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BATM.py
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BATM.py
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#!/usr/bin/env python
# -*- encoding: utf-8 -*-
'''
@File : BATM.py
@Time : 2020/10/11 20:41:22
@Author : Leilan Zhang
@Version : 1.0
@Contact : [email protected]
@Desc : None
'''
import os
import re
import pickle
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.data import Dataset, DataLoader
import numpy as np
from tqdm import tqdm
import matplotlib.pyplot as plt
from .gan import Generator, Encoder, Discriminator
import sys
sys.path.append('..')
from utils import evaluate_topic_quality, smooth_curve
# BATM model
class BATM:
def __init__(self, bow_dim=2000, n_topic=20, hid_dim=1024, device=None, taskname=None):
self.n_topic = n_topic
self.bow_dim = bow_dim
self.device = device
self.id2token = None
self.taskname = taskname
self.generator = Generator(n_topic=n_topic,hid_dim=hid_dim,bow_dim=bow_dim)
self.encoder = Encoder(bow_dim=bow_dim,hid_dim=hid_dim,n_topic=n_topic)
self.discriminator = Discriminator(bow_dim=bow_dim,n_topic=n_topic,hid_dim=hid_dim)
if device!=None:
self.generator = self.generator.to(device)
self.encoder = self.encoder.to(device)
self.discriminator = self.discriminator.to(device)
def train(self,train_data,batch_size=256, learning_rate=1e-4,test_data=None,num_epochs=100,is_evaluate=False,log_every=10,beta1=0.5,beta2=0.999,clip=0.01,n_critic=5):
self.generator.train()
self.encoder.train()
self.discriminator.train()
self.id2token = {v:k for k,v in train_data.dictionary.token2id.items()}
data_loader = DataLoader(train_data, batch_size=batch_size,shuffle=True,num_workers=4, collate_fn=train_data.collate_fn)
optim_G = torch.optim.Adam(self.generator.parameters(),lr=learning_rate,betas=(beta1,beta2))
optim_E = torch.optim.Adam(self.encoder.parameters(),lr=learning_rate,betas=(beta1,beta2))
optim_D = torch.optim.Adam(self.discriminator.parameters(),lr=learning_rate,betas=(beta1,beta2))
Gloss_lst, Eloss_lst, Dloss_lst = [], [], []
c_v_lst, c_w2v_lst, c_uci_lst, c_npmi_lst, mimno_tc_lst, td_lst = [], [], [], [], [], []
for epoch in range(num_epochs):
epochloss_lst = []
for iter, data in enumerate(data_loader):
txts, bows_real = data
bows_real = bows_real.to(self.device)
bows_real /= torch.sum(bows_real,dim=1,keepdim=True)
# Train Discriminator
optim_D.zero_grad()
theta_fake = torch.from_numpy(np.random.dirichlet(alpha=1.0*np.ones(self.n_topic)/self.n_topic,size=(len(bows_real)))).float().to(self.device)
loss_D = -1.0*torch.mean(self.discriminator(self.encoder(bows_real).detach())) + torch.mean(self.discriminator(self.generator(theta_fake).detach()))
loss_D.backward()
optim_D.step()
for param in self.discriminator.parameters():
param.data.clamp_(-clip,clip)
if iter % n_critic==0:
# Train Generator
optim_G.zero_grad()
loss_G = -1.0*torch.mean(self.discriminator(self.generator(theta_fake)))
loss_G.backward()
optim_G.step()
# Train Encoder
optim_E.zero_grad()
loss_E = torch.mean(self.discriminator(self.encoder(bows_real)))
loss_E.backward()
optim_E.step()
Dloss_lst.append(loss_D.item())
Gloss_lst.append(loss_G.item())
Eloss_lst.append(loss_E.item())
print(f'Epoch {(epoch+1):>3d}\tIter {(iter+1):>4d}\tLoss_D:{loss_D.item():<.7f}\tLoss_G:{loss_G.item():<.7f}\tloss_E:{loss_E.item():<.7f}')
if (epoch+1) % log_every == 0:
print(f'Epoch {(epoch+1):>3d}\tLoss_D_avg:{sum(Dloss_lst)/len(Dloss_lst):<.7f}\tLoss_G_avg:{sum(Gloss_lst)/len(Gloss_lst):<.7f}\tloss_E_avg:{sum(Eloss_lst)/len(Eloss_lst):<.7f}')
print('\n'.join([str(lst) for lst in self.show_topic_words()]))
print('='*30)
smth_pts_d = smooth_curve(Dloss_lst)
smth_pts_g = smooth_curve(Gloss_lst)
smth_pts_e = smooth_curve(Eloss_lst)
plt.cla()
plt.plot(np.array(range(len(smth_pts_g)))*log_every, smth_pts_g, label='loss_G')
plt.plot(np.array(range(len(smth_pts_d)))*log_every, smth_pts_d, label='loss_D')
plt.plot(np.array(range(len(smth_pts_e)))*log_every, smth_pts_e, label='loss_E')
plt.legend()
plt.xlabel('epochs')
plt.title('Train Loss')
plt.savefig('batm_trainloss.png')
if test_data!=None:
c_v,c_w2v,c_uci,c_npmi,mimno_tc, td = self.evaluate(test_data,calc4each=False)
c_v_lst.append(c_v), c_w2v_lst.append(c_w2v), c_uci_lst.append(c_uci),c_npmi_lst.append(c_npmi), mimno_tc_lst.append(mimno_tc), td_lst.append(td)
def evaluate(self, test_data, calc4each=False):
topic_words = self.show_topic_words()
return evaluate_topic_quality(topic_words, test_data, taskname=self.taskname,calc4each=calc4each)
def show_topic_words(self, topic_id=None, topK=15):
with torch.no_grad():
topic_words = []
idxes = torch.eye(self.n_topic).to(self.device)
word_dist = self.generator.inference(idxes)
vals, indices = torch.topk(word_dist, topK, dim=1)
vals = vals.cpu().tolist()
indices = indices.cpu().tolist()
if topic_id == None:
for i in range(self.n_topic):
topic_words.append([self.id2token[idx] for idx in indices[i]])
else:
topic_words.append([self.id2token[idx] for idx in indices[topic_id]])
return topic_words
def inference(self, doc_tokenized, dictionary,normalize=True):
doc_bow = torch.zeros(1,self.bow_dim)
for token in doc_tokenized:
try:
idx = dictionary.token2id[token]
doc_bow[0][idx] += 1.0
except:
continue
#print(f'{token} not in the vocabulary.')
doc_bow = doc_bow.to(self.device)
with torch.no_grad():
theta = self.encoder.forward(doc_bow)
if normalize:
theta = F.softmax(theta,dim=1)
return theta.detach().cpu().squeeze(0).numpy()