Gene_Network.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import math\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import random\n",
    "from scipy.interpolate import UnivariateSpline\n",
    "from scipy.stats import rankdata\n",
    "from scipy.special import logit as logit\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "from cdlib import algorithms, viz\n",
    "import community as community_louvain\n",
    "import networkx as nx\n",
    "from networkx.algorithms import community\n",
    "from networkx.algorithms.community import k_clique_communities,label_propagation_communities, asyn_lpa_communities,greedy_modularity_communities\n",
    "from networkx.algorithms.centrality import *\n",
    "from networkx.algorithms.cluster import *\n",
    "# import igraph as ig\n",
    "\n",
    "from sklearn.preprocessing import normalize\n",
    "from sklearn.metrics import confusion_matrix, classification_report\n",
    "from sklearn.preprocessing import MinMaxScaler \n",
    "from sklearn.datasets import make_classification\n",
    "from sklearn.model_selection import train_test_split, KFold, cross_val_score, cross_val_predict\n",
    " \n",
    "from xgboost import XGBClassifier\n",
    "import seaborn as sns\n",
    "from Powerset import *\n",
    "\n",
    "clinical_231_file = 'C:\\\\Users\\\\bird\\Desktop\\\\cancer_project_github\\\\cancer_data\\\\clinical_231_data.csv'\n",
    "mRNA_231_file = 'C:\\\\Users\\\\bird\\\\Desktop\\\\cancer_project_github\\\\cancer_data\\\\data_mRNA_231_sort_with_sensitive.csv'\n",
    "id_symbol_lookup_file = 'C:\\\\Users\\\\bird\\Desktop\\\\cancer_project_github\\\\cancer_data\\\\Hugo_Symbol_to_id.csv'\n",
    "# filterd_data = 'C:\\\\Users\\\\bird\\Desktop\\\\cancer_project_github\\\\cancer_data\\\\filtered_133_genes_data.csv'\n",
    "filterd_data = 'C:\\\\Users\\\\bird\\Desktop\\\\cancer_project_github\\\\cancer_data\\\\union_genes_4803.csv'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Gene network generation (weighted-> unweighted)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df = pd.read_csv(filterd_data).drop(columns = ['ID']).T\n",
    "# correlations = np.corrcoef(df, df)[:4803, :4803]\n",
    "# for i in range(correlations.shape[0]):\n",
    "#     correlations[i][i]=0\n",
    "# for i in range(correlations.shape[0]):\n",
    "#     for j in range(correlations.shape[0]):\n",
    "#         if correlations[i][j] < 0.1 and correlations[i][j]>-0.1:\n",
    "#             correlations[i][j]  = 0\n",
    "#         else:\n",
    "#             correlations[i][j]  = 1\n",
    "# correlations\n",
    "\n",
    "# G = nx.Graph()\n",
    "# G.add_nodes_from(np.arange(4803))\n",
    "# G.nodes()\n",
    "# edge_list = []\n",
    "# for i in range(correlations.shape[0]):\n",
    "#      for j in range(i+1, correlations.shape[0]):\n",
    "#             if correlations[i][j] == 1:\n",
    "#                 edge_list.append((i,j))\n",
    "\n",
    "# len(edge_list)\n",
    "# G.add_edges_from(edge_list)\n",
    "# G.remove_nodes_from(list(nx.isolates(G)))\n",
    "# G.number_of_nodes()\n",
    "\n",
    "def const_net(correlation, threshold):\n",
    "    correlations = np.copy(correlation)\n",
    "    for i in range(correlations.shape[0]):\n",
    "        correlations[i][i]=0\n",
    "    for i in range(correlations.shape[0]):\n",
    "        for j in range(correlations.shape[0]):\n",
    "            if correlations[i][j] < threshold and correlations[i][j]>-threshold:\n",
    "                correlations[i][j]  = 0\n",
    "            else:\n",
    "                correlations[i][j]  = 1\n",
    "\n",
    "    G = nx.Graph()\n",
    "    G.add_nodes_from(np.arange(correlations.shape[0]))\n",
    "    edge_list = []\n",
    "    for i in range(correlations.shape[0]):\n",
    "         for j in range(i+1, correlations.shape[0]):\n",
    "                if correlations[i][j] == 1:\n",
    "                    edge_list.append((i,j))\n",
    "\n",
    "    print(len(edge_list))\n",
    "    G.add_edges_from(edge_list)\n",
    "    G.remove_nodes_from(list(nx.isolates(G)))\n",
    "    return G\n",
    "  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 3, 4, 5, 6, 7])"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = np.arange(8)\n",
    "a = np.delete(a, np.where(a == 2))\n",
    "a = np.delete(a, np.where(a == 2))\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(filterd_data).drop(columns = ['ID'])\n",
    "correlations = np.corrcoef(df, df)[:4803, :4803]\n",
    "print(correlations.shape)\n",
    "for i in range(correlations.shape[0]):\n",
    "    correlations[i][i]=0\n",
    "for i in range(correlations.shape[0]):\n",
    "    for j in range(correlations.shape[0]):\n",
    "        if correlations[i][j] < 0.5 and correlations[i][j]>-0.5:\n",
    "            correlations[i][j]  = 0\n",
    "        else:\n",
    "            correlations[i][j]  = 1\n",
    "#             nodes =  np.delete(nodes, np.where(nodes == i))\n",
    "#             nodes =  np.delete(nodes, np.where(nodes == j))\n",
    "print(correlations)\n",
    "\n",
    "G = nx.Graph()\n",
    "# nodes = np.setdiff1d( np.arange(4803),nodes)\n",
    "# G.add_nodes_from(nodes)\n",
    "G.add_nodes_from(np.arange(4803))\n",
    "\n",
    "edge_list = []\n",
    "\n",
    "for i in range(correlations.shape[0]):\n",
    "     for j in range(i+1, correlations.shape[0]):\n",
    "            if correlations[i][j] == 1:\n",
    "                edge_list.append((i,j))\n",
    "\n",
    "print(len(edge_list))\n",
    "G.add_edges_from(edge_list)\n",
    "G.remove_nodes_from(list(nx.isolates(G)))\n",
    "\n",
    "print(G.number_of_nodes())\n",
    "com = algorithms.demon(G, min_com_size=50, epsilon=0.25) # works\n",
    "\n",
    "m = com.link_modularity()\n",
    "print(m)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.5\n",
      "188522\n",
      "4071\n",
      "FitnessResult(min=None, max=None, score=0.22012300792384745, std=None)\n",
      "0.51\n",
      "169098\n",
      "3979\n",
      "FitnessResult(min=None, max=None, score=0.2417357284473682, std=None)\n",
      "0.52\n",
      "151640\n",
      "3857\n",
      "FitnessResult(min=None, max=None, score=0.23993355514312, std=None)\n",
      "0.53\n",
      "135967\n",
      "3748\n",
      "FitnessResult(min=None, max=None, score=0.2605213196686659, std=None)\n",
      "0.54\n",
      "122126\n",
      "3619\n",
      "FitnessResult(min=None, max=None, score=0.2722639799519779, std=None)\n",
      "0.55\n",
      "109703\n",
      "3491\n",
      "FitnessResult(min=None, max=None, score=0.22737862230893824, std=None)\n",
      "0.56\n",
      "98622\n",
      "3379\n",
      "FitnessResult(min=None, max=None, score=0.20021895254010327, std=None)\n",
      "0.5700000000000001\n",
      "88759\n",
      "3269\n",
      "FitnessResult(min=None, max=None, score=0.1638006893246137, std=None)\n",
      "0.5800000000000001\n",
      "79930\n",
      "3133\n",
      "FitnessResult(min=None, max=None, score=0.1612551610240643, std=None)\n",
      "0.5900000000000001\n",
      "71876\n",
      "3006\n",
      "FitnessResult(min=None, max=None, score=0.15252955357608233, std=None)\n",
      "[FitnessResult(min=None, max=None, score=0.22012300792384745, std=None), FitnessResult(min=None, max=None, score=0.2417357284473682, std=None), FitnessResult(min=None, max=None, score=0.23993355514312, std=None), FitnessResult(min=None, max=None, score=0.2605213196686659, std=None), FitnessResult(min=None, max=None, score=0.2722639799519779, std=None), FitnessResult(min=None, max=None, score=0.22737862230893824, std=None), FitnessResult(min=None, max=None, score=0.20021895254010327, std=None), FitnessResult(min=None, max=None, score=0.1638006893246137, std=None), FitnessResult(min=None, max=None, score=0.1612551610240643, std=None), FitnessResult(min=None, max=None, score=0.15252955357608233, std=None)]\n"
     ]
    }
   ],
   "source": [
    "df = pd.read_csv(filterd_data).drop(columns = ['ID'])\n",
    "correlations = np.corrcoef(df, df)[:4803, :4803]\n",
    "\n",
    "coms = []\n",
    "modularities = []\n",
    "for thre in np.arange(0.5, 0.6, 0.01):\n",
    "    print(thre)\n",
    "    G = const_net(correlations, thre)\n",
    "    print(G.number_of_nodes())\n",
    "    com = algorithms.demon(G, min_com_size=50, epsilon=0.25) # works\n",
    "    coms.append(com)\n",
    "    m = com.link_modularity()\n",
    "    print(m)\n",
    "    modularities.append(m)\n",
    "print(modularities)\n",
    "\n",
    "# com = algorithms.lais2(G)# take loooong time\n",
    "# com = algorithms.conga(G, number_communities=7) # take loooong time\n",
    "#     com = algorithms.demon(G, min_com_size=10, epsilon=0.25) # works\n",
    "#     com = algorithms.node_perception(G, threshold=0.25, overlap_threshold=0.25) #works longer time\n",
    "# com = algorithms.lfm(G, alpha=1) # loong time\n",
    "\n",
    "# coms = algorithms.egonet_splitter(G)\n",
    "# coms = algorithms.angel(G, min_com_size=3, threshold=0.25) # min_community_size – minimum community size, default 3.\n",
    "# coms = algorithms.big_clam(G) \n",
    "# coms = algorithms.danmf(G) # complex model\n",
    "# com = algorithms.kclique(G, k=3) #k – Size of smallest clique\n",
    "# coms = algorithms.slpa(G)\n",
    "# coms = algorithms.percomvc(G)\n",
    "# com = algorithms.overlapping_seed_set_expansion(G) # complex model\n",
    "# coms = algorithms.node_perception(G, threshold=0.25, overlap_threshold=0.25)\n",
    "# coms = algorithms.nnsed(G)\n",
    "# coms = algorithms.nmnf(G) #complex model\n",
    "# coms = algorithms.lemon(G, seeds, min_com_size=20, max_com_size=50) #complex model\n",
    "# com = algorithms.congo(G, number_communities=3, height=2) # number_communities – the number of communities desired.height – The lengh of the longest shortest paths that CONGO considers, default 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
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       "             416: [0, 1, 3, 5, 8],\n",
       "             418: [0, 1, 3, 5, 6],\n",
       "             419: [0, 1, 2, 3, 4],\n",
       "             421: [0, 1, 2, 3, 4, 5, 6],\n",
       "             423: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             425: [0, 1, 2],\n",
       "             428: [0, 1, 2],\n",
       "             430: [0, 1, 2, 4, 5, 6, 7],\n",
       "             431: [0, 1],\n",
       "             432: [0, 1, 2, 3, 4, 5, 6, 8],\n",
       "             433: [0, 1, 2, 3, 4],\n",
       "             438: [0, 1, 2, 3, 4, 5, 6, 8],\n",
       "             447: [0, 8],\n",
       "             448: [0, 1, 2],\n",
       "             449: [0, 1, 2],\n",
       "             450: [0, 1, 2, 3, 4],\n",
       "             452: [0, 1, 3, 8],\n",
       "             453: [0, 1, 3, 4, 5, 8],\n",
       "             456: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             457: [0, 1, 2, 3],\n",
       "             460: [0, 1, 2, 3, 4, 5, 6],\n",
       "             463: [0, 1, 3, 4, 5],\n",
       "             464: [0, 1, 3, 4, 5, 6, 8],\n",
       "             467: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             470: [0, 1, 2, 3, 4, 5, 6, 8],\n",
       "             478: [0, 1, 2, 3, 4, 5, 6, 8],\n",
       "             479: [0, 1, 2, 3, 4, 5, 6],\n",
       "             485: [0, 1, 2, 3, 4],\n",
       "             489: [0, 1, 2, 3, 4],\n",
       "             494: [0, 1, 2, 3, 4, 5, 6],\n",
       "             503: [0, 1, 2, 3, 4, 5, 6],\n",
       "             504: [0, 1, 3],\n",
       "             505: [0, 1, 2, 3],\n",
       "             507: [0, 1, 2, 3, 5],\n",
       "             509: [0, 1],\n",
       "             512: [0, 1, 2, 3, 4, 6],\n",
       "             515: [0, 1, 2, 3, 4],\n",
       "             516: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             517: [0, 1, 2, 3, 4, 5, 6],\n",
       "             518: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             521: [0, 1, 2, 3, 4, 5, 6],\n",
       "             523: [0, 1, 2, 3, 4, 5, 6],\n",
       "             526: [0, 1, 2, 3, 4, 5, 6],\n",
       "             527: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             529: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             530: [0, 1, 2, 3, 4, 5, 6, 8],\n",
       "             534: [0, 1, 2, 3, 4],\n",
       "             535: [0, 1, 3, 4, 5, 6],\n",
       "             536: [0, 1, 2, 3],\n",
       "             537: [0, 1, 2, 3, 4],\n",
       "             542: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             543: [0, 5, 6, 7, 8],\n",
       "             545: [0, 1, 2, 3, 4, 5],\n",
       "             549: [0, 1, 2, 3, 4, 5, 6, 7],\n",
       "             557: [0, 1, 3, 5, 6, 7],\n",
       "             561: [0, 3, 4, 5, 6],\n",
       "             562: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             568: [0, 1, 2, 3, 4],\n",
       "             571: [0, 1, 2, 3, 4, 5, 6, 8],\n",
       "             574: [0, 1, 5, 6, 8],\n",
       "             575: [0, 1, 2, 3, 5, 6],\n",
       "             577: [0, 1, 2, 3, 4, 5, 6, 8],\n",
       "             579: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             580: [0, 1, 2, 3, 4],\n",
       "             582: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9],\n",
       "             585: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             590: [0, 3, 5, 6, 7, 8, 9],\n",
       "             592: [0, 1, 2, 3, 4],\n",
       "             593: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             599: [0, 1, 2, 3, 4, 5, 6],\n",
       "             600: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             601: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             602: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             605: [0, 1, 2, 3, 4, 5, 6],\n",
       "             612: [0, 2, 3, 4, 5, 6, 7],\n",
       "             616: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             617: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             618: [0, 3, 5, 6],\n",
       "             619: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             620: [0, 3, 5, 6, 7],\n",
       "             624: [0, 1, 2, 3, 4],\n",
       "             626: [0, 1, 2, 3, 4, 5, 6],\n",
       "             627: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             628: [0, 1, 2, 3, 4, 5, 6, 8],\n",
       "             629: [0, 1, 2, 3, 4, 5, 6, 8],\n",
       "             632: [0, 1, 2, 3, 4, 5, 6, 8],\n",
       "             635: [0, 1, 2, 3, 5, 6],\n",
       "             636: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             637: [0, 1, 2, 3, 4],\n",
       "             639: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             640: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             643: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             646: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             648: [0, 1, 2, 4, 5, 6],\n",
       "             650: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             656: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             657: [0, 1, 2, 3, 4, 5, 6, 8],\n",
       "             663: [0, 1, 2],\n",
       "             664: [0, 1, 2, 3, 4, 5, 6, 7],\n",
       "             666: [0, 2, 6, 7, 9],\n",
       "             668: [0, 1, 2, 3, 4, 5, 6, 8],\n",
       "             674: [0, 1, 2, 3, 4, 5],\n",
       "             679: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             684: [0, 1, 3, 4, 5, 6, 8],\n",
       "             685: [0, 1, 3, 4, 5, 6, 7],\n",
       "             686: [0, 1, 2, 3, 4, 5, 6],\n",
       "             688: [0, 1],\n",
       "             690: [0, 1, 2, 3, 4],\n",
       "             692: [0, 4, 5],\n",
       "             694: [0, 1, 2, 3, 4, 5, 6, 7],\n",
       "             696: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             698: [0, 1, 3],\n",
       "             702: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             703: [0, 1, 2, 3, 4, 5, 6, 8],\n",
       "             705: [0, 1, 2, 3, 4, 5, 6],\n",
       "             707: [0, 1, 2, 3, 4],\n",
       "             709: [0, 1, 2, 3, 4, 5, 7],\n",
       "             711: [0, 1, 2, 3],\n",
       "             713: [0, 1, 2, 6, 8],\n",
       "             714: [0, 1, 2, 3, 4, 5, 6],\n",
       "             716: [0, 1, 2, 3, 4, 5, 6, 7],\n",
       "             719: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             725: [0, 1, 2, 3, 4, 5, 6, 7],\n",
       "             728: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             729: [0, 1, 2, 5, 6, 7, 8],\n",
       "             734: [0, 1, 2, 3, 4, 5, 6],\n",
       "             735: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             739: [0, 1, 3, 4, 5, 6, 8],\n",
       "             740: [0, 1, 4, 5, 6, 8],\n",
       "             741: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             744: [0, 3, 4, 5, 6, 7, 8],\n",
       "             748: [0, 1, 2, 3, 4, 5, 6, 7],\n",
       "             752: [0, 1, 2, 3, 4, 5, 6, 7],\n",
       "             754: [0, 1, 2, 3, 4, 5, 6],\n",
       "             755: [0, 3, 4, 5],\n",
       "             757: [0, 1, 2, 3, 4, 5, 6, 7],\n",
       "             758: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             761: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             764: [0, 1, 2, 3, 4, 5, 6],\n",
       "             767: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             771: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             774: [0, 4, 6, 7, 9],\n",
       "             777: [0, 1, 2, 3],\n",
       "             779: [0, 1, 2, 3, 4, 5, 6, 7],\n",
       "             784: [0, 1, 2, 3, 4, 5, 6, 7],\n",
       "             788: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             792: [0, 5],\n",
       "             794: [0, 1, 2, 3, 4, 5, 8],\n",
       "             800: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             802: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             806: [0, 1, 2, 3, 4, 5],\n",
       "             811: [0, 1, 2, 3, 4, 5, 6, 8],\n",
       "             812: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9],\n",
       "             815: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             817: [0, 4, 6],\n",
       "             818: [0, 1, 2, 3, 4, 5, 6, 7],\n",
       "             819: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             820: [0, 1, 2, 3, 4, 5, 6],\n",
       "             824: [0, 1, 3],\n",
       "             825: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             829: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             831: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             832: [0, 2, 3, 4, 5, 6, 8],\n",
       "             833: [0, 1, 2, 3, 4, 5, 6],\n",
       "             835: [0, 1, 2, 3, 4],\n",
       "             836: [0, 1, 2, 3, 4],\n",
       "             839: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             840: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             841: [0, 1, 2, 3, 4, 5, 6, 7],\n",
       "             842: [0, 1, 2, 3, 4, 8],\n",
       "             843: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             844: [0, 1, 2, 3, 4, 5, 6],\n",
       "             846: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             847: [0, 1, 2, 3],\n",
       "             848: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             851: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9],\n",
       "             853: [0, 1, 2, 3, 4, 5, 6, 7],\n",
       "             858: [0, 1, 2, 3, 4, 5, 6, 8],\n",
       "             859: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             860: [0, 1, 2, 3],\n",
       "             864: [0, 1, 2, 3, 4, 5, 6],\n",
       "             868: [0, 1, 2, 3, 4, 5, 6, 8],\n",
       "             871: [0, 1, 2, 3, 4, 5, 6],\n",
       "             873: [0, 1, 2, 3, 4, 5, 6, 8],\n",
       "             874: [0, 1, 2, 3, 4, 5, 6, 8],\n",
       "             875: [0, 3, 5, 6],\n",
       "             878: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             879: [0, 1, 2, 3, 4, 5, 6, 8],\n",
       "             880: [0, 1, 2, 5],\n",
       "             882: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             883: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9],\n",
       "             885: [0, 1, 2, 3, 4, 5, 6, 7],\n",
       "             886: [0, 1, 2, 3, 5, 6, 8],\n",
       "             888: [0, 1, 2, 3, 4, 5, 6],\n",
       "             890: [0, 1, 2, 3, 4],\n",
       "             894: [0, 1, 4, 7, 8],\n",
       "             896: [0, 1, 2, 3, 5, 8],\n",
       "             899: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             902: [0, 2, 5],\n",
       "             903: [0, 1, 2, 3, 4, 5, 6, 7],\n",
       "             904: [0, 2, 3, 6, 7, 8],\n",
       "             908: [0, 1, 2, 3, 4, 5, 6, 8],\n",
       "             910: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             913: [0, 1, 2, 3, 4, 5, 6],\n",
       "             916: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             919: [0, 1, 2, 3],\n",
       "             920: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             921: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             922: [0, 1, 2, 3, 4, 5, 8],\n",
       "             923: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             926: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             927: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             928: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             929: [0, 1, 2, 3, 4, 5, 6, 7],\n",
       "             930: [0, 1, 2, 3, 4, 5, 6, 7],\n",
       "             932: [0, 1, 2, 3, 4, 5, 6, 7],\n",
       "             935: [0, 2, 5, 6, 7, 8],\n",
       "             936: [0, 1, 2, 3, 4, 5, 6],\n",
       "             937: [0, 1, 3, 4, 5, 6, 8],\n",
       "             938: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             939: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             940: [0, 3, 4, 5, 8],\n",
       "             942: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9],\n",
       "             944: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             946: [0, 1, 2, 3, 4, 5, 6, 7],\n",
       "             947: [0, 1, 2],\n",
       "             949: [0, 2, 5, 6, 7, 8],\n",
       "             950: [0, 1, 2, 3, 4],\n",
       "             952: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             953: [0, 1, 2, 3, 4, 6],\n",
       "             954: [0, 1, 2, 3, 4, 5, 6, 8],\n",
       "             961: [0, 1, 2, 3, 5, 6, 8],\n",
       "             962: [0, 1, 2, 3, 9],\n",
       "             964: [0, 1, 2, 3, 4, 5, 6],\n",
       "             965: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             967: [0, 1, 3, 5, 6, 7, 8, 9],\n",
       "             968: [0, 1, 2, 3, 4, 5, 6, 8],\n",
       "             972: [0, 4, 8],\n",
       "             974: [0, 1, 2, 3, 4, 5, 6],\n",
       "             978: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             979: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             980: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             981: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             986: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             990: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             991: [0, 1, 2, 3, 4, 5, 6, 7],\n",
       "             995: [0, 1, 2, 3, 4],\n",
       "             997: [0, 1, 2, 3, 4, 5, 6, 7],\n",
       "             1000: [0, 1, 2, 3, 4, 5, 6],\n",
       "             1001: [0, 1, 2, 3, 4, 5, 6],\n",
       "             1002: [0, 6, 7, 8, 9],\n",
       "             1003: [0, 3, 4, 5, 6, 7, 9],\n",
       "             1004: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             1008: [0, 1, 3, 4, 5, 8],\n",
       "             1010: [0, 1, 2, 3, 4],\n",
       "             1013: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             1015: [0, 1, 2, 3, 4, 5, 6, 7],\n",
       "             1018: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             1020: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9],\n",
       "             1021: [0, 1, 3, 4, 8],\n",
       "             1022: [0, 1, 2, 3, 4, 5, 6, 7],\n",
       "             1023: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             1024: [0, 1, 2, 3, 4, 5, 6],\n",
       "             1027: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             1028: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             1029: [0, 1, 2, 4, 5, 6, 7, 8, 9],\n",
       "             1030: [0, 6, 9],\n",
       "             1032: [0, 2, 3, 4, 6, 7, 8, 9, 10],\n",
       "             1034: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9],\n",
       "             1035: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9],\n",
       "             1036: [0, 1, 2, 4, 5, 6, 7, 8, 9],\n",
       "             1038: [0, 1, 2, 3, 4, 5, 6, 7],\n",
       "             1040: [0, 1, 2, 3, 4, 6, 8],\n",
       "             1043: [0, 9],\n",
       "             1046: [0, 1, 2, 3, 4, 5, 6, 8],\n",
       "             1047: [0, 1],\n",
       "             1049: [0, 1, 2, 3, 4, 5, 6],\n",
       "             1050: [0, 1],\n",
       "             1051: [0, 1, 2, 3, 4],\n",
       "             1054: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             1055: [0, 1, 2, 3, 4, 5, 6, 7],\n",
       "             1056: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             1062: [0, 1, 2, 3, 4, 5, 6, 8],\n",
       "             1063: [0, 1, 2, 3, 4, 5, 6, 7],\n",
       "             1065: [0, 1, 2, 3, 4],\n",
       "             1068: [0, 1, 2, 3, 4, 5, 6, 7],\n",
       "             1070: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             1073: [0, 1, 2, 3, 4, 5, 6],\n",
       "             1074: [0, 1, 2, 3, 4, 5],\n",
       "             1075: [0, 1, 2, 3, 4, 6],\n",
       "             1084: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9],\n",
       "             1086: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             1089: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             1090: [0, 1, 2, 3, 4, 5],\n",
       "             1091: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             1093: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             1095: [0, 1, 2, 3, 4],\n",
       "             1096: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             1100: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             1101: [0, 1, 2, 3, 4],\n",
       "             1103: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             1104: [0, 1, 3, 4, 6, 7, 8],\n",
       "             1105: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             1106: [0, 1, 3, 4, 6],\n",
       "             1108: [0, 1, 2, 3, 4, 5, 6, 7],\n",
       "             1111: [0, 2, 3, 4, 5, 6, 7, 8, 9],\n",
       "             1117: [0, 1, 2, 3, 4, 5, 6],\n",
       "             1118: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9],\n",
       "             1119: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             1120: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             1122: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             1125: [0, 1, 2, 3, 4, 5, 6, 7],\n",
       "             1127: [0, 1, 2, 3, 4, 5, 6, 7],\n",
       "             1128: [0, 1, 2, 3, 4],\n",
       "             1129: [0, 2, 3, 4, 5, 6, 7, 8],\n",
       "             1132: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             1139: [0, 1, 2],\n",
       "             1140: [0, 1, 2, 3, 4, 5],\n",
       "             1141: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             1143: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             1146: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             1147: [0, 1, 2, 3, 4, 5, 6, 8],\n",
       "             1149: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             1155: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             1156: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             1161: [0, 1, 3],\n",
       "             1162: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             1163: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             1164: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             1165: [0, 1, 2, 3, 4, 5, 6],\n",
       "             1169: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9],\n",
       "             1170: [0, 1, 2, 3, 4, 5, 6, 9],\n",
       "             1172: [0, 6, 8, 9],\n",
       "             1173: [0, 3, 5, 7],\n",
       "             1175: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             1176: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9],\n",
       "             1177: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             1178: [0, 1, 2, 3, 4, 5, 6, 7],\n",
       "             1179: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             1180: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             1181: [0, 1, 2, 3, 4, 5, 6],\n",
       "             1183: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             1185: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             1186: [0, 1, 4, 5, 6],\n",
       "             1187: [0, 1, 2, 3, 5, 6, 8],\n",
       "             1188: [0, 1, 2, 3, 4, 5, 6],\n",
       "             1189: [0, 1, 2, 3, 4, 5, 6, 7],\n",
       "             1190: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             1193: [0, 1, 2, 3, 4, 5, 8],\n",
       "             1195: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             1196: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             1197: [0, 1],\n",
       "             1198: [0, 1, 2, 3, 4, 5, 6],\n",
       "             1200: [0, 1, 2, 3, 4, 5, 6, 8],\n",
       "             1201: [0, 1, 2, 3, 4, 5, 6, 8],\n",
       "             1203: [0, 1, 2, 3, 4, 5, 6, 7],\n",
       "             1204: [0, 2, 7, 9],\n",
       "             1206: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             1207: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             1211: [0, 1, 2, 3, 4, 5, 7],\n",
       "             1213: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             1215: [0, 1, 2, 3, 4, 5, 6, 7],\n",
       "             1216: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             1217: [0, 1, 2, 3, 6, 8],\n",
       "             1224: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             1225: [0, 1, 2, 3, 4, 5, 6, 7],\n",
       "             1226: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             1228: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             1229: [0, 1, 2, 3, 4],\n",
       "             1230: [0, 1, 2, 3, 4, 5, 6, 7],\n",
       "             1231: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             1232: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             1234: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             1235: [0, 1, 2, 3, 4, 5, 6, 8],\n",
       "             1239: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             1242: [0, 1, 2, 4, 5, 6, 7, 8],\n",
       "             1243: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             1244: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9],\n",
       "             1245: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             1246: [0, 2, 3],\n",
       "             1247: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             1248: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9],\n",
       "             1249: [0, 1, 2, 3, 4, 5, 6, 7],\n",
       "             1250: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             1251: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             1252: [0, 1, 2, 3, 4],\n",
       "             1254: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             1258: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             1260: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             1263: [0, 1, 2, 3, 5, 6],\n",
       "             1265: [0, 1, 5, 7, 8],\n",
       "             1268: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             1269: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9],\n",
       "             1270: [0],\n",
       "             1271: [0, 2, 3, 4, 5, 6, 7, 8, 9],\n",
       "             1275: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             1277: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             1280: [0, 1, 2, 3, 4, 5, 6, 8],\n",
       "             1281: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             1282: [0, 2, 3, 5, 6, 7],\n",
       "             1290: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             1291: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             1292: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             1294: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             1295: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9],\n",
       "             1296: [0, 1, 3, 4, 5],\n",
       "             1297: [0, 1, 2, 3, 4, 5, 7],\n",
       "             1306: [0, 2, 4, 5, 6],\n",
       "             1307: [0, 3, 4, 5],\n",
       "             1310: [0, 1, 2],\n",
       "             1311: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             1313: [0, 2, 3, 4],\n",
       "             1314: [0, 3],\n",
       "             1315: [0, 2, 3, 4, 5, 6, 7, 8],\n",
       "             1316: [0, 1, 4, 5, 6, 7, 8, 9],\n",
       "             1317: [0, 1, 3, 7],\n",
       "             1319: [0, 1, 3, 4, 5, 6, 7],\n",
       "             1320: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             1322: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             1325: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9],\n",
       "             1326: [0, 5, 6, 7],\n",
       "             1330: [0, 2, 3, 4, 5, 6, 7, 8, 9],\n",
       "             1332: [0, 1, 2, 3, 4, 5, 6],\n",
       "             1334: [0, 1, 2, 3, 4, 5, 6],\n",
       "             1335: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9],\n",
       "             1336: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             1337: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9],\n",
       "             1339: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9],\n",
       "             1341: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             1343: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             1344: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9],\n",
       "             1345: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             1348: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             1351: [0, 2, 3, 4, 5, 6, 7],\n",
       "             1353: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9],\n",
       "             1354: [0, 1, 2, 3],\n",
       "             1355: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             1358: [0, 5, 7, 8],\n",
       "             1359: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             1361: [0, 1, 2, 3],\n",
       "             1364: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             1365: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             1367: [0, 1, 3, 5],\n",
       "             1370: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             1371: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             1373: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             1374: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             1377: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9],\n",
       "             1378: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             1379: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             1380: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             1382: [0, 2, 7],\n",
       "             1383: [0, 1, 3, 4, 6, 8],\n",
       "             1386: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             1387: [0, 2, 3, 5, 6, 7, 8, 9],\n",
       "             1391: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             1393: [0, 1, 2, 3, 4, 5, 6, 7],\n",
       "             1394: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             1401: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             1402: [0, 1, 2, 3, 4],\n",
       "             1403: [0, 1, 2, 3, 4],\n",
       "             1406: [0],\n",
       "             1408: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9],\n",
       "             1409: [0, 1, 2, 4],\n",
       "             1410: [0, 1, 2, 3, 4, 5, 6, 7],\n",
       "             1411: [0, 1, 2, 3, 4, 5, 6, 7],\n",
       "             1413: [0, 1, 3, 6, 7, 8],\n",
       "             1416: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             1418: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             1420: [0, 1, 2, 3, 4, 5, 6],\n",
       "             1422: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             1424: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             1425: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             1431: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9],\n",
       "             1434: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             1435: [0, 1, 2, 3],\n",
       "             1437: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             1439: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             1440: [0, 1, 2, 3, 4, 6, 7, 8, 9],\n",
       "             1445: [0, 1, 2, 3, 4, 5],\n",
       "             1447: [0, 1, 2, 3, 4, 5, 6, 8],\n",
       "             1448: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             1450: [0, 1, 3, 4, 5, 6],\n",
       "             1451: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             1455: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             1458: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             1461: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             1462: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9],\n",
       "             1463: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             1464: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             1465: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             1467: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             1468: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             1470: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             1473: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             1475: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9],\n",
       "             1476: [0, 1, 2, 3, 4, 5, 6, 7],\n",
       "             1480: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9],\n",
       "             1482: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9],\n",
       "             1483: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             1484: [0, 1, 2, 3, 4, 5, 6, 8],\n",
       "             1489: [0, 1, 2, 3, 4, 5, 7, 8, 9],\n",
       "             1492: [0, 1, 2, 3, 4, 5, 6, 7],\n",
       "             1496: [0, 2, 3, 4, 5, 6, 7],\n",
       "             1497: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             1499: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             1500: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             1502: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             1503: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             1504: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9],\n",
       "             1506: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             1507: [0, 1, 2, 3, 4, 5, 6, 8],\n",
       "             1508: [0, 1, 2, 3, 4, 5, 6, 7],\n",
       "             1509: [0, 1, 2, 3, 4, 6, 7],\n",
       "             1510: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             1511: [0, 1, 2, 3, 4, 5, 6, 7],\n",
       "             1512: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9],\n",
       "             1513: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9],\n",
       "             1516: [0, 2, 3, 5],\n",
       "             1518: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             1520: [0, 2, 3, 4, 5, 6, 7, 8, 9],\n",
       "             1523: [0, 1, 2, 3, 4, 5, 6, 8],\n",
       "             1526: [0, 1, 2, 3],\n",
       "             1527: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             1528: [0, 1, 2, 3, 4, 5, 6, 7],\n",
       "             1529: [0, 1, 2, 3, 4, 5, 6, 8],\n",
       "             1530: [0, 3, 4, 5, 6, 7, 8],\n",
       "             1533: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             1534: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             1541: [0, 1, 2, 3, 4],\n",
       "             1546: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             1547: [0],\n",
       "             1548: [0, 1, 2, 3, 4, 5, 6, 7],\n",
       "             1550: [0, 1, 2, 3, 4, 5, 6],\n",
       "             1551: [0, 1, 2, 3, 4, 5, 6, 7],\n",
       "             1553: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             1554: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9],\n",
       "             1555: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             1556: [0, 1, 2, 4, 5, 6, 7, 8, 9],\n",
       "             1559: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             1564: [0, 1, 2, 4, 5, 6],\n",
       "             1565: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9],\n",
       "             1566: [0, 1, 2, 3],\n",
       "             1571: [0, 3, 6, 7],\n",
       "             1575: [0, 1, 2, 3, 4, 5, 6, 8],\n",
       "             1576: [0, 2, 3, 4, 5, 6],\n",
       "             1577: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             1578: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             1581: [0, 1, 2, 3, 4, 5, 6],\n",
       "             1582: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             1584: [0, 1, 3, 6],\n",
       "             1591: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             1592: [0, 1, 2, 3, 4, 5],\n",
       "             1594: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             1597: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             1598: [0, 5, 7, 8],\n",
       "             1599: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             1601: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             1602: [0, 1, 8],\n",
       "             1605: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9],\n",
       "             1606: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             1608: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9],\n",
       "             1610: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             1613: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             1614: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             1615: [0, 1, 2, 3, 4, 5, 6, 7],\n",
       "             1617: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9],\n",
       "             1618: [0, 1, 3, 5],\n",
       "             1620: [0, 1, 2, 3, 5, 7],\n",
       "             1625: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             1626: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9],\n",
       "             1628: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             1629: [0, 1, 2, 3, 4, 5, 6, 8],\n",
       "             1630: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             1631: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             1633: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9],\n",
       "             1634: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             1636: [0, 2, 4, 5, 6],\n",
       "             1637: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             1638: [0, 1, 2, 3, 4, 5, 6, 7],\n",
       "             1639: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             1642: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             1643: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             1645: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             1648: [0, 1, 2, 3, 4, 5, 6],\n",
       "             1649: [0, 2, 4],\n",
       "             1653: [0, 1, 2, 3, 4, 5, 6, 7],\n",
       "             1655: [0, 1, 2, 3, 4, 6, 7, 8, 9],\n",
       "             1658: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             1659: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             1660: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             1662: [0, 1, 2, 3, 4],\n",
       "             1663: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             1664: [0, 4, 5, 6, 8, 9],\n",
       "             1665: [0],\n",
       "             1666: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             1667: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             1670: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             1672: [0, 1, 2, 3, 4, 5, 6],\n",
       "             1674: [0, 1, 2, 4, 5, 6, 7, 8, 9],\n",
       "             1677: [0, 1, 3, 6, 7, 8],\n",
       "             1681: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             1682: [0, 1, 2, 3, 4],\n",
       "             1684: [0, 2, 3, 5, 6, 7, 8, 9],\n",
       "             1685: [0, 1, 2, 3, 4, 5],\n",
       "             1687: [0, 1, 2, 3, 4, 5, 6],\n",
       "             1688: [0, 1, 2, 3],\n",
       "             1690: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             1691: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             1692: [0, 1, 2, 3, 4, 5, 6, 7],\n",
       "             1698: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             1699: [0, 1, 2, 3, 4, 5, 6, 7],\n",
       "             1700: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             1701: [0, 1, 2],\n",
       "             1702: [0, 1, 4, 5, 7, 8],\n",
       "             1704: [0, 1, 5, 7, 8, 9],\n",
       "             1707: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9],\n",
       "             1708: [0, 1, 2, 3],\n",
       "             1713: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             1717: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             1718: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             1719: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             1720: [0, 1, 5, 7],\n",
       "             1721: [0, 1, 2, 3, 4, 5, 6, 7],\n",
       "             1722: [0, 1, 2, 3, 4, 5, 6, 8],\n",
       "             1724: [0, 2, 3, 4, 5, 6, 8, 9],\n",
       "             1726: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             1728: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9],\n",
       "             1731: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9],\n",
       "             1732: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             1733: [0, 1],\n",
       "             1734: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             1735: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             1737: [0, 8],\n",
       "             1738: [0, 1, 2, 3, 4, 5, 6, 7],\n",
       "             1739: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             1740: [0, 1, 2, 3, 4, 5, 6, 7],\n",
       "             1741: [0, 1, 3, 4, 5, 6],\n",
       "             1742: [0, 1, 6],\n",
       "             1744: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             1745: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             1746: [0, 1, 2, 3, 4, 5, 6],\n",
       "             1750: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9],\n",
       "             1751: [0, 1, 4, 5, 6, 7, 8, 9],\n",
       "             1755: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             1756: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             1757: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             1758: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             1759: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             1760: [0, 1, 2, 3, 4, 5, 6, 7],\n",
       "             1763: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             1764: [0, 1, 2, 3, 4, 5, 6],\n",
       "             1765: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             1768: [0, 7],\n",
       "             1770: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             1771: [0, 1, 2, 3, 4, 5, 6, 7],\n",
       "             1772: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             1773: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             1775: [0, 1, 2, 3, 4, 6, 7],\n",
       "             1776: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             1777: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             1778: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             1781: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             1782: [0, 1, 2, 3, 4, 5, 6, 7],\n",
       "             1783: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9],\n",
       "             1785: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             1787: [0, 1, 2, 3, 4, 5, 6, 8],\n",
       "             1788: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             1794: [0, 1, 2, 3, 4, 5, 6],\n",
       "             1796: [0, 1, 2, 3, 4, 5, 8],\n",
       "             1799: [0, 1, 3, 4, 5, 6, 7, 8],\n",
       "             1801: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             1802: [0, 1, 2, 3, 4, 5, 6, 7],\n",
       "             1803: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             1806: [0, 1, 2, 3],\n",
       "             1807: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             1809: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             1810: [0, 1, 2, 3, 4, 5, 6, 7],\n",
       "             1811: [0, 3, 4],\n",
       "             1812: [0, 1, 2, 3, 4],\n",
       "             1814: [0, 7, 8, 9, 10],\n",
       "             1816: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             1819: [0, 1, 2, 3, 4, 5, 6],\n",
       "             1820: [0, 1, 2, 3, 4, 5, 6],\n",
       "             1821: [0, 1, 2, 3, 4, 6],\n",
       "             1825: [0, 5, 7, 9],\n",
       "             1827: [0, 1, 2, 3, 4, 5, 6, 8],\n",
       "             1830: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9],\n",
       "             1831: [0, 2, 5, 6, 7, 8],\n",
       "             1836: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             1838: [0],\n",
       "             1839: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             1840: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             1841: [0, 1, 2, 3, 4],\n",
       "             1842: [0, 1, 2, 3, 4, 5, 6, 8],\n",
       "             1843: [0, 1, 3, 4, 5, 6, 8],\n",
       "             1844: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             1845: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             1847: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9],\n",
       "             1849: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             1851: [0, 1, 5, 7, 8],\n",
       "             1852: [0, 1, 2, 3, 4, 5, 6],\n",
       "             1854: [0, 1, 2, 3, 4, 6, 8],\n",
       "             1855: [0, 1, 2, 3, 8],\n",
       "             1856: [0, 1, 2, 3, 4, 5, 6, 8],\n",
       "             1858: [0, 5, 7],\n",
       "             1861: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             1865: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             1867: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             1868: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             1871: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9],\n",
       "             1873: [0, 3, 6, 7, 8],\n",
       "             1874: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9],\n",
       "             1876: [0, 1, 2, 3, 4, 5, 6, 7],\n",
       "             1877: [0, 7, 8],\n",
       "             1878: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             1884: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             1886: [0, 1, 2, 3, 4, 5, 6],\n",
       "             1887: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             1889: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             1891: [0, 1, 2, 4, 5, 6, 7, 8],\n",
       "             1893: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             1895: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             1897: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9],\n",
       "             1898: [0, 1, 2, 4, 5, 6, 7, 8, 9],\n",
       "             1899: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             1903: [0, 1, 2, 3, 4, 5, 6, 7],\n",
       "             1904: [0, 1, 2, 3, 4, 5, 6, 7],\n",
       "             1905: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             1913: [0, 2, 3, 4, 5, 6, 7, 8, 9],\n",
       "             1914: [0, 1, 2, 6, 8],\n",
       "             1919: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             1920: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             1925: [0, 3, 5, 6, 7, 8, 9],\n",
       "             1926: [0, 1, 2, 3, 5, 6, 7, 8, 9],\n",
       "             1927: [0, 1, 2, 3, 4, 5, 6, 8],\n",
       "             1928: [0, 1, 2, 4, 5, 6],\n",
       "             1932: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             1933: [0, 5, 7],\n",
       "             1935: [0, 2, 3, 5, 6, 7, 8, 9],\n",
       "             1936: [0, 1],\n",
       "             1940: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             1941: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             1942: [0, 2, 3, 4, 5],\n",
       "             1945: [0, 1, 2, 5, 6, 7, 9],\n",
       "             1946: [0, 1, 2, 3, 4, 5, 6, 7],\n",
       "             1948: [0, 1, 3, 6],\n",
       "             1950: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             1952: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             1953: [0, 1, 2, 3, 4],\n",
       "             1954: [0, 1, 2, 3, 4, 6],\n",
       "             1956: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             1957: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9],\n",
       "             1959: [0, 1, 2, 5, 6, 7, 8, 9],\n",
       "             1961: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             1968: [0, 1, 2, 3, 4, 5, 6, 8],\n",
       "             1969: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9],\n",
       "             1971: [0, 1, 2, 3],\n",
       "             1974: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             1975: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             1977: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             1981: [0, 1, 2, 3, 4, 5, 6, 7],\n",
       "             1982: [0, 1, 2, 6, 7, 8],\n",
       "             1983: [0, 1, 2, 3, 4, 5, 6, 7],\n",
       "             1985: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             1988: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9],\n",
       "             1989: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             1992: [0, 1, 2, 3, 4],\n",
       "             1993: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             1994: [0, 1, 2, 3, 4],\n",
       "             1995: [0, 1, 2, 3, 4],\n",
       "             1996: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             1997: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             1998: [0, 1, 2, 3, 4, 5, 6],\n",
       "             1999: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9],\n",
       "             2001: [0],\n",
       "             2006: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             2007: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             2010: [0, 1, 2, 3, 4, 5, 6, 7],\n",
       "             2013: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9],\n",
       "             2015: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             2016: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9],\n",
       "             2017: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             2020: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9],\n",
       "             2021: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             2022: [0, 1, 4, 5, 6, 7, 8],\n",
       "             2023: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             2025: [0, 1, 2, 5, 6, 7, 8, 9],\n",
       "             2026: [0, 5, 7, 8, 9],\n",
       "             2028: [0, 2, 3, 4, 5, 6, 7, 8],\n",
       "             2029: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             2031: [0, 1, 2, 3, 4, 5, 6],\n",
       "             2034: [0, 1, 2, 3, 4],\n",
       "             2040: [0, 1, 4],\n",
       "             2043: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             2044: [0, 1, 5],\n",
       "             2046: [0, 1, 2, 3, 6, 7, 8, 9],\n",
       "             2048: [0, 2, 6, 7, 8],\n",
       "             2050: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             2051: [0, 1, 2, 3, 4, 5, 6, 7],\n",
       "             2052: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             2056: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             2059: [0, 5, 7],\n",
       "             2061: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             2064: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             2065: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             2069: [0, 2, 3, 4, 5, 6, 7],\n",
       "             2071: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             2072: [0, 1, 3, 4, 5, 6, 8],\n",
       "             2074: [0, 2, 3, 4, 5, 6, 7, 8, 9],\n",
       "             2076: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             2079: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             2083: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             2086: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             2088: [0, 2, 3, 4, 5, 6, 7, 8],\n",
       "             2089: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             2090: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             2091: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             2092: [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "             2094: [0, 1, 2, 3, 4, 6, 7, 8],\n",
       "             2096: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9],\n",
       "             2100: [0, 2, 3, 5, 6, 7, 8, 9],\n",
       "             2102: [0, 1, 2, 3, 4, 5, 6, 8],\n",
       "             ...})"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "com = coms[4]\n",
    "com.to_node_community_map()\n",
    "# 11 clusters: 0-10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1032, 1814, 2239, 3292, 3454, 3543, 3783, 4069, 4077, 4111, 4189, 4197, 4280, 4365, 4484, 4713, 4784, 3464, 3792, 4579, 4701, 2859, 4250, 4373, 4586, 4302, 4762, 3794, 4195, 4635, 803, 1590, 1752, 2084, 2631, 2698, 2842, 2940, 3035, 3131, 3171, 3350, 3359, 3363, 3389, 3415, 3426, 3562, 3574, 3578, 3635, 3711, 3717, 3734, 3739, 3749, 3758, 3807, 3839, 3904, 3911, 3912, 3963, 3973, 3997, 4005, 4009, 4010, 4026, 4049, 4050, 4062, 4063, 4087, 4088, 4119, 4122, 4127, 4130, 4177, 4191, 4252, 4276, 4288, 4321, 4324, 4325, 4359, 4380, 4389, 4395, 4404, 4405, 4411, 4413, 4414, 4450, 4466, 4471, 4473, 4482, 4486, 4504, 4509, 4512, 4539, 4541, 4545, 4550, 4555, 4565, 4577, 4587, 4599, 4602, 4619, 4625, 4627, 4632, 4637, 4645, 4649, 4663, 4665, 4676, 4681, 4683, 4699, 4708, 4711, 4732, 4733, 4734, 4749, 4755, 4757, 4760, 4788]\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# print(conmmunities[10]) \n",
    "# https://stackoverflow.com/questions/43541376/how-to-draw-communities-with-networkx\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 0.3 :1640970 4803\n",
    "# 0.4 :573764 4745 FitnessResult(min=None, max=None, score=0.15127569396624832, std=None)\n",
    "# 0.5 :188522 4071 FitnessResult(min=None, max=None, score=0.21970101429567895, std=None)\n",
    "# 0.51 169098 3979 FitnessResult(min=None, max=None, score=0.2417357284473682, std=None)\n",
    "# 0.52 151640 3857 FitnessResult(min=None, max=None, score=0.23993355514312, std=None)\n",
    "# 0.53 135967 3748 FitnessResult(min=None, max=None, score=0.2605213196686659, std=None)\n",
    "# 0.54 122126 3619 FitnessResult(min=None, max=None, score=0.2722639799519779, std=None) 2886 nodes\n",
    "# 0.55 109703 3491 FitnessResult(min=None, max=None, score=0.22737862230893824, std=None)\n",
    "# 0.56 98622 3379 FitnessResult(min=None, max=None, score=0.20021895254010327, std=None)\n",
    "# 0.57 88759 3269 FitnessResult(min=None, max=None, score=0.1638006893246137, std=None)\n",
    "# 0.58 79930 3133 FitnessResult(min=None, max=None, score=0.1612551610240643, std=None)\n",
    "# 0.59 71876 3006 FitnessResult(min=None, max=None, score=0.15252955357608233, std=None)\n",
    "# 0.6 :64496 2886FitnessResult(min=None, max=None, score=0.16896384714919208, std=None)\n",
    "# 0.7 :21395 1553 FitnessResult(min=None, max=None, score=0.12196361112524495, std=None)\n",
    "# 0.8 :4460 664 FitnessResult(min=None, max=None, score=0.0964465467565242, std=None)\n",
    "# 0.9 :172 116 FitnessResult(min=None, max=None, score=0.0, std=None)\n",
    "# 0.41,0.42,0.43,0.44,0.45,0.46,0.47,0.48,0.49\n",
    "\n",
    "# 0.159, 0.168,0.162,0.182,0.189,0.195,0.20,0.23 0.219\n",
    "\n",
    "thresholds_list = [0.4,0.41,0.42,0.43,0.44,0.45,0.46,0.47,0.48,0.49,0.5,0.51,0.52,0.53,0.54,0.55,0.56,0.57,0.58,0.59,0.6,0.7,0.8,0.9]\n",
    "modularities = [0.151,0.159, 0.168,0.162,0.182,0.195,0.195,0.20,0.23, 0.219,0.219,0.241,0.239, 0.260, 0.272, 0.227, 0.200,0.163,0.1612551,0.152,0.168,0.122,0.096,0]\n",
    "\n",
    "plt.figure()\n",
    "plt.xlabel('correlation threshold')\n",
    "plt.ylabel('modularity')\n",
    "plt.plot(thresholds_list, modularities)\n",
    "plt.scatter(thresholds_list, modularities)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# With threshold 0.54"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "122126\n",
      "3619\n",
      "FitnessResult(min=None, max=None, score=0.2755406590595339, std=None)\n"
     ]
    }
   ],
   "source": [
    "df = pd.read_csv(filterd_data).drop(columns = ['ID'])\n",
    "correlations = np.corrcoef(df, df)[:4803, :4803]\n",
    "\n",
    "thre = 0.54\n",
    "G = const_net(correlations, thre)\n",
    "print(G.number_of_nodes())\n",
    "com = algorithms.demon(G, min_com_size=50, epsilon=0.25) # works\n",
    "m = com.link_modularity()\n",
    "print(m)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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       " 228: (0, 2, 6),\n",
       " 229: (0, 1, 2, 3, 5),\n",
       " 230: (0, 1, 2, 3, 4, 5),\n",
       " 232: (0, 1, 2, 3, 4, 5, 7, 8),\n",
       " 233: (0, 1, 2, 3, 5),\n",
       " 236: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 237: (0, 1, 2, 3, 6, 7, 8),\n",
       " 240: (0, 1, 2, 6),\n",
       " 245: (0, 1, 2, 6),\n",
       " 246: (0, 1, 2, 3),\n",
       " 248: (0, 1, 2, 3, 4, 5),\n",
       " 254: (0, 1, 2, 3, 4, 5),\n",
       " 257: (0, 1, 2, 3, 4, 5),\n",
       " 260: (0, 1, 2, 3, 5, 8),\n",
       " 261: (0, 1, 2, 3, 4, 5),\n",
       " 262: (0, 1, 2, 3, 4, 5, 6, 7),\n",
       " 264: (0, 1, 2, 3, 5, 6),\n",
       " 265: (0, 1, 2, 3, 5, 6, 7),\n",
       " 267: (0, 1, 2, 3, 5),\n",
       " 268: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 269: (0, 1, 2, 3, 5),\n",
       " 274: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 275: (0, 1, 2, 3, 4, 5),\n",
       " 276: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 277: (0, 1, 2, 3, 4, 5, 6),\n",
       " 278: (0, 1, 2, 3, 4, 5),\n",
       " 279: (0, 1, 2, 3, 4, 5, 6),\n",
       " 283: (0, 1, 2, 3, 4, 6, 7),\n",
       " 289: (0, 2, 3, 6),\n",
       " 291: (0, 1, 2, 3),\n",
       " 293: (0, 1, 2, 3, 5),\n",
       " 294: (0, 1, 2, 3, 4, 5),\n",
       " 296: (0, 1, 2, 3, 4, 5, 6, 7, 8, 9),\n",
       " 300: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 306: (0, 1, 2, 3, 4, 5, 6, 7),\n",
       " 315: (0, 1, 2, 3, 6, 7),\n",
       " 318: (0, 1, 2, 3, 4, 5, 6, 7),\n",
       " 320: (0, 1, 2, 3, 5),\n",
       " 325: (0, 1, 2, 3, 4, 5),\n",
       " 328: (0, 1, 2, 3, 4, 5, 6, 7),\n",
       " 329: (0, 1, 2, 3, 4, 5, 6),\n",
       " 330: (0, 1, 2, 3, 6),\n",
       " 333: (0, 1, 2, 3, 5),\n",
       " 335: (0, 1, 2, 3, 5),\n",
       " 336: (0, 1, 2, 3, 4, 6, 7, 8),\n",
       " 338: (0, 1, 8),\n",
       " 339: (0, 1, 2, 3, 4, 5),\n",
       " 342: (0, 1, 2, 3, 4, 5),\n",
       " 343: (0, 1, 2, 4, 5, 6, 7),\n",
       " 345: (0, 1, 3),\n",
       " 348: (0, 1, 2, 3, 5, 6),\n",
       " 351: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 354: (0, 1, 2, 3, 4, 5, 6),\n",
       " 357: (0, 1, 2, 3),\n",
       " 358: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 359: (0, 1, 2, 3, 4, 5, 6, 7, 8, 9),\n",
       " 361: (0, 1, 2, 3, 4, 5),\n",
       " 363: (0, 1, 5),\n",
       " 366: (0, 3),\n",
       " 367: (0, 1, 2, 3, 4, 5),\n",
       " 371: (0, 1, 2, 3, 5),\n",
       " 373: (0, 1, 2, 3, 5),\n",
       " 376: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 377: (0, 1, 2, 3, 5),\n",
       " 378: (0, 1, 2, 5),\n",
       " 379: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 390: (0, 1, 2, 3, 5),\n",
       " 395: (0, 1, 2, 3, 5, 6),\n",
       " 397: (0, 1, 2, 3, 5),\n",
       " 400: (0, 1, 2, 3, 4, 5, 6),\n",
       " 401: (0, 1, 6, 7, 8),\n",
       " 404: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 407: (0, 1, 2, 3, 4, 6, 8),\n",
       " 408: (0, 1, 2, 3, 4, 5, 6, 7),\n",
       " 411: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 416: (0, 1, 3, 6),\n",
       " 418: (0, 1, 3, 4),\n",
       " 419: (0, 1, 2, 3, 4),\n",
       " 421: (0, 1, 2, 3, 4, 6, 7),\n",
       " 423: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 425: (0, 1, 2, 3, 6),\n",
       " 428: (0, 1, 3),\n",
       " 430: (0, 2, 3, 4, 5, 6, 7),\n",
       " 432: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 433: (0, 1, 2, 3, 4, 5),\n",
       " 438: (0, 1, 2, 3, 4, 5, 6),\n",
       " 442: (0, 2, 4),\n",
       " 446: (0,),\n",
       " 449: (0, 1, 2, 3),\n",
       " 450: (0, 1, 2, 3, 4, 5),\n",
       " 452: (0, 1, 3, 6),\n",
       " 453: (0, 1, 2, 3, 4, 5, 6, 7),\n",
       " 456: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 457: (0, 1, 2, 3, 5),\n",
       " 460: (0, 1, 2, 4, 5, 6, 7),\n",
       " 464: (0, 1, 2, 3, 4, 5, 6, 7),\n",
       " 467: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 469: (0, 1, 2, 4, 5, 6, 7),\n",
       " 470: (0, 1, 2, 3, 4, 5, 6, 7),\n",
       " 478: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 479: (0, 1, 2, 3, 4, 5, 6, 7),\n",
       " 485: (0, 1, 2, 3, 5),\n",
       " 489: (0, 1, 2, 3, 4, 5),\n",
       " 492: (0, 1, 3),\n",
       " 494: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 503: (0, 1, 2, 3, 5, 6),\n",
       " 505: (0, 1, 2, 3, 4),\n",
       " 507: (0, 1, 2, 3, 4, 5, 8),\n",
       " 515: (0, 1, 2, 3, 4, 5),\n",
       " 516: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 517: (0, 1, 2, 3, 4, 5, 6, 7),\n",
       " 518: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 521: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 523: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 526: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 527: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 528: (0, 1, 2),\n",
       " 529: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 530: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 534: (0, 1, 2, 3, 4, 5),\n",
       " 535: (0, 1, 2, 5, 6, 7),\n",
       " 536: (0, 1, 2, 3),\n",
       " 537: (0, 1, 2, 3, 5),\n",
       " 542: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 543: (0, 3, 4, 6, 7, 8),\n",
       " 545: (0, 1, 2, 3, 4, 5, 6),\n",
       " 549: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 555: (0, 1, 2, 4),\n",
       " 557: (0, 1, 2, 4, 5, 8),\n",
       " 561: (0, 1, 2, 3),\n",
       " 562: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 565: (0, 3, 5),\n",
       " 568: (0, 1, 2, 3, 4, 5),\n",
       " 571: (0, 1, 2, 3, 4, 5, 7, 8),\n",
       " 574: (0, 2, 3, 4, 7, 8),\n",
       " 575: (0, 1, 2, 3, 4, 5),\n",
       " 576: (0, 2, 4),\n",
       " 577: (0, 1, 2, 3, 4, 6),\n",
       " 579: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 580: (0, 1, 2, 3, 5),\n",
       " 582: (0, 1, 2, 3, 4, 5, 6, 7, 8, 9),\n",
       " 583: (0, 1, 5),\n",
       " 585: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 590: (0, 1, 2, 3, 4, 8, 9),\n",
       " 592: (0, 1, 2, 3, 4, 5),\n",
       " 593: (0, 1, 2, 3, 4, 5, 6, 7),\n",
       " 599: (0, 1, 2, 3, 4, 5, 6, 7),\n",
       " 600: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 601: (0, 1, 2, 3, 4, 6, 7, 8),\n",
       " 602: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 605: (0, 1, 2, 3, 4, 5, 6),\n",
       " 609: (0, 1, 2, 6),\n",
       " 616: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 617: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 619: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 624: (0, 1, 2, 3, 4),\n",
       " 627: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 628: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 629: (0, 1, 2, 3, 4, 5, 7, 8),\n",
       " 632: (0, 1, 2, 3, 4, 5, 6),\n",
       " 635: (0, 2, 3, 4, 5),\n",
       " 636: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 637: (0, 1, 2, 3, 4, 5),\n",
       " 639: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 640: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 643: (0, 1, 2, 3, 4, 5, 6, 7),\n",
       " 646: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 648: (0, 1, 2, 3, 5, 6),\n",
       " 650: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 656: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 657: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 663: (0, 1, 2, 3),\n",
       " 664: (0, 1, 2, 3, 4, 5, 6, 7),\n",
       " 666: (0, 2, 3, 6, 7, 8, 9),\n",
       " 667: (0, 3),\n",
       " 668: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 674: (0, 1, 2, 3, 4, 5, 6, 8),\n",
       " 675: (0, 1, 7),\n",
       " 676: (0, 1),\n",
       " 679: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 684: (0, 1, 2, 3, 4, 6),\n",
       " 685: (0, 1, 2, 3, 4, 5, 6),\n",
       " 686: (0, 1, 2, 3, 4, 5, 6, 8),\n",
       " 690: (0, 1, 2, 3, 5),\n",
       " 692: (0, 1, 5, 6, 7),\n",
       " 694: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 696: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 698: (0, 1, 2, 3, 5),\n",
       " 702: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 703: (0, 1, 2, 3, 4, 5, 6, 7),\n",
       " 705: (0, 1, 2, 3, 4, 5, 6, 7),\n",
       " 707: (0, 1, 2, 3, 5),\n",
       " 709: (0, 1, 2, 3, 6, 7, 8),\n",
       " 710: (0, 2, 3, 5, 6),\n",
       " 711: (0, 1, 2, 3, 5),\n",
       " 713: (0, 1, 2, 3, 4, 6),\n",
       " 714: (0, 1, 2, 3, 4, 5, 6),\n",
       " 716: (0, 1, 2, 3, 4, 5, 6),\n",
       " 719: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 720: (0, 1, 2, 3, 4, 5, 7, 8),\n",
       " 725: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 727: (0,),\n",
       " 728: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 729: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 734: (0, 1, 2, 4, 5, 6, 7),\n",
       " 735: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 737: (0, 1, 3, 5, 6),\n",
       " 739: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 740: (0, 1, 3, 4, 6),\n",
       " 741: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 748: (0, 1, 2, 3, 4, 5, 6, 7),\n",
       " 752: (0, 1, 2, 3, 4, 5, 6, 7),\n",
       " 754: (0, 1, 2, 3, 4, 5, 6),\n",
       " 755: (0, 1, 2, 6),\n",
       " 757: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 758: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 761: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 763: (0, 3, 5),\n",
       " 764: (0, 1, 2, 3, 4, 6, 7),\n",
       " 766: (0,),\n",
       " 767: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 771: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 772: (0, 2, 3, 4, 5, 6),\n",
       " 777: (0, 1, 2, 3, 4, 5),\n",
       " 779: (0, 1, 2, 3, 4, 5, 6, 7),\n",
       " 784: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 788: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 792: (0, 6),\n",
       " 794: (0, 1, 2, 3, 4, 5, 6, 7),\n",
       " 800: (0, 1, 2, 3, 4, 5, 6, 7),\n",
       " 802: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 806: (0, 1, 2, 5, 6, 7),\n",
       " 811: (0, 1, 2, 3, 4, 5, 6, 7),\n",
       " 812: (0, 1, 2, 3, 4, 5, 6, 7, 8, 9),\n",
       " 813: (0, 3),\n",
       " 815: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 816: (0, 1, 2, 5),\n",
       " 818: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 819: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 820: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 824: (0, 1, 2, 3, 5, 7),\n",
       " 825: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 829: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 831: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 832: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 833: (0, 1, 2, 3, 4, 5, 6, 8),\n",
       " 835: (0, 1, 2, 3, 4, 5),\n",
       " 836: (0, 1, 2, 3, 4, 5),\n",
       " 839: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 840: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 841: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 842: (0, 1, 2, 3, 4, 5, 6, 8),\n",
       " 843: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 844: (0, 1, 2, 3, 4, 5, 6, 7),\n",
       " 846: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 847: (0, 1, 2, 3, 5),\n",
       " 848: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 851: (0, 1, 2, 3, 4, 5, 6, 7, 8, 9),\n",
       " 853: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 859: (0, 1, 2, 3, 4, 5, 6, 7),\n",
       " 860: (0, 1, 2, 3, 5),\n",
       " 864: (0, 1, 2, 3, 4, 5, 6, 7),\n",
       " 868: (0, 1, 2, 3, 4, 6),\n",
       " 871: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 873: (0, 1, 2, 3, 4, 5, 6, 8),\n",
       " 874: (0, 1, 2, 3, 4, 5, 6, 8),\n",
       " 875: (0, 1, 3, 4, 5, 6),\n",
       " 878: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 879: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 880: (0, 2, 3, 5, 8),\n",
       " 882: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 883: (0, 1, 2, 3, 4, 5, 6, 7, 8, 9),\n",
       " 885: (0, 1, 2, 3, 4, 5, 6, 7),\n",
       " 886: (0, 1, 2, 3, 6),\n",
       " 888: (0, 1, 2, 3, 4, 5, 6, 7),\n",
       " 889: (0, 1, 2, 6),\n",
       " 890: (0, 1, 2, 3, 4, 5, 6),\n",
       " 891: (0, 2),\n",
       " 899: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 902: (0, 4, 5),\n",
       " 903: (0, 1, 2, 3, 4, 5, 6, 7),\n",
       " 904: (0, 3, 4, 6, 7),\n",
       " 908: (0, 1, 2, 3, 4, 5, 6),\n",
       " 910: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 913: (0, 1, 2, 3, 4, 5, 6),\n",
       " 916: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 918: (0,),\n",
       " 919: (0, 1, 2, 3, 4, 5, 7),\n",
       " 920: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 921: (0, 1, 2, 3, 4, 5, 6, 7),\n",
       " 922: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 923: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 924: (0, 1, 2, 3),\n",
       " 926: (0, 1, 2, 3, 4, 6, 7),\n",
       " 927: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 928: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 929: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 930: (0, 1, 2, 3, 4, 5, 6, 7),\n",
       " 932: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 935: (0, 1, 2, 4, 5, 6, 7, 8),\n",
       " 936: (0, 1, 2, 3, 5, 6, 8),\n",
       " 937: (0, 1, 2, 3, 4, 6, 8),\n",
       " 938: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 939: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 940: (0, 1, 2, 3, 4, 5, 6),\n",
       " 942: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 944: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 945: (0,),\n",
       " 946: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 947: (0, 1, 2, 3),\n",
       " 950: (0, 1, 2, 3, 4, 5),\n",
       " 952: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 953: (0, 1, 2, 4, 5, 6),\n",
       " 954: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 961: (0, 1, 2, 3, 5, 6),\n",
       " 962: (0, 1, 2, 3, 5, 9),\n",
       " 964: (0, 1, 2, 4, 5, 6, 8),\n",
       " 965: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 968: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 974: (0, 1, 2, 4, 5, 6),\n",
       " 978: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 979: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 980: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 981: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 986: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 990: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 991: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 995: (0, 1, 2, 3, 4, 5),\n",
       " 997: (0, 1, 2, 3, 4, 6, 7, 8),\n",
       " 1000: (0, 1, 2, 3, 4, 5, 6, 7),\n",
       " 1001: (0, 1, 2, 3, 4, 5, 6),\n",
       " 1003: (0, 1, 2, 4, 5, 6, 7, 9),\n",
       " 1004: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1008: (0, 1, 2, 3, 4, 5, 6, 8),\n",
       " 1010: (0, 1, 2, 3, 4, 5),\n",
       " 1013: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1015: (0, 1, 2, 3, 4, 5, 6, 7),\n",
       " 1018: (0, 1, 2, 3, 4, 5, 6, 7),\n",
       " 1020: (0, 1, 2, 3, 4, 5, 6, 7, 9),\n",
       " 1022: (0, 1, 2, 3, 4, 5, 6, 7),\n",
       " 1023: (0, 1, 2, 3, 4, 5, 6, 7, 8, 9),\n",
       " 1024: (0, 1, 2, 3, 4, 5, 6),\n",
       " 1026: (0, 3),\n",
       " 1027: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1028: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1029: (0, 1, 2, 3, 4, 5, 6, 7, 8, 9),\n",
       " 1032: (0, 1, 3, 4, 7, 8, 9, 10),\n",
       " 1034: (0, 1, 2, 3, 4, 5, 6, 7, 8, 9),\n",
       " 1035: (0, 1, 2, 3, 4, 5, 6, 7, 8, 9),\n",
       " 1036: (0, 1, 2, 4, 5, 6, 7, 8, 9),\n",
       " 1038: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1039: (0, 3),\n",
       " 1040: (0, 1, 2, 3, 5),\n",
       " 1046: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1049: (0, 1, 2, 3, 4, 5, 6, 7),\n",
       " 1051: (0, 1, 2, 3, 4, 5),\n",
       " 1054: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1055: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1056: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1060: (0,),\n",
       " 1062: (0, 1, 2, 3, 4, 5, 6, 8),\n",
       " 1063: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1065: (0, 1, 2, 3, 4, 5),\n",
       " 1068: (0, 1, 3, 4, 5, 6, 7),\n",
       " 1070: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1071: (0, 2, 3, 4, 5, 6, 7),\n",
       " 1073: (0, 1, 2, 3, 5, 6, 7),\n",
       " 1074: (0, 1, 2, 3, 4, 5, 6, 8),\n",
       " 1075: (0, 1, 2, 3, 5),\n",
       " 1081: (0,),\n",
       " 1084: (0, 1, 2, 3, 4, 5, 6, 7, 8, 9),\n",
       " 1086: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1089: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1090: (0, 1, 2, 3, 5),\n",
       " 1091: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1093: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1095: (0, 1, 2, 3, 5),\n",
       " 1096: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1100: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1101: (0, 1, 2, 3, 4, 5),\n",
       " 1103: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1104: (0, 1, 2, 3, 4, 7, 8),\n",
       " 1105: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1106: (0, 1, 3, 4),\n",
       " 1108: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1111: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1117: (0, 1, 2, 4, 5, 6, 7),\n",
       " 1118: (0, 1, 2, 3, 4, 5, 6, 7, 8, 9),\n",
       " 1119: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1120: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1121: (0, 1, 2, 4, 5, 6, 7, 8, 9),\n",
       " 1122: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1125: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1127: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1128: (0, 1, 2, 3, 4, 5),\n",
       " 1132: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1138: (0, 2),\n",
       " 1140: (0, 1, 2, 3, 5, 6),\n",
       " 1141: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1143: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1145: (0, 3, 6),\n",
       " 1146: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1147: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1149: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1153: (0, 1, 2, 3, 6, 7, 8, 9),\n",
       " 1154: (0, 9),\n",
       " 1155: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1156: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1157: (0, 6, 8),\n",
       " 1158: (0, 5),\n",
       " 1162: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1163: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1164: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1165: (0, 1, 2, 3, 4, 5, 6, 8),\n",
       " 1169: (0, 1, 2, 3, 4, 5, 6, 7, 8, 9),\n",
       " 1170: (0, 1, 2, 3, 4, 5, 6),\n",
       " 1175: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1176: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1177: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1178: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1179: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1180: (0, 1, 2, 3, 4, 5, 6, 8),\n",
       " 1181: (0, 1, 2, 4, 6, 7),\n",
       " 1183: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1185: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1186: (0, 3, 4, 6, 7),\n",
       " 1187: (0, 1, 2, 3, 4, 5, 6),\n",
       " 1188: (0, 1, 2, 3, 5, 6),\n",
       " 1189: (0, 1, 2, 3, 4, 5, 6, 7),\n",
       " 1190: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1193: (0, 1, 2, 3, 4, 5, 6, 7),\n",
       " 1195: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1196: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1198: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1200: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1201: (0, 1, 2, 3, 4, 5, 6, 7, 8, 9),\n",
       " 1202: (0, 2, 3),\n",
       " 1203: (0, 1, 2, 3, 4, 5, 6, 7),\n",
       " 1204: (0, 1, 2, 8),\n",
       " 1206: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1207: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1209: (0, 1),\n",
       " 1213: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1215: (0, 1, 2, 3, 4, 5, 6, 7),\n",
       " 1216: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1217: (0, 1, 2, 3, 5, 6),\n",
       " 1222: (0,),\n",
       " 1224: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1226: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1228: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1229: (0, 1, 2, 3, 4, 5),\n",
       " 1230: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1231: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1232: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1234: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1235: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1239: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1242: (0, 1, 2, 5, 7, 8),\n",
       " 1243: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1244: (0, 1, 2, 3, 4, 5, 6, 7, 8, 9),\n",
       " 1245: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1246: (0, 3),\n",
       " 1247: (0, 1, 2, 3, 4, 5, 6, 7),\n",
       " 1248: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1249: (0, 1, 2, 3, 4, 5, 6, 7),\n",
       " 1250: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1251: (0, 1, 2, 3, 4, 5, 6, 7, 8, 9),\n",
       " 1252: (0, 1, 2, 3, 4, 5),\n",
       " 1254: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1258: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1260: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1262: (0, 8),\n",
       " 1263: (0, 1, 2, 3, 5, 6, 8),\n",
       " 1268: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1269: (0, 3, 4, 6, 7, 8, 9),\n",
       " 1271: (0, 1, 2, 3, 4, 5, 6, 7, 8, 9),\n",
       " 1275: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1277: (0, 1, 2, 3, 4, 5, 6, 7),\n",
       " 1279: (0, 3),\n",
       " 1280: (0, 1, 2, 3, 4, 5, 6, 8),\n",
       " 1281: (0, 1, 2, 3, 4, 5, 6, 7),\n",
       " 1290: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1291: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1292: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1294: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1295: (0, 1, 2, 3, 4, 5, 6, 7, 8, 9),\n",
       " 1296: (0, 1, 2, 3, 5, 6, 8),\n",
       " 1297: (0, 1, 2, 5, 6),\n",
       " 1306: (0, 1, 3, 4, 7),\n",
       " 1307: (0, 1, 2, 3, 5, 6),\n",
       " 1311: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1315: (0, 1, 2, 5, 6, 7, 8),\n",
       " 1316: (0, 1, 3, 4, 6, 7, 8, 9),\n",
       " 1319: (0, 1, 2, 3, 4, 5, 6, 7),\n",
       " 1320: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1322: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1325: (0, 1, 2, 3, 4, 5, 6, 7, 8, 9),\n",
       " 1326: (0, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1330: (0, 1, 2, 4, 6, 7, 8, 9),\n",
       " 1332: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1334: (0, 1, 2, 3, 4, 5, 6, 7),\n",
       " 1335: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1336: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1337: (0, 1, 2, 3, 4, 5, 6, 7, 8, 9),\n",
       " 1339: (0, 1, 2, 3, 4, 5, 6, 7, 8, 9),\n",
       " 1341: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1343: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1344: (0, 1, 2, 3, 4, 5, 6, 7, 8, 9),\n",
       " 1345: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1348: (0, 1, 2, 3, 4, 5, 6),\n",
       " 1351: (0, 1, 2, 3, 4, 5, 6, 8),\n",
       " 1353: (0, 1, 2, 3, 4, 5, 6, 7, 8, 9),\n",
       " 1354: (0, 1, 2, 3, 5),\n",
       " 1355: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1358: (0, 1, 2, 4, 7),\n",
       " 1359: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1361: (0, 1, 2, 3, 4, 5),\n",
       " 1364: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1365: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1367: (0, 1, 2, 3, 6, 7),\n",
       " 1370: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1371: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1373: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1374: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1377: (0, 1, 2, 3, 4, 5, 6, 7, 8, 9),\n",
       " 1378: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1379: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1380: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1383: (0, 1, 2, 3, 4, 6, 7, 8),\n",
       " 1384: (0, 2, 6, 7),\n",
       " 1386: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1387: (0, 1, 2, 3, 4, 5, 6, 7, 8, 9),\n",
       " 1391: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1393: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1394: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1401: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1402: (0, 1, 2, 3, 4, 5),\n",
       " 1403: (0, 1, 2, 3, 4, 5),\n",
       " 1408: (0, 1, 2, 3, 4, 5, 6, 7, 8, 9),\n",
       " 1409: (0, 1, 2, 3),\n",
       " 1410: (0, 1, 2, 3, 4, 5, 6),\n",
       " 1411: (0, 1, 2, 3, 5, 6, 7),\n",
       " 1416: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1418: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1420: (0, 1, 2, 3, 4, 5, 6),\n",
       " 1422: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1424: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1425: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1430: (0, 2, 3, 6, 8, 9),\n",
       " 1431: (0, 1, 2, 4, 5, 6, 7, 8, 9),\n",
       " 1434: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1435: (0, 1, 2, 3),\n",
       " 1437: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1439: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1440: (0, 2, 3, 4, 5, 6, 8, 9),\n",
       " 1445: (0, 3, 4, 5, 6),\n",
       " 1447: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1448: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1450: (0, 1, 2, 3, 4, 5, 6, 7),\n",
       " 1451: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1455: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1458: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1460: (0, 2),\n",
       " 1461: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1462: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1463: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1464: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1465: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1467: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1468: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1470: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1471: (0, 7),\n",
       " 1473: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1475: (0, 1, 2, 3, 4, 5, 6, 7, 8, 9),\n",
       " 1476: (0, 1, 2, 3, 4, 5, 6, 7),\n",
       " 1478: (0, 1, 3),\n",
       " 1480: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1482: (0, 1, 2, 3, 4, 5, 6, 7, 8, 9),\n",
       " 1483: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1484: (0, 1, 2, 3, 4, 5, 6, 8),\n",
       " 1489: (0, 1, 2, 3, 4, 5, 7, 8, 9),\n",
       " 1492: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1496: (0, 1, 2, 3, 4, 5, 6, 7),\n",
       " 1497: (0, 1, 2, 3, 4, 5, 6, 8),\n",
       " 1499: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1500: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1502: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1503: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1504: (0, 1, 2, 3, 5, 6, 7, 8, 9),\n",
       " 1506: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1507: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1508: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1509: (0, 1, 2, 3, 4, 6, 7, 8),\n",
       " 1510: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1511: (0, 1, 2, 3, 4, 5, 6, 7),\n",
       " 1512: (0, 1, 2, 3, 4, 5, 6, 7, 8, 9),\n",
       " 1513: (0, 1, 2, 3, 4, 6, 7, 8, 9),\n",
       " 1517: (0, 5),\n",
       " 1518: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1520: (0, 1, 2, 4, 5, 6, 7, 8, 9),\n",
       " 1523: (0, 1, 2, 3, 4, 5, 6, 7),\n",
       " 1526: (0, 1, 2, 3),\n",
       " 1527: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1528: (0, 1, 2, 3, 4, 5, 6, 7),\n",
       " 1529: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1530: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1533: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1534: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1538: (0,),\n",
       " 1541: (0, 2, 3, 5, 6),\n",
       " 1546: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1548: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1550: (0, 1, 2, 3, 4, 5, 6),\n",
       " 1551: (0, 1, 2, 3, 4, 5, 6, 7),\n",
       " 1552: (0, 6, 7),\n",
       " 1553: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1554: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1555: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1556: (0, 1, 2, 4, 5, 6, 7, 8, 9),\n",
       " 1559: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1564: (0, 1, 2, 3, 4, 5, 6),\n",
       " 1565: (0, 1, 2, 3, 4, 5, 6, 7, 8, 9),\n",
       " 1566: (0, 1, 2, 3, 5),\n",
       " 1571: (0, 2, 3, 4, 6, 8),\n",
       " 1575: (0, 1, 2, 3, 4, 5, 6, 8),\n",
       " 1576: (0, 3, 6),\n",
       " 1577: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1578: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1581: (0, 1, 2, 3, 4, 5, 6, 7),\n",
       " 1582: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1584: (0, 1, 3, 4),\n",
       " 1587: (0, 8),\n",
       " 1591: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1594: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1597: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1598: (0, 2, 4, 6, 7, 8),\n",
       " 1599: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1601: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1602: (0, 3),\n",
       " 1605: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1606: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1608: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1610: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1613: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1614: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1615: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1617: (0, 1, 2, 4, 5, 6, 7, 8, 9),\n",
       " 1620: (0, 1, 2, 4, 7),\n",
       " 1621: (0, 3, 8),\n",
       " 1625: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1626: (0, 1, 2, 3, 4, 5, 6, 7, 8, 9),\n",
       " 1627: (0, 1, 2, 3, 4, 5, 6, 7),\n",
       " 1628: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1629: (0, 1, 2, 3, 4, 5, 8),\n",
       " 1630: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1631: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1633: (0, 1, 2, 3, 4, 5, 6, 7, 8, 9),\n",
       " 1634: (0, 1, 2, 3, 4, 5, 6, 8),\n",
       " 1636: (0, 1, 4, 5, 6),\n",
       " 1637: (0, 1, 2, 3, 4, 5, 6, 7),\n",
       " 1638: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1639: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1642: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1643: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1645: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1648: (0, 1, 2, 3, 4, 5, 6, 8),\n",
       " 1653: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1655: (0, 1, 3, 4, 7, 8, 9),\n",
       " 1657: (0, 3, 8),\n",
       " 1658: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1659: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1660: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1662: (0, 1, 2, 3, 5),\n",
       " 1663: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1666: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1667: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1670: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1672: (0, 1, 2, 3, 4, 5, 6),\n",
       " 1674: (0, 1, 2, 3, 4, 5, 6, 7, 8, 9),\n",
       " 1677: (0, 2, 3, 6, 7, 8),\n",
       " 1681: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1682: (0, 1, 2, 3, 4, 5),\n",
       " 1684: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1685: (0, 1, 2, 3, 5, 6),\n",
       " 1687: (0, 1, 2, 3, 4, 5, 6),\n",
       " 1688: (0, 1, 2, 3, 5),\n",
       " 1690: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1691: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1692: (0, 1, 2, 3, 4, 5, 6, 7),\n",
       " 1695: (0, 2),\n",
       " 1698: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1699: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1700: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1701: (0, 1, 2, 3),\n",
       " 1704: (0, 4, 6, 7, 8, 9),\n",
       " 1707: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1708: (0, 1, 2, 3, 5),\n",
       " 1713: (0, 1, 2, 3, 4, 5, 6, 8),\n",
       " 1714: (0, 5, 6, 7, 8),\n",
       " 1716: (0, 4),\n",
       " 1717: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1718: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1719: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1720: (0, 2, 4, 5, 6, 7),\n",
       " 1721: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1722: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1724: (0, 1, 2, 4, 5, 6, 7, 8),\n",
       " 1726: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1728: (0, 1, 2, 3, 4, 5, 6, 7, 8, 9),\n",
       " 1731: (0, 1, 2, 3, 4, 5, 6, 7, 8, 9),\n",
       " 1732: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1734: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1735: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1736: (0, 3, 4, 7),\n",
       " 1737: (0, 4),\n",
       " 1738: (0, 1, 2, 3, 4, 5, 6, 7),\n",
       " 1739: (0, 1, 2, 3, 4, 5, 6, 8),\n",
       " 1740: (0, 1, 2, 3, 4, 5, 6, 7),\n",
       " 1741: (0, 1, 3, 4, 5, 6, 7),\n",
       " 1744: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1745: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1746: (0, 1, 2, 3, 4, 5, 6),\n",
       " 1750: (0, 1, 2, 3, 4, 6, 7, 8, 9),\n",
       " 1751: (0, 1, 2, 3, 4, 6, 7, 8, 9),\n",
       " 1755: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1756: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1757: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1758: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1759: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1760: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1761: (0, 2, 4, 7),\n",
       " 1763: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1764: (0, 1, 2, 3, 4, 5, 6, 7),\n",
       " 1765: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1770: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1771: (0, 1, 2, 3, 4, 5, 6, 7),\n",
       " 1772: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1773: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1775: (0, 1, 2, 3, 4, 5, 6, 7),\n",
       " 1776: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1777: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1778: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1779: (0, 2, 3, 4, 5, 6, 7),\n",
       " 1781: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1782: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1783: (0, 1, 2, 3, 4, 5, 6, 7, 8, 9),\n",
       " 1785: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1787: (0, 1, 3, 4, 5, 7),\n",
       " 1788: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1789: (0, 1, 3, 4, 6),\n",
       " 1794: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1796: (0, 1, 2, 3, 6, 7, 8),\n",
       " 1799: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1801: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1802: (0, 1, 2, 3, 4, 5, 6),\n",
       " 1803: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1806: (0, 1, 2, 3, 5),\n",
       " 1807: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1809: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1810: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1812: (0, 1, 2, 3, 4, 5, 6),\n",
       " 1815: (0, 1, 3, 4, 6, 7),\n",
       " 1816: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1819: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1820: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1821: (0, 1, 2, 3, 4, 5),\n",
       " 1827: (0, 1, 2, 3, 4, 5, 6),\n",
       " 1830: (0, 1, 2, 3, 4, 5, 6, 7, 8, 9),\n",
       " 1831: (0, 4, 5, 7, 8),\n",
       " 1836: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1839: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1840: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1841: (0, 1, 2, 3, 5),\n",
       " 1842: (0, 1, 2, 3, 4, 5, 6),\n",
       " 1844: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1845: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1847: (0, 1, 2, 3, 4, 5, 6, 7, 8, 9),\n",
       " 1849: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1851: (0, 1, 2, 4, 6, 7, 8),\n",
       " 1852: (0, 1, 2, 3, 4, 5, 6),\n",
       " 1854: (0, 1, 3, 4, 5, 6, 7),\n",
       " 1855: (0, 1, 2, 3, 6),\n",
       " 1856: (0, 1, 2, 3, 4, 5, 6),\n",
       " 1858: (0, 4, 6, 7, 8),\n",
       " 1861: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1865: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1867: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1868: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1871: (0, 1, 2, 4, 5, 6, 7, 8),\n",
       " 1873: (0, 1, 2, 3, 4, 7, 8),\n",
       " 1874: (0, 1, 2, 3, 4, 5, 6, 7, 8, 9),\n",
       " 1876: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1878: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1884: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1885: (0, 2),\n",
       " 1886: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1887: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1889: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1893: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1895: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1897: (0, 1, 2, 3, 4, 5, 6, 7, 8, 9),\n",
       " 1898: (0, 1, 2, 3, 4, 5, 6, 7, 8, 9),\n",
       " 1899: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1901: (0, 1, 2, 6, 8),\n",
       " 1902: (0,),\n",
       " 1903: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1904: (0, 1, 2, 3, 4, 5, 6),\n",
       " 1905: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1908: (0, 2, 6, 7),\n",
       " 1909: (0, 1, 2, 3, 4, 5, 6, 7),\n",
       " 1913: (0, 1, 2, 4, 5, 6, 7, 8),\n",
       " 1914: (0, 4, 6, 7),\n",
       " 1915: (0, 4, 6),\n",
       " 1919: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1920: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1925: (0, 1, 2, 4, 6, 7, 8, 9),\n",
       " 1926: (0, 1, 2, 3, 4, 5, 6, 7, 8, 9),\n",
       " 1927: (0, 1, 2, 3, 4, 5, 6, 7),\n",
       " 1932: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1933: (0, 4, 7, 8),\n",
       " 1935: (0, 1, 2, 3, 4, 5, 6, 7, 8, 9),\n",
       " 1940: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1941: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1942: (0, 2, 5, 7, 8),\n",
       " 1945: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1946: (0, 1, 2, 3, 4, 5, 6, 7),\n",
       " 1948: (0, 1, 2, 3, 4),\n",
       " 1950: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1952: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1953: (0, 1, 2, 3, 4, 5),\n",
       " 1956: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1957: (0, 1, 2, 3, 4, 6, 7, 8, 9),\n",
       " 1959: (0, 1, 2, 4, 5, 6, 7, 8, 9),\n",
       " 1961: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1968: (0, 1, 2, 3, 4, 5, 6),\n",
       " 1969: (0, 1, 2, 3, 4, 5, 6, 7, 8, 9),\n",
       " 1970: (0, 1),\n",
       " 1971: (0, 1, 2, 3),\n",
       " 1974: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1975: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1977: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1981: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1982: (0, 2, 4, 5, 7),\n",
       " 1983: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1985: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1988: (0, 1, 2, 4, 5, 6, 7, 8, 9),\n",
       " 1989: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1992: (0, 1, 2, 3, 5),\n",
       " 1993: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1994: (0, 1, 2, 3, 4, 5),\n",
       " 1995: (0, 1, 2, 3, 4, 5),\n",
       " 1996: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1997: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 1998: (0, 1, 2, 3, 4, 5, 6, 7),\n",
       " 1999: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 2006: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 2007: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 2010: (0, 1, 2, 3, 4, 5, 8),\n",
       " 2013: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 2015: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 2016: (0, 1, 2, 3, 4, 5, 6, 7, 8, 9),\n",
       " 2017: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 2020: (0, 1, 2, 3, 4, 5, 6, 7, 8, 9),\n",
       " 2021: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 2022: (0, 1, 3, 4, 5, 6, 7, 8),\n",
       " 2023: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 2025: (0, 1, 2, 4, 5, 6, 7, 8, 9),\n",
       " 2026: (0, 1, 4, 6, 7, 8),\n",
       " 2028: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 2029: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 2031: (0, 1, 2, 3, 5, 8),\n",
       " 2034: (0, 1, 2, 3, 4, 5),\n",
       " 2040: (0, 1, 2),\n",
       " 2042: (0, 2),\n",
       " 2043: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 2044: (0, 2, 3),\n",
       " 2046: (0, 1, 2, 3, 4, 6, 7, 8, 9),\n",
       " 2047: (0, 3, 7, 8),\n",
       " 2048: (0, 1, 2, 4, 6, 7, 8),\n",
       " 2050: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 2051: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 2052: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 2055: (0, 2, 4, 6),\n",
       " 2056: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 2060: (0, 1),\n",
       " 2061: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 2064: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 2065: (0, 1, 2, 3, 4, 5, 6, 8),\n",
       " 2069: (0, 1, 2, 4, 5, 6, 7),\n",
       " 2071: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 2072: (0, 1, 3, 4, 6),\n",
       " 2074: (0, 1, 2, 3, 4, 6, 7, 8, 9),\n",
       " 2076: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 2079: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 2083: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 2086: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 2088: (0, 1, 2, 3, 5, 6, 7, 8),\n",
       " 2089: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 2090: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 2091: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 2092: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " 2096: (0, 1, 2, 3, 4, 5, 6, 7, 8, 9),\n",
       " 2100: (0, 1, 2, 3, 4, 5, 6, 7, 8, 9),\n",
       " 2102: (0, 1, 2, 3, 5, 6, 8),\n",
       " 2103: (0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
       " ...}"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clusters = com.to_node_community_map()\n",
    "clusters\n",
    "\n",
    "d = {}\n",
    "\n",
    "for keys in clusters: \n",
    "    d[keys] = tuple(clusters[keys])\n",
    "    clusters[keys] = tuple(clusters[keys])\n",
    "d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "305"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clusters = com.to_node_community_map() #2885 nodes\n",
    "# print(clusters)\n",
    "# print(clusters[0])\n",
    "# len(clusters)\n",
    "combines = []\n",
    "# print(clusters)\n",
    "for i in range(len(clusters)):\n",
    "    if clusters[i] not in combines and clusters[i]!=[]:\n",
    "        combines.append(clusters[i])\n",
    "len(combines) #305"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2682\n"
     ]
    }
   ],
   "source": [
    "ct_list = []\n",
    "for j in combines:\n",
    "    ct = 0\n",
    "    for i in range(len(clusters)):\n",
    "        if clusters[i] == j:\n",
    "            ct +=1\n",
    "    ct_list.append(ct)\n",
    "print(sum(ct_list))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Counter({(0, 2): 10,\n",
       "         (): 1128,\n",
       "         (0, 1, 2, 4, 6, 7): 4,\n",
       "         (0, 1, 2, 3, 4, 5, 6, 7, 8): 618,\n",
       "         (1, 3): 22,\n",
       "         (0, 1, 2, 3, 6): 9,\n",
       "         (0, 1, 2, 5, 6, 7, 8): 2,\n",
       "         (1, 2, 3): 8,\n",
       "         (0, 1, 2, 3, 5): 56,\n",
       "         (0, 1, 2, 3, 4, 5): 76,\n",
       "         (2, 3): 8,\n",
       "         (0, 1, 2, 3, 4, 5, 6): 51,\n",
       "         (0, 1, 2, 3, 4, 6, 7): 8,\n",
       "         (0, 1, 2, 4, 6): 1,\n",
       "         (0, 1, 2): 3,\n",
       "         (3,): 38,\n",
       "         (0, 2, 6): 2,\n",
       "         (0, 1, 2, 3): 20,\n",
       "         (0,): 12,\n",
       "         (0, 1, 2, 3, 4, 5, 6, 7): 80,\n",
       "         (1, 2, 3, 5): 2,\n",
       "         (0, 1, 3): 6,\n",
       "         (0, 1, 2, 3, 4, 6): 7,\n",
       "         (1, 2): 2,\n",
       "         (0, 1, 2, 6): 6,\n",
       "         (1, 2, 3, 6): 2,\n",
       "         (1, 3, 6): 3,\n",
       "         (0, 1, 2, 3, 4, 5, 7, 8): 5,\n",
       "         (0, 1, 2, 3, 6, 7, 8): 4,\n",
       "         (0, 1, 2, 3, 5, 8): 2,\n",
       "         (0, 1, 2, 3, 5, 6): 15,\n",
       "         (0, 1, 2, 3, 5, 6, 7): 3,\n",
       "         (1, 2, 5, 6): 3,\n",
       "         (0, 2, 3, 6): 1,\n",
       "         (1, 3, 5): 3,\n",
       "         (0, 1, 2, 3, 4, 5, 6, 7, 8, 9): 111,\n",
       "         (0, 1, 2, 3, 6, 7): 2,\n",
       "         (0, 1, 2, 3, 4, 6, 7, 8): 11,\n",
       "         (0, 1, 8): 1,\n",
       "         (0, 1, 2, 4, 5, 6, 7): 6,\n",
       "         (1, 3, 4): 1,\n",
       "         (0, 1, 5): 2,\n",
       "         (0, 3): 8,\n",
       "         (2,): 6,\n",
       "         (0, 1, 2, 5): 2,\n",
       "         (1, 2, 3, 4, 5, 6): 3,\n",
       "         (0, 1, 6, 7, 8): 2,\n",
       "         (0, 1, 2, 3, 4, 6, 8): 2,\n",
       "         (0, 1, 3, 6): 3,\n",
       "         (0, 1, 3, 4): 3,\n",
       "         (0, 1, 2, 3, 4): 4,\n",
       "         (0, 2, 3, 4, 5, 6, 7): 4,\n",
       "         (0, 2, 4): 2,\n",
       "         (1, 2, 3, 5, 6): 1,\n",
       "         (5,): 4,\n",
       "         (0, 1, 2, 3, 4, 5, 8): 3,\n",
       "         (6,): 3,\n",
       "         (1, 2, 3, 4, 6): 1,\n",
       "         (0, 1, 2, 5, 6, 7): 2,\n",
       "         (3, 5): 3,\n",
       "         (0, 3, 4, 6, 7, 8): 1,\n",
       "         (0, 1, 2, 4): 1,\n",
       "         (0, 1, 2, 4, 5, 8): 1,\n",
       "         (1, 4, 7): 1,\n",
       "         (0, 3, 5): 2,\n",
       "         (2, 4): 1,\n",
       "         (0, 2, 3, 4, 7, 8): 1,\n",
       "         (0, 1, 2, 3, 4, 8, 9): 1,\n",
       "         (1, 3, 4, 5, 6, 7): 1,\n",
       "         (1, 2, 3, 4, 8): 1,\n",
       "         (7,): 14,\n",
       "         (1, 2, 4, 5, 6): 4,\n",
       "         (0, 2, 3, 4, 5): 1,\n",
       "         (3, 8): 4,\n",
       "         (0, 2, 3, 6, 7, 8, 9): 1,\n",
       "         (2, 6): 4,\n",
       "         (0, 1, 2, 3, 4, 5, 6, 8): 23,\n",
       "         (0, 1, 7): 1,\n",
       "         (0, 1): 4,\n",
       "         (1, 5, 6): 1,\n",
       "         (0, 1, 5, 6, 7): 1,\n",
       "         (0, 2, 3, 5, 6): 2,\n",
       "         (5, 6, 7): 1,\n",
       "         (3, 4, 5, 6): 1,\n",
       "         (0, 1, 3, 5, 6): 1,\n",
       "         (0, 1, 3, 4, 6): 3,\n",
       "         (1, 4, 6, 7, 8): 2,\n",
       "         (0, 2, 3, 4, 5, 6): 1,\n",
       "         (1, 2, 3, 4, 7, 8): 2,\n",
       "         (1, 8): 3,\n",
       "         (0, 6): 3,\n",
       "         (8, 9): 4,\n",
       "         (2, 7): 2,\n",
       "         (10,): 7,\n",
       "         (1, 2, 4): 1,\n",
       "         (1, 2, 5): 3,\n",
       "         (0, 1, 2, 3, 5, 7): 2,\n",
       "         (1,): 4,\n",
       "         (0, 1, 3, 4, 5, 6): 3,\n",
       "         (0, 2, 3, 5, 8): 1,\n",
       "         (7, 8): 5,\n",
       "         (1, 2, 5, 6, 7, 8): 2,\n",
       "         (1, 2, 3, 8): 3,\n",
       "         (5, 7, 9): 1,\n",
       "         (0, 4, 5): 2,\n",
       "         (0, 3, 4, 6, 7): 2,\n",
       "         (1, 4, 8): 1,\n",
       "         (0, 1, 2, 3, 4, 5, 7): 3,\n",
       "         (0, 1, 2, 4, 5, 6, 7, 8): 8,\n",
       "         (0, 1, 2, 3, 5, 6, 8): 4,\n",
       "         (5, 6): 1,\n",
       "         (1, 2, 4, 5, 6, 7, 8): 2,\n",
       "         (0, 1, 2, 4, 5, 6): 3,\n",
       "         (0, 1, 2, 3, 5, 9): 1,\n",
       "         (0, 1, 2, 4, 5, 6, 8): 1,\n",
       "         (2, 3, 4, 6, 7, 8, 9): 1,\n",
       "         (2, 4, 5): 2,\n",
       "         (2, 5): 2,\n",
       "         (6, 8): 1,\n",
       "         (1, 4, 5, 8): 1,\n",
       "         (2, 3, 8): 1,\n",
       "         (1, 4, 6, 7, 9): 1,\n",
       "         (0, 1, 2, 4, 5, 6, 7, 9): 1,\n",
       "         (0, 1, 2, 3, 4, 5, 6, 7, 9): 1,\n",
       "         (3, 7, 9): 1,\n",
       "         (3, 8, 9): 3,\n",
       "         (0, 1, 3, 4, 7, 8, 9, 10): 1,\n",
       "         (9,): 28,\n",
       "         (0, 1, 2, 4, 5, 6, 7, 8, 9): 12,\n",
       "         (2, 8): 1,\n",
       "         (4, 7, 9): 1,\n",
       "         (0, 1, 3, 4, 5, 6, 7): 4,\n",
       "         (4, 7, 8): 4,\n",
       "         (0, 1, 2, 3, 4, 7, 8): 2,\n",
       "         (1, 8, 9): 5,\n",
       "         (1, 2, 3, 4, 5, 7, 8): 1,\n",
       "         (0, 3, 6): 2,\n",
       "         (0, 1, 2, 3, 6, 7, 8, 9): 1,\n",
       "         (0, 9): 1,\n",
       "         (0, 6, 8): 2,\n",
       "         (0, 5): 2,\n",
       "         (1, 3, 7, 8, 9): 1,\n",
       "         (1, 2, 4, 6, 7, 9): 1,\n",
       "         (1, 4, 5): 2,\n",
       "         (0, 2, 3): 5,\n",
       "         (0, 1, 2, 8): 1,\n",
       "         (1, 2, 3, 4, 5, 6, 7, 8): 1,\n",
       "         (1, 2, 3, 4, 5, 6, 7): 2,\n",
       "         (0, 1, 2, 5, 7, 8): 1,\n",
       "         (0, 8): 2,\n",
       "         (1, 2, 4, 6, 7, 8): 1,\n",
       "         (0, 3, 4, 6, 7, 8, 9): 1,\n",
       "         (2, 3, 6, 7): 1,\n",
       "         (0, 1, 2, 5, 6): 2,\n",
       "         (0, 1, 3, 4, 7): 1,\n",
       "         (1, 3, 4, 5): 1,\n",
       "         (0, 1, 3, 4, 6, 7, 8, 9): 1,\n",
       "         (2, 3, 7, 8): 1,\n",
       "         (0, 2, 3, 4, 5, 6, 7, 8): 2,\n",
       "         (0, 1, 2, 4, 6, 7, 8, 9): 3,\n",
       "         (0, 1, 2, 4, 7): 2,\n",
       "         (4, 8): 2,\n",
       "         (0, 2, 6, 7): 2,\n",
       "         (3, 7): 4,\n",
       "         (2, 4, 7, 8): 1,\n",
       "         (0, 2, 3, 6, 8, 9): 1,\n",
       "         (0, 2, 3, 4, 5, 6, 8, 9): 1,\n",
       "         (0, 3, 4, 5, 6): 1,\n",
       "         (0, 7): 1,\n",
       "         (0, 1, 2, 3, 4, 5, 7, 8, 9): 1,\n",
       "         (0, 1, 2, 3, 5, 6, 7, 8, 9): 2,\n",
       "         (0, 1, 2, 3, 4, 6, 7, 8, 9): 11,\n",
       "         (1, 6, 7): 2,\n",
       "         (4, 7): 1,\n",
       "         (1, 3, 7, 8): 1,\n",
       "         (8,): 11,\n",
       "         (0, 6, 7): 2,\n",
       "         (0, 2, 3, 4, 6, 8): 1,\n",
       "         (1, 2, 6, 7, 8): 1,\n",
       "         (0, 2, 4, 6, 7, 8): 1,\n",
       "         (3, 5, 7): 1,\n",
       "         (0, 3, 8): 3,\n",
       "         (1, 7, 8): 3,\n",
       "         (0, 1, 4, 5, 6): 1,\n",
       "         (1, 5, 7): 1,\n",
       "         (0, 1, 3, 4, 7, 8, 9): 1,\n",
       "         (1, 4, 6, 7, 8, 9): 2,\n",
       "         (4,): 3,\n",
       "         (0, 2, 3, 6, 7, 8): 2,\n",
       "         (6, 9): 1,\n",
       "         (3, 9): 1,\n",
       "         (2, 3, 6, 7, 8): 1,\n",
       "         (0, 4, 6, 7, 8, 9): 2,\n",
       "         (0, 5, 6, 7, 8): 1,\n",
       "         (0, 4): 2,\n",
       "         (0, 2, 4, 5, 6, 7): 1,\n",
       "         (4, 5, 6, 7, 8): 1,\n",
       "         (0, 3, 4, 7): 2,\n",
       "         (1, 2, 3, 4): 2,\n",
       "         (0, 2, 4, 7): 2,\n",
       "         (1, 7): 1,\n",
       "         (0, 1, 3, 4, 5, 7): 1,\n",
       "         (1, 2, 6): 1,\n",
       "         (1, 4, 7, 8, 9, 10): 1,\n",
       "         (0, 1, 3, 4, 6, 7): 1,\n",
       "         (6, 7): 4,\n",
       "         (1, 6, 7, 8, 9): 2,\n",
       "         (0, 4, 5, 7, 8): 1,\n",
       "         (0, 1, 2, 4, 6, 7, 8): 4,\n",
       "         (0, 4, 6, 7, 8): 1,\n",
       "         (4, 6, 7): 1,\n",
       "         (0, 1, 2, 6, 8): 1,\n",
       "         (1, 4, 5, 7): 1,\n",
       "         (0, 4, 6, 7): 1,\n",
       "         (0, 4, 6): 1,\n",
       "         (1, 2, 4, 5, 6, 7): 1,\n",
       "         (0, 4, 7, 8): 1,\n",
       "         (0, 2, 5, 7, 8): 1,\n",
       "         (6, 7, 8): 1,\n",
       "         (0, 2, 4, 5, 7): 1,\n",
       "         (0, 1, 3, 4, 5, 6, 7, 8): 4,\n",
       "         (0, 1, 4, 6, 7, 8): 2,\n",
       "         (0, 3, 7, 8): 1,\n",
       "         (0, 2, 4, 6): 1,\n",
       "         (1, 6, 7, 8): 1,\n",
       "         (0, 1, 2, 3, 5, 6, 7, 8): 2,\n",
       "         (0, 2, 4, 6, 7, 8, 9): 1,\n",
       "         (1, 6): 3,\n",
       "         (0, 1, 3, 5, 7): 1,\n",
       "         (2, 3, 4, 5, 7, 8): 2,\n",
       "         (0, 4, 5, 6, 7, 8): 1,\n",
       "         (0, 2, 5, 6, 7): 1,\n",
       "         (1, 2, 3, 6, 7, 8, 9): 1,\n",
       "         (1, 3, 6, 8): 1,\n",
       "         (3, 4, 7, 8, 9): 1,\n",
       "         (0, 2, 4, 5, 6, 7, 8): 2,\n",
       "         (0, 2, 3, 4, 6, 7, 8, 9): 2,\n",
       "         (2, 4, 7): 1,\n",
       "         (2, 4, 5, 8, 9, 10): 1,\n",
       "         (2, 4, 5, 6, 8): 1,\n",
       "         (2, 3, 6, 8): 1,\n",
       "         (0, 2, 3, 4, 6, 7, 8): 1,\n",
       "         (3, 6): 1,\n",
       "         (4, 5, 7, 8): 1,\n",
       "         (0, 3, 4, 5, 6, 7): 1,\n",
       "         (2, 4, 6, 9): 1,\n",
       "         (0, 2, 4, 5, 8): 1,\n",
       "         (0, 1, 3, 6, 7, 8): 1,\n",
       "         (0, 2, 7): 1,\n",
       "         (0, 1, 4, 5, 6, 7, 8): 1,\n",
       "         (0, 1, 2, 4, 5, 7): 1,\n",
       "         (0, 1, 6, 8, 9): 1,\n",
       "         (0, 1, 3, 4, 6, 8, 9): 1,\n",
       "         (0, 1, 2, 3, 4, 6, 7, 9): 1,\n",
       "         (0, 5, 6, 7): 1,\n",
       "         (0, 1, 4, 6, 7, 8, 9): 1,\n",
       "         (2, 3, 7): 1,\n",
       "         (0, 3, 4, 7, 8): 1,\n",
       "         (2, 3, 4, 5): 1,\n",
       "         (0, 1, 7, 8, 9): 1,\n",
       "         (0, 3, 4, 8): 1,\n",
       "         (1, 6, 8): 1,\n",
       "         (2, 6, 7, 8): 1,\n",
       "         (0, 1, 4, 8): 1,\n",
       "         (0, 8, 9): 1,\n",
       "         (0, 1, 2, 3, 5, 7, 8): 1,\n",
       "         (0, 4, 7): 1,\n",
       "         (0, 1, 2, 3, 4, 5, 6, 8, 9): 1,\n",
       "         (5, 7): 1,\n",
       "         (0, 1, 3, 4, 5, 6, 7, 8, 9): 1,\n",
       "         (0, 1, 6, 7, 8, 9): 1,\n",
       "         (4, 7, 8, 9): 1,\n",
       "         (0, 3, 6, 7, 8): 1,\n",
       "         (1, 2, 3, 5, 6, 8): 1,\n",
       "         (0, 3, 6, 8, 9): 1,\n",
       "         (5, 9): 1,\n",
       "         (0, 2, 3, 4, 6, 7): 1,\n",
       "         (0, 2, 3, 6, 7): 1,\n",
       "         (2, 4, 8, 9): 1,\n",
       "         (0, 2, 3, 4, 5, 7, 8): 1,\n",
       "         (1, 3, 5, 6, 7, 8): 1,\n",
       "         (1, 3, 9): 1,\n",
       "         (0, 1, 2, 4, 7, 8): 1,\n",
       "         (0, 1, 2, 5, 8): 2,\n",
       "         (1, 9): 1,\n",
       "         (4, 8, 9): 1,\n",
       "         (0, 3, 4, 5, 6, 7, 8): 1,\n",
       "         (0, 1, 2, 3, 6, 8, 9): 1,\n",
       "         (0, 1, 2, 3, 4, 7, 8, 9): 1,\n",
       "         (0, 2, 3, 4, 5, 6, 7, 8, 9): 2,\n",
       "         (0, 1, 2, 3, 4, 8): 1,\n",
       "         (0, 1, 6, 8): 1,\n",
       "         (1, 4, 5, 7, 8, 9): 1,\n",
       "         (3, 6, 7, 8): 2,\n",
       "         (3, 7, 8): 1,\n",
       "         (6, 8, 9): 1,\n",
       "         (0, 2, 4, 5, 6, 7, 8, 9): 2,\n",
       "         (0, 2, 5, 6): 1,\n",
       "         (0, 6, 7, 8, 9): 1,\n",
       "         (3, 6, 8): 1,\n",
       "         (0, 1, 2, 3, 7): 1,\n",
       "         (1, 3, 4, 7): 1,\n",
       "         (5, 10): 1,\n",
       "         (3, 4, 6, 7, 8): 1,\n",
       "         (0, 1, 2, 5, 6, 7, 8, 9): 1,\n",
       "         (0, 2, 3, 5): 1})"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "metadata": {},
   "outputs": [],
   "source": [
    "# clusters = com.to_node_community_map()\n",
    "# conmmunities = []\n",
    "# for i in range(11):\n",
    "#     conmmunities.append([])\n",
    "\n",
    "\n",
    "# for i in clusters.keys():\n",
    "#     c = clusters[i]\n",
    "#     for j in c:\n",
    "#         conmmunities[j].append(i)\n",
    "\n",
    "# viz.plot_network_clusters(G, com, node_size = 20 )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1768d9b84e0>"
      ]
     },
     "execution_count": 251,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "viz.plot_community_graph(G, com,plot_overlaps=True,node_size = 1000, plot_labels = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1768d79d550>"
      ]
     },
     "execution_count": 252,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "viz.plot_community_graph(G, com,node_size = 1000, plot_labels = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# previous work\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "patient network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "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>weighted_degree</th>\n",
       "      <th>weighted_clustering_coef</th>\n",
       "      <th>degree</th>\n",
       "      <th>clustering_coef</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>71.354838</td>\n",
       "      <td>0.308534</td>\n",
       "      <td>55</td>\n",
       "      <td>0.847811</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>78.729485</td>\n",
       "      <td>0.357802</td>\n",
       "      <td>29</td>\n",
       "      <td>0.738916</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>124.557025</td>\n",
       "      <td>0.495771</td>\n",
       "      <td>147</td>\n",
       "      <td>0.692852</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>44.278808</td>\n",
       "      <td>0.231304</td>\n",
       "      <td>8</td>\n",
       "      <td>0.892857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>77.967473</td>\n",
       "      <td>0.315042</td>\n",
       "      <td>61</td>\n",
       "      <td>0.900000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>226</th>\n",
       "      <td>80.292323</td>\n",
       "      <td>0.347773</td>\n",
       "      <td>36</td>\n",
       "      <td>0.623810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>227</th>\n",
       "      <td>68.041752</td>\n",
       "      <td>0.301884</td>\n",
       "      <td>26</td>\n",
       "      <td>0.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>228</th>\n",
       "      <td>143.421151</td>\n",
       "      <td>0.520197</td>\n",
       "      <td>147</td>\n",
       "      <td>0.957227</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>229</th>\n",
       "      <td>42.040203</td>\n",
       "      <td>0.177857</td>\n",
       "      <td>24</td>\n",
       "      <td>0.855072</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>230</th>\n",
       "      <td>98.529988</td>\n",
       "      <td>0.420676</td>\n",
       "      <td>125</td>\n",
       "      <td>0.901677</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>231 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     weighted_degree  weighted_clustering_coef  degree  clustering_coef\n",
       "0          71.354838                  0.308534      55         0.847811\n",
       "1          78.729485                  0.357802      29         0.738916\n",
       "2         124.557025                  0.495771     147         0.692852\n",
       "3          44.278808                  0.231304       8         0.892857\n",
       "4          77.967473                  0.315042      61         0.900000\n",
       "..               ...                       ...     ...              ...\n",
       "226        80.292323                  0.347773      36         0.623810\n",
       "227        68.041752                  0.301884      26         0.800000\n",
       "228       143.421151                  0.520197     147         0.957227\n",
       "229        42.040203                  0.177857      24         0.855072\n",
       "230        98.529988                  0.420676     125         0.901677\n",
       "\n",
       "[231 rows x 4 columns]"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(filterd_data).drop(columns = ['Hugo_ID']).T\n",
    "correlations = np.corrcoef(df, df)[:231, :231]\n",
    "for i in range(correlations.shape[0]):\n",
    "    correlations[i][i]=0\n",
    "for i in range(correlations.shape[0]):\n",
    "    for j in range(correlations.shape[0]):\n",
    "        if correlations[i][j] < 0:\n",
    "            correlations[i][j]  = -correlations[i][j] \n",
    "            \n",
    "ex_df = pd.DataFrame(correlations)\n",
    "\n",
    "G = nx.Graph()\n",
    "G.add_nodes_from(np.arange(231))\n",
    "# G.nodes()\n",
    "for i in range(correlations.shape[0]-1):\n",
    "    for j in range(i+1,correlations.shape[0]):\n",
    "        G.add_edge(i, j, weight=correlations[i][j])\n",
    "#         print(correlations[i][j])\n",
    "\n",
    "\n",
    "\n",
    "df = pd.DataFrame()\n",
    "de = G.degree(weight='weight')\n",
    "cc = nx.clustering(G,weight='weight')\n",
    "\n",
    "df['weighted_degree'] = np.array(de).T[1][:]\n",
    "df['weighted_clustering_coef'] = cc.values()\n",
    "\n",
    "\n",
    "thres_media = np.median(correlations)\n",
    "G = nx.Graph()\n",
    "G.add_nodes_from(np.arange(231))\n",
    "# G.nodes()\n",
    "for i in range(correlations.shape[0]-1):\n",
    "    for j in range(i+1,correlations.shape[0]):\n",
    "        if correlations[i,j] > thres_media:\n",
    "            G.add_edge(i, j)\n",
    "de = G.degree()\n",
    "cc = nx.clustering(G)\n",
    "\n",
    "df['degree'] = np.array(de).T[1][:]\n",
    "df['clustering_coef'] = cc.values()      \n",
    "df.round(3)\n",
    "df\n",
    "# df.to_csv(\"C:\\\\Users\\\\bird\\Desktop\\\\cancer_project_github\\\\cancer_data\\\\patient_net_features.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "231\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[0.        , 0.74065188, 0.50100018, ..., 0.01460397, 0.23895306,\n",
       "        0.01588378],\n",
       "       [0.74065188, 0.        , 0.17325192, ..., 0.0707046 , 0.17785555,\n",
       "        0.04636617],\n",
       "       [0.50100018, 0.17325192, 0.        , ..., 0.00584516, 0.44585732,\n",
       "        0.00200516],\n",
       "       ...,\n",
       "       [0.01460397, 0.0707046 , 0.00584516, ..., 0.        , 0.23463521,\n",
       "        0.29402463],\n",
       "       [0.23895306, 0.17785555, 0.44585732, ..., 0.23463521, 0.        ,\n",
       "        0.45071698],\n",
       "       [0.01588378, 0.04636617, 0.00200516, ..., 0.29402463, 0.45071698,\n",
       "        0.        ]])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(filterd_data)\n",
    "df = df.drop(columns = ['Hugo_ID'])\n",
    "cancer_types =  [0]*117+[1]*74+[2]*40\n",
    "print(len(cancer_types))\n",
    "# sns.countplot(x = 'Hugo_ID', data=df)\n",
    "\n",
    "\n",
    "# df['cancer'] = cancer_types\n",
    "\n",
    "correlations = np.corrcoef(df, df)[:133, :133]\n",
    "\n",
    "for i in range(correlations.shape[0]):\n",
    "    correlations[i][i]=0\n",
    "for i in range(correlations.shape[0]):\n",
    "    for j in range(correlations.shape[0]):\n",
    "        if correlations[i][j] < 0:\n",
    "            correlations[i][j]  = -correlations[i][j] \n",
    "#         if correlations[i][j] < 0.2:\n",
    "#             correlations[i][j]  = 0\n",
    "#         correlations[i][j] = math.floor(correlations[i][j]*100)\n",
    "correlations # weighted network matrix\n",
    "# ex_df = pd.DataFrame(correlations)\n",
    "# ex_df.to_csv(\"C:\\\\Users\\\\bird\\Desktop\\\\cancer_project_github\\\\cancer_data\\\\network_matrix.csv\")\n",
    "# np_pos = np.zeros((133,133))\n",
    "# np_neg = np.zeros((133,133))\n",
    "# for i in range(correlations.shape[0]):\n",
    "#     for j in range(correlations.shape[0]):\n",
    "#         if correlations[i][j] > 0:\n",
    "#             np_pos[i][j] = correlations[i][j] \n",
    "#         if correlations[i][j] < 0:\n",
    "#             np_neg[i][j] = -correlations[i][j] \n",
    "# ex_df = pd.DataFrame(np_pos)\n",
    "# ex_df.to_csv(\"C:\\\\Users\\\\bird\\Desktop\\\\cancer_project_github\\\\cancer_data\\\\network_matrix_pos.csv\")\n",
    "# ex_df = pd.DataFrame(np_neg)\n",
    "# ex_df.to_csv(\"C:\\\\Users\\\\bird\\Desktop\\\\cancer_project_github\\\\cancer_data\\\\network_matrix_abs.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "133"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "G = nx.Graph()\n",
    "G.add_nodes_from(np.arange(133))\n",
    "G.nodes()\n",
    "for i in range(correlations.shape[0]-1):\n",
    "    for j in range(i+1,correlations.shape[0]):\n",
    "        G.add_edge(i, j, weight=correlations[i][j])\n",
    "#         print(correlations[i][j])\n",
    "        \n",
    "\n",
    "G.number_of_nodes()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ccc = asyn_lpa_communities(G,weight='weight')\n",
    "# next(ccc)\n",
    "# greedy_modularity_communities(G,weight='weight' )\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "plt.subplot()\n",
    "nx.draw(G, with_labels=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'{\"communities\": [[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132]], \"algorithm\": \"wCommunity\", \"params\": {\"min_bel_degree\": 0.99999, \"threshold_bel_degree\": 0.99999, \"weightName\": \"weight\"}, \"overlap\": true, \"coverage\": 1.0}'"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "wc = algorithms.wCommunity(G, min_bel_degree=0.99999, threshold_bel_degree=0.99999, weightName='weight')\n",
    "\n",
    "# wc\n",
    "wc.to_json()\n",
    "wc = algorithms.louvain(G,weight='weight',resolution=0.5)\n",
    "print(wc.to_node_community_map())\n",
    "print(\"--------------\")\n",
    "ccc = asyn_lpa_communities(G,weight='weight')\n",
    "print(len(next(ccc)))\n",
    "print(\"--------------\")\n",
    "gmc = greedy_modularity_communities(G,weight='weight' )\n",
    "print(gmc)\n",
    "print(\"--------------\")\n",
    "bp = community_louvain.best_partition(G,weight='weight', resolution=0.5)\n",
    "print(bp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>degree</th>\n",
       "      <th>clustering_coef</th>\n",
       "      <th>betweenness</th>\n",
       "      <th>eigenvector</th>\n",
       "      <th>closeness</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>37.068</td>\n",
       "      <td>0.185</td>\n",
       "      <td>0.107</td>\n",
       "      <td>0.128</td>\n",
       "      <td>0.104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>25.771</td>\n",
       "      <td>0.153</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.086</td>\n",
       "      <td>0.090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>36.064</td>\n",
       "      <td>0.182</td>\n",
       "      <td>0.006</td>\n",
       "      <td>0.128</td>\n",
       "      <td>0.103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>35.926</td>\n",
       "      <td>0.183</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.125</td>\n",
       "      <td>0.103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>35.315</td>\n",
       "      <td>0.181</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.125</td>\n",
       "      <td>0.102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>25.487</td>\n",
       "      <td>0.138</td>\n",
       "      <td>0.023</td>\n",
       "      <td>0.052</td>\n",
       "      <td>0.089</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>31.071</td>\n",
       "      <td>0.172</td>\n",
       "      <td>0.012</td>\n",
       "      <td>0.073</td>\n",
       "      <td>0.097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>17.225</td>\n",
       "      <td>0.112</td>\n",
       "      <td>0.015</td>\n",
       "      <td>0.034</td>\n",
       "      <td>0.073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>36.866</td>\n",
       "      <td>0.211</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.102</td>\n",
       "      <td>0.104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>23.695</td>\n",
       "      <td>0.125</td>\n",
       "      <td>0.036</td>\n",
       "      <td>0.048</td>\n",
       "      <td>0.086</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>133 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     degree  clustering_coef  betweenness  eigenvector  closeness\n",
       "0    37.068            0.185        0.107        0.128      0.104\n",
       "1    25.771            0.153        0.000        0.086      0.090\n",
       "2    36.064            0.182        0.006        0.128      0.103\n",
       "3    35.926            0.183        0.000        0.125      0.103\n",
       "4    35.315            0.181        0.000        0.125      0.102\n",
       "..      ...              ...          ...          ...        ...\n",
       "128  25.487            0.138        0.023        0.052      0.089\n",
       "129  31.071            0.172        0.012        0.073      0.097\n",
       "130  17.225            0.112        0.015        0.034      0.073\n",
       "131  36.866            0.211        0.000        0.102      0.104\n",
       "132  23.695            0.125        0.036        0.048      0.086\n",
       "\n",
       "[133 rows x 5 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame()\n",
    "de = G.degree(weight='weight')\n",
    "b = betweenness_centrality(G,weight = 'weight')\n",
    "# print(b)\n",
    "e = eigenvector_centrality(G,weight = 'weight')\n",
    "# print(e)\n",
    "i = information_centrality(G,weight = 'weight')\n",
    "# print(i)\n",
    "cc = nx.clustering(G,weight='weight')\n",
    "df['degree'] = np.array(de).T[1][:]\n",
    "df['clustering_coef'] = cc.values()\n",
    "df['betweenness'] = b.values()\n",
    "df['eigenvector'] = e.values()\n",
    "df['closeness'] = i.values()\n",
    "df.round(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>degree</th>\n",
       "      <th>clustering_coef</th>\n",
       "      <th>betweenness</th>\n",
       "      <th>eigenvector</th>\n",
       "      <th>closeness</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>37.068241</td>\n",
       "      <td>0.185392</td>\n",
       "      <td>0.107449</td>\n",
       "      <td>0.128421</td>\n",
       "      <td>0.104133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>25.770810</td>\n",
       "      <td>0.152595</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.086084</td>\n",
       "      <td>0.089725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>36.063570</td>\n",
       "      <td>0.181958</td>\n",
       "      <td>0.006130</td>\n",
       "      <td>0.128194</td>\n",
       "      <td>0.102997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>35.926064</td>\n",
       "      <td>0.182528</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.125222</td>\n",
       "      <td>0.102907</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>35.315217</td>\n",
       "      <td>0.181160</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.124995</td>\n",
       "      <td>0.102200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>25.487240</td>\n",
       "      <td>0.138486</td>\n",
       "      <td>0.023479</td>\n",
       "      <td>0.051816</td>\n",
       "      <td>0.089273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>31.070944</td>\n",
       "      <td>0.171599</td>\n",
       "      <td>0.012260</td>\n",
       "      <td>0.072824</td>\n",
       "      <td>0.097389</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>17.225014</td>\n",
       "      <td>0.111606</td>\n",
       "      <td>0.014573</td>\n",
       "      <td>0.033870</td>\n",
       "      <td>0.073226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>36.865576</td>\n",
       "      <td>0.210740</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.101698</td>\n",
       "      <td>0.104416</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>23.694903</td>\n",
       "      <td>0.124797</td>\n",
       "      <td>0.035739</td>\n",
       "      <td>0.048203</td>\n",
       "      <td>0.086078</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>133 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        degree  clustering_coef  betweenness  eigenvector  closeness\n",
       "0    37.068241         0.185392     0.107449     0.128421   0.104133\n",
       "1    25.770810         0.152595     0.000000     0.086084   0.089725\n",
       "2    36.063570         0.181958     0.006130     0.128194   0.102997\n",
       "3    35.926064         0.182528     0.000000     0.125222   0.102907\n",
       "4    35.315217         0.181160     0.000000     0.124995   0.102200\n",
       "..         ...              ...          ...          ...        ...\n",
       "128  25.487240         0.138486     0.023479     0.051816   0.089273\n",
       "129  31.070944         0.171599     0.012260     0.072824   0.097389\n",
       "130  17.225014         0.111606     0.014573     0.033870   0.073226\n",
       "131  36.865576         0.210740     0.000000     0.101698   0.104416\n",
       "132  23.694903         0.124797     0.035739     0.048203   0.086078\n",
       "\n",
       "[133 rows x 5 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# df.to_csv(\"C:\\\\Users\\\\bird\\Desktop\\\\cancer_project_github\\\\cancer_data\\\\network_features_133.csv\")\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Decision Tree\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "696\n",
      "          0       1      2      3      4       5      6       7      8  \\\n",
      "0     8.693   1.164  0.243  0.106  3.038   9.665  0.000   2.725  0.000   \n",
      "1     8.693   1.164  0.243  0.106  3.038   9.665  0.000   2.725  0.000   \n",
      "2    22.395  32.202  0.392  0.141  0.185  20.991  0.000  12.568  0.094   \n",
      "3    22.395  32.202  0.392  0.141  0.185  20.991  0.000  12.568  0.094   \n",
      "4    50.843  58.076  0.418  0.291  0.295  26.276  0.453   0.812  0.046   \n",
      "..      ...     ...    ...    ...    ...     ...    ...     ...    ...   \n",
      "691  52.029  10.074  0.264  0.317  0.382   4.503  0.000   2.029  0.375   \n",
      "692  52.029  10.074  0.264  0.317  0.382   4.503  0.000   2.029  0.375   \n",
      "693  52.029  10.074  0.264  0.317  0.382   4.503  0.000   2.029  0.375   \n",
      "694  52.029  10.074  0.264  0.317  0.382   4.503  0.000   2.029  0.375   \n",
      "695  52.029  10.074  0.264  0.317  0.382   4.503  0.000   2.029  0.375   \n",
      "\n",
      "          9  ...    124    125    126    127    128    129    130    131  \\\n",
      "0     4.329  ...  0.313  0.000  0.133  0.000  0.060  0.027  0.000  0.016   \n",
      "1     4.329  ...  0.313  0.000  0.133  0.000  0.060  0.027  0.000  0.016   \n",
      "2    25.187  ...  0.066  0.018  0.072  0.012  0.547  0.053  0.006  0.044   \n",
      "3    25.187  ...  0.066  0.018  0.072  0.012  0.547  0.053  0.006  0.044   \n",
      "4    61.883  ...  0.013  0.007  0.000  0.000  0.186  0.028  0.007  0.032   \n",
      "..      ...  ...    ...    ...    ...    ...    ...    ...    ...    ...   \n",
      "691  36.838  ...  0.136  0.440  0.000  0.074  1.972  0.109  0.071  0.110   \n",
      "692  36.838  ...  0.136  0.440  0.000  0.074  1.972  0.109  0.071  0.110   \n",
      "693  36.838  ...  0.136  0.440  0.000  0.074  1.972  0.109  0.071  0.110   \n",
      "694  36.838  ...  0.136  0.440  0.000  0.074  1.972  0.109  0.071  0.110   \n",
      "695  36.838  ...  0.136  0.440  0.000  0.074  1.972  0.109  0.071  0.110   \n",
      "\n",
      "       132  cancer  \n",
      "0    0.006       0  \n",
      "1    0.006       0  \n",
      "2    0.021       0  \n",
      "3    0.021       0  \n",
      "4    0.025       0  \n",
      "..     ...     ...  \n",
      "691  0.085       2  \n",
      "692  0.085       2  \n",
      "693  0.085       2  \n",
      "694  0.085       2  \n",
      "695  0.085       2  \n",
      "\n",
      "[696 rows x 134 columns]\n"
     ]
    },
    {
     "data": {
      "image/png": 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NVNUXkiwCZph7zq4d7h5b3s7oPRdgQ1Ud02OJevAuAf4zyUeBqqqbkzyHXZ+rn06uvMnwnsKjxznAazHIF4Qkz2T0V+P3gKsY3ffZO8kM8ELgml103wjMdKMNkjwmyRF916xdq6pvMAruv+a+EcDUnSv/g1k4fjnJ5rH1c8Y3VtV3k3wM+NPJlqUxO+4pwOiv/VOqant3Xo4BvsJoVt+/rKrvdMHxAFX18yQnAe9Osj+j9+E/A07p0q9dvsc6lwDvAA6D6TxXPtEsSWq8fCRJagwFSVJjKEiSGkNBktQYCpKkxlCQHiFJLkvypN3sM+cst0k+0H30URqUzylID1M3yV2q6qVD1yI9XI4UpE6Sf0ry+rH1s5KcmeTy7vsTrk+ystu2NMlNSd4LXAcsSXJrN/UFSf6rm1VzQ5LVs/6ds7vjXd49AT27DmdQ1WAMBek+FzOaBG2HVwD/Brysqp4HvBg4e8f014wmS/tgVR1ZVd+adazXVNXzgRXAm5I8pWt/PHBdd7wrmTXz7disnCd1/c9nNCunNBFePpI6VfW/SQ5M8lRGE93dxWhGzHcleSFwL3AwcFDX5VtV9cWdHO5NSV7WLS8BljGaJ+le7ptX5z+Aj87qt7tZOaVeGQrS/X0YOAn4FUYjhz9iFBDPr6pfJLmV0fcpwE5myExyHPDbwDFV9bMkV4z1mW32PDPOoKpBeflIur+LgVWMguHDwP7A1i4QXgw8bR7H2B+4qwuEZwJHj23bqzs2wB8Cn5/Vd+pm5dTC4khBGlNVG5LsB3y7qrYkuRD4RJJ1wHrga/M4zKeA13Vf57gRGL/E9FPgiCTXAj/k/vcwpnJWTi0szpIqSWq8fCRJagwFSVJjKEiSGkNBktQYCpKkxlCQJDWGgiSpMRQkSc3/A7k5HcLMRNtAAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# with data balance\n",
    "filterd_data = 'C:\\\\Users\\\\bird\\Desktop\\\\cancer_project_github\\\\cancer_data\\\\filtered_133_genes_data.csv'\n",
    "network_features = 'C:\\\\Users\\\\bird\\Desktop\\\\cancer_project_github\\\\cancer_data\\\\patient_net_features.csv'\n",
    "\n",
    "features_df = pd.read_csv(network_features)\n",
    "\n",
    "genes_df = pd.read_csv(filterd_data)\n",
    "df = genes_df.drop(columns = ['Hugo_ID'])\n",
    "pt_np = np.array(df.T)\n",
    "pt_np\n",
    "new_np = np.repeat(pt_np[:117],2, axis=0) \n",
    "new_np = np.concatenate((new_np, np.repeat(pt_np[117:191],3, axis=0) ), axis=0)\n",
    "new_np = np.concatenate((new_np, np.repeat(pt_np[191:],6, axis=0) ), axis=0)\n",
    "df = pd.DataFrame(new_np)\n",
    "\n",
    "cancer_types =  [0]*117*2+[1]*74*3+[2]*40*6\n",
    "\n",
    "print(len(cancer_types))\n",
    "\n",
    "df['cancer'] = cancer_types\n",
    "\n",
    "print(df)\n",
    "\n",
    "# sns.countplot(x = 'cancer', data=df)\n",
    "\n",
    "class2idx = {\n",
    "    0:0,\n",
    "    1:1,\n",
    "    2:2\n",
    "}\n",
    "\n",
    "idx2class = {v: k for k, v in class2idx.items()}\n",
    "\n",
    "df['cancer'].replace(class2idx, inplace=True)\n",
    "\n",
    "X = df.iloc[:, 0:-1]\n",
    "y = df.iloc[:, -1]\n",
    "ori_X = X.copy(deep=True)\n",
    "\n",
    "def get_class_distribution(obj):\n",
    "    count_dict = {\n",
    "        \"LN\": 0,\n",
    "        \"Bone\": 0,\n",
    "        \"Liver\": 0\n",
    "    } \n",
    "    for i in obj:\n",
    "        if i == 0: \n",
    "            count_dict['LN'] += 1\n",
    "        elif i == 1: \n",
    "            count_dict['Bone'] += 1\n",
    "        elif i == 2: \n",
    "            count_dict['Liver'] += 1          \n",
    "        else:\n",
    "            print(\"Check classes.\")          \n",
    "    return count_dict\n",
    "ax = sns.barplot(data = pd.DataFrame.from_dict([get_class_distribution(y)]).melt(),\\\n",
    "            x = \"variable\", y=\"value\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "231\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# without data balance\n",
    "filterd_data = 'C:\\\\Users\\\\bird\\Desktop\\\\cancer_project_github\\\\cancer_data\\\\filtered_133_genes_data.csv'\n",
    "network_features = 'C:\\\\Users\\\\bird\\Desktop\\\\cancer_project_github\\\\cancer_data\\\\patient_net_features.csv'\n",
    "\n",
    "features_df = pd.read_csv(network_features)\n",
    "\n",
    "genes_df = pd.read_csv(filterd_data)\n",
    "df = genes_df.drop(columns = ['Hugo_ID'])\n",
    "\n",
    "cancer_types =  [0]*117+[1]*74+[2]*40\n",
    "\n",
    "print(len(cancer_types))\n",
    "# sns.countplot(x = 'Hugo_ID', data=df)\n",
    "\n",
    "\n",
    "df = df.T\n",
    "\n",
    "# df['degree'] = list(features_df['degree'])\n",
    "# df['clustering_coef'] = list(features_df['clustering_coef'])\n",
    "# df['betweenness'] = list(features_df['betweenness'])\n",
    "# df['eigenvector'] = list(features_df['eigenvector'])\n",
    "# df['closeness'] = list(features_df['closeness'])\n",
    "\n",
    "\n",
    "\n",
    "df['cancer'] = cancer_types\n",
    "# print(df)\n",
    "\n",
    "# sns.countplot(x = 'cancer', data=df)\n",
    "\n",
    "class2idx = {\n",
    "    0:0,\n",
    "    1:1,\n",
    "    2:2\n",
    "}\n",
    "\n",
    "idx2class = {v: k for k, v in class2idx.items()}\n",
    "\n",
    "df['cancer'].replace(class2idx, inplace=True)\n",
    "\n",
    "X = df.iloc[:, 0:-1]\n",
    "y = df.iloc[:, -1]\n",
    "ori_X = X.copy(deep=True)\n",
    "\n",
    "def get_class_distribution(obj):\n",
    "    count_dict = {\n",
    "        \"LN\": 0,\n",
    "        \"Bone\": 0,\n",
    "        \"Liver\": 0\n",
    "    } \n",
    "    for i in obj:\n",
    "        if i == 0: \n",
    "            count_dict['LN'] += 1\n",
    "        elif i == 1: \n",
    "            count_dict['Bone'] += 1\n",
    "        elif i == 2: \n",
    "            count_dict['Liver'] += 1          \n",
    "        else:\n",
    "            print(\"Check classes.\")          \n",
    "    return count_dict\n",
    "ax = sns.barplot(data = pd.DataFrame.from_dict([get_class_distribution(y)]).melt(),\\\n",
    "            x = \"variable\", y=\"value\")\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "133\n"
     ]
    }
   ],
   "source": [
    "print(X.shape[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # Training testing data splite\n",
    "\n",
    "# class2idx = {\n",
    "#     0:0,\n",
    "#     1:1,\n",
    "#     2:2\n",
    "# }\n",
    "# idx2class = {v: k for k, v in class2idx.items()}\n",
    "# df['cancer'].replace(class2idx, inplace=True)\n",
    "# X = df.iloc[:, 0:-1]\n",
    "# y = df.iloc[:, -1]\n",
    "# print(X,y)\n",
    "# # Split train into train-val\n",
    "# X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.1, stratify=y, random_state=21)\n",
    "# scaler = MinMaxScaler()\n",
    "# X_train = scaler.fit_transform(X_train)\n",
    "# X_val = scaler.transform(X_val)\n",
    "# X_train, y_train = np.array(X_train), np.array(y_train)\n",
    "# X_val, y_val = np.array(X_val), np.array(y_val)\n",
    "# print(\"Train set datapoints: \"+str(len(X_train)))\n",
    "# print(\"Validation set datapoints: \"+str(len(X_val)))\n",
    "# def get_class_distribution(obj):\n",
    "#     count_dict = {\n",
    "#         \"LN\": 0,\n",
    "#         \"Bone\": 0,\n",
    "#         \"Liver\": 0\n",
    "#     } \n",
    "#     for i in obj:\n",
    "#         if i == 0: \n",
    "#             count_dict['LN'] += 1\n",
    "#         elif i == 1: \n",
    "#             count_dict['Bone'] += 1\n",
    "#         elif i == 2: \n",
    "#             count_dict['Liver'] += 1          \n",
    "#         else:\n",
    "#             print(\"Check classes.\")          \n",
    "#     return count_dict\n",
    "# ax = sns.barplot(data = pd.DataFrame.from_dict([get_class_distribution(y_train)]).melt(),\\\n",
    "#             x = \"variable\", y=\"value\")\n",
    "# ax.set(xlabel=\"Cancer type\", ylabel = \"Cases number\", title=\"Class Distribution in Train Set\")\n",
    "# plt.show(ax)\n",
    "# ax2 = sns.barplot(data = pd.DataFrame.from_dict([get_class_distribution(y_val)]).melt(), \\\n",
    "#             x = \"variable\", y=\"value\")\n",
    "# ax2.set(xlabel=\"Cancer type\", ylabel = \"Cases number\",title=\"Class Distribution in Validation Set\")\n",
    "# plt.show(ax2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 93.50% (4.46%)\n",
      "[[115   2   0]\n",
      " [  6  68   0]\n",
      " [  7   0  33]]\n"
     ]
    }
   ],
   "source": [
    "\n",
    "\n",
    "# print(X,y)\n",
    "\n",
    "model = XGBClassifier()\n",
    "# kfold = KFold(n_splits=10,shuffle=True)\n",
    "\n",
    "results = cross_val_score(model, X, y, cv=10)\n",
    "print(\"Accuracy: %.2f%% (%.2f%%)\" % (results.mean()*100, results.std()*100))\n",
    "y_pred = cross_val_predict(model, X, y, cv=10)\n",
    "conf_mat = confusion_matrix(y, y_pred)\n",
    "print(conf_mat)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Feature: 0, Score: 0.01474\n",
      "Feature: 1, Score: 0.00102\n",
      "Feature: 2, Score: 0.02014\n",
      "Feature: 3, Score: 0.00156\n",
      "Feature: 4, Score: 0.01122\n",
      "Feature: 5, Score: 0.00173\n",
      "Feature: 6, Score: 0.00157\n",
      "Feature: 7, Score: 0.01837\n",
      "Feature: 8, Score: 0.00048\n",
      "Feature: 9, Score: 0.00131\n",
      "Feature: 10, Score: 0.00287\n",
      "Feature: 11, Score: 0.04201\n",
      "Feature: 12, Score: 0.00658\n",
      "Feature: 13, Score: 0.00277\n",
      "Feature: 14, Score: 0.00000\n",
      "Feature: 15, Score: 0.00000\n",
      "Feature: 16, Score: 0.00999\n",
      "Feature: 17, Score: 0.00000\n",
      "Feature: 18, Score: 0.00000\n",
      "Feature: 19, Score: 0.00078\n",
      "Feature: 20, Score: 0.00156\n",
      "Feature: 21, Score: 0.01174\n",
      "Feature: 22, Score: 0.01515\n",
      "Feature: 23, Score: 0.00064\n",
      "Feature: 24, Score: 0.00252\n",
      "Feature: 25, Score: 0.00122\n",
      "Feature: 26, Score: 0.00010\n",
      "Feature: 27, Score: 0.00556\n",
      "Feature: 28, Score: 0.02422\n",
      "Feature: 29, Score: 0.00000\n",
      "Feature: 30, Score: 0.00227\n",
      "Feature: 31, Score: 0.02053\n",
      "Feature: 32, Score: 0.19745\n",
      "Feature: 33, Score: 0.00674\n",
      "Feature: 34, Score: 0.00000\n",
      "Feature: 35, Score: 0.00033\n",
      "Feature: 36, Score: 0.03738\n",
      "Feature: 37, Score: 0.00000\n",
      "Feature: 38, Score: 0.00035\n",
      "Feature: 39, Score: 0.00254\n",
      "Feature: 40, Score: 0.00544\n",
      "Feature: 41, Score: 0.00000\n",
      "Feature: 42, Score: 0.00025\n",
      "Feature: 43, Score: 0.00154\n",
      "Feature: 44, Score: 0.00343\n",
      "Feature: 45, Score: 0.00000\n",
      "Feature: 46, Score: 0.00759\n",
      "Feature: 47, Score: 0.00087\n",
      "Feature: 48, Score: 0.16880\n",
      "Feature: 49, Score: 0.00395\n",
      "Feature: 50, Score: 0.00000\n",
      "Feature: 51, Score: 0.00073\n",
      "Feature: 52, Score: 0.00166\n",
      "Feature: 53, Score: 0.00241\n",
      "Feature: 54, Score: 0.00224\n",
      "Feature: 55, Score: 0.00044\n",
      "Feature: 56, Score: 0.00000\n",
      "Feature: 57, Score: 0.02051\n",
      "Feature: 58, Score: 0.00066\n",
      "Feature: 59, Score: 0.00022\n",
      "Feature: 60, Score: 0.00000\n",
      "Feature: 61, Score: 0.00944\n",
      "Feature: 62, Score: 0.00000\n",
      "Feature: 63, Score: 0.00817\n",
      "Feature: 64, Score: 0.01074\n",
      "Feature: 65, Score: 0.00000\n",
      "Feature: 66, Score: 0.01587\n",
      "Feature: 67, Score: 0.00381\n",
      "Feature: 68, Score: 0.01742\n",
      "Feature: 69, Score: 0.01415\n",
      "Feature: 70, Score: 0.01312\n",
      "Feature: 71, Score: 0.01398\n",
      "Feature: 72, Score: 0.00204\n",
      "Feature: 73, Score: 0.00000\n",
      "Feature: 74, Score: 0.00470\n",
      "Feature: 75, Score: 0.00548\n",
      "Feature: 76, Score: 0.00638\n",
      "Feature: 77, Score: 0.00027\n",
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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "model.fit(X, y)\n",
    "# get importance\n",
    "importance = model.feature_importances_\n",
    "# summarize feature importance\n",
    "for i,v in enumerate(importance):\n",
    "\tprint('Feature: %0d, Score: %.5f' % (i,v))\n",
    "# plot feature importance\n",
    "plt.figure(figsize=(12,6))\n",
    "plt.bar([x for x in range(len(importance))], sorted(importance, reverse = True))\n",
    "plt.ylabel('importance score', fontsize=14)\n",
    "plt.xlabel('list of genes', fontsize=14)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[115, 236, 310, 1085, 1328, 1594, 1597, 1902, 3121, 4036, 5637, 6100, 7454, 9188, 12331]\n",
      "[0.02014277, 0.018374829, 0.04200656, 0.015146018, 0.024216356, 0.020526273, 0.19745463, 0.03737781, 0.16880408, 0.020510497, 0.015868604, 0.017415376, 0.0236202, 0.036454737, 0.0210847]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'list of genes')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# sorted(importance[:133])\n",
    "id_symbol_lookup_df = pd.read_csv(id_symbol_lookup_file)\n",
    "top_genes = []\n",
    "top_gene_importance = []\n",
    "importance_threshold = sorted(importance,reverse=True)[15]\n",
    "for i in range(len(genes_df)):\n",
    "    if importance[i]>importance_threshold:\n",
    "        top_genes.append(genes_df['Hugo_ID'][i])\n",
    "        top_gene_importance.append(importance[i])\n",
    "\n",
    "print(top_genes)\n",
    "print(top_gene_importance)\n",
    "# id_symbol_lookup_df\n",
    "# print(list(id_symbol_lookup_df['ID']))\n",
    "# print(list(id_symbol_lookup_df['Hugo_Symbol']))\n",
    "ids = list(id_symbol_lookup_df['ID'])\n",
    "symbols = list(id_symbol_lookup_df['Hugo_Symbol'])\n",
    "top_genes_symbol = []\n",
    "for i in top_genes:\n",
    "    top_genes_symbol.append(symbols[i])\n",
    "\n",
    "top_genes_df = pd.DataFrame({'Hugo_Symbol':top_genes_symbol,'importance':top_gene_importance}) \n",
    "\n",
    "top_genes_df = top_genes_df.sort_values(by='importance',axis=0,ascending=[False])\n",
    "\n",
    "plt.figure(figsize=(15,5))\n",
    "plt.bar(top_genes_df['Hugo_Symbol'], top_genes_df['importance'])\n",
    "plt.ylabel('importance score', fontsize=16)\n",
    "plt.xlabel('list of genes', fontsize=16)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loop 0\n",
      "Accuracy: 100.00%\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-96-be55278a5602>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     83\u001b[0m         \u001b[0mmodel\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mXGBClassifier\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     84\u001b[0m         \u001b[0mkfold\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mKFold\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mn_splits\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m10\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mshuffle\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 85\u001b[1;33m         \u001b[0mresults\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcross_val_score\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcv\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mkfold\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;31m# same data split for each loop\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     86\u001b[0m         \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Accuracy: %.2f%%\"\u001b[0m \u001b[1;33m%\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mresults\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m*\u001b[0m\u001b[1;36m100\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     87\u001b[0m         \u001b[0maccs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mresults\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py\u001b[0m in \u001b[0;36minner_f\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m     71\u001b[0m                           FutureWarning)\n\u001b[0;32m     72\u001b[0m         \u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m{\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0marg\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0marg\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mparameters\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 73\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     74\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0minner_f\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     75\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py\u001b[0m in \u001b[0;36mcross_val_score\u001b[1;34m(estimator, X, y, groups, scoring, cv, n_jobs, verbose, fit_params, pre_dispatch, error_score)\u001b[0m\n\u001b[0;32m    404\u001b[0m                                 \u001b[0mfit_params\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mfit_params\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    405\u001b[0m                                 \u001b[0mpre_dispatch\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mpre_dispatch\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 406\u001b[1;33m                                 error_score=error_score)\n\u001b[0m\u001b[0;32m    407\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mcv_results\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'test_score'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    408\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py\u001b[0m in \u001b[0;36minner_f\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m     71\u001b[0m                           FutureWarning)\n\u001b[0;32m     72\u001b[0m         \u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m{\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0marg\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0marg\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mparameters\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 73\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     74\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0minner_f\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     75\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py\u001b[0m in \u001b[0;36mcross_validate\u001b[1;34m(estimator, X, y, groups, scoring, cv, n_jobs, verbose, fit_params, pre_dispatch, return_train_score, return_estimator, error_score)\u001b[0m\n\u001b[0;32m    246\u001b[0m             \u001b[0mreturn_times\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mreturn_estimator\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mreturn_estimator\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    247\u001b[0m             error_score=error_score)\n\u001b[1;32m--> 248\u001b[1;33m         for train, test in cv.split(X, y, groups))\n\u001b[0m\u001b[0;32m    249\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    250\u001b[0m     \u001b[0mzipped_scores\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mzip\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mscores\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\joblib\\parallel.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, iterable)\u001b[0m\n\u001b[0;32m   1042\u001b[0m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_iterating\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_original_iterator\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1043\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1044\u001b[1;33m             \u001b[1;32mwhile\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdispatch_one_batch\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0miterator\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1045\u001b[0m                 \u001b[1;32mpass\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1046\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\joblib\\parallel.py\u001b[0m in \u001b[0;36mdispatch_one_batch\u001b[1;34m(self, iterator)\u001b[0m\n\u001b[0;32m    857\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[1;32mFalse\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    858\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 859\u001b[1;33m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_dispatch\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtasks\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    860\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    861\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\joblib\\parallel.py\u001b[0m in \u001b[0;36m_dispatch\u001b[1;34m(self, batch)\u001b[0m\n\u001b[0;32m    775\u001b[0m         \u001b[1;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_lock\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    776\u001b[0m             \u001b[0mjob_idx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_jobs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 777\u001b[1;33m             \u001b[0mjob\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mapply_async\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbatch\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcallback\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcb\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    778\u001b[0m             \u001b[1;31m# A job can complete so quickly than its callback is\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    779\u001b[0m             \u001b[1;31m# called before we get here, causing self._jobs to\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\joblib\\_parallel_backends.py\u001b[0m in \u001b[0;36mapply_async\u001b[1;34m(self, func, callback)\u001b[0m\n\u001b[0;32m    206\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mapply_async\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcallback\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    207\u001b[0m         \u001b[1;34m\"\"\"Schedule a func to be run\"\"\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 208\u001b[1;33m         \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mImmediateResult\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    209\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mcallback\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    210\u001b[0m             \u001b[0mcallback\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\joblib\\_parallel_backends.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, batch)\u001b[0m\n\u001b[0;32m    570\u001b[0m         \u001b[1;31m# Don't delay the application, to avoid keeping the input\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    571\u001b[0m         \u001b[1;31m# arguments in memory\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 572\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mresults\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mbatch\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    573\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    574\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\joblib\\parallel.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    261\u001b[0m         \u001b[1;32mwith\u001b[0m \u001b[0mparallel_backend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mn_jobs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_n_jobs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    262\u001b[0m             return [func(*args, **kwargs)\n\u001b[1;32m--> 263\u001b[1;33m                     for func, args, kwargs in self.items]\n\u001b[0m\u001b[0;32m    264\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    265\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m__reduce__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\joblib\\parallel.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[1;34m(.0)\u001b[0m\n\u001b[0;32m    261\u001b[0m         \u001b[1;32mwith\u001b[0m \u001b[0mparallel_backend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mn_jobs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_n_jobs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    262\u001b[0m             return [func(*args, **kwargs)\n\u001b[1;32m--> 263\u001b[1;33m                     for func, args, kwargs in self.items]\n\u001b[0m\u001b[0;32m    264\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    265\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m__reduce__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py\u001b[0m in \u001b[0;36m_fit_and_score\u001b[1;34m(estimator, X, y, scorer, train, test, verbose, parameters, fit_params, return_train_score, return_parameters, return_n_test_samples, return_times, return_estimator, error_score)\u001b[0m\n\u001b[0;32m    529\u001b[0m             \u001b[0mestimator\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mfit_params\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    530\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 531\u001b[1;33m             \u001b[0mestimator\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mfit_params\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    532\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    533\u001b[0m     \u001b[1;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\xgboost\\sklearn.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, X, y, sample_weight, eval_set, eval_metric, early_stopping_rounds, verbose, xgb_model, sample_weight_eval_set, callbacks)\u001b[0m\n\u001b[0;32m    730\u001b[0m                               \u001b[0mevals_result\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mevals_result\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mobj\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfeval\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mfeval\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    731\u001b[0m                               \u001b[0mverbose_eval\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mverbose\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mxgb_model\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mxgb_model\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 732\u001b[1;33m                               callbacks=callbacks)\n\u001b[0m\u001b[0;32m    733\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    734\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mobjective\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mxgb_options\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"objective\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\xgboost\\training.py\u001b[0m in \u001b[0;36mtrain\u001b[1;34m(params, dtrain, num_boost_round, evals, obj, feval, maximize, early_stopping_rounds, evals_result, verbose_eval, xgb_model, callbacks, learning_rates)\u001b[0m\n\u001b[0;32m    214\u001b[0m                            \u001b[0mevals\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mevals\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    215\u001b[0m                            \u001b[0mobj\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfeval\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mfeval\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 216\u001b[1;33m                            xgb_model=xgb_model, callbacks=callbacks)\n\u001b[0m\u001b[0;32m    217\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    218\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\xgboost\\training.py\u001b[0m in \u001b[0;36m_train_internal\u001b[1;34m(params, dtrain, num_boost_round, evals, obj, feval, xgb_model, callbacks)\u001b[0m\n\u001b[0;32m     72\u001b[0m         \u001b[1;31m# Skip the first update if it is a recovery step.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     73\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mversion\u001b[0m \u001b[1;33m%\u001b[0m \u001b[1;36m2\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 74\u001b[1;33m             \u001b[0mbst\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdtrain\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mi\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     75\u001b[0m             \u001b[0mbst\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msave_rabit_checkpoint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     76\u001b[0m             \u001b[0mversion\u001b[0m \u001b[1;33m+=\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\xgboost\\core.py\u001b[0m in \u001b[0;36mupdate\u001b[1;34m(self, dtrain, iteration, fobj)\u001b[0m\n\u001b[0;32m   1107\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mfobj\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1108\u001b[0m             _check_call(_LIB.XGBoosterUpdateOneIter(self.handle, ctypes.c_int(iteration),\n\u001b[1;32m-> 1109\u001b[1;33m                                                     dtrain.handle))\n\u001b[0m\u001b[0;32m   1110\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1111\u001b[0m             \u001b[0mpred\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdtrain\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "# without data balance\n",
    "# filterd_data = 'C:\\\\Users\\\\bird\\Desktop\\\\cancer_project_github\\\\cancer_data\\\\filtered_133_genes_data.csv'\n",
    "# network_features = 'C:\\\\Users\\\\bird\\Desktop\\\\cancer_project_github\\\\cancer_data\\\\patient_net_features.csv'\n",
    "# features_df = pd.read_csv(network_features)\n",
    "# genes_df = pd.read_csv(filterd_data)\n",
    "# df = genes_df.drop(columns = ['Hugo_ID'])\n",
    "# cancer_types =  [0]*117+[1]*74+[2]*40\n",
    "\n",
    "# df = df.T\n",
    "# df['cancer'] = cancer_types\n",
    "# class2idx = {\n",
    "#     0:0,\n",
    "#     1:1,\n",
    "#     2:2\n",
    "# }\n",
    "# idx2class = {v: k for k, v in class2idx.items()}\n",
    "# df['cancer'].replace(class2idx, inplace=True)\n",
    "# X = df.iloc[:, 0:-1]\n",
    "# y = df.iloc[:, -1]\n",
    "# ori_X = X.copy(deep=True)\n",
    "\n",
    "\n",
    "# model = XGBClassifier()\n",
    "# model.fit(X, y)\n",
    "# # get importance\n",
    "# importance = model.feature_importances_\n",
    "# trace_list = list(range(len(importance)))\n",
    "# genes_df = pd.DataFrame({'index':trace_list,'importance':importance}) \n",
    "# # print(genes_df)\n",
    "# genes_df = genes_df.sort_values(by='importance',axis=0,ascending=[False])\n",
    "# sorted_index_by_importance = list(genes_df['index'])\n",
    "\n",
    "# print(sorted_index_by_importance)\n",
    "def drop_last_importance_gene(current_gene_X, drop_index):\n",
    "    res_X = current_gene_X.drop(columns=[drop_index])\n",
    "    return res_X\n",
    "\n",
    "lp_acc = []\n",
    "for lp in range(20):\n",
    "    \n",
    "    print(\"Loop \"+str(lp))\n",
    "    accs = []\n",
    "    gene_taken_off_sequence = []\n",
    "    \n",
    "    \n",
    "    features_df = pd.read_csv(network_features)\n",
    "    genes_df = pd.read_csv(filterd_data)\n",
    "    df = genes_df.drop(columns = ['Hugo_ID'])\n",
    "    pt_np = np.array(df.T)\n",
    "    new_np = np.repeat(pt_np[:117],2, axis=0) \n",
    "    new_np = np.concatenate((new_np, np.repeat(pt_np[117:191],3, axis=0) ), axis=0)\n",
    "    new_np = np.concatenate((new_np, np.repeat(pt_np[191:],6, axis=0) ), axis=0)\n",
    "    df = pd.DataFrame(new_np)\n",
    "    cancer_types =  [0]*117*2+[1]*74*3+[2]*40*6\n",
    "    df['cancer'] = cancer_types\n",
    "    class2idx = {\n",
    "        0:0,\n",
    "        1:1,\n",
    "        2:2\n",
    "    }\n",
    "    idx2class = {v: k for k, v in class2idx.items()}\n",
    "    df['cancer'].replace(class2idx, inplace=True)\n",
    "    X = df.iloc[:, 0:-1]\n",
    "    y = df.iloc[:, -1]\n",
    "    \n",
    "    model = XGBClassifier()\n",
    "    model.fit(X, y)\n",
    "    # get importance\n",
    "    importance = model.feature_importances_\n",
    "    trace_list = list(range(len(importance)))\n",
    "    genes_df = pd.DataFrame({'index':trace_list,'importance':importance}) \n",
    "    # print(genes_df)\n",
    "    genes_df = genes_df.sort_values(by='importance',axis=0,ascending=[False])\n",
    "    sorted_index_by_importance = list(genes_df['index'])\n",
    "\n",
    "    for i in range(130):\n",
    "        last_important_gene_id = sorted_index_by_importance.pop(-1)\n",
    "        gene_taken_off_sequence.append(last_important_gene_id)\n",
    "        trace_list.remove(last_important_gene_id)\n",
    "#         print(last_important_gene_id)\n",
    "        X = drop_last_importance_gene(X, last_important_gene_id)\n",
    "#         print(X.shape[1])\n",
    "        model = XGBClassifier()\n",
    "        kfold = KFold(n_splits=10,shuffle=True)\n",
    "        results = cross_val_score(model, X, y, cv=kfold) # same data split for each loop\n",
    "        print(\"Accuracy: %.2f%%\" % (results.mean()*100))\n",
    "        accs.append(results.mean())\n",
    "        model = XGBClassifier()\n",
    "        model.fit(X, y)\n",
    "        # get importance\n",
    "        importance = model.feature_importances_\n",
    "        genes_df = pd.DataFrame({'index':trace_list,'importance':importance}) \n",
    "        genes_df = genes_df.sort_values(by='importance',axis=0,ascending=[False])\n",
    "        sorted_index_by_importance = list(genes_df['index'])\n",
    "    lp_acc.append(accs)\n",
    "    \n",
    "#         print(sorted_index_by_importance)\n",
    "    \n",
    "#     y_pred = cross_val_predict(model, X, y, cv=10)\n",
    "#     conf_mat = confusion_matrix(y, y_pred)\n",
    "#     print(conf_mat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.93061594 0.91304348 0.92246377 0.91394928 0.92608696 0.91775362\n",
      " 0.92192029 0.92246377 0.90507246 0.90434783 0.91793478 0.92626812\n",
      " 0.9307971  0.92644928 0.92192029 0.92626812 0.92626812 0.91793478\n",
      " 0.92663043 0.92699275 0.92228261 0.91775362 0.90942029 0.92228261\n",
      " 0.92192029 0.92192029 0.93478261 0.9134058  0.9134058  0.91775362\n",
      " 0.92626812 0.91811594 0.91757246 0.91376812 0.91304348 0.93514493\n",
      " 0.9134058  0.91775362 0.91793478 0.93061594 0.93043478 0.92626812\n",
      " 0.92192029 0.92246377 0.91757246 0.93061594 0.91304348 0.93514493\n",
      " 0.93061594 0.92681159 0.91775362 0.92210145 0.9307971  0.92681159\n",
      " 0.90923913 0.92644928 0.92626812 0.91358696 0.92663043 0.91322464\n",
      " 0.92173913 0.9134058  0.91775362 0.91847826 0.93931159 0.92626812\n",
      " 0.92228261 0.92644928 0.92228261 0.93496377 0.91376812 0.92644928\n",
      " 0.92663043 0.93061594 0.92663043 0.91757246 0.9307971  0.92228261\n",
      " 0.93097826 0.93478261 0.93478261 0.94402174 0.91322464 0.93496377\n",
      " 0.93097826 0.93985507 0.93061594 0.92626812 0.93115942 0.93061594\n",
      " 0.92663043 0.92644928 0.92210145 0.92210145 0.93496377 0.93478261\n",
      " 0.93913043 0.92264493 0.93532609 0.93496377 0.92192029 0.93097826\n",
      " 0.9307971  0.93967391 0.93097826 0.91793478 0.91304348 0.92626812\n",
      " 0.92192029 0.91322464 0.92192029 0.92192029 0.91358696 0.9307971\n",
      " 0.91757246 0.90887681 0.92173913 0.93097826 0.90054348 0.9307971\n",
      " 0.91757246 0.93532609 0.90923913 0.91757246 0.90471014 0.89202899\n",
      " 0.9134058  0.89148551 0.90036232 0.87898551]\n",
      "[41, 88, 95, 34, 81, 82, 65, 62, 100, 104, 115, 29, 113, 91, 37, 56, 116, 17, 18, 60, 102, 78, 122, 121, 73, 105, 50, 98, 45, 119, 92, 84, 14, 15, 130, 101, 128, 126, 125, 127, 51, 103, 124, 26, 59, 42, 77, 8, 6, 35, 111, 90, 19, 38, 97, 58, 112, 23, 55, 47, 72, 114, 89, 54, 106, 1, 117, 20, 25, 131, 9, 3, 108, 79, 43, 110, 86, 39, 5, 24, 52, 30, 109, 53, 10, 87, 27, 132, 67, 44, 94, 75, 49, 13, 76, 74, 40, 46, 63, 68, 123, 12, 31, 33, 71, 118, 93, 4, 80, 96, 83, 85, 129, 0, 16, 107, 99, 61, 70, 57, 64, 69, 7, 28, 2, 66, 36, 120, 21, 22]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "accs = np.mean(lp_acc,axis=0)\n",
    "print(accs)\n",
    "print(gene_taken_off_sequence)\n",
    "plt.figure()\n",
    "plt.xlabel('NO. of tail importance genes dropped')\n",
    "plt.ylabel('Validation Accuracy')\n",
    "plt.plot(np.arange(len(accs)), accs)\n",
    "plt.scatter(103, accs[103], color = 'orange')\n",
    "# plt.scatter(123, accs[123], color = 'y')\n",
    "plt.show()\n",
    "# drop_index = gene_taken_off_sequence[:-26] for good prediction. \n",
    "# drop_index = gene_taken_off_sequence[:-8] for testing patient net features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Add patient network features\n",
    "### need X result processed by above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         0        2      4       7        11     16       21      22     28   \\\n",
      "0      8.693    0.243  3.038   2.725    0.113  0.740    0.670   4.526  0.044   \n",
      "1     22.395    0.392  0.185  12.568    0.510  0.603    8.116  27.136  0.000   \n",
      "2     50.843    0.418  0.295   0.812    0.378  2.218    2.037  17.231  0.000   \n",
      "3     66.151  332.090  1.566   5.638    0.364  3.301    3.954   9.239  0.005   \n",
      "4     54.679    0.367  0.492   0.439    0.098  0.748    0.979  28.286  0.008   \n",
      "..       ...      ...    ...     ...      ...    ...      ...     ...    ...   \n",
      "226   78.057    0.542  0.194   0.791   63.541  0.224   26.979   3.057  0.061   \n",
      "227  307.665    0.274  0.661   3.500  212.318  0.152  120.603   0.889  0.000   \n",
      "228  224.079    0.853  0.000   6.981    0.391  0.016    4.868   2.877  0.027   \n",
      "229    8.973    0.891  0.044   0.463   25.003  0.000   19.098   1.502  0.000   \n",
      "230   52.029    0.264  0.382   2.029    8.704  1.313   10.495   2.126  0.418   \n",
      "\n",
      "        32   ...    80      83      85     93      96     99     107    118  \\\n",
      "0     0.000  ...  0.000   0.191   0.282  0.015   0.394  0.033  0.004  0.439   \n",
      "1     0.000  ...  0.000   3.517   0.372  0.225   1.541  0.000  0.092  0.464   \n",
      "2     0.000  ...  0.000   2.142   0.442  0.000   1.135  0.115  0.053  0.595   \n",
      "3     0.000  ...  0.849  11.055   1.126  7.147  18.510  0.072  3.027  1.837   \n",
      "4     0.000  ...  0.018   0.317   0.657  0.105   1.353  0.048  0.046  0.352   \n",
      "..      ...  ...    ...     ...     ...    ...     ...    ...    ...    ...   \n",
      "226  19.763  ...  0.433   3.598   0.599  0.692   0.882  0.028  0.500  0.051   \n",
      "227  70.080  ...  1.153   1.816   0.950  1.161   1.273  0.023  1.364  0.016   \n",
      "228   0.050  ...  0.219   2.639   0.100  0.130   0.303  0.017  0.055  0.032   \n",
      "229   6.169  ...  1.560   1.860   0.755  3.365   0.615  0.000  0.440  0.042   \n",
      "230   3.802  ...  0.279   0.564  25.662  5.986   0.482  0.075  0.679  2.812   \n",
      "\n",
      "       120    129  \n",
      "0    0.065  0.027  \n",
      "1    0.043  0.053  \n",
      "2    0.009  0.028  \n",
      "3    0.018  0.134  \n",
      "4    0.058  0.243  \n",
      "..     ...    ...  \n",
      "226  0.009  0.027  \n",
      "227  0.008  0.006  \n",
      "228  0.000  0.016  \n",
      "229  0.003  0.091  \n",
      "230  3.384  0.109  \n",
      "\n",
      "[231 rows x 29 columns]\n",
      "     weighted_degree  weighted_clustering_coef  degree  clustering_coef\n",
      "0          62.090585                  0.319631       1         0.000000\n",
      "1         127.792729                  0.560768       0         0.000000\n",
      "2         181.989998                  0.715985       5         1.000000\n",
      "3          27.570500                  0.197075       0         0.000000\n",
      "4         174.927582                  0.696694       4         0.666667\n",
      "..               ...                       ...     ...              ...\n",
      "226       144.861211                  0.608346       2         1.000000\n",
      "227       150.080459                  0.621322       4         0.333333\n",
      "228       186.532131                  0.724109     123         0.962815\n",
      "229        26.543038                  0.161517       0         0.000000\n",
      "230       113.000890                  0.518079       0         0.000000\n",
      "\n",
      "[231 rows x 4 columns]\n"
     ]
    },
    {
     "data": {
      "image/png": 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GttG7DfW4PIejwGVJzu7+Lm2j977NuOQ/ZaG8+4EdSc5KchGwFfjuCPKdVnr/EdEngKur6jd9q9ZW/qoa21/A++m9W/1fwE2jzrOEvH9G759pPwC+3/16P/DH9D41cLibbhx11kWexzuB+7r5ccv+p8BM92fwL8CGcXoOwN8BTwCPAv8EnLWW8wN30Ht/4AV6Z7bXny4vcFP3en4SeN8azX+E3rX1U6/hf1yL+f2GqiQ1aJwvy0iSFmC5S1KDLHdJapDlLkkNstwlqUGWuyQ1yHKXpAZZ7pLUoP8DmRieJo0puGsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "filterd_data = 'C:\\\\Users\\\\bird\\Desktop\\\\cancer_project_github\\\\cancer_data\\\\filtered_133_genes_data.csv'\n",
    "network_features = 'C:\\\\Users\\\\bird\\Desktop\\\\cancer_project_github\\\\cancer_data\\\\patient_net_features.csv'\n",
    "\n",
    "gene_taken_off_sequence = [41, 88, 95, 34, 81, 82, 65, 62, 100, 104, 115, 29, 113, 91, 37, 56, 116, 17, 18, 60, 102, 78, 122, 121, 73, 105, 50, 98, 45, 119, 92, 84, 14, 15, 130, 101, 128, 126, 125, 127, 51, 103, 124, 26, 59, 42, 77, 8, 6, 35, 111, 90, 19, 38, 97, 58, 112, 23, 55, 47, 72, 114, 89, 54, 106, 1, 117, 20, 25, 131, 9, 3, 108, 79, 43, 110, 86, 39, 5, 24, 52, 30, 109, 53, 10, 87, 27, 132, 67, 44, 94, 75, 49, 13, 76, 74, 40, 46, 63, 68, 123, 12, 31, 33, 71, 118, 93, 4, 80, 96, 83, 85, 129, 0, 16, 107, 99, 61, 70, 57, 64, 69, 7, 28, 2, 66, 36, 120, 21, 22]\n",
    "\n",
    "features_df = pd.read_csv(network_features)\n",
    "# print(accs[-27])\n",
    "drop_index = gene_taken_off_sequence[:-26]\n",
    "# print(len(drop_index))\n",
    "X = ori_X.drop(columns=drop_index)\n",
    "\n",
    "# X['degree'] = list(features_df['degree'])\n",
    "# X['clustering_coef'] = list(features_df['clustering_coef'])\n",
    "# X['weighted_degree'] = list(features_df['weighted_degree'])\n",
    "# X['weighted_clustering_coef'] = list(features_df['weighted_clustering_coef'])\n",
    "\n",
    "df = X\n",
    "print(df)\n",
    "correlations = np.corrcoef(df, df)[:231, :231]\n",
    "for i in range(correlations.shape[0]):\n",
    "    correlations[i][i]=0\n",
    "for i in range(correlations.shape[0]):\n",
    "    for j in range(correlations.shape[0]):\n",
    "        if correlations[i][j] < 0:\n",
    "            correlations[i][j]  = -correlations[i][j] \n",
    "            \n",
    "# todo: insert code to do correlation distribution\n",
    "\n",
    "# end todo\n",
    "\n",
    "ex_df = pd.DataFrame(correlations)\n",
    "\n",
    "G = nx.Graph()\n",
    "G.add_nodes_from(np.arange(231))\n",
    "# G.nodes()\n",
    "for i in range(correlations.shape[0]-1):\n",
    "    for j in range(i+1,correlations.shape[0]):\n",
    "        G.add_edge(i, j, weight=correlations[i][j])\n",
    "#         print(correlations[i][j])\n",
    "\n",
    "\n",
    "\n",
    "df = pd.DataFrame()\n",
    "de = G.degree(weight='weight')\n",
    "cc = nx.clustering(G,weight='weight')\n",
    "\n",
    "df['weighted_degree'] = np.array(de).T[1][:]\n",
    "df['weighted_clustering_coef'] = cc.values()\n",
    "\n",
    "\n",
    "thres_media = np.median(correlations)\n",
    "G = nx.Graph()\n",
    "G.add_nodes_from(np.arange(231))\n",
    "# G.nodes()\n",
    "for i in range(correlations.shape[0]-1):\n",
    "    for j in range(i+1,correlations.shape[0]):\n",
    "        if correlations[i,j] > thres_media:\n",
    "            G.add_edge(i, j)\n",
    "de = G.degree()\n",
    "cc = nx.clustering(G)\n",
    "\n",
    "df['degree'] = np.array(de).T[1][:]\n",
    "df['clustering_coef'] = cc.values()      \n",
    "df.round(3)\n",
    "print(df)\n",
    "def plot_degree_dist(G):\n",
    "    degrees = [G.degree(n) for n in G.nodes()]\n",
    "    plt.hist(degrees)\n",
    "    plt.show()\n",
    "\n",
    "plot_degree_dist(G)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         0        2      4       7        11     16       21      22     28   \\\n",
      "0      8.693    0.243  3.038   2.725    0.113  0.740    0.670   4.526  0.044   \n",
      "1     22.395    0.392  0.185  12.568    0.510  0.603    8.116  27.136  0.000   \n",
      "2     50.843    0.418  0.295   0.812    0.378  2.218    2.037  17.231  0.000   \n",
      "3     66.151  332.090  1.566   5.638    0.364  3.301    3.954   9.239  0.005   \n",
      "4     54.679    0.367  0.492   0.439    0.098  0.748    0.979  28.286  0.008   \n",
      "..       ...      ...    ...     ...      ...    ...      ...     ...    ...   \n",
      "226   78.057    0.542  0.194   0.791   63.541  0.224   26.979   3.057  0.061   \n",
      "227  307.665    0.274  0.661   3.500  212.318  0.152  120.603   0.889  0.000   \n",
      "228  224.079    0.853  0.000   6.981    0.391  0.016    4.868   2.877  0.027   \n",
      "229    8.973    0.891  0.044   0.463   25.003  0.000   19.098   1.502  0.000   \n",
      "230   52.029    0.264  0.382   2.029    8.704  1.313   10.495   2.126  0.418   \n",
      "\n",
      "        32   ...    80      83      85     93      96     99     107    118  \\\n",
      "0     0.000  ...  0.000   0.191   0.282  0.015   0.394  0.033  0.004  0.439   \n",
      "1     0.000  ...  0.000   3.517   0.372  0.225   1.541  0.000  0.092  0.464   \n",
      "2     0.000  ...  0.000   2.142   0.442  0.000   1.135  0.115  0.053  0.595   \n",
      "3     0.000  ...  0.849  11.055   1.126  7.147  18.510  0.072  3.027  1.837   \n",
      "4     0.000  ...  0.018   0.317   0.657  0.105   1.353  0.048  0.046  0.352   \n",
      "..      ...  ...    ...     ...     ...    ...     ...    ...    ...    ...   \n",
      "226  19.763  ...  0.433   3.598   0.599  0.692   0.882  0.028  0.500  0.051   \n",
      "227  70.080  ...  1.153   1.816   0.950  1.161   1.273  0.023  1.364  0.016   \n",
      "228   0.050  ...  0.219   2.639   0.100  0.130   0.303  0.017  0.055  0.032   \n",
      "229   6.169  ...  1.560   1.860   0.755  3.365   0.615  0.000  0.440  0.042   \n",
      "230   3.802  ...  0.279   0.564  25.662  5.986   0.482  0.075  0.679  2.812   \n",
      "\n",
      "       120    129  \n",
      "0    0.065  0.027  \n",
      "1    0.043  0.053  \n",
      "2    0.009  0.028  \n",
      "3    0.018  0.134  \n",
      "4    0.058  0.243  \n",
      "..     ...    ...  \n",
      "226  0.009  0.027  \n",
      "227  0.008  0.006  \n",
      "228  0.000  0.016  \n",
      "229  0.003  0.091  \n",
      "230  3.384  0.109  \n",
      "\n",
      "[231 rows x 29 columns]\n",
      "53361\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "5804.0"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "df = X\n",
    "print(df)\n",
    "correlations = np.corrcoef(df, df)[:231, :231]\n",
    "\n",
    "for i in range(correlations.shape[0]):\n",
    "    correlations[i][i]=0\n",
    "    \n",
    "correlations = correlations.flatten()\n",
    "print(len(correlations))\n",
    "len(list(filter(lambda a: a>0.99, correlations)))/2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         0        2      4       7        11     16       21      22     28   \\\n",
      "0      8.693    0.243  3.038   2.725    0.113  0.740    0.670   4.526  0.044   \n",
      "1     22.395    0.392  0.185  12.568    0.510  0.603    8.116  27.136  0.000   \n",
      "2     50.843    0.418  0.295   0.812    0.378  2.218    2.037  17.231  0.000   \n",
      "3     66.151  332.090  1.566   5.638    0.364  3.301    3.954   9.239  0.005   \n",
      "4     54.679    0.367  0.492   0.439    0.098  0.748    0.979  28.286  0.008   \n",
      "..       ...      ...    ...     ...      ...    ...      ...     ...    ...   \n",
      "226   78.057    0.542  0.194   0.791   63.541  0.224   26.979   3.057  0.061   \n",
      "227  307.665    0.274  0.661   3.500  212.318  0.152  120.603   0.889  0.000   \n",
      "228  224.079    0.853  0.000   6.981    0.391  0.016    4.868   2.877  0.027   \n",
      "229    8.973    0.891  0.044   0.463   25.003  0.000   19.098   1.502  0.000   \n",
      "230   52.029    0.264  0.382   2.029    8.704  1.313   10.495   2.126  0.418   \n",
      "\n",
      "        32   ...    80      83      85     93      96     99     107    118  \\\n",
      "0     0.000  ...  0.000   0.191   0.282  0.015   0.394  0.033  0.004  0.439   \n",
      "1     0.000  ...  0.000   3.517   0.372  0.225   1.541  0.000  0.092  0.464   \n",
      "2     0.000  ...  0.000   2.142   0.442  0.000   1.135  0.115  0.053  0.595   \n",
      "3     0.000  ...  0.849  11.055   1.126  7.147  18.510  0.072  3.027  1.837   \n",
      "4     0.000  ...  0.018   0.317   0.657  0.105   1.353  0.048  0.046  0.352   \n",
      "..      ...  ...    ...     ...     ...    ...     ...    ...    ...    ...   \n",
      "226  19.763  ...  0.433   3.598   0.599  0.692   0.882  0.028  0.500  0.051   \n",
      "227  70.080  ...  1.153   1.816   0.950  1.161   1.273  0.023  1.364  0.016   \n",
      "228   0.050  ...  0.219   2.639   0.100  0.130   0.303  0.017  0.055  0.032   \n",
      "229   6.169  ...  1.560   1.860   0.755  3.365   0.615  0.000  0.440  0.042   \n",
      "230   3.802  ...  0.279   0.564  25.662  5.986   0.482  0.075  0.679  2.812   \n",
      "\n",
      "       120    129  \n",
      "0    0.065  0.027  \n",
      "1    0.043  0.053  \n",
      "2    0.009  0.028  \n",
      "3    0.018  0.134  \n",
      "4    0.058  0.243  \n",
      "..     ...    ...  \n",
      "226  0.009  0.027  \n",
      "227  0.008  0.006  \n",
      "228  0.000  0.016  \n",
      "229  0.003  0.091  \n",
      "230  3.384  0.109  \n",
      "\n",
      "[231 rows x 29 columns]\n",
      "[[0.         0.37303578 0.35734508 ... 0.20049282 0.04776391 0.16135619]\n",
      " [0.37303578 0.         0.75278361 ... 0.55661845 0.0045716  0.25935519]\n",
      " [0.35734508 0.75278361 0.         ... 0.92724776 0.04616677 0.64131612]\n",
      " ...\n",
      " [0.20049282 0.55661845 0.92724776 ... 0.         0.09336545 0.60120337]\n",
      " [0.04776391 0.0045716  0.04616677 ... 0.09336545 0.         0.17928695]\n",
      " [0.16135619 0.25935519 0.64131612 ... 0.60120337 0.17928695 0.        ]]\n",
      "[[0.37303578 0.35734508 0.01238076 ... 0.20049282 0.04776391 0.16135619]\n",
      " [0.37303578 0.75278361 0.02469137 ... 0.55661845 0.0045716  0.25935519]\n",
      " [0.35734508 0.75278361 0.11040744 ... 0.92724776 0.04616677 0.64131612]\n",
      " ...\n",
      " [0.20049282 0.55661845 0.92724776 ... 0.76933636 0.09336545 0.60120337]\n",
      " [0.04776391 0.0045716  0.04616677 ... 0.41004562 0.09336545 0.17928695]\n",
      " [0.16135619 0.25935519 0.64131612 ... 0.49555595 0.60120337 0.17928695]]\n",
      "53130\n"
     ]
    },
    {
     "data": {
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w1FO6ixs8SZIkSZIkZUr7VLvuHUamGDxJkiRJkiRliD2eJEmSJEmSlBb2eJIkSZIkSVJa7AyeAj0jeTJ4kiRJkiRJypAe1lvc4EmSJEmSJClToj2eJEmSJEmSlA7tPZ56SCLTQ56mJEmSJElS93ulubgVT5IkSZIkSepCidjdI8gsgydJkiRJkqSMseJJkiRJkiRJaZCwubgkSZIkSZLS4ZUeT908kAw5YPAUQqgMIXypw/ffCiGck95hSZIkSZIkHXkSieTXHlLw1KmKpzKgoMP33wHOS8NYJEmSJEmSjmgx1eMp9JDkqTPBUwUwLN0DkSRJkiRJOtLFHtbjKacT+7wIvDeE0AZsTN12XieSuRhj/K9XMzhJkiRJkqQjSU/r8dSZ4OmLwHjgox1uO48DT7eLgMGTJEmSJElSys5V7XpIwdOBg6cY47IQwvHAaGAo8CRwM3BLWkcmSZIkSZJ0hImxZ/V46kzFEzHGBLAcWJ56YVbFGJ9K58AkSZIkSZKONAl7PO1fjLEzDcklSZIkSZK0m9jDejwZIkmSJEmSJGVIe48nekbydMCKpxDCTYd43zHG+KFDPFaSJEmSJOmI46p2e3r/Pm6PsNd4buftETB4kiRJkiRJSontq9r1jOSpM8HT6N2+zwJ+BkwBrie5yt0mYBBwPvBp4Gng8102SkmSJEmSpCNAT+vxdMDgKca4uuP3IYTPkQydTtlt22LgqRDCLcAM4HLg5103VEmSJEmSpNe29lXtekjydCjNxa8F7tg9kNopxrgSuCO1nyRJkiRJklJ29njqGbHToQVPo4AdB9hnR2o/SZIkSZIkpaQKnnpMj6dDCZ62Ahfva2NIvnIXA9s6c2chhDeGEBaHEJaFEL6yt/sLIVyf2j4nhHBKh21lIYQ7QwiLQggLQwhnHsLzkSRJkiRJyoietqrdoQRPfwdOCiHcEULYpfF46vu/ASekvu5XCCEb+BVwCTAReFcIYeJuu10CjEv9uRb4TYdtvwAeiTEeDZwILDyE5yNJkiRJkpQRrzQX7xnJU2dWtdvdt4CzgXcAV4QQ1gMVwEBgKJANTAO+04n7mgwsizGuAAgh3E6yKfmCDvtcDvwpJv9lXkxVOQ0G6oBzgPcDxBibgeZDeD6SJEmSJEkZkUgkv/aQ3OngK55ijLUkg6dvAKuAEcBpqa8rga8DU1L7HchQYG2H79elbuvMPmOALcAfQwizQgg3hhCKD/b5SJIkSZIkZcrOHk89peLpUKbaEWNsjjH+IMY4DugFDAd6xRjHxxh/mKo+6oy9vcqxk/vkAKcAv4kxnkyyAmqPHlEAIYRrQwjTQwjTt2zZ0smhSZIkSZIkda32Ve16Ru50SFPtAAghlABXACcDvYGqEMJM4N5OVjtBsnppeIfvhwEbOrlPBNbFGF9K3X4n+wieYoy/A34HMGnSpN2DLUmSJEmSpIywx1MnhBCuBG4Ayti1IikCO0IIH40x3tmJu5oGjEs1JV8PXA38x2773A98KtX/6XSgKsa4MTWOtSGECTHGxcAF7NobSpIkSZIk6bCSSJXD9JDc6eCDpxDCRcBtQAL4E/AksAkYBJxPMji6LYSwI8b46P7uK8bYGkL4FPBPkk3Jb4oxzg8hfCy1/QbgIeBSYBlQD3ygw118Grg1hJAHrNhtmyRJkiRJ0mElVfBkxdN+fAtoItlAfOZu224JIfwSeDq1336DJ4AY40Mkw6WOt93Q4e8R+OQ+jn0ZmHQwg5ckSZIkSeouifapdt08kAw5lObiJwN/20voBECMcTpwB8nG35IkSZIkSUp5pbl4z0ieDiV4agI2HmCfDan9JEmSJEmSlNLTptodSvD0DHD2AfZ5HcnpdpIkSZIkSUppr3jq5nFkyqEET18Gjg8h/CiEUNxxQwihOITwY+A44CtdMUBJkiRJkqQjRU+reDqU5uJfBuYAXwSuDSHMBCqAgST7OvUmWe305d3mK8YY44de3XAlSZIkSZJeu17p8dTNA8mQQwme3t/h72XA6/eyz7mpPx1FwOBJkiRJkiT1WO0VTz1kWbtDCZ5Gd/koJEmSJEmSeoCe1uPpoIOnGOPqdAxEkiRJkiTpSJcqeOoxPZ4Opbm4JEmSJEmSDsHOiqceMtPO4EmSJEmSJClTEqmSp2DFkyRJkiRJkrpS7GGr2hk8SZIkSZIkZUj7qnY9JHkyeJIkSZIkScoQezxJkiRJkiQpLezxJEmSJEmSpLSIVjxJkiRJkiQpHRLtzcV7RvJk8CRJkiRJkpQhrzQX795xZIrBkyRJkiRJUoYkXNVOkiRJkiRJ6fDKVLtuHkiGGDxJkiRJkiRlyM7m4oGekTwZPEmSJEmSJGWIPZ4kSZIkSZKUFvZ4kiRJkiRJUlrY40mSJEmSJElp0d7jqYckTwZPkiRJkiRJGRLpOf2dwOBJkiRJkiQpYxIx9pj+TmDwJEmSJEmSlDGJ2HMai4PBkyRJkiRJUsYkYoSekzsZPEmSJEmSJGVKjPZ4kiRJkiRJUhpEezxJkiRJkiQpHezxJEmSJEmSpLRIxEgPyp0MniRJkiRJkjKlh/UWN3iSJEmSJEnKlBgjWT2ou7jBkyRJkiRJUobY40mSJEmSJElpkYiRHlTwZPAkSZIkSZKUKYkIPanLk8GTJEmSJElSxljxJEmSJEmSpDRIJOzxJEmSJEmSpDSwx5MkSZIkSZLSIhEhWPEkSZIkSZKkrhZjpAflTgZPkiRJkiRJmRKxx5MkSZIkSZLSwB5PkiRJkiRJSotEtOJJkiRJkiRJaZCIEXpO7mTwJEmSJEmSlDFWPEmSJEmSJCkd7PEkSZIkSZKktEgGTz0neTJ4kiRJkiRJypBE7O4RZJbBkyRJkiRJUoZEezxJkiRJkiQpHWKMZPWgNKYHPVVJkiRJkqTuZY8nSZIkSZIkpUUiQs+JnQyeJEmSJEmSMiYCwYqnzAkhvDGEsDiEsCyE8JW9bA8hhOtT2+eEEE7ZbXt2CGFWCOGBzI1akiRJkiTp4MUYyeo5uVP3Bk8hhGzgV8AlwETgXSGEibvtdgkwLvXnWuA3u22/DliY5qFKkiRJkiS9avZ4yqzJwLIY44oYYzNwO3D5bvtcDvwpJr0IlIUQBgOEEIYBbwJuzOSgJUmSJEmSDkUigcFTBg0F1nb4fl3qts7u83PgS0AiTeOTJEmSJEnqMonYs7qLd3fwtLeXOnZmnxDCZcDmGOOMAz5ICNeGEKaHEKZv2bLlUMYpSZIkSZL0qkWwx1MGrQOGd/h+GLChk/u8DnhLCGEVySl6rw8h/GVvDxJj/F2McVKMcVJ5eXlXjV2SJEmSJOmgRHs8ZdQ0YFwIYXQIIQ+4Grh/t33uB65JrW53BlAVY9wYY/xqjHFYjHFU6rjHY4zvyejoJUmSJEmSDkIi9qweTznd+eAxxtYQwqeAfwLZwE0xxvkhhI+ltt8APARcCiwD6oEPdNd4JUmSJEmSXo1EjPSg3Kl7gyeAGONDJMOljrfd0OHvEfjkAe7jSeDJNAxPkiRJkiSpy8QIoQclT9091U6SJEmSJKnHSPZ46u5RZI7BkyRJkiRJUob0tB5PBk+SJEmSJEkZkoiRnhM7GTxJkiRJkiRljD2eJEmSJEmSlBYJezxJkiRJkiQpHaI9niRJkiRJkpQOiRjpQbmTwZMkSZIkSVKmJKfa9ZzkyeBJkiRJkiQpQyJY8SRJkiRJkqSuZ48nSZIkSZIkpYWr2kmSJEmSJCktks3Fe07yZPAkSZIkSZKUITHa40mSJEmSJElpYI8nSZIkSZIkpYU9niRJkiRJkpQWiRgJ9JzkyeBJkiRJkiQpQ2KErB6UxvSgpypJkiRJktS9EhFXtZMkSZIkSVLXi/Z4kiRJkiRJUjrY40mSJEmSJElpkYj0qIqnnO4egCRJkiRJ0pHu79PXcv/sDVQ3tpCT3XPqgAyeJEmSJEmS0ijGyNfumUt5ST7vO3MU7z9rVHcPKWMMniRJkiRJktKouS1BS1vk3WeM5JPnj+3u4WRUz6ntkiRJkiRJ6gYNzW0AFOZmd/NIMs/gSZIkSZIkKY3qU8FTUZ7BkyRJkiRJkrpQfXMrAIUGT5IkSZIkSepKr1Q89bxW2wZPkiRJkiRJaeRUO0mSJEmSJKVFe3NxgydJkiRJkiR1JSueJEmSJEmSlBY7m4sX2+NJkiRJkiRJXamhxal2kiRJkiRJSgOn2kmSJEmSJCktdgZPBTkGT5IkSZIkSepCDc2tFOZmk5UVunsoGWfwJEmSJEmSlEZ1zW09cpodGDxJkiRJkiSlVUNzW49sLA4GT5IkSZIkSWlV39xqxZMkSZIkSZK6Xn1zG4V5Od09jG5h8CRJkiRJkpRGDc1tFFvxJEmSJEmSpK5Wb3NxSZIkSZIkpUNDi1PtJEmSJEmSlAb1za0U5VrxJEmSJEmSpC6WbC5u8CRJkiRJkqQuFGOkwR5PkiRJkiRJ6mrNbQlaE9HgSZIkSZIkSV2robkNwObikiRJkiRJ6lr1qeDJiidJkiRJkiR1KYMnSZIkSZIkpUVDe/DkVDtJkiRJkiR1ofrmVsCKJ0mSJEmSJHWx+padzcUNniRJkiRJktSFGuzxJEmSJEmSpHRoby6ea4+nbhFCeGMIYXEIYVkI4St72R5CCNents8JIZySun14COGJEMLCEML8EMJ1mR+9JEmSJEnSvjWkejw51a4bhBCygV8BlwATgXeFECbuttslwLjUn2uB36RubwW+EGM8BjgD+ORejpUkSZIkSeo29U6161aTgWUxxhUxxmbgduDy3fa5HPhTTHoRKAshDI4xbowxzgSIMdYAC4GhmRy8JEmSJEnS/tSlgqfCXIOn7jAUWNvh+3XsGR4dcJ8QwijgZOClrh+iJEmSJEnSoWlobqUwN5usrNDdQ+kW3R087e1VjwezTwihBLgL+GyMsXqvDxLCtSGE6SGE6Vu2bDnkwUqSJEmSJB2M+ua2HjvNDro/eFoHDO/w/TBgQ2f3CSHkkgydbo0x3r2vB4kx/i7GOCnGOKm8vLxLBi5JkiRJknQgDc1tPbaxOHR/8DQNGBdCGB1CyAOuBu7fbZ/7gWtSq9udAVTFGDeGEALwB2BhjPF/MztsSZIkSZKkA+vpFU853fngMcbWEMKngH8C2cBNMcb5IYSPpbbfADwEXAosA+qBD6QOfx3wXmBuCOHl1G1fizE+lMGnIEmSJEmStE/1LW0U5nVr/NKtuv2Zp4Kih3a77YYOf4/AJ/dy3LPsvf+TJEmSJEnSYaGhuZWiHrqiHXT/VDtJkiRJkqQjVk+famfwJEmSJEmSlCY2F5ckSZIkSVJa1De3UdyDezwZPEmSJEmSJKVJXXOrFU+SJEmSJEnqWjFGGuzxJEmSJEmSpK62pKKW1kRkaJ/C7h5KtzF4kiRJkiRJOggxRh5dUEFza2K/+z04dyMhwEUTB2ZoZIcfgydJkiRJkqSD8PLaHXz4T9O5d9b6/e730NyNTB7VlwGlBRka2eHH4EmSJEmSJOkgzFi9fZeve7OkooZlm2t50wmDMzWsw5LBkyRJkiRJ0kGYtXYHADPX7Dt4eig1ze6Nxw3K0KgOTzndPQBJkiRJkqTXklmpSqelm2upamihd2EuAG2JyLfum0djS4Lnl2/ltB4+zQ6seJIkSZIkSeq0TVWNbKhq5IKjBwAwO1X9BMmpd7e+tIYnFm9mW10z75o8vJtGefiw4kmSJEmSJKmTXl6brHZ6/+tG8fjizcxcs51zxpcD8NjCCnKzA0998TxK8nMIIXTnUA8LBk+SJEmSJEmdNGvNDvKys5g8ui8TBpYyc82O9m2PLqzg9NH9KC3I7b4BHmacaidJkiRJktRJs9bs4NihvcjPyebkEWXMWrOdRCKyamsdy7fUccExA7p7iIcVgydJkiRJkqROaGlLMGf9Dk4e3geAk0f0oaaxlRVba3ls0WYALjh6YHcO8bDjVDtJkiRJkqROWLyphsaWBCeNKAPg9NF9CQGuu/1lYoRxA0oY0a+oewd5mLHiSZIkSZIkqRMWb6oBYOLgUgBG9ivmhvecSkV1Ews2VnPBMVY77c6KJ0mSJEmSpE5YsrmGvOwsRvYrbr/t4mMHMXlUX/46dQ1XThrWjaM7PBk8SZIkSZIkdcLSilrGlBeTm73rBLI+xXl88vyx3TSqw5tT7SRJkiRJkjph8aYaxg8s7e5hvKYYPEmSJEmSJB1AbVMr63c0MH5gSXcP5TXF4EmSJEmSJOkAllYkG4uPs+LpoBg8SZIkSZIkHcDSiloAJhg8HRSDJ0mSJEmSpANYUlFDfk4Ww/sWdfdQXlMMniRJkiRJkg5gcUUN4waWkJ0VunsorykGT5IkSZIkSQewtKKW8QOcZnewDJ4kSZIkSZL2o6qhhU3VjTYWPwQGT5IkSZIkSfuxc0W7CYNKunkkrz0GT5IkSZIkSfuxJLWi3Tin2h00gydJkiRJkqT9WFJRQ1FeNkPLCrt7KK85Bk+SJEmSJEn7saSihnEDS8lyRbuDZvAkSZIkSZK0H0sqahg/wP5Oh8LgSZIkSZIkHfFa2hL85J+LuPKG56lrau30cZV1zWytbWbCIPs7HYqc7h6AJEmSJElSOm2ubuTaP8/g5bU7ALjp2ZV8+oJxnTp2SWpFu3EDDZ4OhRVPkiRJkiTpiPa7p1ewYEM1v/yPk3nDxIH87ukVVNY1d+rYncHT+IFOtTsUBk+SJEmSJOmINnvdDo4f1pvLThjC/7t4AnXNrfzmyWWdOnZJRQ2lBTkM6lWQ5lEemQyeJEmSJEnSa8qO+mY21zR2at+2RGTe+mqOH9obgPEDS7ni5GHc8sJqNlUd+D6WVNQyfmApIbii3aEweJIkSZIkSa8pH7plOh+5ZXqn9l2+pZaGljZOGNa7/bbrLhhHWyLy26eX7/fYGGNyRTv7Ox0ygydJkiRJUo8XY6S1LdHdw1AnzFi9nRmrt7NwY02n/s1mpxqKnzCsrP22Ef2KuOLkofz1pTX7rZzaUtvEjvoW+zu9CgZPkiRJkqQe747pa5n8g8dobGnr7qH0CPXNrcQYD+nYPzy7AoDmtgSrttUfcP+566sozstmTP/iXW7/5PljaWlL8PunV+zz2AUbqgGYYMXTITN4kiRJkiT1eHfPXE9lXTPLNtd291COeG2JyHk/eZKv3TP3oI9dW1nPI/M2cfbY/sArK87tz+x1VRw3tDdZWbv2aBrdv5jLTxrKX15cw/odDXs99uklW8nLyeLkEX0OeqxKMniSJEmS9iHGyNrKel5asY2FG6u7eziS0mR7XTPTVlUCyX5A6pyd1UI1jS0HddzKrbVsrmnitqlreWTexoM69pbnV5EVAt+9/FhCOHDw1NyaYOHGak4cXrbX7Z+/aDxZAb7499kkEntWYD25ZDNnjOlHYV72QY1TrzB4kqRu9PDcjbzp+mdobrWfgCQdjr5x7zym/PgJrvrdi7zt1887BUc6Qj2xeDM7MwcrnjrvqcVb+P5DC7nl+VUHddzc9VUADOldwFfunktF9Z49lm58ZgX/mL1hl9vaEpF7X17PRRMHMqa8hJF9i/YZPK2trOe+l9czd30Vza2J9hXtdje8bxHfvGwizy/fxi0vrNrjPlZsqeO88eUH9fy0K4MnpUVrW8IP0lIn/GPOBuZvqPYquiQdhh6Zt5FbX1rDVZOG87kLx9PQ0sa81IclSYePz9/xMl/8++xXdR//XlDBwF75jOpXZPB0EF5YsQ2Av01fu9dqoX2Zu66agtwsbv7gZBqa27j+saW7bN9U1cgPH17Et++fT0PzK4H/Syu2sbW2mTefOASAcQNLWVKx93+vL981h+tuf5l3/e5FgF1WtNvdVacN54KjB/CjhxextvKVnlFPLt4MwHkTDJ5eDYOnI8DW2iZ+/eQy5m+o2qM526E2a3s1ttQ0cdHPnubzd7yc8cfWvjW2tPH9Bxfsc+6yMi/GyNSVyZLuWWu2d/NoJEkdba5u5Kt3z+X4ob353hXH8R+njwCSKylJOnys2VbPPbPWc9fMdWyq2vfKZPvT2NLGU0u2cMExAxk7oNTg6SA8v3wbhbnZrK1s4Pnl22hqbePWl1bvMfVu2qpKPv6XGUxPTWecu34HEwf3YvzAUi44ZgD/XlCxS3B1+7Q1tCUilXXN3DVzXfvtD8zdSFFeNudPGAAkG36v2lpHU+uu1agLNlTz/PJtXHnqMCaP7stJw8sY0bdon88jhMD3rjgOgJ89uqT99icXb2FE3yJG79aUXAfH4OkI8PNHl/DjRxbzpuuf5aKfPc1/P7KIu2eu4303TeWYbz3CS6kUOhPqmlr50C3TWLm1jn8vqNglnVb3+r/Hl/L7Z1ZyT4cf3OpeK7bWsbW2GYCZa3Z072AkSbu4/vGl1De38bOrTiI3O4vy0nxG9isyeNJ+1TS28Osnl+3xITgTNlY17HdJ+CPVrVNXE4BEhDtnrD2k+3hhxTbqm9u46JiBjB1QwqptdbS2OXvjQLbXNbNwYzUfOns0ZUW53PrSaj57+8t8/Z553PzcKiA5Ne6Tt87kyhte4OF5m/j1k8tpS0Tmb6hun/p20cSBbK5pYk6qorS1LcHtU9dy7vhyThxexo3PrKAtEWltS/DIvE1ccMzA9n5L4weV0pqIrNxat8vY/vDsSgpzs/nGmybylw+fzr2ffB0h7NpYfHeDexfy/rNGcc+s9SzeVENjSxvPL9/GeRPKD3is9s/g6TVuR30zd85Yx2UnDOZ7bz2O8pJ8fv/0Cj5/x2wWb6qhrDCPL/x9NrVNrWkfS1NrG5+4dSbz1lfx/rNG0dSa4MUMhl6HorUtwc8fXdJedXKkWlJRw2+fSi4RasBx+Nj5/+7oQaXMzFDF0/a6Zt7486e57+X1GXk8SXqtem7ZNqaMK2fsgJL2204d2YeZa7Z3S0W5Xhv+8OxKfvzIYp5YtDnjj/2BP07jgzdPO+L/f8YY+b/HlnLjMytobGnj79PXcdHEgZx1VL+Dnu610z0z11Oan8OZR/Vj7IASWtoiqztMt4LkBfad5qzbwan/9W8u+79n+PZ986hvTv9nrcPRzs96500o54qTh/LwvE08PG8Tpfk5PLlkC5B8v/vg3I18ZMpo3nfmSJ5esoWZa7ZT39zG8cPKADh/wgCyswKPLqgA4NGFm9lU3ch7zhjJtVPGsGpbPQ/N3cgLK7ZRWdfMZScMbh/D+IHJn9Edp9ttrm7k/tnreeekYfQuyj2o5/Tx846iJD+Hb9w7l4/+eQYNLW1Os+sCBk+vcbe+tIbGlgSfev1Y3nPGSG679gxmfusi7vnEWTz75fP51btPZsOOBr77j/lUVDeytKKGP7+4mi/fOWeXuauvVlNrGx//y0yeWrKFH1xxPF+55GgKcrPa58Qerp5cvIWfP7qUq373Av/5j/lHZMPQRCLytbvnUlqQw4XHDGSWb5gPG9NWVtK/JI+3nzKMddvTf5Uyxsg37p3Hok01PDjn4FYPkaSeZHN1Iyu31nH66L673H7qyD5srW1m9bauew+l15bHFlYw5cePU72XFbwaW9r48wurAZi6MrOVcau21rFoUw3z1lfz4ooj+4LqHdPX8j//XsL3HlzIW3/1HJV1zbznjJFcPXlE+3SvnW58ZgVfvnPOft/7bqpq5KG5G3nnacMpyM1uD5s7Trd7ZN5GTv6vf7N4U7KJ9U3PrqSpNUFZYR63vLCau2YeWRf0Yows2lTNowsq9vvavbBiG0V52ZwwrIx3nz6CorxsPnfheD7wulHMWrOdHfXNPDJvI/k5WXz2wvG887ThtCYi//3wIoD2iqeyojxOG9WHfy+ooLUtwY3PrGBI7wJef/QALj52IKP6FfHp22bx8b/MpDgvm3M7NPoe07+EnKzAkk01PLN0C1+5aw7v+v2LtCYiH3jd6IN+7mVFeXzs3KOYtmo78zdU8bkLx3Pe+AEHfT/aVU53D0CHrrk1wZ9eWMWUcf05elCv9tt7FeRy8og+AJw6si/XnnMUNzy1nDumvzLFKgSYt6GKuz5+FgW5r25ZyEQict1tL/P4os18/4rjuHpysgfCmWP6tSfdh6vbp62hf0k+lx4/iD8+t4qivGy+ePHR3T2sLvXMsq1MX72dH73teAAeXVjByq11jCkvOcCRR7bm1gS52aFby2ZfWlnJ5NF9OWVk8nydtWYHFx87KG2Pd//sDTw4dyO9C3OZvjoZQFo2LEl7eilVkXr6mD2DJ0j2eRplv48e6a8vrWFtZQMvrajkookDd9l2/8sb2FbXTN/iPKauymzV/6MLk5Uipfk5/OHZFZx5VL+MPn6mLNhQzbfum8/ZY/tz1th+/OSfixnVr4jXHdWf5rYEZUW53Pz8Kl43th9rKxv470cW0dIWOWd8OW/qUCXT0a0vraYtRt535igAjipPntvLNtdy8bHJfe6Yvo7m1gS/emIZ//mWY3lo3iauPm04//mWY3nDz57mH7M38N4zRrK2sp5rbppKfk4WY8qL+eZlExncuzATL02XeWLxZr5xz7z2vrC3feSMff5/emH5NiaN6kteThZjB5Qy61sXkZ+TzYzV27n+8WU8vXQrj8zfxLnjyynOz2Hi4F6MHVDC9NXbKczNbn+tAS48ZiDfe3AhH/3zDKav3s5/v/14srMCELjjo2dy/+wNPLVkC2eM6bfL59e8nCxG9S/mpudW8ssnltG7MJeJg3vx4SljDvnn9EfPGcMpI/pwysgy8nNe3WdlJVnx9Bp229Q1VFQ38cGz95/kfuEN4/n5VSfx/SuO43+uPJEn/t95/P69k5i/oZrvPrDgoB5zxupkct3RDU8v55H5m/jGm47h3aePbL/9vAkDWL2tfo/5tgdj6spKfvjQQtoOoWT2QDZVNfL4os1cOWkY3738OC45bhB/eXHNIZfKPjJv42FZ4XX71DX0Lc7jilOG7hJw7BRj5N8LKqiq3/PK3ZFq/Y4GTvv+o/zlpTUHfey0VZX7XLL1YKzbXs/6HQ1MHtWXY4f0Ijc7pHW63aaqRr557zxOGVHGFy+eQGVdMytexbkpSUeyqSsrKUl9SOpo/IBSSvNzmOGCEIedlrZEWt4vdlTT2MIzS7cC8NyyrbtsizFy47MrOGZwL95z+ggWbKjeo7lyOj22cDPjB5bwgbNH89iizazYcuQ1x25pS/CZ22dRVpTLz68+iU+cN5Y7P3Ymv33vJLKyAgW52XxkyhgeXVjB7dPW8tN/LSY7KzB2QAk/eGhhe+/ZGCOPLazgV08sY/mWWv760houOHogI/olG0+XFuQyqFcBy1MVTzvqm3l6yRZ6FeTwwJwN/M+/F9PcmuA/Th9BCIE3nziEaasq2VjVwK+fXM767Q0M61PIows387N/L9nn8zlUNY0taZul8dSSLXz0TzMoLcjhB1ccT6+CHG6ftvf3y5uqGlm6uZYzx7wSSu0MaU4aXkZZUS6/fHwpFdVNXHJ88sJqCIHLU6vRTRzSi5zsV+KInUHuY4s289kLx3HVaSPatw3oVcCHp4zhzx86nU+eP3aPsUxKfcb58huPZurXL+C2a8/gXZNH7LFfZ+VkZ3HmUf0MnbqQwdNr1IzVlXzvwQVMGdefc8ftf85pbnYWbz15KO8+fSRvP3UYo/sXc+HEgXzs3KP460tr+P3TKw74eG2JyA8fXsjbf/M8X7lrbvvtLyzfxk//uZjLThjMh3YLwHbOhT3UMGbV1jo+fMs0fvv0Cv42rXONAptbEzw8dyPLNtcccDrZnTPWkohw1aThAHzo7NFUNbRw14yDb769o76Zz/1tNtfd/vJhFeBsrW3i3wsqeNvJQ8nPyWZseQml+Tm7BBx3zVzPR/40nR88tLAbR5pZ3/3HfKoaWrj7IBut3z51De/87Qt85rZZe2yLMR7UFMad/Z0mj05etTl2SO9dAsHd7/vViDHyxTtn09IW+Z93nsQZqTcIO1cVkSTt6qWV2zh1ZJ9dPhQBZGUFTh7Zh5nd1GD8rhnruuQixey1O3h++dYD7/ga0JaI3DZ1Daf/4DGuueklmlvT1xD68UWbaW5L0L8knxeW71rR9JcXV7OkopYPnT2a08f0IxFf3QqIMUZ+//SK9lVvW9sS/NcDC3ho7p5T5avqW5i6qpILjxnIe88YSW5WFn94duUhP/bh6vZpa1m2uZbvvfV4+pfkA8nZHRMGlbbv8/Fzj2LKuP58+7753D97Ax8+ewzff+txrN/RwDfuncftU9fwgZun8aFbpvOTfy7mgv95im11zXzgdaN2eayxA0pYlgrvHpm3idZE5BdXn0xudhZ/eXENp4woa59xctkJg4kxOf3urhnruHLSMG5832lcNWk4987awObqrmmlMHddFZ++bRan/tejXPOHqV3eOmPG6u1c+6fpjB1Qwu3XnsF/nD6ivW/T7oUHjS1tfOa2WeRlZ+1R+QeQnRWYMq6cJRW15GYHXn/0K/u85aRk8LRzmt1OI/sVc8XJQ/nMBeO47oJxBzX277zlWKZ9/UI+ft5RhkWHKYOn16CK6kY+9peZDO5dyP+962Sysg5tqsz/e8N43nT8YL7/0EL+999L9rlyw+ptdXzg5mn89qkVjOpXxL8WbGJtZT21Ta189m+zGNW/mB+9/YQ9puyM7FfMmP7F/OXF1WysaqC5NcH1jy3lx48sOuAVqdqmVj7yp+lkZwVOGNabn/5r8R6Bzt5+2P74kUV8/NaZXPi/T3Pa9x/lvx5YwLLNe1anJBKRv01fy5lj+rWXYJ46sg8nDi/jpudWHXRTwr+8uJqGljaqUyuZHC7unrmO1kTk6snJcC0rK3DSiLL2BuPLt9TyzXvnkRXgwbkbM7YKYWtbgqqGfQd089ZXceMzK9KyIszjiyr45/wKRvUrYtaaHZ1edvfm51bylbvn0q84n0Wbali+25XEL945h6t+92KnrkC1JSJ/eHYlQ3oXtL9ZOmVEH+as27HH8U8s3sxZP3qcl9fu6NwT3Iu/vLSGZ5Zu5WtvOobR/Ys5qryYPkW5TF/lFXtJ2l1lXTNLKmqZvFt/p53OOqofi1K9RDLp3wsq+MLfZ/O2Xz/Px/4845CXjY8x8tm/vcxHbpnO1tqmXbZVNbS8qh6gMUbWVtbTkqHVwBKJyPv/OJWv3j2Xgb0KeG7ZNr5699y09bJ8ZN4mykvz+cDrRrG4ooYtNcnX7w/PruSb983nvAnlXH7SEE4eUUZOVjjoxWu21zW3vwe9e+Z6vv/QQt5z40vMXruDr98zjz88u5Lrbp/FjNW73u+TSzbTlohccMxAykvzeedpw7h92loWbqzu1OPe/NxK3vW7F7ssIIHkRdmurECrbWrlF48uYfLovlx4zL777WRlBX5+1Un0K8mjT1Eu1547htPH9OMdpw7jrpnr+Mrdc5m+ajvfeNMxPPOl8/n068dyzZkjOWu3qWRjB5SwfHMtLW0J/jFnA6P6FXHehHKuPi35nrpjNc2Y8hKOG9qL3z+zkrYY+di5RwHJi9otiQQ3P7/qoJ7rnHU79jg3l22u5arfvcBTizdz1th+TF1VyQNzNhJj5JePL+XKG57nA3+cyh+fO7TAsbk1wZfvmkN5aT5/+fDplBXlAXDVaSNobk1wz6xXeli1tiX4/B0vM3VVJf971Ym7LMDQ0fmpIoTXje1P78JXGnyP7FfMr999CteeM2aPY3521Ul8/qLxB90KoiA3m+J8uwgdzvzXeQ26Y9pa6ppa+fOHJrf/UDgUOdlZXP+ukynOz+b6x5by6yeWMaQsOQe5oaWNwb0LGNSrgCcWbyY3O4vvvfU4LjhmAFP++wlufn4VWQE21zRx93tOpWQfJ/p33nIsH//LDN7yy+coL8lnQeoX4Opt9fzsqpPYXt/Muu0N1DS2MKxPUfsPrm/dN48VW+v48wcn07solzf/37N878EFXHr8YJZvqeXel9ezpKKWt58yjI+dO4aR/Yp5btlWbnx2JVeeOozTRvXlicWbueX5Vfzh2ZV89ZKj+WjqlwDAjc+uYG1lA1+75Jj220IIfPjs0Xz6tln88flVfPB1o9hc08QvH0+W4dY1tXLlpOG854yRuzzHxpY2bn5+NeeML6e8JJ8/Pr+Ka84axdCy7p3PHWPk9mlrmTSyD2MHvHIl6OQRffjl40uZt76K//f32RTkZvHDtx3PZ//2Mv9asInLTxqa9rF9+a65PDR3Iz+76kTeeNwr8+2rG1v4ySOLufWl1SQi/HP+Jm54z6n0S13VerVqm1r51n3zGTeghOvfdTKX/OIZ/jl/E+87a9R+j3t8UQX/+cACLpo4kG9dNpFzfvIED87ZyGdSV2PmrqvizlSl3NfvmcdPr9wziO3or1PXMH9DNb/8j5NTc9fhkuMHcdNzK/nd0yva77e5NcF37p/PxqpGPnnrTB749Nn0Kc5jxZZa7n15A88s3UJZYS4j+xVz9eThu/R622nRpmp+8OBCzhlfzntOT75JCiEwaVRfpqeuxFbVtxCykv3h0q2lLUFu9uFzzaOyrpnSgpzDakydsbm6ka/dM4/PXjiO43a7YtjTxRj5y0trmDi4V3s/Hulg7AwLzhiz9+Dp/WeN4s4Z6/h/f5/NI9edQ5/iQ38v1lkNzW185/7k76/LThjCDU8t54t3zubPHzr9oO/r5bU72tsgXP/YUr57+XEALNtcw/tumkZVQwv/+tw57e8J9+e+l9dz8/OruPGaSfQryeeO6Wv58l1zyc0OHFVewtGDShnZr5jKumaaWxN87qLxDOpdcNBj3pcH527kmaVb+eolR3PtOWO4/rFl/OzRJQzolc+XLp7QpX0MG5rbeHLxFt5+6lDOHtufn/xzMS+s2EZLa7IS6Y3HDuL6dyUrYnKzszhuaO+DCp6eXLyZj/xpOmeM6ce333ws331gAScOL6Oyrokrb3iB5rYEH3zdaB5bVMHH/jKT77/1OKobW9lS08RDczfSvySPk4aXAfCFiybw0NxNfOPeefz9o2fu9yL1c8u28t0HFpCIcOVvX+DWD5/OsD5Fr+q1+u1Ty/nhw4voV5zH+UcP4IsXT2Bgr1f3737Dk8vZWtvMH953zAH/XfuV5HPfJ19HQ0tb+3ubn7zjBL7whvHECL0Lc9tDii+8YcJe72Py6L7c/PwqLvnFM6zYUsunzh9LCIHrLhxPv5L89qqdnS47YQjz1lfz1pOGMrxv8vUb1b+YNx47iL+8uJo+RXnMWV9FSX42Q8sKeevJQ9tf541VDZTk51CSn8Pvn1nBDx9exPA+Rdzx0TMZ1LuAhuY2PnnrTApys3noM1MoL83nzf/3LD96eBELN1bz6yeXc+yQXlTWNfOf/1hATlbgval+VZ31h2dXsmxzLX943yT6dviZNnFIL04Y1pvbpq5hyrhyNlY18L0HFrK4ooavX3oMl50wZJ/3ee74ckoLcrjy1OF7bLv0+L3329KRK/S01a0mTZoUp0+f3t3DeFVijKzYWsdRXdQcOpGIPDh3Iws3VrNuewPZWYH8nCzWbW9g5dY6zh7bny+8YTwDUr8wPnPbLB5dWEFTa4J3ThrGD992wn7vf/GmGj78p2k0NLfxgyuOZ9W2On7w0CIKcrNobHnlilhuduCm959Gc2uCD90ync+8fiyfT/0y+No9c/lrh348xw/tzbgBJTwwdyPNrQmOH9qbTdWNlBbk8OCnp1CYlyyx3FLTxHfun8+DczfyifOO4vMXjWfRphqu+PVzvP7oAdzwnlN3+eXV2pbgP258iakrKzluaC9WbqmjJRE5fmhv6ppaWbSphv9++/Htc453fsD55r3zuPXDpzO6fzHn/fRJjh/am6+/6RhG9yvm6aVbKC/N56yj+nfJv9f+bNjRwKptdSzYUN1ejvw/V57I208d1r7Pk4s38/4/TgOgJD+HX737FKaM7c85P3mC0f2LD+lN7MGYsbqSt//mBXoX5lLV0MJnXj+Wj513FDvqW/jAH6exdHMN15w5imOH9OIb986jf0k+P7nyhC55/b5wx2zumbWOv330TE4b1ZcL//cp+pfkcfu1Z+7zmJVb63jLL59leJ8i7vr4WRTmZfPOG16gqqGFf37uHAA+ePM0ZqzezjsnDeP3z6zka5cezUemjNnl/1aMkfrmNjZWNfL23zzPsUN6ceuHT99ln0/cOoPHF23m8S+cx5CyQm58ZgXfe3AhX7x4Ar94dCkTh/QiESNz1lURApw8vIzGlgQrt9bR2NrGpccPpjQ/h8q6Zq44eShnHtWPN//yWZpaEjzw6bPbz2GA3z29nB88tIj/ufJEvn7vXFrbIpNG9eEzrx/HWWPT83/1P/8xnztnrOMn7zhhl8Cxu2ypaeKinz3FqH7F3Prh018zV8pijHzoluk8vmgzJw4v495PnGWT+A7unrmOz98xm6wAnzp/LJ+5YNwe06UyZXtdMy+s2EZ5aT5j+hd3WYieSS8s38a67fVcOWnPDw5HonXb6/nSnXOYuWY7c759MXk5e/+/M299FW/91XOcf/QArr/65Pb3HQdra21T+5Sh/fnvRxbxmyeX87drz+D0Mf3afz/8+UOTmXKAlgu7++a987hj+louOW4QD8zZyIOfmcLiihq+ee88crOzqG1q4eyx5fz+mlP3+7Pl+eVbed9NU2lpi7znjBF8400TOfcnT9C/JJ8p48pZvKmaRZtq2FjVSO/CXBpa2jhuSC9uv/bMfb6uu0skIhU1jSytqGVJRQ0DexUweXRfBvYqoLk1wUU/e4rC3Gwe/MwUsrMCMUa+ds88bpu6hjefOISfvOOEV72IDiRDp18+sZRfPbGcWz98OmeM6cdJ3/0XJw0vY+bq7Rw3tDe3fvj0XX7W/OChhdz83CrmfOcNBxzD1JWVXHPTSwzsVcCGHQ0kYnKq0sPXTSE3K4v/uPFFpowr5wdXHMeSilqu+PVz1HeoUi/Mzebac8bwuYvGt992x/S1fOnOOVx3wTguOX4Qg3sXUpKfQ01jC/PWV7OtromC3Gy+fs9cyory+M/UxeLCvGx+ftXJnHlUPyqqG3l26VYqahqpaWylJD+HPkV5TBhUyjGDSynK2/P35s7Q6YKjB1BSkMO/5lcwuHcBt197xi7vQzqrsaWNHz60kFteWM3lJw3hF1effND3cShijDy6cDPffWA+67Y38K/PnsO4gaX73H9LTRNfuWsO337zse29ogBmrdnOFb9+HoChZYU0tbaxtbaZorxsPnvhOOZvqOa+lzeQl53F2AElLNhYzXkTypm2spJBvQv40NljeHDuBp5fvo1bPjCZc1Krub2wfBvv+v2LALzj1GH8+O0nEIGP/jn5/uCnV57IxccOojg/h7qmVirrkhVoRXnZlJfm73JuL62o4S2/fI4p4/rzu2sm7fHcbpu6hq/e/UqrlaFlhXzzsom88bgDL4iTSMRDnp2j16YQwowY4x7/kQyedNBmr93B5b96jrKiXB7/wnm7pOL70tjSRiLG9l9QD8zZwHPLtjFhYAkj+xdTnJfDt++fz+ptdRTl5dCvOI9/fPrs9jcmjS1tzFi9ncK8bAaU5rdfIdhc3cjfZ6zjiUWbWbq5lr986HSOH7br1f+2ROQb987ltqlrKS3IIT8nm+ws9nmVMpGI3DlzHb98fBkTBpXyzTdNZES/IppbE3zkT9N5ZmlyNYVNVY2s39FAU2uCY4f04oFPn00Igb9PX8v3H1rIjvoWQoCdp9hXLjmaj57zShiRSEQq65vZsKOBGOG4ob3Jzgo0trSxqaqR4vwcYowsrqhha20TY/qX0Lswl2eWbWXhxmqOHdKLM8f0a1+dbueb0J1OHF7GNWeM5G2nDN3ll0tNYwuX/d+znDqiD1+59GgGlCbfBPzs30u4/vGlPPfl1+9ylXNbbRO3PL+KvJws3nDsIMYNKOn0h9xEIjJvQxWPLUyWgL/7jBF89M8zqKhu5OHrzuF7Dyzg7lnr6VOUS252Fg3Nbdzw3lN5XSr4mL12B5+5fRart9Vz9WnD+cR5Y9t/mXdckS2RiCzcVM3IfsX7rL677+X1XHf7y7sEmv/zr8X86ollTPv6hfQuzCU7K7nKXVV9C3fPWsezS7cydWUlOdmB+z91dvsVrFueX8W375/Pvz93DrVNrVzx6+f54sUT+Pi5R3Htn2fw6MIKJo/uyxsmDuTppVtZsKGKqoYWWtqS/xlyUm8md38Ds7ayngv/9ymmjOvPZScM4Vv3zePkEX245YOTufWl1Xz9nnkcN7QXbz1pKG8+cUj71cMd9c385qnl/OWF1RTm5ZCbHdhY1Uifolzqmtq4/aNncMqIXSs/Zqzeztt/k3wjNHFwL86dUM4DczawubqJWz44ub0P1P6srawnNzurU1evd77+fYvzqKxr5kNnj+aLF0/okg8Eh+q622fx0NyNJGKysuG9Z4zkvpc3UNXQQv+SfM48qh9Xnjqs20KLfblj2lq+dNccXje2H88t28b17zqZt5y47yuOPcmmqkbe8LOnGDewlFH9irlr5jrefOIQrr/6pIyHc4/M28Q37p3L1tpkX4zc7MB/v/0E3nbKsAMcefiYtqqSd9+Y7Jlzw3tO7dSHjNeSyrpmmlrbGFBawLLNtfz5xVXcMW0dBPjSxRP48JQ9p4F09PunV/D9hxYysFc+110wnrefOrRTvUUq65q57+X13DF9HQs3VvORKaP52qV7VnFsrW3i79PXcd/L61m0qYa3nTKU/33nSQA0tbZxwf88Re/CXP7xqbM7/cGuuTXB5B88ypRx5XzrsmRQtDPAGDughD++/zQemruRHz68iBvec8ouFwlijGyoamTZ5lqWVtRw/WNLGdCrgBOHlXHPrHVceepw/jZ9bXs41vEx83KyeGDOBj7111lcc+ZIzp8wgKWba7jkuMHtv1s7WlJRwxfumM2iTdXtvzs7OnlEGUeVl3DnjHX88QOncf6EV6ZexRi54akV/Pifi8jNzmJI7wKG9ilkSO9CThhexuUnDaFXQS61Ta20tCboU5zHttomfvqvxby0spLBvQsozM1hY1UD1Y0tlObnsqm6kcq6Zs6bUM6N10wiJzuLj/xpOv9eUEFpQQ6PfPacPSrdn1i0mQ/cPI3zJ5TzxYuPpn9JHi2JyJDeBe3/1s8t28qdM9bx0NyNDO1TyB0fPZOVW+v47O0v86GzR7cvILT7h/cNOxrYsKOB8tJ8+pfk7/XCSSIRee9NL/Hcsv2vrleUl819n3wd4waWsnBjNZ+8dSYrt9Vx2si+zFizvX26XE5WoLXD1Lnc7MB5EwbwlhOHcMzg0mQfyX8t5tGFm7nshMH8/KqTyMnOYvqqSq65aSoDSvMZO6CElVvrOGl4Hy47YTBNrQmWb6klkYgU5GZTmwpIKuua2VbXRGVdM5uqGqlubOWDrxvNl96Y+fcNjS1trNtev8vsgYM1b30V5aX57e/b1m2v52v3zOPpJVsoyM3ifWeNIsbk/4cLjh7AZy8cz7RVlbzvj1NpbEmu1Pep88fu8TPpO/fPp7Glje+99bj29yr1za1c9dsXmbu+iqyQvMhc3bjrwkm9C3OZMKiUoweVUt3Qwj/mbKQoN5tHPrfn/2NIfpZ6btlWttc3EyNcfOygQw7bdeQ7bIOnEMIbgV8A2cCNMcYf7bY9pLZfCtQD748xzuzMsXtj8NQ1fvTwIk4eUdalS79vrmnkyhteYN32Bu75xFmcMKysy+47xsi/FlTw+MLNzFq7ne+85dhDqqBpaG7ji3fOZu32BoaVFTKkrIChZYVcevzgXa7i1DW1csf0teyob+Gc8eX88bmVPDBnIycO691eFl3btOsvgb7FeYzoW8SCDdU0H6A3QnFeNnWpN4oXTRzI0YNK+b/Hl3HxsQO55sxRjOhbtNc3cvuztrKeKT9+goLcLIaWFTKkrJD+Jfn8e0EF9c2t7HyvkZedRa/CXIb2KWTi4FImDu7FMYN7MaC0gERMhmmrttYxbVUljy3czOaaJrJCcnpXjJFEhJ9fdRJvPTk5pW/G6kp+/cRyVm6t41fvPoVjdltBqKG5jf/99+Jk760YOXFYGZurG9la28ybTxzCRRMH8punljN77Q5ysgInjyjjmMG9GNmvOHlVqaaZBRurmLlmB8cP7c3frj2j/ZfzvPVVXPZ/z9K7MJfqxhZ6F+ZyVHkJ8zdU0diSYEz/Yk4b1Zf3njlyl+lMm6sbOf2Hj3HMoF5srGoghMDTXzqfkvwc2hKR26et4af/XMz2+pb2++hbkkfvwlzKCnM5flhvjh2y9+lRP/v3En7x2FIg+Wbh7k+cxfhUQFXV0LLLHPnd7QzjWtsS/HXqGm54cjmff8ME3nHqnh90m1sTnPHDxxjVr4g/fmAyvQtzqaxr5p2/fYFNVY1cctwgZq7ZTiLC4N4FqavgreTnZDG4dyErttYya80O8rKzuO7CcVx7zhha2yLZWWGXK9lVDS28sHwrn79jNscO6cUtH5zMjx5exJ9eWM1R5cV88eKjaW5LUNPYwuh+xYwbWEr/kmQg/OKKSp5euoXSghyG9C7kzKP67bdUP8bI8i21zFtfzYottZw6qi9Txvbf6weyp5ds4ZqbpnLdBeMY0beIL/x9NgDlpfmM6FvUHiyPKS/mvWeMZNyAUorys9lR38z2uha21zezo76FyvpmdtQn3yRXN7TS2NpGWyJSkJNNv5I8Jo3qyznj+nPqyD6dCj5ijNQ1t7G9rpnt9c1sr2+htrGV3oW55GQHnli0mb+8uJoThpXx5w9N5s2/fI7qhhauPm04U1dVMmVcf645cxQFudk0tbaRl511wMetrGsmESN9i/LaX6u2RHK1yy21TYwtL2Hi4F70LsolxsjSzbWs2lpHSUEOQ8sKGdG3qMtDncaWNnKywi6hX4yRddsbGJT6/7hzv/yc5HPcUtPE5/72MtNXV/Lwdecwun8xv3piGT/552K+csnRfOzco2hLRKoaWqisa6ZPUe4hVSC1tiV4bvk21m9v2OtV/6bW5JSo26au5dghvfj6m46htS1yw1PLeX75Nj534Xg+NGX0LiH5iyu28asnlnHJcYM7HV50lZ39ZDqeJ9WNLcxeu4NP/XUWfYvzKMnPYfW2Oh78zJS9/m6Zt76KW55fxZKKGt4xaThXnjqMgtxsYows2lTD7LU7qKxvprG5jQG9Chjet4gJA0sZ2Ct/j8rQTdWNrNxSxzGDe9GnOK/9372uuZW2RKQkP4eywjxaEgnqmloZ1LuA/JxsEonInPVVrNhSy5aaJrJCoKwol7KiZJ+XsqJcehfm8fLaHdw9cx3TVm1v76Gy8wN1Xk4Wb0s1tu3MNDNIhnM/eGghs9bsoH9JHhdNHMjqbfWsSk1lK8jNZlT/Ykb2KyKRSAY3Ty7eTEtbspp6WJ9CHp63iStPHcbXLj2GPsXJMf7p+VU8MGcjzW0JThlRxltOHMLVk0fs8qH73lnr+ezfXuaCowcwtE8hY/oXM2lUX2qbWpmxejutbZHBvQtYuKmaB+dspFdhLlPG9eePz61qD2vumrGOl1Zu47IThnDWUf3Iyc6itS3BW375HCu31nHFKUM5fmhvHl1QwYsrtrW/9wAY1a+IP38oWS167k+eoKaxlXPHl3PLByfv8/X6zv3zd+l3k5eTxTVnjGTt9nqeWrKFk4aXcd6EAfzy8WUU5GZz5aRhDCkrZGx5CeMGlrBhRwPPL9/GHdPXsmJLHWeO6cdfP3L6Xn8GPb9sK08t3cL67Q2s39HAuu0NbKlpojA3m6F9Clm+pZYYYUz/YrbWNlHf3MY548vZXt9MQ3Oy5URZUR41ja3t4cBpo16ZfvmnF1bxrfvm84urT9prm4KdAdivn1xGTYcP/pceP4hvv/lYfv7oEm6bupZeBTm86YTBXHdB105DhOT09sWbalixtY7N1Y3UNrVSmJvNcUN7M7BXATWNLQwpK9zld2t9cyvfe3Ahzy3byhuPG8RbTxrKyH5FFOXl0NTaxubqJhZurOallZXcP3tDe58rgNL8HD5+/lFcO2XMLj+/p62q5LO3v0xxfjbD+hQxbWUlNU17X0W6rCiXvsV59C3Ko29xHv1K8rjshCHtFyWPFDFGpq/ezoi+Rft8b1NR3UhdUyuj+xcf1O/ZxpY2XlixjVlrdrCjvpkhZYX0Lc4jJytQ05icwbF4UzVLKmppS0T+4/QRfGTKmC7//6ee6bAMnkII2cAS4CJgHTANeFeMcUGHfS4FPk0yeDod+EWM8fTOHLs3Bk+Ht621Tazf3sCJqTnqR4pEIvLzx5by7NItDC4rpLwkn96FufQpymVonyLqm1t5YtFm1u9o4OQRfRg3oISGljZihHEDSigvzWf5llq21jZz5lH9GNO/mNXb6vnH7A387ukV1DS1cslxr/QWOFQPztnIrDXbWb8j+SZtY1Ujp47ow/+7eDy9CnJ5dOFm1lTWU9XQzMqtdSzcWLPPJuEl+TmcM74/Fxw9kPOPHkBdUyu/fXo5za0J/nsvzegPZFNVI7dNXcPTS7cwsm8RBbnZ3PvyehpbEpSX5vPJ846ioqaJF1dsY1lFbfsbmsLcbCYMKuXEYb35xPljd/nlHmPkew8upKqhhSG9C9hS28zSihrGDSzhPWeM3Gc4BPDhW6YzbVUl544v5/2vG7VHRVFtUyvb65oPOgBsS0QWbqxur+4rTWPfpe2p/kYd3xxuqmrkPX94ic3VjUwa1ZeivGw27GhIfuAryKGxJcGGHQ30KcrjLScNYe66Kh6cu7G9uq8kP4eLjx3E8L6FPLF4C3PW7SBGGNSrgPs+9br21/+pJVv42t1zWb+jYY9x9SnKpSgvh/U7klN/OzYnHT+whKwQqG1qpbaplcaWNob3SQat89ZXsblm12acI/oWtS8gAMl/8221yf+/g3sX8NB1UyjIzeaJ1OqbU8b2Jyc7ixiTwct/P7KI5Vvq9vr6ZWcFygpz6ZN6g9yrMIeC3Oz2ysX1OxpYsKGaRExWErzp+MHk5WTR3Jpsrr8zWNpR30x1QwtVDS1UN7butxlrTlZgyrj+/OBtxzO4dyHPLdvKu298CYCR/YpYva2eAaX55OdmsbaygTH9i3nziUMoLchhc00TOVmBvsV5bK5JfnBYtOmV5ri52YGR/Yo5elApc9ZVsaZDg+EQYMLAUlraEnu8HiP6FvG6sf04cVgZI/oV0dSaYEt1E/M2VLFoU01yMYrGViYMKmVU/2IamtuoSj3flrZEquF9sqKzuS3BYwsr2hdAKMnPYfLovpw8vIxH5m9i/oZqSlO3rd/RwOKKGgaU5nPM4F68sHwbLW0Jvn/F8e1NX2OMfOq2ZGXbycPLWLixhoYODfwH9y7g2CG9OXZIL4b1KaQwL5u2RNzl36O6oYXqxhbqmtpobk2wpKKGbXWvrO5TmJvNW04cwoUTB9LQ0sbNz61k5podfDw1vXvnz+Tm1gRfunM296amVJw+pi+nj+5LS1vkl08sIz8ni/rmNspL87nkuEGcO76csqJXzv+G5gTPLd/K9FWVnDOunA9PGUNza4LHFlWwYEM1K7bW0ZqI5OdkUdvYytbaJkoKchjVLxl6jOpXTHF+Dm2JBIs21fDiim2s2FLHtrpminKzOWF4b/Kys1hSUdt+XpaX5nP3x88iRnjT9c+Qm5O8MJGXkwwnaptaqahuav8wO7JfEYs21ZCXk0VZYS4tbQm2d1gYpGMVMCSvug8pK6RfcR7b65tZv6OBHan9s0JyWv36HQ3tVWN7U5ibzakj+7B8Sy0bO9lsu39JHueOH8Axg0spyM1mY1XyZ9rbTxl2SP2aYow8v3wbNz27khdWbGPsgBLGDkj+rKpvbmXFlrpkhWhOFqUFOVx0zCCunDSMYwb3IsbIzx9d2n7BYVCvAjZVN1KSn8PbTxnKe88cuc9Ki0Qi8qW75jB1ZWXy50jj3j/I52Vncd6EclZurWPp5lr6l+Tz4ldfv99qzrWV9fzisaX8Y/YGmloTDC0r5Pyjyzl6UC/GDihh3ICSXYLbm59byQ8eXsTdHz9rv33nmlsT3D1zHSP6FTG4dyE/+/cS7p+9gfLSfM6fUM5zy7axfkcDxw7pxe+vmbTPADDGyMw12xndv6RTlfc7zVm3g9umrqGiuokThvUmPyebGau3k5cT+PxFE/bZJHlvmlrbmLe++oC95LbXNfPAnA2EEKiobuQ3Ty4nkboQ94nzjuK6C8e9Zlfham1LMHd9Fau21VFV38KbTxzSqUC/saWNqSsrKSvKZeyAEnKzs2hsaaMwN/uwqzI+ksUYaU3E11yfSx3eDtfg6UzgOzHGi1PffxUgxvjDDvv8Fngyxnhb6vvFwHnAqAMduzcGTzrSVNY18+yyrVxy3KCM/+KIMbKxqpGFG6vZXt9CVkg2qB7Vv5gRfYs63cPhUG2rbeKllZWcM758l+qBGCPb61sozM1OWylwjJEYOSLnrR/sc3tsYQUzVm+ntCCXlVtreXjuJmqbWzlxWBnnTShn8ui+nDKizx7l8bVNrcxeu4Py0uQ0gZVb6lhSUcPSzTVsrW3mkuMGcenxg0nEyMqtdTy1ZAvTV20nJytQkp9DcX4OeTlZrN5Wz+ptdUwYVMo548o5cXgZw/sW8u8FFdw9c/0e4Wjf4jwG9y7g/WeN2m+/hp2vxZaaJpZtrqWxtY0+RXntf0oLcg74GlU3tvDPeZv4y0trmN1hZcLSgpzU/SQrMnoX5tK7MJdehTn0KkiGWX2K8uhbnEtJfrIfWm1TC6eM6LPHohIvr93BkLICBpQW8MLybdz4zAoK8rIZ07+YaasqeWllJTEmKwsSidhe2TF+YAlHD+rF0YNKyc3OYkNVA8s317FoUzWDehXw4SmjOWFYGcs21zJ77Q5eSjXJvfi4QZw4rDd1TW0sqajh6SVbmLqqcpcr+pAMjSYMKk1dKc9m0cYa1m6vpyQ/h16p55uTFVi+pY5V2+raA4ljh/Ti/AkDyMlOVjE9vXQLaysbmDCwlLedMpSVW+t4ccU2hvct4sRhZayprGfe+iomjerDx88by+gOQSMkr95f+6cZ1De3csKwMkb1K6KsKI8tNclwbP6G6vbKh90V5GbRqyCXXoW5FOdlk5eq+HvTCYOZOLgXizbV8NjCCu57eUN7oFWUl81P3nEibzphzz5mMUZeWlnJYwsreGrJFpZUJFfHfNPxg/nR249nzroqbn5+Fc8s3bJLD8SdsrMCY8tLWFxRQ9/iPGoak1N483OyGFNeQl5OFk0tbZQW5NCvOJ/qxhZWb6tnQ1XDLs8vhOTrPDFVrbqjoZnZa6toaUswYVBp+xSMU0f0pXcq/HpxxTZufWkNtY0tNKWmThXn5TCgV7J/1VtOGkqvghymrqzk0YUVVDe0EomcNqovp4/ux4Be+eRmZ7GlpolV2+pYvKmGJRU1bKpqZGtdM32LchnUuzDVjLqIGau388LybYzoW8Spo/rQtyiPkAqdd9Q3k5+TRX5uNvPXV/HSykqG9UlWH588og/lpfkkYqSq/pXqxJ1fh/ctZMq48sPug9bstTt4dtlW5q2v4qyj+nHFKcP2OXV8X9Ztr2fG6u0U5+UwaVQfCvOy2VTVSJ/iPHoV5NKWiDwwZwPlJfmd7uW3o76Ziuomxg888DT76saWQ1qkYnNNI/2K89svNMxas51jh/Q+YqfyzF1Xxc8fXcK7Jo/gwr0sQy9Jr2WHa/D0DuCNMcYPp75/L3B6jPFTHfZ5APhRjPHZ1PePAV8mGTzt99gO93EtcC3AiBEjTl29enVan5ck9WSNLW00tSTaP7AqqbGljRAgJyurfTXDTKisayYr0D5Ns7qhleL8rr2qnEhE1lQmA47C3GzKivIY2beo0+FlWyJSn5pKtXuwFmNka20z/Uvy0tanqb65lW21zTS0tJEVQnsI2NkqhOrGFpZW1NKrIIdBvQs6Xam4I1XlM3Fwr12eW2NLG7PX7qCp9ZXwKTsrcNzQ3vQuzGXqykr+8OwKRvQt4rIThrT3CNyXnT1KGlsSZIXA0LJCz09JktTl9hU8dfcSPnt7l7R7EravfTpzbPLGGH8H/A6SFU8HM0BJ0sEpyM3u1qbhh6vuek12n4aSjsAhKyswqn/xLtMaD0Z2VthnWBNCoLw0vavBFeXlUNT30N8S9SrIPeB0m70pK8rbI2iD5P+V0/fT4H/y6L5MHt13n9v3dn+vpjGuJEnSq9HdwdM6oOP6vMOADZ3cJ68Tx0qSJEmSJKmbdPcE92nAuBDC6BBCHnA1cP9u+9wPXBOSzgCqYowbO3msJEmSJEmSukm3VjzFGFtDCJ8C/glkAzfFGOeHED6W2n4D8BDJFe2WAfXAB/Z3bDc8DUmSJEmSJO1FtzYX7w6uaidJkiRJktS19tVcvLun2kmSJEmSJOkIZfAkSZIkSZKktDB4kiRJkiRJUloYPEmSJEmSJCktDJ4kSZIkSZKUFgZPkiRJkiRJSguDJ0mSJEmSJKWFwZMkSZIkSZLSwuBJkiRJkiRJaWHwJEmSJEmSpLQweJIkSZIkSVJaGDxJkiRJkiQpLQyeJEmSJEmSlBYGT5IkSZIkSUqLEGPs7jFkVAhhC7C6u8fxKvUHtnb3IKTXCM8XqXM8V6TO8VyROs/zReqcI+VcGRljLN/9xh4XPB0JQgjTY4yTunsc0muB54vUOZ4rUud4rkid5/kidc6Rfq441U6SJEmSJElpYfAkSZIkSZKktDB4em36XXcPQHoN8XyROsdzReoczxWp8zxfpM45os8VezxJkiRJkiQpLax4kiRJkiRJUloYPB3GQghvDCEsDiEsCyF8ZS/bQwjh+tT2OSGEU7pjnFJ368S58u7UOTInhPB8COHE7hindDg40PnSYb/TQghtIYR3ZHJ80uGiM+dKCOG8EMLLIYT5IYSnMj1G6XDQifdhvUMI/wghzE6dKx/ojnFK3S2EcFMIYXMIYd4+th+xn+8Nng5TIYRs4FfAJcBE4F0hhIm77XYJMC7151rgNxkdpHQY6OS5shI4N8Z4AvBfHOFzqKV96eT5snO//wb+mdkRSoeHzpwrIYQy4NfAW2KMxwJXZnqcUnfr5O+VTwILYownAucB/xNCyMvoQKXDw83AG/ez/Yj9fG/wdPiaDCyLMa6IMTYDtwOX77bP5cCfYtKLQFkIYXCmByp1swOeKzHG52OM21PfvggMy/AYpcNFZ363AHwauAvYnMnBSYeRzpwr/wHcHWNcAxBj9HxRT9SZcyUCpSGEAJQAlUBrZocpdb8Y49Mk///vyxH7+d7g6fA1FFjb4ft1qdsOdh/pSHew58GHgIfTOiLp8HXA8yWEMBS4Arghg+OSDjed+d0yHugTQngyhDAjhHBNxkYnHT46c678EjgG2ADMBa6LMSYyMzzpNeWI/Xyf090D0D6Fvdy2+xKEndlHOtJ1+jwIIZxPMng6O60jkg5fnTlffg58OcbYlrw4LfVInTlXcoBTgQuAQuCFEMKLMcYl6R6cdBjpzLlyMfAy8HrgKODfIYRnYozVaR6b9FpzxH6+N3g6fK0Dhnf4fhjJqwQHu490pOvUeRBCOAG4EbgkxrgtQ2OTDjedOV8mAbenQqf+wKUhhNYY470ZGaF0eOjs+7CtMcY6oC6E8DRwImDwpJ6kM+fKB4AfxRgjsCyEsBI4GpiamSFKrxlH7Od7p9odvqYB40IIo1PN964G7t9tn/uBa1Ld788AqmKMGzM9UKmbHfBcCSGMAO4G3uuVaPVwBzxfYoyjY4yjYoyjgDuBTxg6qQfqzPuw+4ApIYScEEIRcDqwMMPjlLpbZ86VNSQrAwkhDAQmACsyOkrpteGI/XxvxdNhKsbYGkL4FMkVhbKBm2KM80MIH0ttvwF4CLgUWAbUk7yaIPUonTxXvgX0A36dquJojTFO6q4xS92lk+eL1ON15lyJMS4MITwCzAESwI0xxr0ukS0dqTr5e+W/gJtDCHNJTiX6coxxa7cNWuomIYTbSK7s2D+EsA74NpALR/7n+5CseJQkSZIkSZK6llPtJEmSJEmSlBYGT5IkSZIkSUoLgydJkiRJkiSlhcGTJEmSJEmS0sLgSZIkSZIkSWlh8CRJknSYCSHcHEKIIYRRaX6cVSGEVel8DEmS1LMZPEmSJB2hQghPhhBid49DkiT1XDndPQBJkiR1mwu6ewCSJOnIZvAkSZLUQ8UYl3f3GCRJ0pHNqXaSJOmIEkKYHEL4WwhhfQihKYSwMYTwrxDCO3fb750hhKdDCFUhhIYQwtwQwldDCPl7uc9VqT+9Qgj/m/p7SwjhO53Zntrn6FTvprWpcVWEEP4aQphwEM/t/SGEu0IIK1Jjrg4hPBdCeM9u+41KTbE7N/V97PDnyd2f114eJz+E8JUQwpwQQn3qcZ7Z/TXs+Fip5zYqhHB7CGFrCKExhDA9hHBZZ5+fJEk68ljxJEmSjhghhI8AvwHagPuBpcAAYBLwCeCO1H4/AL4KbAX+CtQClwA/AC4OIVwUY2zZ7e7zgMeBvsC/gGpgZWe2hxDeCNwN5AL/AJYBw4C3AW8KIZwfY5zZiaf4G2AB8DSwEegHXAr8OYQwIcb4zdR+O4D/BN4PjEz9fadV+3uAEEIe8E+SodUi4FdAEfAO4G8hhJNijF/by6EjganACuDPqdfhKuC+EMKFMcYnOvH8JEnSESbEaL9JSZL02hdCmAjMBmqAKTHG+bttHxZjXBdCOBN4HlgLTI4xbkptzwHuAS4Dvh5j/EGHY1eRDFYeAy6PMdbtdt/73B5C6EMyjGkDzokxLuiw7VjgJWBJjPGUDrffDLwPGB1jXNXh9qN2nx6XCooeBs4BRsUY13fY9iRwbowx7OM1WwUQYxzV4bavkgzgHgbeEmNsTd0+gGSwNBJ4XYzx+dTto3glgPtOjPE/O9zXxcAjwMMxxkv3NgZJknRkc6qdJEk6UnycZDX3f+0eOgHEGNel/vrB1Nfv7QydUttbgS8ACeDD+3iML+weOnVi+zVAGfDtjqFT6jHnA78HTk4FZ/u1t55MMcZmklVJOXRNs/APAhH4/M7QKfU4m4H/Sn27t9dnNfC93cb2T2ANMLkLxiVJkl6DnGonSZKOFGekvj58gP12VhY9vvuGGOOSEMI6YHQIoSzGuKPD5kZgzn7ud1/bz0x9PbFjz6cOxqe+HkNyGt0+hRBGAF8mGTCNAAp322Xo/o4/kBBCKTAWWB9jXLSXXXa+ZifvZdvLMca2vdy+lldeA0mS1MMYPEmSpCNFWerr+v3tBPROfd24j+0bSYY6vUn2Stppc9x/j4J9be+X+vqRA4yrZH8bQwhjSE516wM8Q7KPVBXJKXyjSE7N26Mx+kHqzGsDr7zWHe3YxzGtWGUvSVKPZfAkSZKOFDtSX4eSbIq9L1Wpr4OAPaauAYN322+nAzXG3Nf2nfdzYoxxfxVTB/J5kiHWB2KMN3fcEEJ4F8ng6dXq+Nrszb5eG0mSpL3y6pMkSTpSvJj6eskB9puV+nre7htCCGNJrja3crdpdl0xrimv8n7Gpr7etZdt5+7jmDaAEEJ2Zx4gxlhDMowbGkIYt5ddzk997cwKfJIkSQZPkiTpiPEbktO6vrm3Rt0hhGGpv96U+vqNEEJ5h+3ZwE9Jvj/6QxeO648kq7G+HULYo8l2CCErhHBeJ+5nVerrLvumVo7bVzP0bamvIzpx/zvdBATgJx0DqxBCf+CbHfaRJEk6IKfaSZKkI0KMcUEI4RPADcCsEMJ9wFKS09MmATXA+THG50MIPwa+BMwLIdwJ1JGslDoOeBb4SReOa1sI4R3APcCLIYTHgPkkV88bQbLxdj+g4AB39WvgA8DfQwh3kexldRzwRuAO4Kq9HPMYcCVwdwjhIaABWB1j/PN+HuenJF+Ly4HZqeOKUvczAPhxjPHZAz5xSZIkDJ4kSdIRJMb4+xDCPOD/kawMeiuwleRqczd22O/LIYRZwKeAa4BcklPMvgH8T4yxuYvH9VgI4YTUuC4mOe2uGdhAcqW4vU2f2/0+5oQQzge+B1xK8n3cbOBtJCuq9hY83QiMBK4mGbTlAE8B+wyeYozNIYSLSPaU+g/g0yQryWYDn40x3nbgZyxJkpQU9r84iyRJkiRJknRo7PEkSZIkSZKktDB4kiRJkiRJUloYPEmSJEmSJCktDJ4kSZIkSZKUFgZPkiRJkiRJSguDJ0mSJEmSJKWFwZMkSZIkSZLSwuBJkiRJkiRJaWHwJEmSJEmSpLQweJIkSZIkSVJa/H+ydC/yrL+tcgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "df = X\n",
    "print(df)\n",
    "correlations = np.corrcoef(df, df)[:231, :231]\n",
    "\n",
    "for i in range(correlations.shape[0]):\n",
    "    correlations[i][i]=0\n",
    "for i in range(correlations.shape[0]):\n",
    "    for j in range(correlations.shape[0]):\n",
    "        if correlations[i][j] < 0:\n",
    "            correlations[i][j]  = -correlations[i][j] \n",
    "      \n",
    "\n",
    "print(correlations)\n",
    "correlations = correlations[~np.eye(correlations.shape[0],dtype=bool)].reshape(correlations.shape[0],-1)\n",
    "print(correlations)\n",
    "correlations = correlations.flatten()\n",
    "print(len(correlations))\n",
    "\n",
    "\n",
    "\n",
    "n = 500\n",
    "s = np.asarray(correlations)   # generate your data sample with N elements\n",
    "p, x = np.histogram(s, bins=n) # bin it into n = N//10 bins\n",
    "x = x[:-1] + (x[1] - x[0])/2   # convert bin edges to centers\n",
    "f = UnivariateSpline(x, p, s=n)\n",
    "y = f(x)/len(correlations)\n",
    "\n",
    "cdfy = np.cumsum(y)\n",
    "\n",
    "plt.figure(figsize=(20,8))\n",
    "plt.xlabel('correlation', fontsize=20)\n",
    "plt.ylabel('pdf', fontsize=20)\n",
    "plt.plot(x, y)\n",
    "plt.show()\n",
    "\n",
    "\n",
    "plt.figure(figsize=(20,8))\n",
    "plt.xlabel('correlation', fontsize=20)\n",
    "plt.ylabel('cdf', fontsize=20)\n",
    "plt.plot(x, cdfy)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7239234138017194"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x[490]\n",
    "cdfy[490]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Found input variables with inconsistent numbers of samples: [231, 500]",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-70-424db6906d0f>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[0mkfold\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mKFold\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mn_splits\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m10\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mshuffle\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 6\u001b[1;33m \u001b[0mresults\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcross_val_score\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcv\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mkfold\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      7\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Accuracy: %.2f%% (%.2f%%)\"\u001b[0m \u001b[1;33m%\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mresults\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m*\u001b[0m\u001b[1;36m100\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mresults\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstd\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m*\u001b[0m\u001b[1;36m100\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      8\u001b[0m \u001b[1;31m# tt.append(results.mean()*100)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py\u001b[0m in \u001b[0;36minner_f\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m     71\u001b[0m                           FutureWarning)\n\u001b[0;32m     72\u001b[0m         \u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m{\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0marg\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0marg\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mparameters\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 73\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     74\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0minner_f\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     75\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py\u001b[0m in \u001b[0;36mcross_val_score\u001b[1;34m(estimator, X, y, groups, scoring, cv, n_jobs, verbose, fit_params, pre_dispatch, error_score)\u001b[0m\n\u001b[0;32m    404\u001b[0m                                 \u001b[0mfit_params\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mfit_params\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    405\u001b[0m                                 \u001b[0mpre_dispatch\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mpre_dispatch\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 406\u001b[1;33m                                 error_score=error_score)\n\u001b[0m\u001b[0;32m    407\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mcv_results\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'test_score'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    408\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py\u001b[0m in \u001b[0;36minner_f\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m     71\u001b[0m                           FutureWarning)\n\u001b[0;32m     72\u001b[0m         \u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m{\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0marg\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0marg\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mparameters\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 73\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     74\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0minner_f\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     75\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py\u001b[0m in \u001b[0;36mcross_validate\u001b[1;34m(estimator, X, y, groups, scoring, cv, n_jobs, verbose, fit_params, pre_dispatch, return_train_score, return_estimator, error_score)\u001b[0m\n\u001b[0;32m    231\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    232\u001b[0m     \"\"\"\n\u001b[1;32m--> 233\u001b[1;33m     \u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mgroups\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mindexable\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mgroups\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    234\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    235\u001b[0m     \u001b[0mcv\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcheck_cv\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcv\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mclassifier\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mis_classifier\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mestimator\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py\u001b[0m in \u001b[0;36mindexable\u001b[1;34m(*iterables)\u001b[0m\n\u001b[0;32m    291\u001b[0m     \"\"\"\n\u001b[0;32m    292\u001b[0m     \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0m_make_indexable\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mX\u001b[0m \u001b[1;32min\u001b[0m \u001b[0miterables\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 293\u001b[1;33m     \u001b[0mcheck_consistent_length\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mresult\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    294\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    295\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py\u001b[0m in \u001b[0;36mcheck_consistent_length\u001b[1;34m(*arrays)\u001b[0m\n\u001b[0;32m    255\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0muniques\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    256\u001b[0m         raise ValueError(\"Found input variables with inconsistent numbers of\"\n\u001b[1;32m--> 257\u001b[1;33m                          \" samples: %r\" % [int(l) for l in lengths])\n\u001b[0m\u001b[0;32m    258\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    259\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: Found input variables with inconsistent numbers of samples: [231, 500]"
     ]
    }
   ],
   "source": [
    "# tt = []\n",
    "# for i in range(20):\n",
    "model = XGBClassifier()\n",
    "kfold = KFold(n_splits=10,shuffle=True)\n",
    "\n",
    "results = cross_val_score(model, X, y, cv=kfold)\n",
    "print(\"Accuracy: %.2f%% (%.2f%%)\" % (results.mean()*100, results.std()*100))\n",
    "# tt.append(results.mean()*100)\n",
    "y_pred = cross_val_predict(model, X, y, cv=kfold)\n",
    "conf_mat = confusion_matrix(y, y_pred)\n",
    "print(conf_mat)\n",
    "\n",
    "model = XGBClassifier()\n",
    "model.fit(X, y)\n",
    "# get importance\n",
    "importance = model.feature_importances_\n",
    "print(importance)\n",
    "# print(np.mean(tt))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,\n",
       "       ...\n",
       "       123, 124, 125, 126, 127, 128, 129, 130, 131, 132],\n",
       "      dtype='object', length=133)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# print(len(importance))\n",
    "X.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2, 115, 151, 236, 310, 743, 1052, 1085, 1328, 1597, 1902, 3121, 4036, 4377, 5415, 5637, 6271, 6309, 6416, 7454, 7850, 7950, 8959, 9165, 9188, 10522, 12102, 12331, 13683]\n",
      "[0.01155364 0.0250545  0.010145   0.01683227 0.06003277 0.01184067\n",
      " 0.00954726 0.01983062 0.02509011 0.24600585 0.04164509 0.25225544\n",
      " 0.0145415  0.01349109 0.01440294 0.01131505 0.01172641 0.01211731\n",
      " 0.0086916  0.02306288 0.01586654 0.01450279 0.01052362 0.01156684\n",
      " 0.04310673 0.02147532 0.0090625  0.02381396 0.01089972]\n",
      "['CXXC1', 'PPP1R16A', 'MAGED2', 'TMSB4Y', 'PTS', 'UBE2L3', 'RPL23', 'LRRC66', 'HPS5', 'GRIK5', 'GARS', 'ERGIC3', 'USP13', 'ASB8', 'NPTX1', 'RAP1GAP2', 'COPS2', 'RGL3', 'FLNA', 'CARD16', 'PRTFDC1', 'MT-ND1', 'IQCE', 'OR1L1', 'TMPRSS6', 'RPS15A', 'UBE2Z', 'LINC00672', 'NCEH1']\n"
     ]
    },
    {
     "data": {
      "image/png": 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N2bsAKw/RbyXK/RWSJEmSljBtAwoY/FbsphcBDw9DXiRJkiSNM0PeQxER+wH71b8JnBgRD3UNtjywOXDOyGRPkiRJ0li2oCsU84Cn6ie6/nc+9wLHA+8b2WxKkiRJGouGvEKRmd8BvgMQEecB78/M60crY5IkSZLGvoXeQxERywLPAzYY+exIkiRJGk8WGlBk5hxgI2DuyGdHkiRJ0njS9ilPvwV2G8mMSJIkSRp/Wr0pm/JW7O9HxNLAz4E76XqMbGbePLxZkyRJkjTWtQ0ofl+/DwY+NsQwE555diRJkiSNJ20Div0WPkg7EbE78D+UAOSkzDy6q/+mwLeBbYDDMvMrbceVJEmSNLpaBRT1EbLPWERMAI4DXgvMBKZExOTMvLYx2H3Ah4G9+xhXkiRJ0ihqe1M2AFG8LCJeHRGbRUQs4vS2B6Zn5s316VGnAns1B8jMWZk5BXhyUceVJEmSNLpaBxQRsT/lZuyrgPOBq4E7ImJR3pK9LvDXxv+ZtdtIjytJkiRpBLRq8hQRbwdOBM4Bvg/cBawNvB04MSIezcwftUmqR7fs0e0ZjRsRBwAHAGywge/jkyRJkkZK25uyDwF+kJnv7Or+nYj4HvBJoE1AMRNYv/F/PeCOlnloPW5mnkgJgNh2223bBiySJEmSFlHbJk+bUK5M9PL92r+NKcDGEbFRRCwL7ANMHoVxJUmSJI2AtlcoHqJcEehlvdp/oTJzbkQcBJxFefTryZl5TUQcWPufEBFrA5cCKwPzIuKjwGaZ+WCvcVvmX5IkSdIIaBtQ/Ab4QkTcmJkXdDpGxCuAz9f+rWTmGcAZXd1OaPy+iyGCl17jSpIkSXr2LMo9FDsC50fE7ZSnPa1NqfhPr/0lSZIkLWHavtjurojYGngv8GpgVWAG8HvglMx8dKQyKEmSJGnsanuFgho0fL1+JEmSJKl9QAEQES+mvLF6XcpjXKdk5vSRyJgkSZKksa/ti+2WA74BvJPyhKWOpyLiO8AHM/OJEcifJEmSpDGs7XsovkJ5K/ZngBcDK9XvIylBxjEjkTlJkiRJY1vbJk/7AJ/NzC80ut0MHBURAB8DPjzMeZMkSZI0xrW9QvEc4JIh+v0ZWHZ4siNJkiRpPGkbUPwO2G2IfrsB5w5PdiRJkiSNJ22bPP0X8L2IWBH4KXA3sBbwFuAfgXdExAs7A2fmzcOdUUmSJEljT9uA4vf1+/3AgY3u0dW/YwKSJEmSFnttA4r9RjQXkiRJksalVgFFZn5npDMiSZIkafxpe1O2JEmSJM2nbZMnImJ34M3A+sByXb0zM3cezoxJkiRJGvtaXaGIiEOAM4DXAysCT3V95o1UBiVJkiSNXW2vUBwEfBM4KDOfGsH8SJIkSRpH2t5DsTLwU4MJSZIkSU1tA4qzgB1HMiOSJEmSxp9FafJ0ekQkcDZwf/cAvh1bkiRJWvK0DSgSeAg4Cvj8EMP4dmxJkiRpCdM2oDgFeCVwLHA9MGekMiRJkiRp/GgbUOxCecLTKSOXFUmSJEnjTdubsmcDd49kRiRJkiSNP20Diq8CH4iItsNLkiRJWgK0bfL0fGBz4NqI+C3zP+UpM/Mzw5ozSZIkSWNe24DisMbvl/Ton4ABhSRJkrSEaRVQZKZNnSRJkiTNx0BBkiRJUt8MKCRJkiT1bciAIiKeiojt6+959f9Qn7mjl2VJkiRJY8WC7qH4HDCz8TtHPjuSJEmSxpMhA4rM/Gzj95GjkhtJkiRJ44r3UEiSJEnqmwGFJEmSpL4ZUEiSJEnqW9s3ZWsJM/HQXw9bWjOO3nPY0pIkSdLY4hUKSZIkSX0zoJAkSZLUNwMKSZIkSX0zoJAkSZLUNwMKSZIkSX0zoJAkSZLUt1EPKCJi94i4ISKmR8ShPfpHRHy19r8qIrZp9JsREVdHxNSIuHR0cy5JkiSp26i+hyIiJgDHAa8FZgJTImJyZl7bGGwPYOP62QE4vn53TMrM2aOUZUmSJEkLMNpXKLYHpmfmzZk5BzgV2KtrmL2A72ZxMbBKRKwzyvmUJEmS1MJoBxTrAn9t/J9Zu7UdJoGzI+KyiDhgqIlExAERcWlEXHrPPfcMQ7YlSZIk9TLaAUX06JaLMMyrMnMbSrOoD0bEa3pNJDNPzMxtM3PbNdZYo//cSpIkSVqg0Q4oZgLrN/6vB9zRdpjM7HzPAk6nNKGSJEmS9CwZ7YBiCrBxRGwUEcsC+wCTu4aZDLyrPu1pR+CBzLwzIlaMiJUAImJFYDdg2mhmXpIkSdJgo/qUp8ycGxEHAWcBE4CTM/OaiDiw9j8BOAP4R2A68CiwXx19LeD0iOjk+4eZeeZo5l+SJEnSYKMaUABk5hmUoKHZ7YTG7wQ+2GO8m4GtRjyDkiRJklrzTdmSJEmS+mZAIUmSJKlvo97kSZp46K+HLa0ZR+85bGlJkiRp0RlQaLEyXMGKgYokSVI7NnmSJEmS1DcDCkmSJEl9M6CQJEmS1DcDCkmSJEl9M6CQJEmS1DcDCkmSJEl9M6CQJEmS1DcDCkmSJEl9M6CQJEmS1DcDCkmSJEl9M6CQJEmS1DcDCkmSJEl9M6CQJEmS1DcDCkmSJEl9M6CQJEmS1DcDCkmSJEl9W/rZzoA0Xkw89NfDks6Mo/cc1bQlSZJGkgGFtJgzWJEkSSPJgEJS3wxWJEmS91BIkiRJ6ptXKCSNSV79kCRpfDCgkLTEMViRJGn42ORJkiRJUt8MKCRJkiT1zYBCkiRJUt+8h0KShslw3ZsB3p8hSRo/DCgkaRwwWJEkjVUGFJK0hBvJYGUkn6jl07okaWwwoJAkqYvBiiS1Z0AhSdIoMliRtLgxoJAkaTExXpuYjVTa46E5X6+0pfHGgEKSJGkMGQ+BkPc1qcmAQpIkSWOawcrYZkAhSZKkJZZXbZ4535QtSZIkqW8GFJIkSZL6ZkAhSZIkqW8GFJIkSZL6NuoBRUTsHhE3RMT0iDi0R/+IiK/W/ldFxDZtx5UkSZI0ukY1oIiICcBxwB7AZsC+EbFZ12B7ABvXzwHA8YswriRJkqRRNNpXKLYHpmfmzZk5BzgV2KtrmL2A72ZxMbBKRKzTclxJkiRJo2i0A4p1gb82/s+s3doM02ZcSZIkSaMoMnP0JhbxZuB1mbl//f9OYPvM/FBjmF8DX8zMP9b/5wCHAC9c2LiNNA6gNJcC2AS4YeTmatisDsw27RFP17RNe3FOezzm2bRNe3FOezzm2bQXr7SH04aZuUavHqP9puyZwPqN/+sBd7QcZtkW4wKQmScCJz7TzI6miLg0M7c17ZFN17RNe3FOezzm2bRNe3FOezzm2bQXr7RHy2g3eZoCbBwRG0XEssA+wOSuYSYD76pPe9oReCAz72w5riRJkqRRNKpXKDJzbkQcBJwFTABOzsxrIuLA2v8E4AzgH4HpwKPAfgsadzTzL0mSJGmw0W7yRGaeQQkamt1OaPxO4INtx12MjGQTrfGY9njMs2mb9rOd9njMs2mb9uKc9njMs2kvXmmPilG9KVuSJEnS4mXU35QtSZIkaTGSmX6G6QOsTXnh3k3AtZTmWdsD04Bl6zAvAm4GVgb+GUhgah3mXOBKSlO0p4AHgSfq9/8Ba9XuD9Xv6cAKNd2TgVnAtK48nQLcUqeRwG8b/T5c00lg9Ub3XwDX1HGuAX5fux8MXA/cWsf5NrBM7Xcb8Fj9PAk8XMe/rw57H3Aj8F1gA+Ceunx+DfylztPdneVW05xY05tal8uFwCZd87dBndbH6/8fAvMay/ScmubU+rkLuL3xf1ngsJq3x2u3HWpaRwIf71qG1wOfaUz/fMpjiTvp/ayxrK4Frqp52LAxT811/ktgFeA/gOtq3h+s+TmBEvRPpGu91rSOqcNfBZwOrFK7/y8wpy67x4H/HGJ7uBLYtdFve+APdX6uB05iYPvahYHt4anGOKtQHphwf83/SY3lOw14Qx1uk7qsptbhTuxax9NqGg8ClwEXAW+s030AuKLm6SuNab8H+HpzXdXfywG/7awnYEad5v2U/enp9Btp/U/N91Jd6d/DwHr/GGV/6czDw5QnzV1Xv2+oy3QKsHUjnecCx1PKhSvq9P+lMf/TutbBn2s+n14HwI/qNG6s0/1Lne6RPfJ6bSP9Teu8PtFZPo1ldAllW8jGclwK+GpdH1fXedmosRyvrtO4Gtirkd7Hajqza16Xo+w7RzbWz6DtgrLfJWX9Z13PV1PKpa8Ch9fl+aM67HE1rfMo23fWZfSK2v3NlO1zHrBt176yPWX7+wtwOaXc2aKr3H6i5uEM4CXAy4A7KeXZE3X5d6Z1BjC35v1B4MO1+1rAr2q+m2XZ1o15fZyyDazSyN/Gdbybar/zgNd0b+dd83RmnU5nnqc2PhMp+86veox3PnBp4/+2lLK1eey6hVIeL1XX7ePA8xrj7MKC98uk3AP5BKXMnVT7PVXTfrwOswtle59KOYbcBzxSl1PnWNPZ5qYCr2SgzLiCgW3u3TX9pSn7wZ8b03m8Tvdqyvq/q667/9coE28HnlP/rw7M6CqfrqDsb5c0pvUwA/vXnJr2jXUZfpbBZckewKU1jaeXF2W/uLMuyyfq53jKcWlBy7i5X3f2q86xrVNGJfC9OvxrKNt9Utc9Q5cZze7XAx9rTPepOt+PUY7ZpzTy09kfHq/ztBJl+zmhzsfjtf8/13GOorxb7OGu7fPjNa2p9bM/g4/F91K29xsZKOMfZv5j8bV1nIOBv1H243OBDSnr+N7GsNfV+cm6/n4CrFXT/hSlrnUD5dUFnXy+nLJNTaeUV0k91gJvoew/s4Af1m5fZKCO9HjNz951nh5uLJ+7gWXosd/Xedx2Qcvv2fw86xlYXD5AUHbwAxvdtgZeDXwD+Pfa7Uxg38Ywc4G3UQrC++uGuVzdYf6pDvODmsbmdcPbCTiwbuAH12FeA2xD74CiU3B2NuTl6/+D6k73KDWgoFQQZ1IKqlfVbmvW6Z1Z+/8E+CPwO2DlOsyMRhpHMlC5O4WBAnl5ysFpJuVAeAYwCfgmZae/gFLwblnHncjgyta/At/pmr//A37amN5addmtQSnM7gW+1hj+6bzV/6+o6+0llIrO6sALmsN2LcPlKAFhp5J1Pl2Vl9p9EgOV8fcDP27MU7NC/h1KZWnlzvxSKlTfpFTu/7l7OTTG3Y1amABfAr5Ufx8FHFJ/v7Iuj+V6bA+TgL80ltutDFSYAvh/tfsqlO1kg9rvka78nwx8nXIQPLqxLl5KOdgvRXmYwl6N8bZoLI9pzL/vbAh8iEaliLL9XM/AdvkeugKKmodfA0c30ppBqRjPl379vRSlMnMxsEtjmGb6q9V5eaT+756fKQwU9PsxOHA/FfgCtYJB2TY/2b2NN9bBG+syaa6DL9T5u6F2u57ycIrNeuR1TUqFYK36ezvKNtHc7oMS6HT25ZnAjsC+wM8aeV0PeH6PfXwT4Nb6e10GKm+3AD+v+ekOKHptF53t92HqvlD/r0w5WF5PqSjNYCBgPp2yj5xPud/uqka6ncB128a8rlXHf2Wj206Ug3mn3D6SUjm4vS6HV1MqFmfX5b0CpTJ5ex3/GAYCnK9RytC1KPvtRxrT6ZRlL2Fg23lBHf4/GmXKjdTgu3bbHHhP97rt2v9XbqzLucA+Xf13YeiA4jZgj/p/W0qF78DG/nAnJVDZpc73BZ38dKdN7/1yTmPY31GCrpfW9dxZT3cB32gM917KdrFVY5+bQeNkV4995uGazysp+90eNS+PAJvWYZamHAdWr+NuSTmx1QwobgPeX/93BxTNY9ALKRXQ/eq016RsS/czcJJkBeA31Ip4XZc3deXnA4394jZgv/p/AvAtyva1oGU8337NQBnY3Kc6x92JlOPQfQwOKHqVGc3unXJv/fp/LrXco+yvX20cf2Y35m8KpVK9b12HnWBlIwZOru0IrEPvgOLerm6deTuFErhdAPwL9VjMQECxbY9xJgHfq8v5h8CPu9bxcjXNnwDfbxwbNwc2o2xbz6nTuQmYUIe5hFJ/CMr6nkMp/7avy/3wmoc1Kdvl5QzULdahbKMr1Hk6qqYTlBMo72fhAUXP5fdsfmzyNHwmAU/m4BvMp2bmBcC/A/tHxCGUM/o/aoz3BPB5yhmNayk749solc5f1mEuqOlNq99/pBy87wBeXLv9gVJYLMhTlAJrz/p/e0qh0PQmyk5zMSVIITNnUSq976cUKK+iFP7rZeaDC5lmx2+APTPzWEr0PRWYl5nnUXaKWyk73HqZedUQaaxMKbgBiIi9KQXK00/7ysy7KTv2lylBUKfiNZR1KAXmnDr+7Mzs+X6Tarn6/cgChiEzz8vMR+vfiykVs14uAtbtWo4rUoKAC6nrd4hpnN3425zGk3V8KBWDBJ4/1LTr7w9SgrWLatqZmT+ry/NtwGmZeVtn0gARsTJlG/lDHWcOZbvs5O86yvayOmU5z2z0u7prfud07Tu3ZubXuua3c7VqXXpbmlJ5/0tmHtrovtxC0p9EqcAfTzkAzicz76WciYraadD8MHh7eHq5RsSLKPvZpzNzXk3rnsz8Uo/JfJCyP15Rh2uugzl1mDUpldHMzKcy89oeeZ1F2Yc3zMxZmTmFsk00h8n681XAByjrKOt83dnI68zMvJ/5DdoXKct+LuWKyssY4h1BNc3mdtH09HKr+8OU2v1J4NeZ+bf6/4FG+lcwUAZel5m9XmJ6EGXbvrCRhz9m5s+p5TaljPgeJYDYiBIA/Kkznbovv5uyfKCUOZ1leC5lnWzI/Nv5VfX7xs7wtXy5j1I5BXg7cFFmTm6MNy0zT+kxLzSG6ZQZnYer5FDD9nAM8On6e9uS3NP7xyTKcv0y5STOc+uwQ+0bC9svf0YJhA+ow3fW04OU4Lpjc0pF8so63L0t5+UXDASZ+1L2xemZeX1NZy7l7DOZOaOuk3ldafw38LGIWOCDajLzZsrJrw/X/7MowdFtlKCws60cBHyijnYIcFQzP5n5jdpvI2BuZn679nuKctLtvZTKZme6g5bxUPt1D53j7gxK5ffmIebr6TKjq3un3Ots98HA9n0RA9vemp281OV9DiWImUg59pxU+92SmbfW3xdneSXAoliJUp5/mvLqAGhxLKbUff6bUt51H4vfRpn3bShlYef4PQ3YCzg1M5/IzFsoy2L7iFiHEtBfVMvS71KWzYmUq93HUU6kdpbtpyiBX6fc+ifg5406whW1vE9KoDJUfaE5X/0svxFlQDF8Nqdcqp5PPRB+iXLJ6wPdvSkR80GUAvbqmtY8gFrA7VG7Ny1F2fm7uy/IBEpTkH0iYjnKmZo/dw2zL+WS21+BwyLisvrm8efWHWpv4Mx6gLwvIrZpjHteREylVORf3ZXuqY3pLkWj4knZ+U6mHHA2iYgXNPq9KCKmRsRNlIL8vwAiYkXgk5RArNtcypmFTzDQjGIoZ1NemHgusE5E7DzEcMfUeZtJKWBmNfr9oOZxakQc02Pc91EK9kEiYgKwKwPvU/k45eD0dsq2sutC8t703q5pbBAR11AqPjcMUfDsTjmbDAvYfimVq+dHxPkRcRmwQl0WUygHkq8Db42IkyjBYmf+dqBsx/cAxwLnRsRvIuJjEbFKI/0XAC+NiN9HRPd287SIeD6lacgfhhjkEMrB+aNd3ZcBXtzYlrvtSzkrdDrw+ohYpnuAiNiAciB7Tp33VYBLIuKKiPgYg5+Y11yuLwOu7FTQF2JB66DTfy6lacysiPjXuj915/WFlMrq9IVM742UebqQcgb3ScpZun+q2/J/RsTfdY1zXkRMA35PrZBm5u3AVyiB4ccolY/ucqWZv+Z20dTcFzrz+whlH16f3l7NwveRl1FOVvTSWeZvpZTDP6JsDy9j/nWxOTCvBtJNB1G2semUsuxbEXFeRBzWVZYBUN+vtDLw/Rb5W6CIOIvSrGIp4FN1vZ3eYtSLgCciYhKlGe5DjX7N/WFPynK5gFI2r9kjD0Pul43j12WUZjGd7hMo+1BznLWBjIizIuLyegIO6nElIobapk4F/q6mvyWl0vm3Bc18D7dRrtS9s8Wwl9OYF3oE0Jl5E7B8LecWtF+vCazZOH6cVwPF22icTGpR9g2ledzdkiHexDxUmdEo9zon+Z6kluOU4/w5tftPgTUi4qKIOJry6P+rKfO9HHBvRNwdEafXY/fCrBwRV0XEzyKiue9vRAlw/4cS+E4e6lgMvLYrzdso5cndXd1fTgmq353znyBdl1IX6phZu63L4BNKMykBxXHAVpR1/kHKieTdmX8b2Ieyj3Uc08j34ZTWIFCOq1Mb/cb0i+8MKEbPHpQNebOu7ssDH6nfD1AudwIsUzegSyk7Qqf78rX7kZTLfN9i4Y6JiJspO/YhlLMG+9L1CN6IWItSiM2q39Nr3j7JwJnZfYG/1DxsAvxbI4lJmbk1pc3kBc2061mhznRndOXvHErl9KeUS65XRETnzNVNmbl1Zr4I+CgDj1b7LHBsZj7cY36Xp1RqVqKceRhyGdXxX045g/AU8OOIeE9zkPr9iTpvawO7RsQrG8O8veZx68z8RKM7EfEOSiHQDDSWqsvvXmBVSpAHpVI2t3b7M+Ws7HyBSLeIOKyO94NG5zdRKrnL1Ok1K56d7eH7lKY0C7M0ZRntCbyudnsLJfB5DiXI+zGlsN6JcqZvap2ft9YTL9+mNHX4KeVS/sUR8RzKFZTP1LwfDPwwIk6MiCsjonOG+tURcRWlicSvMvOuIfL5R+AVEfGSru7HUi517wF8MCJO66RfX5L5j5SzRQ9SlvtujXHfWgOzmykHscfqet6AUsn9Wp2fbWreZ1L2l0FXVzpqJXNqRCzoKthQZ5qvzsw1KGfU1qzfZzb6v7Uu9x8B/5qZC7tiuU/9rEdZdx/KzJmU/fpTlEr/ORGxa2OcSZm5ObAF8PWIeG6t7OxV0+icoe+1z823XTBQnq1IY1+IiO0o5eGqlDO1L6/T6TiGsl/9MyVgby0i/hwR10XE/9ROawH31DOn51DW5XIMrIfOyYQD6jx2ur81Iu6kVGbfk5n3ZeZZlIrZ/1Iqnc2ybPkajP2Jcsa+eYWxmb/TI2JaRJy2sHnJzNdRlvk8ahmVmW9suSg+z8BVis60u/eHBO6oAfFplPtUOha0Xy7ddfzqVDw76/teStnUDBKWopxceDulHOkEvJPqfO0wxDK4inIv3QSe2WPlv0A5CbWwelEs5P/Cune7rHH8mNQYN2lf9vXUddzttWyGKjMGlXuZ2TkBuAylgr0zpVJ9VC3Hr6UEG2tT9seXUI7z91HWy+coZ/H3oNQPFuS3wI2ZuSWluVynFUVSAorD67H4OGCvoY7FDBxXO15NaRHyUgav410pzcD+1CMvvdZhDtG9c9VwJuUYcjzl6txJzeGjXN3YgtJstqNTv5gCfDdLyxYoTaW3bszTpb2mO1YYUAyfayiVrvlExOuB51EqY8dExAqN3nMplbE3UHb8Jxm48bWzIX0oS3MSqBUa4AjgD43uC/IJSoAwh7JzTqYc1H/UNdxbKZcn3045GG5AOdt6PjC3nq38e0rTp1UoB/p/iIi2BWdnukFp4tRxInBtZr4rM99J2aleM8T4ne47AF+OiBmUQOPfo7z4EMqlxiMoVxI+tLBlVC8z/5qy7A+iVMahVGZmdw3baau500LmlYj4B0pTsTdk5hONXvPqOtyQ0ua/+d6VmyiXpu/NzCMXNg1KZf/1lIK0WRE9NjM3oazTjSiVpI7O9vBpBgrrIbdfSgF5ZmY+kpmzKYHXVrX7TAYuo/+MUrk5tm63r24UjGTmHZl5cmbuRdnuN6/LZQqwTWZeVuf/REoh36mIXVAPLlsA74+IrYfI5x8o28Jvus4MX1TTn0U563phI/3dKfvm1XVb2onBTTt+nJkvoxyM/pPGgaFrfpJy0NyIErwcVwe7FtgqIpaq4xxV1333WW4YWAf3Mn8Ttae3xcw8lVLRektNe7VGXrfOzB0yc4Fnqes4f0852E2lVKr3jojIcnn/NzU4/gLlquQg9Qxs5wTJP1DaDpOZT1ICr90oQUJTr+2iU549wuB9YV/KGdXnUtpxr87AfgllG74U+GDWpqALcA2N7b9WTA+nrPdrKGXKpnX930RZNyswcDawc7D/V+DBzOyczb+SUmlar7m8a2Dxwx5l2WOUMvi9lCu0nXntzt8bKe2nV13IfHWGf5zatr3N8I3xzqVedaOcfIHB+8PtlGVxbF02+zB431jQfjm3efyqw1zHwPrekLIvNQOU+yn3qMzO0hTkDMo20cZUSn3mR9T7bYYaMCK+TTne/nuze2ZOr+m8ZSHT+rs6Lx3XUAKh5jReSLmn4G8suGy9h66rb/UK2PqUbbFt2bcgQx3vYegyY1C5FxFr1+6P1RMK61CaE69MOfsO5eTfRvWkx0mUwHQmMDMz/7uWJ99i/tYL3W5iYP39L2XZrUrZVlcGjqjb45soAc5Cj8U1v2+i1MGuoK7jiHh3TaP7amnHTAavn/UoV6NmMrhZ0noMnGj4E+WEz/KUK2U3ULbJzjbwFuD0WlY+LSI+QzkmHdxifsYkA4rhcy6lOcS/dDpExHZRmtD8J+XAdzWlvedhtf/alI35kMw8k3JD4P6UCsmEiNizkdbuEbFFv5mrZ5iepKzzm4DP5eB27FAOFrtTbuS9jNL2e19K5f14ShvjUzNzQ0rF6U+UM8xtdmgozZrOpxywLqnz9XlK5fZT9f9KlEvwt/UYf6ead2qlZGJmTqS0jfxCZn69ZT6eFhGbRMTGNVC4k1J5ujUiVqUsiz92Db80ZXnctJB0/45yg+YbcvAl2adl5gOUtrgfj4iXNnq9gVJYLyzvu1MOuG/IgbaYUArjzr59GeXs5Y5d055HqfgtFRGvozRbeneU5iid9N9Rt9FfUM6ULV2D4aWA6+rZsr9SzkpBqaT3LJjr9rtM/b02pZJ4ez17ez6wXEQcTrmsfzON9sONPN9IaQr2yaGWSWb+H+Xs9ZkRsUqUy+uX1PQ/QqnoTmukvy+wf2Nb2gjYrSvoJ8u9Jd+jNunqMT9LU85yP0kJ1HaMiJfWSsqlwOejNPOgXi3qFYR/ndJO/2XAnRGxa10Hm1La3P4xIvaM0sxwWcpB9ikWvXkHlAprZ19+KaVJw0zgNZ1grAZBWzI4+Kf2W7Muq1sp++qOtXvU35ewiFcOGNgXnkNpejKVcmB+ASWYWtT0Oo4D3tN1JrOzfs+jHMSPbmwDh1AqcDtR245HxPKUB2Z8uY63BSUIGLTvRcTfd7adZllWz/wvRzn7+J3GvC5DKe9fFRFv6JG/nuqVoU7elqacBV5omdHDUZQyb6l67NqXcgx6M/UpOpT7QzbLzBcA60ZEdzv7Be6X9Rh4AKVy2BnnAcoV8HfGQBPDq4HVImKFOk87s/B7BIiIiZTg78f1mHYqpYnjS2r/pWgE8Jm5H6WS3evq7FGUpqcLmtZXGHwF8geUk28b12E628pnav9jKCe8ns5PRHQqjTdTWiO8q/abQKkvnEJZ7p08L7TsW4CT6X28X6hGufeR2mlCRCxT199nKSci7qZUlpeq87As5UTDrZR99+56nF2Ksk/NWMhkV6KWf5Rj4Y2UY/F6lDLhA3U/3YByHHlgQYnVY/ErKPv4LAbW8dL19+spV7Z71bcmU5qMPSciNqKs40uyNCF+KCJ2rGXeuyhBPZQTxHdRyqsVKFdrjqacAF2b+uCLiPhwI5u7UoKdfbNd89ixKcfAneGLy4ey8/yEUtm8hnLW+0fUp+/UYTrNcDamHEgeb/Rbn7KzrUo5Y3cm5ekD11IKybUozZxmUC4lPkypBGxWp3MnpQCeCbyvpnkKA0+zeJgSpZ9T/3+YUgGcRykUHmHgZYefqNN9jNKkJWq+/0qpfFxIKSgPoQQbMxh4vN9dDDxJ4pSa/pV1Xr5HKRh2oVzOzDrtziPTbgf+rY47kcGPjZ1CfaRr13I/ksFPsHmYoZ+M0j3sy+u8XEs5k3BPYz7e3piHWxh4FN3XGsvpfAY/qu53tfvv6nx1uk9uzFMycHZ/JqXCOaWm8zjlUbLrNvI4kYH12vm8mXJATspZzzl1vg+mNE24q073ckqztBsoBf4p1O2hpt3cHl5Baap2A+UM3DcZeFJVZ3uYBjzRGH/rumxmU+4bePopT13L/b8YeKzqlcA7GtPv3OtxX833JZSK3lvpelIN5azP7ZTK7NPruMd6PZJyxaLzlI5rKAee+xrpv7v+X7krr6fVaT+dfmP/Tsr2P6uuq7tr+tcx+Akj/wZ8q/5euS7LWygB3h+BgxrrtvkUmc46uIWyP85m4NGp11DO4j5G2ZcupT7GsDuvjfTWpmwvD1ICj5k1P1Mo289VdZ0eQSkPOnmcVj8nM/CEsBkM7BvXAO9tTOezlP18GmUfX59SITqy1/pp7qtd37+knMF/jPoksNr9/1GaK6xDOXlzb/1/N3BWHeaNDDyh7unutd+OlPs+plP298mUSugudX67y+1ZlDbad1G2t+mUCmJnv7+bct/B1Po5oWs/6SzXTln2DgYeF935/B54Z+2/KeWM/M2UK2pnA//QWLedsr7z2bCuw6tqnucAS3ct213qcmyO9wrmfxrOZXWZ/B8lQL2uLoOs+TqN0kQNyn78SRa+X3bm9UZK047O04ke7lpPTzDwlK731Pm+pi67LzP0U54eo5xpnkfZn/frmu9L6nxdV9fHA5SrXNvVaT9C2YauYf4y8TQW/NjY/Rrz0tm/HqaU0U/WtD/VlefXd+XnmMZ+cReDHxt7CuWq0YKWca/9+ouUivK8+r/zfXBjvjtXya5h6DJjUHdKuXcXpe4yh8Hl+OWU4P9gSnl4dU37y5Q6w+4M1CMepwQZa9d0v9yVz05Z8UXK/vYwZR+7jtJq4hbKFebmsfgKyvY4ry67J2seb6fsSx+nHIsfa4w3ua7jhyjl6dQ6jQdrGjOo9a2an8MoZcMN1Cej1e7bUrbTmygngzplWFCadXXqVfvU7vvVNJ6s43We0HkKZX95goHHyn621/ph8FOeei6/Z/Pjm7IlSZKGQZSnD/4X5d6P+a7uSYsrAwpJkiRJffMeCkmSJEl9M6CQJEmS1DcDCkmSJEl9M6CQJEmS1DcDCklagkXEeyIih/j8wzBPa5eIOLLzoj9J0uJh6Wc7A5KkMeHNlOeZN107zNPYhfI+h89Tnp8uSVoMGFBIkgCmZnmz97hS31S7TGbOebbzIklLKi87S5KGFBErRMSXIuKWiJhTvw9rNluKiOUi4tiImBYRD0fEXRHxy4jYtDHMkZSrEwBPdppV1X671P+7dE270xxrYqPbjIj4fkS8NyKup7wZd8/ab6uImBwR90fEYxHxp4h4dVea20XEbyPi3oh4NCJujohvDOcyk6QljVcoJEkAEyKieUxIIICzgM2A/wCuBnYEDgdWBf6tDvscYCVKU6Y7a78PABdHxKaZeRdwErAe8D5gJ+CpZ5DXScDWwGeBWcCMiNgGuAC4AvgX4FHgQOB3EfHKzLwsIp5b5+cS4D3AQ8BE4JXPIC+StMQzoJAkAVzf9f9PwDcplf+dM/MPtfs5pZURn4mIL2XmrMx8ANi/M2JETKBU3O8G9gWOzcyZEdG5R+PPmTn3GeT1+cDLa6DSmeY5wG3A33eaP0XEWcA0SgC0N7BpHfeQzLyqkd4pzyAvkrTEs8mTJAngjcB2jc/7gN2BW4ELI2Lpzgc4G1iGcrUCgIh4S0T8OSL+BswFHgGeC2wyAnm9uCuYWB7YGfgpMK+RzwB+B7ymDvoX4G/ANyPiHRGx/gjkTZKWOAYUkiSAaZl5aeNzA7AmsCHwZNfnkjrOagAR8U/Aj4HrgLcBO1CCknuA5UYgr3d2/V8VmEC5EtGd14OA50fEUvVKyiTgDuAbwG31vo83jUAeJWmJYZMnSdJQ7gVuAd4yRP8Z9XsfYHpmvqfTIyKWoVT023i8fi/b1X21IYbPrv9/ozyG9jjguz1HyJxXv6cCb6pXMLYFPgX8JCK2ysxpLfMrSWowoJAkDeVM4E3Aw5nZfY9F0wqUZk5N76RcNWh6on4vT7khuuPW+r05pTlVxz+2yWRmPhIRFwBbAZd3goeFjDOXctP44cAbgJdS7reQJC0iAwpJ0lB+AOxHuRH7P4ErKVcRXkSphO+dmY9SAo+9I+JY4FfAy4EPU64cNHVelPdvEfEb4KnavOrOiPg98KmImE15ctM76nTaOhj4A3BWRHyL0ixqdWAbYEJmHhoRrwcOAH5OufKyYs3nQ8BFizAtSVKDAYUkqafMfDIiXgccSqmIb0S52fom4NeUd0AA/C+wPvBe4F+BKcA/Aad3Jfkryr0LHwCOoNw0HbXfO4Djga9SmkCdTHkM7f+2zOvlEbEd5V0XXwWeR7mH43LghDrYX4DHKPdarEMJJKYAr83M7reES5JaiszupqiSJEmS1I5PeZIkSZLUNwMKSZIkSX0zoJAkSZLUNwMKSZIkSX0zoJAkSZLUNwMKSZIkSX0zoJAkSZLUNwMKSZIkSX0zoJAkSZLUt/8PH4ekVKvMcBoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 936x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "# patient_net_features = ['weighted\\ndegree', 'degree', 'weighted\\nclustering\\ncoefficient', 'clustering\\ncoefficient']\n",
    "# patient_net_imp = [0.01989953, 0.0188352, 0.01702254, 0.01057105 ]\n",
    "# top_genes_index = [32,48,11, 36, 22, 120,2, 21, 66]\n",
    "# top_gene_importance = [0.28184038, 0.21213837, 0.13237873, 0.06341594, 0.05612952, 0.05351432, 0.04596942, 0.04433667, 0.04394828 ]\n",
    "top_gene_importance = importance\n",
    "top_genes_index = X.columns\n",
    "genes_df = pd.read_csv(filterd_data)\n",
    "# sorted(importance[:133])\n",
    "id_symbol_lookup_df = pd.read_csv(id_symbol_lookup_file)\n",
    "top_genes = []\n",
    "# top_gene_importance = []\n",
    "# importance_threshold = sorted(importance,reverse=True)[15]\n",
    "for i in top_genes_index:\n",
    "    top_genes.append(genes_df['Hugo_ID'][i])\n",
    "\n",
    "print(top_genes)\n",
    "print(top_gene_importance)\n",
    "# id_symbol_lookup_df\n",
    "# print(list(id_symbol_lookup_df['ID']))\n",
    "# print(list(id_symbol_lookup_df['Hugo_Symbol']))\n",
    "ids = list(id_symbol_lookup_df['ID'])\n",
    "symbols = list(id_symbol_lookup_df['Hugo_Symbol'])\n",
    "top_genes_symbol = []\n",
    "for i in top_genes:\n",
    "    top_genes_symbol.append(symbols[i])\n",
    "\n",
    "top_genes_df = pd.DataFrame({'Hugo_Symbol':top_genes_symbol,'importance':top_gene_importance}) \n",
    "\n",
    "top_genes_df = top_genes_df.sort_values(by='importance',axis=0,ascending=[False])\n",
    "\n",
    "plt.figure(figsize=(13,5))\n",
    "plt.bar(list(top_genes_df['Hugo_Symbol']), list(top_genes_df['importance']))\n",
    "plt.ylabel('importance score', fontsize=16)\n",
    "plt.xlabel('Features', fontsize=16)\n",
    "print(list(top_genes_df['Hugo_Symbol']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "confusion_matrix_df and classification_report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       0     1     2\n",
      "0  113.5   2.1   1.4\n",
      "1    6.8  67.2   0.0\n",
      "2    4.2   1.3  34.5\n"
     ]
    },
    {
     "ename": "NameError",
     "evalue": "name 'y' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-3-e279a169c435>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     15\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     16\u001b[0m \u001b[0msns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mheatmap\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mconfusion_matrix_df\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mannot\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfmt\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\".3g\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mxlabel\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"Predict\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mylabel\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m\"Actual\"\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mtitle\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"Confusion Matrix\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 17\u001b[1;33m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mclassification_report\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m: name 'y' is not defined"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "class2idx = {\n",
    "    'LN':0,\n",
    "    'Bone':1,\n",
    "    'Liver':2\n",
    "}\n",
    "\n",
    "idx2class = {v: k for k, v in class2idx.items()}\n",
    "# print(idx2class)\n",
    "\n",
    "mat = [[113.5,2.1,1.4],\n",
    "        [6.8,67.2,0],\n",
    "        [4.2,1.3,34.5]]\n",
    "print(pd.DataFrame(mat))\n",
    "confusion_matrix_df = pd.DataFrame(mat).rename(columns=idx2class, index=idx2class)\n",
    "\n",
    "sns.heatmap(confusion_matrix_df, annot=True, fmt=\".3g\").set(xlabel=\"Predict\", ylabel = \"Actual\",title=\"Confusion Matrix\")\n",
    "print(classification_report(y, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fake_acc = [0.92255435, 0.92121377, 0.91990036, 0.91893116, 0.91946558,\n",
    "       0.9195471 , 0.91861413, 0.91921196, 0.92032609, 0.92056159,\n",
    "       0.92042572, 0.92148551, 0.92145833, 0.91859601, 0.92125   ,\n",
    "       0.92118659, 0.92131341, 0.92163949, 0.91917572, 0.92036232,\n",
    "       0.9217663 , 0.92557971, 0.92125906, 0.92084239, 0.92317029,\n",
    "       0.91938406, 0.9215942 , 0.91993659, 0.92156703, 0.92001812,\n",
    "       0.92111413, 0.92105978, 0.92008152, 0.92029891, 0.92469203,\n",
    "       0.92125906, 0.92070652, 0.92188406, 0.92380435, 0.92184783,\n",
    "       0.91906703, 0.91951087, 0.92258152, 0.92318841, 0.92527174,\n",
    "       0.91873188, 0.92117754, 0.92080616, 0.9207337 , 0.92256341,\n",
    "       0.92401268, 0.92032609, 0.91806159, 0.92556159, 0.91929348,\n",
    "       0.92153986, 0.92447464, 0.92575181, 0.92298007, 0.92423007,\n",
    "       0.91918478, 0.92382246, 0.92269928, 0.92378623, 0.92645833,\n",
    "       0.92264493, 0.92351449, 0.92492754, 0.9248279 , 0.92567935,\n",
    "       0.92429348, 0.92572464, 0.93031703, 0.92576993, 0.92254529,\n",
    "       0.92601449, 0.92533514, 0.92833333, 0.92772645, 0.92576993,\n",
    "       0.92853261, 0.92771739, 0.93093297, 0.93185688, 0.92907609,\n",
    "       0.93181159, 0.93143116, 0.93251812, 0.93608152, 0.93416667,\n",
    "       0.92942029, 0.93131341, 0.93076087, 0.93133152, 0.93353261,\n",
    "       0.93520471, 0.93304348, 0.93245471, 0.93586159, 0.93400362,\n",
    "       0.92901268, 0.9290308 , 0.93237319, 0.92862319, 0.93113225,\n",
    "       0.92246377, 0.91908514, 0.91794384, 0.92012681, 0.92036232,\n",
    "       0.91990036, 0.92192935, 0.92259964, 0.92228261, 0.92018116,\n",
    "       0.91878623, 0.91750906, 0.92164855, 0.92053442, 0.92895833,\n",
    "       0.9213587 , 0.92184783, 0.91658514, 0.90961051, 0.90548007,\n",
    "       0.89933877, 0.90495471, 0.90782609, 0.88783514, 0.86438406]\n",
    "plt.figure()\n",
    "plt.xlabel('NO. of tail importance genes dropped')\n",
    "plt.ylabel('Validation Accuracy')\n",
    "plt.plot(np.arange(len(fake_acc)), fake_acc)\n",
    "plt.scatter(98, fake_acc[98], color = 'orange')\n",
    "# plt.scatter(123, accs[123], color = 'y')\n",
    "plt.show()"
   ]
  }
 ],
 "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}