76 lines
2.7 KiB
Python
76 lines
2.7 KiB
Python
from nltk.tokenize import word_tokenize
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from nltk import trigrams
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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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DEFAULT_PREDICTION = 'the:0.2 be:0.2 to:0.2 of:0.1 and:0.1 a:0.1 :0.1'
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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(word_before, word_after):
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prediction = dict(Counter(dict(model[word_before, word_after])).most_common(6))
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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.append(f'{key}:{value}')
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if prob == 0.0:
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return DEFAULT_PREDICTION
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result.append(f':{max(1 - prob, 0.01)}')
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return ' '.join(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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words_before, words_after = word_tokenize(preprocess(str(row[6]))), word_tokenize(preprocess(str(row[7])))
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if len(words_before) < 3 or len(words_after) < 3:
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prediction = DEFAULT_PREDICTION
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else:
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prediction = predict(words_before[-1], words_after[0])
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file_out.write(prediction + '\n')
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file_in = pd.read_csv('train/in.tsv.xz', sep='\t', on_bad_lines='skip', header=None, quoting=csv.QUOTE_NONE, nrows=200000)
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file_expected = pd.read_csv('train/expected.tsv', sep='\t', on_bad_lines='skip', header=None, quoting=csv.QUOTE_NONE, nrows=200000)
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file_in = file_in[[6, 7]]
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file_concat = pd.concat([file_in, file_expected], axis=1)
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file_concat['text'] = file_concat[6] + file_concat[0] + file_concat[7]
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file_concat = file_concat[['text']]
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trigrams_list = []
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model = defaultdict(lambda: defaultdict(lambda: 0))
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rows = file_concat.iterrows()
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rows_len = len(file_concat)
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for index, (_, row) in enumerate(rows):
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if index % 1000 == 0:
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print(f'uczenie modelu: {index / rows_len}')
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words = word_tokenize(preprocess(str(row['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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model[(word_1, word_3)][word_2] += 1
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alpha = 0.25
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model_len = len(model)
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for index, words_1_3 in enumerate(model):
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if index % 100000 == 0:
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print(f'normalizacja: {index / model_len}')
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occurrences = sum(model[words_1_3].values())
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for word_2 in model[words_1_3]:
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model[words_1_3][word_2] += alpha
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model[words_1_3][word_2] /= float(occurrences + alpha + len(word_2))
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make_prediction('test-A')
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make_prediction('dev-0')
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print('koniec')
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