{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Scalable Clustering" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Most of the clustering algorithms that we have seen do not scale to large number of examples or even clusters" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(85345, 2)\n" ] } ], "source": [ "from sklearn.cluster import Birch, MiniBatchKMeans, KMeans\n", "from pylab import *\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "from numpy import loadtxt\n", "import time\n", "from sklearn.metrics import adjusted_mutual_info_score\n", "import warnings\n", "warnings.filterwarnings(\"ignore\")\n", "\n", "%matplotlib inline\n", "\n", "citypath = '/home/bejar/Data/City/' # Adjust the path to your local dataset to run\n", "data = 'LONcrime.csv'\n", "citypos = loadtxt(citypath+data, delimiter=',')\n", "\n", "print(citypos.shape)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's see what happens when we increase the number of clusters" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[]" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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6d4f114dXXklbkbfYIutkkoqJxTFJkiRxzjlpOuWdd0KHDlmnKSyrrZb+tz3/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\nVbj162HOnMYg7KGHYMkS6NEDRo6Ek06CCRNyI/3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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ltimes = []\n", "for nc in range(10, 101, 10):\n", " km = KMeans(n_clusters=nc, n_init=1)\n", " itime = time.perf_counter()\n", " kmlabels = km.fit_predict(citypos)\n", " etime = time.perf_counter()\n", " ltimes.append(etime-itime)\n", " \n", "fig = plt.figure(figsize=(15,5))\n", "ax = fig.add_subplot(111)\n", "plt.plot(range(10, 101, 10), ltimes)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Minibatch K-means is an incremental version of K-means that uses random batches of examples to reduce the amount of memory and time. It is designed for datasets that can not be stored in memory, the size of the batch could improve the results if the clusters are difficult to find, if not small sizes can obtain results good enough in practice." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[]" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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JxdaAAel7xyqrpO8Vhx8Ov/61GyLIokuSpJ+56Sb45ps03F2SVHvff//zHQ/n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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ltimes = []\n", "ltimeskm = []\n", "agree = []\n", "for nc in range(10, 101, 10):\n", " mbkm = MiniBatchKMeans(n_clusters=nc, batch_size=100, n_init=1, max_iter=5000)\n", " itime = time.perf_counter()\n", " mbkmlabels = mbkm.fit_predict(citypos)\n", " etime = time.perf_counter()\n", " ltimes.append(etime-itime)\n", " km = KMeans(n_clusters=nc, n_init=1)\n", " itime = time.perf_counter()\n", " kmlabels = km.fit_predict(citypos)\n", " etime = time.perf_counter()\n", " ltimeskm.append(etime-itime)\n", " agree.append(adjusted_mutual_info_score(mbkmlabels, kmlabels))\n", " \n", "fig = plt.figure(figsize=(15,5))\n", "ax = fig.add_subplot(121)\n", "plt.plot(range(10, 101, 10), ltimeskm, color='b')\n", "plt.plot(range(10, 101, 10), ltimes, color='r')\n", "ax = fig.add_subplot(122)\n", "plt.plot(range(10, 101, 10), agree)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "BIRCH is an algorithm that compresses the data to certain granularity using the Leader algorithm to reduce the data size and uses an indexing datastructure for fast assignment, the adjustment of its parameters is key for good results. Also assumes that the whole dataset does not fits in memory but the compressed data is a good approximation of the data densities." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[]" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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sq2FDOOGENH0xxqzTlI4xY+Bf/4KePav2/hBg0CBYbz37dUlSsfr007SS7mOP\nwb331ryHYZJUk1nokqQMffZZGjF10UVQv37WaX6qrAzefjs1vVVh9OqVpiAeeGDVj7Heeqlf15Qp\nVS+YSZKy8eqr6efA55/Dc8/BccdlnUiSiouFLknK0LXXwlprwVlnZZ2kYgcdBJtvDkOHZp2kNLz8\nclqYoGfP6j+5b9kS+vaFa66B++7LTT5JUn49+CDsvz9ssgm89BLsvnvWiSSp+FjokqSMzJwJN94I\nXbpA48ZZp6lY3brQvj3cdRf8+GPWaWq/Xr1g++1z9/T+/PPT9NNOneC993JzTElS7sWYFqY57jg4\n/PC0GMzPfpZ1KkkqTha6JCkjgwbBrFnQrVvWSVauQwf45ht45JGsk9Ru77wDo0alPix16+bmmCHA\nrbfCBhukfl0WKyWp5pk3Lz30uvRSuOwyGDkS1l4761SSVLwsdElSBhYsgP79U/Fhiy2yTrNyu+wC\nLVqkpvTKn759YcMNU1+0XGrcOPXr+uc/0y9RkqSa45tv4IgjYPBguO02+NvfoI6/oUlStfhtVJIy\nMGoU/Oc/cMklWSdZPWVlMHo0fP111klqp+nT0y84F14IDRrk/vgtWqTVPa+7Lv3bkyRl7+23Ye+9\nYepUGDcOOnbMOpEk1Q4WuiSpwGKE3r2hdeviaTJ76qkp94gRWSepnf7xD6hXD849N3/nOPdcOOkk\n6NwZ3n03f+eRJK3aU0/BXnulKeYTJ8IBB2SdSJJqDwtdklRgzzwDkyalXkzFYqON0tQKpy/m3qxZ\ncP31qT9Lkyb5O08IcMstaXrkySfbr0uSsnLbbXDYYdC8Obz4YlqERJKUOxa6JKnA+vSBXXdNqyoV\nk7Ky9NT5zTezTlK7DByYVuC86KL8n2txv67XXiueabOSVFssWgS/+10aWdupEzz6aH4fcEhSqbLQ\nJUkFNG0aPPxwKjKEkHWayvntb1OhZNiwrJPUHvPnp95ZbdvCllsW5pzNm8PVV6dRZCNHFuacklTq\nZs+GE05IrQv69YMbb4T69bNOJUm1k4UuSSqgfv1g001TYaPYrLVWWiVy6ND0VFrVN2IEfPRR4VdD\n7No1TV884wx4553CnluSSs0nn8D++8Pjj8MDD0D37sX3sEuSiomFLkkqkM8/Tz2uLrwQ1lwz6zRV\n06FDWi3y2WezTlL8YoReveCoo6BZs8Kee3G/ro03TgWvuXMLe35JKhWTJsGee8KXX8Lzz6fR0ZKk\n/LLQJUn5vwBcAAAgAElEQVQFcv31aZrC2WdnnaTq9t0XttnGpvS5MGYM/Otf0LNnNudfd93Ur+v1\n1+3XJUn5cN99aTXFzTaDl16CX/4y60SSVBosdElSAcyeDQMGwFlnFXfj2RBSU/qRI+GHH7JOU9z+\n7//SU/4sl5TffffUr2vAgDSNUpJUfYtH7J5wQhq1+9RTqW2BJKkwLHRJUgHcfjvMmAHdumWdpPra\nt0+rBD7wQNZJitdLL6VffHr2zL5Py9lnw6mnpiLs229nm0WSit28ean/4e9+B5dfnh4irL121qkk\nqbRUq9AVQrgshLAohNAvV4EkqbZZuDA1oT/pJNh666zTVN/228M++zh9sTp6905/jscdl3WSVGi7\n6SbYZBP7dUlSdXz9NRx2GNxxR/oZ+f/+H9RxWIEkFVyVv/WGEFoCXYCpuYsjSbXPfffBe+/Vrj5I\nZWUwdmxqsK/KeecdGDUKevSAunWzTpOsu26ajjptWloNTJJUOW++Ca1awWuvwfjxafEWSVI2qlTo\nCiE0AoYBZwLf5TSRJNUiMabROwcdBHvskXWa3Dn55FSkGT486yTFp29f2HBDOP30rJMs65e/hGuv\nhRtv9O9VkirjySdTkatePZg4EfbbL+tEklTaqjqi63rgoRjjE7kMI0m1zfPPp35MPXpknSS3mjRJ\nS6Q7fbFypk+H225LvdrWWivrND911llw2mnQpQu89VbWaSSp5hs4ME1XbNkSXnwRttsu60SSpEoX\nukIIpwK/An6f+ziSVLv06QM77wxHHpl1ktwrK4MpU+Bf/8o6SfG47rr0xP+cc7JOUrEQ0oiun/0s\n9ZRzZU1JqtjChXDppekBwZlnwujRsN56WaeSJEElC10hhM2Bq4F2Mcb5+YkkSbXDm2/Cgw+m3ly1\nsRntEUdA06YwdGjWSYrDrFlw/fVptFSTJlmnWbF11kn9ut56Cy66KOs0klTzzJoFbdqkhWauuQYG\nDID69bNOJUlarF4l928BbAhMDuG/C6LXBQ4IIZwPrBljjMu/qXv37jRu3HiZbW3btqVt27ZViCxJ\nxaF/f9hoI2jXLusk+bHGGtC2LQwbBlddVXMaq9dUAwemX46Kodn7brul0WdnnQUHHpimM+bT8OHD\nGb5cY7AZM2bk96SSVAUff5ym7r/zTnqYdfTRWSeSJC0vVFCXWvHOITQEtlpu8+3ANODvMcZpy+3f\nHJg0adIkmjdvXs2oklQ8vvgCttoK/vAHuPzyrNPkz8svw557wpgxqUeJKjZ/furb8utfw+DBWadZ\nPTGm6an33QeTJsHPf17Y80+ePJkWLVoAtIgxTi7s2bW6vNdTKXnlFTjmmDR66+GHoVmzrBNJUnHK\n931epSbTxBhnxxhfX/oFzAa+Xr7IJUmlbMCANF2xa9esk+TXHnvATjvZlH5VRoyAjz4qrkUJQoAb\nboAttkj9uubMyTqRJGVn1Cg44ADYcsu0sqJFLkmquXLRNWb1h4RJUgmYMyf1YurcGTbYIOs0+RXC\nklE/M2dmnaZmihF69YKjjiq+X4waNUr9ut55J60UKUmlJsY0Pf/EE9NoriefhE02yTqVJGllql3o\nijEeHGO8OBdhJKk2GDIEvvmmdBp5t2uXVue7994MQ9Tg5QEfeyytTNmzZ9ZJqmbXXeEf/0g9xoYN\nyzqNJBXOjz9Cp07wP/8Df/oT3HknNGiQdSpJ0qrUwnXAJCk7CxemVZjatEk9mUrBllvCQQdlOH3x\n6adh/fXh/fczCrByvXqlPmYHHJB1kqrr1Ak6dICzz4ZpNiqQVAK++goOPRSGD09F/v/939q5grIk\n1UZ+u5akHHroIXj77eLqxZQLZWVpOsdHH2Vw8vvvh7lzYdCgDE6+ci+9BE89lUZz/Xet4iK0uF/X\nVlvBySfbr0tS7fbGG9CqVfr45JO1d/VkSaqtLHRJUg716QP77w977ZV1ksI64QRYay24444MTj52\nbHrMfvvtaUhdDdK7N+ywAxx3XNZJqq9hQ7j7bnj3XbjggqzTSFJ+jBuXilxrrpmazu+zT9aJJEmV\nZaFLknLkxRfh+edLbzQXwDrrwPHHp+mLsZBLlHz0Ebz+OlxyCXz8MTz+eAFPvnJvv51W6erRA+rW\nzTpNbuy6a1pRdNAgV9qUVPvcdBMccUQqdL3wAmyzTdaJJElVYaFLknKkTx/YcUf4zW+yTpKNsrLU\nv2nSpAKe9PHH07y6yy5LSxreemsBT75yffvChhumP5fapGNHOP10OOecVGOUpGK3cCFcfDF07Zpe\nDz8MjRtnnUqSVFUWuiQpB955B+67Lw0sKtVmta1bw6abFnikz9ix0LJlakbfuTM88EDqIJyx6dPT\nTMpu3dKUztrm+uth663hpJNg9uys05SuEMJ5IYT3Qwg/hBAmhBBarmTf20IIi0IIC8s/Ln79a7n9\nTgohTCs/5tQQwpH5vxIpOzNnpunl11wD112XVpmtVy/rVJKk6ijRX8ckKbf694emTdPKdKWqXr3U\nsHf4cJg/vwAnXLgwjeg67LD0efv26eOwYQU4+cpdd1368zjnnKyT5EfDhjByJHzwAZx/ftZpSlMI\n4RSgL3AFsDswFRgTQmi6grdcCGwCbFr+cXPgG+DupY65D3AncAvwK+AB4P4Qwi55ugwpUx9+CPvt\nlxbvHT3a72eSVFtY6JKkavrqK7jttnSD3KBB1mmy1aFD+vN47LECnOzVV+Gbb5YUupo2TY/lb721\nwI3CljVrVhrx1KULNGmSWYy822WXtBLj7benlwquO3BTjHFIjPENoCswB+hc0c4xxpkxxi8Wv4A9\ngfWA25fa7ULg0RhjvxjjmzHGPwGTAX/9V63zwguw557w/ffpv484IutEkqRcsdAlSdV0ww2prnLu\nuVknyd5uu8Evf1mg6YtjxqQu+K1aLdnWuTO89hq88koBAlRs4MBU7OrePbMIBVNWBp06pX/7//53\n1mlKRwihPtACGL94W4wxAuOAvVfzMJ2BcTHGj5batnf5MZY2phLHlIrCLbfAQQelVXEnTkwLbUiS\nag8LXZJUDXPnpmlqnTqlAUVKxY+HHoJvv83zicaOhYMPhvr1l2w79FDYfPPMmtLPnw/9+sFpp8EW\nW2QSoeD+8Q/Ydlv7dRVYU6AuMH257dNJ0xJXKoSwKXAkaYri0jap6jGlYjBvXirMd+kCZ5wB48fD\nRhtlnUqSlGsWuiSpGoYOTVP1SmH0zuo67bRU8Bk5Mo8nmTkzzTVZPG1xsbp107KAw4fDnDl5DFCx\nu+6Cjz6CHj0KfurMrL12+rv+8MP0C2SGs0a1+joC35J6cEklYfp0OOSQNOr2ppvSaOw11sg6lSQp\nH1xTRJKqaNEi6Ns3tYXaYYes09Qcm2yS6k9DhqSn5nnx1FOwYAEcfvhPv9apE1x5JYwaVdDVAWKE\nXr3gqKOgWbOCnbZG2HlnuPHG9Md90EHpr0B59RWwENh4ue0bA5+vxvs7AUNijAuW2/55VY/ZvXt3\nGjduvMy2tm3b0rZt29WII+XXpEnpZ/X8+fDkk7DvvlknkqTSMXz4cIYPH77MthkzZuT1nBa6JKmK\nRo+GN9+EQYOyTlLzlJWlkV3vvgvbbZeHE4wZk+bLVXTwbbdNUxpvvbWgha7HHkvtwf7xj4KdskZp\n3z7VH887D1q2tOdNPsUY54cQJgGtgQcBQgih/PNrV/beEMJBwHZARfN7X6zgGIeWb1+p/v3707x5\n89WJLxXUHXfAmWemBxD33ptmt0uSCqeiB1+TJ0+mRYsWeTunUxclqYr69IG994Z99sk6Sc1z7LGp\nT/ywYXk6wdixP522uLTOndN68e+8k6cAP9WrF+y1FxxwQMFOWeNcdx1sv33q1zVrVtZpar1+wFkh\nhLIQwk7AjcDalK+iGEK4KoQwuIL3nQFMjDFOq+Br1wBHhBAuDiH8PITwZ1LT+xIt36qYLViQppG3\nbw8nnwzPPGORS5JKhYUuSaqCl15KN82l1IupMtZeOxU7hgzJQ8+m99+Ht99eeaGrTRto3Bhuuy3H\nJ6/YSy+l0Uw9e0IIBTlljdSgAdx9d+pTds459uvKpxjj3UAP4C/Aq8BuwOExxi/Ld9kEWGZJhBDC\nusDxwMAVHPNF4DSgCzAFaAMcG2N8PR/XIOXLN9+kaeRXX51et98Oa62VdSpJUqFY6JKkKujbN41c\nOfbYrJPUXB06wHvvpZ7xOfX446np/MEHr3ifBg2gXbv0282C5dsQ5V6vXqlPm/8eYKedUqPnYcOc\n1ptvMcYBMcatY4wNYox7xxhfWeprnWKMBy+3//cxxkYxxhX+zcQYR8UYdyo/5m4xxjH5vAYp1157\nLU2fnjQpzXLv1q20H0BIUimy0CVJlfT++3DPPXDxxaneooodcABsuWUa1ZVTY8ZAq1ZpxNbKdO4M\nn36apjnm0dtvp74vPXr472Gxdu3grLPg/PPhX//KOo2kUnHvvenHQ6NG8Mor0Lp11okkSVmw0CVJ\nldS/PzRpAqefnnWSmq1OnTSq6+67Ye7cHB10wQIYP37l0xYXa94cfvnL1JQ+j/r2hQ03TA34tcQ1\n18COO6YprDNnZp1GUm22aBH86U9wwglpyuILL8A222SdSpKUFQtdklQJ33yT6ibnnZf6UGnlOnSA\n776Dhx/O0QFffhlmzFi9QlcIaVTXgw/CF1/kKMCypk9PsyO7dbP/y/IW9+v65BPo2tV+XZLy4/vv\n4bjj4Mor4W9/gxEjoGHDrFNJkrJkoUuSKuHGG2HhwlTo0qr9/Oew5545nL44diyst15qwLI62rVL\nQ8vytPzjdddB/fqp8bp+6uc/h5tvhjvvhIEVtj+XpKp766202u3TT8NDD8Hvf28/LkmShS5JWm0/\n/gjXXpumLG60UdZpikdZGTz6KHz55ar3XaUxY+CQQ1a/GdYGG8Dxx6dheDkeUjRzJlx/PXTpkqay\nqmJt28LZZ8MFF8DUqVmnkVRbPPJIepACaeXbo4/ONo8kqeaw0CVJq+mOO9JUtYsvzjpJcTnllPSE\n/a67qnmg776DiRNXb9ri0jp3htdfT78J5dDAgTBrFlx0UU4PWyv1759WYzz5ZPt1SaqeGOHvf4ff\n/CYtejJhQho9KknSYha6JGk1LFoEffrAMcd4Q11ZTZum5sDVnr74xBPpL6Kyha5DDknLP+awKf38\n+dCvH5x2GmyxRc4OW2s1aAAjR6ZFMM8+235dkqpm9mw49dQ0RfHyy+H++1e9AK8kqfRY6JKk1fDY\nYzBtGvTokXWS4lRWlpZ6f/31ahxk7NhUZdxqq8q9r04d6NQpDSmbPbsaAZa46y74+GO49NKcHK4k\n7LBDGgU3fHjq2yVJlfHBB7DvvjB6NNxzD/y//5e+vUuStDx/PEjSaujTJ/UC2W+/rJMUp6OPTn2s\nhg6t4gFiTP25Kjuaa7GOHdM8w3vuqWKAZaP06pWuadddq324knLKKalxf7duMGVK1mkkFYsnnoA9\n9khTn198EU44IetEkqSazEKXJK3CpEnw5JNpNJerOVXNmmum6SbDhqXZh5X2zjvpcX5VC11bbw2t\nW+dk+uKjj8Jrr0HPntU+VEnq1w922QVOOgm+/z7rNJJqshjhmmvSt/7dd4eXX4ZmzbJOJUmq6Sx0\nSdIq9O0L22yTFu9T1XXokKb7PfVUFd48dizUrw8HHVT1AJ07w7PPpvXoq6FXr7Sc/f77V+swJWut\nteDuu9PCDl262K9LUsXmzk2zzi+6KL0efRTWXz/rVJKkYmChS5JW4j//Sb+Ud+8O9eplnaa4tWoF\n229fxab0Y8em5iyNGlU9wPHHp/mTt91W5UNMnAhPP51Gczm6r+q23z4NrhsxAm68Mes0kmqaTz6B\nAw9M3yOGDk3tA/wZLElaXRa6JGklrrkG1l03PVVW9YSQmtLfc08le8LPm5catFR12uJia60F7drB\n4MGwYEGVDtG7d2qqfuyx1YuiNHXxvPPSSI1XX806jaSa4oUXoEWLtErrc89B+/ZZJ5IkFRsLXZK0\nAt99B7fckppnV2cgkZZo3z4Vue6/vxJvmjAhNZKvbqEL0vTFzz5Ly2hW0ttvw733pl5tdetWP4rS\nKI1dd01Frxkzsk4jKWu33JJmqO+wQ1qpt0WLrBNJkoqRhS5JWoGbb06DiS64IOsktcc226TeVpWa\nvjh2LDRtmjoRV9fuu6dXFZrS9+0LG22URqUpNxb36/rySzjrLPt1SaVq3jw499zUt+/MM2H8eNh4\n46xTSZKKlYUuSarAvHlp2mKHDrDJJlmnqV3KymDcuDQtZbWMHQuHHgp1cvQjq3NnePjh1A19NX3+\nOdx+O3Trloozyp3ttkt1x5EjYcCArNNIKrTp0+GQQ2DgwPSAacAAWGONrFNJkoqZhS5JqsDw4akQ\nc/HFWSepfU46KS2geOedq7HzV1+l+Su5mLa4WLt2ae7h0KGr/ZbrrkuZu3bNXQwtceKJcP756f+3\nSZOyTiOpUCZNgj32SIvhPvlkGtkpSVJ1WeiSpOXEmHoHHX007LJL1mlqn8aN4bjjUk/4VU5VGz8+\n7XToobkL0KQJtGmThhGtxly5mTPTCIMuXdJblR99+kCzZnDyyfbrkkrBHXfAfvvBppumgte++2ad\nSJJUW1jokqTljB0Lr72Wmo4rPzp0SH/GU6euYsexY+EXv4DNNsttgM6d4Y03UqP7VRg4MPXCv+ii\n3EbQstZcM/Xr+vprOOMM+3VJtdWCBXDJJWlxklNOgWeeyf23eElSabPQJUnL6dMnrfR04IFZJ6m9\nDjssNXZfaVP6GFOh6/DDcx/g4INh661X2ZR+/nzo1y/Ndtxii9zH0LK23RYGDYJRo1LRS1Lt8s03\ncNRRqQfm1VfDbbfZ91CSlHsWuiRpKVOmpEbpPXpACFmnqb3q14fTTkt9uhYsWMFO06bBxx/ntj/X\nYnXqQKdOMGJEGq61AsOHpwiO7iucNm3gwgtTgVFS7fGvf0HLljB5cnqG0a2bP2clSflhoUuSltK3\nL2y5ZWqOrfzq0CGttvX44yvYYezYNJ9t//3zE6BjR5g9Oy33V4EYoVev1Ktt113zE0EV690bdtwx\n6xSScmXUKNh7b2jUCF5+OQ2qlSQpXyx0SVK5jz6Cu+6C7t2hXr2s09R+u++e2m+tcPri2LGpyLX2\n2vkJsOWWqcn9CqYvPvoo/Pvf0LNnfk6vFVtjDbjllqxTSKquRYvgj39MD4+OPhpeeAG22SbrVJKk\n2s5ClySVu/ZaaNgwNcJW/oUAZWVw//0VrLL344/w1FP56c+1tM6d4fnnU2P65fTqBXvtlb8BZVo5\n+/ZIxe3779MKu3/9K1x1VXqQ1LBh1qkkSaXAQpckkQotN90EXbvCOutknaZ0tGuXalqjRi33heee\ngx9+yE9/rqUddxysv37qiLyUiRPh6afTaC57yEhS5bz5ZnpQ8Mwz8PDDcNllfi+VJBWOhS5JAgYO\nhLlz4YILsk5SWjbbDFq3rmD64tixsPHG0KxZfgOsuWZa437w4LTEYrnFPaKOPTa/p5ek2uaRR2DP\nPdN/v/RSWmVRkqRCstAlqeTNn5+WOT/ttFR4UWGVlaXRUx98sNTGsWPTaK5CDAHo3Dl1xX/0UQDe\negvuvTettFi3bv5PL0m1QYxpiuJvfgMHHphGxrqohCQpCxa6JJW8u++Gjz+GSy7JOklpOv741Ldl\n2LDyDdOnw5Qp+e/PtdgvfwktWvy3KX3fvrDRRmlVSEnSqs2eDaecAv/zP/CHP6Tei+uum3UqSVKp\nstAlKXWIPeigZaZulYoYoU+fVFPJ9yw5VaxRIzjhBBg6NP198Pjj6QuHHFK4EGecAaNH88XUzxg8\nGLp1sxm6JK2O99+HffZJUxZHjYK//AXq+BuGJClD/hiSSt3nn8M556S5Y+VTt0rJE0+kwUOXXpp1\nktLWoUOaMvjSS6Rpi7/6VerRVSht20L9+kzuPpT69dOiBJKklXviCWjZEmbNggkToE2brBNJkmSh\nS1L37lCvHvziFzBoUNZpCq5371RTOfjgrJOUtl//OvVHGzI4LunPVUjrrcf8Y05gu6dv5ewukSZN\nCnt6SSomMabelocdBs2bw8svw667Zp1KkqTEQpdUyh55JE1b7N8fzj03rQH++edZpyqYf/4TxoxJ\nTcdd9jxbdeumxQ//ece/Uo+uQvXnWsr963dmh0Vv0XO/Fwp+bkkqFnPnQqdO6TnZRRelW4n11886\nlSRJS1jokkrVrFmpuHXoodCuXZq6Va/eUh3Ba79+/WDzzeHkk7NOIkjTF1t9P4YFazSAffct6Lnn\nzYNLHjqILxptw0YP3VrQc0tSsfj4YzjgABgxIvVV7NMn3TpIklSTWOiSStUVV8AXX8CNN6bhTE2a\npOYagwaVdwSv3T75BO68Mz2Nrl8/6zSCNHv2hHXGMrXJQbDmmgU99113wUef1CF27JyW4Zw5s6Dn\nl6Sa7vnnYY894LPP4Lnn0ihcSZJqIgtdUimaNCk11/jzn2HbbZds79wZpk2DiRMzi1Yo110HDRrA\nWWdlnUT/NWcOe/zwLHd+eRhff12408YIvXrB0UfDxj1PhzlzUrFLkgTAzTenXoo77ACvvAItWmSd\nSJKkFbPQJZWaBQugSxdo1iw12FjawQfDllvW+qb0M2emgWxdusC662adRv/17LPUW/AjYzi8oHWm\nRx+Ff/8bevYEttgi9Qe71emLkjRvXlqY+eyz4cwzYfz4wi6IK0lSVVjokkrNtdfCq6+mx7PLz9mr\nUwc6dkzzuObMySReIdx6K8yeDRdemHUSLWPMGNh8c7Y+YieGDCncaXv1glatYP/9yzd07gwvvphG\nN0pSiZo+HQ45JP3MvPlmGDAA1lgj61SSJK2ahS6plHzwAfzxj3DBBbDnnhXv07FjGvI0alQhkxXM\nggVpkclTT02Dd1SDjB0Lhx1G2emBCRPgrbfyf8qJE+Hpp9Norv+uvHnMMbDBBrV+ZKMkrcikSakf\n11tvwZNPOs1fklRcKlXoCiF0DSFMDSHMKH+9EEI4Il/hJOVQjGn+wfrrw5VXrni/bbZJUxhr6S/5\nd98NH34Il1ySdRIt45NP0vzBww7jt7+Fxo3Til751qsX7Lhjqm3915prpiUghwyB+fPzH0KqohDC\neSGE90MIP4QQJoQQWq5i/zVCCH8NIXwQQpgbQngvhNBxqa+fHkJYFEJYWP5xUQih9g7vVYXuvhv2\n2w823TQVvAq8CK4kSdVW2RFdHwG/A5oDLYAngAdCCDvnOpikHBsxAh57LM09WGedle/buTM89RS8\n+25BohXC1KmpdnH66XDEEfCrX2WdSMt4/PE0pOqQQ2jQAE4+GYYNg0WL8nfKt96C++6DHj2gbt3l\nvti5c1qVdPTo/AWQqiGEcArQF7gC2B2YCowJITRdydtGAr8GOgE7Am2BN5fbZwawyVKvrXKbXDXZ\ns89Cu3Zw/PHwzDOw2WZZJ5IkqfIqVeiKMY6OMT4WY3w3xvhOjPEPwCygVX7iScqJb76Bbt3gxBPh\nt79d9f5t2qQhNbffnvdo+RRjqp8cdlgqbD3zDPTuDSNHZp1MPzFmTJons8EGQCpKfvBBWsI+X/r2\nhY02Suf6iWbNoGVLm9KrJusO3BRjHBJjfAPoCswBOle0c/kI/P2Bo2KMT8YYP4wxTowxvrjcrjHG\n+GWM8Yvy15d5vQrVGB9+CCeckEZwDR4Ma62VdSJJkqqmyj26Qgh1QginAmsDy98kSapJfvc7mDsX\nrrlm9fZv0ADatk2FroUL8xotH+bPT6OBdt89Fbm++gruvBPeeQcuuggaNco6oZaxaNGSimS5ffdN\ns2jz1ZT+88/TL3Lduq3kl7kzzoBHHoFPP81PCKmKQgj1SSPrxy/eFmOMwDhg7xW87bfAK8DvQggf\nhxDeDCH0DiEs/39Ao/KpjR+GEO4PIeySj2tQzTJnThrFtfba6WHQ8mvVSJJUTCpd6Aoh7BpCmAn8\nCAwAji9/kiipJnr6aRg4EP7v/+BnP1v993XqBB9/DOPG5S9bjn3/PfTrB9ttl0bpbLppij9pUqrb\neeNeQ736Knz99TKFrjp10t/hyJHwww+5P+W116Z/D+ecs5KdTj019esq5BKQ0uppCtQFpi+3fTpp\numFFtiWN6PoFcBzQDTgRuH6pfd4kjQg7BmhHuk98IYRQiR8eKjYxwplnwhtvwP33w4YbZp1IkqTq\nqcqIrjeAXwJ7AjcAQ0IIO+U0laTcmDsXzj4b9tkHunSp3HtbtoRf/KIomtJ/+mkatLbllunjr38N\n//wnPPootG691Gp6qpnGjk3D7PZediBKhw6pePngg7k93cyZqVXd2WfDeuutZMfGjdN030GD0m+C\nUnGrAywCTosxvhJjfAy4GDg9hLAmQIxxQoxxWIzxnzHGZ4E2wJfA2ZmlVt716gXDh6dB3PavlCTV\nBvUq+4YY4wLgvfJPXw0h7El6KrjC5+Ldu3encePGy2xr27Ytbdu2rezpJVXGVVfBe+/BqFFpiExl\nhJAacv/+96nH1/rr5ydjNbz2WuqzdMcdabbl2WfDhRfC5ptnnUyVMmZMWulzuSF322+fal9DhsAp\np+TudLfcArNnp2msq9S5c1r+8bnnYP/9cxdCyxg+fDjDhw9fZtuMGTMySlMUvgIWAhsvt31j4PMV\nvOcz4JMY46yltk0DArA58JPVR2KMC0IIrwLbryqQ93rF6ZFH0o/5yy+Hk07KOo0kqTbK4j4vxGo+\npQ4hjAf+E2P8SfPTEEJzYNKkSZNo3rx5tc4jqZJefz09mr3sMvjLX6p2jC++SEsu9e8P55+f23xV\nFGNaELJ37zRia7PNUsHirLPSABwVmZkzUwP6/v3hvPN+8uUbb0z/9D7+GDZZ0YSsSpg3L01tbd16\nNddaiBF22AH226/oF2coNpMnT6ZFixYALWKMk7POU9OEECYAE2OM3co/D8CHwLUxxt4V7H8W0B/Y\nKCOpvTUAACAASURBVMY4p3zbscA9QKMY448VvKcO8G9gdIyxxwpyeK9XpN58E/bcEw48ME1ZrOzz\nMEmSqirf93mV+pEWQvhbCGH/EMJW/5+9O4+3qf7+OP76mAkVDSqpbypTSWhQRIOfBoWkIuO9kQYk\nZM5QhoxpIGSOayhSvpVDpYjKUJqUJs3GDGV27/79sfgmme49+5x9hvfz8bgPD+fu89nrcu69Z6+9\nPmvt79XVD6gKvOh3YCIShowMK2/6z3+gS5esr3PaaTalMQa2L+7bB1On2o7K666zxMfEiVaw1r69\nklxx6913bXrAQf25DnbnnZA9u22r8cPUqfba6dDhOJ9woLJxxgzbRykSO4YAzZ1zjfe3kHgeGxA0\nHsA51885N+Gg46cAm4BxzrlSzrlrgAHAmANJLudcd+dcdefcf5xzlwKTgWLAC1H7qiQqtmyB226z\nm0Uvvqgkl4iIJJbM/lo7DZiA9emaj038+T/P8972OzARCcMLL9hWq5Ejw58PnpJizcI//tif2DLp\nr7+scfgFF1hD+ZNPhjffhJUrrYdTrlyBhCV+CYUsIXv+4XdGFSpkudZJk8I/VUaG9aKpWdPazx23\nJk2s3920aeEHIeITz/OmA+2B3sDHQFmghud5G/YfUgQ4+6DjtwPVgZOApcAkYDbWfuKAk4FRwJfA\nf4H8QCUNHUos6elwzz1WtD17NhQsGHREIiIi/spUjy7P8+6NVCAi4pPff4dHH7UEVbVq4a934422\nZ2zcOLj00vDXO05r18Izz8CIEVZIc9ddMHNmVEOQaJg716q5jjIxoFEjqF0bPvsMLr4466d64w34\n4gt7TWXKWWfZ98GYMbZHViRGeJ43HJuAfbjPNTvMY6uBGkdZ7xGsQb0ksG7d7IbR66/bTSQREZFE\no0JlkUTTpo2VOQ38V4uWrMmRAxo3tr0Nu3b5s+ZRfPWV5RLOOccquZo2he++s4bzSnIlmDVrYPXq\nI25bPOCmm6yNV7hVXQMGwJVXWrutTEtNhQ8/tEyZiEicSkuD/v3t52GNI6Y8RURE4psSXSKJZM4c\n6yU0bJi/UxKbNYPNm+HVV/1b8yCeBwsXWr+QUqXsy+jVC376CYYMsaSXJKB586wB13XXHfWwXLls\n2+rkybblJis++ADee8+KHY9SPHZkNWvCqafGRL86EZGsWLHCcvYNG8IjqtsTEZEEpkSXSKL46y94\n4AHbYnX33f6uXbIkXHWVbV/0UXo6vPQSVKoE11xjlVtjx1qhT6dO1o9LElgoBFdcASeddMxDGzWC\n336Dt7PYEXLgQLjwQkumZkmuXBbExIk2ulFEJI6sX29bwMuUgVGjspjwFxERiRNKdIkkiu7dYeNG\nGD48Mu9gU1Ksn9LPP4e91I4dFmaJElCvnvXLnzPHejA1awa5c/sQr8S2fftg/vxjbls84LLL7PUy\ncWLmT7V6NcyaZdM5s2fP/PP/JyXFvsfmzAljERGR6NqzB+64w/6cNQvy5g06IhERkchSokskESxd\nag2teve2CXaRcOed9u44K5mG/TZsgB49oFgxaNUKKlSAjz6CBQvglls03jypLFtm8+2PM9HlnLWK\nmznTihczY9AgOP10K8gKS5kyVoE2ZkyYC4mIRE+bNrZ9e+ZMKFo06GhEREQiT5eVIvFu717r3l62\nLDz8cOTOU6CAJbvGjoWMjEw99Ztv4P77LcE1aBA0aGCPTZtmlTqShEIhOPHETL0AGja0asCZM4//\nNGvXwoQJdqGXJ08W4jxUaqqNK/v1Vx8WExGJrOeft48RI6wDgYiISDJQoksk3j31lO35Gz3aJiRG\nUkoKfP+9dY4/DkuWQN26tuVs5kzo2tUazD/9NJx3XmRDlRgXCsENN2TqNVusGFSrlrmiwqeftvZa\nLVtmPsTDuusuy5hNmODTgiIikbFwoVVPP/SQ5ehFRESShRJdIvHshx9sL2CbNlCxYuTPV7kynH/+\nUSfPZWTA7Nl26FVXweefw8iR8OOP0K0bFC4c+TAlxm3davtojnPb4sEaN7aG9MfTKu7PP60X3H33\nHVe/++NTsKA1lstCZaOISLT89JPdaKpc2aYXi4iIJBMlukTilefZfsBTT7XeXNHgnHWLnzEDtm37\nx6d27bJJTqVK2WQngFdegVWrbGelL9vGJDG8/baN3KxePdNPrVvXXkuTJx/72NGjbauj7zt6U1Js\nROh77/m8sIhI+HbssN/D+fLZr+ucOYOOSEREJLqU6BKJV2lpNgVx+HDInz96523cGHbvhunTAdi0\nCR5/HM45x7aHXXQRLF4MixZBrVpqMC+HEQrBBRdkaXBCwYJQpw5MmmS53iPZs8eqGO65JwLNl6tU\nsfiPUtkoIhIEz7Nc/NdfW3X1KacEHZGIiEj06RJUJB5t2mRlKnfeaeMKo6loUahRg10jxtKqlfVN\n6tvXKm2+/hpefhkqVYpuSBJnQiGoUSPLT2/UCL78ElasOPIxaWnWL759+yyf5sicsyvJl16ybZgi\nIjHiySdt0Mv48XDJJUFHIyIiEgwlukTiUYcOVrIybFjUT710KQzZkkKeFUtYNmkVHTpYL5Dhw63I\nReSovv3WBhpkoT/XATfcAEWKHLkpfUYGDBwINWtCmTJZPs3RHahsnDo1QicQEcmc//4XunSxfpj1\n6gUdjYiISHCU6BKJN++8A+PG2ZV8kSJROWVGhr2BrlYNLr8cXlh3KztPKMx7zcbRs6e1CRM5LqGQ\nTVqsVi3LS+TIYVsS09Jg795/f/6NN+CLL+DRR7Me5jGdeSbcfDOMGRPBk4iIHJ+vvoIGDeDWW6FX\nr6CjERERCZYSXSLxZNcuGyFXuXJUZoXv3m1tiC6+2Kpjdu2y3Vqfrc5N3nsbkjNt4uEzDSJHEgrZ\nOM4CBcJapnFj2LDB2tQdasAAuPJK+zaJqNRUK3H87LMIn0hE5Mi2bLGemEWLWv9C9cYUEZFkp1+F\nIvGkTx9Ys8bGG0bwnezmzdC/v/UKT02F88+HhQthyRLrxZU9OzZ9cd06K58ROR5799rExTD6cx1Q\ntqx9HLp98YMPbBjio49aK62IuuUWOO00NaUXkcCkp1sl1/r11ny+YMGgIxIREQmeEl0i8eLzzy37\n1KULlCoVkVP8+CO0bWsN5nv2tCquVavszXPlyockDi65BMqX10W+HL8PPoA//wyrP9fBGjeGV1+1\nxOwBAwbAhRdadUPE5cxpQUyaZOWPIiJR1rWrVbZOm2Y3pURERESJLpH4kJEBLVpA8eLQubPvy//2\nm/U8Kl4cJkyANm0s6TVqFJQseZQnpqRY865163yPSRJQKASFC8Oll/qyXIMGViQ2Y4b9/euv4ZVX\nbFZD1LbupKTYFNTXXovSCUVETFqaTVkcONC3+wciIiIJQYkukXgwcqTtGxw1CnLn9nXpffvgjjvg\nrbdg6FCboPjEE3D66cfx5Pr1bR/jiy/6GpMkqFDIRiZmz+7LcmecAdWrW0EVwODB9rpt2NCX5Y9P\nqVJQqZKa0otIVC1fbnn2Ro2sEltERET+pkSXSKz77Tfo1AmaN4drrvF9+d694aOPYOZMaNUK8ufP\nxJMLFYI6dWz7ouf5HpskkD/+sMbtPvTnOljjxrBoESxe/Hc1Yp48vp7i2FJTbe/Qzz9H+cQikozW\nrYPatW1QzMiRUehHKCIiEmeU6BKJda1bQ968tj/BZ++9Z/3te/SwQXhZkpICX35p2TKRI5k/35Kh\n1av7umzt2pacvf12K3Zs2dLX5Y/PnXdCvnyWaRMRiaA9e6wKe98+mDXL3h6IiIjIPynRJRLLZs+G\nl1+GYcPg5JN9XfqPP6wvV+XK1t8+y667zrrXqym9HE0oBKVLQ9Givi6bLx/Uq2cVDvfdByed5Ovy\nx6dAAUt2jR1r/fRERCKkdeu/q7DPOivoaERERGKTEl0isWrbNnjwQbj5ZruI9pHnwb33wvbt1l4r\nrJZJ2bNDkybWFXfHDt9ilATieZboilC35JYt4ZxzbNtiYFJT4YcfYMGCAIMQkUT2/PO2VXHECGsN\nKCIiIoenRJdIrOrWDTZvhuHDfW/AMWqUbXkYMwbOPtuHBZs2hT//tOozkUN9/bX1r/K5P9cBl18O\na9b4XiyWOVddBSVKqLJRRCLi3Xetj2arVtYxQERERI5MiS6RWPTRR/Dsszb+8JxzfF36iy/g4Yet\nCqZOHZ8WPe88uPZaXeTL4c2dC7lyRWSYQsxwzq4+X34ZtmwJOhoRSSA//mh9uapUsemyIiIicnRK\ndInEmr17bcLipZfarVsf7doF9etbXsr3N8spKbZt6/vvfV5Y4l4oZFdo+fIFHUlkNW5s379paUFH\nIiIJYvv2v4duTJ8OOXMGHZGIiEjsU6JLJNYMGQKffw6jR0OOHL4u3aEDrF4NU6dGIOdw++1QsCCM\nH+/zwhLXdu+2BGiE+nPFlCJF4JZbbE+wiEiYPM/uIX3zDbz6KpxyStARiYiIxAclukRiyXffQc+e\n0LYtlC/v69KvvWa7IQcNgosv9nVpky+flYuNHw/p6RE4gcSlxYttSEGE+nPFnNRUWL4cVq4MOhIR\niXP9+1sV14QJEfq9LSIikqCU6BKJFZ5njbNOPx169fJ16d9+g2bN4NZbbZBjxKSkWNPxt96K4Ekk\nrsyda6/pZLlKu+km+3rVr05EwjBnDnTtCo89BnXrBh2NiIhIfFGiSyRWTJ4M8+fb3PATTvBt2fR0\naNQIcue2a2+fBzj+02WXQenSusiXv4VCUL06ZEuSXzc5c0KTJvDii7ZtU0Qkk1atggYN4LbboEeP\noKMRERGJP0ly5SES4zZutO2Kd99tFSE+GjgQ3nkHJk2KQn+PA5PnZs2CP/6I8Mkk5q1fDx9/nBz9\nuQ7WrJm9/l95JehIRCTObNkCtWpBsWL2eztZ7hGIiIj4Sb8+RWJB+/awbx889ZSvy374IXTvDp06\nwXXX+br0kTVsCBkZMGVKlE4oMWv+fPuzevVg44i2kiXh6qtV2SgimZKebq0uN26E2bOhQIGgIxIR\nEYlPSnSJBO2tt6zT7KBB1tvHJ9u22RvmChV8b/l1dKefDjVrwrhxUTypxKS5c+GSS2waYbJJTYV5\n8+DHH4OORETiRJcuttt72jQoXjzoaEREROKXEl0iQdq5E+67D6pWtS1/PnrgAbsrPGWKtQ2KqpQU\nWLECPvkkyieWmOF5dsWWbNsWD6hXz3rtTZgQdCQiEgemTIEBA+yeV7IVwYqIiPhNiS6RID3xhE0p\nHDnS1y7xkyZZb/sRI+C883xb9vjddJNV8aiqK3l9/jmsXZu8ia78+eGuu+x7ICMj6GhEJIYtX25F\noI0bw8MPBx2NiIhI/FOiSyQon31mt2+7doUSJXxb9ttvrZqrcWO45x7fls2cHDksAE2eS16hEOTN\nC5UrBx1JcFJTYc0aePvtoCMRkRi1bh3Urg0XX+z7PS8REZGkpUSXSBDS06F5c7jgAujY0bdl9+yx\nvlxFisCzz/q2bNYcmDz36qsBByKBCIVsS26ePEFHEpwrr7TG9GpKLyKHsWcP1K1rs2hmzUruH5ci\nIiJ+UqJLJAjPP28jEUeNgty5fVu2e3drizVlSgxMaypZEipV0kV+Mtq5E957L3m3LR7gnFV1zZwJ\nmzcHHY2IxBDPg4cegqVL7UfEWWcFHZGIiEjiUKJLJNp+/RU6d7Ym9D5u65o3z3ZC9u0Ll13m27Lh\nSUmxyXu//BJ0JBJNCxfCrl1KdAE0amQVnFOmBB2JJCDn3IPOuR+cczudcx8454760985l8s518c5\nt8Y5t8s5971zrukhx9Rzzq3av+ZK59xNEf0iktSIETB6tN33qlQp6GhEREQSixJdItHWqpVNY+vf\n37clN2ywlljVq0O7dr4tG74777Q+TRMnBh2JRFMoZOUJpUsHHUnwTj8dataEMWOCjkQSjHPuLmAw\n0AO4FFgJzHXOnXKUp80ArgWaARcC9YGvD1rzKmAKMBooB8wGXnHO6ZvZRwsWQJs29tGsWdDRiIiI\nJB4lukSiadYs+3jmGTjpJF+W9Dx7o7xvH0yYANli6bu6YEGoV8+2L3pe0NFItIRCVs2lrsomNRU+\n/tg+RPzTFhjped5Ez/O+AloCO4CUwx3snLsRqALc7HneO57n/eR53oee5y056LDWwBue5w3xPO9r\nz/MeA1YAD0X2S0kea9bYr8VrroFBg4KORkREJDHF0iWxSGLbts0actSsad1nffLMM/Df/1qS64wz\nfFvWPykp8N13tp1NEt9vv9lEUW1b/NuNN9o3p/rViU+cczmBCsBbBx7zPM8D5gNH2gh3K7AM6Oic\n+8U597VzbqBz7uAW6JX2r3GwuUdZUzJh+3absFigAEyfbgOKRURExH9KdIlES5cusHUrPPecb5Uu\nK1dChw62/eHmm31Z0n9VqsD55+siP1nMm2ev7xtuCDqS2JEjBzRpApMnW+8ykfCdAmQH1h3y+Dqg\nyBGecx5W0VUGqA20Ae4AnjvomCKZXFOO04Hq62+/hdmzoXDhoCMSERFJXEp0iUTDkiUwfDj06QPF\nivmy5PbtcPfdUKoUPPmkL0tGhnPQtCnMmGFVbZLYQiGoUAFOOVqboCSUkmKTF2fNCjoSSV7ZgAyg\nged5yzzPexN4BGjinPNv/K8cVr9+9mtw4kS4+OKgoxEREUlsKpoWibS9e6FFC7v4f8i/Nidt28KP\nP8Ly5ZA71i9RmjSB7t1tr8a99wYdjURKRoZVdDVvHnQkseeCC6y6cexYqF8/6Ggk/m0E0oHTD3n8\ndGDtEZ7zO/Cr53l/HfTYKsABRYHv9j83M2v+T9u2bTnxxBP/8Vj9+vWpr9c7r70G3bpBjx5w++1B\nRyMiIhJdaWlppKWl/eOxrVu3RvScSnT5Zfly+OoruOeeoCORWDNoEKxaBcuWQfbsviz50ks2lnz0\naKvoinlFi0KNGnaRr0RX4vrkExsBqv5ch5eaatWNa9bAuecGHIzEM8/z9jrnlgPXA68COOfc/r8/\nfYSnvQ/c4ZzL53nejv2PlcCqvH7Z//clh1mj+v7Hj2ro0KGUL18+s19Kwlu1yt4a1qoFjz0WdDQi\nIiLRd7gbXytWrKBChQoRO6e2Lvph1y4bodOokTVNEjngm2+gVy945BEoV86XJX/6yQpm7rjDrpvj\nRkqKbeFctSroSCRSQiHInx8qqW/1Yd1xh3WhHj8+6EgkMQwBmjvnGjvnSgLPA/mA8QDOuX7OuQkH\nHT8F2ASMc86Vcs5dAwwAxniet3v/McOAG51zjzjnSjjnemJN75+NyleUYDZvtgRXsWK2ZTGmpiKL\niIgkMP3K9cNTT8HPP8PZZ0O7dtZxVMTzoGVLOPNM26/gg3377M5wwYIwapRvPe2j47bboFAhXeQn\nslAIrr0WcuUKOpLYdMIJ1lhv3DhITw86GolznudNB9oDvYGPgbJADc/zNuw/pAhw9kHHb8eqs04C\nlgKTgNlYU/oDxywBGgAtgE+A24Fanud9GemvJ9Gkp9su5Y0brfl8gQJBRyQiIpI8lOgK1++/W4Px\nBx+EZ5+Ft96C118POiqJBRMnwttvw4gRdoHrgz59YPFiG9528sm+LBk9uXNDw4YwYYL1LZPEsn07\nLFqkbYvHkppqZZlvvRV0JJIAPM8b7nneuZ7n5fU8r5LnecsO+lwzz/OuO+T41Z7n1fA8L7/need4\nnvfoQdVcB4552fO8kvvXLOt53txofT2JpHNna1k4fToULx50NCIiIslFia5wde1qF/A9ekDNmnDd\nddC+vS7kk92GDbZd8Z57rDeVDxYtgt69rcdH5cq+LBl9KSmwbh28+WbQkYjfFiywn3tKdB3d5ZdD\n6dLWr05EEtLkyTBwIAweDDfcEHQ0IiIiyUeJrnAsX27bsHr3tvIa5+xdzddf274ySV7t2tmfQ4b4\nstzmzdCgAVx1leVW49Yll8Cll+oiPxGFQnDOOTZdUI7MOavqmjULNm0KOhoR8dmyZTZzpUkTaNPm\n2MeLiIiI/5ToyirPg4cftjvzLVr8/Xi5ctCsmVV4bdkSXHwSnHnzYNIkm7Z42mlhL+d59hL780+7\nS5wj3melpqTAnDlW2SWJIxSy6sW4ahwXkIYNISPDvqFFJGGsXQu1a0PZsvD88/pxKCIiEhQlurJq\nxgzbSzZ06L8zD088YZMY+/QJJjYJzo4d1oC+WjVo2tSXJceMgZdegtGjbXJT3GvQwEZPvfhi0JGI\nX376Cb76StsWj9dpp9lwhjFjNLxEJEHs3g1161oOe9YsyJMn6IhERESSlxJdWbFzJzz6KNx6K1Sv\n/u/Pn3EGdOoETz8N330X/fgkOL17w6+/wsiRvtzKXbUKWreG5s3hjjt8iC8WFCoEderY9kVd5CeG\nUMiSl9ddd+xjxaSmwqefwooVQUciImHyPJtJtGyZJbnOPDPoiERERJJbphJdzrnOzrmPnHPbnHPr\nnHOznHMXRiq4mDVkCPz2m/XjOpJHHrG79h07Ri8uCdbKlbZdsXt3uDD8b4tdu2w0+TnnWOFgQklJ\ngS+/hKVLg45E/BAKWZP1uBsFGqD/+z+7Gla/OpG4N3y4FWiOGgVXXBF0NCIiIpLZiq4qwDPAFcAN\nQE4g5JzL63dgMeu336BfP2jV6uhNl/Pls+NefhkWLoxefBKM9HRrpFWyJHTo4MuSnTpZRdfUqXDC\nCb4sGTuuvx7OPlsX+YkgPR3mz/dtumjSyJHDtjdPnmxVwiISlxYssKbzDz9sDehFREQkeJlKdHme\nd7PneZM8z1vled5nQFOgGFAhEsHFpM6dIW9eq9o5lgYNoGJFq+7KyIh8bBKc4cPho4/sdm6uXGEv\n99//wrBhNp78kkt8iC/WZM9uF/lpadbXTOLX8uU2FlT9uTKvWTPYuhVmzgw6EhHJgjVrrK1AtWr2\n+1pERERiQ7g9uk4CPOAPH2KJfUuXwsSJ1mz+pJOOfXy2bLbNcdkymDIl8vFJMH7+Gbp0gfvvh6uu\nCnu533+3HNAtt1jhYMJq2hS2bdNFfrybOxcKFrSti5I5558PVauqslEkDm3fDrVqwYknwrRpCTAR\nWUREJIFkOdHlnHPAU8Aiz/O+9C+kGOV5Vpdetizce+/xP69KFRvD07mzKlcS0YEOtAUK2FbVMGVk\nQOPG9oZ53LgEH01+3nl2G1wX+fEtFLKtqLrKy5rUVHj7bfj++6AjEZHj5Hl2r+a772D2bChcOOiI\nRERE5GDhVHQNB0oDd/sUS2ybOhUWL7au4NmzZ+65Tz4J69dbdZcklpkz4bXX4Nln7bZumAYPhrfe\ngkmT4NRTfYgv1qWkwDvv6CI/Xm3bBkuWqD9XOOrWtYq48eODjkREjlPfvvDSS/a7+qKLgo5GRERE\nDpWlW/DOuWeBm4Eqnuf9fqzj27Zty4mHJAHq169P/fr1s3L66Nuxw6Yn1q4N112X+ecXLw6tW0P/\n/nb3/owz/I9Rom/rVttbWKsW1KkT9nJLl9oOyA4d4IYbfIgvHtStaxVx48dD795BRyOZ9c471oxe\n/bmyLl8+G686bhz06JH5GykJIi0tjbS0tH88tnXr1oCiETmyV1+Fbt2gZ09ffvWLiIhIBDjP8zL3\nBEty1QKqep531DIM51x5YPny5cspX7581qMMWu/e1pdr1SpLWmXFli3Wj6V2bXjhBX/jk2A88IDd\nzl21CooWDWupP/+ESy+Fk0+G99/3pZ99/LjvPnjjDfjhh6S9yI9bDzwA8+bBN98EHUl8W7rUepy9\n8QbceGPQ0cSMFStWUKFCBYAKnuetCDoeObyEea93HL78Eq680m5GvfSStWIVERGRzIv0+7xM/Yp2\nzg0H7gEaANudc6fv/8jjd2Ax45dfbOvhww9nPckF1ry+Vy/rR/TJJ/7FJ8F4/30YMcL2L4SZ5AJ4\n6CFYt86GECZVkgts++LPP1ufIokvoZCqufxQsaLtf1K/OpGYtXmzFXCfcw5MmKAkl4iISCzL7K/p\nlkBBYAHw20Efd/obVgzp1Any57c69XC1aAElSkC7dtbJVOLTnj32f3n55VbREqbJk22Y5/DhVvSX\ndC6/HEqX1kV+vPnuO/tQf67wOWfb2l95BTZuDDoaETmM7t3t23P2bJs/IyIiIrErU4kuz/OyeZ6X\n/TAfEyMVYKA++MCyEH36WLPgcOXMCYMGWeXKnDnhryfBGDAAVq+G0aPD3mr3/fdw//1wzz3QqJFP\n8cUb56yqa9Ys+OOPoKOR4zVvnk1arFYt6EgSQ8OG9ueLLwYbh4j8y++/W9eJ9u1tYLCIiIjENhVe\nH0lGhm1XLFcOmjXzb92bb7bmDu3bw969/q0r0bF6tfVra98eypYNa6m9e60H9amnWjVXUmvYEPbt\ns72bEh/mzoVKlfy5CSBwyim2L2rsWFX8isSYwYMhTx5rMyAiIiKxT4muI5kyBT78EJ56yt8G2c7Z\nO6ZvvoHnn/dvXYk8z7PG6UWLwmOPhb1cjx6wYoXldpI+V3D66VCzprYvxou9e60yVf25/JWaCp99\nBsuWBR2JiOy3caO15GzVCg4ZIC4iIiIxSomuw9m+3Xpz1a0LVav6v37ZsnZB07OndTeV+DB+PCxY\nYAnKvHnDWurtt6F/f3j8cWtRJdj2xRUrYOXKoCORY/noI9i2Tf25/Fa9uiXSlfAViRnDhtmfbdoE\nG4eIiIgcPyW6DmfAANiwAQYOjNw5Hn/cmpo/8UTkziH+Wb/ehgg0amRbT8OwcaPt1Lv2Wnj0UZ/i\nSwQ33WSVXePGBR2JHEsoBIUKQfnyQUeSWLJnh6ZNraJ4x46goxFJelu2wNNPWy/NU04JOhoRERE5\nXkp0HeqnnyzR9cgj8J//RO48RYpA587wzDPw7beRO4/4o21bmyU+eHBYy3ieFS7t2QOTJmk8+T/k\nzAmNG1sz7t27g45GjmbuXEv4+rmtW0yzZlYt9/LLQUcikvSee85+HbVrF3QkIiIikhm6zD5UOOee\nHwAAIABJREFUx45w0knQpUvkz9W2rSW8OnaM/Lkk69580yoshgyxzvFheO45eO01K1o680yf4ksk\nzZrBpk32jySx6Y8/YOlS9eeKlPPOs3JPbV8UCdT27TB0KNx7L5xxRtDRiIiISGYo0XWwxYth6lTo\n2xcKFIj8+fLmtUZNM2fCu+9G/nySedu3256F66+3bYth+PRTG9b40ENw660+xZdoSpWySX66yI9d\nb79tU2mV6Iqc1FTrB/jdd0FHIpK0Ro6ErVvVYkBERCQeKdF1QEaGdRotXx6aNIneee++27qRP/KI\nxSCxpVcvWLvWGtA7l+VlduyA+vXhwgsj2/otITRrZlvjfvkl6EjkcEIhS0iefXbQkSSu22+38W5K\n+IoEYtcu+13duDEUKxZ0NCIiIpJZSnQdMGmSjXR/6qnoNk7Kls22xK1YYb2JJHYsXmz/N489Buef\nH9ZS7drBDz9YwWCePD7Fl6juugty54aJE4OORA7leZaEVDVXZOXNCw0a2KTX9PSgoxFJOmPH2gya\nTp2CjkRERESyQokugL/+ssbwd94JVapE//xXXw316llfsO3bo39++bf16+31UKmS7TcMw8yZVhA2\ndCiULu1TfImsYEH7fhg71hIrEjtWr7aBHUp0RV5qKvz2myUWRSRq9u6FJ5+0ey4XXBB0NCIiIpIV\nSnSB9cn64w+bthhkDBs2hD3VT3yQng733GPvdqdNs2mAWfTzz9bI9vbboUULH2NMdCkp1p9o4cKg\nI5GDhUKQKxdUrRp0JImvfHkoW1bbF0Wi7MUXLZ8fjZlEIiIiEhlKdK1ZA4MGWdXOOecEF8d551mP\nsCeftLv4EpzHH7eG21OmhDUaMT0dGjaEE06A0aPDavGVfK65BooXt/GUEjtCIahc2V7UElnOWVXX\nq6/aTRARibj0dOjXD+rUgYsuCjoaERERySoluh59FAoVio1GDF27Qr580K1b0JEkr7lzoXdva0J/\n/fVhLdW3LyxaBJMn20tMMsE5a0o/fTr8+WfQ0QjAnj3wzjvathhN99xj3wuTJgUdiUhSmDEDvvnG\n3o6JiIhI/EruRNfChfaupn9/yJ8/6Ghsylbv3taA+OOPg44m+fz8s11Y3nhj2HsWFi+2XFnXrlac\nJFnQpAns3GnJLgne4sXWQ1CJrugpXBhq14YxY9SvTiTCMjKgTx97C1C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VbRCcc1gTrtad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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ltimes = []\n", "ltimeskm = []\n", "agree = []\n", "for nc in range(10, 101, 10):\n", " birch = Birch(threshold=0.02, n_clusters=nc, branching_factor=100)\n", " itime = time.perf_counter()\n", " birchlabels = mbkm.fit_predict(citypos)\n", " etime = time.perf_counter()\n", " ltimes.append(etime-itime)\n", " km = KMeans(n_clusters=nc, n_init=1)\n", " itime = time.perf_counter()\n", " kmlabels = km.fit_predict(citypos)\n", " etime = time.perf_counter()\n", " ltimeskm.append(etime-itime)\n", " agree.append(adjusted_mutual_info_score(birchlabels, kmlabels))\n", " \n", "fig = plt.figure(figsize=(15,5))\n", "ax = fig.add_subplot(121)\n", "plt.plot(range(10, 101, 10), ltimeskm, color='b')\n", "plt.plot(range(10, 101, 10), ltimes, color='r')\n", "ax = fig.add_subplot(122)\n", "plt.plot(range(10, 101, 10), agree)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The implementation of scikit-learn of BIRCH does not always do a good job because the final number of clusters is obtained using agglomerative hierarchical clustering" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now let's see what happens if we increase the size of the dataset. For this we are going to generate randomly spherical blobs of with increasing numbers of examples.\n", "\n", "The three algorithms are computed for the data and the time used and the agreement with the original labels is collected. The size of the batches of minibatch K-means and Birch branching factor are set to a 5% of the size of the dataset, this is just a random guess and these parameter have to be carefully set.\n", "\n", "If you run this cell again it takes some time to complete. You have also this code in the /Code folder so yu can run it outside the notebooks and explore different options." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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7WaD9r8aYPcaY5caY1mn2FzbG9DXG/BE45yJjTOPcXlfy10MPuY6kzz7rO4nI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\nYpAsmW0CWrnSbEOdPdsUzBFCxB29e5vt6Lt3Q8JnV13Ed42PZ2FP5h6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c2Jr5g4Ojn7AL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\nPCWg7Dy6k0nRk5iwegIzNs0gJi6GesXr8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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from amltlearn.datasets import make_blobs\n", "\n", "\n", "ltimes = []\n", "agree = []\n", "step = 10000\n", "limit = 100001\n", "ncenters = 50\n", "for ns in range(step, limit, step):\n", " blobs, labels = make_blobs(n_samples=ns, n_features=20, centers=ncenters, cluster_std=1, center_box=(-3,3))\n", " # KMeans\n", " km = KMeans(n_clusters=ncenters, n_init=1)\n", " itime = time.perf_counter()\n", " kmlabels = km.fit_predict(blobs)\n", " etime = time.perf_counter()\n", " dtime1 = etime-itime\n", " agg1 = adjusted_mutual_info_score(labels, kmlabels)\n", "\n", " # Minibatch Kmeans\n", " itime = time.perf_counter()\n", " mbkm = MiniBatchKMeans(n_clusters=ncenters, batch_size=int(ns/20), n_init=1)\n", " mbkmlabels = mbkm.fit_predict(blobs)\n", " etime = time.perf_counter()\n", " dtime2 = etime-itime\n", " agg2 = adjusted_mutual_info_score(labels, mbkmlabels)\n", "\n", " # Birch\n", " itime = time.perf_counter()\n", " birch = Birch(threshold=5, n_clusters=ncenters, branching_factor=int(ns/20))\n", " birchlabels = birch.fit_predict(blobs)\n", " etime = time.perf_counter()\n", " dtime3 = etime-itime\n", " agg3 = adjusted_mutual_info_score(labels, birchlabels)\n", "\n", " ltimes.append((dtime1, dtime2, dtime3))\n", " agree.append((agg1, agg2, agg3))\n", "\n", "fig = plt.figure(figsize=(15,5))\n", "ax = fig.add_subplot(121)\n", "plt.plot(range(step, limit, step), [x for x,_,_ in ltimes], color='r')\n", "plt.plot(range(step, limit, step), [x for _, x,_ in ltimes], color='g')\n", "plt.plot(range(step, limit, step), [x for _, _, x in ltimes], color='b')\n", "\n", "ax = fig.add_subplot(122)\n", "plt.plot(range(step, limit, step), [x for x,_,_ in agree], color='r')\n", "plt.plot(range(step, limit, step), [x for _, x,_ in agree], color='g')\n", "plt.plot(range(step, limit, step), [x for _, _, x in agree], color='b')\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As you can see K-means time grows faster and Birch and K-means scale in different ways, be aware that changing their parameters also change the time cost. Looking to the agreement to the original partitions you can see that Birch does in general a better job." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You can play with the size of the datasets and specially with the number of clusters, you will see that with a small number of clusters if data fits in memory in general K-means is really fast. " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "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": "3.4.3" }, "name": "" }, "nbformat": 4, "nbformat_minor": 0 }