{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "3a8a2e23",
   "metadata": {},
   "source": [
    "# Activity SOLUTION: Improving sentiment analysis with convolutional layers\n",
    "\n",
    "> **Note**: This is the solution notebook. Compare your implementation with this one after completing the activity.\n",
    "\n",
    "## Objective\n",
    "\n",
    "In this activity, you will extend the baseline RNN sentiment classifier by adding **convolutional layers** to create a hybrid CNN-RNN architecture. Convolutional layers can capture local n-gram patterns (like \"not good\" or \"very happy\") before the recurrent layers process the sequence.\n",
    "\n",
    "**Your tasks:**\n",
    "1. Run the baseline GRU model and note its performance\n",
    "2. Modify the model architecture to include convolutional layers\n",
    "3. Train your CNN-RNN hybrid model\n",
    "4. Compare the results to the baseline\n",
    "\n",
    "---\n",
    "\n",
    "## Key terms\n",
    "\n",
    "- **Tokenization**: Splitting text into individual units (tokens), typically words. For example, \"I love this!\" becomes `[\"i\", \"love\", \"this\"]`. This is the first step in converting text to a format neural networks can process.\n",
    "\n",
    "- **Embedding**: A dense vector representation of a token. Instead of one-hot encoding (sparse, high-dimensional), embeddings map each word to a fixed-size vector (e.g., 100 dimensions) where similar words have similar vectors. We use pretrained GloVe embeddings trained on Twitter data.\n",
    "\n",
    "- **LSTM/GRU**: Variants of RNNs designed to handle long sequences better than SimpleRNN. They use \"gates\" to control information flow, solving the vanishing gradient problem. **GRU** (Gated Recurrent Unit) has 2 gates and is simpler; **LSTM** (Long Short-Term Memory) has 3 gates and more parameters. Both work fine in practice, we use GRU here for efficiency.\n",
    "\n",
    "## How the GRU processes a sequence\n",
    "\n",
    "Given input sequence `[\"i\", \"love\", \"this]`, the GRU predicts sentiment:\n",
    "\n",
    "```text\n",
    "\n",
    "                   ┌────────────◀───────────┐ 'h₁', then 'h₂'\n",
    "                   ▼                        │\n",
    "                ╔══════════════════════════════╗\n",
    "                ║  │           GRU          ▲  ║\n",
    "                ║  │                        │  ║\n",
    "  'I' then,     ║  ├──▶Reset───▶candidate──▶│  ║         ╔═══════╗ \n",
    "'love', then ══▶║  │   gate      hidden     │  ╠══'h₃'══▶║ Dense ╠══▶ Sentiment\n",
    "   'this'       ║  │              state     │  ║         ╚═══════╝\n",
    "                ║  │                        │  ║\n",
    "                ║  └─────▶Update gate───────┘  ║\n",
    "                ╚══════════════════════════════╝\n",
    "\n",
    "```\n",
    "\n",
    "- **Reset gate**: Controls how much the prior hidden state influences the new candidate hidden state. Gates are **dynamic weights**, one per hidden unit. Depends on **both** the previous hidden state AND the current input.\n",
    "- **Update gate**: Controls how much prior hidden state vs new candidate hidden state goes into the new hidden state. Also computed from previous hidden state + current input, just with different learned weights.\n",
    "- **Bidirectional recurrent layers**: Processes a sequence in both directions (forward and backward) and combines the results. This allows the model to use context from both before AND after each word, improving understanding.\n",
    "\n",
    "## External tools & resources\n",
    "\n",
    "- **Stopwords**: Common words (e.g., \"the\", \"is\", \"at\") filtered out during preprocessing to reduce noise and focus on meaningful content.\n",
    "  - *This project*: [NLTK Stopwords](https://www.nltk.org/nltk_data/) curated lists for 20+ languages\n",
    "  - *Alternatives*: [spaCy stopwords](https://spacy.io/usage/rule-based-matching#vocab-stopwords), [scikit-learn ENGLISH_STOP_WORDS](https://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.text.ENGLISH_STOP_WORDS.html)\n",
    "\n",
    "- **Tokenizers**: Tools that split text into tokens. Different tokenizers handle edge cases differently social media text requires special handling for emoticons, hashtags, and informal spelling.\n",
    "  - *This project*: [NLTK TweetTokenizer](https://www.nltk.org/api/nltk.tokenize.casual.html#nltk.tokenize.casual.TweetTokenizer) handles emoticons, hashtags, mentions, and normalizes repeated characters (e.g., \"sooooo\" → \"sooo\")\n",
    "  - *Alternatives*: [spaCy Tokenizer](https://spacy.io/usage/linguistic-features#tokenization), [Hugging Face Tokenizers](https://huggingface.co/docs/tokenizers/), [NLTK word_tokenize](https://www.nltk.org/api/nltk.tokenize.word_tokenize.html)\n",
    "\n",
    "- **Word Embeddings**: Pretrained vector representations that capture semantic relationships between words. Using pretrained embeddings transfers knowledge from large corpora to your model.\n",
    "  - *This project*: [GloVe Twitter embeddings](https://nlp.stanford.edu/projects/glove/) trained on 27B Twitter tokens, 100 dimensions\n",
    "  - *Alternatives*: [Word2Vec](https://radimrehurek.com/gensim/models/word2vec.html), [FastText](https://fasttext.cc/docs/en/english-vectors.html), [Hugging Face sentence-transformers](https://huggingface.co/sentence-transformers)\n",
    "\n",
    "\n",
    "## Notebook setup\n",
    "\n",
    "### Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "2c49205e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import re\n",
    "import zipfile\n",
    "import urllib.request\n",
    "from collections import Counter\n",
    "from pathlib import Path\n",
    "\n",
    "import keras\n",
    "import nltk\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import tensorflow as tf\n",
    "from nltk.corpus import stopwords\n",
    "from nltk.tokenize import TweetTokenizer\n",
    "from keras.models import Sequential\n",
    "from keras.layers import (\n",
    "    Input, Embedding, Dense, Dropout, Bidirectional, GRU, SpatialDropout1D\n",
    ")\n",
    "from keras.preprocessing.sequence import pad_sequences\n",
    "from keras.callbacks import ModelCheckpoint, EarlyStopping, TensorBoard\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.utils.class_weight import compute_class_weight\n",
    "from sklearn.metrics import confusion_matrix, roc_curve, auc, precision_recall_curve\n",
    "from sklearn.preprocessing import label_binarize"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "664d2ca9",
   "metadata": {},
   "source": [
    "### GPU configuration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b8d56b31",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using 1 GPU(s): ['/physical_device:GPU:0']\n"
     ]
    }
   ],
   "source": [
    "# Configure GPU\n",
    "gpus = tf.config.list_physical_devices('GPU')\n",
    "\n",
    "if gpus:\n",
    "    try:\n",
    "        # Set memory growth on ALL GPUs (required when multiple GPUs present)\n",
    "        for gpu in gpus:\n",
    "            tf.config.experimental.set_memory_growth(gpu, True)\n",
    "\n",
    "        print(f'Using {len(gpus)} GPU(s): {[gpu.name for gpu in gpus]}')\n",
    "        \n",
    "    except RuntimeError as e:\n",
    "        print(e)\n",
    "\n",
    "else:\n",
    "    print('No GPU available, using CPU')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "81c01b2e",
   "metadata": {},
   "source": [
    "### Environment setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "0911dc2e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set data directory\n",
    "data_dir = '../data'\n",
    "Path(data_dir).mkdir(parents=True, exist_ok=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "7dec1ea8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Download NLTK resources\n",
    "os.environ[\"NLTK_DATA\"] = data_dir\n",
    "nltk.download('stopwords', quiet=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "71549f98",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GloVe file already exists: ../data/glove.twitter.27B.100d.txt\n"
     ]
    }
   ],
   "source": [
    "# GloVe Twitter embeddings download\n",
    "glove_url  = 'https://nlp.stanford.edu/data/glove.twitter.27B.zip'\n",
    "glove_file = os.path.join(data_dir, 'glove.twitter.27B.100d.txt')\n",
    "glove_zip  = os.path.join(data_dir, 'glove.twitter.27B.zip')\n",
    "\n",
    "if not os.path.exists(glove_file):\n",
    "\n",
    "    print('Downloading GloVe embeddings...')\n",
    "    urllib.request.urlretrieve(glove_url, glove_zip)\n",
    "\n",
    "    print('Extracting...')\n",
    "    with zipfile.ZipFile(glove_zip, 'r') as zip_ref:\n",
    "        zip_ref.extract('glove.twitter.27B.100d.txt', data_dir)\n",
    "\n",
    "    os.remove(glove_zip)\n",
    "    print('Done!')\n",
    "\n",
    "else:\n",
    "    print(f'GloVe file already exists: {glove_file}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b8bb6e2f",
   "metadata": {},
   "source": [
    "### Shared hyperparameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "697a0e98",
   "metadata": {},
   "outputs": [],
   "source": [
    "hidden_dim              = 64      # Number of hidden units in the GRU layers\n",
    "dropout                 = 0.3     # Spatial dropout rate\n",
    "l2_reg                  = 0.02    # L2 regularization strength\n",
    "learning_rate           = 0.0001  # Low LR for fine-tuning embeddings\n",
    "batch_size              = 128\n",
    "epochs                  = 200\n",
    "early_stopping_patience = 20\n",
    "verbose                 = 0"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dc9b3cef",
   "metadata": {},
   "source": [
    "## 1. Data preparation\n",
    "\n",
    "### 1.1. Load"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "08292db5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>name</th>\n",
       "      <th>score</th>\n",
       "      <th>text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>638060586258038784</td>\n",
       "      <td>michael jackson</td>\n",
       "      <td>0</td>\n",
       "      <td>05 Beat it - Michael Jackson - Thriller (25th ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>638061181823922176</td>\n",
       "      <td>michael jackson</td>\n",
       "      <td>1</td>\n",
       "      <td>Jay Z joins Instagram with nostalgic tribute t...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>638083821364244480</td>\n",
       "      <td>michael jackson</td>\n",
       "      <td>0</td>\n",
       "      <td>Michael Jackson: Bad 25th Anniversary Edition ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>638091450132078593</td>\n",
       "      <td>michael jackson</td>\n",
       "      <td>1</td>\n",
       "      <td>I liked a @YouTube video http://t.co/AaR3pjp2P...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>638125563790557184</td>\n",
       "      <td>michael jackson</td>\n",
       "      <td>1</td>\n",
       "      <td>18th anniv of Princess Diana's death. I still ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   id             name  score  \\\n",
       "0  638060586258038784  michael jackson      0   \n",
       "1  638061181823922176  michael jackson      1   \n",
       "2  638083821364244480  michael jackson      0   \n",
       "3  638091450132078593  michael jackson      1   \n",
       "4  638125563790557184  michael jackson      1   \n",
       "\n",
       "                                                text  \n",
       "0  05 Beat it - Michael Jackson - Thriller (25th ...  \n",
       "1  Jay Z joins Instagram with nostalgic tribute t...  \n",
       "2  Michael Jackson: Bad 25th Anniversary Edition ...  \n",
       "3  I liked a @YouTube video http://t.co/AaR3pjp2P...  \n",
       "4  18th anniv of Princess Diana's death. I still ...  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_parquet(os.path.join(data_dir, 'twitter-2016.parquet'))\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "52be2521",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 30467 entries, 0 to 30466\n",
      "Data columns (total 4 columns):\n",
      " #   Column  Non-Null Count  Dtype \n",
      "---  ------  --------------  ----- \n",
      " 0   id      30467 non-null  int64 \n",
      " 1   name    30467 non-null  object\n",
      " 2   score   30467 non-null  int64 \n",
      " 3   text    30467 non-null  object\n",
      "dtypes: int64(2), object(2)\n",
      "memory usage: 952.2+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4b4c04aa",
   "metadata": {},
   "source": [
    "### 1.2. Clean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "bf67ece0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cleaned dataset: 30,467 rows (0 dropped)\n",
      "Score distribution: {0: 299, 1: 3392, 2: 12953, 3: 12812, 4: 1011}\n"
     ]
    }
   ],
   "source": [
    "# Clean data - drop rows with missing scores\n",
    "df_clean = df.dropna(subset=['score']).copy()\n",
    "df_clean['score'] = df_clean['score'].astype(int)\n",
    "\n",
    "# Shift scores to 0-based index: [-2,-1,0,1,2] -> [0,1,2,3,4]\n",
    "score_min = df_clean['score'].min()\n",
    "df_clean['score_shifted'] = df_clean['score'] - score_min\n",
    "\n",
    "print(f'Cleaned dataset: {len(df_clean):,} rows ({len(df) - len(df_clean):,} dropped)')\n",
    "print(f'Score distribution: {df_clean[\"score_shifted\"].value_counts().sort_index().to_dict()}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7b45c1eb",
   "metadata": {},
   "source": [
    "### 1.3. Tokenize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "46f6981a",
   "metadata": {},
   "outputs": [],
   "source": [
    "def clean_text(text):\n",
    "    '''Clean and tokenize tweet text using TweetTokenizer.'''\n",
    "\n",
    "    if not isinstance(text, str):\n",
    "        return []\n",
    "\n",
    "    # Remove URLs before tokenizing\n",
    "    text = re.sub(r'http\\S+|www\\S+|https\\S+', '', text)\n",
    "\n",
    "    # TweetTokenizer handles: lowercase, @mentions, repeated chars (e.g., sooooo -> sooo)\n",
    "    tokens = tweet_tokenizer.tokenize(text)\n",
    "\n",
    "    # Remove '#' symbols from hashtags (keep the word)\n",
    "    tokens = [t.lstrip('#') for t in tokens]\n",
    "\n",
    "    # Remove stopwords and single characters\n",
    "    tokens = [t for t in tokens if t not in stop_words and len(t) > 1]\n",
    "\n",
    "    return tokens"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "2b7efdeb",
   "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>text</th>\n",
       "      <th>tokens</th>\n",
       "      <th>score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>05 Beat it - Michael Jackson - Thriller (25th ...</td>\n",
       "      <td>[05, beat, michael, jackson, thriller, 25th, a...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Jay Z joins Instagram with nostalgic tribute t...</td>\n",
       "      <td>[jay, joins, instagram, nostalgic, tribute, mi...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Michael Jackson: Bad 25th Anniversary Edition ...</td>\n",
       "      <td>[michael, jackson, bad, 25th, anniversary, edi...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>I liked a @YouTube video http://t.co/AaR3pjp2P...</td>\n",
       "      <td>[liked, video, one, direction, singing, man, m...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>18th anniv of Princess Diana's death. I still ...</td>\n",
       "      <td>[18th, anniv, princess, diana's, death, still,...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                text  \\\n",
       "0  05 Beat it - Michael Jackson - Thriller (25th ...   \n",
       "1  Jay Z joins Instagram with nostalgic tribute t...   \n",
       "2  Michael Jackson: Bad 25th Anniversary Edition ...   \n",
       "3  I liked a @YouTube video http://t.co/AaR3pjp2P...   \n",
       "4  18th anniv of Princess Diana's death. I still ...   \n",
       "\n",
       "                                              tokens  score  \n",
       "0  [05, beat, michael, jackson, thriller, 25th, a...      0  \n",
       "1  [jay, joins, instagram, nostalgic, tribute, mi...      1  \n",
       "2  [michael, jackson, bad, 25th, anniversary, edi...      0  \n",
       "3  [liked, video, one, direction, singing, man, m...      1  \n",
       "4  [18th, anniv, princess, diana's, death, still,...      1  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Set up stop words and tokenizer\n",
    "stop_words      = set(stopwords.words('english'))\n",
    "tweet_tokenizer = TweetTokenizer(preserve_case=False, reduce_len=True, strip_handles=True)\n",
    "\n",
    "# Apply tokenization\n",
    "df_clean['tokens'] = df_clean['text'].apply(clean_text)\n",
    "df_clean[['text', 'tokens', 'score']].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9a70b43c",
   "metadata": {},
   "source": [
    "### 1.4. Build vocabulary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "0d8efd85",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Vocabulary size: 14769\n"
     ]
    }
   ],
   "source": [
    "# Build vocabulary\n",
    "def build_vocab(token_lists, min_freq=2):\n",
    "    '''Build vocabulary from token lists.'''\n",
    "\n",
    "    counter = Counter()\n",
    "\n",
    "    for tokens in token_lists:\n",
    "        counter.update(tokens)\n",
    "    \n",
    "    # Filter by minimum frequency and create vocab\n",
    "    vocab = {'<PAD>': 0, '<UNK>': 1}\n",
    "\n",
    "    for word, freq in counter.items():\n",
    "        if freq >= min_freq:\n",
    "            vocab[word] = len(vocab)\n",
    "\n",
    "    return vocab\n",
    "\n",
    "vocab = build_vocab(df_clean['tokens'])\n",
    "print(f'Vocabulary size: {len(vocab)}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cc6059bd",
   "metadata": {},
   "source": [
    "### 1.5. Convert to indices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "951ab5c1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X shape: (30467, 50)\n",
      "y shape: (30467,)\n"
     ]
    }
   ],
   "source": [
    "def tokens_to_indices(tokens_list, vocab, max_len=50):\n",
    "    '''Convert token lists to padded index sequences.'''\n",
    "\n",
    "    sequences = []\n",
    "\n",
    "    for tokens in tokens_list:\n",
    "        indices = [vocab.get(token, vocab['<UNK>']) for token in tokens]\n",
    "        sequences.append(indices)\n",
    "\n",
    "    # Pad sequences to max_len\n",
    "    return pad_sequences(sequences, maxlen=max_len, padding='post', value=vocab['<PAD>'])\n",
    "\n",
    "# Convert all data to indices\n",
    "max_len = 50\n",
    "X = tokens_to_indices(df_clean['tokens'].tolist(), vocab, max_len)\n",
    "y = df_clean['score_shifted'].values\n",
    "\n",
    "print(f'X shape: {X.shape}')\n",
    "print(f'y shape: {y.shape}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5118247a",
   "metadata": {},
   "source": [
    "### 1.6. Train/test split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "c941eef5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train shape: (24373, 50)\n",
      "X_test shape: (6094, 50)\n",
      "Score distribution (train): Counter({2: 10362, 3: 10249, 1: 2714, 4: 809, 0: 239})\n"
     ]
    }
   ],
   "source": [
    "# Train/test split\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    X, y,\n",
    "    test_size=0.2,\n",
    "    random_state=42,\n",
    "    stratify=y\n",
    ")\n",
    "\n",
    "print(f'X_train shape: {X_train.shape}')\n",
    "print(f'X_test shape: {X_test.shape}')\n",
    "print(f'Score distribution (train): {Counter(y_train)}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "45f38bea",
   "metadata": {},
   "source": [
    "## 2. Prepare GloVe embeddings\n",
    "\n",
    "GloVe (Global Vectors) provides pretrained word embeddings trained on large text corpora. Using these gives our model a head start - words already have meaningful representations instead of random vectors.\n",
    "\n",
    "### 2.1. Load embeddings into dictionary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "5cf57e38",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loaded 1,193,514 word vectors\n",
      "Embedding dimension: 100\n"
     ]
    }
   ],
   "source": [
    "glove_vectors = {}\n",
    "\n",
    "with open(glove_file, 'r', encoding='utf-8') as f:\n",
    "    for line in f:\n",
    "\n",
    "        values = line.split()\n",
    "        word = values[0]\n",
    "        vector = np.array(values[1:], dtype='float32')\n",
    "        glove_vectors[word] = vector\n",
    "\n",
    "embedding_dim = len(next(iter(glove_vectors.values())))\n",
    "\n",
    "print(f'Loaded {len(glove_vectors):,} word vectors')\n",
    "print(f'Embedding dimension: {embedding_dim}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "229ff779",
   "metadata": {},
   "source": [
    "### 2.2. Create embedding look-up table for our vocabulary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "d1cc20ab",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Vocabulary coverage: 13,204 / 14,769 (89.4%)\n",
      "Embedding matrix shape: (14769, 100)\n"
     ]
    }
   ],
   "source": [
    "vocab_size = len(vocab)\n",
    "\n",
    "embedding_matrix = np.zeros((vocab_size, embedding_dim))\n",
    "words_found = 0\n",
    "\n",
    "for word, idx in vocab.items():\n",
    "    if word in glove_vectors:\n",
    "        embedding_matrix[idx] = glove_vectors[word]\n",
    "        words_found += 1\n",
    "\n",
    "    else:\n",
    "        # Random initialization for words not in GloVe\n",
    "        embedding_matrix[idx] = np.random.normal(scale=0.6, size=(embedding_dim,))\n",
    "\n",
    "coverage = words_found / vocab_size\n",
    "print(f'Vocabulary coverage: {words_found:,} / {vocab_size:,} ({coverage:.1%})')\n",
    "print(f'Embedding matrix shape: {embedding_matrix.shape}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e889cccf",
   "metadata": {},
   "source": [
    "## 3. Baseline: Bidirectional GRU model\n",
    "\n",
    "First, let's establish our baseline performance with a pure recurrent architecture.\n",
    "\n",
    "### 3.1. Build model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "d90c1cf5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential\"</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1mModel: \"sequential\"\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ embedding (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Embedding</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">50</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">100</span>)        │     <span style=\"color: #00af00; text-decoration-color: #00af00\">1,476,900</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ spatial_dropout1d               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">50</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">100</span>)        │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">SpatialDropout1D</span>)              │                        │               │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ Bidirectional_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Bidirectional</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">50</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)        │        <span style=\"color: #00af00; text-decoration-color: #00af00\">63,744</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ Bidirectional_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Bidirectional</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)            │        <span style=\"color: #00af00; text-decoration-color: #00af00\">74,496</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dropout (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)            │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">5</span>)              │           <span style=\"color: #00af00; text-decoration-color: #00af00\">645</span> │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
       "</pre>\n"
      ],
      "text/plain": [
       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ embedding (\u001b[38;5;33mEmbedding\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m50\u001b[0m, \u001b[38;5;34m100\u001b[0m)        │     \u001b[38;5;34m1,476,900\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ spatial_dropout1d               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m50\u001b[0m, \u001b[38;5;34m100\u001b[0m)        │             \u001b[38;5;34m0\u001b[0m │\n",
       "│ (\u001b[38;5;33mSpatialDropout1D\u001b[0m)              │                        │               │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ Bidirectional_1 (\u001b[38;5;33mBidirectional\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m50\u001b[0m, \u001b[38;5;34m128\u001b[0m)        │        \u001b[38;5;34m63,744\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ Bidirectional_2 (\u001b[38;5;33mBidirectional\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)            │        \u001b[38;5;34m74,496\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dropout (\u001b[38;5;33mDropout\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)            │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense (\u001b[38;5;33mDense\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m)              │           \u001b[38;5;34m645\u001b[0m │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">1,615,785</span> (6.16 MB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m1,615,785\u001b[0m (6.16 MB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">1,615,785</span> (6.16 MB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m1,615,785\u001b[0m (6.16 MB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Convert numpy embedding matrix into a Constant initializer\n",
    "glove_initializer = keras.initializers.Constant(embedding_matrix)\n",
    "\n",
    "# Build Bidirectional GRU model with pretrained GloVe embeddings\n",
    "baseline_model = Sequential([\n",
    "    Input(shape=(max_len,)),\n",
    "    Embedding(\n",
    "        input_dim=vocab_size, \n",
    "        output_dim=embedding_dim,\n",
    "        embeddings_initializer=glove_initializer,\n",
    "        trainable=True  # Set to True to make embeddings trainable\n",
    "    ),\n",
    "    SpatialDropout1D(dropout),\n",
    "    Bidirectional(GRU(hidden_dim, return_sequences=True, name='GRU_1'), name='Bidirectional_1'),\n",
    "    Bidirectional(GRU(hidden_dim, return_sequences=False, name='GRU_2'), name='Bidirectional_2'),\n",
    "    Dropout(dropout),\n",
    "    Dense(len(set(y_train)), activation='softmax', kernel_regularizer=keras.regularizers.l2(l2_reg))\n",
    "])\n",
    "\n",
    "baseline_model.compile(\n",
    "    optimizer=keras.optimizers.Adam(learning_rate=learning_rate),\n",
    "    loss='sparse_categorical_crossentropy',\n",
    "    metrics=['accuracy']\n",
    ")\n",
    "\n",
    "baseline_params = baseline_model.count_params()\n",
    "baseline_model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1f4d6bcf",
   "metadata": {},
   "source": [
    "### 3.2. Define training callbacks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "9e1fb763",
   "metadata": {},
   "outputs": [],
   "source": [
    "callbacks = [\n",
    "    ModelCheckpoint(\n",
    "        '../models/activity_baseline_rnn_classifier.keras',\n",
    "        save_best_only=True,\n",
    "        monitor='val_accuracy',\n",
    "        mode='max',\n",
    "        verbose=verbose\n",
    "    ),\n",
    "    EarlyStopping(\n",
    "        monitor='val_loss',\n",
    "        patience=early_stopping_patience,\n",
    "        restore_best_weights=True,\n",
    "        verbose=verbose\n",
    "    ),\n",
    "    TensorBoard(\n",
    "        log_dir='../logs/activity_baseline_rnn_classifier',\n",
    "        histogram_freq=1,\n",
    "        write_graph=True\n",
    "    )\n",
    "]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d9cae247",
   "metadata": {},
   "source": [
    "### 3.3. Calculate class weights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "f10adb3a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Class weights: {0: 20.39581589958159, 1: 1.7960943257184967, 2: 0.47043041883806214, 3: 0.47561713337886624, 4: 6.025463535228678}\n"
     ]
    }
   ],
   "source": [
    "class_weights = compute_class_weight('balanced', classes=np.unique(y_train), y=y_train)\n",
    "class_weight_dict = dict(enumerate(class_weights))\n",
    "print(f'Class weights: {class_weight_dict}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0791aaeb",
   "metadata": {},
   "source": [
    "### 3.4. Train the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "9fb97944",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "CPU times: user 11min 13s, sys: 42 s, total: 11min 55s\n",
      "Wall time: 10min 29s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "# Training with class weighting and early stopping\n",
    "history = baseline_model.fit(\n",
    "    X_train, y_train,\n",
    "    validation_data=(X_test, y_test),\n",
    "    epochs=epochs,\n",
    "    batch_size=batch_size,\n",
    "    class_weight=class_weight_dict,\n",
    "    callbacks=callbacks,\n",
    "    verbose=verbose\n",
    ")\n",
    "\n",
    "print()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dc5c7fe6",
   "metadata": {},
   "source": [
    "### 3.5. Baseline model learning curves"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "c68536e1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1, 2, figsize=(10, 4))\n",
    "\n",
    "axes[0].plot(history.history['loss'], label='Train')\n",
    "axes[0].plot(history.history['val_loss'], label='Validation')\n",
    "axes[0].set_title('Loss')\n",
    "axes[0].set_xlabel('Epoch')\n",
    "axes[0].set_ylabel('Loss')\n",
    "axes[0].legend()\n",
    "\n",
    "axes[1].plot(history.history['accuracy'], label='Train')\n",
    "axes[1].plot(history.history['val_accuracy'], label='Validation')\n",
    "axes[1].set_title('Accuracy')\n",
    "axes[1].set_xlabel('Epoch')\n",
    "axes[1].set_ylabel('Accuracy')\n",
    "axes[1].legend()\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "72de8d3b",
   "metadata": {},
   "source": [
    "## 4. Baseline evaluation\n",
    "\n",
    "Record the baseline metrics below so you can compare them to your CNN-RNN model later.\n",
    "\n",
    "### 4.1. Make test set predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "7342f545",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m191/191\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 11ms/step\n",
      "Test samples: 6094\n",
      "Prediction distribution:\n",
      "\n",
      "  Actual:    Counter({2: 2591, 3: 2563, 1: 678, 4: 202, 0: 60})\n",
      "  Predicted: Counter({2: 2137, 3: 2137, 1: 1214, 4: 516, 0: 90})\n"
     ]
    }
   ],
   "source": [
    "# Load best model and get predictions\n",
    "baseline_model = keras.saving.load_model('../models/activity_baseline_rnn_classifier.keras')\n",
    "\n",
    "# Get predicted probabilities and classes\n",
    "y_prob_baseline = baseline_model.predict(X_test)\n",
    "y_pred_baseline = y_prob_baseline.argmax(axis=1)\n",
    "\n",
    "# Class labels\n",
    "class_names = ['-2', '-1', '0', '1', '2']\n",
    "n_classes = len(class_names)\n",
    "\n",
    "print(f'Test samples: {len(y_test)}')\n",
    "print(f'Prediction distribution:\\n')\n",
    "print(f'  Actual:    {Counter(y_test)}')\n",
    "print(f'  Predicted: {Counter(y_pred_baseline)}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "408d5b20",
   "metadata": {},
   "source": [
    "### 4.2. Per-class accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "89df382c",
   "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>Class</th>\n",
       "      <th>Samples</th>\n",
       "      <th>Correct</th>\n",
       "      <th>Accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-2</td>\n",
       "      <td>60</td>\n",
       "      <td>25</td>\n",
       "      <td>41.7%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-1</td>\n",
       "      <td>678</td>\n",
       "      <td>425</td>\n",
       "      <td>62.7%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>2591</td>\n",
       "      <td>1324</td>\n",
       "      <td>51.1%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>2563</td>\n",
       "      <td>1362</td>\n",
       "      <td>53.1%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>202</td>\n",
       "      <td>96</td>\n",
       "      <td>47.5%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Overall</td>\n",
       "      <td>6094</td>\n",
       "      <td>3232</td>\n",
       "      <td>53.0%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Class  Samples  Correct Accuracy\n",
       "0       -2       60       25    41.7%\n",
       "1       -1      678      425    62.7%\n",
       "2        0     2591     1324    51.1%\n",
       "3        1     2563     1362    53.1%\n",
       "4        2      202       96    47.5%\n",
       "5  Overall     6094     3232    53.0%"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Per-class accuracy table\n",
    "per_class_stats = []\n",
    "\n",
    "for i, class_name in enumerate(class_names):\n",
    "    mask = y_test == i\n",
    "    n_samples = mask.sum()\n",
    "    n_correct = (y_pred_baseline[mask] == i).sum()\n",
    "    acc = n_correct / n_samples if n_samples > 0 else 0\n",
    "    \n",
    "    per_class_stats.append({\n",
    "        'Class': class_name,\n",
    "        'Samples': n_samples,\n",
    "        'Correct': n_correct,\n",
    "        'Accuracy': f'{acc:.1%}'\n",
    "    })\n",
    "\n",
    "# Add overall accuracy\n",
    "baseline_acc = (y_pred_baseline == y_test).mean()\n",
    "\n",
    "per_class_stats.append({\n",
    "    'Class': 'Overall',\n",
    "    'Samples': len(y_test),\n",
    "    'Correct': (y_pred_baseline == y_test).sum(),\n",
    "    'Accuracy': f'{baseline_acc:.1%}'\n",
    "})\n",
    "\n",
    "accuracy_df = pd.DataFrame(per_class_stats)\n",
    "accuracy_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "296a3976",
   "metadata": {},
   "source": [
    "### 4.3. Confusion matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "02d44a72",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot confusion matrix\n",
    "fig, ax = plt.subplots(figsize=(8, 6))\n",
    "\n",
    "ax.set_title('Confusion matrix')\n",
    "cm = confusion_matrix(y_test, y_pred_baseline)\n",
    "\n",
    "sns.heatmap(\n",
    "    cm, \n",
    "    annot=True, fmt='d', cmap='Blues', \n",
    "    xticklabels=class_names, yticklabels=class_names, ax=ax\n",
    ")\n",
    "\n",
    "ax.set_xlabel('Predicted')\n",
    "ax.set_ylabel('Actual')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7895bdbc",
   "metadata": {},
   "source": [
    "### 4.4. Predicted probability distributions by class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "91f28172",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x250 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Predicted probability distributions for each true class\n",
    "fig, axes = plt.subplots(1, n_classes, figsize=(10, 2.5))\n",
    "\n",
    "plt.suptitle('Predicted probability distributions by true class', y=1.02)\n",
    "\n",
    "for i, class_name in enumerate(class_names):\n",
    "\n",
    "    # Get predictions for samples of this true class\n",
    "    mask = y_test == i\n",
    "    probs_for_class = y_prob_baseline[mask]\n",
    "    \n",
    "    # Plot distribution of predicted probabilities for each predicted class\n",
    "    ax = axes[i]\n",
    "    ax.boxplot([probs_for_class[:, j] for j in range(n_classes)], tick_labels=class_names)\n",
    "    ax.set_title(f'True class: {class_name}')\n",
    "    ax.set_xlabel('Predicted class')\n",
    "    ax.set_ylabel('Probability')\n",
    "    ax.set_ylim(0, 1)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9fa8cfff",
   "metadata": {},
   "source": [
    "### 4.5. Evaluation curves"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "5a0b7d21",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Binarize labels for multiclass ROC/PR\n",
    "y_test_bin = label_binarize(y_test, classes=range(n_classes))\n",
    "\n",
    "# Compute ROC and PR curves for each class\n",
    "fig, axes = plt.subplots(1, 2, figsize=(10, 4))\n",
    "\n",
    "# ROC curves\n",
    "ax_roc = axes[0]\n",
    "ax_roc.set_title('ROC curves (one-vs-rest)')\n",
    "\n",
    "for i, class_name in enumerate(class_names):\n",
    "    fpr, tpr, _ = roc_curve(y_test_bin[:, i], y_prob_baseline[:, i])\n",
    "    roc_auc = auc(fpr, tpr)\n",
    "    ax_roc.plot(fpr, tpr, label=f'Class {class_name} (AUC={roc_auc:.2f})')\n",
    "\n",
    "ax_roc.plot([0, 1], [0, 1], 'k--', alpha=0.5)\n",
    "ax_roc.set_xlabel('False positive rate')\n",
    "ax_roc.set_ylabel('True positive rate')\n",
    "ax_roc.legend(loc='lower right')\n",
    "ax_roc.set_xlim([0, 1])\n",
    "ax_roc.set_ylim([0, 1.05])\n",
    "\n",
    "# PR curves\n",
    "ax_pr = axes[1]\n",
    "ax_pr.set_title('Precision-recall curves (one-vs-rest)')\n",
    "\n",
    "for i, class_name in enumerate(class_names):\n",
    "    precision, recall, _ = precision_recall_curve(y_test_bin[:, i], y_prob_baseline[:, i])\n",
    "    pr_auc = auc(recall, precision)\n",
    "    ax_pr.plot(recall, precision, label=f'Class {class_name} (AUC={pr_auc:.2f})')\n",
    "\n",
    "ax_pr.set_xlabel('Recall')\n",
    "ax_pr.set_ylabel('Precision')\n",
    "ax_pr.set_xlim([0, 1])\n",
    "ax_pr.set_ylim([0, 1.05])\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a59b2536",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "## 5. Activity: Build a CNN-RNN hybrid model (SOLUTION)\n",
    "\n",
    "The solution below demonstrates one effective approach: adding a Conv1D layer before the GRU to capture local n-gram patterns.\n",
    "\n",
    "### Why add convolutions?\n",
    "\n",
    "- **Local pattern detection**: Conv1D layers can detect n-gram patterns (e.g., \"not good\", \"very happy\") that are important for sentiment\n",
    "- **Dimensionality reduction**: Convolutions can reduce sequence length before the RNN, making training faster\n",
    "- **Feature extraction**: The CNN extracts local features, then the RNN captures long-range dependencies\n",
    "\n",
    "### Solution architecture\n",
    "\n",
    "```\n",
    "Embedding (vocab_size × 100)\n",
    "    ↓\n",
    "SpatialDropout1D (0.3)\n",
    "    ↓\n",
    "Conv1D (128 filters, kernel_size=3, relu, padding='same')  ← Captures trigram patterns\n",
    "    ↓\n",
    "MaxPooling1D (pool_size=2)  ← Reduces sequence length by half\n",
    "    ↓\n",
    "Bidirectional GRU (32 units, return_sequences=True)\n",
    "    ↓\n",
    "Bidirectional GRU (16 units)\n",
    "    ↓\n",
    "Dropout (0.3)\n",
    "    ↓\n",
    "Dense (5 classes, softmax)\n",
    "```\n",
    "\n",
    "### Key design decisions\n",
    "\n",
    "1. **128 filters**: Each filter learns a different pattern detector. More filters = more patterns, but more parameters.\n",
    "2. **kernel_size=3**: Captures trigrams like \"not very good\". You could try 4 or 5 for longer patterns.\n",
    "3. **padding='same'**: Output length = input length, so MaxPooling works correctly.\n",
    "4. **MaxPooling1D(pool_size=2)**: Halves the sequence length, making the GRU faster and helping it focus on the most salient features.\n",
    "\n",
    "### 5.1. Define the CNN-RNN model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "ff1c7488",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential_1\"</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1mModel: \"sequential_1\"\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ embedding_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Embedding</span>)         │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">50</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">100</span>)        │     <span style=\"color: #00af00; text-decoration-color: #00af00\">1,476,900</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ spatial_dropout1d_1             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">50</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">100</span>)        │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">SpatialDropout1D</span>)              │                        │               │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ conv1d (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv1D</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">50</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)        │        <span style=\"color: #00af00; text-decoration-color: #00af00\">38,528</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ max_pooling1d (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling1D</span>)    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">25</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)        │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ Bidirectional_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Bidirectional</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">25</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)        │        <span style=\"color: #00af00; text-decoration-color: #00af00\">74,496</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ Bidirectional_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Bidirectional</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)            │        <span style=\"color: #00af00; text-decoration-color: #00af00\">74,496</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dropout_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)            │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">5</span>)              │           <span style=\"color: #00af00; text-decoration-color: #00af00\">645</span> │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
       "</pre>\n"
      ],
      "text/plain": [
       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ embedding_1 (\u001b[38;5;33mEmbedding\u001b[0m)         │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m50\u001b[0m, \u001b[38;5;34m100\u001b[0m)        │     \u001b[38;5;34m1,476,900\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ spatial_dropout1d_1             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m50\u001b[0m, \u001b[38;5;34m100\u001b[0m)        │             \u001b[38;5;34m0\u001b[0m │\n",
       "│ (\u001b[38;5;33mSpatialDropout1D\u001b[0m)              │                        │               │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ conv1d (\u001b[38;5;33mConv1D\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m50\u001b[0m, \u001b[38;5;34m128\u001b[0m)        │        \u001b[38;5;34m38,528\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ max_pooling1d (\u001b[38;5;33mMaxPooling1D\u001b[0m)    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m128\u001b[0m)        │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ Bidirectional_1 (\u001b[38;5;33mBidirectional\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m128\u001b[0m)        │        \u001b[38;5;34m74,496\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ Bidirectional_2 (\u001b[38;5;33mBidirectional\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)            │        \u001b[38;5;34m74,496\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dropout_1 (\u001b[38;5;33mDropout\u001b[0m)             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)            │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_1 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m)              │           \u001b[38;5;34m645\u001b[0m │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">1,665,065</span> (6.35 MB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m1,665,065\u001b[0m (6.35 MB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">1,665,065</span> (6.35 MB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m1,665,065\u001b[0m (6.35 MB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from keras.layers import Conv1D, MaxPooling1D\n",
    "\n",
    "# Model hyperparameters\n",
    "conv_filters     = 128\n",
    "conv_kernel_size = 3\n",
    "max_pool_size    = 2\n",
    "\n",
    "# Convert numpy embedding matrix into a Constant initializer\n",
    "glove_initializer = keras.initializers.Constant(embedding_matrix)\n",
    "\n",
    "# Build Bidirectional GRU model with pretrained GloVe embeddings\n",
    "cnn_rnn_model = Sequential([\n",
    "    Input(shape=(max_len,)),\n",
    "    Embedding(\n",
    "        input_dim=vocab_size, \n",
    "        output_dim=embedding_dim,\n",
    "        embeddings_initializer=glove_initializer,\n",
    "        trainable=True  # Set to True to make embeddings trainable\n",
    "    ),\n",
    "    SpatialDropout1D(dropout),\n",
    "    Conv1D(filters=conv_filters, kernel_size=conv_kernel_size, activation='relu', padding='same'),\n",
    "    MaxPooling1D(pool_size=max_pool_size),\n",
    "    Bidirectional(GRU(hidden_dim, return_sequences=True, name='GRU_1'), name='Bidirectional_1'),\n",
    "    Bidirectional(GRU(hidden_dim, return_sequences=False, name='GRU_2'), name='Bidirectional_2'),\n",
    "    Dropout(dropout),\n",
    "    Dense(len(set(y_train)), activation='softmax', kernel_regularizer=keras.regularizers.l2(l2_reg))\n",
    "])\n",
    "\n",
    "cnn_rnn_model.compile(\n",
    "    optimizer=keras.optimizers.Adam(learning_rate=learning_rate),\n",
    "    loss='sparse_categorical_crossentropy',\n",
    "    metrics=['accuracy']\n",
    ")\n",
    "\n",
    "cnn_rnn_params = cnn_rnn_model.count_params()\n",
    "cnn_rnn_model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a5a622e",
   "metadata": {},
   "source": [
    "### 5.2. Define training callbacks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "2332a279",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define callbacks for CNN-RNN model (saves to different file)\n",
    "cnn_rnn_callbacks = [\n",
    "    ModelCheckpoint(\n",
    "        '../models/activity_cnn_rnn_classifier.keras',\n",
    "        save_best_only=True,\n",
    "        monitor='val_accuracy',\n",
    "        mode='max',\n",
    "        verbose=verbose\n",
    "    ),\n",
    "    EarlyStopping(\n",
    "        monitor='val_loss',\n",
    "        patience=early_stopping_patience,\n",
    "        restore_best_weights=True,\n",
    "        verbose=verbose\n",
    "    ),\n",
    "    TensorBoard(\n",
    "        log_dir='../logs/activity_cnn_rnn_classifier',\n",
    "        histogram_freq=1,\n",
    "        write_graph=True\n",
    "    )\n",
    "]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d4dfef75",
   "metadata": {},
   "source": [
    "### 5.3. Train your CNN-RNN model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "35d2245e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "CPU times: user 9min 34s, sys: 40.9 s, total: 10min 15s\n",
      "Wall time: 8min 19s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "# Train the CNN-RNN model\n",
    "cnn_rnn_history = cnn_rnn_model.fit(\n",
    "    X_train, y_train,\n",
    "    validation_data=(X_test, y_test),\n",
    "    epochs=epochs,\n",
    "    batch_size=batch_size,\n",
    "    class_weight=class_weight_dict,\n",
    "    callbacks=cnn_rnn_callbacks,\n",
    "    verbose=verbose\n",
    ")\n",
    "\n",
    "print()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "34ed90fe",
   "metadata": {},
   "source": [
    "### 5.4. CNN-RNN model learning curves"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "e2bd76b4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1, 2, figsize=(10, 4))\n",
    "\n",
    "axes[0].plot(history.history['loss'], label='Train')\n",
    "axes[0].plot(history.history['val_loss'], label='Validation')\n",
    "axes[0].set_title('Loss')\n",
    "axes[0].set_xlabel('Epoch')\n",
    "axes[0].set_ylabel('Loss')\n",
    "axes[0].legend()\n",
    "\n",
    "axes[1].plot(history.history['accuracy'], label='Train')\n",
    "axes[1].plot(history.history['val_accuracy'], label='Validation')\n",
    "axes[1].set_title('Accuracy')\n",
    "axes[1].set_xlabel('Epoch')\n",
    "axes[1].set_ylabel('Accuracy')\n",
    "axes[1].legend()\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7951f272",
   "metadata": {},
   "source": [
    "### 5.5. Evaluate your CNN-RNN model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "3bc6b841",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m191/191\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 10ms/step\n",
      "CNN-RNN model accuracy: 54.0%\n"
     ]
    }
   ],
   "source": [
    "# Load best CNN-RNN model and get predictions\n",
    "cnn_rnn_model = keras.saving.load_model('../models/activity_cnn_rnn_classifier.keras')\n",
    "\n",
    "# Get predicted probabilities and classes\n",
    "y_prob_cnn_rnn = cnn_rnn_model.predict(X_test)\n",
    "y_pred_cnn_rnn = y_prob_cnn_rnn.argmax(axis=1)\n",
    "\n",
    "# Calculate accuracy\n",
    "cnn_rnn_acc = (y_pred_cnn_rnn == y_test).mean()\n",
    "print(f'CNN-RNN model accuracy: {cnn_rnn_acc:.1%}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5dee91fe",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "## 6. Compare models\n",
    "\n",
    "Now let's compare your CNN-RNN model to the baseline GRU model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "03720dce",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model comparison:\n"
     ]
    },
    {
     "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>Model</th>\n",
       "      <th>Accuracy</th>\n",
       "      <th>Parameters</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Baseline GRU</td>\n",
       "      <td>53.0%</td>\n",
       "      <td>1615785</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CNN-RNN</td>\n",
       "      <td>54.0%</td>\n",
       "      <td>1665065</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Model Accuracy  Parameters\n",
       "0  Baseline GRU    53.0%     1615785\n",
       "1       CNN-RNN    54.0%     1665065"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create comparison dataframe\n",
    "comparison = pd.DataFrame({\n",
    "    'Model': ['Baseline GRU', 'CNN-RNN'],\n",
    "    'Accuracy': [f'{baseline_acc:.1%}', f'{cnn_rnn_acc:.1%}'],\n",
    "    'Parameters': [baseline_params, cnn_rnn_params]\n",
    "})\n",
    "\n",
    "print('Model comparison:')\n",
    "comparison"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "53cf2d4d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1400x500 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Side-by-side confusion matrices\n",
    "fig, axes = plt.subplots(1, 2, figsize=(14, 5))\n",
    "\n",
    "for ax, y_pred, title in zip(\n",
    "    axes, \n",
    "    [y_pred_baseline, y_pred_cnn_rnn], \n",
    "    ['Baseline GRU', 'CNN-RNN']\n",
    "):\n",
    "    cm = confusion_matrix(y_test, y_pred)\n",
    "    sns.heatmap(\n",
    "        cm, annot=True, fmt='d', cmap='Blues',\n",
    "        xticklabels=class_names, yticklabels=class_names, ax=ax\n",
    "    )\n",
    "    ax.set_title(f'{title} (Acc: {(y_pred == y_test).mean():.1%})')\n",
    "    ax.set_xlabel('Predicted')\n",
    "    ax.set_ylabel('Actual')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0d23f8d6",
   "metadata": {},
   "source": [
    "## 7. Reflection\n",
    "\n",
    "**1. Did your CNN-RNN model outperform the baseline?**\n",
    "\n",
    "Results vary by run, but typically the CNN-RNN model shows marginal improvement or similar performance. The convolutional layer helps by:\n",
    "- Detecting local sentiment indicators like \"not good\" or \"very happy\" as fixed patterns\n",
    "- Reducing sequence length before the RNN, which can help with longer sequences\n",
    "- Extracting position-invariant features (the same pattern is detected anywhere in the tweet)\n",
    "\n",
    "However, for short tweets (average ~10-15 tokens), the baseline GRU is already quite capable of capturing the full context, limiting the CNN's advantage.\n",
    "\n",
    "**2. How did the number of parameters change?**\n",
    "\n",
    "The CNN-RNN model typically has **more parameters** due to the Conv1D layer (128 filters × 3 kernel × 100 embedding_dim = 38,400 weights + biases). However, we reduced the GRU sizes to partially compensate.\n",
    "\n",
    "Trade-off: More parameters = more capacity to learn complex patterns, but also higher risk of overfitting on small datasets. With ~15K training samples, regularization (dropout, L2) is important.\n",
    "\n",
    "**3. Which classes improved the most?**\n",
    "\n",
    "Typically, the extreme sentiment classes (-2 and +2) and the neutral class (0) see the most variability:\n",
    "- **Extreme classes**: Often underrepresented, so small changes in the model can have outsized effects\n",
    "- **Neutral class**: Hard to distinguish from mild sentiment; n-gram patterns like \"it's okay\" or \"not bad\" can help\n",
    "\n",
    "The adjacent classes (-1, +1) are hardest to improve because they're semantically closest to neutral.\n",
    "\n",
    "**4. What other architectures could you try?**\n",
    "\n",
    "- **Multiple kernel sizes**: Use parallel Conv1D layers with kernel_size=2,3,4,5 to capture different n-gram lengths, then concatenate (requires Functional API)\n",
    "- **Deeper convolutions**: Stack multiple Conv1D layers with increasing filters (64 → 128 → 256)\n",
    "- **Pure CNN**: Replace GRU entirely with GlobalMaxPooling1D; often competitive and much faster to train\n",
    "- **Attention mechanisms**: Add self-attention after the RNN to let the model focus on important words\n",
    "- **Transformer-based**: Use a pretrained model like BERT or DistilBERT for state-of-the-art results"
   ]
  }
 ],
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