Classical_ML.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "First few rows of df1:\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>v_id</th>\n",
       "      <th>lon</th>\n",
       "      <th>lat</th>\n",
       "      <th>typ</th>\n",
       "      <th>voltage</th>\n",
       "      <th>degree_centrality</th>\n",
       "      <th>pagerank</th>\n",
       "      <th>betweenness</th>\n",
       "      <th>combined_centrality</th>\n",
       "      <th>criticality_label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>9.522576</td>\n",
       "      <td>52.360409</td>\n",
       "      <td>substation</td>\n",
       "      <td>220000.0</td>\n",
       "      <td>0.002092</td>\n",
       "      <td>0.001128</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000277</td>\n",
       "      <td>Low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>9.113210</td>\n",
       "      <td>52.543853</td>\n",
       "      <td>substation</td>\n",
       "      <td>220000.0</td>\n",
       "      <td>0.010460</td>\n",
       "      <td>0.002766</td>\n",
       "      <td>0.000197</td>\n",
       "      <td>0.001358</td>\n",
       "      <td>Severe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>9.389745</td>\n",
       "      <td>52.026313</td>\n",
       "      <td>substation</td>\n",
       "      <td>380000.0</td>\n",
       "      <td>0.010460</td>\n",
       "      <td>0.002086</td>\n",
       "      <td>0.000171</td>\n",
       "      <td>0.001290</td>\n",
       "      <td>Severe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>9.125265</td>\n",
       "      <td>52.538264</td>\n",
       "      <td>substation</td>\n",
       "      <td>380000.0</td>\n",
       "      <td>0.010460</td>\n",
       "      <td>0.002402</td>\n",
       "      <td>0.000395</td>\n",
       "      <td>0.001499</td>\n",
       "      <td>Severe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>10.366275</td>\n",
       "      <td>52.284647</td>\n",
       "      <td>substation</td>\n",
       "      <td>380000.0</td>\n",
       "      <td>0.016736</td>\n",
       "      <td>0.002086</td>\n",
       "      <td>0.000110</td>\n",
       "      <td>0.001843</td>\n",
       "      <td>Severe</td>\n",
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       "      <th>...</th>\n",
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       "    <tr>\n",
       "      <th>474</th>\n",
       "      <td>475</td>\n",
       "      <td>8.872260</td>\n",
       "      <td>50.129725</td>\n",
       "      <td>substation</td>\n",
       "      <td>110000.0</td>\n",
       "      <td>0.002092</td>\n",
       "      <td>0.001128</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000277</td>\n",
       "      <td>Low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>475</th>\n",
       "      <td>476</td>\n",
       "      <td>8.743261</td>\n",
       "      <td>50.162338</td>\n",
       "      <td>auxillary_T_node</td>\n",
       "      <td>220000.0</td>\n",
       "      <td>0.006276</td>\n",
       "      <td>0.003523</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000841</td>\n",
       "      <td>High</td>\n",
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       "    <tr>\n",
       "      <th>476</th>\n",
       "      <td>477</td>\n",
       "      <td>7.957537</td>\n",
       "      <td>47.572159</td>\n",
       "      <td>substation</td>\n",
       "      <td>220000.0</td>\n",
       "      <td>0.002092</td>\n",
       "      <td>0.001128</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000277</td>\n",
       "      <td>Low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>477</th>\n",
       "      <td>478</td>\n",
       "      <td>7.958491</td>\n",
       "      <td>47.579148</td>\n",
       "      <td>generator</td>\n",
       "      <td>380000.0</td>\n",
       "      <td>0.002092</td>\n",
       "      <td>0.001128</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000277</td>\n",
       "      <td>Low</td>\n",
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       "    <tr>\n",
       "      <th>478</th>\n",
       "      <td>479</td>\n",
       "      <td>7.950278</td>\n",
       "      <td>47.591674</td>\n",
       "      <td>auxillary_T_node</td>\n",
       "      <td>220000.0</td>\n",
       "      <td>0.006276</td>\n",
       "      <td>0.003595</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000846</td>\n",
       "      <td>High</td>\n",
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       "<p>479 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     v_id        lon        lat               typ   voltage  \\\n",
       "0       1   9.522576  52.360409        substation  220000.0   \n",
       "1       2   9.113210  52.543853        substation  220000.0   \n",
       "2       3   9.389745  52.026313        substation  380000.0   \n",
       "3       4   9.125265  52.538264        substation  380000.0   \n",
       "4       5  10.366275  52.284647        substation  380000.0   \n",
       "..    ...        ...        ...               ...       ...   \n",
       "474   475   8.872260  50.129725        substation  110000.0   \n",
       "475   476   8.743261  50.162338  auxillary_T_node  220000.0   \n",
       "476   477   7.957537  47.572159        substation  220000.0   \n",
       "477   478   7.958491  47.579148         generator  380000.0   \n",
       "478   479   7.950278  47.591674  auxillary_T_node  220000.0   \n",
       "\n",
       "     degree_centrality  pagerank  betweenness  combined_centrality  \\\n",
       "0             0.002092  0.001128     0.000000             0.000277   \n",
       "1             0.010460  0.002766     0.000197             0.001358   \n",
       "2             0.010460  0.002086     0.000171             0.001290   \n",
       "3             0.010460  0.002402     0.000395             0.001499   \n",
       "4             0.016736  0.002086     0.000110             0.001843   \n",
       "..                 ...       ...          ...                  ...   \n",
       "474           0.002092  0.001128     0.000000             0.000277   \n",
       "475           0.006276  0.003523     0.000000             0.000841   \n",
       "476           0.002092  0.001128     0.000000             0.000277   \n",
       "477           0.002092  0.001128     0.000000             0.000277   \n",
       "478           0.006276  0.003595     0.000000             0.000846   \n",
       "\n",
       "    criticality_label  \n",
       "0                 Low  \n",
       "1              Severe  \n",
       "2              Severe  \n",
       "3              Severe  \n",
       "4              Severe  \n",
       "..                ...  \n",
       "474               Low  \n",
       "475              High  \n",
       "476               Low  \n",
       "477               Low  \n",
       "478              High  \n",
       "\n",
       "[479 rows x 10 columns]"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "# Read the CSV files into DataFrames\n",
    "df1 = pd.read_csv('nodes.csv')\n",
    "df2 = pd.read_csv('edges3.csv')\n",
    "\n",
    "# Optionally, display the first few rows of each DataFrame to verify they're loaded correctly\n",
    "print(\"First few rows of df1:\")\n",
    "df1\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "First few rows of df2:\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 765 entries, 0 to 764\n",
      "Data columns (total 11 columns):\n",
      " #   Column    Non-Null Count  Dtype  \n",
      "---  ------    --------------  -----  \n",
      " 0   l_id      765 non-null    float64\n",
      " 1   v_id_1    765 non-null    float64\n",
      " 2   v_id_2    765 non-null    float64\n",
      " 3   voltage   765 non-null    float64\n",
      " 4   cables    765 non-null    float64\n",
      " 5   wires     765 non-null    float64\n",
      " 6   length_m  765 non-null    float64\n",
      " 7   r         765 non-null    float64\n",
      " 8   x         765 non-null    float64\n",
      " 9   c         765 non-null    float64\n",
      " 10  i_th_max  765 non-null    float64\n",
      "dtypes: float64(11)\n",
      "memory usage: 65.9 KB\n"
     ]
    }
   ],
   "source": [
    "print(\"\\nFirst few rows of df2:\")\n",
    "df2.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Defaulting to user installation because normal site-packages is not writeable\n",
      "Requirement already satisfied: networkx in /Users/mmishkaturrahman/Library/Python/3.9/lib/python/site-packages (3.2.1)\n",
      "\u001b[33mWARNING: You are using pip version 21.2.4; however, version 23.3.2 is available.\n",
      "You should consider upgrading via the '/Library/Developer/CommandLineTools/usr/bin/python3 -m pip install --upgrade pip' command.\u001b[0m\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install networkx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import networkx as nx\n",
    "\n",
    "# Create a directed graph\n",
    "G = nx.DiGraph()\n",
    "\n",
    "# Add edges from df2\n",
    "for idx, row in df2.iterrows():\n",
    "    G.add_edge(row['v_id_1'], row['v_id_2'], weight=row['voltage'], id=row['l_id'])\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of nodes: 479\n",
      "Number of edges: 633\n",
      "Sizes of weakly connected components: [469, 4, 2, 2, 2]\n"
     ]
    }
   ],
   "source": [
    "# Number of nodes\n",
    "num_nodes = G.number_of_nodes()\n",
    "print(f\"Number of nodes: {num_nodes}\")\n",
    "\n",
    "# Number of edges\n",
    "num_edges = G.number_of_edges()\n",
    "print(f\"Number of edges: {num_edges}\")\n",
    "\n",
    "# Find weakly connected components and their sizes\n",
    "weakly_connected_components = nx.weakly_connected_components(G)\n",
    "component_sizes = [len(c) for c in weakly_connected_components]\n",
    "\n",
    "# Print sizes of weakly connected components\n",
    "print(\"Sizes of weakly connected components:\", component_sizes)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "eigen_centrality = nx.eigenvector_centrality(G, max_iter=1000, weight='weight')\n",
    "closeness_centrality = nx.closeness_centrality(G)\n",
    "clustering_coefficient = nx.clustering(G)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1['eigen_centrality'] = df1['v_id'].apply(lambda x: eigen_centrality.get(x, 0))\n",
    "df1['closeness_centrality'] = df1['v_id'].apply(lambda x: closeness_centrality.get(x, 0))\n",
    "df1['clustering_coefficient'] = df1['v_id'].apply(lambda x: clustering_coefficient.get(x, 0))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>v_id</th>\n",
       "      <th>lon</th>\n",
       "      <th>lat</th>\n",
       "      <th>typ</th>\n",
       "      <th>voltage</th>\n",
       "      <th>degree_centrality</th>\n",
       "      <th>pagerank</th>\n",
       "      <th>betweenness</th>\n",
       "      <th>combined_centrality</th>\n",
       "      <th>criticality_label</th>\n",
       "      <th>eigen_centrality</th>\n",
       "      <th>closeness_centrality</th>\n",
       "      <th>clustering_coefficient</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>9.522576</td>\n",
       "      <td>52.360409</td>\n",
       "      <td>substation</td>\n",
       "      <td>220000.0</td>\n",
       "      <td>0.002092</td>\n",
       "      <td>0.001128</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>Low</td>\n",
       "      <td>9.305167e-51</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>9.113210</td>\n",
       "      <td>52.543853</td>\n",
       "      <td>substation</td>\n",
       "      <td>220000.0</td>\n",
       "      <td>0.010460</td>\n",
       "      <td>0.002766</td>\n",
       "      <td>0.000197</td>\n",
       "      <td>0.001358</td>\n",
       "      <td>Severe</td>\n",
       "      <td>1.838136e-31</td>\n",
       "      <td>0.006834</td>\n",
       "      <td>0.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>9.389745</td>\n",
       "      <td>52.026313</td>\n",
       "      <td>substation</td>\n",
       "      <td>380000.0</td>\n",
       "      <td>0.010460</td>\n",
       "      <td>0.002086</td>\n",
       "      <td>0.000171</td>\n",
       "      <td>0.001290</td>\n",
       "      <td>Severe</td>\n",
       "      <td>7.071928e-44</td>\n",
       "      <td>0.004184</td>\n",
       "      <td>0.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>9.125265</td>\n",
       "      <td>52.538264</td>\n",
       "      <td>substation</td>\n",
       "      <td>380000.0</td>\n",
       "      <td>0.010460</td>\n",
       "      <td>0.002402</td>\n",
       "      <td>0.000395</td>\n",
       "      <td>0.001499</td>\n",
       "      <td>Severe</td>\n",
       "      <td>1.813950e-37</td>\n",
       "      <td>0.006538</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>10.366275</td>\n",
       "      <td>52.284647</td>\n",
       "      <td>substation</td>\n",
       "      <td>380000.0</td>\n",
       "      <td>0.016736</td>\n",
       "      <td>0.002086</td>\n",
       "      <td>0.000110</td>\n",
       "      <td>0.001843</td>\n",
       "      <td>Severe</td>\n",
       "      <td>7.071928e-44</td>\n",
       "      <td>0.004184</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>474</th>\n",
       "      <td>475</td>\n",
       "      <td>8.872260</td>\n",
       "      <td>50.129725</td>\n",
       "      <td>substation</td>\n",
       "      <td>110000.0</td>\n",
       "      <td>0.002092</td>\n",
       "      <td>0.001128</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000277</td>\n",
       "      <td>Low</td>\n",
       "      <td>9.305167e-51</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>475</th>\n",
       "      <td>476</td>\n",
       "      <td>8.743261</td>\n",
       "      <td>50.162338</td>\n",
       "      <td>auxillary_T_node</td>\n",
       "      <td>220000.0</td>\n",
       "      <td>0.006276</td>\n",
       "      <td>0.003523</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000841</td>\n",
       "      <td>High</td>\n",
       "      <td>6.141411e-44</td>\n",
       "      <td>0.006276</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>476</th>\n",
       "      <td>477</td>\n",
       "      <td>7.957537</td>\n",
       "      <td>47.572159</td>\n",
       "      <td>substation</td>\n",
       "      <td>220000.0</td>\n",
       "      <td>0.002092</td>\n",
       "      <td>0.001128</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000277</td>\n",
       "      <td>Low</td>\n",
       "      <td>9.305167e-51</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>477</th>\n",
       "      <td>478</td>\n",
       "      <td>7.958491</td>\n",
       "      <td>47.579148</td>\n",
       "      <td>generator</td>\n",
       "      <td>380000.0</td>\n",
       "      <td>0.002092</td>\n",
       "      <td>0.001128</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000277</td>\n",
       "      <td>Low</td>\n",
       "      <td>9.305167e-51</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>478</th>\n",
       "      <td>479</td>\n",
       "      <td>7.950278</td>\n",
       "      <td>47.591674</td>\n",
       "      <td>auxillary_T_node</td>\n",
       "      <td>220000.0</td>\n",
       "      <td>0.006276</td>\n",
       "      <td>0.003595</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000846</td>\n",
       "      <td>High</td>\n",
       "      <td>2.087394e-21</td>\n",
       "      <td>0.009375</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>479 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     v_id        lon        lat               typ   voltage  \\\n",
       "0       1   9.522576  52.360409        substation  220000.0   \n",
       "1       2   9.113210  52.543853        substation  220000.0   \n",
       "2       3   9.389745  52.026313        substation  380000.0   \n",
       "3       4   9.125265  52.538264        substation  380000.0   \n",
       "4       5  10.366275  52.284647        substation  380000.0   \n",
       "..    ...        ...        ...               ...       ...   \n",
       "474   475   8.872260  50.129725        substation  110000.0   \n",
       "475   476   8.743261  50.162338  auxillary_T_node  220000.0   \n",
       "476   477   7.957537  47.572159        substation  220000.0   \n",
       "477   478   7.958491  47.579148         generator  380000.0   \n",
       "478   479   7.950278  47.591674  auxillary_T_node  220000.0   \n",
       "\n",
       "     degree_centrality  pagerank  betweenness  combined_centrality  \\\n",
       "0             0.002092  0.001128     0.000000             0.000277   \n",
       "1             0.010460  0.002766     0.000197             0.001358   \n",
       "2             0.010460  0.002086     0.000171             0.001290   \n",
       "3             0.010460  0.002402     0.000395             0.001499   \n",
       "4             0.016736  0.002086     0.000110             0.001843   \n",
       "..                 ...       ...          ...                  ...   \n",
       "474           0.002092  0.001128     0.000000             0.000277   \n",
       "475           0.006276  0.003523     0.000000             0.000841   \n",
       "476           0.002092  0.001128     0.000000             0.000277   \n",
       "477           0.002092  0.001128     0.000000             0.000277   \n",
       "478           0.006276  0.003595     0.000000             0.000846   \n",
       "\n",
       "    criticality_label  eigen_centrality  closeness_centrality  \\\n",
       "0                 Low      9.305167e-51              0.000000   \n",
       "1              Severe      1.838136e-31              0.006834   \n",
       "2              Severe      7.071928e-44              0.004184   \n",
       "3              Severe      1.813950e-37              0.006538   \n",
       "4              Severe      7.071928e-44              0.004184   \n",
       "..                ...               ...                   ...   \n",
       "474               Low      9.305167e-51              0.000000   \n",
       "475              High      6.141411e-44              0.006276   \n",
       "476               Low      9.305167e-51              0.000000   \n",
       "477               Low      9.305167e-51              0.000000   \n",
       "478              High      2.087394e-21              0.009375   \n",
       "\n",
       "     clustering_coefficient  \n",
       "0                      0.00  \n",
       "1                      0.05  \n",
       "2                      0.05  \n",
       "3                      0.00  \n",
       "4                      0.00  \n",
       "..                      ...  \n",
       "474                    0.00  \n",
       "475                    0.00  \n",
       "476                    0.00  \n",
       "477                    0.00  \n",
       "478                    0.00  \n",
       "\n",
       "[479 rows x 13 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "load_centrality = nx.load_centrality(G)\n",
    "# Calculate the average shortest path length for each node\n",
    "avg_shortest_path_length = {}\n",
    "for node in G.nodes():\n",
    "    path_lengths = nx.single_source_shortest_path_length(G, node)\n",
    "    avg_length = sum(path_lengths.values()) / len(path_lengths)\n",
    "    avg_shortest_path_length[node] = avg_length\n",
    "average_neighbor_degree = nx.average_neighbor_degree(G)\n",
    "# Initialize a dictionary to store node strength\n",
    "node_strength = {node: 0 for node in G.nodes()}\n",
    "\n",
    "# Sum up the weights of the edges for each node\n",
    "for node in G.nodes():\n",
    "    # Sum weights of outgoing edges\n",
    "    node_strength[node] += sum(data['weight'] for _, _, data in G.edges(node, data=True))\n",
    "    # If it's a directed graph, also sum weights of incoming edges\n",
    "    if isinstance(G, nx.DiGraph):\n",
    "        node_strength[node] += sum(data['weight'] for _, _, data in G.in_edges(node, data=True))\n",
    "df1['load_centrality'] = df1['v_id'].apply(lambda x: load_centrality.get(x, 0))\n",
    "df1['avg_shortest_path_length'] = df1['v_id'].apply(lambda x: avg_shortest_path_length.get(x, float('inf')))\n",
    "df1['average_neighbor_degree'] = df1['v_id'].apply(lambda x: average_neighbor_degree.get(x, 0))\n",
    "df1['node_strength'] = df1['v_id'].apply(lambda x: node_strength.get(x, 0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>v_id</th>\n",
       "      <th>lon</th>\n",
       "      <th>lat</th>\n",
       "      <th>typ</th>\n",
       "      <th>voltage</th>\n",
       "      <th>degree_centrality</th>\n",
       "      <th>pagerank</th>\n",
       "      <th>betweenness</th>\n",
       "      <th>combined_centrality</th>\n",
       "      <th>criticality_label</th>\n",
       "      <th>eigen_centrality</th>\n",
       "      <th>closeness_centrality</th>\n",
       "      <th>clustering_coefficient</th>\n",
       "      <th>load_centrality</th>\n",
       "      <th>avg_shortest_path_length</th>\n",
       "      <th>average_neighbor_degree</th>\n",
       "      <th>node_strength</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>9.522576</td>\n",
       "      <td>52.360409</td>\n",
       "      <td>substation</td>\n",
       "      <td>220000.0</td>\n",
       "      <td>0.002092</td>\n",
       "      <td>0.001128</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000277</td>\n",
       "      <td>Low</td>\n",
       "      <td>9.305167e-51</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.222222</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>220000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>9.113210</td>\n",
       "      <td>52.543853</td>\n",
       "      <td>substation</td>\n",
       "      <td>220000.0</td>\n",
       "      <td>0.010460</td>\n",
       "      <td>0.002766</td>\n",
       "      <td>0.000197</td>\n",
       "      <td>0.001358</td>\n",
       "      <td>Severe</td>\n",
       "      <td>1.838136e-31</td>\n",
       "      <td>0.006834</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.000197</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>1.333333</td>\n",
       "      <td>1260000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>9.389745</td>\n",
       "      <td>52.026313</td>\n",
       "      <td>substation</td>\n",
       "      <td>380000.0</td>\n",
       "      <td>0.010460</td>\n",
       "      <td>0.002086</td>\n",
       "      <td>0.000171</td>\n",
       "      <td>0.001290</td>\n",
       "      <td>Severe</td>\n",
       "      <td>7.071928e-44</td>\n",
       "      <td>0.004184</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.000171</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1900000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>9.125265</td>\n",
       "      <td>52.538264</td>\n",
       "      <td>substation</td>\n",
       "      <td>380000.0</td>\n",
       "      <td>0.010460</td>\n",
       "      <td>0.002402</td>\n",
       "      <td>0.000395</td>\n",
       "      <td>0.001499</td>\n",
       "      <td>Severe</td>\n",
       "      <td>1.813950e-37</td>\n",
       "      <td>0.006538</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000395</td>\n",
       "      <td>2.350000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1900000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>10.366275</td>\n",
       "      <td>52.284647</td>\n",
       "      <td>substation</td>\n",
       "      <td>380000.0</td>\n",
       "      <td>0.016736</td>\n",
       "      <td>0.002086</td>\n",
       "      <td>0.000110</td>\n",
       "      <td>0.001843</td>\n",
       "      <td>Severe</td>\n",
       "      <td>7.071928e-44</td>\n",
       "      <td>0.004184</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000110</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>1.333333</td>\n",
       "      <td>2400000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>474</th>\n",
       "      <td>475</td>\n",
       "      <td>8.872260</td>\n",
       "      <td>50.129725</td>\n",
       "      <td>substation</td>\n",
       "      <td>110000.0</td>\n",
       "      <td>0.002092</td>\n",
       "      <td>0.001128</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000277</td>\n",
       "      <td>Low</td>\n",
       "      <td>9.305167e-51</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>220000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>475</th>\n",
       "      <td>476</td>\n",
       "      <td>8.743261</td>\n",
       "      <td>50.162338</td>\n",
       "      <td>auxillary_T_node</td>\n",
       "      <td>220000.0</td>\n",
       "      <td>0.006276</td>\n",
       "      <td>0.003523</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000841</td>\n",
       "      <td>High</td>\n",
       "      <td>6.141411e-44</td>\n",
       "      <td>0.006276</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>660000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>476</th>\n",
       "      <td>477</td>\n",
       "      <td>7.957537</td>\n",
       "      <td>47.572159</td>\n",
       "      <td>substation</td>\n",
       "      <td>220000.0</td>\n",
       "      <td>0.002092</td>\n",
       "      <td>0.001128</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000277</td>\n",
       "      <td>Low</td>\n",
       "      <td>9.305167e-51</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>220000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>477</th>\n",
       "      <td>478</td>\n",
       "      <td>7.958491</td>\n",
       "      <td>47.579148</td>\n",
       "      <td>generator</td>\n",
       "      <td>380000.0</td>\n",
       "      <td>0.002092</td>\n",
       "      <td>0.001128</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000277</td>\n",
       "      <td>Low</td>\n",
       "      <td>9.305167e-51</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>220000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>478</th>\n",
       "      <td>479</td>\n",
       "      <td>7.950278</td>\n",
       "      <td>47.591674</td>\n",
       "      <td>auxillary_T_node</td>\n",
       "      <td>220000.0</td>\n",
       "      <td>0.006276</td>\n",
       "      <td>0.003595</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000846</td>\n",
       "      <td>High</td>\n",
       "      <td>2.087394e-21</td>\n",
       "      <td>0.009375</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>660000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>479 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     v_id        lon        lat               typ   voltage  \\\n",
       "0       1   9.522576  52.360409        substation  220000.0   \n",
       "1       2   9.113210  52.543853        substation  220000.0   \n",
       "2       3   9.389745  52.026313        substation  380000.0   \n",
       "3       4   9.125265  52.538264        substation  380000.0   \n",
       "4       5  10.366275  52.284647        substation  380000.0   \n",
       "..    ...        ...        ...               ...       ...   \n",
       "474   475   8.872260  50.129725        substation  110000.0   \n",
       "475   476   8.743261  50.162338  auxillary_T_node  220000.0   \n",
       "476   477   7.957537  47.572159        substation  220000.0   \n",
       "477   478   7.958491  47.579148         generator  380000.0   \n",
       "478   479   7.950278  47.591674  auxillary_T_node  220000.0   \n",
       "\n",
       "     degree_centrality  pagerank  betweenness  combined_centrality  \\\n",
       "0             0.002092  0.001128     0.000000             0.000277   \n",
       "1             0.010460  0.002766     0.000197             0.001358   \n",
       "2             0.010460  0.002086     0.000171             0.001290   \n",
       "3             0.010460  0.002402     0.000395             0.001499   \n",
       "4             0.016736  0.002086     0.000110             0.001843   \n",
       "..                 ...       ...          ...                  ...   \n",
       "474           0.002092  0.001128     0.000000             0.000277   \n",
       "475           0.006276  0.003523     0.000000             0.000841   \n",
       "476           0.002092  0.001128     0.000000             0.000277   \n",
       "477           0.002092  0.001128     0.000000             0.000277   \n",
       "478           0.006276  0.003595     0.000000             0.000846   \n",
       "\n",
       "    criticality_label  eigen_centrality  closeness_centrality  \\\n",
       "0                 Low      9.305167e-51              0.000000   \n",
       "1              Severe      1.838136e-31              0.006834   \n",
       "2              Severe      7.071928e-44              0.004184   \n",
       "3              Severe      1.813950e-37              0.006538   \n",
       "4              Severe      7.071928e-44              0.004184   \n",
       "..                ...               ...                   ...   \n",
       "474               Low      9.305167e-51              0.000000   \n",
       "475              High      6.141411e-44              0.006276   \n",
       "476               Low      9.305167e-51              0.000000   \n",
       "477               Low      9.305167e-51              0.000000   \n",
       "478              High      2.087394e-21              0.009375   \n",
       "\n",
       "     clustering_coefficient  load_centrality  avg_shortest_path_length  \\\n",
       "0                      0.00         0.000000                  2.222222   \n",
       "1                      0.05         0.000197                  1.500000   \n",
       "2                      0.05         0.000171                  3.000000   \n",
       "3                      0.00         0.000395                  2.350000   \n",
       "4                      0.00         0.000110                  1.500000   \n",
       "..                      ...              ...                       ...   \n",
       "474                    0.00         0.000000                  0.500000   \n",
       "475                    0.00         0.000000                  0.000000   \n",
       "476                    0.00         0.000000                  0.500000   \n",
       "477                    0.00         0.000000                  0.500000   \n",
       "478                    0.00         0.000000                  0.000000   \n",
       "\n",
       "     average_neighbor_degree  node_strength  \n",
       "0                   3.000000       220000.0  \n",
       "1                   1.333333      1260000.0  \n",
       "2                   1.000000      1900000.0  \n",
       "3                   2.000000      1900000.0  \n",
       "4                   1.333333      2400000.0  \n",
       "..                       ...            ...  \n",
       "474                 0.000000       220000.0  \n",
       "475                 0.000000       660000.0  \n",
       "476                 0.000000       220000.0  \n",
       "477                 0.000000       220000.0  \n",
       "478                 0.000000       660000.0  \n",
       "\n",
       "[479 rows x 17 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1.to_excel(\"node3_final.xlsx\", index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_clm = df1.drop(['pagerank', 'degree_centrality', 'betweenness', 'combined_centrality'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>v_id</th>\n",
       "      <th>lon</th>\n",
       "      <th>lat</th>\n",
       "      <th>typ</th>\n",
       "      <th>voltage</th>\n",
       "      <th>criticality_label</th>\n",
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       "      <th>0</th>\n",
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       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>9.113210</td>\n",
       "      <td>52.543853</td>\n",
       "      <td>substation</td>\n",
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       "      <td>1.838136e-31</td>\n",
       "      <td>0.006834</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.000197</td>\n",
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       "      <td>3</td>\n",
       "      <td>9.389745</td>\n",
       "      <td>52.026313</td>\n",
       "      <td>substation</td>\n",
       "      <td>380000.0</td>\n",
       "      <td>Severe</td>\n",
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       "      <td>0.004184</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.000171</td>\n",
       "      <td>3.000000</td>\n",
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       "      <th>3</th>\n",
       "      <td>4</td>\n",
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       "      <td>0.006538</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000395</td>\n",
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       "      <td>2.000000</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>10.366275</td>\n",
       "      <td>52.284647</td>\n",
       "      <td>substation</td>\n",
       "      <td>380000.0</td>\n",
       "      <td>Severe</td>\n",
       "      <td>7.071928e-44</td>\n",
       "      <td>0.004184</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000110</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>1.333333</td>\n",
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       "      <td>0.000000</td>\n",
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       "      <th>475</th>\n",
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       "      <td>auxillary_T_node</td>\n",
       "      <td>220000.0</td>\n",
       "      <td>High</td>\n",
       "      <td>6.141411e-44</td>\n",
       "      <td>0.006276</td>\n",
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       "      <th>478</th>\n",
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      ],
      "text/plain": [
       "     v_id        lon        lat               typ   voltage criticality_label  \\\n",
       "0       1   9.522576  52.360409        substation  220000.0               Low   \n",
       "1       2   9.113210  52.543853        substation  220000.0            Severe   \n",
       "2       3   9.389745  52.026313        substation  380000.0            Severe   \n",
       "3       4   9.125265  52.538264        substation  380000.0            Severe   \n",
       "4       5  10.366275  52.284647        substation  380000.0            Severe   \n",
       "..    ...        ...        ...               ...       ...               ...   \n",
       "474   475   8.872260  50.129725        substation  110000.0               Low   \n",
       "475   476   8.743261  50.162338  auxillary_T_node  220000.0              High   \n",
       "476   477   7.957537  47.572159        substation  220000.0               Low   \n",
       "477   478   7.958491  47.579148         generator  380000.0               Low   \n",
       "478   479   7.950278  47.591674  auxillary_T_node  220000.0              High   \n",
       "\n",
       "     eigen_centrality  closeness_centrality  clustering_coefficient  \\\n",
       "0        9.305167e-51              0.000000                    0.00   \n",
       "1        1.838136e-31              0.006834                    0.05   \n",
       "2        7.071928e-44              0.004184                    0.05   \n",
       "3        1.813950e-37              0.006538                    0.00   \n",
       "4        7.071928e-44              0.004184                    0.00   \n",
       "..                ...                   ...                     ...   \n",
       "474      9.305167e-51              0.000000                    0.00   \n",
       "475      6.141411e-44              0.006276                    0.00   \n",
       "476      9.305167e-51              0.000000                    0.00   \n",
       "477      9.305167e-51              0.000000                    0.00   \n",
       "478      2.087394e-21              0.009375                    0.00   \n",
       "\n",
       "     load_centrality  avg_shortest_path_length  average_neighbor_degree  \\\n",
       "0           0.000000                  2.222222                 3.000000   \n",
       "1           0.000197                  1.500000                 1.333333   \n",
       "2           0.000171                  3.000000                 1.000000   \n",
       "3           0.000395                  2.350000                 2.000000   \n",
       "4           0.000110                  1.500000                 1.333333   \n",
       "..               ...                       ...                      ...   \n",
       "474         0.000000                  0.500000                 0.000000   \n",
       "475         0.000000                  0.000000                 0.000000   \n",
       "476         0.000000                  0.500000                 0.000000   \n",
       "477         0.000000                  0.500000                 0.000000   \n",
       "478         0.000000                  0.000000                 0.000000   \n",
       "\n",
       "     node_strength  \n",
       "0         220000.0  \n",
       "1        1260000.0  \n",
       "2        1900000.0  \n",
       "3        1900000.0  \n",
       "4        2400000.0  \n",
       "..             ...  \n",
       "474       220000.0  \n",
       "475       660000.0  \n",
       "476       220000.0  \n",
       "477       220000.0  \n",
       "478       660000.0  \n",
       "\n",
       "[479 rows x 13 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_clm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "cols = [col for col in df_clm.columns if col != 'criticality_label']\n",
    "cols.append('criticality_label')\n",
    "df_clm = df_clm[cols]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
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       "      <th>475</th>\n",
       "      <td>476</td>\n",
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       "      <td>auxillary_T_node</td>\n",
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       "      <td>0.000000</td>\n",
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       "      <td>High</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>479 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     v_id        lon        lat               typ   voltage  eigen_centrality  \\\n",
       "0       1   9.522576  52.360409        substation  220000.0      9.305167e-51   \n",
       "1       2   9.113210  52.543853        substation  220000.0      1.838136e-31   \n",
       "2       3   9.389745  52.026313        substation  380000.0      7.071928e-44   \n",
       "3       4   9.125265  52.538264        substation  380000.0      1.813950e-37   \n",
       "4       5  10.366275  52.284647        substation  380000.0      7.071928e-44   \n",
       "..    ...        ...        ...               ...       ...               ...   \n",
       "474   475   8.872260  50.129725        substation  110000.0      9.305167e-51   \n",
       "475   476   8.743261  50.162338  auxillary_T_node  220000.0      6.141411e-44   \n",
       "476   477   7.957537  47.572159        substation  220000.0      9.305167e-51   \n",
       "477   478   7.958491  47.579148         generator  380000.0      9.305167e-51   \n",
       "478   479   7.950278  47.591674  auxillary_T_node  220000.0      2.087394e-21   \n",
       "\n",
       "     closeness_centrality  clustering_coefficient  load_centrality  \\\n",
       "0                0.000000                    0.00         0.000000   \n",
       "1                0.006834                    0.05         0.000197   \n",
       "2                0.004184                    0.05         0.000171   \n",
       "3                0.006538                    0.00         0.000395   \n",
       "4                0.004184                    0.00         0.000110   \n",
       "..                    ...                     ...              ...   \n",
       "474              0.000000                    0.00         0.000000   \n",
       "475              0.006276                    0.00         0.000000   \n",
       "476              0.000000                    0.00         0.000000   \n",
       "477              0.000000                    0.00         0.000000   \n",
       "478              0.009375                    0.00         0.000000   \n",
       "\n",
       "     avg_shortest_path_length  average_neighbor_degree  node_strength  \\\n",
       "0                    2.222222                 3.000000       220000.0   \n",
       "1                    1.500000                 1.333333      1260000.0   \n",
       "2                    3.000000                 1.000000      1900000.0   \n",
       "3                    2.350000                 2.000000      1900000.0   \n",
       "4                    1.500000                 1.333333      2400000.0   \n",
       "..                        ...                      ...            ...   \n",
       "474                  0.500000                 0.000000       220000.0   \n",
       "475                  0.000000                 0.000000       660000.0   \n",
       "476                  0.500000                 0.000000       220000.0   \n",
       "477                  0.500000                 0.000000       220000.0   \n",
       "478                  0.000000                 0.000000       660000.0   \n",
       "\n",
       "    criticality_label  \n",
       "0                 Low  \n",
       "1              Severe  \n",
       "2              Severe  \n",
       "3              Severe  \n",
       "4              Severe  \n",
       "..                ...  \n",
       "474               Low  \n",
       "475              High  \n",
       "476               Low  \n",
       "477               Low  \n",
       "478              High  \n",
       "\n",
       "[479 rows x 13 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_clm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_clm.to_excel(\"node3_cml.xlsx\", index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>v_id</th>\n",
       "      <th>lon</th>\n",
       "      <th>lat</th>\n",
       "      <th>typ</th>\n",
       "      <th>voltage</th>\n",
       "      <th>eigen_centrality</th>\n",
       "      <th>closeness_centrality</th>\n",
       "      <th>clustering_coefficient</th>\n",
       "      <th>load_centrality</th>\n",
       "      <th>avg_shortest_path_length</th>\n",
       "      <th>average_neighbor_degree</th>\n",
       "      <th>node_strength</th>\n",
       "      <th>criticality_label</th>\n",
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       "      <td>52.360409</td>\n",
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       "      <td>1.500000</td>\n",
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       "      <td>5</td>\n",
       "      <td>10.366275</td>\n",
       "      <td>52.284647</td>\n",
       "      <td>substation</td>\n",
       "      <td>380000.0</td>\n",
       "      <td>7.071928e-44</td>\n",
       "      <td>0.004184</td>\n",
       "      <td>0.00</td>\n",
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       "      <td>1.500000</td>\n",
       "      <td>1.333333</td>\n",
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       "      <th>...</th>\n",
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       "      <td>476</td>\n",
       "      <td>8.743261</td>\n",
       "      <td>50.162338</td>\n",
       "      <td>auxillary_T_node</td>\n",
       "      <td>220000.0</td>\n",
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       "      <td>0.006276</td>\n",
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       "      <td>0.000000</td>\n",
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       "      <td>High</td>\n",
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       "<p>479 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     v_id        lon        lat               typ   voltage  eigen_centrality  \\\n",
       "0       1   9.522576  52.360409        substation  220000.0      9.305167e-51   \n",
       "1       2   9.113210  52.543853        substation  220000.0      1.838136e-31   \n",
       "2       3   9.389745  52.026313        substation  380000.0      7.071928e-44   \n",
       "3       4   9.125265  52.538264        substation  380000.0      1.813950e-37   \n",
       "4       5  10.366275  52.284647        substation  380000.0      7.071928e-44   \n",
       "..    ...        ...        ...               ...       ...               ...   \n",
       "474   475   8.872260  50.129725        substation  110000.0      9.305167e-51   \n",
       "475   476   8.743261  50.162338  auxillary_T_node  220000.0      6.141411e-44   \n",
       "476   477   7.957537  47.572159        substation  220000.0      9.305167e-51   \n",
       "477   478   7.958491  47.579148         generator  380000.0      9.305167e-51   \n",
       "478   479   7.950278  47.591674  auxillary_T_node  220000.0      2.087394e-21   \n",
       "\n",
       "     closeness_centrality  clustering_coefficient  load_centrality  \\\n",
       "0                0.000000                    0.00         0.000000   \n",
       "1                0.006834                    0.05         0.000197   \n",
       "2                0.004184                    0.05         0.000171   \n",
       "3                0.006538                    0.00         0.000395   \n",
       "4                0.004184                    0.00         0.000110   \n",
       "..                    ...                     ...              ...   \n",
       "474              0.000000                    0.00         0.000000   \n",
       "475              0.006276                    0.00         0.000000   \n",
       "476              0.000000                    0.00         0.000000   \n",
       "477              0.000000                    0.00         0.000000   \n",
       "478              0.009375                    0.00         0.000000   \n",
       "\n",
       "     avg_shortest_path_length  average_neighbor_degree  node_strength  \\\n",
       "0                    2.222222                 3.000000       220000.0   \n",
       "1                    1.500000                 1.333333      1260000.0   \n",
       "2                    3.000000                 1.000000      1900000.0   \n",
       "3                    2.350000                 2.000000      1900000.0   \n",
       "4                    1.500000                 1.333333      2400000.0   \n",
       "..                        ...                      ...            ...   \n",
       "474                  0.500000                 0.000000       220000.0   \n",
       "475                  0.000000                 0.000000       660000.0   \n",
       "476                  0.500000                 0.000000       220000.0   \n",
       "477                  0.500000                 0.000000       220000.0   \n",
       "478                  0.000000                 0.000000       660000.0   \n",
       "\n",
       "    criticality_label  \n",
       "0                 Low  \n",
       "1              Severe  \n",
       "2              Severe  \n",
       "3              Severe  \n",
       "4              Severe  \n",
       "..                ...  \n",
       "474               Low  \n",
       "475              High  \n",
       "476               Low  \n",
       "477               Low  \n",
       "478              High  \n",
       "\n",
       "[479 rows x 13 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_clm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/mmishkaturrahman/Library/Python/3.9/lib/python/site-packages/sklearn/preprocessing/_encoders.py:975: FutureWarning: `sparse` was renamed to `sparse_output` in version 1.2 and will be removed in 1.4. `sparse_output` is ignored unless you leave `sparse` to its default value.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "from sklearn.preprocessing import OneHotEncoder\n",
    "\n",
    "# Reshape the 'typ' column to a 2D array for OneHotEncoder\n",
    "typ_array = df_clm[['typ']].values\n",
    "\n",
    "# Create the encoder\n",
    "encoder = OneHotEncoder(sparse=False)\n",
    "typ_encoded = encoder.fit_transform(typ_array)\n",
    "\n",
    "# Convert the encoded data into a DataFrame\n",
    "typ_encoded_df = pd.DataFrame(typ_encoded, columns=encoder.get_feature_names_out(['typ']))\n",
    "\n",
    "# Concatenate the encoded df with the original df\n",
    "df_clm = pd.concat([df_clm.reset_index(drop=True), typ_encoded_df], axis=1)\n",
    "\n",
    "# Drop the original 'typ' column\n",
    "df_clm.drop('typ', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Encode the Target Variable\n",
    "#Use LabelEncoder to convert the criticality_label into numerical format:\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "\n",
    "label_encoder = LabelEncoder()\n",
    "df_clm['criticality_label'] = label_encoder.fit_transform(df_clm['criticality_label'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Normalize/Standardize Numerical Features\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "# List of numerical columns (exclude 'v_id' and 'criticality_label')\n",
    "num_cols = ['lon', 'lat', 'voltage', 'eigen_centrality', 'closeness_centrality', \n",
    "            'clustering_coefficient', 'load_centrality', 'avg_shortest_path_length', \n",
    "            'average_neighbor_degree', 'node_strength']\n",
    "\n",
    "scaler = StandardScaler()\n",
    "df_clm[num_cols] = scaler.fit_transform(df_clm[num_cols])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Splitting the Data\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X = df_clm.drop('criticality_label', axis=1)\n",
    "y = df_clm['criticality_label']\n",
    "\n",
    "# Stratified split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, stratify=y, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Defaulting to user installation because normal site-packages is not writeable\n",
      "Requirement already satisfied: xgboost in /Users/mmishkaturrahman/Library/Python/3.9/lib/python/site-packages (2.0.3)\n",
      "Requirement already satisfied: numpy in /Users/mmishkaturrahman/Library/Python/3.9/lib/python/site-packages (from xgboost) (1.26.2)\n",
      "Requirement already satisfied: scipy in /Users/mmishkaturrahman/Library/Python/3.9/lib/python/site-packages (from xgboost) (1.11.4)\n",
      "\u001b[33mWARNING: You are using pip version 21.2.4; however, version 23.3.2 is available.\n",
      "You should consider upgrading via the '/Library/Developer/CommandLineTools/usr/bin/python3 -m pip install --upgrade pip' command.\u001b[0m\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
       "              colsample_bylevel=None, colsample_bynode=None,\n",
       "              colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
       "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "              gamma=None, grow_policy=None, importance_type=None,\n",
       "              interaction_constraints=None, learning_rate=None, max_bin=None,\n",
       "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "              max_delta_step=None, max_depth=None, max_leaves=None,\n",
       "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "              multi_strategy=None, n_estimators=None, n_jobs=None,\n",
       "              num_parallel_tree=None, objective=&#x27;multi:softprob&#x27;, ...)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">XGBClassifier</label><div class=\"sk-toggleable__content\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
       "              colsample_bylevel=None, colsample_bynode=None,\n",
       "              colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
       "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "              gamma=None, grow_policy=None, importance_type=None,\n",
       "              interaction_constraints=None, learning_rate=None, max_bin=None,\n",
       "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "              max_delta_step=None, max_depth=None, max_leaves=None,\n",
       "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "              multi_strategy=None, n_estimators=None, n_jobs=None,\n",
       "              num_parallel_tree=None, objective=&#x27;multi:softprob&#x27;, ...)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
       "              colsample_bylevel=None, colsample_bynode=None,\n",
       "              colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
       "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "              gamma=None, grow_policy=None, importance_type=None,\n",
       "              interaction_constraints=None, learning_rate=None, max_bin=None,\n",
       "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "              max_delta_step=None, max_depth=None, max_leaves=None,\n",
       "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "              multi_strategy=None, n_estimators=None, n_jobs=None,\n",
       "              num_parallel_tree=None, objective='multi:softprob', ...)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Model Training\n",
    "%pip install xgboost\n",
    "import xgboost as xgb\n",
    "\n",
    "xgb_clf = xgb.XGBClassifier()\n",
    "xgb_clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>MLPClassifier(max_iter=1000, random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">MLPClassifier</label><div class=\"sk-toggleable__content\"><pre>MLPClassifier(max_iter=1000, random_state=42)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "MLPClassifier(max_iter=1000, random_state=42)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Multilayer perceptron\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "\n",
    "mlp_clf = MLPClassifier(max_iter=1000, random_state=42)\n",
    "mlp_clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {color: black;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "SVC()"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#SVM\n",
    "from sklearn.svm import SVC\n",
    "\n",
    "svm_clf = SVC()\n",
    "svm_clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "XGBoost Accuracy: 0.8263888888888888\n",
      "MLP Accuracy: 0.6875\n",
      "SVM Accuracy: 0.3611111111111111\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "# Evaluate XGBoost\n",
    "y_pred_xgb = xgb_clf.predict(X_test)\n",
    "accuracy_xgb = accuracy_score(y_test, y_pred_xgb)\n",
    "\n",
    "# Evaluate MLP\n",
    "y_pred_mlp = mlp_clf.predict(X_test)\n",
    "accuracy_mlp = accuracy_score(y_test, y_pred_mlp)\n",
    "\n",
    "# Evaluate SVM\n",
    "y_pred_svm = svm_clf.predict(X_test)\n",
    "accuracy_svm = accuracy_score(y_test, y_pred_svm)\n",
    "\n",
    "print(f\"XGBoost Accuracy: {accuracy_xgb}\")\n",
    "print(f\"MLP Accuracy: {accuracy_mlp}\")\n",
    "print(f\"SVM Accuracy: {accuracy_svm}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Assess Feature Importance with XGBoost\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Get feature importances from the trained XGBoost model\n",
    "xgb_feature_importances = xgb_clf.feature_importances_\n",
    "\n",
    "# Create a pandas series to hold the feature importances\n",
    "features = pd.Series(xgb_feature_importances, index=X_train.columns)\n",
    "\n",
    "# Plot the feature importances\n",
    "plt.figure(figsize=(10, 8))\n",
    "features.sort_values(ascending=False).plot(kind='bar')\n",
    "plt.title('Feature Importance in XGBoost Classifier')\n",
    "plt.xlabel('Features')\n",
    "plt.ylabel('Importance Score')\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Feature Importances:\n",
      "eigen_centrality            0.327468\n",
      "node_strength               0.263645\n",
      "voltage                     0.080253\n",
      "closeness_centrality        0.077640\n",
      "typ_auxillary_T_node        0.062690\n",
      "load_centrality             0.040499\n",
      "v_id                        0.034389\n",
      "avg_shortest_path_length    0.028571\n",
      "lon                         0.021662\n",
      "clustering_coefficient      0.017912\n",
      "typ_substation              0.017727\n",
      "lat                         0.015838\n",
      "average_neighbor_degree     0.011705\n",
      "typ_generator               0.000000\n",
      "typ_plant                   0.000000\n",
      "dtype: float32\n"
     ]
    }
   ],
   "source": [
    "# Assuming X_train is a pandas DataFrame with column names\n",
    "feature_importances = xgb_clf.feature_importances_\n",
    "feature_names = X_train.columns\n",
    "importances = pd.Series(feature_importances, index=feature_names).sort_values(ascending=False)\n",
    "\n",
    "print(\"Feature Importances:\")\n",
    "print(importances)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Cross Validation\n",
    "from sklearn.model_selection import GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Define Hyperparameter\n",
    "xgb_param_grid = {\n",
    "    'n_estimators': [100, 200, 300],\n",
    "    'learning_rate': [0.01, 0.1, 0.2],\n",
    "    'max_depth': [3, 4, 5],\n",
    "    'subsample': [0.7, 0.8, 0.9],\n",
    "    'colsample_bytree': [0.7, 0.8, 0.9]\n",
    "}\n",
    "mlp_param_grid = {\n",
    "    'hidden_layer_sizes': [(50,), (100,), (50, 50)],\n",
    "    'activation': ['tanh', 'relu'],\n",
    "    'solver': ['sgd', 'adam'],\n",
    "    'alpha': [0.0001, 0.05],\n",
    "    'learning_rate': ['constant', 'adaptive'],\n",
    "}\n",
    "svm_param_grid = {\n",
    "    'C': [0.1, 1, 10],\n",
    "    'gamma': ['scale', 'auto'],\n",
    "    'kernel': ['rbf', 'linear']\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-4 {color: black;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-4 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-4 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=5, estimator=SVC(), n_jobs=-1,\n",
       "             param_grid={&#x27;C&#x27;: [0.1, 1, 10], &#x27;gamma&#x27;: [&#x27;scale&#x27;, &#x27;auto&#x27;],\n",
       "                         &#x27;kernel&#x27;: [&#x27;rbf&#x27;, &#x27;linear&#x27;]},\n",
       "             scoring=&#x27;accuracy&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=5, estimator=SVC(), n_jobs=-1,\n",
       "             param_grid={&#x27;C&#x27;: [0.1, 1, 10], &#x27;gamma&#x27;: [&#x27;scale&#x27;, &#x27;auto&#x27;],\n",
       "                         &#x27;kernel&#x27;: [&#x27;rbf&#x27;, &#x27;linear&#x27;]},\n",
       "             scoring=&#x27;accuracy&#x27;)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: SVC</label><div class=\"sk-toggleable__content\"><pre>SVC()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC()</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "GridSearchCV(cv=5, estimator=SVC(), n_jobs=-1,\n",
       "             param_grid={'C': [0.1, 1, 10], 'gamma': ['scale', 'auto'],\n",
       "                         'kernel': ['rbf', 'linear']},\n",
       "             scoring='accuracy')"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Run Grid Search CV\n",
    "xgb_grid_search = GridSearchCV(xgb.XGBClassifier(), xgb_param_grid, cv=5, scoring='accuracy', n_jobs=-1)\n",
    "xgb_grid_search.fit(X_train, y_train)\n",
    "mlp_grid_search = GridSearchCV(MLPClassifier(max_iter=1000), mlp_param_grid, cv=5, scoring='accuracy', n_jobs=-1)\n",
    "mlp_grid_search.fit(X_train, y_train)\n",
    "svm_grid_search = GridSearchCV(SVC(), svm_param_grid, cv=5, scoring='accuracy', n_jobs=-1)\n",
    "svm_grid_search.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best XGBoost Accuracy: 0.8472222222222222\n",
      "Best MLP Accuracy: 0.7986111111111112\n",
      "Best SVM Accuracy: 0.8333333333333334\n"
     ]
    }
   ],
   "source": [
    "#Evaluate best Model\n",
    "# Best estimator accuracy for XGBoost\n",
    "best_xgb = xgb_grid_search.best_estimator_\n",
    "best_xgb_accuracy = accuracy_score(y_test, best_xgb.predict(X_test))\n",
    "\n",
    "# Best estimator accuracy for MLP\n",
    "best_mlp = mlp_grid_search.best_estimator_\n",
    "best_mlp_accuracy = accuracy_score(y_test, best_mlp.predict(X_test))\n",
    "\n",
    "# Best estimator accuracy for SVM\n",
    "best_svm = svm_grid_search.best_estimator_\n",
    "best_svm_accuracy = accuracy_score(y_test, best_svm.predict(X_test))\n",
    "\n",
    "print(f\"Best XGBoost Accuracy: {best_xgb_accuracy}\")\n",
    "print(f\"Best MLP Accuracy: {best_mlp_accuracy}\")\n",
    "print(f\"Best SVM Accuracy: {best_svm_accuracy}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best XGBoost Parameters: {'colsample_bytree': 0.7, 'learning_rate': 0.2, 'max_depth': 4, 'n_estimators': 200, 'subsample': 0.9}\n",
      "Best XGBoost Accuracy: 0.8472222222222222\n",
      "Best MLP Parameters: {'activation': 'tanh', 'alpha': 0.05, 'hidden_layer_sizes': (50, 50), 'learning_rate': 'constant', 'solver': 'adam'}\n",
      "Best MLP Accuracy: 0.7916666666666666\n",
      "Best SVM Parameters: {'C': 10, 'gamma': 'scale', 'kernel': 'linear'}\n",
      "Best SVM Accuracy: 0.8333333333333334\n"
     ]
    }
   ],
   "source": [
    "# Best parameters and accuracy for XGBoost\n",
    "best_xgb_params = xgb_grid_search.best_params_\n",
    "best_xgb_accuracy = accuracy_score(y_test, best_xgb.predict(X_test))\n",
    "print(f\"Best XGBoost Parameters: {best_xgb_params}\")\n",
    "print(f\"Best XGBoost Accuracy: {best_xgb_accuracy}\")\n",
    "# Best parameters and accuracy for MLP\n",
    "best_mlp_params = mlp_grid_search.best_params_\n",
    "best_mlp_accuracy = accuracy_score(y_test, best_mlp.predict(X_test))\n",
    "print(f\"Best MLP Parameters: {best_mlp_params}\")\n",
    "print(f\"Best MLP Accuracy: {best_mlp_accuracy}\")\n",
    "# Best parameters and accuracy for SVM\n",
    "best_svm_params = svm_grid_search.best_params_\n",
    "best_svm_accuracy = accuracy_score(y_test, best_svm.predict(X_test))\n",
    "print(f\"Best SVM Parameters: {best_svm_params}\")\n",
    "print(f\"Best SVM Accuracy: {best_svm_accuracy}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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