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main_crf.py
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main_crf.py
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import argparse
import glob
import random
import os
import time
import logging
import sys
from itertools import chain
from torch import nn, optim, max, LongTensor, cuda, sum, transpose, torch, stack, tensor
from torch.autograd import Variable
from torch.nn.utils import clip_grad_norm
from torch.utils.data import DataLoader
from tqdm import tqdm
import matplotlib.pyplot as plt
from corpus.BratWriter import BratFile, Writer
from corpus.WLPDataset import WLPDataset
import config as cfg
from model.SeqNet import SeqNet
from model.multi_batch.BiLSTM_CRF import BiLSTM_CRF
from model.multi_batch.MultiBatchSeqNet import MultiBatchSeqNet
from model.utils import to_scalar
from postprocessing.evaluator import Evaluator
import numpy as np
import pickle
import pandas as pd
from preprocessing.utils import quicksave, quickload, touch
logger = logging.getLogger(__name__)
plt.ion()
plt.legend(loc='upper left')
def argmax(var):
assert isinstance(var, Variable)
_, preds = torch.max(var.data, 1)
preds = preds.cpu().numpy().tolist()
return preds
def train_a_epoch(name, data, tag_idx, model, optimizer):
evaluator = Evaluator(name, [0, 1], main_label_name=cfg.POSITIVE_LABEL, label2id=tag_idx, conll_eval=True)
t = tqdm(data, total=len(data))
for SENT, X, Y, P in t:
# zero the parameter gradients
optimizer.zero_grad()
model.zero_grad()
np.set_printoptions(threshold=np.nan)
nnl = model.neg_log_likelihood(X, Y)
logger.debug("tensor X variable: {0}".format(X))
nnl.backward()
preds = model(X)
for pred, x, y in zip(preds, X, Y):
evaluator.append_data(to_scalar(nnl), pred, x, y)
if cfg.CLIP is not None:
clip_grad_norm(model.parameters(), cfg.CLIP)
optimizer.step()
evaluator.classification_report()
return evaluator, model
def plot_curve(x, y1, y2, xlabel, ylabel, title, savefile):
plt.plot(range(x + 1), y1, 'r', label='dev')
plt.plot(range(x + 1), y2, 'b', label='test')
plt.xlabel(xlabel)
plt.ylabel(ylabel)
plt.title(title)
plt.savefig(savefile)
plt.pause(0.05)
def build_model(train_dataset, dev_dataset, test_dataset,
collate_fn, tag_idx, is_oov, embedding_matrix, model_save_path, plot_save_path):
# init model
model = BiLSTM_CRF(embedding_matrix, tag_idx)
# Turn on cuda
model = model.cuda()
# verify model
print(model)
# remove paramters that have required_grad = False
optimizer = optim.Adadelta(filter(lambda p: p.requires_grad, model.parameters()), lr=cfg.LEARNING_RATE)
# optimizer = optim.SGD(model.parameters(), lr=cfg.LEARNING_RATE, momentum=0.9)
optimizer.zero_grad()
model.zero_grad()
# init loss criteria
best_res_val_0 = 0.0
best_epoch = 0
dev_eval_history = []
test_eval_history = []
for epoch in range(cfg.MAX_EPOCH):
print('-' * 40)
print("EPOCH = {0}".format(epoch))
print('-' * 40)
random.seed(epoch)
train_loader = DataLoader(train_dataset, batch_size=cfg.BATCH_SIZE, shuffle=cfg.RANDOM_TRAIN,
num_workers=28, collate_fn=collate_fn)
train_eval, model = train_a_epoch(name="train", data=train_loader, tag_idx=tag_idx,
model=model, optimizer=optimizer)
dev_loader = DataLoader(dev_dataset, batch_size=cfg.BATCH_SIZE, num_workers=28, collate_fn=collate_fn)
test_loader = DataLoader(test_dataset, batch_size=cfg.BATCH_SIZE, num_workers=28, collate_fn=collate_fn)
dev_eval, _, _ = test("dev", dev_loader, tag_idx, model)
test_eval, _, _ = test("test", test_loader, tag_idx, model)
dev_eval.verify_results()
test_eval.verify_results()
dev_eval_history.append(dev_eval.results[cfg.BEST_MODEL_SELECTOR[0]])
test_eval_history.append(test_eval.results['test_conll_f'])
plot_curve(epoch, dev_eval_history, test_eval_history, "epochs", "fscore", "epoch learning curve",
plot_save_path)
pickle.dump((dev_eval_history, test_eval_history), open("plot_data.p", "wb"))
# pick the best epoch
if epoch < cfg.MIN_EPOCH_IMP or (dev_eval.results[cfg.BEST_MODEL_SELECTOR[0]] > best_res_val_0):
best_epoch = epoch
best_res_val_0 = dev_eval.results[cfg.BEST_MODEL_SELECTOR[0]]
torch.save(model, model_save_path)
print("current dev micro_score: {0}".format(dev_eval.results[cfg.BEST_MODEL_SELECTOR[0]]))
print("current dev macro_score: {0}".format(dev_eval.results[cfg.BEST_MODEL_SELECTOR[1]]))
print("best dev micro_score: {0}".format(best_res_val_0))
print("best_epoch: {0}".format(str(best_epoch)))
# if the best epoch model outperforms MA
if 0 < cfg.MAX_EPOCH_IMP <= (epoch - best_epoch):
break
print("Loading Best Model ...")
model = torch.load(model_save_path)
return model
def test(name, data, tag_idx, model):
pred_list = []
true_list = []
full_eval = Evaluator(name, [0, 1], main_label_name=cfg.POSITIVE_LABEL, label2id=tag_idx, conll_eval=True)
only_ents_eval = Evaluator("test_ents_only", [0, 1], skip_label=['B-Action', 'I-Action'],
main_label_name=cfg.POSITIVE_LABEL, label2id=tag_idx, conll_eval=True)
for SENT, X, Y, P in tqdm(data, desc=name, total=len(data)):
np.set_printoptions(threshold=np.nan)
preds = model(X)
for pred, x, y in zip(preds, X, Y):
full_eval.append_data(0, pred, x, y)
only_ents_eval.append_data(0, pred, x, y)
pred_list.append(pred[1:-1])
true_list.append(y[1:-1])
full_eval.classification_report()
only_ents_eval.classification_report()
return full_eval, pred_list, true_list
def dataset_prep(loadfile=None, savefile=None):
start_time = time.time()
if loadfile:
print("Loading corpus ...")
corpus = pickle.load(open(loadfile, "rb"))
train_p = set([os.path.splitext(os.path.basename(f))[0] for f in glob.glob(cfg.TRAIN_ARTICLES_PATH + "/*")])
dev_p = set([os.path.splitext(os.path.basename(f))[0] for f in glob.glob(cfg.DEV_ARTICLES_PATH + "/*")])
test_p = set([os.path.splitext(os.path.basename(f))[0] for f in glob.glob(cfg.TEST_ARTICLES_PATH + "/*")])
corpus.gen_data(train_p, dev_p, test_p)
else:
print("Loading Data ...")
corpus = WLPDataset(min_wcount=cfg.MIN_WORD_COUNT,
lowercase=cfg.LOWERCASE, replace_digits=cfg.REPLACE_DIGITS, gen_ent_feat=True)
train_p = set([os.path.splitext(os.path.basename(f))[0] for f in glob.glob(cfg.TRAIN_ARTICLES_PATH + "/*")])
dev_p = set([os.path.splitext(os.path.basename(f))[0] for f in glob.glob(cfg.DEV_ARTICLES_PATH + "/*")])
test_p = set([os.path.splitext(os.path.basename(f))[0] for f in glob.glob(cfg.TEST_ARTICLES_PATH + "/*")])
corpus.gen_data(train_p, dev_p, test_p)
if savefile:
print("Saving corpus and embedding matrix ...")
pickle.dump(corpus, open(savefile, "wb"))
end_time = time.time()
print("Ready. Input Process time: {0}".format(end_time - start_time))
return corpus
def multi_batchify(samples):
samples = sorted(samples, key=lambda s: len(s.SENT), reverse=True)
SENT, X, Y, P = zip(*[(sample.SENT, sample.X, sample.Y, sample.P)
for sample in samples])
return SENT, X, Y, P
def to_variables(X, C, POS, Y):
if cfg.BATCH_TYPE == "multi":
x_var = X
c_var = C
pos_var = POS
y_var = list(chain.from_iterable(list(Y)))
lm_X = [[cfg.LM_MAX_VOCAB_SIZE - 1 if (x >= cfg.LM_MAX_VOCAB_SIZE) else x for x in x1d] for x1d in X]
else:
x_var = Variable(cuda.LongTensor([X]))
c_var = C
# f_var = Variable(torch.from_numpy(f)).float().unsqueeze(dim=0).cuda()
pos_var = Variable(torch.from_numpy(POS).cuda()).unsqueeze(dim=0)
lm_X = [cfg.LM_MAX_VOCAB_SIZE - 1 if (x >= cfg.LM_MAX_VOCAB_SIZE) else x for x in X]
y_var = Variable(cuda.LongTensor(Y))
return x_var, c_var, pos_var, y_var, lm_X
def single_run(corpus, index, title, overwrite, only_test=False):
if cfg.BATCH_TYPE == "multi":
collate_fn = multi_batchify
else:
collate_fn = lambda x: \
(x[0].X, x[0].C, x[0].POS, x[0].REL, x[0].DEP, x[0].Y)
model_save_path = os.path.join(cfg.MODEL_SAVE_DIR, title + ".m")
plot_save_path = os.path.join(cfg.PLOT_SAVE_DIR, title + ".png")
if not only_test:
the_model = build_model(corpus.train, corpus.dev, corpus.test,
collate_fn, corpus.tag_idx, corpus.is_oov, corpus.embedding_matrix, model_save_path,
plot_save_path)
else:
the_model = torch.load(model_save_path)
print("Testing ...")
test_loader = DataLoader(corpus.test, batch_size=cfg.BATCH_SIZE, num_workers=28, collate_fn=collate_fn)
test_eval, pred_list, true_list = test("test", test_loader, corpus.tag_idx, the_model)
print("Writing Brat File ...")
bratfile_full = Writer(cfg.CONF_DIR, os.path.join(cfg.BRAT_DIR, title), "full_out", corpus.tag_idx)
bratfile_inc = Writer(cfg.CONF_DIR, os.path.join(cfg.BRAT_DIR, title), "inc_out", corpus.tag_idx)
# convert idx to label
test_eval.print_results()
txt_res_file = os.path.join(cfg.TEXT_RESULT_DIR, title + ".txt")
csv_res_file = os.path.join(cfg.CSV_RESULT_DIR, title + ".csv")
test_eval.write_results(txt_res_file, title + "g={0}".format(cfg.LM_GAMMA), overwrite)
test_eval.write_csv_results(csv_res_file, title + "g={0}".format(cfg.LM_GAMMA), overwrite)
test_loader = DataLoader(corpus.test, batch_size=cfg.BATCH_SIZE, num_workers=28, collate_fn=collate_fn)
sents = [(sent, p) for SENT, X, C, POS, Y, P in test_loader for sent, p in zip(SENT, P)]
bratfile_full.from_labels(sents, true_list, pred_list, doFull=True)
bratfile_inc.from_labels(sents, true_list, pred_list, doFull=False)
return test_eval
def main(nrun=1):
for run in range(nrun):
dataset = dataset_prep()
cfg.CATEGORIES = len(dataset.tag_idx.keys()) + 2 # +2 for start and end tags of a seq
dataset.tag_idx['<s>'] = len(dataset.tag_idx.keys())
dataset.tag_idx['</s>'] = len(dataset.tag_idx.keys())
cfg.WORD_VOCAB = len(dataset.word_index.items())
i = 0
if cfg.CHAR_LEVEL != "None":
cfg.CHAR_VOCAB = len(dataset.char_index.items())
if cfg.POS_FEATURE == "Yes":
cfg.POS_VOCAB = len(dataset.pos_ids)
if cfg.DEP_LABEL_FEATURE == "Yes":
cfg.REL_VOCAB = len(dataset.rel_ids)
if cfg.DEP_WORD_FEATURE == "Yes":
cfg.DEP_WORD_VOCAB = dataset.embedding_matrix.shape[0]
test_ev = single_run(dataset, i, "BiLSTM_CRF", overwrite=False, only_test=False)
plt.clf()
if __name__ == '__main__':
# setup_logging()
main(1)