Energy Cost Savings on Cloud Data Centres using Heuristic Algorithm.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>15.26</td>\n",
       "      <td>14.84</td>\n",
       "      <td>0.8710</td>\n",
       "      <td>5.763</td>\n",
       "      <td>3.312</td>\n",
       "      <td>2.221</td>\n",
       "      <td>5.220</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>14.88</td>\n",
       "      <td>14.57</td>\n",
       "      <td>0.8811</td>\n",
       "      <td>5.554</td>\n",
       "      <td>3.333</td>\n",
       "      <td>1.018</td>\n",
       "      <td>4.956</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>14.29</td>\n",
       "      <td>14.09</td>\n",
       "      <td>0.9050</td>\n",
       "      <td>5.291</td>\n",
       "      <td>3.337</td>\n",
       "      <td>2.699</td>\n",
       "      <td>4.825</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>13.84</td>\n",
       "      <td>13.94</td>\n",
       "      <td>0.8955</td>\n",
       "      <td>5.324</td>\n",
       "      <td>3.379</td>\n",
       "      <td>2.259</td>\n",
       "      <td>4.805</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>16.14</td>\n",
       "      <td>14.99</td>\n",
       "      <td>0.9034</td>\n",
       "      <td>5.658</td>\n",
       "      <td>3.562</td>\n",
       "      <td>1.355</td>\n",
       "      <td>5.175</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      "text/plain": [
       "       0      1       2      3      4      5      6  7\n",
       "0  15.26  14.84  0.8710  5.763  3.312  2.221  5.220  1\n",
       "1  14.88  14.57  0.8811  5.554  3.333  1.018  4.956  1\n",
       "2  14.29  14.09  0.9050  5.291  3.337  2.699  4.825  1\n",
       "3  13.84  13.94  0.8955  5.324  3.379  2.259  4.805  1\n",
       "4  16.14  14.99  0.9034  5.658  3.562  1.355  5.175  1"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv('seed.txt', sep='\\t', header=None)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "x = data.drop([7], axis=1)\n",
    "x = x.values.astype(float)\n",
    "minmax = MinMaxScaler()\n",
    "x_scaled = minmax.fit_transform(x)\n",
    "data_normalized = pd.DataFrame(x_scaled)\n",
    "data_normalized"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "x = data.drop([7], axis=1)\n",
    "x = x.values\n",
    "x = pd.json_normalize(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pso import ParticleSwarmOptimizedClustering\n",
    "from particle import quantization_error, calc_sse\n",
    "from utils import normalize\n",
    "from kmeans import KMeans\n",
    "from sklearn.metrics import silhouette_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = data.drop([7], axis=1)\n",
    "x = x.values\n",
    "x = normalize(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# K-Means"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "kmeans = KMeans(n_cluster=3, init_pp=False, seed=2018)\n",
    "kmeans.fit(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Silhouette: 0.42212676242010655\n",
      "SSE: 22.026451226606618\n",
      "Quantization: 0.30155146657476695\n"
     ]
    }
   ],
   "source": [
    "predicted_kmeans = kmeans.predict(x)\n",
    "print('Silhouette:', silhouette_score(x, predicted_kmeans))\n",
    "print('SSE:', kmeans.SSE)\n",
    "print('Quantization:', quantization_error(centroids=kmeans.centroid, data=x, labels=predicted_kmeans))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Silhouette: 0.42212676242010655\n",
      "SSE: 22.024363075666034\n",
      "Quantization: 0.3016646187475438\n"
     ]
    }
   ],
   "source": [
    "kmeans2 = KMeans(n_cluster=3, init_pp=True, seed=2018)\n",
    "kmeans2.fit(x)\n",
    "predicted_kmeans2 = kmeans2.predict(x)\n",
    "print('Silhouette:', silhouette_score(x, predicted_kmeans))\n",
    "print('SSE:', kmeans2.SSE)\n",
    "print('Quantization:', quantization_error(centroids=kmeans2.centroid, data=x, labels=predicted_kmeans2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# PSO"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "pso = ParticleSwarmOptimizedClustering(n_cluster=3, n_particles=10, data=x, hybrid=True, max_iter=2000, print_debug=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Initial global best score 0.301551466574767\n",
      "Iteration 0001/2000 current gbest score 0.301551466574767002\n",
      "Iteration 0051/2000 current gbest score 0.301483340501563446\n",
      "Iteration 0101/2000 current gbest score 0.301384390021302961\n",
      "Iteration 0151/2000 current gbest score 0.301359808928162942\n",
      "Iteration 0201/2000 current gbest score 0.301359511342237540\n",
      "Iteration 0251/2000 current gbest score 0.301355292823660470\n",
      "Iteration 0301/2000 current gbest score 0.301351922639844838\n",
      "Iteration 0351/2000 current gbest score 0.301351821604912640\n",
      "Iteration 0401/2000 current gbest score 0.301351697374445227\n",
      "Iteration 0451/2000 current gbest score 0.301351645466648999\n",
      "Iteration 0501/2000 current gbest score 0.301351617019892493\n",
      "Iteration 0551/2000 current gbest score 0.301351606327527943\n",
      "Iteration 0601/2000 current gbest score 0.301351596478920347\n",
      "Iteration 0651/2000 current gbest score 0.301351589829204569\n",
      "Iteration 0701/2000 current gbest score 0.301351587651789432\n",
      "Iteration 0751/2000 current gbest score 0.301351586711841701\n",
      "Iteration 0801/2000 current gbest score 0.301351586385081249\n",
      "Iteration 0851/2000 current gbest score 0.301351586224875401\n",
      "Iteration 0901/2000 current gbest score 0.301351586088624002\n",
      "Iteration 0951/2000 current gbest score 0.301351585992641391\n",
      "Iteration 1001/2000 current gbest score 0.301351585952734258\n",
      "Iteration 1051/2000 current gbest score 0.301351585919644949\n",
      "Iteration 1101/2000 current gbest score 0.301351585886706852\n",
      "Iteration 1151/2000 current gbest score 0.301351585840477998\n",
      "Iteration 1201/2000 current gbest score 0.301351585818873835\n",
      "Iteration 1251/2000 current gbest score 0.301351585808393441\n",
      "Iteration 1301/2000 current gbest score 0.301351585801022726\n",
      "Iteration 1351/2000 current gbest score 0.301351585794994048\n",
      "Iteration 1401/2000 current gbest score 0.301351585788872445\n",
      "Iteration 1451/2000 current gbest score 0.301351585784704945\n",
      "Iteration 1501/2000 current gbest score 0.301351585783028064\n",
      "Iteration 1551/2000 current gbest score 0.301351585781699294\n",
      "Iteration 1601/2000 current gbest score 0.301351585780793740\n",
      "Iteration 1651/2000 current gbest score 0.301351585780080866\n",
      "Iteration 1701/2000 current gbest score 0.301351585779610576\n",
      "Iteration 1751/2000 current gbest score 0.301351585779383868\n",
      "Iteration 1801/2000 current gbest score 0.301351585779256970\n",
      "Iteration 1851/2000 current gbest score 0.301351585779191522\n",
      "Iteration 1901/2000 current gbest score 0.301351585779159714\n",
      "Iteration 1951/2000 current gbest score 0.301351585779142894\n",
      "Finish with gbest score 0.301351585779136677\n"
     ]
    }
   ],
   "source": [
    "hist = pso.run()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "pso_kmeans = KMeans(n_cluster=3, init_pp=False, seed=2018)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.76541996, 0.80131406, 0.69915987, 0.73701562, 0.77723262,\n",
       "        0.35779836, 0.76076165],\n",
       "       [0.1234663 , 0.17550971, 0.37544388, 0.18818347, 0.1616025 ,\n",
       "        0.50210363, 0.28048571],\n",
       "       [0.37802539, 0.41445385, 0.67032439, 0.35904426, 0.46392445,\n",
       "        0.26414688, 0.31467392]])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pso_kmeans.centroid = pso.gbest_centroids.copy()\n",
    "pso_kmeans.centroid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Silhouette: 0.42212676242010655\n",
      "SSE: 22.058278321675825\n",
      "Quantization: 0.3013515857791367\n"
     ]
    }
   ],
   "source": [
    "predicted_pso = pso_kmeans.predict(x)\n",
    "print('Silhouette:', silhouette_score(x, predicted_pso))\n",
    "print('SSE:', calc_sse(centroids=pso.gbest_centroids, data=x, labels=predicted_pso))\n",
    "print('Quantization:', pso.gbest_score)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Repeated Test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### K-Means++"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "kmeanspp = {\n",
    "    'silhouette': [],\n",
    "    'sse' : [],\n",
    "    'quantization' : [],\n",
    "}\n",
    "for _ in range(20):\n",
    "    kmean_rep = KMeans(n_cluster=3, init_pp=True)\n",
    "    kmean_rep.fit(x)\n",
    "    predicted_kmean_rep = kmean_rep.predict(x)\n",
    "    silhouette = silhouette_score(x, predicted_kmean_rep)\n",
    "    sse = kmean_rep.SSE\n",
    "    quantization = quantization_error(centroids=kmean_rep.centroid, data=x, labels=predicted_kmean_rep)\n",
    "    kmeanspp['silhouette'].append(silhouette)\n",
    "    kmeanspp['sse'].append(sse)\n",
    "    kmeanspp['quantization'].append(quantization)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### PSO "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Initial global best score 0.37052219340762643\n",
      "Iteration 0001/2000 current gbest score 0.361720141201318068\n",
      "Finish with gbest score 0.313525331185252332\n",
      "Initial global best score 0.3762627344894916\n",
      "Iteration 0001/2000 current gbest score 0.375590401664467777\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\mervi\\Desktop\\VIT\\2nd Sem [WINTER 20-21]\\RBL\\RBL [Sem 2]\\Implementation\\particle-swarm-optimized-clustering-master\\particle.py:14: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  dist /= len(idx)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Finish with gbest score 0.313131446854706230\n",
      "Initial global best score 0.363835293819178\n",
      "Iteration 0001/2000 current gbest score 0.353956251099471764\n",
      "Finish with gbest score 0.321180391883586935\n",
      "Initial global best score 0.3638862728668936\n",
      "Iteration 0001/2000 current gbest score 0.363886272866893623\n",
      "Finish with gbest score 0.315559653496116088\n",
      "Initial global best score 0.3850859514190586\n",
      "Iteration 0001/2000 current gbest score 0.379873173636763706\n",
      "Finish with gbest score 0.327789796461660121\n",
      "Initial global best score 0.3654270792822761\n",
      "Iteration 0001/2000 current gbest score 0.361309409719373642\n",
      "Finish with gbest score 0.334930184154607236\n",
      "Initial global best score 0.3695040910012317\n",
      "Iteration 0001/2000 current gbest score 0.369504091001231716\n",
      "Finish with gbest score 0.316912970638799474\n",
      "Initial global best score 0.39986607807325975\n",
      "Iteration 0001/2000 current gbest score 0.369087802844176638\n",
      "Finish with gbest score 0.326584848265292826\n",
      "Initial global best score 0.38003309598689405\n",
      "Iteration 0001/2000 current gbest score 0.380033095986894054\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\mervi\\Desktop\\VIT\\2nd Sem [WINTER 20-21]\\RBL\\RBL [Sem 2]\\Implementation\\particle-swarm-optimized-clustering-master\\particle.py:14: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  dist /= len(idx)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Finish with gbest score 0.322098154473820231\n",
      "Initial global best score 0.3603319034976\n",
      "Iteration 0001/2000 current gbest score 0.360331903497600026\n",
      "Finish with gbest score 0.312285938198056945\n",
      "Initial global best score 0.33123856483527264\n",
      "Iteration 0001/2000 current gbest score 0.331238564835272642\n",
      "Finish with gbest score 0.305037430246008545\n",
      "Initial global best score 0.3789153274525323\n",
      "Iteration 0001/2000 current gbest score 0.378915327452532313\n",
      "Finish with gbest score 0.324936361587446820\n",
      "Initial global best score 0.3851084793450758\n",
      "Iteration 0001/2000 current gbest score 0.385108479345075805\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\mervi\\Desktop\\VIT\\2nd Sem [WINTER 20-21]\\RBL\\RBL [Sem 2]\\Implementation\\particle-swarm-optimized-clustering-master\\particle.py:14: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  dist /= len(idx)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Finish with gbest score 0.328571025756002111\n",
      "Initial global best score 0.3980791953285596\n",
      "Iteration 0001/2000 current gbest score 0.398079195328559621\n",
      "Finish with gbest score 0.317752067492370616\n",
      "Initial global best score 0.3633084852567769\n",
      "Iteration 0001/2000 current gbest score 0.363308485256776892\n",
      "Finish with gbest score 0.336337304250364566\n",
      "Initial global best score 0.39474771567859784\n",
      "Iteration 0001/2000 current gbest score 0.385353286476722456\n",
      "Finish with gbest score 0.308734310453695981\n",
      "Initial global best score 0.35812508639393253\n",
      "Iteration 0001/2000 current gbest score 0.358125086393932535\n",
      "Finish with gbest score 0.314675795911969036\n",
      "Initial global best score 0.37875033569399935\n",
      "Iteration 0001/2000 current gbest score 0.373762690297090472\n",
      "Finish with gbest score 0.312043679084685666\n",
      "Initial global best score 0.3373163303728424\n",
      "Iteration 0001/2000 current gbest score 0.337316330372842421\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\mervi\\Desktop\\VIT\\2nd Sem [WINTER 20-21]\\RBL\\RBL [Sem 2]\\Implementation\\particle-swarm-optimized-clustering-master\\particle.py:14: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  dist /= len(idx)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Finish with gbest score 0.306090790631497855\n",
      "Initial global best score 0.40015393469775007\n",
      "Iteration 0001/2000 current gbest score 0.356488419464995598\n",
      "Finish with gbest score 0.306543432812857508\n",
      "Wall time: 1min 51s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "pso_plain = {\n",
    "    'silhouette': [],\n",
    "    'sse' : [],\n",
    "    'quantization' : [],\n",
    "}\n",
    "for _ in range(20):\n",
    "    pso_rep = ParticleSwarmOptimizedClustering(\n",
    "        n_cluster=3, n_particles=10, data=x, hybrid=False, max_iter=2000, print_debug=2000)\n",
    "    pso_rep.run()\n",
    "    pso_kmeans = KMeans(n_cluster=3, init_pp=False, seed=2018)\n",
    "    pso_kmeans.centroid = pso_rep.gbest_centroids.copy()\n",
    "    predicted_pso_rep = pso_kmeans.predict(x)\n",
    "    \n",
    "    silhouette = silhouette_score(x, predicted_pso_rep)\n",
    "    sse = calc_sse(centroids=pso_rep.gbest_centroids, data=x, labels=predicted_pso_rep)\n",
    "    quantization = pso_rep.gbest_score\n",
    "    pso_plain['silhouette'].append(silhouette)\n",
    "    pso_plain['sse'].append(sse)\n",
    "    pso_plain['quantization'].append(quantization)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### PSO Hybrid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Initial global best score 0.3016646187475438\n",
      "Iteration 0001/2000 current gbest score 0.301664618747543800\n",
      "Finish with gbest score 0.301226178999996985\n",
      "Initial global best score 0.301551466574767\n",
      "Iteration 0001/2000 current gbest score 0.301551466574767002\n",
      "Finish with gbest score 0.301227000695316216\n",
      "Initial global best score 0.3016646187475438\n",
      "Iteration 0001/2000 current gbest score 0.301664618747543800\n",
      "Finish with gbest score 0.301325966928793987\n",
      "Initial global best score 0.301551466574767\n",
      "Iteration 0001/2000 current gbest score 0.301551466574767002\n",
      "Finish with gbest score 0.301304883136897184\n",
      "Initial global best score 0.30166461874754386\n",
      "Iteration 0001/2000 current gbest score 0.301664618747543856\n",
      "Finish with gbest score 0.301417680935965715\n",
      "Initial global best score 0.3016646187475438\n",
      "Iteration 0001/2000 current gbest score 0.301664618747543800\n",
      "Finish with gbest score 0.301280207564644409\n",
      "Initial global best score 0.30155146657476695\n",
      "Iteration 0001/2000 current gbest score 0.301551466574766946\n",
      "Finish with gbest score 0.301221857752872857\n",
      "Initial global best score 0.30155146657476695\n",
      "Iteration 0001/2000 current gbest score 0.301551466574766946\n",
      "Finish with gbest score 0.301182480830895105\n",
      "Initial global best score 0.3016646187475438\n",
      "Iteration 0001/2000 current gbest score 0.301664618747543800\n",
      "Finish with gbest score 0.301322508854178939\n",
      "Initial global best score 0.301551466574767\n",
      "Iteration 0001/2000 current gbest score 0.301551466574767002\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\mervi\\Desktop\\VIT\\2nd Sem [WINTER 20-21]\\RBL\\RBL [Sem 2]\\Implementation\\particle-swarm-optimized-clustering-master\\particle.py:14: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  dist /= len(idx)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Finish with gbest score 0.301093685142135636\n",
      "Initial global best score 0.3016646187475438\n",
      "Iteration 0001/2000 current gbest score 0.301664618747543800\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\mervi\\Desktop\\VIT\\2nd Sem [WINTER 20-21]\\RBL\\RBL [Sem 2]\\Implementation\\particle-swarm-optimized-clustering-master\\particle.py:14: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  dist /= len(idx)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Finish with gbest score 0.301255285838620712\n",
      "Initial global best score 0.301551466574767\n",
      "Iteration 0001/2000 current gbest score 0.301551466574767002\n",
      "Finish with gbest score 0.301144264787732707\n",
      "Initial global best score 0.3016646187475438\n",
      "Iteration 0001/2000 current gbest score 0.301664618747543800\n",
      "Finish with gbest score 0.301374975970117254\n",
      "Initial global best score 0.30166461874754386\n",
      "Iteration 0001/2000 current gbest score 0.301664618747543856\n",
      "Finish with gbest score 0.301282327173265807\n",
      "Initial global best score 0.30155146657476695\n",
      "Iteration 0001/2000 current gbest score 0.301551466574766946\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\mervi\\Desktop\\VIT\\2nd Sem [WINTER 20-21]\\RBL\\RBL [Sem 2]\\Implementation\\particle-swarm-optimized-clustering-master\\particle.py:14: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  dist /= len(idx)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Finish with gbest score 0.301201927508178591\n",
      "Initial global best score 0.30166461874754386\n",
      "Iteration 0001/2000 current gbest score 0.301664618747543856\n",
      "Finish with gbest score 0.301237018900874232\n",
      "Initial global best score 0.30155146657476695\n",
      "Iteration 0001/2000 current gbest score 0.301551466574766946\n",
      "Finish with gbest score 0.301108294138927590\n",
      "Initial global best score 0.30155146657476695\n",
      "Iteration 0001/2000 current gbest score 0.301551466574766946\n",
      "Finish with gbest score 0.301082281520932027\n",
      "Initial global best score 0.301551466574767\n",
      "Iteration 0001/2000 current gbest score 0.301551466574767002\n",
      "Finish with gbest score 0.301228912943299454\n",
      "Initial global best score 0.30166461874754386\n",
      "Iteration 0001/2000 current gbest score 0.301664618747543856\n",
      "Finish with gbest score 0.301082933754605986\n",
      "Wall time: 1min 48s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "pso_hybrid = {\n",
    "    'silhouette': [],\n",
    "    'sse' : [],\n",
    "    'quantization' : [],\n",
    "}\n",
    "for _ in range(20):\n",
    "    pso_rep = ParticleSwarmOptimizedClustering(\n",
    "        n_cluster=3, n_particles=10, data=x, hybrid=True, max_iter=2000, print_debug=2000)\n",
    "    pso_rep.run()\n",
    "    pso_kmeans = KMeans(n_cluster=3, init_pp=False, seed=2018)\n",
    "    pso_kmeans.centroid = pso_rep.gbest_centroids.copy()\n",
    "    predicted_pso_rep = pso_kmeans.predict(x)\n",
    "    \n",
    "    silhouette = silhouette_score(x, predicted_pso_rep)\n",
    "    sse = calc_sse(centroids=pso_rep.gbest_centroids, data=x, labels=predicted_pso_rep)\n",
    "    quantization = pso_rep.gbest_score\n",
    "    pso_hybrid['silhouette'].append(silhouette)\n",
    "    pso_hybrid['sse'].append(sse)\n",
    "    pso_hybrid['quantization'].append(quantization)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# HSA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "from harmony_search import HarmonySearch,harmony_search\n",
    "from objectivefunction import ObjectiveFunction\n",
    "from utils import normalize\n",
    "from kmeans import KMeans\n",
    "from sklearn.metrics import silhouette_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "#from pyharmonysearch import ObjectiveFunctionInterface, harmony_search\n",
    "from math import pow\n",
    "import random\n",
    "from bisect import bisect_left\n",
    "from multiprocessing import cpu_count\n",
    "from pprint import pprint\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import math as math"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "obj_fun = ObjectiveFunction()\n",
    "num_processes = cpu_count()\n",
    "num_iterations = num_processes * 5\n",
    "results = harmony_search(obj_fun, num_processes, num_iterations)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Elapsed time: 0:00:39.500473\n",
      "Best harmony: [1, 1.0007502363917098]\n",
      "Best fitness: -5.628388037735078e-05\n",
      "Harmony memories:\n",
      "[[([5, 25.680590429945248], -4.14820649926468),\n",
      "  ([-8, 64.53668747207348], -4.707756956782439),\n",
      "  ([-3, 9.37643919341076], -3.4394768389466237),\n",
      "  ([-1, 1.0552588930770526], -1.6687165985625283),\n",
      "  ([-6, 35.74541452444519], -4.033910963219958),\n",
      "  ([0, -0.9955013699869317], -4.616132853409955),\n",
      "  ([4, 16.300420349877374], -2.945766448323173),\n",
      "  ([-2, 4.517784787276355], -3.605772497494756),\n",
      "  ([-5, 25.823525298023927], -4.652238789584423),\n",
      "  ([1, 0.4701023726235576], -3.370021407049166),\n",
      "  ([-1, 1.8974109966042993], -4.448921553293275),\n",
      "  ([-8, 63.542184749310266], -4.634335516425975),\n",
      "  ([9, 81.64577918204537], -4.670049978851335),\n",
      "  ([-3, 8.834106054535368], -2.9832588080382076),\n",
      "  ([9, 81.42653593694537], -4.421166702154553),\n",
      "  ([-3, 8.545013967560521], -3.6296926926362145),\n",
      "  ([4, 15.95515102332358], -2.3224997797850713),\n",
      "  ([-3, 9.368396117628436], -3.4200704904352515),\n",
      "  ([-5, 24.72801544324011], -3.79318451098176),\n",
      "  ([8, 64.54110120929147], -4.37297393068772),\n",
      "  ([1, 1.8974109966042993], -4.401028079376534),\n",
      "  ([-3, 8.390252155130831], -3.992297872592298),\n",
      "  ([1, 1.3825352052600692], -2.749404426847821),\n",
      "  ([10, 99.90953883874955], -4.416649319151982),\n",
      "  ([6, 36.52570973909499], -3.9822404890262457),\n",
      "  ([2, 3.143332494590653], -4.322647069640964),\n",
      "  ([6, 36.60939052152925], -4.1452860736466235),\n",
      "  ([3, 8.545013967560521], -3.246538810660634),\n",
      "  ([-5, 24.267520650960858], -4.507034614968144),\n",
      "  ([2, 4.347131156529684], -2.6426226792931398),\n",
      "  ([-1, 0.8146220439020799], -2.1325673719060783),\n",
      "  ([-2, 3.964647570156019], -2.3150055813448462),\n",
      "  ([2, 3.47985675414502], -3.36918713072349),\n",
      "  ([5, 25.57369785745712], -3.910279950491922),\n",
      "  ([-2, 3.980980634553106], -2.3061959286933322),\n",
      "  ([2, 3.0216387595195333], -4.582096746625298),\n",
      "  ([8, 64.53668747207348], -4.366955435913314),\n",
      "  ([0, -0.159930857341692], -1.5168373990267463),\n",
      "  ([-4, 16.300420349877374], -3.556068906248232),\n",
      "  ([10, 100.39053709384046], -4.5773047475118185),\n",
      "  ([1, 1.1380956971402614], -1.0671361233172982),\n",
      "  ([2, 3.526735262951746], -3.194499158828208),\n",
      "  ([-2, 3.142778632393923], -4.4246411892943724),\n",
      "  ([5, 24.72801544324011], -3.1944831236749915),\n",
      "  ([-1, 0.4701023726235576], -3.4989031609349963),\n",
      "  ([0, 0.4701023726235576], -3.182196241811872),\n",
      "  ([2, 4.227640916969768], -1.9715832853582438),\n",
      "  ([1, 1.0552588930770526], -0.2664746716268691),\n",
      "  ([5, 25.823525298023927], -4.440524191756397),\n",
      "  ([-2, 4.2136381518840444], -2.6785613813361215),\n",
      "  ([2, 4.517784787276355], -3.360726318387564),\n",
      "  ([-2, 4.227640916969768], -2.7201130651818723),\n",
      "  ([0, 0.4104077079269075], -2.936165302958173),\n",
      "  ([3, 8.390252155130831], -3.7419282382434775),\n",
      "  ([-8, 63.958538655478634], -4.4088134469082405),\n",
      "  ([-2, 3.143332494590653], -4.423503472526561),\n",
      "  ([1, 1.0835811048313602], -0.5297926717589272),\n",
      "  ([-2, 3.47985675414502], -3.6124005864765767),\n",
      "  ([6, 36.82767534677714], -4.548649018118376),\n",
      "  ([-2, 4.042183914507746], -2.320223445064675),\n",
      "  ([-1, 1.0835811048313602], -1.7402170400341077),\n",
      "  ([-1, 1.3825352052600692], -2.9772280376259643),\n",
      "  ([0, -0.3265990218235295], -2.538975879393783),\n",
      "  ([3, 9.47999831421198], -3.3336262905603684),\n",
      "  ([-6, 36.60939052152925], -4.467466452935349),\n",
      "  ([-5, 24.42893132605306], -4.242936147969455),\n",
      "  ([2, 3.964647570156019], -0.7537621221353429),\n",
      "  ([0, -0.34693218863761643], -2.6416393042543915),\n",
      "  ([8, 63.542184749310266], -4.262109015206902),\n",
      "  ([-1, 1.2142940148582966], -2.260949484288764),\n",
      "  ([2, 4.2136381518840444], -1.8816193677485347),\n",
      "  ([1, 1.2142940148582966], -1.7213714250130911),\n",
      "  ([9, 81.63257843524241], -4.654108412275533),\n",
      "  ([-2, 3.0216387595195333], -4.660785308837224),\n",
      "  ([5, 24.267520650960858], -4.257774905012378),\n",
      "  ([-4, 16.558521874136748], -4.04646068405756),\n",
      "  ([-2, 3.91840295698573], -2.3670430867831826),\n",
      "  ([2, 3.980980634553106], -0.7110723731467323),\n",
      "  ([-1, 1.1380956971402614], -1.9325414935869036),\n",
      "  ([5, 25.539350891853548], -3.8305946713946146),\n",
      "  ([3, 8.834106054535368], -2.0479612091818917),\n",
      "  ([-4, 15.95515102332358], -3.2658030384753567),\n",
      "  ([-2, 4.347131156529684], -3.0933127825442504),\n",
      "  ([3, 9.368396117628436], -2.921631913975854),\n",
      "  ([8, 63.958538655478634], -3.915455194900623),\n",
      "  ([-1, 0.4104077079269075], -3.682909345607917),\n",
      "  ([-5, 25.680590429945248], -4.4226926170245395),\n",
      "  ([1, 0.8146220439020799], -1.4898654754120508),\n",
      "  ([3, 10.026932181659827], -4.7046441449733525),\n",
      "  ([6, 35.74541452444519], -3.480666892361904),\n",
      "  ([-3, 9.47999831421198], -3.6898749125965487),\n",
      "  ([-6, 36.52570973909499], -4.35204505766656),\n",
      "  ([0, 0.8146220439020799], -4.2248011326051635),\n",
      "  ([2, 3.142778632393923], -4.323905442470946),\n",
      "  ([2, 3.91840295698573], -0.9805071047027539),\n",
      "  ([-5, 25.539350891853548], -4.191016518241525),\n",
      "  ([-5, 25.57369785745712], -4.247250512910922),\n",
      "  ([9, 81.41503860340687], -4.4094679567303565),\n",
      "  ([1, 0.4104077079269075], -3.576883279062289),\n",
      "  ([5, 24.42893132605306], -3.9042315917695567)],\n",
      " [([-5, 24.680831776645324], -3.8541149443657243),\n",
      "  ([5, 24.680831776645324], -3.3027328664329687),\n",
      "  ([3, 8.488196926537242], -3.4402334175262173),\n",
      "  ([9, 80.78006900727043], -4.246163442034178),\n",
      "  ([-3, 9.02473889376192], -2.836806954968798),\n",
      "  ([3, 8.282766531275291], -4.033220380980062),\n",
      "  ([-4, 15.70738414569049], -3.542766496474441),\n",
      "  ([-1, 1.0087599986214784], -1.610971487423588),\n",
      "  ([7, 48.36113398300928], -4.354333941786133),\n",
      "  ([3, 9.058871970353664], -1.6764591408516694),\n",
      "  ([-1, 1.8639562921863568], -4.377542188067425),\n",
      "  ([-5, 25.33647999752816], -3.8778844368452448),\n",
      "  ([-9, 80.78006900727043], -4.661899836064841),\n",
      "  ([3, 9.107936219206838], -1.818891825444323),\n",
      "  ([-7, 48.36113398300928], -4.661692085646693),\n",
      "  ([6, 35.825144214634584], -3.369275061999294),\n",
      "  ([0, 0.08515768623933012], -1.0025356378065375),\n",
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      "  ([-7, 48.902162302196075], -4.189006372958761),\n",
      "  ([5, 24.31620266628383], -4.155092774200714),\n",
      "  ([-7, 48.912836154228806], -4.1860080037969025),\n",
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      "  ([-3, 8.880752953332484], -2.9135448325023705),\n",
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      "  ([1, 0.353962355536833], -3.755052509134241),\n",
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      "  ([9, 81.06946863485922], -4.181784292388913),\n",
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      "  ([-2, 4.841984699096315], -4.393137471968379),\n",
      "  ([9, 80.50252192536837], -4.497010684309021),\n",
      "  ([1, 0.42191481385422946], -3.538586895941469),\n",
      "  ([-1, 0.08515768623933012], -4.485188250951344),\n",
      "  ([7, 49.6064221659617], -4.301016997571167),\n",
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      "  ([5, 25.33647999752816], -3.343634611033546),\n",
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      "  ([-3, 9.552705315895054], -3.861746386090594),\n",
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      "  ([-7, 48.84139491797067], -4.212358048427207),\n",
      "  ([-5, 24.18700456156776], -4.635662128361287),\n",
      "  ([-1, 1.6642934970990382], -3.894441045212322),\n",
      "  ([-3, 9.107936219206838], -2.8994979176221016),\n",
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      "  ([4, 15.70738414569049], -2.9211382350991744),\n",
      "  ([7, 48.902162302196075], -3.636459775887835),\n",
      "  ([-3, 8.488196926537242], -3.765707120629041),\n",
      "  ([-3, 8.282766531275291], -4.225992294499404),\n",
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      "  ([-1, 1.7521742561311129], -4.120282109456233),\n",
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      "  ([1, 0.9731137732300112], -0.06979367515130006),\n",
      "  ([-4, 15.815413853081143], -3.381239697502071),\n",
      "  ([-2, 4.134849080751469], -2.4696599623448066),\n",
      "  ([2, 3.8569983135420998], -1.3974687526486764),\n",
      "  ([6, 35.403377316079556], -4.120594704271588)],\n",
      " [([1, 1.7777547396568414], -4.118878520046464),\n",
      "  ([2, 4.425304693055212], -3.0001429419136056),\n",
      "  ([-1, 0.9397082850227844], -1.6796184417475588),\n",
      "  ([-3, 8.50899872603119], -3.7162082256624824),\n",
      "  ([-3, 9.721259663198992], -4.234418777223383),\n",
      "  ([-1, 1.894963790893371], -4.443780250713175),\n",
      "  ([0, 0.23624835582107107], -2.0256884564352196),\n",
      "  ([0, 0.16755712404690115], -1.5701853070047334),\n",
      "  ([-6, 35.4021544962179], -4.4513419083082315),\n",
      "  ([-2, 4.831004420858918], -4.370167021002143),\n",
      "  ([-2, 3.7432469225001768], -2.8089335660953774),\n",
      "  ([9, 81.01776784762345], -4.174872838771326),\n",
      "  ([-8, 64.10190312285859], -4.419303448197564),\n",
      "  ([-7, 49.14619942037288], -4.206741668680798),\n",
      "  ([2, 3.539244559002615], -3.1454255040211296),\n",
      "  ([-7, 48.929563434332145], -4.1819910718630995),\n",
      "  ([3, 8.50899872603119], -3.371020783858104),\n",
      "  ([7, 48.9191798077499], -3.6284176876422305),\n",
      "  ([-3, 9.49905874187406], -3.735428126685866),\n",
      "  ([-7, 48.98187433503814], -4.174892587939904),\n",
      "  ([-1, 1.3605237490244235], -2.8902460478317944),\n",
      "  ([-2, 4.540722324315187], -3.6696472757135283),\n",
      "  ([-2, 3.539244559002615], -3.4413650066371755),\n",
      "  ([3, 8.18670777927207], -4.2647119466176875),\n",
      "  ([0, -0.5857820052310352], -3.5922048774878),\n",
      "  ([-1, 0.28086976323273305], -4.038035724036908),\n",
      "  ([3, 9.19218887851514], -2.162593622065655),\n",
      "  ([4, 15.40342647610197], -3.81968832714071),\n",
      "  ([-2, 4.697320344891409], -4.07117088710268),\n",
      "  ([-2, 4.175905805320767], -2.5721758933889984),\n",
      "  ([0, -0.7532457115795936], -4.073085348040298),\n",
      "  ([-2, 4.310763325549857], -2.9784530667373317),\n",
      "  ([3, 9.374571485239917], -2.946036636730794),\n",
      "  ([-2, 4.110850305616096], -2.418480038486643),\n",
      "  ([-1, 1.9192267169865147], -4.494213772705849),\n",
      "  ([0, -0.4286217377477861], -3.01414469019976),\n",
      "  ([-3, 9.374571485239917], -3.4349667167187476),\n",
      "  ([-1, 1.7777547396568414], -4.181901177437557),\n",
      "  ([1, 1.9192267169865147], -4.448490360659064),\n",
      "  ([2, 4.672185482570457], -3.8540407009437976),\n",
      "  ([-3, 8.280428825043373], -4.230887816882978),\n",
      "  ([9, 81.22161155798132], -4.2472254113425105),\n",
      "  ([4, 16.329343321882146], -3.0371957758722705),\n",
      "  ([7, 48.929563434332145], -3.62423775374179),\n",
      "  ([1, 1.3605237490244235], -2.6388956994868527),\n",
      "  ([-1, 1.2118075803099657], -2.2498428660079575),\n",
      "  ([2, 3.450554108919178], -3.471627224912336),\n",
      "  ([-4, 16.58725277781526], -4.102421563666703),\n",
      "  ([-3, 9.736455155947509], -4.266007007438394),\n",
      "  ([-2, 4.425304693055212], -3.335356971424974),\n",
      "  ([0, -0.7787145949747352], -4.137398337149403),\n",
      "  ([2, 4.677601172951469], -3.8694147280901094),\n",
      "  ([-2, 4.0472733692363505], -2.324686756254675),\n",
      "  ([0, 0.28086976323273305], -2.291401022771303),\n",
      "  ([-3, 8.972477922848892], -2.83765912181145),\n",
      "  ([7, 48.98187433503814], -3.6118054638711334),\n",
      "  ([2, 4.310763325549857], -2.4559398376358286),\n",
      "  ([1, 0.28086976323273305], -3.9648968152654605),\n",
      "  ([2, 4.110850305616096], -1.172104055095037),\n",
      "  ([1, 0.16755712404690115], -4.252716522360122),\n",
      "  ([5, 25.387331681974118], -3.4658166241069837),\n",
      "  ([2, 4.0472733692363505], -0.7990722515794346),\n",
      "  ([-3, 8.18670777927207], -4.420579139405164),\n",
      "  ([-1, 0.16755712404690115], -4.308058651197339),\n",
      "  ([1, 1.894963790893371], -4.395633868683595),\n",
      "  ([-1, 0.23624835582107107], -4.148385321014907),\n",
      "  ([-5, 25.387331681974118], -3.9512933940031583),\n",
      "  ([1, 0.23624835582107107], -4.083143015134065),\n",
      "  ([3, 9.868716868748681], -4.387845917317362),\n",
      "  ([3, 8.280428825043373], -4.039153639534023),\n",
      "  ([-2, 4.672185482570457], -4.0106609565606846),\n",
      "  ([6, 36.69952931345995], -4.31660940810741),\n",
      "  ([-3, 9.19218887851514], -3.0298272041511476),\n",
      "  ([2, 4.540722324315187], -3.4416373256514174),\n",
      "  ([-6, 35.6576125347482], -4.12265529851239),\n",
      "  ([3, 9.736455155947509], -4.081539926194329),\n",
      "  ([2, 3.3344636861272647], -3.8350093066855755),\n",
      "  ([6, 35.6576125347482], -3.630267804618955),\n",
      "  ([-3, 9.678467761771799], -4.143640160167937),\n",
      "  ([3, 9.721259663198992], -4.04342926964238),\n",
      "  ([-7, 48.9191798077499], -4.184386195874735),\n",
      "  ([-2, 3.450554108919178], -3.6935952851468636),\n",
      "  ([4, 16.58725277781526], -3.7951876269692155),\n",
      "  ([3, 8.972477922848892], -1.6244736024013118),\n",
      "  ([2, 4.831004420858918], -4.26348004623894),\n",
      "  ([7, 49.14619942037288], -3.6670792227539266),\n",
      "  ([2, 4.175905805320767], -1.6281193692092681),\n",
      "  ([2, 3.7432469225001768], -2.1508564769996372),\n",
      "  ([-1, 1.6384995169120529], -3.8235887251667418),\n",
      "  ([3, 9.678467761771799], -3.93244995497822),\n",
      "  ([1, 1.6384995169120529], -3.7321344060242927),\n",
      "  ([2, 4.697320344891409], -3.924456712326053),\n",
      "  ([7, 49.618428562286], -4.32075462155344),\n",
      "  ([6, 35.4021544962179], -4.12296319188337),\n",
      "  ([8, 64.10190312285859], -3.9325787734511306),\n",
      "  ([-2, 3.3344636861272647], -3.9944111175659325),\n",
      "  ([-4, 16.329343321882146], -3.606766126621195),\n",
      "  ([1, 0.9397082850227844], -0.31006158957346175),\n",
      "  ([-2, 4.677601172951469], -4.023820786615638),\n",
      "  ([3, 9.888793973338295], -4.430762901012844)]]\n"
     ]
    }
   ],
   "source": [
    "print('Elapsed time: {}\\nBest harmony: {}\\nBest fitness: {}\\nHarmony memories:'.format(results.elapsed_time, results.best_harmony, results.best_fitness))\n",
    "pprint(results.harmony_memories)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Comparison"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "benchmark = {\n",
    "    'method' : ['K-Means++', 'PSO', 'PSO Hybrid','HSA'],\n",
    "    'sse_mean' : [\n",
    "        np.around(np.mean(kmeanspp['sse']), decimals=10),\n",
    "        np.around(np.mean(pso_plain['sse']), decimals=10),\n",
    "        np.around(np.mean(pso_hybrid['sse']), decimals=10),\n",
    "        np.around(np.mean(hsa['sse']), decimals=10),\n",
    "    ],\n",
    "    'sse_stdev' : [\n",
    "        np.around(np.std(kmeanspp['sse']), decimals=10),\n",
    "        np.around(np.std(pso_plain['sse']), decimals=10),\n",
    "        np.around(np.std(pso_hybrid['sse']), decimals=10),\n",
    "        np.around(np.std(hsa['sse']), decimals=10),\n",
    "    ],\n",
    "    'silhouette_mean' : [\n",
    "        np.around(np.mean(kmeanspp['silhouette']), decimals=10),\n",
    "        np.around(np.mean(pso_plain['silhouette']), decimals=10),\n",
    "        np.around(np.mean(pso_hybrid['silhouette']), decimals=10),\n",
    "        np.around(np.mean(hsa['silhouette']), decimals=10),\n",
    "    ],\n",
    "    'silhouette_stdev' : [\n",
    "        np.around(np.std(kmeanspp['silhouette']), decimals=10),\n",
    "        np.around(np.std(pso_plain['silhouette']), decimals=10),\n",
    "        np.around(np.std(pso_hybrid['silhouette']), decimals=10),\n",
    "        np.around(np.std(hsa['silhouette']), decimals=10),\n",
    "    ],\n",
    "    'quantization_mean' : [\n",
    "        np.around(np.mean(kmeanspp['quantization']), decimals=10),\n",
    "        np.around(np.mean(pso_plain['quantization']), decimals=10),\n",
    "        np.around(np.mean(pso_hybrid['quantization']), decimals=10),\n",
    "        np.around(np.mean(hsa['quantization']), decimals=10),\n",
    "    ],\n",
    "    'quantization_stdev' : [\n",
    "        np.around(np.std(kmeanspp['quantization']), decimals=10),\n",
    "        np.around(np.std(pso_plain['quantization']), decimals=10),\n",
    "        np.around(np.std(pso_hybrid['quantization']), decimals=10),\n",
    "        np.around(np.std(hsa['quantization']), decimals=10),\n",
    "    ],\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\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>method</th>\n",
       "      <th>sse_mean</th>\n",
       "      <th>sse_stdev</th>\n",
       "      <th>silhouette_mean</th>\n",
       "      <th>silhouette_stdev</th>\n",
       "      <th>quantization_mean</th>\n",
       "      <th>quantization_stdev</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>K-Means++</td>\n",
       "      <td>22.024363</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.422105</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.301665</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>PSO</td>\n",
       "      <td>27.080251</td>\n",
       "      <td>7.395153</td>\n",
       "      <td>0.395904</td>\n",
       "      <td>0.040273</td>\n",
       "      <td>0.320626</td>\n",
       "      <td>0.011037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>PSO Hybrid</td>\n",
       "      <td>22.070765</td>\n",
       "      <td>0.016733</td>\n",
       "      <td>0.422127</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.301247</td>\n",
       "      <td>0.000147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>HSA</td>\n",
       "      <td>25.060403</td>\n",
       "      <td>5.423699</td>\n",
       "      <td>0.412855</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.321653</td>\n",
       "      <td>0.012078</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       method   sse_mean  sse_stdev  silhouette_mean  silhouette_stdev  \\\n",
       "0   K-Means++  22.024363   0.000000         0.422105          0.000000   \n",
       "1         PSO  27.080251   7.395153         0.395904          0.040273   \n",
       "2  PSO Hybrid  22.070765   0.016733         0.422127          0.000000   \n",
       "3         HSA  25.060403   5.423699         0.412855          0.000000   \n",
       "\n",
       "   quantization_mean  quantization_stdev  \n",
       "0           0.301665            0.000000  \n",
       "1           0.320626            0.011037  \n",
       "2           0.301247            0.000147  \n",
       "3           0.321653            0.012078  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "benchmark "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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>method</th>\n",
       "      <th>sse_mean</th>\n",
       "      <th>sse_stdev</th>\n",
       "      <th>silhouette_mean</th>\n",
       "      <th>silhouette_stdev</th>\n",
       "      <th>quantization_mean</th>\n",
       "      <th>quantization_stdev</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>K-Means++</td>\n",
       "      <td>22.024363</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.422105</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.301665</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>PSO</td>\n",
       "      <td>27.080251</td>\n",
       "      <td>7.395153</td>\n",
       "      <td>0.395904</td>\n",
       "      <td>0.040273</td>\n",
       "      <td>0.320626</td>\n",
       "      <td>0.011037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>PSO Hybrid</td>\n",
       "      <td>22.070765</td>\n",
       "      <td>0.016733</td>\n",
       "      <td>0.422127</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.301247</td>\n",
       "      <td>0.000147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>HSA</td>\n",
       "      <td>25.060403</td>\n",
       "      <td>5.423699</td>\n",
       "      <td>0.412855</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.321653</td>\n",
       "      <td>0.012078</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       method   sse_mean  sse_stdev  silhouette_mean  silhouette_stdev  \\\n",
       "0   K-Means++  22.024363   0.000000         0.422105          0.000000   \n",
       "1         PSO  27.080251   7.395153         0.395904          0.040273   \n",
       "2  PSO Hybrid  22.070765   0.016733         0.422127          0.000000   \n",
       "3         HSA  25.060403   5.423699         0.412855          0.000000   \n",
       "\n",
       "   quantization_mean  quantization_stdev  \n",
       "0           0.301665            0.000000  \n",
       "1           0.320626            0.011037  \n",
       "2           0.301247            0.000147  \n",
       "3           0.321653            0.012078  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "benchmark_df = pd.DataFrame.from_dict(benchmark)\n",
    "benchmark_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "benchmark_df.to_excel('benchmark_res.xlsx', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "benchmark_df.to_csv('benchmark_res.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "sse_mean = [22.02436308, 27.08025074, 22.0707652, 25.06040326]\n",
    "sse_stdev = [0, 7.395153008, 0.016732686, 5.423698526]\n",
    "silhouette_mean = [0.422105257, 0.395904088, 0.422126762, 0.412854659]\n",
    "silhouette_stdev = [0, 0.040273107, 0, 0]\n",
    "quantization_mean = [0.301664619, 0.320625773, 0.301247072, 0.321652545]\n",
    "quantization_stdev = [0, 0.011036628, 0.000147039, 0.012078442]\n",
    "index = ['K-Means++','PSO','PSO Hybrid','HSA']\n",
    "df = pd.DataFrame({'sse_mean' : sse_mean,'sse_stdev' : sse_stdev,'silhouette_mean' : silhouette_mean,\n",
    "                   'silhouette_stdev' : silhouette_stdev,'quantization_mean' : quantization_mean,\n",
    "                   'quantization_stdev' : quantization_stdev}, index=index)\n",
    "ax = df.plot.bar(rot=0)\n",
    "plt.title(\"Benchmark between K-Means++, PSO, PSO Hybrid, HSA\")\n",
    "plt.xlabel(\"Method\")\n",
    "plt.ylabel(\"Standards\")\n",
    "plt.rcParams[\"figure.figsize\"] = [10,10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}