reproducibility/RQ2/K-MeansClusterFinal.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.decomposition import PCA\n",
    "from collections import Counter\n",
    "import re\n",
    "from scipy import stats\n",
    "import shap\n",
    "from lightgbm import LGBMClassifier\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from mpl_toolkits.mplot3d import Axes3D  \n",
    "from datetime import timedelta"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Make sure to read in raw data, before pre-processing. Code requires only numerical data frames with no NaN values. Code also assumes features have the same names as found in the appendix of the \"ICER_Final_Report.docx\" appendix for SLURM data, Due to pre-processing package \"datetime\", data must be read in via pandas\n",
    "\n",
    "\n",
    "Example of loading data.\n",
    "\n",
    "df = pd.read_csv(\"DID_FINAL_SLURM_OCT_2023_aggregated.csv\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Pre-Processing with Shams Code\n",
    "slurm=df\n",
    "slurm = slurm[slurm['State'] != 'Cancelled']\n",
    "slurm = slurm[slurm['Start']!= 'Unknown']\n",
    "\n",
    "slurm['Submit'] = pd.to_datetime(slurm['Submit'])\n",
    "slurm['Start'] = pd.to_datetime(slurm['Start'])\n",
    "slurm['End'] = pd.to_datetime(slurm['Start'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def convert_memory(mem_str):\n",
    "    '''\n",
    "    Convert memory units to MegaBytes unit float.\n",
    "    '''\n",
    "    if isinstance(mem_str, (int, float, complex)):   # if its a numeric type zero\n",
    "        return float(mem_str)\n",
    "    elif mem_str.endswith('M'):\n",
    "        return float(mem_str[:-1]) # remove 'M' and convert to float\n",
    "    elif mem_str.endswith('K'):\n",
    "        return float(mem_str[:-1]) / 1000\n",
    "    elif mem_str.endswith('G'):\n",
    "        return float(mem_str[:-1]) * 1e3  # convert MB to KB\n",
    "    elif mem_str.endswith('T'):\n",
    "        return float(mem_str[:-1]) * 1e6 # convert MB to T\n",
    "    else:\n",
    "        return float(mem_str)    # when its a string zero '0'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# First drop rows with NA requested memory\n",
    "slurm= slurm.dropna(subset=['ReqMem'])\n",
    "slurm= slurm.dropna(subset=['MaxRSS'])\n",
    "\n",
    "\n",
    "slurm['ReqMem_MB'] = slurm['ReqMem'].apply(convert_memory)\n",
    "slurm['MaxRSS_MB'] = slurm['MaxRSS'].apply(convert_memory)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function to parse time data columns: TimeLimit and Elapsed\n",
    "def parse_time_string(time_str):\n",
    "    \"\"\"Convert a time string into a timedelta object.\"\"\"\n",
    "    days = 0\n",
    "    if '-' in time_str:\n",
    "        days, time_str = time_str.split('-')\n",
    "        days = int(days)\n",
    "\n",
    "    parts = time_str.split(':')\n",
    "    hours, minutes, seconds = map(int, parts) if len(parts) == 3 else (int(parts[0]), int(parts[1]), 0)\n",
    "    return timedelta(days=days, hours=hours, minutes=minutes, seconds=seconds)\n",
    "\n",
    "# Convert Timelimit and Elapsed to timedeltas\n",
    "slurm['Timelimit_'] = slurm['Timelimit'].apply(parse_time_string)\n",
    "slurm['total_minutes'] = slurm['Elapsed'].apply(parse_time_string)\n",
    "\n",
    "slurm['total_minutes'] = pd.to_timedelta(slurm['total_minutes']).dt.total_seconds() / 60  # Convert Elapsed time to hours\n",
    "slurm['Timelimit_'] = pd.to_timedelta(slurm['Timelimit_']).dt.total_seconds() / 60  # Convert Elapsed time to hours\n",
    "\n",
    "df=slurm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Feature Set to be used in Numerical Clustering models (Anything using PCA)\n",
    "features3=['CPUTimeRAW','ReqCPUS','AllocCPUS','ReqNodes','NNodes','total_minutes','Timelimit_','ReqMem_MB']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After pre-processing, we use principal component anaylsis to reduce the dimesnions of the data frame so we can visualize the clusters. The data is also scaled used a log transformation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>1st feature</th>\n",
       "      <th>2nd feature</th>\n",
       "      <th>3rd feature</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4.496051</td>\n",
       "      <td>0.778034</td>\n",
       "      <td>1.505580</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.457472</td>\n",
       "      <td>0.793370</td>\n",
       "      <td>1.496210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.445893</td>\n",
       "      <td>0.797972</td>\n",
       "      <td>1.493398</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.459311</td>\n",
       "      <td>0.792638</td>\n",
       "      <td>1.496657</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4.461650</td>\n",
       "      <td>0.791709</td>\n",
       "      <td>1.497225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1449977</th>\n",
       "      <td>-0.339319</td>\n",
       "      <td>2.302176</td>\n",
       "      <td>-0.450351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1449978</th>\n",
       "      <td>0.566273</td>\n",
       "      <td>0.290256</td>\n",
       "      <td>0.546012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1449979</th>\n",
       "      <td>2.818491</td>\n",
       "      <td>0.375392</td>\n",
       "      <td>0.140060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1449980</th>\n",
       "      <td>2.807200</td>\n",
       "      <td>0.379880</td>\n",
       "      <td>0.137318</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1449981</th>\n",
       "      <td>2.789742</td>\n",
       "      <td>0.386820</td>\n",
       "      <td>0.133077</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1449982 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         1st feature  2nd feature  3rd feature\n",
       "0           4.496051     0.778034     1.505580\n",
       "1           4.457472     0.793370     1.496210\n",
       "2           4.445893     0.797972     1.493398\n",
       "3           4.459311     0.792638     1.496657\n",
       "4           4.461650     0.791709     1.497225\n",
       "...              ...          ...          ...\n",
       "1449977    -0.339319     2.302176    -0.450351\n",
       "1449978     0.566273     0.290256     0.546012\n",
       "1449979     2.818491     0.375392     0.140060\n",
       "1449980     2.807200     0.379880     0.137318\n",
       "1449981     2.789742     0.386820     0.133077\n",
       "\n",
       "[1449982 rows x 3 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca3=PCA(3)\n",
    "\n",
    "df_pca3=df[features3]\n",
    "df_pca3=np.log10(df_pca3)\n",
    "df_pca3 = pca3.fit_transform(df_pca3)\n",
    "\n",
    "\n",
    "pca2=PCA(2)\n",
    "\n",
    "df_pca2=df[features3]\n",
    "df_pca2=np.log10(df_pca2)\n",
    "df_pca2 = pca2.fit_transform(df_pca2)\n",
    "\n",
    "\n",
    "df_pca3 = pd.DataFrame(df_pca3, columns=['1st feature', \n",
    "                                          '2nd feature',\n",
    "                                          '3rd feature'])\n",
    "df_pca2 = pd.DataFrame(df_pca2, columns=['1st feature', \n",
    "                                          '2nd feature'])\n",
    "df_pca3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The next few cells use the K-Means algorithm to cluster the data, and then provide visuals for the data frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "kmeans = KMeans(n_clusters=3,algorithm='auto',init='k-means++',max_iter=300)\n",
    "kmeans.fit_predict(df_pca2)\n",
    "labels = kmeans.labels_\n",
    "df_pca2['Cluster'] = labels\n",
    "sns.scatterplot(x=df_pca2['1st feature'], y=df_pca2['2nd feature'], hue='Cluster', data=df_pca2, palette='viridis')\n",
    "plt.title('K-Means Clustering 2D')\n",
    "plt.savefig('KMEANS_Clustering_2D_Simple.png', dpi=200) \n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Explained Variance for Each Principal Component (PC=2):\n",
      "PC1: 67.17%\n",
      "PC2: 13.03%\n",
      "Loadings for PC2:\n",
      "                    PC1       PC2\n",
      "CPUTimeRAW     0.691185 -0.046488\n",
      "ReqCPUS        0.100829  0.416695\n",
      "AllocCPUS      0.100500  0.416593\n",
      "ReqNodes       0.003097  0.009174\n",
      "NNodes         0.006017  0.017755\n",
      "total_minutes  0.590685 -0.463080\n",
      "Timelimit_     0.389598  0.533560\n",
      "ReqMem_MB      0.035431  0.388748\n",
      "   Cluster Label 2D  Percentage (%)\n",
      "0                 1       19.719141\n",
      "1                 0       39.987186\n",
      "2                 2       40.293673\n"
     ]
    }
   ],
   "source": [
    "print(\"Explained Variance for Each Principal Component (PC=2):\")\n",
    "for i, var in enumerate(pca2.explained_variance_ratio_):\n",
    "    print(f\"PC{i + 1}: {var:.2%}\")\n",
    "    \n",
    "    \n",
    "loadings_df = pd.DataFrame(pca2.components_.T, columns=[f'PC{i+1}' for i in range(pca2.n_components_)], index=df[features3].columns)\n",
    "\n",
    "print(\"Loadings for PC2:\")\n",
    "print(loadings_df[['PC1', 'PC2',]])\n",
    "\n",
    "\n",
    "cluster_counts = Counter(labels)\n",
    "percentages = {cluster: count / len(df) * 100 for cluster, count in cluster_counts.items()}\n",
    "table_data = pd.DataFrame(list(percentages.items()), columns=['Cluster Label 2D', 'Percentage (%)'])\n",
    "print(table_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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AS0S2DKfPnXKG0UgSxiSLRFcdSSBTJG0NlaO9vZ2Ojg66urqwWCxkZWWRlZVFcXExNpvNDOeO1bEh/Llpmkbj1mbcPR5yirPIys+Mqk/QbT1JSUn9yG0wGAZDn8/H119/bXotfD4fLperF9n4/X7zXC0Wy6CqlHGvA4FAXL6ld3WJQymVJCJe9ACwDcAdSql0EekGLgTWA39VSllEJCoDacyJoy9JuFwuuru7ycnZ4TIeTJKI1lMRjcQxWFsRwe1209HRgdPpZNWqVYiIqXKMGzeOjIyMXoS2ffv2nWY/WPfZJv5xw0vUbWkkMz+DsinFLLn4CCbtMTRpjRSGgdD45OXlRTQ5BlKlnE6nqVK53W46Ozt7Xce+huKB7Dd9DcWjIXFEC7/fH08SR6lS6gBgr+DfAOlKKTewAFg+3I5jfoahBjzQxfS6ujry8vIi2j9a9WO4qorP5zPtEh0dHbhcLpKTk8nKysJutzNnzhzS09OH7LOv5DQaaNzazCv3vElLXRu5xdk4u1y0NXTw1qPvc97Np2O1jrysSiCg8e3yDTRubSa3OJtZB87AnjSyx8NiseBwOHA4HGF/b2pqoru7m4kTJyIivFa5kYbubg4bX0GhI7UX2Xi9Xnp6egY1FPt8PpKSkujq6hpQjbLb7VF5XqIlokAgEE/EEQDcQCfQAXjQg8KswK+BjwGilTZgFIgj1GgJRO0ei9YWEQlxaJqG0+mko6OD7u5uurq6sFqtZGZmkp2dTUlJCSkpKeaxOzo6YnrzR/oWbKpuwe/zY7XbUBZFSnoyPe09eF3Z+Nw+rGnhJ2akEBHef+Zjvnl/LY5UBx6nl23rajnmosNjQkpDYUNTI6f/+2U8QRK+64uV/GH/RRw3bXrEfQQCAbZs2YLdbicjI2NAQ3Go7SbUKB2OaIygQUP9iOQ+xpPaJSLbge1KqVfRycKNnuCmAUpEhg7HHQCjQhyhMNyN0WAkqooRJWoYMDs7O82babPZwqocw0U0hDAcsVcLaNRvaaSrtQerzYpS4PP4EU1DCwh5ZTk4UiO3VQwEV5ebNR+up2B8vnm/qlZvp62unfzy3BH3PxhaPW5+9dlHJmkACPCHj/8bFXEYhuLU1FRycyMb81CG4u7ublwuF1999VVYQ3HfT0tLC1VVVdjtdpqbm8nJyYkXEskBzgMOAuyAHyhWSl0uIu/EhY0jHKKZNNEQjVKKQCBAc3OzSRQul4uUlBSys7MpKChgypQp2O12Ghoa6O7u7lUTY7B+Ix1DpLkq0SLg13jz7/+lYVMTAM4OJ1n5mTRvb0ETYe6hszjmosOj6nv7xjq+/XA9E/cYx5R5E3eEVWvSa5y6/YmwalisI0drXU463e5+v/mCdrLRJOehDMXNzc10dnYyadKkXscwDMV9P5WVlXz++ee0t7ezYsUKfvKTn3D88cdHNaZQKKWswEqgRkSWDGf/YPLan9D5+Ep0dcUKOIDK4DmNvarSF8MJyx6ovaZpdHV1mSRhxEwYno7S0lKSk5MHfNhibcgcrYfa4/Sw5r31bFq1jYmzx+kT2WKhoCKX0393IlmFmeQWZw95fFe3i/uvfJIPn1+O5r2312/zjpjDb567AqUUqVkpTJo7nsovqkjLSsXV6aJwfD65Jdlh+42lATLJYiXd4cDlcvXabh9G2DjEdmzh7lmooTglJaXXbxUVFdTX15ORkcGFF14YiyFcDnyLXk9jJHADr4vIVyMfko64Iw6jfV+Vo6OjA03TyMjIICsriwkTJpgxFDNmzIjpOGItcUSDpu0tvPSXN1i7Yj3dzU7sNhsVM8tIy0oBkYi8KCLC47/7Jy/e+fqAbb546xvefGQZR5y1CIvFwuIfL2JlcTb1mxvJnT+RvY6Zh80+uo+HiDApI5NjJ03hsbWr8QevpQJ+t/+Bw+pvZ0eO9oXf7+8VyDeC45YDx6BLCz8fZjeGJLEcWKyU6kHPkjXQEHTXRo24IQ6fz0d7ezttbW00NzezYcMGUlJSyMrKorCw0FQ5QtHW1jYqAWBjiTceeg9np4uedhc9HU6+XvYtAjhSHEzdc+KQ+wN8+urnvPiXgUnDPNaD7+Lz+DnynEOwO+zsu3TBCEc/NLx+P180NCAIZRYrSVYrF85fyH6l5Syr2Y4FxSnTZ1KWnT3qYxkKYxzHcQfwS2AkVaIs6J6VfYAlwMHoWbF+oCK4bVlc2jjC2SwMlcOQJLq7u7FarWRlZeFwOMjLy4soizVaxFriGGlOS18EAhpt9e1Ub6zDkWLHXpRFR2MnG1du5qjzDmWvY+YP2Yff5+e9pz7SNdohUDKlmMovtvL1srXsuXj3iMY4ErQ6nZzy8gs0u3W1JCcpiVsX7Mt4m42F5RUsLK8YUf/flchRpdQSoFFEVimlDh5uPyISUEopEbkEuGSQdvFn4zASuGpra82YCUPlyM7OZsKECaSnp5tejsrKyojDlkdL/YgGsVRprFYLOcVZbFhRiSMjCVuqDXuSjZLJRez1vbkkJQ8tAv/32U9orm4dsl1+RS7jZpTR2dJF0/aWiM4Bep9Ht9eLApZtr+I/W7aggMPGT+ToSZOxh7x1161bx/mffkSnpsddpAZ/a/N4+PPqL3hq2rSIj78zMYYSx/7AcUqp76FLCJlKqSdE5PRoOxIRUUolA6cDJ6KrPduAo4H/ikjjcAY4KgFgRvq44eXw+Xy43W4KCwuZOnXqoIw8GnEc0SLWEkc0OPqCw/l62Vo627tJSU2mYkYZjpQkMvOHllh9Xh+f/vtz8spyaKpppbulu1+brIIMymeUstt+0xAEV7eHgorIgvMMdHo9/Gn5h3zb0kKX14OIkGKz4/L7+bqpgW3tbRw1eQrvbN3CX7/8vN/+zkCAdLsdFQhQ18coOhLEQ+RouIpmwzjur4BfAQQljiuHQxohKsif0Wtv5AP5IvKtUuoCoBFoDEolUZ1szImjtbWV1tZWMzNUKcXXX3/dy6U1GEYzcnQ07CEjIhgJgCgI+T2/NIef/e1CHrn2GexWG8lpDg477YCweSl+n59Vb35N49ZmyqYUo4nGtrW1JKclkV2QSXZBBgJM2r+My/58ATabDZ/Hxxt/e4+q1dsBmDJ/ArsfvNuAY9c0jedeX86T29bjtivGpTgodnaxtrmZHIeDVpeTHr+fDu8OG9uDq7/iwdWDG/A1TUMDCpJTBm0XDeLFOBonsRug25kBpgE3AtPZsXqjH93+MSzEnDiKi4vJz883v4cmPUWCsch4DYdYSzK9+vO8hc11FzbpRFQZPvslaPYdhsnSKcUs/eViUu2pFJcVkZLRf3L5vH5+WHBBv+1T95qIp8eLPcmKs9PNgqN2Z9bxk00pz+6wc8zFh9MZlEay8jMGvTZvvL6Cv25Zg8WqsLqFNW433wbcaI1OqlMt4LCCJfrJ6goEyLDbuWr26NtWhovhqiqx8KqEjGEZsGy4uwf/b0GvbJ4HiFIqCT30fNjiXtwFgEXbfrTaRoJhvd28n2F1/gnwAjYsUgm+O/Fa/4hYdrha7Q4bOYXZ/Ujjrkv+xntPfDxg9xs/28KiH+1H45ZGWmrbqdvSzMSO8l5tLBYL2QWRhQb89+sN+NIDSJ0LLaBLCT6rIOkWSAq+WUV6SU2R4NjJUzlr3ESSYxjaHw/G0XjKVQkxev4GuBY9se1y4BF0CeTLYLv4qwAWbch5vKgqoxU5avEtA3woUhEUgsIiDSituhdxhMMHz38yKGkYqFlXzabPtwHw+X++4ot3vmbqx1OomF5GzcY66rc0kpqRwpQ9Jw2YyOZxeVn2j4/58t9f4jl2HJaApsu4NoXF6SeQldSbMKIkj4UlpbR5PWTHOEkw1sQRbWpCnKkqAIhec+NMpdQCoBi4RETqh9htUMRNHMdw2o+1WjMcW4iQjEKhq5e24P9JoIY2fr79yAcRHavyS500lEUhmiB+4abT7uEnfzmLtx79L1ihJlVIW/UVJ5+8iJmFhYC+4tvRLz+PB8jsAFXZgkzMwNrsxl+QjCa67Jv6eRNdBxTvkDiiRH5KCr//8H1TwT5gYxl3HnE0NV1dBESjJD2DlGG8tUdVvYwQcVrIxwZMQo9A9QK7K6X2BN4TEedw+ow7VWW0PCXRjiP2kaMa+NeDdSKQibDDBeq3HoxmmTlk39nFEUYeh+xmkEd7UwfvPP4hWcWZLCvwsDnJi+Zv45O33+Tyffbh0y++4IXOHYt6dWZoMEGR+Y0iqdVD+tetaElW7K1umo8bN2zSWFhYxOdNjb2sch/W1vDzd94k0+HAohTZycmcPWcuOUNUNut32gnjaC+EeEuuAn4MbAW6gj/noqsq8Ukc0V74aGpcjLV0Evm5CVmO97F6qhCxILapoE3HLymIbR8C9sNB9b4VoX173B4+eO5TckpzdiRFD3a04GkayWsAznYXK//zFblHT2VDgR1Lo4eAN0Bnp4cbPO/T7g+AReutbmRnY3XV4s+2k7qxi6RmN/U/nIiWk2wMMnTEg44p025n79JyNjQ3EQhzHz6rqSY/NY3ani4E+HflRv686FDml5QNfrKjiOEaR+NI4lDor5ETgSNFpCpWHcfNGRoYazIwMGKJQwTl+wDlfZNsSzOSXI9oC8CWC+JDVDNe6y+wWFMH7b+9qYtfHf5Hmqp3SChZBRl0NHUNsld4dLV0U/dVFZ5xFdhceikGq1/odHkgSYEK0eeVAhE8Rckor4bFG0AUeCYZKlWf6znE5Q0IfLR1y4CvN5dobO/ZcU5NLhfnvPEal8zdk/Pn7RnR+SWMo/1gPJwbgHKlVC07Xj2axFM9jpFiNKNBd2aUqfK+hcV9D4iLZNUENj/KX4PIHMQ6H4Wgq5uDE8fj/+8fNNW0YHfYsVgVXqcXj9MbkeQRDsmVHaBVoCVZwKvhSQLV4wO7vb+BUym85WlkvV+HpcfP5l9NgfzkPgZRy5CkAeDy+6IergD3f/0F5+4xL2bLcUZ1/GESR7yoKiFoQF9H5QP0JSEFvYTgFSLSPpwO45I4Rqvf0XDHDtSn8r4OWFDSgWABpRD8qMBqBA2vNo+OngAOh5OkpKQBK0zVVzWiUKYwEAhoBHo8wx6zzamR968tNPxoPCQ7wOul9KGt1C4dD+PTepOHx0NSjRNri4ct502F/OzenUXhcRiu78Svabywbg3XfaqXx7QCTx99HNOLi8O2H+vI0XiSOELcrG8B76EX8bGh34409HT7YWHUK4ANZ/+xzj+JzcNnqJd+wIZIACUaGh48nioauqbS6mrA5wvg9XrN+plKKZKSknC73Xi9XvLGZcMKCPgC+Lz+iJLXhkLDedPAeCva7dReOoPcN2pp9QRgcppOHNvacKSk4ZydjXNOzg6pwrg2OynbWMAkDdB9UCe//jJfnd0/+E1E2O8fT5rfk4AVYdpFg13ZOGpARP6tlMpB96zYgI0iMnRC0yAY/YKSw8BYx2ZEOobBHio3h+HzeQkEBBE3EEDDjrKkkpR2IBUlPcyYYmPOnDnsueee7LXXXkyfNJ3PHl/NK9e/x+bl27EpO9+/+mhKZxThc/tHsETwDlReOFEnDaV2fCwWWr9XDlMyIKCwdPhRORl4ipL1qFBLSNudjJLUtLDbHw6TA3Pait4xLl5g70ceHPaxvwPGUcDMd1mOnqr/B+AbpdTJagRvyPg6Q+IjqGs4AWAul8vM0+ns7CQ5uYiKwpPJT38eu7YNUFgsAUSVoqzFEKgFz3qs/mUgGts21HL5odVoQXPV5298zau3vMuFd5xB9draiMYSEcaFiRcxnh+/rqZoGXZdJ0ANYL8YRmUuhicspQ4Qvu2L8P64RyCRjlVafawQ4o69AzhBRL4Nbk9Ht3e8BbQN3MPAiI8zDEE8qCqRwOVy0dzcTHNzMx999BHJycnk5ubuKIZMBxbn6yi/g4CqAK0VKxmg7BDYjPIvI4WXzTSj606bjuZPIlQIdHW5uePc6N6YRtzGgNjQBHsMUPfCZkgWwf0HmjPDeE8lW234tIBZ5SsSLB43gUXl47nm4/f7/XbGjIET82KFXV3iCLFx+IB20IPBRKRbKSXEU5LbSDHWqfIDte0vUSSTnJxMeno6u+++e2+rvwSwOB9F+ZejL2mRhTeQT6rNh+AKbvf2mn/tzVaGNSNDx21hUNLQrJBqSR844sdqGjIGP9AwclN8AT9pdjsdPl9E7SvS0slLTeOPn3zQT1p54Mjv9av3ORrY1Y2jIXgbuF0p9SzQo5Q6GfiaeDKOjhTxEsfh8XioqanpRRS9JAqLhdbWVhoaGvq5CpX7A5T/GyAZJWChFYvKQlQeYq1Aae/0mprffJKGaBb06RHdOH9w9bFs+Xo7X723WreDDIKeWbl4y1L1oArbCO0VUQ41ABGTBsD2nm6eXrfG/G5B155EKfYo7O9Rueg/rw2oDl397tvcdOjhkQ82BN8R4+ivlFKnAyeh24z/C9wvw6w3Cv9DxAGDv0FCJYqmpiaSk5MpKSkZdB2Wfg9VzxNYvbehS4YKyEFUPkI3VtWFWKYhSaegfCsR3CigrcnKrVeUk53vo6fTirNLEc2M/OzVL2hr7IjIzK2lWhEBe60TX3nasNLhxwpa8IMIz3++kvHZ2UzMyePlqs3cN0Ttj3e3Vw3rmN+BADAAlFIW4HVgbXDTppGQBsQhccDO8aqEUz0MicLhcJCTk0NBQUHkY/V8itX7Z0CC014QWkGK0SimqXsuhXlXg9SiJf0Qi/cufD7h6TuL6GixkZwiJDkgp8hHzWYbaJERyNY11fr5hSEODfDnOcCisDa76ZqeTSAniUB2Ejj9umqSbB2GtyR6woml8vnw+m/xaxoOoF6LQE0Xobm52VxDJSkpKeKAsu9IANgP0KuAbUZ/LGYofTGmfw63w7gjjlissBYOHo8Ht9vNN998M6DqYaCxsTE6/VacWJx/DUaD7oACRHXilcNpdR9CUeBrlPef4OtBYze2bvbS2iRYbQplBasFWupU8NUaZYh8H1et0YVzcjrth5ejWRWSlaTPYAuQFne3PmI0+SNXeQAOKSqjo6MDr9drrkNr5ENZrVaTTOx2e6+/jfVpoyGQWBfyiRFuBPYUkWYApVQh8BEQ/8QRzcWPhd2ir0SRlJREIBCI+RKQIj4s7keALf3UfgE06yyc/tPRpBHV8wzIWhSbdZLxJZOSWsKsvTx89bEDd0+ExskIIHYIpNtJXd9Jy6Ea5KeABsqvIUpBUmSh4iFnu8OnugtoOBbAZrGwdOpUfrPfogHbBQJ6AF4oqTidTrxeL52dnf0XiupDLqFSTE9PT1yqKkA14FZK2dHfKR1A5BWqw2CnRI7ujALEg6keGRkZ+P1+vvrqq5gvAZlkbQWtFkUqKpxL3N9NLseTX9Rtyuv61bCSX+qhp9PLtyvTcfcYxtEdLYYLzWGh9fBSOmdnQ17KDkOoVSF+FXS7RtGhUvrQBHYF5njsyGPYozSyrFqr1UpKSkpYL00gEKC8vJyMDD32RURMcgklG4NobrvtNrZu3cr++++PUorPPvtsWCSilKoAHgOK0C/4AyJyZ9Qd7cC3wGvAs8Hv3wc+V0r9AD3Z7floO4w7iSPSSWsQhcvlCh9H0dfTMUqFfLTghBIKgdp+6oqFj/Rp1qfLz5c5+OOFE3F1W4Jp8KETcmSTs3OPXDr3yQNjFfvQpDSHZUfefaSQPuMJ/d53mKFdjxG/REoaQ6HvM2ukAwy01uyDDz7IQQcdxIoVK0YqdfiBX4jI50qpDGCVUuotEVk71I59ETSM+tHXoJ2FflfWo8/9o9EdXvFJHNEuJB2u7UASRVJSEvvuu2/MsycHbRuoRQW2YhMNfyATsU1GiRf8OQht7JDpJezcEQ3uvLoCj0shMtDsip48dGNoEm2HFvcnDf1LmG2RYqDyAfTmu4F+20k4pHxczPoajnF0OOUGw/RRB9QF/+5SSn0LlLHDKxJNXxrwk2B4uSHWynDqjIZip0ockcAgjqFUD+Pm1NXVRXSjYhbz4V+HxfMECCSLm8L0LCTp52D5ArEtAM2NWDOwuJ5B8S3hJlxHi+4WtTk0Av7BvCeRzzwNaCpNovu4cVCYOgA5xL+aMRLslprGrYceEbP+hlvfI5YZukqpCcA84NMR9LEPcC56vVELYFFKOUXkxOH2GTcSh0EUDQ0NtLa24vP5BlU9RhsDjdfifRVUFljS0PweHLYNoFUhSQf0buhbhvKvoy9xBHzQ1WHl6NOaePSmSETqyGT/7mkZdJ81FVJsOouE8wiOQZLaaCPFauWTM88FYOXKlWOeVh9j0khHVyOuEJHOYfaRBjyNvsBTJfpDNMxqLjuwU4gj0hDu3NxcSkpKUEoxb968UR/DsNqKC5Re+1NXSCwofIjWgfK+iZImxDIRNBC0HVJ8MEo7EIAkh0Z6xnAMoeElhu7SJJounKn/pFQwxDL6sPDoEKb/vqGbO4Gnsh2OHSOKg5qjsUqXCHpAngeeFJEXRtCVBT2N/pmYDCyInUYcLpeLpqamIVUPp9NJQ0PDqIwhFjdVbPNQ/o+AQpR0ImJFVAEW999B2kBloNxPorT1fY4f3F9TeD0W/BrDTxntg6azpu8gjb4H3NnYyYf9v733H7W+R4OIIkHQHvEQ8K2I3DbC7pzAU0qp24FX0A2lGuARkRXD7XTUiCNUomhra2P9+vUUFBREpHqMVcargcEeFkk6EpQV5V+NprJp6NqXbLwgrWAp0fcXL4qB80Y2fu1g8+pkrDaNgM/CSKUOa6cPLdsx7MrjI4LpEWKnk1WO1cbBEyb22jbWNUdjhP2BM9DrZnwZ3PZrEfn3MPpyAIcAewO7oT88SUAzcFJI6n1UiDlxBAIBPv74YxwOhylR+Hw+Zs6cGVFG45hmx4oT/OvISXodi9UK/uPA1id9W9mRpKORpKPxaN14/Jsxq7EF1QNRyabL03jsmuttvPBgPh/9O5Omage9C7kP32jpzbKS8XkT2rftdO2ZR6A0fSeoKUH0vZ6Gm3YnzbVlZ54zqv2PFXGIyIfE7ipmo0eNzhjgWMOabDEnDqvVyr777tvrgsfCHTtSDPkA+Ndhcf0N5V9OjsOHJmlYez4ikPo7CFnXtS9EBCyliHUqyvstBDpQ1GGUOhCgvcXKbT+rYMs6B21N9iBPqGGqKTvOQw8rt4LNFsxDSd55pDEgdo7n5tUTTh71Y0T7HBrtx0hKGQg+YINSahx6oWIN/eEMxF2V83AXbjTWSokZxInF8yxoesKYRipKBUDA6rwTLfkHYJmA2Ob0npSan8K0f6HaL8ZC7+UKQq/A+lWp1FcnkZGt0dYU/DXqU+x9TTWga14eXfsVgsuPyrTrHpX/AVw8Zy4V2dk75VhxRgLDgQZMAFagB4Fp6OpLNXBO3Kgq4S50NK7UsSGOLgiSrzK9VYFghfIelP9LFMvRtGqwloD4ETWO9MBZZOXUBPcbGMkpGo7kAFYrWKwSLA8YVcx3r28a4MtNwp/vwJfvQHOk6lmu/wN44pilzCks2inHGkMbRyzRBVwAeNArmxsioQfiSFUJh7goByjd5Gd8iqXrOf2rfW/EsRRUCqhgST9LKaJtR6EXFxY0RC0ASz6i9WBxP4hYpwMWVKAGRU1EKv20uU7mH9TNJ//JJD3TT2ernaH9lgP3qgBrt4+0Vc148xw49yqIoxiN0R3HziIN+G4Qh4h4lVJfAXPQq5x3AZ+KSONI+v3fIA6tFUvPzUwtehflB4Ud0dajaU1IyiWgUtEcp2LxPINYpiOBSjTJwWLNBNssfVxaA4gHLGW6V8X3rb59gEMeVQowGWjkilvsNNUkkVfip7MtmR257YPZA/peA72dPxnaDyyhZ1Yu/hQr5CWPIWmECdzYtedZL3wXiEMplQr8GjgFPdGtHLheKXWpiLw/3H7jkjhifnzvMpS2CV0RyQB8KM2PxfcZmiUX8INlGlrKzwAX1TUdiAjj014FaQRJ0/+3FOjGSPHp+4RBdzecNG0WO0pyTeCOKwOcekUL9iRh63oHFotCLBrijyYaVnBVpFJ76SywW4xNY1vFK1z23ncIYx0WECNMAQ4VkSnGBqXUvsANwMFKKUswnyUq7JQ47jGXOKQTxAa9CjsHQOpQ/m9QgVos3pdR/g+C5JCMJkloyecg1rmIykNLOhqxlIC3HeX5G1DV/zDASdNmoF9WFfKx8vQdqbQ1KzparWiaCpKGmaseEVzl6WS/Xw+Nbl3KGOs5u5Pn1R0HD69u6Eiwq0sc6E+JG0AplRyyLbpqSH0wal6V0Mkfy9qgw4J1GshLWJQPoT0oYKeDKgZLhR57Ieko38eI/cgd+1mykOTv7/jeVoLiMvNr30dK/24L8wtAOp/3EwwjfygFRdbyJqpmZcFhJXHgdjVCYRl1ArOhuOeII9knhpmvkSIa4tA0bafnVEWA7eju2JeA54JJc4cAxpJ38WschdF1xw6tizrAmo3b3UWa1YNgQ2zzUcoRMvmCdof2dyi1/xyrAyxtxi/ZKH1ZCn2Mg47GiCUPTyvDhQDtBxdhObwUbYxWVes/otiPwaoUszIymV5URIYjhbPm7EF2cvLQO8YB4q3eaFANaVVKXYZed3QB+gJMV4vISohzr0q07tiYQtNA24hICh5fPqnphYglD1SuXi0jUAs4UNKC5m/DygtYg1fFGEkoaQwKgV/fW8X1F08mlnJ8525ZNJ02BZTC0u6B1DiqaRnj23XDAYs4csq02Ha6kxBPizEFs2IXKqXaReRL4Cn0nJUUYG+lVJmI1Ay3/51m44hU4hhO3wOSptaFxXkVFtdfUdrHpCVXgb8a5V+FWHLRUs5B7AeD0hB/PRY+N3PFIuIvCfk/+PdBS51Mml8HQ7pbI0PnjEyazpuhB3el2NCKB6q1sbMxOjaWucWlse90JyHOJI7ZwOXARsBInENEXEAFcE1w+7A4YKcRx2j2PWDtDNf9KP8aFEkokkmyutHrtDpAFUJnFzh/hvL+E8WasH30g9DfpqnA6YTLj53EyXNmkpqUQm/j6PDRfEIwicu4ht9h0gA4/rmYZn/vVMTZYkzpgE9EepS+7KOEkEQtekUxGOadjDt3bCz7Vto6PbiLDpA0/Jpgs2YhlqlAEhbt8KGndriuVe/fXU44afpsNE3/YXVL9nBOJfzhk/26IVREH4t1rIxvKuyfsYZzZPVlxhTxpKoAyejkgZGTEuJ2TWOEQsMuTxyDQVRJMOEsE2hAEUBIAksK4m9myIT2CLWNO64qDpJGbGdU7W5JpGz34arwQWawYM2Ye1NGH19++SUOh4OkpCQcDkevvyNdTGksEGeqyhagVenrxL6Anp/iAsYDhwMfBtvFr3F0rCQOLeUnWHuuRqQFlAOPL5Xk9OMRx6Hg/nhHw4GGFuH8/L+76vnlnfVoGhw7AWD3KM5gYGS4kmiqSId0+w7CGDPS2Dkp8zlJycycOROv14vH48Hj8dDd3Y3H4zGXJNA0DaUUdru9F6n4fD46OjrGjGDiSeIQkbVKqYeA24HTgQ1AAbAn8B8RuSnYblgi3k4jjp1iHO16Hfy/NNPUADSKkORfgdLobnuUFO/r+HxN1HUsZnyq0YfRWfD/YXCcxaL38+v7s7j+wjYgZ9jnBOCxKxqXjod0W5ys8Tr6pFGUksqbp5wOgMPhMNczCQdjjRODXAxCqa+v7/XdIJiBpBeHwxEzgokniSOY9fqeUmo+sBg9Q3YV8FsR2TbS/ncacYxm36JpqLZTsbB6x/bg/xYaEPevQbNSkNGD8oODLymyL0NZHIBnxBEJKhgE2tKkePmRXKbuYWHjVyPoEKj+5e5jnIcyuiixJ3H9brszffp00tLSot4/dI0Tg2BqamqYPn16r3ahBGNIMT09PbS1tfUiGMDszyAYQ4KJlGDiaRU3Iz4jKFG8Eev+d1rk6GhKHMr7CRZW95/8EiQEcaK/McVcnDnFsSF2L1CB1gY478CZuHtG/sZxpdsgyxZfpBHjoVy2z344PCOKeo4IQy2iZCAcwQwkwdhstn7Si1KKzZs3fxdC1CPCLi9xAFi0b/Q/BvKA6Oyhp6qEbI4FOtssPHtXEc8/kA1iHXHPAQW1V8yAOHlzAaNCYNmOZMTtjZuJFo5gBpNgQm0wTqeTmpoabrnlFjZt2sT8+fM59thjufbaa4c7lqOAO9Hr1f9NRG4c/pmNDnZpG4fb7cbtdlNZk8xuxSCGGh7yLA5nEYJI4XLBybPHo3u+oi06HB4NP5oMuWnxIW2M0hhSrFb2rxjH6o7VQzeOEDvLaxdKMOnp6eb2CRMmcPPNN3PPPffw5JNPDtLDkP1bgXuAI9CrdK1QSr0c7fKPwaUj89EL9gTQ07lD//eKSGDgHgbHLuVV0TSNtrY2mpubaWlpwW63IyLkFR2HZvkvFr7caWuVedxwwuTd6L3ykcZI3OPbp4B3z/zvJGlkWKwELIqK9HQe/95S4LtR7yIUMbJx7AVsEpHNAEqpZ4ClRL/84/HAo0A9uivWeDADQC5wB/DzYHBY1LVH417icLvdNDU10dzcjNPpJDs7m4KCAiZPnozNZuPLL78kNTUDSXkMrX33ftN2tB7LK08rRCeN2B3Be/78mPU1IsR4Mt992BEcOG5iTPvsi3ggoRh5VcrQM1oNVKMvbRAVRORx4PEI2g2rYHHcSRyapuH3+1m/fr0pVeTn5zN16lTS0tLCPhwSjHGIjbIQGf7yXCOBQCNLxscmZkNDUXLXeuou221s1keBmBPGfUd8j33Ly2PaZzwjntyx0CsPJQXIA7KC3wWoF5Hm4fYdF8bRvlKFz+cjJyfHlCqG6nssolKVgiXjJ6CrjMYYhzfxBEUn0PKTKTuqe+3iSLfZhySNWEoJsZY4hvNM+Xy+WKgqNehJaAbKg9uihohowfobvwbOBDrRVZUi4GfAnUop63BsHWOiqmiaRnt7u7kkpM1m6yVVLF++nMLCwoj7Nm5ygFlYWTP6S5gKHFXW174xfOuKAI6iZCyNfrTxYyVux/a45+8xL6b9DYVYvzyGQ0QxsnGsAKYqpSaiE8YpwI+i7SSEEM5Ct49cDBSKyE1KqWuBpmDT+A459/v9bN++fUBbRV9EeuN6tcl5hkDH+Vi0TwCjpE42Ftwo7GhaF8o68inym3Py6G/fGH6vTgs0/GwGJCWNXS5KDA5pB4rSMjhnjz04cfpuQ7aPtZQw1hJHLFQVEfErpS4F/oP+kP1dRCJM3Q4/LPT1YwUoDm5TwLSQv6PGqBFHqFTR0NCAiDBu3LhBbRUGDCki0geh103OerBXfqVoXjTX3Sjvw5GX6ewrsvTBynezBxoJ0d4HV1kaDedN1UlDKfotq7iLIDcpifdO+/GYHT8eVqqPVa5KcI3Y4awT26ub4P/N6Pr0N8AxSqkH0JdJeLpPu6gwKsRRWVlJbW2tKVVkZWXR1dXFxImRWdZjWtzYkoSk/RyL9+GI+hsK7S0W7FYHvn5Bj8N4Q6Fo9fkhLeQttYu6J9859cxh7TfWnpCBMFxVJV6Mo0bymojcZ2xTSv0ePT7kYRFZHtouWowKcUyYMIEJEyaYF765uTkqd2xMiMPzMTjvw8JXqKAMEotn9G/XFaP5wrlhhxBTwqBdgfvqPcY465URH/ursy8Y1n6xtEuMhnF0rCSOWEMpVQTMQzeKfgaMMJNqlIjDZrMRCOww1EZ7A6JpH444VPcdWHwP6X+HbB+p+SAQgJrNySibYLcJPs/wPSCCwn3elF2aNNKBj4ZJGqOB74KNI1Yw1oRVSi0EbkafCp8APwW2KaV+LiJbhtt/XNYcHZGLNdCExfd0+DrjYULRo4HFAtl5frJy/AT84R7SyB/czdngnpqpF1PeRTFS0oi1OzbWGI6qYrfHTSFpY26fDbwqIotE5GoR2RPdo3Ic9Ir1GFbno4rhLnkwrL6llR2LLoXpl+HXH1cKTvt5I3nFfrSA0Vv0qMxR8P/20hPZLJaxM4gOc9J+eNKpw1ZPRgvxoqrEi8QRAsM4SnA5SIAe9OK7w0ZcFisekY3DUgZkhp3SerNsesdfhPwewfGmzHFzzf1bGG7chgAVTgsZ/63doaYY12dnEsgIJtlgBXaiQTyEiQ+EXd04GoJvgYuUUs8Blyql3gLmAuOUUv8PmDWcTuMyV2VkxJFOIO0mrD1XIbSYm4VkWrtmk55zFPbU6Uj3GcMO3yosD6CsGhIIpxANDkGx/Y/zwGrtbXTZWfEbIzzGnBgNI9aIB4kjzgr5GGJ3E/AQ+jKQyehFfYzlBnOJtziOUIymxAFh1JqkhQTs74HWCaTrkxSo3LyKWcWzsNmTIecbU6FRnpdRrv+A/DfC8cGv79/Kn84z3MsRxpug2HLjXH08cfqmHQwW4PLZOzciNFLEA3HEk6oSTKt3AcuAj9D1d42gYD2SlHr4jkgcA/wA1qx+bcP1K/ZFKN834LMT6Vq8B36vh7vfWM9lS8Yh/lQiJu5wb6Q4lzaOnzyFZHsS3T4ftW5XzIYT76pKtPD7/fFkHD0feB04GD1PpRldR/cBRUqpX4rIfw3vS7Sd7/ISR9i2WifgBJUNKnngtiIgDSA+tJQLsWgNEPgUhTOiY9uTA4g/BuuaGhGjozmJRtD365WbWDpuAoFAgHX+AId0dpKcnIzdbo+biR8PkaOBQICUlJSYjmEEeA5oAV4CVtK7WEwK6AV643rt2OHU44iaOLQmlOcllO8bFE2IdTyoDDTHmWANk6UpGsrzIirwOWDR26acj8WbBd4X9Xql4Z6bkGGNn6rx6/uquP6iCfS2M4d/4ASBr76CefPCE4XTB8m2HbuPdCLEaCJ5gOTkFDp6urGIXlLP4/Hg8/kQEbMGZ3Jycq//HQ7HoOQS7xLHLm7j2A6mJ2WtiPQYvymlMiHCt+MAiLt6HBD9ItUKNxb3s6C1o2Q7aBpKqhFLGZbuG0BrQbGGBeMAjwXx7Y2WehmWwApElYGygNaMCqxES/0tTu+hpMtPdxyj79BDnqeDju3BqzZwy4UzGEpdsQAVLwnby7qhML1/gzYvpGmQlRR3NpBOCZCelsbuWJk5c2av3/x+P263G4/Hg9vtprOz0/zbF4zNN9ZACSWWQCCAz+cjKSlpxAQSLzaOeCGOkMpefwWeB54N2fYg8DDwRlypKn0v+HBuaDTnoqQFpBuwgNYEuEHzobT16PYhQmqRaqAtx+IEsRbqcRQAKhPlWoG43iFZNhDQgvmvKihkWAamhY9eGsg92d9XY3P6yV3ZRuviVLCFEGS3B1uSFX9mjHTkGE6iDKuN/cvGsXt+AdvXrev3u81mIz09vVcNzlCICH6/3yQTj8dDR0cHXq+XNWvW4PfrRagMcukruSQnJw85IUcjrT5axJk7NvQE2oP/G2L/iB+OuFRVordx2EH8KH8l4ESZeSOe8PsAaMv1y+gLyTIJkoUV/R8jpm6wkYjApX9uZfm/ywjv1O1dLtniF7LfrSPt61a6ZqTjLUoj6706ktt9bL9oOuQ74kZFAX3UL5z0QwpT09A0jeph9K2UviiS3W7vRS6tra3MmzfPvN99JZf29nbzu0EuoeuehBJLIBCICxtHvEgc7Hjw6oC5SqlVQKdSqgw9W6BrJJ1/J4yjfslFbLNQ/lXop+RDVwyG7sNQQ0yJxBhqn6UUwvekEIS8XMgtbae1NrtPS2McEFq/Q2lCUqObvEY3urEbvAXJ+CdlxRVpAOxTUkZ+8ugb/ELJZaAAs9C1TwxCMRZW6unpwePxsGLFCux2ey87SyjBRCoRfAdsHMab+hrgMfRiQA3ATOAaEfko2O67YxyNPkRdIUnHI75PUIEtiNjQI2rdgBtFSD3WUKLYsXtE6G/qsKEIAIqnVtYidPPNN23sHhIl1dVm4fYrS/j49dxB+3ZOzdKz6Eby4MWQNCzA4gkTufbAQ0Z1DdZoExr7rt5moLOzk7q6OqZNm9ZrzRO3201ra6tJNIZkYkgu4QjGarUOmzjibUFsEekGvq+UKkGvO7pRRMKL4lHgOyFxiAhY0pDk08D9FErbAloSqGSEfAh8GWwcwhFRDEkfSSbCQsCGUmtQUgsEECSYtp+M2BYzZ86z5n7bN9nZtiGZhuqh39iWgIat3Y8/2QNpqTvdOLqbPYkrDj6MvcsrBmwzFrVdI4UxNqWUSQSDtfV6vSaZhC5s7Xa70TTN/IhIWIIJRxA+ny+e4jgA3UgK7AdMQZ/v+yqlUoCnRaRp0J0HwS4vcYS2Ffs+iKUC8W+no7OTuqZ02lrd7Dv9GmzW7uAOA/clfb8Y0eBMRkv9JRb/G6CKwN9DbWU3j9+aTkeLnfkH9/D9C/NQgVXmrhKA+ioH679Iw24XHCkaHtfAYnLqN2107l2IWOwEHBIMCh598si02vjgzHNG/Tg7A5G+oCIhl5aWFpqamiguLjYJpqury/zbWA7SIJL169ezbds2vv32W3bffXfKy8vHVG1RSlmC6sqNwIHAF+ywa+QC/xpJ/3HpjoXo4zj8fj/Nzc00NDTT3R0gN7eC4pJiZszMxtpzMvgeAU1DBni2JOTjo4i65ulMKE4C1QXWaXosSCAZpIPG+sn88sQuujv1WIy1K3Kp36a45I/rADsKHx6P4tUncmlvstPTZR0yf83m9FP89/V07FtI+0El0asswyCZz888F9DLPEYqYsdz3EWsYbPZyMrKIisrK+zvmqaZEsu2bdvo6upi+fLlfPrpp/z+979n991HtnSGUupm4FjAC1QCZ4tIe5Td7AmcKSLrRzSYPohL4oj0Ifb5fLS3t9PV1cX27dvJz89nwoQJZGZm9nrAtdTLwTud7pYnSE1tw0IGWBaA/SRIKUC5/4nFtwx9dZM8HIH1jM/7CKUFUFiRwNdYAl+hpfwGi+9N3nxyA92dFtJzMvF6/Sg0/vtiNxf93orNpsctOFKElFShqsmGxQZ+39ATztbtJ+P9OtoPKSCqWzPEZA5n3F11xjmIyI4K8YHeqQvG9VNKxS1ZhGIsIkctFgspKSmkpKRw+OGH8/bbb3PCCSdw+OGHx2oIbwG/ChYwvgn4FXB1lH2sA3ZTSum6tf4oaCO1c8QlcQADqjYej4fGxkYaGhrw+Xw4HA4KCwuZOnXqwJ1ZLJD8PbZ1TKAktYScnBx9u/ixuO5F+d4FLCjND2wANJTSUMH8WYWCwHaUtgUt9XI88gxKvYVSVpQKEPD5kYDg8/qxBbURBVx0bS1/vHAc365MQwtERoat09J1aSNGk8AgjUJHMo8dcgQpKSlmXIThYTCutUEkoYRi/Ob3+2lq0lViI2LUYrH0Iph4MwyOBPHgVRGRN0O+fgKcFMXuxuD96EtBrgZa0YMQcpVSp4hI9XDHFrfG0VC4XC4aGhpobGxERCgsLGS33XYjNTWV6urqfm/LiCFtoG0DbKDSweIBTegdJ7NDidE0HwG/n32X7skbf3uPng6nHrTlhtn7peNITQLpMaPJs/MD/OS6Wv7fjyfQXJNEJBbZ1O0eeqJ5+Ia4tjaLBb+m0eHzIiK0trbicrlMI2Conm6QivGx2Wx0dnZSW1uL0+mksLCQ2bNnm4TTl2T8fn+vexdOahktcvkfiBw9B3h2yFZBhGS//gW4D91RZqTTJ7NjXZVhYadEjg5nf5fLxebNm2lsbMRqtVJUVMTuu+9OcnJyv7bDT4izYcZ7iMaO8K8Amliwmon3GqKy8Vv2RAGT95jIz/9+EY//7jl6Ol3MO2wO5958As3Op8m2PY3dFpQCFUya5eEn19Vwx5UVdLYa921gZHb4aHJ5IKWP4S7cNY3gOmuahgCZjmQqKvp7TAw93e1243K56Onpob6+ns7OTrxeL1arlZSUFNLT0xER2traepFLNFIL7FCJfD4ffr+/F9mMhFxGgziixXDS6g8//HDeeeed1WF+ukZEXgJQSl2DLjk8GWm/QfdrPdCG/tD52bFS/a6hqkQCEaG7u5v6+nqqq6txOByMGzeO+fPnk5SUNOB+IyIOlY3Y90cFmkDqAdG9MloSXt8WknCDSsZvmYHf/gtsthJz1z2P2J05i2aYatPGzdspLlpMTtp2kPcQNJMi9juqm7kHrOfMvafT3TYYeejbJ1/zFZUnZMCCCRCwQXoYF1+EkyQAJFks3H74kWF/N/R0pRQdHR00NTWRkpLCjBkzyMvL0wPs/H5TSnG73bS0tJh/G1JLKJmESi6hSW5+v5/W1lZqa2txu92MGzfOvCeh9yWcvWVnSC19ES0RaZoWtcTx9ttvA8weZAw/BpYAh0UZrPUQ8AN078me6EFNFvRHIkcpNd5IhBsOxpQ4RISOjg4aGhpobm4mLS2NoqIiysvLycjIoLi4eOhORgKlkKQlBCyTwLcGDRuaZQ9EVdDma8LV48ftNt7GnQQCn5i7GslbOTk5jB8/nqysLGw2GxI4Hc3ZioWvkWBwGAip6Rq3/LOSy4+dgsc59Cr3k//VBf/6hupzp+CZNXjwWDgU2ZL4/vTpYLFy0m6zKQoTjRkIBGhsbKSuro5AIEBJSQnz58/vF4tgRHRmZmaGPZamaSaRuN1uurq6aGpqMpPcNE0jEAjg9/tJTU2loKCAcePGmepQKBEYsRORSC2AaWsJBAKIiNlupOQSD4V8lFJHAb8EFolIVNmsIvK9YB8Hj7RoTzjsdOLQNI22tjYaGhpoa2szCWLKlCnmRd+8efPI6nFE0NZ4KHVRfjrYp5ttLEqRl7dDujAMg/X19Xi9XnJycsjIyEBETPvL1q1bTTJJSzmH9LQuUh1urI5sSh0/BwUTZnr5y783ceHB0yO+XuUPbaLy2nmQYTdOYtD2+2Hn+uOPN1UPt9vNtnXr2BKMmHQ4HCilcLvdeL1e8vLymDx5cj9PVDSwWCykpqaSmppqbvP7/TQ0NFBbW0tSUhJFRUWkpaWZapFBLG6325z8oVJLqORis9nMsYWqRJqm4XQ6qauro7W1lQkTJpikMlKpJR6Mo8DdgAN4KziWT0Tkokh2DMmEXa+UWiQiw1q4eiDsFOLQNA2fz8fq1atpb28nJyeHoqIiZsyYEfYGjriQzxBjCQQCvd5g4VyOgUDAXL7S5XJRUFDAtGnTSEtLG7R/EellM3D1dKEFi5krBeWTvOQUd9NWn05vR+nAD/Lk331BTyrUz8jitN8fR8CRxMSsHJZOm076IGpcKHp6eqitraWxsZHU1FRyc3OxWCy43W42bdpkkl5SUlI/dSMlJcUknKHOvb29nZqaGrq7uykqKmLOnDn97FLhEAgEBpVaQsfmcDjwer10dHRgsVgYN24c06ZNM5+lgaSWUIlkIGIx/jZUsGgQa+OoiEwZwe7GCa5Ed8d2sKN8oCYikZW6GwCjRhw7ArIa6O7uJhAIUFZWxqxZs4a8IdHmLwyG0IcnNTWVjRs3snHjxl4TxJgkSUlJdHV10dDQgNPpJD8/n8mTJw+YLj7QeIwJl52dDZTg9z5DkvsUAKw2eOzTLdxxVT7v/KMAnTx2qAYpOQ5cbTvsVr967TKmzZlkTt5oRHCv10tDQwN1dXXY7XZKS0uZNGnSgOJ0aCi2QXwdHR1mtKSImMbSUFJRStHa2kpzczMZGRmUlZWRnZ0d1X20Wq2kpaUNSMwGKVVXV1NbW2sabP1+P1u3bqWqqsqUWvp6hwxbi4G+Uovxt/ExnoGCggLT9QyY7ueBpJZ4SnILgR+9HsfX6On1GpCulDoumMcyLKgh3tbDCsdrb2/niy++ID8/n6KiIjIzM1m+fDn77bdfRPtXVVVhs9koLw9TuasPGhoa6OrqYsqUHeQc+hCEkyyMLEuXy4XT6aSlpYWOjg48Hg9WqxWbzWY+fKHEkpKSMuyiM+Kvxt3xPG7nVnx+wZo0g/Ss/XCk964bbkTBGmpG6P/hJm/f/wGampqoq6vD6/VSXFxMcXHxoAbmaGCMzel00tzcTGtrqzlhrFYrVqsVh8PRj1z6qhyRwrDD1NTUYLFYKC8vJz8/PyyBGlJLqCHX+Bhp+Ybrue/YrFareZzk5GTKysrIysoypY++Km5fiAhLlizh0UcfZdq0af1+HwKjEmEXzFPZBz3y1McOd2wS8LEMc91YGCWJIyMjI2KSCIdoJY6+YulQakjoW6Wjo4Pc3FxmzZplPiihxGJ82tracLlceL1eQBebw5FLKLEYb8m6ujo6OjrIyzua4uJisjMyBjzH0PTygYyRfYmlpaWFzs5Oenp6zIK56enp5OTkYLFY6Ozs7DVBhgsRwel0UltbS1tbGwUFBcyfP7+XlBBqKHW5XKbK4XK5wk7eUIIJVYe6u7uprq6mra2NwsJCZs2aNWQ9z0ikFkONND7Nzc3mS8Nms5GcnGxKUC6Xq19cS+h5gp6V++yzz/L444+Tnp4eM4IeKZRSBcAFwHigCvjrMMLVB0TcxnFEYrcwiKKzs5PGxkZTnLdarWHJor29nfr6etrb28nOzqakpISZM2f2axuavh0uTyFUpA9HLIYNxfAi5OXlMXPmzBFJLKGw2WxkZGRgs9no6uqitbWV9PR0Jk6cSG5urjl5DXJpa2ujrq4Ol8tl5qWESgWh/4cTtb1eL/X19dTV1ZGSkkJpaSnTp08Pex7hDKV9r13o5DWunaEOGfEdVquV7OxsKioqSE1NNV8II/GWGGpkUlISXq+X1tZWRIRp06aZUoxRTKjv2Nxut2kXsdvt3H777bS1tVFZWcnSpUv5z3/+Q0FBwbDHNgr4O3r9jXeBo4C7lFLniog3Fp2PiqpiGEND8fHHH0cshWzfvh0RYdy4cf0H1Eey8Pl81NfX93qLiEivt4fH48HpdJKVlUVJSYlpGIwlXC4X9fX1NDQ0kJycTEFBAampqXg8nl7SwWASS0pKypCVww1vRV1dHUopSkpKKCwsjEq37kssof8HQrwvImLGahQUFFBWVkZqamrMc0K6urqorq6mvb2dwsJCCgsLAXqpHIaqZpBHOFvGQMRnwOPxUFNTQ0NDA7m5uZSXlw9p7A5FIBDgrbfe4oknnqC7u5uZM2dSVFRERkYGl1122UguQcxVFaXUFyIyL+T7KmDhSNSTXv3HI3EYYeTjx4/XBxHiy49UDamtraW5uRmHw0FqaqrpQfB4dMNjX+NopBM3FIbxsb6+HqvVSnFxcUST2JBYQies8XdfYjGkKK/Xa0o1xcXFlJSUROStiBY9PT3U1NTQ1NREWloamZmZJoGEejjCSSx9VbXB4Pf7qa+vp7a2FofDQVlZmRlwNhRCPTB9ySWU+IwxBQIB2tvb8fl8lJeXU1JSEpXK1t7ezuOPP87TTz/N3nvvzaWXXsrs2bNjSaCjQRzrgWPQq5l7gPfR11hxontV3CPqfzSIw5gYoYiGOGpqavD5fIwfPz4isgDMqNPm5mZSU1MpLi4mLy8v7AMSzobRd+L2nRjGRyllxnT4fD6Ki4spKioatLZDtDCuX0tLC/X19XR1dWG327HZbOa1CCWW0DEOZ62T0JgLq9VKWVkZBQUFA0plfV3Oof+HEl9fNcjhcODxeKitraWjo4Pi4mJKS0tjeu1Af8GEEqBRSlDTNHN8xrZwbmeLxYKI8O2333LfffexcuVKzjjjDM4555wdCZKxxWgQRxVgrBRmCf5vfGxARpSRqL0QV76j0POora3F5XKRkpJCamqqeYNDJ4WRV2GESRcVFTFx4sQh3yaR2DAMFcPlcpmGuu7ubtP4mJaWZu5rGB9TUlJGHDnodrtNlSclJYWKigry8vJ6TeJwEotRHi+cxBI6eQ1iGUnMRX+Xc//rZxCz2+3udf1gx6R1Op1UV1f3I5eRqJFGiYXOzk6Ki4vZe++9+xksjfGFEp5RpMflcvGHP/yBTZs2oWkaBxxwAJ9++mnMyW20ISITRrP/nSpx7LvvvmFVC+ithhgRmcZNdTqdvQxUhuHRbreTm5tLYWGhadGOlfjY15iam5tLSUmJGTEaSix9bQR9dfDQyRuOWMKFfhcVFQ27DF0osfSVCAx1IxAI4HA4yM3NJScnZ0QSy0Bj6OjooKamhq6uLlO6SEpKGrHLuS+xaJpGQ0MD1dXV2O12KioqyM3Njeo8mpubeeSRR3juuec4+OCD+fGPf0xycjLV1dUceWT4PJ8YIv4LnvTBTiOO5cuXs/fee5tiIERmswD9LdzQ0EBDQwNWq5WCggLS09P7qRt91Yy+n0gMiF1dXabKk5mZSXFxcdQPYWg8Qd/JoWkaVqvVfLMbk7mgoICKioqojHWRQtM0Ghsbqa2tRdM0iouLycnJ6SUVhFPVwsWxDEUsPp+Puro66urqSE1NpaysjJycnGGpTwMRi2EgNbwjbreb7OxsM/AsmkrmX375Jffffz9r1qzh7LPP5swzzxzQDT6KSBAHhCeOTz75hIULFwJERBYej8eswaGUoqioiMLCwiH95OGkAePTVxoItVu0t7fT1NREcnIyxcXF5Ofnj8riOobubbiP09LSzNyRUI9QOPtFJGHfoTBqaRgxFyUlJRER00A2DJfLFdY4mpycbGa+9vT0UFZWRklJyagU7hURmpub2b59Oz6fj9zcXNN2YowzNGO3r7RiqKkvvvgiDz30ELm5uVx66aUcdthhY1mIKEEcsIM4QiWLr776Co/H08tmYfxvqBher9dMU9c0jaKiopgbHg1poLOzk6amJtrb200pwGq19rMNGJ+RqEHhQr8HI6ZQSSp00kbiETLc06ExF5F6KyKFQSyGdNba2orVasVut5v3fKA4keGqQl6vl9raWurq6sz4jsFSAcK5nNva2vj1r3/Nhg0bsFqt7L777vz73/+OhzDxBHEAbNy4kaqqKiZMmEBRUZEZ4x8IBPpJAT09PTidTnw+H0op0tPTyc3NJTMzM2YGRwOGLcHwiBjEFGoQDOdtGY4apGnaqIR+h/MIOZ1Ourq6zLdtSkoKWVlZpKen9/O4jBQiejGf6upqXC4XpaWlFBcX9+o7WoklVLoKJRbDTlJdXU1PTw+lpaWUlJREHbPy2Wefcd9991FVVcX555/Paaedht1up7a21nT5jzESxAHw0Ucf8dhjj1FVVUVDQ4MZqDRhwgQmTpxIcXExGzZsYN999yUlJcUM+rFarb0mg/G3IRGEm7BDie+appluzZ6eHgoKCiguLh6WLWEoNUgphdVqxe/34/V6zejUvl6RWMHIeG1ubiYnJ4fS0lIyMjJ6jTF00hrVtkLdj6GfwQjacKM2NDSQmZlJWVnZsFPx+2UQh4zTIBajTXJyMoWFheTl5UUlsbhcLp577jkefvhhxo0bx2WXXcYBBxwQr3VRE8TRrwMRAoEA1dXVbN68mb/97W+89957TJkyBYfDQVNTkxkuPXHiRMaPH8+kSZOYOHFir1gCQ1oJJZRQ8b2vJOD3+2lra6Ozs5Pc3FyKi4tHVHNiMLhcLurq6kwXamZmJna7vdek6Gu7GK4aFG3MRV8YpQLDxbCE8wgZtgu/309ZWRnFxcWjJtr39PSwfft2WltbKSgoICsry7zvfYmlr8RiqGlut5u///3vvP766xx//PFcdNFFESVLjjESxDEUjMShvuJoZWUllZWVbN682fw0NzdjsVgoKyszpZWJEycyYcIEJkyYYE444+3U2tpqJq4ZlaUMKcCYpIZdZTjGxlAMJ/R7uGqQ1Wqlvb2d2tpaurq6KCoqGrXI0UAgQEdHB7W1tbS2tpKUlITdbjfd4H3dpKHXcjhvc0Olq66uRilFRUUF+fn5g96XUMnPcNd/8cUXXH/99TQ0NJCVlcUPfvADbr755pFcip2JBHHEEoa/f9u2bWzevJlNmzaxefNmtmzZwtatW/F6vSYJiQhXXnkl2dnZTJ06tVfqdV/biiG1DCStDGa3MGpmOp3OmE7gcGpQd3d3r6LB6enpZGZm9gqIS0lJiYn4bXgrjKhdw3bRV30xksD6kp8RfxGpR8jtdpvepby8PMrLywdMjBsI3d3dPPPMMzz22GPMnDmTSy+9lL322gtN0+jq6gobnDaWCAQCLFiwgLKyMl599dXQnxLEsTMhIlx88cXk5eUxYcIEGhsb2bJlC5WVlbS2tmKz2aioqDAllEmTJjFhwgTGjRvXK4LSCJbqqwYZorvNZjNXSTf0+9GyW/SNuTCCwSwWSz/VwgiMMwKmUlNTzclq/D2UVBU6gXNycigrKxtwtfhI0Feq6hvYpZQy1Y38/HzT3hSp7UJE2Lx5Mw888ADLli3j5JNP5oILLqCoqGjYY95ZuO2221i5ciWdnZ1xQxxKqUOAVSLSGdV+uzJxDAbD+1BVVcXmzZtNVWjLli29YgD6qkCTJk0iOzubhoYGM/zbbreTlZWF3W7v5SmA3qHdoWpQtHaA4cZcGAgNoTYIpa8LN1QSMKqViwilpaUUFRWNStyKMba6ujpqampIT083STeUXMKpa6ESVUpKCu+++y733XcfTqeTn/zkJ3z/+9+Pu0WeB0J1dTVnnXUW11xzDbfddtuYEodSKgn4GfBDoBuoBl4SkYjXbfnOEsdQMLwtlZWVpgq0efNmVqxYQXV1NUVFRSxdutSsOmVIK8Ziwn2llb4SS6ihMZRQQgORwtW5iHXMBeyIqwktKmS327FaraYUEM7LMlI1qLOzk+3bt9PV1WW6Ugeb6H3VNbfbTUdHB9dffz2rVq3C7/ez++678/DDD8eLGzVinHTSSfzqV7+iq6uLW265ZayJYzZwGvCoiKxTSp0O/ExE9oy0jzGPfBkrWCwWCgoKKCgoYJ999jG3v/POO4wbN47y8vJe0sqbb77Jli1bzJT/goICU1oJlVpCPTdGsJlBKEYlrO7ubrOWZUZGBvn5+aSnp+NwOGJet9IwPtbU1JjSxZw5c3pJF6HBUkaglLH2SbSu8EAgQENDAzU1NSQlJVFeXs5uu+0WERkaBOZwOKivr+f+++/nk08+4fTTT+exxx4jMzPTJPV4gtvt5qCDDsLj8eD3+znppJO49tprzd9fffVVCgsL2XPPPVm2bNnYDXQHqoD7RGRr8PunwFalVJ6ItETSwf+sxDESGHaITZs29fMEdXd343A4GD9+vEkokyZNMo2KOTk55OTkUFJSQkpKSj+7ihFv0Tc03pBaIvVehMZ45OfnU1paOuw8GCNvJJxBFDA9L0bYfF5eHuPGjYuqyLNxnNdff50HHngAi8XCJZdcwpIlS+IhsnNQiAg9PT1m/tQBBxzAnXfeab6QfvWrX/H4449js9nMqOXvf//7PPHEE0YXo5FWr9DLBiaLyLqB2oiIKKUuBRaIyI+NbUP2nyCO2MLI7DWIxEiistlsTJs2jdbWVvLz802DrSGpTJw40cxbAZ2cwnmCDNuK3W4P615ua2ujpqYGpVTUMR7DOdempia2bduG3+8nOzu7lx0oNOCsr7QSminc0tLCY489xrPPPssBBxzApZdeGrak464Ap9PJAQccwL333svee+/d7/dly5aNqqoSOvGVUscDE9EXnE4G3pGQhZ1CiONe4E0R+Vekx4lvKt8FoZQiNTWV2bNnM3v2bJYsWcK5555LWVkZoBNCXV2dKa2sXr2aV155hS1bttDT00NKSopJKoaxdtKkSb0WrAoNO3c6nbS3t1NZWYnb7TbzbdLS0ujs7MTn85kEM9JaFwaMKNL6+npycnKYMWPGgNLFQGqQy+XioYceYvny5bS1tbHXXnvxyCOPMH/+/BGPbywQCATYc8892bRpE5dccklY0hhNBCUM1ac0YAHwO/QFpn+LXgnMRJA0HOjk8opSKhnYXUQ+G/J4CYkjfiCir59rSCuhnqD6+nqUUhQXFzNx4kTKyspoaGigvLycffbZh4qKClO66Cut9I1gNaSVvhLLUIZLY10TI0cl2hJ8oCervfLKKzz44INkZmZy9tlnM3nyZLZu3cq8efPC1pkdS2zfvp0zzzzTTJ244IILuPzyywds397ezgknnMBdd93F7NkDLgnbF7GUOIqBg4AXgWnohLFcRO4YoP3pwNXAq8BS4A0R+fmQx0kQx64BozhzdXU1H3/8Mb/85S+ZOnUqBQUFbN26FZfLRVpamimljB8/3rSvFBYWmpLGQGUTjXBuQ7UIzVw2ljhIT0+nvLzcXEYiGjQ0NPDwww/zwgsvcMQRR3DJJZcwefLkuFdHjNoi8+fPp6uriz333JMXX3yR3XbbbcB9/vCHP5CamsqVV14Z6WGiughB6cIiIgGllDX4/1xgKvpasy5gLfB7YDrwfeBZEfk4TD9/AY4AHgb+ljCOfodhkEjo216Cy0T0lVQqKytpbGzsFbofGgw3YcKEXt4RQ7VobW2lrq6Onp4e83cRGbDsQLgALhFh5cqV3HfffWzatIlzzz2XM844Y1SKFe0sLF26lEsvvZQjjjjC3GbUNc3OzsblcrF48WKuvvpqlixZEmm3I2ZPpdSnQA9wKtAM3AR0Bf+/EfhSRB4xiCZkv6kisjHkuwVdixl07ieI438ARqKhEbofSixbt241I2IrKirMhZwvvPBCxo0bx4QJE3rZVvx+f1hPUKi08uKLL1JfX89nn33GuHHj+OUvf8khhxwSr5mpEaOqqoqDDjqI1atX96oS9vXXX3PWWWeZ+Twnn3wy/+///b9ouh6UOPp6OpRShejqxQLgOeAZYC5wO2AsibAQuB690vkJwGFABXq189tCjaTBPm1AIBKPCuwk4rj11lu58soraWpqIj8/PxZdJhBDGDU2zj77bJRSzJw5k+bmZiorK2lpaTGX4wwXuh+a2VtdXc3jjz/Ou+++S0FBAaWlpTQ3N3PLLbfEne0iWnR3d7No0SKuueYavv/978e6+37EMYCxE6WUHZ00WoA3gP9Dd7mepZRaC5wvIh8ppXLQiWSViNyllPo+uipzj4xgzVhzHKNNHNu3b+e8885j3bp1rFq1KkEcuxgMKSNc6P62bdvw+XxmiH5OTg4XXHABp5xyCikpgy/XGA8455xzzOCs1atXD9jO5/OxZMkSjjzySH7+8yHthsPBgBKHUsqKHuXZLiIvK6XSgZeBB4AlwBTgThF5Win1B2CiiJwR3O8HwDwRuTrmIxaRwT4jxoknnihffvmljB8/XpqammLRZQJxhEAgIE1NTfLqq6+KpmljPZyo8P7778uqVatk1qxZA7bRNE3OOOMMufzyy0dzKABWCZl7QAZwBrq34x/AN8CPgDTgFWAVcHCf9vnohtGyAfo0pJih5v2Qn1FVOl966SXKysrYY489YtLfVVddxYwZM9h999054YQTaG9vj0m/CQwfFouF/Px8jjnmmLj3kPTFQQcdRG5u7qBtPvroI1P9mjt3LnPnzuXf//53zMciQYOlUsqoLbAXcAX6qvInoxs5FwOTgX8D34rIsuA+PwF+LCLN6C7V+j59WoLfTZYaKUYcAHb44YdTX1/fb/uf/vQnrr/+et58882RHsLEEUccwQ033IDNZuPqq6/mhhtu4KabbopZ/wkk0BcHHHAAMZprJgKBQC+Ds8VisQLnAD8FViilPhCRh5VS/wUKg7s9j656jAeeAL6nlHoKmANsBa4N9tdvwkmM1ovt2+moqCpff/21FBQUyPjx42X8+PFitVqloqJC6urqRtKtiRdeeEF+9KMfxaSvBL57eP3112XatGkyefJkueGGGwZst2XLlkFVlVgiEAiE3Q7sDbwN7IbuKakMEsKJwL3AdL0ZVwF/AzLRl3Wcg27TGLHqEe1n1FSVOXPm0NjYSFVVFVVVVZSXl/P5559TXFwck/7//ve/c/TRRw9r3zfeeIPp06czZcoUbrzxxpiMJ4H4QSAQ4JJLLuH1119n7dq1PP3006xdu3ash2W6o59//nnOO++8UJVnNvCNiKwVkZXo0sVRwFfosRiHBds9D/iBFBHRROQbEdmidIxOMZUBEHe5KoOpPkuXLjX/ttlsnHbaaVH3bzxUb731FuXl5SxcuJDjjjtu0EjABHYtfPbZZ0yZMoVJkyYBcMopp/DSSy/t1HsclBB62X3+/e9/s3z5cr755huWLFnCVVddRWdnJ+gJaC6lVK6ItAKvA3eJyM1Kqe3APKVUqohsBi4KcywBAn23jyZ2GnFUVVVF1O7tt98e9PdHHnmEV199lXfeeWdYxrh4eKgSGF3U1NRQUVFhfi8vL+fTTz/t1+7UU09l2bJlNDc3U15ezrXXXsu555477ONKSESv8WyuXr2a7OxsysvLWb16Nc8//zy33XYbRx11FPn5+bz00kugZ68egl6R615078gbwW5fBB4TEWdINqtFRsNuEQV2qVC+N954gz//+c+8/PLLURe2NRDuoaqpqRn2mLZv384hhxzCbrvtxqxZs7jzzjuH3VcC0eG5555j1qxZWCwWVq5cGfX+Tz/9NHV1dfh8Pqqrq0dEGsayk4bRs7W1ld/97ncsXryYa665hldffZXzzjuPmTNnmot+H3/88Ub8SDdwB7BAKfURer7J8wAisl1EOkKjR8eaNGAXI45LL72Urq4ujjjiCObOnctFF/WT2nY6bDYbt956K2vXruWTTz7hnnvuiQt9+n8Bs2fP5oUXXuCggw7qtb2srIzt27eb36urq82yBrGEoY6Abr/o7Ozkxhtv5NBDD+Waa66hqKiI2tpajj32WB566CEyMjKYOXMmn332GQ0NDQBMnjwZdAPnh8DPgXNEZKGILO9zrLhK/4g7G8dg2LRp04j7iPVDVVJSQklJCYD5YNTU1CRUn52AmTNnht2+cOFCNm7cyJYtWygrK+OZZ57hqaeeitlxDXdqqKr80UcfceWVV3LYYYdx++23c+qpp1JaWgrAfvvtx6uvvsprr73GaaedxjnnnENNTQ0tLS00NjYCvBfsplNEOkCPGJWQZLR4Q8QSx5/+9CdOPPFE/vjHP/LKK6/gdDr7tYkzUgyL0IfK6/XyzDPPcNxxx8Wk76qqKr744ouYFXEJBALMmzcvmizLBNClwLvvvpsjjzySmTNncvLJJzNr1qxh9+fz+Vi1ahWBgD6PDXXkH//4B5988gl+v5+CggJqamqYO3cue+yxB+eddx41NTX09PRQVFTEoYceyjPPPMPMmTPZY489KCoq4qyzzmL58uVIMJU9VKqIZ9KAKIjjqaeeoqCggLy8PK6//nreffddYEcciNfrRSnFyy+/THV19agNeKSI9UNloLu7mxNPPJE77rijV+bkSHDnnXcO+Fb9X8Hhhx9uVlML/QSNigPie9/7Hhs2bKCyspJrrrlmWMeuqqri4osvZv/99+f+++/nww8/BOCLL77goIMO4vHHH+e6667jqquuorCwkIsuusgsRnzGGWewceNGvvnmG6xWKwsWLDCX2zjwwAPp6OhgwYIFgJmZumthiEAPE6WlpWbw1u233y6XXHKJtLa29gtm+etf/yrr1683v2uaJoFAQPx+f9jgl+8CvF6vLF68WG699daY9bl9+3Y59NBD5Z133pFjjjkmZv3uCrjyyitl+vTpMmfOHDn++OOlra1t0PaLFi2SFStWxHwcp59+ulx55ZXS0dEhgUBAmpubRUTkvvvuk4svvlhERCorK+W0006TF154QT744AM59thjpbKyUkREzjvvPLnsssvE7/eL3+8Xp9MpIiI1NTVywQUXyDfffGMcaqcHcI30E7HE0dXVZQZvBQIBRISvvvqKpUuXcs4553D00UejaZoZWOX3+wHdj22xWAYsMWeIf83NzbzxxhthVaB4hohw7rnnMnPmzJhmTl5xxRX8+c9/jnkNi/b2dk466SRmzJjBzJkzWb58+dA77WQcccQRrF69mq+//ppp06Zxww037PQx/Otf/6Krq4ubb77ZXPIiLy+PQCBAV1cXFRUVuFwusx7shg0bmD17NhUVFTz00EMAXHnllZxyyilYrVZziQm/309paSn3339/NKUF4w4RPZX19fV0d3dz1VVXcd111/HWW2+xZMkSnE4n77zzDueeey6XXnopgUCAww8/HIvFwnvvvcchhxzCmWeeyeLFi82LacAgCINQbDZbv6Q1ETFJajAM9ftoYjSSoELX4Yg1Lr/8co466ijWrVvHV199FZeq0OLFi80lEfbZZ58BVd9//etflJeXs3z5co455hiOPPLIqI4jIlRVVfHee7pt0u/3o2m6p9Oo1m5AKWXGaKSmpuJ2u01j/ZQpU1ixYgXZ2dksXrzYNIpOnz6d/fbbr9cx432ph4gxhEgiIiLvvPOOlJaWyvPPPy+//e1v5c033xQRkQcffFBOOukkU7RbvXq1TJgwQUREHn74YSkpKZHq6mr55z//Kfvvv784nU7xer3yyCOPyIIFC2Tu3Lly/PHHi8/nk61bt8oXX3whIiJdXV1DSZH98MQTT4jX6+23fVdL9RYR+b//+z8pKyuT8ePHS1FRkaSkpMhpp5024n7b29tlwoQJu9Q1WbJkiTz++OOj1v+zzz4r2dnZ/ba/+eabcvbZZ8uaNWtERHqp299++6385je/ke9///vy5JNPykEHHSQvvPDCSIYx5qpHtJ+IiOMvf/mLnHzyyb3OtKenR2666Sb51a9+ZW57/vnnZe+99xYRkVtvvVXOP/98ERHZtm2bnHDCCbJmzRpZtmyZTJ8+Xdrb26W9vV2++OIL8fv9cv/998uSJUtEROSiiy6S0047Tfbff385/vjjZePGjSIi4na7Zd26ddLd3d1rLG63W4LxMWHx8ssvy/PPPy9r1qwx9cxdBe+9917MbBxffPGFLFy4UM466yyZO3eunHvuuf2u5c7CYYcdJrNmzer3efHFF802f/zjH+X444+PKdH5/f5+/Y0fP17++9//ioiYL59vvvlGfvazn8ntt98uIjsS1Nrb2+W9996TpqYmueuuu+S8887rRxqapkU75jEngmg/EclNn3zyCVlZWQC4XC5SUlLw+XzU19czZcoUs93atWuZMWMGAI2NjUyYMAHATHJrb29n9erVHHPMMWZ/c+fONZdJnDt3Ln6/n8bGRmw2G2+99Ra33HILf/rTn3j44YfZsmULP/3pT2ltbSUnJ4f777+fSZMmsWXLFrM0naZppl3A7XZzzTXXUF9fj8vlorOzk8zMTO666y7KysrQNA0RMe0w33X4/X4+//xz7rrrLvbee28uv/xybrzxRq677rqdPpbRTi0YCIZq3NHRYVZxP/PMM/nTn/7EG2+8YR5r1qxZLFq0iBtvvJFJkyZx3HHHsWrVKm688UYOPPBADj74YC655JKwY9vV6pIMBxERxy233GL+nZycDOi+7c7OTjP4CeDDDz9kr732AvRy+Iabs6mpCavVyrhx43j55ZfJzs4G9IV9HA4Hfr+furo6Jk2aRH19PQUFBRx99NGkpKSwYMECVq5cic/nY8qUKTz99NPk5eXx8MMPc+ONN/LAAw/wzTffUFioly0wiENE+Mc//sGnn37KG2+8YS4Y9OWXX1JfX09ZWdmAZBHKrBaLZUwfhIMPPpiDDz44Jn2Vl5dTXl5uxpmcdNJJcZkdbKQWvP/++8NOLYAdYeCh9+/VV1/lL3/5Cw6Hg/nz53Pttddy9dVXU1JSQkNDA0VFRWaA19KlS6mtreWFF17g5ptvpqenhwsuuIDzzz8f2EEQoS+r/xVERByh5GBcrPz8fP72t7+ZXhGA448/noULF+LxeBARysvLAdi8eTMej4fc3Fzy8vLYunUrfr8fh8MB6IbStrY2JkyYQEtLCy6Xi4KCAkCPj5g1axabNm1i1apV3H333QQCATZs2MCJJ54IwJo1a0zJxzBubd26lc8//5wzzjiD9PR0U1KaO3cuoOcS/P3vf6ehoYEjjjiCI444wjy3vg9bOBjHibR9PKC4uJiKigrWr1/P9OnTeeedd+IywvXSSy/F4/GYSxDss88+3HfffRHvb0z8vpN527ZtPPnkk/zud79jr732Ij8/n3nz5nH88cdz1FFHccMNN3DHHXeYLw2lFBdffDEXXXQR33777YDX6n+NNCAGIeehbtbQ3JFHHnnE/PuEE06gra0NpRRXXXUVF154IfPnz8fv9/PUU0+Rn5/Ppk2bKC4uprW1lc8++8xMmX/++efZZ5992Lx5M6+99hp33303CxYs4PTTTzeJaf369eZNNSZwY2Mjfr+fqVOnArqkZDwMra2t3HDDDTQ3NzNr1izuvfdekpKSOOCAA7jlllv48ssvsVqtTJ06lXPOOQev10sgEOi1DONgD0s8q0B33XUXp512Gl6vl0mTJvHwww+P9ZD6YTipBVu3buXLL79k6dKl5j1au3Ytd9xxB7NmzeIHP/gB48aN4+9//zuPPvooV155JePHj+fee+/l+OOP5ze/+Q2HH344d9xxR1jPh/F8hVbv+l9GTH1DA13UYCKPifvvvx/Q1Zns7Gy8Xi9nnXUW06dP54knnuDAAw/kz3/+M1dffTVTpkzhuOOOw+v1ArrdoqmpiWXLlnHggQcCug3lhz/8IbBjQufk5NDQ0GBGcfr9fjO/4J133sHpdHLllVcya9YsampqeO211zjooIN4++23KS0t5aqrruLaa6/l97//PePHj+ehhx7itNNO44YbbuDDDz9k9erV5Obm4nQ6WbRoERMnTjTPLxJSGauHb+7cucPKJI1niAiNjY2mZLxlyxaef/553nrrLY4++mi++uorPvjgAx588EFaWlr417/+xauvvkpeXh7Z2dl89NFH7L///qSmpvKvf/2LE044oVf/odJkgjR0xJQ4Brqoxtu37/eioiIAHA4HZ511FqCnvU+cOJFbbrmFmpoacnJyzMCz0tJSfvrTnzJ//nzcbrcpTaxcudJUVaxWK5qmMXXqVOrr61m7di0LFizotS5qdXW1uTQiwPjx4/H7/axdu5bc3Fx+9KMfMWfOHIqLi9m2bRt///vfOeOMM/jDH/5AVVUVdXV13H333RxyyCE0NDTw5ptvctNNN1FRUcF7773HM888g1KKs88+m7333rsXaQ1FKqFqT9/rlsAOhL6klFIsXLiQ999/n//85z/MmDGD1157jXHjxnHFFVfQ2dnJ1Vdfzeuvv47dbicvL4+MjAzWrl1LcnIy//znP9l///1ZtWoVeXl5ieseAXZKNErfmxDupkgw2CsrK4vU1FQyMzP75Xzceuut5t833XQTGRkZAPz61782vTmw443/29/+lscee4yuri7mzp2L1+vFbrczadIkHn30UX7xi18A8P7773PiiScSCATw+/1mkZ+2tjZOPfVUs9/Ozk6sVisNDQ3MmzeP3/3ud+Tn57Pvvvvy5Zdf0tTUxL333sv++++PiHDnnXdy2223UV9fzz333EN3dzdNTU0ceuihXHLJJaZnyel0kpqaao47XPal8TB3dHTQ0dFBfn7+iAyHuzoM0lixYgWrV6/m7LPP5ssvv+SDDz7gn//8J0ceeSRNTU00NTVRUFBAUVERGzZs4Ic//CGffvop8+bNo7S0lFtvvZXvfe97AOTl5QH/G16RkSJuFHClFDabjSuuuIILLrgA6B8RGhpFmpeXR1JSEgDXXnttWL108eLFLF26lJUrV/LHP/6R+++/n5KSEtNjc8EFF3DttdfS1NTEkUceicfjwefzmWn27e3tppG2s7MTt9tNfn4+W7duZdGiRaYUU1FRQVpaGg8++CAHHnggl19+OVdccQXr1q1jzZo1WCwWli1bxnHHHcf999/Piy++yKpVqwD473//y0UXXcTs2bM57rjj2LBhA1arlWXLlvHtt9/S2NhoSiIAGzZs4D//+Q8+n6/f+Wqaxuuvv86tt97KihUrzLD/XR2appnGaOP+t7W1ceyxx/LTn/6UpqYmAoEAixcvRtM0vvzySxYtWkRPT48ZFTp16lS2bNnCzJkzue6667j99tt56623OO2008jJyRmzc9tVEdfxr32Zf7B8l4F++8EPfsAPfvCDftuvu+463nzzTRobG3nssccoKCjg888/R9M00tLScLvdbNu2zVx3o7W1FZvNRkpKimnAPe+88wBdp87Ly6O5uZk5c+bgdrtJTk5m/Pjx2Gw2KisrOeCAAzjqqKPIyckhKyuLnp4eurq6uOqqq7j77rtZuHAh9913H//5z3+YNm0av/jFL8x1Wz/44AMeeeQRjjzySO666y7WrVuH1+vlRz/6EdnZ2eZ16unpMdWot956i3vvvbeX7WVXg9/vx2azmZJYT0+PuWD1u+++y/Tp03nllVfM9uPGjWPPPffkqaee4s9//jNPPfUUN910Ex999BGvvPIKd999NwApKSksXrwYCO+yTWBoxDVxRIrBDFYDeTgmTZrUr4LYkUceyaJFi8z9fv3rX5tBbF1dXWRlZSEidHR0ICLcc8899PT0YLVa2WOPPdhtt9145513OPjgg/F4PKxdu5Zx48ZRVVVFRkaG+WbLyMggLS2Nd999l/b2ds4//3ySk5NxOp2UlpZy2WWXAXDIIYdw6aWX8o9//IPnn3+eAw88kPT0dBwOB2+//TYVFRUsWbLEfOiTk5M555xzSE5OZsOGDbvkqvDt7e387Gc/4+GHHzalyDVr1nDTTTexbt06LrzwQs4991w2bdrEtm3bgB2EkpaWxmGHHcZ1111HXV0dixcvxufzMW7cONasWRN2Wcp483rtKvhOEMdgGCrIq28bI8AtNTW1l31j6dKlLF26lMrKStLT0znkkEP44osv8Pv9PPfccwD86Ec/4ic/+QlnnnkmdrudQw45hIkTJ/L888+bAWgtLS0EAgFycnLweDzMnz+fp59+mra2Nmpra7FYLPj9flpbWznrrLMQESZMmEBVVRWpqalMnTqV3XffPWzZREN1qq6uxmq1xqwuyM6AQfDZ2dnmuWmaxh133MHTTz/Nb3/7W9LT07nhhhsYN24chx12GO+++y5tbW0mIbtcLmbMmEFRURGffPIJCxYs4N133yU3N5eUlJSEKzWG+M4Tx0CINMjLIBXj782bN+NyuTjwwANNT5CB6dOnc9999/HZZ59htVo55ZRT6OrqAjCjZbdu3Wo+7Ha7nQ0bNtDa2kpubq45ASorK8nIyCAjIwMRoampyXxb1tbWmlJQOOt/IBCgp6eH7OxskwTjHUaELugqYXFxMYsWLeL9998nJyeHbdu2ceyxx6KU4oMPPmDlypWceuqplJWV8bOf/Yzzzz+f+++/n+TkZB544AHuvPNO03BeUVGBw+Ewo5QTiBGGSGZJoA+6u7tl/fr10tHRISIScYEiI+mpublZ3n77bens7BQRkUcffVRmzpwps2fPlh/+8IdSU1Mjr732msyZM8fs/4EHHpCLL75YfD6f/PSnP5WXXnppwOM0NTXJL37xC/nb3/42ktMcVbz00kvyox/9SO655x5zIfLPP/9cLrnkElmwYIGIiKSkpMi3334rHR0dcsIJJ5iJZG+//bZcdNFFsnLlSmlpaZG77rpLDjvsMLn11lvNayqy4774fL6dfHbDwpgnrUX7SSh4USItLY1p06aZakBf0VdETLE7FIZkkJeXx2GHHWa+Ec8880zWrl3Le++9xx/+8AdKSkooKCgw8yECgQDbt28nJSUFm81GRkYGTz75JN9++20vz4pxvIyMDBwOh7kERN9xjCXWrl1LdnY2zzzzDIsWLeLDDz/khhtu4NNPP+XCCy/EarWyYsUKAH784x9zww03kJmZyUEHHWQaQQ8++GC8Xi/vvfceKSkpXHrppbz99tv8/Oc/NyU06F3nJYFRwBDMksAoIDTlOpL0a7/fb75NP/vsMznnnHPk5ptvNqUeo48XXnhBjjvuODnwwAPNdPB4q72RlJQktbW1IiLy1ltvybnnnivvvPOOnHvuuXLGGWeY7aqrqyUzM1M8Ho+sXbtWDj30UPn4449FRL8GjY2NvfoNly6/C2HMJYhoPwmJYwwQapcIZ2cJTaAD/e1pSCgLFy7koYce4sorrzSlHqOPnJwcKioqKC8v5/bbb+eee+6JOzfjOeecw9VXXw3oS1U0NDQwe/ZsTj75ZFpbW80qcGVlZey5555cf/31zJw5k6OOOsrsY+HChWZ8jYG+AXMJjC6UDC7Kxo+cmwAw9rkuI8W2bduYM2cOt9xyC88++yy77747t912G7W1tVx77bXsvffenHPOOQC8+eabPPnkkzz66KNjPOpRxy7HeAni+A7BiLA03rzxSi7HHXcca9eu5aWXXjJrtvh8Ph5//HGeeuqpAYv8fIfrXiSII4EEhsKbb77JZZddxvr164EdhFBVVUVlZSWHHXZYr/bfYcIwkCCOBBKIBCUlJdx9992ceOKJ/wvEMBR2OeL4n75bCYwdrrnmGlpbW4FE2PeuiKEkjgQSSCCBfkhQfQJjBqVU4vnbRZGQOBJIIIGokWD8BBJIIGokiCOBBBKIGgniSCCBBKJGgjgSSCCBqJEgjgQSSCBq/H8rjku5tb0OWwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Explained Variance for Each Principal Component (PC=3):\n",
      "PC1: 67.17%\n",
      "PC2: 13.03%\n",
      "PC3: 12.63%\n",
      "Loadings for PC3:\n",
      "                    PC1       PC2       PC3\n",
      "CPUTimeRAW     0.691185 -0.046488  0.297456\n",
      "ReqCPUS        0.100829  0.416695  0.280669\n",
      "AllocCPUS      0.100500  0.416593  0.283571\n",
      "ReqNodes       0.003097  0.009174  0.013853\n",
      "NNodes         0.006017  0.017755  0.024448\n",
      "total_minutes  0.590685 -0.463080  0.013884\n",
      "Timelimit_     0.389598  0.533560 -0.736595\n",
      "ReqMem_MB      0.035431  0.388748  0.456922\n",
      "   Cluster Label 3D  Percentage (%)\n",
      "0                 0       19.716314\n",
      "1                 2       40.135326\n",
      "2                 1       40.148360\n"
     ]
    }
   ],
   "source": [
    "kmeans = KMeans(n_clusters=3, algorithm='auto', init='k-means++', max_iter=300)\n",
    "kmeans.fit_predict(df_pca3)\n",
    "labels2 = kmeans.labels_\n",
    "df_pca3['Cluster']=labels\n",
    "\n",
    "\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111, projection='3d')\n",
    "\n",
    "ax.scatter(df_pca3['1st feature'], df_pca3['2nd feature'], df_pca3['3rd feature'],\n",
    "           c=df_pca3['Cluster'], cmap='viridis', alpha=0.5)\n",
    "\n",
    "ax.set_xlabel('Principal Component 1')\n",
    "ax.set_ylabel('Principal Component 2')\n",
    "ax.set_zlabel('Principal Component 3')\n",
    "plt.title('K-Means Clustering Results in 3D')\n",
    "plt.savefig('KMEANS_Clustering_3D_Simple2.png', dpi=200)  \n",
    "ax.view_init(elev=10, azim=-60)\n",
    "plt.show()\n",
    "\n",
    "\n",
    "print(\"Explained Variance for Each Principal Component (PC=3):\")\n",
    "for i, var in enumerate(pca3.explained_variance_ratio_):\n",
    "    print(f\"PC{i + 1}: {var:.2%}\")\n",
    "    \n",
    "    \n",
    "loadings_df = pd.DataFrame(pca3.components_.T, columns=[f'PC{i+1}' for i in range(pca3.n_components_)], index=df[features3].columns)\n",
    "\n",
    "print(\"Loadings for PC3:\")\n",
    "print(loadings_df[['PC1', 'PC2','PC3']])\n",
    "\n",
    "\n",
    "cluster_counts = Counter(labels2)\n",
    "percentages = {cluster: count / len(df) * 100 for cluster, count in cluster_counts.items()}\n",
    "table_data = pd.DataFrame(list(percentages.items()), columns=['Cluster Label 3D', 'Percentage (%)'])\n",
    "print(table_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is to try get more of understanding of how the features effected the clustering. This takes the data and uses the numerica features to try to predict the cluster label via a classification tree. The plot shown at the end will show which clusters were effected the most by the numeric features given, via a SHAP value. \n",
    "\n",
    "Note: The final plotted image is shown in the report, but this takes a very long time to load: ~20-30 minuted given the size of the dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[LightGBM] [Warning] Unknown parameter: colsample_by_tree\n",
      "[LightGBM] [Warning] Unknown parameter: colsample_by_tree\n",
      "[LightGBM] [Warning] Unknown parameter: colsample_by_tree\n",
      "[LightGBM] [Warning] Unknown parameter: colsample_by_tree\n",
      "[LightGBM] [Warning] Unknown parameter: colsample_by_tree\n",
      "CV F1 score for K-Means (3D) clusters is 0.9959805296503047\n",
      "[LightGBM] [Warning] Unknown parameter: colsample_by_tree\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "lgbm_data=df[features3]\n",
    "for c in lgbm_data.select_dtypes(include='object'):\n",
    "    lgbm_data[c] = lgbm_data[c].astype('category')\n",
    "clf_km = LGBMClassifier(colsample_by_tree=0.8)\n",
    "cv_scores_km = cross_val_score(clf_km, lgbm_data, labels2, scoring='f1_weighted')\n",
    "print(f'CV F1 score for K-Means (3D) clusters is {np.mean(cv_scores_km)}')\n",
    "\n",
    "\n",
    "clf_km.fit(lgbm_data, labels2)\n",
    "explainer_km = shap.TreeExplainer(clf_km)\n",
    "shap_values_km = explainer_km.shap_values(lgbm_data)\n",
    "shap.summary_plot(shap_values_km, lgbm_data, plot_type=\"bar\", plot_size=(15, 10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[LightGBM] [Warning] Unknown parameter: colsample_by_tree\n",
      "[LightGBM] [Warning] Unknown parameter: colsample_by_tree\n",
      "[LightGBM] [Warning] Unknown parameter: colsample_by_tree\n",
      "[LightGBM] [Warning] Unknown parameter: colsample_by_tree\n",
      "[LightGBM] [Warning] Unknown parameter: colsample_by_tree\n",
      "CV F1 score for K-Means clusters (2D) is 0.9965203703085367\n",
      "[LightGBM] [Warning] Unknown parameter: colsample_by_tree\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "lgbm_data=df[features3]\n",
    "for c in lgbm_data.select_dtypes(include='object'):\n",
    "    lgbm_data[c] = lgbm_data[c].astype('category')\n",
    "clf_km = LGBMClassifier(colsample_by_tree=0.8)\n",
    "cv_scores_km = cross_val_score(clf_km, lgbm_data, labels, scoring='f1_weighted')\n",
    "print(f'CV F1 score for K-Means clusters (2D) is {np.mean(cv_scores_km)}')\n",
    "\n",
    "\n",
    "clf_km.fit(lgbm_data, labels)\n",
    "explainer_km = shap.TreeExplainer(clf_km)\n",
    "shap_values_km = explainer_km.shap_values(lgbm_data)\n",
    "shap.summary_plot(shap_values_km, lgbm_data, plot_type=\"bar\", plot_size=(15, 10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>1st feature</th>\n",
       "      <th>2nd feature</th>\n",
       "      <th>Cluster</th>\n",
       "      <th>Group</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4.496051</td>\n",
       "      <td>0.778034</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.457472</td>\n",
       "      <td>0.793370</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.445893</td>\n",
       "      <td>0.797972</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.459311</td>\n",
       "      <td>0.792638</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4.461650</td>\n",
       "      <td>0.791709</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1449976</th>\n",
       "      <td>0.500355</td>\n",
       "      <td>0.089999</td>\n",
       "      <td>0</td>\n",
       "      <td>group_140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1449977</th>\n",
       "      <td>-0.339319</td>\n",
       "      <td>2.302176</td>\n",
       "      <td>0</td>\n",
       "      <td>group_140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1449978</th>\n",
       "      <td>0.566273</td>\n",
       "      <td>0.290256</td>\n",
       "      <td>0</td>\n",
       "      <td>group_140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1449979</th>\n",
       "      <td>2.818491</td>\n",
       "      <td>0.375392</td>\n",
       "      <td>1</td>\n",
       "      <td>group_140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1449980</th>\n",
       "      <td>2.807200</td>\n",
       "      <td>0.379880</td>\n",
       "      <td>1</td>\n",
       "      <td>group_140</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1449981 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         1st feature  2nd feature  Cluster      Group\n",
       "0           4.496051     0.778034        1        NaN\n",
       "1           4.457472     0.793370        1        NaN\n",
       "2           4.445893     0.797972        1        NaN\n",
       "3           4.459311     0.792638        1        NaN\n",
       "4           4.461650     0.791709        1        NaN\n",
       "...              ...          ...      ...        ...\n",
       "1449976     0.500355     0.089999        0  group_140\n",
       "1449977    -0.339319     2.302176        0  group_140\n",
       "1449978     0.566273     0.290256        0  group_140\n",
       "1449979     2.818491     0.375392        1  group_140\n",
       "1449980     2.807200     0.379880        1  group_140\n",
       "\n",
       "[1449981 rows x 4 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y=df['Group']\n",
    "df_pca2['Group']=y\n",
    "df_pca2 = df_pca2.drop(df_pca2.index[-1])\n",
    "df_pca2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These just give a break down of how the \"Group\" feature was allocated throughout the clustering. Because of how many unique groups are present in the SLURMS dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Group    group_000  group_001  group_002  group_003  group_004  group_005  \\\n",
      "Cluster                                                                     \n",
      "0         2.283380   0.234558   4.559642   0.545553   0.297506   0.000375   \n",
      "1         3.129869   0.098438   4.206488   0.575902   0.605356   0.003876   \n",
      "2         2.689208   0.041334   4.951432   0.685322   0.462117   0.002893   \n",
      "\n",
      "Group    group_006  group_007  group_008  group_009  ...  group_144  \\\n",
      "Cluster                                              ...              \n",
      "0         0.000375   0.004871   2.414898   8.446709  ...   0.008243   \n",
      "1         0.000775   0.005426   2.299733   6.635663  ...   0.000775   \n",
      "2         0.000413   0.005373   2.013392  10.396396  ...   0.008680   \n",
      "\n",
      "Group    group_145  group_146  group_147  group_148  group_149  group_150  \\\n",
      "Cluster                                                                     \n",
      "0         0.008993   1.305431   0.001124   0.018735   0.004122   0.000749   \n",
      "1         0.025578   0.601480   0.001550   0.031004   0.001550   0.000000   \n",
      "2         0.016947   1.112719   0.002893   0.020254   0.002067   0.000000   \n",
      "\n",
      "Group    group_152  group_153  group_154  \n",
      "Cluster                                   \n",
      "0         0.001873   0.000375   4.625213  \n",
      "1         0.000775   0.000000   6.141146  \n",
      "2         0.002893   0.000413   6.035630  \n",
      "\n",
      "[3 rows x 129 columns]\n"
     ]
    }
   ],
   "source": [
    "group_count_per_cluster = df_pca2.groupby(['Cluster', 'Group']).size().unstack(fill_value=0)\n",
    "percentage_breakdown = group_count_per_cluster.div(group_count_per_cluster.sum(axis=1), axis=0) * 100\n",
    "print(percentage_breakdown)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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