{ "nbformat": 4, "nbformat_minor": 0, "metadata": { "colab": { "provenance": [] }, "kernelspec": { "name": "python3", "display_name": "Python 3" }, "language_info": { "name": "python" }, "accelerator": "GPU", "gpuClass": "standard" }, "cells": [ { "cell_type": "code", "execution_count": 6, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "ECxQCLFdh2dg", "outputId": "a5bf4387-212b-4f01-e3cc-df308fa015b8" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Drive already mounted at /content/gdrive; to attempt to forcibly remount, call drive.mount(\"/content/gdrive\", force_remount=True).\n" ] } ], "source": [ "from google.colab import drive\n", "drive.mount('/content/gdrive')" ] }, { "cell_type": "code", "source": [ "root_path = '/content/gdrive/MyDrive/challenging-america-word-gap-prediction'" ], "metadata": { "id": "uWXe1O7FjKVZ" }, "execution_count": 7, "outputs": [] }, { "cell_type": "code", "source": [ "import torch\n", "torch.cuda.is_available()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "Lo2e9lxajQGy", "outputId": "751a36fc-203b-40eb-e59e-139d6ed92231" }, "execution_count": 8, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "True" ] }, "metadata": {}, "execution_count": 8 } ] }, { "cell_type": "code", "source": [ "import torch\n", "import csv\n", "torch.cuda.empty_cache()\n", "from torch.utils.data import DataLoader\n", "import pandas as pd\n", "from os.path import exists\n", "from torchtext.vocab import build_vocab_from_iterator\n", "import itertools\n", "import regex as re\n", "from csv import QUOTE_NONE\n", "from torch import nn\n", "\n", "ENCODING = \"utf-8\"\n", "\n", "REP = re.compile(r\"[{}\\[\\]\\&%^$*#\\(\\)@\\t\\n0123456789]+\")\n", "REM = re.compile(r\"'s|[\\-]\\\\n|\\-\\\\n|\\p{P}\")\n", "\n", "def read_csv(fname):\n", " return pd.read_csv(fname, sep=\"\\t\", on_bad_lines='skip', header=None, quoting=QUOTE_NONE, encoding=ENCODING)\n", "\n", "def clean_text(text):\n", " res = str(text).lower().strip()\n", " res = res.replace(\"’\", \"'\")\n", " res = REM.sub(\"\", res)\n", " res = REP.sub(\" \", res)\n", " res = res.replace(\"'t\", \" not\")\n", " res = res.replace(\"'s\", \" is\")\n", " res = res.replace(\"'ll\", \" will\")\n", " res = res.replace(\"'ve'\", \"have\")\n", " return res.replace(\"'m\", \" am\")\n", "\n", "def get_words_from_line(line, specials = True):\n", " line = line.rstrip()\n", " if specials:\n", " yield ''\n", " for m in re.finditer(r'[\\p{L}0-9\\*]+|\\p{P}+', line):\n", " yield m.group(0).lower()\n", " if specials:\n", " yield ''\n", "\n", "\n", "def get_word_lines_from_data(d):\n", " for line in d:\n", " yield get_words_from_line(line)\n", "\n", "\n", "\n", "\n", "class Bigrams(torch.utils.data.IterableDataset):\n", " def __init__(self, data, vocabulary_size):\n", " self.vocab = build_vocab_from_iterator(\n", " get_word_lines_from_data(data),\n", " max_tokens = vocabulary_size,\n", " specials = [''])\n", " self.vocab.set_default_index(self.vocab[''])\n", " self.vocabulary_size = vocabulary_size\n", " self.data = data\n", "\n", " @staticmethod\n", " def look_ahead_iterator(gen):\n", " w1 = None\n", " for item in gen:\n", " if w1 is not None:\n", " yield (w1, item)\n", " w1 = item\n", "\n", " def __iter__(self):\n", " return self.look_ahead_iterator(\n", " (self.vocab[t] for t in itertools.chain.from_iterable(get_word_lines_from_data(self.data))))\n", "\n", "class SimpleBigramNeuralLanguageModel(torch.nn.Module):\n", " def __init__(self, vocabulary_size, embedding_size):\n", " super(SimpleBigramNeuralLanguageModel, self).__init__()\n", " self.model = nn.Sequential(\n", " nn.Embedding(vocabulary_size, embedding_size),\n", " nn.Linear(embedding_size, vocabulary_size),\n", " nn.Softmax(),\n", " )\n", "\n", " def forward(self, x):\n", " return self.model(x)\n", "\n", "\n" ], "metadata": { "id": "GDsznRxrjNSi" }, "execution_count": 9, "outputs": [] }, { "cell_type": "code", "source": [ "\n", "\n", "data = read_csv(\"/content/gdrive/MyDrive/challenging-america-word-gap-prediction/train/in.tsv.xz\")\n", "train_words = read_csv(\"/content/gdrive/MyDrive/challenging-america-word-gap-prediction/train/expected.tsv\")\n", "\n", "train_data = data[[6, 7]]\n", "train_data = pd.concat([train_data, train_words], axis=1)\n", "train_data = train_data[6] + train_data[0] + train_data[7]\n", "train_data = train_data.apply(clean_text)\n", "\n", "vocab_size = 30000\n", "embed_size = 150\n", "\n", "train_dataset = Bigrams(train_data, vocab_size)\n", "\n", "\n", "\n", "device = 'cuda' if torch.cuda.is_available() else 'cpu'\n", "model = SimpleBigramNeuralLanguageModel(vocab_size, embed_size).to(device)\n", "print(device)\n", "if(not exists('model1.bin')):\n", " data = DataLoader(train_dataset, batch_size=8000)\n", " optimizer = torch.optim.Adam(model.parameters())\n", " criterion = torch.nn.NLLLoss()\n", "\n", " model.train()\n", " step = 0\n", " for i in range(2):\n", " print(f\"EPOCH {i}=========================\")\n", " for x, y in data:\n", " x = x.to(device)\n", " y = y.to(device)\n", " optimizer.zero_grad()\n", " ypredicted = model(x)\n", " loss = criterion(torch.log(ypredicted), y)\n", " if step % 100 == 0:\n", " print(step, loss)\n", " step += 1\n", " loss.backward()\n", " optimizer.step()\n", "\n", " torch.save(model.state_dict(), 'model1.bin')\n", "else:\n", " print(\"Loading model1\")\n", " model.load_state_dict(torch.load('model1.bin'))\n", "\n", "\n", "\n", "vocab = train_dataset.vocab\n", "\n", "def predict(tokens):\n", " ixs = torch.tensor(vocab.forward(tokens)).to(device)\n", " out = model(ixs)\n", " top = torch.topk(out[0], 8)\n", " top_indices = top.indices.tolist()\n", " top_probs = top.values.tolist()\n", " top_words = vocab.lookup_tokens(top_indices)\n", " result = \"\"\n", " for word, prob in list(zip(top_words, top_probs)):\n", " result += f\"{word}:{prob} \"\n", " # result += f':0.01'\n", " return result\n", "\n", "DEFAULT_PREDICTION = \"from:0.2 the:0.2 to:0.2 a:0.1 and:0.1 of:0.1 :0.1\"\n", "\n", "def predict_file(result_path, data):\n", " with open(result_path, \"w+\", encoding=\"UTF-8\") as f:\n", " for row in data:\n", " result = {}\n", " before = None\n", " for before in get_words_from_line(clean_text(str(row)), False):\n", " pass\n", " before = [before]\n", " print(before)\n", " if(len(before) < 1):\n", " result = DEFAULT_PREDICTION\n", " else:\n", " result = predict(before)\n", " result = result.strip()\n", " f.write(result + \"\\n\")\n", " print(result)\n", "\n" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "UAGoWjAvgdHR", "outputId": "7488a193-d5d3-4e15-8c94-e6a92fcb6e1c" }, "execution_count": 10, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "cuda\n", "Loading model1\n" ] } ] }, { "cell_type": "code", "source": [ "dev_data = pd.read_csv(\"/content/gdrive/MyDrive/challenging-america-word-gap-prediction/dev-0/in.tsv.xz\", sep='\\t', header=None, quoting=csv.QUOTE_NONE)[6]\n", "dev_data = dev_data.apply(clean_text)\n", "predict_file(\"/content/gdrive/MyDrive/challenging-america-word-gap-prediction/dev-0/out.tsv\", dev_data)\n", "\n", "test_data = pd.read_csv(\"/content/gdrive/MyDrive/challenging-america-word-gap-prediction/test-A/in.tsv.xz\", sep='\\t', header=None, quoting=csv.QUOTE_NONE)[6]\n", "test_data = test_data.apply(clean_text)\n", "predict_file(\"/content/gdrive/MyDrive/challenging-america-word-gap-prediction/test-A/out.tsv\", test_data)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "NcAFyvb6gi6O", "outputId": "dac6e397-e648-46d7-aa36-ad7675cec7d3" }, "execution_count": 11, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "['fromn']\n", "to:0.6022941470146179 :0.08413857221603394 the:0.02710115723311901 a:0.024180108681321144 cents:0.01099067460745573 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Change the call to include dim=X as an argument.\n", " input = module(input)\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "\u001b[1;30;43mStrumieniowane dane wyjściowe obcięte do 5000 ostatnich wierszy.\u001b[0m\n", "['wellnspread']\n", ":0.13795071840286255 the:0.050966303795576096 of:0.046845439821481705 and:0.042942408472299576 to:0.03077627159655094 in:0.023394089192152023 a:0.01842011697590351 that:0.01086820662021637\n", "['from']\n", "the:0.25287488102912903 :0.15713191032409668 a:0.034414686262607574 his:0.014321111142635345 tho:0.014128337614238262 this:0.012060044333338737 which:0.011743685230612755 to:0.011461544781923294\n", "['of']\n", "the:0.24628451466560364 :0.16549083590507507 a:0.030842546373605728 this:0.018929913640022278 his:0.013870411552488804 tho:0.011849009431898594 said:0.010169874876737595 their:0.00833516288548708\n", "['that']\n", "the:0.1410231739282608 :0.11953254044055939 he:0.044784024357795715 it:0.03494413569569588 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Change the call to include dim=X as an argument.\n", " input = module(input)\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "\u001b[1;30;43mStrumieniowane dane wyjściowe obcięte do 5000 ostatnich wierszy.\u001b[0m\n", "['employed']\n", "in:0.22053653001785278 by:0.08955898135900497 :0.08067802339792252 to:0.05950067192316055 as:0.03592720627784729 and:0.035128992050886154 at:0.02778000943362713 the:0.02518022619187832\n", "['the']\n", ":0.22546915709972382 same:0.00775930006057024 state:0.007242937106639147 first:0.006000572349876165 city:0.005373408552259207 most:0.005041860044002533 people:0.005007922183722258 united:0.0049338326789438725\n", "['man']\n", ":0.12974177300930023 who:0.11130981147289276 of:0.04574340209364891 and:0.042749278247356415 in:0.04084893316030502 is:0.02529185637831688 to:0.02484465390443802 was:0.01994374394416809\n", "['acre']\n", ":0.12164300680160522 of:0.08069809526205063 and:0.07071764022111893 in:0.05700080841779709 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