BERT-Sentiment-Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": "device(type='cuda', index=0)"
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from transformers import BertModel, BertTokenizer, AdamW, get_linear_schedule_with_warmup\n",
    "import torch\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "from pylab import rcParams\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import confusion_matrix, classification_report\n",
    "from collections import defaultdict\n",
    "from textwrap import wrap\n",
    "\n",
    "from torch import nn, optim\n",
    "from torch.utils.data import Dataset, DataLoader\n",
    "import torch.nn.functional as F\n",
    "\n",
    "from data_loader import PrepareDataset\n",
    "from model import SentimentClassifier\n",
    "\n",
    "import logging\n",
    "\n",
    "\n",
    "logging.basicConfig(level=logging.ERROR)\n",
    "\n",
    "%matplotlib inline\n",
    "%config InlineBackend.figure_format='retina'\n",
    "\n",
    "sns.set(style='whitegrid', palette='muted', font_scale=1.2)\n",
    "\n",
    "HAPPY_COLORS_PALETTE = [\"#01BEFE\", \"#FFDD00\", \"#FF7D00\", \"#FF006D\", \"#ADFF02\", \"#8F00FF\"]\n",
    "\n",
    "sns.set_palette(sns.color_palette(HAPPY_COLORS_PALETTE))\n",
    "\n",
    "rcParams['figure.figsize'] = 12, 8\n",
    "\n",
    "RANDOM_SEED = 42\n",
    "np.random.seed(RANDOM_SEED)\n",
    "torch.manual_seed(RANDOM_SEED)\n",
    "\n",
    "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
    "device"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "outputs": [
    {
     "data": {
      "text/plain": "   Unnamed: 0                 date  rating  \\\n0           0  2015-04-15 05:21:16     2.0   \n1           1  2013-12-07 03:16:52     1.0   \n2           2  2015-12-05 03:18:11     5.0   \n3           3  2011-05-27 05:30:52     1.0   \n4           4  2017-01-14 21:56:57     4.0   \n\n                                                text  \n0  As someone who has worked with many museums, I...  \n1  I am actually horrified this place is still in...  \n2  I love Deagan's. I do. I really do. The atmosp...  \n3  Dismal, lukewarm, defrosted-tasting TexMex glo...  \n4  Oh happy day, finally have a Canes near my cas...  ",
      "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>Unnamed: 0</th>\n      <th>date</th>\n      <th>rating</th>\n      <th>text</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>2015-04-15 05:21:16</td>\n      <td>2.0</td>\n      <td>As someone who has worked with many museums, I...</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>2013-12-07 03:16:52</td>\n      <td>1.0</td>\n      <td>I am actually horrified this place is still in...</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2</td>\n      <td>2015-12-05 03:18:11</td>\n      <td>5.0</td>\n      <td>I love Deagan's. I do. I really do. The atmosp...</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>3</td>\n      <td>2011-05-27 05:30:52</td>\n      <td>1.0</td>\n      <td>Dismal, lukewarm, defrosted-tasting TexMex glo...</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>4</td>\n      <td>2017-01-14 21:56:57</td>\n      <td>4.0</td>\n      <td>Oh happy day, finally have a Canes near my cas...</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# df = pd.read_csv(\"data/google_play/reviews.csv\")\n",
    "# df = df.rename({'content': 'text', 'score':'rating'}, axis=1)\n",
    "df = pd.read_csv(\"data/yelp/reviews.csv\")\n",
    "df = df.rename({'stars': 'rating'}, axis=1)\n",
    "df.head()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "outputs": [
    {
     "data": {
      "text/plain": "(19999, 4)"
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 19999 entries, 0 to 19998\n",
      "Data columns (total 4 columns):\n",
      " #   Column      Non-Null Count  Dtype  \n",
      "---  ------      --------------  -----  \n",
      " 0   Unnamed: 0  19999 non-null  int64  \n",
      " 1   date        19999 non-null  object \n",
      " 2   rating      19999 non-null  float64\n",
      " 3   text        19999 non-null  object \n",
      "dtypes: float64(1), int64(1), object(2)\n",
      "memory usage: 625.1+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/muzamil/anaconda3/lib/python3.8/site-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 864x576 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "image/png": {
       "width": 739,
       "height": 487
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(df.rating)\n",
    "plt.xlabel('review score');"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "outputs": [],
   "source": [
    "def to_sentiment(rating):\n",
    "  rating = int(rating)\n",
    "  if rating <= 3:\n",
    "    return 0\n",
    "  elif rating == 4:\n",
    "    return 1\n",
    "  else:\n",
    "    return 2\n",
    "\n",
    "df['sentiment'] = df.rating.apply(to_sentiment)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "outputs": [],
   "source": [
    "sentiments = ['negative', 'neutral', 'positive']"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/muzamil/anaconda3/lib/python3.8/site-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 864x576 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "image/png": {
       "width": 739,
       "height": 487
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.countplot(df.sentiment)\n",
    "plt.xlabel('review sentiment')\n",
    "ax.set_xticklabels(sentiments);"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "outputs": [],
   "source": [
    "PRE_TRAINED_BERT_MODEL = 'bert-base-cased'\n",
    "tokenizer = BertTokenizer.from_pretrained(PRE_TRAINED_BERT_MODEL)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/muzamil/anaconda3/lib/python3.8/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 864x576 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "image/png": {
       "width": 750,
       "height": 487
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "token_lens = []\n",
    "\n",
    "for text in df.text:\n",
    "  tokens = tokenizer.encode(text, max_length=512)\n",
    "  token_lens.append(len(tokens))\n",
    "\n",
    "sns.distplot(token_lens)\n",
    "plt.xlim([0, 256]);\n",
    "plt.xlabel('Token count');"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "outputs": [],
   "source": [
    "MAX_LEN = 160"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "outputs": [],
   "source": [
    "df_train, df_test = train_test_split(df, test_size=0.1, random_state=RANDOM_SEED)\n",
    "df_val, df_test = train_test_split(df_test, test_size=0.5, random_state=RANDOM_SEED)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "outputs": [
    {
     "data": {
      "text/plain": "((17999, 5), (1000, 5), (1000, 5))"
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.shape, df_val.shape, df_test.shape"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "outputs": [],
   "source": [
    "def create_data_loader(df, tokenizer, max_len, bs):\n",
    "    ds = PrepareDataset(\n",
    "        reviews=df[\"text\"].to_numpy(),\n",
    "        targets=df[\"sentiment\"].to_numpy(),\n",
    "        tokenizer=tokenizer,\n",
    "        max_len=max_len\n",
    "    )\n",
    "\n",
    "    return DataLoader(ds, batch_size=bs, num_workers=4)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "outputs": [],
   "source": [
    "BATCH_SIZE = 16\n",
    "\n",
    "train_data_loader = create_data_loader(df_train, tokenizer, MAX_LEN, BATCH_SIZE)\n",
    "val_data_loader = create_data_loader(df_val, tokenizer, MAX_LEN, BATCH_SIZE)\n",
    "test_data_loader = create_data_loader(df_test, tokenizer, MAX_LEN, BATCH_SIZE)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "outputs": [
    {
     "data": {
      "text/plain": "dict_keys(['review_text', 'input_ids', 'attention_mask', 'targets'])"
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = next(iter(train_data_loader))\n",
    "data.keys()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([16, 160])\n",
      "torch.Size([16, 160])\n",
      "torch.Size([16])\n"
     ]
    }
   ],
   "source": [
    "print(data['input_ids'].shape)\n",
    "print(data['attention_mask'].shape)\n",
    "print(data['targets'].shape)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "outputs": [],
   "source": [
    "model = SentimentClassifier(PRE_TRAINED_BERT_MODEL, len(sentiments))\n",
    "model = model.to(device)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([16, 160])\n",
      "torch.Size([16, 160])\n",
      "torch.Size([16])\n"
     ]
    }
   ],
   "source": [
    "input_ids = data['input_ids'].to(device)\n",
    "attention_mask = data['attention_mask'].to(device)\n",
    "targets = data['targets'].to(device)\n",
    "\n",
    "print(input_ids.shape) # batch size x seq length\n",
    "print(attention_mask.shape) # batch size x seq length\n",
    "print(targets.shape)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "outputs": [],
   "source": [
    "EPOCHS = 10\n",
    "\n",
    "optimizer = AdamW(model.parameters(), lr=2e-5, correct_bias=False)\n",
    "total_steps = len(train_data_loader) * EPOCHS\n",
    "\n",
    "scheduler = get_linear_schedule_with_warmup(\n",
    "  optimizer,\n",
    "  num_warmup_steps=0,\n",
    "  num_training_steps=total_steps\n",
    ")\n",
    "\n",
    "loss_fn = nn.CrossEntropyLoss().to(device)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "outputs": [],
   "source": [
    "def train_epoch(\n",
    "  model,\n",
    "  data_loader,\n",
    "  loss_fn,\n",
    "  optimizer,\n",
    "  device,\n",
    "  scheduler,\n",
    "  n_examples\n",
    "):\n",
    "  model = model.train()\n",
    "\n",
    "  losses = []\n",
    "  correct_predictions = 0\n",
    "\n",
    "  for d in data_loader:\n",
    "    input_ids = d[\"input_ids\"].to(device)\n",
    "    attention_mask = d[\"attention_mask\"].to(device)\n",
    "    targets = d[\"targets\"].to(device)\n",
    "\n",
    "    outputs = model(input_ids=input_ids, attention_mask=attention_mask)\n",
    "\n",
    "    _, preds = torch.max(outputs, dim=1)\n",
    "    loss = loss_fn(outputs, targets)\n",
    "\n",
    "    correct_predictions += torch.sum(preds == targets)\n",
    "    losses.append(loss.item())\n",
    "\n",
    "    loss.backward()\n",
    "    nn.utils.clip_grad_norm_(model.parameters(), max_norm=1.0)\n",
    "    optimizer.step()\n",
    "    scheduler.step()\n",
    "    optimizer.zero_grad()\n",
    "\n",
    "  return correct_predictions.double() / n_examples, np.mean(losses)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "outputs": [],
   "source": [
    "def eval_model(model, data_loader, loss_fn, device, n_examples):\n",
    "  model = model.eval()\n",
    "\n",
    "  losses = []\n",
    "  correct_predictions = 0\n",
    "\n",
    "  with torch.no_grad():\n",
    "    for d in data_loader:\n",
    "      input_ids = d[\"input_ids\"].to(device)\n",
    "      attention_mask = d[\"attention_mask\"].to(device)\n",
    "      targets = d[\"targets\"].to(device)\n",
    "\n",
    "      outputs = model(input_ids=input_ids, attention_mask=attention_mask)\n",
    "      _, preds = torch.max(outputs, dim=1)\n",
    "\n",
    "      loss = loss_fn(outputs, targets)\n",
    "\n",
    "      correct_predictions += torch.sum(preds == targets)\n",
    "      losses.append(loss.item())\n",
    "\n",
    "  return correct_predictions.double() / n_examples, np.mean(losses)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "----------\n",
      "Train loss 0.6432836941348182 accuracy 0.7201511195066392\n",
      "Val   loss 0.559965714339226 accuracy 0.744\n",
      "\n",
      "Epoch 2/10\n",
      "----------\n",
      "Train loss 0.4404999771018823 accuracy 0.8218234346352575\n",
      "Val   loss 0.6387954940871586 accuracy 0.747\n",
      "\n",
      "Epoch 3/10\n",
      "----------\n",
      "Train loss 0.30167894969218306 accuracy 0.8928829379409967\n",
      "Val   loss 0.8711256829519121 accuracy 0.726\n",
      "\n",
      "Epoch 4/10\n",
      "----------\n",
      "Train loss 0.22247904135079846 accuracy 0.9296072004000222\n",
      "Val   loss 1.0680668707937002 accuracy 0.728\n",
      "\n",
      "Epoch 5/10\n",
      "----------\n",
      "Train loss 0.1603876303436959 accuracy 0.9554975276404245\n",
      "Val   loss 1.3535771932903795 accuracy 0.721\n",
      "\n",
      "Epoch 6/10\n",
      "----------\n",
      "Train loss 0.12492692004162301 accuracy 0.9676648702705706\n",
      "Val   loss 1.5260661568063947 accuracy 0.735\n",
      "\n",
      "Epoch 7/10\n",
      "----------\n",
      "Train loss 0.08450687795574777 accuracy 0.9804433579643314\n",
      "Val   loss 1.7807937473047613 accuracy 0.729\n",
      "\n",
      "Epoch 8/10\n",
      "----------\n",
      "Train loss 0.06992896628219428 accuracy 0.9844435801989\n",
      "Val   loss 1.7961964217700168 accuracy 0.738\n",
      "\n",
      "Epoch 9/10\n",
      "----------\n",
      "Train loss 0.05159025283394537 accuracy 0.9882215678648814\n",
      "Val   loss 1.888123500804853 accuracy 0.737\n",
      "\n",
      "Epoch 10/10\n",
      "----------\n",
      "Train loss 0.03260594028159459 accuracy 0.9932218456580921\n",
      "Val   loss 1.9480634919411155 accuracy 0.743\n",
      "\n",
      "CPU times: user 1h 17s, sys: 10.3 s, total: 1h 27s\n",
      "Wall time: 1h 43s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "history = defaultdict(list)\n",
    "best_accuracy = 0\n",
    "\n",
    "for epoch in range(EPOCHS):\n",
    "\n",
    "  print(f'Epoch {epoch + 1}/{EPOCHS}')\n",
    "  print('-' * 10)\n",
    "\n",
    "  train_acc, train_loss = train_epoch(\n",
    "    model,\n",
    "    train_data_loader,\n",
    "    loss_fn,\n",
    "    optimizer,\n",
    "    device,\n",
    "    scheduler,\n",
    "    len(df_train)\n",
    "  )\n",
    "\n",
    "  print(f'Train loss {train_loss} accuracy {train_acc}')\n",
    "\n",
    "  val_acc, val_loss = eval_model(\n",
    "    model,\n",
    "    val_data_loader,\n",
    "    loss_fn,\n",
    "    device,\n",
    "    len(df_val)\n",
    "  )\n",
    "\n",
    "  print(f'Val   loss {val_loss} accuracy {val_acc}')\n",
    "  print()\n",
    "\n",
    "  history['train_acc'].append(train_acc)\n",
    "  history['train_loss'].append(train_loss)\n",
    "  history['val_acc'].append(val_acc)\n",
    "  history['val_loss'].append(val_loss)\n",
    "\n",
    "  if val_acc > best_accuracy:\n",
    "    torch.save(model.state_dict(), 'best_model_state.bin')\n",
    "    best_accuracy = val_acc"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 864x576 with 1 Axes>",
      "image/png": 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     },
     "metadata": {
      "image/png": {
       "width": 728,
       "height": 503
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_accuracy = torch.stack(history['train_acc'])\n",
    "val_accuracy = torch.stack(history['val_acc'])\n",
    "plt.plot(train_accuracy.cpu().numpy(), label='train accuracy')\n",
    "plt.plot(val_accuracy.cpu().numpy(), label='validation accuracy')\n",
    "\n",
    "plt.title('Training history')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.xlabel('Epoch')\n",
    "plt.legend()\n",
    "plt.ylim([0, 1]);"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "# model = SentimentClassifier(PRE_TRAINED_BERT_MODEL, len(sentiments))\n",
    "# model.load_state_dict(torch.load('best_model_state.bin'))\n",
    "# model = model.to(device)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "outputs": [
    {
     "data": {
      "text/plain": "0.745"
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_acc, _ = eval_model(\n",
    "  model,\n",
    "  test_data_loader,\n",
    "  loss_fn,\n",
    "  device,\n",
    "  len(df_test)\n",
    ")\n",
    "\n",
    "test_acc.item()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "outputs": [],
   "source": [
    "def get_predictions(model, data_loader):\n",
    "  model = model.eval()\n",
    "\n",
    "  review_texts = []\n",
    "  predictions = []\n",
    "  prediction_probs = []\n",
    "  real_values = []\n",
    "\n",
    "  with torch.no_grad():\n",
    "    for d in data_loader:\n",
    "\n",
    "      texts = d[\"review_text\"]\n",
    "      input_ids = d[\"input_ids\"].to(device)\n",
    "      attention_mask = d[\"attention_mask\"].to(device)\n",
    "      targets = d[\"targets\"].to(device)\n",
    "\n",
    "      outputs = model(input_ids=input_ids, attention_mask=attention_mask)\n",
    "      _, preds = torch.max(outputs, dim=1)\n",
    "\n",
    "      probs = F.softmax(outputs, dim=1)\n",
    "\n",
    "      review_texts.extend(texts)\n",
    "      predictions.extend(preds)\n",
    "      prediction_probs.extend(probs)\n",
    "      real_values.extend(targets)\n",
    "\n",
    "  predictions = torch.stack(predictions).cpu()\n",
    "  prediction_probs = torch.stack(prediction_probs).cpu()\n",
    "  real_values = torch.stack(real_values).cpu()\n",
    "  return review_texts, predictions, prediction_probs, real_values"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "outputs": [],
   "source": [
    "y_review_texts, y_pred, y_pred_probs, y_test = get_predictions(\n",
    "  model,\n",
    "  test_data_loader\n",
    ")"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "    negative       0.85      0.82      0.84       329\n",
      "     neutral       0.49      0.54      0.51       228\n",
      "    positive       0.81      0.80      0.80       443\n",
      "\n",
      "    accuracy                           0.74      1000\n",
      "   macro avg       0.72      0.72      0.72      1000\n",
      "weighted avg       0.75      0.74      0.75      1000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, y_pred, target_names=sentiments))"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score, roc_curve, auc\n",
    "\n",
    "def evaluate_roc(probs, y_true):\n",
    "    \"\"\"\n",
    "    - Print AUC and accuracy on the test set\n",
    "    - Plot ROC\n",
    "    @params    probs (np.array): an array of predicted probabilities with shape (len(y_true), 2)\n",
    "    @params    y_true (np.array): an array of the true values with shape (len(y_true),)\n",
    "    \"\"\"\n",
    "    preds = probs[:, 1]\n",
    "    fpr, tpr, threshold = roc_curve(y_true, preds)\n",
    "    roc_auc = auc(fpr, tpr)\n",
    "    print(f'AUC: {roc_auc:.4f}')\n",
    "\n",
    "    # Get accuracy over the test set\n",
    "    y_pred = np.where(preds >= 0.5, 1, 0)\n",
    "    accuracy = accuracy_score(y_true, y_pred)\n",
    "    print(f'Accuracy: {accuracy*100:.2f}%')\n",
    "\n",
    "    # Plot ROC AUC\n",
    "    plt.title('Receiver Operating Characteristic')\n",
    "    plt.plot(fpr, tpr, 'b', label = 'AUC = %0.2f' % roc_auc)\n",
    "    plt.legend(loc = 'lower right')\n",
    "    plt.plot([0, 1], [0, 1],'r--')\n",
    "    plt.xlim([0, 1])\n",
    "    plt.ylim([0, 1])\n",
    "    plt.ylabel('True Positive Rate')\n",
    "    plt.xlabel('False Positive Rate')\n",
    "    plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "evaluate_roc(y_pred_probs, y_test)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 864x576 with 2 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "image/png": {
       "width": 713,
       "height": 512
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def show_confusion_matrix(confusion_matrix):\n",
    "  hmap = sns.heatmap(confusion_matrix, annot=True, fmt=\"d\", cmap=\"Blues\")\n",
    "  hmap.yaxis.set_ticklabels(hmap.yaxis.get_ticklabels(), rotation=0, ha='right')\n",
    "  hmap.xaxis.set_ticklabels(hmap.xaxis.get_ticklabels(), rotation=30, ha='right')\n",
    "  plt.ylabel('True sentiment')\n",
    "  plt.xlabel('Predicted sentiment');\n",
    "\n",
    "cm = confusion_matrix(y_test, y_pred)\n",
    "df_cm = pd.DataFrame(cm, index=sentiments, columns=sentiments)\n",
    "show_confusion_matrix(df_cm)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "outputs": [],
   "source": [
    "idx = 2\n",
    "\n",
    "review_text = y_review_texts[idx]\n",
    "true_sentiment = y_test[idx]\n",
    "pred_df = pd.DataFrame({\n",
    "  'sentiments': sentiments,\n",
    "  'values': y_pred_probs[idx]\n",
    "})"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This food is delicious and its authentic. The owner sticks by his word\n",
      "and makes sure his customers are satisfied. This is one of the only\n",
      "Greek places that has authentic tzatziki sauce. I eat here at least 3\n",
      "times a week. This place is a hidden gem I'm the university circle\n",
      "Uptown district. If eating in this area be sure to shop by this place.\n",
      "P.S the chicken wings are to die for literally. Im a life time\n",
      "customer.\n",
      "\n",
      "True sentiment: positive\n"
     ]
    }
   ],
   "source": [
    "print(\"\\n\".join(wrap(review_text)))\n",
    "print()\n",
    "print(f'True sentiment: {sentiments[true_sentiment]}')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 864x576 with 1 Axes>",
      "image/png": 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     },
     "metadata": {
      "image/png": {
       "width": 769,
       "height": 487
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x='values', y='sentiments', data=pred_df, orient='h')\n",
    "plt.ylabel('sentiment')\n",
    "plt.xlabel('probability')\n",
    "plt.xlim([0, 1]);"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "outputs": [],
   "source": [
    "review_text = \"I love completing my todos! Best app ever!!!\""
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "outputs": [],
   "source": [
    "encoded_review = tokenizer.encode_plus(\n",
    "  review_text,\n",
    "  max_length=MAX_LEN,\n",
    "  add_special_tokens=True,\n",
    "  return_token_type_ids=False,\n",
    "  pad_to_max_length=True,\n",
    "  return_attention_mask=True,\n",
    "  return_tensors='pt',\n",
    ")"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Review text: I love completing my todos! Best app ever!!!\n",
      "Sentiment  : positive\n"
     ]
    }
   ],
   "source": [
    "input_ids = encoded_review['input_ids'].to(device)\n",
    "attention_mask = encoded_review['attention_mask'].to(device)\n",
    "\n",
    "output = model(input_ids, attention_mask)\n",
    "_, prediction = torch.max(output, dim=1)\n",
    "\n",
    "print(f'Review text: {review_text}')\n",
    "print(f'Sentiment  : {sentiments[prediction]}')"
   ],
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