bert_Inference.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Classifying OUV using Pretrained BERT"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/opt/miniconda3/bin/python'"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import sys\n",
    "sys.executable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from argparse import Namespace\n",
    "from collections import Counter\n",
    "import json\n",
    "import os\n",
    "import re\n",
    "import string\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "import torch.optim as optim\n",
    "from torch.utils.data import Dataset, DataLoader\n",
    "from tqdm.notebook import tqdm\n",
    "\n",
    "from torch.autograd import Variable\n",
    "\n",
    "from sklearn.multiclass import OneVsRestClassifier\n",
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "import random\n",
    "\n",
    "from scipy.special import softmax\n",
    "\n",
    "import pickle\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from transformers import  BertTokenizer, BertModel, BertConfig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PyTorch version 1.7.0\n",
      "GPU-enabled installation? False\n"
     ]
    }
   ],
   "source": [
    "print(\"PyTorch version {}\".format(torch.__version__))\n",
    "print(\"GPU-enabled installation? {}\".format(torch.cuda.is_available()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cpu\n"
     ]
    }
   ],
   "source": [
    "device = torch.device(\"cuda:0\") if torch.cuda.is_available() else torch.device(\"cpu\")\n",
    "print(device)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Vectorization Classes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### BERT Vectorizer and Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The Vectorizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "class OuvVectorizer(object):\n",
    "    \"\"\" The Vectorizer which coordinates the Vocabularies and puts them to use\"\"\"\n",
    "    def __init__(self, tokenizer):\n",
    "        \"\"\"\n",
    "        Args:\n",
    "            review_vocab (Vocabulary): maps words to integers\n",
    "        \"\"\"\n",
    "        self.tokenizer = tokenizer\n",
    "        \n",
    "    def vectorize(self, data):\n",
    "        \"\"\"Create a collapsed one-hit vector for the ouv data\n",
    "        \n",
    "        Args:\n",
    "            data (str): the ouv description data\n",
    "            vector_length (int): an argument for forcing the length of index vector\n",
    "        Returns:\n",
    "            the vectorized data (np.ndarray)\n",
    "        \"\"\"\n",
    "        \n",
    "        vector_length = args.max_length\n",
    "                \n",
    "        inputs = self.tokenizer.encode_plus(\n",
    "            data,\n",
    "            None,\n",
    "            add_special_tokens=True,\n",
    "            max_length = vector_length,\n",
    "            pad_to_max_length = True,\n",
    "            return_token_type_ids = True,\n",
    "            truncation=True\n",
    "        )\n",
    "        \n",
    "        ids = inputs['input_ids']\n",
    "        mask = inputs['attention_mask']\n",
    "        token_type_ids = inputs['token_type_ids']\n",
    "        \n",
    "        return {\n",
    "            'ids': torch.tensor(ids, dtype=torch.long),\n",
    "            'mask': torch.tensor(mask, dtype = torch.long),\n",
    "            'token_type_ids': torch.tensor(token_type_ids, dtype=torch.long)\n",
    "        }"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "class OuvDataset(Dataset):\n",
    "    def __init__(self, ouv_df, vectorizer):\n",
    "        \"\"\"\n",
    "        Args:\n",
    "            ouv_df (pandas.DataFrame): the dataset\n",
    "            vectorizer (ReviewVectorizer): vectorizer instantiated from dataset\n",
    "        \"\"\"\n",
    "        self.ouv_df = ouv_df\n",
    "        self._vectorizer = vectorizer\n",
    "        \n",
    "        # +0 if not using begin_seq and end seq, +1 if only using begin_seq, +2 if using both begin and end seq tokens\n",
    "        measure_len = lambda context: len(context.split(\" \"))\n",
    "        self._max_seq_length = max(map(measure_len, ouv_df.data)) + 2\n",
    "\n",
    "        self.train_df = self.ouv_df[self.ouv_df.split=='train']\n",
    "        self.train_size = len(self.train_df)\n",
    "\n",
    "        self.val_df = self.ouv_df[self.ouv_df.split=='dev']\n",
    "        self.validation_size = len(self.val_df)\n",
    "\n",
    "        self.test_df = self.ouv_df[self.ouv_df.split=='test']\n",
    "        self.test_size = len(self.test_df)\n",
    "\n",
    "        self._lookup_dict = {'train': (self.train_df, self.train_size),\n",
    "                             'val': (self.val_df, self.validation_size),\n",
    "                             'test': (self.test_df, self.test_size)}\n",
    "\n",
    "        self.set_split('train')\n",
    "\n",
    "    @classmethod\n",
    "    def load_dataset_and_make_vectorizer(cls, ouv_csv):\n",
    "        \"\"\"Load dataset and make a new vectorizer from scratch\n",
    "        \n",
    "        Args:\n",
    "            ouv_csv (str): location of the dataset\n",
    "            \n",
    "        Returns:\n",
    "            an instance of OuvDataset\n",
    "        \"\"\"\n",
    "        ouv_df = pd.read_csv(ouv_csv)\n",
    "        train_ouv_df = ouv_df[ouv_df.split=='train']\n",
    "        tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')\n",
    "        vectorizer = OuvVectorizer(tokenizer)\n",
    "        return cls(ouv_df, vectorizer)\n",
    "    \n",
    "    @classmethod\n",
    "    def load_dataset_and_load_vectorizer(cls, ouv_csv, vectorizer_filepath):\n",
    "        \"\"\"Load dataset and the corresponding vectorizer. \n",
    "        Used in the case in the vectorizer has been cached for re-use\n",
    "        \n",
    "        Args:\n",
    "            ouv_csv (str): location of the dataset\n",
    "            vectorizer_filepath (str): location of the saved vectorizer\n",
    "        Returns:\n",
    "            an instance of OuvDataset\n",
    "        \"\"\"\n",
    "        ouv_df = pd.read_csv(ouv_csv)\n",
    "        vectorizer = cls.load_vectorizer_only(vectorizer_filepath)\n",
    "        return cls(ouv_df, vectorizer)\n",
    "\n",
    "    def save_vectorizer(self, vectorizer_filepath):\n",
    "        \"\"\"saves the vectorizer to disk using json\n",
    "        \n",
    "        Args:\n",
    "            vectorizer_filepath (str): the location to save the vectorizer\n",
    "        \"\"\"\n",
    "        with open(vectorizer_filepath, \"w\") as fp:\n",
    "            json.dump(self._vectorizer.tokenizer.vocab, fp)\n",
    "\n",
    "    def get_vectorizer(self):\n",
    "        \"\"\" returns the vectorizer \"\"\"\n",
    "        return self._vectorizer\n",
    "\n",
    "    def set_split(self, split=\"train\"):\n",
    "        \"\"\" selects the splits in the dataset using a column in the dataframe \n",
    "        \n",
    "        Args:\n",
    "            split (str): one of \"train\", \"val\", or \"test\"\n",
    "        \"\"\"\n",
    "        self._target_split = split\n",
    "        self._target_df, self._target_size = self._lookup_dict[split]\n",
    "\n",
    "    def __len__(self):\n",
    "        return self._target_size\n",
    "\n",
    "    def __getitem__(self, index):\n",
    "        \"\"\"the primary entry point method for PyTorch datasets\n",
    "        \n",
    "        Args:\n",
    "            index (int): the index to the data point \n",
    "        Returns:\n",
    "            a dictionary holding the data point's features (x_data) and component for labels (y_target and y_fuzzy)\n",
    "        \"\"\"\n",
    "        row = self._target_df.iloc[index]\n",
    "        \n",
    "        tokenized = self._vectorizer.vectorize(row.data)\n",
    "        ouv_vector = tokenized['ids']\n",
    "        mask = tokenized['mask']\n",
    "        token_type_ids = tokenized['token_type_ids']\n",
    "\n",
    "        true_label = \\\n",
    "            np.fromstring(row.true[1:-1],dtype=float, sep=' ')\n",
    "        \n",
    "        if len(true_label)==10:\n",
    "            true_label = np.append(true_label,0.0)\n",
    "        \n",
    "        fuzzy_label = \\\n",
    "            np.fromstring(row.fuzzy[1:-1],dtype=float, sep=' ')\n",
    "\n",
    "        return {'x_data': ouv_vector,\n",
    "                'y_target': true_label,\n",
    "                'y_fuzzy': fuzzy_label,\n",
    "                'mask': mask,\n",
    "                'token_type_ids': token_type_ids,\n",
    "                #'text' : row.data,\n",
    "                'id' : row.id\n",
    "               }\n",
    "\n",
    "    def get_num_batches(self, batch_size):\n",
    "        \"\"\"Given a batch size, return the number of batches in the dataset\n",
    "        \n",
    "        Args:\n",
    "            batch_size (int)\n",
    "        Returns:\n",
    "            number of batches in the dataset\n",
    "        \"\"\"\n",
    "        return len(self) // batch_size  \n",
    "    \n",
    "def generate_batches(dataset, batch_size, shuffle=True,\n",
    "                     drop_last=True, device=\"cpu\"):\n",
    "    \"\"\"\n",
    "    A generator function which wraps the PyTorch DataLoader. It will \n",
    "      ensure each tensor is on the write device location.\n",
    "    \"\"\"\n",
    "    dataloader = DataLoader(dataset=dataset, batch_size=batch_size,\n",
    "                            shuffle=shuffle, drop_last=drop_last)\n",
    "\n",
    "    for data_dict in dataloader:\n",
    "        out_data_dict = {}\n",
    "        for name, tensor in data_dict.items():\n",
    "            out_data_dict[name] = data_dict[name].to(device)\n",
    "        yield out_data_dict"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The Model: BERT Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "class BERTClassifier(nn.Module):\n",
    "    \n",
    "    def __init__(self, num_classes, dropout_p):\n",
    "        \"\"\"\n",
    "        Args:\n",
    "            num_classes (int): the number of classes in classification\n",
    "            dropout_p (float): a dropout parameter \n",
    "            \n",
    "        \"\"\"\n",
    "        super(BERTClassifier, self).__init__()\n",
    "\n",
    "        self._dropout_p = dropout_p\n",
    "        \n",
    "        self.bert = BertModel.from_pretrained('bert-base-uncased',output_attentions=True)\n",
    "        #for param in self.bert.parameters():\n",
    "        #    param.requires_grad = False        \n",
    "        self.fc = nn.Linear(768, num_classes)\n",
    "\n",
    "    def forward(self, ids, mask, token_type_ids, apply_softmax=False):\n",
    "        \"\"\"The forward pass of the classifier\n",
    "        \n",
    "        Args:\n",
    "            x_in (torch.Tensor): an input data tensor. \n",
    "                x_in.shape should be (batch, dataset._max_seq_length)\n",
    "            apply_softmax (bool): a flag for the softmax activation\n",
    "                should be false if used with the Cross Entropy losses\n",
    "        Returns:\n",
    "            the resulting tensor. tensor.shape should be (batch, num_classes)\n",
    "        \"\"\"\n",
    "        \n",
    "        model_outs = self.bert(ids,attention_mask = mask, token_type_ids = token_type_ids)\n",
    "        output_1 = model_outs[1]\n",
    "        y_out = self.fc(F.dropout(output_1, self._dropout_p, self.training))\n",
    "        \n",
    "        if apply_softmax:\n",
    "            y_out = F.softmax(y_out, dim=1)\n",
    "            \n",
    "        return y_out, model_outs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training Routine"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Helper Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_train_state(args):\n",
    "    return {'stop_early': False,\n",
    "            'early_stopping_step': 0,\n",
    "            'early_stopping_best_1_acc_val': 0,\n",
    "            'learning_rate': args.learning_rate,\n",
    "            'epoch_index': 0,\n",
    "            'train_loss': [],\n",
    "            'train_1_acc': [],\n",
    "            'train_k_acc': [],\n",
    "            'train_k_jac': [],\n",
    "            'val_loss': [],\n",
    "            'val_1_acc': [],\n",
    "            'val_k_acc': [],\n",
    "            'val_k_jac': [],\n",
    "            'test_loss': -1,\n",
    "            'test_1_acc': -1,\n",
    "            'test_k_acc':-1,\n",
    "            'test_k_jac':-1,\n",
    "            'model_filename': args.model_state_file}\n",
    "\n",
    "def update_train_state(args, model, train_state):\n",
    "    \"\"\"Handle the training state updates.\n",
    "\n",
    "    Components:\n",
    "     - Early Stopping: Prevent overfitting.\n",
    "     - Model Checkpoint: Model is saved if the model is better\n",
    "\n",
    "    :param args: main arguments\n",
    "    :param model: model to train\n",
    "    :param train_state: a dictionary representing the training state values\n",
    "    :returns:\n",
    "        a new train_state\n",
    "    \"\"\"\n",
    "\n",
    "    # Save one model at least\n",
    "    if train_state['epoch_index'] == 0:\n",
    "        torch.save(model.state_dict(), train_state['model_filename'])\n",
    "        train_state['stop_early'] = False\n",
    "\n",
    "    # Save model if performance improved\n",
    "    elif train_state['epoch_index'] >= 1:\n",
    "        acc_tm1, acc_t = train_state['val_1_acc'][-2:]\n",
    "\n",
    "        # If accuracy worsened\n",
    "        if acc_t <= train_state['early_stopping_best_1_acc_val']:\n",
    "            # Update step\n",
    "            train_state['early_stopping_step'] += 1\n",
    "        # Loss decreased\n",
    "        else:\n",
    "            # Save the best model from sklearn\n",
    "            if acc_t > train_state['early_stopping_best_1_acc_val']:\n",
    "                train_state['early_stopping_best_1_acc_val'] = acc_t\n",
    "                torch.save(model.state_dict(), train_state['model_filename'])\n",
    "                \n",
    "            # Reset early stopping step\n",
    "            train_state['early_stopping_step'] = 0\n",
    "\n",
    "        # Stop early ?\n",
    "        train_state['stop_early'] = \\\n",
    "            train_state['early_stopping_step'] >= args.early_stopping_criteria\n",
    "\n",
    "    return train_state"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Evaluation Metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def compute_cross_entropy(y_pred, y_target):\n",
    "    y_target = y_target.cpu().float()\n",
    "    y_pred = y_pred.cpu().float()\n",
    "    criterion = nn.BCEWithLogitsLoss()\n",
    "    return criterion(y_target, y_pred)\n",
    "\n",
    "def compute_1_accuracy(y_pred, y_target):\n",
    "    y_target_indices = y_target.max(dim=1)[1]\n",
    "    y_pred_indices = y_pred.max(dim=1)[1]\n",
    "    n_correct = torch.eq(y_pred_indices, y_target_indices).sum().item()\n",
    "    return n_correct / len(y_pred_indices) * 100\n",
    "\n",
    "def compute_k_accuracy(y_pred, y_target, k=3):\n",
    "    y_pred_indices = y_pred.topk(k, dim=1)[1]\n",
    "    y_target_indices = y_target.max(dim=1)[1]\n",
    "    n_correct = torch.tensor([y_pred_indices[i] in y_target_indices[i] for i in range(len(y_pred))]).sum().item()\n",
    "    return n_correct / len(y_pred_indices) * 100\n",
    "\n",
    "def compute_k_jaccard_index(y_pred, y_target, k=3):\n",
    "    y_target_indices = y_target.topk(k, dim=1)[1]\n",
    "    y_pred_indices = y_pred.max(dim=1)[1]\n",
    "    jaccard = torch.tensor([len(np.intersect1d(y_target_indices[i], y_pred_indices[i]))/\n",
    "                            len(np.union1d(y_target_indices[i], y_pred_indices[i]))\n",
    "                            for i in range(len(y_pred))]).sum().item()\n",
    "    return jaccard / len(y_pred_indices)\n",
    "\n",
    "def compute_jaccard_index(y_pred, y_target, k=3, multilabel=False):\n",
    "    \n",
    "    threshold = 1.0/(k+1)\n",
    "    threshold_2 = 0.5\n",
    "    \n",
    "    if multilabel:\n",
    "        y_pred_indices = y_pred.gt(threshold_2)\n",
    "    else:\n",
    "        y_pred_indices = y_pred.gt(threshold)\n",
    "    \n",
    "    y_target_indices = y_target.gt(threshold)\n",
    "        \n",
    "    jaccard = ((y_target_indices*y_pred_indices).sum(axis=1)/((y_target_indices+y_pred_indices).sum(axis=1)+1e-8)).sum().item()\n",
    "    return jaccard / len(y_pred_indices)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def softmax_sensitive(T):\n",
    "    T = torch.exp(T) - 1 + 1e-9\n",
    "    if len(T.shape)==1:\n",
    "        return T/T.sum()\n",
    "    return  T/(T.sum(axis=1).unsqueeze(1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def cross_entropy(pred, soft_targets):\n",
    "    logsoftmax = nn.LogSoftmax(dim=1)\n",
    "    return torch.mean(torch.sum(- soft_targets * logsoftmax(pred), 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# convert a df to tensor to be used in pytorch\n",
    "def df_to_tensor(df):\n",
    "    device = args.device\n",
    "    return torch.from_numpy(df.values).float().to(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_prior():\n",
    "    prior = pd.read_csv(args.prior_csv,sep=';',names=classes[:-1], skiprows=1)\n",
    "    prior['Others'] = 1\n",
    "    prior = prior.T\n",
    "    prior['Others'] = 1\n",
    "    prior = df_to_tensor(prior)\n",
    "    return(prior)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def compute_fuzzy_label(y_target, y_fuzzy, fuzzy=False, how='uni', lbd=0):\n",
    "    '''\n",
    "    Using two sets of prediction labels and fuzziness parameters to compute the fuzzy label in the form as \n",
    "    a distribution over classes\n",
    "    \n",
    "    Args:\n",
    "    y_target (torch.Tensor) of shape (n_batch, n_classes): the true label of the ouv description\n",
    "    y_fuzzy (torch.Tensor) of shape (n_batch, n_classes): the fuzzy label of the ouv description\n",
    "    fuzzy (bool): whether or not to turn on the fuzziness option\n",
    "    how (string): the way fuzziness weights are used, one of the options in {'uni', 'prior'}\n",
    "    lbd (float): the scaler applied to the fuzziness of the label\n",
    "    \n",
    "    Returns:\n",
    "    A pytorch Tensor of shape (n_batch, n_classes): The processed label in the form of distribution that add to 1\n",
    "    '''\n",
    "    assert y_target.shape == y_fuzzy.shape, 'target labels must have the same size'\n",
    "    assert how in {'uni', 'prior'}, '''how must be one of the two options in {'uni', 'prior'}'''\n",
    "    \n",
    "    if not fuzzy:\n",
    "        return softmax_sensitive(y_target)\n",
    "    \n",
    "    if how == 'uni':\n",
    "        y_label = y_target + lbd * y_fuzzy\n",
    "        return softmax_sensitive(y_label)\n",
    "    \n",
    "    ### TO DO ###\n",
    "    elif how == 'prior':\n",
    "        prior = get_prior()\n",
    "        y_inter = torch.matmul(y_target.float(),prior)\n",
    "        y_inter = y_inter/(y_inter.max(dim=1, keepdim=True)[0])\n",
    "        y_label = y_target + lbd * y_fuzzy * y_inter\n",
    "        return softmax_sensitive(y_label)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### General Utilities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def set_seed_everywhere(seed, cuda):\n",
    "    np.random.seed(seed)\n",
    "    torch.manual_seed(seed)\n",
    "    random.seed(seed)\n",
    "    if cuda:\n",
    "        torch.cuda.manual_seed_all(seed)\n",
    "\n",
    "def handle_dirs(dirpath):\n",
    "    if not os.path.exists(dirpath):\n",
    "        os.makedirs(dirpath)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Settings and Some Prep Work"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Expanded filepaths: \n",
      "\tmodel_storage/bert/vectorizer.json\n",
      "\tmodel_storage/bert/model.pth\n",
      "Using CUDA: False\n"
     ]
    }
   ],
   "source": [
    "args = Namespace(\n",
    "    # Data and Path information\n",
    "    model_state_file='model.pth',\n",
    "    ouv_csv='Data/ouv_with_splits_full.csv',\n",
    "    big_ouv_csv='Data/all_with_splits_full.csv',\n",
    "    prior_csv = 'Data/Coappearance_matrix.csv',\n",
    "    sm_csv = 'Data/Social_media.csv',\n",
    "    save_dir='model_storage/bert/',\n",
    "    vectorizer_file='vectorizer.json',\n",
    "    # Model hyper parameters\n",
    "    max_length=200,\n",
    "    use_glove=False,\n",
    "    # Training hyper parameters\n",
    "    batch_size=64,\n",
    "    early_stopping_criteria=5,\n",
    "    learning_rate=2e-5,\n",
    "    l2=1e-5,\n",
    "    dropout_p=0.2,\n",
    "    k = 3,\n",
    "    fuzzy = True,\n",
    "    fuzzy_how = 'uni',\n",
    "    fuzzy_lambda = 0.2,\n",
    "    num_epochs=100,\n",
    "    seed=1337,\n",
    "    # Runtime options\n",
    "    catch_keyboard_interrupt=True,\n",
    "    cuda=True,\n",
    "    expand_filepaths_to_save_dir=True,\n",
    "    reload_from_files=False,\n",
    ")\n",
    "\n",
    "classes = ['Criteria i', 'Criteria ii', 'Criteria iii', 'Criteria iv', 'Criteria v', 'Criteria vi', \n",
    "              'Criteria vii', 'Criteria viii', 'Criteria ix', 'Criteria x', 'Others']\n",
    "\n",
    "if args.expand_filepaths_to_save_dir:\n",
    "    args.vectorizer_file = os.path.join(args.save_dir,\n",
    "                                        args.vectorizer_file)\n",
    "\n",
    "    args.model_state_file = os.path.join(args.save_dir,\n",
    "                                         args.model_state_file)\n",
    "    \n",
    "    print(\"Expanded filepaths: \")\n",
    "    print(\"\\t{}\".format(args.vectorizer_file))\n",
    "    print(\"\\t{}\".format(args.model_state_file))\n",
    "    \n",
    "# Check CUDA\n",
    "if not torch.cuda.is_available():\n",
    "    args.cuda = False\n",
    "\n",
    "print(\"Using CUDA: {}\".format(args.cuda))\n",
    "\n",
    "args.device = torch.device(\"cuda\" if args.cuda else \"cpu\")\n",
    "\n",
    "# Set seed for reproducibility\n",
    "set_seed_everywhere(args.seed, args.cuda)\n",
    "\n",
    "# handle dirs\n",
    "handle_dirs(args.save_dir)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Initialization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Not using pre-trained embeddings\n"
     ]
    }
   ],
   "source": [
    "if args.reload_from_files:\n",
    "    # training from a checkpoint\n",
    "    dataset = OuvDataset.load_dataset_and_load_vectorizer(args.ouv_csv, args.vectorizer_file)\n",
    "\n",
    "else:\n",
    "    # create dataset and vectorizer\n",
    "    dataset = OuvDataset.load_dataset_and_make_vectorizer(args.ouv_csv)\n",
    "    dataset.save_vectorizer(args.vectorizer_file)    \n",
    "\n",
    "vectorizer = dataset.get_vectorizer()\n",
    "set_seed_everywhere(args.seed, args.cuda)\n",
    "\n",
    "# Use GloVe or randomly initialized embeddings\n",
    "if args.use_glove:\n",
    "    words = vectorizer.ouv_vocab._token_to_idx.keys()\n",
    "    embeddings = make_embedding_matrix(glove_filepath=args.glove_filepath, \n",
    "                                       words=words)\n",
    "    print(\"Using pre-trained embeddings\")\n",
    "else:\n",
    "    print(\"Not using pre-trained embeddings\")\n",
    "    embeddings = None\n",
    "\n",
    "classifier = BERTClassifier(\n",
    "                    num_classes=len(classes),\n",
    "                    dropout_p=args.dropout_p,\n",
    "                        )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Training Loop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "def initialization(embeddings=embeddings):\n",
    "    if args.reload_from_files:\n",
    "        # training from a checkpoint\n",
    "        dataset = OuvDataset.load_dataset_and_load_vectorizer(args.ouv_csv, args.vectorizer_file)\n",
    "\n",
    "    else:\n",
    "        # create dataset and vectorizer\n",
    "        dataset = OuvDataset.load_dataset_and_make_vectorizer(args.ouv_csv, cutoff=args.frequency_cutoff)\n",
    "        dataset.save_vectorizer(args.vectorizer_file)    \n",
    "    \n",
    "    vectorizer = dataset.get_vectorizer()\n",
    "    set_seed_everywhere(args.seed, args.cuda)\n",
    "    classifier = GRUClassifier(embedding_size=args.embedding_size, \n",
    "                                num_embeddings=len(vectorizer.ouv_vocab),\n",
    "                                hidden_dim=args.hidden_dim, \n",
    "                                num_classes=len(classes), \n",
    "                                dropout_p=args.dropout_p,\n",
    "                                pretrained_embeddings=embeddings,\n",
    "                                padding_idx=0,\n",
    "                                bi=args.bi, freeze=args.freeze)\n",
    "    return dataset, vectorizer, classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "def training_loop(embeddings= embeddings, verbose=True):\n",
    "    \n",
    "    dataset,vectorizer,classifier = initialization(embeddings)\n",
    "    classifier = classifier.to(args.device)\n",
    "\n",
    "    loss_func = cross_entropy\n",
    "    optimizer = optim.Adam(classifier.parameters(), lr=args.learning_rate, weight_decay=args.l2)\n",
    "    scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer=optimizer,\n",
    "                                               mode='min', factor=0.5,\n",
    "                                               patience=1)\n",
    "\n",
    "    train_state = make_train_state(args)\n",
    "\n",
    "    if verbose:\n",
    "        epoch_bar = tqdm(desc='training routine', \n",
    "                              total=args.num_epochs)\n",
    "        train_bar = tqdm(desc='split=train',\n",
    "                              total=dataset.get_num_batches(args.batch_size), \n",
    "                              leave=True)\n",
    "        val_bar = tqdm(desc='split=val',\n",
    "                            total=dataset.get_num_batches(args.batch_size), \n",
    "                            leave=True)\n",
    "\n",
    "    dataset.set_split('train')\n",
    "    dataset.set_split('val')\n",
    "\n",
    "    try:\n",
    "        for epoch_index in range(args.num_epochs):\n",
    "            train_state['epoch_index'] = epoch_index\n",
    "\n",
    "            # Iterate over training dataset\n",
    "\n",
    "            # setup: batch generator, set loss and acc to 0, set train mode on\n",
    "            dataset.set_split('train')\n",
    "            batch_generator = generate_batches(dataset, \n",
    "                                               batch_size=args.batch_size, \n",
    "                                               device=args.device)\n",
    "            running_loss = 0.0\n",
    "            running_1_acc = 0.0\n",
    "            running_k_acc = 0.0\n",
    "            running_k_jac = 0.0\n",
    "            classifier.train()\n",
    "\n",
    "            for batch_index, batch_dict in enumerate(batch_generator):\n",
    "\n",
    "                # step 1. zero the gradients\n",
    "                optimizer.zero_grad()\n",
    "\n",
    "                # step 2. get the data compute fuzzy labels\n",
    "                X = batch_dict['x_data']\n",
    "\n",
    "                y_target = batch_dict['y_target']\n",
    "                y_fuzzy = batch_dict['y_fuzzy']\n",
    "\n",
    "                Y = compute_fuzzy_label(y_target, y_fuzzy, fuzzy= args.fuzzy, \n",
    "                                        how=args.fuzzy_how, lbd = args.fuzzy_lambda)\n",
    "\n",
    "                # step 3. compute the output\n",
    "                y_pred = classifier(X)\n",
    "\n",
    "                # step 4. compute the loss\n",
    "                loss = loss_func(y_pred, Y)\n",
    "                loss_t = loss.item()\n",
    "                running_loss += (loss_t - running_loss) / (batch_index + 1)\n",
    "\n",
    "                # step 5. use loss to produce gradients\n",
    "                loss.backward()\n",
    "\n",
    "                # step 6. use optimizer to take gradient step\n",
    "                optimizer.step()\n",
    "\n",
    "                # -----------------------------------------\n",
    "                # compute the accuracies\n",
    "                acc_1_t = compute_1_accuracy(y_pred, Y)\n",
    "                acc_k_t = compute_k_accuracy(y_pred, Y, args.k)\n",
    "                jac_k_t = compute_jaccard_index(y_pred, Y, len(classes))\n",
    "\n",
    "                running_1_acc += (acc_1_t - running_1_acc) / (batch_index + 1)\n",
    "                running_k_acc += (acc_k_t - running_k_acc) / (batch_index + 1)\n",
    "                running_k_jac += (jac_k_t - running_k_jac) / (batch_index + 1)\n",
    "\n",
    "                # update bar\n",
    "                if verbose:\n",
    "                    train_bar.set_postfix(loss=running_loss, \n",
    "                                      acc_1=running_1_acc,\n",
    "                                      acc_k=running_k_acc,\n",
    "                                      jac_k=running_k_jac,\n",
    "                                      epoch=epoch_index)\n",
    "                    train_bar.update()\n",
    "\n",
    "            train_state['train_loss'].append(running_loss)\n",
    "            train_state['train_1_acc'].append(running_1_acc)\n",
    "            train_state['train_k_acc'].append(running_k_acc)\n",
    "            train_state['train_k_jac'].append(running_k_jac)\n",
    "\n",
    "            # Iterate over val dataset\n",
    "\n",
    "            # setup: batch generator, set loss and acc to 0; set eval mode on\n",
    "            dataset.set_split('val')\n",
    "            batch_generator = generate_batches(dataset, \n",
    "                                               batch_size=args.batch_size, \n",
    "                                               device=args.device)\n",
    "            running_loss = 0.0\n",
    "            running_1_acc = 0.0\n",
    "            running_k_acc = 0.0\n",
    "            running_k_jac = 0.0\n",
    "            classifier.eval()\n",
    "\n",
    "            for batch_index, batch_dict in enumerate(batch_generator):\n",
    "\n",
    "                # step 2. get the data compute fuzzy labels\n",
    "                X = batch_dict['x_data']\n",
    "\n",
    "                y_target = batch_dict['y_target']\n",
    "                y_fuzzy = batch_dict['y_fuzzy']\n",
    "\n",
    "                Y = compute_fuzzy_label(y_target, y_fuzzy, fuzzy= args.fuzzy, \n",
    "                                        how=args.fuzzy_how, lbd = args.fuzzy_lambda)\n",
    "\n",
    "                # step 3. compute the output\n",
    "                y_pred = classifier(X)\n",
    "\n",
    "                # step 4. compute the loss\n",
    "                loss = loss_func(y_pred, Y)\n",
    "                loss_t = loss.item()\n",
    "                running_loss += (loss_t - running_loss) / (batch_index + 1)\n",
    "\n",
    "                # -----------------------------------------\n",
    "                # compute the accuracies\n",
    "                acc_1_t = compute_1_accuracy(y_pred, Y)\n",
    "                acc_k_t = compute_k_accuracy(y_pred, Y, args.k)\n",
    "                jac_k_t = compute_jaccard_index(y_pred, Y, len(classes))\n",
    "\n",
    "                running_1_acc += (acc_1_t - running_1_acc) / (batch_index + 1)\n",
    "                running_k_acc += (acc_k_t - running_k_acc) / (batch_index + 1)\n",
    "                running_k_jac += (jac_k_t - running_k_jac) / (batch_index + 1)\n",
    "\n",
    "                # update bar\n",
    "                if verbose:\n",
    "                    val_bar.set_postfix(loss=running_loss, \n",
    "                                    acc_1=running_1_acc,\n",
    "                                    acc_k=running_k_acc,\n",
    "                                    jac_k=running_k_jac,\n",
    "                                    epoch=epoch_index)\n",
    "                    val_bar.update()\n",
    "\n",
    "            train_state['val_loss'].append(running_loss)\n",
    "            train_state['val_1_acc'].append(running_1_acc)\n",
    "            train_state['val_k_acc'].append(running_k_acc)\n",
    "            train_state['val_k_jac'].append(running_k_jac)\n",
    "\n",
    "            train_state = update_train_state(args=args, model=classifier,\n",
    "                                             train_state=train_state)\n",
    "\n",
    "            scheduler.step(train_state['val_loss'][-1])\n",
    "\n",
    "            if train_state['stop_early']:\n",
    "                break\n",
    "\n",
    "            if verbose:\n",
    "                train_bar.n = 0\n",
    "                val_bar.n = 0\n",
    "                epoch_bar.update()\n",
    "\n",
    "    except KeyboardInterrupt:\n",
    "        print(\"Exiting loop\")\n",
    "        pass\n",
    "    \n",
    "    return train_state"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading Trained Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Option 1 LS Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "from collections import OrderedDict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(args.save_dir+'hyperdict_fuzzy.p', 'rb') as fp:\n",
    "    hyperdict_fuzzy = pickle.load(fp)\n",
    "    train_state = hyperdict_fuzzy[('uni', 0.2)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "BERTClassifier(\n",
       "  (bert): BertModel(\n",
       "    (embeddings): BertEmbeddings(\n",
       "      (word_embeddings): Embedding(30522, 768, padding_idx=0)\n",
       "      (position_embeddings): Embedding(512, 768)\n",
       "      (token_type_embeddings): Embedding(2, 768)\n",
       "      (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "      (dropout): Dropout(p=0.1, inplace=False)\n",
       "    )\n",
       "    (encoder): BertEncoder(\n",
       "      (layer): ModuleList(\n",
       "        (0): BertLayer(\n",
       "          (attention): BertAttention(\n",
       "            (self): BertSelfAttention(\n",
       "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (output): BertSelfOutput(\n",
       "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "          )\n",
       "          (intermediate): BertIntermediate(\n",
       "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
       "          )\n",
       "          (output): BertOutput(\n",
       "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
       "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "        )\n",
       "        (1): BertLayer(\n",
       "          (attention): BertAttention(\n",
       "            (self): BertSelfAttention(\n",
       "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (output): BertSelfOutput(\n",
       "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "          )\n",
       "          (intermediate): BertIntermediate(\n",
       "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
       "          )\n",
       "          (output): BertOutput(\n",
       "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
       "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "        )\n",
       "        (2): BertLayer(\n",
       "          (attention): BertAttention(\n",
       "            (self): BertSelfAttention(\n",
       "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (output): BertSelfOutput(\n",
       "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "          )\n",
       "          (intermediate): BertIntermediate(\n",
       "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
       "          )\n",
       "          (output): BertOutput(\n",
       "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
       "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "        )\n",
       "        (3): BertLayer(\n",
       "          (attention): BertAttention(\n",
       "            (self): BertSelfAttention(\n",
       "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (output): BertSelfOutput(\n",
       "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "          )\n",
       "          (intermediate): BertIntermediate(\n",
       "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
       "          )\n",
       "          (output): BertOutput(\n",
       "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
       "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "        )\n",
       "        (4): BertLayer(\n",
       "          (attention): BertAttention(\n",
       "            (self): BertSelfAttention(\n",
       "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (output): BertSelfOutput(\n",
       "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "          )\n",
       "          (intermediate): BertIntermediate(\n",
       "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
       "          )\n",
       "          (output): BertOutput(\n",
       "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
       "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "        )\n",
       "        (5): BertLayer(\n",
       "          (attention): BertAttention(\n",
       "            (self): BertSelfAttention(\n",
       "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (output): BertSelfOutput(\n",
       "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "          )\n",
       "          (intermediate): BertIntermediate(\n",
       "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
       "          )\n",
       "          (output): BertOutput(\n",
       "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
       "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "        )\n",
       "        (6): BertLayer(\n",
       "          (attention): BertAttention(\n",
       "            (self): BertSelfAttention(\n",
       "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (output): BertSelfOutput(\n",
       "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "          )\n",
       "          (intermediate): BertIntermediate(\n",
       "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
       "          )\n",
       "          (output): BertOutput(\n",
       "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
       "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "        )\n",
       "        (7): BertLayer(\n",
       "          (attention): BertAttention(\n",
       "            (self): BertSelfAttention(\n",
       "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (output): BertSelfOutput(\n",
       "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "          )\n",
       "          (intermediate): BertIntermediate(\n",
       "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
       "          )\n",
       "          (output): BertOutput(\n",
       "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
       "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "        )\n",
       "        (8): BertLayer(\n",
       "          (attention): BertAttention(\n",
       "            (self): BertSelfAttention(\n",
       "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (output): BertSelfOutput(\n",
       "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "          )\n",
       "          (intermediate): BertIntermediate(\n",
       "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
       "          )\n",
       "          (output): BertOutput(\n",
       "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
       "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "        )\n",
       "        (9): BertLayer(\n",
       "          (attention): BertAttention(\n",
       "            (self): BertSelfAttention(\n",
       "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (output): BertSelfOutput(\n",
       "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "          )\n",
       "          (intermediate): BertIntermediate(\n",
       "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
       "          )\n",
       "          (output): BertOutput(\n",
       "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
       "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "        )\n",
       "        (10): BertLayer(\n",
       "          (attention): BertAttention(\n",
       "            (self): BertSelfAttention(\n",
       "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (output): BertSelfOutput(\n",
       "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "          )\n",
       "          (intermediate): BertIntermediate(\n",
       "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
       "          )\n",
       "          (output): BertOutput(\n",
       "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
       "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "        )\n",
       "        (11): BertLayer(\n",
       "          (attention): BertAttention(\n",
       "            (self): BertSelfAttention(\n",
       "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (output): BertSelfOutput(\n",
       "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "          )\n",
       "          (intermediate): BertIntermediate(\n",
       "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
       "          )\n",
       "          (output): BertOutput(\n",
       "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
       "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "        )\n",
       "      )\n",
       "    )\n",
       "    (pooler): BertPooler(\n",
       "      (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "      (activation): Tanh()\n",
       "    )\n",
       "  )\n",
       "  (fc): Linear(in_features=768, out_features=11, bias=True)\n",
       ")"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "state_dict = torch.load(args.save_dir+'1337/model.pth',map_location=torch.device('cpu'))\n",
    "state_dict_n = {key[7:]:value for key,value in state_dict.items()}\n",
    "state_dict_n = OrderedDict(state_dict_n)\n",
    "classifier.load_state_dict(state_dict_n,strict=False)\n",
    "classifier.eval()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Option 2 Baseline w/o LS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(args.save_dir+'hyperdict_fuzzy.p', 'rb') as fp:\n",
    "    hyperdict_fuzzy = pickle.load(fp)\n",
    "    train_state = hyperdict_fuzzy[('uni', 0)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "BERTClassifier(\n",
       "  (bert): BertModel(\n",
       "    (embeddings): BertEmbeddings(\n",
       "      (word_embeddings): Embedding(30522, 768, padding_idx=0)\n",
       "      (position_embeddings): Embedding(512, 768)\n",
       "      (token_type_embeddings): Embedding(2, 768)\n",
       "      (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "      (dropout): Dropout(p=0.1, inplace=False)\n",
       "    )\n",
       "    (encoder): BertEncoder(\n",
       "      (layer): ModuleList(\n",
       "        (0): BertLayer(\n",
       "          (attention): BertAttention(\n",
       "            (self): BertSelfAttention(\n",
       "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (output): BertSelfOutput(\n",
       "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "          )\n",
       "          (intermediate): BertIntermediate(\n",
       "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
       "          )\n",
       "          (output): BertOutput(\n",
       "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
       "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "        )\n",
       "        (1): BertLayer(\n",
       "          (attention): BertAttention(\n",
       "            (self): BertSelfAttention(\n",
       "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (output): BertSelfOutput(\n",
       "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "          )\n",
       "          (intermediate): BertIntermediate(\n",
       "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
       "          )\n",
       "          (output): BertOutput(\n",
       "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
       "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "        )\n",
       "        (2): BertLayer(\n",
       "          (attention): BertAttention(\n",
       "            (self): BertSelfAttention(\n",
       "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (output): BertSelfOutput(\n",
       "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "          )\n",
       "          (intermediate): BertIntermediate(\n",
       "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
       "          )\n",
       "          (output): BertOutput(\n",
       "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
       "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "        )\n",
       "        (3): BertLayer(\n",
       "          (attention): BertAttention(\n",
       "            (self): BertSelfAttention(\n",
       "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (output): BertSelfOutput(\n",
       "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "          )\n",
       "          (intermediate): BertIntermediate(\n",
       "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
       "          )\n",
       "          (output): BertOutput(\n",
       "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
       "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "        )\n",
       "        (4): BertLayer(\n",
       "          (attention): BertAttention(\n",
       "            (self): BertSelfAttention(\n",
       "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (output): BertSelfOutput(\n",
       "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "          )\n",
       "          (intermediate): BertIntermediate(\n",
       "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
       "          )\n",
       "          (output): BertOutput(\n",
       "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
       "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "        )\n",
       "        (5): BertLayer(\n",
       "          (attention): BertAttention(\n",
       "            (self): BertSelfAttention(\n",
       "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (output): BertSelfOutput(\n",
       "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "          )\n",
       "          (intermediate): BertIntermediate(\n",
       "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
       "          )\n",
       "          (output): BertOutput(\n",
       "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
       "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "        )\n",
       "        (6): BertLayer(\n",
       "          (attention): BertAttention(\n",
       "            (self): BertSelfAttention(\n",
       "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (output): BertSelfOutput(\n",
       "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "          )\n",
       "          (intermediate): BertIntermediate(\n",
       "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
       "          )\n",
       "          (output): BertOutput(\n",
       "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
       "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "        )\n",
       "        (7): BertLayer(\n",
       "          (attention): BertAttention(\n",
       "            (self): BertSelfAttention(\n",
       "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (output): BertSelfOutput(\n",
       "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "          )\n",
       "          (intermediate): BertIntermediate(\n",
       "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
       "          )\n",
       "          (output): BertOutput(\n",
       "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
       "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "        )\n",
       "        (8): BertLayer(\n",
       "          (attention): BertAttention(\n",
       "            (self): BertSelfAttention(\n",
       "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (output): BertSelfOutput(\n",
       "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "          )\n",
       "          (intermediate): BertIntermediate(\n",
       "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
       "          )\n",
       "          (output): BertOutput(\n",
       "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
       "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "        )\n",
       "        (9): BertLayer(\n",
       "          (attention): BertAttention(\n",
       "            (self): BertSelfAttention(\n",
       "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (output): BertSelfOutput(\n",
       "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "          )\n",
       "          (intermediate): BertIntermediate(\n",
       "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
       "          )\n",
       "          (output): BertOutput(\n",
       "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
       "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "        )\n",
       "        (10): BertLayer(\n",
       "          (attention): BertAttention(\n",
       "            (self): BertSelfAttention(\n",
       "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (output): BertSelfOutput(\n",
       "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "          )\n",
       "          (intermediate): BertIntermediate(\n",
       "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
       "          )\n",
       "          (output): BertOutput(\n",
       "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
       "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "        )\n",
       "        (11): BertLayer(\n",
       "          (attention): BertAttention(\n",
       "            (self): BertSelfAttention(\n",
       "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (output): BertSelfOutput(\n",
       "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "          )\n",
       "          (intermediate): BertIntermediate(\n",
       "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
       "          )\n",
       "          (output): BertOutput(\n",
       "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
       "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "        )\n",
       "      )\n",
       "    )\n",
       "    (pooler): BertPooler(\n",
       "      (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "      (activation): Tanh()\n",
       "    )\n",
       "  )\n",
       "  (fc): Linear(in_features=768, out_features=11, bias=True)\n",
       ")"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "state_dict = torch.load(args.save_dir+'baseline/model.pth',map_location=torch.device('cpu'))\n",
    "state_dict_n = {key[7:]:value for key,value in state_dict.items()}\n",
    "state_dict_n = OrderedDict(state_dict_n)\n",
    "classifier.load_state_dict(state_dict_n,strict=False)\n",
    "classifier.eval()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "109490699"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def count_parameters(model):\n",
    "    return sum(p.numel() for p in model.parameters() if p.requires_grad)\n",
    "count_parameters(classifier)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [],
   "source": [
    "#train_state = make_train_state(args)\n",
    "set_seed_everywhere(args.seed, args.cuda)\n",
    "# compute the loss & accuracy on the test set using the best available model\n",
    "loss_func = cross_entropy\n",
    "#train_state = hyperdict[best_config]\n",
    "#classifier.load_state_dict(torch.load(train_state['model_filename']))\n",
    "#classifier = classifier.to(args.device)\n",
    "\n",
    "dataset.set_split('test')\n",
    "batch_generator = generate_batches(dataset, \n",
    "                                   batch_size=args.batch_size, \n",
    "                                   device=args.device)\n",
    "running_loss = 0.\n",
    "running_1_acc = 0.\n",
    "running_k_acc = 0.\n",
    "running_k_jac = 0.\n",
    "classifier.eval()\n",
    "\n",
    "for batch_index, batch_dict in enumerate(batch_generator):\n",
    "    \n",
    "    # get the data compute fuzzy labels\n",
    "    X = batch_dict['x_data']\n",
    "    mask = batch_dict['mask']\n",
    "    token_type_ids = batch_dict['token_type_ids']\n",
    "\n",
    "    y_target = batch_dict['y_target']\n",
    "    y_fuzzy = batch_dict['y_fuzzy']\n",
    "\n",
    "    Y = compute_fuzzy_label(y_target, y_fuzzy, fuzzy= args.fuzzy, \n",
    "                            how=args.fuzzy_how, lbd = args.fuzzy_lambda)\n",
    "\n",
    "    # step 3. compute the output\n",
    "    with torch.no_grad():\n",
    "        y_pred = classifier(X, mask, token_type_ids)[0]\n",
    "\n",
    "    # compute the output\n",
    "    #y_pred = classifier(X)\n",
    "\n",
    "    # compute the loss\n",
    "    loss = loss_func(y_pred, Y)\n",
    "    loss_t = loss.item()\n",
    "    running_loss += (loss_t - running_loss) / (batch_index + 1)\n",
    "\n",
    "    # compute the accuracy\n",
    "    acc_1_t = compute_1_accuracy(y_pred, y_target)\n",
    "    acc_k_t = compute_k_accuracy(y_pred, y_target, args.k)\n",
    "    jac_k_t = compute_jaccard_index(y_pred, y_target, args.k)\n",
    "\n",
    "    running_1_acc += (acc_1_t - running_1_acc) / (batch_index + 1)\n",
    "    running_k_acc += (acc_k_t - running_k_acc) / (batch_index + 1)\n",
    "    running_k_jac += (jac_k_t - running_k_jac) / (batch_index + 1)\n",
    "\n",
    "train_state['test_loss'] = running_loss\n",
    "train_state['test_1_acc'] = running_1_acc\n",
    "train_state['test_k_acc'] = running_k_acc\n",
    "train_state['test_k_jac'] = running_k_jac"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'stop_early': True,\n",
       " 'early_stopping_step': 10,\n",
       " 'early_stopping_best_k_acc_val': 93.9453125,\n",
       " 'learning_rate': 2e-05,\n",
       " 'epoch_index': 29,\n",
       " 'train_loss': [1.9384734945777904,\n",
       "  1.4072733713924295,\n",
       "  1.1899583553609676,\n",
       "  1.0696189326583827,\n",
       "  0.969181185954117,\n",
       "  0.8754922606869212,\n",
       "  0.7988645281382007,\n",
       "  0.7705735202161464,\n",
       "  0.7478130243170605,\n",
       "  0.7364640901071308,\n",
       "  0.7254302265538413,\n",
       "  0.7218846836447511,\n",
       "  0.7179453043291322,\n",
       "  0.7163840365825659,\n",
       "  0.7156912600755493,\n",
       "  0.7150583410396265,\n",
       "  0.71247800533912,\n",
       "  0.7128028032780985,\n",
       "  0.7112062406387958,\n",
       "  0.7133496683735651,\n",
       "  0.7136671717785539,\n",
       "  0.7135898698063848,\n",
       "  0.7119978615706831,\n",
       "  0.710358968590015,\n",
       "  0.7129910518835733,\n",
       "  0.7121140727612363,\n",
       "  0.7107224952304333,\n",
       "  0.7116938700600858,\n",
       "  0.7111992317646455,\n",
       "  0.712164424643171],\n",
       " 'train_1_acc': [34.799107142857146,\n",
       "  58.549107142857146,\n",
       "  70.42410714285715,\n",
       "  77.52232142857142,\n",
       "  83.43750000000001,\n",
       "  89.35267857142854,\n",
       "  94.4642857142857,\n",
       "  96.00446428571432,\n",
       "  97.29910714285714,\n",
       "  97.9464285714286,\n",
       "  98.25892857142857,\n",
       "  98.28125000000001,\n",
       "  98.61607142857142,\n",
       "  98.75000000000001,\n",
       "  98.72767857142856,\n",
       "  98.77232142857143,\n",
       "  98.79464285714285,\n",
       "  98.81696428571428,\n",
       "  98.74999999999999,\n",
       "  98.74999999999997,\n",
       "  98.77232142857143,\n",
       "  98.63839285714285,\n",
       "  98.90625,\n",
       "  98.79464285714286,\n",
       "  98.92857142857142,\n",
       "  98.79464285714286,\n",
       "  98.75,\n",
       "  98.6607142857143,\n",
       "  98.92857142857142,\n",
       "  99.08482142857139],\n",
       " 'train_k_acc': [68.99553571428571,\n",
       "  91.27232142857142,\n",
       "  95.64732142857139,\n",
       "  96.91964285714286,\n",
       "  98.23660714285718,\n",
       "  98.99553571428571,\n",
       "  99.39732142857144,\n",
       "  99.55357142857142,\n",
       "  99.6205357142857,\n",
       "  99.66517857142857,\n",
       "  99.77678571428571,\n",
       "  99.77678571428568,\n",
       "  99.82142857142858,\n",
       "  99.70982142857146,\n",
       "  99.70982142857144,\n",
       "  99.77678571428571,\n",
       "  99.91071428571429,\n",
       "  99.75446428571426,\n",
       "  99.79910714285714,\n",
       "  99.84374999999996,\n",
       "  99.84375,\n",
       "  99.75446428571428,\n",
       "  99.82142857142858,\n",
       "  99.86607142857146,\n",
       "  99.82142857142856,\n",
       "  99.84374999999999,\n",
       "  99.82142857142858,\n",
       "  99.82142857142857,\n",
       "  99.86607142857143,\n",
       "  99.84374999999999],\n",
       " 'train_k_jac': [0.2590620617781367,\n",
       "  0.31700202226638796,\n",
       "  0.32711256657327936,\n",
       "  0.32734162509441367,\n",
       "  0.3309677949973515,\n",
       "  0.3268680934395108,\n",
       "  0.32358684156622197,\n",
       "  0.31952646715300415,\n",
       "  0.31598214464528224,\n",
       "  0.3128576785326005,\n",
       "  0.3099861851760318,\n",
       "  0.3075717496020454,\n",
       "  0.30616603110517787,\n",
       "  0.3073527898107257,\n",
       "  0.3055171140602658,\n",
       "  0.3048256831509727,\n",
       "  0.3049080588987896,\n",
       "  0.3059773632458278,\n",
       "  0.30435427555016104,\n",
       "  0.3053082508700235,\n",
       "  0.30438297518662055,\n",
       "  0.3043106973171234,\n",
       "  0.30608896740845265,\n",
       "  0.3054389915296009,\n",
       "  0.30508290954998557,\n",
       "  0.3050765310014997,\n",
       "  0.3042968771287374,\n",
       "  0.30610863310950154,\n",
       "  0.3043021908828191,\n",
       "  0.3042554233755384],\n",
       " 'val_loss': [1.54212753051368,\n",
       "  1.2628084325594995,\n",
       "  1.1716130438359071,\n",
       "  1.1450273683185195,\n",
       "  1.18617004201939,\n",
       "  1.2066747567964455,\n",
       "  1.211391011460802,\n",
       "  1.240820411240497,\n",
       "  1.2204740069752777,\n",
       "  1.2303455526745064,\n",
       "  1.2042868329549994,\n",
       "  1.217607510804781,\n",
       "  1.2206128917911339,\n",
       "  1.235375488790092,\n",
       "  1.202693902303832,\n",
       "  1.2059950331359384,\n",
       "  1.2306231157400482,\n",
       "  1.2270288659343156,\n",
       "  1.2043630857056502,\n",
       "  1.2118795746867375,\n",
       "  1.2108343326703683,\n",
       "  1.2164301840480998,\n",
       "  1.2362817097339152,\n",
       "  1.2109667746804211,\n",
       "  1.222257065103675,\n",
       "  1.2128276737524724,\n",
       "  1.2174610803184422,\n",
       "  1.2058006253940732,\n",
       "  1.2069273441732007,\n",
       "  1.2146932638849632],\n",
       " 'val_1_acc': [50.9765625,\n",
       "  66.2109375,\n",
       "  71.6796875,\n",
       "  73.6328125,\n",
       "  70.3125,\n",
       "  72.65625,\n",
       "  70.703125,\n",
       "  70.1171875,\n",
       "  72.85156249999999,\n",
       "  71.2890625,\n",
       "  72.4609375,\n",
       "  72.0703125,\n",
       "  70.8984375,\n",
       "  71.484375,\n",
       "  71.875,\n",
       "  71.09375,\n",
       "  70.70312499999999,\n",
       "  70.8984375,\n",
       "  72.4609375,\n",
       "  71.6796875,\n",
       "  72.265625,\n",
       "  71.09375,\n",
       "  70.703125,\n",
       "  71.875,\n",
       "  71.48437500000001,\n",
       "  72.0703125,\n",
       "  71.2890625,\n",
       "  72.4609375,\n",
       "  72.265625,\n",
       "  72.4609375],\n",
       " 'val_k_acc': [86.5234375,\n",
       "  92.1875,\n",
       "  93.359375,\n",
       "  92.96875,\n",
       "  92.96874999999999,\n",
       "  92.578125,\n",
       "  93.359375,\n",
       "  93.359375,\n",
       "  92.96875,\n",
       "  93.35937500000001,\n",
       "  93.75,\n",
       "  92.7734375,\n",
       "  93.5546875,\n",
       "  93.5546875,\n",
       "  93.75,\n",
       "  93.359375,\n",
       "  93.5546875,\n",
       "  92.7734375,\n",
       "  93.1640625,\n",
       "  93.9453125,\n",
       "  93.5546875,\n",
       "  93.9453125,\n",
       "  93.75,\n",
       "  93.359375,\n",
       "  93.1640625,\n",
       "  93.359375,\n",
       "  93.1640625,\n",
       "  92.96875,\n",
       "  93.1640625,\n",
       "  92.7734375],\n",
       " 'val_k_jac': [0.30928200110793114,\n",
       "  0.31985677033662796,\n",
       "  0.3244466185569763,\n",
       "  0.33525391295552254,\n",
       "  0.330533854663372,\n",
       "  0.32353981211781496,\n",
       "  0.31788503751158714,\n",
       "  0.31555059924721723,\n",
       "  0.30609188601374626,\n",
       "  0.30511533468961716,\n",
       "  0.30369697883725166,\n",
       "  0.2950195409357548,\n",
       "  0.3000558018684387,\n",
       "  0.2967913039028644,\n",
       "  0.29597749933600426,\n",
       "  0.29593563824892044,\n",
       "  0.29525670036673546,\n",
       "  0.29614490643143654,\n",
       "  0.2979399114847183,\n",
       "  0.2989629879593849,\n",
       "  0.2947312220931053,\n",
       "  0.29658203572034836,\n",
       "  0.2940569259226322,\n",
       "  0.2931547611951828,\n",
       "  0.29404762387275696,\n",
       "  0.2935732901096344,\n",
       "  0.29546596482396126,\n",
       "  0.29561012610793114,\n",
       "  0.2917829304933548,\n",
       "  0.29482888057827955],\n",
       " 'test_loss': 1.3138385342412966,\n",
       " 'test_1_acc': 66.9921875,\n",
       " 'test_k_acc': 94.53125,\n",
       " 'test_k_jac': 0.25075335428118706,\n",
       " 'model_filename': 'model_storage/bert/unfreeze/multigroup/model.pth'}"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# LS Model\n",
    "train_state"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'stop_early': True,\n",
       " 'early_stopping_step': 10,\n",
       " 'early_stopping_best_k_acc_val': 93.9453125,\n",
       " 'learning_rate': 2e-05,\n",
       " 'epoch_index': 29,\n",
       " 'train_loss': [1.9384734945777904,\n",
       "  1.4072733713924295,\n",
       "  1.1899583553609676,\n",
       "  1.0696189326583827,\n",
       "  0.969181185954117,\n",
       "  0.8754922606869212,\n",
       "  0.7988645281382007,\n",
       "  0.7705735202161464,\n",
       "  0.7478130243170605,\n",
       "  0.7364640901071308,\n",
       "  0.7254302265538413,\n",
       "  0.7218846836447511,\n",
       "  0.7179453043291322,\n",
       "  0.7163840365825659,\n",
       "  0.7156912600755493,\n",
       "  0.7150583410396265,\n",
       "  0.71247800533912,\n",
       "  0.7128028032780985,\n",
       "  0.7112062406387958,\n",
       "  0.7133496683735651,\n",
       "  0.7136671717785539,\n",
       "  0.7135898698063848,\n",
       "  0.7119978615706831,\n",
       "  0.710358968590015,\n",
       "  0.7129910518835733,\n",
       "  0.7121140727612363,\n",
       "  0.7107224952304333,\n",
       "  0.7116938700600858,\n",
       "  0.7111992317646455,\n",
       "  0.712164424643171],\n",
       " 'train_1_acc': [34.799107142857146,\n",
       "  58.549107142857146,\n",
       "  70.42410714285715,\n",
       "  77.52232142857142,\n",
       "  83.43750000000001,\n",
       "  89.35267857142854,\n",
       "  94.4642857142857,\n",
       "  96.00446428571432,\n",
       "  97.29910714285714,\n",
       "  97.9464285714286,\n",
       "  98.25892857142857,\n",
       "  98.28125000000001,\n",
       "  98.61607142857142,\n",
       "  98.75000000000001,\n",
       "  98.72767857142856,\n",
       "  98.77232142857143,\n",
       "  98.79464285714285,\n",
       "  98.81696428571428,\n",
       "  98.74999999999999,\n",
       "  98.74999999999997,\n",
       "  98.77232142857143,\n",
       "  98.63839285714285,\n",
       "  98.90625,\n",
       "  98.79464285714286,\n",
       "  98.92857142857142,\n",
       "  98.79464285714286,\n",
       "  98.75,\n",
       "  98.6607142857143,\n",
       "  98.92857142857142,\n",
       "  99.08482142857139],\n",
       " 'train_k_acc': [68.99553571428571,\n",
       "  91.27232142857142,\n",
       "  95.64732142857139,\n",
       "  96.91964285714286,\n",
       "  98.23660714285718,\n",
       "  98.99553571428571,\n",
       "  99.39732142857144,\n",
       "  99.55357142857142,\n",
       "  99.6205357142857,\n",
       "  99.66517857142857,\n",
       "  99.77678571428571,\n",
       "  99.77678571428568,\n",
       "  99.82142857142858,\n",
       "  99.70982142857146,\n",
       "  99.70982142857144,\n",
       "  99.77678571428571,\n",
       "  99.91071428571429,\n",
       "  99.75446428571426,\n",
       "  99.79910714285714,\n",
       "  99.84374999999996,\n",
       "  99.84375,\n",
       "  99.75446428571428,\n",
       "  99.82142857142858,\n",
       "  99.86607142857146,\n",
       "  99.82142857142856,\n",
       "  99.84374999999999,\n",
       "  99.82142857142858,\n",
       "  99.82142857142857,\n",
       "  99.86607142857143,\n",
       "  99.84374999999999],\n",
       " 'train_k_jac': [0.2590620617781367,\n",
       "  0.31700202226638796,\n",
       "  0.32711256657327936,\n",
       "  0.32734162509441367,\n",
       "  0.3309677949973515,\n",
       "  0.3268680934395108,\n",
       "  0.32358684156622197,\n",
       "  0.31952646715300415,\n",
       "  0.31598214464528224,\n",
       "  0.3128576785326005,\n",
       "  0.3099861851760318,\n",
       "  0.3075717496020454,\n",
       "  0.30616603110517787,\n",
       "  0.3073527898107257,\n",
       "  0.3055171140602658,\n",
       "  0.3048256831509727,\n",
       "  0.3049080588987896,\n",
       "  0.3059773632458278,\n",
       "  0.30435427555016104,\n",
       "  0.3053082508700235,\n",
       "  0.30438297518662055,\n",
       "  0.3043106973171234,\n",
       "  0.30608896740845265,\n",
       "  0.3054389915296009,\n",
       "  0.30508290954998557,\n",
       "  0.3050765310014997,\n",
       "  0.3042968771287374,\n",
       "  0.30610863310950154,\n",
       "  0.3043021908828191,\n",
       "  0.3042554233755384],\n",
       " 'val_loss': [1.54212753051368,\n",
       "  1.2628084325594995,\n",
       "  1.1716130438359071,\n",
       "  1.1450273683185195,\n",
       "  1.18617004201939,\n",
       "  1.2066747567964455,\n",
       "  1.211391011460802,\n",
       "  1.240820411240497,\n",
       "  1.2204740069752777,\n",
       "  1.2303455526745064,\n",
       "  1.2042868329549994,\n",
       "  1.217607510804781,\n",
       "  1.2206128917911339,\n",
       "  1.235375488790092,\n",
       "  1.202693902303832,\n",
       "  1.2059950331359384,\n",
       "  1.2306231157400482,\n",
       "  1.2270288659343156,\n",
       "  1.2043630857056502,\n",
       "  1.2118795746867375,\n",
       "  1.2108343326703683,\n",
       "  1.2164301840480998,\n",
       "  1.2362817097339152,\n",
       "  1.2109667746804211,\n",
       "  1.222257065103675,\n",
       "  1.2128276737524724,\n",
       "  1.2174610803184422,\n",
       "  1.2058006253940732,\n",
       "  1.2069273441732007,\n",
       "  1.2146932638849632],\n",
       " 'val_1_acc': [50.9765625,\n",
       "  66.2109375,\n",
       "  71.6796875,\n",
       "  73.6328125,\n",
       "  70.3125,\n",
       "  72.65625,\n",
       "  70.703125,\n",
       "  70.1171875,\n",
       "  72.85156249999999,\n",
       "  71.2890625,\n",
       "  72.4609375,\n",
       "  72.0703125,\n",
       "  70.8984375,\n",
       "  71.484375,\n",
       "  71.875,\n",
       "  71.09375,\n",
       "  70.70312499999999,\n",
       "  70.8984375,\n",
       "  72.4609375,\n",
       "  71.6796875,\n",
       "  72.265625,\n",
       "  71.09375,\n",
       "  70.703125,\n",
       "  71.875,\n",
       "  71.48437500000001,\n",
       "  72.0703125,\n",
       "  71.2890625,\n",
       "  72.4609375,\n",
       "  72.265625,\n",
       "  72.4609375],\n",
       " 'val_k_acc': [86.5234375,\n",
       "  92.1875,\n",
       "  93.359375,\n",
       "  92.96875,\n",
       "  92.96874999999999,\n",
       "  92.578125,\n",
       "  93.359375,\n",
       "  93.359375,\n",
       "  92.96875,\n",
       "  93.35937500000001,\n",
       "  93.75,\n",
       "  92.7734375,\n",
       "  93.5546875,\n",
       "  93.5546875,\n",
       "  93.75,\n",
       "  93.359375,\n",
       "  93.5546875,\n",
       "  92.7734375,\n",
       "  93.1640625,\n",
       "  93.9453125,\n",
       "  93.5546875,\n",
       "  93.9453125,\n",
       "  93.75,\n",
       "  93.359375,\n",
       "  93.1640625,\n",
       "  93.359375,\n",
       "  93.1640625,\n",
       "  92.96875,\n",
       "  93.1640625,\n",
       "  92.7734375],\n",
       " 'val_k_jac': [0.30928200110793114,\n",
       "  0.31985677033662796,\n",
       "  0.3244466185569763,\n",
       "  0.33525391295552254,\n",
       "  0.330533854663372,\n",
       "  0.32353981211781496,\n",
       "  0.31788503751158714,\n",
       "  0.31555059924721723,\n",
       "  0.30609188601374626,\n",
       "  0.30511533468961716,\n",
       "  0.30369697883725166,\n",
       "  0.2950195409357548,\n",
       "  0.3000558018684387,\n",
       "  0.2967913039028644,\n",
       "  0.29597749933600426,\n",
       "  0.29593563824892044,\n",
       "  0.29525670036673546,\n",
       "  0.29614490643143654,\n",
       "  0.2979399114847183,\n",
       "  0.2989629879593849,\n",
       "  0.2947312220931053,\n",
       "  0.29658203572034836,\n",
       "  0.2940569259226322,\n",
       "  0.2931547611951828,\n",
       "  0.29404762387275696,\n",
       "  0.2935732901096344,\n",
       "  0.29546596482396126,\n",
       "  0.29561012610793114,\n",
       "  0.2917829304933548,\n",
       "  0.29482888057827955],\n",
       " 'test_loss': 1.363605621549176,\n",
       " 'test_1_acc': 67.578125,\n",
       " 'test_k_acc': 93.5546875,\n",
       " 'test_k_jac': 0.308756522834301,\n",
       " 'model_filename': 'model_storage/bert/unfreeze/multigroup/model.pth'}"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#baseline\n",
    "train_state"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Inference"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "def preprocess_text(text):\n",
    "    text = text.lower()\n",
    "    text = re.sub(r\"([.,!?])\", r\" \\1 \", text)\n",
    "    text = re.sub(r\"[^a-zA-Z.,!?]+\", r\" \", text)\n",
    "    return text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "def predict_rating(text, classifier, vectorizer, classes=classes, k=1):\n",
    "    \"\"\"Predict the rating of a review\n",
    "    \n",
    "    Args:\n",
    "        text (str): the text of the description\n",
    "        classifier (ReviewClassifier): the trained model\n",
    "        vectorizer (ReviewVectorizer): the corresponding vectorizer\n",
    "        classes (list of str): The name of the ouv classes\n",
    "        k (int): show the largest k prediction, default to 1\n",
    "    \"\"\"\n",
    "    ouv = preprocess_text(text)\n",
    "    tokenized = vectorizer.vectorize(ouv)\n",
    "    X = tokenized['ids']\n",
    "    mask = tokenized['mask']\n",
    "    token_type_ids = tokenized['token_type_ids']\n",
    "    \n",
    "    X = X.view(1,-1)\n",
    "    mask = mask.unsqueeze(0)\n",
    "    classifier.eval()\n",
    "    with torch.no_grad():\n",
    "        result = classifier(X, mask, token_type_ids, apply_softmax=True)[0]\n",
    "    \n",
    "    if k==1:\n",
    "        pred_id = result.argmax().item()\n",
    "        return (classes[pred_id], result[0][pred_id])\n",
    "    else:\n",
    "        pred_indices = [i.item() for i in result.topk(k)[1][0]]\n",
    "        output = []\n",
    "        for pred_id in pred_indices:\n",
    "            output.append((classes[pred_id],result[0][pred_id].item()))\n",
    "        return output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is a very old building dating back to 13th century -> Criteria iv with a probability of 0.55\n"
     ]
    }
   ],
   "source": [
    "test_ouv = 'this is a very old building dating back to 13th century'\n",
    "\n",
    "prediction = predict_rating(test_ouv,classifier,vectorizer,classes)\n",
    "print('{} -> {} with a probability of {:0.2f}'.format(test_ouv, prediction[0],prediction[1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Top 3 predictions:\n",
      "===================\n",
      "this is a very old building dating back to 13th century -> Criteria iv with a probability of 0.55\n",
      "this is a very old building dating back to 13th century -> Criteria iii with a probability of 0.26\n",
      "this is a very old building dating back to 13th century -> Criteria i with a probability of 0.07\n"
     ]
    }
   ],
   "source": [
    "test_ouv = 'this is a very old building dating back to 13th century'\n",
    "k=3\n",
    "predictions = predict_rating(test_ouv,classifier,vectorizer,classes,k=k)\n",
    "\n",
    "print(\"Top {} predictions:\".format(k))\n",
    "print(\"===================\")\n",
    "for prediction in predictions:\n",
    "    print('{} -> {} with a probability of {:0.2f}'.format(test_ouv, prediction[0],prediction[1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Top 3 predictions:\n",
      "===================\n",
      "The particular layout of the complex is unique to this site -> Criteria iv with a probability of 0.44\n",
      "The particular layout of the complex is unique to this site -> Criteria iii with a probability of 0.29\n",
      "The particular layout of the complex is unique to this site -> Criteria i with a probability of 0.16\n"
     ]
    }
   ],
   "source": [
    "test_ouv = 'The particular layout of the complex is unique to this site'\n",
    "k=3\n",
    "predictions = predict_rating(test_ouv,classifier,vectorizer,classes,k)\n",
    "\n",
    "print(\"Top {} predictions:\".format(k))\n",
    "print(\"===================\")\n",
    "for prediction in predictions:\n",
    "    print('{} -> {} with a probability of {:0.2f}'.format(test_ouv, prediction[0],prediction[1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Top 3 predictions:\n",
      "===================\n",
      "the lagoon of venice also has one of the highest concentrations of masterpieces in the world from \n",
      "torcellos cathedral to the church of santa maria della salute . the years of the republics extraordinary golden \n",
      "age are represented by monuments of incomparable beauty -> Criteria i with a probability of 0.85\n",
      "the lagoon of venice also has one of the highest concentrations of masterpieces in the world from \n",
      "torcellos cathedral to the church of santa maria della salute . the years of the republics extraordinary golden \n",
      "age are represented by monuments of incomparable beauty -> Criteria iv with a probability of 0.06\n",
      "the lagoon of venice also has one of the highest concentrations of masterpieces in the world from \n",
      "torcellos cathedral to the church of santa maria della salute . the years of the republics extraordinary golden \n",
      "age are represented by monuments of incomparable beauty -> Criteria ii with a probability of 0.03\n"
     ]
    }
   ],
   "source": [
    "test_ouv = '''the lagoon of venice also has one of the highest concentrations of masterpieces in the world from \n",
    "torcellos cathedral to the church of santa maria della salute . the years of the republics extraordinary golden \n",
    "age are represented by monuments of incomparable beauty'''\n",
    "k=3\n",
    "predictions = predict_rating(test_ouv,classifier,vectorizer,classes,k)\n",
    "\n",
    "print(\"Top {} predictions:\".format(k))\n",
    "print(\"===================\")\n",
    "for prediction in predictions:\n",
    "    print('{} -> {} with a probability of {:0.2f}'.format(test_ouv, prediction[0],prediction[1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Top 3 predictions:\n",
      "===================\n",
      "the lagoon of venice also has one of the highest concentrations of masterpieces in the world -> Criteria i with a probability of 0.87\n",
      "the lagoon of venice also has one of the highest concentrations of masterpieces in the world -> Criteria ii with a probability of 0.05\n",
      "the lagoon of venice also has one of the highest concentrations of masterpieces in the world -> Criteria vi with a probability of 0.03\n"
     ]
    }
   ],
   "source": [
    "test_ouv = '''the lagoon of venice also has one of the highest concentrations of masterpieces in the world'''\n",
    "k=3\n",
    "predictions = predict_rating(test_ouv,classifier,vectorizer,classes,k)\n",
    "\n",
    "print(\"Top {} predictions:\".format(k))\n",
    "print(\"===================\")\n",
    "for prediction in predictions:\n",
    "    print('{} -> {} with a probability of {:0.2f}'.format(test_ouv, prediction[0],prediction[1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Top 3 predictions:\n",
      "===================\n",
      "from torcellos cathedral to the church of santa maria della salute -> Criteria i with a probability of 0.70\n",
      "from torcellos cathedral to the church of santa maria della salute -> Criteria iv with a probability of 0.13\n",
      "from torcellos cathedral to the church of santa maria della salute -> Criteria ii with a probability of 0.08\n"
     ]
    }
   ],
   "source": [
    "test_ouv = '''from torcellos cathedral to the church of santa maria della salute'''\n",
    "k=3\n",
    "predictions = predict_rating(test_ouv,classifier,vectorizer,classes,k)\n",
    "\n",
    "print(\"Top {} predictions:\".format(k))\n",
    "print(\"===================\")\n",
    "for prediction in predictions:\n",
    "    print('{} -> {} with a probability of {:0.2f}'.format(test_ouv, prediction[0],prediction[1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Top 3 predictions:\n",
      "===================\n",
      "the years of the republics extraordinary golden age are represented by monuments of incomparable beauty -> Criteria iv with a probability of 0.59\n",
      "the years of the republics extraordinary golden age are represented by monuments of incomparable beauty -> Criteria i with a probability of 0.15\n",
      "the years of the republics extraordinary golden age are represented by monuments of incomparable beauty -> Criteria iii with a probability of 0.12\n"
     ]
    }
   ],
   "source": [
    "test_ouv = '''the years of the republics extraordinary golden age are represented by monuments of incomparable beauty'''\n",
    "k=3\n",
    "predictions = predict_rating(test_ouv,classifier,vectorizer,classes,k)\n",
    "\n",
    "print(\"Top {} predictions:\".format(k))\n",
    "print(\"===================\")\n",
    "for prediction in predictions:\n",
    "    print('{} -> {} with a probability of {:0.2f}'.format(test_ouv, prediction[0],prediction[1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Elapsed: 0.4411201477050781\n"
     ]
    }
   ],
   "source": [
    "import time\n",
    "class Timer(object):\n",
    "    def __init__(self, name=None):\n",
    "        self.name = name\n",
    "\n",
    "    def __enter__(self):\n",
    "        self.tstart = time.time()\n",
    "\n",
    "    def __exit__(self, type, value, traceback):\n",
    "        if self.name:\n",
    "            print('[%s]' % self.name,)\n",
    "        print('Elapsed: %s' % (time.time() - self.tstart))\n",
    "        \n",
    "set_seed_everywhere(args.seed, args.cuda)        \n",
    "test_ouv = 'The particular layout of the complex is unique to this site'\n",
    "k=3\n",
    "with Timer():\n",
    "    predictions = predict_rating(test_ouv,classifier,vectorizer,classes,k)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Interpretability"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "ouv_df = pd.read_csv(args.ouv_csv)\n",
    "word_counts = Counter()\n",
    "for data in ouv_df.data:\n",
    "    for word in data.split(' '):\n",
    "        if word not in string.punctuation:\n",
    "            word_counts[word] += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "def ngrams_iterator(token_list, ngrams):\n",
    "    def _get_ngrams(n):\n",
    "        return zip(*[token_list[i:] for i in range(n)])\n",
    "    for x in token_list:\n",
    "        yield x\n",
    "    for n in range(2, ngrams+1):\n",
    "        for x in _get_ngrams(n):\n",
    "            yield ' '.join(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "ouv_df = pd.read_csv(args.ouv_csv)\n",
    "word_counts = Counter()\n",
    "for data in ouv_df.data:\n",
    "    token_list = data.split(' ')\n",
    "    for word in ngrams_iterator(token_list, 5):\n",
    "        temp = 0\n",
    "        for element in word:\n",
    "            if element in string.punctuation:\n",
    "                temp = 1\n",
    "                break\n",
    "        if temp==0:\n",
    "            word_counts[word] += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2353"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vocab = [word for word, count in word_counts.items() if count>15 and count<600]\n",
    "len(vocab)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "def infer_tokens_importance(vocab, classifier, vectorizer, classes, k=50):\n",
    "    \"\"\"Predict the rating of a review\n",
    "    \n",
    "    Args:\n",
    "        vocab (list of str): the whole vocabulary\n",
    "        classifier (ReviewClassifier): the trained model\n",
    "        vectorizer (ReviewVectorizer): the corresponding vectorizer\n",
    "        classes (list of str): The name of the ouv classes\n",
    "        k (int): show the largest k prediction, default to 1\n",
    "    \"\"\"\n",
    "    try:\n",
    "        with open(args.save_dir+'completed.p', 'rb') as fp:\n",
    "            results = pickle.load(fp)\n",
    "    except:\n",
    "        results = {}\n",
    "    classifier.eval()\n",
    "    for token in vocab:\n",
    "        if token in results.keys():\n",
    "            continue\n",
    "        vectorized_token = vectorizer.vectorize(token)['ids'].unsqueeze(0)\n",
    "        masks = vectorizer.vectorize(token)['mask'].unsqueeze(0)\n",
    "        token_type_ids = vectorizer.vectorize(token)['token_type_ids'].unsqueeze(0)\n",
    "        result = classifier(vectorized_token, masks, token_type_ids, apply_softmax = True)[0]\n",
    "        results[token] = result\n",
    "        if len(results)%20==0:\n",
    "            with open(args.save_dir+'completed.p', 'wb') as fp:\n",
    "                pickle.dump(results,fp, protocol=pickle.HIGHEST_PROTOCOL)\n",
    "            print(len(results))\n",
    "        \n",
    "    results = torch.stack(list(results.values()))\n",
    "    \n",
    "    vocab_id = results.topk(k, dim=0)[1]\n",
    "    vocab_weight = results.topk(k, dim=0)[0]\n",
    "    return vocab_id, vocab_weight"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_top_k_DataFrame(vocab, classifier, vectorizer, classes, k=10):\n",
    "    \n",
    "    vocab_id = infer_tokens_importance(vocab, classifier, vectorizer, classes, k)[0]\n",
    "    df = pd.DataFrame(columns = classes)\n",
    "    for i in range(len(classes)):\n",
    "        indices = vocab_id[:,:,i].squeeze().tolist()\n",
    "        words = pd.Series([vocab[j] for j in indices])\n",
    "        df[classes[i]] = words\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Criteria i</th>\n",
       "      <th>Criteria ii</th>\n",
       "      <th>Criteria iii</th>\n",
       "      <th>Criteria iv</th>\n",
       "      <th>Criteria v</th>\n",
       "      <th>Criteria vi</th>\n",
       "      <th>Criteria vii</th>\n",
       "      <th>Criteria viii</th>\n",
       "      <th>Criteria ix</th>\n",
       "      <th>Criteria x</th>\n",
       "      <th>Others</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>a masterpiece of</td>\n",
       "      <td>interchange of human values</td>\n",
       "      <td>neolithic</td>\n",
       "      <td>are outstanding examples of</td>\n",
       "      <td>traditional human settlement</td>\n",
       "      <td>directly and tangibly associated with</td>\n",
       "      <td>exceptional natural</td>\n",
       "      <td>tectonic</td>\n",
       "      <td>evolutionary processes</td>\n",
       "      <td>the conservation of</td>\n",
       "      <td>surviving</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>masterpiece of</td>\n",
       "      <td>in the development of</td>\n",
       "      <td>bears exceptional testimony to the</td>\n",
       "      <td>an outstanding example of a</td>\n",
       "      <td>traditional human</td>\n",
       "      <td>directly and tangibly associated</td>\n",
       "      <td>exceptional natural beauty</td>\n",
       "      <td>formation</td>\n",
       "      <td>ecological and</td>\n",
       "      <td>of mammals</td>\n",
       "      <td>date</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>a masterpiece</td>\n",
       "      <td>interchange of human</td>\n",
       "      <td>traditions of</td>\n",
       "      <td>are outstanding examples</td>\n",
       "      <td>example of a traditional</td>\n",
       "      <td>and tangibly associated with</td>\n",
       "      <td>lake</td>\n",
       "      <td>geological</td>\n",
       "      <td>and marine</td>\n",
       "      <td>endangered species</td>\n",
       "      <td>played</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>is a masterpiece</td>\n",
       "      <td>influence on the development of</td>\n",
       "      <td>bears exceptional testimony to</td>\n",
       "      <td>outstanding examples of the</td>\n",
       "      <td>settlement</td>\n",
       "      <td>directly associated with</td>\n",
       "      <td>natural beauty</td>\n",
       "      <td>geological processes</td>\n",
       "      <td>ecological processes</td>\n",
       "      <td>species of mammals</td>\n",
       "      <td>record</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>is a masterpiece of</td>\n",
       "      <td>the influence of</td>\n",
       "      <td>tradition of</td>\n",
       "      <td>outstanding example of a</td>\n",
       "      <td>of a traditional</td>\n",
       "      <td>tangibly associated with</td>\n",
       "      <td>peak</td>\n",
       "      <td>volcanic</td>\n",
       "      <td>and ecological</td>\n",
       "      <td>mammals</td>\n",
       "      <td>ages</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>of human creative genius</td>\n",
       "      <td>influence on the development</td>\n",
       "      <td>bronze age</td>\n",
       "      <td>the historic centre</td>\n",
       "      <td>vernacular</td>\n",
       "      <td>profound influence on</td>\n",
       "      <td>spectacular</td>\n",
       "      <td>glacial</td>\n",
       "      <td>ecosystems and</td>\n",
       "      <td>bird species</td>\n",
       "      <td>well as the</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>masterpieces of</td>\n",
       "      <td>century ad</td>\n",
       "      <td>inscriptions</td>\n",
       "      <td>an outstanding example of an</td>\n",
       "      <td>socio economic</td>\n",
       "      <td>islam</td>\n",
       "      <td>scenic</td>\n",
       "      <td>fossil</td>\n",
       "      <td>undisturbed</td>\n",
       "      <td>bird species</td>\n",
       "      <td>result of</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>masterpieces</td>\n",
       "      <td>to the development of</td>\n",
       "      <td>cultural tradition</td>\n",
       "      <td>outstanding examples of</td>\n",
       "      <td>of traditional</td>\n",
       "      <td>orthodox</td>\n",
       "      <td>cliffs</td>\n",
       "      <td>lava</td>\n",
       "      <td>lowland</td>\n",
       "      <td>the endangered</td>\n",
       "      <td>the same</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>a unique artistic</td>\n",
       "      <td>the development of architecture</td>\n",
       "      <td>the roman</td>\n",
       "      <td>historic centre</td>\n",
       "      <td>a traditional</td>\n",
       "      <td>directly and tangibly</td>\n",
       "      <td>mountain</td>\n",
       "      <td>dunes</td>\n",
       "      <td>extent</td>\n",
       "      <td>the conservation</td>\n",
       "      <td>variety</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>a unique artistic achievement</td>\n",
       "      <td>the persian</td>\n",
       "      <td>unique testimony to the</td>\n",
       "      <td>exceptional example of</td>\n",
       "      <td>socio</td>\n",
       "      <td>exceptional witness to</td>\n",
       "      <td>gorges</td>\n",
       "      <td>limestone</td>\n",
       "      <td>rainforests</td>\n",
       "      <td>threatened species</td>\n",
       "      <td>greek</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>masterpiece</td>\n",
       "      <td>important interchange of</td>\n",
       "      <td>the site of</td>\n",
       "      <td>outstanding example of an</td>\n",
       "      <td>way of life</td>\n",
       "      <td>witness to</td>\n",
       "      <td>national park</td>\n",
       "      <td>sandstone</td>\n",
       "      <td>climate change</td>\n",
       "      <td>biological diversity</td>\n",
       "      <td>survived</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>unique artistic</td>\n",
       "      <td>influenced by</td>\n",
       "      <td>of the roman</td>\n",
       "      <td>outstanding example of the</td>\n",
       "      <td>settlements</td>\n",
       "      <td>tangibly associated</td>\n",
       "      <td>peaks</td>\n",
       "      <td>karst</td>\n",
       "      <td>and biological processes</td>\n",
       "      <td>critically endangered</td>\n",
       "      <td>german</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>the monumental</td>\n",
       "      <td>the development of the</td>\n",
       "      <td>bce</td>\n",
       "      <td>historic centre of</td>\n",
       "      <td>land use</td>\n",
       "      <td>unity</td>\n",
       "      <td>lakes</td>\n",
       "      <td>rugged</td>\n",
       "      <td>processes of</td>\n",
       "      <td>the diversity of</td>\n",
       "      <td>numbers</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>unique artistic achievement</td>\n",
       "      <td>that was</td>\n",
       "      <td>cultural tradition of</td>\n",
       "      <td>the historic centre of</td>\n",
       "      <td>community</td>\n",
       "      <td>directly and</td>\n",
       "      <td>sea level</td>\n",
       "      <td>glaciers</td>\n",
       "      <td>rainforest</td>\n",
       "      <td>species such</td>\n",
       "      <td>highest</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>and decorative</td>\n",
       "      <td>and a</td>\n",
       "      <td>civilisation</td>\n",
       "      <td>an exceptional example of</td>\n",
       "      <td>vestiges</td>\n",
       "      <td>and tangibly associated</td>\n",
       "      <td>mountains</td>\n",
       "      <td>geomorphological</td>\n",
       "      <td>the natural</td>\n",
       "      <td>of birds</td>\n",
       "      <td>world</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>human creative genius</td>\n",
       "      <td>influenced the</td>\n",
       "      <td>a unique testimony</td>\n",
       "      <td>is an exceptional example of</td>\n",
       "      <td>the traditional</td>\n",
       "      <td>testify to</td>\n",
       "      <td>the mountains</td>\n",
       "      <td>andes</td>\n",
       "      <td>geomorphological</td>\n",
       "      <td>conservation of</td>\n",
       "      <td>achieved</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>magnificent</td>\n",
       "      <td>an important interchange of</td>\n",
       "      <td>the kingdom</td>\n",
       "      <td>unique example of</td>\n",
       "      <td>a type of</td>\n",
       "      <td>philosophy</td>\n",
       "      <td>the lake</td>\n",
       "      <td>ongoing</td>\n",
       "      <td>property is a</td>\n",
       "      <td>species of</td>\n",
       "      <td>most complete</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>and landscape</td>\n",
       "      <td>to the development</td>\n",
       "      <td>a unique testimony to</td>\n",
       "      <td>an outstanding example of the</td>\n",
       "      <td>the landscape</td>\n",
       "      <td>is directly and</td>\n",
       "      <td>slopes</td>\n",
       "      <td>characteristic of the</td>\n",
       "      <td>and fauna</td>\n",
       "      <td>species of</td>\n",
       "      <td>rocky</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>painted</td>\n",
       "      <td>in the development</td>\n",
       "      <td>the ancient</td>\n",
       "      <td>is an outstanding example of</td>\n",
       "      <td>migration</td>\n",
       "      <td>witness to the</td>\n",
       "      <td>waterfalls</td>\n",
       "      <td>climate change</td>\n",
       "      <td>the americas</td>\n",
       "      <td>of species</td>\n",
       "      <td>source</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>frescoes</td>\n",
       "      <td>the interchange of</td>\n",
       "      <td>archaeological site</td>\n",
       "      <td>an outstanding example of</td>\n",
       "      <td>human settlement</td>\n",
       "      <td>associated with the</td>\n",
       "      <td>bay</td>\n",
       "      <td>processes of</td>\n",
       "      <td>basin</td>\n",
       "      <td>plant species</td>\n",
       "      <td>china</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       Criteria i                      Criteria ii  \\\n",
       "0                a masterpiece of      interchange of human values   \n",
       "1                  masterpiece of            in the development of   \n",
       "2                   a masterpiece             interchange of human   \n",
       "3                is a masterpiece  influence on the development of   \n",
       "4             is a masterpiece of                 the influence of   \n",
       "5        of human creative genius     influence on the development   \n",
       "6                 masterpieces of                       century ad   \n",
       "7                    masterpieces            to the development of   \n",
       "8               a unique artistic  the development of architecture   \n",
       "9   a unique artistic achievement                      the persian   \n",
       "10                    masterpiece         important interchange of   \n",
       "11                unique artistic                    influenced by   \n",
       "12                 the monumental           the development of the   \n",
       "13    unique artistic achievement                         that was   \n",
       "14                 and decorative                            and a   \n",
       "15          human creative genius                   influenced the   \n",
       "16                    magnificent      an important interchange of   \n",
       "17                  and landscape               to the development   \n",
       "18                        painted               in the development   \n",
       "19                       frescoes               the interchange of   \n",
       "\n",
       "                          Criteria iii                    Criteria iv  \\\n",
       "0                            neolithic    are outstanding examples of   \n",
       "1   bears exceptional testimony to the    an outstanding example of a   \n",
       "2                        traditions of       are outstanding examples   \n",
       "3       bears exceptional testimony to    outstanding examples of the   \n",
       "4                         tradition of       outstanding example of a   \n",
       "5                           bronze age            the historic centre   \n",
       "6                         inscriptions   an outstanding example of an   \n",
       "7                   cultural tradition        outstanding examples of   \n",
       "8                            the roman                historic centre   \n",
       "9              unique testimony to the         exceptional example of   \n",
       "10                         the site of      outstanding example of an   \n",
       "11                        of the roman     outstanding example of the   \n",
       "12                                 bce             historic centre of   \n",
       "13               cultural tradition of         the historic centre of   \n",
       "14                        civilisation      an exceptional example of   \n",
       "15                  a unique testimony   is an exceptional example of   \n",
       "16                         the kingdom              unique example of   \n",
       "17               a unique testimony to  an outstanding example of the   \n",
       "18                         the ancient   is an outstanding example of   \n",
       "19                 archaeological site      an outstanding example of   \n",
       "\n",
       "                      Criteria v                            Criteria vi  \\\n",
       "0   traditional human settlement  directly and tangibly associated with   \n",
       "1              traditional human       directly and tangibly associated   \n",
       "2       example of a traditional           and tangibly associated with   \n",
       "3                     settlement               directly associated with   \n",
       "4               of a traditional               tangibly associated with   \n",
       "5                     vernacular                  profound influence on   \n",
       "6                 socio economic                                  islam   \n",
       "7                 of traditional                               orthodox   \n",
       "8                  a traditional                  directly and tangibly   \n",
       "9                          socio                 exceptional witness to   \n",
       "10                   way of life                             witness to   \n",
       "11                   settlements                    tangibly associated   \n",
       "12                      land use                                  unity   \n",
       "13                     community                           directly and   \n",
       "14                      vestiges                and tangibly associated   \n",
       "15               the traditional                             testify to   \n",
       "16                     a type of                             philosophy   \n",
       "17                 the landscape                        is directly and   \n",
       "18                     migration                         witness to the   \n",
       "19              human settlement                    associated with the   \n",
       "\n",
       "                  Criteria vii          Criteria viii  \\\n",
       "0          exceptional natural               tectonic   \n",
       "1   exceptional natural beauty              formation   \n",
       "2                         lake             geological   \n",
       "3               natural beauty   geological processes   \n",
       "4                         peak               volcanic   \n",
       "5                  spectacular                glacial   \n",
       "6                       scenic                 fossil   \n",
       "7                       cliffs                   lava   \n",
       "8                     mountain                  dunes   \n",
       "9                       gorges              limestone   \n",
       "10               national park              sandstone   \n",
       "11                       peaks                  karst   \n",
       "12                       lakes                 rugged   \n",
       "13                   sea level               glaciers   \n",
       "14                   mountains       geomorphological   \n",
       "15               the mountains                  andes   \n",
       "16                    the lake                ongoing   \n",
       "17                      slopes  characteristic of the   \n",
       "18                  waterfalls         climate change   \n",
       "19                         bay           processes of   \n",
       "\n",
       "                 Criteria ix             Criteria x         Others  \n",
       "0     evolutionary processes    the conservation of      surviving  \n",
       "1             ecological and             of mammals           date  \n",
       "2                 and marine     endangered species         played  \n",
       "3       ecological processes     species of mammals         record  \n",
       "4             and ecological                mammals           ages  \n",
       "5             ecosystems and           bird species    well as the  \n",
       "6                undisturbed           bird species      result of  \n",
       "7                    lowland         the endangered       the same  \n",
       "8                     extent       the conservation        variety  \n",
       "9                rainforests     threatened species          greek  \n",
       "10            climate change   biological diversity       survived  \n",
       "11  and biological processes  critically endangered         german  \n",
       "12              processes of       the diversity of        numbers  \n",
       "13                rainforest           species such        highest  \n",
       "14               the natural               of birds          world  \n",
       "15          geomorphological        conservation of       achieved  \n",
       "16             property is a             species of  most complete  \n",
       "17                 and fauna             species of          rocky  \n",
       "18              the americas             of species         source  \n",
       "19                     basin          plant species          china  "
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "make_top_k_DataFrame(vocab, classifier, vectorizer, classes, k=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "make_top_k_DataFrame(vocab, classifier, vectorizer, classes, k=50).to_csv(args.save_dir+'top_words.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## BERT Attention"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "from bertviz import head_view"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "require.config({\n",
       "  paths: {\n",
       "      d3: '//cdnjs.cloudflare.com/ajax/libs/d3/3.4.8/d3.min',\n",
       "      jquery: '//ajax.googleapis.com/ajax/libs/jquery/2.0.0/jquery.min',\n",
       "  }\n",
       "});\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%javascript\n",
    "require.config({\n",
    "  paths: {\n",
    "      d3: '//cdnjs.cloudflare.com/ajax/libs/d3/3.4.8/d3.min',\n",
    "      jquery: '//ajax.googleapis.com/ajax/libs/jquery/2.0.0/jquery.min',\n",
    "  }\n",
    "});"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "def show_head_view(model, tokenizer, sentence_a, sentence_b=None):\n",
    "    inputs = tokenizer.encode_plus(sentence_a, sentence_b, return_tensors='pt', add_special_tokens=True)\n",
    "    input_ids = inputs['input_ids']\n",
    "    if sentence_b:\n",
    "        token_type_ids = inputs['token_type_ids']\n",
    "        attention = model(input_ids, token_type_ids=token_type_ids)[-1]\n",
    "        sentence_b_start = token_type_ids[0].tolist().index(1)\n",
    "    else:\n",
    "        attention = model(input_ids)[-1]\n",
    "        sentence_b_start = None\n",
    "    input_id_list = input_ids[0].tolist() # Batch index 0\n",
    "    tokens = tokenizer.convert_ids_to_tokens(input_id_list)    \n",
    "    head_view(attention, tokens, sentence_b_start)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "              <span style=\"user-select:none\">\n",
       "                Layer: <select id=\"layer\"></select>\n",
       "              </span>\n",
       "              <div id='vis'></div> \n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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0.0014486040454357862, 0.001597759430296719, 0.9568340182304382], [0.01685730740427971, 0.0436115562915802, 0.002587023191154003, 0.0022372128441929817, 0.0033890099730342627, 0.04729565232992172, 0.05432368442416191, 0.024079173803329468, 0.005411825608462095, 0.004672291222959757, 0.005314578767865896, 0.001965109957382083, 0.003813783871009946, 0.015371494926512241, 0.7690702676773071], [0.013433187268674374, 0.03823290765285492, 0.0029920341912657022, 0.0034588261041790247, 0.0027731142472475767, 0.02521379664540291, 0.0324866883456707, 0.008823661133646965, 0.0037267697043716908, 0.001982538728043437, 0.0062019480392336845, 0.0026874279137700796, 0.0029698233120143414, 0.005305343773216009, 0.8497119545936584], [0.020512616261839867, 0.01699083484709263, 0.0035777604207396507, 0.004528617020696402, 0.004939126782119274, 0.04132378473877907, 0.020804988220334053, 0.016228249296545982, 0.009826763533055782, 0.005341439973562956, 0.008472545072436333, 0.005107529927045107, 0.004619515035301447, 0.018332751467823982, 0.8193934559822083], [0.014113511890172958, 0.0073749953880906105, 0.00028677372029051185, 0.0005210893577896059, 0.000522421149071306, 0.015898512676358223, 0.01075479481369257, 0.01280383113771677, 0.005883971229195595, 0.004793544299900532, 0.005703432019799948, 0.002287394367158413, 0.001269331551156938, 0.014760227873921394, 0.9030262231826782], [0.0021232208237051964, 0.0026345793157815933, 0.00025014005950652063, 0.0006809842889197171, 0.0005052211927250028, 0.007087933365255594, 0.0040666647255420685, 0.002095218049362302, 0.0016279974952340126, 0.0005344810779206455, 0.0013263728469610214, 0.0009303283295594156, 0.0006428243359550834, 0.0034255776554346085, 0.9720684885978699], [0.0025595317129045725, 0.004974059294909239, 0.0007923386292532086, 0.0017817659536376595, 0.0010706746252253652, 0.005723791662603617, 0.005978525150567293, 0.0026894202455878258, 0.0023192381486296654, 0.0008672068361192942, 0.0025332937948405743, 0.0016736641991883516, 0.0015958142466843128, 0.0053662266582250595, 0.9600744247436523], [0.0006510563543997705, 0.001352450461126864, 0.0005688093951903284, 0.0013182100374251604, 0.0007585608400404453, 0.005853972397744656, 0.0012963517801836133, 0.000932259950786829, 0.0011179312132298946, 0.000392441259464249, 0.0013451800914481282, 0.0013693784130737185, 0.0011534610530361533, 0.0023769724648445845, 0.9795129895210266], [0.0008530855411663651, 0.0007128557190299034, 0.0016543661477044225, 0.003568971063941717, 0.0023291853722184896, 0.003735298290848732, 0.0009546275832690299, 0.0005828219000250101, 0.0009597637690603733, 0.00035036291228607297, 0.0009355708607472479, 0.0014680303866043687, 0.0019932014402002096, 0.0038317653816193342, 0.9760701060295105], [0.014880981296300888, 0.010864433832466602, 0.0031053873244673014, 0.005422388203442097, 0.005288921296596527, 0.03878868371248245, 0.013223041780292988, 0.01536309439688921, 0.019540175795555115, 0.014549373649060726, 0.01987200416624546, 0.015493148937821388, 0.010436298325657845, 0.03678734973073006, 0.7763846516609192], [0.002277572173625231, 0.007350052706897259, 0.0027602340560406446, 0.0022019436582922935, 0.0016340423608198762, 0.01582973077893257, 0.007527559529989958, 0.003630109131336212, 0.007612062152475119, 0.001841096905991435, 0.0028290615882724524, 0.0016395821003243327, 0.0026847466360777617, 0.002845322946086526, 0.9373369216918945]]]], \"left_text\": [\"[CLS]\", \"from\", \"tor\", \"##cel\", \"##los\", \"cathedral\", \"to\", \"the\", \"church\", \"of\", \"santa\", \"maria\", \"della\", \"salute\", \"[SEP]\"], \"right_text\": [\"[CLS]\", \"from\", \"tor\", \"##cel\", \"##los\", \"cathedral\", \"to\", \"the\", \"church\", \"of\", \"santa\", \"maria\", \"della\", \"salute\", \"[SEP]\"]}}, \"default_filter\": \"all\"}"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "/**\n",
       " * @fileoverview Transformer Visualization D3 javascript code.\n",
       " *\n",
       " *\n",
       " *  Based on: https://github.com/tensorflow/tensor2tensor/blob/master/tensor2tensor/visualization/attention.js\n",
       " *\n",
       " * Change log:\n",
       " *\n",
       " * 12/19/18  Jesse Vig   Assorted cleanup. Changed orientation of attention matrices.\n",
       " */\n",
       "\n",
       "requirejs(['jquery', 'd3'], function($, d3) {\n",
       "\n",
       "const TEXT_SIZE = 15;\n",
       "const BOXWIDTH = 110;\n",
       "const BOXHEIGHT = 22.5;\n",
       "const MATRIX_WIDTH = 115;\n",
       "const CHECKBOX_SIZE = 20;\n",
       "const TEXT_TOP = 30;\n",
       "const HEAD_COLORS = d3.scale.category10();\n",
       "\n",
       "var params = window.params;\n",
       "var config = {};\n",
       "initialize();\n",
       "\n",
       "function lighten(color) {\n",
       "  var c = d3.hsl(color);\n",
       "  var increment = (1 - c.l) * 0.6;\n",
       "  c.l += increment;\n",
       "  c.s -= increment;\n",
       "  return c;\n",
       "}\n",
       "\n",
       "function transpose(mat) {\n",
       "  return mat[0].map(function(col, i) {\n",
       "    return mat.map(function(row) {\n",
       "      return row[i];\n",
       "    });\n",
       "  });\n",
       "}\n",
       "\n",
       "function zip(a, b) {\n",
       "  return a.map(function (e, i) {\n",
       "    return [e, b[i]];\n",
       "  });\n",
       "}\n",
       "\n",
       "function render() {\n",
       "\n",
       "  var attnData = config.attention[config.filter];\n",
       "  var leftText = attnData.left_text;\n",
       "  var rightText = attnData.right_text;\n",
       "  var attentionHeads = attnData.attn[config.layer];\n",
       "\n",
       "  $(\"#vis svg\").empty();\n",
       "  $(\"#vis\").empty();\n",
       "\n",
       "  var height = config.initialTextLength * BOXHEIGHT + TEXT_TOP;\n",
       "  var svg = d3.select(\"#vis\")\n",
       "            .append('svg')\n",
       "            .attr(\"width\", \"100%\")\n",
       "            .attr(\"height\", height + \"px\");\n",
       "\n",
       "  var attData = [];\n",
       "  for (var i=0; i < config.nHeads; i++) {\n",
       "    var att = attentionHeads[i];\n",
       "    var att_trans = transpose(att);\n",
       "    attData.push(zip(att_trans, att));\n",
       "  }\n",
       "\n",
       "  renderText(svg, leftText, true, attData, 0);\n",
       "  renderText(svg, rightText, false, attData, MATRIX_WIDTH + BOXWIDTH);\n",
       "\n",
       "  renderAttentionHighlights(svg, attData);\n",
       "\n",
       "  svg.append(\"g\").classed(\"attentionHeads\", true);\n",
       "\n",
       "  renderAttention(svg, attentionHeads);\n",
       "\n",
       "  drawCheckboxes(0, svg, attentionHeads);\n",
       "\n",
       "}\n",
       "\n",
       "function renderText(svg, text, isLeft, attData, leftPos) {\n",
       "  // attData: list of tuples (att, att_trans), one for each layer. att and att_trans are attention matrics for each layer.\n",
       "  //           att is of shape [nHeads, source_len, target_len)\n",
       "  var id = isLeft ? \"left\" : \"right\";\n",
       "  var textContainer = svg.append(\"svg:g\")\n",
       "                         .attr(\"id\", id);\n",
       "\n",
       "  textContainer.append(\"g\").classed(\"attentionBoxes\", true)\n",
       "               .selectAll(\"g\")\n",
       "               .data(attData)\n",
       "               .enter()\n",
       "               .append(\"g\")\n",
       "               .selectAll(\"rect\")\n",
       "               .data(function(d) {return d;})\n",
       "               .enter()\n",
       "               .append(\"rect\")\n",
       "               .attr(\"x\", function(d, i, j) {\n",
       "                 return leftPos + boxOffsets(j);\n",
       "               })\n",
       "               .attr(\"y\", function(d, i) {\n",
       "                 return (+1) * BOXHEIGHT;\n",
       "               })\n",
       "               .attr(\"width\", BOXWIDTH / activeHeads())\n",
       "               .attr(\"height\", function() { return BOXHEIGHT; })\n",
       "               .attr(\"fill\", function(d, i, j) {\n",
       "                  return HEAD_COLORS(j);\n",
       "                })\n",
       "               .style(\"opacity\", 0.0);\n",
       "\n",
       "  var tokenContainer = textContainer.append(\"g\").selectAll(\"g\")\n",
       "                                    .data(text)\n",
       "                                    .enter()\n",
       "                                    .append(\"g\");\n",
       "\n",
       "  tokenContainer.append(\"rect\")\n",
       "                .classed(\"background\", true)\n",
       "                .style(\"opacity\", 0.0)\n",
       "                .attr(\"fill\", \"lightgray\")\n",
       "                .attr(\"x\", leftPos)\n",
       "                .attr(\"y\", function(d, i) {\n",
       "                  return TEXT_TOP + i * BOXHEIGHT;\n",
       "                })\n",
       "                .attr(\"width\", BOXWIDTH)\n",
       "                .attr(\"height\", BOXHEIGHT);\n",
       "\n",
       "  var textEl = tokenContainer.append(\"text\")\n",
       "                              .text(function(d) { return d; })\n",
       "                              .attr(\"font-size\", TEXT_SIZE + \"px\")\n",
       "                              .style(\"cursor\", \"default\")\n",
       "                              .style(\"-webkit-user-select\", \"none\")\n",
       "                              .attr(\"x\", leftPos)\n",
       "                              .attr(\"y\", function(d, i) {\n",
       "                                return TEXT_TOP + i * BOXHEIGHT;\n",
       "                              });\n",
       "\n",
       "  if (isLeft) {\n",
       "    textEl.style(\"text-anchor\", \"end\")\n",
       "           .attr(\"dx\", BOXWIDTH - 0.5 * TEXT_SIZE)\n",
       "           .attr(\"dy\", TEXT_SIZE);\n",
       "  } else {\n",
       "    textEl.style(\"text-anchor\", \"start\")\n",
       "           .attr(\"dx\", + 0.5 * TEXT_SIZE)\n",
       "           .attr(\"dy\", TEXT_SIZE);\n",
       "  }\n",
       "\n",
       "  tokenContainer.on(\"mouseover\", function(d, index) {\n",
       "    textContainer.selectAll(\".background\")\n",
       "                 .style(\"opacity\", function(d, i) {\n",
       "                   return i == index ? 1.0 : 0.0;\n",
       "                 });\n",
       "\n",
       "    svg.selectAll(\".attentionHeads\").style(\"display\", \"none\");\n",
       "\n",
       "    svg.selectAll(\".lineHeads\")  // To get the nesting to work.\n",
       "       .selectAll(\".attLines\")\n",
       "       .attr(\"stroke-opacity\", function(d) {\n",
       "          return 1.0;\n",
       "        })\n",
       "       .attr(\"y1\", function(d, i) {\n",
       "        if (isLeft) {\n",
       "          return TEXT_TOP + index * BOXHEIGHT + (BOXHEIGHT/2);\n",
       "        } else {\n",
       "          return TEXT_TOP + i * BOXHEIGHT + (BOXHEIGHT/2);\n",
       "        }\n",
       "     })\n",
       "     .attr(\"x1\", BOXWIDTH)\n",
       "     .attr(\"y2\", function(d, i) {\n",
       "       if (isLeft) {\n",
       "          return TEXT_TOP + i * BOXHEIGHT + (BOXHEIGHT/2);\n",
       "        } else {\n",
       "          return TEXT_TOP + index * BOXHEIGHT + (BOXHEIGHT/2);\n",
       "        }\n",
       "     })\n",
       "     .attr(\"x2\", BOXWIDTH + MATRIX_WIDTH)\n",
       "     .attr(\"stroke-width\", 2)\n",
       "     .attr(\"stroke\", function(d, i, j) {\n",
       "        return HEAD_COLORS(j);\n",
       "      })\n",
       "     .attr(\"stroke-opacity\", function(d, i, j) {\n",
       "      if (isLeft) {d = d[0];} else {d = d[1];}\n",
       "      if (config.headVis[j]) {\n",
       "        if (d) {\n",
       "          return d[index];\n",
       "        } else {\n",
       "          return 0.0;\n",
       "        }\n",
       "      } else {\n",
       "        return 0.0;\n",
       "      }\n",
       "     });\n",
       "\n",
       "    function updateAttentionBoxes() {\n",
       "      var id = isLeft ? \"right\" : \"left\";\n",
       "      var leftPos = isLeft ? MATRIX_WIDTH + BOXWIDTH : 0;\n",
       "      svg.select(\"#\" + id)\n",
       "         .selectAll(\".attentionBoxes\")\n",
       "         .selectAll(\"g\")\n",
       "         .selectAll(\"rect\")\n",
       "         .attr(\"x\", function(d, i, j) { return leftPos + boxOffsets(j); })\n",
       "         .attr(\"y\", function(d, i) { return TEXT_TOP + i * BOXHEIGHT; })\n",
       "         .attr(\"width\", BOXWIDTH/activeHeads())\n",
       "         .attr(\"height\", function() { return BOXHEIGHT; })\n",
       "         .style(\"opacity\", function(d, i, j) {\n",
       "            if (isLeft) {d = d[0];} else {d = d[1];}\n",
       "            if (config.headVis[j])\n",
       "              if (d) {\n",
       "                return d[index];\n",
       "              } else {\n",
       "                return 0.0;\n",
       "              }\n",
       "            else\n",
       "              return 0.0;\n",
       "         });\n",
       "    }\n",
       "\n",
       "    updateAttentionBoxes();\n",
       "  });\n",
       "\n",
       "  textContainer.on(\"mouseleave\", function() {\n",
       "    d3.select(this).selectAll(\".background\")\n",
       "                   .style(\"opacity\", 0.0);\n",
       "    svg.selectAll(\".attLines\").attr(\"stroke-opacity\", 0.0);\n",
       "    svg.selectAll(\".attentionHeads\").style(\"display\", \"inline\");\n",
       "    svg.selectAll(\".attentionBoxes\")\n",
       "       .selectAll(\"g\")\n",
       "       .selectAll(\"rect\")\n",
       "       .style(\"opacity\", 0.0);\n",
       "  });\n",
       "}\n",
       "\n",
       "function renderAttentionHighlights(svg, attention) {\n",
       "  var line_container = svg.append(\"g\");\n",
       "  line_container.selectAll(\"g\")\n",
       "                .data(attention)\n",
       "                .enter()\n",
       "                .append(\"g\")\n",
       "                .classed(\"lineHeads\", true)\n",
       "                .selectAll(\"line\")\n",
       "                .data(function(d){return d;})\n",
       "                .enter()\n",
       "                .append(\"line\").classed(\"attLines\", true);\n",
       "}\n",
       "\n",
       "function renderAttention(svg, attentionHeads) {\n",
       "  var line_container = svg.selectAll(\".attentionHeads\");\n",
       "  line_container.html(null);\n",
       "  for(var h=0; h<attentionHeads.length; h++) {\n",
       "    for(var s=0; s<attentionHeads[h].length; s++) {\n",
       "      for(var a=0; a<attentionHeads[h][s].length; a++) {\n",
       "        line_container.append(\"line\")\n",
       "        .attr(\"y1\", TEXT_TOP + s * BOXHEIGHT + (BOXHEIGHT/2))\n",
       "        .attr(\"x1\", BOXWIDTH)\n",
       "        .attr(\"y2\", TEXT_TOP + a * BOXHEIGHT + (BOXHEIGHT/2))\n",
       "        .attr(\"x2\", BOXWIDTH + MATRIX_WIDTH)\n",
       "        .attr(\"stroke-width\", 2)\n",
       "        .attr(\"stroke\", HEAD_COLORS(h))\n",
       "        .attr(\"stroke-opacity\", function() {\n",
       "          if (config.headVis[h]) {\n",
       "            return attentionHeads[h][s][a]/activeHeads();\n",
       "          } else {\n",
       "            return 0.0;\n",
       "          }\n",
       "        }());\n",
       "      }\n",
       "    }\n",
       "  }\n",
       "}\n",
       "\n",
       "// Checkboxes\n",
       "function boxOffsets(i) {\n",
       "  var numHeadsAbove = config.headVis.reduce(\n",
       "      function(acc, val, cur) {return val && cur < i ? acc + 1: acc;}, 0);\n",
       "  return numHeadsAbove * (BOXWIDTH / activeHeads());\n",
       "}\n",
       "\n",
       "function activeHeads() {\n",
       "  return config.headVis.reduce(function(acc, val) {\n",
       "    return val ? acc + 1: acc;\n",
       "  }, 0);\n",
       "}\n",
       "\n",
       "function drawCheckboxes(top, svg, attentionHeads) {\n",
       "  var checkboxContainer = svg.append(\"g\");\n",
       "  var checkbox = checkboxContainer.selectAll(\"rect\")\n",
       "                                  .data(config.headVis)\n",
       "                                  .enter()\n",
       "                                  .append(\"rect\")\n",
       "                                  .attr(\"fill\", function(d, i) {\n",
       "                                    return HEAD_COLORS(i);\n",
       "                                  })\n",
       "                                  .attr(\"x\", function(d, i) {\n",
       "                                    return i * CHECKBOX_SIZE;\n",
       "                                  })\n",
       "                                  .attr(\"y\", top)\n",
       "                                  .attr(\"width\", CHECKBOX_SIZE)\n",
       "                                  .attr(\"height\", CHECKBOX_SIZE);\n",
       "\n",
       "  function updateCheckboxes() {\n",
       "    checkboxContainer.selectAll(\"rect\")\n",
       "                              .data(config.headVis)\n",
       "                              .attr(\"fill\", function(d, i) {\n",
       "      var headColor = HEAD_COLORS(i);\n",
       "      var color = d ? headColor : lighten(headColor);\n",
       "      return color;\n",
       "    });\n",
       "  }\n",
       "\n",
       "  updateCheckboxes();\n",
       "\n",
       "  checkbox.on(\"click\", function(d, i) {\n",
       "    if (config.headVis[i] && activeHeads() == 1) return;\n",
       "    config.headVis[i] = !config.headVis[i];\n",
       "    updateCheckboxes();\n",
       "    renderAttention(svg, attentionHeads);\n",
       "  });\n",
       "\n",
       "  checkbox.on(\"dblclick\", function(d, i) {\n",
       "    // If we double click on the only active head then reset\n",
       "    if (config.headVis[i] && activeHeads() == 1) {\n",
       "      config.headVis = new Array(config.nHeads).fill(true);\n",
       "    } else {\n",
       "      config.headVis = new Array(config.nHeads).fill(false);\n",
       "      config.headVis[i] = true;\n",
       "    }\n",
       "    updateCheckboxes();\n",
       "    renderAttention(svg, attentionHeads);\n",
       "  });\n",
       "}\n",
       "\n",
       "function initialize() {\n",
       "  config.attention = params['attention'];\n",
       "  config.filter = params['default_filter'];\n",
       "  config.nLayers = config.attention[config.filter]['attn'].length;\n",
       "  console.log('num layers')\n",
       "  console.log(config.nLayers)\n",
       "  config.nHeads = config.attention[config.filter]['attn'][0].length;\n",
       "  config.headVis  = new Array(config.nHeads).fill(true);\n",
       "  config.layer = 0;\n",
       "  config.initialTextLength = config.attention[config.filter].right_text.length;\n",
       "  console.log('initial text length')\n",
       "  console.log(config.initialTextLength)\n",
       "}\n",
       "\n",
       "$(\"#layer\").empty();\n",
       "for(var i=0; i<config.nLayers; i++) {\n",
       "  $(\"#layer\").append($(\"<option />\").val(i).text(i));\n",
       "}\n",
       "\n",
       "$(\"#layer\").on('change', function(e) {\n",
       "  config.layer = +e.currentTarget.value;\n",
       "  render();\n",
       "});\n",
       "\n",
       "$(\"#filter\").on('change', function(e) {\n",
       "  config.filter = e.currentTarget.value;\n",
       "  render();\n",
       "});\n",
       "\n",
       "render();\n",
       "\n",
       "});"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model_version = 'bert-base-uncased'\n",
    "do_lower_case = True\n",
    "model = BertModel.from_pretrained(model_version, output_attentions=True)\n",
    "model.load_state_dict(torch.load(args.save_dir+'1337/model.pth',map_location=torch.device('cpu')),strict=False)\n",
    "tokenizer = BertTokenizer.from_pretrained(model_version, do_lower_case=do_lower_case)\n",
    "sentence_a = '''The particular layout of the complex is unique to this site'''\n",
    "sentence_b = '''this is a very old building dating back to 13th century'''\n",
    "sentence_c = '''With the unusualness of an archaeological site which still breathes life, Venice bears testimony unto itself.'''\n",
    "sentence_d = '''This mistress of the seas is a link between the East and the West, between Islam and Christianity'''\n",
    "sentence_e = '''It was from Venice that Marco Polo (1254-1324) set out in search of China, Annam, Tonkin, Sumatra, India and Persia.\n",
    "'''\n",
    "sentence_f = '''the lagoon of venice also has one of the highest concentrations of masterpieces in the world'''\n",
    "sentence_f1 = '''from torcellos cathedral to the church of santa maria della salute'''\n",
    "sentence_g = '''the years of the republics extraordinary golden age are represented by monuments of incomparable beauty'''\n",
    "sentence_h = '''san marco , palazzo ducale , san zanipolo , scuola di san marco , frari and scuola di san rocco , san giorgio maggiore , etc'''\n",
    "show_head_view(model, tokenizer, sentence_f1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Confusion Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset.set_split('test')\n",
    "batch_generator = generate_batches(dataset, \n",
    "                                   batch_size=args.batch_size, \n",
    "                                   device=args.device)\n",
    "set_seed_everywhere(args.seed, args.cuda)\n",
    "conf_mat_test = np.zeros((len(classes)-1,len(classes)-1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "for batch_index, batch_dict in enumerate(batch_generator):\n",
    "    \n",
    "    # get the data compute fuzzy labels\n",
    "    X = batch_dict['x_data']\n",
    "    mask = batch_dict['mask']\n",
    "    token_type_ids = batch_dict['token_type_ids']\n",
    "\n",
    "    y_target = batch_dict['y_target']\n",
    "    y_fuzzy = batch_dict['y_fuzzy']\n",
    "\n",
    "    Y = compute_fuzzy_label(y_target, y_fuzzy, fuzzy= args.fuzzy, \n",
    "                            how=args.fuzzy_how, lbd = args.fuzzy_lambda)\n",
    "\n",
    "    # compute the output\n",
    "    classifier.eval()\n",
    "    with torch.no_grad():\n",
    "        y_pred = classifier(X, mask, token_type_ids)[0]\n",
    "    \n",
    "    conf_mat_test = np.add(conf_mat_test,confusion_matrix(y_target.argmax(axis=1), \n",
    "                                                          y_pred.argmax(axis=1),labels=range(len(classes)-1)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[21.,  2.,  2.,  9.,  0.,  2.,  0.,  0.,  0.,  0.],\n",
       "       [ 0., 52.,  3., 13.,  3.,  2.,  1.,  0.,  0.,  0.],\n",
       "       [ 1.,  7., 36.,  9.,  6.,  3.,  0.,  1.,  0.,  0.],\n",
       "       [ 2., 11., 11., 52.,  4.,  1.,  0.,  0.,  0.,  0.],\n",
       "       [ 1.,  1.,  3., 10., 15.,  0.,  1.,  0.,  0.,  1.],\n",
       "       [ 1.,  2.,  9.,  2.,  1., 27.,  1.,  0.,  0.,  0.],\n",
       "       [ 0.,  0.,  1.,  2.,  0.,  0., 28.,  6.,  2.,  3.],\n",
       "       [ 0.,  0.,  0.,  0.,  0.,  0.,  1., 26.,  3.,  0.],\n",
       "       [ 0.,  0.,  0.,  0.,  0.,  0.,  2.,  3., 32., 10.],\n",
       "       [ 0.,  0.,  0.,  1.,  0.,  0.,  0.,  0.,  9., 54.]])"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "conf_mat_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [],
   "source": [
    "set_seed_everywhere(args.seed, args.cuda)\n",
    "dataset.set_split('val')\n",
    "batch_generator = generate_batches(dataset, \n",
    "                                   batch_size=args.batch_size, \n",
    "                                   device=args.device)\n",
    "conf_mat_val = np.zeros((len(classes)-1,len(classes)-1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "for batch_index, batch_dict in enumerate(batch_generator):\n",
    "    \n",
    "    # get the data compute fuzzy labels\n",
    "    X = batch_dict['x_data']\n",
    "    mask = batch_dict['mask']\n",
    "    token_type_ids = batch_dict['token_type_ids']\n",
    "\n",
    "    y_target = batch_dict['y_target']\n",
    "    y_fuzzy = batch_dict['y_fuzzy']\n",
    "\n",
    "    Y = compute_fuzzy_label(y_target, y_fuzzy, fuzzy= args.fuzzy, \n",
    "                            how=args.fuzzy_how, lbd = args.fuzzy_lambda)\n",
    "\n",
    "    # compute the output\n",
    "    classifier.eval()\n",
    "    with torch.no_grad():\n",
    "        y_pred = classifier(X, mask, token_type_ids)[0]\n",
    "    \n",
    "    conf_mat_val = np.add(conf_mat_val,confusion_matrix(y_target.argmax(axis=1), y_pred.argmax(axis=1),labels=range(len(classes)-1)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[16.,  3.,  7.,  8.,  0.,  1.,  0.,  0.,  0.,  0.],\n",
       "       [ 1., 53.,  4.,  7.,  0.,  1.,  0.,  0.,  0.,  0.],\n",
       "       [ 1.,  6., 55., 10.,  3.,  3.,  0.,  1.,  0.,  0.],\n",
       "       [ 5.,  7.,  8., 54.,  4.,  3.,  0.,  1.,  0.,  0.],\n",
       "       [ 1.,  1.,  5.,  4., 13.,  0.,  0.,  0.,  0.,  1.],\n",
       "       [ 2.,  3.,  3.,  4.,  0., 28.,  0.,  0.,  0.,  0.],\n",
       "       [ 0.,  0.,  0.,  0.,  0.,  0., 33.,  2.,  2.,  2.],\n",
       "       [ 0.,  0.,  0.,  0.,  0.,  0.,  4., 31.,  4.,  1.],\n",
       "       [ 0.,  0.,  1.,  0.,  0.,  0.,  7.,  0., 24.,  8.],\n",
       "       [ 0.,  0.,  0.,  0.,  0.,  0.,  1.,  0.,  5., 60.]])"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "conf_mat_val"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "set_seed_everywhere(args.seed, args.cuda)\n",
    "dataset.set_split('train')\n",
    "batch_generator = generate_batches(dataset, \n",
    "                                   batch_size=args.batch_size, \n",
    "                                   device=args.device)\n",
    "conf_mat_train = np.zeros((len(classes)-1,len(classes)-1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "for batch_index, batch_dict in enumerate(batch_generator):\n",
    "    \n",
    "    # get the data compute fuzzy labels\n",
    "    X = batch_dict['x_data']\n",
    "    mask = batch_dict['mask']\n",
    "    token_type_ids = batch_dict['token_type_ids']\n",
    "\n",
    "    y_target = batch_dict['y_target']\n",
    "    y_fuzzy = batch_dict['y_fuzzy']\n",
    "\n",
    "    Y = compute_fuzzy_label(y_target, y_fuzzy, fuzzy= args.fuzzy, \n",
    "                            how=args.fuzzy_how, lbd = args.fuzzy_lambda)\n",
    "\n",
    "    # compute the output\n",
    "    classifier.eval()\n",
    "    with torch.no_grad():\n",
    "        y_pred = classifier(X, mask, token_type_ids)[0]\n",
    "    \n",
    "    conf_mat_train = np.add(conf_mat_train,confusion_matrix(y_target.argmax(axis=1), y_pred.argmax(axis=1),labels=range(len(classes)-1)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[322.,   6.,   1.,   1.,   0.,   0.,   0.,   0.,   0.,   0.],\n",
       "       [  0., 622.,   0.,   2.,   2.,   2.,   0.,   0.,   0.,   0.],\n",
       "       [  0.,   1., 641.,   2.,   0.,   0.,   0.,   0.,   0.,   1.],\n",
       "       [  0.,   2.,   0., 768.,   0.,   0.,   0.,   0.,   0.,   0.],\n",
       "       [  0.,   0.,   1.,   3., 203.,   0.,   0.,   0.,   0.,   0.],\n",
       "       [  0.,   0.,   0.,   0.,   0., 323.,   0.,   0.,   0.,   0.],\n",
       "       [  0.,   0.,   0.,   0.,   0.,   0., 378.,   1.,   2.,   1.],\n",
       "       [  0.,   0.,   0.,   0.,   0.,   0.,   0., 260.,   0.,   0.],\n",
       "       [  0.,   1.,   0.,   0.,   0.,   0.,   0.,   0., 366.,   2.],\n",
       "       [  0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   1., 565.]])"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "conf_mat_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.concat([pd.DataFrame(conf_mat_test),pd.DataFrame(conf_mat_val),pd.DataFrame(conf_mat_train)],axis=1).to_csv(args.save_dir+'confusion_matrix.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.concat([pd.DataFrame(conf_mat_test),pd.DataFrame(conf_mat_val),pd.DataFrame(conf_mat_train)],axis=1).to_csv(args.save_dir+'baseline_confusion_matrix.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "def per_class_metrics(confusion_matrix, classes):\n",
    "    '''\n",
    "    Compute the per class precision, recall, and F1 for all the classes\n",
    "    \n",
    "    Args:\n",
    "    confusion_matrix (np.ndarry) with shape of (n_classes,n_classes): a confusion matrix of interest\n",
    "    classes (list of str) with shape (n_classes,): The names of classes\n",
    "    \n",
    "    Returns:\n",
    "    metrics_dict (dictionary): a dictionary that records the per class metrics\n",
    "    '''\n",
    "    num_class = confusion_matrix.shape[0]\n",
    "    metrics_dict = {}\n",
    "    for i in range(num_class):\n",
    "        key = classes[i]\n",
    "        temp_dict = {}\n",
    "        row = confusion_matrix[i,:]\n",
    "        col = confusion_matrix[:,i]\n",
    "        val = confusion_matrix[i,i]\n",
    "        precision = val/row.sum()\n",
    "        recall = val/col.sum()\n",
    "        F1 = 2*(precision*recall)/(precision+recall)\n",
    "        temp_dict['precision'] = precision\n",
    "        temp_dict['recall'] = recall\n",
    "        temp_dict['F1'] = F1\n",
    "        metrics_dict[key] = temp_dict\n",
    "    \n",
    "    return metrics_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [],
   "source": [
    "metrics_dict = {}\n",
    "metrics_dict['test'] = per_class_metrics(conf_mat_test, classes[:-1])\n",
    "metrics_dict['val'] = per_class_metrics(conf_mat_val, classes[:-1])\n",
    "metrics_dict['train'] = per_class_metrics(conf_mat_train, classes[:-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [],
   "source": [
    "metrics_df = pd.DataFrame.from_dict({(i,j): metrics_dict[i][j] \n",
    "                           for i in metrics_dict.keys() \n",
    "                           for j in metrics_dict[i].keys()},\n",
    "                       orient='index')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "metrics_df.to_csv(args.save_dir+'per_class_metrics.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [],
   "source": [
    "metrics_df.to_csv(args.save_dir+'baseline_per_class_metrics.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Try on totally Unseen Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "#args.ouv_csv='Data/ouv_with_splits_full.csv',\n",
    "new_ouv_csv='Data/sd_full.csv'\n",
    "#args.reload_from_files=True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "def compute_jac_k_accuracy(y_pred, y_target, k=3, multilabel=False):\n",
    "    \n",
    "    y_pred_indices = y_pred.topk(k, dim=1)[1]\n",
    "    y_target_indices = y_target.topk(k, dim=1)[1]\n",
    "        \n",
    "    n_correct = torch.tensor([torch.tensor([y_pred_indices[j][i] in y_target_indices[j] for i in range(k)]).sum()>0 \n",
    "                              for j in range(len(y_pred))]).sum().item()\n",
    "    return n_correct / len(y_pred_indices) * 100\n",
    "\n",
    "def compute_jac_1_accuracy(y_pred, y_target, k=3, multilabel=False):\n",
    "    \n",
    "    y_pred_indices = y_pred.topk(1, dim=1)[1]\n",
    "    y_target_indices = y_target.topk(k, dim=1)[1]\n",
    "        \n",
    "    n_correct = torch.tensor([torch.tensor([y_pred_indices[j] in y_target_indices[j] for i in range(k)]).sum()>0 \n",
    "                              for j in range(len(y_pred))]).sum().item()\n",
    "    return n_correct / len(y_pred_indices) * 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Elapsed: 1635.7847800254822\n"
     ]
    }
   ],
   "source": [
    "with Timer():\n",
    "    loss_func = cross_entropy\n",
    "    set_seed_everywhere(args.seed, args.cuda)\n",
    "    train_state = make_train_state(args)\n",
    "    dataset = OuvDataset.load_dataset_and_make_vectorizer(new_ouv_csv)\n",
    "\n",
    "    dataset.set_split('val')\n",
    "    verbose=False\n",
    "    try:\n",
    "        # Iterate over training dataset\n",
    "\n",
    "        # setup: batch generator, set loss and acc to 0, set train mode on\n",
    "        dataset.set_split('val')\n",
    "        batch_generator = generate_batches(dataset, \n",
    "                                           batch_size=args.batch_size, \n",
    "                                           device=args.device)\n",
    "        running_loss = 0.0\n",
    "        running_1_acc = 0.0\n",
    "        running_k_acc = 0.0\n",
    "        running_k_jac = 0.0\n",
    "        classifier.eval()\n",
    "\n",
    "        for batch_index, batch_dict in enumerate(batch_generator):\n",
    "\n",
    "            # step 2. get the data compute fuzzy labels\n",
    "            X = batch_dict['x_data']\n",
    "            mask = batch_dict['mask']\n",
    "            token_type_ids = batch_dict['token_type_ids']\n",
    "\n",
    "            y_target = batch_dict['y_target']\n",
    "            y_fuzzy = batch_dict['y_fuzzy']\n",
    "\n",
    "            Y = compute_fuzzy_label(y_target, y_fuzzy, fuzzy= args.fuzzy, \n",
    "                                    how=args.fuzzy_how, lbd = args.fuzzy_lambda)\n",
    "\n",
    "            # step 3. compute the output\n",
    "            with torch.no_grad():\n",
    "                y_pred = classifier(X, mask, token_type_ids)[0]\n",
    "\n",
    "            # step 4. compute the loss\n",
    "            loss = loss_func(y_pred, Y)\n",
    "            loss_t = loss.item()\n",
    "            running_loss += (loss_t - running_loss) / (batch_index + 1)\n",
    "\n",
    "            # -----------------------------------------\n",
    "            # compute the accuracies\n",
    "            acc_1_t = compute_jac_1_accuracy(y_pred, y_target)\n",
    "            acc_k_t = compute_jac_k_accuracy(y_pred, y_target, args.k)\n",
    "            jac_k_t = compute_jaccard_index(y_pred, y_target, len(classes))\n",
    "\n",
    "            running_1_acc += (acc_1_t - running_1_acc) / (batch_index + 1)\n",
    "            running_k_acc += (acc_k_t - running_k_acc) / (batch_index + 1)\n",
    "            running_k_jac += (jac_k_t - running_k_jac) / (batch_index + 1)\n",
    "\n",
    "            # update bar\n",
    "            if verbose:\n",
    "                val_bar.set_postfix(loss=running_loss, \n",
    "                                acc_1=running_1_acc,\n",
    "                                acc_k=running_k_acc,\n",
    "                                jac_k=running_k_jac,\n",
    "                                epoch=epoch_index)\n",
    "                val_bar.update()\n",
    "\n",
    "        train_state['val_loss'].append(running_loss)\n",
    "        train_state['val_1_acc'].append(running_1_acc)\n",
    "        train_state['val_k_acc'].append(running_k_acc)\n",
    "        train_state['val_k_jac'].append(running_k_jac)\n",
    "\n",
    "    except KeyboardInterrupt:\n",
    "        print(\"Exiting loop\")\n",
    "        pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'stop_early': False,\n",
       " 'early_stopping_step': 0,\n",
       " 'early_stopping_best_1_acc_val': 0,\n",
       " 'learning_rate': 2e-05,\n",
       " 'epoch_index': 0,\n",
       " 'train_loss': [],\n",
       " 'train_1_acc': [],\n",
       " 'train_k_acc': [],\n",
       " 'train_k_jac': [],\n",
       " 'val_loss': [2.1934949217579707],\n",
       " 'val_1_acc': [71.51041666666667],\n",
       " 'val_k_acc': [96.14583333333333],\n",
       " 'val_k_jac': [0.44643012732267384],\n",
       " 'test_loss': -1,\n",
       " 'test_1_acc': -1,\n",
       " 'test_k_acc': -1,\n",
       " 'test_k_jac': -1,\n",
       " 'model_filename': 'model_storage/bert/model.pth'}"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_state"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'stop_early': False,\n",
       " 'early_stopping_step': 0,\n",
       " 'early_stopping_best_1_acc_val': 0,\n",
       " 'learning_rate': 2e-05,\n",
       " 'epoch_index': 0,\n",
       " 'train_loss': [],\n",
       " 'train_1_acc': [],\n",
       " 'train_k_acc': [],\n",
       " 'train_k_jac': [],\n",
       " 'val_loss': [2.437905519070895],\n",
       " 'val_1_acc': [71.56250000000001],\n",
       " 'val_k_acc': [95.96354166666666],\n",
       " 'val_k_jac': [0.45349258879820503],\n",
       " 'test_loss': -1,\n",
       " 'test_1_acc': -1,\n",
       " 'test_k_acc': -1,\n",
       " 'test_k_jac': -1,\n",
       " 'model_filename': 'model_storage/bert/model.pth'}"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Baseline\n",
    "train_state"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Predicting scores for Social Media Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "sm_df = pd.read_csv(args.sm_csv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "def record_ouv(line, classifier=classifier, vectorizer=vectorizer, classes=classes, k=11):\n",
    "    predictions = predict_rating(line['processed'], classifier, vectorizer, classes, k)\n",
    "    for key, value in predictions:\n",
    "        line[key] = value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_dict = {}\n",
    "for i in range(len(sm_df)):\n",
    "    new_dict[i]={}\n",
    "    line = sm_df.iloc[i].copy()\n",
    "    predictions = predict_rating(line['processed'], classifier, vectorizer, classes, k=11)\n",
    "    for key, value in predictions:\n",
    "        new_dict[i][key] = value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "des_dict = {}\n",
    "for i in range(len(des_df)):\n",
    "    des_dict[i]={}\n",
    "    line = des_df.iloc[i].copy()\n",
    "    predictions = predict_rating(line['data'], classifier, vectorizer, classes, k=11)\n",
    "    for key, value in predictions:\n",
    "        des_dict[i][key] = value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Criteria i</th>\n",
       "      <th>Criteria ii</th>\n",
       "      <th>Criteria iii</th>\n",
       "      <th>Criteria iv</th>\n",
       "      <th>Criteria v</th>\n",
       "      <th>Criteria vi</th>\n",
       "      <th>Criteria vii</th>\n",
       "      <th>Criteria viii</th>\n",
       "      <th>Criteria ix</th>\n",
       "      <th>Criteria x</th>\n",
       "      <th>Others</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.121917</td>\n",
       "      <td>0.024367</td>\n",
       "      <td>0.051884</td>\n",
       "      <td>0.767751</td>\n",
       "      <td>0.004244</td>\n",
       "      <td>0.014229</td>\n",
       "      <td>0.001415</td>\n",
       "      <td>0.000845</td>\n",
       "      <td>0.000945</td>\n",
       "      <td>0.002275</td>\n",
       "      <td>0.010129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.129583</td>\n",
       "      <td>0.243989</td>\n",
       "      <td>0.266616</td>\n",
       "      <td>0.259280</td>\n",
       "      <td>0.017020</td>\n",
       "      <td>0.055353</td>\n",
       "      <td>0.003414</td>\n",
       "      <td>0.001681</td>\n",
       "      <td>0.001967</td>\n",
       "      <td>0.002403</td>\n",
       "      <td>0.018694</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.230085</td>\n",
       "      <td>0.086930</td>\n",
       "      <td>0.017167</td>\n",
       "      <td>0.547663</td>\n",
       "      <td>0.008494</td>\n",
       "      <td>0.053089</td>\n",
       "      <td>0.011696</td>\n",
       "      <td>0.006186</td>\n",
       "      <td>0.003875</td>\n",
       "      <td>0.012006</td>\n",
       "      <td>0.022811</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.058064</td>\n",
       "      <td>0.023713</td>\n",
       "      <td>0.133313</td>\n",
       "      <td>0.084863</td>\n",
       "      <td>0.122985</td>\n",
       "      <td>0.125263</td>\n",
       "      <td>0.339578</td>\n",
       "      <td>0.020654</td>\n",
       "      <td>0.016124</td>\n",
       "      <td>0.036024</td>\n",
       "      <td>0.039419</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.060112</td>\n",
       "      <td>0.512026</td>\n",
       "      <td>0.020254</td>\n",
       "      <td>0.350749</td>\n",
       "      <td>0.015139</td>\n",
       "      <td>0.017242</td>\n",
       "      <td>0.001813</td>\n",
       "      <td>0.002387</td>\n",
       "      <td>0.003894</td>\n",
       "      <td>0.003338</td>\n",
       "      <td>0.013045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1127</th>\n",
       "      <td>0.053496</td>\n",
       "      <td>0.228884</td>\n",
       "      <td>0.014969</td>\n",
       "      <td>0.604132</td>\n",
       "      <td>0.009759</td>\n",
       "      <td>0.058910</td>\n",
       "      <td>0.002598</td>\n",
       "      <td>0.003048</td>\n",
       "      <td>0.003529</td>\n",
       "      <td>0.002367</td>\n",
       "      <td>0.018309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1128</th>\n",
       "      <td>0.698811</td>\n",
       "      <td>0.033695</td>\n",
       "      <td>0.030942</td>\n",
       "      <td>0.020628</td>\n",
       "      <td>0.007408</td>\n",
       "      <td>0.041588</td>\n",
       "      <td>0.049546</td>\n",
       "      <td>0.023215</td>\n",
       "      <td>0.016675</td>\n",
       "      <td>0.055115</td>\n",
       "      <td>0.022377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1129</th>\n",
       "      <td>0.036197</td>\n",
       "      <td>0.006216</td>\n",
       "      <td>0.038991</td>\n",
       "      <td>0.017800</td>\n",
       "      <td>0.042295</td>\n",
       "      <td>0.123313</td>\n",
       "      <td>0.563167</td>\n",
       "      <td>0.059646</td>\n",
       "      <td>0.028864</td>\n",
       "      <td>0.055149</td>\n",
       "      <td>0.028361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1130</th>\n",
       "      <td>0.294645</td>\n",
       "      <td>0.068841</td>\n",
       "      <td>0.351736</td>\n",
       "      <td>0.073772</td>\n",
       "      <td>0.004323</td>\n",
       "      <td>0.172625</td>\n",
       "      <td>0.005515</td>\n",
       "      <td>0.003727</td>\n",
       "      <td>0.002251</td>\n",
       "      <td>0.005164</td>\n",
       "      <td>0.017400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1131</th>\n",
       "      <td>0.046666</td>\n",
       "      <td>0.087098</td>\n",
       "      <td>0.019651</td>\n",
       "      <td>0.073331</td>\n",
       "      <td>0.008387</td>\n",
       "      <td>0.728658</td>\n",
       "      <td>0.005269</td>\n",
       "      <td>0.003832</td>\n",
       "      <td>0.004549</td>\n",
       "      <td>0.005575</td>\n",
       "      <td>0.016985</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1132 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Criteria i  Criteria ii  Criteria iii  Criteria iv  Criteria v  \\\n",
       "0       0.121917     0.024367      0.051884     0.767751    0.004244   \n",
       "1       0.129583     0.243989      0.266616     0.259280    0.017020   \n",
       "2       0.230085     0.086930      0.017167     0.547663    0.008494   \n",
       "3       0.058064     0.023713      0.133313     0.084863    0.122985   \n",
       "4       0.060112     0.512026      0.020254     0.350749    0.015139   \n",
       "...          ...          ...           ...          ...         ...   \n",
       "1127    0.053496     0.228884      0.014969     0.604132    0.009759   \n",
       "1128    0.698811     0.033695      0.030942     0.020628    0.007408   \n",
       "1129    0.036197     0.006216      0.038991     0.017800    0.042295   \n",
       "1130    0.294645     0.068841      0.351736     0.073772    0.004323   \n",
       "1131    0.046666     0.087098      0.019651     0.073331    0.008387   \n",
       "\n",
       "      Criteria vi  Criteria vii  Criteria viii  Criteria ix  Criteria x  \\\n",
       "0        0.014229      0.001415       0.000845     0.000945    0.002275   \n",
       "1        0.055353      0.003414       0.001681     0.001967    0.002403   \n",
       "2        0.053089      0.011696       0.006186     0.003875    0.012006   \n",
       "3        0.125263      0.339578       0.020654     0.016124    0.036024   \n",
       "4        0.017242      0.001813       0.002387     0.003894    0.003338   \n",
       "...           ...           ...            ...          ...         ...   \n",
       "1127     0.058910      0.002598       0.003048     0.003529    0.002367   \n",
       "1128     0.041588      0.049546       0.023215     0.016675    0.055115   \n",
       "1129     0.123313      0.563167       0.059646     0.028864    0.055149   \n",
       "1130     0.172625      0.005515       0.003727     0.002251    0.005164   \n",
       "1131     0.728658      0.005269       0.003832     0.004549    0.005575   \n",
       "\n",
       "        Others  \n",
       "0     0.010129  \n",
       "1     0.018694  \n",
       "2     0.022811  \n",
       "3     0.039419  \n",
       "4     0.013045  \n",
       "...        ...  \n",
       "1127  0.018309  \n",
       "1128  0.022377  \n",
       "1129  0.028361  \n",
       "1130  0.017400  \n",
       "1131  0.016985  \n",
       "\n",
       "[1132 rows x 11 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score_df = pd.DataFrame(new_dict).reindex(classes).T\n",
    "score_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = score_df[classes].to_numpy().argsort()[:, :-4:-1]\n",
    "c = np.array(classes)[a]\n",
    "d = score_df[classes].to_numpy()[np.arange(a.shape[0])[:, None], a]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1 = pd.DataFrame(c).rename(columns=lambda x : f'max_{x+1}_col')\n",
    "df2 = pd.DataFrame(d).rename(columns=lambda x : f'max_{x+1}_val')\n",
    "c = score_df.columns.tolist() + [y for x in zip(df2.columns, df1.columns) for y in x]\n",
    "\n",
    "score_df = pd.concat([score_df, df1, df2], axis=1).reindex(c, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Criteria i</th>\n",
       "      <th>Criteria ii</th>\n",
       "      <th>Criteria iii</th>\n",
       "      <th>Criteria iv</th>\n",
       "      <th>Criteria v</th>\n",
       "      <th>Criteria vi</th>\n",
       "      <th>Criteria vii</th>\n",
       "      <th>Criteria viii</th>\n",
       "      <th>Criteria ix</th>\n",
       "      <th>Criteria x</th>\n",
       "      <th>Others</th>\n",
       "      <th>max_1_val</th>\n",
       "      <th>max_1_col</th>\n",
       "      <th>max_2_val</th>\n",
       "      <th>max_2_col</th>\n",
       "      <th>max_3_val</th>\n",
       "      <th>max_3_col</th>\n",
       "      <th>max_1</th>\n",
       "      <th>max_3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.121917</td>\n",
       "      <td>0.024367</td>\n",
       "      <td>0.051884</td>\n",
       "      <td>0.767751</td>\n",
       "      <td>0.004244</td>\n",
       "      <td>0.014229</td>\n",
       "      <td>0.001415</td>\n",
       "      <td>0.000845</td>\n",
       "      <td>0.000945</td>\n",
       "      <td>0.002275</td>\n",
       "      <td>0.010129</td>\n",
       "      <td>0.767751</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.121917</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>0.051884</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.767751</td>\n",
       "      <td>0.941553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.129583</td>\n",
       "      <td>0.243989</td>\n",
       "      <td>0.266616</td>\n",
       "      <td>0.259280</td>\n",
       "      <td>0.017020</td>\n",
       "      <td>0.055353</td>\n",
       "      <td>0.003414</td>\n",
       "      <td>0.001681</td>\n",
       "      <td>0.001967</td>\n",
       "      <td>0.002403</td>\n",
       "      <td>0.018694</td>\n",
       "      <td>0.266616</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.259280</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.243989</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.266616</td>\n",
       "      <td>0.769884</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.230085</td>\n",
       "      <td>0.086930</td>\n",
       "      <td>0.017167</td>\n",
       "      <td>0.547663</td>\n",
       "      <td>0.008494</td>\n",
       "      <td>0.053089</td>\n",
       "      <td>0.011696</td>\n",
       "      <td>0.006186</td>\n",
       "      <td>0.003875</td>\n",
       "      <td>0.012006</td>\n",
       "      <td>0.022811</td>\n",
       "      <td>0.547663</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.230085</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>0.086930</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.547663</td>\n",
       "      <td>0.864678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.058064</td>\n",
       "      <td>0.023713</td>\n",
       "      <td>0.133313</td>\n",
       "      <td>0.084863</td>\n",
       "      <td>0.122985</td>\n",
       "      <td>0.125263</td>\n",
       "      <td>0.339578</td>\n",
       "      <td>0.020654</td>\n",
       "      <td>0.016124</td>\n",
       "      <td>0.036024</td>\n",
       "      <td>0.039419</td>\n",
       "      <td>0.339578</td>\n",
       "      <td>Criteria vii</td>\n",
       "      <td>0.133313</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.125263</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>0.339578</td>\n",
       "      <td>0.598154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.060112</td>\n",
       "      <td>0.512026</td>\n",
       "      <td>0.020254</td>\n",
       "      <td>0.350749</td>\n",
       "      <td>0.015139</td>\n",
       "      <td>0.017242</td>\n",
       "      <td>0.001813</td>\n",
       "      <td>0.002387</td>\n",
       "      <td>0.003894</td>\n",
       "      <td>0.003338</td>\n",
       "      <td>0.013045</td>\n",
       "      <td>0.512026</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.350749</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.060112</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>0.512026</td>\n",
       "      <td>0.922888</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1127</th>\n",
       "      <td>0.053496</td>\n",
       "      <td>0.228884</td>\n",
       "      <td>0.014969</td>\n",
       "      <td>0.604132</td>\n",
       "      <td>0.009759</td>\n",
       "      <td>0.058910</td>\n",
       "      <td>0.002598</td>\n",
       "      <td>0.003048</td>\n",
       "      <td>0.003529</td>\n",
       "      <td>0.002367</td>\n",
       "      <td>0.018309</td>\n",
       "      <td>0.604132</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.228884</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.058910</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>0.604132</td>\n",
       "      <td>0.891926</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1128</th>\n",
       "      <td>0.698811</td>\n",
       "      <td>0.033695</td>\n",
       "      <td>0.030942</td>\n",
       "      <td>0.020628</td>\n",
       "      <td>0.007408</td>\n",
       "      <td>0.041588</td>\n",
       "      <td>0.049546</td>\n",
       "      <td>0.023215</td>\n",
       "      <td>0.016675</td>\n",
       "      <td>0.055115</td>\n",
       "      <td>0.022377</td>\n",
       "      <td>0.698811</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>0.055115</td>\n",
       "      <td>Criteria x</td>\n",
       "      <td>0.049546</td>\n",
       "      <td>Criteria vii</td>\n",
       "      <td>0.698811</td>\n",
       "      <td>0.803473</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1129</th>\n",
       "      <td>0.036197</td>\n",
       "      <td>0.006216</td>\n",
       "      <td>0.038991</td>\n",
       "      <td>0.017800</td>\n",
       "      <td>0.042295</td>\n",
       "      <td>0.123313</td>\n",
       "      <td>0.563167</td>\n",
       "      <td>0.059646</td>\n",
       "      <td>0.028864</td>\n",
       "      <td>0.055149</td>\n",
       "      <td>0.028361</td>\n",
       "      <td>0.563167</td>\n",
       "      <td>Criteria vii</td>\n",
       "      <td>0.123313</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>0.059646</td>\n",
       "      <td>Criteria viii</td>\n",
       "      <td>0.563167</td>\n",
       "      <td>0.746126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1130</th>\n",
       "      <td>0.294645</td>\n",
       "      <td>0.068841</td>\n",
       "      <td>0.351736</td>\n",
       "      <td>0.073772</td>\n",
       "      <td>0.004323</td>\n",
       "      <td>0.172625</td>\n",
       "      <td>0.005515</td>\n",
       "      <td>0.003727</td>\n",
       "      <td>0.002251</td>\n",
       "      <td>0.005164</td>\n",
       "      <td>0.017400</td>\n",
       "      <td>0.351736</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.294645</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>0.172625</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>0.351736</td>\n",
       "      <td>0.819006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1131</th>\n",
       "      <td>0.046666</td>\n",
       "      <td>0.087098</td>\n",
       "      <td>0.019651</td>\n",
       "      <td>0.073331</td>\n",
       "      <td>0.008387</td>\n",
       "      <td>0.728658</td>\n",
       "      <td>0.005269</td>\n",
       "      <td>0.003832</td>\n",
       "      <td>0.004549</td>\n",
       "      <td>0.005575</td>\n",
       "      <td>0.016985</td>\n",
       "      <td>0.728658</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>0.087098</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.073331</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.728658</td>\n",
       "      <td>0.889086</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1132 rows × 19 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Criteria i  Criteria ii  Criteria iii  Criteria iv  Criteria v  \\\n",
       "0       0.121917     0.024367      0.051884     0.767751    0.004244   \n",
       "1       0.129583     0.243989      0.266616     0.259280    0.017020   \n",
       "2       0.230085     0.086930      0.017167     0.547663    0.008494   \n",
       "3       0.058064     0.023713      0.133313     0.084863    0.122985   \n",
       "4       0.060112     0.512026      0.020254     0.350749    0.015139   \n",
       "...          ...          ...           ...          ...         ...   \n",
       "1127    0.053496     0.228884      0.014969     0.604132    0.009759   \n",
       "1128    0.698811     0.033695      0.030942     0.020628    0.007408   \n",
       "1129    0.036197     0.006216      0.038991     0.017800    0.042295   \n",
       "1130    0.294645     0.068841      0.351736     0.073772    0.004323   \n",
       "1131    0.046666     0.087098      0.019651     0.073331    0.008387   \n",
       "\n",
       "      Criteria vi  Criteria vii  Criteria viii  Criteria ix  Criteria x  \\\n",
       "0        0.014229      0.001415       0.000845     0.000945    0.002275   \n",
       "1        0.055353      0.003414       0.001681     0.001967    0.002403   \n",
       "2        0.053089      0.011696       0.006186     0.003875    0.012006   \n",
       "3        0.125263      0.339578       0.020654     0.016124    0.036024   \n",
       "4        0.017242      0.001813       0.002387     0.003894    0.003338   \n",
       "...           ...           ...            ...          ...         ...   \n",
       "1127     0.058910      0.002598       0.003048     0.003529    0.002367   \n",
       "1128     0.041588      0.049546       0.023215     0.016675    0.055115   \n",
       "1129     0.123313      0.563167       0.059646     0.028864    0.055149   \n",
       "1130     0.172625      0.005515       0.003727     0.002251    0.005164   \n",
       "1131     0.728658      0.005269       0.003832     0.004549    0.005575   \n",
       "\n",
       "        Others  max_1_val     max_1_col  max_2_val     max_2_col  max_3_val  \\\n",
       "0     0.010129   0.767751   Criteria iv   0.121917    Criteria i   0.051884   \n",
       "1     0.018694   0.266616  Criteria iii   0.259280   Criteria iv   0.243989   \n",
       "2     0.022811   0.547663   Criteria iv   0.230085    Criteria i   0.086930   \n",
       "3     0.039419   0.339578  Criteria vii   0.133313  Criteria iii   0.125263   \n",
       "4     0.013045   0.512026   Criteria ii   0.350749   Criteria iv   0.060112   \n",
       "...        ...        ...           ...        ...           ...        ...   \n",
       "1127  0.018309   0.604132   Criteria iv   0.228884   Criteria ii   0.058910   \n",
       "1128  0.022377   0.698811    Criteria i   0.055115    Criteria x   0.049546   \n",
       "1129  0.028361   0.563167  Criteria vii   0.123313   Criteria vi   0.059646   \n",
       "1130  0.017400   0.351736  Criteria iii   0.294645    Criteria i   0.172625   \n",
       "1131  0.016985   0.728658   Criteria vi   0.087098   Criteria ii   0.073331   \n",
       "\n",
       "          max_3_col     max_1     max_3  \n",
       "0      Criteria iii  0.767751  0.941553  \n",
       "1       Criteria ii  0.266616  0.769884  \n",
       "2       Criteria ii  0.547663  0.864678  \n",
       "3       Criteria vi  0.339578  0.598154  \n",
       "4        Criteria i  0.512026  0.922888  \n",
       "...             ...       ...       ...  \n",
       "1127    Criteria vi  0.604132  0.891926  \n",
       "1128   Criteria vii  0.698811  0.803473  \n",
       "1129  Criteria viii  0.563167  0.746126  \n",
       "1130    Criteria vi  0.351736  0.819006  \n",
       "1131    Criteria iv  0.728658  0.889086  \n",
       "\n",
       "[1132 rows x 19 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score_df['max_1'] = score_df['max_1_val']\n",
    "score_df['max_3'] = score_df['max_1_val']+score_df['max_2_val']+score_df['max_3_val']\n",
    "score_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "sns.set(style=\"white\")\n",
    "plt.figure()\n",
    "score_df['max_1'].hist(alpha=.7, bins=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(style=\"white\")\n",
    "plt.figure()\n",
    "score_df['max_3'].hist(alpha=.7, bins=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "sm_df = sm_df.merge(score_df, how = 'left', left_index=True, right_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "sm_df.to_csv(args.save_dir+'social_media_pred.csv', encoding='utf-8-sig')\n",
    "score_df.to_csv(args.save_dir+'social_media_score.csv', encoding='utf-8-sig')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## END"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}