challenging-america-word-gap-prediction/cw7zad1.ipynb

Imports

import itertools
import lzma

import regex as re
import torch
from torch import nn
from torch.utils.data import IterableDataset, DataLoader
from torchtext.vocab import build_vocab_from_iterator

from google.colab import drive

Definitions

Functions

def clean_line(line: str):
    # Preprocessing
    separated = line.split('\t')
    prefix = separated[6].replace(r'\n', ' ')
    suffix = separated[7].replace(r'\n', ' ')
    return prefix + ' ' + suffix

def get_words_from_line(line):
    line = clean_line(line)
    for word in line.split():
        yield word

def get_word_lines_from_file(file_name):
    with lzma.open(file_name, mode='rt', encoding='utf-8') as fid:
        for line in fid:
            yield get_words_from_line(line)

def look_ahead_iterator(gen):
    prev = None
    for item in gen:
        if prev is not None:
            yield (prev, item)
        prev = item

def prediction(word: str) -> str:
    ixs = torch.tensor(vocab.forward([word])).to(device)
    out = model(ixs)
    top = torch.topk(out[0], 5)
    top_indices = top.indices.tolist()
    top_probs = top.values.tolist()
    top_words = vocab.lookup_tokens(top_indices)
    zipped = list(zip(top_words, top_probs))
    for index, element in enumerate(zipped):
        unk = None
        if '<unk>' in element:
            unk = zipped.pop(index)
            zipped.append(('', unk[1]))
            break
    if unk is None:
        zipped[-1] = ('', zipped[-1][1])
    return ' '.join([f'{x[0]}:{x[1]}' for x in zipped])

def create_outputs(folder_name):
    print(f'Creating outputs in {folder_name}')
    with lzma.open(f'{folder_name}/in.tsv.xz', mode='rt', encoding='utf-8') as fid:
        with open(f'{folder_name}/out.tsv', 'w', encoding='utf-8', newline='\n') as f:
            for line in fid:
                separated = line.split('\t')
                prefix = separated[6].replace(r'\n', ' ').split()[-1]
                output_line = prediction(prefix)
                f.write(output_line + '\n')

Classes

class Bigrams(IterableDataset):
    def __init__(self, text_file, vocabulary_size):
        self.vocab = build_vocab_from_iterator(
            get_word_lines_from_file(text_file),
            max_tokens=vocabulary_size,
            specials=['<unk>'])
        self.vocab.set_default_index(self.vocab['<unk>'])
        self.vocabulary_size = vocabulary_size
        self.text_file = text_file

    def __iter__(self):
        return look_ahead_iterator(
            (self.vocab[t] for t in itertools.chain.from_iterable(get_word_lines_from_file(self.text_file))))

class SimpleBigramNeuralLanguageModel(nn.Module):
    def __init__(self, vocabulary_size, embedding_size):
        super(SimpleBigramNeuralLanguageModel, self).__init__()
        self.model = nn.Sequential(
            nn.Embedding(vocabulary_size, embedding_size),
            nn.Linear(embedding_size, vocabulary_size),
            nn.Softmax()
        )

    def forward(self, x):
        return self.model(x)

Training

Params

vocab_size = 15000
embed_size = 150
batch_size = 3000
device = 'cuda'
path_to_train = 'train/in.tsv.xz'
path_to_model = 'model1.bin'

Colab

drive.mount('/content/drive')
%cd /content/drive/MyDrive/

Run

vocab = build_vocab_from_iterator(
    get_word_lines_from_file(path_to_train),
    max_tokens=vocab_size,
    specials=['<unk>']
)

vocab.set_default_index(vocab['<unk>'])

train_dataset = Bigrams(path_to_train, vocab_size)

model = SimpleBigramNeuralLanguageModel(vocab_size, embed_size).to(device)
data = DataLoader(train_dataset, batch_size=batch_size)
optimizer = torch.optim.Adam(model.parameters())
criterion = torch.nn.NLLLoss()

model.train()
step = 0
for x, y in data:
    x = x.to(device)
    y = y.to(device)
    optimizer.zero_grad()
    ypredicted = model(x)
    loss = criterion(torch.log(ypredicted), y)
    if step % 100 == 0:
        print(step, loss)
    step += 1
    loss.backward()
    optimizer.step()

torch.save(model.state_dict(), path_to_model)

model = SimpleBigramNeuralLanguageModel(vocab_size, embed_size).to(device)
model.load_state_dict(torch.load(path_to_model))
model.eval()

create_outputs('dev-0')

create_outputs('test-A')