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      "name": "python3"
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    "language_info": {
      "name": "python",
      "version": "3.10.0"
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    "colab": {
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      "gpuType": "T4"
    },
    "accelerator": "GPU"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ukOZw8cj1Iwy"
      },
      "source": [
        "# 🧠 MindScan — Notebook 1: Classical Models\n",
        "### NCI H9DAI Research Project 2026\n",
        "\n",
        "---\n",
        "\n",
        "## What this notebook does\n",
        "Trains **4 classical ML models** on **3 datasets**. No GPU needed. Runs in ~20 minutes.\n",
        "\n",
        "| | Dataset | Task | File to upload |\n",
        "|---|---|---|---|\n",
        "| **D1** | Zenodo — depression types | 6-class | `dataset.csv` |\n",
        "| **D2** | Kaggle — binary depression | Binary | `DA_DB_2.csv` |\n",
        "| **D3** | Kaggle — suicide risk | Binary | `DA_DB_3.csv` |\n",
        "\n",
        "## 4 Models trained per dataset\n",
        "1. Logistic Regression\n",
        "2. Random Forest\n",
        "3. SVM (LinearSVC)\n",
        "4. XGBoost\n",
        "\n",
        "## Output\n",
        "All files saved to **Google Drive → MindScan_Models/classical/**\n",
        "\n",
        "## ⚠️ No GPU needed — use standard CPU runtime"
      ],
      "id": "ukOZw8cj1Iwy"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ndVjWOve1Iw1"
      },
      "source": [
        "---\n",
        "# ⚙️ SECTION 0 — Setup"
      ],
      "id": "ndVjWOve1Iw1"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "O1f7mVvC1Iw2",
        "outputId": "eeced7cd-1c7c-4ce5-ebfa-ff3a0583ef92"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "✅ Libraries installed!\n"
          ]
        }
      ],
      "source": [
        "!pip install -q scikit-learn xgboost imbalanced-learn matplotlib seaborn pandas numpy wordcloud\n",
        "print('✅ Libraries installed!')"
      ],
      "id": "O1f7mVvC1Iw2"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "h4O48ndd1Iw3",
        "outputId": "653e0b55-9566-4a8f-d0df-7f78e57724cc"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "✅ Imports done!\n"
          ]
        }
      ],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import re, string, os, joblib, warnings\n",
        "from collections import Counter\n",
        "warnings.filterwarnings('ignore')\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.svm import LinearSVC\n",
        "from xgboost import XGBClassifier\n",
        "from sklearn.metrics import (\n",
        "    classification_report, confusion_matrix,\n",
        "    f1_score, accuracy_score, cohen_kappa_score\n",
        ")\n",
        "from imblearn.over_sampling import SMOTE\n",
        "\n",
        "# Storage for final comparison\n",
        "ALL_RESULTS = {}\n",
        "print('✅ Imports done!')"
      ],
      "id": "h4O48ndd1Iw3"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zFe0_MkX1Iw3",
        "outputId": "8bc3692c-172d-42d7-dd69-86381776e73e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n",
            "✅ Drive mounted. Saving to: /content/drive/MyDrive/MindScan_Models/classical\n"
          ]
        }
      ],
      "source": [
        "# ─────────────────────────────────────────────────────────────────\n",
        "# MOUNT GOOGLE DRIVE — saves everything here so nothing is lost\n",
        "# even if Colab disconnects mid-run\n",
        "# ─────────────────────────────────────────────────────────────────\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive')\n",
        "\n",
        "SAVE_DIR = '/content/drive/MyDrive/MindScan_Models/classical'\n",
        "os.makedirs(SAVE_DIR, exist_ok=True)\n",
        "print(f'✅ Drive mounted. Saving to: {SAVE_DIR}')"
      ],
      "id": "zFe0_MkX1Iw3"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sgaJfx4Z1Iw4",
        "outputId": "e294bc98-2c6f-4dc0-c801-de85dd3abe63"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "✅ All utility functions ready!\n"
          ]
        }
      ],
      "source": [
        "# ─────────────────────────────────────────────────────────────────\n",
        "# SHARED UTILITY FUNCTIONS — used by all 3 datasets\n",
        "# ─────────────────────────────────────────────────────────────────\n",
        "\n",
        "def clean_text(text):\n",
        "    \"\"\"\n",
        "    Cleans one text entry.\n",
        "    Works on tweets AND Reddit posts.\n",
        "    Steps: lowercase → remove URLs → remove @mentions →\n",
        "           remove # symbol → remove punctuation → trim spaces\n",
        "    \"\"\"\n",
        "    text = str(text).lower()\n",
        "    text = re.sub(r'http\\S+|www\\S+|https\\S+', '', text)\n",
        "    text = re.sub(r'@\\w+', '', text)\n",
        "    text = re.sub(r'#', '', text)\n",
        "    text = text.translate(str.maketrans('', '', string.punctuation))\n",
        "    text = re.sub(r'\\s+', ' ', text).strip()\n",
        "    return text\n",
        "\n",
        "\n",
        "def make_tfidf(X_train, X_test, max_features=50000):\n",
        "    \"\"\"\n",
        "    Converts text to TF-IDF number vectors.\n",
        "    Fit ONLY on training data, transform both sets.\n",
        "    ngram_range=(1,2) captures single words AND word pairs.\n",
        "    \"\"\"\n",
        "    tfidf = TfidfVectorizer(\n",
        "        max_features=max_features,\n",
        "        ngram_range=(1, 2),\n",
        "        sublinear_tf=True,\n",
        "        min_df=2\n",
        "    )\n",
        "    Xtr = tfidf.fit_transform(X_train)\n",
        "    Xte = tfidf.transform(X_test)\n",
        "    return tfidf, Xtr, Xte\n",
        "\n",
        "\n",
        "def apply_smote(X_train, y_train):\n",
        "    \"\"\"\n",
        "    Balances classes by creating synthetic minority samples.\n",
        "    ONLY applied to training set — never the test set.\n",
        "    \"\"\"\n",
        "    before = Counter(y_train)\n",
        "    smote = SMOTE(random_state=42)\n",
        "    Xb, yb = smote.fit_resample(X_train, y_train)\n",
        "    after = Counter(yb)\n",
        "    print(f'   SMOTE: {sum(before.values())} → {sum(after.values())} samples')\n",
        "    return Xb, yb\n",
        "\n",
        "\n",
        "def evaluate_model(name, y_true, y_pred, label_names, ds_key, results_store):\n",
        "    \"\"\"\n",
        "    Prints + stores metrics for one model.\n",
        "    Accuracy, Macro F1, Cohen's Kappa, per-class breakdown.\n",
        "    Saves confusion matrix chart.\n",
        "    \"\"\"\n",
        "    acc   = accuracy_score(y_true, y_pred)\n",
        "    macro = f1_score(y_true, y_pred, average='macro')\n",
        "    kappa = cohen_kappa_score(y_true, y_pred)\n",
        "\n",
        "    results_store[name] = {\n",
        "        'accuracy': round(acc, 4),\n",
        "        'macro_f1': round(macro, 4),\n",
        "        'kappa':    round(kappa, 4)\n",
        "    }\n",
        "\n",
        "    print(f'\\n{\"=\"*55}')\n",
        "    print(f'  {name}  [{ds_key}]')\n",
        "    print(f'{\"=\"*55}')\n",
        "    print(f'  Accuracy     : {acc*100:.2f}%')\n",
        "    print(f'  Macro F1     : {macro:.4f}   ← main metric')\n",
        "    print(f\"  Cohen's Kappa: {kappa:.4f}   ← agreement beyond chance\")\n",
        "    print(f'{\"=\"*55}\\n')\n",
        "    print(classification_report(y_true, y_pred, target_names=label_names, digits=3))\n",
        "\n",
        "    # Confusion matrix\n",
        "    cm = confusion_matrix(y_true, y_pred)\n",
        "    plt.figure(figsize=(max(6, len(label_names)*1.4), max(5, len(label_names)*1.1)))\n",
        "    sns.heatmap(cm, annot=True, fmt='d',\n",
        "                xticklabels=label_names, yticklabels=label_names,\n",
        "                cmap='Blues', linewidths=0.4)\n",
        "    plt.title(f'Confusion Matrix — {name}  [{ds_key}]', fontsize=12, fontweight='bold')\n",
        "    plt.ylabel('Actual'); plt.xlabel('Predicted')\n",
        "    plt.xticks(rotation=30, ha='right')\n",
        "    plt.tight_layout()\n",
        "    chart_path = os.path.join(SAVE_DIR, f'cm_{ds_key}_{name.replace(\" \",\"_\")}.png')\n",
        "    plt.savefig(chart_path, dpi=120, bbox_inches='tight')\n",
        "    plt.show()\n",
        "\n",
        "\n",
        "def save_pkl(obj, filename):\n",
        "    \"\"\"\n",
        "    Saves a file to BOTH local Colab storage AND Google Drive.\n",
        "    Local copy: used immediately in this session.\n",
        "    Drive copy: safe even if Colab disconnects.\n",
        "    \"\"\"\n",
        "    joblib.dump(obj, filename)\n",
        "    joblib.dump(obj, os.path.join(SAVE_DIR, filename))\n",
        "    size_kb = os.path.getsize(filename) / 1024\n",
        "    print(f'   Saved: {filename}  ({size_kb:.0f} KB)')\n",
        "\n",
        "\n",
        "def summary_table(results_store, ds_key):\n",
        "    \"\"\"\n",
        "    Prints final comparison of all 4 models for one dataset.\n",
        "    \"\"\"\n",
        "    print(f'\\n{\"═\"*58}')\n",
        "    print(f'  SUMMARY — {ds_key}')\n",
        "    print(f'{\"═\"*58}')\n",
        "    print(f'  {\"Model\":<24} {\"Accuracy\":>10} {\"Macro F1\":>10} {\"Kappa\":>8}')\n",
        "    print(f'  {\"-\"*54}')\n",
        "    best = max(results_store.values(), key=lambda x: x['macro_f1'])['macro_f1']\n",
        "    for name, s in results_store.items():\n",
        "        star = ' ★' if s['macro_f1'] == best else ''\n",
        "        print(f'  {name:<24} {s[\"accuracy\"]:>10.4f} {s[\"macro_f1\"]:>10.4f} {s[\"kappa\"]:>8.4f}{star}')\n",
        "    print(f'{\"═\"*58}\\n')\n",
        "\n",
        "\n",
        "print('✅ All utility functions ready!')"
      ],
      "id": "sgaJfx4Z1Iw4"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6QVSB5Jv1Iw4"
      },
      "source": [
        "---\n",
        "# 📁 DATASET 1 — Depression Type (6 Classes)\n",
        "**Source:** Nusrat et al. (2024) · Zenodo 14233292  \n",
        "**Upload:** `dataset.csv`  \n",
        "**Task:** Which of 6 psychiatric depression types does this text suggest?"
      ],
      "id": "6QVSB5Jv1Iw4"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299
        },
        "id": "74z8IbCK1Iw4",
        "outputId": "77fa7240-8c4c-47ea-f963-7155d640ed01"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "✅ Dataset 1 loaded: 14,983 rows\n",
            "Labels\n",
            "postpartum          3746\n",
            "major depressive    2517\n",
            "bipolar             2443\n",
            "psychotic           2312\n",
            "no                  1985\n",
            "atypical            1980\n",
            "Name: count, dtype: int64\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                              Tweets      Labels\n",
              "0  I been going thru depression  I didnt even rea...  postpartum\n",
              "1  @keshab_mahanta hello Health minister sir,I m ...  postpartum\n",
              "2  FYI in case anyone is concerned - I am NOT act...  postpartum"
            ],
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              "      <th>0</th>\n",
              "      <td>I been going thru depression  I didnt even rea...</td>\n",
              "      <td>postpartum</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>@keshab_mahanta hello Health minister sir,I m ...</td>\n",
              "      <td>postpartum</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>FYI in case anyone is concerned - I am NOT act...</td>\n",
              "      <td>postpartum</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "                                                    [key], {});\n",
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              "\n",
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              "type": "dataframe",
              "variable_name": "df1",
              "summary": "{\n  \"name\": \"df1\",\n  \"rows\": 14983,\n  \"fields\": [\n    {\n      \"column\": \"Tweets\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 14983,\n        \"samples\": [\n          \"i have a healthy head of hair, boyish good looks, and severe atypical depression\",\n          \"dunlap mil sack amp lawson mil sacks he isn playing up to his contact amp has become distraction in the locker room seems like simple decision trade him save money and hopefully get draft pick\",\n          \"still laughing abt dude saying nvm being silly when asked him if he wanted his feelings hurt after he questioned me on how many dudes talking to got the whole squad laughing bro\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Labels\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 6,\n        \"samples\": [\n          \"postpartum\",\n          \"major depressive\",\n          \"atypical\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 5
        }
      ],
      "source": [
        "# ─────────────────────────────────────────────────────────────────\n",
        "# D1 — LOAD\n",
        "# Upload dataset.csv using the folder icon on the left sidebar.\n",
        "# ─────────────────────────────────────────────────────────────────\n",
        "df1 = pd.read_csv('DA_1_DB.csv')\n",
        "df1 = df1.dropna(subset=['Tweets', 'Labels'])\n",
        "df1 = df1[df1['Labels'].str.strip() != ''].reset_index(drop=True)\n",
        "\n",
        "print(f'✅ Dataset 1 loaded: {len(df1):,} rows')\n",
        "print(df1['Labels'].value_counts())\n",
        "df1.head(3)"
      ],
      "id": "74z8IbCK1Iw4"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 541
        },
        "id": "8pfINjoZ1Iw5",
        "outputId": "dd1d9eef-a85c-4e6b-bd99-5dd218966ad5"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dataset 1 — Feature Statistics:\n",
            "       word_count  char_count  hashtag_count  mention_count\n",
            "count    14983.00    14983.00       14983.00       14983.00\n",
            "mean        31.42      173.17           0.07           0.32\n",
            "std         12.18       66.24           0.41           0.79\n",
            "min          1.00        6.00           0.00           0.00\n",
            "25%         22.00      124.00           0.00           0.00\n",
            "50%         31.00      168.00           0.00           0.00\n",
            "75%         41.00      235.00           0.00           0.00\n",
            "max         76.00      411.00           7.00          20.00\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1800x500 with 3 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Imbalance ratio: 1.89x\n",
            "Largest class:  postpartum (3746)\n",
            "Smallest class: atypical (1980)\n"
          ]
        }
      ],
      "source": [
        "# ─────────────────────────────────────────────────────────────────\n",
        "# D1 — EDA\n",
        "# Check: class distribution, tweet length, imbalance ratio\n",
        "# ─────────────────────────────────────────────────────────────────\n",
        "label_counts1 = df1['Labels'].value_counts()\n",
        "df1['word_count'] = df1['Tweets'].apply(lambda x: len(str(x).split()))\n",
        "df1['char_count'] = df1['Tweets'].apply(lambda x: len(str(x)))\n",
        "df1['hashtag_count'] = df1['Tweets'].apply(lambda x: str(x).count('#'))\n",
        "df1['mention_count'] = df1['Tweets'].apply(lambda x: len(re.findall(r'@\\w+', str(x))))\n",
        "df1['exclamation_count'] = df1['Tweets'].apply(lambda x: str(x).count('!'))\n",
        "df1['question_count'] = df1['Tweets'].apply(lambda x: str(x).count('?'))\n",
        "\n",
        "print('Dataset 1 — Feature Statistics:')\n",
        "print(df1[['word_count','char_count','hashtag_count','mention_count']].describe().round(2))\n",
        "\n",
        "colors = ['#6c8fff','#a78bfa','#2dd4bf','#fbbf24','#f472b6','#94a3b8']\n",
        "fig, axes = plt.subplots(1, 3, figsize=(18, 5))\n",
        "fig.suptitle('Dataset 1 EDA — Depression Type (6 Classes)', fontsize=14, fontweight='bold')\n",
        "\n",
        "# Class distribution\n",
        "label_counts1.plot(kind='bar', ax=axes[0], color=colors, edgecolor='none')\n",
        "axes[0].set_title('Tweets per Class', fontweight='bold')\n",
        "axes[0].set_xlabel('Depression Type'); axes[0].set_ylabel('Count')\n",
        "axes[0].set_xticklabels(label_counts1.index, rotation=30, ha='right')\n",
        "for i, v in enumerate(label_counts1.values):\n",
        "    axes[0].text(i, v+20, str(v), ha='center', fontsize=9, fontweight='bold')\n",
        "\n",
        "# Word count histogram\n",
        "axes[1].hist(df1['word_count'], bins=30, color='#6c8fff', edgecolor='none', alpha=0.85)\n",
        "axes[1].axvline(df1['word_count'].mean(), color='#f472b6', linestyle='--', linewidth=2,\n",
        "                label=f'Mean: {df1[\"word_count\"].mean():.1f} words')\n",
        "axes[1].set_title('Tweet Length Distribution', fontweight='bold')\n",
        "axes[1].set_xlabel('Word Count'); axes[1].legend(fontsize=9)\n",
        "\n",
        "# Box plot per class\n",
        "df1.boxplot(column='word_count', by='Labels', ax=axes[2],\n",
        "            medianprops=dict(color='#f472b6', linewidth=2))\n",
        "axes[2].set_title('Word Count per Class', fontweight='bold')\n",
        "axes[2].set_xlabel(''); plt.sca(axes[2]); plt.xticks(rotation=30, ha='right')\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.savefig(os.path.join(SAVE_DIR, 'eda_d1.png'), dpi=130, bbox_inches='tight')\n",
        "plt.show()\n",
        "\n",
        "print(f'\\nImbalance ratio: {label_counts1.max()/label_counts1.min():.2f}x')\n",
        "print(f'Largest class:  {label_counts1.idxmax()} ({label_counts1.max()})')\n",
        "print(f'Smallest class: {label_counts1.idxmin()} ({label_counts1.min()})')"
      ],
      "id": "8pfINjoZ1Iw5"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7mgWuJTd1Iw5",
        "outputId": "6db9387b-261f-485c-c02f-a68c96599334"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cleaning tweets...\n",
            "\n",
            "Before: I been going thru depression  I didnt even realize it till a couple weeks ago I always said Im strong Im not gone go thru that I got this but your emotions change your body changes your mind changes after a baby like I just pushed a whole human outta me I need time\n",
            "After:  i been going thru depression i didnt even realize it till a couple weeks ago i always said im strong im not gone go thru that i got this but your emotions change your body changes your mind changes after a baby like i just pushed a whole human outta me i need time\n",
            "\n",
            "Label encoding:\n",
            "  0 → atypical  (1980 tweets)\n",
            "  1 → bipolar  (2443 tweets)\n",
            "  2 → major depressive  (2517 tweets)\n",
            "  3 → no  (1985 tweets)\n",
            "  4 → postpartum  (3746 tweets)\n",
            "  5 → psychotic  (2312 tweets)\n",
            "   Saved: le_d1.pkl  (1 KB)\n",
            "\n",
            "✅ D1 preprocessed: 14983 samples, 6 classes\n"
          ]
        }
      ],
      "source": [
        "# ─────────────────────────────────────────────────────────────────\n",
        "# D1 — PREPROCESS: Clean text + encode labels\n",
        "# ─────────────────────────────────────────────────────────────────\n",
        "print('Cleaning tweets...')\n",
        "df1['clean'] = df1['Tweets'].apply(clean_text)\n",
        "\n",
        "# Show before/after example\n",
        "print('\\nBefore:', df1['Tweets'].iloc[0])\n",
        "print('After: ', df1['clean'].iloc[0])\n",
        "\n",
        "# Encode labels to numbers\n",
        "le1 = LabelEncoder()\n",
        "df1['label_enc'] = le1.fit_transform(df1['Labels'])\n",
        "\n",
        "print('\\nLabel encoding:')\n",
        "for i, c in enumerate(le1.classes_):\n",
        "    print(f'  {i} → {c}  ({(df1[\"Labels\"]==c).sum()} tweets)')\n",
        "\n",
        "# Save label encoder immediately\n",
        "save_pkl(le1, 'le_d1.pkl')\n",
        "\n",
        "X1 = df1['clean']\n",
        "y1 = df1['label_enc']\n",
        "print(f'\\n✅ D1 preprocessed: {len(X1)} samples, {len(le1.classes_)} classes')"
      ],
      "id": "7mgWuJTd1Iw5"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sddhr2s31Iw6",
        "outputId": "e1869655-c37c-482d-ba03-c46fceba090c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Split: 11986 train / 2997 test\n",
            "TF-IDF shape: (11986, 34615)  (tweets × features)\n",
            "Applying SMOTE...\n",
            "   SMOTE: 11986 → 17982 samples\n",
            "   Saved: tfidf_d1.pkl  (1338 KB)\n",
            "✅ D1 ready for model training.\n"
          ]
        }
      ],
      "source": [
        "# ─────────────────────────────────────────────────────────────────\n",
        "# D1 — SPLIT → TF-IDF → SMOTE\n",
        "#\n",
        "# 80/20 split (same as Tumaliuan et al. 2024 — keeps results comparable)\n",
        "# TF-IDF converts text to numbers the models can read\n",
        "# SMOTE balances classes (fixes the imbalance the base paper ignored)\n",
        "# ─────────────────────────────────────────────────────────────────\n",
        "X1_tr, X1_te, y1_tr, y1_te = train_test_split(\n",
        "    X1, y1, test_size=0.2, stratify=y1, random_state=42\n",
        ")\n",
        "print(f'Split: {len(X1_tr)} train / {len(X1_te)} test')\n",
        "\n",
        "tfidf1, X1_tr_tf, X1_te_tf = make_tfidf(X1_tr, X1_te)\n",
        "print(f'TF-IDF shape: {X1_tr_tf.shape}  (tweets × features)')\n",
        "\n",
        "print('Applying SMOTE...')\n",
        "X1_bal, y1_bal = apply_smote(X1_tr_tf, y1_tr)\n",
        "\n",
        "# Save TF-IDF — needed to process new text in the UI later\n",
        "save_pkl(tfidf1, 'tfidf_d1.pkl')\n",
        "print('✅ D1 ready for model training.')"
      ],
      "id": "sddhr2s31Iw6"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "jiMUE3W-1Iw6",
        "outputId": "e0a96c20-8a69-43c7-a155-955d6ca9e684"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Training Logistic Regression...\n",
            "\n",
            "=======================================================\n",
            "  Logistic Regression  [D1]\n",
            "=======================================================\n",
            "  Accuracy     : 91.52%\n",
            "  Macro F1     : 0.9179   ← main metric\n",
            "  Cohen's Kappa: 0.8971   ← agreement beyond chance\n",
            "=======================================================\n",
            "\n",
            "                  precision    recall  f1-score   support\n",
            "\n",
            "        atypical      0.995     0.962     0.978       396\n",
            "         bipolar      0.965     0.945     0.955       489\n",
            "major depressive      0.855     0.831     0.843       504\n",
            "              no      0.922     0.985     0.952       397\n",
            "      postpartum      0.916     0.921     0.919       749\n",
            "       psychotic      0.855     0.866     0.860       462\n",
            "\n",
            "        accuracy                          0.915      2997\n",
            "       macro avg      0.918     0.918     0.918      2997\n",
            "    weighted avg      0.916     0.915     0.915      2997\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 840x660 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "   Saved: logistic_regression_d1.pkl  (1624 KB)\n",
            "\n",
            "Training Random Forest...\n",
            "\n",
            "=======================================================\n",
            "  Random Forest  [D1]\n",
            "=======================================================\n",
            "  Accuracy     : 90.89%\n",
            "  Macro F1     : 0.9129   ← main metric\n",
            "  Cohen's Kappa: 0.8891   ← agreement beyond chance\n",
            "=======================================================\n",
            "\n",
            "                  precision    recall  f1-score   support\n",
            "\n",
            "        atypical      0.990     0.962     0.976       396\n",
            "         bipolar      0.969     0.949     0.959       489\n",
            "major depressive      0.928     0.746     0.827       504\n",
            "              no      0.935     0.985     0.960       397\n",
            "      postpartum      0.839     0.955     0.893       749\n",
            "       psychotic      0.867     0.859     0.863       462\n",
            "\n",
            "        accuracy                          0.909      2997\n",
            "       macro avg      0.921     0.909     0.913      2997\n",
            "    weighted avg      0.912     0.909     0.908      2997\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 840x660 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "   Saved: random_forest_d1.pkl  (246200 KB)\n",
            "\n",
            "Training SVM...\n",
            "\n",
            "=======================================================\n",
            "  SVM  [D1]\n",
            "=======================================================\n",
            "  Accuracy     : 92.36%\n",
            "  Macro F1     : 0.9269   ← main metric\n",
            "  Cohen's Kappa: 0.9072   ← agreement beyond chance\n",
            "=======================================================\n",
            "\n",
            "                  precision    recall  f1-score   support\n",
            "\n",
            "        atypical      0.995     0.970     0.982       396\n",
            "         bipolar      0.963     0.947     0.955       489\n",
            "major depressive      0.857     0.853     0.855       504\n",
            "              no      0.958     0.982     0.970       397\n",
            "      postpartum      0.922     0.925     0.923       749\n",
            "       psychotic      0.870     0.883     0.876       462\n",
            "\n",
            "        accuracy                          0.924      2997\n",
            "       macro avg      0.927     0.927     0.927      2997\n",
            "    weighted avg      0.924     0.924     0.924      2997\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 840x660 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "   Saved: svm_d1.pkl  (1623 KB)\n",
            "\n",
            "Training XGBoost...\n",
            "\n",
            "=======================================================\n",
            "  XGBoost  [D1]\n",
            "=======================================================\n",
            "  Accuracy     : 91.76%\n",
            "  Macro F1     : 0.9217   ← main metric\n",
            "  Cohen's Kappa: 0.9000   ← agreement beyond chance\n",
            "=======================================================\n",
            "\n",
            "                  precision    recall  f1-score   support\n",
            "\n",
            "        atypical      0.992     0.965     0.978       396\n",
            "         bipolar      0.959     0.955     0.957       489\n",
            "major depressive      0.861     0.845     0.853       504\n",
            "              no      0.944     0.980     0.962       397\n",
            "      postpartum      0.903     0.911     0.907       749\n",
            "       psychotic      0.873     0.874     0.874       462\n",
            "\n",
            "        accuracy                          0.918      2997\n",
            "       macro avg      0.922     0.922     0.922      2997\n",
            "    weighted avg      0.918     0.918     0.918      2997\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 840x660 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "   Saved: xgboost_d1.pkl  (3102 KB)\n",
            "\n",
            "══════════════════════════════════════════════════════════\n",
            "  SUMMARY — D1 — Depression Type\n",
            "══════════════════════════════════════════════════════════\n",
            "  Model                      Accuracy   Macro F1    Kappa\n",
            "  ------------------------------------------------------\n",
            "  Logistic Regression          0.9152     0.9179   0.8971\n",
            "  Random Forest                0.9089     0.9129   0.8891\n",
            "  SVM                          0.9236     0.9269   0.9072 ★\n",
            "  XGBoost                      0.9176     0.9217   0.9000\n",
            "══════════════════════════════════════════════════════════\n",
            "\n",
            "\n",
            "✅ Dataset 1 complete! All files saved to Drive.\n"
          ]
        }
      ],
      "source": [
        "# ─────────────────────────────────────────────────────────────────\n",
        "# D1 — TRAIN + EVALUATE 4 CLASSICAL MODELS\n",
        "#\n",
        "# Model 1: Logistic Regression — simple linear classifier, good baseline\n",
        "# Model 2: Random Forest       — 300 decision trees voting together\n",
        "#                                 (used in Tumaliuan 2024 — kept for comparison)\n",
        "# Model 3: SVM (LinearSVC)     — gold standard for text classification\n",
        "# Model 4: XGBoost             — gradient boosting, strong on imbalanced data\n",
        "# ─────────────────────────────────────────────────────────────────\n",
        "d1_results = {}\n",
        "\n",
        "models_d1 = {\n",
        "    'Logistic Regression': LogisticRegression(\n",
        "        max_iter=1000, class_weight='balanced', random_state=42, n_jobs=-1),\n",
        "    'Random Forest': RandomForestClassifier(\n",
        "        n_estimators=300, class_weight='balanced', random_state=42, n_jobs=-1),\n",
        "    'SVM': LinearSVC(\n",
        "        C=1.0, class_weight='balanced', max_iter=2000, random_state=42),\n",
        "    'XGBoost': XGBClassifier(\n",
        "        n_estimators=300, learning_rate=0.1, max_depth=6,\n",
        "        eval_metric='mlogloss', random_state=42, n_jobs=-1)\n",
        "}\n",
        "\n",
        "for name, model in models_d1.items():\n",
        "    print(f'\\nTraining {name}...')\n",
        "    model.fit(X1_bal, y1_bal)\n",
        "    preds = model.predict(X1_te_tf)\n",
        "    evaluate_model(name, y1_te, preds, le1.classes_, 'D1', d1_results)\n",
        "    safe = name.lower().replace(' ', '_')\n",
        "    save_pkl(model, f'{safe}_d1.pkl')\n",
        "\n",
        "ALL_RESULTS['D1 Depression Type'] = d1_results\n",
        "summary_table(d1_results, 'D1 — Depression Type')\n",
        "print('\\n✅ Dataset 1 complete! All files saved to Drive.')"
      ],
      "id": "jiMUE3W-1Iw6"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1qRqUXJw1Iw7"
      },
      "source": [
        "---\n",
        "# 📁 DATASET 2 — Binary Depression Detection\n",
        "**Source:** albertobellardini/depression-twitter · Kaggle  \n",
        "**Upload:** `DA_DB_2.csv`  \n",
        "**Columns:** `Index` | `message to examine` | `label (depression result)`  \n",
        "**Task:** Is this text depressed or not?"
      ],
      "id": "1qRqUXJw1Iw7"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ec1BMGiG1Iw7",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0285aed7-cda0-42fe-e627-74e0b5969538"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Raw shape: (10314, 3)\n",
            "Columns: ['Index', 'message to examine', 'label (depression result)']\n",
            "   Index                                 message to examine  \\\n",
            "0    106  just had a real good moment. i missssssssss hi...   \n",
            "1    217         is reading manga  http://plurk.com/p/mzp1e   \n",
            "2    220  @comeagainjen http://twitpic.com/2y2lx - http:...   \n",
            "\n",
            "   label (depression result)  \n",
            "0                          0  \n",
            "1                          0  \n",
            "2                          0  \n"
          ]
        }
      ],
      "source": [
        "# ─────────────────────────────────────────────────────────────────\n",
        "# D2 — LOAD + STANDARDISE COLUMNS\n",
        "# We know the exact column names from the dataset.\n",
        "# ─────────────────────────────────────────────────────────────────\n",
        "df2 = pd.read_csv('DA_DB_2.csv')\n",
        "print(f'Raw shape: {df2.shape}')\n",
        "print(f'Columns: {list(df2.columns)}')\n",
        "print(df2.head(3))"
      ],
      "id": "ec1BMGiG1Iw7"
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gPmWIwC-3qhP",
        "outputId": "93916604-9e04-41b7-cd69-943a43300cbf"
      },
      "id": "gPmWIwC-3qhP",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "S1KSidUN1Iw8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "943b9fef-5bc3-488c-e63b-2ae014a83d0e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Label distribution:\n",
            "label\n",
            "0    8000\n",
            "1    2314\n",
            "Name: count, dtype: int64\n",
            "\n",
            "Total clean rows: 10,314\n",
            "                                                text label\n",
            "0  just had a real good moment. i missssssssss hi...     0\n",
            "1         is reading manga  http://plurk.com/p/mzp1e     0\n",
            "2  @comeagainjen http://twitpic.com/2y2lx - http:...     0\n"
          ]
        }
      ],
      "source": [
        "# ─────────────────────────────────────────────────────────────────\n",
        "# Rename to standard: text + label\n",
        "# ─────────────────────────────────────────────────────────────────\n",
        "df2 = df2.rename(columns={\n",
        "    'message to examine': 'text',\n",
        "    'label (depression result)': 'label'\n",
        "})\n",
        "if 'Index' in df2.columns:\n",
        "    df2 = df2.drop(columns=['Index'])\n",
        "\n",
        "df2 = df2[['text','label']].dropna().reset_index(drop=True)\n",
        "df2['label'] = df2['label'].astype(str).str.strip().str.lower()\n",
        "\n",
        "print('Label distribution:')\n",
        "print(df2['label'].value_counts())\n",
        "print(f'\\nTotal clean rows: {len(df2):,}')\n",
        "print(df2.head(3))"
      ],
      "id": "S1KSidUN1Iw8"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "mj-pXDIF1Iw8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 461
        },
        "outputId": "bb94d6cd-6ad8-4732-fd69-f99079bfc31f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dataset 2 Statistics:\n",
            "       word_count  char_count\n",
            "count    10314.00    10314.00\n",
            "mean        15.07       92.62\n",
            "std         10.13       64.67\n",
            "min          1.00        1.00\n",
            "25%          8.00       48.00\n",
            "50%         13.00       78.00\n",
            "75%         20.00      119.00\n",
            "max         92.00      874.00\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1600x400 with 3 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Imbalance ratio: 3.46x\n"
          ]
        }
      ],
      "source": [
        "# ─────────────────────────────────────────────────────────────────\n",
        "# D2 — EDA\n",
        "# ─────────────────────────────────────────────────────────────────\n",
        "label_counts2 = df2['label'].value_counts()\n",
        "df2['word_count'] = df2['text'].apply(lambda x: len(str(x).split()))\n",
        "df2['char_count'] = df2['text'].apply(lambda x: len(str(x)))\n",
        "\n",
        "print('Dataset 2 Statistics:')\n",
        "print(df2[['word_count','char_count']].describe().round(2))\n",
        "\n",
        "fig, axes = plt.subplots(1, 3, figsize=(16, 4))\n",
        "fig.suptitle('Dataset 2 EDA — Binary Depression Detection', fontsize=13, fontweight='bold')\n",
        "\n",
        "label_counts2.plot(kind='bar', ax=axes[0], color=['#ef4444','#22c55e'], edgecolor='none')\n",
        "axes[0].set_title('Class Distribution', fontweight='bold')\n",
        "axes[0].set_xticklabels(label_counts2.index, rotation=0)\n",
        "for i, v in enumerate(label_counts2.values):\n",
        "    axes[0].text(i, v+20, str(v), ha='center', fontsize=10, fontweight='bold')\n",
        "\n",
        "axes[1].hist(df2['word_count'], bins=30, color='#6c8fff', edgecolor='none', alpha=0.85)\n",
        "axes[1].axvline(df2['word_count'].mean(), color='#f472b6', linestyle='--', linewidth=2,\n",
        "                label=f'Mean: {df2[\"word_count\"].mean():.1f}')\n",
        "axes[1].set_title('Text Length Distribution', fontweight='bold')\n",
        "axes[1].set_xlabel('Words'); axes[1].legend(fontsize=9)\n",
        "\n",
        "for lbl, color in zip(df2['label'].unique(), ['#ef4444','#22c55e']):\n",
        "    axes[2].hist(df2[df2['label']==lbl]['word_count'], bins=20,\n",
        "                 alpha=0.65, color=color, label=lbl, edgecolor='none')\n",
        "axes[2].set_title('Word Count by Class', fontweight='bold')\n",
        "axes[2].set_xlabel('Words'); axes[2].legend(fontsize=9)\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.savefig(os.path.join(SAVE_DIR, 'eda_d2.png'), dpi=130, bbox_inches='tight')\n",
        "plt.show()\n",
        "print(f'Imbalance ratio: {label_counts2.max()/label_counts2.min():.2f}x')"
      ],
      "id": "mj-pXDIF1Iw8"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Z4cJazh11Iw8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4cd0089a-c733-442e-dba5-11c1bd553bf2"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cleaning text...\n",
            "\n",
            "Before: just had a real good moment. i missssssssss him so much, \n",
            "After:  just had a real good moment i missssssssss him so much\n",
            "   Saved: le_d2.pkl  (0 KB)\n",
            "\n",
            "Label encoding:\n",
            "  0 → 0  (8000 samples)\n",
            "  1 → 1  (2314 samples)\n",
            "   Saved: tfidf_d2.pkl  (569 KB)\n",
            "TF-IDF shape: (8251, 15240)\n",
            "Applying SMOTE...\n",
            "   SMOTE: 8251 → 12800 samples\n",
            "✅ D2 ready.\n"
          ]
        }
      ],
      "source": [
        "# ─────────────────────────────────────────────────────────────────\n",
        "# D2 — PREPROCESS + SPLIT + TF-IDF + SMOTE\n",
        "# ─────────────────────────────────────────────────────────────────\n",
        "print('Cleaning text...')\n",
        "df2['clean'] = df2['text'].apply(clean_text)\n",
        "\n",
        "print('\\nBefore:', str(df2['text'].iloc[0])[:80])\n",
        "print('After: ', str(df2['clean'].iloc[0])[:80])\n",
        "\n",
        "le2 = LabelEncoder()\n",
        "df2['label_enc'] = le2.fit_transform(df2['label'])\n",
        "save_pkl(le2, 'le_d2.pkl')\n",
        "\n",
        "print('\\nLabel encoding:')\n",
        "for i, c in enumerate(le2.classes_):\n",
        "    print(f'  {i} → {c}  ({(df2[\"label\"]==c).sum()} samples)')\n",
        "\n",
        "X2 = df2['clean']; y2 = df2['label_enc']\n",
        "\n",
        "X2_tr, X2_te, y2_tr, y2_te = train_test_split(\n",
        "    X2, y2, test_size=0.2, stratify=y2, random_state=42)\n",
        "\n",
        "tfidf2, X2_tr_tf, X2_te_tf = make_tfidf(X2_tr, X2_te)\n",
        "save_pkl(tfidf2, 'tfidf_d2.pkl')\n",
        "print(f'TF-IDF shape: {X2_tr_tf.shape}')\n",
        "\n",
        "print('Applying SMOTE...')\n",
        "X2_bal, y2_bal = apply_smote(X2_tr_tf, y2_tr)\n",
        "print('✅ D2 ready.')"
      ],
      "id": "Z4cJazh11Iw8"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "B4rZ3AFU1Iw9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "c210b0fd-8c68-4e99-dd42-47180c05c0b4"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Training Logistic Regression...\n",
            "\n",
            "=======================================================\n",
            "  Logistic Regression  [D2]\n",
            "=======================================================\n",
            "  Accuracy     : 98.89%\n",
            "  Macro F1     : 0.9839   ← main metric\n",
            "  Cohen's Kappa: 0.9678   ← agreement beyond chance\n",
            "=======================================================\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0      0.990     0.996     0.993      1600\n",
            "           1      0.985     0.965     0.975       463\n",
            "\n",
            "    accuracy                          0.989      2063\n",
            "   macro avg      0.987     0.981     0.984      2063\n",
            "weighted avg      0.989     0.989     0.989      2063\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x500 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "   Saved: logistic_regression_d2.pkl  (120 KB)\n",
            "\n",
            "Training Random Forest...\n",
            "\n",
            "=======================================================\n",
            "  Random Forest  [D2]\n",
            "=======================================================\n",
            "  Accuracy     : 99.18%\n",
            "  Macro F1     : 0.9880   ← main metric\n",
            "  Cohen's Kappa: 0.9760   ← agreement beyond chance\n",
            "=======================================================\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0      0.989     1.000     0.995      1600\n",
            "           1      1.000     0.963     0.981       463\n",
            "\n",
            "    accuracy                          0.992      2063\n",
            "   macro avg      0.995     0.982     0.988      2063\n",
            "weighted avg      0.992     0.992     0.992      2063\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x500 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "   Saved: random_forest_d2.pkl  (73424 KB)\n",
            "\n",
            "Training SVM...\n",
            "\n",
            "=======================================================\n",
            "  SVM  [D2]\n",
            "=======================================================\n",
            "  Accuracy     : 97.09%\n",
            "  Macro F1     : 0.9589   ← main metric\n",
            "  Cohen's Kappa: 0.9177   ← agreement beyond chance\n",
            "=======================================================\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0      0.987     0.975     0.981      1600\n",
            "           1      0.917     0.957     0.937       463\n",
            "\n",
            "    accuracy                          0.971      2063\n",
            "   macro avg      0.952     0.966     0.959      2063\n",
            "weighted avg      0.972     0.971     0.971      2063\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x500 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "   Saved: svm_d2.pkl  (120 KB)\n",
            "\n",
            "Training XGBoost...\n",
            "\n",
            "=======================================================\n",
            "  XGBoost  [D2]\n",
            "=======================================================\n",
            "  Accuracy     : 99.27%\n",
            "  Macro F1     : 0.9895   ← main metric\n",
            "  Cohen's Kappa: 0.9789   ← agreement beyond chance\n",
            "=======================================================\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0      0.991     0.999     0.995      1600\n",
            "           1      0.998     0.970     0.984       463\n",
            "\n",
            "    accuracy                          0.993      2063\n",
            "   macro avg      0.995     0.985     0.989      2063\n",
            "weighted avg      0.993     0.993     0.993      2063\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x500 with 2 Axes>"
            ],
            "image/png": 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dq+PHj5tt1+J5Xqh///7y8Dj/o9SjR49Lvtv1hX8px8bGqlOnTmrZsqVSU1ML7J9/XunSQZGVwPZCZ8+e1aOPPirDMMxfRr///rulzE+1atVknF8sUeTNStZn3759euGFFySdD0w/++wzM6A8duyYRo4c6dT/wp+PVatW6csvv7R4Fwp3YbB/uZ+r7Oxsff/99+bj22+/XZUqVTJfB+np6dqxY4fZXtDr0zAMVa1aVRMnTlRiYqKOHTumzZs3m/0+/fRT82urr4niZBiGRo8ebd6nqlWrOr3dRWpqqtq0aWPep8GDB+vDDz+8qj80AFfilXgDePLJJzV//nwdPHhQJ0+eVPPmzfXkk0+qfv36+vPPP7V27Vq9//77Wrt2rYKCgvTQQw9p27ZtkqTo6Gj9+eefcjgceuaZZ8xzPvjgg8Uy1ttuu03Jyck6d+6cHnroIbVu3VrvvPPORZkgSZo0aZLWrl2rLl26KDw8XP7+/lq5cqXZnpmZWeh1KlSooA4dOmjFihXKzMxUr169NHLkSO3du1fvvPOO2a+4nueFqlatqrffflspKSl64IEHLts3X2xsrKKiovTVV185Pe8LXZilmjdvnkqVKqVSpUqpZcuWVzXmmJgY7d27V9L5CfKfffaZ/ve//2nu3Lnq2bOnunTpclXnvxqPP/64Ofl3xIgRatiwoaZMmaIVK1bo2LFjmjdvnvr27WuW+dq3b68uXbroiy++kHQ+AB0yZIjatWsnLy8vS8HQ0aNHtX79euXk5Ojbb79VfHy82ZYfoDzwwAOKiYlRdna2Pv30U40fP14tWrTQ3LlzdeTIEUlSnTp11KBBAzkcDvXu3VtxcXGSzmc9xo8frxMnTmj8+PHmufNfnwsXLlRcXJy6d++u6tWrKyAgwJwgLDn/LFz4mpg1a5Y6d+4sPz8/NW3atAh3uei2b99ufvTEe++9p8TERLPtjTfeMAObo0ePqlWrVtqzZ48kqV27dnrooYecstbh4eFOGT3gmruG84twFYryDs8ZGRlGq1atCu13qaXuFypssu2lJuG+++67F12vTJkyxs0333zRZMYXX3yx0DF6eHgY69evNwyj8KXue/fuvaKl7hdOer3U8y/IXyc8X8qFY8mf8Lxx48aLlko7HA4jIiLCfPz++++b57hwkvqF21+v8dcJ5vkKmhy7Zs0acwwRERFGbm6usW/fPsPf39+QZISFhRknTpy47L0oDh988IE53mrVqhnp6elm27x588y2W265xant1KlTRvv27S/58yHJiIyMNI+x8g7PjRs3dlpaX5Sl7sePH7/sUvf89/r58MMPLzmOhQsXmue9cML05b7/l1OUCc8FbV5eXkZcXJzTMZdbki/JGD9+fJHHArgSZa8bRJ06dbRt2zZNnjxZLVu2VFBQkLy9vVWlShV16NBBc+fOVZ06dSRJPj4+io+P18SJE3XHHXfIz89Pvr6+ql+/vmJjY/X111+by9hd7V//+pfGjBmj4OBg+fn56Z577lFiYqJuvfXWi/p27txZgwYNUr169VS+fHmVKlVKQUFBat++vVauXKm77rrrkte65ZZb9N1332nIkCGqXr26vLy8VK5cObVu3VofffSRJk6cWCzP8Wo0a9ZMS5YsUf369eXr66u6detq8eLFBX5GmHR+XtDrr7+uW2+91SUlg/T0dPXv31+GYcjLy0uzZs2Sh4eHqlevrpdfflnS+fLXhW8BcK0cP35co0aNMh/PmDFDpUuXNh/36dPHXPq/b98+TZgwwWwrV66cVqxYoU8++URdu3ZVaGiovLy85Ofnpxo1aqhXr16aO3euPvvss8uOw8/PT/Xq1dNzzz2nNWvWOC2tf+KJJxQfH69OnTopKChInp6eCgsLU9++fZWcnKw777zT7BsUFKQNGzZozJgxqlmzpnx8fOTv768777xTM2bM0IIFC8xyZUREhIYPH67GjRurYsWKKlWqlAICAtSqVSt99NFH6t27t3neQYMGKSYmRuHh4U4lsGvBy8tLlSpVUtOmTTVq1Cjt3LlTgwYNuqZjAFzBYRhFnMkKALCFfv36ae7cuU77pkyZYmnul6tUq1ZNBw4ccNq3f/9+VatW7ZqNAe6HzA8AAHArZH4AwE3t2bPnopWGt9xyS6EfbloctmzZooyMDKd9d9555w3xQby4cRH8AAAAt0LZCwAAuBWCHwAA4FYIfgAAgFsh+AEAAG6Fj7cAAMAm/BoNcen5zn3/lkvPd72wbfDj6hcA4I7Off+WMnJKehTAjc/Xtr9tb0x8OwAAsAsHs1msIPgBAMAu/v/nxeHSCBEBAIBbIfMDAIBdUPayhLsEAADcCpkfAADsgjk/lhD8AABgF5S9LOEuAQAAt0LmBwAAu6DsZQnBDwAAdkHZyxLuEgAAcCtkfgAAsAvKXpYQ/AAAYBeUvSzhLgEAALdC5gcAALug7GUJmR8AAOBWyPwAAGAXzPmxhOAHAAC7oOxlCSEiAABwK2R+AACwC8pelhD8AABgFwQ/lnCXAACAWyHzAwCAXXgw4dkKgh8AAOyCspcl3CUAAOBWyPwAAGAXvM+PJWR+AACAWyHzAwCAXTDnxxKCHwAA7IKylyWEiAAAwK2Q+QEAwC4oe1lC8AMAgF1Q9rKEEBEAALgVMj8AANgFZS9LCH4AALALyl6WECICAAC3QuYHAAC7oOxlCXcJAAC4FTI/AADYBXN+LCH4AQDALih7WcJdAgAAboXMDwAAdkHmxxKCHwAA7II5P5YQIgIAALdC8AMAgF04PFy7FUFCQoK6du2qsLAwORwOLV26tNC+jz/+uBwOh6ZOneq0Py0tTX369FG5cuUUGBioAQMG6MyZM059tm3bplatWsnX11dVqlTRpEmTijROieAHAAD7cDhcuxVBenq6GjRooLfffvuS/ZYsWaINGzYoLCzsorY+ffpox44dio+P1/Lly5WQkKCBAwea7adPn1b79u1VtWpVJScn67XXXtOECRM0c+bMIo2VOT8AAOCqderUSZ06dbpkn99++01Dhw7VypUr1aVLF6e2nTt3asWKFdq8ebOaNm0qSZo+fbo6d+6s119/XWFhYZo/f76ysrI0e/ZseXt7q27dutq6dasmT57sFCRdDpkfAADswsVlr8zMTJ0+fdppy8zMvKKh5eXl6ZFHHtFTTz2lunXrXtSelJSkwMBAM/CRpMjISHl4eGjjxo1mn9atW8vb29vs06FDB+3evVsnTpywPBaCHwAAUKDY2FgFBAQ4bbGxsVd0rldffVWenp4aNmxYge0pKSkKDg522ufp6amgoCClpKSYfUJCQpz65D/O72MFZS8AAOzCxUvdx4wZo1GjRjnt8/HxKfJ5kpOT9eabb+q7776T4zpYjk/mBwAAm3A4HC7dfHx8VK5cOaftSoKfxMREHT16VOHh4fL09JSnp6cOHDig0aNHq1q1apKk0NBQHT161Om4nJwcpaWlKTQ01OyTmprq1Cf/cX4fKwh+AABAsXrkkUe0bds2bd261dzCwsL01FNPaeXKlZKkiIgInTx5UsnJyeZxq1evVl5enpo3b272SUhIUHZ2ttknPj5eNWvWVPny5S2Ph7IXAAA2UZIlpTNnzuiXX34xH+/fv19bt25VUFCQwsPDVaFCBaf+Xl5eCg0NVc2aNSVJtWvXVseOHfXYY48pLi5O2dnZGjJkiHr37m0ui3/ooYf0wgsvaMCAAYqJidGPP/6oN998U1OmTCnSWAl+AACwixKcTrNlyxa1bdvWfJw/VygqKkpz5syxdI758+dryJAhateunTw8PNSjRw9NmzbNbA8ICNDXX3+t6OhoNWnSRBUrVtS4ceOKtMxdkhyGYRhFOuIG4ddoSEkPAbjhnfv+LWXklPQogBuf7zVKNfj3fN+l50tf/KhLz3e9IPMDAIBNXA8rqW4EBD8AANgEwY81rPYCAABuhcwPAAA2QebHGjI/AADArZD5AQDAJsj8WEPwAwCAXRD7WELZCwAAuBUyPwAA2ARlL2sIfgAAsAmCH2soewEAALdC5gcAAJsg82MNwQ8AADZB8GMNZS8AAOBWyPwAAGAXJH4sIfMDAADcCpkfAABsgjk/1hD8AABgEwQ/1lD2AgAAboXMDwAANkHmxxqCHwAA7ILYxxLKXgAAwK2Q+QEAwCYoe1lD8AMAgE0Q/FhD2QsAALgVMj8AANgEmR9ryPwAAAC3QuYHAACbIPNjDcEPAAB2QexjCWUvAADgVsj8AABgE5S9rCH4AQDAJgh+rKHsBQAA3AqZHwAAbILMjzUEPwAA2AWxjyWUvQAAgFsh8wMAgE1Q9rKGzA8AAHArZH5wRe5qfKtG9o1U4zrhqlwpQL1GztTna7eZ7TNfeFiP3NvC6Zivv/lJ3Ya8Yz5uWOtmvTS8u5rUDVdurqGlq7Yq5o3/Kv1cltnn7ma3a/wT/1Dd28KUfi5L8z/fqPFvf67c3Lzif5LAdSp5y2bNmf2edv70o/744w9Nmfa27mkXWdLDwnWAzI81ZH5wRfz9fLT95980IvajQvus/GaHqkWOMbeoMe+bbZUrBeiLuKHae+gPtX7kdXWLflt1bg3VrH8/Yvapf/tNWjp9sL7+9ie1eHCiHnlmtrq0qa+XhnUr1ucGXO/OnTurmjVraszY8SU9FFxnHA6HS7eiSEhIUNeuXRUWFiaHw6GlS5eabdnZ2YqJiVH9+vXl7++vsLAw9e3bV7///rvTOdLS0tSnTx+VK1dOgYGBGjBggM6cOePUZ9u2bWrVqpV8fX1VpUoVTZo0qcj3icwPrsjX3/ykr7/56ZJ9srJylHr8zwLbOrWqp+ycXI2I/ViGYUiShr78kbYsfla3VKmofYeO6YH2jfXjnt8VO3OFJGnfoWN67s2lmvdqf7387pc6czbTtU8KuEG0bNVGLVu1KelhAE7S09PVoEED9e/fX/fff79T29mzZ/Xdd9/p+eefV4MGDXTixAkNHz5c9957r7Zs2WL269Onj44cOaL4+HhlZ2fr0Ucf1cCBA7VgwQJJ0unTp9W+fXtFRkYqLi5O27dvV//+/RUYGKiBAwdaHmuJBj/Hjh3T7NmzlZSUpJSUFElSaGio/va3v6lfv36qVKlSSQ4PV6lV0xo6sCpWJ0+f1drNP+uFt5cr7VS6JMnH21PZ2blm4CNJ5zLPl7v+1vBW7Tt0TD7ensrIzHY657nMbPn5eqtR7XAlJu+5dk8GAG4AJVn26tSpkzp16lRgW0BAgOLj4532vfXWW2rWrJkOHjyo8PBw7dy5UytWrNDmzZvVtGlTSdL06dPVuXNnvf766woLC9P8+fOVlZWl2bNny9vbW3Xr1tXWrVs1efLkIgU/JVb22rx5s26//XZNmzZNAQEBat26tVq3bq2AgABNmzZNtWrVcooGcWOJ/3an/vX8h+o8aLrGvvmZWjW5TZ+9NVgeHud/MNdu2q2QCuU0sm87eXmWUmBZP7OcFVopwDxHiwa3qFfHJvLwcCisUoCeHXj+B6typXIl88QA4HrmcPFWjE6dOiWHw6HAwEBJUlJSkgIDA83AR5IiIyPl4eGhjRs3mn1at24tb29vs0+HDh20e/dunThxwvK1SyzzM3ToUPXs2VNxcXEXRaqGYejxxx/X0KFDlZSUdMnzZGZmKjPTufzh4+Pj8vGiaBavTDa/3vHL79q+5zftXP6CWjetobWbftbOfSl6bNyHmjj6fv176L3KzcvTOwvXKeXYaRl55yczr9qwS89OXappz/bWey/2VWZ2jibOWqGWjW9TXp5R2KUBAC5S2O/Yq/09m5GRoZiYGD344IMqV+78H7MpKSkKDg526ufp6amgoCCzOpSSkqLq1as79QkJCTHbypcvb+n6JZb5+eGHHzRy5MgCU3QOh0MjR47U1q1bL3ue2NhYBQQEOG2xsbHFMGJcjV9/O64/TvypW6v8XynzoxVbVP3vz+rWDmN1090xeinuS1UqX0b7Dx83+0ybt1qhrZ/S7Z3H6ea2z5gryvYfPnbNnwMAXO9cPeG5OH7HZmdnq1evXjIMQzNmzHDRMy+aEsv8hIaGatOmTapVq1aB7Zs2bTKjuUsZM2aMRo0a5bTPx8dHr3422iXjhGvcFByoCgH+Sjl2+qK2o2nnJ0X37dZCGVnZWrVh10V9jvxxSpLUq2NTHTqSpu93HSreAQPADcjVc34K+x17pfIDnwMHDmj16tVm1kc6HxccPXrUqX9OTo7S0tIUGhpq9klNTXXqk/84v48VJRb8PPnkkxo4cKCSk5PVrl07M9BJTU3VqlWrNGvWLL3++uuXPY8r0m8oOn8/b6csTrWbKuiO22/SidNnlXYqXc8N6qylq7Yq5dhp3VKlol4e3l17Dx1T/Lc7zWMe/2drbfhhn86czVK7FrX0yojuen76Zzp15pzZZ2Tfdvr6253Ky8tTt3YN9eSjf9fDT8+m7AW3djY9XQcPHjQf/3b4sHbt3KmAgABVDgsrwZHBblz5OzY/8NmzZ4/WrFmjChUqOLVHRETo5MmTSk5OVpMmTSRJq1evVl5enpo3b272ee6555SdnS0vLy9JUnx8vGrWrGm55CVJDuPC5TbX2EcffaQpU6YoOTlZubm5kqRSpUqpSZMmGjVqlHr16nXF5/ZrNMRVw0QBWjWpoa//M/yi/R8u26Bhr3ykjycPVINaNyuwrJ+O/HFK/0vapX+/s9zM8kjSf158RB1b1lOZ0t7a/Wuqpn6wSgu/2Ox0vq/eHaqGtavIx8tT23/+TS/P/OqyS+zhOue+f0sZOSU9CvzV5k0b9a9H+160/95u9+nFVyaWwIhwOb7XKNVw25NfufR8v7xe8Oqtgpw5c0a//PKLJKlRo0aaPHmy2rZtq6CgIFWuXFkPPPCAvvvuOy1fvtypshMUFGROYO7UqZNSU1MVFxdnLnVv2rSpudT91KlTqlmzptq3b6+YmBj9+OOP6t+/v6ZMmVKk1V4lGvzky87O1rFj5+dwVKxY0YzmrgbBD3D1CH4A17hWwU+Np1a49Hx7Xutoue/atWvVtm3bi/ZHRUVpwoQJF01UzrdmzRrdfffdks6/yeGQIUP0+eefy8PDQz169NC0adNUpkwZs/+2bdsUHR2tzZs3q2LFiho6dKhiYmKK9Lyui+CnOBD8AFeP4AdwDXcIfm4kvMMzAAA2wUd7WcNnewEAALdC5gcAAJvgU92tIfgBAMAmiH2soewFAADcCpkfAABsIv/Do3FpBD8AANgEZS9rKHsBAAC3QuYHAACbYLWXNQQ/AADYBLGPNZS9AACAWyHzAwCATVD2sobMDwAAcCtkfgAAsAkyP9YQ/AAAYBPEPtZQ9gIAAG6FzA8AADZB2csagh8AAGyC2Mcayl4AAMCtkPkBAMAmKHtZQ/ADAIBNEPtYQ9kLAAC4FTI/AADYBGUva8j8AAAAt0LmBwAAmyDxYw3BDwAANkHZyxrKXgAAwK2Q+QEAwCZI/FhD8AMAgE1Q9rKGshcAAHArZH4AALAJEj/WEPwAAGATlL2soewFAADcCpkfAABsgsSPNWR+AACAWyHzAwCATTDnxxqCHwAAbILgxxrKXgAAwK2Q+QEAwCZI/FhD5gcAAJtwOBwu3YoiISFBXbt2VVhYmBwOh5YuXerUbhiGxo0bp8qVK8vPz0+RkZHas2ePU5+0tDT16dNH5cqVU2BgoAYMGKAzZ8449dm2bZtatWolX19fValSRZMmTSryfSL4AQAAVy09PV0NGjTQ22+/XWD7pEmTNG3aNMXFxWnjxo3y9/dXhw4dlJGRYfbp06ePduzYofj4eC1fvlwJCQkaOHCg2X769Gm1b99eVatWVXJysl577TVNmDBBM2fOLNJYKXsBAGATJVn26tSpkzp16lRgm2EYmjp1qsaOHatu3bpJkj744AOFhIRo6dKl6t27t3bu3KkVK1Zo8+bNatq0qSRp+vTp6ty5s15//XWFhYVp/vz5ysrK0uzZs+Xt7a26detq69atmjx5slOQdDlkfgAAsImSLHtdyv79+5WSkqLIyEhzX0BAgJo3b66kpCRJUlJSkgIDA83AR5IiIyPl4eGhjRs3mn1at24tb29vs0+HDh20e/dunThxwvJ4yPwAAIACZWZmKjMz02mfj4+PfHx8inSelJQUSVJISIjT/pCQELMtJSVFwcHBTu2enp4KCgpy6lO9evWLzpHfVr58eUvjIfMDAIBNOByu3WJjYxUQEOC0xcbGlvTTvGpkfgAAQIHGjBmjUaNGOe0ratZHkkJDQyVJqampqly5srk/NTVVDRs2NPscPXrU6bicnBylpaWZx4eGhio1NdWpT/7j/D5WkPkBAMAmPBwOl24+Pj4qV66c03YlwU/16tUVGhqqVatWmftOnz6tjRs3KiIiQpIUERGhkydPKjk52eyzevVq5eXlqXnz5mafhIQEZWdnm33i4+NVs2ZNyyUvieAHAADbcHXZqyjOnDmjrVu3auvWrZLOT3LeunWrDh48KIfDoREjRuill17SsmXLtH37dvXt21dhYWHq3r27JKl27drq2LGjHnvsMW3atEnffPONhgwZot69eyssLEyS9NBDD8nb21sDBgzQjh079NFHH+nNN9+8KDt1OZS9AADAVduyZYvatm1rPs4PSKKiojRnzhw9/fTTSk9P18CBA3Xy5Em1bNlSK1askK+vr3nM/PnzNWTIELVr104eHh7q0aOHpk2bZrYHBATo66+/VnR0tJo0aaKKFStq3LhxRVrmLkkOwzCMq3y+1yW/RkNKegjADe/c928pI6ekRwHc+HyvUaqhwzsbXXq+lU80d+n5rhdkfgAAsAkPPtvLEub8AAAAt0LmBwAAm3DluzLbGcEPAAA2QexjDWUvAADgVsj8AABgEw6R+rGCzA8AAHArZH4AALAJlrpbQ/ADAIBNsNrLGspeAADArZD5AQDAJkj8WEPwAwCATXgQ/VhC2QsAALgVMj8AANgEiR9rCH4AALAJVntZQ9kLAAC4FTI/AADYBIkfa8j8AAAAt0LmBwAAm2CpuzUEPwAA2AShjzWUvQAAgFsh8wMAgE2w1N0agh8AAGzCg9jHEspeAADArZD5AQDAJih7WWMp+Fm2bJnlE957771XPBgAAHDliH2ssRT8dO/e3dLJHA6HcnNzr2Y8AAAAxcpS8JOXl1fc4wAAAFeJspc1THgGAABu5YomPKenp2vdunU6ePCgsrKynNqGDRvmkoEBAICiYam7NUUOfr7//nt17txZZ8+eVXp6uoKCgnTs2DGVLl1awcHBBD8AAJQQyl7WFLnsNXLkSHXt2lUnTpyQn5+fNmzYoAMHDqhJkyZ6/fXXi2OMAAAALlPk4Gfr1q0aPXq0PDw8VKpUKWVmZqpKlSqaNGmSnn322eIYIwAAsMDh4s2uihz8eHl5ycPj/GHBwcE6ePCgJCkgIECHDh1y7egAAIBlHg6HSze7KvKcn0aNGmnz5s2qUaOG2rRpo3HjxunYsWP68MMPVa9eveIYIwAAgMsUOfPzyiuvqHLlypKkl19+WeXLl9fgwYP1xx9/aObMmS4fIAAAsMbhcO1mV0XO/DRt2tT8Ojg4WCtWrHDpgAAAwJVhtZc1vMkhAABwK0XO/FSvXv2SkeW+ffuuakAAAODKkPixpsjBz4gRI5weZ2dn6/vvv9eKFSv01FNPuWpcAAAAxaLIwc/w4cML3P/2229ry5YtVz0gAABwZUpyeXpubq4mTJigefPmKSUlRWFhYerXr5/Gjh1rVowMw9D48eM1a9YsnTx5UnfddZdmzJihGjVqmOdJS0vT0KFD9fnnn8vDw0M9evTQm2++qTJlyrhsrC6b89OpUyf997//ddXpAABAEZXkaq9XX31VM2bM0FtvvaWdO3fq1Vdf1aRJkzR9+nSzz6RJkzRt2jTFxcVp48aN8vf3V4cOHZSRkWH26dOnj3bs2KH4+HgtX75cCQkJGjhwoKtukaQr/GDTgnzyyScKCgpy1ekAAMAN5Ntvv1W3bt3UpUsXSVK1atW0cOFCbdq0SdL5rM/UqVM1duxYdevWTZL0wQcfKCQkREuXLlXv3r21c+dOrVixQps3bzZXl0+fPl2dO3fW66+/rrCwMJeM9Yre5PDCCc+GYSglJUV//PGH3nnnHZcMCgAAFJ2rl7pnZmYqMzPTaZ+Pj498fHwu6vu3v/1NM2fO1M8//6zbb79dP/zwg9avX6/JkydLkvbv36+UlBRFRkaaxwQEBKh58+ZKSkpS7969lZSUpMDAQKe31YmMjJSHh4c2btyo++67zyXPq8jBT7du3ZxuroeHhypVqqS7775btWrVcsmgXOHc92+V9BAAW/B1WX4YQHFz9fvXxMbG6oUXXnDaN378eE2YMOGivs8884xOnz6tWrVqqVSpUsrNzdXLL7+sPn36SJJSUlIkSSEhIU7HhYSEmG0pKSkKDg52avf09FRQUJDZxxWK/N9aQU/4enQ22yjpIQA3vNJeDq3edbykhwHc8O6pVaGkh3BFxowZo1GjRjntKyjrI0kff/yx5s+frwULFqhu3braunWrRowYobCwMEVFRV2L4VpW5OCnVKlSOnLkyEWR2fHjxxUcHKzc3FyXDQ4AAFjn6rJXYSWugjz11FN65pln1Lt3b0lS/fr1deDAAcXGxioqKkqhoaGSpNTUVPNjsvIfN2zYUJIUGhqqo0ePOp03JydHaWlp5vGuUOQMmWEUnFHJzMyUt7f3VQ8IAABcGQ+Ha7eiOHv2rDw8nMOKUqVKKS8vT9L5N0kODQ3VqlWrzPbTp09r48aNioiIkCRFRETo5MmTSk5ONvusXr1aeXl5at68+RXelYtZzvxMmzZN0vmo8j//+Y/Tevvc3FwlJCRcV3N+AADAtdO1a1e9/PLLCg8PV926dfX9999r8uTJ6t+/v6Tz8cOIESP00ksvqUaNGqpevbqef/55hYWFqXv37pKk2rVrq2PHjnrssccUFxen7OxsDRkyRL1793bZSi+pCMHPlClTJJ3P/MTFxalUqVJmm7e3t6pVq6a4uDiXDQwAABRNUbM1rjR9+nQ9//zzeuKJJ3T06FGFhYVp0KBBGjdunNnn6aefVnp6ugYOHKiTJ0+qZcuWWrFihXx9fc0+8+fP15AhQ9SuXTvzTQ7zEzCu4jAKq2MVom3btvr0009Vvnx5lw7E1ZjwDFw9JjwDrnGtJjyPWrbLpeebfK89KzpFnvC8Zs2a4hgHAAC4Sq6e8GxXRZ7w3KNHD7366qsX7Z80aZJ69uzpkkEBAICiK8kJzzeSIgc/CQkJ6ty580X7O3XqpISEBJcMCgAAoLgUuex15syZApe0e3l56fTp0y4ZFAAAKDqqXtYUOfNTv359ffTRRxftX7RokerUqeOSQQEAgKLzcDhcutlVkTM/zz//vO6//37t3btX99xzjyRp1apVWrBggT755BOXDxAAAMCVihz8dO3aVUuXLtUrr7yiTz75RH5+fmrQoIFWr16toKCg4hgjAACwwNUfbGpXV/R5zV26dFGXLl0knX9r6oULF+rJJ59UcnIyn+0FAEAJsXGlyqWuOEhMSEhQVFSUwsLC9MYbb+iee+7Rhg0bXDk2AAAAlytS5iclJUVz5szRe++9p9OnT6tXr17KzMzU0qVLmewMAEAJs/MkZVeynPnp2rWratasqW3btmnq1Kn6/fffNX369OIcGwAAgMtZzvx89dVXGjZsmAYPHqwaNWoU55gAAMAVIPFjjeXMz/r16/Xnn3+qSZMmat68ud566y0dO3asOMcGAACKgI+3sMZy8NOiRQvNmjVLR44c0aBBg7Ro0SKFhYUpLy9P8fHx+vPPP4tznAAAAC5R5NVe/v7+6t+/v9avX6/t27dr9OjRmjhxooKDg3XvvfcWxxgBAIAFvMOzNVf1fkg1a9bUpEmTdPjwYS1cuNBVYwIAAFfA4XDtZlcueTPIUqVKqXv37lq2bJkrTgcAAFBsrugdngEAwPXHzpOUXYngBwAAm3CI6McKPgMNAAC4FTI/AADYBGUva8j8AAAAt0LmBwAAmyDzYw3BDwAANuGw85vzuBBlLwAA4FbI/AAAYBOUvawh+AEAwCaoellD2QsAALgVMj8AANiEnT+J3ZUIfgAAsAnm/FhD2QsAALgVMj8AANgEVS9rCH4AALAJDz7V3RLKXgAAwK2Q+QEAwCYoe1lD5gcAALgVMj8AANgES92tIfgBAMAmeJNDayh7AQAAt0LwAwCATTgcrt2K6rffftPDDz+sChUqyM/PT/Xr19eWLVvMdsMwNG7cOFWuXFl+fn6KjIzUnj17nM6RlpamPn36qFy5cgoMDNSAAQN05syZq701Tgh+AACwCQ+Hw6VbUZw4cUJ33XWXvLy89NVXX+mnn37SG2+8ofLly5t9Jk2apGnTpikuLk4bN26Uv7+/OnTooIyMDLNPnz59tGPHDsXHx2v58uVKSEjQwIEDXXaPJMlhGIbh0jNeJ85m2/JpAddUaS+HVu86XtLDAG5499SqcE2u896mgy4934Bm4Zb7PvPMM/rmm2+UmJhYYLthGAoLC9Po0aP15JNPSpJOnTqlkJAQzZkzR71799bOnTtVp04dbd68WU2bNpUkrVixQp07d9bhw4cVFhZ29U9KZH4AALCNkix7LVu2TE2bNlXPnj0VHBysRo0aadasWWb7/v37lZKSosjISHNfQECAmjdvrqSkJElSUlKSAgMDzcBHkiIjI+Xh4aGNGzde3c25AMEPAAA24eHiLTMzU6dPn3baMjMzC7z2vn37NGPGDNWoUUMrV67U4MGDNWzYMM2dO1eSlJKSIkkKCQlxOi4kJMRsS0lJUXBwsFO7p6engoKCzD6uQPADAAAKFBsbq4CAAKctNja2wL55eXlq3LixXnnlFTVq1EgDBw7UY489pri4uGs86ssj+AEAwCYcDodLtzFjxujUqVNO25gxYwq8duXKlVWnTh2nfbVr19bBg+fnIYWGhkqSUlNTnfqkpqaabaGhoTp69KhTe05OjtLS0sw+rkDwAwAACuTj46Ny5co5bT4+PgX2veuuu7R7926nfT///LOqVq0qSapevbpCQ0O1atUqs/306dPauHGjIiIiJEkRERE6efKkkpOTzT6rV69WXl6emjdv7rLnxTs8AwBgEyX5/s4jR47U3/72N73yyivq1auXNm3apJkzZ2rmzJnnx+ZwaMSIEXrppZdUo0YNVa9eXc8//7zCwsLUvXt3SeczRR07djTLZdnZ2RoyZIh69+7tspVeEsEPAAC2UZIfb3HnnXdqyZIlGjNmjP7973+revXqmjp1qvr06WP2efrpp5Wenq6BAwfq5MmTatmypVasWCFfX1+zz/z58zVkyBC1a9dOHh4e6tGjh6ZNm+bSsfI+PwAKxfv8AK5xrd7nZ17yYZee7+EmN7v0fNcLMj8AANgEH2tqDcEPAAA2wYe6W8NqLwAA4FbI/AAAYBMOUj+WEPwAAGATlHOs4T4BAAC3QuYHAACboOxlDZkfAADgVsj8AABgE+R9rCH4AQDAJih7WUPZCwAAuBUyPwAA2AQZDWsIfgAAsAnKXtYQJAIAALdC5gcAAJsg72MNwQ8AADZB1csayl4AAMCtkPkBAMAmPCh8WULmBwAAuBUyPwAA2ARzfqwh+AEAwCYclL0soewFAADcCpkfAABsgrKXNQQ/AADYBKu9rKHsBQAA3AqZHwAAbIKylzUEPwAA2ATBjzWUvQAAgFsh8wMAgE3wPj/WkPkBAABuhcwPAAA24UHixxKCHwAAbIKylzWUvQAAgFsh8wMAgE2w1N0agh8AAGyCspc1lL0AAIBbIfMDAIBNsNrLGoIfAABsgrKXNZS9UGySt2zW8OjH9fe2rdSoXi2tWfW/Qvu+9MJ4NapXS/M/nHsNRwhc/1Z+8oEGd/ubPv7P1IvaDMPQ9BdGaXC3v2nrhnVObbt+2KLXnh6oEf+MVEzUP7Rk7tvKzc25RqMGrm8EPyg2586d0+01a2nMc+Mu2W/1/+K1fdsPqhQcfI1GBtwYft3zkxJXfqabqt1WYPvqZR/JUcDynsP79+jtf49WncYt9OzUORrw1Ivatmm9ln4wo7iHjBLmcLh2u1ITJ06Uw+HQiBEjzH0ZGRmKjo5WhQoVVKZMGfXo0UOpqalOxx08eFBdunRR6dKlFRwcrKeeeko5Oa4P2gl+UGxatmqt6GEjdE/k3wvtczQ1Va/GvqRXXn1Nnp5UYYF8GefO6v3JL6hP9DMqXabsRe2H9v2s/322UI8Mffaiti3rV+mmareqS+/+Cq58s26v10j3RUVr3Zf/VcbZ9GsxfLixzZs3691339Udd9zhtH/kyJH6/PPPtXjxYq1bt06///677r//frM9NzdXXbp0UVZWlr799lvNnTtXc+bM0bhxl/4D+koQ/KDE5OXlaeyYpxXVb4Buva1GSQ8HuK4sevcN1WvyN9VueOdFbVmZGZr9xgT1HjRaAeUrXNSek50lT28fp31e3j7KzsrSwb27i23MKHkOF29FdebMGfXp00ezZs1S+fLlzf2nTp3Se++9p8mTJ+uee+5RkyZN9P777+vbb7/Vhg0bJElff/21fvrpJ82bN08NGzZUp06d9OKLL+rtt99WVlbWldyOQl3Xwc+hQ4fUv3//kh4Gisn7781SqVKl9ODDj5T0UIDryuaEeB3at1vd+z5eYPvi997ULbXqq0Hz1gW212nUXPt2bdfmhK+Vl5urk8f/0JcfzZYknTpxrNjGjZLn4XC4dCuq6OhodenSRZGRkU77k5OTlZ2d7bS/Vq1aCg8PV1JSkiQpKSlJ9evXV0hIiNmnQ4cOOn36tHbs2HGFd6Rg13WdIS0tTXPnztXs2bML7ZOZmanMzEynfT4+PpKHd3EPD1fhpx0/auG8D7Vg8X8LnLMAuKu0P1K1+D9TNezfb8rrL9kbSfphY6J2b0vWs1PmFHqOOo2a6/5+0Vow4zXNmfKiPL281LlXP/3y0w9yOK7rv3lxnSnsd6yPz8WvzUWLFum7777T5s2bL2pLSUmRt7e3AgMDnfaHhIQoJSXF7HNh4JPfnt/mSiUa/CxbtuyS7fv27bvsOWJjY/XCCy847Rs/fryefm78VY0Nxev775KVlnZcnf9+j7kvNzdXk197VfM/nKsvv15dgqMDSs7Bvbv056kTih35qLkvLy9Xv+zYqnVf/FetO92nYym/afRDHZyOm/nqc7qtTgONevltSVJktwfV7t7eOpV2TKXLlNPxo0e09MM4VQwNu6bPB9eWq/+ULOx37IQJE5z2HTp0SMOHD1d8fLx8fX1dPArXK9Hgp3v37nI4HDIMo9A+l8sKjBkzRqNGjXLa5+Pjo1yXjBDFpUvXe9W8RYTTvicG/UtdunZTt+73ldCogJJX646mGjvtQ6d9H057WSE3V1X7+x9WmXKBatmhm1P7S8Me0QP9h+mOZi2d9jscDgVWqCRJ2pIQr/IVQxR+S83ifQIoWS6Ofgr7HftXycnJOnr0qBo3bmzuy83NVUJCgt566y2tXLlSWVlZOnnypFP2JzU1VaGhoZKk0NBQbdq0yem8+avB8vu4SokGP5UrV9Y777yjbt26Fdi+detWNWnS5JLnKCz9dja78IAK18bZs+k6dPCg+fi33w5r966dKhcQoMqVwxQYWN6pv6enpypWrKhq1W+51kMFrhu+pf11U9VbnfZ5+/rJv2yAub+gSc5BlUJUMeT/sjpffzpfdRu3kMPDoa1J67Ty0w/1r6delEepUsX7BGArhf2O/at27dpp+/btTvseffRR1apVSzExMapSpYq8vLy0atUq9ejRQ5K0e/duHTx4UBER5/8QjoiI0Msvv6yjR48q+P+/9Ul8fLzKlSunOnXquPR5lWjw06RJEyUnJxca/FwuK4Tr208//qjH+keZj9+YNFGS1LVbd/375YklNSzALez4LkkrPpmrnOws3VSthh5/9lXVaxJx+QNxQyupd3guW7as6tWr57TP399fFSpUMPcPGDBAo0aNUlBQkMqVK6ehQ4cqIiJCLVq0kCS1b99ederU0SOPPKJJkyYpJSVFY8eOVXR0tKUArCgcRglGF4mJiUpPT1fHjh0LbE9PT9eWLVvUpk2bIp+bzA9w9Up7ObR61/GSHgZww7un1sXZuuKwad8pl56v2S0BV3zs3XffrYYNG2rq1KmSzr/J4ejRo7Vw4UJlZmaqQ4cOeuedd5xKWgcOHNDgwYO1du1a+fv7KyoqShMnTnT5+8CVaPBTnAh+gKtH8AO4hjsGP9ez63qpOwAAsI43DrGGN3wAAABuhcwPAAB2QerHEoIfAABsoqRWe91oKHsBAAC3QuYHAACb4KMSrSH4AQDAJoh9rKHsBQAA3AqZHwAA7ILUjyUEPwAA2ASrvayh7AUAANwKmR8AAGyC1V7WkPkBAABuhcwPAAA2QeLHGoIfAADsgujHEspeAADArZD5AQDAJljqbg3BDwAANsFqL2soewEAALdC5gcAAJsg8WMNwQ8AAHZB9GMJZS8AAOBWyPwAAGATrPayhswPAABwK2R+AACwCZa6W0PwAwCATRD7WEPZCwAAuBUyPwAA2AWpH0sIfgAAsAlWe1lD2QsAALgVMj8AANgEq72sIfgBAMAmiH2soewFAADcCpkfAADsgtSPJWR+AACAWyHzAwCATbDU3RqCHwAAbILVXtZQ9gIAAG6FzA8AADZB4scagh8AAOyC6McSyl4AAOCqxcbG6s4771TZsmUVHBys7t27a/fu3U59MjIyFB0drQoVKqhMmTLq0aOHUlNTnfocPHhQXbp0UenSpRUcHKynnnpKOTk5Lh0rwQ8AADbhcPG/oli3bp2io6O1YcMGxcfHKzs7W+3bt1d6errZZ+TIkfr888+1ePFirVu3Tr///rvuv/9+sz03N1ddunRRVlaWvv32W82dO1dz5szRuHHjXHaPJMlhGIbh0jNeJ85m2/JpAddUaS+HVu86XtLDAG5499SqcE2us/9YhkvPV72i7xUf+8cffyg4OFjr1q1T69atderUKVWqVEkLFizQAw88IEnatWuXateuraSkJLVo0UJfffWV/vGPf+j3339XSEiIJCkuLk4xMTH6448/5O3t7ZLnReYHAAAUKDMzU6dPn3baMjMzLR176tQpSVJQUJAkKTk5WdnZ2YqMjDT71KpVS+Hh4UpKSpIkJSUlqX79+mbgI0kdOnTQ6dOntWPHDlc9LYIfAADswuHiLTY2VgEBAU5bbGzsZceRl5enESNG6K677lK9evUkSSkpKfL29lZgYKBT35CQEKWkpJh9Lgx88tvz21yF1V4AAKBAY8aM0ahRo5z2+fj4XPa46Oho/fjjj1q/fn1xDe2qEPwAAGAXLl7q7uPjYynYudCQIUO0fPlyJSQk6Oabbzb3h4aGKisrSydPnnTK/qSmpio0NNTss2nTJqfz5a8Gy+/jCpS9AACwiZJc7WUYhoYMGaIlS5Zo9erVql69ulN7kyZN5OXlpVWrVpn7du/erYMHDyoiIkKSFBERoe3bt+vo0aNmn/j4eJUrV0516tS5ijvjjMwPAAC4atHR0VqwYIE+++wzlS1b1pyjExAQID8/PwUEBGjAgAEaNWqUgoKCVK5cOQ0dOlQRERFq0aKFJKl9+/aqU6eOHnnkEU2aNEkpKSkaO3asoqOji5yBuhSWugMoFEvdAde4VkvdD6ZZW4llVXiQ9YDDUcinqr7//vvq16+fpPNvcjh69GgtXLhQmZmZ6tChg9555x2nktaBAwc0ePBgrV27Vv7+/oqKitLEiRPl6em6fA3BD4BCEfwArnGtgp9DLg5+qhQh+LmRMOcHAAC4Feb8AABgE4VUnvAXBD8AANgG0Y8VlL0AAIBbIfMDAIBNUPayhuAHAACbIPaxhrIXAABwK2R+AACwCcpe1pD5AQAAboXMDwAANlHUDyN1VwQ/AADYBbGPJZS9AACAWyHzAwCATZD4sYbgBwAAm2C1lzWUvQAAgFsh8wMAgE2w2ssagh8AAOyC2McSyl4AAMCtkPkBAMAmSPxYQ+YHAAC4FTI/AADYBEvdrSH4AQDAJljtZQ1lLwAA4FbI/AAAYBOUvawh8wMAANwKwQ8AAHArlL0AALAJyl7WEPwAAGATrPayhrIXAABwK2R+AACwCcpe1pD5AQAAboXMDwAANkHixxqCHwAA7ILoxxLKXgAAwK2Q+QEAwCZY6m4NwQ8AADbBai9rKHsBAAC3QuYHAACbIPFjDcEPAAB2QfRjCWUvAADgVsj8AABgE6z2sobMDwAAcCtkfgAAsAmWulvjMAzDKOlBwP1kZmYqNjZWY8aMkY+PT0kPB7gh8XMEXBmCH5SI06dPKyAgQKdOnVK5cuVKejjADYmfI+DKMOcHAAC4FYIfAADgVgh+AACAWyH4QYnw8fHR+PHjmaQJXAV+joArw4RnAADgVsj8AAAAt0LwAwAA3ArBDwAAcCsEPwAAwK0Q/AAAALdC8AOXS0pK0pEjR0p6GAAAFIjgBy6zatUq3XLLLXrwwQfVrFkzPfbYY0pNTS3pYQE3pISEBK1cuVI5OTklPRTAdgh+4BKHDh3S2LFj9fDDD2vVqlWaMmWKVq9erejoaGVlZZX08IAbxrFjxxQVFaW7775bTz/9tA4fPlzSQwJsh+AHLrFr1y798MMPioqK0q233qoHHnhAkyZN0h9//KHp06eX9PCAG0JOTo4WL16s1NRULVq0SL/88osWLVrEHxCAi3mW9ABgD2lpaapdu7Zyc3PNfd27d9euXbs0e/Zs9e3bV5UqVSrBEQLXP09PTzVu3Fg333yzunbtql27dmny5Mnq2LGjGjZsWNLDA2yDzA9com7duvrpp5+0a9cuc1+pUqXUpUsXValSRXFxcSU4OuDG0axZM3Xt2lWSNG7cOHl5eWnGjBn6888/S3hkgH0Q/MAl6tWrp7Zt22ry5Mk6c+aMub9hw4YKDg7Wli1bxMfIAZfncDgkySx1vfnmm5o9e7Y2bNhQksMCbIXgBy4TGxurb775RvPmzXOaoxAeHq6ffvrJ/E8dwOV5e3tLkh544AHdeeedmjRpko4ePSpJSklJKcmhATc85vzAZRo0aKCYmBi9+OKL8vLyUu/evZWXl6ctW7bo4YcfLunhATecnJwceXp6atasWWrQoIEWLVqkvXv3KikpSe+++64aNWpU0kMEbkgOg1oEXCw6OlpLlixReHi4UlJS5O/vr8WLF6tOnTolPTTghtWsWTNt2bJF4eHhevfdd9WhQ4eSHhJwwyL4gctlZGRo586d+u677+Tj40PWB7gKe/fuVffu3bVv3z5NmzZNAwYMKOkhATc8gh8AuI79+uuvmjNnjmJiYuTn51fSwwFsgeAHAAC4FVZ7AQAAt0LwAwAA3ArBDwAAcCsEPwAAwK0Q/AAAALdC8AMAANwKwQ8AAHArBD8AJEn9+vVT9+7dzcd33323RowYcc3HsXbtWjkcDp08efKaXxuAeyD4Aa5z/fr1k8PhkMPhkLe3t2677Tb9+9//Vk5OTrFe99NPP9WLL75oqS8BC4AbCZ/qDtwAOnbsqPfff1+ZmZn68ssvFR0dLS8vL40ZM8apX1ZWlry9vV1yzaCgIJecBwCuN2R+gBuAj4+PQkNDVbVqVQ0ePFiRkZFatmyZWap6+eWXFRYWppo1a0qSDh06pF69eikwMFBBQUHq1q2bfv31V/N8ubm5GjVqlAIDA1WhQgU9/fTT+usn3fy17JWZmamYmBhVqVJFPj4+uu222/Tee+/p119/Vdu2bSVJ5cuXl8PhUL9+/SRJeXl5io2NVfXq1eXn56cGDRrok08+cbrOl19+qdtvv11+fn5q27at0zgBoDgQ/AA3ID8/P2VlZUmSVq1apd27dys+Pl7Lly9Xdna2OnTooLJlyyoxMVHffPONypQpo44dO5rHvPHGG5ozZ45mz56t9evXKy0tTUuWLLnkNfv27auFCxdq2rRp2rlzp959912VKVNGVapU0X//+19J0u7du3XkyBG9+eabkqTY2Fh98MEHiouL044dOzRy5Eg9/PDDWrdunaTzQdr999+vrl27auvWrfrXv/6lZ555prhuGwCcZwC4rkVFRRndunUzDMMw8vLyjPj4eMPHx8d48sknjaioKCMkJMTIzMw0+3/44YdGzZo1jby8PHNfZmam4efnZ6xcudIwDMOoXLmyMWnSJLM9OzvbuPnmm83rGIZhtGnTxhg+fLhhGIaxe/duQ5IRHx9f4BjXrFljSDJOnDhh7svIyDBKly5tfPvtt059BwwYYDz44IOGYRjGmDFjjDp16ji1x8TEXHQuAHAl5vwAN4Dly5erTJkyys7OVl5enh566CFNmDBB0dHRql+/vtM8nx9++EG//PKLypYt63SOjIwM7d27V6dOndKRI0fUvHlzs83T01NNmza9qPSVb+vWrSpVqpTatGljecy//PKLzp49q7///e9O+7OystSoUSNJ0s6dO53GIUkRERGWrwEAV4LgB7gBtG3bVjNmzJC3t7fCwsLk6fl/P7r+/v5Ofc+cOaMmTZpo/vz5F52nUqVKV3R9Pz+/Ih9z5swZSdIXX3yhm266yanNx8fnisYBAK5A8APcAPz9/XXbbbdZ6tu4cWN99NFHCg4OVrly5QrsU7lyZW3cuFGtW7eWJOXk5Cg5OVmNGzcusH/9+vWVl5endevWKTIy8qL2/MxTbm6uua9OnTry8fHRwYMHC80Y1a5dW8uWLXPat2HDhss/SQC4Ckx4BmymT58+qlixorp166bExETt379fa9eu1bBhw3T48GFJ0vDhwzVx4kQtXbpUu3bt0hNPPHHJ9+ipVq2aoqKi1L9/fy1dutQ858cffyxJqlq1qhwOh5YvX64//vhDZ86cUdmyZfXkk09q5MiRmjt3rvbu3avvvvtO06dP19y5cyVJjz/+uPbs2aOnnnpKu3fv1oIFCzRnzpzivkUA3BzBD2AzpUuXVkJCgsLDw3X//ferdu3aGjBggDIyMsxM0OjRo/XII48oKipKERERKlu2rO67775LnnfGjBl64IEH9MQTT6hWrVp67LHHlJ6eLkm66aab9MILL+iZZ55RSEiIhgwZIkl68cUX9fzzzys2Nla1a9dWx44d9cUXX6h69eqSpPDwcP33v//V0qVL1aBBA8XFxemVV14pxrsDAJLDKGyGIwAAgA2R+QEAAG6F4AcAALgVgh8AAOBWCH4AAIBbIfgBAABuheAHAAC4FYIfAADgVgh+AACAWyH4AQAAboXgBwAAuBWCHwAA4FYIfgAAgFv5f7JveFqIyDXNAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "   Saved: xgboost_d2.pkl  (362 KB)\n",
            "\n",
            "══════════════════════════════════════════════════════════\n",
            "  SUMMARY — D2 — Binary Depression\n",
            "══════════════════════════════════════════════════════════\n",
            "  Model                      Accuracy   Macro F1    Kappa\n",
            "  ------------------------------------------------------\n",
            "  Logistic Regression          0.9889     0.9839   0.9678\n",
            "  Random Forest                0.9918     0.9880   0.9760\n",
            "  SVM                          0.9709     0.9589   0.9177\n",
            "  XGBoost                      0.9927     0.9895   0.9789 ★\n",
            "══════════════════════════════════════════════════════════\n",
            "\n",
            "\n",
            "✅ Dataset 2 complete! All files saved to Drive.\n"
          ]
        }
      ],
      "source": [
        "# ─────────────────────────────────────────────────────────────────\n",
        "# D2 — TRAIN + EVALUATE 4 CLASSICAL MODELS\n",
        "# ─────────────────────────────────────────────────────────────────\n",
        "d2_results = {}\n",
        "\n",
        "models_d2 = {\n",
        "    'Logistic Regression': LogisticRegression(\n",
        "        max_iter=1000, class_weight='balanced', random_state=42, n_jobs=-1),\n",
        "    'Random Forest': RandomForestClassifier(\n",
        "        n_estimators=300, class_weight='balanced', random_state=42, n_jobs=-1),\n",
        "    'SVM': LinearSVC(\n",
        "        C=1.0, class_weight='balanced', max_iter=2000, random_state=42),\n",
        "    'XGBoost': XGBClassifier(\n",
        "        n_estimators=300, learning_rate=0.1, max_depth=6,\n",
        "        eval_metric='logloss', random_state=42, n_jobs=-1)\n",
        "}\n",
        "\n",
        "for name, model in models_d2.items():\n",
        "    print(f'\\nTraining {name}...')\n",
        "    model.fit(X2_bal, y2_bal)\n",
        "    preds = model.predict(X2_te_tf)\n",
        "    evaluate_model(name, y2_te, preds, le2.classes_, 'D2', d2_results)\n",
        "    safe = name.lower().replace(' ', '_')\n",
        "    save_pkl(model, f'{safe}_d2.pkl')\n",
        "\n",
        "ALL_RESULTS['D2 Binary Depression'] = d2_results\n",
        "summary_table(d2_results, 'D2 — Binary Depression')\n",
        "print('\\n✅ Dataset 2 complete! All files saved to Drive.')"
      ],
      "id": "B4rZ3AFU1Iw9"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "K6yeUGba1Iw9"
      },
      "source": [
        "---\n",
        "# 📁 DATASET 3 — Suicide Risk Detection\n",
        "**Source:** nikhileswarkomati/suicide-watch · Kaggle (Reddit)  \n",
        "**Upload:** `DA_DB_3.csv`  \n",
        "**Columns:** `text` | `class` (suicide / non-suicide)  \n",
        "**Task:** Is there suicide risk in this text?\n",
        "\n",
        "**Note:** This dataset has ~232K rows. We sample 25K per class for Colab speed.  \n",
        "Reddit posts are longer than tweets — we use `max_features=60000` for TF-IDF."
      ],
      "id": "K6yeUGba1Iw9"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "zQQzqpac1Iw9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 403
        },
        "outputId": "b34b30ef-5463-4f9e-9793-4bbb729f7946"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Raw shape: (232074, 3)\n",
            "Columns: ['Unnamed: 0', 'text', 'class']\n",
            "\n",
            "Full distribution:\n",
            "label\n",
            "suicide        116037\n",
            "non-suicide    116037\n",
            "Name: count, dtype: int64\n",
            "\n",
            "Using sample:\n",
            "label\n",
            "non-suicide    25000\n",
            "suicide        25000\n",
            "Name: count, dtype: int64\n",
            "Total: 50,000 rows\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                                text        label\n",
              "0  Used to keep it cool Used to be a fool \\n\\nAll...  non-suicide\n",
              "1  Well, haven't mat a girl like this in a while ...  non-suicide\n",
              "2  My girlfriend just left me👍👍👍👍👍😁😁😁😁😁 I don't w...  non-suicide"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-0bd6f869-73e7-4c1f-b964-4959baaf1f76\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
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              "      <td>non-suicide</td>\n",
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              "    <tr>\n",
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              "      <td>My girlfriend just left me👍👍👍👍👍😁😁😁😁😁 I don't w...</td>\n",
              "      <td>non-suicide</td>\n",
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              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "      display:flex;\n",
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              "\n",
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              "      const buttonEl =\n",
              "        document.querySelector('#df-0bd6f869-73e7-4c1f-b964-4959baaf1f76 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
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              "        const element = document.querySelector('#df-0bd6f869-73e7-4c1f-b964-4959baaf1f76');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df3",
              "summary": "{\n  \"name\": \"df3\",\n  \"rows\": 50000,\n  \"fields\": [\n    {\n      \"column\": \"text\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 50000,\n        \"samples\": [\n          \"My own mother told me she wished she aborted me.Every day this house is hell. Absolute hell. From the minute I wake up in my tiny room there's nothing but negative words thrown at me. All my 52 year old mother can do is drink and curse at me and is in deep debt. We don't even own a house we have to live with my christian grandmother is equally as mentally abusive. I've always been told I'm ugly, just the other day she sat and told me how disgusting I looked and how her 50 year old friend looked so much better than me. She comments on my body a lot it's horrible and I hate it. They'll both be extremly nice to strangers but when it comes to me I get nothing but hate. I can't take this anymore. I have health issues too which I get mocked for. I've tried and tried to apply for jobs for years, no one ever answers apart from rejection emails. I have no friends and no family and every weekday/weekends are spent with these two people who hate me. I live in a tiny house with mold/spiders too which is equally stressful as I find insects in my room daily. I don't know how much more I can take.\",\n          \"When u like giving awards but are poor in coins This is not Poggies \\ud83d\\ude14\",\n          \"Welcome to The Vibe\\u2122\\ufe0f zone Feel free to vibe in the comments as long as you want.\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"label\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"suicide\",\n          \"non-suicide\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 16
        }
      ],
      "source": [
        "# ─────────────────────────────────────────────────────────────────\n",
        "# D3 — LOAD + STANDARDISE\n",
        "# ─────────────────────────────────────────────────────────────────\n",
        "df3 = pd.read_csv('DA_DB_3.csv')\n",
        "print(f'Raw shape: {df3.shape}')\n",
        "print(f'Columns: {list(df3.columns)}')\n",
        "\n",
        "# Standardise column names\n",
        "col_map = {}\n",
        "for col in df3.columns:\n",
        "    if col.lower() == 'class':\n",
        "        col_map[col] = 'label'\n",
        "    elif col.lower() == 'text':\n",
        "        col_map[col] = 'text'\n",
        "df3 = df3.rename(columns=col_map)\n",
        "\n",
        "df3 = df3[['text','label']].dropna().reset_index(drop=True)\n",
        "df3['label'] = df3['label'].astype(str).str.strip().str.lower()\n",
        "\n",
        "print(f'\\nFull distribution:')\n",
        "print(df3['label'].value_counts())\n",
        "\n",
        "# Sample 25K per class — keeps Colab RAM under control\n",
        "# Remove this block if you have Colab Pro or run locally\n",
        "df3 = df3.groupby('label').apply(\n",
        "    lambda x: x.sample(min(25000, len(x)), random_state=42)\n",
        ").reset_index(drop=True)\n",
        "\n",
        "print(f'\\nUsing sample:')\n",
        "print(df3['label'].value_counts())\n",
        "print(f'Total: {len(df3):,} rows')\n",
        "df3.head(3)"
      ],
      "id": "zQQzqpac1Iw9"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Jgm1Y0Z51Iw-",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 514
        },
        "outputId": "e26a9e2d-f283-417c-d476-3c2f5a64a5f3"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dataset 3 Statistics:\n",
            "       word_count  char_count\n",
            "count    50000.00    50000.00\n",
            "mean       131.48      686.67\n",
            "std        226.47     1182.62\n",
            "min          1.00        4.00\n",
            "25%         26.00      138.00\n",
            "50%         60.00      313.00\n",
            "75%        154.00      796.00\n",
            "max       9684.00    38785.00\n",
            "\n",
            "Word count by class:\n",
            "  non-suicide: avg 62.2 words\n",
            "  suicide: avg 200.8 words\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1600x400 with 3 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ],
      "source": [
        "# ─────────────────────────────────────────────────────────────────\n",
        "# D3 — EDA\n",
        "# Reddit posts are much longer than tweets — important to show in paper\n",
        "# ─────────────────────────────────────────────────────────────────\n",
        "df3['word_count'] = df3['text'].apply(lambda x: len(str(x).split()))\n",
        "df3['char_count'] = df3['text'].apply(lambda x: len(str(x)))\n",
        "label_counts3 = df3['label'].value_counts()\n",
        "\n",
        "print('Dataset 3 Statistics:')\n",
        "print(df3[['word_count','char_count']].describe().round(2))\n",
        "print('\\nWord count by class:')\n",
        "for lbl in df3['label'].unique():\n",
        "    mean_wc = df3[df3['label']==lbl]['word_count'].mean()\n",
        "    print(f'  {lbl}: avg {mean_wc:.1f} words')\n",
        "\n",
        "fig, axes = plt.subplots(1, 3, figsize=(16, 4))\n",
        "fig.suptitle('Dataset 3 EDA — Suicide Risk Detection (Reddit)', fontsize=13, fontweight='bold')\n",
        "\n",
        "label_counts3.plot(kind='bar', ax=axes[0], color=['#ef4444','#3b82f6'], edgecolor='none')\n",
        "axes[0].set_title('Class Distribution', fontweight='bold')\n",
        "axes[0].set_xticklabels(label_counts3.index, rotation=0)\n",
        "for i, v in enumerate(label_counts3.values):\n",
        "    axes[0].text(i, v+50, str(v), ha='center', fontsize=10, fontweight='bold')\n",
        "\n",
        "axes[1].hist(df3['word_count'], bins=40, color='#ef4444', edgecolor='none', alpha=0.7)\n",
        "axes[1].axvline(df3['word_count'].mean(), color='#3b82f6', linestyle='--', linewidth=2,\n",
        "                label=f'Mean: {df3[\"word_count\"].mean():.1f}')\n",
        "axes[1].set_title('Reddit Post Length\\n(longer than tweets)', fontweight='bold')\n",
        "axes[1].set_xlabel('Words'); axes[1].legend(fontsize=9)\n",
        "\n",
        "for lbl, color in zip(['suicide','non-suicide'], ['#ef4444','#3b82f6']):\n",
        "    if lbl in df3['label'].values:\n",
        "        axes[2].hist(df3[df3['label']==lbl]['word_count'], bins=25,\n",
        "                     alpha=0.65, color=color, label=lbl, edgecolor='none')\n",
        "axes[2].set_title('Word Count by Class', fontweight='bold')\n",
        "axes[2].legend(fontsize=9)\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.savefig(os.path.join(SAVE_DIR, 'eda_d3.png'), dpi=130, bbox_inches='tight')\n",
        "plt.show()"
      ],
      "id": "Jgm1Y0Z51Iw-"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "3AaIHOVH1Iw_",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6efa6d4c-f7d2-41f8-9dc3-6a6a25802093"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cleaning text...\n",
            "\n",
            "Before: Used to keep it cool Used to be a fool \n",
            "\n",
            "All about the bounce in my step\n",
            "\n",
            "Watch \n",
            "After:  used to keep it cool used to be a fool all about the bounce in my step watch it \n",
            "   Saved: le_d3.pkl  (0 KB)\n",
            "\n",
            "Label encoding:\n",
            "  0 → non-suicide\n",
            "  1 → suicide\n",
            "   Saved: tfidf_d3.pkl  (2300 KB)\n",
            "TF-IDF shape: (40000, 60000)\n",
            "\n",
            "Dataset is balanced — SMOTE not needed.\n",
            "✅ D3 ready.\n"
          ]
        }
      ],
      "source": [
        "# ─────────────────────────────────────────────────────────────────\n",
        "# D3 — PREPROCESS + SPLIT + TF-IDF\n",
        "# No SMOTE needed — dataset is already balanced (25K each class)\n",
        "# max_features=60000 because Reddit posts are longer than tweets\n",
        "# ─────────────────────────────────────────────────────────────────\n",
        "print('Cleaning text...')\n",
        "df3['clean'] = df3['text'].apply(clean_text)\n",
        "\n",
        "print('\\nBefore:', str(df3['text'].iloc[0])[:80])\n",
        "print('After: ', str(df3['clean'].iloc[0])[:80])\n",
        "\n",
        "le3 = LabelEncoder()\n",
        "df3['label_enc'] = le3.fit_transform(df3['label'])\n",
        "save_pkl(le3, 'le_d3.pkl')\n",
        "\n",
        "print('\\nLabel encoding:')\n",
        "for i, c in enumerate(le3.classes_):\n",
        "    print(f'  {i} → {c}')\n",
        "\n",
        "X3 = df3['clean']; y3 = df3['label_enc']\n",
        "\n",
        "X3_tr, X3_te, y3_tr, y3_te = train_test_split(\n",
        "    X3, y3, test_size=0.2, stratify=y3, random_state=42)\n",
        "\n",
        "# Larger vocabulary for longer Reddit posts\n",
        "tfidf3, X3_tr_tf, X3_te_tf = make_tfidf(X3_tr, X3_te, max_features=60000)\n",
        "save_pkl(tfidf3, 'tfidf_d3.pkl')\n",
        "print(f'TF-IDF shape: {X3_tr_tf.shape}')\n",
        "print('\\nDataset is balanced — SMOTE not needed.')\n",
        "print('✅ D3 ready.')"
      ],
      "id": "3AaIHOVH1Iw_"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "kjIj2Hm-1Iw_",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "3950e85e-1cfd-4170-a1d3-d9120246dba6"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Training Logistic Regression...\n",
            "\n",
            "=======================================================\n",
            "  Logistic Regression  [D3]\n",
            "=======================================================\n",
            "  Accuracy     : 93.18%\n",
            "  Macro F1     : 0.9318   ← main metric\n",
            "  Cohen's Kappa: 0.8636   ← agreement beyond chance\n",
            "=======================================================\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            " non-suicide      0.923     0.942     0.933      5000\n",
            "     suicide      0.941     0.921     0.931      5000\n",
            "\n",
            "    accuracy                          0.932     10000\n",
            "   macro avg      0.932     0.932     0.932     10000\n",
            "weighted avg      0.932     0.932     0.932     10000\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x500 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "   Saved: logistic_regression_d3.pkl  (470 KB)\n",
            "\n",
            "Training Random Forest...\n",
            "\n",
            "=======================================================\n",
            "  Random Forest  [D3]\n",
            "=======================================================\n",
            "  Accuracy     : 88.00%\n",
            "  Macro F1     : 0.8800   ← main metric\n",
            "  Cohen's Kappa: 0.7600   ← agreement beyond chance\n",
            "=======================================================\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            " non-suicide      0.874     0.889     0.881      5000\n",
            "     suicide      0.887     0.871     0.879      5000\n",
            "\n",
            "    accuracy                          0.880     10000\n",
            "   macro avg      0.880     0.880     0.880     10000\n",
            "weighted avg      0.880     0.880     0.880     10000\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x500 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "   Saved: random_forest_d3.pkl  (341926 KB)\n",
            "\n",
            "Training SVM...\n",
            "\n",
            "=======================================================\n",
            "  SVM  [D3]\n",
            "=======================================================\n",
            "  Accuracy     : 93.68%\n",
            "  Macro F1     : 0.9368   ← main metric\n",
            "  Cohen's Kappa: 0.8736   ← agreement beyond chance\n",
            "=======================================================\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            " non-suicide      0.931     0.943     0.937      5000\n",
            "     suicide      0.942     0.930     0.936      5000\n",
            "\n",
            "    accuracy                          0.937     10000\n",
            "   macro avg      0.937     0.937     0.937     10000\n",
            "weighted avg      0.937     0.937     0.937     10000\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x500 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "   Saved: svm_d3.pkl  (469 KB)\n",
            "\n",
            "Training XGBoost...\n",
            "\n",
            "=======================================================\n",
            "  XGBoost  [D3]\n",
            "=======================================================\n",
            "  Accuracy     : 91.62%\n",
            "  Macro F1     : 0.9162   ← main metric\n",
            "  Cohen's Kappa: 0.8324   ← agreement beyond chance\n",
            "=======================================================\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            " non-suicide      0.899     0.938     0.918      5000\n",
            "     suicide      0.935     0.894     0.914      5000\n",
            "\n",
            "    accuracy                          0.916     10000\n",
            "   macro avg      0.917     0.916     0.916     10000\n",
            "weighted avg      0.917     0.916     0.916     10000\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x500 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "   Saved: xgboost_d3.pkl  (701 KB)\n",
            "\n",
            "══════════════════════════════════════════════════════════\n",
            "  SUMMARY — D3 — Suicide Risk\n",
            "══════════════════════════════════════════════════════════\n",
            "  Model                      Accuracy   Macro F1    Kappa\n",
            "  ------------------------------------------------------\n",
            "  Logistic Regression          0.9318     0.9318   0.8636\n",
            "  Random Forest                0.8800     0.8800   0.7600\n",
            "  SVM                          0.9368     0.9368   0.8736 ★\n",
            "  XGBoost                      0.9162     0.9162   0.8324\n",
            "══════════════════════════════════════════════════════════\n",
            "\n",
            "\n",
            "✅ Dataset 3 complete! All files saved to Drive.\n"
          ]
        }
      ],
      "source": [
        "# ─────────────────────────────────────────────────────────────────\n",
        "# D3 — TRAIN + EVALUATE 4 CLASSICAL MODELS\n",
        "# No class_weight='balanced' needed — data is already balanced\n",
        "# ─────────────────────────────────────────────────────────────────\n",
        "d3_results = {}\n",
        "\n",
        "models_d3 = {\n",
        "    'Logistic Regression': LogisticRegression(\n",
        "        max_iter=1000, random_state=42, n_jobs=-1),\n",
        "    'Random Forest': RandomForestClassifier(\n",
        "        n_estimators=300, random_state=42, n_jobs=-1),\n",
        "    'SVM': LinearSVC(\n",
        "        C=1.0, max_iter=2000, random_state=42),\n",
        "    'XGBoost': XGBClassifier(\n",
        "        n_estimators=300, learning_rate=0.1, max_depth=6,\n",
        "        eval_metric='logloss', random_state=42, n_jobs=-1)\n",
        "}\n",
        "\n",
        "for name, model in models_d3.items():\n",
        "    print(f'\\nTraining {name}...')\n",
        "    model.fit(X3_tr_tf, y3_tr)   # no SMOTE — already balanced\n",
        "    preds = model.predict(X3_te_tf)\n",
        "    evaluate_model(name, y3_te, preds, le3.classes_, 'D3', d3_results)\n",
        "    safe = name.lower().replace(' ', '_')\n",
        "    save_pkl(model, f'{safe}_d3.pkl')\n",
        "\n",
        "ALL_RESULTS['D3 Suicide Risk'] = d3_results\n",
        "summary_table(d3_results, 'D3 — Suicide Risk')\n",
        "print('\\n✅ Dataset 3 complete! All files saved to Drive.')"
      ],
      "id": "kjIj2Hm-1Iw_"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yLQ8SXg91Iw_"
      },
      "source": [
        "---\n",
        "# 📊 CLASSICAL MODEL SUMMARY\n",
        "All 12 classical models trained. Here's the full cross-dataset comparison."
      ],
      "id": "yLQ8SXg91Iw_"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Yk8FGWp01Iw_",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ec197289-f376-47f6-9854-d57d44178d5e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "CLASSICAL MODELS — MASTER RESULTS\n",
            "             Dataset               Model  Accuracy  Macro F1  Cohen's Kappa\n",
            "  D1 Depression Type Logistic Regression    0.9152    0.9179         0.8971\n",
            "  D1 Depression Type       Random Forest    0.9089    0.9129         0.8891\n",
            "  D1 Depression Type                 SVM    0.9236    0.9269         0.9072\n",
            "  D1 Depression Type             XGBoost    0.9176    0.9217         0.9000\n",
            "D2 Binary Depression Logistic Regression    0.9889    0.9839         0.9678\n",
            "D2 Binary Depression       Random Forest    0.9918    0.9880         0.9760\n",
            "D2 Binary Depression                 SVM    0.9709    0.9589         0.9177\n",
            "D2 Binary Depression             XGBoost    0.9927    0.9895         0.9789\n",
            "     D3 Suicide Risk Logistic Regression    0.9318    0.9318         0.8636\n",
            "     D3 Suicide Risk       Random Forest    0.8800    0.8800         0.7600\n",
            "     D3 Suicide Risk                 SVM    0.9368    0.9368         0.8736\n",
            "     D3 Suicide Risk             XGBoost    0.9162    0.9162         0.8324\n",
            "\n",
            "Saved: classical_results.csv\n"
          ]
        }
      ],
      "source": [
        "# ─────────────────────────────────────────────────────────────────\n",
        "# MASTER COMPARISON TABLE — All 12 classical models\n",
        "# ─────────────────────────────────────────────────────────────────\n",
        "import pandas as pd\n",
        "rows = []\n",
        "for ds, res in ALL_RESULTS.items():\n",
        "    for model, scores in res.items():\n",
        "        rows.append({'Dataset': ds, 'Model': model,\n",
        "                     'Accuracy': scores['accuracy'],\n",
        "                     'Macro F1': scores['macro_f1'],\n",
        "                     \"Cohen's Kappa\": scores['kappa']})\n",
        "\n",
        "master = pd.DataFrame(rows)\n",
        "print('CLASSICAL MODELS — MASTER RESULTS')\n",
        "print(master.to_string(index=False))\n",
        "\n",
        "# Save table\n",
        "master.to_csv(os.path.join(SAVE_DIR, 'classical_results.csv'), index=False)\n",
        "print('\\nSaved: classical_results.csv')"
      ],
      "id": "Yk8FGWp01Iw_"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Ms0kxw7C1IxA",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 316
        },
        "outputId": "ab543b6e-a630-44d1-e992-79068e9f0e62"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1800x500 with 3 Axes>"
            ],
            "image/png": 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KCmbOnCml131u1/Tp07F//34EBQVh7ty5aNWqFTQaDQAgNjZWev5guXLlsH79euzfvx+rV6/G+PHjUaNGDchkslzrlpycjPfffx8PHz4EkDkd4bx587Bnzx4sX74c77zzjpRHfttRUAqFQnpmyqZNm/DgwQN8//33AIAKFSqgffv2BreLiorCgAEDpCnxWrdujR07dmDx4sXSs73Onj2LCRMmAAAyMjIwdOhQaWo6Dw8PbNq0CatXr0ZsbKzBMmJiYtCnTx/pmT5DhgzBH3/8gVWrVqFChQoAgDVr1mDFihW5trEwPsPi0KdPH1hbW0MIgUWLFuH8+fM4evQogMxn1uSkQoUK+Prrr7F582bs3bsXwcHBCAoKQqtWrQAAN2/exI8//iilnzFjBvbu3QsAkMlkGDRoELZv344tW7ZgxIgRsLS0NFhOREQEOnTogKCgIGzduhXvvPNOvo+L/KhYsSJkMpneq6im7Dp79qz0d4sWLfJ0fGzfvl1vesCRI0di165dGD9+vLT9smXLcpx+9NatW5g3bx6CgoKkKU2fPHmCr776Ct27d8fOnTvRsWNHKf2CBQtyrMuTJ0+wcuVKBAUFoX79+gAAIQQ++eQTxMfHAwBsbW0xc+ZMbNiwAX/88QeCg4OxY8cO9O7dGwDw+PFjzJkzx2D+T58+RWpqKubPn4+9e/diwYIFUKlUOdZHe75VKBRYuHAhDhw4gE2bNuGrr76Cv78/TE1NpbS9e/dGdHQ0AKBy5cpYt24dNm3aBA8PDwCZ3+devXohIyMjWzn3799H1apVsWXLFsyfP1/KNzQ0FPv27cuxflpXrlyBEEIqOyfOzs56U+6mpKQAyDy/aM+fhcXOzk6acnTZsmVITk6WjrPGjRsX2vMljx49iuDgYMyZM0ea4lapVOLjjz+W0iQlJaFnz554/vw5AMDX1xe//fYbfv31V5QtWxYAEBkZiUGDBuVYjva7W65cOZw7dw4AUKNGDXz77bc5bvPLL79k++7LZDI8efLEYPrk5GQcPXoU+/fv15uC1dra+qWeu9ikSRN8//332Lp1K/bv348DBw5gzZo1cHd3BwBs27YNp0+ffmE+eT3XFnQ/37hxA++//z527tyJ9957T1r+zTffwNLSEhs2bNA7V+3Zs0cvHsuL69ev44MPPsDvv/+ud4z8/PPPUr0jIyP14pX27dvj999/xzfffIMrV67kq7y8ioiIwKeffoqdO3eia9eu0vLczpc5qVSpEvr37w8AWLp0KW7fvp1j2vx+ZgsXLsz2W3DkyBHp5ePjU+AY6MKFC/jwww+xa9cuzJ49WzofXrx4EXPnzn2pegPA0KFDERERAQAoVaoUli9fjq1bt8LPzw9A5jT+vXr1kn5rsrp79y6aNWuGv//+GwAwfvx4LFy48KXjsA0bNkh/V65cWXouqy7daYEvXbr0UuUQERFRMSrmjkciIiLKhzt37ujdofvjjz/mabucRvwJIcSxY8dE165dRbly5fRGYGhfdnZ2UtqePXtKy9etWydiYmIMlpeYmCjkcrkAILy8vMTZs2dFYmJijvUzdEf677//Li0zMTER586dy7WN+WlHQUf8de/eXURFRUnl9OvXTxop8PXXX2cbdaW1YMECaZmjo6PePlm0aJG0zsrKSqSlpYkzZ87o5aM7YmfXrl1667QjuxYuXCgtq1Gjhjhy5Ij0mjRpkrSuQYMGBtumHQWX38/QkKSkJL3yX+b1IoZGEmhH81hZWYmuXbtKn39CQkKOI/6EEOLXX38VzZs3Fw4ODlLbdV9dunQRQgiRkZGhN3Jr5MiRudZRt8w6depkW5/f4yI3WY89Qy/dETJZFWTEn+7IVO0oqxfp3LmztE379u311mk/OwCiTZs20nLd88W4ceOk5UOHDpWWlylTRqSmpgohMkcO5+U8sHPnTmndvXv39M4jv/32m7Tu999/F23bthXOzs4GR+PWrl1bSpv189i+fXu2fZDTiL9GjRoJAEKj0Yg9e/aIJ0+eGNyHFy9e1Cvj7Nmz0rqsozpOnTqVrUxTU1O9kSmtWrWS1n3//fcGy9SlO5Vu9+7dc02bkpIiypQpo1eniRMnGkxbkBF/48ePF+fPn5feDxgwQPp7/fr1eu3POo1ebuehrCPxsr7q1q2b7by1bds2ab1SqRT37t2T1un+zslkMmlqxBeVA0DUr19fXL58Ocd9kNNLd+Sy7vfd0KtFixbiypUrOe7z3Eb8xcfHi9mzZ4s6deoIKysrg6OoXnR85edc+zL7Wbf9uueMU6dO6dVz165dUl66IyF1v895GfHn6ekpjdBKT08XZmZm0rpLly4JIYT47rvvpGVOTk560zyPGTPG4HGbm7yM+NM9v0ZFRem1PS8jzbPGR7du3RIqlUr67uU04u9lPrMXxW8FjYHq1aunl9+wYcOkdTVr1nypej969EhvNOmWLVuk9DExMdLoZABi48aNQgj93w57e3u90YIzZ8584WeSm3Xr1kkxjqmpqQgODjaYTjfO1D1GiIiIqGRQgIiIiEoMGxsbvffa0XAva9++fWjdujXS09NzTPP48WPp748++ggbNmxAenq6NJrC1tYWNWvWRLt27TB06FCYmZlBrVYjICAAy5cvx+XLl1GnTh2YmJigfPnyqF+/Pvr374+WLVvmWrfQ0FDp74oVK8LHx6fQ2lEYSpUqhW7dumH16tVYuXIlAECtVmPgwIG4fPmywW3Cw8Olv+vWrQu1Wi291971DQBxcXG4d+8erl+/Li0zMzNDzZo1pfeNGzc2WIbufrty5Qreeustg+leNHKgMD7D+/fv51h+Xol/RxLlxyeffILvv/8ecXFx2Lx5MwBgwIAB0ohUQ6ZOnYpZs2blmq/2GIqNjUVMTIy0vEuXLnmum6G0+T0uypUrl+fyNm3aJI2E09KOSChsNjY20kjUvJ6bdNuu21bte+3np5tOV6NGjaS/7e3tpb/r1asHhSLzvzoODg7S8kePHuVYF93yS5cuDTc3N6lc7UiN5cuXY8CAAbm2KadzjUqlQrt27XLdVteQIUPw119/ITExUfquOTk5wdvbG++99x4+/PBDKBQKvX2j0WhQu3Zt6b2npydsbGykUV7h4eGoV6+eXjnVqlXTOyZ092Nu+8uQF31fZ8yYgXv37uktmz9/Pvr166c3uqQweHt7w8/PD0ePHsXPP/8MAChTpgy6dOmCNWvWGNxm+fLlmDFjRrbleT0PhYaG4s6dO3rLdD+fSpUqoXTp0tJ73WNOCIGrV6/CyckpW75HjhwBkHlszZs3DwcPHsTJkyfRqlUr3Lhxw+DI0datW+Pzzz/Ptjyn0ciGnD9/Xu9cl1dCCLRp0waHDh3KNd2Lfpfzc64t6H729fWVzhm63wEAaNiwofR3Xs8nhjRv3lwaoWViYgJbW1skJCTo5aU91wBAnTp19D5bPz8/fPfdd/kqMy/efvtt6e+sbX/06FG+jhkgcxTvxx9/jPnz5+OXX35B9+7dDaYrjO9GVgWNgQz9Di1atAjAf59NfuutUqn0RlvrpnFwcEDVqlVx4cKFbHlrPXz4UPpNHThwIKZMmWKw7nkxb948jB49GkIIqFQqrF+/Hv7+/gbTvkz8RURERK8PTvVJRERUgpibm6Nq1arS+z///LNA+QUGBkqdZb6+vti8eTOOHDmCdevWSWl0/+PftGlTnDlzBsOHD0fjxo3h4OCAx48f49ChQxg7dix69eolpV22bBlWr16NHj16oEaNGlAqlbh58yY2bNiAVq1aYdu2bQWqe0HaUVg+/fRTvfc9e/bMdtHsdaWdoio3r/IzLEyVK1eWpugEMi+wDh06NMf0qamp0hReANCrVy/s3r0bR44cwbhx46TlhqZJzC/dC4SvQt26deHn56f3qlixYpGUVadOHenv/fv3v5KLhtqpUIHMz1kr600SheXrr7+W/m7VqhW2b9+OI0eOYN68edLynI6TUqVK5Wtatj59+uDQoUMYPHgwfH19YWNjg+joaOzduxeDBw/G2LFjX74hOuzs7PTeazs/gLydNx0dHaW/c+sIOXnypN7+0x6HiYmJ6Nu3b643brysrOfoIUOG6E2RWlBCCERHR6Nv374AMqfrCwgI0Ot8KAza72779u2xdu1aafndu3dz7FxzcnLK9t338/ODXC43mL5ChQoQQuCff/6RphF/+PAhunTpku/Ov+PHj0v1ksvlmDVrFvbv348jR47gnXfekdIVxjm1sOR0LgFyPp/k9xyXl++a7jniVU2nrVsv3Trp1iu/Jk6cCAsLC6SlpWHatGkFql9RyEsM9LrQ/c6uWbPmhR3qhgghMHLkSIwaNQpCCFhbW2P37t3o1KlTjtvons/z0ulKRERErxd2/BEREZUwus/a+fPPP3N89lVERIT0DKWc3Lp1S/p7ypQpeO+99+Dn5yc9Uy4rIQS8vb0xf/58HD16FDExMbh+/TosLCwAZD6zR3v3uomJCXr16oV169bh8uXLiI+PR2BgoJSXbqecIdpnUwGZz0q5ePGiwfq8TDsKi6+vL3x9faX3WS8yZ1WtWjXp77Nnz0rPoAGAY8eOSX9bWVmhdOnSqFSpkrQsISFBbyThX3/9ZbAM3ee0NGrUCEIIg6+8XPQq6Gfo6uqaY/l5fb0s3c+iffv2cHV1zTHtw4cP9Z6rs2TJErRq1Qp+fn4GR645ODjodXYEBQVlS5NT3Q1dyM3vcfG60j03RURE5Pisu/v370v7Vbftum3N+l43XVHRLS8qKkp6lhIA6blkuueawMBAtG/fHn5+fnn6PuX3Ir4QAk2aNMGSJUtw8uRJPH78GCdPnpTWa79/uvsmMTER58+fl96HhobqPdOtKPaj7vM+c3rmWWJiIgICAqTOvR49euDQoUNSZ8vJkyfx1VdfFXrdunTpIo1mVCqVuT5HD8h8dm1+z0OOjo5YtmyZ1JGZkpKi9zxO3X1+48YNREVFSe91jzmZTKZ3Y09OstYnv6POXqR8+fJYs2aN9Nk8fvz4haOhs9L9nnh7e2Py5Mlo3rw5GjRooLfuRfJzri3s/VxcdJ+Tee7cOenZr8B/Iz9LAicnJwwfPhxAZkewIS/zmWXtlM3aeVzQGCi33yHt70B+6125cmW9euumefjwod5509A52sXFRRrll5iYiHbt2uHEiRMG629IcnIyunfvjvnz5wPI/I4fO3YMzZo1y3U73Xp5eXnluTwiIiJ6PXCqTyIiohJm+PDhWL9+vdQR9sEHH2Dv3r1o164drKyscPfuXfzxxx/YtGkTHjx4AKVSmWNebm5u0n/s582bB1NTU9y4cQOTJ082mH7kyJG4ceMG3n33XZQrVw7W1tY4d+6c1NknhEBycjLMzMzg7u6ONm3aoE6dOihTpgzS09Nx+PBhKS/dzg1DWrRogQoVKuCff/5BRkYGWrVqhYkTJ6J69eq4f/8+1q9fj6+++gq1atXKdzsK09y5c7Fv3z7Y2trC29s717TdunXDxIkTkZCQgOjoaHTt2hVDhgzBnTt3MGnSJCld7969oVAoULt2bbi5uUkdED179sT06dORnJxscAo3AOjevTs+//xzPH/+HH/99Re6du2Knj17wtraGnfv3sXVq1exa9cudOrU6YV34Rf0MyxOrVq1whdffIGUlBR07tw517SlSpWCubm51Pn3+eefo3379jhw4ABWrFiRLb1MJsOAAQOk0Uvz589HQkIC2rZti7S0NBw7dgxqtTrPF8vze1wUtvDwcGl6Md1pxmJjY7F161YAmRcKdaeQNKR79+5YuXIl9uzZAwAYO3YsTp06hffffx+Ojo548OABDh48iDVr1uDIkSOwt7dHv379pIv5O3bswJgxY9CiRQscPnwYW7ZskfLW7VQsKoMGDcIXX3wBa2trfPPNN9KNEzY2NtIIKDc3N4SFhQEAZs+ejQEDBuDs2bP44osvCr0+77//PhQKBZo2bYqyZcvC3Nwce/fuldZrv381a9ZE7dq1ce7cOQCZvwkzZsyAXC7Xm7ayRo0aeqMyC4uDgwM8PT1x5coV3L59G1FRUdmmlx0/frx0ji5dujT+97//wc7ODosWLUKfPn0AADNnzkTbtm1znNZ52bJl+OOPP7ItHz58eI4d4gqFAkuXLsXp06dRsWJFlCpVqiBNzZFKpcKkSZMwcOBAAJnH8vnz5+Hj44N3330XZcqUwb1796Tz0fjx4/H8+XNMnDhRyqN169Y5jqo5evQogP+m+tSle5OMrujoaGk7XWXLln3hqF8HBwd8+umnmD17NgDgxx9/xMSJE/N844Gbm5v096VLl7B48WJUrFgRy5Yty7Fz2JD8nGsLYz+/Drp06YJx48YhPT0dUVFR6NGjBwYMGICQkBBpysmSYuzYsVi8eHGOU7q+zGdmZ2cHmUwmdfjOmzcPvr6+MDExQePGjQscA506dQqDBg1Cly5dcP78eSxdulRa161bt5eud8eOHaXfuk8++QRPnz6FnZ0d5syZg8TERACZNxG0adPG4L6aOXMmYmNj8cMPP+D58+do3bo1Dhw4kOs0+EBmR2GrVq2k2M3GxgaBgYF4/Pix3vmhSpUq2b4Xp06dkv7OaTpQIiIieo0VxoMCiYiI6NW6f/++aNKkiQCQ6+vx48dCCCEOHjwoLatQoYKUz+7duw1u17RpU733WoMHD861vA4dOkhpVSpVrmm3bNkipa1QoYK0/ODBg9LykydPCltb2xzzOH/+/Eu1IzIy0uDy3AQEBEjpu3fvnmta3f2dNf9169YJU1PTHNtUp04d8fTpU73PSKFQZEtXtWpVvfeRkZHSNr/99ptQq9W57v9p06YZbJvu8vx8hsUl62cZFhaWa3p/f3+DbZ0wYcILjyF/f38pfWJionj77bdz3DfDhw83WOaKFSsM1iu/x0VOsh57usdFTqZNm/bCc0lAQMAL8xFCiGfPnon33nvvhflpv7tCCDFs2LBc03700Ud6ZeR0vtBth259c/q+Z13u4+OTrWyZTCZ+/fVXaZslS5a88DjRPcfmdO7VtWLFCoPHWMuWLXPdL5999pmUNiQkRDg5OeWY1t7eXly8ePGFZQqR8/kgN7Nnz5a2+emnn/TW7d+/X8hkMmn9zp079dZ37dpVWufp6SmSkpKEENk/nxcdS7rfs/Hjx+daX9325/S5vGg73WNJCCFSUlJE+fLlpXWdOnWS1gUHBwsLC4sc21CxYkVx+/btHMvJ6dWrVy+9Oujug7ycm3S/M1n3Q2xsrF6dR4wYkW1/6B4rusdRenq6aNSoUbayzc3NRb169fJ1fOXnXJvf/Zzfc0bWfax7Ps9peW7fp5zOZTNnzjRYf29v73wftzmVkdvvkm6ZefkNyS0++vLLL7O1Q/d3Or+fmRBCNGzYMFs6uVwurS9IDJQ1ttK+vLy8RHx8/EvX+/79+6Jy5co5pjczMxN79+6V0hv67UhPTxc9evSQljs4OIgrV67k+tnk9Tya9RiIi4sTGo1GABBVqlTJtQwiIiJ6PXGqTyIiohLI2dkZBw8eRFBQELp27Yry5ctDrVbDwsICVatWRc+ePbFt2za9Z9YY0qpVK2zZsgXe3t7QaDQoX748pk6dimXLlhlM/8EHH2DgwIHw8vKCvb095HI5zM3N4ePjg5kzZ2LDhg1S2q+++godOnSAq6srLCwsIJfL4ejoiFatWmHXrl3o0qXLC9vp6+uLK1euYMyYMahRowbMzMygVqtRsWJF9OrVC2XKlHmpdhSnHj164NSpU+jduzfKlSsHU1NTWFhYoE6dOvj2229x9OhRWFlZSelbtWqFP/74Aw0aNIBKpZJGSemOvMuqc+fOOH/+PAYNGgR3d3eo1WqYm5vD3d0d7dq1w5IlS3J95p1WYXyGJcWsWbMwa9YsuLm5Qa1Wo2bNmlizZg0CAgIMpler1di7dy9+/vlnNGvWDHZ2dlAoFHB0dETz5s3RokWLfJWf3+PidWVhYYHNmzdj//796NOnDypVqgQzMzOYmZmhUqVK6NSpE9auXas3SmnhwoUICgpCq1at4ODgAIVCATs7O7Ro0QIbN258Zd/jgwcP4pNPPoGzszNUKhVq166NLVu2SCPSAGDw4MH44YcfUK1aNajValSuXBnz58/H1KlTC70+H3/8Mfr06YNq1arB1tYWcrkc1tbWaNCgARYsWKA38svDwwOXLl3C6NGjpbqp1WpUqVIFw4cPx6VLl1CzZs1Cr6PWwIEDpWfn6T6DLi4uDv3795dG5wwcODDbiJalS5dKI8lCQkL0RrmWJKamphg/frz0ftu2bbh06RKAzNEyFy5cwODBg+Hm5galUgmNRgMvLy9MnjwZ586dg4uLywvLkMvlsLe3R9OmTbFs2TL88ssvRdYee3t7fPzxx9L7pUuXIjo6Ok/bmpiYYNu2bejXr580orpZs2YIDg7OcYRiTvJzri2s/VzcpkyZgh9++AFVq1aFUqlExYoV8eWXX+rNYmBubl6MNcy74cOH5zrS9mU+s1WrVqFNmzawtLQ0mGdBYqAePXpg8+bNqFOnDtRqNRwcHPDRRx/h4MGDMDMze+l6Ozs748yZM5gxYwa8vb1hZmYGpVIJV1dXDBgwAOfPn9d7/qUhJiYm+PXXX9GyZUsAmaPyW7RogYiIiFy3exlbt26VRiIOGTKk0PMnIiKioicT2v+FERERERERvSFu3rypN+Uh/1tUMJ999hkWLlwImUyGK1eu5LuDh4gyCSEMPhN09OjRmDt3LgCgU6dOBp95SFQY6tevj1OnTqF06dKIiIgoMR3NRERE9B+O+CMiIiIiIqICmTZtGmxsbCCEwFdffVXc1SEqsVasWIHBgwdj3759uHnzJkJCQvD111/rPeOvb9++xVhDMmb79++Xnu83a9YsdvoRERGVUBzxR0REREREbxyO+COi19GSJUv0plnN6pNPPtHrBCQiIiIiykpR3BUgIiIiIiIiIqLM5xu///77OHPmDB48eIC0tDQ4OjrC19cXH330EVq3bl3cVSQiIiKi1xxH/BEREREREREREREREREZAT7jj4iIiIiIiIiIiIiIiMgIsOOPiIiIiIiIiIiIiIiIyAiw44+IiIiIiIiIiIiIiIjICLDjj4iIiIiIiIiIiIiIiMgIsOOPiIiIiIiIiIiIiIiIyAiw44+IiIiIiIiIiIiIiIjICLDjj4iIiIiIiIiIiIiIiMgIsOOPiIiIiIiIiIiIiIiIyAiw44+IiIiIiIiIiIiIiIjICLDjj4iIiIiIiIiIiIiIiMgIsOOPiIiIiIiIiIiIiIiIyAiw44+IiIiIiIiIiIiIiIjICLDjj4iIiIiIiIiIiIiIiMgIsOOPiIiIiIiIiIiIiIiIyAiw44+IiIiIiIiIiIiIiIjICLDjj4iIiIiIiIiIiIiIiMgIsOOPiIiIiIiIiIiIiIiIyAiw44+IiIiIiIiIiIiIiIjICLDjj4iIiIiIiIiIiIiIiMgIsOOPiKgQyGQyyGQyrFy5srirQkRERCXczZs3pdgiODi4uKtDedCvXz/IZDI0bdq0uKtCREREr4n8XivKawy4cuVKKd3NmzcLpa55MX36dMhkMri6uuZ5G8ZIRMWDHX9ERq5p06ZSMCCXy2FpaYmqVauif//+OHfuXLb0n376KWrVqgWFQgGZTAZnZ+c8laP9IdeWY2FhgUqVKqF79+44ePBgYTfrtVO/fn3Ur18fjo6Or6Q87b7O7cVOSCIiorzJT7wUEhKCfv36oVq1arCysoK1tTXq1KmDn3/++YXl6MZLMpkMCoUCTk5OaNu2Lc6fPy+lU6lUUmxhZWVV6O0tCsHBwXptU6lUcHZ2RpMmTfD9998jKSmpuKtYpCpVqoT69evDw8OjuKtCRERkdPITq929exdt27aFi4sLVCoVbGxsUKtWLQQGBiIjIyPXclJSUvDll1/Cw8MD5ubmsLKygru7Ozp37oyLFy/mu975vVZUHDGgbmej9qXRaFC9enVMnz5db5+5uLigfv368PHxeSV1I6KXpyjuChDRq6FUKuHj44M7d+4gIiIC165dw+rVq/HDDz9g4MCBUrpVq1ZBqVTCzs4OMTExL1VWvXr18ODBA9y8eRN///03Nm7ciKlTp2LGjBmF1ZwXSklJgVKpfGXlnThx4pWVBWQGj1o3btxAbGxstuWvqhOSiIjIWOQlXjp9+jR++eUX2Nraws3NDdeuXcO5c+cwcOBAPHz4EOPGjctTWfXr10dKSgouXryIXbt24fTp0/jnn3+g0WhQunTpVx5bAIUXP7m5ucHc3Bw3btzAkSNHcOTIEaxYsQIHDhyAra1tIdT0xdLT0wEAcrn8lZQ3ZcoUTJky5ZWURURE9KbKS6wWExODAwcOoEKFCnB2dsbNmzdx6dIljBs3Dunp6ZgwYUKO+Y8dOxbff/89AKBy5cpQq9W4efMmtm7dil69eqFWrVr5qm9+47niigG1ypYtizJlyuDGjRsIDw/HjBkzYGFhgTFjxgAABg4cqHcNkYheXxzxR/SG0AYPd+7cwalTp1ChQgWkpaXh448/Rnh4uJTu8uXLiI6ORps2bV66rBMnTiAyMhJhYWHw9vYGAMycORP79u2T0ty7dw8ffvghypQpA6VSCTc3N8yaNQtpaWlSGu0dXX379sW0adPg7OwMc3Nz9OjRA0+ePJHSubq6QiaTYezYsfjwww9hY2ODli1bAgCSk5Mxbdo0VK5cGUqlEk5OTvjwww+ljjIAiIqKQq9evVC6dGnp7vTmzZtj165dADIvHE2cOBFubm5Qq9Wws7ND3bp1ERgYKOVhaJTdlStX0KVLF9jb20ttnDhxIhITE3NsY+nSpWFra4vevXvj2bNnue5j7att27bZlpuamqJdu3bo3bu3tC49PR1OTk6QyWT4+uuv9eo9d+5c9OrVC5aWlnB0dMTUqVMhhJC2ffr0KYYPH44KFSpAqVTCxcUFo0aNQkJCQo51JCIiKmnyEi+VL18emzZtQkxMDC5cuICwsDBYW1sDANasWZPnsk6cOIFz585JN0bFxMQgNDQUgOFpnnSnVtq0aROqVasGc3NzNGnSBFevXpXy3bdvH9566y04OTlBqVTCysoKb731Fnbv3i2l0c3/p59+wttvvw21Wo0JEybAwsJCWq51+fJlKX1eLkZNmTIFly5dwqNHjzBq1CgAwIULF/Dpp59KaTIyMrBgwQLUqFEDarUatra2eP/99xEZGSml0Z3GKjg4GD4+PlCr1ahZsyYOHTokpdPdN7/++isqVaoEpVKJ27dvAwB2794Nf39/WFpaQqPR4K233so2I8WcOXNQrVo1mJmZwdraGrVq1cLYsWOl9bt27ULDhg1hY2MDMzMzuLu7o3v37nj8+DEAw9NYJSYmYtKkSXB3d5durOvUqRMuX75ssI0HDx5E7dq1odFoULt27WK98EdERPQ6ykusVqNGDTx79gzh4eE4c+YMIiMjYWZmBgA4duxYrvlv2LABADB16lRcu3YNly5dwtOnT3H06FG9Tj/tdajp06dLywzFAoauFUVERKBnz55wdnaWrq9oO9Zymupz4cKFKFu2LMzNzdGrVy88ffrUYP3zEvPkZuDAgTh16hQiIyNhbm4OADnGXFovipGyio6ORvXq1SGTyeDr66t3fY+ICpEgIqPm7+8vAIgKFSroLd+6dasAIACIMWPGZNsuICBAABClSpXKUzna9FlPKxcuXJCWd+3aVQghRGxsrChXrpwAICwtLUXNmjWFQqEQAET//v2z1V2lUgkrKytRtWpVKa/OnTtL6SpUqCAACKVSKTQajfDy8hKtW7cWQgjRpk0bAUDI5XJRs2ZNYWVlJQAIDw8PkZCQIIQQonPnzgKAsLCwELVr1xblypUTMplMTJs2TQghxIIFC/TycHd3F0qlUvj7+0t10NZrxYoVQgghQkNDhYWFhZRv9erVhUwmEwDEO++8k62NpqamwtLSUlSsWFHK6/PPP3/pfb9mzRoBQGg0GvHkyRMhhBAHDx4UAISJiYm4ffu2Xr1VKpUoU6aMKFu2rLRswYIFQgghkpOThbe3twAg1Gq1qFmzplCr1QKAaN68ucjIyMhTPYmIiF5XLxsvaXl5eQkAol69ermWk/U3Ozk5WQwYMECKYx48eCCEECIyMlJKd/DgQSGEENOmTRMAhEKhEKampqJatWpSbNGoUSOpjHnz5glTU1Ph5uYmfHx8pHhEoVCICxcuZMtfqVQKe3t74eHhIWbOnCk++ugjAUA0bNhQylNbdpUqVXJsmzbO0I2HhBAiIyND+Pj4SHXQxiUff/yxlN7T01PY29sLAMLZ2VnaDytWrJDSmJmZierVqwuNRiMACHNzc3H37l29+pmamgqZTCaqVKkiSpcuLSIjI8X69eul/VShQgUp1pLL5eLAgQNCCCG2bdsmlePh4SGqVasmNBqNdDxER0cLpVIpAIjy5cuLmjVrChsbGwFAREZG6n22uvFhixYtBAAhk8lEtWrV9GLDsLCwbG1UqVSiatWqUlxcoUIFkZqamusxRURE9CZ4mVitTZs2ok6dOlKMAUB8/fXXuZbj6OgoAIgGDRqIHTt2iKioKIPptNehtNeNhDAcC2SNjSIiIqQYQi6Xi+rVqwtnZ2dRq1YtIYThGHD79u3SMkdHR1GuXDlhbm4uLdPGInmJeQzRLVPbnqdPn0plDB06VEqrjbleNkZ69OiRqFWrlgAg6tevL8WFRFT4OOKP6A311ltvSX9r7y4vCrVq1YKlpaVeOYsWLcLt27dRqlQp3LhxAxcvXsTmzZsBZN71fP36db08NBoNrl69ivDwcGlKhqCgIL2RigBgZWWFq1ev4tKlS9ixYwcOHTokjdo7cOAALl68iPDwcGg0GoSGhmLt2rUAMu+2AoAlS5bg7NmzuHXrFu7evYsePXrore/fvz8uXryIiIgIPHz4UG/EX1Zff/01nj9/DgsLC4SGhiI0NBRz584FkHknftY7rtRqNcLCwnD9+nXUqVMHALB///487+esunbtCkdHRyQmJmLdunUAgC1btgAAmjdvDhcXF730vr6+uHnzJiIjI6Vj48svvwQArFu3DhcuXIBSqcSlS5dw8eJF6Q70AwcO4MCBAy9dTyIiotdZXuKlw4cPIyQkBADw0Ucf5Tlv7XPwfv75Z8hkMvzwww9wcnJ64XZpaWnYsmULwsLCMGLECADAX3/9Jc0o0LlzZ0RHR+PGjRs4d+4cbt26BUtLS6SlpUnxlq6GDRvizp07CAkJweeff46PP/4YAHD8+HEp1tLGEH379s1z+3Tb6efnJ9X92rVriIyMxJIlSwAAv/zyC65cuYKbN2/CxcUFUVFRWLhwYbZ85s6di9DQUJw+fRoKhQLx8fHSVFxaqampWLx4Ma5evYq7d++ifPnymDBhAoQQ+PDDDxEZGYkbN26gc+fOSE9Px9SpUwH8F+u1aNECISEhCAsLw+PHj6VY8datW0hJSYGlpSXCw8Nx8eJFPHr0CKdOncpxavWDBw/izz//lOoeFhaGsLAwWFhY4Pnz5/jqq6+ybRMYGIjw8HDMmTMHAPDPP/9ki4uJiIjoP7nFamfPnsXZs2fx8OFDAMC4ceNeOCX70KFDAWTOzNC+fXs4OzujWrVqmDVrVqE8r/jLL7/EkydPYGpqisOHDyM0NBT379/HihUrctzm22+/BZD5POG///4bkZGRqFevXrZ0eYl5XuSnn36Cr68vKlasiPj4eDRs2DDXqczzEyM9f/4crVu3xsWLF9GgQQPs3btXmjWDiAofO/6I3lAveqBxYRI6U0YCwKlTpwAADx48kKae7NSpk5T25MmTeumbNWsGZ2dnAMAHH3wgLdedJgkA3nvvPZQrVw5A5vNctOUAgL+/P2QyGcqUKSNdGNN2XrVv3x4AEBAQAHd3d7Rr1w6rV69GmTJlAADt2rWTprwqW7YsmjVrhtmzZ8POzi7HNp8+fRpAZhCqrVPPnj2l9WfOnNFL37x5c5QtWxYmJiaoVq2atH9ellKpxIcffggAWL58OYQQCAoKktqZVdeuXWFqagpTU1N07dpVKj8mJkbajykpKahSpQpkMpk0hSvw6p9vSERE9Kq8KF7atWsX2rZti4yMDHz22Wf56virX78+6tatCysrKwghMHLkSFy8ePGF21lbW0uxi4eHh7Q8OjoaQOY05/369YOTkxPkcjns7Oyk6cPv3buXLb8hQ4ZArVYDyIyffHx8pGcGL1++HNeuXcOVK1cgk8nQp0+fPLdPV9b9eObMGSk+DAgIgEwmg6WlJe7cuQPAcGyhjQE9PT3h5eUFIHssqNFoMGjQIACZHY4PHz7EzZs3pbaYmJjAxMREiom0MWfLli2hVCrx559/wtHREX5+fhg3bpw0LZinpyfc3Nzw7NkzODk5oXbt2ujXrx/u378vTYOVlTYWBP6LAV1cXKQLlFljQQDS/tX9XAsSDxIRERm73GK1qKgoxMfH4/fff4eFhQW+++47/Pzzz7nmN336dPz2229o3749rKysAABXr17F1KlTMWTIkALXVxt7+Pv7o1GjRtJyHx+fHLfR3mDWsmVLWFhYQC6Xo0uXLnppYmJi8hTzvMjdu3dx+vRpPHr0CAqFAq1atZKuxxmSnxjp7NmzOHnyJCpUqIA9e/ZI+5eIioaiuCtARMXjyJEj0t+6FxcK2/nz5/H8+XOD5VhaWhosW3uRJb9KlSqV4zrtBSxd2uDliy++QOPGjbFnzx5cuXIFhw8fxs6dOxEcHIydO3eiZcuWOHfuHDZt2oSLFy/i/PnzCA4OlkYnWlhYvFR9ddnY2Eh/KxSZp+asHab5NWTIEAQGBuL06dP4+eefcffuXVhaWmYLEPNK+xDtrGxtbQtUTyIiotdVbvHSDz/8gE8//RTp6emYOXNmrndDG6Lt3IqJiYGrqyvi4uIQGBiI1atX57qdoZgB+C9uaNu2La5fvw6FQgEvLy+o1WqcP38eKSkpSE9Pz5afofhp6NChOHnyJFatWiXN3NCsWTOUL18+X23U1kv7PB25XI4qVapIF6YAwNvbGyqVSm+bChUq5LscAHB0dISJieF7W93c3AyOzktJSUGNGjUQEhKCtWvX4vz587h48SKOHTuGn376CWFhYShfvjzOnj2LVatW4eTJkwgNDcWqVavw66+/YuPGjXj//fdfqr5ZaT9bQ58rERERZfeia1tmZmZo27Yt3nnnHQQFBWHq1KkYOHBgrnl27twZnTt3RkZGBs6ePYsBAwbg8uXL2Lp1q5RGJpMBgF5sldNz91613GIepVKZ67bTpk3D2LFjMWbMGCxZsgTTpk2Dl5cXOnfubDC9Wq3Oc4xkbm6O+Ph4/PPPP1i1ahU++eSTgjWUiHLFEX9Eb6AzZ85g5MiRADIvwPTv379IyomIiJBGnQH/TX+lnZJAoVBg/fr1OHHiBE6cOIF9+/Zh6NCh2QKK4OBg6W5n7YOWgcwHNuvSBl5aulMfTJw4USrn6NGjmD59OgYMGAAg8+HO/v7++P7773HgwAEsW7YMQObUXQBw6dIlODo64osvvsDvv/+Os2fPAsi8A/vq1asG264t+8iRI9Ld69rpogCgbt26Oey1wuPq6opWrVoBgDQV2HvvvWewY/W3335DWloa0tLS8NtvvwHIvBDo6OgotSU9PR2LFy+W9mNwcDDGjh2rN5KRiIjIWOQULwkhMG7cOAwdOhRyuRyrV6/Od6efLplMJnXuFHQKqYcPH0pTQ86cORMXLlzA+vXrs8VIWcvPqlu3brCzs0NUVBS++eYbAC83zWdycjLGjh2LCxcuSPlaW1ujTp06Urn9+vWTYovjx48jMDAQn332Wba8tDFgWFiYNNJPO/Ivp7Y4OjpKnYi1a9fG0aNHpbJ+/fVXzJo1C0qlEhEREZDJZJg6dao0nbyVlRUSEhJw+vRpxMXFISwsDMOGDcPq1atx7tw5vPvuuwD+ixez0o1DtTHgnTt3pAuUryIWJCIiMmY5xWpbt27FtWvXpHTR0dHSSPv4+Phc85w8ebIUt5iYmKBevXqoUqUKAOhNS6mdnl1bTmxsLIKDg19YZ+1N6YcOHdIbhZfbrA+enp4AgL179yI+Ph7p6enSSD6tvMY8eWFubo558+ZJs2BNnTo1xxuR8hMj1a1bF5MnTwYAfPrpp9JjaYioaLDjj+gNcf/+fTRo0ADlypWDr68v/vnnHygUCixZskTvrqimTZvC3d1d6vyJjY2Fu7s73N3d8zw1QIMGDeDm5oZq1apJAdPUqVOlH/9PPvkEZcuWxePHj1G1alV4e3ujUqVKsLe3NzgNpXaKyWrVqknPnevYsSOqV6+eaz2aNm2Kli1bAgA6deqEatWqwdPTEzY2NmjdurV0t/mECRNgb28Pd3d31KlTR+qsrFmzJgBg48aNKFeuHMqXL486depIF5nMzMxQqVIlg2VPmDBBeoZL9erV4eHhgVGjRgEA3nnnHTRr1ixP+7KgtM/p0Qa3hvYvkBkwu7q6wtXVFYcOHQIA6XmKH3zwAWrWrIn09HTUq1cPNWrUQNWqVWFjY4OuXbviyZMnRd8QIiKiVyAv8dL69eul5/xaWVlh4cKFaNCggfTKqwYNGsDX1xfu7u7SNOTaqc9flp2dnfQcX+0d2rVr19YbQZYXarVaungWHx8Pc3NzvPfee3neftasWahVqxbs7Oyk59X5+Pjgf//7H4DMO9G1N4SNGDECbm5uqFmzJmxsbNCkSROcO3cuW55jxoyBp6cn6tati7S0NJiZmeHTTz99YV20sePmzZtRpkwZ+Pj4wNnZGVWrVsWaNWsAZF58c3d3R5kyZVC7dm1UrFgRcXFxkMvl8PDwQHR0NBo1agR7e3vUrFkT1apVw549ewD8Fy9m1axZM7Ro0QIAMGrUKHh4eMDDw0N6BvTEiRPzvD+JiIgoU15ita1bt6Jq1aooW7YsatWqhQoVKuD27dsAcr4movXTTz/Bx8cHjo6OqFOnDsqVKyc961j3pue3334bQOb1orfeegteXl6Ii4t7Yf0///xz2NjYIDU1FY0bN4anpyfKli2ba73GjBkDALh+/Trc3Nzg5uaGv/76K1u6vMQ8eaVWq6VO1StXrmD79u0G0+U3Rpo1axb69+8PIQQCAgKwe/fufNWLiPKOHX9Eb4iUlBScOnUKT548gbu7OwICAnDy5MlsUxzcvHkTN27ckJ4Fk56ejhs3buDGjRvSRakXOXXqFB48eIDy5cuja9euOHjwIGbMmCGtd3R0xIkTJ9C/f3/Y29sjJCQEiYmJeOuttzBv3rxs+XXt2hXjxo3DkydPoNFo8P7772P58uV5qsvWrVsxdepUVK5cGX///TeioqJQvXp1TJ48WRox2L17d9StWxdxcXG4fPkybGxs0KNHD+nuoyZNmqBVq1bIyMjAlStXIIRA8+bNsXv3br3ptnRVr14dx48fR+fOnaU7yV1dXTFhwgRs27YtT3UvDG3atJHu+qpQoQL8/f0Npvvyyy/x9ttv4+nTp7C3t8ekSZOku+1VKhUOHTqEzz77DOXKlcO1a9fw+PFj1K1bF1988UWuU6wSERGVJHmJl5KTk6W/Y2NjcfLkSb1XXp08eRKnT59GamoqfHx8sHjxYvTu3btA9ZfJZNiyZQvq1asHuVyO9PR0rFmzBg4ODvnOa8iQIdIIui5duuRravO///4bYWFhsLCwQOPGjTF//nwcP35cb3rwH374AfPmzYOXlxfu3buHf/75B66urhg1ahSaNm2aLc/du3dDrVYjLS0NNWrUwI4dO1C2bNkX1qVnz574/fff4e/vj8TERFy9ehWWlpbo27ev9Ln6+PhIMVtoaCji4+PRoEEDbNq0CdWrV4e9vT369euHUqVKITIyErdv35ZuSMtturDt27fj888/R8WKFREREQGFQoGOHTvir7/+kp7pTERERHmXl1itRYsWaNSoEZKTkxESEgJTU1P4+vpiwYIFmD9/fq75z549Gx07doSlpSXCw8MRHR2NqlWrYtq0aZg1a5aUbuLEiejduzdsbGxw7do19O3bFz169Hhh/d3d3XHq1Cl88MEHsLe3R0REBID/OhIN6dixI+bNmwdnZ2c8e/YMdevWxezZs7Oly0vMkx+DBw+Wrnl98cUXBtO8TIy0bNkytG7dGqmpqejatas0JTwRFS6Z4EMDiOg11bRpUxw6dAgBAQFYuXJlcVenxGrVqhX27NmDKVOmYObMmXrrtBf0VqxYgX79+hVD7YiIiOh1lJycjFKlSuHp06fYv38/mjdv/srrsHLlSr0pVomIiIiIiOjF8jfnCxERlRhffPEFDh8+jL1798LMzIwPTiYiIqI86d27N0JCQvD06VPUqVOnWDr9iIiIiIiI6OWw44+IyEjt27cPR44cQeXKlTFv3jxOyUlERER5smbNGpiamqJRo0b45Zdfirs6RERERERElA+c6pOIiIiIiIiIiIiIiIjICJgUdwWIiIiIiIiIiIiIiIiIqODY8UdERERERERERERERERkBNjxR0RERERERERERERERGQEFMVdgVctIyMD9+7dg6WlJWQyWXFXh4iIiEoIIQSePXuGMmXKwMTkzb13irEUERERvQzGUpkYSxEREdHLyE8s9cZ1/N27dw/lypUr7moQERFRCXX79m24uLgUdzWKDWMpIiIiKgjGUoyliIiI6OXlJZZ64zr+LC0tAWTuHCsrq2KuDREREZUUcXFxKFeunBRLvKkYSxEREdHLYCyVibEUERERvYz8xFJvXMefdhoFKysrBlhERESUb2/6lEyMpYiIiKggGEsxliIiIqKXl5dY6s2dVJ2IiIiIiIiIiIiIiIjIiLDjj4iIiIiIiIiIiIiIiMgIvHFTfRIRERWl9PR0pKamFnc1qABMTU0hl8uLuxpERERERERERPnC61IlX2Fcl2LHHxERUSF5/vw57ty5AyFEcVeFCkAmk8HFxQUWFhbFXRUiIiIiIiIiojzhdSnjUBjXpdjxR0REVAjS09Nx584dmJmZwdHRMU8P2qXXjxACMTExuHPnDipXrsyRf0RERERERET02uN1KeNQWNel2PFHRERUCFJTUyGEgKOjIzQaTXFXhwrA0dERN2/eRGpqKjv+iIiIiIiIiOi1x+tSxqMwrkuZFHKdiIiI3mi8o6rk42dIRERERERERCURr2mUfIXxGbLjj4iIyEi5urqiatWq8Pb2RvXq1dGzZ0/Ex8cXWVkXLlwAAAwcOBAHDx4s1PxPnjyJWrVqoUqVKmjevDnu3r1rMF1iYiL69u2LGjVqoEaNGujQoQNiYmIAADdv3kTTpk1hbW0Nb2/vQq0fERERERERERH9h9eliu+6FDv+iIiIjNiGDRtw4cIFhISE4OnTp1i5cmWRl/nTTz+hWbNmhZZfRkYGevXqhfnz5+PatWto06YNRowYYTDt0qVLkZCQgMuXL+PKlSsoVaoUAgMDAQBWVlaYPXs21q5dW2h1IyIiIiIiIiIiw3hdqniuS7Hjj4iI6A2QkpKChIQE2NraAgAuX74MPz8/1K5dGx4eHpg9e7aUdseOHahZsya8vb1Ro0YNbNu2DQAQFRWFbt26wdfXF15eXpg8ebLBspo2bYqtW7cCAPr164fBgwfj7bffRpUqVdClSxekpKQAyJx/fsKECfD19YW3tze6deuGx48fZ8vv7NmzUCgUUtA2ePBg7NixA0lJSdnSymQyJCQkIDU1FWlpaXj+/DlcXFwAAHZ2dvDz84O5uflL7kUiMkYpKSkYP348XFxcoFKp4OHhgV9//TXH9EIILFy4EJ6entBoNChbtizGjRuH1NRUKc3Vq1fRpUsXODs7Q6VSoWzZsujbty+io6OlNKtXr0bdunVhbW0Nc3NzeHp6Yv78+UXZVCIqgKI4V9y/fx/9+/eHi4sL1Go1ateujZ07d+rlc+/ePfTo0QO2trbQaDTw9/fHyZMni6ydREREREWB16Ve7XUpdvwREREVEZGQgIyHD/Vfz55lrktPz77u4UNp24ynT7OtE8nJ+a5D9+7d4e3tDWdnZ5iYmKBbt24AMqdA2L9/P86dO4ezZ89iy5YtOHHiBABg8uTJWLp0KS5cuIBLly7B398fABAQEIBPPvkEp06dwvnz53HmzBls2rTphXW4cOECduzYgbCwMDx48ABbtmwBAAQGBsLc3BynTp3ChQsXcgzabt26hQoVKkjvLS0tYWVlhXv37mVLO3jwYFhaWsLJyQmlSpXC06dPMWzYsHzvNyJ6c4wdOxbffvstTE1N0aNHD9y6dQsBAQHYsWOHwfTz5s3DZ599hnv37qFPnz6wtrZGYGAgxowZI6Xp0qULgoKCYGVlhf79+yMtLQ2rVq3C4MGDAQCnTp1Cnz59cPbsWfj7+6Ndu3YIDQ3FyJEjpf/UEtHrpbDPFRkZGWjbti1WrlwJZ2dn9OrVC+Hh4ejQoQPOnj0LILPzsG3bttiwYQOqVauGNm3a4PDhw3j77bdx//79V9Z2IiIiKpl4XSrTm3hdqlg7/g4fPoz27dujTJkykMlkUi9sboKDg1G7dm2oVCq4u7u/kqGhRERELyMtIgLJO3fqvdL+nW9cxMdnW5esc4d36rFj2dZl5DB/eG60UyrExsbC1dUV48ePB5A55/jAgQPh5eWFBg0a4J9//pHmQn/77bcxfPhwfPvtt7h06RJsbGwQHx+P/fv3Y/jw4fD29kbdunVx/fp1XL169YV16Ny5M8zMzCCXy+Hr64sbN24AALZu3YrVq1fD29sb3t7eWLduHSIjI/PdRl179+5FRkYGoqKicP/+fdjY2GDq1KkFyvN1xlgqf4prZJfW8uXLIZPJIJPJ0KNHjyJpI+VPTEwMli5dCgDYvn07fvnlF+lO0xkzZhjcZv369QCA8ePHY9myZdI0LUuWLEF0dDSEELh+/ToAYM6cOViyZAlGjx4NANI5Trvezs4O27dvx4YNG1C9enW9NET0+iiKc0V4eDjOnz8PAPj999/x888/Y9CgQcjIyMCsWbMAZN7tfuHCBTg7O+Pw4cPYsmULOnbsiPj4eMyZM6dI2/ymYCxFRETGjNelMr2J16UUr7xEHfHx8ahVqxY+/PBDdOnS5YXpIyMj0bZtWwwZMgRr1qzB/v37MXDgQJQuXRotW7Z8BTUmIiLKO0XlypD/O5xfolQCAGTm5lC1bZvjtqaNGwNpaXrLZBYWL18XhQLvvfcexo4dizlz5uDzzz+Hg4MDzp8/D4VCgS5dukhTFMydOxchISE4ePAgAgIC0KtXLwwdOhQAcOLECajV6nyVrZteLpcj7d92aTtV3n333Vy3L1++PP755x/p/bNnz/D06VOUKVMmW9ply5ahZ8+eUpm9evXCl19+ma/6liSMpfJn7Nix+P777+Hq6ooePXpgy5YtCAgIgK2tLdq3b58t/bx58zB69GjY2NigT58+OHr0KAIDA5GcnIwFCxYAyBzZFRoaisqVK6NTp04ICgrCqlWr8OzZMwQFBUl5hYeH49NPP4VCoZC+A1T8QkJCkJycDLVaDS8vLwBAgwYNAAAXL15Eeno65HK53jba88ulS5eQkJCAM2fOAMjsWA4JCUGzZs0wbtw4zJ49G6NHj8bOnTsRFBQECwsLqYOgbdu28PLywuXLl9GhQwdoNBqEhYXBx8cHvXv3flXNJ6I8Kopzhe5d46dPn0azZs0QEhICAFKH4Llz5wAA3t7eMDU1lcrdtm2btI4KhrEUEREZM16XyvQmXpcq1hF/rVu3xuzZs9G5c+c8pV+yZAkqVqyIOXPmoHr16hg2bBi6du2KefPmFXFNiYiI8k9mZgYTe3v9l6Vl5jq5PPs6e3tpWxNr62zrZCpVgepz4MABVK1aFQDw+PFjuLi4QKFQ4OrVq9i3b5+ULjw8HJ6enhg2bBg+/vhjnDhxAhYWFmjWrBm+/vprKd29e/dw586dl65Pp06dMG/ePCQkJAAAEhISpAteuurUqYPU1FQcPHgQQOaDktu3b28w0HNzc8PevXshhIAQAjt37kSNGjVeuo6vO8ZSeVdcI7sAIDk5GT169EDlypXRtWvXomkgvZSoqCgAgIXOf2C1f6elpSE2NjbbNhMnToRCocC6detgbm6Ojz76KFt+nTt3hoeHByIiIrB06VJER0ejcePG8Pb2BgBYW1tj4MCBUKlU2LFjBzZu3Ai1Wo0uXbpIz7wgotdHUZwr3NzcpI7+Dh06wNLSEn/++adeebmVy6k+CwdjKSIiMma8LpU7Y74uVawj/vLr+PHjaNGihd6yli1bYsSIEcVTISIiotdc9+7dodFokJaWhgoVKmDJkiUAMudL79OnD3755RdUqlQJzZs3l7b5/PPPcfXqVSiVSpiZmeGHH34AAKxZswajRo1CjRo1IJPJYG5ujqVLl0oPKc6v8ePHIzk5GfXr14dMJpOWeXp66qUzMTHB6tWrMXjwYCQlJaFMmTJYtWqVtL5NmzaYOXMm6tati+nTp2PQoEFSUFWtWjWpsychIQFVqlRBcnIynj59ChcXF/Tp0wdfffXVS9W/JHqTY6niGtkFAKNHj8aNGzdw9uxZox6BWhI5OzsDAJ4/fy4te/bvMy8UCgUcHByybdO6dWuEhYUhKCgIT58+hZ+fH7p27Yr4+Hg4OTkhLS0Nbdq0wYMHDzB37lwMGTIEkyZNwrx589CtWzecPHkSO3bswPDhw2FjY4PLly/D0tISfn5+mDJlCuzt7fHxxx+/mh1ARHlSFOcKAFi1ahV69OiBkydPwtzcHHK5HGPHjpXW51Zu6dKli6Cl9CJvcixFRET0MnhdqniuS5Wojr+oqCiUKlVKb1mpUqUQFxeHxMREaDSabNskJycjWeehk3FxcQAyH6SdkZFRtBUmIqI3RkZGhnQ3jxCiuKsDIOfnRAkh4O3tjcuXLxtcp33Icdbljo6OeoGN7jptWUII6Q4oIQRWrFgh/Q1kPjhZ+14ul2PGjBnZRlsZ2n8NGjTAxYsXDabb+e8c9EII2NraGnywsxACGo0Gt2/fNrgu63shRLZYwRjihjc5ltI+dNvCwkKqt5mZGYDM0RrR0dHZ9s348eNx/PhxrFu3DuvWrcuWX0ZGBjp27IjffvsNoaGhiIiIAAC8++67qFmzJjIyMrB161b873//w4oVK+Du7i4db9pjjIpX9erVoVQqkZSUhIsXL8LLywvHjx8HANSsWRMymQyhoaEAMqd3MTMzQ2pqKtzc3KTRnStXrkR8fDwsLS1Rr149PHr0CA8ePAAA+Pr6QqVSoU6dOgCA0NBQZGRkIDw8HADg5OSESpUqAci8M/TGjRsICQnhsUH0mimKc0VGRgZSUlLQunVrtG7dGunp6WjdujUAoEWLFsjIyECtWrUAZE79mZycDFNTU6lcb2/vEneuKGn1NeRNjqWIiOj1xutSvC6lq0R1/L2Mr776yuD0TTExMdKcsURERAWVmpqKjIwMpKWl8fldJVxaWhoyMjLw8OFD6Xk6wH932L9pjCWW0o7ee/78OaKjowEAt27dApA5WiMjI0NarlWnTh0cPnwYu3fvRlxcHHx9ffHRRx8hISEBSqUS9+7dQ5s2bRATE4Pp06ejb9+++Prrr7Fs2TK899572LVrF3788Ueo1WqsWbMGa9askS4MHz58GL1798bcuXNf4V4gQ3r37o3ly5ejffv2aNiwIX7//XcAwLBhwxAdHS3d7bllyxY0atQIhw8fxpQpU1C3bl08fPhQmpJm3Lhx0hQxVapUwbVr19CnTx80btxYStOgQQMpTxMTE1y7dg0tW7aEubm5NMWft7d3tmORiIpfYZ8rEhISMGLECDx48AAuLi44e/YswsLCYGtri6FDhyI6Ohr169eHh4cHQkND4efnBycnJ+zatQsajQZ9+/YtcecKxlL6SlosRURErzdelzIehXFdqkR1/Dk7O0t3z2o9ePAAVlZWBu+qAjLn1R81apT0Pi4uDuXKlYOjoyOsrKyKtL5ERPTmSEpKwrNnz6BQKKBQlKifV8pCoVDAxMQE9vb2evO15/fh0a+jNzmWaty4sTRa48GDB/Dy8sK1a9cAZI7WKF26tDQKS3e0hpOTE+rXrw8gc7RGQkICLC0t0bJlSyQlJSEmJgZA5uiMChUqoEmTJli2bBkiIiLg5OQklant1NGKiorCX3/9JU3nRsVn0aJFsLW1xdq1axEUFIRKlSphzJgxCAgI0EtnY2MDJycnVK9eHebm5ti2bRvS09Ph4+ODkSNH4oMPPpDS7ty5E5MmTcKRI0ewceNGODk54cMPP8SXX34JR0dHtGrVCuvWrcOcOXNw+vRppKWloUaNGhg8eDA+/PDDV70LiCgPiuJc4evri/nz5+PYsWOwtLTEe++9hy+++AKVK1eW0uzevRujR4/G3r17ceXKFfj5+eHrr79GzZo1X1nbCwtjqUwlNZYiIqLXG69LGY/CuC5Voo6Ahg0bYteuXXrL9u3bh4YNG+a4jUqlgsrAQydNTExgYmJS6HUkIqI3k4mJCWQymfSikkv7GWaNFYwhbniTY6lSpUph0KBBWLRoETp16gR/f39s3rwZADBlyhSYmJhIozUOHjyIpk2b4siRIxg+fDgaNmyI2NhY7NixAwAwe/ZsWFlZwcrKShqJ0a9fPzRv3lxK4+/vDxMTE2zbtk2vHv369cMvv/yC7t27Y/369a9wD1BONBoNvvvuO3z33XcG12eddsXLywvnzp3LNU93d3ds2LAh1zTdunVDt27d8ldZIio2RXGuGDVqlF6HkCHly5c3OF1USVSS4oacvMmxFBERvd54Xcp4FMZ1qWKNMJ4/f44LFy7gwoULADLnfL1w4YI07dLEiRPRt29fKf2QIUPw999/Y9y4cQgPD8fixYuxceNGjBw5sjiqT0RERFSsGEvlz3fffYcxY8YgOTkZa9euRbly5bB8+XJ06tTJYPrSpUtDpVJh/fr12LNnD3x8fLB27Vp89tlnUpodO3age/fuiI+Px8qVKyGXyzFgwADpOQJERET0+mIsRURERMZIJorxSY/BwcFo1qxZtuUBAQFYuXIl+vXrh5s3byI4OFhvm5EjRyI0NBQuLi6YMmUK+vXrl+cy4+LiYG1tjadPn3JKBSIiKjRJSUmIjIyEq6trjtP8UMmQmJiImzdvomLFinrTKLyOMQRjKXqTfDTW8CgbypsfA8cUdxVKjJSUFEyZMgVr1qxBTEwMKlWqhAkTJuhd/NclhMCiRYuwZMkS/P3337Czs0OvXr3wxRdfSM/kmD59usFnfK1atQq9e/cGAJw5cwaTJk3CmTNnkJycjCZNmmDu3LmoVq1a0TXWCNUMCS7uKpRolzybFlner2MMwViKiIiMBa9LGY/CuC5VrFN9Nm3aNNt0GLpWrlxpcJvz588XYa2IiIjyz9TUFDKZDDExMXB0dOS0CiWUEAIxMTGQyWR6D1B+XTGWIiIqfGPHjsX3338PV1dX9OjRA1u2bEFAQABsbW3Rvn37bOnnzZuH0aNHw8bGBn369MHRo0cRGBiI5ORkLFiwQC/tO++8Aw8PD+l99erVAQA3b97E22+/jbi4OLRv3x4KhQJBQUG4cOECIiIiYG5uXrSNJnpDMZYiIiJjwetSxqGwrkuVqGf8ERERva7kcjlcXFxw584d3Lx5s7irQwUgk8ng4uICuVxe3FV5Y/1v9Z7irkKJ9knvlsVdBaISKyYmBkuXLgUAbN++HV5eXvDx8cHIkSMxY8YMgx1/2ud1jh8/HhMmTMCFCxfg4+ODJUuWYNKkSXBycpLS9uzZ0+DIoF27diEuLg5Vq1bF9u3bAQDe3t64ePEifvzxR4wYMaLwG0tERERERoPXpYxHYVyXYscfERFRIbGwsEDlypWRmppa3FWhAjA1NWWnHxHRGyokJATJyclQq9Xw8vICADRo0AAAcPHiRaSnp2f7jdBOv3Pp0iUkJCTgzJkzADKnDA0JCdHr+Bs+fDiGDBkCFxcX9OrVC59//jlUKpWUR3R0NG7evAm5XI579+4BAEcWEREREVGe8LqUcSiM61Ls+CMiIipEcrmcnUZEREQlVFRUFIDMiyZa2r/T0tIQGxuLUqVK6W0zceJEHD9+HOvWrcO6desM5qdQKNC4cWNUr14dT548wbZt2zBz5kw8efIECxYswPvvv4/vvvsOYWFhqFixosE8iIiIiIhehNelCGDHHxEREREREREAwNnZGQDw/PlzadmzZ88AZHbeOTg4ZNumdevWCAsLQ1BQEJ4+fQo/Pz907doV8fHx0mi/SZMmYfLkydI2CxYswIgRI7BhwwYsWLAAlpaWOHfuHNatW4eIiAhUqFABx44dw6pVq/RGDBIREREREb0IO/6IiIiIiIiIAHh6ekKpVCIpKQmXL1+Gl5cXTpw4AQCoWbMm5HI5wsPDAQDly5eHmZkZUlNT4e7ujrFjxwIAVq5cifj4eFhaWqJ+/foAgIiICFSpUkUqRwgBAEhKSpKWmZiYoH///gAynzU4ZcoUAMA777xTxK0mIiIiIiJjwo4/IiIiIiIiIgCOjo4YNGgQFi1ahA4dOsDf3x+bN28GAKkjrnr16gCAgwcPomnTpjh06BCGDx+Ohg0bIjY2Fjt27AAAzJ49W5omtGXLlrCzs4O3tzfi4uKwbds2AEDfvn2lsmvXrg0PDw9YWFhg9+7diImJQf369fHBBx+8svYTEREREVHJZ1LcFSAiIiIiIiJ6XXz33XcYM2YMkpOTsXbtWpQrVw7Lly9Hp06dDKYvXbo0VCoV1q9fjz179sDHxwdr167FZ599JqUZPHgwAGDLli3YvXs3qlatirlz52Lu3LlSmlq1auHgwYNYtWoVFAoFRowYgX379sHU1LRI20tERERUmFJSUjB+/Hi4uLhApVLBw8MDv/76a47phRBYuHAhPD09odFoULZsWYwbNw6pqalSmm+++Qbu7u4wMzODhYUFPDw8EBgYKK1fuXIlZDKZwdf06dOLsrlEryWO+CMiIiIiIiL6l0qlQmBgoN7FJF3aaTq1PD09ce7cuVzznDBhAiZMmJBrmjVr1uSvokRERESvobFjx+L777+Hq6srevTogS1btiAgIAC2trZo3759tvTz5s3D6NGjYWNjgz59+uDo0aMIDAxEcnIyFixYAAD4+++/4enpiXfffRc3b97E7t27MW7cOJQvXx7du3eHh4cHhg8fLuWZmJiIZcuWAYDedOtEbwqO+CMiIiIiIiIiIqJcFcconps3b6J79+6oVKkSzMzM4ODggJYtW+L06dNF2lYiejkxMTFYunQpAGD79u345ZdfMHv2bADAjBkzDG6zfv16AMD48eOxbNkyrF27FgCwZMkSREdHAwCWLl2Kbdu2YfHixdi1axdq1KgBALhx4wYAwNfXF/Pnz5de3t7eAIBy5cqhW7duRdNYotcYR/wRERERERFRifPp/OKuQcm2cERx14CISpriGMVz8+ZNBAUF4a233kLz5s2xZ88e7N27F6dOnUJ4eDhKlSr1qncDEeUiJCQEycnJUKvV8PLyAgA0aNAAAHDx4kWkp6dDLpfrbaNWqwEAly5dQkJCAs6cOQMg82aDkJAQODk5AQD++OMP7Nq1C9evX8eVK1dQsWJF9O7dO1sdMjIyMG/ePADAiBEjoFCwC4TePBzxR0RkZIriLswpU6agTp06sLOzg5mZGTw9PfH9999L64ODg3OcS71fv35F2VwiIiIiIiIqYsU1iqdKlSr4+++/sX//fvz4448IDg4GADx58gR//fVX0TSWiF5aVFQUAMDCwkJapv07LS0NsbGx2baZOHEiFAoF1q1bB3Nzc3z00UfZ8gOAEydOYOHChdi9ezdkMhlat24Ne3v7bPlt374dERERsLa21suL6E3C7m4iIiNTFHdhrlq1CmZmZujYsSP++ecfHDx4EMOHD4dKpcLgwYPh4uKiN5c6ACxevBipqamcS52IiIiIiKiEK65RPGXKlNHLMzk5Wfq7bNmyRdBSIioIZ2dnAMDz58+lZc+ePQMAKBQKODg4ZNumdevWCAsLQ1BQEJ4+fQo/Pz907doV8fHx0nkCAKZPn44pU6bgxo0b6NGjBxYvXgyFQiFdu9L67rvvAABDhgyBpaVlobeRqCTgiD8iIiNSVHdhbty4EaGhoVixYgUOHDiAt956CwCwa9cuAIC7u7veXOotW7ZEamoqzM3NMWTIkKJrMOVZcYwEffr0KQICAlCtWjVYWFjAxsYGfn5+2Lt3b5G2lYiIiIiICtfrMIrn8ePH6NOnDwCgb9++8PX1LZzGEVGh8fT0hFKpRFJSEi5fvgwg8zsOADVr1oRcLkd4eDjCw8ORkJAAAEhNTYW7uzvGjh2L2bNnIyoqCvHx8bC0tET9+vWRnp6O+Ph4AIBcLkeVKlVQt25dAJk3Fug6efIkjh07BqVSic8+++xVNZvotcOOvxKkKC7a/vnnn3jrrbdgZmYGmUwGV1fXbPns3r0bfn5+sLOzg0ajQeXKlTFlyhSkp6cXRTOJqADychdmVi+6CxNAtv9Qae+yzOkOyzlz5gAAPvzwQ9jZ2RW0WVQIxo4di2+//Rampqbo0aMHbt26hYCAAOzYscNg+nnz5uGzzz7DvXv30KdPH1hbWyMwMBBjxoyR0qxatQqJiYno2LEjGjRogNDQUAwfPlzqfH78+DFWrVoFBwcHfPDBByhTpgyOHTuG9u3bS/8BICIiIiKi119BRvF8++23mDRpEnbv3g1zc3MAyDaKJy0tDVevXoW3tzcWL16Mzz//XC+vGzduoEGDBjh79iwCAgKwfPnyQm8jERWco6MjBg0aBADo0KED+vXrhylTpgCA9G/16tVRvXp1nDp1CgBw6NAheHp6YuDAgejUqRMGDBgAAJg9ezYsLCzw7NkzODs7o0OHDhg6dCjatWuHn376CQDw7rvv6pWvHe3Xq1evbCOGid4k7PgrQYriou21a9cQHx8vdRBkde/ePXTs2BHHjh1DzZo10b17d/zzzz+YPXs2Fi1aVCTtJKKXV5R3YWrNnDkTp06dQunSpTFp0qRs6y9cuID9+/dDLpdj5MiRBW4TFVxxjQS1s7NDaGgojh49ih9//BEnT56Eubk5UlJS8OeffxZdg4mIiIiIqFAV5yieo0ePokGDBoiIiMCMGTOwcuXKbNOKEtHr47vvvsOYMWOQnJyMtWvXoly5cli+fDk6depkMH3p0qWhUqmwfv167NmzBz4+Pli7dq00Yk+lUqFZs2Y4e/YsfvrpJxw/fhz16tXDDz/8gAkTJkj5REZGIigoCDKZDKNHj34VTaV8KK5BTUDm9at69epBo9HA2toa9erVyzZa1Niw46+EKKqLtkOHDsW5c+fQv39/g3ncvHlT+jLt2LEDK1euRIsWLQBknkyp+BXXSbN79+4oW7YsVCoV7Ozs0LhxY/z+++9F0UTKh6K8CzMtLQ1DhgzBtGnT4OrqisOHDxsc8ae9u6pr166oWLFi4TWOXlpxjQS1srJCtWrVpPUZGRnSuYbP4yAiIiIiKjmKaxRPaGgoWrRogdjYWFSpUgWPHj3CiBEjMGLECKkcInq9qFQqBAYG4t69e0hJSUFYWJjetWchBIQQaNq0KYDMGwvOnTuH58+fIzExEWfOnMEHH3wgpddoNNi+fTvu3r2LlJQUPHz4ECdPnsSQIUMgk8mkdBUrVkRaWhoyMjLg6en5ytpLeVMcg5oA4H//+x+6d++Oy5cvo127dujevTtMTU3x4MGDQm/j60RR3BWgvCnKhyjnxtfXF82aNcPBgwfRvn17uLq64s8//4Sbmxs++eSTwmwivaSxY8fi+++/h6urK3r06IEtW7YgICAAtra2aN++fbb08+bNw+jRo2FjY4M+ffrg6NGjCAwMRHJysvQwXN2TZk6B9N9//40mTZrA1tYWx48fx19//YXOnTsjMjISLi4uRdpmylnWuzC9vLwM3oUJAOXLl4eZmZneXZgAsHLlSr27MAEgLi4O77//Pvbu3YsGDRpg27ZtBs8hd+7cwcaNGwFAyo+KX15GgpYqVUpvm4kTJ+L48eNYt24d1q1bZzA/XS8aCZqUlIQPPvgAKSkpaN68Obp06VLgdhERERER0avz3XffQa1WY82aNVi7di0qVaqEcePG5WkUT3p6Onx8fDB69Gjpgr7uKJ4//vgDlpaWqFevHvr374/BgwcDAKKjo6UbDK9evYqrV69K+Xt7e/M5f0REJUDWQU1eXl7w8fHByJEjMWPGDIPXsHUHNU2YMAEXLlyAj48PlixZgkmTJsHJyQlDhw7F0KFDsWTJEoPXsJ8/fy5NHf3HH39Inc1vAnb8lRCv4qKtIQqFAoMGDcL58+dx6NAhHDp0CCYmJmjXrh07d14DxXXSBIDTp09Lfz98+BAODg5IS0vDrVu3eGwUI+1dmIsWLUKHDh3g7++PzZs3A9C/CxMADh48iKZNm+LQoUMYPnw4GjZsiNjYWOlOG+1dmADQtm1bHD16FKampqhVqxa+/PJLAIC7uzuGDRsmlb9gwQKkpqaiWbNmqFOnzitrN+WuICNBg4KC8PTpU/j5+aFr166Ij4/PNhJ02LBhWLp0KVxdXbFv375so/mio6PRsWNHnDhxAu+++y5+++03KBQMQYiIiIiIShLtKJ7AwECD64UQeu+1o3hyoh3Fk5umTZtmy5eICsen84u7BiXbwhHFXYOSo7gGNZ04cQJxcXHQaDQIDAxE+/btYWdnh4CAAEydOtWor00Zb8uMTFFetM3N5cuX8cEHH8DExAQnTpxA1apV0a5dO3z//fdQq9X45ptvCqF19LKK66SptWjRIoSFheGvv/4CkDkVh3aEGBWfwr4LEwBu374NIPMZDdrOZgDw9/eXOv6ePXuGH3/8EQD0ht1T8SvOkaBhYWFo27YtIiMjMWjQIPzvf/8z6sCKiIiIiIiIiIj+U1yDmrSPO0tMTERERAS6d++OjRs3YtasWVAqlZg8efJLt+l1xytvJURRXbR9Ee0UChqNBnXq1IFCoYCnpyeOHTuG0NDQImgp5UdxnTS1Nm/ejEOHDgEArK2t0a5dOz5g+zVQ2HdhApnP+3wRS0tLPHnyJK/VpFeouEaCPnz4EI0aNcKTJ09QunRpaDQaqVO4VatWaNWq1SvdD0REREREb4L/rd5T3FUo0T7p3bK4q0BEZFSKa1CT7nXx1atXw9fXF+XLl8e0adMQFBTEjj8qfkV10fbo0aP46aefpA6+2NhY9OvXD0BmR6Gvry80Gg3i4+PRvHlzuLm5SVNF+vv7v7L2k2HFddLUCg4ORnJyMo4fP46OHTvis88+g4uLCzp37lyAVhFRUSiOkaDPnj2TOoPv378vPUcUAGxsbNjxR0RERERERERk5IprUJOnpycUCgXS0tKyrdMdSGOM2PFXghTFRdvr16/jl19+kd7Hx8dL71euXIny5cvj999/x6xZs3Dp0iWcPn0aFSpUQO/evTFq1KgibS+9WHGdNOPj46HRaGBiYgKVSoUmTZqgdOnSiIuLw6VLl9jxl0ecS71gOJd6/hTHSFBXV1c+j4OIiIiIiIiI6A1WXIOanJ2dMXDgQCxZsgS9e/dGkyZNsHHjRgDAgAEDXln7iwM7/kqQorho269fP+nLkJPmzZujefPm+aorvRrFddLct28fPvnkE7z11luws7PD6dOncfXqVchkMrRo0eJV7gIiIiIiIiIiIiIieo0Vx6AmAJg/fz7MzMywdu1arF27FpUrV8aoUaPQt2/fImvr64Adf0QlXHGcNCtWrAh3d3f8+eefiIuLg729PVq3bo2RI0eicePGRdpeImNXMyS4uKtQol3ybFrcVSAiIiIiIiIiIh3FNahJpVJhzpw5mDNnTr7qW9Kx468I8CHKBcOHKOdPcZw0a9WqhUOHDuW7rkREREREREREREREVHTY8UdERERERERERERERPSG40xUBfO6zETFjj+iV+Sjsd8VdxVKtB8DxxR3FYiIiIiIiIiIiIiIXmsmxV0BIiIiIiIiIiIiIip5UlJSMH78eLi4uEClUsHDwwO//vprjunT0tIwefJkVK5cGRqNBra2tvDz88OePf89Ounq1avo0qULnJ2doVKpULZsWfTt2xfR0dFSmnv37qFHjx6wtbWFRqOBv78/Tp48WaRtJSIqKdjxR0RERERERERERET5NnbsWHz77bcwNTVFjx49cOvWLQQEBGDHjh0G08+bNw9ffPEFbt++jR49esDT0xPHjh1Dhw4dpI69Ll26ICgoCFZWVujfvz/S0tKwatUqDB48GAAghEDbtm2xYcMGVKtWDW3atMHhw4fx9ttv4/79+6+s7UREryt2/BERERERERERERFRvsTExGDp0qUAgO3bt+OXX37B7NmzAQAzZswwuE1ERAQAoG3btlixYgV+++03AJkjB+/duwchBK5fvw4AmDNnDpYsWYLRo0cDACIjIwEAO3bswIULF+Ds7IzDhw9jy5Yt6NixI+Lj4zFnzpyiazARUQnBjj8iIiIiIiIiIiIiypeQkBAkJydDrVbDy8sLANCgQQMAwMWLF5Genp5tm0GDBsHW1hY7d+5E//790aVLFwBAnz594O3tDZlMhnHjxgEARo8ejSFDhmDOnDmwsLCQOhPPnTsHAPD29oapqaleudp1RERvMnb8EREREREREREREVG+REVFAQAsLCykZdq/09LSEBsbm20bDw8PdO7cGcnJyVi5ciWOHTsGFxcXdOjQQUrTuXNneHh4ICIiAkuXLkV0dDQaN24Mb2/vF5bLqT6JiNjxR0RERERERERERET55OzsDAB4/vy5tOzZs2cAAIVCAQcHh2zbTJkyBcuXL0fjxo3x6NEjnDx5Evfu3UO3bt0QEhKCtLQ0tGnTBqGhoZg7dy4SEhIwcuRI7NmzB926dXthuaVLly6axhIRlSDs+CMiIiIiIiIiIiKifPH09IRSqURSUhIuX74MADhx4gQAoGbNmpDL5QgPD0d4eDgSEhIAAFevXpW2tbW1Re3ataHRaCCEQHh4OJ48eYIHDx4AyJy+U6PRoF69egCA0NBQAICPjw8A4Pz580hNTdUrV7uOiOhNpijuChARERERERERERFRyeLo6IhBgwZh0aJF6NChA/z9/bF582YAmSP7AKB69eoAgIMHD6Jp06bw9/fHzp078csvvyA5ORmRkZGIj4+HRqOBr68vHBwc4OHhgdDQUPTt2xfNmzfHjh07AAD+/v4AgPbt26NmzZq4dOkS/P39Ubp0aWzfvh1mZmYYPXp0MewJIqLXC0f8EREREREREREREVG+fffddxgzZgySk5Oxdu1alCtXDsuXL0enTp0Mph89ejRmzZoFV1dXbNy4EZcvX4a/vz9+//13lCtXDgCwY8cOdO/eHfHx8Vi5ciXkcjkGDBiAFStWAABMTEywc+dOvP/++wgJCcGuXbvg5+eHP//8E2XKlHlVTSciem1xxB8RERERERERERER5ZtKpUJgYCACAwMNrhdC6L03MTHB5MmTMXny5BzzdHNzw/r163Mt18XFBRs3bsx/hYmI3gAc8UdERERERERERERERERkBDjij4iIiIiIiIiIiMiIfTT2u+KuQon2Y+CY4q4CEVGeccQfERERERERERERERERkRFgxx8RERERERERERERERGREWDHHxEREREREREREREREZERYMcfERERERERERERERERkRFgxx8RERERERERERERERGREWDHHxEREREREREREREREZERYMcfERERERERERERERERkRFgxx8RERERERERERERERGREWDHHxEREREREREREREREZERYMcfERERERERERERERERkRFgxx8RERERERERERERERGREWDHHxEREREREREREREREZERYMcfERERERERERERERERkRFgxx8RERERERERERERERGREWDHHxEREREREREREREREZERYMcfERERERERERERERERkRFgxx8RERERERERERERERGREWDHHxEREREREREREREREZERYMcfERERERERERERERERkRFgxx8RERERERERERERERGREWDHHxEREREREREREREREZERYMcfERERERERERERERERkRFgxx8RERERERERERERERGREWDHHxEREREREREREREREZERUBR3Bf73v/8hMDAQUVFRqFWrFhYuXAhfX98c08+fPx8//PADbt26BQcHB3Tt2hVfffUV1Gp1vsrNePQIGamp/y1QKmFiaQmRng7x5Em29Cb29pnbPX0KpKXprZNZWECmUkEkJkIkJECd8Py/ckzkSFFrACGgTozPlm+y2gzCxATK5ESYpKfrrUszVSLNVAl5WipMU5L11gkTEySrzQBAr7z/8tVAmMhhmpIEeZb6pilMkaZUwSQ9DcrkJP0NZTIkacwz802MB4TQW52iUiNDroAiJRmKtFS9dekKBVKVasgy0qFKSsxWpyQzCwCAKikBsowMvXWpShXSFaZQpKYg4+FD/Q1NTWFiZZXjZyOztYXMxAQZcXFAqn6dZGZmkGk0EMnJEM+z7CeFAibW1gCQvUwAMhsbyORyZDx7BqSk6K/TaCAzM4NISYF49kx/QxMTmNjaZub7+DHwb1stUzM/wwS5KdJNTKBKT4MyQ/8zTzUxQZLcFCYiA+ZZ9i8APDNVAQDM01JgkuWzSZQrkGYihzI9Daos+abJTJCoMIVMCFik6bcFAJ4plIBMBrO0FMiz5JskVyDVRA7TjHSo0/WPpXSZDAkKJSAELA3k+1yhhJDJoElLhULof+bJJnKkyBVQZKRDkyXfDJkM8QolgP/2m+5nJLO2hkyhgIiPh0jSP4ZlajVk5uYQqakQcXH6Fcrhs5G2tbSETKmESEiASMxyDBfiOULLIglINzFFotIKMpEB8+TH2fKNV9lAyOTQpMRBnqF/TKQozJCi0ECRngx1qv7xnSGTI0Fl82852Y/vBKU1MkwUUKc+hyJd//ySKlcj2dQc8vQUaFL1j28hM0G8KnMfmic/hizL55poaol0uRKq1HiYput/NmlyFZJMLWCSkQazlKfZ6vRcnbkPzZKfwEToH8NJphZIk6ugTEuEMi1zH2Zom2Uk5wip2LR0pCnkMEtKhiZZP98kpSniNWrI09Nh8zxBb52QyfDIKvM8a/08Hop0/XyfmWmQYqqAJjkFZkn6n3myqQLPzTQwyciA7bPsv1UPrSwAmQxW8QkwTdP/bJ5r1EhWmkKVkgqLRP3PPFUhR5y5GSAE7OOy/1Y9tjRHhokJLBISoUrV/94kqFVIVCmhTE2DZYL+9zFNboKnFpm/VXZxzyHTOW9lPHxYZOcIkeX8+LpgLMVYCmAsJe0HxlIAGEvpYiyVibHUv/uBsRQAxlJZMZZiLAUwlpL2A2MpAIyldDGWysRY6t/9wFgKQMmIpYq142/Dhg0YNWoUlixZgvr162P+/Plo2bIlrl69Cicnp2zp165diwkTJmD58uVo1KgRrl27hn79+kEmk2Hu3Ln5Kjt5zx4kazTSe3nFilC+9RZEfDySd+7Mll7Tty8AIPXYMWTExuqtU/r5Qe7mhvR//kHqqVOofO26tO65pQ0iK3nCJCMdla9dzJZvqGc9pJsoUfruTVjFPdJbd79MRcQ6lYHFs6co/89VvXWJGgtcr1oLAOAecTnbSe5aVW8ka8zhFHUHdo8e6K2LcXJBVJkK0CTGw+36Fb11qaYqhHvWBQC4/h0G01T9L+Hf7jUQb2ENh9goOEbf0Vv3yK4U7pZ3hzI5KVtbhcwEV2o1BACU+ycCmkT9L9qtClXx1NYBNo9js+1/uYsLlM2bAykpBj8bdY8egFKJtJMnkX7/vt46U19fKKpVQ8bdu0g5elRvnYmDA1Rt2gCAwXxVnTpBZmWFtAsXkB4ZqbdOUasWTGvVQkZMDFL279fP19ISqs6dAQApe/dCJGfuwwaxtwEAp+zL4qlSgwrxT1Ah/onetrfNrBFu7QjztFQpvVaaiQkOlnIDANR6HAXzLAHNBdvSiFGbo2ziM7g/0/8xeKC2wCVbZygz0rPlCwD7nd2QARk8nsbANkX/RBZq7YS7ZlZwSoqHx9NovXWPlRqcsS8LEwiD+R52ckWyXIHKzx6iVJL+Z37d0h6RFrawTUmC92P9zy1eocRfjuUBAHUf3YUiI0PvM1K1bQuZvT3SrlxB2lX974aienWY1qsH8fgxkv/4Q2+dTKWCunt3AEBqcHDmD6MO5dtvQ162LNIiIpB2Uf8YLsxzhFa9W8Ajs9K46PIOTDLSUO9W9nyPur2PVIUG7jFn4BCv/5277lgXt209YJdwH573D+ute6ayw5kK7QAAdW/vznaOOFWhPeJVtnB9dAmln17XW/ePXQ387VAblsmP4HNnr966ZIUZ/nLrCgCodXc/VGn6P/LnXd7FEzNnlH16FRUe6Z9f7lu7I7xUI2hSn2Vrq5CZILhybwCAR9RRWCbrnw9DSjdBtKUrSj2LhHvMmcy6/JuFsZwjtOzcbBBtawXPyDuocfOu3rqw8qVxwrMybJ4noMNf5/XWpSjkWPNOYwBA8/Nh2QKwP2t74nYpe1S+E4U6127qrbvp7ICDPh5Qp6RmyxcAfn3XD+lyGRpdiUDpR/rB8bEalXGtXGlUeBCLxlci9Nbdt7PGH/VrQZ4hDOa7oVl9JKhVqHc1Eq5R+t+bs1VccalSeZR69BQtzoXorXtiYYagtzJ/q1qfvAilTtCXfP1xkZ0jMnK5AFRcGEsxltJiLJWJsVQmxlL/YSyVibFUJsZSmRhL/YexFGMpLcZSmRhLZWIs9R/GUpkYS2ViLJWpJMRSMlGMt1zVr18f9erVw6JFiwAAGRkZKFeuHD799FNMmDAhW/phw4YhLCwM+3UO1tGjR+PkyZM4muWLkZO4uDhYW1vjcWQkrCwt/1tRiHdNrNgSLK3jnVX/yeudVR91aKS/oZHcNTFm1hIAvLNKK793Vn03ZYi03ljurJr0I++s0pXfO6u++OjflUZyjtCqe+cC76zCy99Ztb9qoyI7RzwTAjaOjnj69CmsrKyytac4MJZiLKXFWOrf/cBYCgBjKV2MpTIxlvp3PzCWAsBYShdjKcZSWoyl/t0PjKUAMJbSxVgqE2Opf/cDYykAJSOWKrYRfykpKTh79iwmTpwoLTMxMUGLFi1w/Phxg9s0atQIq1evxqlTp+Dr64u///4bu3btQp8+fXIsJzk5Gck6vdZx2p1pYwNk2TkZGRmATAb8u3OzrQMA3aDsXwKAyMgAVCpApUKSmXn2FDIYWP7f+hRVTlNCCKQrFEhXGPqoMg+o3PJNVaqQqlQZXJchl+ew7b/5asxyzDdNqUSaUmlwnTAxyTXfZLXGwLp/8zU1zXn/5/DZSPvfwiLndbnlC+Ser7l55svQOoUi93z/PTkDwHPT/z4HGYAUuQIp8uyfqwyZP2K66XXXAcgMagyQAUiVK5CaQ76QyXLNNzGXfNNM5HhuIs9Xvtr1SQrTHNel55YvdPabzn6W9r9Gk/nSIa2Ty/P82WTbVq3OfBnathDOEVrxam1bMwAZEK/Onq92fZIy+/GtXZcuN0W8PPu2MmT8W07O+SabmiHZNPt3XYYMZMgVueaboMq+D7XrU0w1SDHN/l2XIQPCxMRgnbT5JqoM/3DJkIFUhQqpCu0x8d86YzhHSOvum8BECCSplEhSZf9OmojM8+xjq+zt0f7H65m54fO3iRBIVpoiWZn9O2kiBCCTGc4XAITAczPD528TIZBqqsBj05zrZChf7foEjRoJBrI2EQJpCnmubX1imeX3xta26M4RWQOyYsZYSn89Y6l/82UsxVgqS50YSzGW0l3PWIqxVNZ8GUsxltKuZyz1b76MpRhLZakTYynGUrrrGUsxlsqa7+saSxVbx19sbCzS09NRqlQpveWlSpVCeHi4wW169uyJ2NhY+Pn5QQiBtLQ0DBkyBJ9//nmO5Xz11VeYMWNGtuUxMTFIytLbWljU8owXJ6IcRUdHvzhRCeRol1MgTHlhjMeFfU7/16A8McJDAgBQOSHtxYkoR0V5rniW9S65YsZYinJijL+ZAGOpgjLG44KxVMEY4SEBgLFUQTGWYixFxvmbCTCWKihjPC4YSxWMER4SABhLFdTrEksV6zP+8is4OBhffvklFi9ejPr16+P69esYPnw4Zs2ahSlTphjcZuLEiRg1apT0Pi4uDuXKlYOjo2ORTS2RlG5SJPm+KQzNo28MYh5lH6ZMeWeMx8XD7DOPUD4Y4SEBAIiILVE/za+dojxXqA3ccVnSMJZ6MxjjbybAWKqgjPG4YCxVMEZ4SABgLFVQjKVyx1jqzWCMv5kAY6mCMsbjgrFUwRjhIQGAsVRBvS6xVLF9ig4ODpDL5XjwQP8Bvw8ePICzs7PBbaZMmYI+ffpg4MCBAAAvLy/Ex8dj0KBBmDRpEkxMsgc2KpUKKlX2od4mJiYG0xcO2YuTUI6K7nMpXsX2ME0jYYzHBY+JgjHCQwJA5rME6OUV5bnidTsPMZainLxux2ph4e9mwRjjccFjomCM8JAAwFiqoBhLMZai1+9YLSz83SwYYzwueEwUjBEeEgAYSxXU6xJLFdvhqVQqUadOHb0HImdkZGD//v1o2LChwW0SEhKyNU4uz5yDWQieqoiIiOjNwViKiIiI6OUxliIiIiJjVazjNkeNGoWAgADUrVsXvr6+mD9/PuLj49G/f38AQN++fVG2bFl89dVXAID27dtj7ty58PHxkaZUmDJlCtq3by8FWkRERERvCsZSRERERC+PsRQREREZo2Lt+OvevTtiYmIwdepUREVFwdvbG3/88Yf0YOVbt27p3Uk1efJkyGQyTJ48GXfv3oWjoyPat2+PL774oriaQERERFRsGEsRERERvTzGUkRERGSMiv1JjcOGDcOwYcMMrgsODtZ7r1AoMG3aNEybNu0V1IyIiIjo9cdYioiIiOjlMZYiIiIiY2Okj6AkIiIiIiIiIiIiIiIierOw44+IiIiIiIiIiIiIiIjICLDjj4iIiIiIiIiIiIiIiMgIsOOPiIiIiIiIiIiIiIiIyAiw44+IiIiIiIiIiIiIiIjICLDjj4iIiIiIiIiIiIiIiMgIsOOPiIiIiIiIiIiIiIiIyAiw44+IiIiIiIiIiIiIiIjICLDjj4iIiIiIiIiIiIiIiMgIsOOPiIiIiIiIiIiIiIiIyAiw44+IiIiIiIiIiIiIiIjICLDjj4iIiIiIiIiIiIiIiMgIsOOPiIiIiIiIiIiIiIiIyAiw44+IiIiIiIiIiIiIiIjICLDjj4iIiIiIiIiIiIiIiMgIsOOPiIiIiIiIiIiIiIiIyAiw44+IiIiIiIiIiIiIiIjICLDjj4iIiIiIiIiIiIiIiMgIsOOPiIiIiIiIiIiIiIiIyAiw44+IiIiIiIiIiIiIiIjICLDjj4iIiIiIiIiIiIiIiMgIsOOPiIiIiIiIiIiIiIiIyAiw44+IiIiIiIiIiIiIiIjICLDjj4iIiIiIiIiIiIiIiMgIsOOPiIiIiIiIiIiIiIiIyAiw44+IiIiIiIiIiIiIiIjICLDjj4iIiIiIiIiIiIiIiMgIsOOPiIiIiIiIiIiIiIiIyAiw44+IiIiIiIiIiIiIiIjICLDjj4iIiIiIiIiIiIiIiMgIsOOPiIiIiIiIiIiIiIiIyAiw44+IiIiIiIiIiIiIiIjICLDjj4iIiIiIiIiIiIiIiMgIsOOPiIiIiIiIiIiIiIiIyAiw44+IiIiIiIiIiIiIiIjICLDjj4iIiIiIiIiIiIiIiMgIsOOPiIiIiIiIiIiIiIiIyAiw44+IiIiIiIiIiIiIiIjICLDjj4iIiIiIiIiIiIiIiMgIsOOPiIiIiIiIiIiIiIiIyAiw44+IiIiIiIiIiIiIiIjICLDjj4iIiIiIiIiIiIiIiMgIsOOPiIiIiIiIiIiIiIiIyAiw44+IiIiIiIiIiIiIiIjICLDjj4iIiIiIiIiIiIiIiMgIsOOPiIiIiIiIiIi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NG5bDDjssc+fOTbL2z4vvfve7GTRoUNO3tR588MFcdNFFmTdvXsaNG5e6urpW+QYxrA9ZikSWYv3JUpsGWQremSxFIkux/mSpTYMs1Q5UefDIOnj75HnBggVFjx49ij/+8Y9FURTFf/3XfxW777570djYWPzsZz8rBg8eXCxevLhobGwsjjvuuKJ///5FURRN34woiqK46aabioMPPrhpny+99FJRFM1/s+qtj7///vsXl1xySdN9L7zwQus+Wd7RW1+Pxx9/vNh5552LF154oVi0aFFRX19fFMWbr+e73/3u4rnnniuK4s1vSowcObJYunRpUV9fX7z//e8vrr/++qIoiuLss88ujj322KKxsbF45ZVXioEDBzZ9s+rf//3fi9GjRxfLli0rVq5cWXzoQx8qLr300qZ9vu997yveeOONYvny5cXOO+9cHHnkkcWKFSuK1157rdhuu+2KRx99dMP+cXhHDQ0Nxcc+9rFim222KY488sjiq1/9avH8888XRVEU//Zv/1YcccQRTesOHDiwuPXWW4ui+Ofnx3PPPVcMHjy4KIqiGDduXHHbbbcVo0eP9s2qdmjx4sVFz549iwMOOKBp2Yc+9KHi8ssvX+t2EyZMKHbYYYdi+PDhxQ477FD069eveOaZZ4qiKIrZs2cXvXv3buqZiy++uDjkkEOKolj750X37t2LefPmFUVRFCtWrCiWLFlSFEW5v1lF+yJL8VayFJWQpTYdshT8kyzFW8lSVEKW2nTIUtXljL+N0IMPPpihQ4dm6NChSZKjjz468+bNy9y5czNjxox8/OMfz5ZbbpmamppVpudvNXz48Dz++OM59dRTc8MNN6RTp07v+LivvfZa7rvvvnz2s59tWvbWb1yw4YwbNy677757Bg0alNNPPz29evXKSy+9lI9//OMZMmRIDjzwwLz00kt59NFHm7b56Ec/mm7duqVDhw4ZOXJknn766STJjBkzcsIJJ6SmpiZbbbVV/u///b9N29x555057rjj0qVLl3Ts2DEnnnhi7rjjjqb7jzjiiHTt2jWdO3fO0KFDM3bs2HTq1Cmbb755Bg0alL/85S8b7o/COqmtrc1PfvKT3H///TnkkEPy61//OoMHD85TTz2Vo48+OjNmzMgLL7yQ+++/P6+99lrGjh27yvZ9+/ZN375987Of/SyPPPJIDjrooCo9E97Jvffemy5duuSvf/1rFi9e3Ow6V1xxRerq6vLud787t912W9Pyz33uc5k1a1bmzp2bCy+8MEcddVSS5O67784hhxySPn36JElOPfXU3HXXXWloaFjr58UHP/jBHHvssfnGN76RZ555JltssUUbP3tYO1kKWYqWkqU2HbIUrJkshSxFS8lSmw5ZqroM/kqupqam2eU77bRTHnvssaYP2CFDhuTll1/ewNXRUjfccEMef/zx/PKXv8wXv/jFzJ49OyeffHLTJRFmzZqVXXfdNcuWLWvapmvXrk3/3aFDh9TX1ze77zX1THP3vX2f6/oYVN/AgQPz6U9/OjfffHPe97735ZZbbkmPHj3y4Q9/ONddd12uueaaHHfccamtXf0wMXHixEycODGf+MQnmr2f6lu0aFFOPvnk3HTTTTnssMOa/sd4jz32yEMPPdS03mc+85nMmjUrO+200yqfF281bty4PPLII1m4cOFq963r58VPfvKTXHrppVm5cmUOPfTQTJ8+vaVPDTY4WaqcZCkqJUuVmywFrUeWKidZikrJUuUmS1Wfd8ZG6H3ve19mz57d9K2Z6dOnp0+fPunTp08OPPDA/OQnP8lrr72WoihyzTXXNLuP559/PjU1NTn88MPzta99LUVR5Lnnnkv37t3z6quvNrvNFltskf322y9f//rXm5Y194ZjwxkzZkxOOeWUnHvuuXn55ZfTv3//1NTU5J577skf/vCHdd7H9773vaZrJv/oRz9a5b4f/OAHWbFiRerr6/Pd73636br7bJzmzp2bX//61023X3755TzzzDPZeeedkyQnnHBCvvOd7+THP/5xJk6c2Ow+jjzyyJx99tk5+eSTN0jNrL/TTjstxxxzTEaOHJmvfvWrueuuu/LLX/4yp512WmbMmJFrr722ad3GxsY1hqvkzW9f9uzZM9tuu20OOOCA3HbbbZk3b16SZNq0afngBz+YDh06rPHzor6+Pk8//XT22muvnH322TnqqKOaQt7ajjnQlmQp/kGWYn3JUpsGWQrWTpbiH2Qp1pcstWmQpaqvY7ULYN3841T1f/jGN76R8ePHp76+Pttss01+/OMfp6amJh/+8Ifz4IMPpq6uLltvvXVGjx7d9MPJbzV79uxMmTIlRVGkvr4+xx57bIYNG5b6+voMHjw4Q4YMyU477ZRbbrllle2uu+66nH766Rk8eHA6deqUI444IhdeeGFbP33W4rzzzst73vOefP7zn88Xv/jFfOUrX0ldXV323nvvdd7+U5/6VAYOHJhevXpl3333zfLly5MkJ510Up5++unsueeeSZL9998/Z511Vls9FTaA+vr6XHTRRXnmmWfSrVu31NfXZ8KECTniiCOSvHnq+/Lly7PXXntlp512anYfXbp0yRe+8IUNWTbr4cYbb8yjjz6a73//+0ne/JHka665JuPHj88f//jH3HvvvTnnnHPy5S9/Odtuu226dOmS0aNHZ9SoUU37uOyyy3LttdemKIp06dIlN954Y2prazNkyJBcdtllOeSQQ5Ik/fr1y3e+850ka/68aGhoyPHHH59FixalY8eO6dWrV773ve8lSc4444yceOKJ6datW6699trU1dVtwL8UmxpZijWRpVgfslT5yVLQPFmKNZGlWB+yVPnJUu1DTVEURbWLoHUtWbIkW265ZYqiyGc/+9m88cYb+da3vlXtsgAANgqyFABAy8lSAFBdzvgrofHjx2fOnDlZtmxZBg8enGnTplW7JACAjYYsBQDQcrIUAFSXM/4AAAAAAACgBGqrXQAAAAAAAABQOYO/KhswYEB222231NXVZbfddsull15a7ZLWqK6uLkuWLKl2GazBW3uprq4un/rUp9r08ebMmeNyHQBUnSxFa5GlANgUyVK0FlkKoP1wqc8qGzBgQG6++ebU1dVl7ty5GTRoUO64446MHDmy4n3X19enY0c/47ipeGsvra+W9MrMmTNz1llnZdasWev9eADQWmQpWossBcCmSJaitchSAO2HM/7akT59+mTgwIH529/+lvnz5+df/uVfMnLkyAwdOjTnnntu03r3339/6urqMnTo0Bx//PEZPnx4Zs6cmSTZf//9c8YZZ2SfffbJwQcfnCT52te+lpEjR2bPPffMIYcckr/97W9Jkp/+9KcZNmxY6urqMmTIkPzP//xPkuTiiy/O7rvv3vQNnX+sX1NTk1deeSVJ8vDDD+f9739/hg0blpEjR+bXv/51kje/bbP11lvnggsuyIgRI/Ke97wnt95664b489GM22+/PXvuuWeGDRuW0aNH57HHHkvyZjgaPHhwTjjhhNTV1eW///u/85e//CWHHXZY3vve92bYsGG56qqrkiRvvPFGxo0bl0GDBmX48OFNfXXyySfnySefTF1dXQ4//PCqPUcA+AdZitYmSwGwKZGlaG2yFECVFFRV//79i9///vdFURTF448/Xuy8887FCy+8UBx88MHFzJkzi6IoipUrVxZjx44t/t//+3/F8uXLi759+xZ33XVXURRFcddddxVJirvvvrsoiqIYPXp0MXbs2GLFihVFURTFD3/4w+JTn/pUUV9fXxRFUfzgBz8oDj300KIoimLYsGHF/fffXxRFUTQ0NBQvv/xysWjRomKrrbYqXn/99aIoimLp0qXFG2+8URRFUSQpXn755WL58uVFv379ittuu60oiqK49957i969exdLliwpnnnmmSJJceONNxZFURS/+MUvil133bUt/4T8r/79+xe77rprMXz48GL48OHF9OnTix49ehR//OMfi6Ioiv/6r/8qdt9996KxsbG4++67i5qamqYeq6+vL0aMGFE8/vjjRVG8+boPHTq0eOihh4qbbrqpOPjgg5se56WXXiqKoijuvvvuYvjw4Rv2SQLA28hStBZZCoBNkSxFa5GlANoP59u3A+PGjUttbW2efPLJXHHFFenWrVtmzJiRBQsWNK3z2muv5cknn8wTTzyRjh075oADDkiSHHDAAdl5551X2d8xxxyTTp06JUluvvnm/Pa3v82IESOSJA0NDU3rffCDH8yZZ56Zo446KgcffHDq6urS0NCQXXbZJcccc0wOPvjgHHbYYenbt+8q+3/yySdTW1ubsWPHJkn23Xff9O7dO7NmzUrfvn3TtWvXfOxjH0uS7LPPPnn66adb+S/Gmtxwww1Nl1T46U9/mqFDh2bo0KFJkqOPPjqnnXZa5s6dmyTZaaedMnr06CRvvqZ/+tOf8olPfKJpX0uWLMljjz2WUaNG5fHHH8+pp56a0aNH59BDD92wTwoA3oEsRWuRpQDYFMlStBZZCqB9MPhrB/5xULzzzjvzkY98JAceeGCS5De/+U26du26yrp//OMfV9u+pqZmldtbbLFF038XRZEpU6bkpJNOWm27yy+/PH/6059y9913Z8KECTn66KPz+c9/Pr/5zW9y//33Z+bMmXnf+96XH/3oRxk1atRan8Nba+jSpUvT7Q4dOqwS6mg/3t4nPXr0WON10R977LHcddddufPOO/P5z3/e9dMBaFdkKapBlgKgLGQpqkGWAmg7fuOvHRkzZkxOOeWUnHvuuTnggANy6aWXNt03b968PP/889ltt92ycuXK/OpXv0qS/OpXv8pTTz21xn0eeeSRmTZtWhYtWpQkWblyZX7/+98nSZ544okMHjw4kyZNyimnnJLf/OY3WbJkSRYsWJBRo0blvPPOy7777tu0/j/stttuaWxszB133JHkzWu7z58/v0U/3kvbed/73pfZs2fn0UcfTZJMnz49ffr0SZ8+fVZ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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Chart saved: /content/drive/MyDrive/MindScan_Models/classical/classical_comparison.png\n"
          ]
        }
      ],
      "source": [
        "# ─────────────────────────────────────────────────────────────────\n",
        "# GROUPED BAR CHART — Classical models comparison\n",
        "# XLM-RoBERTa results will be added in Notebook 2\n",
        "# ─────────────────────────────────────────────────────────────────\n",
        "fig, axes = plt.subplots(1, 3, figsize=(18, 5))\n",
        "fig.suptitle('Classical Models — Macro F1 Comparison (XLM-RoBERTa coming in Notebook 2)',\n",
        "             fontsize=13, fontweight='bold')\n",
        "\n",
        "model_colors = {\n",
        "    'Logistic Regression': '#94a3b8',\n",
        "    'Random Forest':   ()     '#64748b',\n",
        "    'SVM':                  '#6c8fff',\n",
        "    'XGBoost':              '#2dd4bf',\n",
        "}\n",
        "\n",
        "for ax, (ds_name, ds_res) in zip(axes, ALL_RESULTS.items()):\n",
        "    models = list(ds_res.keys())\n",
        "    f1s    = [ds_res[m]['macro_f1'] for m in models]\n",
        "    colors = [model_colors.get(m, '#6b7280') for m in models]\n",
        "    bars = ax.bar(range(len(models)), f1s, color=colors, edgecolor='none', width=0.6)\n",
        "    for bar, score in zip(bars, f1s):\n",
        "        ax.text(bar.get_x()+bar.get_width()/2, bar.get_height()+0.005,\n",
        "                f'{score:.3f}', ha='center', fontsize=9, fontweight='bold')\n",
        "    ax.set_title(ds_name, fontsize=10, fontweight='bold')\n",
        "    ax.set_xticks(range(len(models)))\n",
        "    ax.set_xticklabels([m.replace(' ','\\n') for m in models], fontsize=8)\n",
        "    ax.set_ylabel('Macro F1'); ax.set_ylim(0, 1.05)\n",
        "    ax.grid(axis='y', alpha=0.3)\n",
        "    ax.axhline(0.81, color='#ef4444', linestyle='--', alpha=0.5,\n",
        "               linewidth=1, label='Baseline 0.81')\n",
        "    ax.legend(fontsize=8)\n",
        "\n",
        "plt.tight_layout()\n",
        "chart_path = os.path.join(SAVE_DIR, 'classical_comparison.png')\n",
        "plt.savefig(chart_path, dpi=150, bbox_inches='tight')\n",
        "plt.show()\n",
        "print(f'Chart saved: {chart_path}')"
      ],
      "id": "Ms0kxw7C1IxA"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "G7hGeH7t1IxA",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d00122bc-d554-4455-d0dd-e0d3dad509da"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Files in /content/drive/MyDrive/MindScan_Models/classical:\n",
            "  ✅ classical_comparison.png  (0.1 MB)\n",
            "  ✅ classical_results.csv  (0.0 MB)\n",
            "  ✅ cm_D1_Logistic_Regression.png  (0.1 MB)\n",
            "  ✅ cm_D1_Random_Forest.png  (0.1 MB)\n",
            "  ✅ cm_D1_SVM.png  (0.1 MB)\n",
            "  ✅ cm_D1_XGBoost.png  (0.1 MB)\n",
            "  ✅ cm_D2_Logistic_Regression.png  (0.0 MB)\n",
            "  ✅ cm_D2_Random_Forest.png  (0.0 MB)\n",
            "  ✅ cm_D2_SVM.png  (0.0 MB)\n",
            "  ✅ cm_D2_XGBoost.png  (0.0 MB)\n",
            "  ✅ cm_D3_Logistic_Regression.png  (0.0 MB)\n",
            "  ✅ cm_D3_Random_Forest.png  (0.0 MB)\n",
            "  ✅ cm_D3_SVM.png  (0.0 MB)\n",
            "  ✅ cm_D3_XGBoost.png  (0.0 MB)\n",
            "  ✅ eda_d1.png  (0.1 MB)\n",
            "  ✅ eda_d2.png  (0.1 MB)\n",
            "  ✅ eda_d3.png  (0.1 MB)\n",
            "  ✅ le_d1.pkl  (0.0 MB)\n",
            "  ✅ le_d2.pkl  (0.0 MB)\n",
            "  ✅ le_d3.pkl  (0.0 MB)\n",
            "  ✅ logistic_regression_d1.pkl  (1.6 MB)\n",
            "  ✅ logistic_regression_d2.pkl  (0.1 MB)\n",
            "  ✅ logistic_regression_d3.pkl  (0.5 MB)\n",
            "  ✅ random_forest_d1.pkl  (240.4 MB)\n",
            "  ✅ random_forest_d2.pkl  (71.7 MB)\n",
            "  ✅ random_forest_d3.pkl  (333.9 MB)\n",
            "  ✅ svm_d1.pkl  (1.6 MB)\n",
            "  ✅ svm_d2.pkl  (0.1 MB)\n",
            "  ✅ svm_d3.pkl  (0.5 MB)\n",
            "  ✅ tfidf_d1.pkl  (1.3 MB)\n",
            "  ✅ tfidf_d2.pkl  (0.6 MB)\n",
            "  ✅ tfidf_d3.pkl  (2.2 MB)\n",
            "  ✅ xgboost_d1.pkl  (3.0 MB)\n",
            "  ✅ xgboost_d2.pkl  (0.4 MB)\n",
            "  ✅ xgboost_d3.pkl  (0.7 MB)\n",
            "\n",
            "✅ Notebook 1 complete!\n",
            "Next step: Run Notebook 2 (xlmr_transformer_notebook.ipynb) with T4 GPU enabled.\n"
          ]
        }
      ],
      "source": [
        "# ─────────────────────────────────────────────────────────────────\n",
        "# FINAL FILE LIST — confirm everything is in Drive\n",
        "# ─────────────────────────────────────────────────────────────────\n",
        "print(f'Files in {SAVE_DIR}:')\n",
        "for f in sorted(os.listdir(SAVE_DIR)):\n",
        "    size = os.path.getsize(os.path.join(SAVE_DIR, f)) / 1024 / 1024\n",
        "    print(f'  ✅ {f}  ({size:.1f} MB)')\n",
        "\n",
        "print(f'\\n✅ Notebook 1 complete!')\n",
        "print(f'Next step: Run Notebook 2 (xlmr_transformer_notebook.ipynb) with T4 GPU enabled.')"
      ],
      "id": "G7hGeH7t1IxA"
    }
  ]
}