{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "3a8a2e23",
   "metadata": {},
   "source": [
    "# Sentiment analysis: Twitter\n",
    "\n",
    "**Note**: this notebook is part of the complete lesson 34 repository [`gperdrizet/RNNs`](https://github.com/gperdrizet/RNNs) and is intended to be run in the environment set-up by that repository.\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 well 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** (0-1) — one per hidden unit — computed fresh for each input. Depend 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",
    "## 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",
    "\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 tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import (\n",
    "    Embedding, Dense, GRU, Dropout, Bidirectional, SpatialDropout1D\n",
    ")\n",
    "from tensorflow.keras.preprocessing.sequence import pad_sequences\n",
    "from tensorflow.keras.callbacks import ModelCheckpoint, EarlyStopping, TensorBoard\n",
    "from tensorflow.keras.regularizers import l2\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": [
    "### Configuration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b8d56b31",
   "metadata": {},
   "outputs": [],
   "source": [
    "# GloVe download settings\n",
    "glove_dir = '../data'\n",
    "glove_url = 'https://nlp.stanford.edu/data/glove.twitter.27B.zip'\n",
    "\n",
    "# Download NLTK resources\n",
    "nltk.download('stopwords', quiet=True)\n",
    "from nltk.corpus import stopwords\n",
    "from nltk.tokenize import TweetTokenizer\n",
    "\n",
    "# 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",
    "# Force CPU only\n",
    "tf.config.set_visible_devices([], 'GPU')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dc9b3cef",
   "metadata": {},
   "source": [
    "## 1. Data preparation\n",
    "\n",
    "### 1.1. Load"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "08292db5",
   "metadata": {},
   "outputs": [
    {
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       "    <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",
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       "      <th>1</th>\n",
       "      <td>638061181823922176</td>\n",
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       "      <td>1</td>\n",
       "      <td>Jay Z joins Instagram with nostalgic tribute t...</td>\n",
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       "    <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",
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       "      <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",
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       "      <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",
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       "  </tbody>\n",
       "</table>\n",
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       "                   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": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_parquet('../data/twitter-2016.parquet')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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": 5,
   "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": "5756badc",
   "metadata": {},
   "source": [
    "### 1.3. Tokenize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "9b37de69",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "        text-align: right;\n",
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       "<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",
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       "    <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",
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       "      <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",
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       "                                                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": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Clean and tokenize text using NLTK's TweetTokenizer\n",
    "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 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\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": 7,
   "id": "0d8efd85",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Vocabulary size: 14965\n",
      "\n",
      "Example vocab entries:\n",
      " sardinia\n",
      " #lambily\n",
      " elha's\n",
      " ballon\n",
      " d'or\n",
      " #argentina\n",
      " #bolivia\n",
      " arg\n",
      " bafana\n",
      " iniesta\n",
      " kluivert\n",
      " mascherano\n",
      " gunny\n",
      " apoel\n",
      " bowles\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)}\\n')\n",
    "\n",
    "print(f'Example vocab entries:')\n",
    "for key, _ in list(vocab.items())[-15:]:\n",
    "    print(f' {key}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cc6059bd",
   "metadata": {},
   "source": [
    "### 1.5. Convert to indices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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": "1d62669b",
   "metadata": {},
   "source": [
    "### 1.6. Train/test split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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=315,\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. Download embeddings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "74bb2bfa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GloVe file already exists: ../data/glove.twitter.27B.100d.txt\n"
     ]
    }
   ],
   "source": [
    "# Download GloVe embeddings (Twitter-specific, 100d)\n",
    "glove_dir = '../data'\n",
    "glove_file = os.path.join(glove_dir, 'glove.twitter.27B.100d.txt')\n",
    "glove_zip = os.path.join(glove_dir, 'glove.twitter.27B.zip')\n",
    "glove_url = 'https://nlp.stanford.edu/data/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', glove_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": "84295ee4",
   "metadata": {},
   "source": [
    "### 2.2. Load embeddings into dictionary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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",
    "print(f'Loaded {len(glove_vectors):,} word vectors')\n",
    "print(f'Embedding dimension: {len(next(iter(glove_vectors.values())))}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a2d98274",
   "metadata": {},
   "source": [
    "### 2.3. Create embedding look-up table for our vocabulary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "d1cc20ab",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Vocabulary coverage: 12,976 / 14,965 (86.7%)\n",
      "Embedding matrix shape: (14965, 100)\n"
     ]
    }
   ],
   "source": [
    "embedding_dim = 100  # GloVe Twitter uses 100d\n",
    "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. Bidirectional GRU model\n",
    "\n",
    "### 3.1. Build model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d90c1cf5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " embedding (Embedding)       (None, None, 100)         1496500   \n",
      "                                                                 \n",
      " spatial_dropout1d (Spatial  (None, None, 100)         0         \n",
      " Dropout1D)                                                      \n",
      "                                                                 \n",
      " bidirectional (Bidirection  (None, None, 128)         63744     \n",
      " al)                                                             \n",
      "                                                                 \n",
      " bidirectional_1 (Bidirecti  (None, 64)                31104     \n",
      " onal)                                                           \n",
      "                                                                 \n",
      " dropout (Dropout)           (None, 64)                0         \n",
      "                                                                 \n",
      " dense (Dense)               (None, 5)                 325       \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 1591673 (6.07 MB)\n",
      "Trainable params: 1591673 (6.07 MB)\n",
      "Non-trainable params: 0 (0.00 Byte)\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# Model hyperparameters\n",
    "hidden_dim = 64\n",
    "output_dim = len(set(y_train))\n",
    "dropout = 0.3            # Spatial dropout rate\n",
    "recurrent_dropout = 0.3  # Dropout on recurrent connections\n",
    "l2_reg = 0.01            # L2 regularization strength\n",
    "learning_rate = 0.0001   # Lower LR for fine-tuning embeddings\n",
    "\n",
    "# Build Bidirectional GRU model with pretrained GloVe embeddings\n",
    "model = Sequential([\n",
    "    Embedding(\n",
    "        vocab_size, \n",
    "        embedding_dim,\n",
    "        weights=[embedding_matrix],\n",
    "        trainable=True  # Fine-tune embeddings with lower learning rate\n",
    "    ),\n",
    "    SpatialDropout1D(dropout),\n",
    "    Bidirectional(GRU(\n",
    "        hidden_dim,\n",
    "        recurrent_dropout=recurrent_dropout,\n",
    "        return_sequences=True\n",
    "    )),\n",
    "    Bidirectional(GRU(\n",
    "        hidden_dim // 2, # Second layer, smaller\n",
    "        recurrent_dropout=recurrent_dropout\n",
    "    )),\n",
    "    Dropout(dropout),\n",
    "    Dense(output_dim, activation='softmax', kernel_regularizer=l2(l2_reg))\n",
    "])\n",
    "\n",
    "model.compile(\n",
    "    optimizer=tf.keras.optimizers.Adam(learning_rate=learning_rate),\n",
    "    loss='sparse_categorical_crossentropy',\n",
    "    metrics=['accuracy']\n",
    ")\n",
    "\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1f4d6bcf",
   "metadata": {},
   "source": [
    "### 3.2. Define training callbacks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9e1fb763",
   "metadata": {},
   "outputs": [],
   "source": [
    "callbacks = [\n",
    "    ModelCheckpoint(\n",
    "        '../models/rnn_classifier.keras',\n",
    "        save_best_only=True,\n",
    "        monitor='val_accuracy',\n",
    "        verbose=0\n",
    "    ),\n",
    "    EarlyStopping(\n",
    "        monitor='val_loss',\n",
    "        patience=5,\n",
    "        restore_best_weights=True,\n",
    "        verbose=0\n",
    "    ),\n",
    "    TensorBoard(\n",
    "        log_dir='../logs/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": 15,
   "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": 16,
   "id": "9fb97944",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/30\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n",
      "I0000 00:00:1771281987.172455  650996 service.cc:145] XLA service 0x73f1347ff120 initialized for platform Host (this does not guarantee that XLA will be used). Devices:\n",
      "I0000 00:00:1771281987.172539  650996 service.cc:153]   StreamExecutor device (0): Host, Default Version\n",
      "I0000 00:00:1771281987.280725  650989 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "381/381 [==============================] - ETA: 0s - loss: 1.7288 - accuracy: 0.2081\n",
      "Epoch 1: val_accuracy improved from -inf to 0.27010, saving model to ../models/rnn_classifier.keras\n",
      "381/381 [==============================] - 89s 116ms/step - loss: 1.7288 - accuracy: 0.2081 - val_loss: 1.6367 - val_accuracy: 0.2701\n",
      "Epoch 2/30\n",
      "381/381 [==============================] - ETA: 0s - loss: 1.6515 - accuracy: 0.2456\n",
      "Epoch 2: val_accuracy did not improve from 0.27010\n",
      "381/381 [==============================] - 31s 81ms/step - loss: 1.6515 - accuracy: 0.2456 - val_loss: 1.6410 - val_accuracy: 0.2204\n",
      "Epoch 3/30\n",
      "381/381 [==============================] - ETA: 0s - loss: 1.5695 - accuracy: 0.2612\n",
      "Epoch 3: val_accuracy did not improve from 0.27010\n",
      "381/381 [==============================] - 31s 81ms/step - loss: 1.5695 - accuracy: 0.2612 - val_loss: 1.5556 - val_accuracy: 0.2699\n",
      "Epoch 4/30\n",
      "381/381 [==============================] - ETA: 0s - loss: 1.4788 - accuracy: 0.2861\n",
      "Epoch 4: val_accuracy did not improve from 0.27010\n",
      "381/381 [==============================] - 30s 78ms/step - loss: 1.4788 - accuracy: 0.2861 - val_loss: 1.5243 - val_accuracy: 0.2465\n",
      "Epoch 5/30\n",
      "381/381 [==============================] - ETA: 0s - loss: 1.4290 - accuracy: 0.2941\n",
      "Epoch 5: val_accuracy improved from 0.27010 to 0.31605, saving model to ../models/rnn_classifier.keras\n",
      "381/381 [==============================] - 30s 79ms/step - loss: 1.4290 - accuracy: 0.2941 - val_loss: 1.4624 - val_accuracy: 0.3160\n",
      "Epoch 6/30\n",
      "381/381 [==============================] - ETA: 0s - loss: 1.3816 - accuracy: 0.3083\n",
      "Epoch 6: val_accuracy improved from 0.31605 to 0.32130, saving model to ../models/rnn_classifier.keras\n",
      "381/381 [==============================] - 30s 79ms/step - loss: 1.3816 - accuracy: 0.3083 - val_loss: 1.4376 - val_accuracy: 0.3213\n",
      "Epoch 7/30\n",
      "381/381 [==============================] - ETA: 0s - loss: 1.3401 - accuracy: 0.3347\n",
      "Epoch 7: val_accuracy did not improve from 0.32130\n",
      "381/381 [==============================] - 30s 78ms/step - loss: 1.3401 - accuracy: 0.3347 - val_loss: 1.4899 - val_accuracy: 0.2790\n",
      "Epoch 8/30\n",
      "381/381 [==============================] - ETA: 0s - loss: 1.2960 - accuracy: 0.3412\n",
      "Epoch 8: val_accuracy improved from 0.32130 to 0.32622, saving model to ../models/rnn_classifier.keras\n",
      "381/381 [==============================] - 30s 78ms/step - loss: 1.2960 - accuracy: 0.3412 - val_loss: 1.4191 - val_accuracy: 0.3262\n",
      "Epoch 9/30\n",
      "381/381 [==============================] - ETA: 0s - loss: 1.2638 - accuracy: 0.3600\n",
      "Epoch 9: val_accuracy improved from 0.32622 to 0.36659, saving model to ../models/rnn_classifier.keras\n",
      "381/381 [==============================] - 30s 78ms/step - loss: 1.2638 - accuracy: 0.3600 - val_loss: 1.3746 - val_accuracy: 0.3666\n",
      "Epoch 10/30\n",
      "381/381 [==============================] - ETA: 0s - loss: 1.2243 - accuracy: 0.3798\n",
      "Epoch 10: val_accuracy did not improve from 0.36659\n",
      "381/381 [==============================] - 30s 77ms/step - loss: 1.2243 - accuracy: 0.3798 - val_loss: 1.3778 - val_accuracy: 0.3623\n",
      "Epoch 11/30\n",
      "381/381 [==============================] - ETA: 0s - loss: 1.2048 - accuracy: 0.3801\n",
      "Epoch 11: val_accuracy improved from 0.36659 to 0.44289, saving model to ../models/rnn_classifier.keras\n",
      "381/381 [==============================] - 30s 78ms/step - loss: 1.2048 - accuracy: 0.3801 - val_loss: 1.2572 - val_accuracy: 0.4429\n",
      "Epoch 12/30\n",
      "381/381 [==============================] - ETA: 0s - loss: 1.1713 - accuracy: 0.4064\n",
      "Epoch 12: val_accuracy did not improve from 0.44289\n",
      "381/381 [==============================] - 30s 78ms/step - loss: 1.1713 - accuracy: 0.4064 - val_loss: 1.3396 - val_accuracy: 0.3935\n",
      "Epoch 13/30\n",
      "381/381 [==============================] - ETA: 0s - loss: 1.1648 - accuracy: 0.4054\n",
      "Epoch 13: val_accuracy did not improve from 0.44289\n",
      "381/381 [==============================] - 30s 78ms/step - loss: 1.1648 - accuracy: 0.4054 - val_loss: 1.3352 - val_accuracy: 0.3966\n",
      "Epoch 14/30\n",
      "381/381 [==============================] - ETA: 0s - loss: 1.1256 - accuracy: 0.4103\n",
      "Epoch 14: val_accuracy did not improve from 0.44289\n",
      "381/381 [==============================] - 30s 80ms/step - loss: 1.1256 - accuracy: 0.4103 - val_loss: 1.2651 - val_accuracy: 0.4427\n",
      "Epoch 15/30\n",
      "381/381 [==============================] - ETA: 0s - loss: 1.1072 - accuracy: 0.4290\n",
      "Epoch 15: val_accuracy did not improve from 0.44289\n",
      "381/381 [==============================] - 29s 77ms/step - loss: 1.1072 - accuracy: 0.4290 - val_loss: 1.2848 - val_accuracy: 0.4250\n",
      "Epoch 16/30\n",
      "381/381 [==============================] - ETA: 0s - loss: 1.0798 - accuracy: 0.4344\n",
      "Epoch 16: val_accuracy improved from 0.44289 to 0.45766, saving model to ../models/rnn_classifier.keras\n",
      "381/381 [==============================] - 30s 80ms/step - loss: 1.0798 - accuracy: 0.4344 - val_loss: 1.2202 - val_accuracy: 0.4577\n",
      "Epoch 17/30\n",
      "381/381 [==============================] - ETA: 0s - loss: 1.0614 - accuracy: 0.4427\n",
      "Epoch 17: val_accuracy improved from 0.45766 to 0.46275, saving model to ../models/rnn_classifier.keras\n",
      "381/381 [==============================] - 30s 78ms/step - loss: 1.0614 - accuracy: 0.4427 - val_loss: 1.2183 - val_accuracy: 0.4628\n",
      "Epoch 18/30\n",
      "381/381 [==============================] - ETA: 0s - loss: 1.0402 - accuracy: 0.4468\n",
      "Epoch 18: val_accuracy did not improve from 0.46275\n",
      "381/381 [==============================] - 29s 77ms/step - loss: 1.0402 - accuracy: 0.4468 - val_loss: 1.2220 - val_accuracy: 0.4583\n",
      "Epoch 19/30\n",
      "381/381 [==============================] - ETA: 0s - loss: 1.0331 - accuracy: 0.4482\n",
      "Epoch 19: val_accuracy improved from 0.46275 to 0.48047, saving model to ../models/rnn_classifier.keras\n",
      "381/381 [==============================] - 29s 77ms/step - loss: 1.0331 - accuracy: 0.4482 - val_loss: 1.1829 - val_accuracy: 0.4805\n",
      "Epoch 20/30\n",
      "381/381 [==============================] - ETA: 0s - loss: 1.0280 - accuracy: 0.4574\n",
      "Epoch 20: val_accuracy did not improve from 0.48047\n",
      "381/381 [==============================] - 29s 75ms/step - loss: 1.0280 - accuracy: 0.4574 - val_loss: 1.1895 - val_accuracy: 0.4710\n",
      "Epoch 21/30\n",
      "381/381 [==============================] - ETA: 0s - loss: 0.9886 - accuracy: 0.4644\n",
      "Epoch 21: val_accuracy did not improve from 0.48047\n",
      "381/381 [==============================] - 29s 75ms/step - loss: 0.9886 - accuracy: 0.4644 - val_loss: 1.2117 - val_accuracy: 0.4593\n",
      "Epoch 22/30\n",
      "381/381 [==============================] - ETA: 0s - loss: 0.9966 - accuracy: 0.4647\n",
      "Epoch 22: val_accuracy improved from 0.48047 to 0.48080, saving model to ../models/rnn_classifier.keras\n",
      "381/381 [==============================] - 29s 77ms/step - loss: 0.9966 - accuracy: 0.4647 - val_loss: 1.1698 - val_accuracy: 0.4808\n",
      "Epoch 23/30\n",
      "381/381 [==============================] - ETA: 0s - loss: 0.9612 - accuracy: 0.4814\n",
      "Epoch 23: val_accuracy did not improve from 0.48080\n",
      "381/381 [==============================] - 30s 79ms/step - loss: 0.9612 - accuracy: 0.4814 - val_loss: 1.2147 - val_accuracy: 0.4591\n",
      "Epoch 24/30\n",
      "381/381 [==============================] - ETA: 0s - loss: 0.9486 - accuracy: 0.4754\n",
      "Epoch 24: val_accuracy did not improve from 0.48080\n",
      "381/381 [==============================] - 30s 79ms/step - loss: 0.9486 - accuracy: 0.4754 - val_loss: 1.1859 - val_accuracy: 0.4706\n",
      "Epoch 25/30\n",
      "381/381 [==============================] - ETA: 0s - loss: 0.9430 - accuracy: 0.4796\n",
      "Epoch 25: val_accuracy did not improve from 0.48080\n",
      "381/381 [==============================] - 30s 79ms/step - loss: 0.9430 - accuracy: 0.4796 - val_loss: 1.1798 - val_accuracy: 0.4751\n",
      "Epoch 26/30\n",
      "381/381 [==============================] - ETA: 0s - loss: 0.9364 - accuracy: 0.4858\n",
      "Epoch 26: val_accuracy improved from 0.48080 to 0.48113, saving model to ../models/rnn_classifier.keras\n",
      "381/381 [==============================] - 29s 76ms/step - loss: 0.9364 - accuracy: 0.4858 - val_loss: 1.1703 - val_accuracy: 0.4811\n",
      "Epoch 27/30\n",
      "381/381 [==============================] - ETA: 0s - loss: 0.9136 - accuracy: 0.4936\n",
      "Epoch 27: val_accuracy improved from 0.48113 to 0.48753, saving model to ../models/rnn_classifier.keras\n",
      "381/381 [==============================] - 29s 77ms/step - loss: 0.9136 - accuracy: 0.4936 - val_loss: 1.1638 - val_accuracy: 0.4875\n",
      "Epoch 28/30\n",
      "381/381 [==============================] - ETA: 0s - loss: 0.8920 - accuracy: 0.4955\n",
      "Epoch 28: val_accuracy improved from 0.48753 to 0.48769, saving model to ../models/rnn_classifier.keras\n",
      "381/381 [==============================] - 29s 76ms/step - loss: 0.8920 - accuracy: 0.4955 - val_loss: 1.1611 - val_accuracy: 0.4877\n",
      "Epoch 29/30\n",
      "381/381 [==============================] - ETA: 0s - loss: 0.8821 - accuracy: 0.5071\n",
      "Epoch 29: val_accuracy improved from 0.48769 to 0.50181, saving model to ../models/rnn_classifier.keras\n",
      "381/381 [==============================] - 29s 76ms/step - loss: 0.8821 - accuracy: 0.5071 - val_loss: 1.1370 - val_accuracy: 0.5018\n",
      "Epoch 30/30\n",
      "381/381 [==============================] - ETA: 0s - loss: 0.8833 - accuracy: 0.5005\n",
      "Epoch 30: val_accuracy did not improve from 0.50181\n",
      "381/381 [==============================] - 29s 76ms/step - loss: 0.8833 - accuracy: 0.5005 - val_loss: 1.1833 - val_accuracy: 0.4769\n",
      "Restoring model weights from the end of the best epoch: 29.\n"
     ]
    }
   ],
   "source": [
    "# Training with class weighting and early stopping\n",
    "history = model.fit(\n",
    "    X_train, y_train,\n",
    "    validation_data=(X_test, y_test),\n",
    "    epochs=30,\n",
    "    batch_size=64,\n",
    "    class_weight=class_weight_dict,\n",
    "    callbacks=callbacks\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a03ea58",
   "metadata": {},
   "source": [
    "### 4.5. Learning curves"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "66abaf38",
   "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. Evaluation\n",
    "\n",
    "### 4.1. Make test set predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "7342f545",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "191/191 [==============================] - 6s 16ms/step\n",
      "Test samples: 6094\n",
      "Prediction distribution:\n",
      "  Actual:    Counter({2: 2591, 3: 2563, 1: 678, 4: 202, 0: 60})\n",
      "  Predicted: Counter({2: 2217, 3: 1957, 1: 990, 4: 773, 0: 157})\n"
     ]
    }
   ],
   "source": [
    "# Load best model and get predictions\n",
    "model = tf.keras.models.load_model('../models/rnn_classifier.keras')\n",
    "\n",
    "# Get predicted probabilities and classes\n",
    "y_prob = model.predict(X_test)\n",
    "y_pred = y_prob.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",
    "print(f'  Actual:    {Counter(y_test)}')\n",
    "print(f'  Predicted: {Counter(y_pred)}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "01c774bb",
   "metadata": {},
   "source": [
    "### 4.2. Per-class accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "1397dc2e",
   "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>23</td>\n",
       "      <td>38.3%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-1</td>\n",
       "      <td>678</td>\n",
       "      <td>337</td>\n",
       "      <td>49.7%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>2591</td>\n",
       "      <td>1365</td>\n",
       "      <td>52.7%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>2563</td>\n",
       "      <td>1204</td>\n",
       "      <td>47.0%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>202</td>\n",
       "      <td>129</td>\n",
       "      <td>63.9%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Overall</td>\n",
       "      <td>6094</td>\n",
       "      <td>3058</td>\n",
       "      <td>50.2%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Class  Samples  Correct Accuracy\n",
       "0       -2       60       23    38.3%\n",
       "1       -1      678      337    49.7%\n",
       "2        0     2591     1365    52.7%\n",
       "3        1     2563     1204    47.0%\n",
       "4        2      202      129    63.9%\n",
       "5  Overall     6094     3058    50.2%"
      ]
     },
     "execution_count": 27,
     "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[mask] == i).sum()\n",
    "    accuracy = 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'{accuracy:.1%}'\n",
    "    })\n",
    "\n",
    "# Add overall accuracy\n",
    "overall_acc = (y_pred == y_test).mean()\n",
    "per_class_stats.append({\n",
    "    'Class': 'Overall',\n",
    "    'Samples': len(y_test),\n",
    "    'Correct': (y_pred == y_test).sum(),\n",
    "    'Accuracy': f'{overall_acc:.1%}'\n",
    "})\n",
    "\n",
    "accuracy_df = pd.DataFrame(per_class_stats)\n",
    "accuracy_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "408d5b20",
   "metadata": {},
   "source": [
    "### 4.3. Confusion matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "89df382c",
   "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('Test set classification performance')\n",
    "\n",
    "cm = confusion_matrix(y_test, y_pred)\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": "296a3976",
   "metadata": {},
   "source": [
    "### 4.4. Predicted probability distributions by class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "02d44a72",
   "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",
    "    ax.set_title(f'True class: {class_name}')\n",
    "\n",
    "    # Get predictions for samples of this true class\n",
    "    mask = y_test == i\n",
    "    probs_for_class = y_prob[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_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": "7895bdbc",
   "metadata": {},
   "source": [
    "### 4.5. Evaluation curves"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "91f28172",
   "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[:, 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[:, 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()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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