HybridSVD.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_movielens_data,\n",
    "                                get_bookcrossing_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 = 10 # 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 = ['ML1M', 'ML10M', 'BX']"
   ]
  },
  {
   "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 = {'ML1M': [r for r in ranks_grid if r <= 1000],\n",
    "             'ML10M': [r for r in ranks_grid if r <= 1000],\n",
    "             'BX': [r for r in ranks_grid if r <= 2000]}\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": [
    "## Movielens1M"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "lbl = 'ML1M'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_dict[lbl], meta_dict[lbl] = get_movielens_data('../datasets/movielens/ml-1m.zip',\n",
    "                                                    meta_path='data/meta_info_ml1m.csv',\n",
    "                                                    implicit=True,\n",
    "                                                    filter_no_meta=True)"
   ]
  },
  {
   "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": [
    "## Movielens10M"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "lbl = 'ML10M'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_dict[lbl], meta_dict[lbl] = get_movielens_data('../datasets/movielens/ml-10m.zip',\n",
    "                                                    meta_path='data/meta_info_ml10m.csv',\n",
    "                                                    implicit=True,\n",
    "                                                    filter_no_meta=True)"
   ]
  },
  {
   "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": [
    "## BookCrossing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "lbl = 'BX'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_dict[lbl], meta_dict[lbl] = get_bookcrossing_data('../datasets/bookcrossing/BX-CSV-Dump.zip',\n",
    "                                                       get_books_meta=True,\n",
    "                                                       implicit=True,\n",
    "                                                       pcore=5,\n",
    "                                                       filter_no_meta=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "similarities[lbl], sim_indices[lbl], feature_idx[lbl] = get_similarity_data(meta_dict[lbl])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 23,
     "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": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ML1M\n",
      "{'userid': 6038, 'movieid': 3522}\n",
      "density 2.699052132255699\n",
      "similarity matrix density 51.672185638677114\n",
      "ML10M\n",
      "{'userid': 69797, 'movieid': 10258}\n",
      "density 0.6991397242349022\n",
      "similarity matrix density 51.91219966091452\n",
      "BX\n",
      "{'userid': 7160, 'isbn': 16273}\n",
      "density 0.18925598044812894\n",
      "similarity matrix density 0.4827469267285518\n"
     ]
    }
   ],
   "source": [
    "print_data_stats(data_labels, all_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Standard experiment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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        \n",
    "    return model_config, model_scores"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "config = {}\n",
    "scores = {}\n",
    "data_models = {}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
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       "           ML1M [█████████████████████████████████████████████] 100/100 [11:31<00:00, 6.91s/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": 28,
   "metadata": {},
   "outputs": [
    {
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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"
    },
    {
     "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);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'ML1M': {'HybridSVD': {'features_weight': 0.9, 'rank': 50}},\n",
       " 'ML10M': {'HybridSVD': {'features_weight': 0.1, 'rank': 30}},\n",
       " 'BX': {'HybridSVD': {'features_weight': 0.3, 'rank': 1250}}}"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "config"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### saving data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "save_results(experiment_name, config=config, tuning=scores)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## cross-validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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=\"3\" value=\"3\" class=\"Progress-main\"/></progress>\n",
       "<span class=\"Progress-label\"><strong>100%</strong></span>\n",
       "<span class=\"Iteration-label\">3/3</span>\n",
       "<span class=\"Time-label\">[35:21<29:16, 707.11s/it]</span></div>"
      ],
      "text/plain": [
       "\u001b[A\u001b[A\u001b[2K\r",
       " [████████████████████████████████████████████████████████████] 3/3 [35:21<29:16, 707.11s/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\">ML1M</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\">[00:49<00:08, 9.89s/it]</span></div>"
      ],
      "text/plain": [
       "\u001b[A\u001b[2K\r",
       "           ML1M [█████████████████████████████████████████████] 5/5 [00:49<00:08, 9.89s/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\">ML10M</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\">[05:16<01:06, 63.22s/it]</span></div>"
      ],
      "text/plain": [
       "\u001b[A\u001b[2K\r",
       "          ML10M [█████████████████████████████████████████████] 5/5 [05:16<01:06, 63.22s/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\">BX</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\">[29:16<08:36, 351.14s/it]</span></div>"
      ],
      "text/plain": [
       "\u001b[A\u001b[2K\r",
       "             BX [█████████████████████████████████████████████] 5/5 [29:16<08:36, 351.14s/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": 32,
   "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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3Urg9s3i9LaXazcysMvxErplZQhz6ZmYJceibmSXEoW9mlhCHvplZQhz6ZmYJceibmSXEoW9mlhCHvplZQhz6ZmYJceibmSXEoW9mlhCHvplZQhz6ZmYJceibmSXEoW9mlhCHvplZQhz6ZmYJceibmSXEoW9mlhCHvplZQhz6ZmYJceibmSVkYKUL6Ak1Mx+rdAkANN90aqVLMDP7FJ/pm5klJFfoS5oiaY2kJkkzSywfLGl+tnyZpJp2y8ZLek7SKkmvSBpSvvLNzKwrOg19SVXAbOAUYCzQIGlsUbeLgY0RcThwK3Bztu5A4F7gryLiKGAi8EnZqjczsy7Jc6ZfDzRFxNqI2AbMA6YW9ZkKzM2mFwCTJAmYDKyMiF8DRERrROwoT+lmZtZVeUL/MODtdvMtWVvJPhGxHdgEjACOAELS45JekvS9UjuQNENSo6TG9evXd/U1mJlZTnlCXyXaImefgcCJwHnZv2dImrRHx4g5EVEXEXXV1dU5SjIzs+7IE/otwOh286OAdR31ya7jDwc2ZO1LIuK9iPgjsAj46t4WbWZm3ZMn9F8ExkiqlbQfMA1YWNRnITA9mz4LeDoiAngcGC/pc9kfg68Dvy1P6WZm1lWdPpwVEdslXU4hwKuAOyJilaRZQGNELARuB+6R1EThDH9atu5GSf9A4Q9HAIsiom88OWVmlqBcT+RGxCIKl2bat13dbnorcHYH695L4bZNMzOrMD+Ra2aWEIe+mVlCHPpmZglx6JuZJcShb2aWEIe+mVlCHPpmZglx6JuZJcShb2aWEIe+mVlCHPpmZglx6JuZJcShb2aWEIe+mVlCHPpmZglx6JuZJcShb2aWEIe+mVlCHPpmZglx6JuZJcShb2aWEIe+mVlCHPpmZglx6JuZJcShb2aWkFyhL2mKpDWSmiTNLLF8sKT52fJlkmqKln9e0hZJf1uess3MrDs6DX1JVcBs4BRgLNAgaWxRt4uBjRFxOHArcHPR8luBxXtfrpmZ7Y08Z/r1QFNErI2IbcA8YGpRn6nA3Gx6ATBJkgAknQ6sBVaVp2QzM+uuPKF/GPB2u/mWrK1kn4jYDmwCRkg6APivwHWftQNJMyQ1Smpcv3593trNzKyL8oS+SrRFzj7XAbdGxJbP2kFEzImIuoioq66uzlGSmZl1x8AcfVqA0e3mRwHrOujTImkgMBzYABwHnCXp74GDgJ2StkbET/a6cjMz67I8of8iMEZSLfB7YBpwblGfhcB04DngLODpiAjgz3Z1kHQtsMWBb2ZWOZ2GfkRsl3Q58DhQBdwREaskzQIaI2IhcDtwj6QmCmf403qyaDMz6548Z/pExCJgUVHb1e2mtwJnd7KNa7tRn5mZlZGfyDUzS4hD38wsIQ59M7OEOPTNzBLi0DczS4hD38wsIQ59M7OEOPTNzBLi0DczS4hD38wsIQ59M7OEOPTNzBLi0DczS4hD38wsIQ59M7OEOPTNzBLi0DczS4hD38wsIQ59M7OEOPTNzBLi0DczS4hD38wsIQ59M7OEOPTNzBLi0DczS0iu0Jc0RdIaSU2SZpZYPljS/Gz5Mkk1Wft/lLRc0ivZvyeXt3wzM+uKTkNfUhUwGzgFGAs0SBpb1O1iYGNEHA7cCtyctb8H/OeIGAdMB+4pV+FmZtZ1ec7064GmiFgbEduAecDUoj5TgbnZ9AJgkiRFxMsRsS5rXwUMkTS4HIWbmVnX5Qn9w4C32823ZG0l+0TEdmATMKKozzeBlyPi4+IdSJohqVFS4/r16/PWbmZmXZQn9FWiLbrSR9JRFC75XFpqBxExJyLqIqKuuro6R0lmZtYdeUK/BRjdbn4UsK6jPpIGAsOBDdn8KOBh4IKIeGNvCzYzs+7LE/ovAmMk1UraD5gGLCzqs5DCB7UAZwFPR0RIOgh4DPh+RDxbrqLNzKx7Og397Br95cDjwGrggYhYJWmWpNOybrcDIyQ1AX8D7Lqt83LgcOAqSSuyn0PL/irMzCyXgXk6RcQiYFFR29XtprcCZ5dY73rg+r2s0czMysRP5JqZJcShb2aWEIe+mVlCHPpmZglx6JuZJcShb2aWEIe+mVlCHPpmZglx6JuZJcShb2aWEIe+mVlCHPpmZglx6JuZJcShb2aWEIe+mVlCHPpmZglx6JuZJcShb2aWEIe+mVlCHPpmZglx6JuZJcShb2aWEIe+mVlCHPpmZglx6JuZJSRX6EuaImmNpCZJM0ssHyxpfrZ8maSadsu+n7WvkfTn5SvdzMy6qtPQl1QFzAZOAcYCDZLGFnW7GNgYEYcDtwI3Z+uOBaYBRwFTgJ9m2zMzswrIc6ZfDzRFxNqI2AbMA6YW9ZkKzM2mFwCTJClrnxcRH0fE74CmbHtmZlYBA3P0OQx4u918C3BcR30iYrukTcCIrP35onUPK96BpBnAjGx2i6Q1uarvWSOB9/ZmA7q5TJVUno/Fbj4Wu/lY7NYXjsUX8nTKE/oq0RY5++RZl4iYA8zJUUuvkdQYEXWVrqMv8LHYzcdiNx+L3frTschzeacFGN1ufhSwrqM+kgYCw4ENOdc1M7Nekif0XwTGSKqVtB+FD2YXFvVZCEzPps8Cno6IyNqnZXf31AJjgBfKU7qZmXVVp5d3smv0lwOPA1XAHRGxStIsoDEiFgK3A/dIaqJwhj8tW3eVpAeA3wLbgb+OiB099FrKrU9dbqowH4vdfCx287HYrd8cCxVOyM3MLAV+ItfMLCEOfTOzhDj0i0i6Q9K7kn5T6VoqSdIQSS9I+rWkVZKuq3RNva3U74KkQyQ9Ien17N+DK1ljb5A0WtIvJK3OfheuzNpTPBYl3xfZjS7LsmMxP7vppU9y6O/pLgpDRqTuY+DkiPgKMAGYIun4CtfU2+5iz9+FmcBTETEGeCqb39dtB74bEUcCxwN/nQ2xkuKx6Oh9cTNwa3YsNlIYmqZPcugXiYilFO5ASloUbMlmB2U/SX3q38HvQvshR+YCp/dqURUQEe9ExEvZ9AfAagpP1qd4LDp6X5xMYQga6OPHwqFvHZJUJWkF8C7wREQsq3RNfcCfRMQ7UAhD4NAK19OrshF0jwGWkeixKH5fAG8A70fE9qxLyeFm+gqHvnUoInZExAQKT1LXSzq60jVZ5UgaCjwI/JeI2Fzpeiql+H0BHFmqW+9WlZ9D3zoVEe8Dv8SfdQD8QdKfAmT/vlvhenqFpEEUAv++iHgoa07yWOzS7n1xPHBQNgQN9PHhZhz6VpKkakkHZdP7A/8BeLWyVfUJ7YccmQ48UsFaekU2TPrtwOqI+Id2i1I8FqXeF6uBX1AYggb6+LHwE7lFJP0cmEhhqNQ/ANdExO0VLaoCJI2n8IFUFYWTgwciYlZlq+pdpX4XgH8BHgA+D7wFnB0R+/QH/5JOBP4f8AqwM2v+bxSu66d2LEq+LyR9kcJ3jRwCvAycHxEfV67Sjjn0zcwS4ss7ZmYJceibmSXEoW9mlhCHvplZQhz6ZmYJcehbsiQdJOmyStdh1psc+paygwCHviWl0+/INduH3QR8KRs864ms7RQK46ZcHxHzJU0EZgGtwL8FlgKXRcTO9huSdCFwGvA54EvAwxHxvd54EWZd4TN9S9lM4I1s8KznKYyP/hUKj9b/j13jylAYVOu7wDgKgX5mB9ubAJyT9TtH0ugerN2sWxz6ZgUnAj/PRlD8A7AEODZb9kJErI2IHcDPs76lPBURmyJiK/Bb4As9XrVZFzn0zQr0GcuKxyoJSWdIWpH91GXt7cda2YEvn1of5NC3lH0ADMuml1K4JFMlqRo4CXghW1affQfqAAqXb56JiIcjYkL209j7pZt1j0PfkhURrcCz2Ref/3tgJfBr4GngexHxr1nX5yh86Psb4HfAwxUo16wsPMqm2WfI7t7524j4T5WuxawcfKZvZpYQn+mbmSXEZ/pmZglx6JuZJcShb2aWEIe+mVlCHPpmZgn5/+9s69rJd7/2AAAAAElFTkSuQmCC\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": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>BX</th>\n",
       "      <th>ML10M</th>\n",
       "      <th>ML1M</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>type</th>\n",
       "      <th>metric</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>relevance</th>\n",
       "      <th>hr</th>\n",
       "      <td>0.060056</td>\n",
       "      <td>0.255093</td>\n",
       "      <td>0.229451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ranking</th>\n",
       "      <th>mrr</th>\n",
       "      <td>0.028553</td>\n",
       "      <td>0.112703</td>\n",
       "      <td>0.097901</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        BX     ML10M      ML1M\n",
       "type      metric                              \n",
       "relevance hr      0.060056  0.255093  0.229451\n",
       "ranking   mrr     0.028553  0.112703  0.097901"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat({lbl: res.mean(level='top-n').loc[10, :'ranking'] for lbl, res in result.items()}, axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### saving data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "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": 35,
   "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": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "config_cold = {}\n",
    "scores_cold = {}\n",
    "data_models_cold = {}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "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=\"3\" value=\"3\" class=\"Progress-main\"/></progress>\n",
       "<span class=\"Progress-label\"><strong>100%</strong></span>\n",
       "<span class=\"Iteration-label\">3/3</span>\n",
       "<span class=\"Time-label\">[02:51:58<01:03:29, 3439.45s/it]</span></div>"
      ],
      "text/plain": [
       "\u001b[A\u001b[A\u001b[2K\r",
       " [████████████████████████████████████████████████████████████] 3/3 [02:51:58<01:03:29, 3439.45s/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\">ML1M</span>\n",
       "<progress style=\"width:45ex\" max=\"100\" value=\"100\" class=\"Progress-main\"/></progress>\n",
       "<span class=\"Progress-label\"><strong>100%</strong></span>\n",
       "<span class=\"Iteration-label\">100/100</span>\n",
       "<span class=\"Time-label\">[13:36<00:00, 8.16s/it]</span></div>"
      ],
      "text/plain": [
       "\u001b[A\u001b[2K\r",
       "           ML1M [█████████████████████████████████████████████] 100/100 [13:36<00:00, 8.16s/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\">ML10M</span>\n",
       "<progress style=\"width:45ex\" max=\"100\" value=\"100\" class=\"Progress-main\"/></progress>\n",
       "<span class=\"Progress-label\"><strong>100%</strong></span>\n",
       "<span class=\"Iteration-label\">100/100</span>\n",
       "<span class=\"Time-label\">[01:34:46<00:04, 56.86s/it]</span></div>"
      ],
      "text/plain": [
       "\u001b[A\u001b[2K\r",
       "          ML10M [█████████████████████████████████████████████] 100/100 [01:34:46<00:04, 56.86s/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\">BX</span>\n",
       "<progress style=\"width:45ex\" max=\"120\" value=\"120\" class=\"Progress-main\"/></progress>\n",
       "<span class=\"Progress-label\"><strong>100%</strong></span>\n",
       "<span class=\"Iteration-label\">120/120</span>\n",
       "<span class=\"Time-label\">[01:03:26<00:01, 31.72s/it]</span></div>"
      ],
      "text/plain": [
       "\u001b[A\u001b[2K\r",
       "             BX [█████████████████████████████████████████████] 120/120 [01:03:26<00:01, 31.72s/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": 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"
    },
    {
     "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": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'ML1M': {'HybridSVD(cs)': {'features_weight': 0.7, 'rank': 5}},\n",
       " 'ML10M': {'HybridSVD(cs)': {'features_weight': 0.9, 'rank': 60}},\n",
       " 'BX': {'HybridSVD(cs)': {'features_weight': 0.9, 'rank': 750}}}"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "config_cold"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### saving data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "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": 41,
   "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=\"3\" value=\"3\" class=\"Progress-main\"/></progress>\n",
       "<span class=\"Progress-label\"><strong>100%</strong></span>\n",
       "<span class=\"Iteration-label\">3/3</span>\n",
       "<span class=\"Time-label\">[24:26<15:00, 488.79s/it]</span></div>"
      ],
      "text/plain": [
       "\u001b[A\u001b[A\u001b[2K\r",
       " [████████████████████████████████████████████████████████████] 3/3 [24:26<15:00, 488.79s/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\">ML1M 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\">[00:20<00:06, 4.06s/it]</span></div>"
      ],
      "text/plain": [
       "\u001b[A\u001b[2K\r",
       "     ML1M folds [█████████████████████████████████████████████] 5/5 [00:20<00:06, 4.06s/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\">ML10M 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\">[09:06<01:56, 109.12s/it]</span></div>"
      ],
      "text/plain": [
       "\u001b[A\u001b[2K\r",
       "    ML10M folds [█████████████████████████████████████████████] 5/5 [09:06<01:56, 109.12s/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\">BX 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\">[15:00<03:14, 180.07s/it]</span></div>"
      ],
      "text/plain": [
       "\u001b[A\u001b[2K\r",
       "       BX folds [█████████████████████████████████████████████] 5/5 [15:00<03:14, 180.07s/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": 42,
   "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"
    },
    {
     "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": 43,
   "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"
    },
    {
     "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": 44,
   "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
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}