3. 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: f27953c25cb37eda249660943e02750136eeb23f\n",
      "\n",
      "torchview : 0.2.6\n",
      "matplotlib: 3.8.0\n",
      "re        : 2.2.1\n",
      "nltk      : 3.8.1\n",
      "numpy     : 1.26.4\n",
      "torch     : 2.6.0\n",
      "watermark : 2.4.3\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": "d66499adefe345eda02b6815475c3161",
       "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": "a3e6b187b47d4071b296e3f8145991cc",
       "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": "cb2ca1594411418496188a2536d9e6e3",
       "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",
    "    # <===> #\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": [
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      "mps\n"
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   "source": [
    "# define training device\n",
    "device = torch.device('mps' if torch.backends.mps.is_available() else 'cpu')\n",
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    "Hyperparameters"
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    "vocab_size = len(word_list)\n",
    "embedding_size = 50"
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    "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 "
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     "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": 31,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-14T13:40:05.951802Z",
     "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",
     "shell.execute_reply.started": "2022-06-14T13:40:05.951765Z"
    }
   },
   "outputs": [],
   "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": 32,
   "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()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3495841c798e468bafb32ee154fb1eed",
       "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: 0.784 Train Acc: 0.580 | Val Loss: 0.609 Val Acc: 0.663\n",
      "Epoch 2/8 | Train Loss: 0.462 Train Acc: 0.784 | Val Loss: 0.465 Val Acc: 0.778\n",
      "Epoch 3/8 | Train Loss: 0.291 Train Acc: 0.879 | Val Loss: 0.417 Val Acc: 0.819\n",
      "Epoch 4/8 | Train Loss: 0.197 Train Acc: 0.924 | Val Loss: 0.436 Val Acc: 0.826\n",
      "Epoch 5/8 | Train Loss: 0.133 Train Acc: 0.952 | Val Loss: 0.469 Val Acc: 0.833\n",
      "Epoch 6/8 | Train Loss: 0.095 Train Acc: 0.969 | Val Loss: 0.665 Val Acc: 0.786\n",
      "Epoch 7/8 | Train Loss: 0.062 Train Acc: 0.981 | Val Loss: 0.585 Val Acc: 0.832\n",
      "Epoch 8/8 | Train Loss: 0.039 Train Acc: 0.990 | Val Loss: 0.617 Val Acc: 0.827\n",
      "CPU times: user 41.7 s, sys: 25.8 s, total: 1min 7s\n",
      "Wall time: 1min 29s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "history = train_model(FF_model, epochs, optim)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
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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",
    "    # <===>\n",
    "    nn.Conv1d(max_words, max_words, kernel_size=3, padding='same'),\n",
    "    nn.MaxPool1d(2),\n",
    "    # <===>\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": {},
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    "# 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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      "text/plain": [
       "Training:   0%|          | 0/16 [00:00<?, ?it/s]"
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     "text": [
      "Epoch 1/16 | Train Loss: 0.699 Train Acc: 0.508 | Val Loss: 0.693 Val Acc: 0.500\n",
      "Epoch 2/16 | Train Loss: 0.650 Train Acc: 0.587 | Val Loss: 0.551 Val Acc: 0.718\n",
      "Epoch 3/16 | Train Loss: 0.392 Train Acc: 0.825 | Val Loss: 0.389 Val Acc: 0.831\n",
      "Epoch 4/16 | Train Loss: 0.250 Train Acc: 0.899 | Val Loss: 0.376 Val Acc: 0.846\n",
      "Epoch 5/16 | Train Loss: 0.164 Train Acc: 0.938 | Val Loss: 0.434 Val Acc: 0.844\n",
      "Epoch 6/16 | Train Loss: 0.103 Train Acc: 0.965 | Val Loss: 0.518 Val Acc: 0.826\n",
      "Epoch 7/16 | Train Loss: 0.062 Train Acc: 0.980 | Val Loss: 0.543 Val Acc: 0.846\n",
      "Epoch 8/16 | Train Loss: 0.038 Train Acc: 0.989 | Val Loss: 0.573 Val Acc: 0.848\n",
      "Epoch 9/16 | Train Loss: 0.026 Train Acc: 0.993 | Val Loss: 0.738 Val Acc: 0.851\n",
      "Epoch 10/16 | Train Loss: 0.016 Train Acc: 0.995 | Val Loss: 1.065 Val Acc: 0.838\n",
      "Epoch 11/16 | Train Loss: 0.016 Train Acc: 0.995 | Val Loss: 1.312 Val Acc: 0.843\n",
      "Epoch 12/16 | Train Loss: 0.029 Train Acc: 0.990 | Val Loss: 1.471 Val Acc: 0.837\n",
      "Epoch 13/16 | Train Loss: 0.008 Train Acc: 0.998 | Val Loss: 1.520 Val Acc: 0.845\n",
      "Epoch 14/16 | Train Loss: 0.003 Train Acc: 0.999 | Val Loss: 1.828 Val Acc: 0.846\n",
      "Epoch 15/16 | Train Loss: 0.001 Train Acc: 1.000 | Val Loss: 1.944 Val Acc: 0.848\n",
      "Epoch 16/16 | Train Loss: 0.000 Train Acc: 1.000 | Val Loss: 1.994 Val Acc: 0.848\n",
      "CPU times: user 1min 28s, sys: 52.6 s, total: 2min 20s\n",
      "Wall time: 3min 52s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "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": {
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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
}