{
 "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": [
    "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_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",
    "\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 = ['ML1M', 'ML10M', 'BX']"
   ]
  },
  {
   "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, 10, 15, 20, 30, 50, 60, 75, 100, 125, 150, 200, 250, 300, 350, 400,\n",
    "              500, 750, 1000, 1250, 1500, 1750, 2000, 2500, 3000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "fm_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]}"
   ]
  },
  {
   "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)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_data = [data_dict, similarities, sim_indices, meta_dict]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Movielens1M"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "lbl = 'ML1M'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_dict[lbl], meta_dict[lbl] = get_movielens_data('/mnt/bulky/datasets/recsys/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": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# not used actually, simply to onform with general pipeline\n",
    "itemid = meta_dict[lbl].index.name\n",
    "sim_indices[lbl] = {itemid: meta_dict[lbl].index}\n",
    "similarities[lbl] = {itemid: sp.sparse.eye(len(meta_dict[lbl].index))}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Movielens10M"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "lbl = 'ML10M'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_dict[lbl], meta_dict[lbl] = get_movielens_data('/mnt/bulky/datasets/recsys/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": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# not used actually, simply to onform with general pipeline\n",
    "itemid = meta_dict[lbl].index.name\n",
    "sim_indices[lbl] = {itemid: meta_dict[lbl].index}\n",
    "similarities[lbl] = {itemid: sp.sparse.eye(len(meta_dict[lbl].index))}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 20,
     "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": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "lbl = 'BX'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/evfro/miniconda3/envs/polara_dev/lib/python3.6/site-packages/pandas/io/parsers.py:1990: DeprecationWarning: invalid escape sequence '\\8'\n",
      "  data = self._reader.read(nrows)\n",
      "/home/evfro/miniconda3/envs/polara_dev/lib/python3.6/site-packages/pandas/io/parsers.py:1990: DeprecationWarning: invalid escape sequence '\\9'\n",
      "  data = self._reader.read(nrows)\n"
     ]
    }
   ],
   "source": [
    "data_dict[lbl], meta_dict[lbl] = get_bookcrossing_data('/mnt/bulky/datasets/recsys/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": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# not used actually, simply to onform with general pipeline\n",
    "itemid = meta_dict[lbl].index.name\n",
    "sim_indices[lbl] = {itemid: meta_dict[lbl].index}\n",
    "similarities[lbl] = {itemid: sp.sparse.eye(len(meta_dict[lbl].index))}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 24,
     "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": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ML1M\n",
      "{'userid': 6038, 'movieid': 3522}\n",
      "density 2.699052132255699\n",
      "similarity matrix density 0.028392958546280524\n",
      "ML10M\n",
      "{'userid': 69797, 'movieid': 10258}\n",
      "density 0.6991397242349022\n",
      "similarity matrix density 0.009748488984207448\n",
      "BX\n",
      "{'userid': 7160, 'isbn': 16273}\n",
      "density 0.18925598044812894\n",
      "similarity matrix density 0.0005841769822585451\n"
     ]
    }
   ],
   "source": [
    "print_data_stats(data_labels, all_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Standard experiment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "config = {}\n",
    "scores = {}\n",
    "times = {}\n",
    "data_models = {}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'with_data_feedback': False,\n",
       " 'ranking_optimization': True,\n",
       " 'solver': 'adagrad',\n",
       " 'sgd_step_size': 0,\n",
       " 'seed': 42,\n",
       " 'max_iterations': 25,\n",
       " 'other_tc_params': {}}"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fm_init_config['ML1M']['FM']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<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\">[09:24:40<13:48, 11293.31s/it]</span></div>"
      ],
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       "\u001b[A\u001b[A\u001b[2K\r",
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     },
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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\">ML1M</span>\n",
       "<progress style=\"width:45ex\" max=\"60\" value=\"60\" class=\"Progress-main\"/></progress>\n",
       "<span class=\"Progress-label\"><strong>100%</strong></span>\n",
       "<span class=\"Iteration-label\">60/60</span>\n",
       "<span class=\"Time-label\">[55:09<00:50, 55.15s/it]</span></div>"
      ],
      "text/plain": [
       "\u001b[A\u001b[2K\r",
       "           ML1M [█████████████████████████████████████████████] 60/60 [55:09<00:50, 55.15s/it]"
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     },
     "metadata": {},
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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\">ML10M</span>\n",
       "<progress style=\"width:45ex\" max=\"60\" value=\"60\" class=\"Progress-main\"/></progress>\n",
       "<span class=\"Progress-label\"><strong>100%</strong></span>\n",
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       "\u001b[A\u001b[2K\r",
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     "metadata": {},
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     "data": {
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       "<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=\"60\" value=\"60\" class=\"Progress-main\"/></progress>\n",
       "<span class=\"Progress-label\"><strong>100%</strong></span>\n",
       "<span class=\"Iteration-label\">60/60</span>\n",
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       "\u001b[A\u001b[2K\r",
       "             BX [█████████████████████████████████████████████] 60/60 [13:48<00:13, 13.79s/it]"
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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=60, record_time_as='param')\n",
    "del model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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"
     ]
    },
    {
     "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('tuning', scores);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'ML1M': {'FM': {'regularization': 3e-08,\n",
       "   'linear_regularization': 1e-10,\n",
       "   'rank': 40,\n",
       "   'adagrad_momentum_weighting': 0.99}},\n",
       " 'ML10M': {'FM': {'regularization': 1e-06,\n",
       "   'linear_regularization': 3e-07,\n",
       "   'rank': 40,\n",
       "   'adagrad_momentum_weighting': 0.99}},\n",
       " 'BX': {'FM': {'regularization': 1e-06,\n",
       "   'linear_regularization': 3e-08,\n",
       "   'rank': 40,\n",
       "   'adagrad_momentum_weighting': 0.99}}}"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "config"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### saving data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "rank_config = {}\n",
    "rank_scores = {}"
   ]
  },
  {
   "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=\"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\">[08:47:04<21:52, 10541.42s/it]</span></div>"
      ],
      "text/plain": [
       "\u001b[A\u001b[A\u001b[2K\r",
       " [████████████████████████████████████████████████████████████] 3/3 [08:47:04<21:52, 10541.42s/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=\"20\" value=\"20\" class=\"Progress-main\"/></progress>\n",
       "<span class=\"Progress-label\"><strong>100%</strong></span>\n",
       "<span class=\"Iteration-label\">20/20</span>\n",
       "<span class=\"Time-label\">[54:37<00:38, 163.84s/it]</span></div>"
      ],
      "text/plain": [
       "\u001b[A\u001b[2K\r",
       "           ML1M [█████████████████████████████████████████████] 20/20 [54:37<00:38, 163.84s/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=\"20\" value=\"20\" class=\"Progress-main\"/></progress>\n",
       "<span class=\"Progress-label\"><strong>100%</strong></span>\n",
       "<span class=\"Iteration-label\">20/20</span>\n",
       "<span class=\"Time-label\">[07:30:36<44:57, 1351.79s/it]</span></div>"
      ],
      "text/plain": [
       "\u001b[A\u001b[2K\r",
       "          ML10M [█████████████████████████████████████████████] 20/20 [07:30:36<44:57, 1351.79s/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=\"24\" value=\"24\" class=\"Progress-main\"/></progress>\n",
       "<span class=\"Progress-label\"><strong>100%</strong></span>\n",
       "<span class=\"Iteration-label\">24/24</span>\n",
       "<span class=\"Time-label\">[21:51<00:40, 54.65s/it]</span></div>"
      ],
      "text/plain": [
       "\u001b[A\u001b[2K\r",
       "             BX [█████████████████████████████████████████████] 24/24 [21:51<00:40, 54.65s/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": 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": 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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": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'ML1M': {'FM': {'rank': 750}},\n",
       " 'ML10M': {'FM': {'rank': 750}},\n",
       " 'BX': {'FM': {'rank': 750}}}"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rank_config"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### saving data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "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": 38,
   "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\">[05:52:21<11:55, 7047.02s/it]</span></div>"
      ],
      "text/plain": [
       "\u001b[A\u001b[A\u001b[2K\r",
       " [████████████████████████████████████████████████████████████] 3/3 [05:52:21<11:55, 7047.02s/it]\u001b[B"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "<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\">[33:45<06:50, 404.94s/it]</span></div>"
      ],
      "text/plain": [
       "\u001b[A\u001b[2K\r",
       "           ML1M [█████████████████████████████████████████████] 5/5 [33:45<06:50, 404.94s/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\">[05:06:41<01:01:12, 3680.21s/it]</span></div>"
      ],
      "text/plain": [
       "\u001b[A\u001b[2K\r",
       "          ML10M [█████████████████████████████████████████████] 5/5 [05:06:41<01:01:12, 3680.21s/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\">[11:55<01:45, 143.05s/it]</span></div>"
      ],
      "text/plain": [
       "\u001b[A\u001b[2K\r",
       "             BX [█████████████████████████████████████████████] 5/5 [11:55<01:45, 143.05s/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": 39,
   "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, target_metric);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "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>0.061034</td>\n",
       "      <td>0.257876</td>\n",
       "      <td>0.190189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ranking</th>\n",
       "      <th>mrr</th>\n",
       "      <td>0.024515</td>\n",
       "      <td>0.109986</td>\n",
       "      <td>0.082559</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        BX     ML10M      ML1M\n",
       "type      metric                              \n",
       "relevance hr      0.061034  0.257876  0.190189\n",
       "ranking   mrr     0.024515  0.109986  0.082559"
      ]
     },
     "execution_count": 40,
     "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": 41,
   "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": 42,
   "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": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "config_cold = {}\n",
    "scores_cold = {}\n",
    "data_models_cold = {}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "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=\"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\">[13:44:00<55:24, 16480.13s/it]</span></div>"
      ],
      "text/plain": [
       "\u001b[A\u001b[A\u001b[2K\r",
       " [████████████████████████████████████████████████████████████] 3/3 [13:44:00<55:24, 16480.13s/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=\"60\" value=\"60\" class=\"Progress-main\"/></progress>\n",
       "<span class=\"Progress-label\"><strong>100%</strong></span>\n",
       "<span class=\"Iteration-label\">60/60</span>\n",
       "<span class=\"Time-label\">[56:30<00:55, 56.51s/it]</span></div>"
      ],
      "text/plain": [
       "\u001b[A\u001b[2K\r",
       "           ML1M [█████████████████████████████████████████████] 60/60 [56:30<00:55, 56.51s/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\">ML10M</span>\n",
       "<progress style=\"width:45ex\" max=\"60\" value=\"60\" class=\"Progress-main\"/></progress>\n",
       "<span class=\"Progress-label\"><strong>100%</strong></span>\n",
       "<span class=\"Iteration-label\">60/60</span>\n",
       "<span class=\"Time-label\">[11:52:03<11:58, 712.04s/it]</span></div>"
      ],
      "text/plain": [
       "\u001b[A\u001b[2K\r",
       "          ML10M [█████████████████████████████████████████████] 60/60 [11:52:03<11:58, 712.04s/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=\"60\" value=\"60\" class=\"Progress-main\"/></progress>\n",
       "<span class=\"Progress-label\"><strong>100%</strong></span>\n",
       "<span class=\"Iteration-label\">60/60</span>\n",
       "<span class=\"Time-label\">[55:23<00:53, 55.39s/it]</span></div>"
      ],
      "text/plain": [
       "\u001b[A\u001b[2K\r",
       "             BX [█████████████████████████████████████████████] 60/60 [55:23<00:53, 55.39s/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=60)\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"
    },
    {
     "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_cold);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'ML1M': {'FM(cs)': {'regularization': 1e-10,\n",
       "   'linear_regularization': 1e-06,\n",
       "   'rank': 40,\n",
       "   'adagrad_momentum_weighting': 0.99}},\n",
       " 'ML10M': {'FM(cs)': {'regularization': 3e-06,\n",
       "   'linear_regularization': 1e-06,\n",
       "   'rank': 40,\n",
       "   'adagrad_momentum_weighting': 0.95}},\n",
       " 'BX': {'FM(cs)': {'regularization': 3e-06,\n",
       "   'linear_regularization': 3e-07,\n",
       "   'rank': 40,\n",
       "   'adagrad_momentum_weighting': 0.95}}}"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "config_cold"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### saving data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "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": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "rank_config_cold = {}\n",
    "rank_scores_cold = {}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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\">[10:16:25<43:13, 12328.43s/it]</span></div>"
      ],
      "text/plain": [
       "\u001b[A\u001b[A\u001b[2K\r",
       " [████████████████████████████████████████████████████████████] 3/3 [10:16:25<43:13, 12328.43s/it]\u001b[B"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "<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=\"20\" value=\"20\" class=\"Progress-main\"/></progress>\n",
       "<span class=\"Progress-label\"><strong>100%</strong></span>\n",
       "<span class=\"Iteration-label\">20/20</span>\n",
       "<span class=\"Time-label\">[47:00<00:45, 141.01s/it]</span></div>"
      ],
      "text/plain": [
       "\u001b[A\u001b[2K\r",
       "           ML1M [█████████████████████████████████████████████] 20/20 [47:00<00:45, 141.01s/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=\"20\" value=\"20\" class=\"Progress-main\"/></progress>\n",
       "<span class=\"Progress-label\"><strong>100%</strong></span>\n",
       "<span class=\"Iteration-label\">20/20</span>\n",
       "<span class=\"Time-label\">[08:46:12<11:03, 1578.59s/it]</span></div>"
      ],
      "text/plain": [
       "\u001b[A\u001b[2K\r",
       "          ML10M [█████████████████████████████████████████████] 20/20 [08:46:12<11:03, 1578.59s/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=\"24\" value=\"24\" class=\"Progress-main\"/></progress>\n",
       "<span class=\"Progress-label\"><strong>100%</strong></span>\n",
       "<span class=\"Iteration-label\">24/24</span>\n",
       "<span class=\"Time-label\">[43:13<01:20, 108.05s/it]</span></div>"
      ],
      "text/plain": [
       "\u001b[A\u001b[2K\r",
       "             BX [█████████████████████████████████████████████] 24/24 [43:13<01:20, 108.05s/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": null,
   "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', {lbl: v.sort_index() for lbl, scr in rank_scores_cold.items() for k, v in scr.items()});"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'ML1M': {'FM(cs)': {'rank': 5}},\n",
       " 'ML10M': {'FM(cs)': {'rank': 75}},\n",
       " 'BX': {'FM(cs)': {'rank': 150}}}"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rank_config_cold"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### saving data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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": null,
   "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=\"1\" class=\"Progress-main\"/></progress>\n",
       "<span class=\"Progress-label\"><strong>33%</strong></span>\n",
       "<span class=\"Iteration-label\">1/3</span>\n",
       "<span class=\"Time-label\">[02:37<02:37, 156.74s/it]</span></div>"
      ],
      "text/plain": [
       "\u001b[A\u001b[A\u001b[2K\r",
       " [████████████████████########################################] 1/3 [02:37<02:37, 156.74s/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\">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\">[02:37<00:31, 31.35s/it]</span></div>"
      ],
      "text/plain": [
       "\u001b[A\u001b[2K\r",
       "           ML1M [█████████████████████████████████████████████] 5/5 [02:37<00:31, 31.35s/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=\"0\" class=\"Progress-main\"/></progress>\n",
       "<span class=\"Progress-label\"><strong>0%</strong></span>\n",
       "<span class=\"Iteration-label\">0/5</span>\n",
       "<span class=\"Time-label\">[0<0, 0.00s/it]</span></div>"
      ],
      "text/plain": [
       "\u001b[2K\r",
       "          ML10M [#############################################] 0/5 [0<0, 0.00s/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": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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dnDfdAlxZom1NiW6LndcgIuYAc7KMy8zMup+/mW1mZqky7VFY71Iz+8lyD4H1d15e7iGYWcJ7FGZmlspBYWZmqRwUZmaWykFhZmapHBRmZpbKQWFmZqkcFGZmlspBYWZmqRwUZmaWykFhZmapHBRmZpbKQWFmZqkcFGZmlspBYWZmqRwUZmaWykFhZmapHBRmZpbKQWFmZqkcFGZmlipTUEiaIGmtpCZJs4ss7yupPln+kqSapHygpGckfSDp3oI2n5L0WtLmHyQpKf+IpKclvZX8PLnzq2lmZh3VZlBIqgDmAROBYcA0ScMKqs0AdkTEmcA9wF1JeQvwbeBbRbr+ATATqE1eE5Ly2cDSiKgFlibzZmZWJln2KMYCTRGxLiL2AguBSQV1JgELkulFwHhJiog/RMTz5ALjAEmDgAER8a8REcBPgMlF+lqQV25mZmWQJShOB97Nm29OyorWiYhWYCcwsI0+m0v0+dGI2JT0tQk4tVgHkmZKapTUuHXr1gyrYWZmHZElKFSkLDpQpzP1D68ccV9EjI6I0dXV1e1pamZm7ZAlKJqBIXnzg4GNpepI6gOcCGxvo8/BJfrcnBya2n+IakuGMZqZWTfJEhTLgVpJQyVVAXVAQ0GdBmB6Mj0FWJaceygqOaS0S9IFydVOVwO/KNLX9LxyMzMrgz5tVYiIVkmzgKeACuCBiFgtaS7QGBENwHzgIUlN5PYk6va3l7QeGABUSZoMXBIRbwD/BXgQ6A/8MnkB3Ak8JmkGsAG4sitW1MzMOqbNoACIiMXA4oKym/OmWyjxgR4RNSXKG4Fzi5RvA8ZnGZeZmXU/fzPbzMxSOSjMzCyVg8LMzFI5KMzMLJWDwszMUjkozMwslYPCzMxSOSjMzCyVg8LMzFI5KMzMLJWDwszMUjkozMwslYPCzMxSOSjMzCyVg8LMzFI5KMzMLJWDwszMUjkozMwsVaZHofYGNbOfLPcQWH/n5eUegpnZYbxHYWZmqTIFhaQJktZKapI0u8jyvpLqk+UvSarJWzYnKV8r6dKk7JOSVua93pd0U7LsVkm/y1t2WdesqpmZdUSbh54kVQDzgC8CzcBySQ0R8UZetRnAjog4U1IdcBcwVdIwoA44B/gzYImkP4+ItcDIvP5/BzyR1989EXF351fPzMw6K8sexVigKSLWRcReYCEwqaDOJGBBMr0IGC9JSfnCiNgTEW8DTUl/+cYDv42Idzq6EmZm1n2yBMXpwLt5881JWdE6EdEK7AQGZmxbBzxaUDZL0ipJD0g6udigJM2U1CipcevWrRlWw8zMOiJLUKhIWWSsk9pWUhVwBfCzvOU/AD5B7tDUJuB7xQYVEfdFxOiIGF1dXV169GZm1ilZgqIZGJI3PxjYWKqOpD7AicD2DG0nAq9ExOb9BRGxOSL2RcSHwP0cfqjKzMx6UJagWA7UShqa7AHUAQ0FdRqA6cn0FGBZRERSXpdcFTUUqAVezms3jYLDTpIG5c1+CXg968qYmVnXa/Oqp4holTQLeAqoAB6IiNWS5gKNEdEAzAcektREbk+iLmm7WtJjwBtAK3B9ROwDkHQ8uSuprit4y7+TNJLcIar1RZabmVkPyvTN7IhYDCwuKLs5b7oFuLJE29uB24uU/zu5E96F5V/LMiYzM+sZ/ma2mZmlclCYmVkqB4WZmaVyUJiZWSoHhZmZpXJQmJlZKgeFmZmlclCYmVkqB4WZmaVyUJiZWSoHhZmZpXJQmJlZKgeFmZmlclCYmVkqB4WZmaVyUJiZWSoHhZmZpXJQmJlZKgeFmZmlclCYmVkqB4WZmaXKFBSSJkhaK6lJ0uwiy/tKqk+WvySpJm/ZnKR8raRL88rXS3pN0kpJjXnlH5H0tKS3kp8nd24VzcysM9oMCkkVwDxgIjAMmCZpWEG1GcCOiDgTuAe4K2k7DKgDzgEmAP+Y9Lfff4iIkRExOq9sNrA0ImqBpcm8mZmVSZY9irFAU0Ssi4i9wEJgUkGdScCCZHoRMF6SkvKFEbEnIt4GmpL+0uT3tQCYnGGMZmbWTbIExenAu3nzzUlZ0ToR0QrsBAa20TaA/ytphaSZeXU+GhGbkr42AacWG5SkmZIaJTVu3bo1w2qYmVlHZAkKFSmLjHXS2n42Is4nd0jrekkXZxjLwU4i7ouI0RExurq6uj1NzcysHbIERTMwJG9+MLCxVB1JfYATge1pbSNi/88twBMcPCS1WdKgpK9BwJbsq2NmZl0tS1AsB2olDZVURe7kdENBnQZgejI9BVgWEZGU1yVXRQ0FaoGXJf2JpBMAJP0JcAnwepG+pgO/6NiqmZlZV+jTVoWIaJU0C3gKqAAeiIjVkuYCjRHRAMwHHpLURG5Poi5pu1rSY8AbQCtwfUTsk/RR4Inc+W76AI9ExK+St7wTeEzSDGADcGUXrq+ZmbVTm0EBEBGLgcUFZTfnTbdQ4gM9Im4Hbi8oWwecV6L+NmB8lnGZmVn38zezzcwslYPCzMxSOSjMzCyVg8LMzFI5KMzMLJWDwszMUjkozMwslYPCzMxSOSjMzCyVg8LMzFI5KMzMLJWDwszMUjkozMwslYPCzMxSOSjMzCyVg8LMzFI5KMzMLJWDwszMUjkozMwslYPCzMxSOSjMzCxVpqCQNEHSWklNkmYXWd5XUn2y/CVJNXnL5iTlayVdmpQNkfSMpDWSVku6Ma/+rZJ+J2ll8rqs86tpZmYd1aetCpIqgHnAF4FmYLmkhoh4I6/aDGBHRJwpqQ64C5gqaRhQB5wD/BmwRNKfA63ANyPiFUknACskPZ3X5z0RcXdXraSZmXVclj2KsUBTRKyLiL3AQmBSQZ1JwIJkehEwXpKS8oURsSci3gaagLERsSkiXgGIiF3AGuD0zq+OmZl1tSxBcTrwbt58M4d/qB+oExGtwE5gYJa2yWGqUcBLecWzJK2S9ICkk4sNStJMSY2SGrdu3ZphNczMrCOyBIWKlEXGOqltJf0p8DhwU0S8nxT/APgEMBLYBHyv2KAi4r6IGB0Ro6urq9PXwMzMOixLUDQDQ/LmBwMbS9WR1Ac4Edie1lZSJbmQ+GlE/Hx/hYjYHBH7IuJD4H5yh77MzKxMsgTFcqBW0lBJVeROTjcU1GkApifTU4BlERFJeV1yVdRQoBZ4OTl/MR9YExHfz+9I0qC82S8Br7d3pczMrOu0edVTRLRKmgU8BVQAD0TEaklzgcaIaCD3of+QpCZyexJ1SdvVkh4D3iB3pdP1EbFP0kXA14DXJK1M3upvImIx8HeSRpI7RLUeuK4L19fMzNqpzaAASD7AFxeU3Zw33QJcWaLt7cDtBWXPU/z8BRHxtSxjMjOznuFvZpuZWSoHhZmZpXJQmJlZKgeFmZmlclCYmVkqB4WZmaVyUJiZWSoHhZmZpXJQmJlZKgeFmZmlclCYmVkqB4WZmaVyUJiZWSoHhZmZpXJQmJlZKgeFmZmlclCYmVkqB4WZmaVyUJiZWSoHhZmZpXJQmJlZqkxBIWmCpLWSmiTNLrK8r6T6ZPlLkmryls1JytdKurStPiUNTfp4K+mzqnOraGZmndFmUEiqAOYBE4FhwDRJwwqqzQB2RMSZwD3AXUnbYUAdcA4wAfhHSRVt9HkXcE9E1AI7kr7NzKxMsuxRjAWaImJdROwFFgKTCupMAhYk04uA8ZKUlC+MiD0R8TbQlPRXtM+kzReSPkj6nNzx1TMzs87qk6HO6cC7efPNwKdL1YmIVkk7gYFJ+YsFbU9Ppov1ORD4fUS0Fql/CEkzgZnJ7AeS1mZYl+52CvBeRxvrri4cSfl5W+R0ajuAt0U+b4uDumhbfCxLpSxBoSJlkbFOqfJiezJp9Q8vjLgPuK/YsnKR1BgRo8s9jiOBt0WOt8NB3hYHHW3bIsuhp2ZgSN78YGBjqTqS+gAnAttT2pYqfw84Kemj1HuZmVkPyhIUy4Ha5GqkKnInpxsK6jQA05PpKcCyiIikvC65KmooUAu8XKrPpM0zSR8kff6i46tnZmad1eahp+ScwyzgKaACeCAiVkuaCzRGRAMwH3hIUhO5PYm6pO1qSY8BbwCtwPURsQ+gWJ/JW/43YKGk24BXk76PFkfUobAy87bI8XY4yNvioKNqWyj3T7yZmVlx/ma2mZmlclCYmVkqB0UnSXpA0hZJr5d7LOUmqZ+klyX9RtJqSd8p95h6UrHfBUkfkfR0ckuapyWdXM4x9hRJQyQ9I2lN8rtwY1Le67ZHqb+Lo+l2RQ6KznuQ3O1JDPYAX4iI84CRwARJF5R5TD3pQQ7/XZgNLE1uSbM0me8NWoFvRsTZwAXA9cltenrj9ij1d3HU3K7IQdFJEfEcuSu9er3I+SCZrUxeveZqiRK/C/m3t+k1t6SJiE0R8UoyvQtYQ+4uC71ue6T8XRw1tytyUFiXSm76uBLYAjwdES+Ve0xl9tGI2AS5D0/g1DKPp8cld5MeBbxEL90ehX8XwG/JeLuiI4GDwrpUROyLiJHkvlU/VtK55R6TlY+kPwUeB26KiPfLPZ5yKfy7AM4uVq1nR5Wdg8K6RUT8HngWn7/ZLGkQQPJzS5nH02MkVZILiZ9GxM+T4l67PeCQv4sLOIpuV+SgsC4jqVrSScl0f+A/Am+Wd1Rll397m15zS5rkkQHzgTUR8f28Rb1ue5T4u1jDUXS7In8zu5MkPQqMI3fb4M3ALRFxNN12pMtIGkHupFwFuX9CHouIueUdVc8p9rsA/B/gMeAMYANwZUQc8xc/SLoI+BfgNeDDpPhvyJ2n6FXbo9TfhaSPk3sWz0fI3a7oP0fEnvKNtDQHhZmZpfKhJzMzS+WgMDOzVA4KMzNL5aAwM7NUDgozM0vloDBrB0knSfqrco/DrCc5KMza5yTAQWG9SpvPzDazQ9wJfCK5wdvTSdlEcvfpuS0i6iWNA+YC24BPAs8BfxURH+Z3JOka4ArgeOATwBMR8dc9sRJm7eE9CrP2mQ38NrnB24vkni9wHrnbMvz9/vsYkbvx2zeB4eRC4Msl+hsJTE3qTZU0pBvHbtYhDgqzjrsIeDS5M+hm4J+BMcmylyNiXUTsAx5N6hazNCJ2RkQL8AbwsW4ftVk7OSjMOk4pywrvjROSviRpZfIanZTn39tnHz4cbEcgB4VZ++wCTkimnyN3uKhCUjVwMfBysmxs8kzk48gdWno+Ip6IiJHJq7Hnh27WMQ4Ks3aIiG3AC5JeBz4DrAJ+AywD/joi/i2p+q/kTny/DrwNPFGG4Zp1Cd891qyLJVc9fSsi/qLcYzHrCt6jMDOzVN6jMDOzVN6jMDOzVA4KMzNL5aAwM7NUDgozM0vloDAzs1T/H+4eqIcU8OqOAAAAAElFTkSuQmCC\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": 61,
   "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": null,
   "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
}