wygladzanie

This commit is contained in:
Maciej(Linux) 2022-04-12 20:13:36 +02:00
parent 4e4ebdcadc
commit ac33d50cec
4 changed files with 18082 additions and 18001 deletions

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128
run.py
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from nltk import tris, word_tokenize
from re import T
import pandas as pd
import csv
import regex as re
from collections import Counter, defaultdict
from nltk.tokenize import RegexpTokenizer
from nltk import trigrams
import regex as re
import lzma
train = pd.read_csv(
'train/in.tsv.xz',
sep='\t',
on_bad_lines='skip',
header=None,
quoting=csv.QUOTE_NONE,
nrows=40000)
class GapEssa:
def __init__(self):
self.alpha = 0.0001
self.vocab = set()
self.model = defaultdict(lambda: defaultdict(lambda: 0))
self.tokenizer = RegexpTokenizer(r"\w+")
labels = pd.read_csv(
'train/expected.tsv',
sep='\t',
on_bad_lines='skip',
header=None,
quoting=csv.QUOTE_NONE,
nrows=40000)
def data_preprocessing(text):
return re.sub(r'\p{P}', '', text.lower().replace('-\\n', '').replace('\\n', ' '))
def predict(before, after):
prediction = dict(Counter(dict(tri[before, after])).most_common(5))
result = ''
prob = 0.0
for key, value in prediction.items():
prob += value
result += f'{key}:{value} '
if prob == 0.0:
return 'to:0.015 be:0.015 the:0.015 not:0.01 and:0.02 a:0.02 :0.9'
result += f':{max(1 - prob, 0.01)}'
return result
def make_prediction(file):
data = pd.read_csv(f'{file}/in.tsv.xz', sep='\t', on_bad_lines='skip', header=None, quoting=csv.QUOTE_NONE)
with open(f'{file}/out.tsv', 'w', encoding='utf-8') as file_out:
for _, row in data.iterrows():
before, after = word_tokenize(data_preprocessing(str(row[6]))), word_tokenize(data_preprocessing(str(row[7])))
if len(before) < 3 or len(after) < 3:
prediction = 'to:0.015 be:0.015 the:0.015 not:0.01 and:0.02 a:0.02 :0.9'
def read_file(self, f, mode=0):
for line in f:
text = line.split("\t")
if(mode==0):
yield re.sub(r"[^\w\d'\s]+", '', re.sub(' +', ' ', ' '.join([text[6], text[7]]).replace("\\n"," ").replace("\n","").lower()))
else:
prediction = predict(before[-1], after[0])
file_out.write(prediction + '\n')
yield re.sub(r"[^\w\d'\s]+", '', re.sub(' +', ' ', text[7].replace("\\n"," ").replace("\n","").lower()))
def train(self, f):
with lzma.open(f, mode='rt') as file:
for index, text in enumerate(self.read_file(file)):
tokens = self.tokenizer.tokenize(text)
for w1, w2, w3 in trigrams(tokens, pad_right=True, pad_left=True):
if w1 and w2 and w3:
self.model[(w2, w3)][w1] += 1
self.vocab.add(w1)
self.vocab.add(w2)
self.vocab.add(w3)
if index == 40000:
break
train = train[[6, 7]]
train = pd.concat([train, labels], axis=1)
train['line'] = train[6] + train[0] + train[7]
for pair in self.model:
num_n_grams = float(sum(self.model[pair].values()))
for word in self.model[pair]:
self.model[pair][word] = (self.model[pair][word] + self.alpha) / (num_n_grams + self.alpha*len(self.vocab))
def out(self, input_f, output_f):
with open(output_f, 'w') as out_f:
with lzma.open(input_f, mode='rt') as in_f:
for _, text in enumerate(self.read_file(in_f, mode=1)):
t = self.tokenizer.tokenize(text)
if len(t) < 4:
# p = 'the:0.2 be:0.2 to:0.2 of:0.1 and:0.1 a:0.1 :0.1'
p = 'the:0.03 be:0.03 to:0.03 of:0.025 and:0.025 a:0.025 in:0.020 that:0.020 have:0.015 I:0.010 it:0.010 for:0.010 not:0.010 on:0.010 with:0.010 he:0.010 as:0.010 you:0.010 do:0.010 at:0.010 :0.77'
else:
p = self.pred(t[0], t[1])
out_f.write(p + '\n')
tri = defaultdict(lambda: defaultdict(lambda: 0))
def pred(self, w1, w2):
total = 0.0
line = ''
rows = train.iterrows()
rows_len = len(train)
for index, (_, row) in enumerate(rows):
text = data_preprocessing(str(row['line']))
words = word_tokenize(text)
for word_1, word_2, word_3 in tris(words, pad_right=True, pad_left=True):
if word_1 and word_2 and word_3:
tri[(word_1, word_3)][word_2] += 1
p = dict(self.model[w1, w2])
m = dict(Counter(p).most_common(6))
model_len = len(tri)
for index, words_1_3 in enumerate(tri):
count = sum(tri[words_1_3].values())
for word_2 in tri[words_1_3]:
tri[words_1_3][word_2] += 0.25
tri[words_1_3][word_2] /= float(count + 0.25 + len(word_2))
for word, prob in m.items():
total += prob
line += f'{word}:{prob} '
if total == 0.0:
return 'the:0.03 be:0.03 to:0.03 of:0.025 and:0.025 a:0.025 in:0.020 that:0.020 have:0.015 I:0.010 it:0.010 for:0.010 not:0.010 on:0.010 with:0.010 he:0.010 as:0.010 you:0.010 do:0.010 at:0.010 :0.77'
if 1 - total >= 0.01:
line += f":{1-total}"
else:
line += f":0.01"
make_prediction('test-A')
make_prediction('dev-0')
return line
wp = GapEssa()
wp.train('train/in.tsv.xz')
wp.out('dev-0/in.tsv.xz', 'dev-0/out.tsv')
wp.out('test-A/in.tsv.xz', 'test-A/out.tsv')

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run_old.py Normal file
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from nltk import trigram as tris
from nltk import word_tokenize
import pandas as pd
import csv
import regex as re
from collections import Counter, defaultdict
train = pd.read_csv(
'train/in.tsv.xz',
sep='\t',
on_bad_lines='skip',
header=None,
quoting=csv.QUOTE_NONE,
nrows=30000)
labels = pd.read_csv(
'train/expected.tsv',
sep='\t',
on_bad_lines='skip',
header=None,
quoting=csv.QUOTE_NONE,
nrows=30000)
def data_preprocessing(text):
return re.sub(r'\p{P}', '', text.lower().replace('-\\n', '').replace('\\n', ' '))
def predict(before, after):
prediction = dict(Counter(dict(tri[before, after])).most_common(5))
result = ''
prob = 0.0
for key, value in prediction.items():
prob += value
result += f'{key}:{value} '
if prob == 0.0:
return 'to:0.015 be:0.015 the:0.015 not:0.01 and:0.02 a:0.02 :0.9'
result += f':{max(1 - prob, 0.01)}'
return result
def make_prediction(file):
data = pd.read_csv(f'{file}/in.tsv.xz', sep='\t', on_bad_lines='skip', header=None, quoting=csv.QUOTE_NONE)
with open(f'{file}/out.tsv', 'w', encoding='utf-8') as file_out:
for _, row in data.iterrows():
before, after = word_tokenize(data_preprocessing(str(row[6]))), word_tokenize(data_preprocessing(str(row[7])))
if len(before) < 3 or len(after) < 3:
prediction = 'to:0.015 be:0.015 the:0.015 not:0.01 and:0.02 a:0.02 :0.9'
else:
prediction = predict(before[-1], after[0])
file_out.write(prediction + '\n')
train = train[[6, 7]]
train = pd.concat([train, labels], axis=1)
train['line'] = train[6] + train[0] + train[7]
tri = defaultdict(lambda: defaultdict(lambda: 0))
rows = train.iterrows()
rows_len = len(train)
for index, (_, row) in enumerate(rows):
text = data_preprocessing(str(row['line']))
words = word_tokenize(text)
for word_1, word_2, word_3 in tris(words, pad_right=True, pad_left=True):
if word_1 and word_2 and word_3:
tri[(word_1, word_3)][word_2] += 1
model_len = len(tri)
for index, words_1_3 in enumerate(tri):
count = sum(tri[words_1_3].values())
for word_2 in tri[words_1_3]:
tri[words_1_3][word_2] += 0.25
tri[words_1_3][word_2] /= float(count + 0.25 + len(word_2))
make_prediction('test-A')
make_prediction('dev-0')

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