126 lines
3.4 KiB
Python
126 lines
3.4 KiB
Python
from cmath import log10
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import csv
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import pandas as pd
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import regex as re
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import os
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import kenlm
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from nltk import word_tokenize
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from collections import Counter, defaultdict
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from english_words import english_words_set
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# nltk.download("punkt")
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# train set
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train_data = pd.read_csv(
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"train/in.tsv.xz",
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sep="\t",
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error_bad_lines=False,
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warn_bad_lines=False,
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header=None,
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quoting=csv.QUOTE_NONE,
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nrows=35_000
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)
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# training labels
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train_labels = pd.read_csv(
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"train/expected.tsv",
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sep="\t",
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error_bad_lines=False,
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warn_bad_lines=False,
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header=None,
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quoting=csv.QUOTE_NONE,
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nrows=35_000
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)
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# dev set
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dev_data = pd.read_csv(
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"dev-0/in.tsv.xz",
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sep="\t",
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error_bad_lines=False,
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warn_bad_lines=False,
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header=None,
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quoting=csv.QUOTE_NONE,
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)
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# test set
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test_data = pd.read_csv(
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"test-A/in.tsv.xz",
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sep="\t",
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error_bad_lines=False,
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warn_bad_lines=False,
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header=None,
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quoting=csv.QUOTE_NONE,
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)
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def prepare_text(text):
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text = text.lower().replace("-\\n", "").replace("\\n", " ")
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text = re.sub(r"\p{P}", "", text)
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return text
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def predict(word1, word2):
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predictions = []
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for word in english_words_set:
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sentence = word1 + ' ' + word + ' ' + word2
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text_score = model.score(sentence, bos=False, eos=False)
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if len(predictions) < 12:
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predictions.append((word, text_score))
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else:
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worst_score = None
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for score in predictions:
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if not worst_score:
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worst_score = score
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else:
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if worst_score[1] > score[1]:
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worst_score = score
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if worst_score[1] < text_score:
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predictions.remove(worst_score)
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predictions.append((word, text_score))
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probs = sorted(predictions, key=lambda tup: tup[1], reverse=True)
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pred_str = ''
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for word, prob in probs:
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pred_str += f'{word}:{prob} '
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pred_str += f':{log10(0.99)}'
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return pred_str
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def write_output():
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with open("dev-0/out.tsv", "w") as file:
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for _, row in dev_data.iterrows():
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text = prepare_text(str(row[7]))
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words = word_tokenize(text)
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if len(words) < 3:
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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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else:
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prediction = predict(words[0], words[1])
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file.write(prediction + "\n")
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with open("test-A/out.tsv", "w") as file:
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for _, row in test_data.iterrows():
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text = prepare_text(str(row[7]))
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words = word_tokenize(text)
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if len(words) < 3:
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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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else:
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prediction = predict(words[0], words[1])
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file.write(prediction + "\n")
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if __name__ == "__main__":
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print("Preparing data...")
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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["final"] = train_data[6] + train_data[0] + train_data[7]
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train = train_data[['final']]
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with open("./train_data.txt", 'a') as f:
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for _, row in train_data.iterrows():
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text = prepare_text(str(row["final"]))
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f.write(text + '\n')
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print("Preparing model...")
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os.system('sh ./kenlm.sh')
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model=kenlm.Model("kenlm_model.binary")
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print("Writing outputs...")
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write_output() |