:, idFrom, :]
restId = (decoderInput != eosToken).cpu().view(-1)
localRestId = localRestId[restId.numpy().astype('bool')]
decoderInput = decoderInput[restId]
hidden = hidden[:, restId, :]
encoderOutput = encoderOutput[restId]
beamWidth = len(localRestId)
if beamWidth < 1: #直到搜索宽度为0
break
lens = [
i.index(eosToken) if eosToken in i else maxAnswerLength
for i in Y.tolist()
]
ans = [''.join(id2seq(self.id2word, i[:l])) for i, l in zip(Y, lens)]
prob = [prob[i, 0] / np.power(lens[i], alpha) for i in range(len(ans))]
if isRandomChoose or allRandomChoose: #对于回答方面做的策略，会去prob最大的那个，同时也可以给出概率
prob = [np.exp(p) for p in prob]
prob = [p / sum(prob) for p in prob]
if showInfo:
for i in range(len(ans)):
print((ans[i], prob[i]))
return random_pick(ans, prob)
else:
ansAndProb = list(zip(ans, prob))
ansAndProb.sort(key=lambda x: x[1], reverse=True)
if showInfo:
for i in ansAndProb:
print(i)
return ansAndProb[0][0]