starting_kit.ipynb

{
 "cells": [
  {
   "attachments": {
    "image-2.png": {
     "image/png": 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Oza0Zt+MBnX7pRE/wNXIv7c8lKKR99r0ACerSqhQFWc+/hfCiIljyfCQ+RscivKIUqd+MQEXra5H+0VcI3bYN4WUlKlfmbXbn6BiEVZUjYdIsAYDlyqV2IKgCYblyseAIk4f5I2xLte85XRPPlQRBE0SzX7zacWF2EhavDlP1gHdNqg+CD0xVwfGBqUKhnpocb+qBKc1NRtDC9VBunaCK3NQ/XjcG/3p0OmLi4sBxEMS0cZnZJyfFCx/Hzm/NF6L8Bf2m4dXvl+LH30JRlJ2E2DjjnCxBwpqQiBMnTjhiCNIzswRgeQNWG0JCsXXbtjptk0vj7wQnr9oODkFpWVmdth2/UF/I0qym+qHRpawEufc8LXRn1KOVKxcJsMi7/RFYCvIFd+ULy2r4pkpkP/IKttmANrfffxC78k8BXmKteAmEYVurkfXs22IeKvdH8LP/XIkq5TQU/+NmRKakICJVdSMzs059dY0EQR+AIEXUysIU9B+xQhWFqQvUOED7/d2ToPSZCOoGyQ2auYmbilLx9eTVUDr71/ZBcfvGcejw5jwhFifEW021rY2HY1vwWwiUbgGqkee2ICg3jYPSyQ/fTl2DivwUw+I8C9mnpqU7Yof4fuL4CcTGWU1nDNf0gPZcptZRRWWVKI6l6SO1ORrZkxNkO5426gjJkRppW5xrjUd4fh7ybu2HaqWVDTQoPl4mOMPMp99VuSYv1yqtthSvC26+S+jryKmR66xodRFil/+BsMpyr/R0kUlJQq9ZdM2tDqKwPQjy+FJUKacjMWg6wipKjdPLSzo43h8Jgj4gKDkjAsddn/xcF5zsAZDHNJZ08QdF4spCVQfkeEPcfacITbB9Z8zK+v2w7ZvHYeBUtW2z+jvq/mjgYTtKN5uBh2Mn0Hb2xzkPTRNif1Z6gqGIGOoDM7NzXOLIps2sOhZq6oHgIo6Js+LI0aNO28/Ny/OK09QLghUVlaJ2rrt76Oy/SA0Eez+CqhoQJFi0RoVyoQAs609LEF5Z5pX4KPSLIWEoOb8dKpTzbWBLzuxMVJx+AeJ+W4PwsmLTfZBjjVuyAhR5ycnW5QDrAqGwFv/3S4Rt3WTqnjujo9nfJAj6EAQf/HKRagl2BD/777f44blBy0COzuhNI8dZmpcsrhcWZ/t2yX3eMh4PD1gkLNBG29bOJ6DnZSah539/qg+07K+zP574binoE8lztes87QmC7qr07T9wAOGRkYg0YdllzRmK2sePH3cKgjTEkBOk4cTTOJ39z5KQxSUlTtu2//HIkSOiH8O6wTirMFLk3v2UTYy0B4wrVbG428MIryhDpNU8l28pyIN1ya+Cw6wLUrY+uj4kgNZsH9Q3Jkybg0rlbNTXB9rP6Qoxz4w3/4ewbZtN3RNn98nsbxIEfQiCD+kBwc7+ePCLX4QRg64uRm6cBoLPD17mHGx7BKLNy3NAnR6NGkba1s6ly81vay2qe0+fibUiNwGQQEt9Z0c/jJ6zFuUGxGKCYF5+gT1m1DmmrpBAZqaIVg0neKTWhaVO44CwRlNnp83TyJ4W7IzMLMcmnX7PpqEnxCBHS51gaRGy+70iDCKOHBQ5q0rlLFjn/aKKjyb1dpaiAsT/tBhlglP7Zx1OrVz5JyqUc2H9ZZkYixH6aOdSr5kwa75op1y5pE77ded0uZhnysBRqpjfxO5dEgQbGwS7+aPnfxeIGGOr1RgIEjSLc5Lx6NeLnYMgLcb3TMayP8LBED5tcRrZ52YkYfGacFV07+vCwNPJD+1enStcdfTOgSJlUXGxU+DQfszKNgEgMbE1VtmDhw5pTdXbHz5yRJxnmEuLiRV6vqTk1HptOvthy9atwppshOb01yMXlfHqh05BsEy5UnBOOQ+8KFxLhH+fiXVLJ+W4latRdlpr4bdXF5haC44z89UPEbZtkyn/QUtxIaw/LwUBlZ+67dtzggTB05B7139gyctFZHyCqbVqiMZu6CVB0A1x9BJZ0wnq4gR7BuK6l+ciKdFqmFsjCBZlJ+P+z39xDoLUC3b1x5AZf6C6MNWw8YLzzctKwoKVIVDo0uPMyk2OsGcgrn5xDmiAoS+hHjoRBD2JlOUVFVgfHKKrPfs+NcMIRWp3W3ZOrim9oyZuO7NsO/ZHsOXYjIItXWRSPx9kA0EaRexB4wpUKP+HstNbI3b1X7BkZxumEccUSUtsnBVFV3YXIqtjH4zsKLqyJyLT0kAjhz2N9RzTwTt+7kKhbyxX/lVvDvb9UQ9ZfElHRCYnm+pLz3j0niNBsLFBsHcQ/vnkTOHPZ9RNhuJwSV4ynvpuqXMQJEB19MMjXy8WoqreRWB/Hl1sZi3dqLr60JrNNh0/twfhwidmgFlz6Ldof72rY4JgYVGRI2bU+b57zx4hDhu15NKxm0DF691t27ZvF87ZRttXQTDBpc7Rsc/E5BTDVuLwqgok+U0RYi+twvaAweNy5TJUKn9D4oRpgmt0RWd3v0cmJiAiKxP53e4X1tn6fVyMCuUfsC5cAnJ17tpy9h8TJySPnoAq5QxhAXZsv/Z7a8HZ5t72qIgeibKacxdzNgYzv0kQbGwQ7DMRZzw0Fb+tixDipJGbRhAsy0/Gy8OWuwbBnoG48oUfDHFp9mOgw/Wkn9dDoVvMPZPrA6AGiJ398dWk1SrHqYOGngwjBJLjJ46LUDSjekGCGl1wCHLutv379wsOjZyj/Zw9HasPiRVHXVifHfuko7bRuOLwynIkBU5HpXKOCwC5TIBL0ugJCKuuMDT+mvlZ40HjSO6dT7oQu1WDReoXQxC2pcpwH/QRTH/7cyfGnbpcLUGeQJk0bhLCNpmci441VzNvD+dKEPRAID2ENCQOU8S8cyLm/xYqHJv1tG9/Dt1XPpuwCkonOz9BDZi4Z9KGf0/Bir8shkGW/dDYMX7eWiHyKvc64QLZBw0knf3R7Z35QvfIZA72Y3R2LIwLWdmOeFHvOw0QRkPpCIJ0xvbECR44cEBEkBgGQVskyCE3Okf7iVC3SdB3RgdXv4VtqkLq18NcghO5KEaRpH32nXcgWJiP3Dv/46IflUPLeupNYYkWqa/0Ph9Wq3Cvyb1Xdfiu5focAfAKVCrnouT89iKtFkPsXNGksX6XIKj3Jrs5TwNBuqfUc12xBygeM7tMr0AELdwg3F2M3uiqghSMmP2XGn1ynxNOje3fOkFElWhJXY30wfaHs306SlPH6Dh+7XuvQFz9wmzEWeOEftNTHyFhFlGbxR4snB0XFBUZDqPjIo6JtYIuKu42/k9/Qp7vabz2/7MGBiNBDh8+7K75mv9oBTcEgjSMVJYh/a3PXICTCiT0rWM0BrlG+/HpPra54uTc/5xLbo1Am3Pfs6qYygQObta9/X+qbi8ZRVf3FGK7OxCkL2Re9wcFV8qsOfbtNMWxBEGdN9ndzdFAUJdhhCDVIxDDZv6JclvmaXdtO/5H6/DkXzZAuT0Iyt1OQJAg1cUfnwetMuUvyGiQb6bQUdoDCN46AVc8O1tkxElJ8vywhFoiRWyvJ+NCVVW1sK4a0dtpccOe2saJE2IMRqM61EJAcdDLCZaUlhoDcoJgeSkyX37fLQgK8OjNMLo8mNWjhVdXIPvJ/m5BMI99MBu0AZ9ExjfH/rZGGG+oV/QIgr36wZLf9JZhPl8SBBsbBIUFNwD/m2AOpAiCkzyBYCc/PPP9MlQUGI/LpDj8kd/vULp6AMHbg3Deo9NFMoeMVM9vcy60OGs8jh49VsMxOTvYsXMnQsItwunY8QXg6jv1gSmpac6aq/cb9XU831Vbzn6nk3WsGLvziBTHTgy7yRAEy4oFOJETcwUgzNFXdGkXMXaRCsvE2hUg+MTrbkDwdJEGi2JqZIJ+NytLUSHif1qiwz2GYv1pyO/yb0TkZDeLTDISBE0sJMcHxRAnSBDs5Ic3R/0qwtMc2/L0neF5InErHZldWW+7+KPPRwtRkJUkkip4atP+f3KCBGgRMqeJvs72fSeh1b+n4Ne14chJ9/yw1IKg86gODUjMGC9ohEjPyNSacLvPpahq0JlZA0FGnujZCPSGxG4BgkXIecS5s7QGimqUx6WIW7wCloJcQ0Au7nEss8iUiQQKFK21du33NMwUXdZVpMI3ArTkBON+W42yM64SFmb7Nh2PBUd768PNwkeQdJEg2NggSEDp4Idnv19qCgTzs5Kw8LdQ93583QPQ8Y15Ih0Wq97Zg5yn48r8FAya/odN5+hGJygMPJMwd0WIAFtP7YqFxvjeI+65KTN6OxpSmJJLz8b4XobBeRqv/f/0+aN47ixBg6s+M7Oz9esFNU7w8dfdisOMwiA3mBg0A+EVJYbmIOZjtYJRI8xW44rjJNCyn7hlK1WRWOfzYcnOQszaDSj5+3WqT6ODn6M9ELLv3HueQnhRAaKs+lys7O+Hr48lCOq8ye4Ib4gTJAh29gf1h3RHMRo6R2dmFnBy68zcPQC39P9RDZ8zCILkBIU4zOQJzjhA7TdhgAnEuB/XoSzfs9itOQ970qtRrxefqGaedkdz+/8o3urlBDdv3iLAyUiCiRpn7P37XWFevd8Li4oNcZzM4JL+3gCX4KSCiM21ZEyQGjliYu3SAJPT7yWX4jD7IZeY+uVQQ24yEelpIvkra4uoqfXrW4U1IBTGl4deEPHSZnWb9vff22MJgiYWkiPRDYNglwD0/Xgh8jOThHXVsT133ynikvuim43LiI7uAej81jykpcSL+sfu2nP8jzpBiuqMPPEIgr0CMcYWQ+zYjuN3YyBoLIbYiE5w06bNhkGQXGC4JQJ79uytB3aufqiorMRGAxxnWFUlUv+nRYy4AhCGm7VCysCRpt1kqBPMfP5dGwhe7kQktrnJPPG6ITcZYR1OTETRVYxGOctJu7VzElbuJ/sLY5AoIeCDZ9BxvRn5LkHQBzfAMAh2D0TXd+aLaAurAQscbyy5R5FUlc7MrqzDXf1xx4cLkZuZaCjTC1N1MUvNi0OWOc8go3GB3FMf2XciZi7diMJsz7kR9YrDdEimEcJIxhcjIMjEq0YNL6ofYgR273YfkWIPiFu2bDXk7xhWVYG0TwZ64ATVxANpH39r2k2GUR0Zr33kBgRVf8Tc2x8TorNekFJzFaaj4Ka7POQSVDnNzOfeFaU5zcZBGwE5T+dKEGwKEOwZiHavzUUy44cNiqvk1EbPXSt8DYXPoT0waced/PDgF4tQmutZTLVfIBoIPjlwiWcQZLKGeydj0Zow5GZ6jjMlqFnjXae70gCE4jLHZDHgy0cQTMvI0Jpwu2dUiRkQpFvNzp073bZt/+fOnbvA2iR6XX30gaAKUBmvf6JyaQazEJGuBMH0d7+w5S10xgmyj9NRcPOdIgGrXgtxZEKCqCLHxLC0/mqir7M9OUHOIWyz8agU+/Xqq2MJgk0Eglc9P1sNbUs0ZrhgHsLvp/0BpYsbF5bO/nhl2ArDLjLRtnyFIkFDJ4diURrAavs+E3HmQ1OxZmOEyIjjaUESEBhT62nbt3+/4agOGkbc1QCx75NARkDTC06cF88lcG5zSN9v367j8d59+wyF6OkFQQJI9uOvqSAY4zlSx/G+EATThNjNDNbOQZD5ACnW8lrdFmKG5JUUIuf+593qGzWdI4G4OeQS5BwlCDYFCN46AZc9M0uUaWTuP8eF6u47w+Y+CfjdddgcQaqTH14YslyAoBEDAEGQfoj3fcYM2R5A8LYg/PM/MxESEY3UFM9z0Cuy7tq92zBI0eXFXa5Ce4BiISYueiZdcEdn+/80ENzuITbZvp9Dh20crc7iS0ZAkLG/LFsZaSCiQ5uPAEEhdrsGwQrlPJRccCOig0PV2iZ6nhFW+6uuQNbTb3kAQZtI/8lAyQnaLxgjx6woZjTAXlsADbU3rBO8LQgXPD4DG8OjkZai30WA4mqN4YK1jDWuzHHfxR99P1oIutOw+JLeeTMEjuU8e7//k+os7diu/fdegWj74g9C58i0YJ76CAkNQ5oOXz7q3Xh/jXBqBE4bO+gAACAASURBVMH8gkJdy8iMH6IGgjsMgCCzXLPmCGOaPdGG/wsQ/PQ7DzpBxt2egfxO9yIiPV3UD9bTtv05AgQ//tatOMwawWWtrkTs6j+FiGt/vbtjpsrP6P+JBxCkhVtB6lfDTVu43Y3BzH+SE9TzlvNwjhkQ/McTMxBsMQiCNnHVZWZpDaR6BuL6V+aIfIV68/1x8WggeKseEOwegE5v6rdAM5Y2K8d12UoNwWp0gjo5KI6bmZyLij2nv2cfZtrXQNCIOMy+klJSwZhpPQ+mXk6QzsyFV/cQzsws1q6nbftzGHec8u1IWyot5+KwlhSVCVJF+JyH9a+1L8qGfviNW6OLmkGmlRiD6Ww4OsejjcvTXoKgDwjaWCAYYxNXH/t6iftEDQxpe2wGNgqQ9SyqaoskPsGKlKR4dHhjHpTuga45TYJtF3/coUWl6FDQ60mlReAQfoIG0+wTBItLSjUcdbs344xNEGRUCotBGdlYU0VXIgU6SzOzdP9PPHKCLJBUfEF7RIeGIyIj3RwIDhlvy/lXP28hdXaaU3bClNmqG4vOZ4SglvoVM+HQMOK8bVaaY17E5FGBauEonW1ra7Qh9hIEfXATDIMgjQoPMqegsXRXdKwuyErGXR//7B4E6UN472T8sioMuRmeQ9q0hcW6JBRtb2IB+R6eQfCRrxajhIXkYz0XkqfIWlCoT2RlaU7qELVxedozAoRZqfVsDH1TxdQI3e2bBUHhK6gnRE8DwVdcpdev9bFjcoLS065E7O9rYMk1nmFacIKDxroFQc14wWLwYZsrddMprLIMySMCUKmc6SInIudhA8HREyQI6lmwzs45JXSCtwfh3Eem46+QSN2ZmQkEBFvq+XpRXLWvO6yJwdrelk4rYIG+aA4NZCg6M8zuhlfnqvkEtfac7bv446Why8F0XXpBkMXQ9Ww5ubmGoi0IggQcPRs5zYSkZEOZn02DYEWFvnloIPjaRx45QVVUvRjx8xepfnwGX+JGQJDZZkR4nk69MrnZpHGThbO0q2pz5DIrlL8jYfJsUXBeW3tNuZecoMFF5OxmGeYEbwvCRU/OQGhEDFINGEZY1Ijp7Du9RXE1wL242j0QY+auFXWKnY3Z2W+0VEdEx4oUWcxJ6NLwQlDs4o/3xv8mLNA02Dhrz/43coJl5fq4NSOpqGj9psjJSBC9W3Kqfl0d52AWBMmdct72dHB6bAgE/4VK5XwkmgQRIyCY9fSbCC8vEfpHp+N2uO88N3HSLJEd2z0InitBUO9idXbeKcEJ3hYEM4YRcmnU2d3cn+KqGxBkppruas5CI+m0hLtObCyueuEHkZjVEwh+O2WN7iQQBIPK6mpnt7Teb5UVVfrAgwBlA0HGBOvdjIrbZkHQsDisixNkrZGzkDR+kqmoEUMg+NQbxkCQJTfnsNDSeUKv6MxRmgWYqNeMnzkPLMykB1wb+hzJCTq8zcwQ3AwnaMZFRtXZxePG1+d6BsFuAaIGCF1q9M4pJTleFE+6+D8zofR2wwlS3O4ZiNFz/9LtkE0QrN60SRdOMdOLLg7K3pHZgPtKY4EgxX9d8zDACdLBmfHDycP8TPnZGQLB595RizrpFYd11B1mDeUK5SJYf1lqqpiT3rVs5DwJgk0Egqo4TEdj/X6CmmN16+dnu+fUyAl2CwA5NSMgmJ4Sj3WhkTi33zQodwS5FodZjvOOIEz5ZQOKcz3HDXNBEgz0WlcJlrTGkgPztJh5DiNAtu/YoQtgeVJjgaCaSUZHrRENBF/5wKNOUAXB05A83N+Un50REGTRJOHGosP6z/skiq/PnCd0ftT9OecECYIXmq5o52k9mPlfgmBTgGCvQFz53GzE66zPod3YpKR40E2GtT3c6uxMgiBLgIZYonHBY9PVanPODCL8jbkE756MBStDhaFGG5+rPYGK1l4mL9CzESwNgaAlAjt37dLTtDinsUCQvosbQwyAoA4XGY0TTB0wFGFV5R5fEo73pMFBcNZ8VCjnuhGHJQjqXqiuTjwldIImQZAgxVq/lzw9y724ahIEaXT5Y2Mkzn7YAyfI5An3MXlCuC4XHA0EmXZez2YUBIPDwqG3bfZvFgQ3b9Gvd2Q/ukGQESMsvv71cJ2cYCuIsphmQfD7MbpcZNLf+p8hTpAJW+lgTZGXVmzJCWKXAkD/61nP0wGIQjnNNWzuwS91VJsjJ2UDQVp79YScaW/zjJQErA2NxPmPTncvrtpA8OtJqw2JwwyZY6nO0x6YWlO606lxpO9EnHb/FPy+zoKsdM/ivFGR1RAI2iJGqqr06RvNg2AYqqv198F+ikt0coIEwS3VwseO+j5n4FH7m6oTTPvwG/Oc4OBxDQOCudmIXfUHSs+82mV2adUw8g9YFyyGpaTIMCerPQu+3Etx2IfisBEQvOKZWWC9XiMgmJacIOKNL3pypntxlUDbLUBkmzFiHaZj9aLVYTiNIEqHa1fi8B0TcU6/afgrWJ+fI0GQMbRMjqBnMwSCWoaXBjSM8IGjTrO8XJ8vojZH3S4yNk4w5btRtpjeWufoWvCr/Y2xtxlvfqo6G+twVLcHDIrDif5TbWUxXUV1qDn/0t/5QtU76tQJMoIlOtSCkgtvErWFnY29JiRv0TJTfo72c/HVsQRBH4AgnYXL8pLx1MClnrOvEFh6BqLtSz8Yju3NTE3An8GRAoDcGi5YNL1nIPznrzPECdZkrWbCVOr9XIHgbUG48MmZCA6PRnqy57A8DQR36NTbUezUqxNk6nsu4n379mnY43Gflma84pwAwYoGBEFdSVVVIBQg+PpHtpyCnn007cFCgGDgNF0gmPnS+zag1ZeEIzIlWSR1KLrSdXZpUY7ztKsQ+/sfotCS/dia6liCoA9AkDePef5eH7FCFFFyCR4aqPQIREcDyQe0xZGTkYhf11qEPs4tp0ad3d2T8dNvoSKFv3a9pz0r2U1fvAGKqGTnoqYx59B7Ai5nzeGoGOG36KldgiAXml5OsKqatYd1GBSY785EESSjOkHOjyCo19lbQ2HdnKCwDpch441PdegE1cSqrFHMCA2jmZnDqyqQ6MeoDoa2uecEs+gnWFas21laZJfOyhAlO5mY1RknqILglaJGsSXPRMU8Hz2v9mtWgqCPiLq5OA0f+f8GpaOHPHwEkR6BuOWNH0FDh5FqcNTZMd641f1T3Ovsbg/C+Y9rWWo8c2ragmBq/QkL10HpHQTlHjcg2CsQ1740R7c4rxUq0sutlZaW6a4Ix9olLG/JxAh6Nqa4YoZro3rldRuDUVZerqeLmnOqqjfp9xMsL0Hmi+/pBkFhGKnWH9er3WMWWkrUyQlm0U/QQMRIJBOrFhUg556nXabTkiBYszzMHzRH6zAXWFVhKgZMWq0WKKJOTeP6nO1NgmBWeiJWrbeg1QMeQLBHIDqYANnKghSMnrNWdcR2N4eeAWj/2lwkJsQLkV57wFztjYJgbn6+PvBgGv7IKETHxuHw4cO6FpWZfIKcF0PzWEbzBE7o6ocnVbOok96wucoypL/9Px0gaIsY8ZsichC6ormr35l0Ie73P1B67jVu6wNT5M5+5GWElxZBb52RqJg44cBN8GQGbMkJtiDrMBdceUEKhszU6vW64aJsnOBN/ecKUdIIJ8jCSTRcCC6NIq8zgOVvndUaIyzKpCe5gfbAEASHzPhTGFUUdyDY1R/d352P7AwWcvKsk9JAcO9efdXaUgxkkTEKgqwxQpcaiujavPXsWR6AiReMbFu2btOt29SbT7A2gcIvpgwLEWlpiIqIQvGFLI15rlOgIngRBHPvflrtw0AGa9YNYSEoFQTr5yuUnKCRFeTi3ObKCZbkpQhDhHBididKEqS6BaDHu/NFbQ5rvD6lMx9UGi5+XBEKhQDoznDR0Q/MOcgawkbS6/P8ryat8gyCnf3x7//9IlLx6wFZgiBBh1yYp602I7O+VFdGQbC8vEK3qG0Pjqxjkpmtr8C7NkcWW6LYrSeVf1h1JdI+91Ry8wpUKBeg9Mw2iP1rPSKyMw0BOedjyc9D3NJfUdaKeQMvdAOCrZDX/UFY8nNhpDYwDS9JYyaKDNjOdI4cf9lpVxvOWm1/L3x9LHWCPtIJsjbH9MUbVdcVV6UwNc6tawD6fLwQeZmJhuoOM43W/JWhKgC6A0EBsguQkZYA+iLqXTQMsfs44DfPINjFH898v1RUs9MDggQqjuHQIc8i64EDBwVgEjj1jNsoCObmF4hM1Hratj+HYm1eXr6Gb7r2O3boL+oUtqUKKUPov+feT1BklmYRpLg4mMkszUzR1kXLhFHEPQgqyO37JCyFBYZAUDhML1ounKWdOUwzZK681WWIXbEKloI8XffY/j40xLEEQR+BYCEB6rdQKH09cGkEwq4BuP3Dn0S0BVPa672xukGwRwBufv1HtaRnguf6H1r/ZfkpeGPUCs+F17v4492xKwWnqSeNlhEQ3Ldvv6EqbZqofeDAAV3AlJ6RKcRhbc569wRBvXVMtIHoNoww7pYc1LhJtsLll7rk0JiVuaB9H0QmJYEFz/WOXzuPwGNdskJUmnMHghRnaahhsXYjFuiI1BREWa0ourQrWLXOUS9Yk7V62hyZT1BbKEb3zVUcpg/f2tAonMdojtvdJB9oDBDsHoDOb+mv/8EHhGDGBKmvjVjuPms1x9/FH99MWa07jZYREDSa/p4gyDe5XstzSmqaoazVGniYAUFm0tZlGCEIlhYjYfpcIe4yqsIRPLTvotBSx3tB3V5koud6z9r4tb0AwcXLPYDg5ahWWiHlu9Egh6pdq2cvLMSFBcjt84QLIw8LLZ2GlMFjTSWA0DMGo+dITtBHnCDTXDHLy3Uvz3Gf5soGgr0/+Ak56cbF4Xm/6hCHu/oLTpN+hUzzpXdRkBPsP9IDJ2hLozV27lphDNLTthEQ5EsxPjFRd+Zn6tuod9utMxolMdlYVmltfgQz1gwxsiUbAFyCU9wigtOlLuNuVYNFK+Td+oioAhdp9RyyqI1f26sg6J4TVNNdXYD4H38WpT21a3XtWXqzqgJZT73p0kJMLjP9nc/B6nS62vTRM+qqLwmCPiKwVqmt69vzVZ0awc7Vp4s/7v/fLyjKSTIEUrrF4c5+uM+A4UJbHNQJCl/Hrm7KeTKapO9EzFiyEXSu1q51tzcCgmoNkHjdfnw0uLAwup5UWsLoksAymPqMLvZzomEkKytbNwYeOXpEuO5oc7dvy9lxRFYWYtZuQOnf2ghuUOP8HPfkovK6PwRLdhYi442DYEROtjBKlP6NLjIXOOU4hfHlb23FeCKys3TdY/s5hW2tFo7frtxk+Hv2Y6+qRZwMWJ7t+/DlsQRBH4JgXkYi7vhQR83ezv54ZMBi0DmZxZP03lBydiv+CvccMdLJD/8ZuFTo7PS2zfNoHR4w0YN12JZBZskf+jLIsF0NCPQYRujvR78/Lkw9YzeSWfrwkSPCsVpv2/b9MxVYWnqGbhDcvWePmINeVxzhuhIVjaLLu4h8fI7gp32nmJp791MILyo04L9X68bEesXRlggUi/je85yCII0vRVd0RVR0jBC77emg59hT6U0CeX73B2DJyUZkgnEg1zMGI+dIEPQhCDIBwW0f6APBh79cBPrxGQHB9JQErA+LwvnM9+dO79jFH2+O+tVQ3DAXDf0EB03XfB1dcLJ3TMRZTJ4QEqW7SJQGgocPe47qOHjwkAA/7Ro9i1lvtbmDB41Znu37ZuJWI36C27ZtF7pHvSAoQs4y0t2GnBEIyUXl3P+calQw8ALV5kJjSmRCIgrb9LIZYWoTM2hAW0Xjyw23C8NLZLJxvaMovfnlUJulu35oXpVyBgquv0OUDOW8tbE11V6CYBOB4ENfGAdBq9UKAqGoMdLNRY0ROjl3C8BngasMg2BVQQpG/fCXWsTJlbN07wm47JnZsOiMG+bC1gCNAOdp22uzDusFD7ZPECwu9Vx3mMYTLni97jf2D6V4UOKsOHr0qKcpiP+ZCUevUUT0Q+63tFgAnCsxsg4Isj6HGRBMSABF3LweD7l0x2Hcb36X+8R5Zji1sMpyJA9zXdtYgGD7vojIzBAJF+zp3BTHEgSbCATNcIJcIOTWnv5+qRqjzBhiR70jwat7AIbO/NOwOMxMOCzTKRy+aQBxbJvfewSIusRJiQyZ0yfKE3QIhHoiRphkgVyXURDUU3d41y79zsuOD6OYQ1SULodvoiCLShkGwfJSsMwlozU0rsxxT4DMevotVZ9mAgQJnOEVZcju97JLwwV9FfN6PgRLQT6i4vTHnms0Y2KHxIkzhYuMs6pzEgR1vUddn9RcXWRoGNEtDnfww7ODlop4Y23h6N0TBGmZZaosFlivB1RMo9VrAib8tB7lefqLLLF/OnxPWbQByu0TXSdQ6OKPW99bgLxMY0YdGhb0pNfftm0bQsIMgmBICPTUNFZFVGNta/eFoKzXAMPVqzuDjPYSJjiVlQiDgUcQ/A9LYRrPICPmEh2L8E0VyOj/icvQNjVk7inDIXMarSzFzDC9zKXDtARB1/im65/mDIJ0eSFA0Bm6HjjZc1Ud/PDSkGXYVJxqWB+ipdNqRY6vr5PEp4xWuSMIUxdtFKCmLUw9+8LsJMxZEew+IqWLPx4eoIryeqJFtH4pspaUeS6+Xm2gvojWNjmuvPwCj+tn02ZbnkINeAzumUShskpf2dDiklLDnCBdSwhOKgi2dsoNkhPMfOG/5kHQ5pitpvI/zWk6Lfaf/fBLsJgUuWm5jlm/ESV/v16U13TkZiUIelyq7k9oziBI7qjPRws9R1x08MMLg5fpdjbWHnbumURhyRo3FuK7JgtwnLU0GEXZ+lxYtPbzM5NEDkLBYbpK0NDFHy8NXYbSvBRDyRnICWZkZrm/uYKD0l9uUxu3arlN99h2ZVWVqZA5rR8COeuG6NkKCouNgaDILl2BlGF+wpmYBZUcwYPfCVAZ73xuKqu0Ng8RnTLedU5BIQ73elhNehpvXByOTElxm1xVgqCeFeTmnOYKgqwCRxeTJ79d4jmnYAc/PD/IHAgWZCdh7vIQNf09ffbsOUweCxeWKSLbTG6GMcse8xUu+5P5Ct3UGDEYMlfz4EVECveUo0ePubm7QElpKdYHhxjikOn3Z01IAP0A3W2l5frzFGrjtt+T48zJzXPXRc1/hsVhGpBYqOinxTYx8mLXIPjGZ96BYGkx4mcvcBmdwuLuRVf3QKTViogUYyoV0ovGFLq/5He73wbodS3QEgRrlom5g+YKgrz5zC79Jf3sOvvDbSqqDn54cYg5ECQnuJicIDPVOOPWmFjhrkmYuyJEZJ2xf4g9HWemJWDVhgic9dA0Nbu0I8Dyexd/fOz/u+6i61qfmnHEUyaZouJiwyCoWZ8PHXZvfSYXt0FPfj8XYrLgODMydS1c6jbJ/Rox8NTU6Pi/G52KkVq5zbSPvkW4iUpz2r1gtuiEaQzRO89pacxK5e8ouqwTomhJT0839EISfVjjRR7CnAdfcGp8kSCoawm5Pqk5g2BRdhLm/RpiS0/vhEsjiFCX11lNQFBZaFwnmJYSj9CIaFHjQ7nNSYwydYK3T8CURRuFH6K28PXs2fbGsCic94gbP8Qu/vh68mpUGxw7gUpP8tO8ggLDYqRegKXeUG/afmf0MgKCu3btRlhEpCEQFEkRkpJQ2O4OWw2QuhyUCoIKUj8fpJbCdAHWzsZu/xsBVE3WwBT49cVu1Vm6C6Kiok05S7Mv5hVkAgZn7j4SBF3jm65/mjMIkpNaGxKFs/u5qdtrA8H3xq+EGRCkFTorLQGdGZ7nzADD9nsEYszctYZdZFJsdY3/9dRM13WNu/ljyIw/DINguM40+MwqHWyQW9MA9pCH7NKM+GBCVXtAMHJMzi47J1fXOt29e49xEIxXffjyezzowoeP5TZPV5MPmEitr801vKrMVnHOeZ0RAYKXd0ZUZJR5ENxSjfQPvnJqgZYgqGsJuT6pOYNgYoIViYlWtHnpB9WFxZk4qXGC48yBIBdyZWEKnhu0zLmvINvvHoiRP/xlGASZAIJhaK2fm636CjqOXyRPmIAxc/4y7IhNbo0fTymvmHnFqMhKEPRUZ4T6wliKaTrD8TTAsN83OAgmMz1WkkiVpRZCqssJqjV7L0D8rPlq2nuTnCD9/6wi5x+LpF9UT/foE06QxeS/HCI5wZZSfF17UJixhdmfhYW4i4skBDYQfGeseRCsKkzBiNku0uBTV3i76iLDsDxtbHr2BHBmum7z4hznIM62ewcJH8SSXGMKc+FnFxaOHTt2uH7DASgpKTGsEyS4EtzcpdPau1eNFomM1pes1Rm9jMQP77JllTaiE6RBISIzE/kd7xUcn6N1mKnpS09nVua/YMkxnthAm5MI0cvMQGGbW2391HXHIbdZcNOdiExPNx3RQZ/HhGlzRAp/5hC0n4vkBN0+Ap7/bM6cIP3mqCt7eehy1xbi+yYLMfaLoFWGOTVtEdMpe/mfdJNxUiSdxpK7JwndJLPOaNfo2dPCTYfpez/7GUonPyiOESk0utw9SWS3Nto2+6eLSWVlldubXF5p3EWG3Cu5tC1bt7pse/OWraJYkh46uDqHHCdBjRliPG1btxo3jLDfsKpyERHiTJcmDBaXd1ETG6Qa1yfXzIvpx7ZtQtqnA22cmiMIMj75eRHGxwzWNdcZ4DxFQojYOBRf2AHkLCUIeloxBv5vziDIxVJdlIqBU9e4rjpny8c37se1IompmQUmxO4EK655yQnH1nciTntgKpb9GQ66vBhpPzJaHf+3U9Y4T6zKtu+fKmofZ6Ubz/5BFxPq/NxtFZVVhg0jnCPdatxFjTC22KjrjTPaEcgrKj0XYS+vMA7m7I8ZWFIGjnQaOqem0WL2FXNptOznw5Ra0aHhKD3r6nqWaAJw+tufI2xzJaJiazPQ2F/v6TgyPl7UM8nv+XA9/abgBG+QCRTcPQdu/2vuIFhba2SC89AzipS3BWHm0mDD1ltt4THzDPu5jWm7yLHZ6+76TMTZD0/Dmg2RurO8aO1yTxecFX9Z1JA8x4iUOybi7H7TRQbtjFTjIMiY4MTkFLf3t9iEOMxxM5ojy00hJJbL5Dn2czVzHBoeIXSLx0+4L73JBKxmLNHhJYWwzvvFqQ8fwSn70VdMFV2vN9foWIRtqUb2I685cIOq8SV5uJ93mZ8ZBljtPLmqSNDA7NjMiZhg7EVdbx4GuFNX18oECj4goj1xGTr3+/oInE6H4z5Owtoort47WTgzM0LD/lojx9QL/nfsyvpia5+JOIsguDFSFFoy0ibP1azPN/X/sX5yWBuX+fu6CJjhBDVx0p0/X74JwwjHTXBKSExyCrAnTpwAX54EYaP0cHb++o3BqN60yWlf2o9m0/hHpKchOiISxRfcXK8kJiM50t8fIOqROBuXod+iYwRIJU6ehSqFVmK1rkmN8eWHBbAUF3lFL4Js2gdf17MQi4iU3o+IiBQziWENzVPH8y1BUAeRjBA9icYFaxxaPz8bSq/AulwaObY7JuKcfipI0aXGSNv251InJ/SC1NPdaQe2fSfhtPunYOkf4WCcsf01eo41kbj/iF/r6zVtfS1YGQrGGetpz/Ec6u6oL3O1MSIjOMQ4x6ZZn5kz0HFjolaOgyDsOB4z32kgSUxyzdEyOzat1aYs0XFWETmSe8djDpZVcminIXmYlxya3XqPZA1iShUX1ertyml8Oe0qxK76ExG5OV7Ri/rN5KFMqUV/xNq8ggz9y7nvWViYGNZqPCzPzD1zd40EQbtF4Y5Qev+jhTg/KxG931/gXK/WOwj0wxP5+JLNLwBybIxVvuWteWo/mhGDOsfeExC0cL3IXK133PbnseDS5F821E+pRUNMV398O3WN0H0SMO2v03NMnRpr/7raMrOyhZFDT1t1z4kR1znLVENgjI6O8RkIaoYYpv1yttFKrXK9ZizRMUJMTX/3Cwcx9TLhQJ3kN0UYT+rO3fh90K4nUGU/9GIN4DKKpOT/bkRMSKjI96edZ2YfXl6KhMmzhWHEPqUWxfqsZ942XMnOzBj0XCNB0McgSAsxRVUmSFA6OujryAn2DETbl+YgKckKFmfSc5OcnUMA2lKchs+DVkHp4GDJ7RogojoqCoy5sWj9pKUkINgSjfMen1E3gzWBtqs/Or01T+gbjdQ01toWxhE3BYvMg6BqHCksLq6HS4cOHRLV9HzFCXIuNLK4ylyz1WBWaY022p75+FT3Epas1MpvEgTPRNLYib4DQVqJt1QjdcBQOxA8HyXntUN0cAgisowXd9fmwL2luBDWn5ZA1Bq2q6AnDC/vDZCFluqtVJ0/NHfDCG8+Y4i/nrzGeQxx7wm49OlZiIyJQbIXnCD7yU5LxLrQSJxOcNXS7du4tQ/9fjPs0KwtYPoK0vDR+e159fWCvSfggidtnGyScU6Wb12Kiq6SHWTl5JjkBGNFOntWeHPcCIKsl+FLEGTShriEBFDf6LhVVVcLdyCNnkb3Qi8YGYWS8xlD/Hebe4lNHB4ZKELSjLbp6nxLSRHi5y8SFmLqA7VaxLErV6tZZLxgEiz5eYhbsQplSuuadikWC7F+uD/CqstNMwGu5mPmd8kJenGTXRG8ODdZ5POjWCoSHRCktE+fiTjzwalYuS7CsAuLs/42F6ei39eLodw0DsoDU9V+uvij/0jjNUa09inSs5h8j3cXQOk4vq6/oM2ww0JLFMe1a4zsyQ0ywYCzrbCoyDSAaE7TRxxS4BNwhWHERJU5V/OivyANLXv27Kk3DRpNOEdX13r83Zb9mQWVyDURRNS44dOE+0yYFyFzjn0zXpmgW3hlT9B1hY7NFcr5SJg6R/UT9OL5EBX01gej9JzratxwypV/okK5SJQXtRTmm6eRF+NypIEEQR8SUyMu/fNoIW5F8dHRQnxbEC58YgZCIqKR5iUnSJGYQMR45TOE680EFbA6+YuwOpbQ1MZkdM8anvwXTgAAIABJREFUxAMmra7vgsM5dfLDpwG/m8qHyHFQL5iT5zwlFXP+eQMgNLw4S3eVnpFpmsN0RTuKxM7S+u/YsdNwiYA6fVBM3VyF5KF+2CRAkEaF1gIQc/q9jPCSIph1Yq7TD9c+wXzbZmS8/T/RV6nSGlsUBWkffIXQHVtMrx/2w+LwzIxT0L6PAFg1H+IZKLjxLpGdhgBcbzwN8Dx66kOCYAMQnc7MtBJf8swstWiRZrSw6QSvf3mu+N8bnaB2YyOiYrCzPB3vjv8Nyo3jVBDs7I9Xhi03LQ6zbc6BFmjhkH3z+Fouk3O4cZxIGUbdpzYOI3t3IjENG0bT69v3TQ7tr/Ub6nCarC0SbokwVWDJvm3HYyZjcCZ+Mz5as1Y7XqP3OwHCkpuD3NsfF87GpcqVAqTyuz2ggqAPrarsJzrMIvIYblUUFF/bEXG/rvJaHOZcRTaZl96v0TkKy/BDLyKsosxUyVC99DNyngTBBgBB3oCqwlS8NfpXKAQQexDs7o9u7yxAZmoCWD3OyM1ydi65QabAorvN5QTd68ZAaTMKr49YYZpTYz+0XtNKzMgTkbLrlvGqaH/jWNzw6lxR9Y5A6WxMen4jN8h0947b9u3bRS0PIzG3jv0Fh4YjKSUVdI3ZuWsXIqNiEGqJEKm8HM/15jvHSCB0VkCK5TnNFHnXxhMdGQXm/YuKtYokB+TOKpW/iaSrrOGhneeTPWOvK0qR+sUwFPa9B5ExieJ7tEVf7WeXYyBHywgYu2zZFO9zHn4JYRWlEgQdF7/e7yeDYYQuFDQskNMTufl6BECh87TGCb4yF8zYkhBvHkTsFx4BqygnGas2RqLbq3Nx9cPTMHXRBsHJ2Z9n9DgiOkZEpqwKicQdTN110zhc89QsWKJjhZ8g+zXapnY+fe2SnESP0O2E4OENCDJvIY0gqq9eFMIskT4HQM6D/TDjjWPZAJYWjYtPMOcn6PBSJjilvzsA5ddei6SRE9WM0tHm6a7Rv87e1h5D6cLzCoVrTHSEb3wqGTWSPCJAcIL0QaRRhJXoOK86Y3CYd2P+JznBBiI+xdStpWn4fsYfUG4cq3JR5Ajp2NwrUNTyYMYZX93s8MgYUe0uLT0R0dY4pCTFg2Pwpn1ymQTC0rxkpKYmYMaCdQiNjgUjXcK9bJvjWr8xBJsduMHde/Z4DYLanLm4KZZq3xtiT7CmDlNL3sB6w0ymyr4Jkl71SXBiintmc4lPRnhRgVpr2NcgyGeAbcZZwZhfceyj54JuNtHBYSi56AZQ1C66uDMiUlMQkZrmHW18ND7eHwmCPiSm/YInN0hOj8aJOz/9GUrb0WpShWtHo8sb8xAfHye4QftrvD0maMXHW0W79Ffkd2/b5PXk+NhuUV4KUpPjxXdftEvdX2yctY4QwFRbIRZzZTF9MSajbWhcJ8EwJS1dADg5Wa8BUFuX0TFgaBmLF4mx+eieGp2n6fMjo4RuMWb1OqQOG61GomRmiKzVptvUaOOjvQRBHxHS2Q0leNBSnJqcgEe/WIQbH5uBx79chPgEqwhp80acdNbfyfdbnHA6LigqqgFCGjGY6MAbcbix6UDAI8fJusQct88AsAHXZqPSiBmqc7IRVl0l9sxY3aj9e6CjBEEPBPL2ZlFMJfeUn52M5ARyU8lgRAZ/97btU+F6ggb1g/QP3LZ9OzKyskUyhFNhbnIOvpFEGpqOEgQbGAR5AymW0gGZhhKmwaKo3NA39mRpn1wTgZD+fRSPfSpKSjrLdaZjDUgQ1EGkkwVQTvZxnkwi8MlOazn+WkZEgqCXIMgqagyfoh+a/EgayDWgYw2Eqxx/c3npSRA0CYIEPjr8UpxjtmQ6x8qPpIFcA57XQGJSMqwJiYJ5oHsRi181pTFJgqBOEKzVXYUL8IuLj0dJaSmYoYQbs4nIj6SBXAOe1wCfFya12L59B1gLmgwF47Dp4N4UYChB0AMI8qbQ/YGRAVTeM8qhqnoTTpw4XuPWIQ8kBSQFzFNg167dIukFQZAO9IzwaUydpQRBFyBI8CNx+Iainic9MxPbtruvmWt+GcgrJQUkBfYfOAD6jPLZIxiSQ2wMMJQg6AQE6aZB8KOuglXDGMolN0kBSYHGocDRo0dRVl4Oa3yiUD1RAmtII4oEQREIT18+1WmX4BdrtYKlHzV9X+PcetmLpICkgD0FmLObsdjMCEQg3BAcKlRTvtYbtmgQtNf3sUYsLXtM6nn8+DH7eyGPJQUkBZqYAtt37EBGVpZwpqeoTNc0X4FhiwRBEo9KWLq4UO9AC5WzKmVNfN9l95ICkgIOFNizd69QUdFYyfrPVF15qzdscSAo9H0bVXM807DvdlE20YH28qukgKRAM6IAVVVFJSUiZyRVWIw/NwuGpzwIMqUUiUMikY2OibWCZRkPOCnS3YzusRyKpICkgA4KsNB9RWUVEhKTRF5HqrWo3zciKp+yIEgikBgkCn38SCQSi0STm6SApMCpR4EtW7YiNS1dJOKgqssSpS8S5ZQDQXt9H/O7kSha1t9T77bLGUkKSAo4UmDnzl3Iys6p8fMlyLkTlU8pEKSnOUVeGj2yc3JFkR1HAsnvkgKSAi2DAvv27Ud+YaEAQBH04ML5+pQAQXJ8BD/q/woKi8CSh3KTFJAUkBQgBY4cOYKSsjIw3p9isqPz9UkJghy0pu9jFgprYqIogk1Pc7lJCkgKSAo4owCTW1RXbxJZn1jCgbYCutpQcoy1xqMB8GOXAmCXs8F481tsfAL+WLtOWHtZ2Gbzlvq1a71pX14rKSAp0PAUSCze3vCduOlh27btSM/IEn7Cf65dJ0TmBjCa+h4EmX6HiE1jx46dO91MUf4lKSAp0Fwp8G5QKJTW3yMqZ1OTD5F5ATKzsoVLTQOEyfoeBInUB6V/X5MvHDkASQGzFBi7LBnKdcOgXP49lJtGIbuseWRmOnjoEI763n3O9yBolvDyOkkBSYGmp8CsddlQ2gyFcsX3ULqNw3PfrUFR9e6mH1jDjUCCYMPRVrYsKXByUWBJdDGUf34D5cKv0eONnxGSWHZyTcDcaCUImqObvEpS4NSiwKq4EihnfIpWncYgYEnSqTU597ORIOiePvJfSYFTnwLTVqVDufhbPP7hUpTvanH+uxIET/0lLmcoKeCeAl//EI2AnxPcn3Tq/itB8NS9t3JmkgKSAjoo0LxAcFlMMQqrTmlLlI57Ik+RFJAUaEQKNC8QVHoHYOgCayPOv7YrS1I5xvySgDfGbsDDA1ej38DVeOz7NXjHPxijF8bjz6gi7Nyr1i+uvcrcUUx6FfyXJOGd8RtFP+zr0e/X4E2/jRi5wIrfwwuwdddBQ43TjaF0615U79iP6u0mPrsOIr98J6q27dPdb37lLpRt21fTZ9X2/ajYtg+Hj/q+1Omx4yfEvMptc6vadQC55Tux58AR3ePliZt27BfzrN51sA6dKrftw7bdB3GkAcZuP8Cjx46DdKvYeUCl24794Jz42wkW7GigTayPLd6sjwMoqNgJ0snoRpoXVOxC9a4DdWiue53u2I+yrXtRUOnz4DZOpfmA4G9RRVDO/hzt31lklMZenT/h5wRc99xcKJ3HQrl2qOok2m4ElPYjodw0EgqPrx0mFMfDf/IOoGetTEOHV+ZD6TZebZMOqaKvEXX7uuhrfDw53NC8PpwcDqXDaJz9yAyc3W+6wc8MnH3fZCg9/RGdWa2732eHrxV0OvuxmaK/s+6fijP6BiEmy/dRBgSP9v0X4ozeATi73wyc1WcC/vnUDwK4dA8YQFL+Fih3TRTztafT3x6ahv97fBauenEeur79Cx76ZhUGz47GqvB87Nt/2EgXbs/lPLq/vwRndvcT8+D9OqPjWPxn6J8NCoJfz46Gcsso8+vj/qniGVmfYNxtJj53C5S+QfVobk9/t8ePzRTPyTuBIW5pa/LP5gOCvf/3qwoMN4+CJb3S5Hz0X1awZS96vLoAytWDoXQco35uGK7uu4wVnvLK9cOhdB0H5d7JAiAHz4vT34HdmZv2HMRd/10C5ZohUG4ZDaXTGCj2fd08CqIvAjH7um4Y3gsKtWvB82Fc9iYBgmK8twVAuT1QdXq97DvV85/e/5d/B+XKQVCuGgyl7VAo7UeogHzHBOEf9sA3qzx3ZHdGIgGFIM759A5Q2+L9S6uyO8t3h9e8NB9K2yFqX5cMxOvjN5pqvMeHS1UacMx9g1S6kS6tv1edhFsPUl+IfPndPAqX9JuOD8ZvRNn2/ab6c7xo1M8JUHhf2H9PP7Huf40sdDzNp99zyncK52fxsme/vOd8CTuuD86d64MO07y3XP889+JvcetHS02PqdN/F6vPmkZzPnOOfdNB+6pB6nnXD6tdVzeOhNJ9PEo2N0hZ3eYBgunF26AQeO6eJBbEA9+vMU1sPRdSbPy/eyarQMA+249EmxfmYcDMKPwcnIvfo4sxaWUq3p0QirZ88HgTzvsSo36O19N8nXN27juEKx6fCeXqIer8bhyJy576AZ9MsWD++hysiikGXRQ+mmLBTf0Xqg/kuV+I/+s0pOPLZRxr9/EqkN49CQ99u0rM4bVxG/D6uA3o77cRTw39S4jgvT5ahtbP/wjl1gAolwyE0ssfm3cad494LSBEXbQPTFX7vXkUllsKdIzW+CmtOT9y0XxR3DIay8LN9TPox1j1JfTvKVBuC8R1r/6EZ4avxWOD1uD2T5bjmhfnQbl9gnpOL38ot/oLYPh73yAsMtmn/WzjczerAHjXRNH/mf2mY/se36ha7PtxPG731i9QOo+BQq6/bxDu+WIl/jshFNr6eMMvGM8M+wuPfLcGvT9ejqtfmCfGJ8Cq4xgUbzIPQt/NjVVf9DU0XyDWJl9k7J99vzhqnVBB3TfgN7TrvxCncZxkRM7/EuOWJztOx1ffmwcIvjx+o8ol8UHqM0GIZQ0ZqtP+jZ9VUCKL324EPgkKxZFjzvVY1NMs3JCDK/oGGebOeJdu//xXKHy7Pqj29dqItdh30LUea6WlADc8MFUsRqN3ueM7i1Sxvu9E8ZAVewh3OnbsuBBdn/58JQb9ZBzgOT7qLk8jN8U3/P1TBHfxlkEuVs888yp2qRxJnyAo90yC0nUs/ogt1nNpvXOCVqaqXA4fyGuG4IuZkfXOodj83awonE6g6jAGyiMzVCnh2mH4Nbqo3vlGfsgq3QHl3knqfHr44bLnfsShow1fUqLPZyuESKzcNUnMJdaT2uLECSTmbcFzX/+Oz2ZHG5livXMDl9vikUnza4fhw0lh9c5x/KGoajfGzo/DA1+sbEhVQdOD4I69h3AaFzUfIhKIQHj1YLxvUCfmSEBX32f9kaGKhDYAfHKQPq5z15FjSCnc5qpZp7//Hl2kcpt8o908Cnd+usLpeY4/UgOVXLDV8WeP32/7ZLn6oN6pchjRWfr1ex4bd3PCgDkxKqjcN0Vwluf8ewp2+1CPxq6Dfk1R1SVcI+Teu49HWIo5tcmMNRm1IHjtMLw/0fUDmVm6Axc/PF19uXDfYTROv28KdnjBuVGsO/OhaSqX1d0PrZ+fh4NHGj7P5u2fLlclDdKvyzisijb3EnGzFFz+5a8lZeD9azcCr4/f4PLcRv6j6UFw1KJElSvTxCkSqZc/znpgqluOySyhOr+3WNWF8W3Y0x/UlTTUdu+A31S9C0Gw01hEZDQsKIlFTl2LDQQjMxpGN+dIrxWWAig3j1bFVN7Hqwbjkxn1uSvH64x8b//2L6o+ldwmH+KefvDIybjoYCojJKjv4lq7bhj+64FzjczZJO6f6Nf2kh6zKNFF655/rty+TxhFKIorPfxw+XM/4sDhhgdB8ZLsMFqlX5dxoDGysTa/pUnqs2ADQYrAzWRrehC85Nm5qj6QxOnhJyx3Ct+S1wyB3zLf6gG27jqAMx6cpnKd3cbhipfnN9h9OHj4mLBeijn18sd5T8xuEFC3n4AjJ9hYIDjnrywVoKirI0j18kerPkHY4qMQrL/iS1XQYvtcJwTBHn6IMvlSMQqCpHEPclE0anF+7UaAeiuzW/nWvTiLXKUEQbMk9OV1TQuCVGwLCxUXdu8AvOkXjMvorkJOptMYXPHCPF9OVoiYwhDA9juPRfs3f/Zp+/aNlW3eqyqgaVnr4YeLn/sRx441oCMYgGYBgryXNm7wDQ8clj293B3fTMsiLdlst4lA8IsZkbXGlE5j0e29Je6G7PY/CYIjhDHELZEa78+mBcGe1GFxcff0x/mPzRTTfnnMeijXDVcX+/XDscKHrgNpRdvUty9B8NYAnPPIDOw16Gyr997QqVShjowg2GcCWt09CSVeWNf09NtsQJBARS6nd4BwjtUzdlfnhKdWqmuEemO220QgOGRenOo7yv47jUG39yUIurpnzn6X4rATqtDqRD2ZQleVa4bgg0mqc/C6+DJ1sRFA2o1A90+WO7na3E/7Dh7F3+l4SbGbolX7EVgW0TD+WYxwuJKuFl3G1QD65NUZ5gau86pmBYLk2q4ZghfGeqf76fnpCig32HGBTQSCz/PlTD0ixeEbhuPBb37XeVfqnyY5QckJilXx3Oj1qkMmubKefsivqA2JuZpOzJoCt+MYJOT5rlDTI9+vEQ+nQt1gxzFo89zc+qvUR7+8RR86Op6yr+7jcXG/GTjQgGFZzQIEaQSiWwnBglxw9/GmMxNHZVarPpp8YZETpC6QVv1G1gkeP3EC/3h0ptB1CpG8zRAErUgxvUokCEoQFKFOrfigMKrh+uG4++u6b9WA5SlQ2gxRwaPNEDw1cq3pBed4oYisYEgcH1CC03XD0M8LJbdj+/bf8+ilT497OttSEX7DcNz6ziI490i0v9LccZOD4D2T8DfOk6BF38GHposX3WPD/jQ1Ia4LoTMmV8mXZROJw3RkF067NNh1GI2zHprmlRpFgqAEQQxeEKdyY1zU7UbgT2tJnYeEYuuZNiujAMo7JoDB+b7aPpgeAeWygarOjmNoOxT3fbgMW3yUIMF+nMPoAsSQNXIyfJivHYae/Rei1EQgun27zo4dQTCqkVxkaqzDd04UsamMQjiDc717ogqGnccaLtaTwHhTTRq4ZTTu//p3PPLdavWl0oic4Kj5ccLHU9w/qlHaDvXaWbq5gODv0kWGj1HjG0YYgXHRUz+oi7njGFz1ygJnzzP6U5RkrC25tauHYICXHuuOnfz7y5VqLC3FNz6wbYfhkoenY2UD6AifH7lO7YsPL7mJG4bj/Hsn46d12Y7D8up7DQgyYuTWAPBha4zNHgSpb80s2QGR0IGxqeQGrx2G+79bbWgoD3y3xuZoPkX4IKYWbRNhVqx+5lNx+KrB+HZuTJ2xHTh0FKsjCnH3R8tUizClFvpf3jIak9d4r9dtchC0RYz4Us1Uh4BOvtQxjLQdio+nGEsQ4qRJX/3U+CD4c0iuKuJQt0NfwKXOfQEzS7ercaJcgN3H47xHZ+LwEd+GFj01cLUaPUJLZj9bWNRNo9B/xFps2W0slZWnO8IUXSJ+mLGo7IsGk3Yj8Oy3q1Cy2TdgVQOC5DrvnCicgBmuNPqXBNefRYkYNTta/E9jjpmtDgh2GoPcip3Yte+wGqJFMZb38OZRIoOLnvZFWBnVCLyu3QjQi4DbXZ+vVH31fMkJdh2HOz//FTNWZ+DzGZEipdnlT89RQY+Ay5dkL3/c8+FSlPpIGmlyEKS64rZAvDp2AyasSHG9NrhuuD7mxIgUd4ePmFfk1AHBHn7o+t5iBC5PAZ3O3a7PxYkYMCkMGQ1XCL7xQbDzB0tVZfetATjjvsniYXH1YIjMMrTIkVNrMxQz//D+LezY16AfmGJotJo1hvosPnhthuDiB6Zi9qp0x9O9+h6wOFHN5EG3IHKEBKu2Q3Hu3ZMQtNh8BII2qBoQ5AuGHDQz1VAUZ+wyM3QwUwp1rfydelGRLmwUFOVjPDfCvN61Dgh2GYvVMWo41oOD1qjZWMgNXjccd3y5Uhuq2/1jQ/9SjWZUVdwwoiarkMj+wnvlSxCktwBfSBd+DeWCr6D83wAo//pWFYHvDFL1kD398eAXK5GW7xsDXZODIFVNXH9MDOK4Pih9OVkfVEccN/mS5M2uA4KMw2b2HNE316dtjTKjE6UHPh/a2rxgAJTufqjYajyPodtFVvtn44JgbPZmVc/DN1GbIULkrR1L/aNfIwrVG8KH4ZbRuK5/wzg3r00ow7VPzlazoTBAn2DIrDZth+LxL1Zisw91hVHZm3ALXWe42GgY6jddcLqkx70fLEXJdvM3uwYESa/7poiohH88ORt/f2QGzntsJv5GMOKDzblxoXMhdh6Lfz45G7SAmt0cQXCFRXU7EkkP2BdpSuBqP8JjlAczlXDRi3G2G4HOdv54t368zPecYMcxQtdI4J63Lltk9Pl0qgW9P1iijoF04ouRnGn7EXhiwG/Y7qVvaZODoFgfk+utDxHFwrnypcB5MyNRt3E498GpYASUN1sdELxvMs54cCoueGIW/v7oDJz76Ezhsyu47p7+KgAyvRyliBuG43fbS9Wb/t1c27gg+J8Ra1U9T98gnHl7ILbqEDlvZmYU6mMInO1HYJ2JpI5uCFDzF11X3h+3QQXpW0apb0r2efVgXP7wdOR6yMhS05COA8LNl7Q48sHi25hcGxdmm6E4/+5JiDORPIHd1oAgOcxu4zF/fbbIhs0Mvlt2HkDZlr1Izt+KP+JK8NWMKFzEN/L5X2JBcK6OUbs+xREEV0bWxqQ+M3qdytXZdKGaaOuqNfoVCm6V9LhhONYnltec2iAgePVgsNCQsy2neDs+CQxR7xONIhzTlYPQ5snZqNph3lDX5CDIF1K38QL0mS1dWx/ktlIKt+LPuBJ8MysKV5AxOPtzTPKBRFQHBG8ahWeH/wUmT2HWaaZwq9q+DzllOxGaUoEJS5Nw58fLofzjK3TyIjLH2T118lvjgeCmHQeg9J2gcj+dx6LLh8sEAbLLdgjLobM9LcIfEyyom6GId/1w3NlA7iwacdZbS9H2P7PVB5FgQq7w+uG48IGpYrzaeb7YR2ZUo8PL89W++AYmV9h+JM7sMwF5JlKJ1wHBTmMRllrhdphcfEHLU7xOU+QOBEsZPkgAIdfLl8r1w7HBDtjsB1i6eY/KnZJzJBf4Qd2ojAYBQR0JFNbEFkPp5aeOjevhmsEiy7VZ8bBZgGCnsQJw7OnveMxMQBOWJOGQD3TxdUDw+uF4d4LnLNE/rExFXPZmx2H5+nvjgeC3TKpIfRTB7N7Jgv09nbo+PhgEGyef0+6fqrLJfAPzQ1Gu2zhkl+7wNSHqtLf/6DG8PORPVZ9Ffzcu/NaDcNdXdf0Z61xk8gtVzf9lPkWKH5p43GYobjFRZqAOCHYZB6byaozNHQiy/zcmhKriP7nB9iPQ4d3FTofFPIRCTVDDBdZN5d5UIMjBLokoULl2rlPq1K74HmNNZpJpFiAos8hoa7BxQPDo8RO4gKw1swLzQaAYyIyxjKagMtTVh6m2WfeD+gGCpXgLD8Hr/p7fItoMvdl/owXN00JIEeLGkQhPM5fDztM4xv2SoD5k7Ichg9cNM5w5ubmCILPJCIBnzkjOjYlJHXzUKJIJB3a+CNqNQCc7XaBGu6YEQY7hLqZGo/qCL++OY3DZC/NM6VIlCLZAZ2mmkefCF4un63jc8ekKfDkzCv+bHuHxw6y/LLAiIhD4Fu4dgNPvmeRVUkvtodKzf5xhdtrCbzMUr3gZC+uuzzfoG0mrGB+y64eJqnfuznf8r7mCIMf58fRI9WVH/edNo3BD/4V1hv8+1R58GQoucATWJ9blAnlyU4PgMkuh+kLmS5FK+94BSMo3nvxWgmALBMFbmAqJlduYOv9Wf6EQrfME6PjSk8VxhLsMnacHY9hCc+ngdXRV55QEFhOitYwcWqcxuPGNhrFQs1NhGWU4H0Xw7uNx6bNzQS5a79acQXD3vsNqqnpapAl0bYdgoc0gw1KXZ9pecBSXnXGBpEFTgyDLYrbiOHmPCIQm65xIEGxhIBiRXqW6NVCcZZzu4D/0PtN1zltOnQxFaD5Ancfi4mfm4IQXbh11GnfzhWnUz6JzM0W5nn644InZ2OumRoibpjz+RUX7JS/8qBoS7giE8u+poGFB79acQZBzGMgQNHJ75AZvGYWrX1TzRY5YGK+qRmy6QFceAE0NgjQknaGtBY71ppH4xYRlXYJgCwPBR4WBYZiqC7ppJIKTal0e9D7c2nmXPPejAEABhNcOFZXhtP8aar/7wGGcR30mIz16+uEfDZwhui0z6NA/6/ZA0DDE5Kx6t+YOggxHO5uiPudHA1m38RiywIr2byxUk0y0H4GOblwimhoERdlKWvG1Yk90DI+tG/eu515JEGxBIFi+dZ+qEKf40HE02jrogfQsGPtzRvwcr1oP+SDdOBId3Dww9td5c8yiOEyIKkT5TmNxYwNmo2Z5zrMftXGdNnHYSChbcwdB3ofRS1hTZjCUB6apxi6KxwQWD1wgr21qEPxpY66qH9aS5d4zSfheGl1fEgRbEAh+PitKBS2KP22GgCmyvNlYm1UAEkVT6mZuHgXmnGvI7afgXFUXyYe07VC8OHpdg3W3lgllmTmFqoMbhovMKUY6OxlA8Oix4/g/Aj31rHyZ8QVD2gou0LnrjEaDpgbBXnTgZUEp20u4ixMLtjZWd3sJgi0EBJnsgEkPhOjTOwB/u3+q2zhhd4vG/r+XGNXRdqiqV2o71JCOkRETVQaL/3R46+danWY7/REr1Tv2o8Jguqy7vlipchoUFa8dhp825NhP3eNxTbU5viCakZ+g48Ans9obwwbpb0cAZHbxdiPA8EV3Wx0QbMRqcxzTNJbpZEwrDSIc8zVD8OPaLHfDdfkfSy+cTd2il4WWjEgJHIx4SWrpyaSfoHZ/Gs5PcAbr+xKsuNCvH47X/IK1Tr3aJ9Jaq2UYoZjdyx8UWfVs8bmbcUm/6Vils2j3QNbL+WCrAAAgAElEQVTTvX6YDXCH1Ilj9dQfi8f/68nZWLBeX7qsCayrS/cYAmC7EbjyxfmGIzkcOcFVJpylC6p2G44QYMyt4GDpNtJ1rK56thc/ZcvUQinhxpG42YUDtT2de39iFzvcfTxCkt1HxNhfa39cp+6wjoiROVzLLAVBCYS+qm2H4rrX67r42Lfv6bh0yx78jf6ydiBopO5wWuFWcG3Sqm5ku501fexA0Ew+wYKqXaBu1+jmv8z7kpucLxkZH28NB4JtX/tJJTj1JzePwjJLgc/GftO7i9VFSbHkmsF4T2eh9k10yOVivn44vp4WIeJpnQ2KYUKfTw5XuTKKa53HCiV+roEaxQw4FxEurQfho4BgtwaO4T/GqrSiawxDzG4ZhdjsTc6G5va3DoyzZsICcoIdx8Ca47mN/YeOIj57Eyb/moq7X5qHfgNX44jBEgCz/shU/eeo2+s0Fit05GSctyFH5QbJWV03TFdBLQHyzCLD+XWtzVbjlihO/py4MrVWxXH1YAyYHVXvrF37DuHP2BI8TT9ROutrAHjdMJzVZwJy7MpB1LvYww8MEVXuo6tNoFhXfOG52+gEUVK9W0gGzwz9E8pVg/DgN6sMvyT7fLZClWps+QSZZd3TRsBLyt2Mab+l4b7XfhLpzMyE0TFll8gQQy762mH4cLLrgvfamOgtUVq9G8stBXh39Hpc+uRspBVt1/721b5hQHBlVJEaBsYFzgf7jgm6c8npmRnzvolIEhK0l7/wMdNTNa58y16cSTGEoHbdMFz46Ay84RcMhn2R0PzQifva539Ux08A5APQYQx+t5bqGVrNOXv2H8Glz81RQfe6YTjnwWl4afQ6kbdO64tv85v5sqD/I0MCCdDtRmDuRnMJDa5kHDKjcmx0v/uLlXhlzPp6n5dGr8cj361Br4+W4V/PzFGNV+R4L/jKqZNyzaRcHDAfnLgf7Pf64SCnpWe75pUFIki+nU5j0w1v/qzeO+pMTfrocVxD6KrDMEWun94BuOH1n2po9NTQP0UluX88Mas2wQVBlwacqwbhuqd+QFKJdw9iQu5mFVT50rhzIs7pNwMvjFpXMwbesxdGrhPZbVjf+NpXF+BMSgjk4pgOrcs4kXRAD43tz7mJL8lOY2rWB/MzOlsfrPj42KA/0PvjZbiE64MSF+l1zhemmZnv+aLXXNxuCxRzctY3f3t62F8CbNu8wnmrLnFM93b/V+ZrPdvTweHY9yBIPcWlz8xVLYAMgaLo2mUcWF3OV9vXs6Oh/PMbdSGxj4u/wat+Gz02X1y9WwCayB9oA1ABQOQuOoxRFxmjQ8hNcdzXDEGbJ2Yh2ESonEgqeluAWinN9rAJnZINVMWCZl8EPgLXNUNw6UPTsFKnqO44WWb/EGPmuClm0TGdUTqXf+fk8714oAVwMTkFXVZaf2+6st/dDCdjvkL2e9l3eHyIPl/QhSF5UFp9gp9D8hynU+97QeUulUvmC4xc2SUD8WaAufDJ3qxgx5BMtkMRnuCi0Ykgw1BNPvRUT3DfboTIOj5wRiQOGnBerzcJ2w9MJCr6Y/8EGAIs6aeNgXt+Z1gpdad8SWri+GXf4dnR61017fJ3lnsVNbfZjrY+mLvPvk/7Y9KB6iyuD67Pqwaj/du/uGzf0x/dmaWbXgGcMxkjZoay78/+mPkvmWeAOliOl8DdZRzyKnZ66sbM/74HQaZv4gTP6TddvOHOeWgazunhh6Vh+WYG6PQacjLndB6rtv/IDJxz9ySc2TdIpAFyeoHtx30Hj4jkoeeRYyHwiYU+Qt3zmB/WPO4wWmSS+X5WFA4cM5dNlyLlOxNCcdG/1TjT2r7s+uOb8ZbRuPLxWRgwORzb9h92N3y3/w2YFYWzSHfSo5/Bz4PTcE4v/zppq9x2ZvcnVQTn3j8F5zApBvu9cyIufHQmqnQYhagyeGtimC4d0+B5cTizg21+nOMdE3DFM3MM68VYe1qM9T7beB1p9f/tXQeUVEXWHsVd1/U3gO4aCBOIQxAckqCgIgiISBBRBFnXRdTVlRUFJE0eYCKZgWGAIQkiQSQrSkZykCAIKDk4knPQ7z9fVb+enqbDe6/fG6bP1junT/frfl3hVtVXt27d+m6TEYJ7Maz9eDzzn+l4O2Uxpn6zC2RUseJiv6jx72liTBhuq0bDcHf9wdi67zfDRek3ZQP+JMalwb5B+XAMP56M6TrMHJ4KRlbov7J/eJO5exu431cdgLb9Awvd6qlcju+sB0EuOU+cOCe4AnPPXJLvuRcEr52Pghj66cTpi8g9fQkifeZx/goOHT6je4lA6vxZy/aAtrh2SYvQsvdctOw9B4wFkjhuDRau/hlGd968VeDMpWuYs3If+o1fK5YeLfvIvF6JX4jY7NWYs2Kv4Y0IT3nRYJx78gJyz1zOkwtlo+d16qKQoad0/X3HEzWHj51F7rkrMq9zV3Dw2FnLQEPLnzRbub9dyOtX565g/9Gz4MRm5KJx/Yhred3lc+oi6Ip1/Yb+44pG8qeLEDfNyKWpq21cy3fyoumJklyBuXyZyZdjzeAmjKtMTp7zI3PXOnr6bB+rNItpPQi6Vl59VhJQElASKOQSUCBYyBtIFU9JQEnAXgkoELRXvip1JQElgUIuAQWChbyBVPGUBJQE7JWAAkF75atSVxJQEijkElAgWMgbSBVPSUBJwF4JKBC0V74qdSUBJYFCLgEFgoW8gVTxlASUBOyVgAJBe+WrUlcSUBIo5BJQIOirgejdnzhjK65ev+HrMfWbkoDtEug3cyvIQ6guyyVgLQiSNGD+2v3YsDdXsMZs2vMrVm87iqMnLwZcchIwMK1NjrTX7DwOPVRAgWRMOisGXV9ugkBBT76U0/pdJ5yyorwCea2jjHYcwzWd551ZvzUu+fP/rCvB347r+G8XMGvpHnQbuRKvRM8TLDaVOn2Gyp0+Q7V3pqJB15l4J/1bZM7Ygu17jYey9FRmUoUtWHcAG37Kk+33249iv4HYLZ7S5Xc8h7zqhyPOPrl21wnBdG51ADCS84aExYK8jVZfBNZlmw9j4z7HmN2bi5VbDmN7gEw5ruXkMcFlmw5h477fzPXvvblY/cMRrNt1wjVZqz5bD4Kk/REMGOQsI5NJuXiEB0BAqdVUMMeQ2YJpMtBN8T4g5Y+dV8e0bxES8j7a2pDPFDKokMVDY/QQ9WLdzL4GCQKIEu1ydIMgOekEUwnlSWaPsFhBI2U1CM5ZsQ+ten6Fu8mmQ+IK1psvsoSQSYfMOmQV0X4jkcUT6ajfeSqyZv8QUBNfunpDEBaEPNRLxjKhfCsmoVjrMUCAx4OTp26SLC9kgqH8SsWgiQmeP38V7JOzFiG3/Qd1us7096jh3wW3Ye00yRRD2ZCVKSIWjBVu1bVq+zHJBkO2HDP9m7J9qBd6jV1jVZFc07EWBJmyiJ3LyGzs3M8MlkItm4BlAWpTh3PPS/Aj7RPpeKr0x5qd9sUXITGAiDFLGp8nB+KYBdqsq+RrkuAyLFbSG3Hwk/aK96ROIuUQaZzIZ0ieQY3Pjt/zd9IMkeaIYEGaI3YsctM92ge9x33vmo3Pz7NX/yJpmtjxOZAjYjHBIKW/rwyWbT2Cp991xJxmWUmDxX7Bz5Qr82UH5yDUvhd8iMNkfSiDiFhUeS1HaHO+8vL1G0kl7qR8mC9lyrqGxWJagES/ZJa5k8S+jJfCulRIxLy1xqPP+So7iTyKthot6b6qJQvt09fzZn5r2GO27Fdsi4pJ+NvLDGdrJiXv/6nOuOHkrGQeZHEiXZ3Wn9mnXV8cBxwPnBg5QZdLQLGXsnDVINmv99Lk+8V6EGTy8zYexB0EK3K1sZOUTwSD1AR6lWZoRg6kmqm4p9VocKlj15VIotBQR4zcUjHoPkY/uPgr06Y9uZJLjQ1fNwNh7XLQsPtsvJmyGB9lrkT/KRvQLulrCW4kEK2dLkgoEydvEKSvb2csQYvoeaj53ueSpp2cbwTGOukg24re6/ipS7hD0LynO4F096FTev/u87luI1ZIzY5ceJy0qPnVG4QGH89CwsR1+HzpHmHOIC0UJ8jR83fg3bRvEdZ2rAREDgBS75NUkxpj+QT0zl7tM09fP37/0wncRX5HDkISlFZKQlkLVihPcjIjH2GdDNzRNBPHTwdu+nGtx3gycFN2HEfhsXjVhlVJkkaKS029fCJaxs53LYIln9mvxSTEPGqkovYHX4h+TrJV7UWyW5Iak+iY4+Hhl8fIti/WA7O//8WScnhIxB4QZEb1SLRJDYeVJvJHxGF0gDYNZ6CYasko03Gih/pY99VDWoxjDpioFNzTfJQlGyS///GHYBEu/0Im0qdvxtY9ubjhwQZHDjYxW9KsUC4BLWM8d0zy9k35ZhciW41GjQ+NTTS04d6vxVSmdtRoOKhxB3q90HWmMFeIWZx9oEYq3s9Ygp2/+GdkvnH9d3y26EeUfy1HasbU4BjXg2aDR/vgnzrIc72Vv0HPrxBSIUH2SYJrWAySpm3y9riu78l2zCDsZDgv2iobFw1Se/nLhGFlhZbMfkgZ1B+EXw0GC/OXx+CZWyXYcJyWT8A/kv0TFPtL0/13LmWdIFgyGgmT1rk/ctP9xcvXMX7udryjM3zGTQno+8I+EGzMDkdbD0GQsxhjZ9RIxT4Dmop7HUgXL2bdaskoZ8Es7p6+dj9t2V45+zI+CssvBkwssubv0B4x/U5722YdRv+F6w9IxmcHxX+jHrP95knNzshFbr17X8qSy1GCYMNhOPirfk3SU14kIhU2YcqtTBzKtRmD9Tpinbinxa2Z91IWy3bgUpMaK1cWD/fGpznmbEPCBuoEweFOs8qmn42TlGrlbRUzXwJI7TQUbTka5y9ZQ77K9LnJJfo7zSHaOAqNQW+T9dfK7P6e9sXmfCD4xgDrCUy7kw2e5gjWIyIOXTNXuBfjVt3bB4INXDVBVpxLm7LxKNdhgunK1vtophME7dQEo/7zhewULDc1EbF86gcuxwvq4o6moL03AIJGy+YJBI0sp93zezVpkQAp0dZl4lG14wRcCNC9yBm/hLYh9qG6A8WycNHGQ+7Z+71nvA6htbFdtT5ZIRGPtMgyTaJKclxBf187TWjV5yxioGZlmjOoEqn+qUSwH7LMUckiHwYhsupyB8EONrA4u4PgR/9zIKhpVGzM4n1Nq7cFAYKbaa+j0Z6zLwGodroMhs4OWD4B35gYfGY6a7CBIE0d3LEXpo9KSXj4pSxc9rDMNyOL7tmrpeFcA67IJBRrNRo0LRi5bgJBLb2S0Xg5fqGRpJzPMiiQHSBI1xURjY7gz77Ifsj350cITWrsQuvcZRQIAmecLWrhB6cmSOCjMZo7xpzJudsZHouZJgydBQGCDGIjdl7rD8LDbccidsJauWnAAVMmDvW7+1+WWiHGYAJB2hZvo6aixYOu3A8b9lnj56fJMuqdqXI51Xi41MxL9MWgWVu1n3W9O0FQ06xqpkkti+BSKhqjTNis7QLBbhrwNxiKIo1HIG7iOhE/RgBhxSSU/ddkXXXW85ACQbtBsG4GHnt7CiIZLpE7fgx5WW0A7ngyA8cNGnjtBkEanIXdiTao0nF4NVFqB6EMZUntkAM9KgVG4g/r6YSengkmEPxg+HIZCpJ2u/BYtE/91lOVAvpu9+EzcqNI292t0h8PvTbOUJpOEHxqoNhYi/zXZNmu3HipnirMD7uPGtMJ7ABBBmO6h7Za2tEjk4RTOStan+Yg2jS5gRGZiCVbzAWfdxeaAkG7QbDaALHdzZB/jDYltIUXMsVgqdZ5qnt7+Ly3GwRjJq6TvkssZ4VEfLPxoChP8tSNcjlGTTY0Bm+aDPXos3JuPwYLCFILvJ1aMh1hqeXXTBO+om7VseS2RcJC6T9GTY6uQxWT8M0m/bZBJwjWSEXlTlPAPsmA3s7JuWw8yryeY6isdoDgmIU7ERIeJzXe0nEY51j6Tv52d567TJl4PPvpV4bK6u1hBYJ2g2BUMkq9Pl7If/6Gg9KfTWw2DBPaQ7ex+v3v7ARBHnV6oHW2BOlK/RDhsgly+vwVFOGgo79b7XTc3mAoOPjtvIIFBDO/2ibNB9xBj0yy5VSDJmexW8rVBJev3KwKj8X7BtwnnCBYKw13vzhSJMujgiIoOB3OudQu2Rf/zFiiZen33Q4QrMAg6fSsqJ4qdu+1yIe0gRbT+ignnBopYDzmQC8FggUAgmHt83aE30j9Vh7VolZFt4yy8Vi89YiudrQTBCd/t9sxmIcLbW/ol/mPa3VIXSwB3KEN0pnazitYQPB5njag6wNBKSIOyZ9vtFMsKMljmTyhQe2zUpI4Eqc3Q1cQpGuQtpPbZdQquZynhkk3nPBYTFv5s65krQbBpVuP5LmWhcbgk6xV+crRe9wauSqh6SE0Bv8aqB+w8yXkcqNAsABAMLzDBLhu6ZdsM0buqNE+WLmfCIZ9/rL/0x92gmDVf0+TTqm10vDnxiNuOo2yZV+uPK3CHeNqyXigzRiXbmT9x2AAwcvXbuDeVtnSdkW5PJ5smZ3Km0TfSvtOLgmpedZMw70tsnD+kr7Yw64geF+LLBH3V8unMm3WPJlBUw03eJ7IwJFT/k9/WA2CjfvMleWg7bNuBg65Oa8fP3VRHiV7MkNsNhZpOMwJ5lpdjL4XOAiGx+HjkSuNFtOu5wvATzAqGe4guP3gKWmQph2JRumwGNTXcTjcLhAkO4U43cIlb2gM3h2yzKPAq384XYK3Q+v57LvdHp+z4stgAMGNe36Vrhs0b1CrbzBU2NmsqL+3NEZw+V3WsTnAPJ8dgl2HTnt7PN/37iDIoODadYguKXRDIQASCEvH4TEdO7BWguDBX8+Lo4/Ctlo6Dk36ztWKl++9Bd1y6D/oWJX0nxqY9l3gIFgqBtHjzTm85xOENTe3BgRZ9sFztsllMYGHfoTF+yDRzxEmu0CwRfwCuRTmEbXqqdh92POgmrZ0rzwEThCskITK7xrb2DHSZsEAgtOW7ZHO62zDmmko2jobZy/q08qMyML1WXGShiQLBF6+aqWJkxWuz3j77AsE+Z/JPClEYgqmy6Vxib74r9ty1D1tK0GwC8/X8jw5l/oVk7xq1XknSYaK1cnf2451L5ah+wIHwToZKPvmJMEC1SZ+Aby/FqJNzHw88+F07DhgzZl2D4K5dSDIwjTrO0/uxlIb5KZDZBLW/PSrh3LKr+wAQR41E1oMtYoy8Xiq25de8+cPXAYLm9SzQ4X2yA5pxxUMIDhq3vY8Dbp6Ckq+nmMX04dTxDxy6GSfYRtEpWDZFn02ZX8gyEzy2ay5WVI2Hgs3e3dFsQoEr1y7gbtoHqLvYmWeTvLtBxjZeYoYLyENhwutlROS2avAQbD5KGlaKtoDIX/riZAHHS/SnZFZhjZmjWLtzo/wQMvRuGDxmWwXWd1aELxy/XcU5dKDO2HNRopGfaDZSK8DyQ4Q7DHmeyl4andl4zFnjW+2ipgJ6+RszaVIRByaxcxzkad1H4MBBAWfHlli6FJUI1U4l1+ykdmH0t1z5IzcvKDNjHbI6qlYse2oLsHrAUEmVPKVsZLKjH3zsf6465khOO3FG8AqEMzkyohaKDXQ8FgM/2qbzzqRdcd5pK5CAqq9P83n875+LHAQfGYwaJOl7zBNZXyV7jhR9B/h+kQPgHLx0u3q8WTsPGibFkix3FoQZAlW0x5H1CdDBne8SkbjxWjPwGI1CNL14J6XRonzmJx9a+lkYXmweZacsakp1E4HbTlWX8EAgmTBEW1HEHwiHXc2zbSc4cRdrj8dPp0HggTCpwaCm1Z6Lr0guOPQaQdlW5rTZv1klxkes7AKBKn5CbIE2tDbS5cyjxm6fEkSEcE0xMmgcj/TzMsFDoIRcfB0dpgTKCe5mcv24lUSUzzYE62TFrnU2JaPtx4EWa0Y8pkV7yttIbQvlYzG0Lk3M7ZYDYLjeN6Vsy9tMI/1R9vERZi3Zj+mLtnj+bV0D2iTivrgC2lA56xdKhofDLeeESMYQHDsop15y2G6ltTJ0MWQE0hXFptYNJ3UGyyA909NRkAve45eEGT5hnGpz6UZ+yP7R4m+op+6l90KEOR5dDGZMK+qA/Bs9y+xYP0BTF3qpR8ukf3w+Z5z5AajQ3tsHrvAvXi67gscBMNi0UMHN+TXa/dbQu3mRwiFAwRZSMEVSECifZC7xlUHYPvB/BsUVoNgxc4O1mMuhdmRqFk8NkDOyCTKvOnVX/6uOesKN41U3Nl4BC5d9e/i46cx8v0cDCA4d81+OQg5ePmqkGgpM3U+gThuZqzYh5DKDofpx5MRaoCVyAgIMrsXabPmRkUT8hnSZp2IlT8ez1csK0DwOdLOkXVZ64fcmPHZD/vJ37UNHII0bdp10k0FYypwEPSiCeYTbMHdFB4QPH3xKv5MbUI7X1wuASXb5N/1shIEV20/Km2RXMpxANMuSfsWO2OZeB8vR2wMllPrtGExGDTT2GF+f20cDCDotM+R6YQTQkQcOhk4beFPBp5+j+fRRlKvM78y8SCfn97LKAiSzr0YbdUV+8kz7xWTcF+TTNA/UrsCBUEGIQqplS6PHBLMOPGSSVtPP2R/Zb9lH6YpqVQ0/mvC/06B4C1wltY6kPv7fC4L2PjiCNMIhJSIRvvkPILHp/5rHZ/gC9Hz5FKYHahOOj7NXo0hs7Zi4Iwtfl98rlP6dzK+B4GwSn8UdxwNdK+T2ftgAEHWjfUWvnUcwFEptjuR1/vEwQlIEAyPg/vJHl/yNgqCTEvYrMkcrdmsw2LQpPccZzatyflHMDLJJ/je0OWSQZsrkRqpeGfQUlEnvf3w41ErpX8jJ/LqqbiraSa402zkUiBYiECQDec8wkRwod9eqWhMW7lPtGndLo54DgHS6wuuNtqVqMFUSESVTp8Z6TPOZ4txU4XOtfRzLBuPr0zQgzkTc/sQLCDYXqMeY3txQimbgBmO9nKrUsC3JIEVlF0kVnUEaTISi9cMCLLQcVM2Sp9WAj3BplQ0hsyVu7etY83zCV64fF2cThJuMdUG4O+ts03JqDw3SBhnhkAaFouhBiP0KRAsZCDIXlCJO2XaEaaoFNxed6AgLOiU8Z1kBg4QBLvyrCiN3g6D8pgFO011vr7jXdxlyiWgppcdRDOJBwsIMs600IQIDgTCyCSUe8u3j5sZefA/MZPWS62J7knhMXi+V55GpidNsyDItLk7LDbRaLMmN2bVASCxRjTDYXJ5bkITpAlFpEnihtAY0P3KzCXcZTT3msr9UOqNvLP6etJTIFgIQfAgjzCxo/FkAH21IhNBe6A4rM9NkwBAkHaev9LOQ/64GqmCTcRsKL8Tpy/JzRQulRhetOoAy3ZHgwUEOcgefT1HkBkIEKRWHBaLbAvj1jIPttFd1MTIsccNgchEEbhezyDXngkEBM8wvCbbmLySdGqOTMJTXWaIqH+kvDcDggQrYQPnOeB6g8BzwWYu8g/+Hx2QGeGR2ni5BEOhPxUIFkIQZEeYxCNZ3Jljh+fMT/Dj7i0/BwCCWfN2SDYY0pSXisH7w5eb6XfO/7ycuEjO5jRMh1kXEjGYQHD81z8KCirhSkLtunoK7qiTgUMWxmruSBYf9gdOiiWj0bSvZ19SZ8N4+BAICDK5BZsOydMMtFmzvTlRc1eWfdKgJujUoB0bSi/FmXNv0ar5EVc3lA/LUjYedXWcxdf+q0CwkIIgG6gDo42V7CuXrZzhuOSiNhAACJYhmzDtJ9yJrpEqnDO1zmDmff3uE3I2p8Oqw05FDTHQK5hAkHWtxGNc2qF+AkS5BIS/nI2LFgTMHqvF3mX7V+mPIvUGgfZBo1egIMj8umStkn1Ss4EKM8AwwyBYn0GfeDyMmnPFfmCg+kCugycc5AsEZSoO1ZKxTUeIU+apQNAmEHyO8VgZc9YDi4yRxg7lESbuvnGGIwAGAIKkI3c6pZZLwNMWxQvhkSWRrsO203Ns4AwZwQaCe46eleaLqgNkW1EWpeNQuV0ODp68YKTJ8z07efFu2Y+4kUXNq3Qc5qw/kO8ZvTdWgCDzErRb9GIg2Gt90oAmSNYbsawnMWrl/ihvcmPOvd7NoudLWzrHSlgsXtcZP9gqEPQV+Op/MtqcEwQfTxbnAmEsMJizfbeRdosDSwuKw05XNRnl3zQe/rJhrznSiM00yifg86XmD507Cwggh8tBzupMNyoF9zbPgsYG7Pqckc9fk4Wb9i9qmJFJaNLTGip11zKQSopnOLmUE8u6RsNxKIAjgF+uPSDlQNJTAgQHY5l43NdwGKYsNk471nPECjm5kOKK5pDScWBUO7NXQ5dA6fe3yMKp88a1SeZ9MPcCbiMo0wbH5T/bnXGHW2Xjgg5uw7e4wRcWI/9bJl64ZJmtk+v/GA6CRLNixcTy1R+Mkzo05vTpW6RvorAnxuPNFP0xYi5fv4GEzJVInLwhH2eoa7n4mSdEnGOEfIKjjPMJrv3xOOhXafFln7N0LR4to0NxVDLCOkyAweiI+eo5cLbjcDmXDuxwjpMCRkKvMjiS2GihXbFuBv7UaDiOWmSzOnfpmmQAYcfjMiksFiPn3XzsL1+l/NzMXv1z3mmMSkl4uussP/8w/vOZC1dwL2Ns1Ep1gOCwgECQJZi0dI8sN7V3AgTBkJNYhUQ07ToTs5fvxdWr3v3YOGizvvwB5duNl8trhxsTNyTGBbjZIoOvJ4oJ9f5Wo3E2gBjBE5fskT6tmommdhruIwj6YTvhjvLtPHFEYLfhqOEj7XLywLlUNPqMX+u3Y4hdaraXY1PlHz40SJ6M+vnoWXG+9/2MJXiUm0TFejjj8XjLrNfYNXkgyADyfsJq3LjxBw6cOI/FGw4gYdJ61H1zEu5sMgK/HDvnLQuz39sHgiU6TJCg80Q6/vLiKJy7FFhMjoqdpkitgCDDw/rNRhqK8/HqgG8QEupwi4lKRgmdh9T1SvZp4cSbJEGQbgoBpj92wU45OztAP+LjgIYAAAUwSURBVNyE5uuv7PtPnBPxUoQWyKVZnQxs1UlG4Cvt5TuPI5zR0kr0lRo8gZBgwZMQlfuhRJsxaBk9X7iXZEzfAr4+GbUSjT+djfs4qPgcJ1D6YIbGoFLbcVi164SvLHX9VvW9aTLduhko0mg4DueaX6Yzw/pscw3snxyI254bCn/B67uTtUjTAh39+NS5K7rKr+ehdklfyzJxw6V6Cv7aZASuXvc+6TDN3gQo2nPZ154eLIK7V3/vc1R5e4oISMWgVI91niLuH2k7TrYl24d1L9oDz+pYpfx7yNI8EHxqIIq/Nk54fNDrw/X1ZJfpIjolIzzexrFODxEyyhTpgm5Zq/WIwOgz9oAgjbzCz49HeuhS8EhvMIZHIBcprpiOGBxsgId6YZLOJdaqHcfkzhnZasTAihYNbiXtU1WeQ37gU5k+87i3m2m/L8qpIWN3aOk5jvR9v9Na7sLB9FN78FNhRKchnR261xj9ga98tefl3/9Aj6HL8Bca6blryTpQq+Pynh2bbUETAo8o0ieUn8kYzU0rDq6y8XjkxVFIMek75162HftPStsi86Vm+mBPDJ/tm67KPQ33+zU/HpeeAVyCMs2/9cSgGVvcH3Pe/8Swoey79CtkH2Gda6aCmxpWXc1o8rm/u0yfcr77Y3Bn3dtFK1Wp9hPEeBJlcoT5DHmkjyQ1IbGJeDnuw2Jl2/FsM23+oTFY7ofK7PqNP3A/wzA82keWi7Zd1p38gTe9eguCZdFntMmQPpDPDMZ5P1q2tzr6+d4eEOTMEvbMEER0nChfDYbirTT9dgZvhX6iywxEvJQl03x2iDi65u1Z1+87D1yC4vUGIeIfkxDxxgREtB2L0u1yAt4Z1vK4eu0G6n44XZaN6XeciPBmI4XT96nzxmf5Y6cuIpKya50ty9txIkKfHYIYHUsbrUx63hlXOaLRMJnHGxMR0TQTDbp9CfqdWXXxRAeBrE7nqSjCE0AEAQIRgY+dm4BA0CP4RaWgaLORaPbxLGTP3oYLPpbNRsuXNm0zSml9knVtOAztLKBpIvlBRLORiGCajYYLlmRv5u/o8WvxsGs/bJeDsJajsXK7Pj5EPXVu3mcuIl7IdPabiJajEfrKGK9hD7b9/BtKthyNiFfHOfrBBP3vTTPxQrT/s9trdh5H8RdHIaJdjv60OY746jABEQ2GInbyej3VN/OMPSDoyS5yUUcgJaM14LEjPZfHQDx/ANduWDPYGUSKITvdr0uXr+GyiYFMu8t1D+c/rZ4JL3ogQKV27BoUy71Ogdz/cvgM5qzch4wvNuOTUavw4eCl+HDYchAcsuduF24iZ3QY8s2UwZPs7OiTnmSqlVeLbqfda+9WTjqeNuSuXbnu1VYpZGDRONDq4/4u8MCIAd89AXvv7QFBe8usUlcSUBJQErBMAgoELROlSkhJQEkgGCWgQDAYW02VWUlAScAyCSgQtEyUKiElASWBYJSAAsFgbDVVZiUBJQHLJKBA0DJRqoSUBJQEglECCgSDsdVUmZUElAQsk4ACQctEqRJSElASCEYJKBAMxlZTZVYSUBKwTAIKBC0TpUpISUBJIBgloEAwGFtNlVlJQEnAMgkoELRMlCohJQElgWCUgALBYGw1VWYlASUByySgQNAyUaqElASUBIJRAgoEg7HVVJmVBJQELJOAAkHLRKkSUhJQEghGCSgQDMZWU2VWElASsEwCCgQtE6VKSElASSAYJSBAMBgLrsqsJKAkoCRgiQRCGEsawBn1UjJQfUD1gf/BPnDw/wEnvZgemhIdIgAAAABJRU5ErkJggg=="
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "![image-2.png](attachment:image-2.png)\n",
    "\n",
    "## RAMP Challenge: French deputies votes prediction\n",
    "\n",
    "Team members: *Florian Eisenbarth, Nicolas Oulianov, Nicolas Boussenina, Armand Foucault, Eloi Alardet*\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Introduction\n",
    "\n",
    "This group project is about predicting positions of deputies on votes in the French National Assembly. \n",
    "\n",
    "### Context"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### The National Assembly\n",
    "\n",
    "We will be studying votes in the French National Assembly. \n",
    "\n",
    "The National Assembly is the French lower house of the parliament, the higher house being the Senate. The Assembly is composed of deputies elected every five years during legislatives elections. One deputy represents one electoral district, composed of 63 000 to 150 000 inhabitants. In total, there are 577 seats available for the deputies in the Assembly. \n",
    "\n",
    "Most of the times, deputies campaign in their district under the banner of a **political party**. But once elected, inside the Assembly, deputies join or create **parliamentary groups** of at least 15 deputies, that are not exactly the same as political parties.\n",
    "\n",
    "These groups are used for various reasons.\n",
    "\n",
    "- Small parties can group their voting forces.\n",
    "- Likely-minded deputies that are not in the same political parties can work together.\n",
    "- You get various advantages: more time to talk at the Assembly and one representative at the President's conference (a weekly reunion of all the parliamentary groups presidents).\n",
    "\n",
    "Some groups corresponds to exactly one party. For example, the group \"En marche\" or the group \"Les Républicains\". But this is far from the case of every group. \n",
    "\n",
    "If a deputy does not belong to any parliamentary groups, he is part of the **‘non inscrit’** group, which is not a parliamentary group strictly speaking.\n",
    "\n",
    "A deputy can also be a member of a **parliamentary commission**, which are specialized groups that review the laws for a specific subject (finance, security, health…). \n",
    "\n",
    "How these groups vote is the focus of this project. \n",
    "\n",
    "\n",
    "#### What do we vote in the Assembly ?\n",
    "\n",
    "In the French legislative system, the government and deputies propose laws. Before a law is adopted, the text is called a: \n",
    "\n",
    "+ **project of law**, when it is proposed by the government\n",
    "+ **proposition of law**, when it is proposed by a parliament member\n",
    "\n",
    "Please note that that those are literal French translations, as we are not legalists. \n",
    "\n",
    "All members of the parliament, commissions and the government can propose **amendments** which are modifications about parts or articles of the discussed text of law. We call those who propose amendments (or any kind of vote) **demandeurs** (literally *askers*), and we will refer to them as **demandeur groups**. The groups that vote on these amendments will be refered as **voting groups**. \n",
    "\n",
    "An amendment is basically a rewrite of one particular aspect of the text of law. There also exists **sub-amendments**, which are amendments of an amendment. Even a small amendment, that adds a comma in a text, is important. Everything that is voted ultimately becomes law and is enforced.  \n",
    "\n",
    "All text of laws and amendments are voted by absolute majority, where one deputy equals one vote. However, for most of the votes, you don't need every deputy to be present to vote. \n",
    "\n",
    "Given a vote topic, we will try to predict the position of each group on this topic.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Objective"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Citizens scrutinize the deputies they voted for with great interest. A concern is that deputies don't vote thinking about the vote topic itself, but only with partisanship reasoning: they vote for an allied political group, and against an ennemy political group. These \"friendship rules\" undermine Democracy and are disapproved by citizens. Indeed, it's seen more as a political warfare than a democratic co-construction of the French society. \n",
    "\n",
    "Our goal is to study to which extent political groups' votes in the Assembly are reducible to those \"friendship rules\", and are thus predictible. In other words, **our project is a [Multilabel classification task](https://scikit-learn.org/stable/modules/multiclass.html) to predict if a set of parliamentary group vote for a given vote topic, or not.**\n",
    "\n",
    "Note that to simplify the problem, we reduce the number of positions to two (**for** or **not for**). But in reality, there can be 4 possible positions: for, against, abstention, or non-presence. We also don't consider every parliamentary group for prediction, but only the largest ones. \n",
    "\n",
    "**The metric used for evaluation is a weighted F1-score:** several F1-scores are computed for each class (parliamentary group), and then averaged, using the logarithm of the share of deputies in Assembly for this parliamentary group as weights. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Most of the data we use in this project comes from government's [open data, freely available on the National Assembly website](https://data.assemblee-nationale.fr/). Some additional features were created using aggregated data from [nosdeputes.fr](https://www.nosdeputes.fr/). \n",
    "\n",
    "Data from the government is very large, as everything ever said or proposed is recorded, and not in the format we want. We will describe below the processing we performed. \n",
    "\n",
    "#### Processing\n",
    "\n",
    "1. We gather the various stacks of individual .csv files into aggregate ones with only relevant information. We create aggregate .csv :\n",
    "    - one .csv with all the **actors**, which is another name for deputies and scenators. \n",
    "    - one .csv with all information about **votes** : topic, date...\n",
    "    - one .csv with the **results of votes** \n",
    "2. We filter these aggregate .csv to keep only relevant information. We keep only data relevant to the latest election. In otherwords, we select data in the timeframe between July, 4th 2017 and November, 20th 2020. \n",
    "3. We format this data in a \"prediction-ready\" format, with a features DataFrame X and a target np.array y. \n",
    "\n",
    "#### Description\n",
    "\n",
    "Here are the **features** we made available for the problem:\n",
    "+ **vote_uid (str)**: the id of the vote\n",
    "+ **date (str)**: date of the vote\n",
    "+ **libelle (str)**: short description of the topic of the vote\n",
    "+ **code_type_vote (str)**: code of the type of the vote\n",
    "+ **libelle_type_vote (str)**: same as code_type_vote but more explicit\n",
    "+ **demandeur (str)** : the deputy or group which has asked for this vote\n",
    "+ **presence_per_party (dict)**: number of deputies physically present for the vote by group\n",
    " \n",
    "Our **target** corresponds to the results of the vote. \n",
    "- There are 10 columns, one for each parliamentary group considered. \n",
    "- Each line corresponds to a vote. The index is the vote_id. \n",
    "- The values are 1 if the group voted for, 0 if the group did something else (*against, abstention, non-presence*) \n",
    "\n",
    "We also provided another .csv file with information about political parties and their members : birth date, sex, twitter... This .csv can be used for further feature engineering. \n",
    "\n",
    "#### Train-test split\n",
    "\n",
    "The train-test split is made temporally. First 80% of votes are in the train dataset, last 20% of votes are in the test dataset. This is because votes are temporally bounded and, ultimately, we want to predict future political decisions."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Exploration\n",
    "\n",
    "To better understand the problem, let's drill down in the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from problem import get_train_data, get_test_data, get_actor_party_data\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import re\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "X_train, y_train = get_train_data()\n",
    "X_test, y_test = get_test_data()\n",
    "\n",
    "X = X_train.append(X_test)\n",
    "y = np.concatenate([y_train, y_test])\n",
    "\n",
    "actors = get_actor_party_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "            code_type_vote         libelle_type_vote  \\\n",
       "VTANR5L15V1            SPS   scrutin public solennel   \n",
       "VTANR5L15V2            SPO  scrutin public ordinaire   \n",
       "VTANR5L15V3            SPO  scrutin public ordinaire   \n",
       "VTANR5L15V4            SPO  scrutin public ordinaire   \n",
       "VTANR5L15V5            SPO  scrutin public ordinaire   \n",
       "\n",
       "                                                     demandeur  \\\n",
       "VTANR5L15V1                          Conference des Presidents   \n",
       "VTANR5L15V2   President(e) du groupe \"La Republique en Marche\"   \n",
       "VTANR5L15V3              President du groupe \"Nouvelle Gauche\"   \n",
       "VTANR5L15V4  President(e) du groupe \"de la Gauche democrate...   \n",
       "VTANR5L15V5              Président du groupe \"Nouvelle Gauche\"   \n",
       "\n",
       "                                                       libelle nb_votants  \\\n",
       "VTANR5L15V1  la declaration de politique generale du Gouver...        566   \n",
       "VTANR5L15V2  l'ensemble du projet de loi prorogeant l'appli...        150   \n",
       "VTANR5L15V3  la motion de renvoi en commission deposee par ...        225   \n",
       "VTANR5L15V4  l'amendement n? 13 de M. Gilbert Collard et le...        202   \n",
       "VTANR5L15V5  l'amendement n° 132 de M. Prud'homme et les am...        182   \n",
       "\n",
       "                   date                                 presence_per_party  \\\n",
       "VTANR5L15V1  2017-07-04  {'SOC': 22, 'FI': 17, 'Dem': 51, 'LT': 17, 'GD...   \n",
       "VTANR5L15V2  2017-07-06  {'SOC': 3, 'FI': 11, 'Dem': 9, 'LT': 1, 'GDR':...   \n",
       "VTANR5L15V3  2017-07-10  {'SOC': 4, 'FI': 15, 'Dem': 15, 'LT': 4, 'GDR'...   \n",
       "VTANR5L15V4  2017-07-10  {'SOC': 0, 'FI': 15, 'Dem': 11, 'LT': 4, 'GDR'...   \n",
       "VTANR5L15V5  2017-07-10  {'SOC': 4, 'FI': 14, 'Dem': 9, 'LT': 2, 'GDR':...   \n",
       "\n",
       "                vote_uid  \n",
       "VTANR5L15V1  VTANR5L15V1  \n",
       "VTANR5L15V2  VTANR5L15V2  \n",
       "VTANR5L15V3  VTANR5L15V3  \n",
       "VTANR5L15V4  VTANR5L15V4  \n",
       "VTANR5L15V5  VTANR5L15V5  "
      ],
      "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>code_type_vote</th>\n      <th>libelle_type_vote</th>\n      <th>demandeur</th>\n      <th>libelle</th>\n      <th>nb_votants</th>\n      <th>date</th>\n      <th>presence_per_party</th>\n      <th>vote_uid</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>VTANR5L15V1</th>\n      <td>SPS</td>\n      <td>scrutin public solennel</td>\n      <td>Conference des Presidents</td>\n      <td>la declaration de politique generale du Gouver...</td>\n      <td>566</td>\n      <td>2017-07-04</td>\n      <td>{'SOC': 22, 'FI': 17, 'Dem': 51, 'LT': 17, 'GD...</td>\n      <td>VTANR5L15V1</td>\n    </tr>\n    <tr>\n      <th>VTANR5L15V2</th>\n      <td>SPO</td>\n      <td>scrutin public ordinaire</td>\n      <td>President(e) du groupe \"La Republique en Marche\"</td>\n      <td>l'ensemble du projet de loi prorogeant l'appli...</td>\n      <td>150</td>\n      <td>2017-07-06</td>\n      <td>{'SOC': 3, 'FI': 11, 'Dem': 9, 'LT': 1, 'GDR':...</td>\n      <td>VTANR5L15V2</td>\n    </tr>\n    <tr>\n      <th>VTANR5L15V3</th>\n      <td>SPO</td>\n      <td>scrutin public ordinaire</td>\n      <td>President du groupe \"Nouvelle Gauche\"</td>\n      <td>la motion de renvoi en commission deposee par ...</td>\n      <td>225</td>\n      <td>2017-07-10</td>\n      <td>{'SOC': 4, 'FI': 15, 'Dem': 15, 'LT': 4, 'GDR'...</td>\n      <td>VTANR5L15V3</td>\n    </tr>\n    <tr>\n      <th>VTANR5L15V4</th>\n      <td>SPO</td>\n      <td>scrutin public ordinaire</td>\n      <td>President(e) du groupe \"de la Gauche democrate...</td>\n      <td>l'amendement n? 13 de M. Gilbert Collard et le...</td>\n      <td>202</td>\n      <td>2017-07-10</td>\n      <td>{'SOC': 0, 'FI': 15, 'Dem': 11, 'LT': 4, 'GDR'...</td>\n      <td>VTANR5L15V4</td>\n    </tr>\n    <tr>\n      <th>VTANR5L15V5</th>\n      <td>SPO</td>\n      <td>scrutin public ordinaire</td>\n      <td>Président du groupe \"Nouvelle Gauche\"</td>\n      <td>l'amendement n° 132 de M. Prud'homme et les am...</td>\n      <td>182</td>\n      <td>2017-07-10</td>\n      <td>{'SOC': 4, 'FI': 14, 'Dem': 9, 'LT': 2, 'GDR':...</td>\n      <td>VTANR5L15V5</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 2
    }
   ],
   "source": [
    "# X the features vectors with info about votes\n",
    "\n",
    "X.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "\"l'amendement n° 118 de M. Quatennens et les amendements identiques suivants à l'article premier du projet de loi d'habilitation à prendre par ordonnances les mesures pour le renforcement du dialogue social (première lecture).\""
      ]
     },
     "metadata": {},
     "execution_count": 3
    }
   ],
   "source": [
    "X['libelle'].iloc[10] # Example of the libelle of a vote"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "             SOC   FI  Dem   LT  GDR  LaREM  Agir ens  UDI-I   LR   NI\n",
       "VTANR5L15V1  0.0  0.0  1.0  1.0  0.0    1.0       1.0    0.0  0.0  1.0\n",
       "VTANR5L15V2  1.0  0.0  1.0  1.0  0.0    1.0       1.0    1.0  1.0  1.0\n",
       "VTANR5L15V3  1.0  1.0  0.0  0.0  1.0    0.0       0.0    0.0  0.0  0.0\n",
       "VTANR5L15V4  0.0  1.0  0.0  0.0  1.0    0.0       0.0    0.0  0.0  0.0\n",
       "VTANR5L15V5  1.0  1.0  0.0  1.0  1.0    0.0       0.0    0.0  0.0  0.0"
      ],
      "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>SOC</th>\n      <th>FI</th>\n      <th>Dem</th>\n      <th>LT</th>\n      <th>GDR</th>\n      <th>LaREM</th>\n      <th>Agir ens</th>\n      <th>UDI-I</th>\n      <th>LR</th>\n      <th>NI</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>VTANR5L15V1</th>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>VTANR5L15V2</th>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>VTANR5L15V3</th>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>VTANR5L15V4</th>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>VTANR5L15V5</th>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 4
    }
   ],
   "source": [
    "# y the mutlilabel target vector\n",
    "# To each of the 10 columns corresponds a political group, as defined in Context:\n",
    "y_columns = [\"SOC\", \"FI\", \"Dem\", \"LT\", \"GDR\", \"LaREM\", \"Agir ens\", \"UDI-I\", \"LR\", \"NI\"]\n",
    "\n",
    "# Those corresponds to :\n",
    "# Socialistes et apparentés, France insoumise, Mouvement démocrate et indépendant,\n",
    "# Libertés et Territoires, Gauche Démocratique et République, La République En Marche,\n",
    "# Agir Ensemble, UDI et Indépendants, Les Républicains, Non inscrits\n",
    "\n",
    "# It's an numpy array, but we convert it into a dataframe for easier manipulation.\n",
    "y = pd.DataFrame(y, columns=y_columns)\n",
    "y.index = X.index\n",
    "y.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "   Unnamed: 0 membre_acteurRef membre_civ membre_prenom membre_nom  \\\n",
       "0           0         PA719108         M.       Raphaël     Gérard   \n",
       "1           0         PA719044        Mme         Nadia    Essayan   \n",
       "2           0            PA672         M.  Jean-Jacques     Bridey   \n",
       "3           0         PA719294        Mme      Mireille     Clapot   \n",
       "4           0         PA719890        Mme      Danielle  Brulebois   \n",
       "5           0         PA721210         M.        Alexis   Corbière   \n",
       "6           0         PA720124        Mme          Aude     Amadou   \n",
       "7           0           PA2952         M.      Jean-Luc   Warsmann   \n",
       "\n",
       "                  slug  custom_id      membre_fullname membre_birthDate  \\\n",
       "0       raphael gerard        313       Raphaël Gérard       1968-10-17   \n",
       "1        nadia essayan        427        Nadia Essayan       1957-06-06   \n",
       "2  jean-jacques bridey        527  Jean-Jacques Bridey       1953-05-07   \n",
       "3      mireille clapot        571      Mireille Clapot       1963-10-14   \n",
       "4   danielle brulebois        184   Danielle Brulebois       1947-07-04   \n",
       "5      alexis corbiere        397      Alexis Corbière       1968-08-17   \n",
       "6          aude amadou        499          Aude Amadou       1980-02-29   \n",
       "7    jean-luc warsmann        497    Jean-Luc Warsmann       1965-10-22   \n",
       "\n",
       "  membre_sex             membre_parti  \n",
       "0          H  La République en Marche  \n",
       "1          F      Mouvement Démocrate  \n",
       "2          H  La République en Marche  \n",
       "3          F  La République en Marche  \n",
       "4          F  La République en Marche  \n",
       "5          H      La France Insoumise  \n",
       "6          F  La République en Marche  \n",
       "7          H           Non déclaré(s)  "
      ],
      "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>Unnamed: 0</th>\n      <th>membre_acteurRef</th>\n      <th>membre_civ</th>\n      <th>membre_prenom</th>\n      <th>membre_nom</th>\n      <th>slug</th>\n      <th>custom_id</th>\n      <th>membre_fullname</th>\n      <th>membre_birthDate</th>\n      <th>membre_sex</th>\n      <th>membre_parti</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>PA719108</td>\n      <td>M.</td>\n      <td>Raphaël</td>\n      <td>Gérard</td>\n      <td>raphael gerard</td>\n      <td>313</td>\n      <td>Raphaël Gérard</td>\n      <td>1968-10-17</td>\n      <td>H</td>\n      <td>La République en Marche</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>0</td>\n      <td>PA719044</td>\n      <td>Mme</td>\n      <td>Nadia</td>\n      <td>Essayan</td>\n      <td>nadia essayan</td>\n      <td>427</td>\n      <td>Nadia Essayan</td>\n      <td>1957-06-06</td>\n      <td>F</td>\n      <td>Mouvement Démocrate</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0</td>\n      <td>PA672</td>\n      <td>M.</td>\n      <td>Jean-Jacques</td>\n      <td>Bridey</td>\n      <td>jean-jacques bridey</td>\n      <td>527</td>\n      <td>Jean-Jacques Bridey</td>\n      <td>1953-05-07</td>\n      <td>H</td>\n      <td>La République en Marche</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0</td>\n      <td>PA719294</td>\n      <td>Mme</td>\n      <td>Mireille</td>\n      <td>Clapot</td>\n      <td>mireille clapot</td>\n      <td>571</td>\n      <td>Mireille Clapot</td>\n      <td>1963-10-14</td>\n      <td>F</td>\n      <td>La République en Marche</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0</td>\n      <td>PA719890</td>\n      <td>Mme</td>\n      <td>Danielle</td>\n      <td>Brulebois</td>\n      <td>danielle brulebois</td>\n      <td>184</td>\n      <td>Danielle Brulebois</td>\n      <td>1947-07-04</td>\n      <td>F</td>\n      <td>La République en Marche</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>0</td>\n      <td>PA721210</td>\n      <td>M.</td>\n      <td>Alexis</td>\n      <td>Corbière</td>\n      <td>alexis corbiere</td>\n      <td>397</td>\n      <td>Alexis Corbière</td>\n      <td>1968-08-17</td>\n      <td>H</td>\n      <td>La France Insoumise</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>0</td>\n      <td>PA720124</td>\n      <td>Mme</td>\n      <td>Aude</td>\n      <td>Amadou</td>\n      <td>aude amadou</td>\n      <td>499</td>\n      <td>Aude Amadou</td>\n      <td>1980-02-29</td>\n      <td>F</td>\n      <td>La République en Marche</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>0</td>\n      <td>PA2952</td>\n      <td>M.</td>\n      <td>Jean-Luc</td>\n      <td>Warsmann</td>\n      <td>jean-luc warsmann</td>\n      <td>497</td>\n      <td>Jean-Luc Warsmann</td>\n      <td>1965-10-22</td>\n      <td>H</td>\n      <td>Non déclaré(s)</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 5
    }
   ],
   "source": [
    "# actors is data to be used for feature engineering\n",
    "# It was downloaded from mesdeputes.fr \n",
    "# It can give good insight about who's inside the parliament and who they are. \n",
    "\n",
    "actors.head(8)"
   ]
  },
  {
   "source": [
    "#### Exploring deputies data"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's get a sense of scale of the different political parties (**not** groups) in the French National Assembly. In 2017, the French voted massively for Macron's \"La République En Marche\" and the \"Mouvement démocrate\". Notice how some parties only have 1 deputie: this is especially the case for oversea parties. That's why they tend to group with others. \n",
    "\n",
    "We study parties since they are more fine-grained than groups. Note also that a deputy can be in several groups. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "                                                    parti_nb_membres\n",
       "parti                                                               \n",
       "La République en Marche                                          287\n",
       "Les Républicains                                                  91\n",
       "Mouvement Démocrate                                               53\n",
       "Parti socialiste                                                  21\n",
       "Union des démocrates                                              20\n",
       "La France Insoumise                                               17\n",
       "Parti communiste français                                         15\n",
       "Régions et peuples solidaires                                      9\n",
       "Rassemblement national                                             5\n",
       "Parti radical de gauche                                            3\n",
       "Parti progressiste martiniquais                                    3\n",
       "Non rattachée                                                      3\n",
       "Debout la France                                                   3\n",
       "Calédonie Ensemble                                                 2\n",
       "Cap sur l'avenir                                                   1\n",
       "Non rattaché                                                       1\n",
       "Non déclaré(s)                                                     1\n",
       "Europe Écologie Les Verts                                          1\n",
       "Tapura Huiraatira                                                  1\n",
       "Tavini Huiraatira No Te Ao Maohi - Front de lib...                 1"
      ],
      "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>parti_nb_membres</th>\n    </tr>\n    <tr>\n      <th>parti</th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>La République en Marche</th>\n      <td>287</td>\n    </tr>\n    <tr>\n      <th>Les Républicains</th>\n      <td>91</td>\n    </tr>\n    <tr>\n      <th>Mouvement Démocrate</th>\n      <td>53</td>\n    </tr>\n    <tr>\n      <th>Parti socialiste</th>\n      <td>21</td>\n    </tr>\n    <tr>\n      <th>Union des démocrates</th>\n      <td>20</td>\n    </tr>\n    <tr>\n      <th>La France Insoumise</th>\n      <td>17</td>\n    </tr>\n    <tr>\n      <th>Parti communiste français</th>\n      <td>15</td>\n    </tr>\n    <tr>\n      <th>Régions et peuples solidaires</th>\n      <td>9</td>\n    </tr>\n    <tr>\n      <th>Rassemblement national</th>\n      <td>5</td>\n    </tr>\n    <tr>\n      <th>Parti radical de gauche</th>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>Parti progressiste martiniquais</th>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>Non rattachée</th>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>Debout la France</th>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>Calédonie Ensemble</th>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>Cap sur l'avenir</th>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>Non rattaché</th>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>Non déclaré(s)</th>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>Europe Écologie Les Verts</th>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>Tapura Huiraatira</th>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>Tavini Huiraatira No Te Ao Maohi - Front de libération de Polynésie</th>\n      <td>1</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 6
    }
   ],
   "source": [
    "effectif_table = actors[[\"membre_acteurRef\", 'membre_parti']].groupby(\"membre_parti\").count().reset_index().rename({\"membre_parti\": \"parti\", \"membre_acteurRef\": \"parti_nb_membres\"}, axis=1).set_index('parti').sort_values('parti_nb_membres', ascending=False)\n",
    "effectif_table"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's plot the average age and the share of women in each party."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Mean age')"
      ]
     },
     "metadata": {},
     "execution_count": 7
    },
    {
     "output_type": "display_data",
     "data": {
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "from datetime import datetime\n",
    "from dateutil.relativedelta import relativedelta\n",
    "\n",
    "def yearsago(years, from_date=None):\n",
    "    if from_date is None:\n",
    "        from_date = datetime.now()\n",
    "    return from_date - relativedelta(years=years)\n",
    "\n",
    "def num_years(begin, end=None):\n",
    "    if end is None:\n",
    "        end = datetime.now()\n",
    "    num_years = int((end - begin).days / 365.25)\n",
    "    if begin > yearsago(num_years, end):\n",
    "        return num_years - 1\n",
    "    else:\n",
    "        return num_years\n",
    "\n",
    "# Count the number of actors of each sex in each party\n",
    "parite_table = pd.pivot_table(actors, values=\"membre_acteurRef\", columns=\"membre_sex\", index=\"membre_parti\", aggfunc='count', fill_value=0).reset_index().rename({\"membre_parti\": \"parti\"}, axis=1).set_index('parti')\n",
    "# Compute the share of women of each party\n",
    "parite_table[\"parti_share_women\"] = parite_table[\"F\"] / (parite_table[\"F\"] + parite_table[\"H\"])\n",
    "parite_table.drop([\"F\", \"H\"], axis=1, inplace=True)\n",
    "\n",
    "# Compute age of actors\n",
    "actors['membre_birthDate'] = pd.to_datetime(actors['membre_birthDate'])\n",
    "actors[\"membre_age\"] = actors[\"membre_birthDate\"].apply(num_years)\n",
    "# Compute the average age of actors of each party\n",
    "age_table = actors[[\"membre_acteurRef\", 'membre_parti', \"membre_age\"]].groupby(\"membre_parti\").mean().reset_index().rename({\"membre_parti\": \"parti\", \"membre_age\": \"parti_mean_age\"}, axis=1).set_index('parti')\n",
    "\n",
    "# Group these features together and display them\n",
    "parti_features = effectif_table.join(parite_table).join(age_table)\n",
    "\n",
    "# Plot\n",
    "plt.figure(figsize=(8,10))\n",
    "p1 = sns.scatterplot(x='parti_share_women', y='parti_mean_age', data=parti_features.reset_index(), size = \"parti_nb_membres\", hue=\"parti\", legend=False, sizes=(20,100))  \n",
    "\n",
    "for line in range(0, parti_features.shape[0]):\n",
    "    # Jitter the text a bit to avoid text clusters\n",
    "     p1.text(parti_features['parti_share_women'][line]+0.01, parti_features['parti_mean_age'][line] + 1.2*(0.5 - np.random.random()), \n",
    "     parti_features.reset_index()['parti'][line][:25], horizontalalignment='left', \n",
    "     size='medium', color='black', weight='light')\n",
    "\n",
    "plt.title('Partis positioning according to age and share of women')\n",
    "plt.xlabel('Share of women')\n",
    "plt.ylabel('Mean age')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For the share of women, \"Les Républicains\" are way behind the other big parties by having only 25% of women, when \"La République En Marche\", \"Parti socialiste\" and \"La France Insoumise\" have roughly 50% of women. Parties with 100% of women have only 1 member. \n",
    "\n",
    "Regarding the age of the big parties, \"Mouvement démocrate\", \"Parti socialiste\" and \"Les Républicains\" are the oldest in the assembly. This is the traditional \"left vs center vs right\" paradigma. On the countrary, \"La République En Marche\" and \"Front National\", the two parties that dueled in the Presidential election last round, are younger. This corresponds to new \"liberals vs nationalists\" paradigma."
   ]
  },
  {
   "source": [
    "#### Exploring votes position repartition\n",
    "\n",
    "Let's compare the count of \"for\" and \"other\" positions. The repartition seems roughly balanced. "
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "votes_for = y.sum()\n",
    "votes_other = y.shape[0] - votes_for\n",
    "\n",
    "plt.bar([1, 2], [votes_for.sum(), votes_other.sum()])\n",
    "plt.xticks([1, 2], ('For', 'Other (not for)'))\n",
    "plt.title(\"Number of votes per position\")\n",
    "plt.show()"
   ]
  },
  {
   "source": [
    "But if we do a breakdown per group, the story is not the same. \"LaRem\" (La République En Marche) and \"Dem\" (Démocrates et apparentés) rarely vote *for*. And the Left leaning groups (SOC Socialistes, FI France Insoumise, GDR Gauche Démocrate et Républiquaine) very often vote for, despite being a minority in the Assembly. \n",
    "\n",
    "The intuition we get is that left-leaning groups propose a lot of amendments that the majority groups (La République En Marche and Démocrates et appartentés) systematically block. Those kind \"useless amendments\" (that demandeur group *know* will be blocked, but that are proposed nonetheless, just to **delay** the proposition of law), are known in US politics as [Filibuster](https://en.wikipedia.org/wiki/Filibuster) (*obstruction parlementaire* in French). In the next graph, we will confirm this intuition. \n",
    "\n",
    "From a machine learning point of view, this means that classes are imbalanced. Beware !"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 720x432 with 1 Axes>",
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "ind = np.arange(len(y.columns))\n",
    "\n",
    "plt.figure(figsize=(10,6))\n",
    "\n",
    "p1 = plt.bar(ind, votes_for, width=0.35)\n",
    "p2 = plt.bar(ind, votes_other, width=0.35, bottom=votes_for)\n",
    "\n",
    "\n",
    "plt.ylabel('Number of votes')\n",
    "plt.title('Repartition of \\'For\\' votes')\n",
    "plt.xticks(ind, y.columns)\n",
    "plt.legend((p1[0], p2[0]), (\"For\", 'Other'))\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "source": [
    "#### Vote demandeur repartition\n",
    "\n",
    "Let's now look at the most common demandeur. We recall that the **demandeur** is the group that asks for an amendment. \n",
    "\n",
    "To clean up demandeur's column, we use a custom Transformer available in `estimator.py`.\n",
    "\n",
    "We see that the group La France Insoumise, despite having 17 deputies in the Assemblée, is the leading demandeur. This means that most amendments are proposed by this minority group. This confirm our previous analysis of fillibusting. Depending on your point of view, you can say that :\n",
    "- France Insoumise defends democracy by preventing bad laws from being voted, and asking for radical changes.\n",
    "- France Insoumise asks for useless votes that will be, most of the time, discarded, just to slow down laws' adoptions. "
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "from estimator import FindGroupVoteDemandeurTransformer\n",
    "from ast import literal_eval\n",
    "\n",
    "demandeur = FindGroupVoteDemandeurTransformer()\n",
    "X_b = demandeur.transform(X.copy())\n",
    "# Won't explode without this trick\n",
    "X_b['demandeur_group'] = X_b['demandeur_group'].apply(literal_eval)\n",
    "X_b = X_b[[\"demandeur_group\", \"vote_uid\"]].explode('demandeur_group', ignore_index=True)\n",
    "demandeur_table = X_b.groupby(\"demandeur_group\").count()\n",
    "\n",
    "# Plot\n",
    "demandeur_table.plot(kind='bar', figsize=(10,6))\n",
    "plt.title(\"Amount of propositions for each demandeur group\")\n",
    "plt.xticks(rotation=90)\n",
    "plt.xlabel(\"Demandeur group\")\n",
    "plt.show()"
   ]
  },
  {
   "source": [
    "Now, let's cross this information with the voting groups, to see which group vote most for which group's propositions. To do that, we compute some kind of confusion matrix that indicates the share of times a voting group voted for this demandeur group. "
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "                       Agir ens       Dem        FI       GDR        LR  \\\n",
       "demandeur_group_sigle                                                     \n",
       "Agir ens               0.800000  0.400000  0.200000  0.600000  0.600000   \n",
       "Dem                    0.534884  0.976744  0.488372  0.488372  0.744186   \n",
       "Eco                    0.244444  0.111111  0.822222  0.800000  0.377778   \n",
       "FI                     0.213650  0.215134  0.821958  0.707715  0.265579   \n",
       "GDR                    0.310662  0.255515  0.757353  0.783088  0.393382   \n",
       "LC                     0.666667  0.500000  0.500000  0.500000  0.833333   \n",
       "LR                     0.353805  0.261682  0.377837  0.455274  0.837116   \n",
       "LT                     0.401869  0.476636  0.514019  0.598131  0.598131   \n",
       "LaREM                  0.784661  0.843658  0.286136  0.336283  0.533923   \n",
       "NG                     0.323529  0.205882  0.676471  0.806723  0.420168   \n",
       "SOC                    0.364035  0.300439  0.708333  0.686404  0.493421   \n",
       "UDI Agir I             0.594444  0.394444  0.450000  0.511111  0.683333   \n",
       "UDI-I                  0.557377  0.327869  0.540984  0.540984  0.606557   \n",
       "UNK                    0.833333  0.841667  0.241667  0.275000  0.375000   \n",
       "\n",
       "                             LT     LaREM        NI       SOC     UDI-I  \n",
       "demandeur_group_sigle                                                    \n",
       "Agir ens               0.600000  0.200000  0.800000  0.200000  0.600000  \n",
       "Dem                    0.604651  0.441860  0.581395  0.697674  0.697674  \n",
       "Eco                    0.733333  0.066667  0.955556  0.711111  0.200000  \n",
       "FI                     0.369436  0.182493  0.298220  0.584570  0.213650  \n",
       "GDR                    0.534926  0.233456  0.426471  0.748162  0.321691  \n",
       "LC                     0.555556  0.277778  0.388889  0.555556  0.777778  \n",
       "LR                     0.463284  0.189586  0.576769  0.477971  0.491322  \n",
       "LT                     0.822430  0.242991  0.616822  0.570093  0.532710  \n",
       "LaREM                  0.587021  0.852507  0.687316  0.471976  0.563422  \n",
       "NG                     0.554622  0.172269  0.302521  0.823529  0.331933  \n",
       "SOC                    0.594298  0.234649  0.500000  0.826754  0.344298  \n",
       "UDI Agir I             0.633333  0.244444  0.522222  0.572222  0.738889  \n",
       "UDI-I                  0.655738  0.295082  0.672131  0.606557  0.655738  \n",
       "UNK                    0.533333  0.833333  0.775000  0.366667  0.550000  "
      ],
      "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>Agir ens</th>\n      <th>Dem</th>\n      <th>FI</th>\n      <th>GDR</th>\n      <th>LR</th>\n      <th>LT</th>\n      <th>LaREM</th>\n      <th>NI</th>\n      <th>SOC</th>\n      <th>UDI-I</th>\n    </tr>\n    <tr>\n      <th>demandeur_group_sigle</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>Agir ens</th>\n      <td>0.800000</td>\n      <td>0.400000</td>\n      <td>0.200000</td>\n      <td>0.600000</td>\n      <td>0.600000</td>\n      <td>0.600000</td>\n      <td>0.200000</td>\n      <td>0.800000</td>\n      <td>0.200000</td>\n      <td>0.600000</td>\n    </tr>\n    <tr>\n      <th>Dem</th>\n      <td>0.534884</td>\n      <td>0.976744</td>\n      <td>0.488372</td>\n      <td>0.488372</td>\n      <td>0.744186</td>\n      <td>0.604651</td>\n      <td>0.441860</td>\n      <td>0.581395</td>\n      <td>0.697674</td>\n      <td>0.697674</td>\n    </tr>\n    <tr>\n      <th>Eco</th>\n      <td>0.244444</td>\n      <td>0.111111</td>\n      <td>0.822222</td>\n      <td>0.800000</td>\n      <td>0.377778</td>\n      <td>0.733333</td>\n      <td>0.066667</td>\n      <td>0.955556</td>\n      <td>0.711111</td>\n      <td>0.200000</td>\n    </tr>\n    <tr>\n      <th>FI</th>\n      <td>0.213650</td>\n      <td>0.215134</td>\n      <td>0.821958</td>\n      <td>0.707715</td>\n      <td>0.265579</td>\n      <td>0.369436</td>\n      <td>0.182493</td>\n      <td>0.298220</td>\n      <td>0.584570</td>\n      <td>0.213650</td>\n    </tr>\n    <tr>\n      <th>GDR</th>\n      <td>0.310662</td>\n      <td>0.255515</td>\n      <td>0.757353</td>\n      <td>0.783088</td>\n      <td>0.393382</td>\n      <td>0.534926</td>\n      <td>0.233456</td>\n      <td>0.426471</td>\n      <td>0.748162</td>\n      <td>0.321691</td>\n    </tr>\n    <tr>\n      <th>LC</th>\n      <td>0.666667</td>\n      <td>0.500000</td>\n      <td>0.500000</td>\n      <td>0.500000</td>\n      <td>0.833333</td>\n      <td>0.555556</td>\n      <td>0.277778</td>\n      <td>0.388889</td>\n      <td>0.555556</td>\n      <td>0.777778</td>\n    </tr>\n    <tr>\n      <th>LR</th>\n      <td>0.353805</td>\n      <td>0.261682</td>\n      <td>0.377837</td>\n      <td>0.455274</td>\n      <td>0.837116</td>\n      <td>0.463284</td>\n      <td>0.189586</td>\n      <td>0.576769</td>\n      <td>0.477971</td>\n      <td>0.491322</td>\n    </tr>\n    <tr>\n      <th>LT</th>\n      <td>0.401869</td>\n      <td>0.476636</td>\n      <td>0.514019</td>\n      <td>0.598131</td>\n      <td>0.598131</td>\n      <td>0.822430</td>\n      <td>0.242991</td>\n      <td>0.616822</td>\n      <td>0.570093</td>\n      <td>0.532710</td>\n    </tr>\n    <tr>\n      <th>LaREM</th>\n      <td>0.784661</td>\n      <td>0.843658</td>\n      <td>0.286136</td>\n      <td>0.336283</td>\n      <td>0.533923</td>\n      <td>0.587021</td>\n      <td>0.852507</td>\n      <td>0.687316</td>\n      <td>0.471976</td>\n      <td>0.563422</td>\n    </tr>\n    <tr>\n      <th>NG</th>\n      <td>0.323529</td>\n      <td>0.205882</td>\n      <td>0.676471</td>\n      <td>0.806723</td>\n      <td>0.420168</td>\n      <td>0.554622</td>\n      <td>0.172269</td>\n      <td>0.302521</td>\n      <td>0.823529</td>\n      <td>0.331933</td>\n    </tr>\n    <tr>\n      <th>SOC</th>\n      <td>0.364035</td>\n      <td>0.300439</td>\n      <td>0.708333</td>\n      <td>0.686404</td>\n      <td>0.493421</td>\n      <td>0.594298</td>\n      <td>0.234649</td>\n      <td>0.500000</td>\n      <td>0.826754</td>\n      <td>0.344298</td>\n    </tr>\n    <tr>\n      <th>UDI Agir I</th>\n      <td>0.594444</td>\n      <td>0.394444</td>\n      <td>0.450000</td>\n      <td>0.511111</td>\n      <td>0.683333</td>\n      <td>0.633333</td>\n      <td>0.244444</td>\n      <td>0.522222</td>\n      <td>0.572222</td>\n      <td>0.738889</td>\n    </tr>\n    <tr>\n      <th>UDI-I</th>\n      <td>0.557377</td>\n      <td>0.327869</td>\n      <td>0.540984</td>\n      <td>0.540984</td>\n      <td>0.606557</td>\n      <td>0.655738</td>\n      <td>0.295082</td>\n      <td>0.672131</td>\n      <td>0.606557</td>\n      <td>0.655738</td>\n    </tr>\n    <tr>\n      <th>UNK</th>\n      <td>0.833333</td>\n      <td>0.841667</td>\n      <td>0.241667</td>\n      <td>0.275000</td>\n      <td>0.375000</td>\n      <td>0.533333</td>\n      <td>0.833333</td>\n      <td>0.775000</td>\n      <td>0.366667</td>\n      <td>0.550000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 11
    }
   ],
   "source": [
    "map_group_sigle = {'Agir Ensemble': 'Agir ens', \n",
    "'Gauche démocrate et républicaine': 'GDR',\n",
    "'La France insoumise': 'FI',\n",
    "'La République en Marche': 'LaREM',\n",
    "'Les Républicains': 'LR',\n",
    "'Libertés et Territoires': 'LT',\n",
    "'Socialistes et apparentés': 'SOC',\n",
    "'UDI Agir et Indépendants': 'UDI Agir I',\n",
    "'UDI et Indépendants': 'UDI-I',\n",
    "'Écologie Démocratie Solidarité': 'Eco',\n",
    "'Non inscrits': 'NI',\n",
    "'Nouvelle Gauche': 'NG',\n",
    "\"Les Constructifs : républicains UDI indépendants\": \"LC\",\n",
    "\"Mouvement Démocrate et apparentés\": \"Dem\",\n",
    "np.nan: 'UNK'}\n",
    "\n",
    "X_b['demandeur_group_sigle'] = X_b['demandeur_group'].apply(lambda x: map_group_sigle[x])\n",
    "\n",
    "partisanship_table = X_b.set_index('vote_uid').join(y).groupby('demandeur_group_sigle').sum()\n",
    "opposition_table =  X_b.set_index('vote_uid').join(1 - y).groupby('demandeur_group_sigle').sum()\n",
    "\n",
    "# Normalize by the number of votes\n",
    "partisanship_table_norm = partisanship_table / (partisanship_table + opposition_table)\n",
    "partisanship_table_norm.sort_index(axis=1, inplace=True)\n",
    "\n",
    "partisanship_table_norm"
   ]
  },
  {
   "source": [
    "Since numbers between 0 and 1 are hard to read, we plot the results below. \n",
    "- Blue square means bottom group agrees with left group. \n",
    "- Red square means bottom group disagrees or ignores left group.  \n",
    "\n",
    "Note here that we have some missing data, labeled UNK. The feature extraction can probably still be perfected. We guess that those might be propositions coming directly from the _Government_ or from _Ministers_."
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "cmap = sns.diverging_palette(20, 230, as_cmap=True)\n",
    "\n",
    "plt.figure(figsize=(10,10))\n",
    "sns.heatmap(partisanship_table_norm.sort_index(axis=1), cmap=cmap, center=0.5, square=True, linewidths=.5,)\n",
    "plt.ylabel('Demandeur group')\n",
    "plt.xlabel('Voting group')\n",
    "plt.title('Partisanship between groups\\n1=support, 0=ignore or disagree')\n",
    "# plt.xticks(rotation=60)\n",
    "plt.show()"
   ]
  },
  {
   "source": [
    "Now, the story is pretty clear. LaREM (La République En Marche) only agrees with proposals from their own members. \n",
    "\n",
    "In general, if the voting and the demandeur groups are the same, there is support. This is logical: groups vote for their own amendments. Let's inspect where  intra-group support is low. This is the case for FI (France Insoumise) and UDI-I (UDI Indépendants). It might be the sign of idelogical tensions inside the group. France insoumise, because of an intern crisis. UDI, by nature of the Centrist ideology. \n",
    "\n",
    "LaREM is often criticized for becomming more and more right-leaning on the political spectrum, since Macron appointed as ministers many politicians from LR. But, on this chart, we see that LaREM (center right) and LR (traditional right) agree mostly only on voting against FI, GDR, and SOC proposals. The support, is less clear. LR supports LaREM on a bit over half of theirs proposals, and LaREM support less than half of LR proposals. What seems to be an alliance from the outside, is much less so inside the Assembly. \n",
    "\n",
    "This chart also reveals ideological contradictions between public relations and votes. Notably, LaREM rarely votes for Eco (Ecologistes), despite environment being one of the main campaign points of Emmanuel Macron. \n",
    "\n",
    "The proposals from UDI Agir-I (UDI Agir et Indépendants) are notably cleaving. It's probably an outlier, and values are extreme due to small number of votes involving this group.  \n",
    "\n",
    "To sum up, our statistical analysis and this chart **reveals lots of complex partisan and ideological tensions**. Will the machine learning model be able to pick up on them ?"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Sample estimator\n",
    "\n",
    "Using NLP, we extract features from X, and create a basic fully-connected neural network to fit a classifier. \n",
    "\n",
    "### Feature extraction\n",
    "\n",
    "The first step is to extract features from X. We created several custom Transformers that mostly use regex. \n",
    "\n",
    "1. `FindGroupVoteDemandeurTransformer` on the column demandeur (ex: Le Président du groupe \"La France Insoumise\" M. Mélenchon) to find demandeur's group (La France Insoumise). \n",
    "2. `DecomposeVoteObjetTransformer` on the column libelle, to find:\n",
    "    - libelle_type: the string with the type of vote (ex: l'amendement, le sous-amendement, l'article...)\n",
    "    - libelle_desc: the string with a description of the object vote (ex: loi bioéthique)\n",
    "    - libelle_auteur: the list of actors mentioned in the libelle (ex: M. Melenchon). These auteurs can be different from demandeurs. \n",
    "3. `FindPartyActorTransformer` on the column libelle_auteur to find the corresponding party (ex: Rassemblement National). \n",
    "\n",
    "This way, we extracted meaningful data from unstructured text data. This should give a upstart to our model. \n",
    "\n",
    "### Numerical encoding\n",
    "\n",
    "Now, we need to encode data numerically.\n",
    "\n",
    "- `demandeur_group` is a list of strings, each string being a category. We One Hot encode it : to each category corresponds a column, and if the category is present in the list we write 1, else 0. \n",
    "- `auteur_parti` is pretty much the same as `demandeur_group`.\n",
    "- `libelle_type` is a category. We One Hot encode it.\n",
    "- `libelle_desc` is a text. We encode it using TF-IDF, [which is a very classic way to encode text](https://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.text.TfidfTransformer.html). But you could imagine something else.  \n",
    "- `presence_per_party` is a dictionary. We create one column for each party with corresponding attendance. \n",
    "\n",
    "We then normalize each dimension between 0 and 1.  \n",
    "\n",
    "### Neural Network model\n",
    "\n",
    "Using Keras, we create a fully connected network with 64 hidden relu-activated Dense units and a dropout of 20%. As an output, we use 10 sigmoid activated units, one for each party. \n",
    "\n",
    "We take care of class imbalance by assigning weights to classes according to their observed frequency.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "Pipeline(steps=[('find_group_vote_demandeur',\n",
       "                 FindGroupVoteDemandeurTransformer()),\n",
       "                ('decompose_vote_object', DecomposeVoteObjetTransformer()),\n",
       "                ('find_party_actor',\n",
       "                 FindPartyActorTransformer(actors=     Unnamed: 0 membre_acteurRef membre_civ membre_prenom  \\\n",
       "0             0         PA719108         M.       Raphaël   \n",
       "1             0         PA719044        Mme         Nadia   \n",
       "2             0            PA672         M.  Jean-Jacques   \n",
       "3             0         PA719294        Mme      Mireille   \n",
       "4             0         PA719890        Mme      Danielle   \n",
       "..          ...              ......\n",
       "                                                  FunctionTransformer(func=<function get_estimator.<locals>.encode_party_presence at 0x7f5fabe56ef0>),\n",
       "                                                  ['presence_per_party']),\n",
       "                                                 ('tfidfvectorizer',\n",
       "                                                  TfidfVectorizer(binary=True),\n",
       "                                                  'libelle_desc')])),\n",
       "                ('densify',\n",
       "                 <estimator.DenseTransformer object at 0x7f5fabe2e210>),\n",
       "                ('normalize', Normalizer()),\n",
       "                ('nn',\n",
       "                 NeuralNet(batch_size=500, epochs=1000,\n",
       "                           neuralNet=<function get_estimator.<locals>.create_nn_model at 0x7f5fabe560e0>,\n",
       "                           verbose=0))])"
      ]
     },
     "metadata": {},
     "execution_count": 13
    }
   ],
   "source": [
    "from estimator import get_estimator\n",
    "\n",
    "model = get_estimator()\n",
    "\n",
    "model.fit(X_train, y_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "WARNING:tensorflow:From /home/haxxor/.conda/envs/deep/lib/python3.7/site-packages/tensorflow/python/keras/wrappers/scikit_learn.py:264: Sequential.predict_proba (from tensorflow.python.keras.engine.sequential) is deprecated and will be removed after 2021-01-01.\nInstructions for updating:\nPlease use `model.predict()` instead.\n"
     ]
    }
   ],
   "source": [
    "y_pred = model.predict(X_test)"
   ]
  },
  {
   "source": [
    "We compare the model accuracies for each class, independently. "
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "# Naive prediction accuracy per class\n",
    "\n",
    "ind = np.arange(len(y_columns))\n",
    "\n",
    "accuracies = np.mean(y_pred == y_test, axis=0)\n",
    "\n",
    "plt.figure(figsize=(10,5))\n",
    "plt.bar(ind, accuracies)\n",
    "\n",
    "plt.ylabel('Accuracy')\n",
    "plt.xlabel('Voting group')\n",
    "plt.title(\"Model accuracy per class\")\n",
    "plt.xticks(ind, y.columns)\n",
    "plt.show()"
   ]
  },
  {
   "source": [
    "We however, won't be evaluating our model using accuracy, but using a custom metric defined below. "
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "source": [
    "### Evaluation\n",
    "\n",
    "For evaluation, we use a custom metric, which is a weighted F1-score. \n",
    "- F1-score is generally the go-to when we talk of classification, since it encompasses very well two important metrics : precision and recall. \n",
    "- We don't have as many positive and negative examples inside of each class (data imbalance). F1-score is a fairer metrics than accuracy, that favours models that always yield the same value. \n",
    "\n",
    "Let's first compare accuracy with F1-score. "
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "               SOC        FI       Dem        LT       GDR    LaREM  Agir ens  \\\n",
       "F1 score  0.766667  0.824691  0.779747  0.719755  0.784504  0.80814  0.641758   \n",
       "\n",
       "             UDI-I        LR        NI  \n",
       "F1 score  0.619543  0.652459  0.615385  "
      ],
      "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>SOC</th>\n      <th>FI</th>\n      <th>Dem</th>\n      <th>LT</th>\n      <th>GDR</th>\n      <th>LaREM</th>\n      <th>Agir ens</th>\n      <th>UDI-I</th>\n      <th>LR</th>\n      <th>NI</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>F1 score</th>\n      <td>0.766667</td>\n      <td>0.824691</td>\n      <td>0.779747</td>\n      <td>0.719755</td>\n      <td>0.784504</td>\n      <td>0.80814</td>\n      <td>0.641758</td>\n      <td>0.619543</td>\n      <td>0.652459</td>\n      <td>0.615385</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 16
    }
   ],
   "source": [
    "from problem import CustomF1Score\n",
    "from sklearn.metrics import f1_score, accuracy_score, precision_score, recall_score\n",
    "\n",
    "# Let's compute the individual F1 of each class\n",
    "class_f1_scores = []\n",
    "for i in range(10):\n",
    "    class_f1_scores.append(f1_score(y_test[:,i], y_pred[:,i]))\n",
    "\n",
    "f1_df = pd.DataFrame(class_f1_scores, y.columns, columns=[\"F1 score\"]).transpose()\n",
    "f1_df"
   ]
  },
  {
   "source": [
    "On this graph, you see how F1-score is a bit more pessimistic about LREM, that has lots of negative examples. Probably our model outputs lots of zeros, without much thinking. F1-score helps us alleviate this bias, and see the model's performances more realistically. "
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "plt.figure(figsize=(10,5))\n",
    "\n",
    "plt.bar(ind-0.2, accuracies, width=0.4)\n",
    "plt.bar(ind+0.2, class_f1_scores, width=0.4)\n",
    "\n",
    "plt.ylabel('Score')\n",
    "plt.xlabel('Voting group')\n",
    "plt.title(\"Comparison of model accuracy and F1-score per class\")\n",
    "plt.xticks(ind, y.columns)\n",
    "plt.legend([\"Accuracy\", \"F1-score\"])\n",
    "plt.show()"
   ]
  },
  {
   "source": [
    "Now, about our custom metric. \n",
    "\n",
    "For each of the 10 class, we compute an F1-score, and then average all of those. The weights are the log-proportions of the share of deputies of this group. \n",
    "\n",
    "$$\\forall g^* \\in \\text{Groups}, \\text{weight}_{g^*} = \\frac{\\log (\\text{# deputies in group } g^*)} {\\sum_{g \\in \\text{Groups}} \\log (\\text{# deputies in group } g)}$$\n",
    "\n",
    "$$F1_{log-weighted} = \\sum_{g \\in \\text{Groups}} \\text{weight}_g \\times F1(\\text{class } g)$$\n",
    "\n",
    "This way :\n",
    "- We focus more on big groups (in Democracy, majority groups are the ones that matter)\n",
    "- Using the log-proportion, smaller groups are not discarded (we won't evaluate the model only on its performances with LREM). \n",
    "\n",
    "A good score is close to 1, a bad score is close to 0. \n",
    "\n",
    "Below, we compare $F1_{\\text{non-weighted}}$, $F1_{\\text{linear-weighted}}$ (same formulas as above but without the logarithms), and $F1_{\\text{log-weighted}}$ that we just presented. "
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Non-weighted average of F1 scores: 0.721\nLinear-weighted average of F1 scores: 0.752\nLog-weighted average of F1 scores: 0.725\n"
     ]
    }
   ],
   "source": [
    "# Basic average\n",
    "non_weighted_score = np.mean(class_f1_scores)\n",
    "\n",
    "# Linear weights average \n",
    "linear_score = CustomF1Score(weights_type=\"linear\")(y_test, y_pred)\n",
    "\n",
    "# Log weights average : the metric that will be used in the RAMP challenge. \n",
    "log_score = CustomF1Score(weights_type=\"log\")(y_test, y_pred)\n",
    "\n",
    "\n",
    "print(f\"Non-weighted average of F1 scores: {non_weighted_score:.3f}\")\n",
    "print(f\"Linear-weighted average of F1 scores: {linear_score:.3f}\")\n",
    "print(f\"Log-weighted average of F1 scores: {log_score:.3f}\")"
   ]
  },
  {
   "source": [
    "We see that Log-weighted is more optimistic on the performance of our model than non-weighted F1, while still being more pessimistic than linear-weighted F1. This is kind of a compromise between the two. \n",
    "\n",
    "On the graph below, we compare the scoring weights. "
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
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eyv7778/8+fPZYYcdOOyww0Y73BFLVXW28aUxffr0mjlzZtdhSFoBOCdMK7prr72WLbbYouswlhtDHa8kl1XV9KHqOydMkiSpAyZhkiRJHTAJkyRJ6oBJmCRJUgdMwiRJkjpgEiZJktQBkzBJkjRmrbbaagut+9znPsdXvvKVDqIZXd6sVZIkjcho3ztvae+b1+8brFYVVcVKK/W3r8qeMEmStFw57rjj+NjHPgbAbrvtxtFHH82OO+7IZpttxkUXXQTAI488wvve9z522GEHtt12W0455RQA5s2bxx577MF2223HNttsw3e+8x0AZs+ezeabb87rX/96tt56a2655Za+74c9YZIkabk2f/58Lr30Us477zyOP/54fvzjH/OlL32J1VdfnRkzZvDggw/y/Oc/nxe/+MWsv/76fOtb3+LJT34yf/7zn9lpp53Yb7/9ALj++us5/fTT2WmnnZZJ3CZhkiRpufaKV7wCgO23357Zs2cD8KMf/Ygrr7ySs88+G4C5c+dy/fXXM2XKFD74wQ9y4YUXstJKK3Hrrbdy++23A7DhhhsuswQMTMIkSdJybpVVVgFgwoQJzJ8/H2jmdX36059mzz33XKDuaaedxh133MFll13GyiuvzNSpU3nggQcAWHXVVZdp3M4JkyRJ486ee+7JZz/7WR5++GEAfvvb33Lfffcxd+5cnva0p7Hyyivz05/+lJtvvrmzGO0JkyRJY9b999/PlClTHls+6qijRvS+N73pTcyePZvtttuOqmLttdfm29/+Nq95zWvYd9992WabbZg+fTrPetaz+hX6sFJVnW18aUyfPr1mzpzZdRiSVgCjfTk+LP0l+VIXrr32WrbYYouuw1huDHW8klxWVdOHqu9wpCRJUgdMwiRJkjpgEiZJktQBkzBJkqQOmIRJkiR1wCRMkiSpAyZhkiRpzFpttdW6DqFvvFmrJEkameNWH+X25o5ue8sZe8IkSdJyZdasWey0005su+22vPzlL+fuu+8GYMaMGWy77bZMmzaN973vfWy99dYdR7p4JmGSJGm58vrXv54TTzyRK6+8km222Ybjjz8egEMPPZRTTjmFWbNmMWHChI6jHJ5JmCRJWm7MnTuXe+65h1133RWAgw8+mAsvvJB77rmHe++9l5133hmAV7/61V2GOSImYZIkSR0wCZMkScuN1Vdfnac85SlcdNFFAJxxxhnsuuuurLHGGkyePJlLLrkEgDPPPLPLMEfEqyMlSdKYdf/99zNlypTHlo866ihOP/10DjvsMO6//3423nhjTj31VAC+9KUv8eY3v5mVVlqJXXfdldVXH+WrOUeZSZgkSRqZDm4p8eijjw65/uKLL15o3VZbbcWVV14JwEc+8hGmT5/e19ger74ORybZK8l1SW5Icsxi6v1jkkoyto+WJEkas84991ymTZvG1ltvzUUXXcSHPvShrkNarL71hCWZAJwM/D0wB5iR5JyqumZQvcnAu4BL+hWLJEka/w444AAOOOCArsMYsX72hO0I3FBVN1XVQ8CZwEuHqPd/gROBB/oYiyRJ0pjSzyRsPeCWnuU57brHJNkOWL+qzu1jHJIkaSlVVdchLBeW5jh1douKJCsBJwHvGUHdtySZmWTmHXfc0f/gJEkSkyZN4s477zQRG0ZVceeddzJp0qQlel8/r468FVi/Z3lKu27AZGBr4IIkAM8AzkmyX1XN7G2oqj4PfB5g+vTpngmSJC0DU6ZMYc6cOdgBMrxJkyYtcCuNkehnEjYD2DTJRjTJ14HAY88QqKq5wFoDy0kuAN47OAGTJEndWHnlldloo426DmPc6ttwZFXNBw4HfghcC3y9qq5OckKS/fq1XUmSpOVBX2/WWlXnAecNWvfhRdTdrZ+xSJIkjSU+O1KSJKkDJmGSJEkdMAmTJEnqgEmYJElSB0zCJEmSOmASJkmS1AGTMEmSpA6YhEmSJHXAJEySJKkDJmGSJEkdMAmTJEnqgEmYJElSB0zCJEmSOmASJkmS1AGTMEmSpA6YhEmSJHXAJEySJKkDJmGSJEkdMAmTJEnqgEmYJElSB0zCJEmSOmASJkmS1AGTMEmSpA6YhEmSJHXAJEySJKkDJmGSJEkdMAmTJEnqgEmYJElSB0zCJEmSOmASJkmS1AGTMEmSpA6YhEmSJHXAJEySJKkDJmGSJEkdMAmTJEnqgEmYJElSB0zCJEmSOmASJkmS1AGTMEmSpA6YhEmSJHXAJEySJKkDJmGSJEkdMAmTJEnqgEmYJElSB0zCJEmSOmASJkmS1IG+JmFJ9kpyXZIbkhwzRPlhSX6dZFaSnyfZsp/xSJIkjRV9S8KSTABOBvYGtgQOGiLJ+mpVbVNV04CPAif1Kx5JkqSxpJ89YTsCN1TVTVX1EHAm8NLeClX1l57FVYHqYzySJEljxhP62PZ6wC09y3OA5w6ulOQdwFHARGD3PsYjSZI0ZnQ+Mb+qTq6qTYCjgQ8NVSfJW5LMTDLzjjvuWLYBSpIk9UE/k7BbgfV7lqe06xblTOBlQxVU1eeranpVTV977bVHL0JJkqSO9DMJmwFsmmSjJBOBA4Fzeisk2bRn8SXA9X2MR5Ikaczo25ywqpqf5HDgh8AE4MtVdXWSE4CZVXUOcHiSFwEPA3cDB/crHkmSpLGknxPzqarzgPMGrftwz+t39XP7kiRJY1XnE/MlSZJWRCZhkiRJHTAJkyRJ6oBJmCRJUgdMwiRJkjpgEiZJktQBkzBJkqQOmIRJkiR1wCRMkiSpAyNKwpKcOJJ1kiRJGpmR9oT9/RDr9h7NQCRJklYki312ZJK3AW8HNk5yZU/RZOAX/QxMkiRpPBvuAd5fBb4P/CtwTM/6e6vqrr5FJUmSNM4tNgmrqrnAXOCgJBOAp7fvWS3JalX1+2UQoyRJ0rgzXE8YAEkOB44DbgcebVcXsG1/wpIkSRrfRpSEAUcCm1fVnX2MRZIkaYUx0qsjb6EZlpQkSdIoGO7qyKPalzcBFyQ5F3hwoLyqTupjbJIkSePWcMORk9t/f9/+TGx/JEmS9DgMd3Xk8csqEEmSpBXJSK+O/C7N1ZC95gIzgVOq6oHRDkySJGk8G+nE/JuAecAX2p+/APcCm7XLkiRJWgIjvUXF86pqh57l7yaZUVU7JLm6H4FJkiSNZyPtCVstyQYDC+3r1drFh0Y9KkmSpHFupD1h7wF+nuRGIMBGwNuTrAqc3q/gJEmSxqsRJWFVdV6STYFntauu65mM/8l+BCZJkjSeDXez1t2r6vwkrxhUtEkSquqbfYxNkiRp3BquJ2xX4Hxg3yHKCjAJkyRJWgrD3az12PbfQ5dNOJIkSSuGEV0dmeTpSb6U5Pvt8pZJ3tjf0CRJksavkd6i4jTgh8C67fJvgSP7EI8kSdIKYaRJ2FpV9XXgUYCqmg880reoJEmSxrmRJmH3JVmT9vmRSXaieXakJEmSlsJwt6g4Evhf4P3Ad4CNk/wCWBvYv+/RSZIkjVPD3aJiCs3NWJ8F/Ab4H+BC4GtV9ef+hiZJkjR+DXeLivcCJJkITAeeB+wGfCDJPVW1Zd8jlCRJGodG+uzIJwJPBlZvf24Dft2voCRJksa74eaEfR7YCrgXuIRmfthJVXX3MohNkiRp3Bru6sgNgFWAPwK3AnOAe/ockyRJ0rg33JywvZKEpjfsecB7gK2T3AX8cuCxRpIkSVoyw84Jq6oCrkpyD829weYC+wA7AiZhkiRJS2G4OWFH0PSAPQ94mGZO2P8CX8aJ+ZIkSUttuJ6wqcA3gHdX1R/6H44kSdKKYbg5YUctq0AkSZJWJCN9dqQkSZJGkUmYJElSB/qahCXZK8l1SW5IcswQ5UcluSbJlUl+kmTDfsYjSZI0VvQtCUsyATgZ2BvYEjgoyeBnTV4BTK+qbYGzgY/2Kx5JkqSxpJ89YTsCN1TVTVX1EHAm8NLeClX106q6v128GJjSx3gkSZLGjH4mYesBt/Qsz2nXLcobge/3MR5JkqQxY9g75i8LSV4LTAd2XUT5W4C3AGywwQbLMDJJkqT+6GdP2K3A+j3LU9p1C0jyIuD/APtV1YNDNVRVn6+q6VU1fe211+5LsJIkSctSP5OwGcCmSTZKMhE4EDint0KS5wCn0CRgf+pjLJIkSWNK35KwqpoPHA78ELgW+HpVXZ3khCT7tdX+DVgN+EaSWUnOWURzkiRJ40pf54RV1XnAeYPWfbjn9Yv6uX1JkqSxyjvmS5IkdcAkTJIkqQMmYZIkSR0wCZMkSeqASZgkSVIHxsQd8yVJWtFNPebcUW1v9kdeMqrtafTZEyZJktQBkzBJkqQOmIRJkiR1wCRMkiSpAyZhkiRJHTAJkyRJ6oBJmCRJUgdMwiRJkjpgEiZJktQBkzBJkqQOmIRJkiR1wCRMkiSpAz7AW5KWpeNW70Obc0e/TUl9Z0+YJElSB0zCJEmSOmASJkmS1AGTMEmSpA6YhEmSJHXAJEySJKkDJmGSJEkdMAmTJEnqgEmYJElSB0zCJEmSOmASJkmS1AGTMEmSpA6YhEmSJHXgCV0HIEmS+uC41fvQ5tzRb3MFZk+YJElSB0zCJEmSOmASJkmS1AGTMEmSpA6YhEmSJHXAJEySJKkDJmGSJEkdMAmTJEnqgEmYJElSB7xjvgSjf2dp7yotSRqGPWGSJEkdMAmTJEnqgEmYJElSB/qahCXZK8l1SW5IcswQ5S9McnmS+Ule2c9YJEmSxpK+JWFJJgAnA3sDWwIHJdlyULXfA4cAX+1XHJIkSWNRP6+O3BG4oapuAkhyJvBS4JqBClU1uy17tI9xSJIkjTn9TMLWA27pWZ4DPLeP2xvTDjjllwut22fbdXjdzlP560OPcMiply5U/srtp7D/9PW5676HeNt/XrZQ+Wt32pB9n70ut93zV9591qyFyt+8y8a8aMunc+Md8/jgN3+9UPk7d9+UF2y6FlffNpcTvnvNQuXv32tztt/wqVx281189AfXLVT+4X23ZKt1V+fn1/+ZT59//ULl//KKbdhk7dX48TW384WLblqo/BMHTGPdNZ7Id391G/958c0LlX/2tdvz1FUn8o2Zt3D2ZXMWKj/t0B154sQJnPHL2Xzvyj8sVH7WW3cG4PMX3shPrv3TAmWTVp7A6W/YEYBP/eR6fvHghxYof0rm8bmJnwTgxIcP4PJHN12gfJ3cxScnfgaA4x9+Hdc8uuEC5Rt/80r+9RXbAvCBb17JTXfct0D5lus+mWP33QqAI8+8gj/MfWCB8u02fApH7/UsAA474zLuvv+hBcqf/8y1OGKPJqaDv3wpDzz8yALle2zxNN7ywk0Az73Hc+71w8EPvZ/TJ34UgE/Nfzm/eGSrBcqX6tzr+Yw3XntVz73l+NwbTQcM+r02KQ89/nOvfX38d6/mmtv+skB5v8+9gd/p48lyMTE/yVuSzEwy84477ug6HEmSpMctVdWfhpOdgeOqas92+QMAVfWvQ9Q9DfheVZ09XLvTp0+vmTNnjnK0Wp5MPebcUW9z9qRXj26D3qx1XFguzjXwfBsnRvt881wbG5JcVlXThyrr53DkDGDTJBsBtwIHAn04I5YTo31HdvDLIEnScqxvw5FVNR84HPghcC3w9aq6OskJSfYDSLJDkjnA/sApSa7uVzySJEljSV+fHVlV5wHnDVr34Z7XM4Ap/YxBkiRpLPIB3pIkaewbh9N6lourIyVJksYbkzBJkqQOOBwpSVp6oz1E5FXfWoHYEyZJktQBe8Kk8WYcTl6Vlgm/O1rGTMK0dPxlJUnS42ISJmlsM+GXNE45J0ySJKkDJmGSJEkdMAmTJEnqgEmYJElSB0zCJEmSOmASJkmS1AGTMEmSpA6YhEmSJHXAJEySJKkDJmGSJEkdMAmTJEnqgEmYJElSB0zCJEmSOmASJkmS1IEndB3AWDT1mHNHvc3Zk0a9SUmStByzJ0ySJKkDJmGSJEkdMAmTJEnqgHPCJEnSqHJu9cjYEyZJktQBkzBJkqQOmIRJkiR1wCRMkiSpA07Ml6QVhJOlpbHFnjBJkqQO2BMmdWy0eyfsmdCKwu+OlncmYSsAhyAkSRp7TMIkjRoTfkkaOeeESZIkdcAkTJIkqQMmYZIkSR0wCZMkSeqASZgkSVIHTMIkSZI6YBImSZLUAZMwSZKkDpiESZIkdcAkTJIkqQN9TcKS7JXkuiQ3JDlmiPJVkpzVll+SZGo/45EkSRor+paEJZkAnAzsDWwJHJRky0HV3gjcXVXPBD4BnNiveCRJksaSfvaE7QjcUFU3VdVDwJnASwfVeSlwevv6bGCPJOljTJIkSWNCP5Ow9YBbepbntOuGrFNV84G5wJp9jEmSJGlMSFX1p+HklcBeVfWmdvl1wHOr6vCeOle1dea0yze2df48qK23AG9pFzcHrutL0P21FvDnYWstP9yfsWs87Qu4P2PdeNqf8bQv4P6MFRtW1dpDFTyhjxu9FVi/Z3lKu26oOnOSPAFYHbhzcENV9Xng832Kc5lIMrOqpncdx2hxf8au8bQv4P6MdeNpf8bTvoD7szzo53DkDGDTJBslmQgcCJwzqM45wMHt61cC51e/uuYkSZLGkL71hFXV/CSHAz8EJgBfrqqrk5wAzKyqc4AvAWckuQG4iyZRkyRJGvf6ORxJVZ0HnDdo3Yd7Xj8A7N/PGMaQ5Xo4dQjuz9g1nvYF3J+xbjztz3jaF3B/xry+TcyXJEnSovnYIkmSpA6YhI2CJP8nydVJrkwyK8lzk0xM8sn2kUzXJ/lOkik973lGkjOT3JjksiTnJdmsy/0YTpJH2v0b+JmaZLck3+s6tqH0xHt1kl8leU+ScXfOJ5nX8/qSdp9/n+SO3s+qwxAXKcnTk3w1yU3t9+CXSV7enldzk1zRPvrswiT79LzvuCS3tvt2TZKDlmHM84av9VjdQ3o+h98keXdPWe8+DPys0e57JXlTT91p7br3jtI+vKxt71kjrP/FIZ54slxof09dNWjdcUnem+S0JL9rfz/8NslXBv2enp1krSHaPCTJfyyL+EdqqPOyy+/JaGjP0Y/3LL83yXHt6+NG6/vQpXH3B2lZS7IzsA+wXVVtC7yI5ga0/wJMBjavqk2BbwPfTAv4FnBBVW1SVdsDHwCe3sU+LIG/VtW0np/ZXQc0jIF4twL+nuYRWsd2HFNfVdVzq2oa8GHgrLH8WbXfg28DF1bVxu334ECa29kAXFRVz6mqzYEjgP9IskdPE59o9/WlwClJVl520S+Rs9o4nw/8nyS9t+75xKDv1D3t+quAV/XUOwj41SjGdBDw8/bfYVXVm6rqmsHr0zyebnn3vqp6Ns09KK8Azm+v6B8vlpfvyVAeBF4xVCI8XpiEPX7rAH+uqgcB2hvN3gMcCry7qh5p159Kc0LtDvwd8HBVfW6gkar6VVVdtIxjX2FU1Z9obvh7eJsHT0jyb0lmpOnBfCtA2wvxs7bn8qYkH0nymiSXJvl1kk263ZNxZXfgoUHfg5ur6tODK1bVLOAE4PAhyq4H7gee0r9QFy/Jvm0v5BVJfpxkof9QVdWdwA00vzOGczMwqe0pDLAX8P1RinU14AU0z+49sGf9Skk+0/bY/U+a3vlXtmUXJJnevp6X5ONJfgXsPKjtTZL8oO3VvGigp63tcfpUkv9tv1cD7a7T9nLOSnJVkl1GYx+XRjU+AfyR5j9s48pY+J4shfk0k/HfPVzF5ZVJ2OP3I2D9tiv7M0l2BZ4J/L6q/jKo7kxgK2Br4LJlHOdoeGLPsMm3ug5mSVXVTTS3S3kazR+guVW1A7AD8OYkG7VVnw0cBmwBvA7YrKp2BL4IvHOZBz5+bQVcvgT1LwcWGj5Lsh1wfZtod+XnwE5V9Rya5+S+f3CFJBsAk4Are1a/u+c79dNBbzmb5urx59Hs+4OjFOtLgR9U1W+BO5Ns365/BTAV2JLmvN956LezKnBJVT27qn4+qOzzwDvbXs33Ap/pKVuHJvnbB/hIu+7VwA/bnppnA7OWfrdGzZDn2fJujHxPlsbJwGuSrN51IP3Q11tUrAiqal77S2wXmh6us2iGIsejv7a/LMeDFwPbDvyPnOZpDZsCDwEzquoP8NijtH7U1vk1zWesPkhyMs0f6YeA9w1VZdDyu5McCmwG7Nvn8IYzBTgryTrAROB3PWUHJHkhzR/2w9tb8wz4RFV9bBFtfp3m98mzgK/RJGOj4SDg39vXZ7bLl9Ec+29U1aPAH4dICgc8Avz34JVtD9vzgG80nXcArNJT5dtt29f09BTOAL7cDpF9u+3xHG2LugXAotYPPs+Wd2Ppe7LEquovSb5CMyXhr13HM9rsCRsFVfVIVV1QVcfSDJfsC2yQZPKgqtsDV7c/26NlKsnGNH9A/kTzi/adPXNxNqqqgWSrt8fh0Z7lR/E/LqPpamC7gYWqegewBzDkM9aA5wDX9ix/op3v94/Al5JM6legI/Bp4D+qahvgrTQ9XgPOaueLPg/4SJJnjKTBqvoj8DDNfMafjEaQSZ5KMwz8xSSzaZLdV6UnaxqBBwamWQyyEnDPoDluW/SU936vAlBVFwIvpHmE3WlJXr8EcYzUnSw8BPdUFv0MwsHnGUne0dNjuW4fYuynsfQ9WVqfpBm9WLXjOEadSdjjlGTzJJv2rJpG84Dx04GTBiautr9cngSc3/6skubB5APtbNvlfIjxLsnawOdo/lAWzZMc3jYwSTXJZknG3Rd8jDufZt7T23rWPWmoikm2Bf6JZmhiAe3TN2byt0egdWF1/vZs3CHjqKqZwBnAu5ag3Q8DRy8i6VkarwTOqKoNq2pqVa1P02u3C/AL4B/buWFPB3Zbkobb6Re/S7I/NBdeJHn24t6TZEPg9qr6As1w/3aLq780qmoe8Icku7fbfCrNHLsFhlLbeI+gGTb9waA2Tu5JLG8b7RiXhTHyPVkqVXUXTc/wG7uOZbT5v/rHbzXg00nWoJlEeAPNBPB7gY8Bv03yKPAb4OUDz8ZM8nLgk0mOBh4AZgNHLuvgx7knJpkFrEzz2ZwBnNSWfZFm/svlbS/AHcDLln2Io+JJSeb0LJ9E8xiwMa2qKsnLgE8keT/NZ3AfcHRbZZckV9AkZn8CjqiqRfUInQB8NckX2iGvfhrqeB9HMwx3N01yudFQbwROpDnnBqYsvDvJa3vKX9Zbuar+d1Qi/puD2hh6/Xe7fqAn8hqaK7wvB+YuYfuvAT6b5EM037szWfxVnbsB70vyMDAP6EdPGG27JycZ+P4fX1U3th2A/5bkn2jOs4uBv6uqh/oURz8NdV4Otiy/J6Pt4wxxYc7yzjvmS5KAZl5XO891TeBS4PntsKikPrAnTJI04Httr/5E4P+agEn9ZU+YJElSB5yYL0mS1AGTMEmSpA6YhEmSJHXAJEzSmJDkp0n2HLTuyCSfXcx7PjhoebRv6SBJfWMSJmms+Bo9D5RuHdiuX5QFkrCqGq1H+4zIwM2YJWlpmIRJGivOBl6SZCJAkqnAusBFSQ5K8uskVyU5sS3/CH97qPx/tevmtf/uluSCJGcn+U2S/xp4NE+Sf2jXXZbkU0m+NziQJE9K8vUk1yT5VpJLkkwf2EaSjyf5FbBzkqPauK5KcuRA7Emu6mnvvUmOa19fkOTf27ivSrJjfw6npLHOJEzSmNA+muRSYO921YE0jypZh+Yu77vTPBZshyQvq6pjaB8qX1WvGaLJ59A8hWJLYGPg+e1z804B9q6q7Vn0cyrfDtxdVVvSPC6p91mvqwKXVNWzaR4ofCjwXGAn4M1JnjOC3X1SVU1rt/PlEdSXNA6ZhEkaS3qHJAeGIncALqiqO6pqPvBfNA99Hs6lVTWnfTzLLJrHVD0LuKmqftezvaG8gOaRO1TVVcCVPWWP0DzqZ6Det6rqvvYZhd+keQ7jcL7Wtn0h8OT2BqmSVjAmYZLGku8AeyTZjqa36LLH0daDPa8fYfSeEPLACB6oPZ8Ff79OGlQ++C7Z3jVbWgGZhEkaM9repJ/SDNEN9FJdCuyaZK12IvxBwM/asoeTrLwEm7gO2LidbwZwwCLq/QJ4FUCSLYFtFlHvIuBl7RyyVYGXt+tuB56WZM0kqwD7DHrfAW3bLwDmVtWSPihb0jjgsyMljTVfA75FOyxZVX9IcgxNchbg3Kr6Tlv388CVSS5fxLywBVTVX5O8HfhBkvuAGYuo+hng9CTXAL8BrgYWSpSq6vIkp9EkigBfrKorAJKc0K6/tW2j1wNJrgBWBt4wXNySxiefHSlphZJktaqa114teTJwfVV9YlCdCcDKVfVAkk2AHwObV9VDo7D9C4D3VtXMx9uWpOWbPWGSVjRvTnIwMBG4guZqycGeBPy0HeoM8PbRSMAkqZc9YZIkSR1wYr4kSVIHTMIkSZI6YBImSZLUAZMwSZKkDpiESZIkdcAkTJIkqQP/H4kg0n0TJJuwAAAAAElFTkSuQmCC\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "plt.figure(figsize=(10,5))\n",
    "plt.bar(ind-0.2, CustomF1Score(weights_type=\"linear\").weights_, width=0.4)\n",
    "plt.bar(ind+0.2, CustomF1Score(weights_type=\"log\").weights_, width=0.4)\n",
    "plt.plot(ind, .1*np.ones(ind.shape[0]), \"--\")\n",
    "\n",
    "plt.ylabel('Weight')\n",
    "plt.xlabel('Voting group')\n",
    "plt.title(\"Scoring weights for each political group\")\n",
    "plt.xticks(ind, y.columns)\n",
    "plt.legend(['Uniform', 'Linear', 'Log'])\n",
    "plt.show()"
   ]
  },
  {
   "source": [
    "## Conclusion \n",
    "\n",
    "We found that the voting position of political groups is, to an extent, predictible. \n",
    "\n",
    "Rivalry between political groups is the main driver of their votes. When a political group votes, it doesn't try to chose the best option for the nation, as it reaffirms its position : its ideology, its support of a group, its opposition to another group. \n",
    "\n",
    "Paradoxically, we can say that this is reassuring for us, citizens. This means that :\n",
    "- Deputies don't vote randomly, which is always good to know. \n",
    "- Deputies defend the positions they have been elected for.\n",
    "- Rivalry in the National Assembly means that there is a political debate, and this is what democracy is all about.\n",
    "\n",
    "We conclude this study of National Assembly with a broader understanding of forces driving votes in the National Assembly. "
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