{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Use K-medoid algorithm to find the suitable human model representitives"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os, sys\n",
    "lib_path = os.path.abspath(os.path.join('..', '..', 'utils'))\n",
    "sys.path.append(lib_path)\n",
    "from GraphType import GraphStat\n",
    "import readCSV as reader\n",
    "from scipy import stats\n",
    "from ipywidgets import interact, fixed, interactive\n",
    "import ipywidgets as widgets\n",
    "from pyclustering.cluster.kmedoids import kmedoids\n",
    "from pyclustering.utils.metric import distance_metric, type_metric\n",
    "import random\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define a new distance metric"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def ks_value(dest1, dest2):\n",
    "    value, p = stats.ks_2samp(dest1, dest2)\n",
    "    return value\n",
    "\n",
    "\n",
    "ks_metric = distance_metric(type_metric.USER_DEFINED, func=ks_value)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Read Human Models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "304"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Progress Widge\n",
    "w = widgets.FloatProgress(\n",
    "    value=0,\n",
    "    min=0,\n",
    "    max=1.0,\n",
    "    step=0.1,\n",
    "    description='Loading Files...:',\n",
    "    bar_style='info',\n",
    "    orientation='horizontal'\n",
    ")\n",
    "\n",
    "humanFiles = reader.readmultiplefiles('../input/Human/', 1300, False)\n",
    "modelToFileName = {}\n",
    "for name in humanFiles:\n",
    "    modelToFileName[GraphStat(name)] = name\n",
    "\n",
    "models = list(modelToFileName.keys())\n",
    "len(humanFiles)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Find Representative by K-medroid for different dists on GraphStat"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Returns the index of the representative"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def findRep(graphStats, func):\n",
    "    out_ds = list(map(func, models))\n",
    "\n",
    "    #choose a random starting point\n",
    "    start_index = random.randint(0, len(out_ds))\n",
    "\n",
    "    # start with one initial metrid [start_index]\n",
    "    outdegree_kmedoid = kmedoids(out_ds, [start_index], metric=ks_metric)\n",
    "\n",
    "    outdegree_kmedoid.process()\n",
    "    centoids = outdegree_kmedoid.get_medoids()\n",
    "    return centoids[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Find representative for out degree"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### For Yakindumm\n",
    "#### For all human models\n",
    "* the rep found is ../input/humanOutput\\R_20158_run_1.csv\n",
    "* the average distance between it and others is 0.05515988287586802\n",
    "\n",
    "#### For human models with $100 \\pm 10$ nodes\n",
    "* the rep found is ../input/human_output_100\\R_2015225_run_1.csv\n",
    "* the average distance between it and others is $0.046150929558524685$\n",
    "\n",
    "#### for human model with $100 \\pm 10$ nodes and new metric\n",
    "* the rep found is ../input/human_output_100\\R_2015248_run_1.csv\n",
    "* average distance:  0.052753778714861366\n",
    "* median:  0.0468131868131868\n",
    "* std:  0.0246917800149673\n",
    "* max: 0.15993907083015996\n",
    "* min: 0.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "../input/Human\\33_run_1.csv\n",
      "../input/Human\\33_run_1.csv\n"
     ]
    }
   ],
   "source": [
    "od_rep_index = findRep(models, lambda m: m.out_d)\n",
    "print(list(modelToFileName.values())[od_rep_index])\n",
    "od_rep_model = models[od_rep_index]\n",
    "print(modelToFileName[od_rep_model])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "average distance:  0.04615092955852465\n",
      "median:  0.04402137483980782\n",
      "std:  0.017305709419913242\n",
      "max: 0.1411706837186424\n",
      "min: 0.0\n"
     ]
    }
   ],
   "source": [
    "distances = []\n",
    "for model in models:\n",
    "    distances.append(ks_value(od_rep_model.out_d, model.out_d))\n",
    "print('average distance: ', np.mean(distances))\n",
    "print('median: ', np.median(distances))\n",
    "print('std: ', np.std(distances))\n",
    "print('max:', max(distances))\n",
    "print('min:', min(distances))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Find Representative for node activities"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### For Yakindumm\n",
    "#### For all human models\n",
    "* the rep found is ../input/humanOutput\\R_2016176_run_1.csv\n",
    "* the average distance between it and others is 0.05275267434589047\n",
    "\n",
    "#### For human models with $100 \\pm 10$ nodes\n",
    "* the rep found is ../input/human_output_100\\R_2017419_run_1.csv\n",
    "* the average distance between it and others is $0.04679429311806747$\n",
    "\n",
    "#### for human model with $100 \\pm 10$ nodes and new metric\n",
    "* the rep found is ../input/human_output_100\\R_2017131_run_1.csv\n",
    "* average distance:  0.024629205820449567\n",
    "* median:  0.023787888564682946\n",
    "* std:  0.013845547883198073\n",
    "* max: 0.09044674910251294\n",
    "* min: 0.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "../input/Human\\288_run_1.csv\n",
      "../input/Human\\288_run_1.csv\n"
     ]
    }
   ],
   "source": [
    "na_rep_index = findRep(models, lambda m: m.na)\n",
    "print(list(modelToFileName.values())[na_rep_index])\n",
    "na_rep_model = models[na_rep_index]\n",
    "print(modelToFileName[na_rep_model])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "average distance:  0.046794293118067494\n",
      "median:  0.03898868458274401\n",
      "std:  0.02880119213919405\n",
      "max: 0.18702970297029703\n",
      "min: 0.0\n"
     ]
    }
   ],
   "source": [
    "distances = []\n",
    "for model in models:\n",
    "    distances.append(ks_value(na_rep_model.na, model.na))\n",
    "print('average distance: ', np.mean(distances))\n",
    "print('median: ', np.median(distances))\n",
    "print('std: ', np.std(distances))\n",
    "print('max:', max(distances))\n",
    "print('min:', min(distances))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Find Representative for MPC"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### For Yakindumm\n",
    "\n",
    "#### For all human models\n",
    "* the rep found is ../input/humanOutput\\R_2015246_run_1.csv\n",
    "* the average distance between it and others is 0.08556632702185384\n",
    "\n",
    "#### For human models with $100 \\pm 10$ nodes\n",
    "* the rep found is ../input/human_output_100\\R_2016324_run_1.csv\n",
    "* the average distance between it and others is $0.07028909225833631$\n",
    "\n",
    "#### for human model with $100 \\pm 10$ nodes and new metric\n",
    "* average distance:  0.054782550772603904\n",
    "* median:  0.048330503678551184\n",
    "* std:  0.028208257424907526\n",
    "* max: 0.21181525241675614\n",
    "* min: 0.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "../input/Human\\151_run_1.csv\n",
      "../input/Human\\151_run_1.csv\n"
     ]
    }
   ],
   "source": [
    "mpc_rep_index = findRep(models, lambda m: m.mpc)\n",
    "print(list(modelToFileName.values())[mpc_rep_index])\n",
    "mpc_rep_model = models[mpc_rep_index]\n",
    "print(modelToFileName[mpc_rep_model])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "average distance:  0.07028909225833632\n",
      "median:  0.06254480286738351\n",
      "std:  0.037281890512224164\n",
      "max: 0.21961550993809065\n",
      "min: 0.0\n"
     ]
    }
   ],
   "source": [
    "distances = []\n",
    "for model in models:\n",
    "    distances.append(ks_value(mpc_rep_model.mpc, model.mpc))\n",
    "print('average distance: ', np.mean(distances))\n",
    "print('median: ', np.median(distances))\n",
    "print('std: ', np.std(distances))\n",
    "print('max:', max(distances))\n",
    "print('min:', min(distances))"
   ]
  }
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