e_ml_models.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "u0qfi0nidPcW"
   },
   "source": [
    "# Forecasting AI and ML Job Trends\n",
    "\n",
    "At this stage, train various Traditionl Machine Learning Classification models, and evaluate their performance"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dependencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "HOluzVpD1iw_",
    "outputId": "47da2da4-182c-44bd-ade3-23765332d32f"
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "from imblearn.pipeline import Pipeline\n",
    "from imblearn.under_sampling import RandomUnderSampler\n",
    "from imblearn.over_sampling import SMOTE, ADASYN\n",
    "\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import (\n",
    "    accuracy_score,\n",
    "    precision_score,\n",
    "    recall_score,\n",
    "    f1_score,\n",
    "    roc_curve,\n",
    "    roc_auc_score,\n",
    "    auc\n",
    ")\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "from lightgbm import LGBMClassifier\n",
    "\n",
    "import warnings"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Suppress specific FutureWarnings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "warnings.filterwarnings(\"ignore\", category=FutureWarning, module=\"sklearn\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "9_VzZB8_hPL4"
   },
   "source": [
    "## Data Loading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1,296,381 job postings loaded from data/c_job_postings_dataset_cleaned.parquet\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>text</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>285061</th>\n",
       "      <td>parttime middle school mathematics teacher 202...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>786709</th>\n",
       "      <td>emerging sale executive commercial line produc...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>633504</th>\n",
       "      <td>restoration superintendent orderly blueteam re...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1185254</th>\n",
       "      <td>supervisormanager parttime manager sale claire...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>602779</th>\n",
       "      <td>midday meal supervisor lunchtime cover driver ...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                      text  label\n",
       "285061   parttime middle school mathematics teacher 202...      0\n",
       "786709   emerging sale executive commercial line produc...      0\n",
       "633504   restoration superintendent orderly blueteam re...      0\n",
       "1185254  supervisormanager parttime manager sale claire...      0\n",
       "602779   midday meal supervisor lunchtime cover driver ...      0"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "project_root = os.path.abspath(os.path.join(os.getcwd()))\n",
    "data_dir = os.path.join(project_root, \"data\")\n",
    "\n",
    "data_filename = os.path.join(data_dir, \"c_job_postings_dataset_cleaned.parquet\")\n",
    "data = pd.read_parquet(data_filename)\n",
    "\n",
    "print(f\"{len(data):,} job postings loaded from {os.path.relpath(data_filename, project_root)}\")\n",
    "data.sample(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Handling Class Imballance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "label\n",
       "Not AI    1274405\n",
       "AI          21976\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labels = data[\"label\"].map({0: \"Not AI\", 1: \"AI\"})\n",
    "labels.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1, 2, figsize=(12, 5))\n",
    "\n",
    "# Visualize the class imbalance before handling - Pie Chart\n",
    "labels.value_counts().plot(kind='pie', autopct='%1.1f%%', startangle=90, colors=['skyblue', 'salmon'], ax=axes[0])\n",
    "axes[0].set_title('Class Distribution Before Handling Imbalance')\n",
    "axes[0].set_ylabel('')\n",
    "\n",
    "# Visualize the class imbalance before handling - Bar Chart\n",
    "labels.value_counts().plot(kind='bar', color=['skyblue', 'salmon'], ax=axes[1])\n",
    "axes[1].set_title('Class Distribution Before Handling Imbalance')\n",
    "axes[1].set_xlabel('Class')\n",
    "axes[1].set_ylabel('Frequency')\n",
    "axes[1].tick_params(axis='x', rotation=0)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The dataset exhibits a significant class imbalance, with the \"Not AI\" class comprising `1,274,405` instances and the \"AI\" class comprising only `21,976` instances. \n",
    "- This imbalance can lead to biased model performance, where the model may favor the majority class.\n",
    "- To address this issue, we will apply various resampling techniques such as `undersampling the majority class` and `oversampling the minority class`. This will help create a more balanced training set, improving the model's ability to learn from both classes effectively."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train/Test Split\n",
    "When dealing with imbalanced datasets, it is crucial to **split the data into training and testing sets before applying oversampling or undersampling techniques:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_size = 0.25"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    data[\"text\"], data[\"label\"], test_size=test_size, random_state=42\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Apply oversampling and undersampling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will use the best combination from our previous experiment:\n",
    "- For undersampling, **RandomUnderSampler**\n",
    "- For oversampling, **ADASYN** or **SMOTE**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "undersampler = RandomUnderSampler(sampling_strategy=0.35, random_state=42)\n",
    "oversampler = ADASYN(random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "class ImbalanceHandler:\n",
    "    def __init__(self, data, undersample_method=None, oversample_method=None):\n",
    "        self.data = data\n",
    "        self.text_column = 'text'\n",
    "        self.label_column = 'label'\n",
    "\n",
    "        # Vectorize text data\n",
    "        self.vectorizer = TfidfVectorizer(max_features=5000)\n",
    "\n",
    "        # Set undersampling and oversampling methods\n",
    "        self.undersample = undersample_method if undersample_method else RandomUnderSampler(sampling_strategy=0.35, random_state=42)\n",
    "        self.oversample = oversample_method if oversample_method else SMOTE(random_state=42)\n",
    "\n",
    "        # Create pipeline\n",
    "        self.pipeline = Pipeline(\n",
    "            [\n",
    "                (\"tfidf\", self.vectorizer),\n",
    "                (\"undersample\", self.undersample),\n",
    "                (\"oversample\", self.oversample),\n",
    "            ]\n",
    "        )\n",
    "\n",
    "    def handle_imbalance(self):\n",
    "        # Extract features and labels\n",
    "        X = self.data[self.text_column]\n",
    "        y = self.data[self.label_column]\n",
    "\n",
    "        # Apply the pipeline\n",
    "        X_res, y_res = self.pipeline.fit_resample(X, y)\n",
    "\n",
    "        return X_res, y_res"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Handle imballances"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = pd.DataFrame({\"text\": X_train, \"label\": y_train})\n",
    "imbalance_handler = ImbalanceHandler(\n",
    "    dataset, undersampler, oversampler\n",
    ")\n",
    "X_res, y_res = imbalance_handler.handle_imbalance()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Review class distribution after handling imballance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Class distribution after handling imbalance:\n",
      "label\n",
      "0    47220\n",
      "1    46428\n",
      "Name: count, dtype: int64\n"
     ]
    },
    {
     "data": {
      "image/png": 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1adKkwmPExcXp8OHDJ3yud955R82bN3dP/926datatmyp8PBwvfvuuyf9GrZs2aK5c+eqQYMGHpfU1FRJ/1vorVzz5s2r9fjx8fFKTU2tcOnWrVuFsYWFhZo4caL7WOXyn4Hs7OxKp7f++f0s/4e1/P0sf////P3RoEEDj3+Eq6uqz9uqVasK961s26k8d0xMjCSpcePGlW7/8/dWcnKyIiIiPLadfvrpkuTx8/LWW2+pU6dOCg0NVf369dWgQQPNnj270q/DH9X013Dbtm2SdNzTMW7ZskWSNGLEiArfx6+//rqKi4tPmBtAYGB/7X/YX2N/7Y/YX/PE/trxXw/7a4HL0jVdoqOjlZycrJ9++umkH+PJJ5/U//3f/+nGG2/U3/72N9WrV082m03jxo3z+Aewbdu22rx5s7744gvNnTtX//3vf/Xyyy9r4sSJevTRRyVJV111lfr06aOPP/5YX331lZ599lk9/fTT+uijj3TxxRefdMY9e/YoJyfnuL+IwsLCtGTJEi1atEizZ8/W3LlzNXPmTJ133nn66quvZLfbT/g81Tmut6r+vHhcOafTWaVMNeFYz2P+aRG3P8vNzdXnn3+uoqKiCv8gSUePWS0/Trq6XC6XLrjgAt1///2V3l7+C76cN7425e644w69+eabGjdunFJSUhQTEyPDMDR8+PBKdwJP9v08VVY97/GeuyYzvfPOO7rhhhs0ePBg3XfffUpISJDdbtfkyZPd/6geixVfw/LHffbZZ495/PIf/4IFIHCxv/Y/7K8dG/trx8f+2sk/N/tr7K/5OssX0h00aJBee+01LV++XCkpKdW+/4cffqj+/fvrjTfe8NienZ2t+Ph4j20REREaNmyYhg0bppKSEg0ZMkRPPPGEJkyY4J7S1bBhQ91+++26/fbblZWVpa5du+qJJ544pX/E//3vf0uS0tLSjjvOZrPp/PPP1/nnn6/nnntOTz75pB566CEtWrRIqampJ/WPzfGUN6flTNPU1q1b1alTJ/e2uLg4ZWdnV7jvzp071aJFC/f16mRr2rSpvv76a+Xl5Xn89WTTpk3u22vCRx99pKKiIr3yyisVvhc2b96shx9+WEuXLtU555xzzMc41utq2bKl8vPz3X8psdKHH36oESNGaMqUKe5tRUVFlX7dqqL8/d+yZYvH13j//v1V+mvVySp/3q1bt1a4rbJttSk9PV0FBQUefz359ddfJck9DfbDDz9UixYt9NFHH3l830yaNOmEj1/TX8Py6d0//fTTMT88lI+Jjo6uE9/HAOo29tf+h/019tdOBvtr3sf+Guoqy08Zff/99ysiIkKjR49WZmZmhdu3bdumF1544Zj3t9vtFdrCWbNmae/evR7bDh486HE9JCRE7dq1k2maKi0tldPprDA1KyEhQcnJySouLq7uy3JbuHCh/va3v6l58+a69tprjznu0KFDFbaVt5nlz1/+C+Rkf7D/7O233/aYKvzhhx9q3759HjssLVu21Pfff++xCvoXX3xR4VSF1ck2YMAAOZ1OTZs2zWP7888/L8MwTmmH6Y/eeecdtWjRQrfeequuuOIKj8u9996ryMjIE05ZjYiIqPQ1XXXVVVq+fLnmzZtX4bbs7GyVlZXVyGuoisp+Bv7xj3/I6XSe1OOlpqYqODhY//jHPzwe988r/Ne05ORkdejQQW+//bby8/Pd27/55hv36QKtUlZWpldffdV9vaSkRK+++qoaNGjgnkJc/teMP75nP/zwg5YvX37Cx6/pr+GFF16oqKgoTZ48WUVFRR63lT9Pt27d1LJlS/3973/3eL/LVXZKQwCBi/21o9hfY3/tZLG/5n3sr6GusnymS8uWLfXee+9p2LBhatu2ra6//np16NBBJSUlWrZsmWbNmqUbbrjhmPcfNGiQHnvsMY0cOVJnn322NmzYoHfffdej8ZWOflMnJSWpd+/eSkxM1C+//KJp06Zp4MCBioqKUnZ2tho1aqQrrrhCnTt3VmRkpL7++mutXLnSo808njlz5mjTpk0qKytTZmamFi5cqPnz56tp06b67LPPKiyQ9EePPfaYlixZooEDB6pp06bKysrSyy+/rEaNGrmb/ZYtWyo2NlbTp09XVFSUIiIi1KtXr2off1quXr16OuecczRy5EhlZmZq6tSpatWqlcdpEkePHq0PP/xQF110ka666ipt27ZN77zzjsdCadXNdskll6h///566KGHtGPHDnXu3FlfffWVPv30U40bN67CY5+M9PR0LVq0qMLib+UcDofS0tI0a9Ysvfjii8d8nG7duumVV17R448/rlatWikhIUHnnXee7rvvPn322WcaNGiQbrjhBnXr1k0FBQXasGGDPvzwQ+3YsaPCX2u8ZdCgQfr3v/+tmJgYtWvXTsuXL9fXX39dpdMzVqZBgwa699573afpGzBggNauXas5c+Z4/TU9+eSTuuyyy9S7d2+NHDlShw8f1rRp09ShQ4dK/6GpLcnJyXr66ae1Y8cOnX766Zo5c6bWrVun1157zX36xEGDBumjjz7S5ZdfroEDB2r79u2aPn262rVrd8LsNf01jI6O1vPPP6/Ro0erR48euuaaaxQXF6f169fryJEjeuutt2Sz2fT666/r4osvVvv27TVy5Eiddtpp2rt3rxYtWqTo6Gh9/vnnJ/X8APwP+2tHsb/G/trJYn/N+9hfQ51VOydJOrFff/3VvOmmm8xmzZqZISEhZlRUlNm7d2/zH//4h1lUVOQeV9kpCO+55x6zYcOGZlhYmNm7d29z+fLlFU6R9+qrr5rnnnuuWb9+fdPhcJgtW7Y077vvPjMnJ8c0zaOnFLvvvvvMzp07m1FRUWZERITZuXNn8+WXXz5h9vLTgpVfQkJCzKSkJPOCCy4wX3jhBY9TnJX78ykIFyxYYF522WVmcnKyGRISYiYnJ5tXX311hVPcffrpp2a7du3MoKAgj1P+VXbqtXLHOgXhf/7zH3PChAlmQkKCGRYWZg4cONDcuXNnhftPmTLFPO2000yHw2H27t3bXLVqVYXHPF62P5+SzjRNMy8vz7z77rvN5ORkMzg42GzdurX57LPPepwKzTSPnqqtstNCHuvUiH/MLMlcsGDBMcfMmDHDlGR++umnxzwFYUZGhjlw4EAzKirKlOTxmvPy8swJEyaYrVq1MkNCQsz4+Hjz7LPPNv/+97+7T0tXfvq9Z5999pg5KnttAwcOrPS2ynIePnzYHDlypBkfH29GRkaaaWlp5qZNmyq8R8c6VWZlpxF0Op3mo48+6v656tevn/nTTz9VeMzqnIKwsvdAfzoVn2ma5vvvv2+2adPGdDgcZocOHczPPvvMHDp0qNmmTZtjv2m/O9YpCP/8PXSsTJW9v+WPuWrVKjMlJcUMDQ01mzZtak6bNs3jvi6Xy3zyySfNpk2bmg6HwzzzzDPNL774otLv/z+/bm98DU3TND/77DPz7LPPNsPCwszo6GizZ8+e5n/+8x+PMWvXrjWHDBni/t3YtGlT86qrrjruzw6AwMX+Gvtr7K/977Wxv8b+GvtrqArDNGthVSQAOAVdunRRgwYNNH/+fKujAAAAoBLsrwGVs3xNFwAoV1paWuH46sWLF2v9+vXq16+fNaEAAADgxv4aUD3MdAFQZ+zYsUOpqam67rrrlJycrE2bNmn69OmKiYnRTz/9dNLHzAIAAKBmsL8GVI/lC+kCQLm4uDh169ZNr7/+uvbv36+IiAgNHDhQTz31FP+AAwAA1AHsrwHVw0wXAAAAAAAAL2BNFwAAAAAAAC+gdAEAAAAAAPAC1nQBAAB1nsvlUnp6uqKiomQYhtVxAABAADNNU3l5eUpOTpbNdvy5LJQuAACgzktPT1fjxo2tjgEAAOC2e/duNWrU6LhjKF0AAECdFxUVJenozk10dLTFaQAAQCDLzc1V48aN3fsnx0PpAgAA6rzyQ4qio6MpXQAAQJ1QlUOeWUgXAAAAAADACyhdAAAAAAAAvIDSBQAAAAAAwAsoXQAAAAAAALyA0gUAAAAAAMALKF0AAAAAAAC8gNIFAAAAAADACyhdAAAAAAAAvIDSBQAAAAAAwAsoXQAAAAAAALyA0gUAAAAAAMALKF0AAAAAAAC8gNIFAAAAAADACyhdAAAAAAAAvIDSBQAAAAAAwAsoXQAAAAAAALyA0gUAAAAAAMALgqwOAAAAgOp7au0BqyPABzx4ZrzVEQAgoDHTBQAAAAAAwAsoXQAAAAAAALyA0gUAAAAAAMALKF0AAAAAAAC8gNIFAAAAAADACzh7EQAAAAAEsNJH77E6Auq44ElTrI7gs5jpAgAAAAAA4AWULgAAAAAAAF5A6QIAAAAAAOAFlC4AAAAAAABeQOkCAAAAAADgBZQuAAAAAAAAXkDpAgAAAAAA4AWULgAAAAAAAF5A6QIAAAAAAOAFlC4AAAAAAABeQOkCAAAAAADgBZQuAAAAAAAAXkDpAgAAAAAA4AVBVgcAfInLNFXiNFXiOnopc0mmacqUZEpqkJOlIFeZZBiSYciwB0khIVKIQ3I4ZAQFW/0SAAAAAAC1hNIFAa3UZaqg1KW8UlfF/5a5dKTM9b+SxWmqzDz+4931/fsystKPPcBmO1rA/H4xwsKkqGgZkdFSZLSMqOj/XY+KkhEeWbMvGAAAAABQayhd4PecpqnsYqcOFTt1qOj3/xY7dbjIpfwyV+2GcbmkosKjFx2dHfPH/1YQ4pDqxcuo38B9Ufn/h4bVRmIAAAAAwEmidIFfKSpzKaOwTBlHjl6yCp3KLnaqlquVmlNSLGXslZmxt2IxEx4pI6mhjIaNZDRsLKPhaTLqxVuREgAAAABQCUoX+KxSl6n0glJlHCnTvt9LluwSn61Xqu9Ivszftsj8bcv/toWGHS1fGjaSkdxERtPmRw9VAgAAAADUOkoX+Iwyl6m9BaXalX/0kl5QJucJ1lgJOEWFMrdvlbl96/+21W8gW7NWMpq1PHqhhAEAAACAWkHpgjrLNE2lHynTjrxS7cwrVXpB6QkXskUlDu6X6+B+afXyo9frN5CtaUsZLc+Q0fJ0GY5Qa/MBAAAAgJ+idEGdUuoytT23RFtySrQtt0RHaFlqXnkJs+Z7yW4/Ovvl9Paynd5ORmw9q9MBAAAAgN+gdIHl8ktd2ppToi05xdqZx2yWWuV0ytz2q8xtv8o152MpoeHR8qVNB9lOa2J1OgAAAADwaZQusESR06XNh0v00+Ei7c4vszoOymXtkytrn/TdAjnj6svW4UzZOnWVEZ9odTIAAAAA8DmULqg1TtPUb7kl+ulQsbbllDCjpa47fFCub7+W69uvpYaNZOvYVbYOXWRExVidDAAAAAB8AqULvC69oFQ/HSrWL4eLVcjphnzTvj1y7dsj1/zPZTRrJduZPWW06yTDzq8QAAAAADgWPjHBK0qcpjYeLtLaA0XKKnRaHQc1xTRlbt8i5/Yt0txPZTuzh2zdUmTE1bc6GQAAAADUOZQuqFEHi8q0en+RfjpUrBIXs1r82pF8uZYukmvZYhktz5Ct+9kyTm8rw7BZnQwAAAAA6gRKF5wy0zS1LbdUq/YXakdeqdVxUNtMU+bWTXJu3STFxMnW4+yjBYwj1OpkAAAAAGApShecNJdp6ufDxfo+s1AHijiECJJyDsv19Wy5vl0gW/ezZTvrXBmRUVanAgAAAABLULqg2spcpjYcKtL3mYXKKXFZHQd1UXGRXEsXyvXDEtk695Dt7H4y6sVbnQoAAAAAahWlC6qsxGlq7YFCrcwqUn4ZZQuqoKxMrtXL5Vrzg4x2nWTvkyojsaHVqQAAAACgVlC64IRKXaZWZRXqh6xCFXHKZ5wM0yVz4zqVbVwvo2MX2ftdxMwXAAAAAH6P0gXH5DJN/XiwWN9lHFF+KTNbUBNMmRvWqmzjj7J17SXbuRfIiIq2OhQAAAAAeAWlCyq1ObtYS9KP6GAxC+TCC1xOuVYtk2v9Ktl69pat93kywsKtTgUAAAAANYrSBR5255dqcXqB9haUWR0FgaC0RK6li+Ra/b1sfc6Xrde5Mux2q1MBAAAAQI2gdIEkKbfEqQV7C7Q5u8TqKAhERYVyzf9CrrUrZL9osGwtz7A6EQAAAACcMkqXAOc0Ta3MKtTSjCNi2RZY7kCWnO+8JlebjrKnXSojtp7ViQAAAADgpFG6BLCdeSWav6dAB4pYtwV1i7lpg8q2bpKtd3/ZzjlPRlCw1ZEAAAAAoNooXQJQfqlLC/cW6OfDxVZHAY6trFSub76Sa/0q2QcOla1VG6sTAQAAAEC1ULoEmHUHirRob4GKXabVUYCqyT4k57v/lKtLD9nTLpMRGmZ1IgAAAACoEkqXAJFb4tScXfnanldqdRTgpJjrVqps22bZB10p2+ntrI4DAAAAACdE6RIANhws0td7C1TsZHYLfFxerpz/eUOuTt1kv2iwjLBwqxMBAAAAwDFRuvix/FKX5u7K19ZcTgMN/2L+uFpl236VfdBQ2dp0tDoOAAAAAFTKZnUAeMcvh4v1+i+HKVzgvwry5Jw5Q84vPpRZxmFzAAAAAOoeZrr4mVKXqfl78vXjQc5MhMDgWr1crt07FHTFdTIaJFkdBwAAAADcmOniRw4WlentzdkULgg8WftU9s8X5Fr9vdVJAAAAAMCNmS5+YuOhIs3bXaASTgWNQFVaIucXs+Ta/qvsg67k1NIAAAAALMdMFx9X6jL15a48fb4zn8IFkGRuXK+yV5+TuW+P1VGAOuWpp56SYRgaN26ce1tRUZHGjBmj+vXrKzIyUkOHDlVmZqbH/Xbt2qWBAwcqPDxcCQkJuu+++1RWVuYxZvHixeratascDodatWqlGTNmVHj+l156Sc2aNVNoaKh69eqlFStWeONlAgAA1CmULj7scLGTw4mAymQfUtm/psn101qrkwB1wsqVK/Xqq6+qU6dOHtvvvvtuff7555o1a5a++eYbpaena8iQIe7bnU6nBg4cqJKSEi1btkxvvfWWZsyYoYkTJ7rHbN++XQMHDlT//v21bt06jRs3TqNHj9a8efPcY2bOnKnx48dr0qRJWrNmjTp37qy0tDRlZWV5/8UDAABYiNLFR+3IK9Fbm7O1v8hpdRSgbiorlfO/78g5/wuZpsvqNIBl8vPzde211+qf//yn4uLi3NtzcnL0xhtv6LnnntN5552nbt266c0339SyZcv0/fdH10f66quv9PPPP+udd95Rly5ddPHFF+tvf/ubXnrpJZWUHD073vTp09W8eXNNmTJFbdu21dixY3XFFVfo+eefdz/Xc889p5tuukkjR45Uu3btNH36dIWHh+tf//pX7b4ZAAAAtYzSxQet2V+oD7bmqsjJ4UTAibiWLZLzvTdkFhVaHQWwxJgxYzRw4EClpqZ6bF+9erVKS0s9trdp00ZNmjTR8uXLJUnLly9Xx44dlZiY6B6Tlpam3Nxcbdy40T3mz4+dlpbmfoySkhKtXr3aY4zNZlNqaqp7TGWKi4uVm5vrcQEAAPA1lC4+xGWa+mp3vr7aUyD+bg9Unbl1k8r+OVXm/swTDwb8yPvvv681a9Zo8uTJFW7LyMhQSEiIYmNjPbYnJiYqIyPDPeaPhUv57eW3HW9Mbm6uCgsLdeDAATmdzkrHlD9GZSZPnqyYmBj3pXHjxlV70QAAAHUIpYuPKCpz6YNtuVpzoMjqKIBvOnRAZa+/INfWTVYnAWrF7t27ddddd+ndd99VaGio1XGqbcKECcrJyXFfdu/ebXUkAACAaqN08QGHipx669ds7cgrtToK4NtKiuX8z7/kWr/K6iSA161evVpZWVnq2rWrgoKCFBQUpG+++UYvvviigoKClJiYqJKSEmVnZ3vcLzMzU0lJSZKkpKSkCmczKr9+ojHR0dEKCwtTfHy87HZ7pWPKH6MyDodD0dHRHhcAAABfQ+lSx2UcKdM7W7J1uJgDioAa4XLK+cl/5PxugdVJAK86//zztWHDBq1bt8596d69u6699lr3/wcHB2vBgv/9LGzevFm7du1SSkqKJCklJUUbNmzwOMvQ/PnzFR0drXbt2rnH/PExyseUP0ZISIi6devmMcblcmnBggXuMQAAAP4qyOoAOLadeSX67295KnGxYC5Q01wLvpRyc2S7eLAMg/4Z/icqKkodOnTw2BYREaH69eu7t48aNUrjx49XvXr1FB0drTvuuEMpKSk666yzJEkXXnih2rVrp7/85S965plnlJGRoYcfflhjxoyRw+GQJN16662aNm2a7r//ft14441auHChPvjgA82ePdv9vOPHj9eIESPUvXt39ezZU1OnTlVBQYFGjhxZS+8GAACANShd6qjN2cX6bEeeOEER4D2ulUtl5ufJPuRaGUH8OkTgef7552Wz2TR06FAVFxcrLS1NL7/8svt2u92uL774QrfddptSUlIUERGhESNG6LHHHnOPad68uWbPnq27775bL7zwgho1aqTXX39daWlp7jHDhg3T/v37NXHiRGVkZKhLly6aO3duhcV1AQAA/I1hmiYf6+uY9QeKNHd3vvjC+J67vn9LQVnpVsdANRlNW8g+/EYZoWFWRwFwDLm5uYqJiVFOTg7ru/zuqbUHrI4AH/DgmfFWR4APKH30HqsjoI4LnjTF6gh1SnX2S5hTX8cszziiORQuQK0yd/4m579flVl4xOooAAAAAPwIpUsd8k16gb7Zx4c+wApm+m6Vvf2KzCP5VkcBAAAA4CcoXeqIb9ILtDyz0OoYQGDLSFfZjFdkFuRZnQQAAACAH6B0qQMWU7gAdcf+DJW9NZ3iBQAAAMApo3Sx2JL0An1P4QLULe7ihUONAAAAAJw8ShcLLc04omUULkDdtD9DZf+eLrOIn1EAAAAAJ4fSxSI/ZB7RtyyaC9Rtmfvk/M8bMktLrU4CAAAAwAdRuljgx4NFWpRO4QL4AnPXdjlnvSXT5bQ6CgAAAAAfQ+lSy37LLdHcXawTAfgSc8svcn46U6ZpWh0FAAAAgA+hdKlF+46U6uPtuXJZHQRAtZk/rpbrq8+sjgEAAADAh1C61JLDxU7N2parUhoXwGe5vl8i57cLrI4BAAAAwEdQutSCglKXZm7N0ZEyDk0AfJ1r4Zdy/bja6hgAAAAAfACli5eVOE19+FuuskuY4gL4C+fnH8i1Z6fVMQAAAADUcZQuXvblrjztO1JmdQwANamsTM6Zb8rMOWx1EgAAAAB1GKWLFy3LOKJN2SVWxwDgDfl5Knv/TZml/IwDAAAAqByli5dszSnRt/uOWB0DgDdl7JXz4/c4lTQAAACASlG6eMHBojJ9viNPfAwD/J/5ywa5Fs21OgYAAACAOojSpYYVO1367295KnZRuQCBwvXt13L98qPVMQAAAADUMZQuNcg0TX22I0+Hip1WRwFQy5yfzpR5+KDVMQAAAADUIZQuNWh5ZqG25ZZaHQOAFYqL5Pzw3zKdnK0MAAAAwFGULjVkb0GpvmPhXCCgmem75Zr3mdUxAAAAANQRlC41oMjp0qc78uSyOggAy7lWLpXr5/VWxwAAAABQB1C61IC5u/KVW0LlAuAo52cfyDx0wOoYAAAAACxG6XKK1h0o0qbsEqtjAKhLiotU9uG/ZTpZVBsAAAAIZJQup+BAYZm+3pNvdQwAddG+PXItmW91CgAAAAAWonQ5SU6XqU935KnMtDoJgLrK9d0Cmem7rY4BAAAAwCKULidpaeYR7S/i0AEAx+FyqeyT/8gs4zTSAAAAQCCidDkJmUfK9H1modUxAPiC/ZlyLZxjdQoAAAAAFqB0qSaXaerLXXlycVgRgCpyff+NXLu2Wx0DAAAAQC2jdKmm7zMLlVnIYUUAqsE05fz0fZmlnOkMAAAACCSULtVwoKhMSzOOWB0DgC86dECuxfOsTgEAAACgFlG6VJFpmvpyZ76cHFYE4CS5vv9WZlaG1TEAAAAA1BJKlypac6BI6Uc4AwmAU+Byyvnlf61OAQAAAKCWULpUQWGZS9/u47AiAKfO3PmbXOtXWR0DAAAAQC2gdKmCb9KPqIjjigDUEOf8z2UWUuQCAAAA/o7S5QQyjpRp/cEiq2MA8CcF+XIt+NLqFAAAAAC8jNLlBL7eky/muACoaa4138uVvtvqGAAAAAC8iNLlODYeKtKeAhbPBeAFpinXV59bnQIAAACAF1G6HEOJ09SidNZcAOA95s5tcv36s9UxAAAAAHgJpcsx/JB1RPmlLqtjAPBzzq9ny3TxuwYAAADwR5QulThS5tLKLBbPBVAL9mfIXLfC6hQAAAAAvIDSpRLLM46oxMXyuQBqh3PxPJmlJVbHAAAAAFDDKF3+JK/EqbUHmOUCoBbl5cq1/BurUwAAAACoYZQuf7I0o1BlTHIBUMtcSxfJPFJgdQwAAAAANYjS5Q8OFzv14yFmuQCwQEmxXN8vsToFAAAAgBpE6fIH3+07IpZyAWAV14rvZBYVWh0DAAAAQA2hdPndoSKnfj5cbHUMAIGsuEiuFUutTgEAAACghlC6/O6HrCNikgsAq7l+WCKzhAIYAAAA8AeULpLyS1366RAfcgDUAUcK5Fq13OoUAAAAAGoApYukVfsL5WSaC4A6wrX8G5llpVbHAAAAAHCKAr50KXa6tPYAZywCUIfk58q1dqXVKQAAAACcooAvXdYdKFIx01wA1DGuFd/KNPndBAAAAPiygC5dnKapVfuZ5QKgDjqQJfO3X61OAQAAAOAUBHTp8svhYuWVuqyOAQCVcq34zuoIAAAAAE5BQJcua5jlAqAOM7f8IvPwQatjAAAAADhJAVu6ZBwpU/qRMqtjAMCxmaZcK5danQIAAADASQrY0mXtgUKrIwDACbnWrpBZWmJ1DAAAAAAnISBLl2KnSz8fLrY6BgCcWFGhzB9XW50CAAAAwEkIyNLl58PFYv1cAL7CtXaF1REAAAAAnISALF3WH2SWCwDfYe7dJfNAptUxAAAAAFRTwJUumUfKlMECugB8jGs9hxgBAAAAvibgSpeNrOUCwAe5flwl0+S4SAAAAMCXBFTpYpqmfqF0AeCLcnNkbt9qdQoAAAAA1RBQpcvugjLlsYIuAB/lWr/K6ggAAAAAqiGgShdmuQDwZeYvG2SW8HsMAAAA8BUBU7q4TFObsvmwAsCHlZbI3LzR6hQAAAAAqihgSpcdeaUqLDOtjgEAp8S1aYPVEQAAAABUUcCULj9zaBEAP2Bu2SSzrNTqGAAAAACqICBKF6dpakt2idUxAODUlZbI3Par1SkAAAAAVEFAlC6780tV7OLQIgD+gUOMAAAAAN8QEKXLthxmuQDwH+avP8t0uayOAQAAAOAEAqN0yWX9AwB+5EiBzF2/WZ0Cddwrr7yiTp06KTo6WtHR0UpJSdGcOXPctxcVFWnMmDGqX7++IiMjNXToUGVmZno8xq5duzRw4ECFh4crISFB9913n8rKyjzGLF68WF27dpXD4VCrVq00Y8aMClleeuklNWvWTKGhoerVq5dWrFjhldcMAABQ1/h96XK42KlDxU6rYwBAjTI3/WR1BNRxjRo10lNPPaXVq1dr1apVOu+883TZZZdp48ajpx2/++679fnnn2vWrFn65ptvlJ6eriFDhrjv73Q6NXDgQJWUlGjZsmV66623NGPGDE2cONE9Zvv27Ro4cKD69++vdevWady4cRo9erTmzZvnHjNz5kyNHz9ekyZN0po1a9S5c2elpaUpKyur9t4MAAAAiximafr1Yier9hfq6z0FVsdAgLjr+7cUlJVudQwEggaJCr79fqtTwMfUq1dPzz77rK644go1aNBA7733nq644gpJ0qZNm9S2bVstX75cZ511lubMmaNBgwYpPT1diYmJkqTp06frgQce0P79+xUSEqIHHnhAs2fP1k8//a8EHD58uLKzszV37lxJUq9evdSjRw9NmzZNkuRyudS4cWPdcccdevDBB6ucPTc3VzExMcrJyVF0dHRNvSU+7am1B6yOAB/w4JnxVkeADyh99B6rI6COC540xeoIdUp19kv8fqYL67kA8Ev7M2Xm5VqdAj7C6XTq/fffV0FBgVJSUrR69WqVlpYqNTXVPaZNmzZq0qSJli9fLklavny5Onbs6C5cJCktLU25ubnu2TLLly/3eIzyMeWPUVJSotWrV3uMsdlsSk1NdY85luLiYuXm5npcAAAAfI1fly6lLlO781nPBYB/MrdvsToC6rgNGzYoMjJSDodDt956qz7++GO1a9dOGRkZCgkJUWxsrMf4xMREZWRkSJIyMjI8Cpfy28tvO96Y3NxcFRYW6sCBA3I6nZWOKX+MY5k8ebJiYmLcl8aNG1f79QMAAFjNr0uX3fmlKvPrg6cABDIXpQtO4IwzztC6dev0ww8/6LbbbtOIESP0888/Wx2rSiZMmKCcnBz3Zffu3VZHAgAAqLYgqwN40x5muQDwY+ZvlC44vpCQELVq1UqS1K1bN61cuVIvvPCChg0bppKSEmVnZ3vMdsnMzFRSUpIkKSkpqcJZhsrPbvTHMX8+41FmZqaio6MVFhYmu90uu91e6ZjyxzgWh8Mhh8NR/RcNAABQh/j1TJddlC4A/FlutsyD+61OAR/icrlUXFysbt26KTg4WAsWLHDftnnzZu3atUspKSmSpJSUFG3YsMHjLEPz589XdHS02rVr5x7zx8coH1P+GCEhIerWrZvHGJfLpQULFrjHAAAA+DO/nelS5jK170iZ1TEAwKtcv22RvX4Dq2OgDpowYYIuvvhiNWnSRHl5eXrvvfe0ePFizZs3TzExMRo1apTGjx+vevXqKTo6WnfccYdSUlJ01llnSZIuvPBCtWvXTn/5y1/0zDPPKCMjQw8//LDGjBnjnoFy6623atq0abr//vt14403auHChfrggw80e/Zsd47x48drxIgR6t69u3r27KmpU6eqoKBAI0eOtOR9AQAAqE1+W7rsO1ImJ+u5APBz5s5tUo+zrY6BOigrK0vXX3+99u3bp5iYGHXq1Enz5s3TBRdcIEl6/vnnZbPZNHToUBUXFystLU0vv/yy+/52u11ffPGFbrvtNqWkpCgiIkIjRozQY4895h7TvHlzzZ49W3fffbdeeOEFNWrUSK+//rrS0tLcY4YNG6b9+/dr4sSJysjIUJcuXTR37twKi+sCAAD4I8M0Tb+sJpZnHNE3+45YHQMB5q7v31JQVrrVMRBIYusp+K6HrE4BeF1ubq5iYmKUk5Oj6Ohoq+PUCU+tPWB1BPiAB8+MtzoCfEDpo/dYHQF1XPCkKVZHqFOqs1/it2u67C5gPRcAASD7kMyCfKtTAAAAAKiEX5YupmlqbwHruQAIDObeXVZHAAAAAFAJvyxdDhU7VcyCLgACBKWL//ntt9+sjgAAAIAa4JelS2ah0+oIAFBrKF38T6tWrdS/f3+98847KioqsjoOAAAATpJfli5ZnCoaQAAx03dbHQE1bM2aNerUqZPGjx+vpKQk3XLLLVqxYoXVsQAAAFBNflm6ZBZSugAIIIVHZB7cb3UK1KAuXbrohRdeUHp6uv71r39p3759Ouecc9ShQwc999xz2r+frzcAAIAv8MvSJYvSBUCAMbP2WR0BXhAUFKQhQ4Zo1qxZevrpp7V161bde++9aty4sa6//nrt28fXHQAAoC7zu9Ilv9SlgjIW0QUQWMz9mVZHgBesWrVKt99+uxo2bKjnnntO9957r7Zt26b58+crPT1dl112mdURAQAAcBxBVgeoacxyARCIzAOULv7kueee05tvvqnNmzdrwIABevvttzVgwADZbEf/VtK8eXPNmDFDzZo1szYoAAAAjovSBQD8ADNd/Msrr7yiG2+8UTfccIMaNmxY6ZiEhAS98cYbtZwMAAAA1eF3pcuhIk4XDSAAHdgv03TJMPzuqNGAtGXLlhOOCQkJ0YgRI2ohDQAAAE6W3+2dHy6hdAEQgMpKpezDVqdADXnzzTc1a9asCttnzZqlt956y4JEAAAAOBl+V7pkF7usjgAAluAQI/8xefJkxcfHV9iekJCgJ5980oJEAAAAOBl+VbqUuUzllVK6AAhM5oEsqyOghuzatUvNmzevsL1p06batWuXBYkAAABwMvyqdMnh0CIAgSyHw4v8RUJCgn788ccK29evX6/69etbkAgAAAAnw69Kl8McWgQggJm52VZHQA25+uqrdeedd2rRokVyOp1yOp1auHCh7rrrLg0fPtzqeAAAAKgivzp7UXYxM10ABLCcbKsToIb87W9/044dO3T++ecrKOjoP9Uul0vXX389a7oAAAD4EP8qXTi8CEAAM3NzrI6AGhISEqKZM2fqb3/7m9avX6+wsDB17NhRTZs2tToaAAAAqsGvSpd8FtEFEMgK8mU6y2TY/epXe0A7/fTTdfrpp1sdAwAAACfJr/bMC8ooXQAEMlPKzZHiWGjV1zmdTs2YMUMLFixQVlaWXC7Pf98WLlxoUTIAAABUh1+VLkfKTKsjAIClzNxsGZQuPu+uu+7SjBkzNHDgQHXo0EGGYVgdCQAAACfBv0oXDi8CEOhY18UvvP/++/rggw80YMAAq6MAAADgFPjNKaNdpqlCJzNdAAQ2s/CI1RFQA0JCQtSqVSurYwAAAOAU+U3pwqFFACCpqMjqBKgB99xzj1544QWZJv+2AQAA+DK/ObzoCIvoAoBUVGh1AtSA7777TosWLdKcOXPUvn17BQcHe9z+0UcfWZQMAAAA1UHpAgB+xKR08QuxsbG6/PLLrY4BAACAU+Q3pUsJ67kAgFRM6eIP3nzzTasjAAAAoAb4zZouLOkCAKrRw4uWLFmiSy65RMnJyTIMQ5988kmNPTZOrKysTF9//bVeffVV5eXlSZLS09OVn59vcTIAAABUld/MdClz0boAvu6Zb1fo4QXf6Y5eZ2rKxf0lSdsOZeuBr77Rsl3pKi5z6sJWzTR1QH8lRkYc97H25ubpr/O/1bytO3SktFQt68Xq9cvS1O20JEnSc0tXacrSlZKke8/pobvP7u6+74o9+3TH7AVaOvoaBdl9rJuuwYV0CwoK1LlzZ914440aMmRIjT0uTmznzp266KKLtGvXLhUXF+uCCy5QVFSUnn76aRUXF2v69OlWRwQAAEAV+E3pUkrpAvi0VXsz9PrqH9UxMd69raCkVAP//V91TGygeSOukCQ9snCZLn/vE303+hrZbEalj3W4sEj93pipvs0b6/NrL1d8RLi2Hjys2LBQSdKPGfv16KJl+uSawTIlDX7vY6W2bKqOiQ1U5nRpzBdf65VLLvC9wkWSWVxzpcvFF1+siy++uMYeD1V31113qXv37lq/fr3q16/v3n755ZfrpptusjAZAAAAqsNvShdmugC+K7+4RNf/90u9cskFmrzkB/f2Zbv2akd2rlbccp2iQx2SpH9dfpESnnpJi7bv0vktm1b6eM9+t1KNYqL0+uA097bmcTHu/9984JA6Jsarf4smkqSOiQ20+cBhdUxsoCnLVqlP00bq/vuMGJ/jdFqdADXg22+/1bJlyxQSEuKxvVmzZtq7d69FqQAAAFBdvvdn3GPwtTVdvp7+jCZ0beBxeW5Iivv20uIifTr5fj3W/3RN6t1U79x7g/IOZlX58T9+4l5N6NpA3737vynoZSXFmvnw7XqkT3P9fXAvbf3hG4/7LHlrmj57+sFTf3FANd355UINOL1FhRKl2OmUIckRZHdvCw2yy2YYWrrr2B88v9i8Td2SEzX8g8912jOvqMf0f+uN1T+6b++QGK8tBw9rV3audmbnasvBw2qfUF/bDmXrrbU/6dHzetf4a6w1zjKrE6AGuFwuOSsp0Pbs2aOoqCgLEgEAAOBk1Hrp8tJLL6lZs2YKDQ1Vr169tGLFihp5XF+c6ZLYso3++tVP7sstb3zhvm32lP/TL99+pWuffkM3//Mz5e7P1Lv33lClx924cLZ2b1il6Aaef6lf8dHbSv9lvW6bMUc9h1yv9/96q0zz6Pt2aO9Orfj437pwzEM19vqAqpi5YZPW7svU4+efU+G2Xo0aKiIkWH+d/62OlJSqoKRUD3y1RE7TVEZ+wTEfc/vhHL26cr1a1YvTF38Zqlu6d9bdcxbp7XUbJUltG9TX384/Rxf/+78a8O//6vHzz1HbBvU15vOvNfmCc/XV1h3q8tJb6jH93/p2xx6vvXavcLqsToAacOGFF2rq1Knu64ZhKD8/X5MmTdKAAQOsCwYAAIBqqdXDi2bOnKnx48dr+vTp6tWrl6ZOnaq0tDRt3rxZCQkJp/TYvrimi81uV1R8YoXtRXm5WvXJuxr25HS17NlHknTFIy/q+aFna9ePq9SkU/cK9ymXk7VPnz0zQTe+9IFm3HmNx21Z27eobd80JbZso3qnNdWcqY+oIPugIuPi9cmT9+niOycqNJK/oKL27M7J0z1zF+vLvwxVaHDFX0cNIsL1nysH6Y7ZCzTth7WyGYaGdWyjMxsmyGZUvp6LJLlMU92SE/V46tEi58yGCdqYdUD/XPWjru/SXpJ0c4/OurlHZ/d93l63UZGOYJ3VuKE6/GOGlt18jfbm5um6D2fr13Gj5AjykaMxXRxe5A+mTJmitLQ0tWvXTkVFRbrmmmu0ZcsWxcfH6z//+Y/V8QAAAFBFtfop4rnnntNNN92kkSNHSpKmT5+u2bNn61//+pcefDDwDms5sGu7nrywg4IcoWrSqbsuGvuwYhs20t5f1stZVqpWvfq6xyY0b63YpEbHLV1cLpc+ePh2nXv9GCW2bFPh9oat22vtlx+otKhQvy5fpKj4REXE1tfaLz9UsCNU7c8b6LXXClRmTXqmsgqOqNer77i3OU1T3+7co5dXrFP+/92lC1o106a7RulAQaGCbIZiw0LV+Nnpat4h5piP2zAqQm0b1PfY1qZBfX38y5ZKxx8oKNQTi7/XgpFXacWeDLWuH6vW9ePUun6cSl0u/Xrw6HovPsH0vQIaFTVq1Ejr16/X+++/rx9//FH5+fkaNWqUrr32WoWFhVkdDwAAAFVUa6VLSUmJVq9erQkTJri32Ww2paamavny5bUVo85o3LGrrnz0RcU3baW8A5la8Nrf9eqoSzRu1rfKO5gle3CIwqI8P1RG1m9w3HVdlsx4UbagIJ199c2V3t79smuUsWWjnr/iHIXH1tM1T7+uwtxsfT39ad302if66qUntX7ex6rfqJmGPvKiYhIa1uhrDgjHmX2Bis5r0URrbrveY9tNn87TGfH1dG/vHrLb/ncEZHzE0Q+ai37bpayCIxp0RstjPm5K42T9evCwx7YtBw+rSUx0pePvnbdYd6Z0VaOYKK1Kz1Cp63+H6JS5XHL60ky6Gixd8vPztXXrVvf17du3a926dapXr56aNGlSY8+DygUFBem6666zOgYAAABOQa2VLgcOHJDT6VRioufhNImJidq0adMpP76vfdQ9o3eq+/8bnt5ejTt209MDz9SP8z9RsKP6f8Xc+/N6Lf3Pa7rjvYUyjvHB3x4crMsmPOOx7cNJd+js4aOVvnmDfl48R3fNXKxvZkzT589M0HV/n1HtHIHOtNlPPAhuUY4QdfjDKaIlKSI4WPXDQt3b31r7k9rE11N8RLi+352ue+Yu1l0p3XRGfD33fdLemqXL2rTS7b3OlCTdldJN577xvp5a8oOuaH+6Vv5+OuqXL7mgQoavt+3UloOH9a/BF0mSuicnafOBQ5q7Zbv25OTJbhg6Iz7OW29BnbZq1Sr179/ffX38+PGSpBEjRmjGjBkWpQoMb7/99nFvv/766497OwAAAOoGH1mk4MSOVTT4irCoGMU3aamDu7erda9+cpaWqDAvx2O2S/7B/YqqX/naN9vXLlfBoQN6ekAX9zaX06kvn5+kpe+9pgdmr6lwn20rv1Pmb5s1ZOJUfTn1EZ3RO1UhYRHqdOFlenX0GzX+GgOBi9Klxm0+cFgPf/2dDhUWqWlstB7s00t3pXT1GPPboRwdOFLovt79tCTNGnapHl7wrZ745ns1i4vRlIv66ZpObT3uV1haqru+XKh3rxgom+3o75BGMVGaevF5uumTeXIE2fXG5RcpLDjY+y+0DurXr597sW3UrrvuusvjemlpqY4cOaKQkBCFh4dTugAAAPiIWitd4uPjZbfblZmZ6bE9MzNTSUlJx7hX1fn6ua+Lj+Tr0J4dihp4pU5r21n2oGBtW7FEHc6/RJK0f8dWZWfsOeZ6LmcOvMpjDRhJenPMVTpz4JXqduk1FcaXFhfps6ce0LAnXpHNbpfpcsr5+4crZ1mpzEpOVYoTM+2ULqfq65FXeVx/8oI+evKCPse9z5a7R1fYNvCMFhp4Rovj3i8sOFgb7xhZYfuN3Trqxm4dq5C2DgoKzILI3xw+fLjCti1btui2227TfffdZ0EiAAAAnIxa6ypCQkLUrVs3LViwwL3N5XJpwYIFSklJOeXHt/lY6/Ll85P02+qlOpy+SzvXr9A799wgm82uzhcNUWhUtLoPvlazp0zUtpXfae/P6/XhI3eqSaceHqXLc0NStHHhbElSRGw9JbVq63GxBQUrsn6CGjRrVeH5F/5zis44J1XJbTpJkpp27qmNC2dr368btXzmG2rapWftvBF+xmX3m8lj8FUBOisnELRu3VpPPfVUhVkwAAAAqLtq9RPi+PHjNWLECHXv3l09e/bU1KlTVVBQ4D6b0amw+9jhRTmZ6Xp/wi06knNYEXH11axLL9321hxFxh1dx2LgPX+TYRh6976RKisp0ekp/XXZhKc9HmP/jq0qys+t9nNnbP1FG+Z/qjvfX+Te1iH1Uv22epleHX2JGjRtpWFPTD+1FxigXMx0gdUoXfxaUFCQ0tPTrY4BAACAKjLMWj5gf9q0aXr22WeVkZGhLl266MUXX1SvXr1O+XG/zzyixelHaiAhcPJu2vSlon9db3UMBLKk0xR8y3irU+AUffbZZx7XTdPUvn37NG3aNDVu3Fhz5syxKJl1cnNzFRMTo5ycHEVHV34mskDz1NoDVkeAD3jwzPgTD0LAK330HqsjoI4LnjTF6gh1SnX2S2r9WIixY8dq7NixNf64ITbfmukC/8RCurCawUwXvzB48GCP64ZhqEGDBjrvvPM0ZQo7PQAAAL7CbxagCLX72KIu8EscXgTLsZCuX3C5XFZHAAAAQA3wm6bCYWemC6zntPlNjwlfxUwXAAAAoM7wm0+IoUGULrAehxfBciEOqxOgBowfX/V1eZ577jkvJgEAAMCp8JvShZkuqAvKOLwIFjPCI62OgBqwdu1arV27VqWlpTrjjDMkSb/++qvsdru6du3qHmf42Jn7AAAAAo3flC6s6YK6wGlQusBiEZQu/uCSSy5RVFSU3nrrLcXFxUmSDh8+rJEjR6pPnz665x7OMgEAAOAL/KapYKYL6gInhxfBYgali1+YMmWKJk+e7C5cJCkuLk6PP/44Zy8CAADwIX5TugTbDLGsC6xG6QLLRURYnQA1IDc3V/v376+wff/+/crLy7MgEQAAAE6G35QukhQR7FcvBz6ozO43R+zBVzHTxS9cfvnlGjlypD766CPt2bNHe/bs0X//+1+NGjVKQ4YMsToeAAAAqsivPiFGh9iUU+KyOgYCWBlrusBiRkSU1RFQA6ZPn657771X11xzjUpLSyVJQUFBGjVqlJ599lmL0wEAAKCq/Kt0CbZLKrM6BgIYhxfBcsx08Qvh4eF6+eWX9eyzz2rbtm2SpJYtWyqCw8cAAAB8il8djxMV4lcvBz6ojNIFVgpxyHCEWp0CNWjfvn3at2+fWrdurYiICJmmaXUkAAAAVINftRTRrOkCi5VyeBGsFFfP6gSoIQcPHtT555+v008/XQMGDNC+ffskSaNGjeJ00QAAAD7Er1oKZrrAasx0gZWMuPpWR0ANufvuuxUcHKxdu3YpPDzcvX3YsGGaO3euhckAAABQHX64pgtgHWa6wEqULv7jq6++0rx589SoUSOP7a1bt9bOnTstSgUAAIDq8qupIdHMdIHFKF1gKUoXv1FQUOAxw6XcoUOH5HA4LEgEAACAk+FXLUVYkE1hQYbVMRDAygy/+pGCj2Gmi//o06eP3n77bfd1wzDkcrn0zDPPqH///hYmAwAAQHX41eFFklTfYdeeMk4bDWuU2vzuRwo+hNLFfzzzzDM6//zztWrVKpWUlOj+++/Xxo0bdejQIS1dutTqeAAAAKgiv/uzfHwoH3phnRKb3/1IwVcYhhTL2Yv8RYcOHfTrr7/qnHPO0WWXXaaCggINGTJEa9euVcuWLa2OBwAAgCryu4YiPpQ1NWCdUv/7kYKvqBcvw87vP39QWlqqiy66SNOnT9dDDz1kdRwAAACcAr/7szylC6xUYrCmEKxhJCZbHQE1JDg4WD/++KPVMQAAAFAD/K50qU/pAguVGMx0gTUoXfzLddddpzfeeMPqGAAAADhFfvcJMSrELofdULHTtDoKAlAJZy+CRYwkShd/UlZWpn/961/6+uuv1a1bN0VERHjc/txzz1mUDAAAANXhd6WLdPQQo70FnMEItY+ZLrCKkdjQ6gioAb/99puaNWumn376SV27dpUk/frrrx5jDA5jBAAA8Bl++QmxYXgQpQssYRqGZLNJLpfVURBIwsJlxMRZnQI1oHXr1tq3b58WLVokSRo2bJhefPFFJSYmWpwMAAAAJ8Mvj4VIDg+2OgICmMkZZFDLWM/Ff5im56Gxc+bMUUFBgUVpAAAAcKr8s3SJ8MsJPPAVdr7/ULsoXfzXn0sYAAAA+Ba/LF1iHXaFBXHMOyxC6YJaZjRuanUE1BDDMCqs2cIaLgAAAL7LL0sXSUoO54MvrGEG8b2H2mU0aW51BNQQ0zR1ww03aMiQIRoyZIiKiop06623uq+XX6pi8uTJ6tGjh6KiopSQkKDBgwdr8+bNHmOKioo0ZswY1a9fX5GRkRo6dKgyMzM9xuzatUsDBw5UeHi4EhISdN9996mszHPdtMWLF6tr165yOBxq1aqVZsyYUSHPSy+9pGbNmik0NFS9evXSihUrqvfmAAAA+CC/LV0asq4LrMKaLqhNsfVkRMVYnQI1ZMSIEUpISFBMTIxiYmJ03XXXKTk52X29/FIV33zzjcaMGaPvv/9e8+fPV2lpqS688EKPNWLuvvtuff7555o1a5a++eYbpaene5Q6TqdTAwcOVElJiZYtW6a33npLM2bM0MSJE91jtm/froEDB6p///5at26dxo0bp9GjR2vevHnuMTNnztT48eM1adIkrVmzRp07d1ZaWpqysrJq4F0DAACouwzTTw8Y/y23RB9sy7U6BgLQuGVvyn4gw+oYCBBG5+4KGny11THgA/bv36+EhAR98803Ovfcc5WTk6MGDRrovffe0xVXXCFJ2rRpk9q2bavly5frrLPO0pw5czRo0CClp6e7z6A0ffp0PfDAA9q/f79CQkL0wAMPaPbs2frpp5/czzV8+HBlZ2dr7ty5kqRevXqpR48emjZtmiTJ5XKpcePGuuOOO/Tggw9WKX9ubq5iYmKUk5Oj6OjomnxrfNZTaw9YHQE+4MEz462OAB9Q+ug9VkdAHRc8aYrVEeqU6uyX+O1Ml+TwIHEUPKzA2YtQm2yNObQIVZOTkyNJqlevniRp9erVKi0tVWpqqntMmzZt1KRJEy1fvlyStHz5cnXs2NHjlNVpaWnKzc3Vxo0b3WP++BjlY8ofo6SkRKtXr/YYY7PZlJqa6h4DAADgr/x28YnQIJuSwoO070jZiQcDNcjFQrqoRazngqpwuVwaN26cevfurQ4dOkiSMjIyFBISotjYWI+xiYmJysjIcI/5Y+FSfnv5bccbk5ubq8LCQh0+fFhOp7PSMZs2bTpm5uLiYhUXF7uv5+YyexUAAPgev53pIknNoljXBbWPmS6oNeERMhoknngcAt6YMWP0008/6f3337c6SpVNnjzZYx2bxo0bWx0JAACg2ihdgBrGTBfUFqNJC6sjwAeMHTtWX3zxhRYtWqRGjRq5tyclJamkpETZ2dke4zMzM5WUlOQe8+ezGZVfP9GY6OhohYWFKT4+Xna7vdIx5Y9RmQkTJignJ8d92b17d/VeOAAAQB3g16XLaRHBCvbrV4i6yGVjpgtqh61VG6sjoA4zTVNjx47Vxx9/rIULF6p5c89D0bp166bg4GAtWLDAvW3z5s3atWuXUlJSJEkpKSnasGGDx1mG5s+fr+joaLVr18495o+PUT6m/DFCQkLUrVs3jzEul0sLFixwj6mMw+FQdHS0xwUAAMDX+PWf5INshhpFBGt7XqnVURBATEoX1BKjNaULjm3MmDF677339OmnnyoqKsq9BktMTIzCwsIUExOjUaNGafz48apXr56io6N1xx13KCUlRWeddZYk6cILL1S7du30l7/8Rc8884wyMjL08MMPa8yYMXI4HJKkW2+9VdOmTdP999+vG2+8UQsXLtQHH3yg2bNnu7OMHz9eI0aMUPfu3dWzZ09NnTpVBQUFGjlyZO2/MQAAALXIr0sX6eghRpQuqE1ODi9CbUhoKCM61uoUqMNeeeUVSVK/fv08tr/55pu64YYbJEnPP/+8bDabhg4dquLiYqWlpenll192j7Xb7friiy902223KSUlRRERERoxYoQee+wx95jmzZtr9uzZuvvuu/XCCy+oUaNGev3115WWluYeM2zYMO3fv18TJ05URkaGunTporlz51ZYXBcAAMDfGKZpmlaH8KbMI2V6c3O21TEQQG7c+pXifl5tdQz4OdvZ/WW/YJDVMYBak5ubq5iYGOXk5HCo0e+eWnvA6gjwAQ+eGW91BPiA0kfvsToC6rjgSVOsjlCnVGe/xO9XPEkIsyuKhV1Qi1w2ZrrA+4zWba2OAAAAAOAE/L6NMAxDrWNCrI6BAOJkTRd4myNURpNmVqcAAAAAcAJ+X7pI0umxlC6oPZy9CN5mtDhdBt9nAAAAQJ0XEKVLk8hghdkNq2MgQJTxYRheZmvb0eoIAAAAAKogIEoXm2GoFYcYoZZweBG8KihYxhntrU4BAAAAoAoConSRpDNiHVZHQIBwspAuvMho3VZGCL/PAAAAAF8QMKVLs6hghdg4xAjeV2YLmB8rWMDWoYvVEQAAAABUUcB8OgyyGWoZHWx1DAQAZrrAa0IcMlq3szoFAAAAgCoKmNJFktrXC7U6AgIAC+nCW4zT28kIpjwGAAAAfEVAlS4tooMVGRRQLxkWKDMoXeAdtvZdrI4AAAAAoBoCqoGwGYba12MBSngXM13gFWHhMlq1sToFAAAAgGoIqNJFkjrWp3SBd5UZAfdjhVpg69RNRhDrBQEAAAC+JOA+HcaHBqlhOB9c4D1lLKQLL7B1PcvqCAAAAACqKeBKF0nqyCFG8KIS1nRBDTMaNZWRkGR1DAAAAADVFJClS7s4h4IMq1PAX7GmC2oas1wAAAAA3xSQpUtokE1nxDLbBd5RykwX1CRHqIwOXaxOAQAAAOAkBGTpIkldG4RaHQF+itIFNcnW4UwZwSFWxwAAAABwEgK2dDktIlhJLKgLLyjl7EWoQbZuHFoEAAAA+KqA/nTYLZ7ZLqh5JazpghpiNGspo2Ejq2MAAAAAOEkBXbq0jXMoghV1UcM4exFqii2ln9URAAAAAJyCgC5dgmyGzowPszoG/EyJKF1QA+ITZbRua3UKAAAAAKcgoEsX6eiCukx2QU1iTRfUBHvKuTIMfjkBAAAAvizgPx2GB9nUoR5ru6DmcHgRTllElIxO3a1OAQAAAOAUBXzpIklnJYbxRqDmGIZkp3jBybP16C0jiLOrAQAAAL6OrkFSrMOu9vUcVseAHzHtfGDGSQoOka3H2VanAAAAAFADKF1+d3ZSuFg9ATWGWQo4SbYevWWER1gdAwAAAEANoHT5XRyzXVCTmOmCkxHikK13f6tTAAAAAKghlC5/cHYis11QM0wba7qg+my9+jDLBQAAAPAjlC5/UC/UrnZxzHbBqTNZSBfV5QiVLaWv1SkAAAAA1CBKlz85OymM2S44ZSyki+qypfSVERZudQwAAAAANYjS5U/qhwapU31mu+DUULqgWsLCZTvrXKtTAAAAAKhhlC6V6NMwQsG8MzgFHF6E6rCd3V+GI9TqGAAAAABqGNVCJSKDbeqVwDR/nDwXC+miqmLryXZWH6tTAAAAAPACSpdj6JUYpkimu+AkcXgRqsqeOkhGULDVMQAAAAB4Aa3CMQTbDJ3bkNkuODkuDi9CFRhNW8jWvrPVMQAAAAB4CaXLcXSs51BCGB+eUX0uZrrgRAxD9rTLrE4BAAAAwIsoXY7DMAydd1qE1THgg1jTBSdidOkho2Ejq2MAAAAA8CJKlxNoFhWiNrEhVseAj3FSuuB4QhyynzfA6hQAAAAAvIzSpQpSG0XKYTesjgEfwuFFOB5b3wtkREZZHQMAAACAl1G6VEFksE19WVQX1cBMFxxT0mmyndXX6hQAAAAAagGlSxWdGR+q5HBmL6BqKF1QKZtNQZdeJcPGr14AAAAgELDnX0WGYeiiJpG8YagSShdUxnbWuSyeCwAAAAQQOoRqSAgLUo+EMKtjwAdQuqCCuPqy9bvI6hQAAAAAahGlSzWd0zBcsSG8bTg+Shf8mf2SK2UEB1sdAwAAAEAtoj2opmCboUFNo8S5jHA8ZTbW/8H/GF16yNa8tdUxAAAAANQySpeT0CgyWL0SOcwIx1bGTBeUi4mTPe0yq1MAAAAAsACly0nq0zBcCWF8sEblygy+NyDJMGS//GoZoZS0AAAAQCCidDlJdsPQJU2jFMRxRqgEa7pAkmy9z5OtaUurYwAAAACwCKXLKWgQFqRzkyOsjoE6iMOLYCQ3lq1fmtUxAAAAAFiI0uUU9WgQqqaRnJEEnko5vCiwBYfIPuRaGXa+DwAAAIBARulyigzD0KBmkQrnOCP8QSkzXQKa/aLBMuo3sDoGAAAAAItRutSAqGC7Lm3GaaTxP8x0CVxGu06yde1ldQwAAAAAdQClSw1pFhWiPg3DrY6BOoKzFwWo+ETZLx1mdQoAAAAAdQSlSw1KSQxTy2jWdwEzXQKSI1RBw26Q4Qi1OgkAAACAOoLSpQYZv59GOiaEtzXQsaZLoDFkv2y4jPgEq4MAAAAAqENoB2pYaJBNlzePlp0FXgJaCTNdAoqtd3/Z2na0OgYAAACAOobSxQuSwoOU1jjS6hiwUInBj1agMFqcLtv5F1sdAwAAAEAdxCdDL+lUP1S9EsKsjgGLsKZLgIitJ/vQ62RQsgEAAACoBJ8UvKhfcrjOiA2xOgYswEyXABAapqBrRskIj7A6CQAAAIA6ik+GXmQYhgY1jVLD8CCro6CWlYivuV+z22UfdoOMBklWJwEAAABQh1G6eFmwzdDQFtGKDuatDiROm00yWE3ZX9kvHSZbs1ZWxwAAAABQx9EE1ILIYJuuaBmtEBsfwgOKndku/sjW7yLZOnWzOgYAAAAAH0DpUksSwoI0uHmU6F0Ch2lnMV1/Y3TpIXvfC6yOAQAAAMBHULrUohbRIbqkaZToXQIEM138itHydNkvudLqGAAAAAB8CKVLLWsb59BFjSOtjoFaYAZRuvgLo3Ez2a+6QYaN2UsAAAAAqo7SxQKd40N13mmcZtbvcXiRf0g6TfZrRssIcVidBAAAAICPoXSxSM+EMPVOCrM6BrzI5PAi3xefoKDrbpYRys8qAAAAgOqjdLFQn4YR6t4g1OoY8BKTQ1F8W714BV1/m4wIDgcEAAAAcHIoXSx2/mkR6lKf4sUfMdPFh8XVV9CI22RERVudBAAAAIAPo3SxmGEYSmscoa7xFC/+xsWaLr4prr6Crr9VRnSs1UkAAAAA+Dj+FF8HGIahCxtHymZIq/YXWR0HNYSZLj6oQaKC/nKLjKgYq5MAAAAA8APMdKlDUhtFKiWRBTv9BTNdfIuR3FhBN4yhcIFfWbJkiS655BIlJyfLMAx98sknHrebpqmJEyeqYcOGCgsLU2pqqrZs2eIx5tChQ7r22msVHR2t2NhYjRo1Svn5+R5jfvzxR/Xp00ehoaFq3LixnnnmmQpZZs2apTZt2ig0NFQdO3bUl19+WeOvFwAAoK6hdKlj+iZHqG/DcKtjoAa4DEoXX2E0aSH79bfKCOdU7vAvBQUF6ty5s1566aVKb3/mmWf04osvavr06frhhx8UERGhtLQ0FRX9b9bltddeq40bN2r+/Pn64osvtGTJEt18883u23Nzc3XhhReqadOmWr16tZ599lk98sgjeu2119xjli1bpquvvlqjRo3S2rVrNXjwYA0ePFg//fST9148AABAHWCYpmlaHQIVrdlfqK/2FFgdA6fgpk1fKvrX9VbHwAkYrdrIftUNMoKDrY4CeJVhGPr44481ePBgSUdnuSQnJ+uee+7RvffeK0nKyclRYmKiZsyYoeHDh+uXX35Ru3bttHLlSnXv3l2SNHfuXA0YMEB79uxRcnKyXnnlFT300EPKyMhQSEiIJOnBBx/UJ598ok2bNkmShg0bpoKCAn3xxRfuPGeddZa6dOmi6dOnVyl/bm6uYmJilJOTo+hoFrmWpKfWHrA6AnzAg2fGWx0BPqD00XusjoA6LnjSFKsj1CnV2S9hpksd1bVBmAY3i1KQYXUSnCwOL6r7jHadZB9+I4ULAtL27duVkZGh1NRU97aYmBj16tVLy5cvlyQtX75csbGx7sJFklJTU2Wz2fTDDz+4x5x77rnuwkWS0tLStHnzZh0+fNg95o/PUz6m/HkqU1xcrNzcXI8LAACAr6F0qcPaxDk0vFWMwmhefJLTxkK6dZntrHNlv+IvMijHEKAyMjIkSYmJiR7bExMT3bdlZGQoISHB4/agoCDVq1fPY0xlj/HH5zjWmPLbKzN58mTFxMS4L40bN67uSwQAALAcpUsd1ygyWNefHqs4B18qX+Oy8WG+TjJssg0cKnvaZTIMfq6AumrChAnKyclxX3bv3m11JAAAgGrjE4cPiHPY9ZfTY3VaBDMnfImT0qXucYTKfs0o2bufbXUSwHJJSUmSpMzMTI/tmZmZ7tuSkpKUlZXlcXtZWZkOHTrkMaayx/jjcxxrTPntlXE4HIqOjva4AAAA+BpKFx8RHmTT1a1i1CY25MSDUSc4OWylbomJU9CNY2Vr1cbqJECd0Lx5cyUlJWnBggXubbm5ufrhhx+UkpIiSUpJSVF2drZWr17tHrNw4UK5XC716tXLPWbJkiUqLS11j5k/f77OOOMMxcXFucf88XnKx5Q/DwAAgL+idPEhQTZDlzWLUu+kMKujoAqcnDK6zjCSGyto9J0yEhpaHQWoVfn5+Vq3bp3WrVsn6ejiuevWrdOuXbtkGIbGjRunxx9/XJ999pk2bNig66+/XsnJye4zHLVt21YXXXSRbrrpJq1YsUJLly7V2LFjNXz4cCUnJ0uSrrnmGoWEhGjUqFHauHGjZs6cqRdeeEHjx49357jrrrs0d+5cTZkyRZs2bdIjjzyiVatWaezYsbX9lgAAANQqjlfxMYZhqE/DCDUMD9bnO/NU7OSM33WV086PV11gdOkh+4ChnKEIAWnVqlXq37+/+3p5ETJixAjNmDFD999/vwoKCnTzzTcrOztb55xzjubOnavQ0FD3fd59912NHTtW559/vmw2m4YOHaoXX3zRfXtMTIy++uorjRkzRt26dVN8fLwmTpyom2++2T3m7LPP1nvvvaeHH35Yf/3rX9W6dWt98skn6tChQy28CwAAANYxTNPkU7uPOlzs1Ee/5Wp/kdPqKKjEpVnr1Pr7OVbHCFz2INkvvly2bmdZnQRADcjNzVVMTIxycnJY3+V3T609YHUE+IAHz4y3OgJ8QOmj91gdAXVc8KQpVkeoU6qzX8LhRT4szmHX9WfEql2cw+ooqASHF1kott7R9VsoXAAAAABYiOMffFywzdClzaLUMDxIi/YWyGV1ILiVcfYiSxit28p++TUywsKtjgIAAAAgwFG6+IkeCWFqGB6kz3fmKaeE6qUuoHSpZYYhW98LZTv3AhmGYXUaAAAAAODwIn/SKDJYN7aJVXsON6oTKF1qUWw92W+4Xfa+F1K4AAAAAKgzmOniZxx2my5pFqWWMSGatzufsxtZqJQ1XWqF0aWH7BcNluEIPfFgAAAAAKhFlC5+ql2cQ6dFBOmLnXnanV9mdZyAxEwXLwuPkH3QlbK17Wh1EgAAAACoFKWLH4sJseuaVjH6PrNQ32UcEZNealcZM128xmjVRvbLhsmI5LSxAAAAAOouShc/ZxiGUpLC1TomRHN352tPAbNeaguHF3mBI1S21EGyd0+xOgkAAAAAnBClS4CIDwvSta1jtPZAkb5JP6JiF9NevK2Uw4tqlNG+89G1W5jdAgAAAMBHULoEEMMw1LVBmFrHhOirPQXaklNidSS/xkyXGhITJ/vAobK1bmt1EgAAAACoFkqXABQVYtfQFtHanF2s+bsLlF/msjqSXyoxOCP7KTFssp3VR7Z+aTJCOA06AAAAAN9D6RLAzoh1qFlUsJZlFGrV/kIW2q1hZQY/XifLaNRU9gFDZDRsZHUUAAAAADhpfCoMcA67Tf1Pi1CX+FAt2lugXznkqMYw0+UkxNaT/fwBsnU40+okAAAAAHDKKF0gSYpz2DWkRbR25pVowd4CZRU6rY7k80pYSLfqHKGynXO+bGedKyOIX0sAAAAA/AOfbuChaVSIRp4RrB8PFmvJvgIVlHHM0ckqEaXLCRk22br2kq3/RTIiIq1OAwAAAAA1itIFFRiGoc7xoWob59Dq/YX6IatQRSz4Um3FnL3oOAwZbTrI3v8iGQlJVocBAAAAAK+gdMExhdgNpSSFq2uDUK3aX6SVlC/VYxiSzSa5ODvU//xetvS9UEZSstVhAAAAAMCrKF1wQg67Tb2TwtWtQahWZRVp5f5CFVO+VIlpD5LhYnFiyhYAAAAAgYjSBVUWarfpnIbh6t4gVCv3F2r1/iJmvpxIUJBUGsiliyGjTfvfy5bTrA4DAAAAALWK0gXVFhpkU5+GETorMVwbDh6d+XK4mENoKmUP0B+x4BDZOneXrVcfGfEJVqcBAAAAAEsE6CdC1IRgm6GuDcJ0ZnyotuaWaEVWoXbnl1kdq04x7XYZVoeoTVExsvXsLVu3FBlh4VanAQAAAABLUbrglBmGodYxDrWOcSjjSJlWZhXql+xiuTjySKY9MM5gZCQ3lu2sc2W06ywjQF4zAAAAAJwIpQtqVFJ4kC5pFqXzSiO04VCR1h8sCuhDj0x/PrzIESpb+y4yuvaS7bQmVqcBAAAAgDrHjz8RwkoRwTadlRiuXglh2pVfqh8PFuvXnGKVBlj/4o+li9G0pWxn9pTRrpOM4BCr4wAAAABAneV/nwhRpxiGoaZRIWoaFaJiZ4Q2HS7RhkNF2lMQGGu/mDY/OdQmKubowrhn9pRRL97qNAAAAADgEyhdUGscdps6x4eqc3yockuc2pxdos3ZxX5dwLh8eaZLdIxsbTsfndHSuJkMI6CWBAYAAACAU+bDnwjhy6JD7OqREKYeCWHK+72A2ZRdrL0FZfKn9Xd9bqZLTJxsbTsdLVoaNaVoAQAAAIBTQOkCy0WF2NU9IUzdE8KUX+rSlpxibc8t1c68UhX7+CmQXHX9TD6GISO5sYyWZ8ho3VbGaU0oWgAAAACghlC6oE6JDLbpzPgwnRkfJpdpam9Bmbbnlmh7XqkyjvjeLJg6eXhRVLSMlmfI1vIMGS1OlxEeYXUiAAAAAPBLdfATIXCUzTDUODJYjSODda6kwjKXduSVald+qfYWlGp/obPOlzCuunB4UVT00TVZGjeXrUVrGQkNrU4EAAAAAAGB0gU+IyzIprZxDrWNc0iSSpym0o+Uam9BmfYWlCq9oExFzrpVw9T6TBebTUpMlq1R09+LlmYyYuvVbgYAAAAAgCRKF/iwELuhZlEhahYVIkkyTVOHip3KKnRqf2GZ9hcd/W92icuyjE5vznQJC5eR0FBGQpKU0FBGYpKMxGQZIQ7vPScAAAAAoMooXeA3DMNQ/dAg1Q8Ncs+GkY7OiDl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      "text/plain": [
       "<Figure size 1200x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Review class distribution after handling imbalance\n",
    "y_res_series = pd.Series(y_res)\n",
    "class_distribution = y_res_series.value_counts()\n",
    "\n",
    "print(\"Class distribution after handling imbalance:\")\n",
    "print(class_distribution)\n",
    "\n",
    "# Visualize the class distribution after handling imbalance\n",
    "fig, axes = plt.subplots(1, 2, figsize=(12, 5))\n",
    "\n",
    "# Pie Chart\n",
    "class_distribution.plot(kind='pie', autopct='%1.1f%%', startangle=90, colors=['skyblue', 'salmon'], ax=axes[0])\n",
    "axes[0].set_title('Class Distribution After Handling Imbalance')\n",
    "axes[0].set_ylabel('')\n",
    "\n",
    "# Bar Chart\n",
    "class_distribution.plot(kind='bar', color=['skyblue', 'salmon'], ax=axes[1])\n",
    "axes[1].set_title('Class Distribution After Handling Imbalance')\n",
    "axes[1].set_xlabel('Class')\n",
    "axes[1].set_ylabel('Frequency')\n",
    "axes[1].tick_params(axis='x', rotation=0)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Classification Models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Evaluate with different ML models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "logreg_model = LogisticRegression(random_state=42, max_iter=1000)\n",
    "rf_model = RandomForestClassifier(random_state=42)\n",
    "lgbm_model = LGBMClassifier(random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "vectorizer = TfidfVectorizer(max_features=5000)\n",
    "X_test_vectorized = vectorizer.fit_transform(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Function to evaluate the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def fit_and_evaluate_model(model, X_train, y_train, X_test, y_test):\n",
    "    model.fit(X_train, y_train)\n",
    "    y_pred = model.predict(X_test)\n",
    "    y_pred_proba = model.predict_proba(X_test)[:, 1]\n",
    "\n",
    "    accuracy = accuracy_score(y_test, y_pred)\n",
    "    precision = precision_score(y_test, y_pred, zero_division=0)\n",
    "    recall = recall_score(y_test, y_pred, zero_division=0)\n",
    "    f1 = f1_score(y_test, y_pred)\n",
    "    fpr, tpr, _ = roc_curve(y_test, y_pred_proba)\n",
    "    roc_auc = roc_auc_score(y_test, y_pred_proba)\n",
    "\n",
    "    return accuracy, precision, recall, f1, fpr, tpr, roc_auc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fit and evaluate the Logistic Regression model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "logreg_metrics = fit_and_evaluate_model(logreg_model, X_res, y_res, X_test_vectorized, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fit and evaluate the Random Forest model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "rf_metrics = fit_and_evaluate_model(rf_model, X_res, y_res, X_test_vectorized, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fit and evaluate the LGBM model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[LightGBM] [Info] Number of positive: 46428, number of negative: 47220\n",
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 1.314729 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 535047\n",
      "[LightGBM] [Info] Number of data points in the train set: 93648, number of used features: 4990\n",
      "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.495771 -> initscore=-0.016915\n",
      "[LightGBM] [Info] Start training from score -0.016915\n"
     ]
    }
   ],
   "source": [
    "lgbm_metrics = fit_and_evaluate_model(lgbm_model, X_res, y_res, X_test_vectorized, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " Save the metrics for different models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Model</th>\n",
       "      <th>Accuracy</th>\n",
       "      <th>Precision</th>\n",
       "      <th>Recall</th>\n",
       "      <th>F1 Score</th>\n",
       "      <th>ROC AUC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Logistic Regression</td>\n",
       "      <td>0.966698</td>\n",
       "      <td>0.024724</td>\n",
       "      <td>0.025509</td>\n",
       "      <td>0.025111</td>\n",
       "      <td>0.537199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Random Forest</td>\n",
       "      <td>0.962039</td>\n",
       "      <td>0.088694</td>\n",
       "      <td>0.135621</td>\n",
       "      <td>0.107249</td>\n",
       "      <td>0.745807</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>LightGBM</td>\n",
       "      <td>0.964298</td>\n",
       "      <td>0.029945</td>\n",
       "      <td>0.035786</td>\n",
       "      <td>0.032606</td>\n",
       "      <td>0.500042</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Model  Accuracy  Precision    Recall  F1 Score   ROC AUC\n",
       "0  Logistic Regression  0.966698   0.024724  0.025509  0.025111  0.537199\n",
       "1        Random Forest  0.962039   0.088694  0.135621  0.107249  0.745807\n",
       "2             LightGBM  0.964298   0.029945  0.035786  0.032606  0.500042"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics_df = pd.DataFrame({\n",
    "    \"Model\": [\"Logistic Regression\", \"Random Forest\", \"LightGBM\"],\n",
    "    \"Accuracy\": [logreg_metrics[0], rf_metrics[0], lgbm_metrics[0]],\n",
    "    \"Precision\": [logreg_metrics[1], rf_metrics[1], lgbm_metrics[1]],\n",
    "    \"Recall\": [logreg_metrics[2], rf_metrics[2], lgbm_metrics[2]],\n",
    "    \"F1 Score\": [logreg_metrics[3], rf_metrics[3], lgbm_metrics[3]],\n",
    "    \"ROC AUC\": [logreg_metrics[6], rf_metrics[6], lgbm_metrics[6]]\n",
    "})\n",
    "\n",
    "metrics_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Save the results to file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model performance saved to data/e_ml_models_performance.csv\n"
     ]
    }
   ],
   "source": [
    "metrics_filename = os.path.join(data_dir, \"e_ml_models_performance.csv\")\n",
    "metrics_df.to_csv(metrics_filename, index=False)\n",
    "\n",
    "print(f\"Model performance saved to {os.path.relpath(metrics_filename, project_root)}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Comparative analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ROC Curve Review\n",
    "\n",
    "ROC curve sorted by ROC AUC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_roc_curves(models, X_test, y_test):\n",
    "    plt.figure(figsize=(10, 8))\n",
    "\n",
    "    for model_name, model in models.items():\n",
    "        y_pred_proba = model.predict_proba(X_test)[:, 1]\n",
    "        fpr, tpr, _ = roc_curve(y_test, y_pred_proba)\n",
    "        roc_auc = auc(fpr, tpr)\n",
    "        plt.plot(fpr, tpr, label=f'{model_name} (AUC = {roc_auc:.2f})')\n",
    "\n",
    "    plt.plot([0, 1], [0, 1], 'k--', lw=2)\n",
    "    plt.xlim([0.0, 1.0])\n",
    "    plt.ylim([0.0, 1.05])\n",
    "    plt.xlabel('False Positive Rate')\n",
    "    plt.ylabel('True Positive Rate')\n",
    "    plt.title('Receiver Operating Characteristic (ROC) Curve')\n",
    "    plt.legend(loc='lower right')\n",
    "    plt.grid()\n",
    "    plt.show()\n",
    "\n",
    "models = {\n",
    "    \"Logistic Regression\": logreg_model,\n",
    "    \"Random Forest\": rf_model,\n",
    "    \"LightGBM\": lgbm_model\n",
    "}\n",
    "\n",
    "plot_roc_curves(models, X_test_vectorized, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Accuracy Comparison"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6))\n",
    "barplot = sns.barplot(x=\"Model\", y=\"Accuracy\", hue=\"Model\", data=metrics_df, palette=\"viridis\", dodge=False, legend=False)\n",
    "plt.title(\"Accuracy Comparison of Different Models\")\n",
    "plt.xlabel(\"Model\")\n",
    "plt.ylabel(\"Accuracy\")\n",
    "plt.ylim(0, 1)\n",
    "\n",
    "# Add accuracy values inside the bars with some padding\n",
    "for p in barplot.patches:\n",
    "    barplot.annotate(f'{p.get_height() * 100:.2f}%', \n",
    "                     (p.get_x() + p.get_width() / 2., p.get_height()), \n",
    "                     ha='center', va='center', \n",
    "                     xytext=(0, -10),  # Adjusted to move text inside the bars\n",
    "                     textcoords='offset points', \n",
    "                     color='white', weight='bold')\n",
    "\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "accelerator": "GPU",
  "colab": {
   "provenance": []
  },
  "gpuClass": "standard",
  "kernelspec": {
   "display_name": "dtm",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.11"
  },
  "widgets": {
   "application/vnd.jupyter.widget-state+json": {
    "01459f7e5c1f4e9292d94bac5933abcf": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "HBoxModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "HBoxModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "HBoxView",
      "box_style": "",
      "children": [
       "IPY_MODEL_60343a190caf4fa29e13aa0b106a3395",
       "IPY_MODEL_209071bd71254493a3c52020bf46d209",
       "IPY_MODEL_ada55d2b9a63438b9693e99b1aad0064"
      ],
      "layout": "IPY_MODEL_98a0ac38b1b6462c8ea8d1ecd46154c7"
     }
    },
    "058d492b49424a23bd9765d0cba430ea": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
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