FactorizationMachines_AMZ.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "env: MKL_NUM_THREADS=24\n",
      "env: OMP_NUM_THREADS=24\n"
     ]
    }
   ],
   "source": [
    "%env MKL_NUM_THREADS=24\n",
    "%env OMP_NUM_THREADS=24"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from collections import defaultdict\n",
    "\n",
    "import numpy as np\n",
    "import scipy as sp\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 random_grid, find_optimal_config\n",
    "from polara.recommender.coldstart.models import ItemColdStartEvaluationMixin\n",
    "from polara.recommender.external.turi.turiwrapper import (TuriFactorizationRecommender,\n",
    "                                                          ColdStartRecommendationsMixin)\n",
    "\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",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "seed = 42"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "experiment_name = 'fm'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Experiment setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_labels = ['AMZe', 'AMZvg']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# according to https://apple.github.io/turicreate/docs/api/generated/turicreate.recommender.ranking_factorization_recommender.RankingFactorizationRecommender.html\n",
    "init_config = dict(with_data_feedback = False, # implicit case\n",
    "                   ranking_optimization = True,\n",
    "                   solver = 'adagrad',\n",
    "                   sgd_step_size = 0, # let Turi autotune it\n",
    "                   seed = seed,\n",
    "                   max_iterations = 25,\n",
    "                   other_tc_params = {}\n",
    "                   )\n",
    "fm_init_config = dict.fromkeys(data_labels, {'FM': init_config, # standard scenario\n",
    "                                             'FM(cs)': init_config}) # cold start"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "params = {\n",
    "          'regularization': [1e-10, 3e-10, 1e-9, 3e-9, 1e-8, 3e-8, 1e-7, 3e-7, 1e-6, 3e-6],\n",
    "          'linear_regularization': [1e-10, 3e-10, 1e-9, 3e-9, 1e-8, 3e-8, 1e-7, 3e-7, 1e-6, 3e-6],\n",
    "          'rank': [40] # for initial tuning (exploration)\n",
    "         }\n",
    "\n",
    "if init_config['solver'] == 'adagrad':\n",
    "    params.update({\n",
    "                   'adagrad_momentum_weighting': [0.9, 0.95, 0.99]\n",
    "                  })"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "ranks_grid = [1, 5, 15, 30, 50, 75, 100, 150, 200, 250, 300, 400,\n",
    "              500, 750, 1000, 1250, 1500, 1750, 2000, 2500, 3000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "fm_ranks = {'AMZe': ranks_grid,\n",
    "            'AMZvg': ranks_grid\n",
    "           }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "topk_values = [1, 3, 10, 20, 30]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "target_metric = 'mrr'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_data = [data_dict, similarities, sim_indices, meta_dict]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Amazon Electronics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "lbl = 'AMZe'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "similarities[lbl], sim_indices[lbl], feature_idx[lbl] = get_similarity_data(meta_dict[lbl])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 17,
     "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": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "lbl = 'AMZvg'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "similarities[lbl], sim_indices[lbl], feature_idx[lbl] = get_similarity_data(meta_dict[lbl])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 21,
     "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": 22,
   "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": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "def prepare_recommender_models(data_label, data_models, config):\n",
    "    data_model = data_models[data_label]\n",
    "    fm = TuriFactorizationRecommender(data_model, item_side_info=meta_dict[data_label])\n",
    "    fm.method = 'FM'\n",
    "    models = [fm]\n",
    "    apply_config(models, config, data_label)\n",
    "    return models\n",
    "\n",
    "def fine_tune_fm(model, params, label, ntrials=60, record_time_as=None):\n",
    "    param_grid, param_names = random_grid(params, n=ntrials)\n",
    "    best_fm_config, fm_scores = find_optimal_config(model, param_grid, param_names,\n",
    "                                                    target_metric,\n",
    "                                                    return_scores=True,\n",
    "                                                    force_build=True,\n",
    "                                                    iterator=lambda x: track(x, label=label))\n",
    "    model_config = {model.method: dict(zip(param_names, best_fm_config))}\n",
    "    model_scores = {model.method: fm_scores}\n",
    "    try:\n",
    "        if record_time_as:\n",
    "            save_training_time(f'{experiment_name}_{record_time_as}', model, fm_scores.index, label)\n",
    "    finally:\n",
    "        return model_config, model_scores"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "config = {}\n",
    "scores = {}\n",
    "times = {}\n",
    "data_models = {}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
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       "<span class=\"Progress-label\"><strong>100%</strong></span>\n",
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       "           AMZe [█████████████████████████████████████████████] 30/30 [01:06:48<02:10, 133.61s/it]"
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       "<span class=\"Progress-label\"><strong>100%</strong></span>\n",
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   ],
   "source": [
    "for label in track(data_labels):\n",
    "    data_models[label] = prepare_data_model(label, *all_data, seed)\n",
    "    model, = prepare_recommender_models(label, data_models, fm_init_config)\n",
    "    config[label], scores[label] = fine_tune_fm(model, params, label, ntrials=30, record_time_as='param')\n",
    "del model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "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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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "report_results('tuning', scores);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'AMZe': {'FM': {'regularization': 1e-06,\n",
       "   'linear_regularization': 1e-08,\n",
       "   'rank': 40,\n",
       "   'adagrad_momentum_weighting': 0.99}},\n",
       " 'AMZvg': {'FM': {'regularization': 3e-06,\n",
       "   'linear_regularization': 3e-06,\n",
       "   'rank': 40,\n",
       "   'adagrad_momentum_weighting': 0.99}}}"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "config"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### saving data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "save_results(f'{experiment_name}_param', config=config, tuning=scores)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## rank estimation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "rank_config = {}\n",
    "rank_scores = {}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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\">[04:08:56<15:27, 7468.04s/it]</span></div>"
      ],
      "text/plain": [
       "\u001b[A\u001b[A\u001b[2K\r",
       " [████████████████████████████████████████████████████████████] 2/2 [04:08:56<15:27, 7468.04s/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=\"21\" value=\"21\" class=\"Progress-main\"/></progress>\n",
       "<span class=\"Progress-label\"><strong>100%</strong></span>\n",
       "<span class=\"Iteration-label\">21/21</span>\n",
       "<span class=\"Time-label\">[03:53:29<05:06, 667.08s/it]</span></div>"
      ],
      "text/plain": [
       "\u001b[A\u001b[2K\r",
       "           AMZe [█████████████████████████████████████████████] 21/21 [03:53:29<05:06, 667.08s/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=\"21\" value=\"21\" class=\"Progress-main\"/></progress>\n",
       "<span class=\"Progress-label\"><strong>100%</strong></span>\n",
       "<span class=\"Iteration-label\">21/21</span>\n",
       "<span class=\"Time-label\">[15:27<00:24, 44.16s/it]</span></div>"
      ],
      "text/plain": [
       "\u001b[A\u001b[2K\r",
       "          AMZvg [█████████████████████████████████████████████] 21/21 [15:27<00:24, 44.16s/it]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for label in track(data_labels):\n",
    "    model, = prepare_recommender_models(label, data_models,\n",
    "                                        [fm_init_config, config]) # initiate with optimal config\n",
    "    rank_config[label], rank_scores[label] = fine_tune_fm(model, {'rank': fm_ranks[label]},\n",
    "                                                          label, ntrials=0, record_time_as='rank')\n",
    "del model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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', {lbl: v.sort_index() for lbl, scr in rank_scores.items() for k, v in scr.items()});"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'AMZe': {'FM': {'rank': 1750}}, 'AMZvg': {'FM': {'rank': 2000}}}"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rank_config"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### saving data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "save_results(f'{experiment_name}_rank', config=rank_config, tuning=rank_scores)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## cross-validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "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\">[02:08:11<09:06, 3845.47s/it]</span></div>"
      ],
      "text/plain": [
       "\u001b[A\u001b[A\u001b[2K\r",
       " [████████████████████████████████████████████████████████████] 2/2 [02:08:11<09:06, 3845.47s/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\">[01:59:05<23:46, 1429.06s/it]</span></div>"
      ],
      "text/plain": [
       "\u001b[A\u001b[2K\r",
       "           AMZe [█████████████████████████████████████████████] 5/5 [01:59:05<23:46, 1429.06s/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\">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\">[09:06<01:50, 109.10s/it]</span></div>"
      ],
      "text/plain": [
       "\u001b[A\u001b[2K\r",
       "          AMZvg [█████████████████████████████████████████████] 5/5 [09:06<01:50, 109.10s/it]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "result = {}\n",
    "for label in track(data_labels):\n",
    "    models = prepare_recommender_models(label, data_models, [fm_init_config, config, rank_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": 35,
   "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": "iVBORw0KGgoAAAANSUhEUgAAAX0AAAEWCAYAAACKSkfIAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvOIA7rQAAGBtJREFUeJzt3X+QXWWd5/H3l05IZBTQEMdZQug4ZHSCgSQbIquoSGYwFCPBmWAS3SVjYcGUw65bM9Rs3Br5kWVRRmuy7srUDiUMyA8TDII9axQRVAZUIAwxEgK7TQzSC0LSiVkZCRj47h/3JFxvbtM33Td9u/O8X1W3cs5znnPv95zq/vTJc899bmQmkqQyHNLpAiRJI8fQl6SCGPqSVBBDX5IKYuhLUkEMfUkqiKEvSQUx9FWEiPheROyIiAl1bddFREbEWQ19/1vV/qfV+saIeL7h8WJEvDLChyENm6Gvg15EdAPvARI4q2Hz/waW1fUdB5wDPLGnLTOPz8zX73kAbwE2A//lwFYutZ+hrxKcC/wIuI66gK/8I/DuiHhjtb4A2AD8/DWe70vAU8BlABGxKSL+aM/GiBgXEdsiYk61fm5EPBkR/RHx6YjYEhF/0I4Dk/aXoa8SnAvcVD0+EBG/XbdtF9ADLKnr++WBnigi/gPwbuAjmblneOcrwNK6bh8AtmXmP0fEDODvgI8CvwMcARw97COShsjQ10EtIk4BjgVuycyHqA3bfKSh25eBcyPiCOB9wO0DPNfJwBXAOZm5rW7TzcBZEXFYtf6Rqg1gEfCPmXlvZr4EXExtmEnqCENfB7tlwLfrQvpmGoZ4MvNeYDLw18D/yswXGp8kIo4Cvgp8KjN/1LB/L7AJ+GAV/Gfxauj/K2pDQXv6/grob8NxSUMyrtMFSAdKRLwO+DDQFRF7xugnAEdGxIkN3W+kdhX+/ibPcwi1EL8vM//HAC+3Z4jnEODR6g8BwDPA2xpqmjS0I5KGz9DXwexs4GVgJvBSXfst1Mbu6/134J+Ae5o8z6XAMcAfv8ZrrQL+K/AmXr3KB1gD/Cgi3gWso/bmb7R8BFKbObyjg9ky4B8y82eZ+fM9D+CL1N5Y3XvRk5nbM/OubP4FE38NvBX4eZP79adW+z8D/BB4F7C67nk3Av+e2h+FZ4BfAs8BLx6IA5YGE36JijRyIuL1wC+A6Zn5007Xo/J4pS8dYBHxwYg4LCJ+C/g88BNgS2erUqkMfenAWwg8XT2mA0sGGEaSDjiHdySpIF7pS1JBRt0tm0cddVR2d3d3ugxJGlMeeuihbZk5ebB+oy70u7u7WbduXafLkKQxJSKebKWfwzuSVBBDX5IKYuhLUkFG3Zh+M7/+9a/p6+tj165dnS5lWCZOnMiUKVMYP358p0uRVKgxEfp9fX284Q1voLu7m4ixOVdVZtLf309fXx/Tpk3rdDmSCjUmhnd27drFpEmTxmzgA0QEkyZNGvP/W5E0to2J0AfGdODvcTAcg6SxbcyEviRp+Az919Dd3c22bduG3UeSRosx8UauJB0o3cu/0ekS2PLZM0fstQ66K/0tW7bw9re/nY9//OO84x3v4KMf/Sjf+c53ePe738306dN54IEH2L59O2effTYnnHACJ598Mhs2bACgv7+f008/ndmzZ3PBBRdQPwPpjTfeyLx585g1axYXXHABL7/8cqcOUZKG7KALfYDe3l4++clPsmHDBh577DFuvvlm7r33Xj7/+c9zxRVXcMkllzB79mw2bNjAFVdcwbnn1r4u9bLLLuOUU07h4Ycf5qyzzuJnP/sZAJs2bWL16tXcd999rF+/nq6uLm666aZOHqIkDclBObwzbdo0Zs6cCcDxxx/P/PnziQhmzpzJli1bePLJJ7n11lsBOO200+jv72fnzp3cc889fO1rXwPgzDPP5I1vfCMAd911Fw899BAnnXQSAC+88AJvfvObO3BkkjQ8LYV+RCwAvgB0AV/KzM82bJ8AfBn410A/sDgzt1TbTgD+HjgceAU4KTMP6M3qEyZM2Lt8yCGH7F0/5JBD2L17N+PG7XvYe26nbHZbZWaybNkyPvOZzxygiiVpZAw6vBMRXcBVwBnADGBpRMxo6HYesCMzjwNWAldW+44DbgT+LDOPB04Fft226ofove99797hme9973scddRRHH744b/R/s1vfpMdO3YAMH/+fNasWcNzzz0HwPbt23nyyZZmMZWkUaWVK/15QG9mbgaIiFXUvvPz0bo+C4FLq+U1wBejdsl8OrAhM38MkJn9bap7WC699FI+9rGPccIJJ3DYYYdx/fXXA3DJJZewdOlS5syZw/ve9z6mTp0KwIwZM7j88ss5/fTTeeWVVxg/fjxXXXUVxx57bCcPQxqy0u5Y0ataCf2jgafq1vuAdw7UJzN3R8ROYBLwe0BGxB3AZGBVZv5N4wtExPnA+cDeoB2q7u5uHnnkkb3r1113XdNtX//61/fZd9KkSXz729/eu75y5cq9y4sXL2bx4sX77LNly5Zh1StJI6mVu3eazR3Q+G3qA/UZB5wCfLT690MRMX+fjplXZ+bczJw7efKg3/YlSRqiVkK/Dzimbn0K8PRAfapx/COA7VX79zNzW2b+ClgLzBlu0ZKkoWkl9B8EpkfEtIg4FFgC9DT06QGWVcuLgLuz9smmO4ATIuKw6o/B+/jN9wIkSSNo0DH9aoz+QmoB3gVcm5kbI2IFsC4ze4BrgBsiopfaFf6Sat8dEfG31P5wJLA2Mzv/DpIkFaql+/Qzcy21oZn6tovrlncB5wyw743UbtuUJHXYQTkNgySpuTE5DUO77zFu5X7hrq6uvVM7ANx+++1s2bKF97///XzpS1/ivPPOA+Dhhx9mzpw5fO5zn+Oiiy5qa52SNFxe6bfoda97HevXr9/76O7uBmDmzJmsXr16b79Vq1Zx4okndqhKSXpthv4wTZ06lV27dvHss8+SmXzrW9/ijDPO6HRZktTUmBze6YQXXniBWbNmAbVZPG+77ba92xYtWsRXv/pVZs+ezZw5c35jwjdJGk0M/RbtGd5p5sMf/jCLFy/mscceY+nSpfzgBz8Y4eokqTWGfhu85S1vYfz48dx555184QtfMPRHKScZkwz9tlmxYgXPPfccXV1dnS5FkgY0JkN/NF4tvetd7+p0CZI0qDEZ+p3w/PPP79N26qmncuqpp+7Tfumllx74giRpCLxlU5IKYuhLUkHGTOjXZmoe2w6GY5A0to2J0J84cSL9/f1jOjQzk/7+fiZOnNjpUiQVbEy8kTtlyhT6+vrYunVrp0sZlokTJzJlypROlyGpYGMi9MePH8+0adM6XYYkjXljYnhHktQehr4kFcTQl6SCGPqSVBBDX5IKYuhLUkEMfUkqiKEvSQVpKfQjYkFEPB4RvRGxvMn2CRGxutp+f0R0V+3dEfFCRKyvHv+zveVLkvbHoJ/IjYgu4CrgD4E+4MGI6MnMR+u6nQfsyMzjImIJcCWwuNr2RGbOanPdkqQhaOVKfx7Qm5mbM/MlYBWwsKHPQuD6ankNMD8ion1lSpLaoZXQPxp4qm69r2pr2iczdwM7gUnVtmkR8XBEfD8i3tPsBSLi/IhYFxHrxvqkapI0mrUS+s2u2BvnOB6ozzPA1MycDfwFcHNEHL5Px8yrM3NuZs6dPHlyCyVJkoaildDvA46pW58CPD1Qn4gYBxwBbM/MFzOzHyAzHwKeAH5vuEVLkoamldB/EJgeEdMi4lBgCdDT0KcHWFYtLwLuzsyMiMnVG8FExFuB6cDm9pQuSdpfg969k5m7I+JC4A6gC7g2MzdGxApgXWb2ANcAN0REL7Cd2h8GgPcCKyJiN/Ay8GeZuf1AHIgkaXAtfYlKZq4F1ja0XVy3vAs4p8l+twK3DrNGSVKb+IlcSSqIoS9JBTH0Jakghr4kFcTQl6SCGPqSVBBDX5IKYuhLUkEMfUkqiKEvSQVpaRoGjV3dy7/R6RIA2PLZMztdgiS80pekohj6klQQQ1+SCmLoS1JBDH1JKoihL0kFMfQlqSCGviQVxNCXpIIY+pJUEENfkgpi6EtSQQx9SSpIS6EfEQsi4vGI6I2I5U22T4iI1dX2+yOiu2H71Ih4PiIuak/ZkqShGDT0I6ILuAo4A5gBLI2IGQ3dzgN2ZOZxwErgyobtK4FvDr9cSdJwtHKlPw/ozczNmfkSsApY2NBnIXB9tbwGmB8RARARZwObgY3tKVmSNFSthP7RwFN1631VW9M+mbkb2AlMiojfAv4TcNnwS5UkDVcroR9N2rLFPpcBKzPz+dd8gYjzI2JdRKzbunVrCyVJkoaila9L7AOOqVufAjw9QJ++iBgHHAFsB94JLIqIvwGOBF6JiF2Z+cX6nTPzauBqgLlz5zb+QZEktUkrof8gMD0ipgH/F1gCfKShTw+wDPghsAi4OzMTeM+eDhFxKfB8Y+BLkkbOoKGfmbsj4kLgDqALuDYzN0bECmBdZvYA1wA3REQvtSv8JQeyaEnS0LRypU9mrgXWNrRdXLe8CzhnkOe4dAj1SZLayE/kSlJBDH1JKoihL0kFMfQlqSCGviQVxNCXpIIY+pJUEENfkgpi6EtSQQx9SSqIoS9JBTH0Jakghr4kFcTQl6SCGPqSVBBDX5IKYuhLUkEMfUkqiKEvSQUx9CWpIIa+JBXE0Jekghj6klQQQ1+SCmLoS1JBWgr9iFgQEY9HRG9ELG+yfUJErK623x8R3VX7vIhYXz1+HBEfam/5kqT9MWjoR0QXcBVwBjADWBoRMxq6nQfsyMzjgJXAlVX7I8DczJwFLAD+PiLGtat4SdL+aeVKfx7Qm5mbM/MlYBWwsKHPQuD6ankNMD8iIjN/lZm7q/aJQLajaEnS0LRy1X008FTdeh/wzoH6ZObuiNgJTAK2RcQ7gWuBY4F/V/dHYK+IOB84H2Dq1Kn7ewz76F7+jWE/Rzts+eyZnS5Bkn5DK1f60aSt8Yp9wD6ZeX9mHg+cBHwqIibu0zHz6sycm5lzJ0+e3EJJkqShaCX0+4Bj6tanAE8P1Kcasz8C2F7fITM3Af8CvGOoxUqShqeV0H8QmB4R0yLiUGAJ0NPQpwdYVi0vAu7OzKz2GQcQEccCbwO2tKVySdJ+G3RMvxqjvxC4A+gCrs3MjRGxAliXmT3ANcANEdFL7Qp/SbX7KcDyiPg18ArwiczcdiAORJI0uJZun8zMtcDahraL65Z3Aec02e8G4IZh1ihJahM/kStJBTH0Jakghr4kFcTQl6SCGPqSVBBDX5IKYuhLUkEMfUkqiKEvSQUx9CWpIIa+JBXE0Jekghj6klQQQ1+SCmLoS1JBDH1JKoihL0kFMfQlqSCGviQVxNCXpIIY+pJUEENfkgpi6EtSQQx9SSpIS6EfEQsi4vGI6I2I5U22T4iI1dX2+yOiu2r/w4h4KCJ+Uv17WnvLlyTtj0FDPyK6gKuAM4AZwNKImNHQ7TxgR2YeB6wErqzatwEfzMyZwDLghnYVLknaf61c6c8DejNzc2a+BKwCFjb0WQhcXy2vAeZHRGTmw5n5dNW+EZgYERPaUbgkaf+1EvpHA0/VrfdVbU37ZOZuYCcwqaHPnwAPZ+aLjS8QEedHxLqIWLd169ZWa5ck7adWQj+atOX+9ImI46kN+VzQ7AUy8+rMnJuZcydPntxCSZKkoWgl9PuAY+rWpwBPD9QnIsYBRwDbq/UpwG3AuZn5xHALliQNXSuh/yAwPSKmRcShwBKgp6FPD7U3agEWAXdnZkbEkcA3gE9l5n3tKlqSNDSDhn41Rn8hcAewCbglMzdGxIqIOKvqdg0wKSJ6gb8A9tzWeSFwHPDpiFhfPd7c9qOQJLVkXCudMnMtsLah7eK65V3AOU32uxy4fJg1SpLaxE/kSlJBDH1JKoihL0kFMfQlqSCGviQVxNCXpIIY+pJUEENfkgpi6EtSQQx9SSqIoS9JBTH0Jakghr4kFcTQl6SCGPqSVBBDX5IKYuhLUkEMfUkqiKEvSQUx9CWpIIa+JBXE0Jekghj6klQQQ1+SCtJS6EfEgoh4PCJ6I2J5k+0TImJ1tf3+iOiu2idFxHcj4vmI+GJ7S5ck7a9BQz8iuoCrgDOAGcDSiJjR0O08YEdmHgesBK6s2ncBnwYualvFkqQha+VKfx7Qm5mbM/MlYBWwsKHPQuD6ankNMD8iIjP/JTPvpRb+kqQOayX0jwaeqlvvq9qa9snM3cBOYFKrRUTE+RGxLiLWbd26tdXdJEn7qZXQjyZtOYQ+A8rMqzNzbmbOnTx5cqu7SZL2Uyuh3wccU7c+BXh6oD4RMQ44AtjejgIlSe3TSug/CEyPiGkRcSiwBOhp6NMDLKuWFwF3Z2bLV/qSpJExbrAOmbk7Ii4E7gC6gGszc2NErADWZWYPcA1wQ0T0UrvCX7Jn/4jYAhwOHBoRZwOnZ+aj7T8USdJgBg19gMxcC6xtaLu4bnkXcM4A+3YPoz5JUhv5iVxJKoihL0kFMfQlqSCGviQVxNCXpIIY+pJUEENfkgpi6EtSQQx9SSqIoS9JBTH0Jakghr4kFcTQl6SCGPqSVBBDX5IKYuhLUkEMfUkqiKEvSQUx9CWpIIa+JBXE0Jekghj6klQQQ1+SCmLoS1JBDH1JKkhLoR8RCyLi8YjojYjlTbZPiIjV1fb7I6K7btunqvbHI+ID7StdkrS/Bg39iOgCrgLOAGYASyNiRkO384AdmXkcsBK4stp3BrAEOB5YAPxd9XySpA5o5Up/HtCbmZsz8yVgFbCwoc9C4PpqeQ0wPyKial+VmS9m5k+B3ur5JEkdMK6FPkcDT9Wt9wHvHKhPZu6OiJ3ApKr9Rw37Ht34AhFxPnB+tfp8RDzeUvUH1lHAtuE8QVzZpko6z3PxqmGdi4PoPIDnot5oOBfHttKpldCPJm3ZYp9W9iUzrwaubqGWERMR6zJzbqfrGA08F6/yXLzKc/GqsXQuWhne6QOOqVufAjw9UJ+IGAccAWxvcV9J0ghpJfQfBKZHxLSIOJTaG7M9DX16gGXV8iLg7szMqn1JdXfPNGA68EB7Spck7a9Bh3eqMfoLgTuALuDazNwYESuAdZnZA1wD3BARvdSu8JdU+26MiFuAR4HdwJ9n5ssH6FjabVQNN3WY5+JVnotXeS5eNWbORdQuyCVJJfATuZJUEENfkgpi6DeIiGsj4rmIeKTTtXRSREyMiAci4scRsTEiLut0TSOt2c9CRLwpIu6MiP9T/fvGTtY4EiLimIj4bkRsqn4WPlm1l3gumv5eVDe63F+di9XVTS+jkqG/r+uoTRlRuheB0zLzRGAWsCAiTu5wTSPtOvb9WVgO3JWZ04G7qvWD3W7gLzPz94GTgT+vplgp8VwM9HtxJbCyOhc7qE1NMyoZ+g0y8x5qdyAVLWuer1bHV4+i3vUf4GehfsqR64GzR7SoDsjMZzLzn6vlXwKbqH2yvsRzMdDvxWnUpqCBUX4uDH0NKCK6ImI98BxwZ2be3+maRoHfzsxnoBaGwJs7XM+IqmbQnQ3cT6HnovH3AngC+EVm7q66NJ1uZrQw9DWgzHw5M2dR+yT1vIh4R6drUudExOuBW4H/mJn/r9P1dErj7wXw+826jWxVrTP0NajM/AXwPXyvA+DZiPgdgOrf5zpcz4iIiPHUAv+mzPxa1Vzkudij7vfiZODIagoaGOXTzRj6aioiJkfEkdXy64A/AB7rbFWjQv2UI8uAr3ewlhFRTZN+DbApM/+2blOJ56LZ78Um4LvUpqCBUX4u/ERug4j4CnAqtalSnwUuycxrOlpUB0TECdTekOqidnFwS2au6GxVI6vZzwJwO3ALMBX4GXBOZh7Ub/xHxCnAPwE/AV6pmv8ztXH90s5F09+LiHgrte8aeRPwMPBvM/PFzlU6MENfkgri8I4kFcTQl6SCGPqSVBBDX5IKYuhLUkEMfRUrIo6MiE90ug5pJBn6KtmRgKGvogz6HbnSQeyzwO9Wk2fdWbWdQW3elMszc3VEnAqsAPqBtwH3AJ/IzFfqnygi/hQ4CzgM+F3gtsz8q5E4CGl/eKWvki0Hnqgmz/oRtfnRT6T20frP7ZlXhtqkWn8JzKQW6H88wPPNAhZX/RZHxDEHsHZpSAx9qeYU4CvVDIrPAt8HTqq2PZCZmzPzZeArVd9m7srMnZm5C3gUOPaAVy3tJ0NfqonX2NY4V0lGxIciYn31mFu118+18jIOn2oUMvRVsl8Cb6iW76E2JNMVEZOB9wIPVNvmVd+Begi14Zt7M/O2zJxVPdaNfOnS0Bj6KlZm9gP3VV98/m+ADcCPgbuBv8rMn1ddf0jtTd9HgJ8Ct3WgXKktnGVTeg3V3TsXZeYfdboWqR280pekgnilL0kF8Upfkgpi6EtSQQx9SSqIoS9JBTH0Jakg/x9+eRthxz+i+QAAAABJRU5ErkJggg==\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": 36,
   "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>AMZe</th>\n",
       "      <th>AMZvg</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>type</th>\n",
       "      <th>metric</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>0.037263</td>\n",
       "      <td>0.132833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ranking</th>\n",
       "      <th>mrr</th>\n",
       "      <td>0.014212</td>\n",
       "      <td>0.056403</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      AMZe     AMZvg\n",
       "type      metric                    \n",
       "relevance hr      0.037263  0.132833\n",
       "ranking   mrr     0.014212  0.056403"
      ]
     },
     "execution_count": 36,
     "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": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "save_results(experiment_name, cv=result)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Cold start"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "import gc\n",
    "del data_models, models\n",
    "gc.collect()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "class TuriRecommenderColdStart(ItemColdStartEvaluationMixin,\n",
    "                               ColdStartRecommendationsMixin,\n",
    "                               TuriFactorizationRecommender): pass\n",
    "\n",
    "def prepare_cold_start_recommender_models(data_label, data_models, config):\n",
    "    data_model = data_models[data_label]\n",
    "    fm = TuriRecommenderColdStart(data_model, item_side_info=meta_dict[data_label])\n",
    "    fm.method = 'FM(cs)'\n",
    "    models = [fm]\n",
    "    apply_config(models, config, data_label)\n",
    "    return models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "config_cold = {}\n",
    "scores_cold = {}\n",
    "data_models_cold = {}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "scrolled": true
   },
   "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\">[23:50:05<23:31, 42902.75s/it]</span></div>"
      ],
      "text/plain": [
       "\u001b[A\u001b[A\u001b[2K\r",
       " [████████████████████████████████████████████████████████████] 2/2 [23:50:05<23:31, 42902.75s/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=\"30\" value=\"30\" class=\"Progress-main\"/></progress>\n",
       "<span class=\"Progress-label\"><strong>100%</strong></span>\n",
       "<span class=\"Iteration-label\">30/30</span>\n",
       "<span class=\"Time-label\">[23:26:29<48:23, 2812.98s/it]</span></div>"
      ],
      "text/plain": [
       "\u001b[A\u001b[2K\r",
       "           AMZe [█████████████████████████████████████████████] 30/30 [23:26:29<48:23, 2812.98s/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=\"30\" value=\"30\" class=\"Progress-main\"/></progress>\n",
       "<span class=\"Progress-label\"><strong>100%</strong></span>\n",
       "<span class=\"Iteration-label\">30/30</span>\n",
       "<span class=\"Time-label\">[23:30<00:39, 47.01s/it]</span></div>"
      ],
      "text/plain": [
       "\u001b[A\u001b[2K\r",
       "          AMZvg [█████████████████████████████████████████████] 30/30 [23:30<00:39, 47.01s/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, =  prepare_cold_start_recommender_models(label, data_models_cold, fm_init_config)\n",
    "    config_cold[label], scores_cold[label] = fine_tune_fm(model, params, label, ntrials=30)\n",
    "del model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "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": [
    "# (multiple) last cats, default neg sampling\n",
    "report_results('tuning', scores_cold);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'AMZe': {'FM(cs)': {'regularization': 3e-06,\n",
       "   'linear_regularization': 3e-08,\n",
       "   'rank': 40,\n",
       "   'adagrad_momentum_weighting': 0.95}},\n",
       " 'AMZvg': {'FM(cs)': {'regularization': 1e-09,\n",
       "   'linear_regularization': 1e-10,\n",
       "   'rank': 40,\n",
       "   'adagrad_momentum_weighting': 0.95}}}"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "config_cold"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### saving data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "save_results(f'{experiment_name}_coldstart_param', config=config_cold, tuning=scores_cold)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## rank estimation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "rank_config_cold = {}\n",
    "rank_scores_cold = {}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "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\">[19:42:33<28:59, 35476.52s/it]</span></div>"
      ],
      "text/plain": [
       "\u001b[A\u001b[A\u001b[2K\r",
       " [████████████████████████████████████████████████████████████] 2/2 [19:42:33<28:59, 35476.52s/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=\"21\" value=\"21\" class=\"Progress-main\"/></progress>\n",
       "<span class=\"Progress-label\"><strong>100%</strong></span>\n",
       "<span class=\"Iteration-label\">21/21</span>\n",
       "<span class=\"Time-label\">[19:13:34<46:09, 3295.92s/it]</span></div>"
      ],
      "text/plain": [
       "\u001b[A\u001b[2K\r",
       "           AMZe [█████████████████████████████████████████████] 21/21 [19:13:34<46:09, 3295.92s/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=\"21\" value=\"21\" class=\"Progress-main\"/></progress>\n",
       "<span class=\"Progress-label\"><strong>100%</strong></span>\n",
       "<span class=\"Iteration-label\">21/21</span>\n",
       "<span class=\"Time-label\">[28:59<01:01, 82.79s/it]</span></div>"
      ],
      "text/plain": [
       "\u001b[A\u001b[2K\r",
       "          AMZvg [█████████████████████████████████████████████] 21/21 [28:59<01:01, 82.79s/it]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for label in track(data_labels):\n",
    "    model, = prepare_cold_start_recommender_models(label, data_models_cold,\n",
    "                                                   [fm_init_config, config_cold]) # initiate with optimal config\n",
    "    rank_config_cold[label], rank_scores_cold[label] = fine_tune_fm(model, {'rank': fm_ranks[label]},\n",
    "                                                                    label, ntrials=0)\n",
    "del model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "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": [
    "# (multiple) last cats, default neg sampling\n",
    "report_results('rank', {lbl: v.sort_index() for lbl, scr in rank_scores_cold.items() for k, v in scr.items()});"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'AMZe': {'FM(cs)': {'rank': 1000}}, 'AMZvg': {'FM(cs)': {'rank': 50}}}"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rank_config_cold"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### saving data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "save_results(f'{experiment_name}_coldstart_rank', config=rank_config_cold, tuning=rank_scores_cold)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## cross validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "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\">[05:11:58<03:29, 9359.12s/it]</span></div>"
      ],
      "text/plain": [
       "\u001b[A\u001b[A\u001b[2K\r",
       " [████████████████████████████████████████████████████████████] 2/2 [05:11:58<03:29, 9359.12s/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\">[05:08:29<01:06:39, 3701.76s/it]</span></div>"
      ],
      "text/plain": [
       "\u001b[A\u001b[2K\r",
       "           AMZe [█████████████████████████████████████████████] 5/5 [05:08:29<01:06:39, 3701.76s/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\">[03:29<00:42, 41.88s/it]</span></div>"
      ],
      "text/plain": [
       "\u001b[A\u001b[2K\r",
       "          AMZvg [█████████████████████████████████████████████] 5/5 [03:29<00:42, 41.88s/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,\n",
    "                                                        [fm_init_config, config_cold, rank_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=label))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "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": [
    "# (multiple) last cats, default neg sampling\n",
    "report_results('topn', result_cold, target_metric);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "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": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "save_results(f'{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": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}