166 lines
5.0 KiB
Python
166 lines
5.0 KiB
Python
import csv
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from lib2to3.pytree import Base
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from logging import raiseExceptions
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import pandas as pd
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import regex as re
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import nltk
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import sys
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from nltk import trigrams, word_tokenize
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from collections import Counter, defaultdict
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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=100_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=100_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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class Model():
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def __init__(self, vocab_size, alpha):
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self.alpha = alpha
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self.model = defaultdict(lambda: defaultdict(lambda: 0))
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self.vocab = set()
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self.vocab_size = vocab_size
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def train(self, corpus: list):
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for _, row in corpus[:self.vocab_size].iterrows():
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text = prepare_text(str(row["final"]))
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words = word_tokenize(text)
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for w1, w2, w3 in trigrams(words, pad_right=True, pad_left=True):
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if all([w1, w2, w3]):
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self.vocab.add(w1)
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self.vocab.add(w2)
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self.vocab.add(w3)
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self.model[(w2, w3)][w1] += 1
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self.model[(w1, w2)][w3] += 1
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for w_pair in self.model:
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ngram_count = float(sum(self.model[w_pair].values()))
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denominator = ngram_count + self.alpha * len(self.vocab)
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for w3 in self.model[w_pair]:
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nominator = self.model[w_pair][w3] + self.alpha
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self.model[w_pair][w3] = nominator / denominator
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def predict(self, word1, word2):
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raw_prediction = dict(self.model[word1, word2])
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prediction = dict(Counter(raw_prediction).most_common(6))
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total_prob = 0.0
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str_prediction = ""
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for word, prob in prediction.items():
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total_prob += prob
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str_prediction += f"{word}:{prob} "
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remaining_prob = 1 - total_prob
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str_prediction += f":{remaining_prob}"
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return str_prediction
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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 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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# left_text, right_text = prepare_text(str(row[6])), prepare_text(str(row[7]))
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# left_words, right_words = word_tokenize(left_text), word_tokenize(right_text)
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# if len(left_words) < 2 or len(right_words) < 2:
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# prediction = ':1.0'
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# else:
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# prediction = model.predict(left_words[len(left_words) - 1], right_words[0])
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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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# left_text, right_text = prepare_text(str(row[6])), prepare_text(str(row[7]))
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# left_words, right_words = word_tokenize(left_text), word_tokenize(right_text)
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# if len(left_words) < 2 or len(right_words) < 2:
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# prediction = ':1.0'
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# else:
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# prediction = model.predict(left_words[len(left_words) - 1], right_words[0])
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# file.write(prediction + '\n')
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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 = model.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 = model.predict(words[0], words[1])
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file.write(prediction + "\n")
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if __name__ == "__main__":
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# Preapare train data
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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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# declare model
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print("Preparing model...")
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model = Model(100_000, 0.0001)
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# train model
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print("Model training...")
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model.train(train_data)
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# write outputs
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print("Writing outputs...")
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write_output()
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