49 lines
1.5 KiB
Python
49 lines
1.5 KiB
Python
from numpy.lib.shape_base import split
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import pandas as pd
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import numpy as np
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import gensim
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import torch
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import pandas as pd
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from sklearn.model_selection import train_test_split
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from collections import Counter
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from torchtext.vocab import Vocab
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# Functions from jupyter
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def build_vocab(dataset):
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counter = Counter()
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for document in dataset:
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counter.update(document)
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return Vocab(counter, specials=['<unk>', '<pad>', '<bos>', '<eos>'])
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def data_process(dt, vocab):
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return [torch.tensor([vocab['<bos>']] + [vocab[token] for token in document] + [vocab['<eos>']], dtype=torch.long) for document in dt]
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def labels_process(dt, vocab):
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return [torch.tensor([0] + document + [0], dtype=torch.long) for document in dt]
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# Load data
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def load_data():
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train = pd.read_csv('train/train.tsv', sep='\t',
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names=['labels', 'document'])
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Y_train = [y.split(sep=" ") for y in train['labels'].values]
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X_train = [x.split(sep=" ") for x in train['document'].values]
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dev = pd.read_csv('dev-0/in.tsv', sep='\t', names=['document'])
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exp = pd.read_csv('dev-0/expected.tsv', sep='\t', names=['labels'])
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X_dev = [x.split(sep=" ") for x in dev['document'].values]
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Y_dev = [y.split(sep=" ") for y in exp['labels'].values]
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test = pd.read_csv('test-A/in.tsv', sep='\t', names=['document'])
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X_test = test['document'].values
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return X_train, Y_train, X_dev, Y_dev, X_test
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if __name__ == "__main__":
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X_train, Y_train, X_dev, Y_dev, X_test = load_data()
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