attention_score_binary_classification.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load processed data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import yaml\n",
    "import numpy as np\n",
    "import torch\n",
    "from collections import Counter\n",
    "from torch import nn\n",
    "from matplotlib import pyplot as plt\n",
    "import torch.nn.functional as F\n",
    "%matplotlib inline\n",
    "import copy\n",
    "import argparse\n",
    "from helper import data_generator\n",
    "import sys; sys.argv=['']; del sys\n",
    "file = 'args/sst/sst_attn_simple.yaml'\n",
    "with open(file) as f:\n",
    "    args = yaml.load(f, Loader=yaml.Loader)\n",
    "    parser = argparse.ArgumentParser(description='attention')\n",
    "    config = parser.parse_args()\n",
    "    for k, v in args['common'].items():\n",
    "        setattr(config, k, v)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import pickle\n",
    "with open(config.dic_path, 'rb') as f:\n",
    "    vocab, word2id, id2word = pickle.load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train_dg = data_generator(config, config.train_path)\n",
    "train_eval_dg = data_generator(config, config.train_path, False)\n",
    "dev_dg = data_generator(config, config.dev_path, False)\n",
    "test_dg = data_generator(config, config.test_path, False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "16174"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(vocab)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#scale = 1\n",
    "from attn_model import SimpleAttnClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix, f1_score, recall_score, precision_score\n",
    "def evaluate(dg, model):\n",
    "    #Make prediction\n",
    "    model.eval()\n",
    "    count = 0\n",
    "    #record the gold and the prediction\n",
    "    gold_labels = []\n",
    "    pred_labels = []\n",
    "    dg.reset_samples()\n",
    "    while dg.index<dg.data_len:\n",
    "        sent_ids, label_list, sent_lens = next(dg.get_ids_samples())\n",
    "        outputs, _, _, _ =  model(sent_ids.to(device), sent_lens.to(device))\n",
    "        preds = (outputs>0.5).squeeze()\n",
    "\n",
    "        gold_labels += list(label_list.cpu().numpy())\n",
    "        pred_labels += list(preds.cpu().numpy())\n",
    "        num = (preds.cpu() == label_list.bool()).sum().cpu().item()\n",
    "        count += num\n",
    "        #print(num)\n",
    "    accuracy = count*1.0/dg.data_len\n",
    "    f1 = f1_score(gold_labels, pred_labels, average='binary')\n",
    "    precision = precision_score(gold_labels, pred_labels, average='binary')\n",
    "    recall = recall_score(gold_labels, pred_labels, average='binary')\n",
    "    print('Index', dg.index)\n",
    "    print('Evaluation accuracy:', accuracy)\n",
    "    return accuracy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "torch.manual_seed(222)\n",
    "loss_function = nn.BCELoss()\n",
    "scale = 10\n",
    "model = SimpleAttnClassifier(config.vocab_size, 100, 100, 1, scale)\n",
    "device = torch.device(\"cuda\") if torch.cuda.is_available() else torch.device(\"cpu\")\n",
    "model = model.to(device)\n",
    "optimizer = torch.optim.Adagrad(model.parameters(), lr=0.02, weight_decay=0.0000, lr_decay=0.001)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 0\n",
      "####################\n",
      "Sample Loss:0.695\n",
      "Sample Loss:0.620\n",
      "Index 6920\n",
      "Evaluation accuracy: 0.7566473988439306\n",
      "Dev Accuracy\n",
      "Index 872\n",
      "Evaluation accuracy: 0.7350917431192661\n",
      "Epoch: 1\n",
      "####################\n",
      "Sample Loss:0.599\n",
      "Sample Loss:0.482\n",
      "Index 6920\n",
      "Evaluation accuracy: 0.8228323699421966\n",
      "Dev Accuracy\n",
      "Index 872\n",
      "Evaluation accuracy: 0.768348623853211\n",
      "Epoch: 2\n",
      "####################\n",
      "Sample Loss:0.504\n",
      "Sample Loss:0.435\n",
      "Index 6920\n",
      "Evaluation accuracy: 0.8625722543352601\n",
      "Dev Accuracy\n",
      "Index 872\n",
      "Evaluation accuracy: 0.786697247706422\n",
      "Epoch: 3\n",
      "####################\n",
      "Sample Loss:0.407\n",
      "Sample Loss:0.315\n",
      "Index 6920\n",
      "Evaluation accuracy: 0.8854046242774567\n",
      "Dev Accuracy\n",
      "Index 872\n",
      "Evaluation accuracy: 0.7855504587155964\n",
      "Epoch: 4\n",
      "####################\n",
      "Sample Loss:0.376\n",
      "Sample Loss:0.276\n",
      "Index 6920\n",
      "Evaluation accuracy: 0.9030346820809249\n",
      "Dev Accuracy\n",
      "Index 872\n",
      "Evaluation accuracy: 0.7912844036697247\n",
      "Epoch: 5\n",
      "####################\n",
      "Sample Loss:0.291\n",
      "Sample Loss:0.327\n",
      "Index 6920\n",
      "Evaluation accuracy: 0.9160404624277456\n",
      "Dev Accuracy\n",
      "Index 872\n",
      "Evaluation accuracy: 0.7901376146788991\n",
      "Epoch: 6\n",
      "####################\n",
      "Sample Loss:0.252\n",
      "Sample Loss:0.296\n",
      "Index 6920\n",
      "Evaluation accuracy: 0.9260115606936417\n",
      "Dev Accuracy\n",
      "Index 872\n",
      "Evaluation accuracy: 0.7935779816513762\n",
      "Epoch: 7\n",
      "####################\n",
      "Sample Loss:0.274\n",
      "Sample Loss:0.325\n",
      "Index 6920\n",
      "Evaluation accuracy: 0.9342485549132948\n",
      "Dev Accuracy\n",
      "Index 872\n",
      "Evaluation accuracy: 0.8004587155963303\n",
      "Epoch: 8\n",
      "####################\n",
      "Sample Loss:0.229\n",
      "Sample Loss:0.217\n",
      "Index 6920\n",
      "Evaluation accuracy: 0.940606936416185\n",
      "Dev Accuracy\n",
      "Index 872\n",
      "Evaluation accuracy: 0.8004587155963303\n",
      "Epoch: 9\n",
      "####################\n",
      "Sample Loss:0.238\n",
      "Sample Loss:0.194\n",
      "Index 6920\n",
      "Evaluation accuracy: 0.9465317919075145\n",
      "Dev Accuracy\n",
      "Index 872\n",
      "Evaluation accuracy: 0.8004587155963303\n",
      "Epoch: 10\n",
      "####################\n",
      "Sample Loss:0.211\n",
      "Sample Loss:0.226\n",
      "Index 6920\n",
      "Evaluation accuracy: 0.9505780346820809\n",
      "Dev Accuracy\n",
      "Index 872\n",
      "Evaluation accuracy: 0.7981651376146789\n",
      "Epoch: 11\n",
      "####################\n",
      "Sample Loss:0.221\n",
      "Sample Loss:0.207\n",
      "Index 6920\n",
      "Evaluation accuracy: 0.9550578034682081\n",
      "Dev Accuracy\n",
      "Index 872\n",
      "Evaluation accuracy: 0.7958715596330275\n",
      "Epoch: 12\n",
      "####################\n",
      "Sample Loss:0.136\n",
      "Sample Loss:0.159\n",
      "Index 6920\n",
      "Evaluation accuracy: 0.9591040462427746\n",
      "Dev Accuracy\n",
      "Index 872\n",
      "Evaluation accuracy: 0.7981651376146789\n",
      "Epoch: 13\n",
      "####################\n",
      "Sample Loss:0.155\n",
      "Sample Loss:0.181\n",
      "Index 6920\n",
      "Evaluation accuracy: 0.9614161849710983\n",
      "Dev Accuracy\n",
      "Index 872\n",
      "Evaluation accuracy: 0.7981651376146789\n",
      "Epoch: 14\n",
      "####################\n",
      "Sample Loss:0.175\n",
      "Sample Loss:0.122\n",
      "Index 6920\n",
      "Evaluation accuracy: 0.9634393063583815\n",
      "Dev Accuracy\n",
      "Index 872\n",
      "Evaluation accuracy: 0.7947247706422018\n",
      "Epoch: 15\n",
      "####################\n",
      "Sample Loss:0.185\n",
      "Sample Loss:0.164\n",
      "Index 6920\n",
      "Evaluation accuracy: 0.9648843930635839\n",
      "Dev Accuracy\n",
      "Index 872\n",
      "Evaluation accuracy: 0.7935779816513762\n",
      "Epoch: 16\n",
      "####################\n",
      "Sample Loss:0.164\n",
      "Sample Loss:0.141\n",
      "Index 6920\n",
      "Evaluation accuracy: 0.9666184971098266\n",
      "Dev Accuracy\n",
      "Index 872\n",
      "Evaluation accuracy: 0.7947247706422018\n",
      "Epoch: 17\n",
      "####################\n",
      "Sample Loss:0.248\n",
      "Sample Loss:0.176\n",
      "Index 6920\n",
      "Evaluation accuracy: 0.9690751445086705\n",
      "Dev Accuracy\n",
      "Index 872\n",
      "Evaluation accuracy: 0.7947247706422018\n",
      "Epoch: 18\n",
      "####################\n",
      "Sample Loss:0.119\n",
      "Sample Loss:0.143\n",
      "Index 6920\n",
      "Evaluation accuracy: 0.9706647398843931\n",
      "Dev Accuracy\n",
      "Index 872\n",
      "Evaluation accuracy: 0.7947247706422018\n",
      "Epoch: 19\n",
      "####################\n",
      "Sample Loss:0.145\n",
      "Sample Loss:0.202\n",
      "Index 6920\n",
      "Evaluation accuracy: 0.9728323699421966\n",
      "Dev Accuracy\n",
      "Index 872\n",
      "Evaluation accuracy: 0.7981651376146789\n",
      "Epoch: 20\n",
      "####################\n",
      "Sample Loss:0.137\n",
      "Sample Loss:0.118\n",
      "Index 6920\n",
      "Evaluation accuracy: 0.9736994219653179\n",
      "Dev Accuracy\n",
      "Index 872\n",
      "Evaluation accuracy: 0.7970183486238532\n",
      "Epoch: 21\n",
      "####################\n",
      "Sample Loss:0.141\n",
      "Sample Loss:0.124\n",
      "Index 6920\n",
      "Evaluation accuracy: 0.9747109826589595\n",
      "Dev Accuracy\n",
      "Index 872\n",
      "Evaluation accuracy: 0.7970183486238532\n",
      "Epoch: 22\n",
      "####################\n",
      "Sample Loss:0.154\n",
      "Sample Loss:0.126\n",
      "Index 6920\n",
      "Evaluation accuracy: 0.9755780346820809\n",
      "Dev Accuracy\n",
      "Index 872\n",
      "Evaluation accuracy: 0.7958715596330275\n",
      "Epoch: 23\n",
      "####################\n",
      "Sample Loss:0.115\n",
      "Sample Loss:0.113\n",
      "Index 6920\n",
      "Evaluation accuracy: 0.9764450867052024\n",
      "Dev Accuracy\n",
      "Index 872\n",
      "Evaluation accuracy: 0.7947247706422018\n",
      "Epoch: 24\n",
      "####################\n",
      "Sample Loss:0.116\n",
      "Sample Loss:0.109\n",
      "Index 6920\n",
      "Evaluation accuracy: 0.9771676300578035\n",
      "Dev Accuracy\n",
      "Index 872\n",
      "Evaluation accuracy: 0.7935779816513762\n",
      "Epoch: 25\n",
      "####################\n",
      "Sample Loss:0.132\n",
      "Sample Loss:0.108\n",
      "Index 6920\n",
      "Evaluation accuracy: 0.9778901734104046\n",
      "Dev Accuracy\n",
      "Index 872\n",
      "Evaluation accuracy: 0.7935779816513762\n",
      "Epoch: 26\n",
      "####################\n",
      "Sample Loss:0.109\n",
      "Sample Loss:0.118\n",
      "Index 6920\n",
      "Evaluation accuracy: 0.9787572254335261\n",
      "Dev Accuracy\n",
      "Index 872\n",
      "Evaluation accuracy: 0.7912844036697247\n",
      "Epoch: 27\n",
      "####################\n",
      "Sample Loss:0.075\n",
      "Sample Loss:0.106\n",
      "Index 6920\n",
      "Evaluation accuracy: 0.9793352601156069\n",
      "Dev Accuracy\n",
      "Index 872\n",
      "Evaluation accuracy: 0.7901376146788991\n",
      "Epoch: 28\n",
      "####################\n",
      "Sample Loss:0.113\n",
      "Sample Loss:0.116\n",
      "Index 6920\n",
      "Evaluation accuracy: 0.9797687861271677\n",
      "Dev Accuracy\n",
      "Index 872\n",
      "Evaluation accuracy: 0.7889908256880734\n",
      "Epoch: 29\n",
      "####################\n",
      "Sample Loss:0.104\n",
      "Sample Loss:0.104\n",
      "Index 6920\n",
      "Evaluation accuracy: 0.9809248554913295\n",
      "Dev Accuracy\n",
      "Index 872\n",
      "Evaluation accuracy: 0.7889908256880734\n",
      "Epoch: 30\n",
      "####################\n",
      "Sample Loss:0.119\n",
      "Sample Loss:0.102\n",
      "Index 6920\n",
      "Evaluation accuracy: 0.9817919075144509\n",
      "Dev Accuracy\n",
      "Index 872\n",
      "Evaluation accuracy: 0.7889908256880734\n",
      "Epoch: 31\n",
      "####################\n",
      "Sample Loss:0.091\n",
      "Sample Loss:0.081\n",
      "Index 6920\n",
      "Evaluation accuracy: 0.9822254335260115\n",
      "Dev Accuracy\n",
      "Index 872\n",
      "Evaluation accuracy: 0.7901376146788991\n",
      "Epoch: 32\n",
      "####################\n",
      "Sample Loss:0.125\n",
      "Sample Loss:0.092\n",
      "Index 6920\n",
      "Evaluation accuracy: 0.9826589595375722\n",
      "Dev Accuracy\n",
      "Index 872\n",
      "Evaluation accuracy: 0.7901376146788991\n",
      "Epoch: 33\n",
      "####################\n",
      "Sample Loss:0.088\n",
      "Sample Loss:0.111\n",
      "Index 6920\n",
      "Evaluation accuracy: 0.9833815028901735\n",
      "Dev Accuracy\n",
      "Index 872\n",
      "Evaluation accuracy: 0.7878440366972477\n",
      "Epoch: 34\n",
      "####################\n",
      "Sample Loss:0.091\n",
      "Sample Loss:0.075\n",
      "Index 6920\n",
      "Evaluation accuracy: 0.9838150289017341\n",
      "Dev Accuracy\n",
      "Index 872\n",
      "Evaluation accuracy: 0.786697247706422\n",
      "Epoch: 35\n",
      "####################\n",
      "Sample Loss:0.130\n",
      "Sample Loss:0.124\n",
      "Index 6920\n",
      "Evaluation accuracy: 0.9841040462427746\n",
      "Dev Accuracy\n",
      "Index 872\n",
      "Evaluation accuracy: 0.7844036697247706\n",
      "Epoch: 36\n",
      "####################\n",
      "Sample Loss:0.062\n",
      "Sample Loss:0.095\n",
      "Index 6920\n",
      "Evaluation accuracy: 0.9845375722543352\n",
      "Dev Accuracy\n",
      "Index 872\n",
      "Evaluation accuracy: 0.7844036697247706\n",
      "Epoch: 37\n",
      "####################\n",
      "Sample Loss:0.091\n",
      "Sample Loss:0.072\n",
      "Index 6920\n",
      "Evaluation accuracy: 0.9848265895953757\n",
      "Dev Accuracy\n",
      "Index 872\n",
      "Evaluation accuracy: 0.7844036697247706\n",
      "Epoch: 38\n",
      "####################\n",
      "Sample Loss:0.077\n",
      "Sample Loss:0.060\n",
      "Index 6920\n",
      "Evaluation accuracy: 0.9854046242774567\n",
      "Dev Accuracy\n",
      "Index 872\n",
      "Evaluation accuracy: 0.7844036697247706\n",
      "Epoch: 39\n",
      "####################\n",
      "Sample Loss:0.084\n",
      "Sample Loss:0.079\n",
      "Index 6920\n",
      "Evaluation accuracy: 0.9856936416184972\n",
      "Dev Accuracy\n",
      "Index 872\n",
      "Evaluation accuracy: 0.7844036697247706\n"
     ]
    }
   ],
   "source": [
    "loop_num = int(train_dg.data_len/config.batch_size)+1\n",
    "best_model = None\n",
    "best_acc = 0\n",
    "for i in range(40):\n",
    "    print('Epoch:', i)\n",
    "    print('#'*20)\n",
    "\n",
    "    total_loss = 0\n",
    "    model.train()\n",
    "    #shuffle the training set given the random seed\n",
    "    train_dg.shuffle_data()\n",
    "    #sequential sampling, use all the dataset\n",
    "    train_dg.reset_samples()\n",
    "    #model.embeddings.required_grad = config.update_emb\n",
    "    for j in range(loop_num):\n",
    "        model.zero_grad()\n",
    "        # generate dataset\n",
    "        sent_ids,  label_list, sent_lens = next(train_dg.get_sequential_ids_samples())\n",
    "        ####check whether the seed works\n",
    "        #if (i<2)&(j==0):\n",
    "            #print(sent_ids)\n",
    "        preds, _, _, _ =  model(sent_ids.to(device), sent_lens.to(device))\n",
    "        temp = model.decoder(model.V.transpose(0, 1))\n",
    "        loss = loss_function(preds.squeeze(), label_list.to(device).float())#+ 0.0001*temp[0, 0].abs()\n",
    "        #loss = loss_function(preds.squeeze(), label_list.to(device).float())\n",
    "\n",
    "        # Do the backward pass and update the gradient\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "\n",
    "        # Get the Python number from a 1-element Tensor by calling tensor.item()\n",
    "        total_loss += loss.cpu().item()*len(sent_ids)\n",
    "        if j%50 == 0:\n",
    "            l = loss.cpu().item()\n",
    "            print('Sample Loss:{:.3f}'.format(l))\n",
    "\n",
    "    #Dev Evaluation\n",
    "#     print('Training Accuracy')\n",
    "    evaluate(train_eval_dg, model)\n",
    "    print('Dev Accuracy')\n",
    "    dev_acc = evaluate(dev_dg, model)\n",
    "    if best_acc < dev_acc:\n",
    "        best_acc = dev_acc\n",
    "        best_model = copy.deepcopy(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best dev: 0.8004587155963303\n",
      "Index 1821\n",
      "Evaluation accuracy: 0.8182317408017573\n"
     ]
    }
   ],
   "source": [
    "#Test performance       \n",
    "print('Best dev:', best_acc)\n",
    "test_acc = evaluate(test_dg, best_model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Select Tokens\n",
    "We will select polarity tokens as well as neutral tokens based on their frequency across different types of instances."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "texts, labels = zip(*train_dg.data_batch)\n",
    "text_length = [len(t) for t in texts]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Calculate the times that a token appears in a specified label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "word_label_pair = []\n",
    "word_doc_freq = {}\n",
    "vocab = []\n",
    "for i, text in enumerate(texts):\n",
    "    for w in text:\n",
    "        word_doc_freq[w] = word_doc_freq.get(w, 0) + 1\n",
    "        \n",
    "for i, text in enumerate(texts):\n",
    "    label = labels[i]\n",
    "    for w in text:\n",
    "        word_doc_freq[w] = word_doc_freq.get(w, 0) + 1\n",
    "    word_label_pair += [(id2word[item], label) for item in text]\n",
    "    vocab += [id2word[item] for item in text]\n",
    "vocab = set(vocab)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "word_label_pair_freq = Counter(word_label_pair)\n",
    "#Get the ratio, a word appears in positive sentences: a word appears in negative sentences\n",
    "token_freq_pos = {}\n",
    "token_freq_neg = {}\n",
    "pos_tokens = []\n",
    "neg_tokens = []\n",
    "neutral_tokens = []\n",
    "total_diff_freq = 0\n",
    "#Note here, 1 means positive and 0 means negative\n",
    "for w in vocab:\n",
    "    freq_pos = word_label_pair_freq[(w, 1)]\n",
    "    freq_neg = word_label_pair_freq[(w, 0)]\n",
    "    token_freq_pos[word2id[w]] = freq_pos\n",
    "    token_freq_neg[word2id[w]] = freq_neg\n",
    "\n",
    "    if freq_pos-freq_neg !=0:\n",
    "        #We use 10\n",
    "        rate = (freq_pos-freq_neg)/(freq_pos+freq_neg)*1.0\n",
    "        if rate>0.5 and freq_pos>5:#positive tokens\n",
    "            pos_tokens.append(w)\n",
    "        if rate<-0.5 and freq_neg>5:\n",
    "            neg_tokens.append(w)\n",
    "\n",
    "    if abs(freq_pos-freq_neg)/(freq_pos+freq_neg)<0.1 and abs(freq_pos-freq_neg)<5:\n",
    "        neutral_tokens.append(w)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#print(pos_tokens)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "412 265 1384\n"
     ]
    }
   ],
   "source": [
    "print(len(pos_tokens), len(neg_tokens), len(neutral_tokens))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pos_tokens_index = [word2id[w] for w in pos_tokens]\n",
    "neg_tokens_index = [word2id[w] for w in neg_tokens]\n",
    "neutral_tokens_index = [word2id[w] for w in neutral_tokens]\n",
    "pos_tokens_index_label1 = [token_freq_pos[w] for w in pos_tokens_index]\n",
    "pos_tokens_index_label0 = [token_freq_neg[w] for w in pos_tokens_index]\n",
    "pos_tokens_index_diff = [token_freq_pos[w]-token_freq_neg[w] for w in pos_tokens_index]\n",
    "\n",
    "neg_tokens_index_label1 = [token_freq_pos[w] for w in pos_tokens_index]\n",
    "neg_tokens_index_label0 = [token_freq_neg[w] for w in pos_tokens_index]\n",
    "neg_tokens_index_diff = [token_freq_pos[w]-token_freq_neg[w] for w in neg_tokens_index]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def get_sentiment_scores(model, pos_tokens_index, neg_tokens_index, neutral_tokens_index):\n",
    "    pos_token_sentiment_score = []\n",
    "    for w in pos_tokens_index:\n",
    "        seq_embs = model.embeddings(torch.LongTensor([w]).to(device))                        \n",
    "        hidden_vecs = model.affine(seq_embs)\n",
    "        hidden_vecs = seq_embs\n",
    "        pos_token_sentiment_score.append(model.decoder(hidden_vecs).item())\n",
    "\n",
    "    neg_token_sentiment_score = []\n",
    "    for w in neg_tokens_index:\n",
    "        seq_embs = model.embeddings(torch.LongTensor([w]).to(device))                        \n",
    "        hidden_vecs = model.affine(seq_embs)\n",
    "        hidden_vecs = seq_embs\n",
    "        neg_token_sentiment_score.append(model.decoder(hidden_vecs).item())\n",
    "\n",
    "    neutral_token_sentimetn_score = []\n",
    "    for w in neutral_tokens_index:\n",
    "        seq_embs = model.embeddings(torch.LongTensor([w]).to(device))                        \n",
    "        hidden_vecs = model.affine(seq_embs)\n",
    "        hidden_vecs = seq_embs\n",
    "        neutral_token_sentimetn_score.append(model.decoder(hidden_vecs).item())\n",
    "\n",
    "    plt.plot(pos_tokens_index, pos_token_sentiment_score, '+',label='Pos')\n",
    "    plt.plot(neg_tokens_index, neg_token_sentiment_score, '.', label='Neg')\n",
    "    plt.plot(neutral_tokens_index, neutral_token_sentimetn_score, 'x', label='Neutral')\n",
    "    plt.xlabel('Token Index')\n",
    "    plt.ylabel('Polarity Score')\n",
    "    plt.grid(linestyle='--')\n",
    "    plt.legend()\n",
    "    return pos_token_sentiment_score, neg_token_sentiment_score, neutral_token_sentimetn_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def get_attn_scores(model, pos_tokens_index, neg_tokens_index, neutral_tokens_index):\n",
    "    pos_token_attn_score = []\n",
    "    for w in pos_tokens_index:\n",
    "        seq_embs = model.embeddings(torch.LongTensor([w]).to(device))                        \n",
    "        hidden_vecs = model.affine(seq_embs)\n",
    "        #hidden_vecs = torch.tanh(model.attn_linear(hidden_vecs))\n",
    "        hidden_vecs = seq_embs\n",
    "        attn_score = torch.matmul(hidden_vecs, model.V)\n",
    "        #attn_score = torch.matmul(hidden_vecs, model.decoder.weight.transpose(0, 1))\n",
    "        pos_token_attn_score.append(attn_score[0].item()/scale)\n",
    "\n",
    "    neg_token_attn_score = []\n",
    "    for w in neg_tokens_index:\n",
    "        seq_embs = model.embeddings(torch.LongTensor([w]).to(device))                        \n",
    "        hidden_vecs = model.affine(seq_embs)\n",
    "        #hidden_vecs = torch.tanh(model.attn_linear(hidden_vecs))\n",
    "        hidden_vecs = seq_embs\n",
    "        attn_score = torch.matmul(hidden_vecs, model.V)\n",
    "        #attn_score = torch.matmul(hidden_vecs, model.decoder.weight.transpose(0, 1))\n",
    "        neg_token_attn_score.append(attn_score[0].item()/scale)\n",
    "\n",
    "    neutral_token_attn_score = []\n",
    "    for w in neutral_tokens_index:\n",
    "        seq_embs = model.embeddings(torch.LongTensor([w]).to(device))                        \n",
    "        hidden_vecs = model.affine(seq_embs)\n",
    "        #hidden_vecs = torch.tanh(model.attn_linear(hidden_vecs))\n",
    "        hidden_vecs = seq_embs\n",
    "        attn_score = torch.matmul(hidden_vecs, model.V)\n",
    "        #attn_score = torch.matmul(hidden_vecs, model.decoder.weight.transpose(0, 1))\n",
    "        neutral_token_attn_score.append(attn_score[0].item()/scale)\n",
    "        \n",
    "    plt.plot(pos_tokens_index, pos_token_attn_score, '+',label='Pos')\n",
    "    plt.plot(neg_tokens_index, neg_token_attn_score, '.', label='Neg')\n",
    "    plt.plot(neutral_tokens_index, neutral_token_attn_score, 'x', label='Neutral')\n",
    "\n",
    "    plt.xlabel('Token Index')\n",
    "    plt.ylabel('Attention Score')\n",
    "    plt.grid(linestyle='--')\n",
    "\n",
    "    plt.legend()\n",
    "    return pos_token_attn_score, neg_token_attn_score, neutral_token_attn_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#torch.save(best_model,'imgs/sst_scores_img/best_model_10.pt' )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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AzV3ZqUcVZ2szyFk/YJt9ZL9fTaOlRSaR/aS41tbNkEcScqxEPcJQZ2zJNZns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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ = get_sentiment_scores(best_model, pos_tokens_index, neg_tokens_index, neutral_tokens_index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Nx2lIKc9LKcuc/28ETEKI7gFuVv1cArmmdHR0dGqjTSsNIURvIYRw/p+A2t7TLXW+gQPr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\nplBNoejZScx/4XWe6vM5d7TvpdBm5pmoMOS2ZFqCLpAetIBPCgaTmXC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e0HNT5hIXFEfO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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_, _, neutral_token_attn_score= get_attn_scores(best_model, pos_tokens_index, neg_tokens_index, neutral_tokens_index)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Calculate attention weights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dg = test_dg\n",
    "dg.reset_samples()\n",
    "model.eval()\n",
    "  \n",
    "best_model.eval()\n",
    "def generate_weights(dg, model):\n",
    "    #model.embeddings.required_grad = config.update_emb\n",
    "    important_word_inds = []\n",
    "    token_attn_weights = {}  \n",
    "    while dg.index<dg.data_len:\n",
    "        # generate dataset\n",
    "        sent_ids,  label_list, sent_lens = next(dg.get_sequential_ids_samples())\n",
    "        ####check whether the seed works\n",
    "        #if (i<2)&(j==0):\n",
    "            #print(sent_ids)\n",
    "        preds, _, attn, _ =  best_model(sent_ids.to(device), sent_lens.to(device))\n",
    "        for i in range(len(sent_ids)):\n",
    "            for j in range(sent_lens[i]):\n",
    "                w = sent_ids[i, j].item()\n",
    "                token_attn_weights[w] = token_attn_weights.get(w, []) + [attn[i, 0, j].item()]\n",
    "        #Get important words\n",
    "        values, indices = torch.topk(attn.squeeze(1), k=2, dim=1, largest=True, sorted=True, out=None)\n",
    "        for i in range(len(indices)):\n",
    "            important_word_inds += list(sent_ids[i][indices[i]].numpy())\n",
    "    return token_attn_weights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# important_words = [id2word[item] for item in important_word_inds]\n",
    "# from collections import Counter\n",
    "# import_word_freq = Counter(important_words)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "token_attn_weights = generate_weights(test_dg, best_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "avg_pos_attn_weights = {}\n",
    "for w in pos_tokens_index:\n",
    "    if w in token_attn_weights.keys():\n",
    "        avg_pos_attn_weights[w] = np.mean(token_attn_weights[w])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "avg_neg_attn_weights = {}\n",
    "for w in neg_tokens_index:\n",
    "    if w in token_attn_weights.keys():\n",
    "        avg_neg_attn_weights[w] = np.mean(token_attn_weights[w])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "avg_neutral_attn_weights = {}\n",
    "for w in neutral_tokens_index:\n",
    "    if w in token_attn_weights.keys():\n",
    "        avg_neutral_attn_weights[w] = np.mean(token_attn_weights[w])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x12c0c9240>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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gofW4seFhallTWZ6i1jAMw4SHr3AXQgwAuBPAbgBHAfxYCHGEiO4hohuT3cBkYtnVPUwX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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = []\n",
    "keys, avg_weights = zip(*avg_pos_attn_weights.items())\n",
    "data += [avg_weights]\n",
    "plt.plot(keys, avg_weights, '+', label='pos')\n",
    "keys, avg_weights = zip(*avg_neg_attn_weights.items())\n",
    "data += [avg_weights]\n",
    "plt.plot(keys, avg_weights, '+', label='neg')\n",
    "keys, avg_weights = zip(*avg_neutral_attn_weights.items())\n",
    "data += [avg_weights]\n",
    "plt.plot(keys, avg_weights, '+', label='neutral')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Average Attention Weight')"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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enh6OHz9OV1cXnZ2d/NVf/RWHDh1i2bJlfPzjH6erqyv0b01NTaWyspKmpiZG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\nvbYRI1fsAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.boxplot(data)\n",
    "plt.grid(linestyle='--')\n",
    "plt.xticks([1, 2, 3], ['Pos', 'Neg', 'Neutral'])\n",
    "plt.ylabel('Average Attention Weight')\n",
    "#plt.savefig(\"imgs/sst_scores_img/sst_attn_weight_d\"+str(scale)+\".pdf\",  dpi=900)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "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": "3.6.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}