6. Text Classification.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"width: 100%; overflow: hidden;\">\n",
    "    <div style=\"width: 150px; float: left;\"> <img src=\"data/D4Sci_logo_ball.png\" alt=\"Data For Science, Inc\" align=\"left\" border=\"0\"> </div>\n",
    "    <div style=\"float: left; margin-left: 10px;\"> <h1>Natural Language Processing</h1>\n",
    "<h1>Text Classification</h1>\n",
    "        <p>Bruno Gonçalves<br/>\n",
    "        <a href=\"http://www.data4sci.com/\">www.data4sci.com</a><br/>\n",
    "            @bgoncalves, @data4sci</p></div>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "import gzip\n",
    "from collections import Counter\n",
    "from pprint import pprint\n",
    "\n",
    "import string\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "np.random.seed(123456)\n",
    "\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt \n",
    "\n",
    "from tqdm.notebook import tqdm\n",
    "tqdm.pandas()\n",
    "\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "from torch.optim import Adam\n",
    "from torch.utils.data import TensorDataset, DataLoader\n",
    "\n",
    "import torchview\n",
    "from torchview import draw_graph\n",
    "\n",
    "import re\n",
    "import nltk\n",
    "from nltk.tokenize import word_tokenize, sent_tokenize\n",
    "from nltk.corpus import stopwords\n",
    "from nltk.stem.wordnet import WordNetLemmatizer\n",
    "stopwords = set(stopwords.words('english'))\n",
    "\n",
    "\n",
    "import watermark\n",
    "\n",
    "%load_ext watermark\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We start by print out the versions of the libraries we're using for future reference"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Python implementation: CPython\n",
      "Python version       : 3.11.7\n",
      "IPython version      : 8.12.3\n",
      "\n",
      "Compiler    : Clang 14.0.6 \n",
      "OS          : Darwin\n",
      "Release     : 24.3.0\n",
      "Machine     : arm64\n",
      "Processor   : arm\n",
      "CPU cores   : 16\n",
      "Architecture: 64bit\n",
      "\n",
      "Git hash: 9c8c00758f3a2fa8e55e08f5aad405a157ca5dd2\n",
      "\n",
      "matplotlib: 3.8.0\n",
      "torch     : 2.3.0\n",
      "watermark : 2.4.3\n",
      "torchview : 0.2.6\n",
      "re        : 2.2.1\n",
      "nltk      : 3.8.1\n",
      "numpy     : 1.26.4\n",
      "pandas    : 2.2.3\n",
      "\n"
     ]
    }
   ],
   "source": [
    "%watermark -n -v -m -g -iv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load default figure style"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.style.use('./d4sci.mplstyle')\n",
    "colors = plt.rcParams['axes.prop_cycle'].by_key()['color']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "# IMDB Dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The IMDB dataset can be downloaded from [Kaggle](https://www.kaggle.com/datasets/lakshmi25npathi/imdb-dataset-of-50k-movie-reviews). We load it directly from the data directory in the repository"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>review</th>\n",
       "      <th>sentiment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>One of the other reviewers has mentioned that ...</td>\n",
       "      <td>positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A wonderful little production. &lt;br /&gt;&lt;br /&gt;The...</td>\n",
       "      <td>positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>I thought this was a wonderful way to spend ti...</td>\n",
       "      <td>positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Basically there's a family where a little boy ...</td>\n",
       "      <td>negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Petter Mattei's \"Love in the Time of Money\" is...</td>\n",
       "      <td>positive</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                              review sentiment\n",
       "0  One of the other reviewers has mentioned that ...  positive\n",
       "1  A wonderful little production. <br /><br />The...  positive\n",
       "2  I thought this was a wonderful way to spend ti...  positive\n",
       "3  Basically there's a family where a little boy ...  negative\n",
       "4  Petter Mattei's \"Love in the Time of Money\" is...  positive"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv('data/IMDB Dataset.csv')\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In total, we have 50,000 reviews"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(50000, 2)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Extract the text of a couple of them. A positive one"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"One of the other reviewers has mentioned that after watching just 1 Oz episode you'll be hooked. They are right, as this is exactly what happened with me.<br /><br />The first thing that struck me about Oz was its brutality and unflinching scenes of violence, which set in right from the word GO. Trust me, this is not a show for the faint hearted or timid. This show pulls no punches with regards to drugs, sex or violence. Its is hardcore, in the classic use of the word.<br /><br />It is called OZ as that is the nickname given to the Oswald Maximum Security State Penitentary. It focuses mainly on Emerald City, an experimental section of the prison where all the cells have glass fronts and face inwards, so privacy is not high on the agenda. Em City is home to many..Aryans, Muslims, gangstas, Latinos, Christians, Italians, Irish and more....so scuffles, death stares, dodgy dealings and shady agreements are never far away.<br /><br />I would say the main appeal of the show is due to the fact that it goes where other shows wouldn't dare. Forget pretty pictures painted for mainstream audiences, forget charm, forget romance...OZ doesn't mess around. The first episode I ever saw struck me as so nasty it was surreal, I couldn't say I was ready for it, but as I watched more, I developed a taste for Oz, and got accustomed to the high levels of graphic violence. Not just violence, but injustice (crooked guards who'll be sold out for a nickel, inmates who'll kill on order and get away with it, well mannered, middle class inmates being turned into prison bitches due to their lack of street skills or prison experience) Watching Oz, you may become comfortable with what is uncomfortable viewing....thats if you can get in touch with your darker side.\""
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.review[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And a negative one"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"Basically there's a family where a little boy (Jake) thinks there's a zombie in his closet & his parents are fighting all the time.<br /><br />This movie is slower than a soap opera... and suddenly, Jake decides to become Rambo and kill the zombie.<br /><br />OK, first of all when you're going to make a film you must Decide if its a thriller or a drama! As a drama the movie is watchable. Parents are divorcing & arguing like in real life. And then we have Jake with his closet which totally ruins all the film! I expected to see a BOOGEYMAN similar movie, and instead i watched a drama with some meaningless thriller spots.<br /><br />3 out of 10 just for the well playing parents & descent dialogs. As for the shots with Jake: just ignore them.\""
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.review[3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We transform the sentiment label into a numeric value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "49d5f05fd193405eb0acf1f8d6a8e377",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/50000 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data['label'] = data['sentiment'].progress_apply(lambda x: 1 if x=='positive' else 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our classes are balanced"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>label</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sentiment</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>negative</th>\n",
       "      <td>25000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>positive</th>\n",
       "      <td>25000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           label\n",
       "sentiment       \n",
       "negative   25000\n",
       "positive   25000"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[['sentiment', 'label']].groupby('sentiment').count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def keep_words(text):\n",
    "    if len(set(text) & set(string.ascii_letters)) != len(set(text)):\n",
    "        return None\n",
    "    \n",
    "    return text\n",
    "\n",
    "def preprocess_pipeline(text):\n",
    "    lemmatizer = WordNetLemmatizer()\n",
    "    \n",
    "    tokens = word_tokenize(text)\n",
    "    no_stopwords = [token for token in tokens if token not in stopwords]\n",
    "    lemmas = [lemmatizer.lemmatize(t) for t in no_stopwords]\n",
    "    words = [word for word in lemmas if keep_words(word) is not None]\n",
    "\n",
    "    return ' '.join(words)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2b96bfd21f7049e993120ec57302fc73",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/50000 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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>review</th>\n",
       "      <th>sentiment</th>\n",
       "      <th>label</th>\n",
       "      <th>processed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>One of the other reviewers has mentioned that ...</td>\n",
       "      <td>positive</td>\n",
       "      <td>1</td>\n",
       "      <td>One reviewer mentioned watching Oz episode hoo...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A wonderful little production. &lt;br /&gt;&lt;br /&gt;The...</td>\n",
       "      <td>positive</td>\n",
       "      <td>1</td>\n",
       "      <td>A wonderful little production br br The filmin...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>I thought this was a wonderful way to spend ti...</td>\n",
       "      <td>positive</td>\n",
       "      <td>1</td>\n",
       "      <td>I thought wonderful way spend time hot summer ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Basically there's a family where a little boy ...</td>\n",
       "      <td>negative</td>\n",
       "      <td>0</td>\n",
       "      <td>Basically family little boy Jake think zombie ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Petter Mattei's \"Love in the Time of Money\" is...</td>\n",
       "      <td>positive</td>\n",
       "      <td>1</td>\n",
       "      <td>Petter Mattei Love Time Money visually stunnin...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                              review sentiment  label  \\\n",
       "0  One of the other reviewers has mentioned that ...  positive      1   \n",
       "1  A wonderful little production. <br /><br />The...  positive      1   \n",
       "2  I thought this was a wonderful way to spend ti...  positive      1   \n",
       "3  Basically there's a family where a little boy ...  negative      0   \n",
       "4  Petter Mattei's \"Love in the Time of Money\" is...  positive      1   \n",
       "\n",
       "                                           processed  \n",
       "0  One reviewer mentioned watching Oz episode hoo...  \n",
       "1  A wonderful little production br br The filmin...  \n",
       "2  I thought wonderful way spend time hot summer ...  \n",
       "3  Basically family little boy Jake think zombie ...  \n",
       "4  Petter Mattei Love Time Money visually stunnin...  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['processed'] = data['review'].progress_apply(preprocess_pipeline)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'One reviewer mentioned watching Oz episode hooked They right exactly happened br br The first thing struck Oz brutality unflinching scene violence set right word GO Trust show faint hearted timid This show pull punch regard drug sex violence Its hardcore classic use br br It called OZ nickname given Oswald Maximum Security State Penitentary It focus mainly Emerald City experimental section prison cell glass front face inwards privacy high agenda Em City home many Aryans Muslims gangsta Latinos Christians Italians Irish scuffle death stare dodgy dealing shady agreement never far br br I would say main appeal show due fact go show would dare Forget pretty picture painted mainstream audience forget charm forget romance OZ mess around The first episode I ever saw struck nasty surreal I could say I ready I watched I developed taste Oz got accustomed high level graphic violence Not violence injustice crooked guard sold nickel inmate kill order get away well mannered middle class inmate turned prison bitch due lack street skill prison experience Watching Oz may become comfortable uncomfortable viewing thats get touch darker side'"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.processed[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Save processed data for later use"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "data[['processed', 'label']].to_csv('data/imdb_processed.csv', index=False, header=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Extract all the tokens"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "reviews = data.processed.values\n",
    "words = ' '.join(reviews)\n",
    "words = words.split()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Build vocabulary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "counter = Counter(words)\n",
    "vocab = sorted(counter, key=counter.get, reverse=True)\n",
    "word_dict = dict(zip(vocab, range(1, len(vocab)+1)))\n",
    "word_dict['<PAD>'] = 0\n",
    "\n",
    "word_list = ['<PAD>']\n",
    "word_list.extend(vocab)\n",
    "\n",
    "vocab_size = len(vocab)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Tokenize reviews"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "dcba5ad3d4854c309f9991f585c468e5",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/50000 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "reviews_enc = [[word_dict[word] for word in review.split()] for review in tqdm(reviews)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Make sure all the reviews have the same length. We truncate reviews that are too long and pad the ones that are too short"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def pad_features(reviews, pad_id, seq_length=128):\n",
    "    features = np.full((len(reviews), seq_length), pad_id, dtype=int)\n",
    "\n",
    "    for i, row in enumerate(reviews):\n",
    "        features[i, :len(row)] = np.array(row)[:seq_length]\n",
    "\n",
    "    return features\n",
    "\n",
    "max_words = 500\n",
    "features = pad_features(reviews_enc, pad_id=word_dict['<PAD>'], seq_length=max_words)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we have a matrix where each row corresponds to a unique review and each column to each of the words"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(50000, 500)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Train, test and valication splits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_size = .7     # 70% used for training\n",
    "val_size = .5       # 50% of test data set used for validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "labels = data.label.to_numpy()\n",
    "\n",
    "# Training dataset\n",
    "split_id = int(len(features) * train_size)\n",
    "X_train, X_remain = features[:split_id], features[split_id:]\n",
    "y_train, y_remain = labels[:split_id], labels[split_id:]\n",
    "\n",
    "# Testing and Validation\n",
    "split_val_id = int(len(X_remain) * val_size)\n",
    "X_validation, X_test = X_remain[:split_val_id], X_remain[split_val_id:]\n",
    "y_validation, y_test = y_remain[:split_val_id], y_remain[split_val_id:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of training data: (35000, 500)\n",
      "Shape of test data: (7500, 500)\n",
      "Shape of validation data: (7500, 500)\n"
     ]
    }
   ],
   "source": [
    "print('Shape of training data:', X_train.shape)\n",
    "print('Shape of test data:', X_test.shape)\n",
    "print('Shape of validation data:', X_validation.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# PyTorch"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tensor datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "trainset = TensorDataset(torch.from_numpy(X_train), torch.from_numpy(y_train))\n",
    "validset = TensorDataset(torch.from_numpy(X_validation), torch.from_numpy(y_validation))\n",
    "testset = TensorDataset(torch.from_numpy(X_test), torch.from_numpy(y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Loaders"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "batch_size = 128\n",
    "hidden_size = 32\n",
    "output_size = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "trainloader = DataLoader(trainset, shuffle=True, batch_size=batch_size)\n",
    "valloader = DataLoader(validset, shuffle=True, batch_size=batch_size)\n",
    "testloader = DataLoader(testset, shuffle=True, batch_size=batch_size)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, we are ready to define our models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Feed Forward Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We'll start by training a simple FF model without any fancyness. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our model will have an embedding layer to turn our numerical ids into vectors, a hidden dense layer of 32 neurons and ReLu activation followed by an output layer with just one neuron and a sigmoid activation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "FF_model = nn.Sequential(\n",
    "    nn.Embedding(vocab_size, 50),\n",
    "    nn.Flatten(),\n",
    "    nn.Linear(50*max_words, hidden_size),\n",
    "    nn.ReLU(),\n",
    "    nn.Linear(hidden_size, output_size),\n",
    "    nn.Sigmoid()\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sequential(\n",
      "  (0): Embedding(114608, 50)\n",
      "  (1): Flatten(start_dim=1, end_dim=-1)\n",
      "  (2): Linear(in_features=25000, out_features=32, bias=True)\n",
      "  (3): ReLU()\n",
      "  (4): Linear(in_features=32, out_features=1, bias=True)\n",
      "  (5): Sigmoid()\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "print(FF_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mps\n"
     ]
    }
   ],
   "source": [
    "# define training device\n",
    "device = torch.device('mps' if torch.backends.mps.is_available() else 'cpu')\n",
    "print(device)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Hyperparameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "vocab_size = len(word_list)\n",
    "output_size = 1\n",
    "embedding_size = 50\n",
    "hidden_size = 32"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then, we define some training config for our model. Since we have *binary classification* task, we will use **Binary Cross Entropy Loss (BCELoss)** as our loss function. And we will use **Adam** as optimizer as it shown in many references that it could find converge point quickly. Based on "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-14T13:35:12.7247Z",
     "iopub.status.busy": "2022-06-14T13:35:12.724126Z",
     "iopub.status.idle": "2022-06-14T13:35:12.730816Z",
     "shell.execute_reply": "2022-06-14T13:35:12.7297Z",
     "shell.execute_reply.started": "2022-06-14T13:35:12.724668Z"
    }
   },
   "outputs": [],
   "source": [
    "# training config\n",
    "lr = 0.001\n",
    "criterion = nn.BCELoss()  # we use BCELoss cz we have binary classification problem\n",
    "optim = Adam(FF_model.parameters(), lr=lr)\n",
    "grad_clip = 5\n",
    "epochs = 8\n",
    "print_every = 1\n",
    "es_limit = 5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**This is our main part!**\n",
    "<br>\n",
    "So, here we define training and validation loop. We will use only 5 epochs and see if we need more or less epochs to obtain good performance. We log our loss and accuracy for each epochs, so we can plot them later to see how the performances. Inspired by https://github.com/LukeDitria/pytorch_tutorials"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
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    "execution": {
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     "iopub.status.busy": "2022-06-14T13:40:05.951377Z",
     "iopub.status.idle": "2022-06-14T13:43:52.973828Z",
     "shell.execute_reply": "2022-06-14T13:43:52.972745Z",
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   "source": [
    "def train_model(model, epochs, optim):\n",
    "    \n",
    "    history = {\n",
    "        'train_loss': [],\n",
    "        'train_acc': [],\n",
    "        'val_loss': [],\n",
    "        'val_acc': [],\n",
    "        'epochs': epochs\n",
    "    }\n",
    "\n",
    "    # train loop\n",
    "    model = model.to(device)\n",
    "\n",
    "    epochloop = tqdm(range(epochs), position=0, desc='Training', leave=True)\n",
    "\n",
    "    # early stop trigger\n",
    "    es_trigger = 0\n",
    "    val_loss_min = torch.inf\n",
    "\n",
    "    for e in epochloop:\n",
    "\n",
    "        #################\n",
    "        # training mode #\n",
    "        #################\n",
    "\n",
    "        model.train()\n",
    "\n",
    "        train_loss = 0\n",
    "        train_acc = 0\n",
    "\n",
    "        for id, (feature, target) in enumerate(trainloader):\n",
    "            # add epoch meta info\n",
    "            epochloop.set_postfix_str(f'Training batch {id}/{len(trainloader)}')\n",
    "\n",
    "            # move to device\n",
    "            feature, target = feature.to(device), target.to(device)\n",
    "\n",
    "            # reset optimizer\n",
    "            optim.zero_grad()\n",
    "\n",
    "            # forward pass\n",
    "            out = model(feature)\n",
    "\n",
    "            # acc\n",
    "            predicted = torch.tensor([1 if i == True else 0 for i in out > 0.5], device=device)\n",
    "            equals = predicted == target\n",
    "            acc = torch.mean(equals.type(torch.FloatTensor))\n",
    "            train_acc += acc.item()\n",
    "\n",
    "            # loss\n",
    "            loss = criterion(out.squeeze(), target.float())\n",
    "            train_loss += loss.item()\n",
    "            loss.backward()\n",
    "\n",
    "            # clip grad\n",
    "            nn.utils.clip_grad_norm_(model.parameters(), grad_clip)\n",
    "\n",
    "            # update optimizer\n",
    "            optim.step()\n",
    "\n",
    "            # free some memory\n",
    "            del feature, target, predicted\n",
    "\n",
    "        history['train_loss'].append(train_loss / len(trainloader))\n",
    "        history['train_acc'].append(train_acc / len(trainloader))\n",
    "\n",
    "        ####################\n",
    "        # validation model #\n",
    "        ####################\n",
    "\n",
    "        model.eval()\n",
    "\n",
    "        val_loss = 0\n",
    "        val_acc = 0\n",
    "\n",
    "        with torch.no_grad():\n",
    "            for id, (feature, target) in enumerate(valloader):\n",
    "                # add epoch meta info\n",
    "                epochloop.set_postfix_str(f'Validation batch {id}/{len(valloader)}')\n",
    "\n",
    "                # move to device\n",
    "                feature, target = feature.to(device), target.to(device)\n",
    "\n",
    "                # forward pass\n",
    "                out = model(feature)\n",
    "\n",
    "                # acc\n",
    "                predicted = torch.tensor([1 if i == True else 0 for i in out > 0.5], device=device)\n",
    "                equals = predicted == target\n",
    "                acc = torch.mean(equals.type(torch.FloatTensor))\n",
    "                val_acc += acc.item()\n",
    "\n",
    "                # loss\n",
    "                loss = criterion(out.squeeze(), target.float())\n",
    "                val_loss += loss.item()\n",
    "\n",
    "                # free some memory\n",
    "                del feature, target, predicted\n",
    "\n",
    "            history['val_loss'].append(val_loss / len(valloader))\n",
    "            history['val_acc'].append(val_acc / len(valloader))\n",
    "\n",
    "        # reset model mode\n",
    "        model.train()\n",
    "\n",
    "        # add epoch meta info\n",
    "        epochloop.set_postfix_str(f'Val Loss: {val_loss / len(valloader):.3f} | Val Acc: {val_acc / len(valloader):.3f}')\n",
    "\n",
    "        # print epoch\n",
    "        if (e+1) % print_every == 0:\n",
    "            epochloop.write(f'Epoch {e+1}/{epochs} | Train Loss: {train_loss / len(trainloader):.3f} Train Acc: {train_acc / len(trainloader):.3f} | Val Loss: {val_loss / len(valloader):.3f} Val Acc: {val_acc / len(valloader):.3f}')\n",
    "            epochloop.update()\n",
    "            \n",
    "    return history"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_history(history):    \n",
    "    fig, ax_lst = plt.subplots(1, 2, sharex=True, sharey=True)\n",
    "    \n",
    "    epochs = range(history['epochs'])\n",
    "    \n",
    "    ax_lst[0].plot(epochs, history['train_loss'], label='Training')\n",
    "    ax_lst[0].plot(epochs, history['val_loss'], label='Testing')\n",
    "    ax_lst[0].set_ylabel('Loss')\n",
    "    ax_lst[0].set_xlabel('Epoch')\n",
    "    ax_lst[0].set_xticks(epochs)\n",
    "    \n",
    "    ax_lst[1].plot(epochs, history['train_acc'], label='Training')\n",
    "    ax_lst[1].plot(epochs, history['val_acc'], label='Testing')\n",
    "    ax_lst[1].set_ylabel('Accuracy')\n",
    "    ax_lst[1].set_xlabel('Epoch')\n",
    "    ax_lst[1].set_xticks(epochs)\n",
    "\n",
    "    ax_lst[1].legend()\n",
    "    \n",
    "    fig.tight_layout()"
   ]
  },
  {
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     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "draw_graph(FF_model, input_data=torch.as_tensor(X_test[:128])).visual_graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a277d69ffa6b48eb8c49423d287cc38b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Training:   0%|          | 0/8 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/8 | Train Loss: 0.902 Train Acc: 0.554 | Val Loss: 0.603 Val Acc: 0.664\n",
      "Epoch 2/8 | Train Loss: 0.490 Train Acc: 0.763 | Val Loss: 0.553 Val Acc: 0.723\n",
      "Epoch 3/8 | Train Loss: 0.323 Train Acc: 0.859 | Val Loss: 0.439 Val Acc: 0.809\n",
      "Epoch 4/8 | Train Loss: 0.217 Train Acc: 0.914 | Val Loss: 0.529 Val Acc: 0.795\n",
      "Epoch 5/8 | Train Loss: 0.148 Train Acc: 0.945 | Val Loss: 0.501 Val Acc: 0.815\n",
      "Epoch 6/8 | Train Loss: 0.102 Train Acc: 0.964 | Val Loss: 0.549 Val Acc: 0.824\n",
      "Epoch 7/8 | Train Loss: 0.070 Train Acc: 0.976 | Val Loss: 0.603 Val Acc: 0.825\n",
      "Epoch 8/8 | Train Loss: 0.055 Train Acc: 0.981 | Val Loss: 0.673 Val Acc: 0.821\n"
     ]
    }
   ],
   "source": [
    "history = train_model(FF_model, epochs, optim)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
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      "text/plain": [
       "<Figure size 3840x2640 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_history(history)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Convolutional Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we add a convolution layer between the embedding layer and the dense layer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "Conv_model = nn.Sequential(\n",
    "    nn.Embedding(vocab_size, 50),\n",
    "    nn.Conv1d(max_words, max_words, kernel_size=3, padding='same'),\n",
    "    nn.MaxPool1d(2),\n",
    "    nn.Flatten(),\n",
    "    nn.Linear(25*max_words, hidden_size),\n",
    "    nn.ReLU(),\n",
    "    nn.Linear(hidden_size, output_size),\n",
    "    nn.Sigmoid()\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/svg+xml": [
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       "<title>model</title>\n",
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   "source": [
    "# training config\n",
    "lr = 0.001\n",
    "criterion = nn.BCELoss()  # we use BCELoss cz we have binary classification problem\n",
    "optim = Adam(Conv_model.parameters(), lr=lr)\n",
    "grad_clip = 5\n",
    "epochs = 16\n",
    "print_every = 1\n",
    "es_limit = 5"
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       "Training:   0%|          | 0/16 [00:00<?, ?it/s]"
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      "Epoch 1/16 | Train Loss: 0.654 Train Acc: 0.610 | Val Loss: 0.496 Val Acc: 0.766\n",
      "Epoch 2/16 | Train Loss: 0.397 Train Acc: 0.821 | Val Loss: 0.419 Val Acc: 0.816\n",
      "Epoch 3/16 | Train Loss: 0.266 Train Acc: 0.891 | Val Loss: 0.392 Val Acc: 0.833\n",
      "Epoch 4/16 | Train Loss: 0.185 Train Acc: 0.929 | Val Loss: 0.431 Val Acc: 0.838\n",
      "Epoch 5/16 | Train Loss: 0.119 Train Acc: 0.958 | Val Loss: 0.498 Val Acc: 0.838\n",
      "Epoch 6/16 | Train Loss: 0.076 Train Acc: 0.975 | Val Loss: 0.582 Val Acc: 0.837\n",
      "Epoch 7/16 | Train Loss: 0.047 Train Acc: 0.986 | Val Loss: 0.688 Val Acc: 0.841\n",
      "Epoch 8/16 | Train Loss: 0.030 Train Acc: 0.992 | Val Loss: 0.822 Val Acc: 0.838\n",
      "Epoch 9/16 | Train Loss: 0.019 Train Acc: 0.995 | Val Loss: 1.103 Val Acc: 0.834\n",
      "Epoch 10/16 | Train Loss: 0.015 Train Acc: 0.996 | Val Loss: 1.367 Val Acc: 0.833\n",
      "Epoch 11/16 | Train Loss: 0.019 Train Acc: 0.994 | Val Loss: 1.651 Val Acc: 0.836\n",
      "Epoch 12/16 | Train Loss: 0.023 Train Acc: 0.991 | Val Loss: 1.842 Val Acc: 0.830\n",
      "Epoch 13/16 | Train Loss: 0.013 Train Acc: 0.995 | Val Loss: 1.982 Val Acc: 0.826\n",
      "Epoch 14/16 | Train Loss: 0.007 Train Acc: 0.998 | Val Loss: 2.202 Val Acc: 0.826\n",
      "Epoch 15/16 | Train Loss: 0.009 Train Acc: 0.997 | Val Loss: 2.145 Val Acc: 0.833\n",
      "Epoch 16/16 | Train Loss: 0.015 Train Acc: 0.995 | Val Loss: 2.248 Val Acc: 0.831\n"
     ]
    }
   ],
   "source": [
    "conv_history = train_model(Conv_model, epochs, optim)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Compile and fit the model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This model takes significantly longer to train, but produces much better results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 3840x2640 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_history(conv_history)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<center>\n",
    "     <img src=\"data/D4Sci_logo_full.png\" alt=\"Data For Science, Inc\" align=\"center\" border=\"0\" width=300px> \n",
    "</center>"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.7"
  },
  "varInspector": {
   "cols": {
    "lenName": 16,
    "lenType": 16,
    "lenVar": 40
   },
   "kernels_config": {
    "python": {
     "delete_cmd_postfix": "",
     "delete_cmd_prefix": "del ",
     "library": "var_list.py",
     "varRefreshCmd": "print(var_dic_list())"
    },
    "r": {
     "delete_cmd_postfix": ") ",
     "delete_cmd_prefix": "rm(",
     "library": "var_list.r",
     "varRefreshCmd": "cat(var_dic_list()) "
    }
   },
   "types_to_exclude": [
    "module",
    "function",
    "builtin_function_or_method",
    "instance",
    "_Feature"
   ],
   "window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}