{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "3a8a2e23",
   "metadata": {},
   "source": [
    "# Sentiment analysis: Twitter\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",
    "## 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": "dcce1685",
   "metadata": {},
   "source": [
    "### Environment setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "1d7906ec",
   "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": "7726801b",
   "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": "6b44da09",
   "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": "dc9b3cef",
   "metadata": {},
   "source": [
    "## 1. Data preparation\n",
    "\n",
    "### 1.1. Load"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "08292db5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\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>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": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_parquet('../data/twitter-2016.parquet')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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": 8,
   "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": 9,
   "id": "3240a32f",
   "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": 10,
   "id": "9b37de69",
   "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": 10,
     "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": 11,
   "id": "0d8efd85",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Vocabulary size: 14769\n",
      "\n",
      "Example vocab entries:\n",
      " tatum\n",
      " carey's\n",
      " sardinia\n",
      " lambily\n",
      " elha's\n",
      " ballon\n",
      " d'or\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": 12,
   "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": 13,
   "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. Load embeddings into dictionary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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",
    "        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": "a2d98274",
   "metadata": {},
   "source": [
    "### 2.2. Create embedding look-up table for our vocabulary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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": [
    "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": 16,
   "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": [
    "# Model hyperparameters\n",
    "hidden_dim              = 64\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\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",
    "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",
    "model.compile(\n",
    "    optimizer=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": 17,
   "id": "9e1fb763",
   "metadata": {},
   "outputs": [],
   "source": [
    "callbacks = [\n",
    "    ModelCheckpoint(\n",
    "        '../models/demo_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/demo_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": 18,
   "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": 19,
   "id": "9fb97944",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "CPU times: user 12min 14s, sys: 46.6 s, total: 13min 1s\n",
      "Wall time: 10min 54s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "# 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=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": "3a03ea58",
   "metadata": {},
   "source": [
    "### 4.5. Learning curves"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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": 21,
   "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 10ms/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: 2065, 3: 1564, 1: 1082, 4: 991, 0: 392})\n"
     ]
    }
   ],
   "source": [
    "# Load best model and get predictions\n",
    "model = keras.saving.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": 22,
   "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>48</td>\n",
       "      <td>80.0%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-1</td>\n",
       "      <td>678</td>\n",
       "      <td>341</td>\n",
       "      <td>50.3%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>2591</td>\n",
       "      <td>1294</td>\n",
       "      <td>49.9%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>2563</td>\n",
       "      <td>978</td>\n",
       "      <td>38.2%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>202</td>\n",
       "      <td>151</td>\n",
       "      <td>74.8%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Overall</td>\n",
       "      <td>6094</td>\n",
       "      <td>2812</td>\n",
       "      <td>46.1%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Class  Samples  Correct Accuracy\n",
       "0       -2       60       48    80.0%\n",
       "1       -1      678      341    50.3%\n",
       "2        0     2591     1294    49.9%\n",
       "3        1     2563      978    38.2%\n",
       "4        2      202      151    74.8%\n",
       "5  Overall     6094     2812    46.1%"
      ]
     },
     "execution_count": 22,
     "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": 23,
   "id": "89df382c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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x4sX1008/qV69eg/Uaaldu7Zq166t8ePHa9myZeratau+/PJLvfTSS/855CQjvr6+6tmzp3r27KnY2Fg1bNhQo0eP1ksvvaRixYpJujMN4r1zmmckM8e+u99Dhw5lqtaff/5ZN27c0DfffKOGDRualp85cybD9hUrVlTFihX1zjvv6I8//lC9evU0Z84cvffee5LuTLPYpEkTNWnSRB9++KEmTJigt99+W1u2bPnP8/0vxYsX14YNGxQZGXnf9P3bb79VUlKS1q5da5bG3h0qda8Hvb5FihSRJB0/flxPPPGE2brjx4+b1meVuwnwvfWdOHFCkkyz7xQvXlyxsbEWX9OMFClSRMePH0+3/O4Qqqw+34ysXLlSjRs31qeffmq2PCoqSv7+/pne392boA8dOnTfa5aZ9yYA/BPDZgALdO7cWampqRo3bly6dbdv31ZUVJSkO39S/2cyV6VKFUkyDZ3JmzevJJm2+S83btwwe+7h4aESJUqY9hcYGKjHH39cc+fO1ZUrV9Jtf+3aNdO/737L6IMcOyAgQA0bNtSCBQt0/vx5s3X/lj7eTSzvbZOcnKxZs2aZtYuJidHt27fNllWsWFEODg6mc4uMjEy3/39eT0t07NhRRqPR9KVZ97pbf0bnEx0drYULF6bbxt3d/YGubY0aNRQYGKg5c+aYnccPP/ygo0ePqlWrVpk9lX91+fJlrVq1yvQ8JiZGn332mapUqaLg4GBJd37Gt23bpg0bNqTbPioqKt1rlRktW7bUzp07tW3bNtOyuLg4zZs3T0WLFlW5cuUeet8PytHRMd3P7YoVKx56zHm1atUUGhqqqVOnpnvN7x4nM+9NAPgnknfAAo0aNdIrr7yisLAw7du3T82aNZOTk5NOnjypFStWaNq0aXrmmWe0ePFizZo1Sx06dFDx4sV169YtzZ8/X15eXmrZsqWkO0MCypUrp+XLl6tUqVLy9fVVhQoV7jt2tly5cnr88cdVvXp1+fr6avfu3Vq5cqX69etnajNz5kzVr19fFStW1Msvv6xixYopIiJC27Zt08WLF01zWVepUkWOjo6aNGmSoqOj5eLiYpq/PCPTp09X/fr1Va1aNfXu3VuhoaE6e/asvvvuO+3bty/DberWrat8+fKpe/fuGjBggAwGg5YsWZKu47R582b169dPnTp1UqlSpXT79m0tWbJEjo6O6tixo6Q74/23bt2qVq1aqUiRIrp69apmzZqlggULmt38+LAaN26sF154QdOnT9fJkyfVokULpaWl6ddff1Xjxo3Vr18/NWvWTM7OzmrTpo1eeeUVxcbGav78+QoMDEzXIatevbpmz56t9957TyVKlFBgYGC6ZF26k8ROmjRJPXv2VKNGjfTss8+apoosWrSoBg8ebPG53atUqVLq1auXdu3apaCgIC1YsEARERFmH0DeeOMNrV27Vq1bt1aPHj1UvXp1xcXF6eDBg1q5cqXOnj37UAm1JA0fPlxffPGFnnrqKQ0YMEC+vr5avHixzpw5o6+//toqX8DUunVrjR07Vj179lTdunV18OBBLV261JSOZ5aDg4Nmz56tNm3aqEqVKurZs6fy58+vY8eO6fDhw6YPQQ/63gSAdGwxxQ1gr/45VeRd8+bNM1avXt3o5uZm9PT0NFasWNH45ptvGi9fvmw0Go3GvXv3Gp999llj4cKFjS4uLsbAwEBj69atjbt37zbbzx9//GGsXr260dnZ+T+njXzvvfeMNWvWNPr4+Bjd3NyMZcqUMY4fP96YnJxs1u6vv/4yduvWzRgcHGx0cnIyFihQwNi6dWvjypUrzdrNnz/fWKxYMaOjo+MDTRt56NAhY4cOHYw+Pj5GV1dXY+nSpY0jR440rc9oqsjff//dWLt2baObm5sxJCTE+Oabbxo3bNhgdrzTp08bX3zxRWPx4sWNrq6uRl9fX2Pjxo2NP/30k2k/mzZtMrZr184YEhJidHZ2NoaEhBifffZZs+kMLZkq0mi8M+Xf+++/byxTpozR2dnZGBAQYHzqqaeMe/bsMbVZu3atsVKlSkZXV1dj0aJFjZMmTTIuWLAg3XmHh4cbW7VqZfT09DRKMk0b+c+pIu9avny5sWrVqkYXFxejr6+vsWvXrsaLFy+mq9nd3T3d6/Luu+9m+DP6T0WKFDG2atXKuGHDBmOlSpWMLi4uxjJlymQ4ReetW7eMI0aMMJYoUcLo7Oxs9Pf3N9atW9f4wQcfmH7e7l7v999/P8PjKYOpIo3GOz+fzzzzjOnnqGbNmsZ169aZtbl7nTKq7X7XoVGjRsby5cvf97zvSkxMNL7++uvG/PnzG93c3Iz16tUzbtu2zdioUSOz6T3vV0NGP2dGo9H422+/GZ988kmjp6en0d3d3VipUiXjxx9/nO7cH+S9CQD3MhiNmbjLBgCQKxQtWlQVKlTQunXrbF0KACATGPMOAAAA2Ak67wAAAICdoPMOAAAA2AnGvAMAAAB2guQdAAAAsBN03gEAAAA7QecdAAAAsBO58htWoxPSbF0CspiLE58zAQB4GK45rLfnVrXffzeyUMKfM7L9GLZCjwgAAACwEznssxgAAAByNQPZsSW4egAAAICdIHkHAACA9RgMtq7ArpG8AwAAAHaC5B0AAADWw5h3i3D1AAAAADtB8g4AAADrYcy7RUjeAQAAADtB8g4AAADrYcy7Rbh6AAAAgJ0geQcAAID1MObdIiTvAAAAgJ0geQcAAID1MObdIlw9AAAAwE6QvAMAAMB6GPNuEZJ3AAAAwE6QvAMAAMB6GPNuEa4eAAAAYCdI3gEAAGA9jHm3CMk7AAAAYCdI3gEAAGA9jHm3CFcPAAAAsBMk7wAAALAexrxbhOQdAAAAsBMk7wAAALAexrxbhKsHAAAA2AmSdwAAAFgPybtFuHoAAACAnSB5BwAAgPU4MNuMJUjeAQAAADtB8g4AAADrYcy7Rbh6AAAAeGRt3bpVbdq0UUhIiAwGg1avXm1al5KSomHDhqlixYpyd3dXSEiIunXrpsuXL5vtIzIyUl27dpWXl5d8fHzUq1cvxcbGmrU5cOCAGjRoIFdXVxUqVEiTJ09+qHrpvAMAAMB6DIbsf2RCXFycKleurJkzZ6ZbFx8fr71792rkyJHau3evvvnmGx0/flxt27Y1a9e1a1cdPnxYGzdu1Lp167R161b17t3btD4mJkbNmjVTkSJFtGfPHr3//vsaPXq05s2bl/nLZzQajZneKoeLTkizdQnIYi5OfM4EAOBhuOawQdJuTSZk+zESNr31UNsZDAatWrVK7du3v2+bXbt2qWbNmjp37pwKFy6so0ePqly5ctq1a5dq1KghSVq/fr1atmypixcvKiQkRLNnz9bbb7+t8PBwOTs7S5KGDx+u1atX69ixY5mqkR4RAAAArMfgkO2PpKQkxcTEmD2SkpKypPzo6GgZDAb5+PhIkrZt2yYfHx9Tx12SmjZtKgcHB+3YscPUpmHDhqaOuyQ1b95cx48f182bNzN1fDrvAAAAyFXCwsLk7e1t9ggLC7N4v4mJiRo2bJieffZZeXl5SZLCw8MVGBho1i5Pnjzy9fVVeHi4qU1QUJBZm7vP77Z5UDnsDykAAADI1TI5Jv1hjBgxQkOGDDFb5uLiYtE+U1JS1LlzZxmNRs2ePduifVmCzjsAAAByFRcXF4s76/e623E/d+6cNm/ebErdJSk4OFhXr141a3/79m1FRkYqODjY1CYiIsKszd3nd9s8KIbNAAAAwHqsMOY9K93tuJ88eVI//fST/Pz8zNbXqVNHUVFR2rNnj2nZ5s2blZaWplq1apnabN26VSkpKaY2GzduVOnSpZUvX75M1UPnHQAAAI+s2NhY7du3T/v27ZMknTlzRvv27dP58+eVkpKiZ555Rrt379bSpUuVmpqq8PBwhYeHKzk5WZJUtmxZtWjRQi+//LJ27typ33//Xf369VOXLl0UEhIiSXruuefk7OysXr166fDhw1q+fLmmTZuWbmjPg2CqSNgFpooEAODh5LipIpt/kO3HSNgw9IHb/vzzz2rcuHG65d27d9fo0aMVGhqa4XZbtmzR448/LunOlzT169dP3377rRwcHNSxY0dNnz5dHh4epvYHDhxQ3759tWvXLvn7+6t///4aNmxY5k5MdN5hJ+i8AwDwcOi85y457OUEAABArpbFY9IfNVw9O7J4wXzVrFJWH07++5vJrl+/pnffflMtmjRQw9rV9EKXp7X5px9tWCUyKyIiQiOGDVXDurVUs1oldWzfRocPHbR1WXhIX325TM90aKO6Naupbs1qeuG5/9Nvv/5i67LwkPbs3qX+r72qpo/XV+XypbV500+2LgkW+HT+XD3XuaPqPFZVjzeoo0H9X9PZM6dtXRaQKSTvduLIoYP6ZuVylShV2mz5mHeG69atW5oydaZ88uXT+h/W6a03B2vxshUqXaacjarFg4qJjlaP559VjZq1NHPOfOXzzafz587Jy8vb1qXhIQUGBWvg4KEqXKSIjEajvl2zWgP79dXyr1epRImSti4PmZSQEK/SpUur/dMdNWRgP1uXAwvt3rVT//dsV5WvWFGpt1P18bQP9erLvfTN2u+UN29eW5f36LDCPO+5mc077xcvXpSPj4/ZgH7pzrQ8d79K9lEXHx+nkW+9obdHjdWC+XPM1h3Yv0/D3h6l8hUrSZJ6vdxHX3y+WEePHKbzbgcWfDpfQcHBGjf+7299K1iwkA0rgqUeb/yE2fP+Awfrqy+/0IH9++i826H6DRqpfoNGti4DWWT2vE/Nno8dP1GNG9TR0SOHVb3GYzaqCsgcmw2buXLlimrWrKkiRYrIx8dH3bp1U2xsrGl9ZGRkhnf+PoomTxineg0aqWbtuunWVapcRRs3/KDo6CilpaXpx/XfKTkpWdVr1LRBpcisX7ZsVvnyFTR08AA93qCOOndsr69XfGXrspBFUlNT9cP33ykhIV6VK1e1dTkA/iH21i1Jkpc3f+20Kjub5z2nsVnyPnz4cDk4OGjHjh2KiorS8OHD1bhxY/3444+myepz4UQ4mfbj+u90/NgRLVq6IsP1EyZ/pLeGDdGTjerIMU8eubq6avKHH6tQ4SJWrhQP4+LFC/pq+Rd6oXtP9er9qg4fPKhJYe/JyclJbdt3sHV5eEgnTxzXC891UXJykvLmzauPps9U8RIlbF0WgHukpaVp8qQJqlK1mkqWLGXrcoAHZrPO+08//aRVq1apRo0akqTff/9dnTp10hNPPKFNmzZJkgwPMCYqKSlJSUlJ5svSnLL0K3FtJSL8ij6cHKaP53x63/OZM2u6Ym/d0oy5C+Tjk0+/bNmkt94crHkLP1cJfhnleGlpRpWvUEEDBt35koayZcvp1KmTWvHVl3Te7VjRoqH66uvVio29pY0/btDIt4bp00Wf04EHcpAJ743RXydPatGSZbYu5dHDmHeL2OzvCtHR0WZfB+vi4qJvvvlGRYsWVePGjXX16tUH2k9YWJi8vb3NHh++PzG7yraqo0cOKzLyhro921F1qldQneoVtHfPLi3/4nPVqV5BFy+c14ovl+qd0e+pZq06KlW6jF5+ta/Kli+vFcv5ZWQPAgICVKx4cbNlxYoV05Url21UEbKCk7OzChcponLlK2jg4NdVqnQZLf38M1uXBeB/Jrw3Vlt/+VnzFy5WUHCwrcsBMsVmyXuxYsV04MABlSz59w1cefLk0YoVK9SpUye1bt36gfYzYsSIdF8tm5jmlKW12spjteroi5VrzJaNHfW2ioaGqlvPl5SYmChJcnAw/wzm4OAoYxpfVGUPqlStprNnzpgtO3f2rEJCCtioImSHtLQ0pfzva7QB2I7RaFTY+HHavGmjPl20hAkCbCWXj0nPbja7ek899ZTmzZuXbvndDnyVKlUeaD8uLi7y8vIye+SGITOS5O7uruIlSpk93Nzc5O3to+IlSqlo0VAVKlRYYe+9q8MHD+jihfNa+tlC7dz+hxo1bmLr8vEAnu/WXQcP7Ncn8+bo/Llz+n7dt1q58iv937PP2bo0PKRpH03Rnt27dOnSRZ08cVzTPpqi3bt2qmXrNrYuDQ8hPi5Ox44e1bGjRyVJly5e1LGjR3XlMn8ds0cTxo3R9+vWauLkKXLP667r167p+rVrpjAMsAcGo43uCr19+7bi4+Pl5eVlWvb777+rRo0acnFx0e3bt3Xp0iUVKZL5Gy+jE3Jv6vxqr24qVbqMhrz5liTp/Lmzmjn9Q+3/c6/i4+NVsHBhPd+tp1q2bmfjSrOWi1Pu/ZT+y89bNH3qhzp/7qwKFCyoF7r1VMdOnW1dFh7SuyPf0s7t23Xt2lV5eHqqVKnS6tnrZdWpW8/WpeEh7Nq5Qy/17JZuedt2HTRuQu4YovkoqVy+dIbLx74XpnYdnrZyNdbjavOJwc25tZmV7cdI+Pa1bD+Grdis854RLy8v7du3T8WKFbNoP7m58/6oys2ddwAAshOd99wlR72cOehzBAAAALIDs81YJEd13gEAAJDLccOqRXLU1Zs7d66CgoJsXQYAAACQI+Wo5P2555hhAwAAIFdj2IxFclTyDgAAAOD+clTyDgAAgFyOMe8W4eoBAAAAdoLkHQAAANbDmHeLkLwDAAAAdoLkHQAAAFZjIHm3CMk7AAAAYCdI3gEAAGA1JO+WIXkHAAAA7ATJOwAAAKyH4N0iJO8AAACAnSB5BwAAgNUw5t0yJO8AAACAnSB5BwAAgNWQvFuG5B0AAACwEyTvAAAAsBqSd8uQvAMAAAB2guQdAAAAVkPybhmSdwAAAMBOkLwDAADAegjeLULyDgAAANgJkncAAABYDWPeLUPyDgAAANgJkncAAABYDcm7ZUjeAQAAADtB8g4AAACrIXm3DMk7AAAAYCdI3gEAAGA1JO+WIXkHAAAA7ATJOwAAAKyH4N0iJO8AAACAnSB5BwAAgNUw5t0yJO8AAACAnSB5BwAAgNWQvFuG5B0AAACwEyTvAAAAsBqSd8uQvAMAAAB2guQdAAAA1kPwbhGSdwAAAMBOkLwDAADAahjzbhmSdwAAAMBO5MrkPTohxdYlIIuduxBv6xKQhUID3G1dArKQd95c+b+SR5YDqWgulLNeU5J3y5C8AwAAAHaCuAQAAABWQ/JuGZJ3AAAAwE6QvAMAAMBqSN4tQ/IOAAAA2AmSdwAAAFgPwbtFSN4BAAAAO0HyDgAAAKthzLtlSN4BAAAAO0HnHQAAAFZjMBiy/ZEZW7duVZs2bRQSEiKDwaDVq1ebrTcajRo1apTy588vNzc3NW3aVCdPnjRrExkZqa5du8rLy0s+Pj7q1auXYmNjzdocOHBADRo0kKurqwoVKqTJkyc/1PWj8w4AAIBHVlxcnCpXrqyZM2dmuH7y5MmaPn265syZox07dsjd3V3NmzdXYmKiqU3Xrl11+PBhbdy4UevWrdPWrVvVu3dv0/qYmBg1a9ZMRYoU0Z49e/T+++9r9OjRmjdvXqbrNRiNRmPmTzNnOx+ZZOsSkMXOXY+3dQnIQqEB7rYuAVnIOy+3T+UmDoxHznXcnXPWa1qo75psP8aFme0eajuDwaBVq1apffv2ku6k7iEhIXr99dc1dOhQSVJ0dLSCgoK0aNEidenSRUePHlW5cuW0a9cu1ahRQ5K0fv16tWzZUhcvXlRISIhmz56tt99+W+Hh4XJ2dpYkDR8+XKtXr9axY8cyVSPJOwAAAHKVpKQkxcTEmD2SkjIf7p45c0bh4eFq2rSpaZm3t7dq1aqlbdu2SZK2bdsmHx8fU8ddkpo2bSoHBwft2LHD1KZhw4amjrskNW/eXMePH9fNmzczVROddwAAAFiPIfsfYWFh8vb2NnuEhYVlutTw8HBJUlBQkNnyoKAg07rw8HAFBgaarc+TJ498fX3N2mS0j3uP8aD4WycAAABylREjRmjIkCFmy1xcXGxUTdai8w4AAACrscY87y4uLlnSWQ8ODpYkRUREKH/+/KblERERqlKliqnN1atXzba7ffu2IiMjTdsHBwcrIiLCrM3d53fbPCiGzQAAAAAZCA0NVXBwsDZt2mRaFhMTox07dqhOnTqSpDp16igqKkp79uwxtdm8ebPS0tJUq1YtU5utW7cqJSXF1Gbjxo0qXbq08uXLl6ma6LwDAADAanLaPO+xsbHat2+f9u3bJ+nOTar79u3T+fPnZTAYNGjQIL333ntau3atDh48qG7duikkJMQ0I03ZsmXVokULvfzyy9q5c6d+//139evXT126dFFISIgk6bnnnpOzs7N69eqlw4cPa/ny5Zo2bVq6oT0PgmEzAAAAeGTt3r1bjRs3Nj2/26Hu3r27Fi1apDfffFNxcXHq3bu3oqKiVL9+fa1fv16urq6mbZYuXap+/fqpSZMmcnBwUMeOHTV9+nTTem9vb/3444/q27evqlevLn9/f40aNcpsLvgHxTzvsAvM8567MM977sI877kL87znPjltnveiA9dl+zHOTmud7cewFX7jAgAAwGqsccNqbsaYdwAAAMBOkLwDAADAegjeLULyDgAAANgJkncAAABYDWPeLUPyDgAAANgJkncAAABYDcm7ZUjeAQAAADtB8g4AAACrIXi3DMk7AAAAYCdI3gEAAGA1jHm3DMk7AAAAYCdI3gEAAGA1BO+WIXkHAAAA7ATJOwAAAKyGMe+WIXkHAAAA7ATJOwAAAKyG4N0yJO8AAACAnSB5BwAAgNU4OBC9W4LkHQAAALATJO8AAACwGsa8W4bkHQAAALATJO8AAACwGuZ5twzJOwAAAGAnSN5zmAN/7taKpYt04vhRRV6/ptETp6peoycybDt10jh9t3qF+gx8Q093ecG0/PkOLRQRftmsba8+A9WlW69srR3pbfn+a/38wze6EXFFkhRSuJjadHlRFWvUNWtnNBo1bfRgHdq7XX3fmqSqdRqZ1i2bO0Wnjh7Q5XOnlb9QUb07fYlVzwF/O/Dnbi3/fJFOHj+iG9evacykqarfqIlp/eL5s7Tlpx90LSJCeZzyqFTpcnrx1QEqW6GSqc3ShfO0/Y+t+uvEceVxctLan/6wxangPlYu/0Irv/pSVy5fkiQVK15CL73ymuo1aChJ6v1iN+3dvctsm6c7/Z/eGjna2qXiASz4ZK42/7RRZ8+clourqypXrqoBg19X0dBiZu327/tTMz+eqkMHD8jRwUGlSpfVzLmfyNXV1UaV524E75ah857DJCYmqFjJ0mreuoPGjBh833a//bxJRw8fkJ9/YIbru7/cVy3bdTQ9d8ubN8trxX/L5x+ojt37KiikoIxG6Y9N32nG+Dc1aupnKlDk7/95bFzz5b/+Nqv/ZBudOX5YF8+eskbZuI+EhAQVL1lKT7XpoHeHD0q3vmDhIur/+lvKX6CgkpOStPKLJRo28BV9tvI7+eTzlSSl3E5RoyeaqVyFyvrh21VWPgP8l8CgYPUbNESFCxeR0WjUurVr9PrAflr61dcqXqKkJKlDx056pW9/0zaurm62Khf/Yc/uXerc5TmVr1BRqampmjHtI732ykv6evU60/8X9+/7U/37vKyevXpr2Ih35OjoqBPHj8vBgcEJyJlybOc9IiJCc+fO1ahRo2xdilXVrNNANes0+Nc2169GaOaHYQqbOkfvvN4vwzZ58+aVr59/dpSITKhS0/y1fLpbH/38wyqdPn7I1Hk/f/qENq5epnc+WqTXu7VKt4/nXnldkrQm+iaddxurVbeBatW9//uzSXPz16/PoDf0w7ff6PSpE6r2WG1JUo+X+0qS1q9bnW114uE1fLyx2fO+Awbp66++1MED+02dd1dXV/n7B9iiPGTSzDmfmD0f816YmjSqqyNHDqt6jcckSVPen6guz72gni/1NrX7ZzKPrMWYd8vk2I+V4eHhGjNmjK3LyHHS0tI0aexb6tS1h4oWK3Hfdl8uWaCnmzfQq90666vPFyr19m0rVomMpKWmaufWjUpOTFDxMhUlSUmJiZr/wSg99+ob8s7nZ+MKkZVSUlL03eqVcvfwVPGSpW1dDh5CamqqNvzwnRIS4lWpchXT8h++X6cmDeuoc4c2mjHtQyUmJNiuSGTKrdhbkiRvb29JUuSNGzp0YL98fX3V4/kuatqonl7q8bz+3LvHlmUC/8pmyfuBAwf+df3x48etVIl9Wb5kgRwc86hD5673bdO+83MqWbqsPL28dfjAPi2YM02RN67r1YFvWLFS3HXx7CmFvfGyUpKT5eLmptfenqSQwqGSpOWfTFXxMhVVtXZDG1eJrLLtt1/03sg3lJSYKF//AE2ePk/ePvlsXRYy4dSJE+r5wrNKTk6SW968en/qxypW/E5Y0qJla+XPH6KAgECdPHlcH380RefOntH7H31s46rxX9LS0vTBpAmqUrWaSpQsJUm6ePGCJGnu7Bka9PqbKl2mrNatXaNXX+qhFau+VeEiRW1Yce5F8m4Zm3Xeq1SpIoPBIKPRmG7d3eUP8uImJSUpKSnpH8skFxeXLKs1pzhx7IhWfbVUsxYt/9dr88yz3Uz/LlailJycnDR10ji92GegnJ2drVEq7hFcoIhGTftMCfFx2vP7Zi34aKzeDJutq1cu6NiB3Ro17TNbl4gsVKX6Y5r32UpFR9/Ud2u+1ri3h2rGp0uVz5e/rNiLIqFFtWzFN4qNjdWmjRs0+p0RmrfgMxUrXkJPP9PZ1K5EqVLy9w9Qn5d76uKF8ypYqLANq8Z/mTh+rP46dVILFi8zLTMa0yTduem4XYc794mVKVtOO3ds05pVX6v/oNdtUivwb2zWeff19dXkyZPVpEmTDNcfPnxYbdq0+c/9hIWFpRteM+jNtzV42MgsqTMnObRvj6JuRqprh+amZWmpqZr78RR9s3ypPl+1PsPtypSvqNTU24q4ckmFioRaq1z8Tx4nJwWFFJIkFS1RRmdPHtFPa5fL2cVF18IvaUCXJ83az5o4QiXLVdabYbNtUS4s5OaWVwUKFVaBQoVVrkJldXumlX74dpWe6/6SrUvDA3JyclahwkUkSWXLldeRQwf1xdIlentU+qGcFSremUnownk67znZxPFj9esvP+uTRZ8rKDjYtNz/f5M+FPvHMNTQYsUVfuWKVWt8lBC8W8Zmnffq1avr8uXLKlKkSIbro6KiMkzl/2nEiBEaMmSI2bKIuCwpMcdp+lQbVf3fTW93jRjUR02faq3mrdrdd7u/Tt65a96HMdU5gtFo1O2UZLXr+rIaNGtrtu7dfl31f70GqnLNf79pGfYjzZimlORkW5cBC6SlGe/7Gh4/fkyS5B/ADaw5kdFo1KQJ47Rl80+av+AzFShY0Gx9SIECCggM1LmzZ8yWnz93VnXr83sYOZPNOu+vvvqq4uLu38suXLiwFi5c+J/7cXFxSTdEJup20n1a53wJ8fG6dPG86Xn45Us6deKYvLy8FRicX17ePmbt8+TJI19fP1OifuTgfh07fECVq9dU3rzuOnJov+ZMm6wmzVvJ08vLmqcCSV8vnqWK1evINyBIiQnx2vHLjzp+cK8GjZkq73x+Gd6k6hcQrIDgENPziMsXlJSYoJibkUpOTtL50yckSSGFQpXHyclq54L7vz89vbzl5e2tpYvmq26Dx+XnF6Do6Jtas/JLXb92VY2aNDNtExF+RbdionU14orS0lJ16sSdzl+BgoWZ0jUHmDHtQ9Wt10DB+UMUHxen9T+s057dO/XxnPm6eOG81n+/TvUaNJK3t49OnjiuD9+fqGrVa6hkKW5Kzokmjh+rH75fp4+mzVRed3ddv35NkuTh4SlXV1cZDAZ169FLc2d9rFKlS6tUmbJat2a1zp45rckfTrNx9bkXY94tY7POe4cOHdIt+/3331WjRg25uLgoX7586t69uw0qs60Txw5raN+/v0xpzvT3JUlPtmyrN0e+95/bOzk7actP6/XZp3OUkpys4JACevr/XlDHe8bBw3puRd/Upx+NUXTkDbm5e6hg0eIaNGaqylet9cD7WPzxBJ049Kfp+diBd17LiZ98I/+gkPtthmxw/Ohhvd73RdPz2dPuvD+btWyrwcNG6cLZMxr9/VrFRN2Ul7ePSpctr6lzFpvNDLVo3gz9+P1a0/NXunWSJE2ZuUBVqj9mpTPB/URG3tC77wzX9WvX5OHhqZKlSunjOfNVu049hYdf0c7t2/TF558pISFBQcHBeqLpk+rVu4+ty8Z9rFj+hSTp5RfN/x84etwEtW3/tCSp6wvdlZyUpCmTJyo6JlqlSpXWrHkLVIhhUMihDMYHGZtiJV5eXtq3b5+KFbNsftXzkfabvCNj567H27oEZKHQAHdbl4As5J03x35lCB6CA6loruPunLNe02pjN2f7MfaOyvjb6XODHDXPew76HAEAAADkOMQlAAAAsBrGvFsmRyXvc+fOVVBQkK3LAAAAAHKkHJW8P/fcc7YuAQAAANmI4N0yOSp5BwAAAHB/OSp5BwAAQO7GmHfLkLwDAAAAdoLkHQAAAFZD8G4ZkncAAADATpC8AwAAwGoY824ZkncAAADATpC8AwAAwGoI3i1D8g4AAADYCZJ3AAAAWA1j3i1D8g4AAADYCZJ3AAAAWA3Bu2VI3gEAAAA7QfIOAAAAq2HMu2VI3gEAAAA7QfIOAAAAqyF4twzJOwAAAGAnSN4BAABgNYx5twzJOwAAAGAnSN4BAABgNSTvliF5BwAAAOwEyTsAAACshuDdMiTvAAAAgJ0geQcAAIDVMObdMiTvAAAAgJ0geQcAAIDVELxbhuQdAAAAsBMk7wAAALAaxrxbhuQdAAAAVmMwZP8jM1JTUzVy5EiFhobKzc1NxYsX17hx42Q0Gk1tjEajRo0apfz588vNzU1NmzbVyZMnzfYTGRmprl27ysvLSz4+PurVq5diY2Oz4pKZofMOAACAR9akSZM0e/ZszZgxQ0ePHtWkSZM0efJkffzxx6Y2kydP1vTp0zVnzhzt2LFD7u7uat68uRITE01tunbtqsOHD2vjxo1at26dtm7dqt69e2d5vQbjvR8rconzkUm2LgFZ7Nz1eFuXgCwUGuBu6xKQhbzzMgIzN3FgSEOu4+6cs17TJ2dsz/ZjbOxX+4Hbtm7dWkFBQfr0009Nyzp27Cg3Nzd9/vnnMhqNCgkJ0euvv66hQ4dKkqKjoxUUFKRFixapS5cuOnr0qMqVK6ddu3apRo0akqT169erZcuWunjxokJCQrLs3EjeAQAAkKskJSUpJibG7JGUlHG4W7duXW3atEknTpyQJO3fv1+//fabnnrqKUnSmTNnFB4erqZNm5q28fb2Vq1atbRt2zZJ0rZt2+Tj42PquEtS06ZN5eDgoB07dmTpudF5BwAAgNVYY8x7WFiYvL29zR5hYWEZ1jN8+HB16dJFZcqUkZOTk6pWrapBgwapa9eukqTw8HBJUlBQkNl2QUFBpnXh4eEKDAw0W58nTx75+vqa2mQV/tYJAACAXGXEiBEaMmSI2TIXF5cM23711VdaunSpli1bpvLly2vfvn0aNGiQQkJC1L17d2uUmyl03gEAAGA11pgq0sXF5b6d9X964403TOm7JFWsWFHnzp1TWFiYunfvruDgYElSRESE8ufPb9ouIiJCVapUkSQFBwfr6tWrZvu9ffu2IiMjTdtnFYbNAAAA4JEVHx8vBwfzLrGjo6PS0tIkSaGhoQoODtamTZtM62NiYrRjxw7VqVNHklSnTh1FRUVpz549pjabN29WWlqaatWqlaX1krwDAADAahxy1uQ3atOmjcaPH6/ChQurfPny+vPPP/Xhhx/qxRdflHTnLwWDBg3Se++9p5IlSyo0NFQjR45USEiI2rdvL0kqW7asWrRooZdffllz5sxRSkqK+vXrpy5dumTpTDMSnXcAAAA8wj7++GONHDlSr732mq5evaqQkBC98sorGjVqlKnNm2++qbi4OPXu3VtRUVGqX7++1q9fL1dXV1ObpUuXql+/fmrSpIkcHBzUsWNHTZ8+PcvrZZ532AXmec9dmOc9d2Ge99yFed5zn5w2z3vLOTuz/Rjfv1oz249hK4x5BwAAAOwEcQkAAACshj/uWCZXdt6dHfmDQm6z71qUrUtAFmr2fyNtXQKy0Aczhtq6BGShtmXz/3cj2BV33webMhH2IVd23gEAAJAzGUT0bgkiagAAAMBOkLwDAADAanLaPO/2huQdAAAAsBMk7wAAALAaA9PNWITkHQAAALATJO8AAACwGoJ3y5C8AwAAAHaC5B0AAABW40D0bhGSdwAAAMBOkLwDAADAagjeLUPyDgAAANgJkncAAABYDfO8W4bkHQAAALATJO8AAACwGoJ3y5C8AwAAAHaC5B0AAABWwzzvliF5BwAAAOwEyTsAAACshtzdMiTvAAAAgJ0geQcAAIDVMM+7ZUjeAQAAADtB8g4AAACrcSB4twjJOwAAAGAnSN4BAABgNYx5twzJOwAAAGAnSN4BAABgNQTvliF5BwAAAOwEyTsAAACshjHvliF5BwAAAOzEAyXva9eufeAdtm3b9qGLAQAAQO7GPO+WeaDOe/v27R9oZwaDQampqZbUAwAAAOA+HqjznpaWlt11AAAA4BHAmHfLMOYdAAAAsBMPNdtMXFycfvnlF50/f17Jyclm6wYMGJAlhQEAACD3IXe3TKY773/++adatmyp+Ph4xcXFydfXV9evX1fevHkVGBhI5x0AAADIJpkeNjN48GC1adNGN2/elJubm7Zv365z586pevXq+uCDD7KjRgAAAOQSDgZDtj9ys0x33vft26fXX39dDg4OcnR0VFJSkgoVKqTJkyfrrbfeyo4aAQAAAOghOu9OTk5ycLizWWBgoM6fPy9J8vb21oULF7K2OgAAAOQqBkP2P3KzTI95r1q1qnbt2qWSJUuqUaNGGjVqlK5fv64lS5aoQoUK2VEjAAAAAD1E8j5hwgTlz59fkjR+/Hjly5dPffr00bVr1zRv3rwsLxAAAAC5h8FgyPZHbpbp5L1GjRqmfwcGBmr9+vVZWhAAAACAjD3UPO8AAADAw8jlwXi2y3TnPTQ09F//HHH69GmLCgIAAACQsUx33gcNGmT2PCUlRX/++afWr1+vN954I6vqemTt37tbX3y+UCeOHdGN69f03uRpavB4E9P6rVs2as03X+nE0SOKiYnWJ5+vVMlSZUzrY6KjtWDeTO3e8YciIq7Ixyef6jd6Qr1e7S8PD09bnNIjbcfqJdq1dqnZMp/ggnp+widKjL2lHWuW6MKhPboVeU1unt4qVrWOanXoLpe87mbbHP3tR+378RtFhV+Ss1telajRQI1e6GfNU3kk1atWXIO7NVW1coWVP8BbnQfP07c/H5Ak5cnjoNGvtVHz+uUVWtBPMbGJ2rzjmEZOX6sr16JN+6hSpqDeG9he1csXVmqqUas37dOwKV8rLiE53fF8vd21c/lwFQjKp+AGbyg6NsFq5/oo2r56iXau/dxsWb7ggnphwqdKjI3R9jVLdP7QXt2KvCo3T28Vr1pXtf/x/rx146q2LPlYF4/tl5OLq8rWe1J1O74oB0dHa58OJB34c7e+WrpIJ48f1Y3r1zRm4lTVa/SEaf3iT2bp543rde1quPI4Oalk6XJ68dX+Klu+kqnNyeNHNH/mVB0/elgODg5q0Lip+gx4Q25589rilHKl3D4Pe3bLdOd94MCBGS6fOXOmdu/ebXFBj7qExASVKFlaLdt00Mhhg9KvT0hQxcrV1LhJc70/YXS69devX9WN61fVZ+BQFQ0tpogrVzRl4ljduH5NYyd+lP0ngHR8CxRRu6FhpucODnf+px4XdUNxUTdU7/9elm9I4TudgM8+VlxUpJ7q+46p/Z8bvta+Dd+obueXFFystFKSEnXreoTVz+NR5O7mooMnLumzNdu0/MPeZuvyujqrStlCmjj/Bx04cUn5vPLqgzee0Yqpr6h+18mSpPwB3vpuTn+t/HGvBk/8Sl7urnr/jY6aP/YFPffGp+mON+fd53Tw5GUVCMpnlfPDnfdnh6ETTc//fn9GKi7qhuqbvT+nKzbqhlr1HSlJSktL1dqpI5XXO586vfWR4qIi9eMn78vB0VF1O75ok/N51CUmJqhYydJq0bqDRo8YnG59wUJF1O/1t5S/QEElJyXq6y+XaNjAV/XZinXyyeer69eu6s3+vdWoaXP1f32E4uLiNHvqZE1+7x29O+FDG5wRkF6WjXl/6qmnNGLECC1cuDCrdvlIql23gWrXbXDf9c1btpUkXbl8KcP1xYqX1LhJU03PCxQsrJf6DND4d4fr9u3bypOH2xyszcHBUe7evumW+xUsqpb/6wRIkndgiOo83V0/zn9faampcnB0VGLcLe1Y9ZlaDRitQuWqmtr6FypmldofdT/+fkQ//n4kw3UxsYlq3WeG2bLBE7/Sb0vfVKHgfLoQflNPNaiglNupGhT2lYxGoySp//jl2r3iLRUr5K/TF66btn25U315e+bVhHk/qEX98tl3UjDzb+/PVn1HmZ77BIaoztM9tGH+ZNP78/yhvYq8fF4dhk5UXu98CihcXHU6dNPvKz9VrXYvyDGPkzVPBZJq1mmgmnXu///QJs1bmT1/deAb+uHbVTp96oSqPVZb23/fKsc8eTRg6Num77QZ+OY76v3CM7p04bwKFCqcrfU/KgjeLZNlPbmVK1fK1zf9L8B/c/36dS1YsEDbtm1TeHi4JCk4OFh169ZVjx49FBAQkFXlPdLiYm8pr7sHHXcbiYq4pAWDn1MeJ2cFlyirOh17ytMvMMO2SQlxcnbNa/qT+4XDf8qYlqa4mze09O2XlZyYoPzFy6pel97y9OX9kdN4ebopLS1NUbfuDHdxcc6jlJRUU8ddkhKS7gyXqVuluKnzXqZYsEa8/JQadftARQv4W7/wR1hUxCV9OvhZOTo5K3+Jsqrb8cUHfn+G/3VEfgWLKq/3338pKVyhhrYs+Vg3Lp1TYJESVjkHPJyUlBR9t3ql3D08Vbxk6f8tSzb7MkpJcnFxlSQdOvAnnXfkCJme571q1aqqVq2a6VG1alXlz59fb731lt56660H3s+uXbtUqlQpTZ8+Xd7e3mrYsKEaNmwob29vTZ8+XWXKlGEYThaIirqpzxbMVZv2z9i6lEdScLEyatrrdbUd8p4adeunmGvh+mbiUCUnxKdrm3ArWru//ULlGz1lWhZz7YqMRqN2f/el6j/7ip567W0lxt3Smg9GKPV2ijVPBf/BxTmP3hvQTl+t36NbcYmSpJ93HleQn5cGd2sipzyO8vF003sD2kmSggO8JUnOTnm0OKyH3pq6WhfCb9qs/kdRcLEyerLXULUbMl6Nu/VX9LVwrZz4+n3fn7u+XaYK97w/46JvKq+X+RCnvF4+kqT4aF7LnGr7b7+o9RO11LJRDX395eeaNG2uvH3uvI5Vq9dU5I0bWv75QqWkpOhWTIw+mT1VknTj+jUbVp27MM+7ZTIdxbZr187sojg4OCggIECPP/64ypQp8y9bmuvfv786deqkOXPmpLvIRqNRr776qvr3769t27b9636SkpKUlJT0j2UOcnFxeeBacqu42FgNH/yaioQWV8/er9m6nEdSkUqPmf7tX6iYgouV0eI3uunUrq0q17CFaV1yQpzWTR2lfPkLq2a7503LjUaj0lJvq+FzfVS4QnVJUvNXhmvB4Od08dh+Fanw9/cuwHby5HHQ55N7yWAwaMCE5ablR0+H6+VRSzTx9ac1tn9bpaaladYXvyj8eoyMaWmSpHED2ur4mQh9+f0uW5X/yCqawftz4Rsv6OSurSp/z/szKSFOa6eOlG/+wqrV7gVblIosVLn6Y5q7eIWio2/q+zXf6L13hurjT5Yqn6+fihYroTdHjtOc6R/o0znT5ejgoPadnlM+Xz+zNB6wpUx33kePHp0lB96/f78WLVqU4acjg8GgwYMHq2rVqhlsaS4sLExjxowxW/b6sHc0dMSo+2zxaIiPi9MbA19R3rzuem/yNOVh7GWO4JLXQz5BBRR19bJpWXJCvNZ++I6cXN3Usv8oOd4zvCnv/8bi+ob8/adaNy8fuXp6KfYGKVBOkCePg5ZO6qXC+fPpqd4fm1L3u5av363l63cr0NdTcQlJMhqlAc8/oTMXb0iSGj1WShVKhKjDriqSZPqdeHHLRE36dIPem/O9Vc/nUXbn/Vkw3ftzzYdvy9nVTa36v2v2/nT3zqeIM8fN9hEfEyVJZkNpkLO4ueVVgUKFVaBQYZWrUFndO7XWD9+u0nPdX5J0Z1x8k+atdDPyhlxd3SSD9PWXS5Q/pKCNK889+BhkmUx33h0dHXXlyhUFBpqPCbxx44YCAwOVmpr6QPsJDg7Wzp0775vW79y5U0FBQf+5nxEjRmjIkCFmy24mPto/FnGxsRo64BU5OztpwpSP+StEDpKcmKDoa1dU2vvO9J/JCXFa8+HbcszjpFYDRiuPk7NZ+/wly0mSboZflMf/xrgnxt5S4q2Y+47LhfXc7bgXLxygFr2nKzI67r5tr0bekiR1a1dbickp2rT9mCTp2aGfyM3l7w/X1csX0bwxz6tpr6k6fYEPaNZ05/15WWX+9/5Muuf92XrAmHTvz+Di5bRr3ZeKj4kyDZe5cHivnN3ymn3gRs6WZkxTSkr6qVvz+fpJkn74dpWcnZ1VvWZta5eWa+X2YS3ZLdOd93tvvLpXUlKSnJ2dM1yXkaFDh6p3797as2ePmjRpYuqoR0REaNOmTZo/f74++OCD/9yPi4tLus5pvNF+xwLHx8fr0sXzpudXLl/SyRPH5OXlraDg/IqJjlZExBXduHZVknTh3BlJkq+vv/z8/f/Xce+txMQEvTN2muJi4xQXe6dD4ZMvnxyZe9iqfls+X6FVasnTL1BxUZHauXqJDAZHlar1+J2O+5S3dTs5Uc1eflPJifFKTrwz1tbN01sODo7KF1xQoVXr6Ncv5qhx94Fyds2rbV8vVL78BVWgTGUbn13u5+7mrOKF/r4xuGgBP1UqVUA3Y+J15Xq0lr3/kqqWKaSnB86Ro4NBQX53vkshMjpeKbfvBBmv/l9Dbd9/WrHxyWpSu4wmDGqvkR+vMc3hfubidbNj+vl4SJKOnQ5nnvds9uvyeQqtUltefoGKi7qh7fe8P5MS4rR6ylu6nZx03/dn4QrV5BtSWD/On6x6nXopPuamtq1apEpPtEnX0Yd1JGTw/9BTJ47J08tbXt7eWrZovuo0eFx+fgGKjo7SmpVf6vq1q2r0RDPTNqtXfKHylSrLzS2v9uzcrnkzPtRLrw2Uh6eXLU4JSOeBO+/Tp0+XdOfT0ieffCIPDw/TutTUVG3dujVTY9779u0rf39/ffTRR5o1a5YpsXd0dFT16tW1aNEide7c+YH3l1scP3pIg/r8PT/wzKl35otu0aqdRrw7Xr//ukUTx/49B/iYt+98MVaPl/qoZ+++OnH8iI4cuvMlMs893dJs31+u3qD8IQWy+xRwj7ib17VhzkQlxt2Sm6e3QkqWV6d3PpKbl48uHtuviNN30tclw83nhO42eZG8/IMlSU++NFS/fjFX66aOkgwGFShdUW2GjDf78z2yR7VyRfTjJ39/t8XkoR0lSUvWbtd7c75Xm8fvfLHLzuUjzLZr9tI0/brnpCSpRoUieufVVvLI66zjZyPUb/wX+uI7xrfnBLE3r2vDnDAl3PP+7PzOVOX9x/vzs+E9zbbrMXmxvPyD5eDgqDYDx2rLko+1YsJg5XF2Vdl6TVW7fXdbnA4kHT92WEP79jI9nzP9fUlSs5ZtNejNkbpw7qx+/P51xUTflJe3j0qVLa+PZi9S0WJ/zwx07MhBLf5klhIT4lWoSKgGDRupJ59qY/Vzyc0cCN4tYjDeL0r/h9DQUEnSuXPnVLBgQbME19nZWUWLFtXYsWNVq1atTBeRkpKi69fvpE/+/v5ycrJsfHZ4tP0m78jYikMXbV0CstCb/abYugRkoQ9mDLV1CchCbcvmt3UJyGKFfHPW8NlBa45l+zGmtnvwQNnePHB0d+bMneEZjRs31jfffKN8+bLuZhwnJyflz88vCwAAgNyO5N0ymf67+5YtW7KjDgAAAAD/IdPTsnTs2FGTJk1Kt3zy5Mnq1KlTlhQFAACA3IkvabJMpjvvW7duVcuWLdMtf+qpp7R169YsKQoAAABAepkeNhMbG5vhlJBOTk6KiYnJkqIAAACQOzHm3TKZTt4rVqyo5cuXp1v+5Zdfqly5cllSFAAAAID0Mp28jxw5Uk8//bT++usvPfHEE5KkTZs2admyZVq5cmWWFwgAAIDcI5cPSc92me68t2nTRqtXr9aECRO0cuVKubm5qXLlytq8ebN8fX2zo0YAAAAAeojOuyS1atVKrVq1kiTFxMToiy++0NChQ7Vnzx7TN6UCAAAA/+RA9G6RTI95v2vr1q3q3r27QkJCNGXKFD3xxBPavn17VtYGAAAAZLtLly7p+eefl5+fn9zc3FSxYkXt3r3btN5oNGrUqFHKnz+/3Nzc1LRpU508edJsH5GRkeratau8vLzk4+OjXr16KTY2NstrzVTnPTw8XBMnTlTJkiXVqVMneXl5KSkpSatXr9bEiRP12GOPZXmBAAAAyD0crPDIjJs3b6pevXpycnLSDz/8oCNHjmjKlCnKly+fqc3kyZM1ffp0zZkzRzt27JC7u7uaN2+uxMREU5uuXbvq8OHD2rhxo9atW6etW7eqd+/emazmvz3wsJk2bdpo69atatWqlaZOnaoWLVrI0dFRc+bMyfKiAAAAAGuYNGmSChUqpIULF5qWhYaGmv5tNBo1depUvfPOO2rXrp0k6bPPPlNQUJBWr16tLl266OjRo1q/fr127dqlGjVqSJI+/vhjtWzZUh988IFCQkKyrN4H/nDyww8/qFevXhozZoxatWolR0fHLCsCAAAAjwaDIfsfSUlJiomJMXskJSVlWM/atWtVo0YNderUSYGBgapatarmz59vWn/mzBmFh4eradOmpmXe3t6qVauWtm3bJknatm2bfHx8TB13SWratKkcHBy0Y8eOLL1+D9x5/+2333Tr1i1Vr15dtWrV0owZM3T9+vUsLQYAAACwVFhYmLy9vc0eYWFhGbY9ffq0Zs+erZIlS2rDhg3q06ePBgwYoMWLF0u6M2xckoKCgsy2CwoKMq0LDw9XYGCg2fo8efLI19fX1CarPPCwmdq1a6t27dqaOnWqli9frgULFmjIkCFKS0vTxo0bVahQIXl6emZpcQAAAMhdrDHbzIgRIzRkyBCzZS4uLhm2TUtLU40aNTRhwgRJUtWqVXXo0CHNmTNH3bt3z/ZaMyvTs824u7vrxRdf1G+//aaDBw/q9ddf18SJExUYGKi2bdtmR40AAADAA3NxcZGXl5fZ436d9/z586tcuXJmy8qWLavz589LkoKDgyVJERERZm0iIiJM64KDg3X16lWz9bdv31ZkZKSpTVZ56KkiJal06dKaPHmyLl68qC+++CKragIAAEAuZY0x75lRr149HT9+3GzZiRMnVKRIEUl3bl4NDg7Wpk2bTOtjYmK0Y8cO1alTR5JUp04dRUVFac+ePaY2mzdvVlpammrVqvWQVypjD/UlTf/k6Oio9u3bq3379lmxOwAAAMAqBg8erLp162rChAnq3Lmzdu7cqXnz5mnevHmSJIPBoEGDBum9995TyZIlFRoaqpEjRyokJMTU9y1btqxatGihl19+WXPmzFFKSor69eunLl26ZOlMM1IWdd4BAACAB+GQw75g9bHHHtOqVas0YsQIjR07VqGhoZo6daq6du1qavPmm28qLi5OvXv3VlRUlOrXr6/169fL1dXV1Gbp0qXq16+fmjRpIgcHB3Xs2FHTp0/P8nrpvAMAAOCR1rp1a7Vu3fq+6w0Gg8aOHauxY8fet42vr6+WLVuWHeWZofMOAAAAq7HGbDO5mUU3rAIAAACwHpJ3AAAAWA3Bu2VI3gEAAAA7QfIOAAAAq8lps83YG5J3AAAAwE6QvAMAAMBqDCJ6twTJOwAAAGAnSN4BAABgNYx5twzJOwAAAGAnSN4BAABgNSTvliF5BwAAAOwEyTsAAACsxsBXrFqE5B0AAACwEyTvAAAAsBrGvFuG5B0AAACwEyTvAAAAsBqGvFuG5B0AAACwEyTvAAAAsBoHoneLkLwDAAAAdoLkHQAAAFbDbDOWIXkHAAAA7ATJOwAAAKyGIe+WIXkHAAAA7ATJOwAAAKzGQUTvlsiVnfc8jvxQ5DbNigfZugRkobNj+9u6BGShD1YctnUJyEL76sbaugRksfmdK9i6BGShXNl5BwAAQM7EmHfLMOYdAAAAsBMk7wAAALAa5nm3DMk7AAAAYCdI3gEAAGA1Dgx6twjJOwAAAGAnSN4BAABgNQTvliF5BwAAAOwEyTsAAACshjHvliF5BwAAAOwEyTsAAACshuDdMiTvAAAAgJ0geQcAAIDVkBxbhusHAAAA2AmSdwAAAFiNgUHvFiF5BwAAAOwEyTsAAACshtzdMnTeAQAAYDV8SZNlGDYDAAAA2AmSdwAAAFgNubtlSN4BAAAAO0HyDgAAAKthyLtlSN4BAAAAO0HyDgAAAKvhS5osQ/IOAAAA2AmSdwAAAFgNybFluH4AAACAnSB5BwAAgNUw5t0yJO8AAACAnSB5BwAAgNWQu1uG5B0AAACwEyTvAAAAsBrGvFuG5B0AAACwEyTvAAAAsBqSY8tw/QAAAAA7QfIOAAAAq2HMu2VI3gEAAAA7QfIOAAAAqyF3twzJOwAAAGAnSN4BAABgNQx5twzJOwAAAGAn6LwDAADAahxkyPaHJSZOnCiDwaBBgwaZliUmJqpv377y8/OTh4eHOnbsqIiICLPtzp8/r1atWilv3rwKDAzUG2+8odu3b1tUS0bovAMAAACSdu3apblz56pSpUpmywcPHqxvv/1WK1as0C+//KLLly/r6aefNq1PTU1Vq1atlJycrD/++EOLFy/WokWLNGrUqCyvkTHvOVxqaqo+nTtTP/6wTjduXJe/f6BatmmnHi+9apon1Wg06pM5M/TtqpW6FXtLlSpX1dARo1SocBEbV48Vn3+qbVs369L5s3J2cVGZCpXV/ZWBKli4qKlNclKSFsz6UL9u3qCUlGRVfayOXh38lvL5+pnatG1UNd2+h44KU8MmLaxxGriHl2setS4boDKB7nJ2NOh6XIq+3HdFF6OT5GCQnirjr7KBHvLN66TE22k6eS1O3x29ppikVElScT83vVa3cIb7nrr1nC5EJ1rzdB557i6OGtKytJpXDJKfh4sOX4rR2G8O68CFaEnSmamtMtwubM1RzdtyWpIUGuCuEW3LqHqor5zyGHTs8i19+P0JbT91w2rngTt83PKoY6VgVQj2kLOjg67GJmvRros6d/PO+2p+5woZbrdif7h+PH5dktS3XmEV8nGVl2sexSWn6mhEnL4+EK7oxKxPUB9VOXXMe2xsrLp27ar58+frvffeMy2Pjo7Wp59+qmXLlumJJ56QJC1cuFBly5bV9u3bVbt2bf344486cuSIfvrpJwUFBalKlSoaN26chg0bptGjR8vZ2TnL6qTznsN9vvhTrV65XO+MmaDQ4iV07MghjR/zjjw8PNXp2eclSUsXf6qVXy7VO2MmKH+BApo/+2MN6ddbn69YKxcXFxufwaPt0P69atXh/1SyTHmlpt7Wkvkz9O7QPpq5+Bu5urlJkj6Z8YF2b/9Nb46ZLHd3D82dOlFhI1/X5JmLzPY1cPgYVatZ1/Tc3cPTmqcCSW5ODupfr7BOXY/X/B0XFZeUKn8PJyWkpEmSnB0dVNDbVRtP3NDlmES5OTmqfYVAvVizoKb+ek6SdDYyQaN/PGW23xal/VXSPy8ddxuY2KWSSgV7asjn+xURk6j2NQpoyWu11GziL4qITtJjI38ya/942QBN6lJJPxy4Ylr26cs1dOZavLrO3K7ElFS92ChUn75cQ43e+1nXbyVZ+5QeWXmdHDTsiWI6fjVO0349p9ik2wr0cFF8cpqpzetrj5ltUyHYQ90fK6C9F6NNy45fi9MPR68pKvG28rnl0TOV8+vVuoU1afNpq50LLJeUlKSkJPP3n4uLy7/2i/r27atWrVqpadOmZp33PXv2KCUlRU2bNjUtK1OmjAoXLqxt27apdu3a2rZtmypWrKigoCBTm+bNm6tPnz46fPiwqlZNH8I9LIbN5HCH9u9Tg8efUN0GjZQ/pIAaN22umrXr6sjhg5LupO5fLVui7r1eUYPHn1CJkqU1ckyYrl+7ql9/3mTj6jHm/Zlq8lRbFQ4trtASpTVwxBhdiwjXqRNHJElxsbf00/er1avvEFWuVlMlSpfTwOFjdOzQfh07fMBsX+4ensrn5296OPPBzOqeKO6rqIQULd8frgtRiYpMSNGJa/G6EZ8iSUq8naa52y9q/5VbuhaXovNRiVp18KoK+bjKx+1OVpJqlG4lpZoeccmpKh/soZ0XYmx5ao8kFycHtagUrInfHtPO05E6dz1e09af1Lnr8Xq+3p2/XF6/lWT2eLJikLaduqELNxIkSfncnRQa6KE5m07p2JVbOns9XpPWHVNelzwqnd/Dlqf3yGlRJkA341O0aNclnY1M0PW4FB2JiNW1uGRTm5jE22aPKgW8dPxqnK7HpZja/HTihk5HJigyPkV/3UjQ+mPXVMzPTY45NC22RwYr/BcWFiZvb2+zR1hY2H1r+vLLL7V3794M24SHh8vZ2Vk+Pj5my4OCghQeHm5qc2/H/e76u+uyEp33HK5C5SravXO7zp87K0k6eeKYDuz7U7XrNpAkXb50UTduXFeNWrVN23h4eqpchUo6dGC/LUrGv4iLjZUkeXp6S5JOnTiq27dvq3L1v1+/gkVCFRAUrOP/6LzPmRqmrm0b6/VXntfG71bLaDRar3BIksoFe+hCdKK6VQ/R6GbFNaRhEdUq7P2v27g6OSjNaDSl8/9UPthD7s6O2nUhOsP1yD55HAzK4+igpJRUs+WJKamqUcw3XXt/D2c1Lheor7ZfMC27GZeivyJi9fRjBeXm7ChHB4Oeq1tE128l6SCvqVVVDvHU2ZsJeqVOIU1pW0YjnyyuBsXy3be9p4ujKub31G9nbt63TV5nR9Uq7KO/rscrlV+5dmXEiBGKjo42e4wYMSLDthcuXNDAgQO1dOlSubq6WrnSzGPYTA73Qo+XFB8bq+c6tpaDg6PS0lLV+7WBat6ytSQp8sadMXq+vv5m2/n6+unG/9YhZ0hLS9MnMz5Q2YpVVKRYCUlS1I0byuPkJA9P8yEwPvn8dDPy7/Gyz73YR5Wq1ZSLi6v27d6mOVPDlJgQrzbPPGfVc3jU+eV1Ut0iPvrl9E1tOnlDhXxc1aFCoFLTjNp9MX1ynsfBoFZlA7Tv0i0l3c64816rkLeOX41jPK0NxCWlas+Zm+rfvKRORcTq+q0kta1WQNWK5tO563Hp2nesWVBxibe1/oB5ivb8rB2a26u6Dk1srjSjUTdik9V9zk7FJPCaWlOAh7Me9/DVxhM39P3Rayrq66YuVfLrdqpR285FpWtft2g+JaWkam8G792OlYLUuISfXPI46K/r8fr4t3NWOINHhzXGvP/XEJl77dmzR1evXlW1atVMy1JTU7V161bNmDFDGzZsUHJysqKioszS94iICAUHB0uSgoODtXPnTrP93p2N5m6brJKjO+8XLlzQu+++qwULFty3TUZjmpJSHHPNWO/NG9frx/XfafT4yQotVkInTxzTtCkT5R8QoJZt2tu6PGTCnI/CdP7MKU38eGGmt+3Svbfp38VLlVFiQoJWffkZnXcrMxgMuhiVqB+O3flgfCkmScGeLqpTxCdd593BIHWrHiKDQVp5MCKj3cnbNY9KB7rrsz2Xs712ZGzI5/s0+dlK2jG2qW6npunwxRh9u/eyKhRK/xeVTrUKac2ey0r+xwexsc+U143YZHX+eJsSU1L1f7UL65OXa6jdh7/rWgxj3q3FIOnszUSt+t/77UJUogp4u6pRcd8MO+/1QvNpx/lo3U5LH6lvOHZdv52+KV93J7Upd+e+FTrwuVeTJk108OBBs2U9e/ZUmTJlNGzYMBUqVEhOTk7atGmTOnbsKEk6fvy4zp8/rzp16kiS6tSpo/Hjx+vq1asKDAyUJG3cuFFeXl4qV65cltabo4fNREZGavHixf/aJqMxTdOmTLJShdlv5rQper5HLzVt3lLFS5ZSi1Zt9X/PddOShZ9Iknz97iTukZHmKXtk5A35+fmn2x9sY87Uidq97Ve9N3W+/AP/HhPn4+en2ykpir11y6x91M0bZrPN/FOpchV1/VqEUpKT79sGWS8m8bYi/nEDYkRssvK5mecgdzvu+dzyaO62C/dN3R8r5K245FQdDo/Ntprx787fiFeXGdtV7s31qjtms9p/9LvyOBp0/nq8WbvHiuVT8SAPLd9+3mx53ZJ+eqJ8kAYs/lN7ztzU4YsxGrXykBJT0tTxsYLWPJVHXnTibV2JMb/p+0pMknzzOqVrW9I/r/J7uejX0xkPmYlNTlVEbLKORsRp/vYLqhTiqWJ+btlS96Mop83z7unpqQoVKpg93N3d5efnpwoVKsjb21u9evXSkCFDtGXLFu3Zs0c9e/ZUnTp1VLv2nWGvzZo1U7ly5fTCCy9o//792rBhg9555x317ds3ywNlmybva9eu/df1p0//953dI0aM0JAhQ8yW3UpxtKiunCQxMUEOBvPPWA4OjjIa73QGQgoUlJ+fv/bs3KFSpctKujOu+sihA+rwzP9ZvV6YMxqNmjttkrb/ulkTps1XcP4CZutLlCqrPHny6MDeHarb6M5d7BfPn9W1iHCVLl8po11Kks6cOi4PTy85ZeHUU/hvZyMTFOBhfs0DPJx0857hEXc77v7uzpq97YLi7zPWXZJqFvLSnosxyiD4g5UlJKcqITlVXm551LBMgCauPWq2vnPtQjpwPkpHL5t/0HZzvvP/m7R/3INiNBrlwA2OVnXqeryCPc07SUGezqYbyu9VPzSfzkYm6OIDzPB0d1rmPA45Ou9ENvvoo4/k4OCgjh07KikpSc2bN9esWbNM6x0dHbVu3Tr16dNHderUkbu7u7p3766xY8dmeS027by3b99eBoPhX2+8M/zHwKiMxjQlx+aecYb1GjyuxQvmKSg4v0KLl9CJY0e1fOlitWrXQdKd69P5uRe0+NO5Kli4sEJCCmr+7I/lHxCoBo83sXH1mPNRmLZu+kFvj/9Ibm7uuvm/+xDyenjIxcVV7h6eatqyvT6dOUUent7K6+6uedMmqUz5Sirzv877zt9/UdTNGypdrpKcnJ21b/d2rfj8U3X4v262PLVH0tbTN9W/fmE1KeGrfZdvqXA+V9Uu7KOV/xsD7WCQutcIUUFvV32y85IcDHduipOk+ORUsxveSvrnlZ+7s3ac56ZGW2pYxl+SQaevxqqov7tGtCujvyJitWLHRVMbD5c8alk5v8avOZpu+71nbyo6PkUfdK2sjzecVGJKmrrUKaSCvnm15chVK54JfjpxQ8OaFFPLsgHadSFaob5ualjMV0t2XzJr55rHQdULeWvF/ivp9hHq66aivm46dT1eccmpCvRwVrsKgbp6K0mnb8Sna4+Hk1Pneb/Xzz//bPbc1dVVM2fO1MyZM++7TZEiRfT9999nc2U27rznz59fs2bNUrt27TJcv2/fPlWvXt3KVeUsg998W/NnT9cHE8fp5s1I+fsHql3HTur5ch9Tm67deykhIUGTx49W7K1bqlSlmqZ8PDfXjPu3Zz+sWSFJemvgy2bLBw4foyZPtZUkvdRvqBwcHDRx1ND/fUlTXfUZ/Pcd8Y558ui7VV/p0xlTZJRR+QsUUq++r6tZ66cF67oQnaiFuy6pVdkAPVnKT5HxKVpz+Kr2XrqTxnq75lGF4Ds3Hw9tVNRs21l/nNdf/5teUJJqFvLWmcgEXY1l6JMtebo66Y3WpRXs46rouBStPxCuD747bjYOuk21/DIYDPp2b/p7E27GpajH3J0a2qq0lvatrTyOBp0Mj1XvT3enS+mRvc7eTNDs38+rQ8UgtS4XoOtxyVq+70q6D8iP/W+GqJ0ZfHBOTk1TtQJeals+UC55HBSdcFuHwm/pu6PXMhwbD9iCwWjD+ebatm2rKlWq3PdPCvv371fVqlWVlnb/Pztn5HouSt5xxw06OLnKvF0X/rsR7MY3m079dyPYjaZ1+Xbu3OZ+3yxrKz8evZbtx2hWNiDbj2ErNk3e33jjDcXFpZ+O664SJUpoy5YtVqwIAAAAyLls2nlv0KDBv653d3dXo0aNrFQNAAAAspshk7PBwBy3TgMAAAB2Ikd/SRMAAAByF6ZRtQzJOwAAAGAnSN4BAABgNYx5twzJOwAAAGAnSN4BAABgNfbwDas5Gck7AAAAYCdI3gEAAGA1jHm3DMk7AAAAYCdI3gEAAGA1zPNuGZJ3AAAAwE6QvAMAAMBqGPNuGZJ3AAAAwE6QvAMAAMBqmOfdMiTvAAAAgJ0geQcAAIDVELxbhuQdAAAAsBMk7wAAALAaBwa9W4TkHQAAALATJO8AAACwGnJ3y5C8AwAAAHaC5B0AAADWQ/RuEZJ3AAAAwE6QvAMAAMBqDETvFiF5BwAAAOwEyTsAAACshmneLUPyDgAAANgJkncAAABYDcG7Zei8AwAAwHrovVuEYTMAAACAnSB5BwAAgNUwVaRlSN4BAAAAO0HyDgAAAKthqkjLkLwDAAAAdoLkHQAAAFZD8G4ZkncAAADATpC8AwAAwHqI3i1C8g4AAADYCZJ3AAAAWA3zvFuG5B0AAACwEyTvAAAAsBrmebcMyTsAAABgJ0jeAQAAYDUE75YheQcAAADshMFoNBptXURWS0ixdQXIaoyPy11y32+dR1tqGi9obnIjNtnWJSCLFfFzsXUJZvZfuJXtx6hcyDPbj2ErJO8AAACAnWDMOwAAAKyGed4tQ/IOAAAA2AmSdwAAAFgN97FZhuQdAAAAsBMk7wAAALAagnfLkLwDAAAAdoLkHQAAANZD9G4RkncAAADATpC8AwAAwGqY590yJO8AAACAnSB5BwAAgNUwz7tlSN4BAAAAO0HyDgAAAKsheLcMyTsAAABgJ0jeAQAAYD1E7xYheQcAAADsBMk7AAAArIZ53i1D8g4AAIBHVlhYmB577DF5enoqMDBQ7du31/Hjx83aJCYmqm/fvvLz85OHh4c6duyoiIgIszbnz59Xq1atlDdvXgUGBuqNN97Q7du3s7xeOu8AAACwGoMh+x+Z8csvv6hv377avn27Nm7cqJSUFDVr1kxxcXGmNoMHD9a3336rFStW6JdfftHly5f19NNPm9anpqaqVatWSk5O1h9//KHFixdr0aJFGjVqVFZdNhOD0Wg0ZvlebSwhxdYVIKvxhQ65S+77rfNoS03jBc1NbsQm27oEZLEifi62LsHM8fD4bD9G6eC8D73ttWvXFBgYqF9++UUNGzZUdHS0AgICtGzZMj3zzDOSpGPHjqls2bLatm2bateurR9++EGtW7fW5cuXFRQUJEmaM2eOhg0bpmvXrsnZ2TlLzksieQcAAIAVGazwSEpKUkxMjNkjKSnpgeqLjo6WJPn6+kqS9uzZo5SUFDVt2tTUpkyZMipcuLC2bdsmSdq2bZsqVqxo6rhLUvPmzRUTE6PDhw9n8gr9OzrvAAAAyFXCwsLk7e1t9ggLC/vP7dLS0jRo0CDVq1dPFSpUkCSFh4fL2dlZPj4+Zm2DgoIUHh5uanNvx/3u+rvrshKzzQAAAMB6rDAUdsSIERoyZIjZMheX/x4+1LdvXx06dEi//fZbdpVmMTrvAAAAyFVcXFweqLN+r379+mndunXaunWrChYsaFoeHBys5ORkRUVFmaXvERERCg4ONrXZuXOn2f7uzkZzt01WYdgMAAAArMZghf8yw2g0ql+/flq1apU2b96s0NBQs/XVq1eXk5OTNm3aZFp2/PhxnT9/XnXq1JEk1alTRwcPHtTVq1dNbTZu3CgvLy+VK1fOgquVHsk7AAAAHll9+/bVsmXLtGbNGnl6eprGqHt7e8vNzU3e3t7q1auXhgwZIl9fX3l5eal///6qU6eOateuLUlq1qyZypUrpxdeeEGTJ09WeHi43nnnHfXt2zfTfwH4L0wVCbvAVJG5S+77rfNoY6rI3IWpInOfnDZV5KmrCdl+jBKBbg/c1nCfTsbChQvVo0cPSXe+pOn111/XF198oaSkJDVv3lyzZs0yGxJz7tw59enTRz///LPc3d3VvXt3TZw4UXnyZG1WTucddoHOe+6S+37rPNrovOcudN5zHzrvuQvDZgAAAGA15HGW4YZVAAAAwE6QvAMAAMB6iN4tQvIOAAAA2AmSdwAAAFhNZudhhzmSdwAAAMBOkLwDAADAapj+2TIk7wAAAICdIHkHAACA1RC8W4bkHQAAALATJO8AAACwHqJ3i5C8AwAAAHaC5B0AAABWwzzvliF5BwAAAOwEybudearZE7py+VK65Z27PKe33nnXBhUhK306f56mT52irs9305sj3rZ1OXgAe3bv0uKFn+rokUO6du2aPpw2U080aWpabzQaNXvmdH2zcoVu3YpRlarV9NbI0SpSpKjtisZ9rVj+hVZ+9YXp92yx4iX08it9Va9BQ0lSUlKSPvpgkn5c/52Sk1NUp249DX/nXfn5+duybPzPgT93a8WyRTp5/Kgir1/Tu2FTVa/RE6b177/3jjZ+v9Zsmxq16mrCR3NMz5ctmqedf/yqv04eVx4nJ6368Xer1f+oYJ53y9B5tzNLv1yptLRU0/NTJ0/q1Zd76slmLWxYFbLCoYMHtHLFlypVqrStS0EmJCTEq1Tp0mrfoaOGDOqXbv2iBfO1bOkSjRs/UQUKFNSsGdP02iu99M2a7+Xi4mKDivFvgoKC1H/Q6ypcuIiMRqPWrV2tIQP7atlX36h4iZKaMjlMv/36iyZ+ME2enh6aNGGc3hjcXws++8LWpUNSYmKCipUoreatO2jsiMEZtqlRu56Gvj3O9NzJydls/e3bKWrwRDOVrVBZ69etytZ6gYdB593O+Pr6mj1f8Mk8FSpUWDUeq2mjipAV4uPiNGLYG3p3zHuaP3e2rctBJtRv0Ej1GzTKcJ3RaNTSJZ/p5d591PiJO2n8uAmT1aRRXW3Z9JNatGxlzVLxABo+/oTZ874DBmvlV1/q4IH9CgwK1ppVX2v8xPdVs1ZtSdK748L0TLuWOrh/nypWrmKDinGvmnUaqGadBv/axsnJWb7/8peSbi/1lST9+N2aLK0NfyN4twxj3u1YSkqyvl+3Vu06dJSBv0HZtQnvjVXDho1Uu05dW5eCLHTp4kVdv35Nte55XT09PVWxUmXt3/+nDSvDg0hNTdWGH75TQkK8KlWuoqNHDuv27RTVqv336xkaWkzB+UN04MA+2xWKTDnw5251atlIL3Zpo+nvj1NMdJStSwIyheTdjm3e9JNu3bqltu072LoUWOCH77/T0aNHtGz5SluXgix2/fo1SZKfn5/Zcl8/P924ft0WJeEBnDxxXD1feFbJyUlyy5tXH0ydoWLFS+j4saNycnKSp5eXWXs/Xk+7UaNWPdVv1ETBIQV0+eJFLZw7XW8PeU1T5y2Ro6Ojrct7ZJA3WsbmnfeEhATt2bNHvr6+KleunNm6xMREffXVV+rWrdt9t09KSlJSUpLZsjQHl0diLOnqb75WvfoNFRgYZOtS8JDCr1zR5InjNXf+gkfiZxawB0VDQ/XFilWKjb2lnzZu0LvvDNf8BUtsXRayQOMnnzL9O7R4KRUrUUrdO7XUgT93qWqN2jas7FFD790SNh02c+LECZUtW1YNGzZUxYoV1ahRI125csW0Pjo6Wj179vzXfYSFhcnb29vs8f6ksOwu3eYuX76kHdv/UIeOz9i6FFjgyJHDirxxQ106Pa1qlcqpWqVy2r1rp5YtXaJqlcopNTX1v3eCHMvfP0CSdOPGDbPlkTduyM+f2UlyKicnZxUqXERly1VQ/4Gvq1SpMvpi6Wfy8w9QSkqKbsXEmLW/wetpt/IXKChvn3y6dPGCrUsBHphNO+/Dhg1ThQoVdPXqVR0/flyenp6qV6+ezp8//8D7GDFihKKjo80ebwwbkY1V5wxrVn0jX18/NWj4uK1LgQVq1a6tlau/1fKvV5se5ctXUMvWbbT869X8GdfOFShYUP7+Adq5fZtpWWxsrA4e2K/KlavasDJkRlpampKTk1W2XHnlyeOknTv+fj3Pnjmt8CuXValSFdsViId27Wq4YqKjmOrTygyG7H/kZjYdNvPHH3/op59+kr+/v/z9/fXtt9/qtddeU4MGDbRlyxa5u7v/5z5cXNIPkUlIya6Kc4a0tDStXf2N2rRrrzx5bD7yCRZwd/dQyZKlzJa55c0rH2+fdMuRM8XHx5kFDpcuXdSxY0fl7e2t/PlD1PWFbpo/b7YKFymiAgUKauaMaQoIDFTje+aCR87x8bQpqlevoYLz51dcXJzW/7BOe3bv1Iw5n8jT01PtOnTUhx9Mkpe3tzw8PDQ57D1VqlyFmWZyiIT4eF2++Pf7MfzKJf114pg8vbzl6eWtJQtmq8HjTZXPz19XLl3Q/JkfKaRgYVWvVc+0zdXwK7oVE62rEVeUlpaqv04ckySFFCwst7x5rX5OwD/ZtOeXkJBg1vk0GAyaPXu2+vXrp0aNGmnZsmU2rC7n2r7tD125clntO3S0dSnAI+/woUN6+cW/78uZMvnOsL027Tpo3PiJ6vHiy0pISNC40aN061aMqlarrllzPuEehxzqZmSkRr0zTNevXZOHh6dKliqtGXM+Ue06dzp3r785Qg4ODnpzyEAlJyerTr36Gv72KBtXjbtOHDusN/r1Mj2fO/19SdKTLdtqwBvv6Mypk9r4/VrFxd6Sn3+gqtWsox69+8nZ+e+53hd/MtPsi5z69OgsSXp/xqeqXO0xK51J7pbLg/FsZzAajUZbHbxmzZrq37+/XnjhhXTr+vXrp6VLlyomJibT435ze/L+KMrtfwJ71Njutw6yQ2oaL2huciM22dYlIIsV8ctZYcHlqOz/GQvxcf7vRnbKpmPeO3TooC++yPhb6WbMmKFnn31WNvxsAQAAgCzGmHfL2DR5zy4k77lPbn8jPmpy32+dRxvJe+5C8p775LTk/Up09v+M5ffOvck7dzsCAADAagyMereITYfNAAAAAHhwJO8AAACwHoJ3i5C8AwAAAHaC5B0AAABWQ/BuGZJ3AAAAwE6QvAMAAMBqmP7ZMiTvAAAAgJ0geQcAAIDVMM+7ZUjeAQAAADtB8g4AAADrIXi3CMk7AAAAYCdI3gEAAGA1BO+WIXkHAAAA7ATJOwAAAKyGed4tQ/IOAAAA2AmSdwAAAFgN87xbhuQdAAAAsBMk7wAAALAaxrxbhuQdAAAAsBN03gEAAAA7QecdAAAAsBOMeQcAAIDVMObdMiTvAAAAgJ0geQcAAIDVMM+7ZUjeAQAAADtB8g4AAACrYcy7ZUjeAQAAADtB8g4AAACrIXi3DMk7AAAAYCdI3gEAAGA9RO8WIXkHAAAA7ATJOwAAAKyGed4tQ/IOAAAA2AmSdwAAAFgN87xbhuQdAAAAsBMk7wAAALAagnfLkLwDAAAAdoLkHQAAANZD9G4RkncAAADATpC8AwAAwGqY590yJO8AAACAnSB5BwAAgNUwz7tlSN4BAAAAO2EwGo1GWxeBzEtKSlJYWJhGjBghFxcXW5eDLMBrmrvweuYuvJ65D68p7BWddzsVExMjb29vRUdHy8vLy9blIAvwmuYuvJ65C69n7sNrCnvFsBkAAADATtB5BwAAAOwEnXcAAADATtB5t1MuLi569913uckmF+E1zV14PXMXXs/ch9cU9oobVgEAAAA7QfIOAAAA2Ak67wAAAICdoPMOAAAA2Ak673YoJSVFw4YNU8WKFeXu7q6QkBB169ZNly9ftnVpeEjffPONmjVrJj8/PxkMBu3bt8/WJcECM2fOVNGiReXq6qpatWpp586dti4JD2nr1q1q06aNQkJCZDAYtHr1aluXBAuEhYXpsccek6enpwIDA9W+fXsdP37c1mUBmULn3Q7Fx8dr7969GjlypPbu3atvvvlGx48fV9u2bW1dGh5SXFyc6tevr0mTJtm6FFho+fLlGjJkiN59913t3btXlStXVvPmzXX16lVbl4aHEBcXp8qVK2vmzJm2LgVZ4JdfflHfvn21fft2bdy4USkpKWrWrJni4uJsXRrwwJhtJpfYtWuXatasqXPnzqlw4cK2LgcP6ezZswoNDdWff/6pKlWq2LocPIRatWrpscce04wZMyRJaWlpKlSokPr376/hw4fbuDpYwmAwaNWqVWrfvr2tS0EWuXbtmgIDA/XLL7+oYcOGti4HeCAk77lEdHS0DAaDfHx8bF0K8MhKTk7Wnj171LRpU9MyBwcHNW3aVNu2bbNhZQAyEh0dLUny9fW1cSXAg6PzngskJiZq2LBhevbZZ+Xl5WXrcoBH1vXr15WamqqgoCCz5UFBQQoPD7dRVQAykpaWpkGDBqlevXqqUKGCrcsBHhiddzuwdOlSeXh4mB6//vqraV1KSoo6d+4so9Go2bNn27BKPKh/ez0BANbRt29fHTp0SF9++aWtSwEyJY+tC8B/a9u2rWrVqmV6XqBAAUl/d9zPnTunzZs3k7rbifu9nrB//v7+cnR0VEREhNnyiIgIBQcH26gqAP/Ur18/rVu3Tlu3blXBggVtXQ6QKXTe7YCnp6c8PT3Nlt3tuJ88eVJbtmyRn5+fjapDZmX0eiJ3cHZ2VvXq1bVp0ybTTY1paWnatGmT+vXrZ9viAMhoNKp///5atWqVfv75Z4WGhtq6JCDT6LzboZSUFD3zzDPau3ev1q1bp9TUVNN4Wl9fXzk7O9u4QmRWZGSkzp8/b5qr/+68w8HBwSS2dmbIkCHq3r27atSooZo1a2rq1KmKi4tTz549bV0aHkJsbKxOnTplen7mzBnt27dPvr6+zOxlh/r27atly5ZpzZo18vT0NP2/09vbW25ubjauDngwTBVph+5OJ5iRLVu26PHHH7duQbDYokWLMuzcvfvuuxo9erT1C4JFZsyYoffff1/h4eGqUqWKpk+fbjZUCvbj559/VuPGjdMt7969uxYtWmT9gmARg8GQ4fKFCxeqR48e1i0GeEh03gEAAAA7wWwzAAAAgJ2g8w4AAADYCTrvAAAAgJ2g8w4AAADYCTrvAAAAgJ2g8w4AAADYCTrvAAAAgJ2g8w4AAADYCTrvAJAFevToofbt25ueP/744xo0aJDV6/j5559lMBgUFRVl9WMDALIfnXcAuVqPHj1kMBhkMBjk7OysEiVKaOzYsbp9+3a2Hvebb77RuHHjHqgtHW4AwIPKY+sCACC7tWjRQgsXLlRSUpK+//579e3bV05OThoxYoRZu+TkZDk7O2fJMX19fbNkPwAA3IvkHUCu5+LiouDgYBUpUkR9+vRR06ZNtXbtWtNQl/HjxyskJESlS5eWJF24cEGdO3eWj4+PfH191a5dO509e9a0v9TUVA0ZMkQ+Pj7y8/PTm2++KaPRaHbMfw6bSUpK0rBhw1SoUCG5uLioRIkS+vTTT3X27Fk1btxYkpQvXz4ZDAb16NFDkpSWlqawsDCFhobKzc1NlStX1sqVK82O8/3336tUqVJyc3NT48aNzeoEAOQ+dN4BPHLc3NyUnJwsSdq0aZOOHz+ujRs3at26dUpJSVHz5s3l6empX3/9Vb///rs8PDzUokUL0zZTpkzRokWLtGDBAv3222+KjIzUqlWr/vWY3bp10xdffKHp06fr6NGjmjt3rjw8PFSoUCF9/fXXkqTjx4/rypUrmjZtmiQpLCxMn332mebMmaPDhw9r8ODBev755/XLL79IuvMh4+mnn1abNm20b98+vfTSSxo+fHh2XTYAQA7AsBkAjwyj0ahNmzZpw4YN6t+/v65duyZ3d3d98sknpuEyn3/+udLS0vTJJ5/IYDBIkhYuXCgfHx/9/PPPatasmaZOnaoRI0bo6aefliTNmTNHGzZsuO9xT5w4oa+++kobN25U06ZNJUnFihUzrb87xCYwMFA+Pj6S7iT1EyZM0E8//aQ6deqYtvntt980d+5cNWrUSLNnz1bx4sU1ZcoUSVLp0qV18OBBTZo0KQuvGgAgJ6HzDiDXW7dunTw8PJSSkqK0tDQ999xzGj16tPr27auKFSuajXPfv3+/Tp06JU9PT7N9JCYm6q+//lJ0dLSuXLmiWrVqmdblyZNHNWrUSDd05q59+/bJ0dFRjRo1euCaT506pfj4eD355JNmy5OTk1W1alVJ0tGjR83qkGTq6AMAcic67wByvcaNG2v27NlydnZWSEiI8uT5+1efu7u7WdvY2FhVr15dS5cuTbefgICAhzq+m5tbpreJjY2VJH333XcqUKCA2ToXF5eHqgMAYP/ovAPI9dzd3VWiRIkHalutWjUtX75cgYGB8vLyyrBN/vz5tWPHDjVs2FCSdPv2be3Zs0fVqlXLsH3FihWVlpamX375xTRs5l53k//U1FTTsnLlysnFxUXnz5+/b2JftmxZrV271mzZ9u3b//skAQB2ixtWAeAeXbt2lb+/v9q1a6dff/1VZ86c0c8//6wBAwbo4sWLkqSBAwdq4sSJWr16tY4dO6bXXnvtX+doL1q0qLp3764XX3xRq1evNu3zq6++kiQVKVJEBoNB69at07Vr1xQbGytPT08NHTpUgwcP1uLFi/XXX39p7969+vjjj7V48WJJ0quvvqqTJ0/qjTfe0PHjx7Vs2TItWrQouy8RAMCG6LwDwD3y5s2rrVu3qnDhwnr66adVtmxZ9erVS4mJiaYk/vXXX9cLL7yg7t27q06dOvL09FSHDh3+db+zZ8/WM888o9dee01lypTRyy+/rLi4OElSgQIFNGbMGA0fPlxBQUHq16+fJGncuHEaOXKkwsLCVLZsWbVo0ULfffedQkNDJUmFCxfW119/rdWrV6ty5cqaM2eOJkyYkI1XBwBgawbj/e6wAgAAAJCjkLwDAAAAdoLOOwAAAGAn6LwDAAAAdoLOOwAAAGAn6LwDAAAAdoLOOwAAAGAn6LwDAAAAdoLOOwAAAGAn6LwDAAAAdoLOOwAAAGAn6LwDAAAAdoLOOwAAAGAn/h9xL0xNGXo6SgAAAABJRU5ErkJggg==",
      "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",
    "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": 24,
   "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",
    "    # 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.set_title(f'True class: {class_name}')\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": 25,
   "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"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}