HybridSVD_AMZ.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "env: MKL_NUM_THREADS=16\n",
      "env: OMP_NUM_THREADS=16\n"
     ]
    }
   ],
   "source": [
    "%env MKL_NUM_THREADS=16\n",
    "%env OMP_NUM_THREADS=16"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from ipypb import track\n",
    "\n",
    "from polara.evaluation import evaluation_engine as ee\n",
    "from polara.evaluation.pipelines import (find_optimal_config,\n",
    "                                         set_config)\n",
    "\n",
    "from hybrids import HybridSVD, HybridSVDColdStart\n",
    "from data_preprocessing import (get_amazon_data,\n",
    "                                get_similarity_data,\n",
    "                                prepare_data_model,\n",
    "                                prepare_cold_start_data_model)\n",
    "from utils import (report_results, save_results,\n",
    "                   apply_config, print_data_stats,\n",
    "                   save_training_time, save_cv_training_time)\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from polara.recommender import defaults\n",
    "defaults.memory_hard_limit = 15 # allowed memory usage during recommendations generation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "seed = 42"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "experiment_name = 'hybridsvd'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Experiment setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_labels = ['AMZe', 'AMZvg']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "ranks_grid = [1, 5, 10, 15, 20, 30, 50, 60, 75, 100, 125, 150, 200, 250, 300,\n",
    "              350, 400, 500, 750, 1000, 1250, 1500, 1750, 2000, 2500, 3000]\n",
    "svd_ranks = {'AMZe': ranks_grid,\n",
    "             'AMZvg': ranks_grid\n",
    "            }\n",
    "\n",
    "hybridsvd_weights = [0.1, 0.3, 0.5, 0.7, 0.9]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "topk_values = [1, 3, 10, 20, 30]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "target_metric = 'mrr'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_dict = dict.fromkeys(data_labels)\n",
    "meta_dict = dict.fromkeys(data_labels)\n",
    "similarities = dict.fromkeys(data_labels)\n",
    "sim_indices = dict.fromkeys(data_labels)\n",
    "feature_idx = dict.fromkeys(data_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_data = [data_dict, similarities, sim_indices, meta_dict]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Amazon Electronics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "lbl = 'AMZe'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_dict[lbl], meta_dict[lbl] = get_amazon_data('/mnt/bulky/datasets/recsys/amazon/ratings_Electronics.csv',\n",
    "                                                 meta_path='/mnt/bulky/datasets/recsys/amazon/meta/meta_Electronics.json.gz',\n",
    "                                                 implicit=True,\n",
    "                                                 pcore=5,\n",
    "                                                 filter_no_meta=True,\n",
    "                                                 flat_categories=True) # take only bottom level categories"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "similarities[lbl], sim_indices[lbl], feature_idx[lbl] = get_similarity_data(meta_dict[lbl])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(meta_dict[lbl].applymap(len).sum(axis=1)==0).mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Amazon Video Games"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "lbl = 'AMZvg'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_dict[lbl], meta_dict[lbl] = get_amazon_data('/mnt/bulky/datasets/recsys/amazon/ratings_Video_Games.csv',\n",
    "                                                 meta_path='/mnt/bulky/datasets/recsys/amazon/meta/meta_Video_Games.json.gz',\n",
    "                                                 implicit=True,\n",
    "                                                 pcore=5,\n",
    "                                                 filter_data={'categories': ['Games']}, # filter uniformative category\n",
    "                                                 filter_no_meta=True,\n",
    "                                                 flat_categories=True) # take only bottom level categories"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "similarities[lbl], sim_indices[lbl], feature_idx[lbl] = get_similarity_data(meta_dict[lbl])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(meta_dict[lbl].applymap(len).sum(axis=1)==0).mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AMZe\n",
      "{'userid': 124895, 'asin': 44843}\n",
      "density 0.019153791836615672\n",
      "similarity matrix density 1.1054998336712965\n",
      "AMZvg\n",
      "{'userid': 14251, 'asin': 6858}\n",
      "density 0.13281340440589384\n",
      "similarity matrix density 9.081814734274188\n"
     ]
    }
   ],
   "source": [
    "print_data_stats(data_labels, all_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Standard experiment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "def prepare_recommender_models(data_label, data_models, config):\n",
    "    data_model = data_models[data_label]\n",
    "    svd = HybridSVD(data_model)\n",
    "    models = [svd]\n",
    "    apply_config(models, config, data_label)\n",
    "    return models\n",
    "\n",
    "\n",
    "def fine_tune_hybridsvd(model, ranks, weights, label, record_time=False):\n",
    "    param_grid = [(w, r) for w in weights for r in reversed(list(sorted(ranks)))]\n",
    "    param_names = ('features_weight', 'rank')\n",
    "    best_svd_config, svd_scores = find_optimal_config(model, param_grid, param_names,\n",
    "                                                      target_metric,\n",
    "                                                      return_scores=True,\n",
    "                                                      force_build=False,\n",
    "                                                      iterator=lambda x: track(x, label=label))\n",
    "    model_config = {model.method: dict(zip(param_names, best_svd_config))}\n",
    "    model_scores = {model.method: svd_scores}\n",
    "    try:\n",
    "        if record_time:\n",
    "            max_rank = max(ranks)\n",
    "            save_training_time(f'{experiment_name}_rank_{max_rank}', model, svd_scores.xs(max_rank, level='rank').index, label)\n",
    "    finally:\n",
    "        return model_config, model_scores"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "config = {}\n",
    "scores = {}\n",
    "data_models = {}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
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       "           AMZe [█████████████████████████████████████████████] 130/130 [05:11:54<00:08, 143.95s/it]"
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   "source": [
    "for label in track(data_labels):\n",
    "    data_models[label] = prepare_data_model(label, *all_data, seed)\n",
    "    config[label], scores[label] = fine_tune_hybridsvd(HybridSVD(data_models[label]),\n",
    "                                                       svd_ranks[label],\n",
    "                                                       hybridsvd_weights,\n",
    "                                                       label, record_time=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
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     "name": "stderr",
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     "text": [
      "/home/evfro/miniconda3/envs/polara_dev/lib/python3.6/site-packages/pandas/plotting/_core.py:998: UserWarning: Attempted to set non-positive left xlim on a log-scaled axis.\n",
      "Invalid limit will be ignored.\n",
      "  ax.set_xlim(left, right)\n"
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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YV1uZE4//C799gyc2H+JjZ9XTHQzz4GsNfPvKk/jwabPSHzyEr/zhTf6wfj8bbrk46xsHqOHPCYnkFaV4+ffH3qLc7+WzSSZfk3HLhxbx/I5mvvDbjay5+eyYgXp+exM33fsaC6ZVcu8NKzKWZspKvJw6Z/IR20SEU2ZP4pTZk5hbU8F/P/k2DW29KecNRgv72nrpH4iycFolVaV+HljXwF+3HsYYjtD4ARbZht/nEcJRQ2N7X14mepu6gnz9j5sYiBqW1k3k2NpKaicEaOoK8tzbTbT1hli5dOjyJXfsa7Wko7tf3BPb1tDel6R1Ym57eAtv7m/n1T3WnF9DWy/zXDoliSiaJG2jTZIaa+jva/G3Hc089dZhPnnBfGoqAxkdO7G8hG9etoStBzr50dM7ASsFwSfuWce8KRX88sYVVJfnvt7B5cssg/T71/fnvO98sO1gF2Bp+KfWW0EGj2y0vPqhHv9Se43ER86wAhkcI5pr/t+zO+kJRQiFo7yyp5Xl9ZOonRDgUGeQnzy7k//+89tZ993SM/jUcNaxNVSV+ujIMDXHXX/bHTP6AO/Yi92ypSgMf2lpKS0tLWqc8oSTj7+0tDR94zFMxF6sNXNiGR8/uz6rPi5efAwrl87gB09v596X93Lj3euYU1POrz6xgkm2TJNr6iaVc+a8Gn7/ekNR/I28fcgy/AumTWDqhFLm1JTz9DZrxf7QeYoV82p48nPncuM5Vp2IfW3DM3jJeHl3C4umV8U+nzpnEnWTyjjU1c+uph5aurOTfIwxtPYMGvn6KRXUVAZo6cnM8Jf5vUd8Hq7hLwqpp66ujoaGBootnUMx4VTgGs88+uYBthzo5HurllI65A8tE2790GL+tqOZrz20iWNrK/jVJ87I+OkhU648tY4v/PYNXtvbxvL6yekPGEHePtTNzIllMcnr1DmTYk8rQz1+sG4Q4UgUn0fYN0yDl4ho1LDzcA+rTp9FbyjMnpZeTp0ziagxGDO4orgvFKGsJLProrM/zEDEcN2K2fxp00E+dlY92w52HXEzcENFwEufnSLEI7CraXj5nIrC8Pv9fq0MpeQVYwx3PLeTebUVfOikGcPqa1JFCd/58FLueXEP/3HFidROyK/RB7hkyTHc8sdNPPhaQxEY/i6OO2ZC7PPyOZP5/ev7qSjxUlWa2CT5vB5mTCxjX1vupZ6Dnf30DUQ4traScMQg0szMiWUcmnzkXEJzd5BZkzObQ3GeFJbXT+Kbl58IwOSKkoxuYMYYOvvCVAZ8vPv4qbT2hHhu+/Cc4KKQehQl37y0q4VN+zv5+3fNy8kq2XMX1nLnx05jalVh5LOKgI9Llkzn0Y0HRkVa42QMRKLsbOpm4bRBw3+arfMfU12asiDQrMllefH4dzZZawqOra3kKx84gYc+dTYiwpyaI418cxZyj+PZ11QM3vxrKkp462AXD6xzl/Y5GI4SikT51AXz+f7qU3j38VPZ1dRDY4YTxPGo4VcU4I7ndjGlsoTLT8kucmM0cOWpM+kKhnli88GRHkpS9rb0MBAxLJw2GJFybG0l1WX+hDJPPLMmldOQB41/x2HL8M+fWkmp30t1mTUBX1NRQkWctNPcnXmtBOeYyXHzOxPLrff/68GNrvro7LMy/VaVWU9DzlNHNjciBzX8yrhn28EuntnWxPVn1g9L2x9pzphbw8yJZfxuFEf3bDlgTezGSz0ej3DrpYu46dxjUx47a3I5zd2hnKdK3tnUTVWpjymVR06+i8gR0s7fdjSztyUzbd2J6JkSN8fT2pOZwe7stw1/qXVDmmhHhrX3DmTUTzxq+JVxz8+e30Wp3xMLGSxWPB7himUzeWF7Uyy75GjjrQOd+DzC/KlHxqBffkod5y2sTXls3SQr4qchxzr/zsM9zKutTCgzzZ1SEfPW735xDxf832cy6rvNlnomVQyG8cbf4PpC6WW5jj7rRldlP4lMsg1/2zCqtanhV8Y1hzv7eWjDfj68fNYRj+PFyiVLphM1lnc6Gtl6wEpXEPBl/mQ12/a+hxvRMpT97X2xvofyLxcfx4+uWxb7nGmG6M7+MKV+zxHfd/7USr5tp6X4ykNvEknT6aDHb0k9jlSkHr+iZMndL+4hHDWxOPFi5/hjJlBV6uOV3aMzCe7WA12cEBcvnwknTK+ixOfJaXWyaNRwoKOPGRMT5zmaP7WSM+YNJukbKgelo7NvICbRxONEev3+9f28/k7bUfvj6eq3PP4JtuF35iDU8CtKFvQEw/zy5b28b/ExY6bSlccjnD53MmtHoeFv6wlxsLOfE6ZPSN84AaV+L8tmT+SlHNU8hsHc+zMnuou+io/OcUNXfzhmsOOZGhfimy73UI9djMepW+D3eijze3ly68GsF+yp4VfGLQ+s20dnf5i/P3feSA8lp6yYW8Pu5h4OjzKdf+sBK69+th4/wJnzprDlQCcdw/B249lvh0Qm8/gdHv3MORxbWxFbROWWzv6BmDYfT/zajmCaPocafoC+gQib9nfyq7XvZDQeBzX8yrgkHIly5wu7WT5nEstmj62iNKfPtRZwjTavf6udo+f4Y7I3/GfMm4wx8EqO5B6n3GM6w794RjWnz62h18VkbDzJpJ74J4dgOHXZzp6gdc7yuIizL11ipfV++I2hVXDdoYZfGZf8afNBGtr6xpy3D7B4RhUVJd5Rp/NvPdDJlMrAsFYynzxrIgGfh5dzJPc4i6BmuKhlUOb3Zrw4LpnU4/UIj3z6HMCF4Q9ZE8S+OEnoH887lvMW1tKf5thkqOFXxh3GGH763C7mTqngPSdMG+nh5Byf18Op9ZNZuzt3Wngu2HqgM2t936HU7+XkWRPTToi6pbGjz0oVUZY+e01ZiYe+gUhGunoyqQcgYOfTD4ZT30y6g+GEqbxLfB5CavgVxR2v7G7ljYYOPvGuuWOuiLnDirmTeftQd8bJwPLFQCTK9kPdR2TAzJY5k8vZn6NY/sZ2K6InVaoIh/ISH5GoIRRxb2w7+8MJpR4gFuIZHEjdX28wfIS+71Di86S9aSRDDb8y7rjjuV1MrijhymVjNxvpClvnHy1yz/62PkKR6FELt7Jh+sQymrqDDGRggJPR2N6fVt93cFZ194fcnbd/wMrvn0jqAQj4HY8/dX/dwQjlJUf3EVCPX1HcseNwF3996zDXnzmnqNMzpOPEumoCPs+oMfxO6oJcZCqdXl2KMeRkdXJrT4gal7H55XbeHreRPbGFV8OUenqCYSoDR1+rAZ8n7U0jGWr4lXHFz57fTcDn4e+KPD1DOgI+L6fMnsgre0aHzu8kK5uSg7oETnnGAx3DN/xd/YmjbhLhFENxmyvIWXiVLNV0TOpxMbmbSOoJ+Lzq8StKOg519vP79fu56tS6vBdGGQ2smFvDlsbOmOc5krTYht+td50Kp0rXcA2/MYbuYOKom0Q4T4iuPf6+I5OrDcUplp5O4+9JofGr4VeUNPzo6R1Eo4Z/PC91Fsixwoq5k4kaeG1PbiJghoNTkCQX+ZCm26tsDwwjHz1AbyhC1JAwYiYRjtTjNqTTScCWrGqX1yP4vZJW6ukNRY5ID+1Q4tXJXUVJyYGOPn7zyj6uOrUu4ypKxcopsyfh98qoWMjV0hNiQqkvq+RsQ6kq9VMZ8A3b4++2V8RWuvT4HQPudhFXv22Uh9bLjSfg86aVevoHIgnnowI+D1FjLUbMFDX8yrjgR0/vJGoMn7pg/kgPpWCUlXhZOG0Cm/Z3jPRQaOkJ5UTfdzimupQDHcPz+B0N3q3H7xhwN6mUAfptCSdVEEHARUhmKBylJEE+n5hUlIXco4ZfGfM0tvdx/6v7uHr5rHHj7TssnlHF5saOrJN55YqW7iA1OUx7Pb26NGcev1uNvyzDqB7nBlHqT25mAz5PWo0/FInGjHw8zrZsdH41/MqY54dP78BguPnd48fbd1g8o5q23oGcRMAMh5Zu92GTbsiJ4Y95/JlF9bj2+MOO4U/h8ftTSz3RqGEgYhIafkc2y2RBmYMafmVM09DWywPr9nHNabOY6XKhzlhi8Qxrpezmxs4RHUdLT5DJGaY0TsX06jKau4NZR7UAdAetqJtMJ3fdevy5kHoco57K40/3xJAINfzKmOaHT+9AkHGl7cdzwvQqRGBz48jp/JGoobUnlHERk1TMmDj8RVxDC5yko9Sf4eTugEupJ8XNy9mXSON3FoCFIplH9qjhV8Ys+1p7+e26BladPisW+z3eqAj4mFtTMaIef3tviKghpxp/3SRrrmZ3c/ZlGGNRPS49/oDPg0fch3P2D0QQSWy0B/v0pvTYnSeaQAqPv189fkUZ5AdP7cDjET55/vj09h0Wzahiywga/pYeZ/FW7qSek+qq8Qis25v9GgVH40+0OCoRIkKZ35tBVE+EUp83ZQK4gN+d1JMoDDY2uZsvjV9E3ici20Rkh4h8KcH+gIjcb+9fKyL1Q/bPFpFuEflCxiNUlCx4p6WXB19v4NrTZ3NMtbuyemOVxTOq2d/eR9sIZepsthdv5XJyd0KpnxOmV/HqMNYodAfDBHyehPp5MspKvPRmoPEnW7zlkC6O3/H4E0/u5jGqR0S8wA+BS4BFwGoRWTSk2Y1AmzFmPvBd4FtD9n8XeDzj0SlKltz+1HZ8HuGfzh8fq3RT4UzwbjkwMl5/Sw7z9MRzWv1k1u9ryzpLZ1cG6Rocqkr9dPS5S4HRNxChNM1NJeD3pJSOnKeBVIY/X3H8pwM7jDG7jDEh4D5g5ZA2K4F77PcPAheK/XwjIpcBu4DNGY9OUbJgT3MPv1+/n2tXzGZa1fj29iE+smdkJniddA251PjBMvz9A9Gs5y+6+xMXOEnFlMoAzV1BV22TrbiNJ510FEoxuVvi9R7RJhPcGP6ZwL64zw32toRtjDFhoAOoEZEK4H8D30h1AhG5SUTWici6pqYmt2NXlITc/tQOy9sfJzl50lFTGeCYqtIRm+Bt6QnhEZhYnmvDb9VKXpdl/V0rQZu7GH6H2gkBmrrdGv4ogTSGvzyNdJRS6onl889PVE+imYmhywCTtfkG8F1jTHeqExhj7jDGLDfGLK+trXUxJEVJzO7mHv6wvoGPnDGHqertx7BW8I6M4W/uDjG5ogRvjqudTa0qZUZ19je07Dz+kow8/rIUoZxgVfVKFR6ayvA7TwH58vgbgFlxn+uAoaXdY21ExAdUA63ACuDbIrIH+Gfg/4jIzRmPUlFc8v2/bqfE5xk3GTjdsnhGFbuaul1HpOQSK11DftJgT64sca25D6Wzf8B1gjaH2gkBOvvDrkI63Ug95SVWTv1INHFKjWCKBVyOx58vw/8qsEBE5opICbAKWDOkzRrgo/b7q4CnjMW7jDH1xph64H+AfzfG/CDjUSqKC3Y2dfPHDfu5/sz6nFR6GkssmlFN1MBbBwvv9bdkUOUqU6pK/bG895nSHQwzIQuNHwZDVFPRH3Zn+CF5cZfUGn8eJ3dtzf5m4AlgK/CAMWaziNwmIpfaze7E0vR3AJ8Hjgr5VJR8890n3ybg83LTufNGeiijjpFM3dDSHcxb4ZvqMvdRNkPpDoYz9vgdw9/kQu7pH4imXLUL6VM9B10s4MrG43f1rY0xjwGPDdl2S9z7fuDqNH3cmvHoFMUlf958kEc2HuAzFy7IedjgWKBuUhnVZf6RMfw9oZxH9DhUlfqzqjAWCkfp6BtgUoYTzs6TpBudvy9kLeBKRXkawz+4cjdRPn5N0qaMY5q7g3z592+yaHoVN4/TnDzpEBFOqqvm2W2HC6rzB8MRuvrD+TP8ZT46+9zVwI3nYEc/xsDMSZml8pjiGH4XkT3BcITSNAu4yvyW751W6kng8fu91mR50OWCsnjU8CtFjTGGr/zhTbr6w3z3mqUZrcIcb9x8wXwaO/r58TM7CnbOXU1WLp26yfnJlVRd5qdvIJKx3NHQ3guQccZWJ9Gca6knjcdfEUid6jmUYgGXiFDi88QmgDNB/0qUoub3r+/nic2H+MJ7F3LcMRNGejijmhXzarhs6Qx+8uwu9gwjuVkmrH+nHYBTZk3KS/9VZVYcfqZyT2O7ldUzU8Mf8HmpKPHS1pv+fH0DkbQavyP19CQz/CmieqzxpC/kkgg1/ErRsr+9j1vXbOYsx9v2AAAgAElEQVT0+snceI5O6Lrh/7z/BEp8Hm59eHNBqnK9/k4bkytKmFOTn8pnVfYCrEwje/a3WWUbs8nj5GZCuTcUJhI1aReIOVJPXxZRPWAZftX4lXFDNGr44m/fIGoM//fqk3O+OGisMrWqlM+9ZyHPbGviz1sO5f18r7/TxrLZE1NmqBwO1TGPPzOdv7G9j9oJgbThlomocmH4nfxE6eY20k3udgXDlHiTJ5IL+LxaelEZP9zz0h5e3NnC1z64iNl58ibHKh89cw4Lp1Vy+1Pb83qe9t4Qu5p6OGV2fmQesCZ3gYxDOve39zEjy4ps1WXp1w602nH+k4Zr+PtTJ5IrSVPIJRlq+JWiY8fhbv7z8bd49/FTuea0WekPUI7A5/Xw7uOn8fbBbsJZZrZ0w/p9tr4/e2LezpGt1NPY3kfdMAx/uhuNY/gnpzP8AUfqydLwez2xCeBMUMOvFBUDkSiff2AD5SVe/vPKE/MmIYx1FkytJBSJsre1N2/nWL+3DY/AyXV5NPxZTO4aY2yPP7tcTpkY/nRSj1PAvSeJxt/VP5BynsAq5KIevzLG+dHTO9nY0ME3Lz+RqRM0CVu2LJhWCcD2QynzJw6L199p5/hjqlxXuMoGR+PPROpp7x0gGI5yTJblODPy+NOkqvB6hIDPM0yPXw2/MobZ2NDO7U9t57KlM3j/idNHejhFzbG1luHfcbgrL/1HooYN+9pZNid/3j5YUS0lXk9Gi7icWrtVGaZrcHCzdqClJ4TfK65yAZWXeFNo/ANpNX41/MqYpX8gwucfeIMplQG+cemSkR5O0VMR8FE3qYzth/Pj8Te299EdDLN4RnVe+ncQEWv1bgZSjyOrZPskUl2eXl5q7QkyuaLElRSZKjWz5fGnkHp0clcZy/zXE9vYcbib/7r6pNgfnjI8Fkyt5O08ST2H7ZWtxxSgJoKb8Mp4eoLDNPwu5KXWnhCTXaaiLivxJk3ZkE7qsWr26uSuMgZ5cWczd76wm+vPnMO7FmihnlyxYNoEdjZ1J80FPxyauqyVsYVIj51paubuoGUoKwOZx/DD4IRyKsPf1jvAJJcOSkUSqScSNWmrhJX6PfTryl1lrNHVP8AXf7uRuVMq+NIlx4/0cMYU86dWEgpH2ZeHyB4nl83UqgIY/jJ/Rgu4em2Pv7wkfx5/byji+omirCRx3V1nLiLVPEGp30ufJmlTxhq3PbyFAx19/PeHT876D1VJzIKpdmRPHnT+w11BPELeKm/FU13mpysjj98yqJmWXYw/H6ReO2CVXXT3RFFe4qN34Ogbl3MzSHUDKfV7XVUDG4oafmXU8ufNB/ntaw188vz5LMvj6s/xynzb8L99KPeRPYc7reIrhUilUVXqy0jjd2SV8jQpk5OfL73h7wu5N/xlSaQex6CnSvQW8GuSNmUMEZ9j/zMXLhjp4YxJJpT6mVFdyo48ePxN3UGmFqj8pTO56zbpXPcwJ3cdQ5wqmqZvIBKrrpWOcr+X3uDRht+RcFLdQMr8XkKR5DV7k6GGXxl1aI79wjF/2gS25yGW/3BXf8EMf3WZn3DUJA2JHEpPMIzPXjiVDU7lq3SG320CuIqAL2FUz6DHn7wfZ1+mkT36F6WMOjTHfuFYMLWSHYe7ieY4sudwZ7BgBe+d6Jl2l3JPbyhCeYk363Qffq8gkrzyVTRqCIXT19t1KCtJPEHb58bw2zevTCN71PArowrNsV9YFkytpH8gSoOdnz4XRKKG5u5gwVJqTLTr5rbZaRLS0R0MZz2xC9aisVQLp/rD6SWaeMr9XgYihoEhCfMc7T7VDcS5KWQ6wauGXxk1aI79whPL2ZNDuae1J0TUFCaUE2CiHWXT7qIqFlhFUsqHmT/IWjiV2PA70ThuNf6yJKmZYxp/in7U8CtFj+bYLzzzp1pSWi5DOg/bi7cKpfE7Oe/b+9x6/O5j7JNhefyJja0biSYeJ0x5qM4f0/hT1O11ngZU6lGKEs2xPzJUl/mZVhXIaZZOJ11DoTR+x+N3UwcXrAVc2a7adUgVRtnvIhonnmTFWBxjnsrjD9jnyHQRlxp+ZcTRHPsjy5zJFexry93q3diq3QJr/O0ZaPzDXQxYmlLqsQ22S8OfTK7pc+PxOxFGaviVYkNz7I8sVWW+WOKyXNBUYI+/xOehosTrOqqnJzS8yV2wPP5kurobbf6IvuzInKHplWNST0mqyV1b6tFwTqWY0Bz7I09FwBdb1JQLDnf2M6HUl1Uh82yZWF5CW687j783GMl61a5DysndDDX+VIZfxCq2kozBpwXV+JUiQXPsjw4qAz66M0hylo7DXYVbteswsdzvOqpnuOGckGZyN5SZxu8sUBx6I3Hy/aSSPp2bxtBQ0HSo4VdGDM2xPzqoLPXRlWOpp9CS3aTyEtpdePzhSJRgOJqjqJ40k7sunypKknj8blb/+r2Jj02HGn5lRNAc+6OHyhIfoXA0qxJ+iTjcVbhVuw7VLj3+nmEmaHMI+LxJo3rc5NgZ2hck8vijafuI3TTU41dGO5pjf3RRaVd4ysUErzGmoHl6HCaV+11p/D3DTMnsEPDnXuoJRY6O6gmkSfvgePwDGd60NcG5UhBC4SjbD3exubGTNRsaOdDRx4P/dJbm2B8FOEawOxiOLYbKlObuIN/7y3ZOrKumfyBasFW7DhPLSujoGyAaNXhSrPjuHWa9XYdUUk+fi2icoX0BRz1B9AbDVKT5+yiJafyZ5VrSvzol5/SGwmw90MWWxg427e9k84EO3j7YHXscrSjx8uVLTtAc+6MEp6brcCJ7nth8kHtf3hv7XGiNf2K5n6ixatQmmy8yxvDHDY2x9sMhVVRP/0AET5ponHiSyTXp6u2ClTAu0bHpcGX4ReR9wPcAL/AzY8x/DtkfAH4BnAq0ANcYY/aIyOnAHU4z4FZjzB8yGqEyqunoHWDzgQ427+9kc2MHmxo72dXUjZPscVK5nyUzq/n4OfUsnlHNkhlV1NdUpPTKlMJSERi+4d95uIcyv5e7P34a6/a2cdGiabkanismOYnaekNJDf9fth7m9qd2sHLpDM6cVzOs8wV8yeP4e0PWpKzbhYjJPP7O/gHmTalMeazfk93kblrDLyJe4IfAe4AG4FURWWOM2RLX7EagzRgzX0RWAd8CrgE2AcuNMWERmQ68ISIPG2NyF0KgFJxI1PDT53fxq7V72dc6mNVxenUpi2dU8YETp7N4RhVLZlYzvbpUV+KOcmJSzzBCOnc1dzOvtoIV82pYMUyjmg0TXaRmdhLR/ccVJ+Jz6Y0nw9L4ExvbAx19HFPl/olnOB6/xyP4PJJxOKcbj/90YIcxZheAiNwHrATiDf9K4Fb7/YPAD0REjDHx68BLgdwm/VYKzt6WHj7/wBu8treNdy2YwrWnz2HxjCoWz6iiprKwuq6SGxzjMpyQzp1N3Zwya+Sku4lxHn8ymrqCVAZ8OZlXKvV5iUQN4Uj0qJvIO629zJrsPsmgIwkNTbvQ2TdAVVl6SarE58mL4Z8J7Iv73ACsSNbG9u47gBqgWURWAHcBc4C/U2+/ODHG8JtX9vFvj27B6xH+55qlrFw6Q735MUBlwDIu2Ub19A9EaGjr46plI5dcz/H4O1KEdOYyzDQQV34x3vAbY9jb0pvR/JWIUOLzEIwz3uFIlJ5QJK3HD1ZkTz4mdxP9ZQ89S9I2xpi1wGIROQG4R0QeN8b0H3GwyE3ATQCzZ892MSSlkBzu7Od//W4jz2xr4uz5NfzXVSczY2LZSA9LyREVdqbKbKWe3c09GAPzaityOayMKHORl74ph1XB4mPvK+K67OgboKs/zOwMPH6rP88ROr0z3+IUdk+F35tcdkqGG6GrAYi/ldcBjcnaiIgPqAZa4xsYY7YCPcBRa/ONMXcYY5YbY5bX1upintHEoxsPcPH/PMfLu1r4xqWLufeGFWr0xxhOyGC2Us/OJiul87G1qSci80kgSdqDeHK5vmDwfEfeaN5ptdTtTKQep7/4sXf2Wf8Xbjz+Em9+NP5XgQUiMhfYD6wCrh3SZg3wUeAl4CrgKWOMsY/ZZ8s/c4DjgD0ZjVAZETp6B7hlzSb+uKGRk+uq+c41S0f0D1vJHx6PUBnIPkPnzsM9iMDcKSPn8QdcFB3PZSqJmNQzJBLHMfyZe/zeIzz+zn5Lshoxjd822jcDT2CFc95ljNksIrcB64wxa4A7gXtFZAeWp7/KPvwc4EsiMgBEgU8aY5ozGqFScJ7f3sQXf7uR5u4gn7toIZ+64NhhR0Eoo5uKgDdrqWdnUzczJ5a5zk2TD5JluHToCYbpCUVytrAsWZqF/Xbt4rpJmT0Vlwz1+B3D71LqyYfHjzHmMeCxIdtuiXvfD1yd4Lh7gXszGpEyYvSFIvzH41v5xUt7Oba2gjuuP4uT6iaO9LCUAlA5jNTMu5q7R/xp0OcRRJJLPYdjxWHyK/Xsb++jqtTHBBcGO54Sr4dQXF/OJHW1C4/f7/XkPo5fGR+sf6eNf3ngDXY193DD2XP5X+87rqD51JWRpbLUn5XGH40adh7u4bTTJ+dhVO4RkZRpFA53WvEkuZ7cHZoHv7G9L6s5sKHrApq7rRvVlAnpU2j4fR5CmrJByYSBSJTv/3U7P3x6B8dUlfLrv1/BWcdOGelhKQVmQpYa/8HOfvoGIiPu8YOTMTOxxn84x+UgB8M5h3r8/czMwvCXDPHam7ut9QiTy9Mb/oDXo0naFPdsP9TF5x7YwKb9nVy5rI6vX7rIlaaojD0qAt5YycRMGA0RPQ4BnydpzpqmfEk9A0M1/l5Oq898IZtVynGwr5aeIJPK/a7m1vw+SZoiOhlq+Mch0ajhrr/t5ttPbKMy4OMnHzmV9y05ZqSHpYwglQF/Vhr/zsO24Z86chE9DgG/J6kBPNwVxO+VYSdnc4ilQ4670XT1D9DZH85K6inxemIhnAAt3SGmuFwJ7/d6Mp6YV8M/zmho6+ULv32Dl3e1ctEJU/mPK04qeNEMZfQxodRHV7+70oXxbG7sZGK5n9pRkK4jVcbMfW291E0qz9lK80T5dQ50WPMIWRl+31CpJ0hNpbsU2dks4FLDP07YeqCTh9bv51dr38EYw7evPImrl9dpygUFsKJ6ekIRjDGurwljDC/saObMeTWj4joq8SYvjrK3pSfj2Pp054Ijw0f3t1uhnNlo/D6vh4FonNTTHeKEGVXuxpJC4kp6voxaK0VFY3sfa95o5KH1+3nrYBc+j/Du46fytQ8uynhloTK2qQj4iESNVe7PZTz+zqYeDnT0c/O7R0cwQLKMmcYY9jb3cmoO6z8MSj2D0TRODH82ht/vEcJxfTV3B5nisiiO3yNEohrVM67p7B/gT28e5A/r9/Py7haMgWWzJ/KvKxfzgZNmMDnLCkvK2KYylqFzwLXhf2F7EwDvmj860qwkC+ds7QnRFQwzpyZ38xCDla8Gz9fY3offK1lNIPu8HsJ2X6FwlM7+sGuN3+vxHHHTcHW+jEeojDpC4SjPvt3EQ+v38+TWQ4TCUeZOqeCfL1zIZafMyOkFr4xNJgScursRmODumBd2NDOnppzZNaPj6THg8ybMx7+nxUqjUD8ld+OMVb4KH2n4j6kuzarIkN87GIvf0mNFILlNc+5Tj3/8YIzh9Xfa+MP6/Tyy8QDtvQPUVJRw7emzueyUmZxcVz0qdFelOKjIsBjLQCTKy7taWbl0Rj6HlREBnydhHP/elh6AnDpAjtQTr63vb+9jRnV2CQz9XiFsa/wtdgy/28ldr1cIq+Ef2+xs6uaP6/fzhw372dfaR6nfw8WLjuHyU2ZyzoIpsQtSUTLBqcLVFXQX2bNhXzvdwTDvWjA69H04OjLGYW9LLx7JPH9OynMlCOdsbO9nxbzsVjD74uSa2Kpdl4bf5xm8abg+X2bDU0aCpq4gj2y0JmnfaOjAI3D2/Cn884ULee+SY2J/tIqSLbGC6y49/ue3N+MROHMUrfJOFs65t6WH6dVlsTQLucApeejcaIwxHOrsz6jkYjz+uNTKjsfvVuP3eTxEVOMfG/SGwjy55RB/WL+f57c3E4kaFs+o4qsfOIEPnTyDaVleYIqSCMd56Am5M/x/29HMSXUTXSURKxTJonr2tfXlNJTTIT4rZv9AlHDUuEqjnAhfnFzjePyuNX6VeoqbcCTKiztbeGj9fv60+SC9oQgzJ5bxD+fO47JTZrJwmstZN0XJkEw0/kjUsLmxg+tWzMn3sDLCiuo5WuNv6Q5yYh6yzFp58C2D60hk2T59+zweIlGDMYa23gH8XqHCZXSVVyd3i49I1PDm/g4efqORNW800tQVZEKpj5VLZ3DZ0pmcVj85qygBRcmETAqu72vtpX8gynGjzBFJJvW09ISYnKNUDfHEr5h1bphuKmYl7sv6Gx+IGHqCYSoDPtfBGarxFwEDkSib9newdncra3e1sG5PG13BMH6vtbjq8lNmcv5xUzUlslJQAj4PPo+4ytC57VAXAAuPGW2G35rcjV99PBCJ0tUfZnJF7lNKBOIqXzl5jrL2+O3J4nA0SncwHFtX4QavR8jQ4VfDn29C4SgbG9pZu7uVl3e18NreNnpD1uPovNoKPnjyDM6YN5nzFtYy0UUKVkXJByJCZanPldTz9kHL8C+YOvIZOeMpiau76zhObT12emOXETKZED8h6/xu2Us9gx5/dzBMZcD9E0o2kXxq+HNM/0CEN/ZZhn7tbsvQO+lWF06r5MpldayYN5nT507OWW5wRckFFSU+V1LPtkNdzJpcFpsXGC0E4hKnOYa/xTb8NXlYsR5f+cr53TLx1If2BdYTSnd/mMqA+yd+bxZS8Oj6nytC+kIR1r/Txsu2dLN+XzuhcBQROG7aBFadNpsVcy1D73aWXlFGggkuPf7th7pHnb4PcQXXB6Jg+1SOxz8pD0/T8UXOYxp/Bp56PI7hD9sev9sYfhh8WsgENfwZ0hMM89reNtbubmHtrlbeaGhnIGLwCCyaUcXfnTEnZuhVulGKCStDZ2rDHwpH2dnUzbtPmFqgUbknUR3cmMefF6knbnJ3mB6/zzs4J9ETDDMngzQY6vHnga7+AdbtbWPtLkuj37S/g3DU4PUIS2ZWc8PZc1kxbzLL6ydr9SqlqKkI+GjvDaVss6elh3DUjE6PP07jd2h1NP48SD0l3qMndysykGjicaJ6wlFDVzCcUXSQevw5oKNvgFdtfX7t7lY27e8gaqwf96S6am46dx4r5tVw6pxJumJWGVNUlvrY19abss02e2J3NK4pcVbmBo8oYRhCBCbmYaFZic9Dn50bqKs/TInPk/XqYJ/HkXqisXBO18fq5G7mtPWEYhOxa3e1svVgJ8ZYd/OlsybyqQvms2JuDcvmTKS8ZNz/XMoYxk3B9bcPdeH1CPNqR1/G10CCqlhtPSGqy9zVrs0Uv1fo6HM8/oFYhtNs+wLraaU3FMlo4lylHhc0dwd5xZ6IXbu7lbdsDybg87Bs9iQ+e+ECVsyt4ZTZEzWWXhlXVAbST+5uO9hFfU35qPzbGCyAPqjxt/aE8laDIj5lQ3d/ZrH3Q3E8/o6+zFcAq9STgMOd/bGIm7W7W9lhF4cu83s5dc4k/uU901kxr4aTZ1XnNImTohQbFXb5xYg9h5WItw91schlScBCE/AfrfG39ATzEsoJR5Y87M5QnhmKM7nbZs+xZNLXmPD4jSFp3Uw3tHSHYrLN2t2t7G62cnFXlHhZXj+ZK5bNZMXcGk6cWR1b8KEoymC6gZ5QOGGgQv9AhL2tvaxcOrPQQ3NFTOMfMrlbn6dCRCVxcfyd/cMz/E44Z3uv7fFnNLk7BjT+TY0dHPfVPw27nwmlPk6vn8zq02exYm4Ni2dU5UXnU5SxQixDZzCx4d92sAtjRufELiQO52ztGeDUOfnz+B2pp7NvgLpJ2WcAdeQaJ6pq3Gn806pK+eJ7j8v6eMezP2F6VVY/iKKMVyrjc/JXH73/z1sO4vUIp8/NrthIvnGe4B0vfCASpa035DqvfaZYGr+VJKepK8gpwyjm7jilrT2Wx5/JRLEzMZzR+TI+Is9MnRDgUxfMH+lhKMq4oyJWhevoCV5jDGveaOSsY2uozaKYeCEYKvXsONxNJGqYn6ecQk7KhoFIlJaeUFZF1h2cil7tfbbGn2GStkxR7UNRFCC+4PrRhn/9vnb2tfaNWn0fjo7q2XqgE4BF0/MzGe1M7jqFU6ZWZW/4ncldR+OvyCB0PBuNXw2/oijAEKlnCGs2NFLi8/DexdMKPSzXDI3q2XqgkxKfh7lT8jW5a5VePNxpG/5hJF30e4/U+DNZuasev6IoWVOZROoJR6I8svEAFx4/lQmjOC2JI5eEYoa/i+OmTchbUIcTidPY3gcwLKnH8dpjHn9GK3fzZPhF5H0isk1EdojIlxLsD4jI/fb+tSJSb29/j4i8JiJv2v++O+MRKopSECqTlF98aVcLzd1BVi6dMRLDco3PaxWTCdrFWLYe6OSE6fmLQHImk/c7hj8HUk9bb4iAz5NRjv1sFnCl7V1EvMAPgUuARcBqEVk0pNmNQJsxZj7wXeBb9vZm4EPGmBOBjwL3ZjxCRVEKQkUSjf+RNw4wIeDj/ONGX0bOoZTYdXcPdwVp6QlxQp70fRj0+BvaLMM/nOihWBx/30DG5RvzpfGfDuwwxuwyxoSA+4CVQ9qsBO6x3z8IXCgiYoxZb4xptLdvBkpFZHSGBCjKOMfv9RDweWKZJh32tvZwwvSqUZmmYShWwfUoe1usZHPH1uavSpg/zuOfXFGSVSUsB8drNyYzmQfyp/HPBPbFfW6wtyVsY4wJAx1AzZA2VwLrjTHBjEepKEpBqAz4jjL8VtKw0W/0wS64PhCNLeIqK8nfuAOx1bahYWfqjZ+HyLSvfGn8iXodWto3ZRsRWYwl//xDwhOI3CQi60RkXVNTk4shKYqSDyoCvlhNaIeeYJjyIklBHvBbIZbOitrheOHp8Psss9cbimRlfI/oK+74TEI5IX8efwMwK+5zHdCYrI2I+LDW/bXan+uAPwDXG2N2JjqBMeYOY8xyY8zy2trazL6Boig5o7zEm9jjz6PnnEsCtsYfClt+ZzarWt3i3FT6BiL4s9DZ44k33s4NxS15mdwFXgUWiMhcESkBVgFrhrRZgzV5C3AV8JQxxojIROBR4MvGmL9lPDpFUQpKZYKc/D3BcNHUoijxeQgODHr8JXn0+B2DGxyIDjs9jFcGj/dmeBPxSB4Mv63Z3ww8AWwFHjDGbBaR20TkUrvZnUCNiOwAPg84IZ83A/OBr4nIBvs1+kMDFGWcUjHE8Btjikrj93sLJ/U4BjoYjg5b6om/cWTalSdfSdqMMY8Bjw3Zdkvc+37g6gTH/RvwbxmPSlGUEaEi4GV/+6DGHwxHCUdN8Xj8cflzYDDyJh/4YlWzIsP2+EUEj0DUZK7ZZ3NqXbmrKEqMipIjPX5nordYNH4nVXIokn+NP17qyUZnH4pj8DOVbrz5kHoURRk/VAwJ53RuAsUS1VPiSD3h/Gv8jqEORYav8cOgwc/Y49dcPYqiDIeKgJfeUARjLI/Z8fiHG6deKEp8Q6SevE7uehK+z5aYx5+x1KOGX1GUYVAR8BGJmliGy56Q7fEXidTjFEcpzOSuJHyfdX+Ox69Sj6IohSSWqM2WeHqDtsZfZB5/ITT++L5zofE7nn6mNxHJwoqr4VcUJYazatTR9ovR43fCOf1eQbLwht2Sc49fJ3cVRRkJnHj9HtvT77UNf6ZpBEaKgKPxh6N5lXlgiMafgycLx+Bn+vSgGr+iKMMilprZNvjODaC8aBZwCQMxjz+/5u1Ijz8Xk7vWvxlP7qrUoyjKcKgYqvEXmccfr/Hn2/DnWuN3niAyHbZ6/IqiDAvHwDuTuo7HX1YEufgBSrxewlFDMByhJI8Tu5B7jd/x3DWqR1GUgjKo8Q96/OUl3qwWCY0EsVTJwUhe0zXA0Dj+3IVzZvpbZzN/rYZfUZQYQ8M5e0KRosnTA4MrdXtC4QJr/DkM58zQkjt5fjI6V2bNFUUZyzhG3tH2e4PhosnMCYMF0HuC+Tf8udb4vVmmbIDMdX41/IqixCjxeSjxeui2tf3uYJF6/MFCa/wjl7Ihm2PU8CuKcgQVAe8RGn9lEXn8jpffXQCPP19x/FlN1qrUoyjKcCiPS81cdBq/LfX0FqXGn31fmd4s1PArinIElQFfbAFX8Wr8hYjqya3GL6jGryjKCGFJPU7KhiLz+G0vPxSJ5l3j93gGo2ly4fHLMPpSjV9RlGERX4ylJxQumupbMOjxQ35TMjs4On8uPH6HbFbiZly8JeMzKIoypqko8cWFc0aKpvoWHGnsC2H4HYObi6iewT4zP0YndxVFGRYVAR89wYid8yaqHn8KHE9/pD1+1fgVRRkWFQEv3cEwfXbZxWLS+OMXVZX48p9mwgnjzIXG76CTu4qiFBzL4w/T7WTmLKKonkCBPX5H4slFHP9gn6rxK4pSYCoDPsJRQ3tvCCguj7/EO3iTKqTUkwuP365vn5XUk+khavgVRTkCp8zi4a4gUFwevz9O3ink5G4uNf5s+lKPX1GUYeEUY2nqtAx/cXn8nrj3+df4/d7cR/VklatHNX5FUYaDk5q5qdv2+IvI8PsLrvHnzuOPLeDSXD2KohSamNTT2Q8Ul9QT7/HnO2UDxJdL1KgeRVGKGMfjP9TpaPzF4/GXjNACrpzG8avGryhKoakYIvWUF9ECLo9HYka4sBp/Dj3+rKJ61PArijIMHE3/cJcl9RTT5C4Mrt4tqMef0zj+/B+jhl9RlCNwNP3DnUFK/Z6cerOFwDH4hUzSltOoHk3ZoChKoXGknmA4WlQRPQ4xj78Ak7u51PidBVzZPD3kxfCLyPtEZJuI7BCRLyXYHxCR++39a0Wk3t5eIyJPi0i3iPwgo5EpijIiBHyDXn55EUX0ODgTvIXQ+PORq9VutMkAAA6QSURBVCc7jz/D9ukaiIgX+CFwCbAIWC0ii4Y0uxFoM8bMB74LfMve3g98DfhCZsNSFGWkEJFYRs6i9viLLDvncAqx5COq53RghzFmlzEmBNwHrBzSZiVwj/3+QeBCERFjTI8x5gWsG4CiKEWCE9JZTBE9Dk6kTSGTtI3FqJ6ZwL64zw32toRtjDFhoAOocTsIEblJRNaJyLqmpia3hymKkiec4ivFFMPvMDIe/8imbMhHsfVEPZos2iTFGHOHMWa5MWZ5bW2t28MURckTFUXt8dsafwHy8XtHST7+fEg9DcCsuM91QGOyNiLiA6qB1oxGoijKqKEyUMQafwHDOf15iOMfLWmZXwUWiMhcESkBVgFrhrRZA3zUfn8V8JQxxrXHryjK6MJZtKVST2ryovEXwONP+79qjAmLyM3AE4AXuMsYs1lEbgPWGWPWAHcC94rIDixPf5VzvIjsAaqAEhG5DLjYGLMlo1EqilJQYpO7RRzOWWxRPQ7Z9JTpU4Kr27kx5jHgsSHbbol73w9cneTY+oxGpCjKiFNRzFKPz4njL4DHn0ONfzgaia7cVRRl2DgGv5gnd/0FmNz15yGqJxs0H7+iKMOmQsM5XZFLjT+LOd24cajHryjKMBkL4ZwF0fi9udf4s0GlHkVRhk0xh3MGCqnx2wbfW4C8QKnIdNGXGn5FUY7CCecsyqiemNRTSI1/pD3+DNvnZxiKohQzc2rK8XqEmRPLRnooGTMh4KPU78FXZBr/sMaRj3BORVHGFyfVTeSNr18ci+cvJq4/s55zFxYm9cuZx9awp2VmTmSlf125hNse2cJxx0zI+NipVaUZtZfRtsB2+fLlZt26dSM9DEVRlKJCRF4zxix301alHkVRlHGGGn5FUZRxhhp+RVGUcYYafkVRlHGGGn5FUZRxhhp+RVGUcYYafkVRlHGGGn5FUZRxhhp+RVGUcYYafkVRlHGGGn5FUZRxhhp+RVGUcYYafkVRlHGGGn5FUZRxhhp+RVGUcYYafkVRlHGGGn5FUZRxhhp+RVGUcYYafkVRlHGGGn5FUZRxhhp+RVGUcYYafkVRlHGGGn5FUZRxhhp+RVGUcYYrwy8i7xORbSKyQ0S+lGB/QETut/evFZH6uH1ftrdvE5H35m7oiqIoSjakNfwi4gV+CFwCLAJWi8iiIc1uBNqMMfOB7wLfso9dBKwCFgPvA35k96coiqKMEG48/tOBHcaYXcaYEHAfsHJIm5XAPfb7B4ELRUTs7fcZY4LGmN3ADrs/RVEUZYTwuWgzE9gX97kBWJGsjTEmLCIdQI29/eUhx84cegIRuQm4yf7YLSLbgGqgI8mYUu2bAjQn+zKjmFTfabSeZzh9ZXqs2/Zu2um1NfrPMxavrVT7c3FtzXHd0hiT8gVcDfws7vPfAbcPabMZqIv7vBPL8P8Q+Ejc9juBK9Od0257R5b71rnpf7S9Un2n0Xqe4fSV6bFu27tpp9fW6D/PWLy2Uu0v9LXlRuppAGbFfa4DGpO1EREf1l2t1eWxyXg4y33FSqG+Uy7PM5y+Mj3WbXs37fTaGv3nGYvXVibnyiti322SN7AM+dvAhcB+4FXgWmPM5rg2nwJONMb8o4isAq4wxnxYRBYDv8bS9WcAfwUWGGMiefk21ljWGWOW56t/Zfyi15aSLwp9baXV+I2l2d8MPAF4gbuMMZtF5Dasx5M1WBLOvSKyA8vTX2Ufu1lEHgC2AGHgU/k0+jZ35Ll/Zfyi15aSLwp6baX1+BVFUZSxha7cVRRFGWeo4VcURRlnFMzwi0iZiDzrrNwVkY+KyHb79dEkx1wtIptFJCoiaSc+RGSWiDwtIlvt4z4bt+9WEdkvIhvs1/vj9iVMK5EsVYWI3CciC7L9LZT8UaDrrFREXhGRN+zjvhG3724R2R13nS21t4uIfN++ljaKyDJ7e62I/Ck3314pFFleZ/fHXRd7RGSDi/PcJSKHRWTTkO0J7ZmInCgid6f9AoWKGwU+BXzWfj8Z2GX/O8l+PynBMScAxwHPAMtdnGM6sMx+PwErGmmR/flW4AsJjlkEvAEEgLlYaxC89msnMA8osds4fZ0H/LSQcbf6GlXXmQCV9ns/sBY4w/58N3BVgmPeDzxuH3sGsDZu38+Bs0f6t9NXfq+zIcf/N3CLi/OcCywDNg3ZntCe2fv+AsxO1W8hpZ7rgD/a798LPGmMaTXGtAFPYuXyOQJjzFZjzDa3JzDGHDDGvG6/7wK2kmCl8BCSpZVIlarieeAiO9RVGV0U4jozxphu+6PffqWLklgJ/MI+9mVgoohMt/c9ZI9bKR4yvs4c7HQ2HwZ+k+4kxpjnsCIlM+Fh7MjKZBTE8ItICTDPGLPH3pQoDUQ6A53pOeuBU7C8MYeb7cfsu0RkUpqxJB2jMSaKdYM4OZdjVoZHIa8zEfHaj+qHsf7o46+zb9rX2XdFJOBiLOuAd+ViXEr+ycF19i7gkDFm+zCHksiegYvrqVAe/xSgPe6zJGiTs7hSEakEfgf8szGm0978Y+BYYClwAOtRK9VY0o3xMNaiNGX0ULDrzBgTMcYsxVqNfrqILLF3fRk4HjgN69H/f7sYi15LxcVwr7PVuPD205DMnoGL66lQhr8PKI37PJxUDikRET+W0f+VMeb3znZjzCH7jzUK/JTBLKHJxpJujKVY30sZPRTsOnMwxrRjzQ28z/58wJZzgljafbrrDPRaKjayvs5sefgK4P7hDCCFPQMX11NBDL+te3lFxPmxngAuFpFJ9iPKxfY2V4jITBH5a4LtgrWKeKsx5jtD9k2P+3g54MySrwFWiVVMZi6wAHgFKzXFAhGZaz/arbLbOizESk6njBIKeJ3VishE+30ZcBHwlv15uv2vAJdx5HV2vR3dcwbQYYw5YO9bGNdOGeUM8zq7CHjLGNPgbEh2naUihT0DN9dTAWfB7wQuivt8A5ZOvgP4eNz2n2FHVthfqAEIAoeAJ+zty533Q85xDtYj1kZgg/16v73vXuBNe98aYHrccV/BiuDZBlwSt/39WJFBO4GvxG2fBrxSqN9OX6PuOjsJWG9fS5uIi84AnrKvs03ALxmM/hGsbLU77f3L4475AvDpkf7t9JXf68z+fDfwj0P6Snid2ft+gyXlDNjX6I329lT27AfAh1KNv2ApG0TkFODzxpi/y0FfNwPvGCtPUMERkc8BncaYO0fi/EpyivE6E5HngJXG8iSVImC0Xmd2MMGzwDnGmHDSdoUy/PagbgDuMflP1JZXROTjwL2pflhl5Cim60xEarFi+B8a6bEomTEarzN7YelMY8wzKdsV0vAriqIoI4/m6lEURRlnqOFXFEUZZ6jhVxRFGWeo4VcURRlnqOFX8oqIfEasNNm/yvC4ehG5Nl/jygciMkNEHnTRrjvJ9stEZFHuR5b6vMr4Qw2/km8+ibWILtPsk/VAxobfyY8+EhhjGo0xVw2ji8uw0oSnRLPCKsNFDb+SN0TkJ1j1DNaIyFfsLIKvish6EVlpt6kXkedF5HX7dZZ9+H8C77KLTHxORD4mIj+I6/sRETnfft8tIreJyFrgTBE5VawiGa+JyBNxaRQ+IyJb7IyG96UY95siMtFOr9AiItfb2+8VkYvszJz/ZX+XjSLyD3HfZZP9vlxEHrD33y8iayWuyIuIfFOsQi4vi8g0+3tfCvyX/Z2PHTKmu0XkOyLyNPAtETldRF60f8sXReQ4u93HROT3IvInsYqCfDvB95siIi+JyAcy+g9Vxg4jvfRZX2P7BezBymb478BH7G0TsVJhVADlQKm9fQGwzn5/PvBIXD8fA34Q9/kR4Hz7vQE+bL/3Ay8Ctfbna4C77PeNQMAZQ4ox/wT4ALAEK2fTT+3t24FK4Cbgq/a2AFYa3LlYTymb7O1fAP6f/X4JEGYwRYTBXlIPfDuur7tJUMQlbt8jgNf+XAX47PcXAb+L+512AdVYybr2ArPsfd1Y6UbWAu8Z6WtDXyP30kdGpVBcDFwqIl+wP5cCs7GM8Q/EKlEYwUowlSkRrIysYFXSWgI8aeVJw4uV6wSsvCa/EpGHsIqfJON5rMpHe7HS394kIjOBVmNMt4hcDJwkIo6sU41103o7ro9zgO8BGGM2icjGuH0hLCMO8BrwHpff87dmcJVoNXDP/2/vjl2biqI4jn9/hS4qODmqg1iEIrrWzclBEaodXERxq7WIg+IiCB0EEdwKgqN/gkuHUq0N6FIwYsFFBREnhyJCxcpxODf0pSb6OjRC3+8zvaR59yaFnHtzXnJO+aVmkAtex3xErAJIWgEOkvXih4F5YCointec03YgB34bFAHnY1OnK0l3ycJox8jU41qf89fpTk1Wy+KuVQKigLcRMdZjjNNkQD8L3JE0Gr3LbiySrfUOkAX8xoEJckHozDEdEV0VGJXNf6g8pp+fEdH5yfwv6r8Pv1eOZ4CFiBgv8z6r/O1H5bg6/jq50Jwi67lYQznHb4MyB0yrbMNLkSvIneuXyLriF8kdOsA3sm9yx0fguKQhSfvprj9e9Q7YJ2mszDMsaVTSEJnyWABukemmPb0GiIhPZHrqcES8B5bI1E0n8M8Bk8reD0gakbR70zBLZHs9yjd1jvb9z2zoes2SrpUCXr3sBT6X48s1xob8ZHAFOCLpds1zbAdy4LdBmSFTDe1yAXSm3D8LXJL0kkzzdHa1bWC9XAC9AbSAD2Qp2gfAcq9JIvsjT5AXQF+TpblPkAvKE0lvyJLKDyObqPTzio3UzQuyld5Suf0YWAGWy2t5xJ+79llyAWqTXbjawOpf5oPs63yzXLA9RHby+trnsfeBe5JabCyW/1Q+GV0ATkq6Wvc821lcpM1sG5SvlQ5HxFoJ4vPASFmY6o7xFDi3lXPM6nCO32x77AIWSjpIwORWA3hEnNmWZ2aN5x2/NZayr8L1TXe3ImLqfzwfs0Fx4Dczaxhf3DUzaxgHfjOzhnHgNzNrGAd+M7OG+Q0+KbkuHuyyWAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "report_results('rank', scores);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'AMZe': {'HybridSVD': {'features_weight': 0.1, 'rank': 1500}},\n",
       " 'AMZvg': {'HybridSVD': {'features_weight': 0.1, 'rank': 150}}}"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "config"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### saving data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "save_results(experiment_name, config=config, tuning=scores)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## cross-validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><span class=\"Text-label\" style=\"display:inline-block; overflow:hidden; white-space:nowrap; text-overflow:ellipsis; min-width:0; max-width:15ex; vertical-align:middle; text-align:right\"></span>\n",
       "<progress style=\"width:60ex\" max=\"2\" value=\"2\" class=\"Progress-main\"/></progress>\n",
       "<span class=\"Progress-label\"><strong>100%</strong></span>\n",
       "<span class=\"Iteration-label\">2/2</span>\n",
       "<span class=\"Time-label\">[01:40:19<00:51, 3009.31s/it]</span></div>"
      ],
      "text/plain": [
       "\u001b[A\u001b[A\u001b[2K\r",
       " [████████████████████████████████████████████████████████████] 2/2 [01:40:19<00:51, 3009.31s/it]\u001b[B"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div><span class=\"Text-label\" style=\"display:inline-block; overflow:hidden; white-space:nowrap; text-overflow:ellipsis; min-width:15ex; max-width:15ex; vertical-align:middle; text-align:right\">AMZe</span>\n",
       "<progress style=\"width:45ex\" max=\"5\" value=\"5\" class=\"Progress-main\"/></progress>\n",
       "<span class=\"Progress-label\"><strong>100%</strong></span>\n",
       "<span class=\"Iteration-label\">5/5</span>\n",
       "<span class=\"Time-label\">[01:39:28<20:22, 1193.51s/it]</span></div>"
      ],
      "text/plain": [
       "\u001b[A\u001b[2K\r",
       "           AMZe [█████████████████████████████████████████████] 5/5 [01:39:28<20:22, 1193.51s/it]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div><span class=\"Text-label\" style=\"display:inline-block; overflow:hidden; white-space:nowrap; text-overflow:ellipsis; min-width:15ex; max-width:15ex; vertical-align:middle; text-align:right\">AMZvg</span>\n",
       "<progress style=\"width:45ex\" max=\"5\" value=\"5\" class=\"Progress-main\"/></progress>\n",
       "<span class=\"Progress-label\"><strong>100%</strong></span>\n",
       "<span class=\"Iteration-label\">5/5</span>\n",
       "<span class=\"Time-label\">[00:51<00:10, 10.21s/it]</span></div>"
      ],
      "text/plain": [
       "\u001b[A\u001b[2K\r",
       "          AMZvg [█████████████████████████████████████████████] 5/5 [00:51<00:10, 10.21s/it]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "result = {}\n",
    "for label in track(data_labels):\n",
    "    models = prepare_recommender_models(label, data_models, config)\n",
    "    result[label] = ee.run_cv_experiment(models,\n",
    "                                         fold_experiment=ee.topk_test,\n",
    "                                         topk_list=topk_values,\n",
    "                                         ignore_feedback=True,\n",
    "                                         iterator=lambda x: track(x, label=label))\n",
    "    save_cv_training_time(experiment_name, models, label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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zqZ02AZA0TVKDpIZNmzZl6K6ZmXVElkAodm0/OlCnQ/UjYk5EVEdEdXl5eRtNmplZZ2QJhCZgRN76cOCN1upI6g8MAba00+bwdto0M7MelCUQXgBGSaqUdARQA9QX1KkHpqblS4An072BoiLiTeAdSWemp4umAI8cdO/NzKzL9G+vQkQ0S7oOeAwoA+ZGxBpJM4GGiKgH7gEWSGokd2ZQs29/SRuAY4AjJE0CxkfEK8B/AeYBRwJL04+ZmZVIu4EAEBFLgCUFZTPylncBk1vZt6KV8gZgdNaOmplZ9/Inlc3MDHAgmJlZ4kAwMzPAgWBmZokDwczMAAeCmZklDgQzMwMcCGZmljgQzMwMcCCYmVniQDAzM8CBYGZmiQPBzMwAB4KZmSUOBDMzAxwIZmaWOBDMzAxwIJiZWZLpKzQPFxXTf1nqLgCw4fsXlLoLZmYH8BmCmZkBDgQzM0syBYKkCZLWSWqUNL3I9oGSFqbtyyVV5G27MZWvk3ReXvnXJa2R9FtJP5M0qCsGZGZmHdNuIEgqA2YD5wNVQK2kqoJqVwJbI2IkcAdwW9q3CqgBTgEmAHdLKpN0PPB3QHVEjAbKUj0zMyuRLGcIY4DGiFgfEe8DdcDEgjoTgflpeTEwTpJSeV1E7I6I14DG1B7kbmgfKak/cBTwRueGYmZmnZElEI4HNuatN6WyonUiohnYDgxtbd+I+FfgduAPwJvA9oj4p2IvLmmapAZJDZs2bcrQXTMz64gsgaAiZZGxTtFySceRO3uoBD4OfETS5cVePCLmRER1RFSXl5dn6K6ZmXVElkBoAkbkrQ/nwMs7LXXSJaAhwJY29v0b4LWI2BQRHwAPA2d1ZABmZtY1sgTCC8AoSZWSjiB387e+oE49MDUtXwI8GRGRymvSU0iVwChgBblLRWdKOirdaxgHrO38cMzMrKPa/aRyRDRLug54jNzTQHMjYo2kmUBDRNQD9wALJDWSOzOoSfuukbQIeAVoBq6NiD3AckmLgRdT+UvAnK4fnpmZZZVp6oqIWAIsKSibkbe8C5jcyr6zgFlFym8Gbj6YzpqZWffxJ5XNzAxwIJiZWeJAMDMzwIFgZmaJA8HMzAAHgpmZJQ4EMzMDHAhmZpY4EMzMDHAgmJlZ4kAwMzPAgWBmZokDwczMAAeCmZklDgQzMwMcCGZmljgQzMwMcCCYmVniQDAzM8CBYGZmSaZAkDRB0jpJjZKmF9k+UNLCtH25pIq8bTem8nWSzssrP1bSYkmvSlor6a+7YkBmZtYx7QaCpDJgNnA+UAXUSqoqqHYlsDUiRgJ3ALelfauAGuAUYAJwd2oP4H8C/zciPgV8Gljb+eGYmVlHZTlDGAM0RsT6iHgfqAMmFtSZCMxPy4uBcZKUyusiYndEvAY0AmMkHQN8DrgHICLej4htnR+OmZl1VJZAOB7YmLfelMqK1omIZmA7MLSNff8C2ATcK+klST+V9JFiLy5pmqQGSQ2bNm3K0F0zM+uILIGgImWRsU5r5f2BzwA/iogzgHeBA+5NAETEnIiojojq8vLyDN01M7OOyBIITcCIvPXhwBut1ZHUHxgCbGlj3yagKSKWp/LF5ALCzMxKJEsgvACMklQp6QhyN4nrC+rUA1PT8iXAkxERqbwmPYVUCYwCVkTEH4GNkj6Z9hkHvNLJsZiZWSf0b69CRDRLug54DCgD5kbEGkkzgYaIqCd3c3iBpEZyZwY1ad81khaRe7NvBq6NiD2p6euBB1LIrAeu6OKxmZnZQWg3EAAiYgmwpKBsRt7yLmByK/vOAmYVKV8FVB9MZ83MrPv4k8pmZgY4EMzMLHEgmJkZ4EAwM7PEgWBmZoADwczMEgeCmZkBDgQzM0scCGZmBjgQzMwscSCYmRngQDAzs8SBYGZmgAPBzMwSB4KZmQEOBDMzSxwIZmYGOBDMzCxxIJiZGeBAMDOzxIFgZmZAxkCQNEHSOkmNkqYX2T5Q0sK0fbmkirxtN6bydZLOK9ivTNJLkh7t7EDMzKxz2g0ESWXAbOB8oAqolVRVUO1KYGtEjATuAG5L+1YBNcApwATg7tTePjcAazs7CDMz67wsZwhjgMaIWB8R7wN1wMSCOhOB+Wl5MTBOklJ5XUTsjojXgMbUHpKGAxcAP+38MMzMrLOyBMLxwMa89aZUVrRORDQD24Gh7ez7A+BbwN62XlzSNEkNkho2bdqUobtmZtYRWQJBRcoiY52i5ZIuBN6KiJXtvXhEzImI6oioLi8vb7+3ZmbWIVkCoQkYkbc+HHijtTqS+gNDgC1t7Hs2cJGkDeQuQZ0r6f4O9N/MzLpIlkB4ARglqVLSEeRuEtcX1KkHpqblS4AnIyJSeU16CqkSGAWsiIgbI2J4RFSk9p6MiMu7YDxmZtZB/durEBHNkq4DHgPKgLkRsUbSTKAhIuqBe4AFkhrJnRnUpH3XSFoEvAI0A9dGxJ5uGouZmXVCu4EAEBFLgCUFZTPylncBk1vZdxYwq422nwaeztIPMzPrPv6kspmZAQ4EMzNLHAhmZgY4EMzMLHEgmJkZ4EAwM7PEgWBmZoADwczMEgeCmZkBDgQzM0scCGZmBjgQzMwscSCYmRngQDAzs8SBYGZmgAPBzMwSB4KZmQEOBDMzSxwIZmYGOBDMzCzJFAiSJkhaJ6lR0vQi2wdKWpi2L5dUkbftxlS+TtJ5qWyEpKckrZW0RtINXTUgMzPrmHYDQVIZMBs4H6gCaiVVFVS7EtgaESOBO4Db0r5VQA1wCjABuDu11wx8IyJOBs4Eri3SppmZ9aAsZwhjgMaIWB8R7wN1wMSCOhOB+Wl5MTBOklJ5XUTsjojXgEZgTES8GREvAkTEO8Ba4PjOD8fMzDoqSyAcD2zMW2/iwDfvljoR0QxsB4Zm2TddXjoDWF7sxSVNk9QgqWHTpk0ZumtmZh2RJRBUpCwy1mlzX0mDgYeAr0XEjmIvHhFzIqI6IqrLy8szdNfMzDoiSyA0ASPy1ocDb7RWR1J/YAiwpa19JQ0gFwYPRMTDHem8mZl1nSyB8AIwSlKlpCPI3SSuL6hTD0xNy5cAT0ZEpPKa9BRSJTAKWJHuL9wDrI2If+yKgZiZWef0b69CRDRLug54DCgD5kbEGkkzgYaIqCf35r5AUiO5M4OatO8aSYuAV8g9WXRtROyRdA7wn4GXJa1KL/X3EbGkqwdoZmbZtBsIAOmNeklB2Yy85V3A5Fb2nQXMKihbRvH7C2ZmViL+pLKZmQEOBDMzSxwIZmYGOBDMzCxxIJiZGeBAMDOzxIFgZmaAA8HMzBIHgpmZAQ4EMzNLHAhmZgY4EMzMLHEgmJkZ4EAwM7PEgWBmZoADwczMEgeCmZkBDgQzM0scCGZmBjgQzMwscSCYmRmQMRAkTZC0TlKjpOlFtg+UtDBtXy6pIm/bjal8naTzsrZpZmY9q91AkFQGzAbOB6qAWklVBdWuBLZGxEjgDuC2tG8VUAOcAkwA7pZUlrFNMzPrQVnOEMYAjRGxPiLeB+qAiQV1JgLz0/JiYJwkpfK6iNgdEa8Bjam9LG2amVkP6p+hzvHAxrz1JuCvWqsTEc2StgNDU/nzBfsen5bbaxMASdOAaWl1p6R1GfrcnYYBb3emAd3WRT0pPR+L/Xws9vOx2K+3HIsTslTKEggqUhYZ67RWXuzMpLDNXGHEHGBOWx3sSZIaIqK61P3oDXws9vOx2M/HYr9D7VhkuWTUBIzIWx8OvNFaHUn9gSHAljb2zdKmmZn1oCyB8AIwSlKlpCPI3SSuL6hTD0xNy5cAT0ZEpPKa9BRSJTAKWJGxTTMz60HtXjJK9wSuAx4DyoC5EbFG0kygISLqgXuABZIayZ0Z1KR910haBLwCNAPXRsQegGJtdv3wukWvuXzVC/hY7OdjsZ+PxX6H1LFQ7g95MzPr6/xJZTMzAxwIZmaWOBAykjRX0luSflvqvpSapEGSVkj6F0lrJH231H3qScX+L0j6qKRfSfpd+ve4UvaxJ0gaIekpSWvT/4MbUnmfOxbQ+u9FenhmeToeC9ODNL2SAyG7eeSm3zDYDZwbEZ8GTgcmSDqzxH3qSfM48P/CdOCJiBgFPJHWD3fNwDci4mTgTODaNAVNXzwW0PrvxW3AHel4bCU31U+v5EDIKCKeJfcEVZ8XOTvT6oD002eeTmjl/0L+9C3zgUk92qkSiIg3I+LFtPwOsJbcTAR97lhAm78X55Kb0gd6+fFwIFiHpEkKVwFvAb+KiOWl7lOJ/VlEvAm5N0rgYyXuT49KMxyfASynDx+Lwt8L4PfAtohoTlXyp+/pdRwI1iERsSciTif3KfMxkkaXuk9WGpIGAw8BX4uIHaXuTykV/l4AJxer1rO9ys6BYJ0SEduAp/H9lT9J+nOA9O9bJe5Pj5A0gFwYPBARD6fiPnks8uX9XpwJHJum9IFePk2PA8EOmqRyScem5SOBvwFeLW2vSi5/+papwCMl7EuPSFPc3wOsjYh/zNvU544FtPp7sRZ4ityUPtDLj4c/qZyRpJ8BY8lNZ/sn4OaIuKeknSoRSaeRuzlWRu6PikURMbO0veo5xf4vAL8AFgGfAP4ATI6Iw/ohBEnnAP8MvAzsTcV/T+4+Qp86FtD674WkvyD3nS8fBV4CLo+I3aXraescCGZmBviSkZmZJQ4EMzMDHAhmZpY4EMzMDHAgmJlZ4kAwKyDpWEnXlLofZj3NgWB2oGMBB4L1Oe1+p7JZH/R94MQ0SdmvUtn55OaguSUiFkoaC8wENgOfBJ4FromIvfkNSfoKcBFwFHAi8POI+FZPDMLsYPkMwexA04Hfp0nKnic3t/2nyU1F8N/3zdNDbvKybwCnknuzv7iV9k4HLk31LpU0ohv7btZhDgSztp0D/CzNYvkn4Bngs2nbiohYHxF7gJ+lusU8ERHbI2IX8ApwQrf32qwDHAhmbVMb2wrnfQlJX5S0Kv1Up/L8eWv24Eu11ks5EMwO9A5wdFp+ltxlnjJJ5cDngBVp25j0fbn9yF0SWhYRP4+I09NPQ8933azjHAhmBSJiM/BrSb8F/hpYDfwL8CTwrYj4Y6r6HLkb0L8FXgN+XoLumnUZz3Zq1gHpKaNvRsSFpe6LWVfxGYKZmQE+QzAzs8RnCGZmBjgQzMwscSCYmRngQDAzs8SBYGZmAPx/4BaOd2LBdEIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "report_results('topn', result, target_metric);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### saving data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "save_results(experiment_name, cv=result)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Cold start"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "import gc\n",
    "gc.collect()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "def prepare_cold_start_recommender_models(data_label, data_models, config):\n",
    "    data_model = data_models[data_label]\n",
    "    svd = HybridSVDColdStart(data_model, item_features=meta_dict[data_label])\n",
    "    models = [svd]\n",
    "    for model in models:\n",
    "        model.verbose = False\n",
    "        model_config = config[data_label].get(model.method, None)\n",
    "        if model_config:\n",
    "            set_config(model, *list(zip(*model_config.items())))\n",
    "    return models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "config_cold = {}\n",
    "scores_cold = {}\n",
    "data_models_cold = {}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<span class=\"Progress-label\"><strong>100%</strong></span>\n",
       "<span class=\"Iteration-label\">2/2</span>\n",
       "<span class=\"Time-label\">[05:48:12<28:38, 10446.13s/it]</span></div>"
      ],
      "text/plain": [
       "\u001b[A\u001b[A\u001b[2K\r",
       " [████████████████████████████████████████████████████████████] 2/2 [05:48:12<28:38, 10446.13s/it]\u001b[B"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
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       "<progress style=\"width:45ex\" max=\"130\" value=\"130\" class=\"Progress-main\"/></progress>\n",
       "<span class=\"Progress-label\"><strong>100%</strong></span>\n",
       "<span class=\"Iteration-label\">130/130</span>\n",
       "<span class=\"Time-label\">[05:19:30<00:12, 147.46s/it]</span></div>"
      ],
      "text/plain": [
       "\u001b[A\u001b[2K\r",
       "           AMZe [█████████████████████████████████████████████] 130/130 [05:19:30<00:12, 147.46s/it]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
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     "data": {
      "text/html": [
       "<div><span class=\"Text-label\" style=\"display:inline-block; overflow:hidden; white-space:nowrap; text-overflow:ellipsis; min-width:15ex; max-width:15ex; vertical-align:middle; text-align:right\">AMZvg</span>\n",
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       "<span class=\"Progress-label\"><strong>100%</strong></span>\n",
       "<span class=\"Iteration-label\">130/130</span>\n",
       "<span class=\"Time-label\">[28:37<00:00, 13.21s/it]</span></div>"
      ],
      "text/plain": [
       "\u001b[A\u001b[2K\r",
       "          AMZvg [█████████████████████████████████████████████] 130/130 [28:37<00:00, 13.21s/it]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for label in track(data_labels):\n",
    "    data_models_cold[label] = prepare_cold_start_data_model(label, *all_data, seed)\n",
    "    model = HybridSVDColdStart(data_models_cold[label],\n",
    "                               item_features=meta_dict[label])\n",
    "    model.use_raw_features = True\n",
    "    config_cold[label], scores_cold[label] = fine_tune_hybridsvd(model,\n",
    "                                                                 svd_ranks[label],\n",
    "                                                                 hybridsvd_weights,\n",
    "                                                                 label)\n",
    "    del model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "report_results('rank', scores_cold);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'AMZe': {'HybridSVD(cs)': {'features_weight': 0.5, 'rank': 1750}},\n",
       " 'AMZvg': {'HybridSVD(cs)': {'features_weight': 0.1, 'rank': 2000}}}"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "config_cold"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### saving data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "save_results(experiment_name+'_coldstart', config=config_cold, tuning=scores_cold)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## cross validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><span class=\"Text-label\" style=\"display:inline-block; overflow:hidden; white-space:nowrap; text-overflow:ellipsis; min-width:0; max-width:15ex; vertical-align:middle; text-align:right\"></span>\n",
       "<progress style=\"width:60ex\" max=\"2\" value=\"2\" class=\"Progress-main\"/></progress>\n",
       "<span class=\"Progress-label\"><strong>100%</strong></span>\n",
       "<span class=\"Iteration-label\">2/2</span>\n",
       "<span class=\"Time-label\">[02:24:31<13:38, 4335.55s/it]</span></div>"
      ],
      "text/plain": [
       "\u001b[A\u001b[A\u001b[2K\r",
       " [████████████████████████████████████████████████████████████] 2/2 [02:24:31<13:38, 4335.55s/it]\u001b[B"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div><span class=\"Text-label\" style=\"display:inline-block; overflow:hidden; white-space:nowrap; text-overflow:ellipsis; min-width:15ex; max-width:15ex; vertical-align:middle; text-align:right\">AMZe folds</span>\n",
       "<progress style=\"width:45ex\" max=\"5\" value=\"5\" class=\"Progress-main\"/></progress>\n",
       "<span class=\"Progress-label\"><strong>100%</strong></span>\n",
       "<span class=\"Iteration-label\">5/5</span>\n",
       "<span class=\"Time-label\">[02:10:53<24:42, 1570.56s/it]</span></div>"
      ],
      "text/plain": [
       "\u001b[A\u001b[2K\r",
       "     AMZe folds [█████████████████████████████████████████████] 5/5 [02:10:53<24:42, 1570.56s/it]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div><span class=\"Text-label\" style=\"display:inline-block; overflow:hidden; white-space:nowrap; text-overflow:ellipsis; min-width:15ex; max-width:15ex; vertical-align:middle; text-align:right\">AMZvg folds</span>\n",
       "<progress style=\"width:45ex\" max=\"5\" value=\"5\" class=\"Progress-main\"/></progress>\n",
       "<span class=\"Progress-label\"><strong>100%</strong></span>\n",
       "<span class=\"Iteration-label\">5/5</span>\n",
       "<span class=\"Time-label\">[13:38<03:33, 163.64s/it]</span></div>"
      ],
      "text/plain": [
       "\u001b[A\u001b[2K\r",
       "    AMZvg folds [█████████████████████████████████████████████] 5/5 [13:38<03:33, 163.64s/it]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "result_cold = {}\n",
    "for label in track(data_labels):\n",
    "    models_cold = prepare_cold_start_recommender_models(label, data_models_cold, config_cold)\n",
    "    result_cold[label] = ee.run_cv_experiment(models_cold,\n",
    "                                              fold_experiment=ee.topk_test,\n",
    "                                              topk_list=topk_values,\n",
    "                                              ignore_feedback=True,\n",
    "                                              iterator=lambda x: track(x, label=f'{label} folds'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "report_results('topn', result_cold, target_metric);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "report_results('topn', result_cold, 'coverage');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### saving data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "save_results(experiment_name+'_coldstart', cv=result_cold)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
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