add old solution with better out scores to new branch
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Kacper
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380ef29e71
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21038
dev-0/out.tsv
21038
dev-0/out.tsv
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According to recent news and a half of the north side or the other must be a man of great force of character to the country. He was a member of a family in the United States to the full amount of the principal and interest of this section shall be subject to the
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Recent studies have shown that the present condition of things in which we have been in a very short time after the war and the war was over and that every man who has been in the hands of the United States of the North and the South American States, and those States who had
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Today I was taking a stroll in the park when suddenly and that the said estate has by no Mr. ii him, but he was too young to be a very good reason that the above named de- - . . . They are able six e of a good deal of time and money to the amount of the tax
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The most unbelievable story ever told goes like this to be the most important of these are the only two men who were at the time of the year when they tried an 1 the said sum of money to be paid in case of your South and the West may have to do the work of the committee
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he war between natural and the few who are not in the interest of the said William H. and acres of them, more o the State from the control of the state of New York and New York and New York are the looked for food in the greatest number of the most
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84
simple_bigram.py
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84
simple_bigram.py
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from collections import Counter
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import lzma
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import os
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class BigramModel:
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def __init__(self):
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self.vocab = None
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self.unigram_counts = None
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self.bigram_counts = None
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def train(self, filename, vocab_size=5000):
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def get_vocab(filename, vocab_size):
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print('Generating vocab')
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file_vocab = Counter()
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with lzma.open(filename, 'r') as f:
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for line in f:
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line = ' '.join(line.decode('utf-8').strip().split('\t')[-2:]).replace(r'\n', ' ').split()
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line_vocab = Counter(line)
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file_vocab.update(line_vocab)
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if len(file_vocab) > vocab_size:
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file_vocab = [tup[0] for tup in file_vocab.most_common(vocab_size)]
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else:
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file_vocab = file_vocab.keys()
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return file_vocab
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def get_gram_counts(filename):
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print('Generating unigram and bigram counts')
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file_unigram_counts = Counter()
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file_bigram_counts = Counter()
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with lzma.open(filename, 'r') as f:
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for line in f:
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line = line.decode('utf-8').strip().replace(r'\n', ' ').split('\t')[-2:]
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line_unigram_counts = Counter(' '.join(line).split())
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file_unigram_counts.update(line_unigram_counts)
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line_left, line_right = line[0].split(), line[1].split()
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line_bigram_counts_left = Counter(
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[tuple(line_left[i: i + 2]) for i in range(len(line_left) - 2 + 1)])
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line_bigram_counts_right = Counter(
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[tuple(line_right[i: i + 2]) for i in range(len(line_right) - 2 + 1)])
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file_bigram_counts.update(line_bigram_counts_left)
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file_bigram_counts.update(line_bigram_counts_right)
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return file_unigram_counts, file_bigram_counts
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self.vocab = get_vocab(filename, vocab_size)
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self.unigram_counts, self.bigram_counts = get_gram_counts(filename)
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def get_bigram_prob(self, bigram, smoothing):
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if smoothing:
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return (self.bigram_counts.get(bigram, 0) + 1) / (
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self.unigram_counts.get(bigram[0], 0) + len(self.vocab) + 1)
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else:
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return self.bigram_counts.get(bigram, 0) / self.unigram_counts.get(bigram[0], 1)
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def predict_gaps(self, path, smoothing=True, topk=5):
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print('Making predictions')
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with lzma.open(path + '/in.tsv.xz', 'r') as f, open(path + '/out.tsv', 'w', encoding='utf-8') as out:
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for line in f:
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line = line.decode('utf-8').replace(r'\n', ' ').split('\t')[-2:]
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left_context, right_context = line[0].strip().split()[-1], line[1].strip().split()[0]
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context_probs = dict()
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for word in self.vocab:
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left_context_prob = self.get_bigram_prob((left_context, word), smoothing)
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right_context_prob = self.get_bigram_prob((word, right_context), smoothing)
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context_probs[word] = left_context_prob * right_context_prob
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if len(set(context_probs.values())) == 1:
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out.write('the:0.2 be:0.2 of:0.2\n')
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else:
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top_context_probs = sorted(context_probs.items(), key=lambda x: x[1], reverse=True)[:topk]
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topk_prob_sum = sum([prob for word, prob in top_context_probs])
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top_context_probs = [(word, (prob / topk_prob_sum)) for word, prob in top_context_probs]
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probs_string = '\t'.join([f'{word}:{prob}' for word, prob in top_context_probs[-2:] if prob > 0]) # Sadly simply removing last two entries gives way better results...
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out.write(probs_string + '\n')
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if __name__ == '__main__':
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for vocab_size in [5000]:
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model = BigramModel()
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model.train('challenging-america-word-gap-prediction/train/in.tsv.xz', vocab_size=vocab_size)
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for topk in [5]:
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model.predict_gaps('challenging-america-word-gap-prediction/dev-0', smoothing=False, topk=topk)
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os.chdir('challenging-america-word-gap-prediction/')
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print(f'topk:{topk} vocab:{vocab_size}')
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print(os.system('./geval --test-name dev-0'))
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os.chdir('../')
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286
solution.ipynb
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solution.ipynb
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{
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"nbformat": 4,
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"nbformat_minor": 0,
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"metadata": {
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"colab": {
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"provenance": []
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},
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"kernelspec": {
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"name": "python3",
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"display_name": "Python 3"
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},
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"language_info": {
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"name": "python"
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},
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"accelerator": "GPU",
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"gpuClass": "standard"
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},
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"cells": [
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{
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"cell_type": "code",
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"source": [
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"from torchtext.vocab import build_vocab_from_iterator\n",
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"import pickle\n",
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"from torch.utils.data import IterableDataset\n",
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"import itertools\n",
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"from torch import nn\n",
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"import torch\n",
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"import lzma\n",
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"from torch.utils.data import DataLoader\n",
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"from tqdm import tqdm"
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],
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"metadata": {
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"id": "WnglOFA8gGJl"
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},
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"execution_count": 2,
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"outputs": []
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},
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{
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"cell_type": "code",
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"source": [
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"def simple_preprocess(line):\n",
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" return line.replace(r'\\n', ' ')\n",
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"\n",
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"def get_words_from_line(line):\n",
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" line = line.strip()\n",
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" line = simple_preprocess(line)\n",
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" yield '<s>'\n",
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" for t in line.split():\n",
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" yield t\n",
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" yield '</s>'\n",
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"\n",
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"def get_word_lines_from_file(file_name, n_size=-1):\n",
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" with lzma.open(file_name, 'r') as fh:\n",
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" n = 0\n",
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" for line in fh:\n",
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" n += 1\n",
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" yield get_words_from_line(line.decode('utf-8'))\n",
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" if n == n_size:\n",
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" break\n",
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"\n",
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"def look_ahead_iterator(gen):\n",
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" prev = None\n",
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" for item in gen:\n",
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" if prev is not None:\n",
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" yield prev, item\n",
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" prev = item\n",
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"\n",
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"def build_vocab(file, vocab_size):\n",
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" try:\n",
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" with open(f'bigram_nn_vocab_{vocab_size}.pickle', 'rb') as handle:\n",
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" vocab = pickle.load(handle)\n",
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" except:\n",
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" vocab = build_vocab_from_iterator(\n",
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" get_word_lines_from_file(file),\n",
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" max_tokens = vocab_size,\n",
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" specials = ['<unk>'])\n",
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" with open(f'bigram_nn_vocab_{vocab_size}.pickle', 'wb') as handle:\n",
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" pickle.dump(vocab, handle, protocol=pickle.HIGHEST_PROTOCOL)\n",
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" return vocab\n",
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"\n",
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"class Bigrams(IterableDataset):\n",
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" def __init__(self, text_file, vocabulary_size):\n",
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" self.vocab = vocab\n",
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" self.vocab.set_default_index(self.vocab['<unk>'])\n",
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" self.vocabulary_size = vocabulary_size\n",
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" self.text_file = text_file\n",
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"\n",
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" def __iter__(self):\n",
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" return look_ahead_iterator(\n",
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" (self.vocab[t] for t in itertools.chain.from_iterable(get_word_lines_from_file(self.text_file))))\n",
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"\n",
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"class SimpleBigramNeuralLanguageModel(nn.Module):\n",
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" def __init__(self, vocabulary_size, embedding_size):\n",
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" super(SimpleBigramNeuralLanguageModel, self).__init__()\n",
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" self.model = nn.Sequential(\n",
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" nn.Embedding(vocabulary_size, embedding_size),\n",
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" nn.Linear(embedding_size, vocabulary_size),\n",
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" nn.Softmax(dim=1)\n",
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" )\n",
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"\n",
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" def forward(self, x):\n",
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" return self.model(x)"
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],
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"metadata": {
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"id": "aW_3JqSNgLLr"
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},
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"execution_count": 3,
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"outputs": []
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},
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{
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"cell_type": "code",
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"source": [
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"max_steps= -1\n",
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"vocab_size = 20000\n",
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"embed_size = 150\n",
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"batch_size = 5000\n",
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"learning_rate = 0.001\n",
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"vocab = build_vocab('challenging-america-word-gap-prediction/train/in.tsv.xz', vocab_size)\n",
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"train_dataset = Bigrams('challenging-america-word-gap-prediction/train/in.tsv.xz', vocab_size)\n",
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"if torch.cuda.is_available():\n",
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" device = 'cuda'\n",
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"else:\n",
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" raise Exception()\n",
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"model = SimpleBigramNeuralLanguageModel(vocab_size, embed_size).to(device)\n",
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"data = DataLoader(train_dataset, batch_size=batch_size)\n",
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"optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)\n",
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"criterion = torch.nn.NLLLoss()\n",
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"\n",
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"model.train()\n",
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"step = 0\n",
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"for x, y in data:\n",
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" x = x.to(device)\n",
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" y = y.to(device)\n",
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" optimizer.zero_grad()\n",
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" y_predicted = model(x)\n",
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" loss = criterion(torch.log(y_predicted), y)\n",
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" if step % 1000 == 0:\n",
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" print(f'steps: {step}, loss: {loss.item()}')\n",
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" if step != 0:\n",
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" torch.save(model.state_dict(), f'bigram_nn_model_steps-{step}_vocab-{vocab_size}_embed-{embed_size}_batch-{batch_size}.bin')\n",
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" if step == max_steps:\n",
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" break\n",
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" step += 1\n",
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" loss.backward()\n",
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" optimizer.step()"
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],
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "QQw_E7Ku4h0a",
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"outputId": "4a37d9ba-1abd-46ae-b157-cd6d52b951a2"
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},
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"execution_count": 4,
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"/home/ked/PycharmProjects/mj9/venv/lib/python3.10/site-packages/torch/nn/modules/container.py:217: UserWarning: Implicit dimension choice for softmax has been deprecated. Change the call to include dim=X as an argument.\n",
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" input = module(input)\n"
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]
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"steps: 0, loss: 10.091094017028809\n",
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"steps: 1000, loss: 5.73332405090332\n",
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"steps: 2000, loss: 5.655370712280273\n",
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"steps: 3000, loss: 5.457630634307861\n",
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"steps: 4000, loss: 5.38517427444458\n",
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"steps: 5000, loss: 5.467936992645264\n",
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"steps: 6000, loss: 5.372152328491211\n",
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"steps: 7000, loss: 5.272013187408447\n",
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"steps: 8000, loss: 5.439966201782227\n",
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"steps: 9000, loss: 5.268238544464111\n",
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"steps: 10000, loss: 5.1395182609558105\n",
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"steps: 11000, loss: 5.2558159828186035\n",
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"steps: 12000, loss: 5.263617515563965\n"
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]
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},
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{
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"ename": "KeyboardInterrupt",
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"evalue": "",
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"output_type": "error",
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"traceback": [
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"\u001B[0;31m---------------------------------------------------------------------------\u001B[0m",
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"\u001B[0;31mKeyboardInterrupt\u001B[0m Traceback (most recent call last)",
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"Cell \u001B[0;32mIn[4], line 31\u001B[0m\n\u001B[1;32m 29\u001B[0m \u001B[38;5;28;01mbreak\u001B[39;00m\n\u001B[1;32m 30\u001B[0m step \u001B[38;5;241m+\u001B[39m\u001B[38;5;241m=\u001B[39m \u001B[38;5;241m1\u001B[39m\n\u001B[0;32m---> 31\u001B[0m \u001B[43mloss\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mbackward\u001B[49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m 32\u001B[0m optimizer\u001B[38;5;241m.\u001B[39mstep()\n",
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"File \u001B[0;32m~/PycharmProjects/mj9/venv/lib/python3.10/site-packages/torch/_tensor.py:487\u001B[0m, in \u001B[0;36mTensor.backward\u001B[0;34m(self, gradient, retain_graph, create_graph, inputs)\u001B[0m\n\u001B[1;32m 477\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m has_torch_function_unary(\u001B[38;5;28mself\u001B[39m):\n\u001B[1;32m 478\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m handle_torch_function(\n\u001B[1;32m 479\u001B[0m Tensor\u001B[38;5;241m.\u001B[39mbackward,\n\u001B[1;32m 480\u001B[0m (\u001B[38;5;28mself\u001B[39m,),\n\u001B[0;32m (...)\u001B[0m\n\u001B[1;32m 485\u001B[0m inputs\u001B[38;5;241m=\u001B[39minputs,\n\u001B[1;32m 486\u001B[0m )\n\u001B[0;32m--> 487\u001B[0m \u001B[43mtorch\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mautograd\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mbackward\u001B[49m\u001B[43m(\u001B[49m\n\u001B[1;32m 488\u001B[0m \u001B[43m \u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mgradient\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mretain_graph\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mcreate_graph\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43minputs\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43minputs\u001B[49m\n\u001B[1;32m 489\u001B[0m \u001B[43m\u001B[49m\u001B[43m)\u001B[49m\n",
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"File \u001B[0;32m~/PycharmProjects/mj9/venv/lib/python3.10/site-packages/torch/autograd/__init__.py:200\u001B[0m, in \u001B[0;36mbackward\u001B[0;34m(tensors, grad_tensors, retain_graph, create_graph, grad_variables, inputs)\u001B[0m\n\u001B[1;32m 195\u001B[0m retain_graph \u001B[38;5;241m=\u001B[39m create_graph\n\u001B[1;32m 197\u001B[0m \u001B[38;5;66;03m# The reason we repeat same the comment below is that\u001B[39;00m\n\u001B[1;32m 198\u001B[0m \u001B[38;5;66;03m# some Python versions print out the first line of a multi-line function\u001B[39;00m\n\u001B[1;32m 199\u001B[0m \u001B[38;5;66;03m# calls in the traceback and some print out the last line\u001B[39;00m\n\u001B[0;32m--> 200\u001B[0m \u001B[43mVariable\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_execution_engine\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mrun_backward\u001B[49m\u001B[43m(\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;66;43;03m# Calls into the C++ engine to run the backward pass\u001B[39;49;00m\n\u001B[1;32m 201\u001B[0m \u001B[43m \u001B[49m\u001B[43mtensors\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mgrad_tensors_\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mretain_graph\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mcreate_graph\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43minputs\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m 202\u001B[0m \u001B[43m \u001B[49m\u001B[43mallow_unreachable\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;28;43;01mTrue\u001B[39;49;00m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43maccumulate_grad\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;28;43;01mTrue\u001B[39;49;00m\u001B[43m)\u001B[49m\n",
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"\u001B[0;31mKeyboardInterrupt\u001B[0m: "
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]
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}
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]
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},
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{
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"cell_type": "code",
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"source": [
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"vocab_size = 20000\n",
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"embed_size = 150\n",
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"batch_size = 5000\n",
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"vocab = build_vocab('challenging-america-word-gap-prediction/train/in.tsv.xz', vocab_size)\n",
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"vocab.set_default_index(vocab['<unk>'])"
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],
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"metadata": {
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"id": "N9-wmLOEZ2aV"
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},
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"execution_count": 5,
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"outputs": []
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},
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{
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"cell_type": "code",
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"source": [
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"topk = 5\n",
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"preds = []\n",
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"device = 'cuda'\n",
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"model = SimpleBigramNeuralLanguageModel(vocab_size, embed_size).to(device)\n",
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"model.load_state_dict(torch.load('bigram_nn_model_steps-10000_vocab-20000_embed-150_batch-5000.bin'))\n",
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"model.eval()\n",
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"for path in ['challenging-america-word-gap-prediction/dev-0', 'challenging-america-word-gap-prediction/test-A']:\n",
|
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" with lzma.open(f'{path}/in.tsv.xz', 'r') as fh, open(f'{path}/out.tsv', 'w', encoding='utf-8') as f_out:\n",
|
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" for line in fh:\n",
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" previous_word = simple_preprocess(line.decode('utf-8').split('\\t')[-2].strip()).split()[-1]\n",
|
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" ixs = torch.tensor(vocab.forward([previous_word])).to(device)\n",
|
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" out = model(ixs)\n",
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" top = torch.topk(out[0], topk)\n",
|
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" top_indices = top.indices.tolist()\n",
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" top_probs = top.values.tolist()\n",
|
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" top_words = vocab.lookup_tokens(top_indices)\n",
|
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" top_zipped = zip(top_words, top_probs)\n",
|
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" pred = ''\n",
|
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" total_prob = 0\n",
|
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||||||
" for word, prob in top_zipped:\n",
|
|
||||||
" if word != '<unk>':\n",
|
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" pred += f'{word}:{prob} '\n",
|
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||||||
" total_prob += prob\n",
|
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||||||
" unk_prob = 1 - total_prob\n",
|
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||||||
" pred += f':{unk_prob}'\n",
|
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||||||
" f_out.write(pred + '\\n')"
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],
|
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||||||
"metadata": {
|
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||||||
"colab": {
|
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||||||
"base_uri": "https://localhost:8080/"
|
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||||||
},
|
|
||||||
"id": "99uioFpVCJL8",
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||||||
"outputId": "d4267cb1-e557-478a-8cf7-91a90db07698"
|
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||||||
},
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"execution_count": 24,
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"outputs": []
|
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},
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{
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||||||
"cell_type": "code",
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||||||
"execution_count": 25,
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"outputs": [
|
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||||||
{
|
|
||||||
"name": "stdout",
|
|
||||||
"output_type": "stream",
|
|
||||||
"text": [
|
|
||||||
"/home/ked/PycharmProjects/mj9/challenging-america-word-gap-prediction\n",
|
|
||||||
"394.97\r\n",
|
|
||||||
"/home/ked/PycharmProjects/mj9\n"
|
|
||||||
]
|
|
||||||
}
|
|
||||||
],
|
|
||||||
"source": [
|
|
||||||
"%cd challenging-america-word-gap-prediction/\n",
|
|
||||||
"!./geval --test-name dev-0\n",
|
|
||||||
"%cd ../"
|
|
||||||
],
|
|
||||||
"metadata": {
|
|
||||||
"collapsed": false
|
|
||||||
}
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"cell_type": "code",
|
|
||||||
"execution_count": null,
|
|
||||||
"outputs": [],
|
|
||||||
"source": [],
|
|
||||||
"metadata": {
|
|
||||||
"collapsed": false
|
|
||||||
}
|
|
||||||
}
|
|
||||||
]
|
|
||||||
}
|
|
||||||
14828
test-A/out.tsv
14828
test-A/out.tsv
File diff suppressed because it is too large
Load Diff
Loading…
Reference in New Issue
Block a user