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JPogodzinski 2022-04-03 17:22:51 +02:00
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Challenging America word-gap prediction
===================================
Guess a word in a gap.
Evaluation metric
-----------------
LikelihoodHashed is the metric

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--metric PerplexityHashed --precision 2 --in-header in-header.tsv --out-header out-header.tsv

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FileId Year LeftContext RightContext
1 FileId Year LeftContext RightContext

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Word
1 Word

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from nltk import trigrams, word_tokenize
from collections import defaultdict, Counter
import pandas as pd
import csv
import regex as re
def preprocess(text):
text = text.lower().replace('-\\n', '').replace('\\n', ' ')
return re.sub(r'\p{P}', '', text)
def predict(before, after):
prediction = dict(Counter(dict(trigram[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:0.02 be:0.02 the:0.02 or:0.01 not:0.01 and:0.01 a:0.01 :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(preprocess(str(row[6]))), word_tokenize(preprocess(str(row[7])))
if len(before) < 3 or len(after) < 3:
prediction = 'to:0.02 be:0.02 the:0.02 or:0.01 not:0.01 and:0.01 a:0.01 :0.9'
else:
prediction = predict(before[-1], after[0])
file_out.write(prediction + '\n')
train_data = pd.read_csv('train/in.tsv.xz', sep='\t', on_bad_lines='skip', header=None, quoting=csv.QUOTE_NONE, nrows=20000)
train_labels = pd.read_csv('train/expected.tsv', sep='\t', on_bad_lines='skip', header=None, quoting=csv.QUOTE_NONE, nrows=20000)
train_data = train_data[[6, 7]]
train_data = pd.concat([train_data, train_labels], axis=1)
train_data['line'] = train_data[6] + train_data[0] + train_data[7]
trigram = defaultdict(lambda: defaultdict(lambda: 0))
rows = train_data.iterrows()
rows_len = len(train_data)
for index, (_, row) in enumerate(rows):
text = preprocess(str(row['line']))
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:
trigram[(word_1, word_3)][word_2] += 1
model_len = len(trigram)
for index, words_1_3 in enumerate(trigram):
count = sum(trigram[words_1_3].values())
for word_2 in trigram[words_1_3]:
trigram[words_1_3][word_2] += 0.25
trigram[words_1_3][word_2] /= float(count + 0.25 + len(word_2))
make_prediction('test-A')
make_prediction('dev-0')

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