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Main_TD_RvNN_Ma.py
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# 基于PyTorch复现并修正后的马金TD方法
# 修正：叶子节点选择错误；SGD->Adagrad
import sys
import numpy as np
from models import TD_RvNN_Ma
import time
import random
from torch import optim
import datetime
from evaluate import *
from config import *
from data_io import loadLabel
from get_args import _ma_args
# str = index:wordfreq index:wordfreq
def str2matrix(Str, MaxL):
word_freq, word_index = [], []
l = 0
for pair in Str.split(' '):
word_freq.append(float(pair.split(':')[1]))
word_index.append(int(pair.split(':')[0]))
l += 1
l_add = [0] * (MaxL - l)
word_freq += l_add
word_index += l_add
return word_freq, word_index
def constructTree(tree):
# tree: {index1:{'parent':, 'maxL':, 'vec':}
# 1. ini tree node
index2node = {}
for i in tree:
node = TD_RvNN_Ma.Node_tweet(idx=i)
index2node[i] = node
# 2. construct tree
for j in tree:
indexC = j
indexP = tree[j]['parent']
nodeC = index2node[indexC]
wordFreq, wordIndex = str2matrix(tree[j]['vec'], tree[j]['maxL'])
nodeC.index = wordIndex
nodeC.word = wordFreq
# nodeC.time = tree[j]['post_t']
# not root node
if not indexP == 'None':
nodeP = index2node[int(indexP)]
nodeC.parent = nodeP
nodeP.children.append(nodeC)
# root node
else:
root = nodeC
# 3. convert tree to DNN input
parent_num = tree[j]['parent_num']
ini_x, ini_index = str2matrix("0:0", tree[j]['maxL'])
x_word, x_index, tree, leaf_idxs = TD_RvNN_Ma.gen_nn_inputs(root, ini_x)
return x_word, x_index, tree, leaf_idxs
################################# loas data ###################################
def loadData(tree_path, train_path, test_path, label_path, train_threshold):
labelDic = {}
for line in open(label_path):
line = line.rstrip()
label, eid = line.split('\t')[0], line.split('\t')[2]
labelDic[eid] = label.lower()
print("loading tree label:", len(labelDic))
treeDic = {}
for line in open(tree_path):
line = line.rstrip()
eid, indexP, indexC = line.split('\t')[0], line.split('\t')[1], int(line.split('\t')[2])
parent_num, maxL = int(line.split('\t')[3]), int(line.split('\t')[4])
Vec = line.split('\t')[5]
if not treeDic.__contains__(eid):
treeDic[eid] = {}
treeDic[eid][indexC] = {'parent': indexP, 'parent_num': parent_num, 'maxL': maxL, 'vec': Vec}
print("reading tree no:", len(treeDic))
tree_train, word_train, index_train, y_train, leaf_idxs_train, c = [], [], [], [], [], 0
l1, l2, l3, l4 = 0, 0, 0, 0
with open(train_path, 'r')as f:
train_ids = [line.strip() for line in f.readlines()]
random.shuffle(train_ids)
for eid in train_ids[:train_threshold]:
# if c > 8: break
eid = eid.rstrip()
if not labelDic.__contains__(eid): continue
if not treeDic.__contains__(eid): continue
if len(treeDic[eid]) <= 0:
continue
# 1. load label
label = labelDic[eid]
y, l1, l2, l3, l4 = loadLabel(label, l1, l2, l3, l4)
y_train.append(y)
# 2. construct tree
x_word, x_index, tree, leaf_idxs = constructTree(treeDic[eid])
tree_train.append(tree)
word_train.append(x_word)
index_train.append(x_index)
leaf_idxs_train.append(leaf_idxs)
c += 1
print("loading train set:", l1, l2, l3, l4)
tree_test, word_test, index_test, leaf_idxs_test, y_test, c = [], [], [], [], [], 0
l1, l2, l3, l4 = 0, 0, 0, 0
for eid in open(test_path):
# if c > 4: break
eid = eid.rstrip()
if not labelDic.__contains__(eid): continue
if not treeDic.__contains__(eid): continue
if len(treeDic[eid]) <= 0:
continue
# 1. load label
label = labelDic[eid]
y, l1, l2, l3, l4 = loadLabel(label, l1, l2, l3, l4)
y_test.append(y)
# 2. construct tree
x_word, x_index, tree, leaf_idxs = constructTree(treeDic[eid])
tree_test.append(tree)
word_test.append(x_word)
index_test.append(x_index)
leaf_idxs_test.append(leaf_idxs)
c += 1
print("loading test set:", l1, l2, l3, l4)
print("train no:", len(tree_train), len(word_train), len(index_train), len(leaf_idxs_train), len(y_train))
print("test no:", len(tree_test), len(word_test), len(index_test), len(leaf_idxs_test), len(y_test))
return tree_train, word_train, index_train, leaf_idxs_train, y_train, tree_test, word_test, index_test, leaf_idxs_test, y_test
##################################### MAIN ####################################
# 1. load tree & word & index & label
print("Step1:processing data")
tree_train, word_train, index_train, leaf_idxs_train, y_train, \
tree_test, word_test, index_test, leaf_idxs_test, y_test = \
loadData(TD_RvNN_TFIDF_path, train_path, test_path, label15_path, _ma_args.train_threshold)
print()
# 2. ini RNN model
print('Step2:build model')
t0 = time.time()
model = TD_RvNN_Ma.RvNN(_ma_args.vocab_dim, _ma_args.random_dim, _ma_args.class_num)
model.to(device)
t1 = time.time()
print('Recursive model established,', (t1 - t0) / 60, 's\n')
# 3. looping SGD
print('Step3:start training')
optimizer = optim.Adagrad(model.parameters(), lr=0.05)
losses_5, losses = [], []
num_examples_seen = 0
indexs = list(range(len(y_train)))
highest_acc = 0
for epoch in range(1, _ma_args.epoch + 1):
# one SGD
random.shuffle(indexs)
for cnt, i in enumerate(indexs):
pred_y, loss = model.forward(torch.Tensor(word_train[i]).cuda(device),
torch.LongTensor(index_train[i]).cuda(device).long(),
torch.LongTensor(tree_train[i]).cuda(device),
torch.Tensor(leaf_idxs_train[i]).cuda(device).long(),
torch.Tensor(y_train[i]).cuda(device))
optimizer.zero_grad()
loss.backward()
optimizer.step()
losses.append(loss.data.cpu().tolist())
num_examples_seen += 1
if (cnt + 1) % 100 == 0:
# print("iteration:%d/%d" % (cnt, len(indexs)))
break
# print("epoch=%d: idx=%d, loss=%f" % (epoch, i, np.mean(losses)))
# cal loss & evaluate
if epoch % 1 == 0:
losses_5.append((num_examples_seen, np.mean(losses)))
time = datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')
# print("%s: Loss after num_examples_seen=%d epoch=%d: %f" % (time, num_examples_seen, epoch, np.mean(losses)))
sys.stdout.flush()
prediction = []
for j in range(len(y_test)):
prediction.append(
model.predict_up(torch.Tensor(word_test[j]).cuda(device),
torch.Tensor(index_test[j]).cuda(device).long(),
torch.Tensor(tree_test[j]).cuda(device).long(),
torch.Tensor(leaf_idxs_test[j]).cuda(device).long())
.cpu().data.numpy().tolist())
# print("predictions:", prediction)
res = evaluation_4class(prediction, y_test)
highest_acc = max(highest_acc, res[1])
# print('results:', res)
# print()
sys.stdout.flush()
# Adjust the learning rate if loss increases
# if len(losses_5) > 1 and losses_5[-1][1] > losses_5[-2][1]:
# lr = lr * 0.5
# print("Setting learning rate to %.12f" % lr)
# sys.stdout.flush()
sys.stdout.flush()
losses = []
print('最高acc：', highest_acc)