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main.py
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main.py
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"""Runs the project"""
import nltk
import scipy.spatial.distance as dist
import numpy as np
import argparse
import time
import os
import utils
from corpus import Corpus, Word
from hdp import HDP
# command line options
parser = argparse.ArgumentParser()
parser.add_argument(
'start_corpus', type=str, help='address of the older (reference) corpus')
parser.add_argument('end_corpus', type=str,
help='address of the newer (focus) corpus')
parser.add_argument('--semeval_mode', type=bool,
help='True if the project is being used for SemEval 2020 Task 1, False if the project is being used for general inference, default False', default=False, metavar='M')
parser.add_argument('targets', type=str,
help='address of the target words', nargs='?')
parser.add_argument('output', type=str, help='address to write output to')
parser.add_argument('--top_k', type=int, metavar='k',
default=25, help='number of words to output')
parser.add_argument('--max_iters', type=int, metavar='N', default=25,
help='maximum number of iterations to run sampling for')
parser.add_argument('--alpha', type=float, default=1.0,
help='alpha value, default 1.0')
parser.add_argument('--gamma', type=float, default=1.0,
help='gamma value, default 1.0')
parser.add_argument('--window_size', metavar='W', type=int, default=10,
help='size of context window to use, default 10')
parser.add_argument('--floor', type=int, metavar='F', default=1,
help='minimum number of occurrences to be considered, default 1')
parser.add_argument('--threshold', type=float, metavar='T', default=0.6,
help='minimum score for before a word is considered to have a novel sense')
args = parser.parse_args()
if args.semeval_mode and 'targets' not in vars(args):
parser.error('targets arg is required when in SemEval mode')
def main():
"""Run the project"""
start_time = time.time()
try:
nltk.data.find('corpora/stopwords')
except LookupError:
nltk.download('stopwords')
try:
nltk.data.find('tokenizers/punkt')
except LookupError:
nltk.download('punkt')
if not os.path.exists(args.output):
os.makedirs(args.output)
save_path = os.path.join(args.output, 'saves')
if not os.path.exists(save_path):
os.makedirs(save_path)
if not os.path.exists(os.path.join(args.output, 'task1')):
os.makedirs(os.path.join(args.output, 'task1'))
if not os.path.exists(os.path.join(args.output, 'task2')):
os.makedirs(os.path.join(args.output, 'task2'))
print('Loading words...')
corpus = Corpus(args.start_corpus, save_path,
args.end_corpus, args.floor, args.window_size)
print('Setting up initial partition...')
for i in corpus.docs:
i.init_partition(args.alpha)
hdp = HDP(corpus.vocab_size, save_path,
alpha=args.alpha, gamma=args.gamma)
hdp.init_partition(corpus.docs)
print('Done')
it = 0
print(f'Running Gibbs sampling for {args.max_iters} iterations...')
while it < args.max_iters:
for j in corpus.docs:
for i in range(len(j.words)):
hdp.sample_table(j, i, corpus.collocations[j.words[i]])
it += 1
corpus.save()
print(f'Iteration {it}/{args.max_iters}')
for i in hdp.senses:
i /= i.sum()
print('Done')
print('Generating scores for word senses...')
words = dict()
for j in corpus.docs:
for i, p in enumerate(j.partition):
origin = j.category
sense = j.topic_to_global_idx[i]
for w in p:
if corpus.idx_to_word[w] in words:
if origin == 'reference':
words[corpus.idx_to_word[w]].senses[sense][0] += 1
else:
words[corpus.idx_to_word[w]].senses[sense][1] += 1
else:
word = Word(corpus.idx_to_word[w], w, hdp.senses.shape[0])
if origin == 'reference':
word.senses[sense][0] += 1
else:
word.senses[sense][1] += 1
words[word.word] = word
print('Done.')
if args.semeval_mode:
targets = utils.get_targets(args.targets)
results = []
for i in range(len(targets)):
t = targets[i][0]
pos = targets[i][1]
recombine = t+'_'+pos
word = words[recombine]
scores = word.senses[~np.all(word.senses == 0, axis=1)]
dist_1 = scores[:, 0]
dist_2 = scores[:, 1]
jensenshannon = dist.jensenshannon(
dist_1, dist_2)
results.append((recombine, jensenshannon))
with open(os.path.join(os.path.join(args.output, 'task1'),
'english.txt'), 'w') as f:
for i in results:
recombine = i[0]
score = i[1]
different = 1 if score > args.threshold else 0
f.write(f'{recombine} {different}\n')
with open(os.path.join(os.path.join(args.output, 'task2'), 'english.txt'), 'w') as f:
for i in results:
recombine = i[0]
jensenshannon = i[1]
f.write(f'{recombine} {jensenshannon:.4f}\n')
else:
for k, v in words.items():
words[k] = v.calculate()
top = sorted(words, key=words.get, reverse=True)[:args.top_k]
with open(os.path.join(args.output, 'out.txt'), 'w') as f:
f.write(f'Top {args.top_k} most differing words:')
f.write('\n'.join(top))
end_time = time.time()
print(f'Ran project in {end_time - start_time} seconds')
if __name__ == '__main__':
main()