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ed_ranker.py
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ed_ranker.py
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import numpy as np
from DCA.vocabulary import Vocabulary
import torch
from torch.autograd import Variable
import DCA.dataset as D
import DCA.utils as utils
import DCA.ntee as ntee
from random import shuffle
import torch.optim as optim
from DCA.abstract_word_entity import load as load_model
from DCA.mulrel_ranker import MulRelRanker
from pprint import pprint
from itertools import count
import copy
import csv
import json
import time
ModelClass = MulRelRanker
wiki_prefix = 'en.wikipedia.org/wiki/'
class EDRanker:
"""
ranking candidates
"""
def __init__(self, config):
print('--- create EDRanker model ---')
config['entity_embeddings'] = config['entity_embeddings'] / \
np.maximum(np.linalg.norm(config['entity_embeddings'],
axis=1, keepdims=True), 1e-12)
config['entity_embeddings'][config['entity_voca'].unk_id] = 1e-10
config['word_embeddings'] = config['word_embeddings'] / \
np.maximum(np.linalg.norm(config['word_embeddings'],
axis=1, keepdims=True), 1e-12)
config['word_embeddings'][config['word_voca'].unk_id] = 1e-10
self.one_entity_once = config['one_entity_once']
self.seq_len = config['seq_len']
self.word_vocab = config['word_voca']
self.ent_vocab = config['entity_voca']
self.output_path = config['f1_csv_path']
print('prerank model')
self.prerank_model = ntee.NTEE(config)
self.args = config['args']
print('main model')
if self.args.mode == 'eval':
print('try loading model from', self.args.model_path)
self.model = load_model(self.args.model_path, ModelClass)
else:
print('create new model')
config['use_local'] = True
config['use_local_only'] = self.args.use_local_only
config['oracle'] = False
self.model = ModelClass(config)
self.load_ent_desc(500, 3)
self.prerank_model.cuda()
self.model.cuda()
def load_ent_desc(self, max_desc_len, n_grams):
ent_desc = json.load(open('../data/ent2desc.json', 'r'))
self.ent_desc = [[self.word_vocab.get_id(Vocabulary.unk_token) for j in range(max_desc_len)] for i in range(self.ent_vocab.size())]
self.desc_mask = [[0 for j in range(max_desc_len-n_grams+1)] for i in range(self.ent_vocab.size())]
for ent in ent_desc:
for i in range(min(len(ent_desc[ent]), max_desc_len)):
self.ent_desc[self.ent_vocab.get_id(ent)][i] = self.word_vocab.get_id(ent_desc[ent][i])
if (i>=n_grams-1):
self.desc_mask[self.ent_vocab.get_id(ent)][i-(n_grams-1)] = 1
self.ent_desc = Variable(torch.LongTensor(self.ent_desc).cuda())
self.desc_mask = Variable(torch.LongTensor(self.desc_mask).cuda())
def get_data_items(self, dataset, predict=False, isTrain=False):
data = []
cand_source = 'candidates'
for doc_name, content in dataset.items():
items = []
for m in content:
try:
named_cands = [c[0] for c in m[cand_source]]
p_e_m = [min(1., max(1e-3, c[1])) for c in m[cand_source]]
etype = [c[2] for c in m[cand_source]]
except:
named_cands = [c[0] for c in m['candidates']]
p_e_m = [min(1., max(1e-3, c[1])) for c in m['candidates']]
etype = [c[2] for c in m['candidates']]
try:
true_pos = named_cands.index(m['gold'][0])
p = p_e_m[true_pos]
except:
true_pos = -1
# select # n_cands_before_rank candidates according to their priority
named_cands = named_cands[:min(self.args.n_cands_before_rank, len(named_cands))]
p_e_m = p_e_m[:min(self.args.n_cands_before_rank, len(p_e_m))]
etype = etype[:min(self.args.n_cands_before_rank, len(etype))]
# guarantee that the ground truth is in the top30 candidates
if true_pos >= len(named_cands):
if not predict:
true_pos = len(named_cands) - 1
p_e_m[-1] = p
named_cands[-1] = m['gold'][0]
else:
true_pos = -1
cands = [self.model.entity_voca.get_id(wiki_prefix + c) for c in named_cands]
mask = [1.] * len(cands)
if len(cands) == 0 and not predict:
continue
elif len(cands) < self.args.n_cands_before_rank:
cands += [self.model.entity_voca.unk_id] * (self.args.n_cands_before_rank - len(cands))
etype += [[0, 0, 0, 1]] * (self.args.n_cands_before_rank - len(etype))
named_cands += [Vocabulary.unk_token] * (self.args.n_cands_before_rank - len(named_cands))
p_e_m += [1e-8] * (self.args.n_cands_before_rank - len(p_e_m))
mask += [0.] * (self.args.n_cands_before_rank - len(mask))
lctx = m['context'][0].strip().split()
lctx_ids = [self.prerank_model.word_voca.get_id(t) for t in lctx if utils.is_important_word(t)]
lctx_ids = [tid for tid in lctx_ids if tid != self.prerank_model.word_voca.unk_id]
lctx_ids = lctx_ids[max(0, len(lctx_ids) - self.args.ctx_window//2):]
rctx = m['context'][1].strip().split()
rctx_ids = [self.prerank_model.word_voca.get_id(t) for t in rctx if utils.is_important_word(t)]
rctx_ids = [tid for tid in rctx_ids if tid != self.prerank_model.word_voca.unk_id]
rctx_ids = rctx_ids[:min(len(rctx_ids), self.args.ctx_window//2)]
ment = m['mention'].strip().split()
ment_ids = [self.prerank_model.word_voca.get_id(t) for t in ment if utils.is_important_word(t)]
ment_ids = [tid for tid in ment_ids if tid != self.prerank_model.word_voca.unk_id]
m['sent'] = ' '.join(lctx + rctx)
mtype = m['mtype']
items.append({'context': (lctx_ids, rctx_ids),
'ment_ids': ment_ids,
'cands': cands,
'named_cands': named_cands,
'p_e_m': p_e_m,
'mask': mask,
'true_pos': true_pos,
'mtype': mtype,
'etype': etype,
'doc_name': doc_name,
'raw': m
})
if len(items) > 0:
# note: this shouldn't affect the order of prediction because we use doc_name to add predicted entities,
# and we don't shuffle the data for prediction
# ----old implementation-----
if self.seq_len == 0:
if len(items) > 100:
print(len(items))
for k in range(0, len(items), 100):
data.append(items[k:min(len(items), k + 100)])
else:
data.append(items)
else:
# ----new implementation----
# each doc is regarded as one batch
# data.append(items)
if isTrain:
for k in range(0, len(items), self.seq_len // 2):
data.append(items[max(0, k - self.seq_len//2) : min(len(items), k + self.seq_len//2)])
else:
if self.one_entity_once:
for k in range(0, len(items)):
data.append(items[max(0, k-self.seq_len+1): k+1])
else:
for k in range(0, len(items), self.seq_len):
data.append(items[k:min(len(items), k + self.seq_len)])
return self.prerank(data, predict)
def prerank(self, dataset, predict=False):
new_dataset = []
has_gold = 0
total = 0
for content in dataset:
items = []
if self.args.keep_ctx_ent > 0:
# rank the candidates by ntee scores
lctx_ids = [m['context'][0][max(len(m['context'][0]) - self.args.prerank_ctx_window // 2, 0):]
for m in content]
rctx_ids = [m['context'][1][:min(len(m['context'][1]), self.args.prerank_ctx_window // 2)]
for m in content]
ment_ids = [[] for m in content]
token_ids = [l + m + r if len(l) + len(r) > 0 else [self.prerank_model.word_voca.unk_id]
for l, m, r in zip(lctx_ids, ment_ids, rctx_ids)]
entity_ids = [m['cands'] for m in content]
entity_ids = Variable(torch.LongTensor(entity_ids).cuda())
entity_mask = [m['mask'] for m in content]
entity_mask = Variable(torch.FloatTensor(entity_mask).cuda())
token_ids, token_offsets = utils.flatten_list_of_lists(token_ids)
token_ids = Variable(torch.LongTensor(token_ids).cuda())
token_offsets = Variable(torch.LongTensor(token_offsets).cuda())
log_probs = self.prerank_model.forward(token_ids, token_offsets, entity_ids, use_sum=True)
log_probs = (log_probs * entity_mask).add_((entity_mask - 1).mul_(1e10))
_, top_pos = torch.topk(log_probs, dim=1, k=self.args.keep_ctx_ent)
top_pos = top_pos.data.cpu().numpy()
else:
top_pos = [[]] * len(content)
# select candidats: mix between keep_ctx_ent best candidates (ntee scores) with
# keep_p_e_m best candidates (p_e_m scores)
for i, m in enumerate(content):
sm = {'cands': [],
'named_cands': [],
'p_e_m': [],
'mask': [],
'etype': [],
'true_pos': -1}
m['selected_cands'] = sm
selected = set(top_pos[i])
idx = 0
while len(selected) < self.args.keep_ctx_ent + self.args.keep_p_e_m:
if idx not in selected:
selected.add(idx)
idx += 1
selected = sorted(list(selected))
for idx in selected:
if idx>len(m['cands'])-1:
continue
sm['cands'].append(m['cands'][idx])
sm['named_cands'].append(m['named_cands'][idx])
sm['p_e_m'].append(m['p_e_m'][idx])
sm['mask'].append(m['mask'][idx])
sm['etype'].append(m['etype'][idx])
if idx == m['true_pos']:
sm['true_pos'] = len(sm['cands']) - 1
if not predict:
if sm['true_pos'] == -1:
continue
# this insertion only makes the performance worse (why???)
# sm['true_pos'] = 0
# sm['cands'][0] = m['cands'][m['true_pos']]
# sm['named_cands'][0] = m['named_cands'][m['true_pos']]
# sm['p_e_m'][0] = m['p_e_m'][m['true_pos']]
# sm['mask'][0] = m['mask'][m['true_pos']]
# if m['true_pos'] != -1:
# sm['true_pos'] = len(selected) - 1
# sm['cands'][-1] = m['cands'][m['true_pos']]
# sm['named_cands'][-1] = m['named_cands'][m['true_pos']]
# sm['p_e_m'][-1] = m['p_e_m'][m['true_pos']]
# sm['mask'][-1] = m['mask'][m['true_pos']]
items.append(m)
if sm['true_pos'] >= 0:
has_gold += 1
total += 1
if predict:
# only for oracle model, not used for eval
if sm['true_pos'] == -1:
sm['true_pos'] = 0 # a fake gold, happens only 2%, but avoid the non-gold
if len(items) > 0:
new_dataset.append(items)
print('recall', has_gold / total)
return new_dataset
# Automatic Order Learning Method - original version
# def train(self, org_train_dataset, org_dev_datasets, config):
# print('extracting training data')
# train_dataset = self.get_data_items(org_train_dataset, predict=False)
# print('#train docs', len(train_dataset))
#
# dev_datasets = []
# for dname, data in org_dev_datasets:
# dev_datasets.append((dname, self.get_data_items(data, predict=True)))
# print(dname, '#dev docs', len(dev_datasets[-1][1]))
#
# print('creating optimizer')
# optimizer = optim.Adam([p for p in self.model.parameters() if p.requires_grad], lr=config['lr'])
#
# for param_name, param in self.model.named_parameters():
# if param.requires_grad:
# print(param_name)
#
# best_f1 = -1
# not_better_count = 0
# is_counting = False
# order_learning = False
# eval_after_n_epochs = self.args.eval_after_n_epochs
#
# rl_acc_threshold = 0.5
# order_learning_count = 0
#
# # optimize the parameters within the disambiguation module first
# self.model.switch_order_learning(0)
#
# for e in range(config['n_epochs']):
# if self.args.method == "SL" or self.args.method == "RL":
# shuffle(train_dataset)
#
# total_loss = 0
#
# if order_learning:
# order_learning_count += 1
#
# if order_learning_count > 5:
# self.model.switch_order_learning(1)
#
# for dc, batch in enumerate(train_dataset): # each document is a minibatch
# self.model.train()
#
# # convert data items to pytorch inputs
# token_ids = [m['context'][0] + m['context'][1]
# if len(m['context'][0]) + len(m['context'][1]) > 0
# else [self.model.word_voca.unk_id]
# for m in batch]
#
# ment_ids = [m['ment_ids'] if len(m['ment_ids']) > 0
# else [self.model.word_voca.unk_id]
# for m in batch]
#
# entity_ids = Variable(torch.LongTensor([m['selected_cands']['cands'] for m in batch]).cuda())
# true_pos = Variable(torch.LongTensor([m['selected_cands']['true_pos'] for m in batch]).cuda())
# p_e_m = Variable(torch.FloatTensor([m['selected_cands']['p_e_m'] for m in batch]).cuda())
# entity_mask = Variable(torch.FloatTensor([m['selected_cands']['mask'] for m in batch]).cuda())
#
# mtype = Variable(torch.FloatTensor([m['mtype'] for m in batch]).cuda())
# etype = Variable(torch.FloatTensor([m['selected_cands']['etype'] for m in batch]).cuda())
#
# token_ids, token_mask = utils.make_equal_len(token_ids, self.model.word_voca.unk_id)
# token_ids = Variable(torch.LongTensor(token_ids).cuda())
# token_mask = Variable(torch.FloatTensor(token_mask).cuda())
#
# ment_ids, ment_mask = utils.make_equal_len(ment_ids, self.model.word_voca.unk_id)
# ment_ids = Variable(torch.LongTensor(ment_ids).cuda())
# ment_mask = Variable(torch.FloatTensor(ment_mask).cuda())
#
# if self.args.method == "SL":
# optimizer.zero_grad()
#
# # get the model output
# if order_learning_count > 5 and len(batch) > 1:
# scores, _ = self.model.forward(token_ids, token_mask, entity_ids, entity_mask, p_e_m, mtype,
# etype,
# ment_ids, ment_mask, gold=true_pos.view(-1, 1),
# method=self.args.method,
# isTrain=False, isDynamic=0, isOrderLearning=order_learning,
# isOrderFixed=False)
#
# loss = self.model.order_loss()
#
# else:
# scores, _ = self.model.forward(token_ids, token_mask, entity_ids, entity_mask, p_e_m, mtype, etype,
# ment_ids, ment_mask, gold=true_pos.view(-1, 1), method=self.args.method,
# isTrain=True, isDynamic=0, isOrderLearning=order_learning,
# isOrderFixed=True)
# if order_learning:
# _, targets = self.model.get_order_truth()
# targets = Variable(torch.LongTensor(targets).cuda())
#
# if scores.size(0) != targets.size(0):
# print("Size mismatch!")
# break
# loss = self.model.loss(scores, targets, method=self.args.method)
# else:
# loss = self.model.loss(scores, true_pos, method=self.args.method)
#
# loss.backward()
# optimizer.step()
# self.model.regularize(max_norm=4)
#
# loss = loss.cpu().data.numpy()
# total_loss += loss
#
# elif self.args.method == "RL":
# action_memory = []
# early_stop_count = 0
#
# for i_episode in count(1): # the actual episode number for one doc is determined by decision accuracy
# optimizer.zero_grad()
#
# # get the model output
# if order_learning_count > 5 and len(batch) > 1:
# scores, actions = self.model.forward(token_ids, token_mask, entity_ids, entity_mask, p_e_m,
# mtype, etype,
# ment_ids, ment_mask, gold=true_pos.view(-1, 1),
# method=self.args.method,
# isTrain=True, isDynamic=0,
# isOrderLearning=order_learning,
# isOrderFixed=False)
#
# loss = self.model.order_loss()
#
# else:
# scores, actions = self.model.forward(token_ids, token_mask, entity_ids, entity_mask, p_e_m,
# mtype, etype,
# ment_ids, ment_mask, gold=true_pos.view(-1, 1),
# method=self.args.method,
# isTrain=True, isDynamic=0,
# isOrderLearning=order_learning,
# isOrderFixed=True)
# if order_learning:
# _, targets = self.model.get_order_truth()
# targets = Variable(torch.LongTensor(targets).cuda())
#
# if scores.size(0) != targets.size(0):
# print("Size mismatch!")
# break
#
# loss = self.model.loss(scores, targets, method=self.args.method)
# else:
# loss = self.model.loss(scores, true_pos, method=self.args.method)
#
# loss.backward()
# optimizer.step()
# self.model.regularize(max_norm=4)
#
# loss = loss.cpu().data.numpy()
# total_loss += loss
#
# # compute accuracy
# correct = 0
# total = 0.
# if order_learning:
# _, targets = self.model.get_order_truth()
# for i in range(len(actions)):
# if targets[i] == actions[i]:
# correct += 1
# total += 1
# else:
# for i in range(len(actions)):
# if true_pos.data[i] == actions[i]:
# correct += 1
# total += 1
#
# if i_episode > len(batch):
# break
#
# if actions == action_memory:
# early_stop_count += 1
# else:
# del action_memory[:]
# action_memory = copy.deepcopy(actions)
# early_stop_count = 0
#
# if correct/total >= rl_acc_threshold or early_stop_count >= 5:
# break
#
# # print('epoch', e, "%0.2f%%" % (dc / len(train_dataset) * 100), loss)
#
# if order_learning_count == 10:
# # Reset to the initial state
# order_learning_count = 0
# self.model.switch_order_learning(0)
#
# print('epoch', e, 'total loss', total_loss, total_loss / len(train_dataset), flush=True)
#
# if (e + 1) % eval_after_n_epochs == 0:
# dev_f1 = 0
#
# if rl_acc_threshold < 0.95:
# rl_acc_threshold += 0.05
#
# for di, (dname, data) in enumerate(dev_datasets):
# predictions = self.predict(data, 0, order_learning)
# f1 = D.eval(org_dev_datasets[di][1], predictions)
#
# # predictions_1 = self.predict(data, 1, order_learning)
# # f1_1 = D.eval(org_dev_datasets[di][1], predictions_1)
# #
# # predictions_2 = self.predict(data, 2, order_learning)
# # f1_2 = D.eval(org_dev_datasets[di][1], predictions_2)
#
# print(dname, utils.tokgreen('micro F1: ' + str(f1)), flush=True)
#
# with open(self.output_path, 'a') as eval_csv_f1:
# eval_f1_csv_writer = csv.writer(eval_csv_f1)
# eval_f1_csv_writer.writerow([dname, e, 0, f1])
# # eval_f1_csv_writer.writerow([dname, e, 1, f1_1])
# # eval_f1_csv_writer.writerow([dname, e, 2, f1_2])
#
# if dname == 'aida-A':
# dev_f1 = f1
#
# if config['lr'] == 2e-4 and dev_f1 >= self.args.dev_f1_change_lr:
# eval_after_n_epochs = 2
# is_counting = True
# best_f1 = dev_f1
# not_better_count = 0
#
# self.model.switch_order_learning(0)
# config['lr'] = 5e-5
# print('change learning rate to', config['lr'])
# optimizer = optim.Adam([p for p in self.model.parameters() if p.requires_grad], lr=config['lr'])
#
# for param_name, param in self.model.named_parameters():
# if param.requires_grad:
# print(param_name)
#
# if dev_f1 >= 0.92 and self.args.order_learning:
# order_learning = True
#
# if is_counting:
# if dev_f1 < best_f1:
# not_better_count += 1
# else:
# not_better_count = 0
# best_f1 = dev_f1
# print('save model to', self.args.model_path)
# self.model.save(self.args.model_path)
#
# if not_better_count == self.args.n_not_inc:
# break
#
# self.model.print_weight_norm()
# Automatic Order Learning Method - final version
# def train(self, org_train_dataset, org_dev_datasets, config):
# print('extracting training data')
# train_dataset = self.get_data_items(org_train_dataset, predict=False)
# print('#train docs', len(train_dataset))
#
# dev_datasets = []
# for dname, data in org_dev_datasets:
# dev_datasets.append((dname, self.get_data_items(data, predict=True)))
# print(dname, '#dev docs', len(dev_datasets[-1][1]))
#
# print('creating optimizer')
# optimizer = optim.Adam([p for p in self.model.parameters() if p.requires_grad], lr=config['lr'])
#
# for param_name, param in self.model.named_parameters():
# if param.requires_grad:
# print(param_name)
#
# best_f1 = -1
# not_better_count = 0
# is_counting = False
# eval_after_n_epochs = self.args.eval_after_n_epochs
#
# order_learning = False
# order_learning_count = 0
#
# # optimize the parameters within the disambiguation module first
# self.model.switch_order_learning(0)
#
# for e in range(config['n_epochs']):
# shuffle(train_dataset)
#
# total_loss = 0
#
# if order_learning:
# order_learning_count += 1
#
# if order_learning_count > 5:
# self.model.switch_order_learning(1)
#
# for dc, batch in enumerate(train_dataset): # each document is a minibatch
# self.model.train()
#
# # convert data items to pytorch inputs
# token_ids = [m['context'][0] + m['context'][1]
# if len(m['context'][0]) + len(m['context'][1]) > 0
# else [self.model.word_voca.unk_id]
# for m in batch]
#
# ment_ids = [m['ment_ids'] if len(m['ment_ids']) > 0
# else [self.model.word_voca.unk_id]
# for m in batch]
#
# entity_ids = Variable(torch.LongTensor([m['selected_cands']['cands'] for m in batch]).cuda())
# true_pos = Variable(torch.LongTensor([m['selected_cands']['true_pos'] for m in batch]).cuda())
# p_e_m = Variable(torch.FloatTensor([m['selected_cands']['p_e_m'] for m in batch]).cuda())
# entity_mask = Variable(torch.FloatTensor([m['selected_cands']['mask'] for m in batch]).cuda())
#
# mtype = Variable(torch.FloatTensor([m['mtype'] for m in batch]).cuda())
# etype = Variable(torch.FloatTensor([m['selected_cands']['etype'] for m in batch]).cuda())
#
# token_ids, token_mask = utils.make_equal_len(token_ids, self.model.word_voca.unk_id)
# token_ids = Variable(torch.LongTensor(token_ids).cuda())
# token_mask = Variable(torch.FloatTensor(token_mask).cuda())
#
# ment_ids, ment_mask = utils.make_equal_len(ment_ids, self.model.word_voca.unk_id)
# ment_ids = Variable(torch.LongTensor(ment_ids).cuda())
# ment_mask = Variable(torch.FloatTensor(ment_mask).cuda())
#
# optimizer.zero_grad()
#
# # get the model output
# # alternate training for parameters within the order learning module and
# # parameters within the disambiguation module
# if order_learning_count > 5 and len(batch) > 1:
# # when starting optimizing the parameters within the order learning module, we have to
# # set isTrain=False to get the rewards from validation, for that SL always give the ground
# # truth answer so that we can't identify whether the present parameters are good or not
# scores, _ = self.model.forward(token_ids, token_mask, entity_ids, entity_mask, p_e_m, mtype,
# etype, ment_ids, ment_mask, gold=true_pos.view(-1, 1),
# method=self.args.method,
# isTrain=False, isDynamic=0, isOrderLearning=order_learning,
# isOrderFixed=False)
#
# loss = self.model.order_loss()
#
# else:
# scores, _ = self.model.forward(token_ids, token_mask, entity_ids, entity_mask, p_e_m, mtype, etype,
# ment_ids, ment_mask, gold=true_pos.view(-1, 1),
# method=self.args.method,
# isTrain=True, isDynamic=0, isOrderLearning=order_learning,
# isOrderFixed=False)
# if order_learning:
# _, targets = self.model.get_order_truth()
# targets = Variable(torch.LongTensor(targets).cuda())
#
# if scores.size(0) != targets.size(0):
# print("Size mismatch!")
# break
# loss = self.model.loss(scores, targets, method=self.args.method)
# else:
# loss = self.model.loss(scores, true_pos, method=self.args.method)
#
# loss.backward()
# optimizer.step()
# self.model.regularize(max_norm=4)
#
# loss = loss.cpu().data.numpy()
# total_loss += loss
#
# # print('epoch', e, "%0.2f%%" % (dc / len(train_dataset) * 100), loss)
#
# if order_learning_count == 10:
# # Reset to the initial state
# order_learning_count = 0
# self.model.switch_order_learning(0)
#
# print('epoch', e, 'total loss', total_loss, total_loss / len(train_dataset), flush=True)
#
# if (e + 1) % eval_after_n_epochs == 0:
# dev_f1 = 0
#
# for di, (dname, data) in enumerate(dev_datasets):
# predictions = self.predict(data, 0, order_learning)
# f1 = D.eval(org_dev_datasets[di][1], predictions)
#
# # predictions_1 = self.predict(data, 1, order_learning)
# # f1_1 = D.eval(org_dev_datasets[di][1], predictions_1)
# #
# # predictions_2 = self.predict(data, 2, order_learning)
# # f1_2 = D.eval(org_dev_datasets[di][1], predictions_2)
#
# print(dname, utils.tokgreen('micro F1: ' + str(f1)), flush=True)
#
# with open(self.output_path, 'a') as eval_csv_f1:
# eval_f1_csv_writer = csv.writer(eval_csv_f1)
# eval_f1_csv_writer.writerow([dname, e, 0, f1])
# # eval_f1_csv_writer.writerow([dname, e, 1, f1_1])
# # eval_f1_csv_writer.writerow([dname, e, 2, f1_2])
#
# if dname == 'aida-A':
# dev_f1 = f1
#
# if config['lr'] == 2e-4 and dev_f1 >= self.args.dev_f1_change_lr:
# eval_after_n_epochs = 2
# is_counting = True
# best_f1 = dev_f1
# not_better_count = 0
#
# self.model.switch_order_learning(0)
# config['lr'] = 5e-5
# print('change learning rate to', config['lr'])
# optimizer = optim.Adam([p for p in self.model.parameters() if p.requires_grad], lr=config['lr'])
#
# for param_name, param in self.model.named_parameters():
# if param.requires_grad:
# print(param_name)
#
# if dev_f1 >= self.args.dev_f1_start_order_learning and self.args.order_learning:
# order_learning = True
#
# if is_counting:
# if dev_f1 < best_f1:
# not_better_count += 1
# else:
# not_better_count = 0
# best_f1 = dev_f1
# print('save model to', self.args.model_path)
# self.model.save(self.args.model_path)
#
# if not_better_count == self.args.n_not_inc:
# break
#
# self.model.print_weight_norm()
# Heuristic Order Learning Method - Based on Mention-Local Similarity or Mention-Topical Similarity
def train(self, org_train_dataset, org_dev_datasets, config):
print('extracting training data')
train_dataset = self.get_data_items(org_train_dataset, predict=False, isTrain=True)
print('#train docs', len(train_dataset))
self.init_lr = config['lr']
dev_datasets = []
for dname, data in org_dev_datasets:
dev_datasets.append((dname, self.get_data_items(data, predict=True, isTrain=False)))
print(dname, '#dev docs', len(dev_datasets[-1][1]))
print('creating optimizer')
optimizer = optim.Adam([p for p in self.model.parameters() if p.requires_grad], lr=config['lr'])
for param_name, param in self.model.named_parameters():
if param.requires_grad:
print(param_name)
best_f1 = -1
not_better_count = 0
is_counting = False
eval_after_n_epochs = self.args.eval_after_n_epochs
order_learning = False
# order_learning_count = 0
rl_acc_threshold = 0.7
# optimize the parameters within the disambiguation module first
# self.model.switch_order_learning(0)
best_aida_A_rlts = []
best_aida_A_f1 = 0.
best_aida_B_rlts = []
best_aida_B_f1 = 0.
best_ave_rlts = []
best_ave_f1 = 0.
# self.records = dict()
# self.records[-1] = dict()
# for di, (dname, data) in enumerate(dev_datasets):
# predictions = self.predict(data, config['isDynamic'], order_learning)
# self.records[-1][dname] = self.record
# json.dump(self.records, open('records.json', 'w'),indent=4)
self.run_time = []
for e in range(config['n_epochs']):
shuffle(train_dataset)
total_loss = 0
# if order_learning:
# order_learning_count += 1
#
# if order_learning_count > 5:
# self.model.switch_order_learning(1)
for dc, batch in enumerate(train_dataset): # each document is a minibatch
self.model.train()
# convert data items to pytorch inputs
token_ids = [m['context'][0] + m['context'][1]
if len(m['context'][0]) + len(m['context'][1]) > 0
else [self.model.word_voca.unk_id]
for m in batch]
ment_ids = [m['ment_ids'] if len(m['ment_ids']) > 0
else [self.model.word_voca.unk_id]
for m in batch]
entity_ids = Variable(torch.LongTensor([m['selected_cands']['cands'] for m in batch]).cuda())
true_pos = Variable(torch.LongTensor([m['selected_cands']['true_pos'] for m in batch]).cuda())
p_e_m = Variable(torch.FloatTensor([m['selected_cands']['p_e_m'] for m in batch]).cuda())
entity_mask = Variable(torch.FloatTensor([m['selected_cands']['mask'] for m in batch]).cuda())
desc_ids = torch.index_select(self.ent_desc, 0, entity_ids.view(-1)).view(entity_ids.size(0), entity_ids.size(1), -1)
desc_mask = torch.index_select(self.desc_mask, 0, entity_ids.view(-1)).view(entity_ids.size(0), entity_ids.size(1), -1)
mtype = Variable(torch.FloatTensor([m['mtype'] for m in batch]).cuda())
etype = Variable(torch.FloatTensor([m['selected_cands']['etype'] for m in batch]).cuda())
token_ids, token_mask = utils.make_equal_len(token_ids, self.model.word_voca.unk_id)
token_ids = Variable(torch.LongTensor(token_ids).cuda())
token_mask = Variable(torch.FloatTensor(token_mask).cuda())
ment_ids, ment_mask = utils.make_equal_len(ment_ids, self.model.word_voca.unk_id)
ment_ids = Variable(torch.LongTensor(ment_ids).cuda())
ment_mask = Variable(torch.FloatTensor(ment_mask).cuda())
# get the model output
# alternate training for parameters within the order learning module and
# parameters within the disambiguation module
# if order_learning_count > 5 and len(batch) > 1:
# # when starting optimizing the parameters within the order learning module, we have to
# # set isTrain=False to get the rewards from validation, for that SL always give the ground
# # truth answer so that we can't identify whether the present parameters are good or not
# scores, _ = self.model.forward(token_ids, token_mask, entity_ids, entity_mask, p_e_m, mtype,
# etype, ment_ids, ment_mask, gold=true_pos.view(-1, 1),
# method=self.args.method,
# isTrain=False, isDynamic=0, isOrderLearning=order_learning,
# isOrderFixed=False)
#
# loss = self.model.order_loss()
#
# else:
# scores, _ = self.model.forward(token_ids, token_mask, entity_ids, entity_mask, p_e_m, mtype, etype,
# ment_ids, ment_mask, gold=true_pos.view(-1, 1),
# method=self.args.method,
# isTrain=True, isDynamic=0, isOrderLearning=order_learning,
# isOrderFixed=False)
# if order_learning:
# _, targets = self.model.get_order_truth()
# targets = Variable(torch.LongTensor(targets).cuda())
#
# if scores.size(0) != targets.size(0):
# print("Size mismatch!")
# break
# loss = self.model.loss(scores, targets, method=self.args.method)
# else:
# loss = self.model.loss(scores, true_pos, method=self.args.method)
# Uniform Training Process
# optimizer.zero_grad()
# scores, _ = self.model.forward(token_ids, token_mask, entity_ids, entity_mask, p_e_m, mtype, etype,
# ment_ids, ment_mask, gold=true_pos.view(-1, 1),
# method=self.args.method,
# isTrain=True, isDynamic=0, isOrderLearning=order_learning,
# isOrderFixed=True, isSort=self.args.sort)
# if order_learning:
# _, targets = self.model.get_order_truth()
# targets = Variable(torch.LongTensor(targets).cuda())
#
# if scores.size(0) != targets.size(0):
# print("Size mismatch!")
# break
# loss = self.model.loss(scores, targets, method=self.args.method)
# else:
# loss = self.model.loss(scores, true_pos, method=self.args.method)
#
# loss.backward()
# optimizer.step()
# self.model.regularize(max_norm=4)
#
# loss = loss.cpu().data.numpy()
# total_loss += loss
# print('epoch', e, "%0.2f%%" % (dc / len(train_dataset) * 100), loss)
if self.args.method == "SL":
optimizer.zero_grad()
scores, _ = self.model.forward(token_ids, token_mask, entity_ids, entity_mask, p_e_m, mtype, etype,
ment_ids, ment_mask, desc_ids, desc_mask, gold=true_pos.view(-1, 1),
method=self.args.method,
isTrain=True, isDynamic=config['isDynamic'], isOrderLearning=order_learning,
isOrderFixed=True, isSort=self.args.sort)
if order_learning:
_, targets = self.model.get_order_truth()
targets = Variable(torch.LongTensor(targets).cuda())
if scores.size(0) != targets.size(0):
print("Size mismatch!")
break
loss = self.model.loss(scores, targets, method=self.args.method)
else:
loss = self.model.loss(scores, true_pos, method=self.args.method)
loss.backward()
optimizer.step()
self.model.regularize(max_norm=4)
loss = loss.cpu().data.numpy()
total_loss += loss
elif self.args.method == "RL":
action_memory = []
early_stop_count = 0
# the actual episode number for one doc is determined by decision accuracy
for i_episode in count(1):
optimizer.zero_grad()
# get the model output
scores, actions = self.model.forward(token_ids, token_mask, entity_ids, entity_mask, p_e_m,
mtype, etype,
ment_ids, ment_mask, desc_ids, desc_mask, gold=true_pos.view(-1, 1),
method=self.args.method,
isTrain=True, isDynamic=config['isDynamic'],
isOrderLearning=order_learning,
isOrderFixed=True, isSort=self.args.sort)
if order_learning:
_, targets = self.model.get_order_truth()
targets = Variable(torch.LongTensor(targets).cuda())
if scores.size(0) != targets.size(0):
print("Size mismatch!")
break
loss = self.model.loss(scores, targets, method=self.args.method)
else:
loss = self.model.loss(scores, true_pos, method=self.args.method)
loss.backward()
optimizer.step()
#self.model.regularize(max_norm=4)
loss = loss.cpu().data.numpy()
total_loss += loss
# compute accuracy
correct = 0
total = 0.
if order_learning:
_, targets = self.model.get_order_truth()
for i in range(len(actions)):
if targets[i] == actions[i]:
correct += 1
total += 1
else:
for i in range(len(actions)):
if true_pos.data[i] == actions[i]:
correct += 1
total += 1
if not config['use_early_stop']:
break
if i_episode > len(batch)/2:
break
if actions == action_memory:
early_stop_count += 1
else:
del action_memory[:]
action_memory = copy.deepcopy(actions)
early_stop_count = 0
if correct/total >= rl_acc_threshold or early_stop_count >= 3:
break
# if order_learning_count == 10:
# # Reset to the initial state
# order_learning_count = 0
# self.model.switch_order_learning(0)
print('epoch', e, 'total loss', total_loss, total_loss / len(train_dataset), flush=True)
if (e + 1) % eval_after_n_epochs == 0:
dev_f1 = 0.
test_f1 = 0.
ave_f1 = 0.
if rl_acc_threshold < 0.92:
rl_acc_threshold += 0.02
temp_rlt = []
#self.records[e] = dict()
for di, (dname, data) in enumerate(dev_datasets):
if dname == 'aida-B':
self.rt_flag = True
else:
self.rt_flag = False
predictions = self.predict(data, config['isDynamic'], order_learning)
#self.records[e][dname] = self.record
f1 = D.eval(org_dev_datasets[di][1], predictions)
# predictions_1 = self.predict(data, 1, order_learning)
# f1_1 = D.eval(org_dev_datasets[di][1], predictions_1)
#
# predictions_2 = self.predict(data, 2, order_learning)
# f1_2 = D.eval(org_dev_datasets[di][1], predictions_2)
print(dname, utils.tokgreen('micro F1: ' + str(f1)), flush=True)
with open(self.output_path, 'a') as eval_csv_f1:
eval_f1_csv_writer = csv.writer(eval_csv_f1)
eval_f1_csv_writer.writerow([dname, e, 0, f1])
#eval_f1_csv_writer.writerow([dname, e, 1, f1_1])
#eval_f1_csv_writer.writerow([dname, e, 2, f1_2])
temp_rlt.append([dname, f1])
if dname == 'aida-A':
dev_f1 = f1
if dname == 'aida-B':
test_f1 = f1
ave_f1 += f1
if dev_f1>best_aida_A_f1:
best_aida_A_f1 = dev_f1
best_aida_A_rlts = copy.deepcopy(temp_rlt)
if test_f1>best_aida_B_f1:
best_aida_B_f1 = test_f1
best_aida_B_rlts = copy.deepcopy(temp_rlt)
if ave_f1 > best_ave_f1:
best_ave_f1 = ave_f1
best_ave_rlts = copy.deepcopy(temp_rlt)