66 lines
2.4 KiB
Python
66 lines
2.4 KiB
Python
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from nltk import trigrams, word_tokenize
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from collections import defaultdict, Counter
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import pandas as pd
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import csv
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import regex as re
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def preprocess(text):
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text = text.lower().replace('-\\n', '').replace('\\n', ' ')
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return re.sub(r'\p{P}', '', text)
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def predict(before, after):
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prediction = dict(Counter(dict(trigram[before, after])).most_common(5))
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result = ''
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prob = 0.0
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for key, value in prediction.items():
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prob += value
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result += f'{key}:{value} '
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if prob == 0.0:
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return 'to:0.02 be:0.02 the:0.02 or:0.01 not:0.01 and:0.01 a:0.01 :0.9'
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result += f':{max(1 - prob, 0.01)}'
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return result
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def make_prediction(file):
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data = pd.read_csv(f'{file}/in.tsv.xz', sep='\t', on_bad_lines='skip', header=None, quoting=csv.QUOTE_NONE)
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with open(f'{file}/out.tsv', 'w', encoding='utf-8') as file_out:
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for _, row in data.iterrows():
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before, after = word_tokenize(preprocess(str(row[6]))), word_tokenize(preprocess(str(row[7])))
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if len(before) < 3 or len(after) < 3:
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prediction = 'to:0.02 be:0.02 the:0.02 or:0.01 not:0.01 and:0.01 a:0.01 :0.9'
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else:
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prediction = predict(before[-1], after[0])
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file_out.write(prediction + '\n')
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train_data = pd.read_csv('train/in.tsv.xz', sep='\t', on_bad_lines='skip', header=None, quoting=csv.QUOTE_NONE, nrows=20000)
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train_labels = pd.read_csv('train/expected.tsv', sep='\t', on_bad_lines='skip', header=None, quoting=csv.QUOTE_NONE, nrows=20000)
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train_data = train_data[[6, 7]]
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train_data = pd.concat([train_data, train_labels], axis=1)
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train_data['line'] = train_data[6] + train_data[0] + train_data[7]
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trigram = defaultdict(lambda: defaultdict(lambda: 0))
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rows = train_data.iterrows()
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rows_len = len(train_data)
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for index, (_, row) in enumerate(rows):
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text = preprocess(str(row['line']))
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words = word_tokenize(text)
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for word_1, word_2, word_3 in trigrams(words, pad_right=True, pad_left=True):
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if word_1 and word_2 and word_3:
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trigram[(word_1, word_3)][word_2] += 1
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model_len = len(trigram)
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for index, words_1_3 in enumerate(trigram):
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count = sum(trigram[words_1_3].values())
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for word_2 in trigram[words_1_3]:
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trigram[words_1_3][word_2] += 0.25
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trigram[words_1_3][word_2] /= float(count + 0.25 + len(word_2))
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make_prediction('test-A')
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make_prediction('dev-0')
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