challenging-america-word-gap-prediction-nn5gram/main.ipynb

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   "cell_type": "code",
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   "id": "f3452caf-df58-4394-b0d6-46459cb47045",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "S:\\WENV_TORCHTEXT\\Lib\\site-packages\\torchtext\\vocab\\__init__.py:4: UserWarning: \n",
      "/!\\ IMPORTANT WARNING ABOUT TORCHTEXT STATUS /!\\ \n",
      "Torchtext is deprecated and the last released version will be 0.18 (this one). You can silence this warning by calling the following at the beginnign of your scripts: `import torchtext; torchtext.disable_torchtext_deprecation_warning()`\n",
      "  warnings.warn(torchtext._TORCHTEXT_DEPRECATION_MSG)\n",
      "S:\\WENV_TORCHTEXT\\Lib\\site-packages\\torchtext\\utils.py:4: UserWarning: \n",
      "/!\\ IMPORTANT WARNING ABOUT TORCHTEXT STATUS /!\\ \n",
      "Torchtext is deprecated and the last released version will be 0.18 (this one). You can silence this warning by calling the following at the beginnign of your scripts: `import torchtext; torchtext.disable_torchtext_deprecation_warning()`\n",
      "  warnings.warn(torchtext._TORCHTEXT_DEPRECATION_MSG)\n"
     ]
    }
   ],
   "source": [
    "from torch.utils.data import IterableDataset, DataLoader\n",
    "from torchtext.vocab import build_vocab_from_iterator\n",
    "\n",
    "import regex as re\n",
    "import itertools\n",
    "from itertools import islice\n",
    "\n",
    "from torch import nn\n",
    "import torch\n",
    "\n",
    "from tqdm.notebook import tqdm\n",
    "device = 'cuda'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "5ee9ad24-a5d2-47e1-a5c6-88981dc22b99",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_words_from_line(line):\n",
    "    line = line.rstrip()\n",
    "    yield '<s>'\n",
    "    for m in re.finditer(r'[\\p{L}0-9\\*]+|\\p{P}+', line):\n",
    "        yield m.group(0).lower()\n",
    "    yield '</s>'\n",
    "\n",
    "def get_word_lines_from_file(file_name):\n",
    "    with open(file_name, 'r', encoding='utf8') as fh:\n",
    "        for line in fh:\n",
    "            yield get_words_from_line(line)\n",
    "\n",
    "def look_ahead_iterator(gen):\n",
    "    prev2, prev1, next1, next2 = None, None, None, None\n",
    "    for item in gen:\n",
    "        if prev2 is not None and prev1 is not None and next1 is not None and next2 is not None:\n",
    "            yield (prev2, prev1, next2, item, next1)\n",
    "        prev2, prev1, next1, next2 = prev1, next1, next2, item"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "93279277-0765-4f85-9666-095fc7808c81",
   "metadata": {},
   "outputs": [],
   "source": [
    "class FiveGrams(IterableDataset):\n",
    "    def __init__(self, text_file, vocabulary_size):\n",
    "        self.vocab = build_vocab_from_iterator(\n",
    "            get_word_lines_from_file(text_file),\n",
    "            max_tokens=vocabulary_size,\n",
    "            specials=['<unk>']\n",
    "        )\n",
    "        self.vocab.set_default_index(self.vocab['<unk>'])\n",
    "        self.vocabulary_size = vocabulary_size\n",
    "        self.text_file = text_file\n",
    "\n",
    "    def __iter__(self):\n",
    "        return look_ahead_iterator(\n",
    "            (self.vocab[t] for t in itertools.chain.from_iterable(get_word_lines_from_file(self.text_file)))\n",
    "        )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "6eb5fbd9-bc0f-499d-85f4-3998a4a3f56e",
   "metadata": {},
   "outputs": [],
   "source": [
    "class SimpleFiveGramNeuralLanguageModel(nn.Module):\n",
    "    def __init__(self, vocabulary_size, embedding_size):\n",
    "        super(SimpleFiveGramNeuralLanguageModel, self).__init__()\n",
    "        self.embedding = nn.Embedding(vocabulary_size, embedding_size)\n",
    "        self.linear1 = nn.Linear(embedding_size * 4, embedding_size)\n",
    "        self.linear2 = nn.Linear(embedding_size, vocabulary_size)\n",
    "        self.softmax = nn.Softmax(dim=1)\n",
    "        self.embedding_size = embedding_size\n",
    "\n",
    "    def forward(self, x):\n",
    "        embeds = self.embedding(x).view(x.size(0), -1)\n",
    "        out = self.linear1(embeds)\n",
    "        out = self.linear2(out)\n",
    "        return self.softmax(out)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "d0dc7c69-3f27-4f00-9b91-5f3a403df074",
   "metadata": {},
   "outputs": [],
   "source": [
    "def train(embed_size,vocab_size,num_epochs,batch_size,train_file_path):\n",
    "    train_dataset = FiveGrams(train_file_path, vocab_size)\n",
    "    model = SimpleFiveGramNeuralLanguageModel(vocab_size, embed_size).to(device)\n",
    "    \n",
    "    data = DataLoader(train_dataset, batch_size=batch_size)\n",
    "    optimizer = torch.optim.Adam(model.parameters())\n",
    "    criterion = torch.nn.CrossEntropyLoss()\n",
    "    \n",
    "    model.train()\n",
    "    step = 0\n",
    "    for _ in range(num_epochs):\n",
    "        for x1, x2, x3, x4, y in tqdm(data, desc=\"Train loop\"):\n",
    "            y = y.to(device)\n",
    "            x = torch.cat((x1.unsqueeze(1), x2.unsqueeze(1), x3.unsqueeze(1), x4.unsqueeze(1)), dim=1).to(device)\n",
    "            optimizer.zero_grad()\n",
    "            ypredicted = model(x)\n",
    "            \n",
    "            loss = criterion(torch.log(ypredicted), y)\n",
    "            if step % 5000 == 0:\n",
    "                print(step, loss)\n",
    "            step += 1\n",
    "            loss.backward()\n",
    "            optimizer.step()\n",
    "        step = 0\n",
    "        break\n",
    "    model.eval()\n",
    "\n",
    "    return model, train_dataset.vocab"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "9a1b2240-d2ed-4c56-8443-12113e66b514",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_gap_candidates(words, model, vocab, n=20):\n",
    "    ixs = vocab(words)\n",
    "    ixs = torch.tensor(ixs).unsqueeze(0).to(device)\n",
    "\n",
    "    out = model(ixs)\n",
    "    top = torch.topk(out[0], n)\n",
    "    top_indices = top.indices.tolist()\n",
    "    top_probs = top.values.tolist()\n",
    "    top_words = vocab.lookup_tokens(top_indices)\n",
    "    return list(zip(top_words, top_probs))\n",
    "\n",
    "def clean(text):\n",
    "    text = text.replace('-\\\\n', '').replace('\\\\n', ' ').replace('\\\\t', ' ')\n",
    "    text = re.sub(r'\\n', ' ', text)\n",
    "    text = re.sub(r'(?<=\\w)[,-](?=\\w)', '', text)\n",
    "    text = re.sub(r'\\s+', ' ', text)\n",
    "    text = re.sub(r'\\p{P}', '', text)\n",
    "    text = text.strip()\n",
    "    return text\n",
    "    \n",
    "def predictor(prefix, suffix, model, vocab):\n",
    "    prefix = clean(prefix)\n",
    "    suffix = clean(suffix)\n",
    "    words = prefix.split(' ')[-2:] + suffix.split(' ')[:2]\n",
    "    candidates = get_gap_candidates(words, model, vocab)\n",
    "\n",
    "    probs_sum = 0\n",
    "    output = ''\n",
    "    for word, prob in candidates:\n",
    "        if word == \"<unk>\":\n",
    "            continue\n",
    "        probs_sum += prob\n",
    "        output += f\"{word}:{prob} \"\n",
    "    output += f\":{1-probs_sum}\"\n",
    "\n",
    "    return output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "40af2781-3807-43e8-b6dd-3b70066e50c1",
   "metadata": {},
   "outputs": [],
   "source": [
    "def generate_result(input_path,model, vocab, output_path='out.tsv'):\n",
    "    lines = []\n",
    "    with open(input_path, encoding='utf-8') as f:\n",
    "        for line in f:\n",
    "            columns = line.split('\\t')\n",
    "            prefix = columns[6]\n",
    "            suffix = columns[7]\n",
    "            lines.append((prefix, suffix))\n",
    "\n",
    "    with open(output_path, 'w', encoding='utf-8') as output_file:\n",
    "        for prefix, suffix in tqdm(lines):\n",
    "            result = predictor(prefix, suffix, model, vocab)\n",
    "            output_file.write(result + '\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "d6b7234f-1f40-468f-8c69-2875bb1ec947",
   "metadata": {},
   "outputs": [],
   "source": [
    "import subprocess\n",
    "\n",
    "def evaluate():\n",
    "    cmd = 'wsl bash -c \"cd /mnt/d/UAM/MODELOWANIE/5GRAM && ./geval -t dev-0\"'\n",
    "    result = subprocess.run(cmd, shell=True, capture_output=True, text=True)\n",
    "    return float(result.stdout)"
   ]
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      "5000 tensor(5.0450, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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      "{'embed_size': 200, 'vocab_size': 20000, 'num_epochs': 1, 'batch_size': 8192, 'train_file_path': 'train/nano.txt', 'perplexity': 294.44}\n"
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     },
     "metadata": {},
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      "0 tensor(9.9541, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "5000 tensor(4.7515, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "10000 tensor(4.5669, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "15000 tensor(4.4938, device='cuda:0', grad_fn=<NllLossBackward0>)\n"
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     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'embed_size': 200, 'vocab_size': 20000, 'num_epochs': 1, 'batch_size': 8192, 'train_file_path': 'train/train.txt', 'perplexity': 178.27}\n"
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       "Train loop: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 tensor(10.3758, device='cuda:0', grad_fn=<NllLossBackward0>)\n"
     ]
    },
    {
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     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'embed_size': 200, 'vocab_size': 30000, 'num_epochs': 1, 'batch_size': 8192, 'train_file_path': 'train/nano.txt', 'perplexity': 297.89}\n"
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       "Train loop: 0it [00:00, ?it/s]"
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 tensor(10.3769, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "5000 tensor(4.8590, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "10000 tensor(4.7090, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "15000 tensor(4.5810, device='cuda:0', grad_fn=<NllLossBackward0>)\n"
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      ]
     },
     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'embed_size': 200, 'vocab_size': 30000, 'num_epochs': 1, 'batch_size': 8192, 'train_file_path': 'train/train.txt', 'perplexity': 177.66}\n"
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       "Train loop: 0it [00:00, ?it/s]"
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     },
     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 tensor(9.2580, device='cuda:0', grad_fn=<NllLossBackward0>)\n"
     ]
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     },
     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'embed_size': 300, 'vocab_size': 10000, 'num_epochs': 1, 'batch_size': 8192, 'train_file_path': 'train/nano.txt', 'perplexity': 271.01}\n"
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       "Train loop: 0it [00:00, ?it/s]"
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     },
     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 tensor(9.2549, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "5000 tensor(4.4134, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "10000 tensor(4.2280, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "15000 tensor(4.1653, device='cuda:0', grad_fn=<NllLossBackward0>)\n"
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'embed_size': 300, 'vocab_size': 10000, 'num_epochs': 1, 'batch_size': 8192, 'train_file_path': 'train/train.txt', 'perplexity': 181.91}\n"
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       "Train loop: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 tensor(9.9555, device='cuda:0', grad_fn=<NllLossBackward0>)\n"
     ]
    },
    {
     "data": {
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     },
     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'embed_size': 300, 'vocab_size': 20000, 'num_epochs': 1, 'batch_size': 8192, 'train_file_path': 'train/nano.txt', 'perplexity': 275.53}\n"
     ]
    },
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       "Train loop: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 tensor(9.9647, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "5000 tensor(4.6888, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "10000 tensor(4.5068, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "15000 tensor(4.4465, device='cuda:0', grad_fn=<NllLossBackward0>)\n"
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'embed_size': 300, 'vocab_size': 20000, 'num_epochs': 1, 'batch_size': 8192, 'train_file_path': 'train/train.txt', 'perplexity': 174.42}\n"
     ]
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      "text/plain": [
       "Train loop: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 tensor(10.3563, device='cuda:0', grad_fn=<NllLossBackward0>)\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "58d964531ad14b69ba72b7d8d538cf2d",
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       "version_minor": 0
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'embed_size': 300, 'vocab_size': 30000, 'num_epochs': 1, 'batch_size': 8192, 'train_file_path': 'train/nano.txt', 'perplexity': 277.32}\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0bddf3d40c054e92805c100cad8aaccd",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Train loop: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 tensor(10.3580, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "5000 tensor(4.8159, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "10000 tensor(4.6442, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "15000 tensor(4.5524, device='cuda:0', grad_fn=<NllLossBackward0>)\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "aedcfa56f6c345dd976b37535d0de2b4",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'embed_size': 300, 'vocab_size': 30000, 'num_epochs': 1, 'batch_size': 8192, 'train_file_path': 'train/train.txt', 'perplexity': 173.38}\n"
     ]
    }
   ],
   "source": [
    "embed_sizes = [100,200,300]\n",
    "vocab_sizes = [10_000, 20_000, 30_000]\n",
    "num_epochss = [1]\n",
    "batch_sizes = [8192]\n",
    "train_file_paths = ['train/nano.txt', 'train/train.txt']\n",
    "\n",
    "results = []\n",
    "\n",
    "for embed_size in embed_sizes:\n",
    "    for vocab_size in vocab_sizes:\n",
    "        for num_epochs in num_epochss:\n",
    "            for batch_size in batch_sizes:\n",
    "                for train_file_path in train_file_paths:\n",
    "                    model, vocab = train(embed_size,vocab_size,num_epochs,batch_size,train_file_path)\n",
    "                    generate_result('dev-0/in.tsv', model, vocab, output_path='dev-0/out.tsv')\n",
    "                    result = evaluate()\n",
    "\n",
    "                    config = {\"embed_size\": embed_size, \"vocab_size\": vocab_size, \"num_epochs\": num_epochs, \"batch_size\": batch_size, \"train_file_path\": train_file_path, \"perplexity\": result }\n",
    "                    print(config)\n",
    "                    results.append( config  )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "5d835463-01ed-4b44-a652-1ea469542d89",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'embed_size': 100,\n",
       "  'vocab_size': 10000,\n",
       "  'num_epochs': 1,\n",
       "  'batch_size': 8192,\n",
       "  'train_file_path': 'train/nano.txt',\n",
       "  'perplexity': 335.54},\n",
       " {'embed_size': 100,\n",
       "  'vocab_size': 10000,\n",
       "  'num_epochs': 1,\n",
       "  'batch_size': 8192,\n",
       "  'train_file_path': 'train/train.txt',\n",
       "  'perplexity': 199.0},\n",
       " {'embed_size': 100,\n",
       "  'vocab_size': 20000,\n",
       "  'num_epochs': 1,\n",
       "  'batch_size': 8192,\n",
       "  'train_file_path': 'train/nano.txt',\n",
       "  'perplexity': 342.38},\n",
       " {'embed_size': 100,\n",
       "  'vocab_size': 20000,\n",
       "  'num_epochs': 1,\n",
       "  'batch_size': 8192,\n",
       "  'train_file_path': 'train/train.txt',\n",
       "  'perplexity': 190.06},\n",
       " {'embed_size': 100,\n",
       "  'vocab_size': 30000,\n",
       "  'num_epochs': 1,\n",
       "  'batch_size': 8192,\n",
       "  'train_file_path': 'train/nano.txt',\n",
       "  'perplexity': 344.0},\n",
       " {'embed_size': 100,\n",
       "  'vocab_size': 30000,\n",
       "  'num_epochs': 1,\n",
       "  'batch_size': 8192,\n",
       "  'train_file_path': 'train/train.txt',\n",
       "  'perplexity': 188.45},\n",
       " {'embed_size': 200,\n",
       "  'vocab_size': 10000,\n",
       "  'num_epochs': 1,\n",
       "  'batch_size': 8192,\n",
       "  'train_file_path': 'train/nano.txt',\n",
       "  'perplexity': 288.34},\n",
       " {'embed_size': 200,\n",
       "  'vocab_size': 10000,\n",
       "  'num_epochs': 1,\n",
       "  'batch_size': 8192,\n",
       "  'train_file_path': 'train/train.txt',\n",
       "  'perplexity': 187.39},\n",
       " {'embed_size': 200,\n",
       "  'vocab_size': 20000,\n",
       "  'num_epochs': 1,\n",
       "  'batch_size': 8192,\n",
       "  'train_file_path': 'train/nano.txt',\n",
       "  'perplexity': 294.44},\n",
       " {'embed_size': 200,\n",
       "  'vocab_size': 20000,\n",
       "  'num_epochs': 1,\n",
       "  'batch_size': 8192,\n",
       "  'train_file_path': 'train/train.txt',\n",
       "  'perplexity': 178.27},\n",
       " {'embed_size': 200,\n",
       "  'vocab_size': 30000,\n",
       "  'num_epochs': 1,\n",
       "  'batch_size': 8192,\n",
       "  'train_file_path': 'train/nano.txt',\n",
       "  'perplexity': 297.89},\n",
       " {'embed_size': 200,\n",
       "  'vocab_size': 30000,\n",
       "  'num_epochs': 1,\n",
       "  'batch_size': 8192,\n",
       "  'train_file_path': 'train/train.txt',\n",
       "  'perplexity': 177.66},\n",
       " {'embed_size': 300,\n",
       "  'vocab_size': 10000,\n",
       "  'num_epochs': 1,\n",
       "  'batch_size': 8192,\n",
       "  'train_file_path': 'train/nano.txt',\n",
       "  'perplexity': 271.01},\n",
       " {'embed_size': 300,\n",
       "  'vocab_size': 10000,\n",
       "  'num_epochs': 1,\n",
       "  'batch_size': 8192,\n",
       "  'train_file_path': 'train/train.txt',\n",
       "  'perplexity': 181.91},\n",
       " {'embed_size': 300,\n",
       "  'vocab_size': 20000,\n",
       "  'num_epochs': 1,\n",
       "  'batch_size': 8192,\n",
       "  'train_file_path': 'train/nano.txt',\n",
       "  'perplexity': 275.53},\n",
       " {'embed_size': 300,\n",
       "  'vocab_size': 20000,\n",
       "  'num_epochs': 1,\n",
       "  'batch_size': 8192,\n",
       "  'train_file_path': 'train/train.txt',\n",
       "  'perplexity': 174.42},\n",
       " {'embed_size': 300,\n",
       "  'vocab_size': 30000,\n",
       "  'num_epochs': 1,\n",
       "  'batch_size': 8192,\n",
       "  'train_file_path': 'train/nano.txt',\n",
       "  'perplexity': 277.32},\n",
       " {'embed_size': 300,\n",
       "  'vocab_size': 30000,\n",
       "  'num_epochs': 1,\n",
       "  'batch_size': 8192,\n",
       "  'train_file_path': 'train/train.txt',\n",
       "  'perplexity': 173.38}]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "b5058255-4478-427a-84f1-fe1f57fc3828",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from scipy.interpolate import griddata\n",
    "\n",
    "# Sample data\n",
    "data = results\n",
    "\n",
    "# Extracting data\n",
    "vocab_size = [item['vocab_size'] for item in data if 'nano' not in item['train_file_path'] ]\n",
    "embed_size = [item['embed_size'] for item in data if 'nano' not in item['train_file_path'] ]\n",
    "perplexity = [item['perplexity'] for item in data if 'nano' not in item['train_file_path'] ]\n",
    "\n",
    "# Plotting\n",
    "grid_x, grid_y = np.meshgrid(np.linspace(min(vocab_size), max(vocab_size), 100),\n",
    "                             np.linspace(min(embed_size), max(embed_size), 100))\n",
    "grid_z = griddata((vocab_size, embed_size), perplexity, (grid_x, grid_y), method='cubic')\n",
    "\n",
    "# Plotting\n",
    "plt.figure(figsize=(10, 6))\n",
    "contour = plt.contourf(grid_x, grid_y, grid_z, cmap='viridis')\n",
    "plt.colorbar(contour, label='Perplexity')\n",
    "plt.scatter(vocab_size, embed_size, c='red')  # Optional: plot actual data points\n",
    "plt.xlabel('Vocab Size')\n",
    "plt.ylabel('Embed Size')\n",
    "plt.title('Embed Size vs Vocab Size with Perplexity for whole training set')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "fe388a52-9bd3-4ee3-9cf1-838c9ff22c55",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Extracting data\n",
    "vocab_size = [item['vocab_size'] for item in data if 'nano' in item['train_file_path'] ]\n",
    "embed_size = [item['embed_size'] for item in data if 'nano' in item['train_file_path'] ]\n",
    "perplexity = [item['perplexity'] for item in data if 'nano' in item['train_file_path'] ]\n",
    "\n",
    "# Plotting\n",
    "grid_x, grid_y = np.meshgrid(np.linspace(min(vocab_size), max(vocab_size), 100),\n",
    "                             np.linspace(min(embed_size), max(embed_size), 100))\n",
    "grid_z = griddata((vocab_size, embed_size), perplexity, (grid_x, grid_y), method='cubic')\n",
    "\n",
    "# Plotting\n",
    "plt.figure(figsize=(10, 6))\n",
    "contour = plt.contourf(grid_x, grid_y, grid_z, cmap='viridis')\n",
    "plt.colorbar(contour, label='Perplexity')\n",
    "plt.scatter(vocab_size, embed_size, c='red')  # Optional: plot actual data points\n",
    "plt.xlabel('Vocab Size')\n",
    "plt.ylabel('Embed Size')\n",
    "plt.title('Embed Size vs Vocab Size with Perplexity for nano training set')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "a310f1f5-0b2f-4994-b36a-e2ff1a7e6b70",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'embed_size': 300,\n",
       " 'vocab_size': 30000,\n",
       " 'num_epochs': 1,\n",
       " 'batch_size': 8192,\n",
       " 'train_file_path': 'train/train.txt',\n",
       " 'perplexity': 173.38,\n",
       " 'logPerplexity': 5.155485717440494}"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from math import log\n",
    "\n",
    "best_model_parameters = min(results, key=lambda x: x['perplexity'])\n",
    "best_model_parameters['logPerplexity'] = log(best_model_parameters['perplexity'])\n",
    "best_model_parameters"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}