3_merge_CG_KG.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "4c92a92e-8a89-4d96-89f1-2510bb39abea",
   "metadata": {},
   "source": [
    "# Merge the Causal Graph and the Knowledge graph\n",
    "\n",
    "1. For each outage, link all the alerts that occurred 1 hour before the start of the outage till the end of the outage to itself. This will link all the alerts that are potentially responsible for the outage.\n",
    "2. Train random forest where the features are the alerts that were fired and the label is the outage cluster label. This will help in identifying the outage cluster during inference time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "55c4723c-7f07-4f29-bd1f-9608dbf1388b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import networkx as nx\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from queue import Queue\n",
    "import random\n",
    "import numpy as np\n",
    "import csv\n",
    "import pickle\n",
    "from datetime import datetime, timedelta   ## dts creation\n",
    "from sklearn import feature_selection      ## mutual information\n",
    "import json"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5e5ff939",
   "metadata": {},
   "outputs": [],
   "source": [
    "global df_alerts_sorted, cso_df, timelist_alerts, numberlist_alerts, cg, kg, cg_alert_id, symptom_cluster"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a754031e-a401-4548-b30e-02c41471e769",
   "metadata": {},
   "outputs": [],
   "source": [
    "# For each outage with a start and end time, find the alerts that were fired within 1 hour ago of the start of the outage and till the outage persists\n",
    "def get_alerts_by_time(start_time, end_time, num):                                           # the input time and the number of different recorded past alert times, we want alerts from\n",
    "    lst = []\n",
    "    low = 0\n",
    "    mx = len(timelist_alerts)\n",
    "    high = mx\n",
    "    while(low<high):\n",
    "        if(timelist_alerts[int((low+high)/2)] > start_time):\n",
    "            high = int((low+high)/2)\n",
    "        else:\n",
    "            low = int((low+high)/2)+1\n",
    "    \n",
    "    ind = low-1\n",
    "    j = 0\n",
    "    k = 0\n",
    "    while(j < low-1 and (start_time - timelist_alerts[low-1-j]) < pd.Timedelta(hours=1)):\n",
    "        lst.append(numberlist_alerts[low - 1 -j])\n",
    "        while(j<low-1 and timelist_alerts[low-1-j] == timelist_alerts[low-2-j]):\n",
    "            lst.append(numberlist_alerts[low-2-j])\n",
    "            j = j + 1\n",
    "        j = j + 1\n",
    "        k = k + 1\n",
    "    \n",
    "    j = low\n",
    "    k = 0\n",
    "    while(j<mx and (end_time - timelist_alerts[j]) > pd.Timedelta(minutes=1)):\n",
    "        lst.append(numberlist_alerts[j])\n",
    "        while(j<mx-1 and timelist_alerts[j] == timelist_alerts[j+1]):\n",
    "            lst.append(numberlist_alerts[j+1])\n",
    "            j = j + 1\n",
    "        j = j + 1\n",
    "        k = k + 1\n",
    "    return list(set(lst))\n",
    "\n",
    "\n",
    "# Wrapper function to get the alerts for each outage\n",
    "def get_alerts_cso(cso, num):\n",
    "    timestr = cso_df[cso_df['cso_number'] == cso]['start_time'].iloc[0]   # gets cso timestr\n",
    "    timeend = cso_df[cso_df['cso_number'] == cso]['end_time'].iloc[0]\n",
    "    \n",
    "    timestr = pd.Timestamp(timestr, tz=\"UTC\")\n",
    "    timeend = pd.Timestamp(timeend, tz=\"UTC\")\n",
    "\n",
    "    if timestr < timelist_alerts[0]:\n",
    "        timestr = timelist_alerts[0]\n",
    "    \n",
    "    if timeend < timelist_alerts[0]:\n",
    "        timeend = timelist_alerts[0]\n",
    "        \n",
    "    return(get_alerts_by_time(timestr, timeend, num))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5419eb62-f1fc-4d42-a0e1-06b394e03bfa",
   "metadata": {},
   "outputs": [],
   "source": [
    "def combine_graphs(show = False):\n",
    "    G = nx.union(cg, kg)   \n",
    "    \n",
    "    # display the combined graph if 'show = True'\n",
    "    if show:\n",
    "        edge_labels = dict()\n",
    "        color_map = []\n",
    "        edge_color = []\n",
    "        i = G.nodes(data = True)[1]\n",
    "\n",
    "        color_map = [i[1]['color'] for i in list(G.nodes(data = True))]\n",
    "        for i in list(G.edges(data = True)):\n",
    "            edge_labels[i[0],i[1]] = i[2]['label']\n",
    "        edge_color = [i[2]['color'] for i in list(G.edges(data = True))]\n",
    "\n",
    "        plt.figure(1, figsize = (35,35))\n",
    "        pos = nx.spring_layout(G)\n",
    "        for i in cg.nodes():\n",
    "            pos[i][0] = pos[i][0]*0.5 - 0.7\n",
    "            pos[i][1] = pos[i][1]*0.8\n",
    "\n",
    "        for i in kg.nodes():\n",
    "            pos[i][0] = pos[i][0]*0.7 + 0.6\n",
    "            pos[i][1] = pos[i][1]*0.9\n",
    "        nx.draw_networkx_nodes(G, pos = pos, node_color = color_map)\n",
    "        nx.draw_networkx_labels(G, pos)\n",
    "        nx.draw_networkx_edges(G, pos, edge_color =  edge_color)\n",
    "        nx.draw_networkx_edge_labels(G, pos, edge_labels = edge_labels)\n",
    "        plt.show()\n",
    "    \n",
    "    return G"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "9481c136-ca58-46f7-aa35-a8e653d37701",
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_cross_edges(no_of_alert_timestamps , G, show = False):\n",
    "    \n",
    "    global inc_dict \n",
    "    global cross_edges\n",
    "    global df_rf\n",
    "    \n",
    "    # map of incident number to index\n",
    "    inc_dict = {}\n",
    "    cross_edges = []\n",
    "    for i, inc in enumerate(numberlist_alerts):\n",
    "        inc_dict[str(inc)] = i\n",
    "    \n",
    "    alert_id_cg = {v:k for k,v in cg_alert_id.items()}\n",
    "    alert_ids = [v for k,v in cg_alert_id.items()]\n",
    "    \n",
    "    df_rf = pd.DataFrame()\n",
    "    \n",
    "    # for each cso, get alert incidents in its neighbourhood\n",
    "    for i,cso in enumerate(cso_df['cso_number'].values):\n",
    "        l = np.zeros(shape=len(alert_ids)+1)\n",
    "        \n",
    "        alerts_timeframe = get_alerts_cso(cso, no_of_alert_timestamps)\n",
    "        for alert in alerts_timeframe:        # append each alert in neighbourhood as cross_edge to a cso \n",
    "            alert_id = df_alerts_sorted['alert_id'].iloc[inc_dict[str(alert)]]\n",
    "            cross_edges.append([alert_id_cg[alert_id], cso])\n",
    "            l[alert_id_cg[alert_id]] = 1\n",
    "        \n",
    "        l[-1] = symptom_cluster[cso + '_sym']\n",
    "        df_rf = pd.concat([df_rf, pd.DataFrame([l])], axis=0, ignore_index=True)\n",
    "       \n",
    "    df_rf.columns = alert_ids + ['cluster_id']\n",
    "    \n",
    "    kwargs = {}\n",
    "    kwargs['label'] = 'caused CSO'\n",
    "    kwargs['color'] = 'magenta'\n",
    "    for edge in cross_edges:\n",
    "        G.add_edge(edge[0], str(edge[1])+'_sym', **kwargs)\n",
    "        \n",
    "    # runs if we want to see the graph\n",
    "    if show:\n",
    "        edge_labels = dict()\n",
    "        color_map = []\n",
    "        edge_color = []\n",
    "\n",
    "        color_map = [i[1]['color'] for i in list(G.nodes(data = True))]\n",
    "        for i in list(G.edges(data = True)):\n",
    "            edge_labels[i[0],i[1]] = i[2]['label']\n",
    "        edge_color = [i[2]['color'] for i in list(G.edges(data = True))]\n",
    "\n",
    "        plt.figure(2, figsize = (35,35))\n",
    "        pos = nx.spring_layout(G)\n",
    "        for i in cg.nodes():\n",
    "            pos[i][0] = pos[i][0]*0.5 - 0.7\n",
    "            pos[i][1] = pos[i][1]*0.8\n",
    "\n",
    "        for i in kg.nodes():\n",
    "            pos[i][0] = pos[i][0]*0.7 + 0.6\n",
    "            pos[i][1] = pos[i][1]*0.9\n",
    "        nx.draw_networkx_nodes(G, pos = pos, node_color = color_map)\n",
    "        nx.draw_networkx_labels(G, pos)\n",
    "        nx.draw_networkx_edges(G, pos, edge_color =  edge_color)\n",
    "        nx.draw_networkx_edge_labels(G, pos, edge_labels = edge_labels)\n",
    "        plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "3b24aeb2-8cfc-44e5-8a23-089919140123",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "def read_files(cg_path, kg_path, alerts_csv, cso_json):\n",
    "    \n",
    "    \n",
    "    cg = pickle.load(open(cg_path, 'rb'))\n",
    "    kg = pickle.load(open(kg_path, 'rb'))\n",
    "    \n",
    "    cg_alert_id = nx.get_node_attributes(cg, \"alert_id\")\n",
    "    alert_ids = [v for k,v in cg_alert_id.items()]\n",
    "    \n",
    "    symptom_cluster = nx.get_node_attributes(kg, \"cluster_id\")\n",
    "    \n",
    "    df_alerts_sorted = pd.read_csv(alerts_csv, index_col=0)\n",
    "    df_alerts_sorted['created_at'] = pd.to_datetime(df_alerts_sorted['created_at'])\n",
    "    df_alerts_sorted = df_alerts_sorted[df_alerts_sorted['alert_id'].isin(alert_ids)]\n",
    "    \n",
    "    with open(cso_json, 'r') as f:\n",
    "        data = json.load(f)\n",
    "    \n",
    "    cso_data = {'cso_number':[], 'start_time':[], 'end_time':[]}\n",
    "    for cso, value in data.items():\n",
    "        cso_data['cso_number'].append(cso)\n",
    "        cso_data['start_time'].append(value['primaryIncident']['u_cso_started'])\n",
    "        cso_data['end_time'].append(value['primaryIncident']['u_cso_ended'])\n",
    "\n",
    "    cso_df = pd.DataFrame(cso_data)\n",
    "    \n",
    "    df_alerts_sorted = df_alerts_sorted.sort_values(by = 'created_at').reset_index(drop=True)\n",
    "    timelist_alerts = list(df_alerts_sorted['created_at'])\n",
    "    numberlist_alerts = list(df_alerts_sorted['incident_number'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "7fc29a11-59bf-4b8a-9e8a-53ce98764770",
   "metadata": {},
   "outputs": [],
   "source": [
    "cg_path = './causal_graph_data/cg_unweighted_filtered.gpickle'\n",
    "kg_path = './CSO_data/KG_pickle_clustered.gpickle'\n",
    "alerts_csv = './Alerts_data/all_alerts_parsed.csv'\n",
    "cso_json = './CSO_data/CSO.json'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "4119337c-e7b8-48a8-a723-cb8aad7dfae8",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "read_files(cg_path, kg_path, alerts_csv, cso_json)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "c0558503-484a-493a-aff4-2b0730e8601f",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "n = 10\n",
    "k = 4\n",
    "G = combine_graphs(False)\n",
    "make_cross_edges(n , G, False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "89fd4f00-a4f0-4dce-bd81-6ec5f1ec93e3",
   "metadata": {},
   "outputs": [],
   "source": [
    "pickle.dump(G, open('cg_kg.gpickle', 'wb'))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7b892df2-5f77-4b5e-9996-f6dfa72ec9fe",
   "metadata": {},
   "source": [
    "# Train Random Forest for Alerts to Symptom Clusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "cb855b06-e87a-4136-8412-9a5b3de59dd4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>9</th>\n",
       "      <th>20</th>\n",
       "      <th>30</th>\n",
       "      <th>33</th>\n",
       "      <th>...</th>\n",
       "      <th>920</th>\n",
       "      <th>922</th>\n",
       "      <th>925</th>\n",
       "      <th>926</th>\n",
       "      <th>927</th>\n",
       "      <th>932</th>\n",
       "      <th>933</th>\n",
       "      <th>934</th>\n",
       "      <th>936</th>\n",
       "      <th>cluster_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>177</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>49.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>44.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>182 rows × 330 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       0    2    3    4    5    6    9   20   30   33  ...  920  922  925  \\\n",
       "0    0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0  0.0   \n",
       "1    0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0  0.0   \n",
       "2    0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0  0.0   \n",
       "3    0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0  0.0   \n",
       "4    0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0  0.0   \n",
       "..   ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...   \n",
       "177  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0  0.0   \n",
       "178  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0  0.0   \n",
       "179  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0  0.0   \n",
       "180  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0  0.0   \n",
       "181  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0  0.0   \n",
       "\n",
       "     926  927  932  933  934  936  cluster_id  \n",
       "0    0.0  0.0  0.0  0.0  0.0  0.0         0.0  \n",
       "1    0.0  0.0  0.0  0.0  0.0  0.0         0.0  \n",
       "2    0.0  0.0  0.0  0.0  0.0  0.0         1.0  \n",
       "3    0.0  0.0  0.0  0.0  0.0  0.0         2.0  \n",
       "4    0.0  0.0  0.0  0.0  0.0  0.0         3.0  \n",
       "..   ...  ...  ...  ...  ...  ...         ...  \n",
       "177  0.0  0.0  0.0  0.0  0.0  0.0        49.0  \n",
       "178  0.0  0.0  0.0  0.0  0.0  0.0        14.0  \n",
       "179  0.0  0.0  0.0  0.0  0.0  0.0         6.0  \n",
       "180  0.0  0.0  0.0  0.0  0.0  0.0         6.0  \n",
       "181  0.0  0.0  0.0  0.0  0.0  0.0        44.0  \n",
       "\n",
       "[182 rows x 330 columns]"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_rf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "894a0fda-f97a-4c90-9b12-5317db485a7a",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "260cb243-900e-44d1-9072-c4a4d1a712ea",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df_rf.loc[:, df_rf.columns != 'cluster_id']\n",
    "y = df_rf['cluster_id']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "4b4fef7a-e5d3-4e7b-a2c0-efbe6312ab2c",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "a5121195-8d6a-4373-894d-e90f8431d6ed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "46"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(np.unique(y_train)), len(np.unique(y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "2acfee46-364a-486d-8279-76f411738404",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-6 {color: black;background-color: white;}#sk-container-id-6 pre{padding: 0;}#sk-container-id-6 div.sk-toggleable {background-color: white;}#sk-container-id-6 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-6 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-6 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-6 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-6 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-6 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-6 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-6 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-6 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 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-6 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-6 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-6 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-6 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 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-6 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-6 div.sk-item {position: relative;z-index: 1;}#sk-container-id-6 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-6 div.sk-item::before, #sk-container-id-6 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-6 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-6 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-6 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-6 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-6 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-6 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-6 div.sk-label-container {text-align: center;}#sk-container-id-6 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-6 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-6\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(max_depth=25, n_estimators=50, random_state=0)</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-6\" type=\"checkbox\" checked><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(max_depth=25, n_estimators=50, random_state=0)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestClassifier(max_depth=25, n_estimators=50, random_state=0)"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf = RandomForestClassifier(max_depth=25, n_estimators=50, random_state=0)\n",
    "clf.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "fb497b53-32b1-4005-8218-8d61ba67233c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.554945054945055"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.score(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "8a0b03cc-9ce1-484c-b034-8e6865a38bec",
   "metadata": {},
   "outputs": [],
   "source": [
    "def gen_labels(X, y, k):\n",
    "    y_pred_prob = clf.predict_proba(X)\n",
    "    ind = np.argpartition(y_pred_prob, -k)\n",
    "    top_k_ind = ind[:, -k:]\n",
    "    \n",
    "    labels = []\n",
    "    acc = 0 \n",
    "    for i in range(len(y)):\n",
    "        if y.iloc[i] in top_k_ind[i]:\n",
    "            labels.append(1)\n",
    "            acc += 1\n",
    "        else:\n",
    "            labels.append(0)\n",
    "    \n",
    "    return labels, acc/len(labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "aaef8ade-6701-41cb-be3a-1dc53ba3b1b7",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.metrics import roc_auc_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "382d7fa9-87bc-4567-b436-76acb089d4ed",
   "metadata": {},
   "outputs": [],
   "source": [
    "acc_list = []\n",
    "\n",
    "# top-10 accuracy computation\n",
    "for i in range(1,11):\n",
    "    labels, acc = gen_labels(X_test, y_test, k=i)\n",
    "    acc_list.append(acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "500d6b76-ffba-4087-ab18-74b5c3f07ac0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.6181818181818182,\n",
       " 0.6545454545454545,\n",
       " 0.6727272727272727,\n",
       " 0.6909090909090909,\n",
       " 0.7272727272727273,\n",
       " 0.7818181818181819,\n",
       " 0.7818181818181819,\n",
       " 0.7818181818181819,\n",
       " 0.7818181818181819,\n",
       " 0.8]"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "acc_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "ae6cf09f-b845-4435-9eae-1f787e927bc4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAY4AAAEWCAYAAABxMXBSAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjYuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/P9b71AAAACXBIWXMAAAsTAAALEwEAmpwYAAAzXElEQVR4nO3dd5wV1f3/8deHXbp0cKUXASkWygK2nxIraiwxRkGNogY00SRqokFjYotR87XEGDUSC8QCKjasiGXViAK7BgsgsCy99w7bPr8/Zja5XneXe3Hvzpb38/G4j71z5syZz9zdnc+dM2dmzN0RERFJVJ2oAxARkepFiUNERJKixCEiIklR4hARkaQocYiISFKUOEREJClKHFJlmdlIM/t31HFUNjPLMrOfhe8vMLN3oo7p+7LAk2a2ycxmRB2PfD9KHNVcuHP9ysx2mtlqM3vEzJonsfxiMzshhSHubf0nm9lHZrbNzNaZ2YdmdkYFr8PNrHsK2txhZtvNbIWZ3WdmaRW5DgB3f8bdT0ognlvM7OmKXLeZjTOz/HAbN5rZVDPrtY/NHQ2cCHRw98EVGKZEQImjGjOz3wB3A9cBzYDDgc7AVDOrF2VsiTCzc4AXgH8BHYAM4I/A6VHGFcvM0suZfZi77wccD5wPjEpy+SqjnDj/Em5jB2AtMG4f2+4MLHb3HRUYm0TF3fWqhi+gKbAdODeufD9gHXBpOD0O+FPM/KHA8vD9U0AxsCts6/qw/AVgNbAF+AjoG7N8K+A1YCswE/gT8O+Y+b2AqcBGYF58fDH1DFgKXFfONo4saRvoAjiQHjM/C/hZ+L478GEY83rgubD8o3C5HeE2nheW/xCYBWwGpgGHxrS7GPgd8CWwJ3adMXUc6B4z/QLw95g4Lwu376Nw/qXAXGATMAXoHLPsicA3Yex/D7fjZ/GfQTjdN+bzXQPcCAwD8oGCcBu/COu2AyaHdXOBUTHt3AJMAp4Of5c/K2Ubx/Htv53TgO0xbb9I8Le2CPhVOW1fDuwGisL4bg3rjQrj2hjG2S7u870SWBC2PxRYDlxPkMBWAWcBpwLzwzZujFl+MPBp+PtdFX6u9eLavyJsfzPwEGAx80eFv69twBxgwN62uza9Ig9Ar338xQU7i0JK36mNByaE7+P/+YcSJo5wejFwQtzylwJNgPrAX4FZMfMmhq9GQB9gGf/buTcOpy8B0oH+BDvxPqXE2Cv85+1azjaOJPHEMQH4PcFRdAPg6Jh68Tv5/uHOZwiQBlwcfg71Yz6TWUBHoGEZsf23zfBzWE2QLEri/Ff4eTQEziTYQfYOP5ebgGnhsq3DndM5QF3gmvD3+p3EEf5OVgG/CbexCTAknHcL8HRcjB8BD4d1+xHs7I6LqV9AsPOtU9p2EvO3Q/CF5Fng47B+DsHRYT2gG5AHnFxW23w3AR5H8LcxgODv7EHCJBvz+U4FWobLDw0/lz+Gn9OocHueDT+HvgRfgLqGyw8kOAJPD38nc4Gr49p/HWgOdArbGhbO+wmwAhhE8AWnO8ERU7nbXZtekQeg1z7+4uBCYHUZ8+4Cpobv//vPH04PZS+JI66t5uE/WTOCnWwBcFDM/P8ecQDnAR/HLf8ocHMp7R4VttugnHWPJPHE8S9gLEEfenw78YnjEeD2uDrzgGNjPpNL9/L5O8G36U3AwvBzqBMTZ7eYum8Bl8VM1wF2hjuji4DPYuYZwTfr0hLHCOA/ZcRzCzGJgyDpFQFNYsruBMbF1P9oL9s4juBIYTNBYpwMHEiQcJfG1b0BeLKstvlu4nicoBusZHq/8G+rS8zne1zc3+0uIC2cbhLWGRJTJwc4q4xtuRp4Oe73F/vl4nlgTPh+CvDrUtood7tr00t9h9XXeqC1maW7e2HcvLbh/KSFJ3jvIPjW1YagKwuCb8YNCb7BLYtZJPZ9Z2CImW2OKUsn6BKLtyEm1kX7Emuc64HbgRlmtgm4192fKKNuZ+BiM/tlTFk9gm6IEsvYuwHunhtbYGalLd8ZeMDM7o2tCrQP1/nfuu7uZlbWujsSJKlEtAM2uvu2mLIlQGbMdCLbeI+73xRbYGYDgXZxv+c0gqORRNtuB3xeMuHu281sA8FnsriMNja4e1H4flf4c03M/F0ECQgz6wncR7C9jQj+DnPi2lsd835nybKU/Tl3Zu/bXSvo5Hj19SlB//vZsYVmth9wCvBeWLSD4B+nxAFx7Xjc9PkEXSsnEBxldClpmuBwvpDgRGmJjjHvlwEfunvzmNd+7v7zUuKfF9b/cVkbGKfkpGqp2+Luq919lLu3I+hTf7ickVTLgDvi4mzk7hNi6sR/LsmKXX4ZcHnc+hq6+zSCrqf/foYWZJ6OlG4ZQffI3tYHsBJoaWZNYso6EXTBlLVMopYBi+K2p4m7n5pE2ysJdsQAmFljgvNnFREfBEeV3wA93L0pwbkgK3+R/1pGcGRVWvnetrtWUOKoptx9C3Ar8KCZDTOzumbWheCQezn/+5Y/CzjVzFqa2QEEh+yx1vDtnVETgoS0gWAn/eeYdRYBLwG3mFmjcGjmRTHLvg70NLOfhvHUNbNBZta7lPgduBb4g5ldYmZNzayOmR1tZmNLqb+OYKdyoZmlmdmlxPxzm9lPzKwkoW0i2OmUHC3Fb+M/gSvMbEh4fUFjMzstbidbkf4B3GBmfcNYm5nZT8J5bwB9zezscPTQr/huci/xOtDWzK42s/pm1sTMhoTz1gBdzKwOgLsvIzjpf6eZNTCzQwnOwVTEkN0ZwDYz+52ZNQx/Hweb2aAk2pgAXGJm/cysPsHf2XR3X1wB8UHwd7wV2B7+nZb25aUsjwG/NbOB4d9HdzPrTMVsd42gxFGNuftfCL5J3UPwTzKd4FvR8e6+J6z2FPAFweH/O8Bzcc3cCdxkZpvN7LcE5wqWEOyk5wCfxdW/iuBIZHXY9gSCREPYLXISMJzgG+VqguHC9cuIfxLBeZFLw/prCM4VvFrGJo8iGHq8geBk6LSYeYOA6Wa2naAv/tfunhfOuwUYH27jue6eHbb1d4Ikk0vQB58S7v4ywecw0cy2Al8THBXi7usJugXvCrerB/BJGe1sIxiBdTrBZ7sA+EE4+4Xw5wYzK+kCGkFwxLgSeJngXNO7FbA9RQSj0voRdDOuJ9jZNkuijXeBPxCMUFpF8CVg+PeNLcZvCY6etxF8UYj/uy8vthcIumufDZd/BWhZEdtdU1h4gkdkn5jZ3cAB7n5x1LGISOXQEYckxcx6mdmh4SH8YILuj5ejjktEKo9GVUmymhB0T7Uj6Fq6l7K7lkSkBlJXlYiIJEVdVSIikpRa0VXVunVr79KlS9RhiIhUKzk5OevdvU18ea1IHF26dCE7OzvqMEREqhUzW1JaubqqREQkKUocIiKSFCUOERFJihKHiIgkRYlDRESSktLEEd61dZ6Z5ZrZmFLmdzKzD8zsP2b2pZmdGjPvhnC5eWZ2cqJtiohIaqUscYQPBHqI4C6gfYARZtYnrtpNwPPu3p/gzpgPh8v2Caf7Ejwi9eHwFsaJtCkiIimUyiOOwUCuu+e5ez7Bc6rPjKvjQNPwfTOC2z8T1pvo7nvcfRHBba8HJ9imiEitt3TDTm59bTaFRcV7r5ykVF4A2J5vP/pxOcEze2PdArwTPsKzMcFT50qWjX0OxPKwjATaBMDMRgOjATIyMsjKykp6A0REqps9hc7reQW8tbiANIPOxavp0iytQtcR9ZXjI4Bx7n6vmR0BPGVmB1dEw+4+FhgLkJmZ6UOHDq2IZkVEqiR357UvV3Hnm3NZtaWAs/q1Y8wpvTmgWYMKX1cqE8cKvv3s5A58+3nCEDzLYRiAu39qZg2A1ntZdm9tiojUKrNXbuHWyXOYsXgjB7dvyoMj+pPZpWXK1pfKxDET6GFmXQl27sMJHuUYaylwPDAufC51A2AdwaM/nzWz+wie+9CD4Hm/lkCbIiK1wsYd+dz7zjwmzFhK80b1uPPsQzg3syNpdSyl601Z4nD3QjO7CpgCpAFPuPtsM7sNyHb3ycBvgH+a2TUEJ8pHevCAkNlm9jzBM68LgSvD5/1SWpup2gYRkaqosKiYZ6Yv5b6p89m+p5CLj+zC1cf3pFmjupWy/lrxIKfMzEzX3XFFpCaYtnA9t06ew7w12ziqeytuPr0vPTOapGRdZpbj7pnx5VGfHBcRkQQs37STP785lze/Wk2HFg35x4UDOblvBmap7ZYqjRKHiEgVtiu/iH98uJB/fLgQM/jNiT0ZdUw3GtSt2CG2yVDiEBGpgtydt75ezR1vzGXF5l2cflg7bjilF+2aN4w6NCUOEZGq5pvVW7ll8mw+y9tIrwOa8NzowxnSrVXUYf2XEoeISBWxeWc+90+dz1OfLaFpw7rcftbBjBjUkfS0qnUjcyUOEZGIFRU7E2Ys5d535rFlVwEXHt6Za0/sSfNG9aIOrVRKHCIiEZqet4FbXpvD3FVbObxbS24+vS+92zbd+4IRUuIQEYnAys27uPOtb3jti5W0a9aAh84fwKmHHBDJ8NpkKXGIiFSi3QVF/POjPB7OWkixO78+vgdXHHsgDetFN7w2WUocIiKVwN2ZMnsNd7w5h2Ubd3HKwQdw46m96diyUdShJU2JQ0QkxRas2catr83h37nr6ZmxH8/+bAhHdm8ddVj7TIlDRCRFtuwq4K/vzudfny6hcb00bj2jLxcM6VTlhtcmS4lDRKSCFRU7z2cv4/+mzGPTznxGDO7Eb086iJaNq+bw2mQpcYhUY1PnrGHctEXUgptcVyurt+4mb90OBnVpwc2nD+bg9s2iDqlCKXGIVFMbd+Tzm+dnsV/9dNq3iP7+RfI/GU0a8Ovje3DGYe2qxfDaZClxiFRTf313Pjvyi5j08yNT9jwGkdJU7zM0IrXU/DXbeGb6Ui4Y0klJQyqdEodINePu3P76HBrXS+PqE3pGHY7UQilNHGY2zMzmmVmumY0pZf79ZjYrfM03s81h+Q9iymeZ2W4zOyucN87MFsXM65fKbRCparLmrePjBev59Qk9a8woHaleUnaOw8zSgIeAE4HlwEwzm+zuc0rquPs1MfV/CfQPyz8A+oXlLYFc4J2Y5q9z90mpil2kqiooKub2N+bQrXVjfnp456jDkVoqlUccg4Fcd89z93xgInBmOfVHABNKKT8HeMvdd6YgRpFq5enPlpC3bge/P6039dLV0yzRSOWoqvbAspjp5cCQ0iqaWWegK/B+KbOHA/fFld1hZn8E3gPGuPueUtocDYwGyMjIICsrK9n4RaqU7fnOPR/vpG+rOtRZPYesNXOjDklqqaoyHHc4MMndi2ILzawtcAgwJab4BmA1UA8YC/wOuC2+QXcfG84nMzPThw4dmpLARSrLLZNns6twMff99GgOOkAjqSQ6qTzWXQF0jJnuEJaVZjild1OdC7zs7gUlBe6+ygN7gCcJusREarTctdt46rMlnD+kk5KGRC6ViWMm0MPMuppZPYLkMDm+kpn1AloAn5bSxnfOe4RHIVhwOeZZwNcVG7ZI1fOnN+bSqF4a12j4rVQBKeuqcvdCM7uKoJspDXjC3Web2W1AtruXJJHhwET3b99tx8y6EByxfBjX9DNm1gYwYBZwRaq2QaQq+GDeWrLmreOm03rTar/6UYcjgnktuDtaZmamZ2dnRx2GSNIKioo55YGPKSwq5p1rjtVIKqlUZpbj7pnx5forFKnCnp2+lNy12/n9aX2UNKTK0F+iSBW1eWc+9787n6O6t+KE3vtHHY7IfylxiFRRD7y3gK27CrjptD418tbcUn0pcYhUQblrt/PUp0sYPrgTvds2jTockW9R4hCpgv785lwa1k3j2hM1/FaqHiUOkSrmw/nreP+btfzy+O601vBbqYKUOESqkMKiYv70+hw6t2rExUd2iTockVIpcYhUIRNmLGXB2u3ceGpv6qenRR2OSKmUOESqiC07C7hv6nyO6NaKk/pkRB2OSJmUOESqiL+9v4DNuwr4ww81/FaqNiUOkSogb912xk9bzPBBHenTTsNvpWpT4hCpAv785lwa1E3j2hMPijoUkb1S4hCJ2McL1vHu3LVcdVx32jTR8Fup+pQ4RCIUDL+dS6eWjbjkqC5RhyOSECUOkQhNnLmMeWu2ceOpvTT8VqoNJQ6RiGzZFQy/HdK1JSf3PSDqcEQSpsQhEpG/v7+ATTvzNfxWqh0lDpEILFq/g3HTFnPuwI4c3L5Z1OGIJCWlicPMhpnZPDPLNbMxpcy/38xmha/5ZrY5Zl5RzLzJMeVdzWx62OZzZlYvldsgkgp/fnMu9dLq8JuTdfdbqX5SljjMLA14CDgF6AOMMLM+sXXc/Rp37+fu/YAHgZdiZu8qmefuZ8SU3w3c7+7dgU3AZanaBpFU+CR3PVPnrOHK47qzf5MGUYcjkrRUHnEMBnLdPc/d84GJwJnl1B8BTCivQQs6go8DJoVF44Gzvn+oIpWjqNi5/fU5dGjRkEuP6hp1OCL7JJWJoz2wLGZ6eVj2HWbWGegKvB9T3MDMss3sMzM7KyxrBWx298K9tSlSFT03cxnfrN7Gjaf2pkFdDb+V6ik96gBCw4FJ7l4UU9bZ3VeYWTfgfTP7CtiSaINmNhoYDZCRkUFWVlZFxiuStJ0Fzp0f76Rnizo0XP8NWVnzog5JZJ+kMnGsADrGTHcIy0ozHLgytsDdV4Q/88wsC+gPvAg0N7P08KijzDbdfSwwFiAzM9OHDh26zxsiUhHufHMu2wvyuPeCIzmkg0ZSSfWVyq6qmUCPcBRUPYLkMDm+kpn1AloAn8aUtTCz+uH71sBRwBx3d+AD4Jyw6sXAqyncBpEKsXj9Dp74ZBHnDOigpCHVXsoSR3hEcBUwBZgLPO/us83sNjOLHSU1HJgYJoUSvYFsM/uCIFHc5e5zwnm/A641s1yCcx6Pp2obRCrKnW/NpW5aHa47WXe/leovpec43P1N4M24sj/GTd9SynLTgEPKaDOPYMSWSLUwbeF6psxew3UnH8T+TTX8Vqo/XTkukkLB8Nu5tG/ekMuO1vBbqRmUOERS6IXsZcxdtZUbTu2l4bdSYyhxiKTItt0F3PPOPDI7t+C0Q9pGHY5IhVHiEEmRhz5YyPrt+fzxdN39VmoWJQ6RFFi6YSdP/HsRPx7QgUM7NI86HJEKtddRVWZ2FHAL0Dmsb4C7e7fUhiZSfd351lzS6hjXD9PwW6l5EhmO+zhwDZADFO2lrkit91neBt76ejW/ObEnGRp+KzVQIolji7u/lfJIRGqAkrvftmvWgFHH6KBcaqZEEscHZvZ/BM/K2FNS6O6fpywqkWrqxZzlzF65lb+N6K/ht1JjJZI4hoQ/M2PKnOC5GCIS2r6nkL9MmceATs05/VANv5Waa6+Jw91/UBmBiFR3D3+Qy/rte3js4kwNv5Uaba/Dcc2smZndFz5UKdvM7jUz3d5TJMayjTt57N+LOLt/e/p1bB51OCIplch1HE8A24Bzw9dW4MlUBiVS3dz11jekmXGdht9KLZDIOY4D3f3HMdO3mtmsFMUjUu3MWLSRN75axTUn9KRts4ZRhyOScokccewys6NLJsILAnelLiSR6qO42Lnt9dm0bdaA0Rp+K7VEIkccPwfGh+c1DNgIjExlUCLVxYufL+frFVt5YHg/GtbT8FupHRIZVTULOMzMmobTW1MdlEh1sCMcftu/U3POOKxd1OGIVJoyE4eZXejuT5vZtXHlALj7fSmOTaRKeyRrIeu27eHRnw7U8FupVco74mgc/mxSGYGIVCfLNu5k7Md5nNWvHQM6tYg6HJFKVWbicPdHw5+37mvjZjYMeABIAx5z97vi5t8PlFxg2AjY392bm1k/4BGgKcGNFe9w9+fCZcYBxwJbwuVGht1pIpViZ34hv3jmc9LrGNcP6xV1OCKVLpELAP9iZk3NrK6ZvWdm68zswgSWSwMeAk4B+gAjzKxPbB13v8bd+7l7P+BBgvthAewELnL3vsAw4K9m1jxm0etKllPSkMpUVOxcPXEWs1du4W/D+9OuuYbfSu2TyHDck8IT4j8EFgPdgesSWG4wkOvuee6eD0wEziyn/ghgAoC7z3f3BeH7lcBaoE0C6xRJqbvemss7c9Zw02l9OKFPRtThiEQikeG4JXVOA15w9y0JnghsDyyLmV7O/26Y+C1m1hnoCrxfyrzBQD1gYUzxHWb2R+A9YIy77ylludHAaICMjAyysrISiVmkTB8sLWD8nHyO75RO14LFZGUtiTokkUgkkjheN7NvCC76+7mZtQF2V3Acw4FJ7v6tB0WZWVvgKeBidy8Oi28AVhMkk7HA74Db4ht097HhfDIzM33o0KEVHLLUJh/OX8fT78xk6EFtePSiTNLT9NRlqb32+tfv7mOAI4FMdy8AdlB+l1OJFUDHmOkOYVlphhN2U5UIrxt5A/i9u38WE88qD+whuGfW4ARiEdln81Zv48pnPqfH/vvx9/MHKGlIrVfedRzHufv7ZnZ2TFlslZe+u9S3zAR6mFlXgoQxHDi/lPX0AloAn8aU1QNeBv7l7pPi6rd191UWBHMW8PVe4hDZZ2u37ebScTNpVC+NJ0YOYr/6iRyki9Rs5f0XHEtwzuH0UuY5e0kc7l5oZlcBUwiG4z7h7rPN7DYg290nh1WHAxPd3WMWPxc4BmhlZiPDspJht8+E3WUGzAKuKC8OkX21K7+IUeOz2bgjn+cvP0IjqERC9u39dc2UmZnp2dnZUYch1UhxsXPls5/z9uzVPHrhQE7qe0DUIYlUOjPLcffM+PJEruP4c+w1FGbWwsz+VMHxiVQpd0/5hre+Xs3vT+2tpCESJ5GzfKe4++aSCXffBJyasohEIjZhxlIe/TCPC4Z04rKju0YdjkiVk0jiSDOz+iUTZtYQqF9OfZFq698L1nPTK19zTM823HpGX928UKQUiQwReQZ4z8xKHhd7CTA+dSGJRGPBmm38/JkcurfZj4fO769htyJlSOR5HHeb2RfACWHR7e4+JbVhiVSuddv2cMm4mdRPT+PxkZk0aVA36pBEqqxEB6XPBQrd/V0za2RmTdx9WyoDE6ksuwuKGPWvbNZv38Nzo4+gQ4tGUYckUqUlMqpqFDAJeDQsag+8ksKYRCpNcbHzm+e/4Ivlm/nref04rGPzqEMSqfIS6cS9EjgK2AoQ3rV2/1QGJVJZ7nlnHm98tYoxw3ox7OC2UYcjUi0kkjj2hLdFB8DM0gmuHBep1p7PXsbDWQsZMbgjo4/pFnU4ItVGIonjQzO7EWhoZicCLwCvpTYskdSalrueG1/6iv/XozW3nXmwht2KJCGRxPE7YB3wFXA58CZwUyqDEkml3LXbueLpHLq2bsxDFwygrobdiiSl3FFV4eNfZ7t7L+CflROSSOps2L6HS8bNoF56HZ4YOYimGnYrkrRyv2qFD1aaZ2adKikekZTZXVDE6KdyWLt1D/+8KJOOLTXsVmRfJHIdRwtgtpnNIHiIEwDufkbKohKpYMXFznWTviRnySYevmAA/Tu1iDokkWorkcTxh5RHIZJi9787n9e+WMn1ww7i1EM07Fbk+0jkliMfmtkBBI9odWCmu69OeWQiFeTFnOU8+H4u52V25OfHHhh1OCLVXiJXjv8MmAGcDZwDfGZml6Y6MJGK8FneBsa89CVHHtiKP/1Iw25FKkIiXVXXAf3dfQOAmbUCpgFPpDIwke8rb912Ln8qh04tG/HIBQM17FakgiTyn7QBiL2h4bawbK/MbJiZzTOzXDMbU8r8+81sVviab2abY+ZdbGYLwtfFMeUDzeyrsM2/mb5CSik27sjn0nEzSa9jPDlyMM0aaditSEVJ5IgjF5huZq8SnOM4E/jSzK4FcPf7SlsovAbkIeBEYDkw08wmu/uckjrufk1M/V8C/cP3LYGbgcxwnTnhspuAR4BRwHSCixGHAW8ls9FSs+0pLOLyp7JZuWU3E0YdTqdWGnYrUpESOeJYSHA33JL7U70KLAKahK+yDAZy3T0vvNfVRIKkU5YRwITw/cnAVHffGCaLqcAwM2sLNHX3z9zdgX8BZyWwDVJLuDtjXvyKmYs3ce9PDmNgZw27FaloiYyqunUf224PLIuZXg4MKa2imXUGugLvl7Ns+/C1vJTy0tocDYwGyMjIICsrK+kNkOrnldx8Xskt4OwedWmyaT5ZWfOjDkmkxkn0QU6pNhyYFF6pXiHcfSwwFiAzM9OHDh1aUU1LFfXKf1bwSu4sfjygA/f85FCNoBJJkVQOM1kBdIyZ7hCWlWY4/+umKm/ZFeH7RNqUWmTGoo1cP+lLDu/WkjvPPkRJQySFErmOo2UpZV0TaHsm0MPMuppZPYLkMLmUtnoR3Nbk05jiKcBJZtbCzFoAJwFT3H0VsNXMDg9HU11EcM5FarHF63dw+VPZdGjRkH9cOJB66Rp2K5JKifyHvWZmTUsmzKwPCTyPw90LgasIksBc4Hl3n21mt5lZ7H2uhgMTw5PdJctuBG4nSD4zgdvCMoBfAI8RjPZaiEZU1WqbdwbDbgGevGQQzRvVizgikZrPYvbXpVcwOw24HjgNOIhgJNMF7j4r5dFVkMzMTM/Ozo46DKlg+YXF/PTx6fxn6WaeGTWEQV2+c3AsIt+DmeW4e2Z8eSKjqt4ws7rAOwTDb3/k7hqqIpFyd8a89CXTF23kgeH9lDREKlGZicPMHuTbzxZvRtA1dJWZ4e6/SnVwImX5+/u5vPT5Cq45oSdn9it1RLaIpEh5RxzxfTs5qQxEJFGvzlrBvVPnc3b/9vzq+O5RhyNS65SZONx9fHyZmQ1w989TG5JI6QqKihk/bTF/mTKPwV1bcuePNexWJArJXgD4GDAgFYGIlOej+eu49bXZLFy3g2N7tuGv5/Wjfnpa1GGJ1ErJJg59vZNKtWTDDm5/fS7vzl1D51aNePziTI7rtb+ONEQilGzi2Nf7VokkZceeQh7OyuWfHy0iPc24fthBXHZ0Vx1liFQBCSUOMzsbOBpwMzN3fzm1YUlt5e5M/mIlf35zLmu27uFH/dsz5pReZDRtEHVoIhLaa+Iws4eB7vzvXlKXm9kJ7n5lSiOTWufrFVu49bXZzFy8iUPaN+PhCwYwsLOuzxCpahI54jgO6F1ySxAzGw/MTmlUUqts2L6He96Zz8SZS2nZqB53nX0IP8nsSFodnccQqYoSfQJgJ2BJON0xLBP5XgqLinn6syXcN3U+O/KLuOTIrvz6hB40a6jHvIpUZYkkjibAXDObEU4PArLNbDKAu59R5pIiZfgkdz23vjab+Wu2c3T31tx8eh96ZJT3QEkRqSoSSRx/THkUUmss27iTO96Yy9uzV9OxZUMe/elATuqToeG1ItVIIjc5/NDMMgiONABmuPva1IYlNc2u/CIe+XAhj364kDpm/Paknvzs/3WjQV0NrxWpbhIZVXUu8H9AFsEFgA+a2XXuPinFsUkN4O688dUq/vzGXFZu2c0Zh7XjhlN70bZZw6hDE5F9lEhX1e+BQSVHGWbWBngXUOKQcs1dtZVbJs9m+qKN9G7blL8O78/grhpeK1LdJZI46sR1TW0gtc8ql2pu04587ps6n2emL6FZw7rc8aODGT6ok4bXitQQiSSOt81sCv+7APA89LhWKUVhUTETZizl3qnz2ba7kIuO6MLVJ/TQ41xFaphETo5fF3PLEYCxid5yxMyGAQ8AacBj7n5XKXXOBW4heGjUF+5+vpn9ALg/plovYLi7v2Jm44BjgS3hvJHV6TG2NdVneRu4ZfJsvlm9jSO6teLmM/rQ64Cme19QRKqdRE6O3+3uvwNeKqWsvOXSgIeAE4HlwEwzm+zuc2Lq9ABuAI5y901mtj+Au38A9AvrtCS44PCdmOZ1cr6KWLF5F39+cy5vfLmK9s0b8sgFAxh28AEaXitSgyXSVXUiEJ8kTimlLN5gINfd8wDMbCJwJjAnps4o4CF33wRQxjDfc4C33H1nArFKJdldUMTYj/J4OCsXd7j6hB5cfsyBNKyn4bUiNV15zxz/OfALoJuZfRkzqwnwSQJttweWxUwvB4bE1ekZrusTgu6sW9z97bg6w4H74sruMLM/Au8BY9x9TynxjwZGA2RkZJCVlZVAyLI37k7OmiImzstn/S5n0AFpnHdQPVqnr2T6tJVRhycilaC8I45nCU6C3wmMiSnf5u4bK3D9PYChQAfgIzM7xN03A5hZW+AQYErMMjcAq4F6wFiCI5/b4ht297HhfDIzM33o0KEVFHLtNX/NNm59bTaf5G6g1wFNePDCvhxxYKuowxKRSlbeM8e3EJyAHrGPba8guCFiiQ5hWazlwHR3LwAWmdl8gkQyM5x/LvByOL8krlXh2z1m9iTw232MTxK0ZWcB9787n6c+W8J+9dO57cy+nD+4E+lpGpUtUhsl+wTAZMwEephZV4KEMRw4P67OKwSJ6Ukza03QdZUXM38EwRHGf5lZW3dfZcHZ17OAr1MSvVBU7Dw3cxn3vDOPzTvzOX9IJ6498SBaNtbwWpHaLGWJw90Lzewqgm6mNOAJd59tZrcB2e4+OZx3kpnNAYoIRkttADCzLgRHLB/GNf1MePW6AbOAK1K1DbVZ9uKN3Dx5NrNXbmVw15bccnpf+rTT8FoRAQufz1SjZWZmenZ2dtRhVAurt+zmzrfm8uqslbRt1oAbT+3NDw9tq+G1IrWQmeW4e2Z8eSq7qqQa2V1QxOP/XsRDH+RSWOz86rjuXDH0QBrV05+IiHyb9gq1nLvz7ty13P76HJZu3Mmwvgfw+9N607Flo6hDE5EqSomjFstdu41bX5vDxwvW02P//Xj6siEc3aN11GGJSBWnxFELbd1dwAPvLmD8tMU0rJfGzaf34cLDO1NXw2tFJAFKHLVIcbEzKWc5f5nyDRt25DN8UCd+e1JPWu1XP+rQRKQaUeKoJXKWbOLW12bz5fItDOzcgnGXDObg9s2iDktEqiEljhpu7dbd3PX2N7z0+QoymtbngeH9OOOwdhpeKyL7TImjhtpTWMSTnyzmwfcWUFDk/GLogVz5g+40rq9fuYh8P9qL1EDvf7OG216bw+INOzmhdwZ/+GFvOrdqHHVYIlJDKHHUIHnrtnP763P4YN46urVpzPhLB3NszzZRhyUiNYwSRw2wbXcBf38/lyc+WUSD9DRuOq03Fx3RhXrpGl4rIhVPiaMaKy52XvrPCu5++xvWbdvDuZkduO7kXrRpouG1IpI6ShzV1BfLNnPz5NnMWraZ/p2a89hFmRzWsXnUYYlILaDEUc2s27aHv7z9DS/kLKdNk/rc+5PD+FH/9tSpo+G1IlI5lDiqifzCYsZPW8zf3lvA7sIiLj+2G788rgf7aXitiFQy7XWqgWm567np1a/JW7eD43rtz02n9aZbm/2iDktEaikljipu2sL1XPTEDDq0aMgTIzM5rldG1CGJSC2nxFGF5a7dzhVP5dC1dWNe/MWRNG1QN+qQRERI6UB/MxtmZvPMLNfMxpRR51wzm2Nms83s2ZjyIjObFb4mx5R3NbPpYZvPmVm9VG5DVDZs38Ol42ZSL70OT4wcpKQhIlVGyhKHmaUBDwGnAH2AEWbWJ65OD+AG4Ch37wtcHTN7l7v3C19nxJTfDdzv7t2BTcBlqdqGqOwuKGL0Uzms2bqbf16UqafxiUiVksojjsFArrvnuXs+MBE4M67OKOAhd98E4O5ry2vQglu6HgdMCovGA2dVZNBRc3eun/QlOUs2cf95/ejfqUXUIYmIfEsqz3G0B5bFTC8HhsTV6QlgZp8AacAt7v52OK+BmWUDhcBd7v4K0ArY7O6FMW22L23lZjYaGA2QkZFBVlbW992eSvHSgnwmLyzgnJ51abRhHllZ86IOSUTkW6I+OZ4O9ACGAh2Aj8zsEHffDHR29xVm1g1438y+ArYk2rC7jwXGAmRmZvrQoUMrOPSK92LOciYv/ILzMjty148P0TMzRKRKSmVX1QqgY8x0h7As1nJgsrsXuPsiYD5BIsHdV4Q/84AsoD+wAWhuZunltFktfZa3gTEvfcmRB7biTz86WElDRKqsVCaOmUCPcBRUPWA4MDmuzisERxuYWWuCrqs8M2thZvVjyo8C5ri7Ax8A54TLXwy8msJtqBR567Zz+VM5dGrZiEcuGEjdNN3VVkSqrpTtocLzEFcBU4C5wPPuPtvMbjOzklFSU4ANZjaHICFc5+4bgN5Atpl9EZbf5e5zwmV+B1xrZrkE5zweT9U2VIaNO/K5dNxM0usYT44cTLNGGnYrIlWbBV/ia7bMzEzPzs6OOozv2FNYxIWPTeeL5VuYMOpwBnbWCCoRqTrMLMfdM+PL1ScSEXdnzItfMXPxJu79yWFKGiJSbShxROSB9xbw8n9W8NuTenL6Ye2iDkdEJGFKHBF45T8r+Ou7C/jxgA5c+YPuUYcjIpIUJY5KNmPRRq6f9CWHd2vJnWfrWg0RqX6UOCrR4vU7uPypbDq0aMg/LhxIvXR9/CJS/WjPVUk27wyG3QI8eckgmjeqkTf1FZFaIOpbjtQK+YXFXP5UDss37eKZUUPo3Kpx1CGJiOwzJY4Uc3fGvPQl0xdt5IHh/RjUpWXUIYmIfC/qqkqxv7+fy0ufr+CaE3pyZr9Sb+QrIlKtKHGk0KuzVnDv1Pmc3b89vzpew25FpGZQ4kiRnCUbuW7Slwzu2pI7dYt0EalBlDhSYMmGHYz6Vw7tmzfk0QsHUj89LeqQREQqjBJHBduys4BLxs2k2J0nRg6iRWMNuxWRmkWJowLlFxZzxdM5LNu4k0cvHEjX1hp2KyI1j4bjVhB35/cvf8WneRu4/7zDGNKtVdQhiYikhI44KsjDWQt5IWc5vzq+Bz/q3yHqcEREUkaJowK8/uVK/m/KPM7s145rTugRdTgiIimlxPE9fb50E9c+/wWZnVtw948P1bBbEanxUpo4zGyYmc0zs1wzG1NGnXPNbI6ZzTazZ8Oyfmb2aVj2pZmdF1N/nJktMrNZ4atfKrehPMs27mTU+GzaNmvA2IsyaVBXw25FpOZL2clxM0sDHgJOBJYDM81ssrvPianTA7gBOMrdN5nZ/uGsncBF7r7AzNoBOWY2xd03h/Ovc/dJqYo9EVt2BcNuC4uDYbctNexWRGqJVB5xDAZy3T3P3fOBicCZcXVGAQ+5+yYAd18b/pzv7gvC9yuBtUCbFMaalIKiYn7xTA5LNuzgHxcO5MA2+0UdkohIpUll4mgPLIuZXh6WxeoJ9DSzT8zsMzMbFt+ImQ0G6gELY4rvCLuw7jez+hUdeHncnT+88jWf5G7gzrMP5YgDNexWRGqXqK/jSAd6AEOBDsBHZnZISZeUmbUFngIudvficJkbgNUEyWQs8DvgtviGzWw0MBogIyODrKysCgn4zbx8np9fwOnd6tJ6Wy5ZWbkV0q6ISHWRysSxAugYM90hLIu1HJju7gXAIjObT5BIZppZU+AN4Pfu/lnJAu6+Kny7x8yeBH5b2srdfSxBYiEzM9OHDh36vTfora9W8fzbn/PDQ9vywPD+1KmjEVQiUvuksqtqJtDDzLqaWT1gODA5rs4rBEcbmFlrgq6rvLD+y8C/4k+Ch0chWDDu9Szg69Rtwv/MWraZq5+bxYBOzbnnJ4cpaYhIrZWyIw53LzSzq4ApQBrwhLvPNrPbgGx3nxzOO8nM5gBFBKOlNpjZhcAxQCszGxk2OdLdZwHPmFkbwIBZwBWp2oYSyzft5Gfjs9m/aX3+qWG3IlLLmbtHHUPKZWZmenZ29j4tu3V3Aec8Mo1VW3bz8i+OpPv+TSo4OhGRqsnMctw9M75cV46Xo6ComCuf+Zy8dTt49MKBShoiIkQ/qqrKcndunjybjxes5y8/PpQju7eOOiQRkSpBRxzlOLDNflz5gwM5d1DHvVcWEakldMRRBjPjsqO7Rh2GiEiVoyMOERFJihKHiIgkRYlDRESSosQhIiJJUeIQEZGkKHGIiEhSlDhERCQpShwiIpKUWnGTQzNbByzZx8VbA+srMJx9pTiqVgygOOIpjm+rCnF83xg6u/t3HttdKxLH92Fm2aXdHVJx1O4YFIfiqA5xpCoGdVWJiEhSlDhERCQpShx7NzbqAEKK43+qQgygOOIpjm+rCnGkJAad4xARkaToiENERJKixCEiIklR4iiDmT1hZmvN7OsIY+hoZh+Y2Rwzm21mv44ojgZmNsPMvgjjuDWKOGLiSTOz/5jZ6xHGsNjMvjKzWWaWHWEczc1skpl9Y2ZzzeyICGI4KPwcSl5bzezqCOK4Jvz7/NrMJphZg8qOIYzj12EMsyvzcyhtn2VmLc1sqpktCH+2qIh1KXGUbRwwLOIYCoHfuHsf4HDgSjPrE0Ece4Dj3P0woB8wzMwOjyCOEr8G5ka4/hI/cPd+EY/VfwB42917AYcRwefi7vPCz6EfMBDYCbxcmTGYWXvgV0Cmux8MpAHDKzOGMI6DgVHAYILfxw/NrHslrX4c391njQHec/cewHvh9PemxFEGd/8I2BhxDKvc/fPw/TaCnUL7COJwd98eTtYNX5GMqjCzDsBpwGNRrL8qMbNmwDHA4wDunu/umyMNCo4HFrr7vt6p4ftIBxqaWTrQCFgZQQy9genuvtPdC4EPgbMrY8Vl7LPOBMaH78cDZ1XEupQ4qgkz6wL0B6ZHtP40M5sFrAWmunskcQB/Ba4HiiNafwkH3jGzHDMbHVEMXYF1wJNh191jZtY4olhKDAcmVPZK3X0FcA+wFFgFbHH3dyo7DuBr4P+ZWSszawScCnSMII4SGe6+Kny/GsioiEaVOKoBM9sPeBG42t23RhGDuxeFXREdgMHhIXmlMrMfAmvdPaey112Ko919AHAKQRfiMRHEkA4MAB5x9/7ADiqoK2JfmFk94AzghQjW3YLg23VXoB3Q2MwurOw43H0ucDfwDvA2MAsoquw4SuPBtRcV0lOgxFHFmVldgqTxjLu/FHU8YVfIB0Rz/uco4AwzWwxMBI4zs6cjiKPkGy7uvpagP39wBGEsB5bHHP1NIkgkUTkF+Nzd10Sw7hOARe6+zt0LgJeAIyOIA3d/3N0HuvsxwCZgfhRxhNaYWVuA8OfaimhUiaMKMzMj6L+e6+73RRhHGzNrHr5vCJwIfFPZcbj7De7ewd27EHSJvO/ulf6t0swam1mTkvfASQRdFJXK3VcDy8zsoLDoeGBOZccRYwQRdFOFlgKHm1mj8P/meCIaQGFm+4c/OxGc33g2ijhCk4GLw/cXA69WRKPpFdFITWRmE4ChQGszWw7c7O6PV3IYRwE/Bb4Kzy8A3Ojub1ZyHG2B8WaWRvBl43l3j2wobBWQAbwc7J9IB55197cjiuWXwDNhN1EecEkUQYQJ9ETg8ijW7+7TzWwS8DnBaMT/EN0tP140s1ZAAXBlZQ1YKG2fBdwFPG9mlxE8WuLcClmXbjkiIiLJUFeViIgkRYlDRESSosQhIiJJUeIQEZGkKHGIiEhSlDhEImBmXaK887LI96HEISIiSVHiEImYmXULb1I4KOpYRBKhK8dFIhTeLmQiMNLdv4g6HpFEKHGIRKcNwb2Dznb3KO8xJZIUdVWJRGcLwc35jo46EJFk6IhDJDr5wI+AKWa23d2jvIuqSMKUOEQi5O47wgdUTQ2Tx+SoYxLZG90dV0REkqJzHCIikhQlDhERSYoSh4iIJEWJQ0REkqLEISIiSVHiEBGRpChxiIhIUv4/RdSO/tRQGbMAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.figure()\n",
    "plt.plot(range(1, len(acc_list)+1), acc_list)\n",
    "plt.xticks(range(1,11))\n",
    "plt.grid(axis='y')\n",
    "plt.xlabel('k')\n",
    "plt.ylabel('top-k precision')\n",
    "plt.title('Outage Cluster Predictor Performance')\n",
    "plt.savefig('Outage_Cluster_Predictor.jpg', dpi=400)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "0afa31ab-fc18-49f6-92d3-ffa16a5def51",
   "metadata": {},
   "outputs": [],
   "source": [
    "cluster_cardinality = df_rf['cluster_id'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "0c0d7402-68e2-4888-9f1e-dc62db07ae9f",
   "metadata": {},
   "outputs": [],
   "source": [
    "sorted_cluster_cardinality = cluster_cardinality.sort_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "cfb50b3b-8e26-4969-8843-631989a65acc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize = (10, 5))\n",
    " \n",
    "# creating the bar plot\n",
    "plt.bar(sorted_cluster_cardinality.index, sorted_cluster_cardinality, width = 0.4)\n",
    "plt.xticks(range(0, len(sorted_cluster_cardinality.index)+1, 2))\n",
    "plt.xlabel('Cluster Number')\n",
    "plt.ylabel('Frequency of the Cluster')\n",
    "plt.savefig('Cluster_Frequency.jpg', dpi=400)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "54c49b9d-86b6-4897-82ed-cf605b6c445b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "pickle.dump(clf, open('random_forest.pickle', 'wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "835586a5-14cd-497e-bf9d-2dd5883d92be",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}