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s440058 2022-04-10 22:50:11 +02:00
parent 32ad4fd656
commit d1010d6e98
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run.py
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#!/usr/bin/env python
# coding: utf-8
# In[1]:
from collections import defaultdict, Counter
import csv
import regex as re
import pandas as pd
from nltk import trigrams, word_tokenize
# In[26]:
def prepare_data():
x_train = pd.read_csv('train/in.tsv.xz', sep='\t', header=None,
quoting=csv.QUOTE_NONE, nrows=10000, error_bad_lines=False)
y_train = pd.read_csv('train/expected.tsv', sep='\t', header=None,
quoting=csv.QUOTE_NONE, nrows=10000, error_bad_lines=False)
x_train = x_train[[6, 7]]
x_train = pd.concat([x_train, y_train], axis=1)
x_train['l'] = x_train[6] + x_train[0] + x_train[7]
return x_train, y_train
x_train, y_train = prepare_data()
# In[39]:
def train(x_train):
model = defaultdict(lambda: defaultdict(lambda: 0))
count = x_train.iterrows()
for i, (_, row) in enumerate(count):
text = re.sub(r'\p{P}', '', str(row['l']).lower().replace(
'-\\n', '').replace('\\n', ' '))
words = word_tokenize(text)
for word_1, word_2, word_3 in trigrams(words, pad_right=True, pad_left=True):
if word_1 and word_2 and word_3:
model[(word_1, word_3)][word_2] += 1
for i, words_1_3 in enumerate(model):
count = sum(model[words_1_3].values())
for word_2 in model[words_1_3]:
model[words_1_3][word_2] += 0.25
model[words_1_3][word_2] /= float(count + 0.25 + len(word_2))
return model
# In[41]:
def predict(before, after):
result = ''
p = 0.0
pred = dict(Counter(dict(model[before, after])).most_common(7))
for key, value in pred.items():
p += value
result += f'{key}:{value} '
if p == 0.0:
result = 'to:0.02 the:0.02 be:0.02 and:0.01 or:0.01 and:0.01 a:0.01 :0.9'
return result
result += f':{max(1 - p, 0.01)}'
return result
# In[42]:
def preprocess(text):
text = text.lower().replace('-\\n', '').replace('\\n', ' ')
return re.sub(r'\p{P}', '', text)
def gap_predict(file):
X_test = pd.read_csv(f'{file}/in.tsv.xz', sep='\t', header=None,
quoting=csv.QUOTE_NONE, error_bad_lines=False)
with open(f'{file}/out.tsv', 'w', encoding='utf-8') as result_file:
for _, row in X_test.iterrows():
before, after = word_tokenize(preprocess(
str(row[6]))), word_tokenize(preprocess(str(row[7])))
if len(before) < 3 or len(after) < 3:
result = 'to:0.02 the:0.02 be:0.02 and:0.01 or:0.01 and:0.01 a:0.01 :0.9'
else:
result = predict(before[-1], after[0])
result_file.write(result + '\n')
# In[43]:
model = train(x_train)
gap_predict('dev-0')
gap_predict('test-A')

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