s460985/retro-gap
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improved

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This commit is contained in:
Michal Maciaszek 2020-12-08 14:14:52 +01:00
parent 44755ca275
commit 07afcec739
6 changed files with 45803 additions and 45817 deletions
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39972
dev-0/out.tsv
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dev-1/out.tsv
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predict.py
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import pickle
import sys
from math import log
import regex as re
def get_prob(count, total, classes):
prob = (count + 1.0) / (total + classes)
def count_prob(bigrams, unigrams):
prob = (bigrams + 1.0) / (unigrams + 1)
if prob > 1.0:
return 1.0
else:
return prob
def main():
ngrams = pickle.load(open('ngrams.pkl', 'rb'))
ngrams = pickle.load(open('ngrams_2.pkl', 'rb'))
vocabulary_size = len(ngrams[1])
# a = ngrams[1]
# print(a)
# lookfor1 = str(".")
# #lookfor = tuple(lookfor1)
# # print(lookfor)
# b = a.get((',',),0)
for line in sys.stdin:
words = re.findall(r'.*\t.*\t.* (.*?) (.*?)\t(.*?) (.*?) ', line.lower())[0]
left_words = [str(words[0]), str(words[1])]
right_words = [str(words[2]), str(words[3])]
words = re.findall(r'.*\t.*\t.* (.*?)\t(.*?) ', line.lower())[0]
#print(words)
left_word = [str(words[0])]
right_word = [str(words[1])]
probabilities = []
for word in ngrams[1].keys():
word = str(word[0])
pre_ngram = tuple(left_words + [word])
post_ngram = tuple([word] + right_words)
pre_ngram_prob = get_prob(ngrams[3].get(pre_ngram, 0), ngrams[2].get(tuple(left_words), 0),
vocabulary_size)
post_ngram_prob = get_prob(ngrams[3].get(post_ngram, 0), ngrams[2].get(post_ngram[0:2], 0),
vocabulary_size)
pre_ngram = tuple(left_word + [word])
post_ngram = tuple([word] + right_word)
#print(pre_ngram)
#print("bigram:", ngrams[2].get(pre_ngram, 0), "unigram", ngrams[1].get(word[0],0))
pre_ngram_prob = count_prob(ngrams[2].get(pre_ngram, 0), ngrams[1].get((word[0],),0) + vocabulary_size)
#if pre_ngram_prob>0:
post_ngram_prob = count_prob(ngrams[2].get(post_ngram, 0), ngrams[1].get((word[0],),0) + vocabulary_size)
probabilities.append((word, pre_ngram_prob * post_ngram_prob))
probabilities = sorted(probabilities, key=lambda t: t[1], reverse=True)[:50]
probability = 1.0

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skrypt.ssh
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xzcat train/train.tsv.xz | ./train.py
xzcat train/train.tsv.xz | python3 ./train.py
cat dev-0/in.tsv | ./predict.py > dev-0/out.tsv
cat dev-1/in.tsv | ./predict.py > dev-1/out.tsv
cat test-A/in.tsv | ./predict.py > test-A/out.tsv
cat dev-0/in.tsv | python3 ./predict.py > dev-0/out.tsv
cat dev-1/in.tsv | python3 ./predict.py > dev-1/out.tsv
cat test-A/in.tsv | python3 ./predict.py > test-A/out.tsv

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test-A/out.tsv
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81
train.py
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import pickle
import sys
from math import log
import regex as re
#!/usr/bin/python3
import sys
import regex as re
import pickle
def into_words(sentence):
return re.findall(r'\p{P}|[^\p{P}\s]+', sentence.lower())
def count_prob(bigrams, unigrams):
prob = (bigrams + 1.0) / (unigrams + 1)
if prob > 1.0:
return 1.0
else:
return prob
def main():
ngrams = pickle.load(open('ngrams_2.pkl', 'rb'))
vocabulary_size = len(ngrams[1])
for line in sys.stdin:
words = re.findall(r'.*\t.*\t.* (.*?)\t(.*?) ', line.lower())[0]
#print(words)
left_word = [str(words[0])]
right_word = [str(words[1])]
probabilities = []
for word in ngrams[1].keys():
word = str(word[0])
pre_ngram = tuple(left_word + [word])
post_ngram = tuple([word] + right_word)
#print(pre_ngram)
pre_ngram_prob = count_prob(ngrams[2].get(pre_ngram, 0), ngrams[1].get(word[0],0) + vocabulary_size * 1000)
#if pre_ngram_prob>0:
post_ngram_prob = count_prob(ngrams[2].get(post_ngram, 0), ngrams[1].get(word[0],0) + vocabulary_size * 1000)
probabilities.append((word, pre_ngram_prob * post_ngram_prob))
probabilities = sorted(probabilities, key=lambda t: t[1], reverse=True)[:50]
probability = 1.0
text = ''
counter = 0
has_log_prob0 = False
for p in probabilities:
word = p[0]
prob = p[1]
if counter == 0 and (probability - prob <= 0.0):
text = word + ':' + str(log(0.95)) + ' :' + str(log(0.05))
has_log_prob0 = True
break
if counter > 0 and (probability - prob <= 0.0):
text += ':' + str(log(probability))
has_log_prob0 = True
break
text += word + ':' + str(log(prob)) + ' '
probability -= prob
counter += 1
if not has_log_prob0:
text += ':' + str(log(0.0001))
print(text)
ngrams = {1: {}, 2: {}}
lowest_ngram = 1
highest_ngram = 2
counter = 0
for line in sys.stdin: #dla kazdej linii z pliku
line = line.split('\t')[4] # podziel na 4
tokens = into_words(line) #na slowa
number_of_tokens = len(tokens) #ile slow?
for n in range(lowest_ngram, highest_ngram+1): #dla kazdego ngram
for i in range(0, number_of_tokens-n+1): #i tyle ile jest slow -n gram + 1
ngram = tuple(tokens[i:i+n])
if ngram in ngrams[n]:
ngrams[n][ngram] += 1
else:
ngrams[n][ngram] = 1
if counter % 1000 == 0:
print('counter = ', counter)
counter += 1
ngrams[1] = dict(sorted(ngrams[1].items(), key=lambda item: ngrams[1][item[0]], reverse=True)[:1000])
ngrams[2] = dict(sorted(ngrams[2].items(), key=lambda item: ngrams[2][item[0]], reverse=True)[:100000])
pickle.dump(ngrams, open('ngrams_2.pkl', 'wb'))
if __name__ == '__main__':