def load_file(file):
from lxml import etree
from efficiency.log import show_time
show_time('[Info] Loading file in')
doc = etree.parse(file)
show_time('[Info] Finished loading file in')
return doc
def run_classifier(file):
import os
from efficiency.log import show_time
cmd = 'cp {} ../1909_prac_cls/data/pubmed/data.csv; ' \
'cd ../1909_prac_cls/; ' \
'python train.py -lower ' \
'-data_dir data/pubmed -train_fname data.csv' \
''.format(file)
import pdb
pdb.set_trace()
os.system(cmd)
show_time('[Info] Finished classification')
def save_csv(txt_native, txt_non_native, file='articles_classified.csv'):
import csv
from efficiency.log import show_time
with open(file, 'w') as f:
writeout = [('native', line) for line in txt_native]
writeout += [('non_native', line) for line in txt_non_native]
writer = csv.writer(f)
writer.writerows(writeout)
show_time('[Info] Saved {}+{} sentences to {}'.format(
len(txt_native), len(txt_non_native), file))
def set_seed(seed=0, verbose=False):
import random
import os
if seed is None:
from efficiency.log import show_time
seed = int(show_time())
if verbose: print("[Info] seed set to: {}".format(seed))
random.seed(seed)
os.environ['PYTHONHASHSEED'] = str(seed)
try:
import numpy as np
np.random.seed(seed)
except ImportError:
pass
try:
import torch
torch.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
except ImportError:
pass
def set_seed(seed):
if not seed:
seed = int(show_time())
print("[Info] seed set to: {}".format(seed))
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
torch.backends.cudnn.deterministic = True
def get_html(url, use_proxy=False):
import time
import requests
from efficiency.log import show_time
time.sleep(interval)
headers = {'User-Agent': next(user_agents)}
if use_proxy:
proxy = next(proxy_pool)
try:
r = requests.get(url,
proxies={
"http": proxy,
"https": proxy
},
headers=headers)
except Exception:
proxy_pool.add_bad_proxy(proxy)
get_html(url)
return
else:
try:
r = requests.get(url, headers=headers)
except:
show_time('[Warn] {} is blocked for {}s'.format(
url, sleeper.block_secs))
save_json()
sleeper.sleep(url)
r = requests.get(url, headers=headers)
if r.status_code == 403:
show_time('[Warn] {} is blocked for {}s'.format(
url, sleeper.block_secs))
save_json()
sleeper.sleep(url)
get_html(url)
else:
if r.status_code == 200:
return r.text
def __init__(self, files, use_all_secs=True, save_to=1):
import pickle
from efficiency.log import show_time
show_time('[Info] files: {}, use_all_secs:{}, save_to:{}'.format(
files, use_all_secs, save_to))
with open('articles{}.pickle'.format(save_to), 'rb') as f:
self.articles = pickle.load(f)
# self.articles = []
# for file in files:
# doc = self.load_file(file)
# self.articles += self.parse(doc, file, use_all_secs=use_all_secs)
# print('[Info] {} articles'.format(len(self.articles)))
# self.save('articles{}.pickle'.format(save_to))
# self.save_ids('pmids{}.txt'.format(save_to))
self.filter_by_ids()
self.articles_non_native, self.articles_native = \
self.split_by_nation()
txt_native = self.get_txt(self.articles_native,
'articles_native{}.txt'.format(save_to))
txt_non_native = self.get_txt(
self.articles_non_native,
'articles_non_native{}.txt'.format(save_to))
import pdb
pdb.set_trace()
txt_native, txt_non_native, eval_native, eval_non_native = \
self.postprocess_txt(txt_native, txt_non_native)
self.save_csv(txt_native,
txt_non_native,
file='articles_classified{}.csv'.format(save_to))
self.save_csv(eval_native,
eval_non_native,
file='articles_eval{}.csv'.format(save_to))
self.run_classifier(file='articles_classified{}.csv'.format(save_to))
def set_seed(seed=0):
import random
if seed is None:
from efficiency.log import show_time
seed = int(show_time())
print("[Info] seed set to: {}".format(seed))
random.seed(seed)
try:
import numpy as np
np.random.seed(seed)
except ImportError:
pass
try:
import torch
torch.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
torch.backends.cudnn.deterministic = True
except ImportError:
pass
parameters = [
hidden_sizes, p_ems, p_ins, p_rnns, p_outs, dropouts, char_methods,
batch_sizes, char_dims, char_hidden_sizes, tag_spaces
]
parameters = list(itertools.product(*parameters)) * num_repeat
import pdb
pdb.set_trace()
dataset_name = 'chemdner'
for param in parameters:
hidden_size, p_em, p_in, p_rnn, p_out, dropout, char_method, batch_size, \
char_dim, char_hidden_size, tag_space = param
st_time = show_time(cat_server=True)
result_file_path = '/afs/csail.mit.edu/u/z/zhijing/proj/ie/data/run/az/hyperp_{}_{}'.format(
dataset_name, st_time)
p_rnn = '{} {}'.format(p_rnn[0], p_rnn[1])
log_msg = '\n{}, char_dim: {}, char_hidden_size: {}'.format(
st_time, char_dim, char_dim)
log_msg += '\nhidden_size: {}\tp_em: {}\tp_in: {}\tp_rnn: {}\tp_out: {}\tdropout: {}\tchar_method: {}\tbatch_size: {}\n'.format(
hidden_size, p_em, p_in, p_rnn, p_out, dropout, char_method,
batch_size)
print(log_msg)
command = 'CUDA_VISIBLE_DEVICES={} python -m pdb -c continue examples/NERCRF_conll.py --cuda --mode LSTM --encoder_mode lstm --char_method {} --num_epochs 150 --batch_size {} --hidden_size {} --num_layers 1 \
--char_dim {} --char_hidden_size {} --tag_space 64 --max_norm 15. --gpu_id {} --results_folder results --tmp_folder tmp --alphabets_folder data/chem/alphabets \
--learning_rate 0.005 --decay_rate 0.01 --schedule 1 --gamma 0. --o_tag O --dataset_name {} \
]
parameters = list(itertools.product(*parameters)) * num_repeat
parameters = [(450, 0.001, 1000, 5, 0.2, 0.33, (0.33, 0.5, 0.5), 0.5, 'gcn')]
dataset_name = '03conll'
results_folder = './data/run/'
for param_i, param in enumerate(parameters):
hidden_size, learning_rate_gcn, gcn_warmup, pretrain_lstm, \
p_em, p_in, p_rnn, p_tag, dropout = param
p_rnn = '{} {} {}'.format(p_rnn[0], p_rnn[1], p_rnn[2])
misc = "{}".format(del_quote(str(param)))
print("\n", misc, "\n")
st_time = show_time()
command = 'CUDA_VISIBLE_DEVICES={gpu_idx} python examples/NERCRF_conll.py ' \
'--cuda --mode LSTM --encoder_mode lstm ' \
'--char_method cnn --num_epochs {max_epochs} --batch_size 1 ' \
'--hidden_size {hidden_size} --num_layers 1 ' \
'--char_dim 30 --char_hidden_size 30 --tag_space 128 ' \
'--max_norm 10. --gpu_id {gpu_idx} ' \
'--alphabets_folder data/alphabets ' \
'--learning_rate 0.01 --decay_rate 0.05 --schedule 1 ' \
'--gamma 0. --o_tag O --dataset_name {dataset_name} ' \
'--dropout {dropout} --p_em {p_em} --p_in {p_in} ' \
'--p_rnn {p_rnn} --p_tag {p_tag} --unk_replace 0.0 ' \
'--bigram ' \
'--seed {seed} ' \
'--learning_rate_gcn {learning_rate_gcn} --gcn_warmup {gcn_warmup} ' \