LCE.ipynb

{
 "cells": [
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "%env MKL_NUM_THREADS=12\n",
    "%env OMP_NUM_THREADS=12"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "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",
    "\n",
    "from lce import LCEModel, LCEModelItemColdStart\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": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "seed = 42"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "experiment_name = 'lce'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Experiment setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_labels = ['ML1M', 'ML10M', 'BX']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "init_config = dict(seed = seed,\n",
    "                   max_iterations = 75,\n",
    "                   alpha = 0.1,\n",
    "                   beta = 0.05,\n",
    "                   max_neighbours=10,\n",
    "                   )\n",
    "lce_init_config = dict.fromkeys(data_labels, {'LCE': init_config,  # standard scenario\n",
    "                                              'LCE(cs)': init_config})  # cold start"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "params = {\n",
    "          'regularization': [1, 3, 10, 30],\n",
    "          'rank': [40] # for initial tuning (exploration)\n",
    "         }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "coeffs = {\n",
    "    'alpha': [0.1, 0.3, 0.5, 0.7, 0.9],\n",
    "    'beta': [0, 0.05, 0.1, 0.3]\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "ranks_grid = [1, 15, 30, 50, 75, 100, 125, 150, 200, 250, 300, 400,\n",
    "              500, 750, 1000, 1250, 1500, 2000, 2500, 3000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "lce_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('../datasets/movielens/ml-1m.zip',\n",
    "                                                    meta_path='data/meta_info_ml1m.csv',\n",
    "                                                    implicit=True,\n",
    "                                                    filter_no_meta=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# not used actually, simply to conform 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('../datasets/movielens/ml-10m.zip',\n",
    "                                                    meta_path='data/meta_info_ml10m.csv',\n",
    "                                                    implicit=True,\n",
    "                                                    filter_no_meta=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# not used actually, simply to conform 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": null,
   "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": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "lbl = 'BX'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_dict[lbl], meta_dict[lbl] = get_bookcrossing_data('../datasets/bookcrossing/BX-CSV-Dump.zip',\n",
    "                                                       get_books_meta=True,\n",
    "                                                       implicit=True,\n",
    "                                                       pcore=5,\n",
    "                                                       filter_no_meta=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# not used actually, simply to conform 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": null,
   "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": null,
   "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": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def prepare_recommender_models(data_label, data_models, config):\n",
    "    data_model = data_models[data_label]\n",
    "    lce = LCEModel(data_model, item_features=meta_dict[data_label])\n",
    "    lce.method = 'LCE'\n",
    "    models = [lce]\n",
    "    apply_config(models, config, data_label)\n",
    "    return models\n",
    "\n",
    "def fine_tune_lce(model, params, label, ntrials=60, record_time_as=None):\n",
    "    param_grid, param_names = random_grid(params, n=ntrials)\n",
    "    best_lce_config, lce_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_lce_config))}\n",
    "    model_scores = {model.method: lce_scores}\n",
    "    try:\n",
    "        if record_time_as:\n",
    "            save_training_time(f'{experiment_name}_{record_time_as}', model, lce_scores.index, label)\n",
    "    finally:\n",
    "        return model_config, model_scores"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "config = {}\n",
    "scores = {}\n",
    "data_models = {}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'seed': 42,\n",
       " 'max_iterations': 75,\n",
       " 'alpha': 0.1,\n",
       " 'beta': 0.05,\n",
       " 'max_neighbours': 10}"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lce_init_config['ML1M']['LCE']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### regularization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
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       "           ML1M [█████████████████████████████████████████████] 4/4 [00:60<00:13, 14.92s/it]"
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       "          ML10M [█████████████████████████████████████████████] 4/4 [05:34<01:26, 83.59s/it]"
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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=\"4\" value=\"4\" class=\"Progress-main\"/></progress>\n",
       "<span class=\"Progress-label\"><strong>100%</strong></span>\n",
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       "             BX [█████████████████████████████████████████████] 4/4 [01:10<00:15, 17.59s/it]"
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   ],
   "source": [
    "_config = {}\n",
    "_scores = {}\n",
    "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, lce_init_config)\n",
    "    _config[label], _ = fine_tune_lce(model, params, label)\n",
    "del model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'ML1M': {'LCE': {'regularization': 30, 'rank': 40}},\n",
       " 'ML10M': {'LCE': {'regularization': 30, 'rank': 40}},\n",
       " 'BX': {'LCE': {'regularization': 10, 'rank': 40}}}"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "_config # will also reuse it in coldstart"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### $\\alpha, \\beta$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
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       "           ML1M [█████████████████████████████████████████████] 20/20 [05:02<00:21, 15.12s/it]"
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       "          ML10M [█████████████████████████████████████████████] 20/20 [24:45<01:25, 74.25s/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, [lce_init_config, _config])\n",
    "    config[label], scores[label] = fine_tune_lce(model, coeffs, label, record_time_as='coeff')\n",
    "    # make sure to save all parameters\n",
    "    config[label][model.method].update(_config[label][model.method])\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('tuning', scores);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'ML1M': {'LCE': {'alpha': 0.1,\n",
       "   'beta': 0.1,\n",
       "   'regularization': 30,\n",
       "   'rank': 40}},\n",
       " 'ML10M': {'LCE': {'alpha': 0.3,\n",
       "   'beta': 0.1,\n",
       "   'regularization': 30,\n",
       "   'rank': 40}},\n",
       " 'BX': {'LCE': {'alpha': 0.1, 'beta': 0.0, 'regularization': 10, 'rank': 40}}}"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "config"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### saving data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "rank_config = {}\n",
    "rank_scores = {}"
   ]
  },
  {
   "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=\"2\" class=\"Progress-main\"/></progress>\n",
       "<span class=\"Progress-label\"><strong>67%</strong></span>\n",
       "<span class=\"Iteration-label\">2/3</span>\n",
       "<span class=\"Time-label\">[02:15:26<01:54:26, 4062.98s/it]</span></div>"
      ],
      "text/plain": [
       "\u001b[A\u001b[A\u001b[2K\r",
       " [████████████████████████████████████████####################] 2/3 [02:15:26<01:54:26, 4062.98s/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\">ML1M</span>\n",
       "<progress style=\"width:45ex\" max=\"15\" value=\"15\" class=\"Progress-main\"/></progress>\n",
       "<span class=\"Progress-label\"><strong>100%</strong></span>\n",
       "<span class=\"Iteration-label\">15/15</span>\n",
       "<span class=\"Time-label\">[21:00<02:34, 84.03s/it]</span></div>"
      ],
      "text/plain": [
       "\u001b[A\u001b[2K\r",
       "           ML1M [█████████████████████████████████████████████] 15/15 [21:00<02:34, 84.03s/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=\"15\" value=\"15\" class=\"Progress-main\"/></progress>\n",
       "<span class=\"Progress-label\"><strong>100%</strong></span>\n",
       "<span class=\"Iteration-label\">15/15</span>\n",
       "<span class=\"Time-label\">[01:54:26<11:46, 457.70s/it]</span></div>"
      ],
      "text/plain": [
       "\u001b[A\u001b[2K\r",
       "          ML10M [█████████████████████████████████████████████] 15/15 [01:54:26<11:46, 457.70s/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=\"18\" value=\"11\" class=\"Progress-main\"/></progress>\n",
       "<span class=\"Progress-label\"><strong>61%</strong></span>\n",
       "<span class=\"Iteration-label\">11/18</span>\n",
       "<span class=\"Time-label\">[20:57<00:18, 114.25s/it]</span></div>"
      ],
      "text/plain": [
       "\u001b[A\u001b[2K\r",
       "             BX [███████████████████████████##################] 11/18 [20:57<00:18, 114.25s/it]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for label in track(data_labels):\n",
    "    model, = prepare_recommender_models(label, data_models,\n",
    "                                        [lce_init_config, config]) # initiate with optimal config\n",
    "    rank_config[label], rank_scores[label] = fine_tune_lce(model, {'rank': lce_ranks[label]},\n",
    "                                                          label, ntrials=0, record_time_as='rank')\n",
    "del model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "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": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "rank_config"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### saving data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "result = {}\n",
    "for label in track(data_labels):\n",
    "    models = prepare_recommender_models(label, data_models, [lce_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": 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, target_metric);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "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>0.043296</td>\n",
       "      <td>0.268563</td>\n",
       "      <td>0.187703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ranking</th>\n",
       "      <th>mrr</th>\n",
       "      <td>0.019476</td>\n",
       "      <td>0.118241</td>\n",
       "      <td>0.084784</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        BX     ML10M      ML1M\n",
       "type      metric                              \n",
       "relevance hr      0.043296  0.268563  0.187703\n",
       "ranking   mrr     0.019476  0.118241  0.084784"
      ]
     },
     "execution_count": 62,
     "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": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "save_results(experiment_name, cv=result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "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": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def prepare_cold_start_recommender_models(data_label, data_models, config):\n",
    "    data_model = data_models[data_label]\n",
    "    lce = LCEModelItemColdStart(data_model, item_features=meta_dict[data_label])\n",
    "    lce.method = 'LCE(cs)'\n",
    "    models = [lce]\n",
    "    apply_config(models, config, data_label)\n",
    "    return models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "config_cold = {}\n",
    "scores_cold = {}\n",
    "data_models_cold = {}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "lce_init_config['ML1M']['LCE(cs)']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### $\\alpha, \\beta$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "_config_cold = {}\n",
    "for label in track(data_labels):\n",
    "    # reuse regularization param from standard scenario\n",
    "    _config_cold[label] = {f'{k}(cs)' if k=='LCE' else k: v for k, v in _config[label].items()}\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, [lce_init_config, _config_cold])\n",
    "    config_cold[label], scores_cold[label] = fine_tune_lce(model, coeffs, label, record_time_as=None)\n",
    "    # make sure to save all parameters\n",
    "    config_cold[label][model.method].update(_config_cold[label][model.method])\n",
    "del model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "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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1+d/BxVNSeHRZHjsrakiM7X/jHe3JL60m9TBfvOD7/2F/XQPb9u5nVAcD0IHim/l0+PPMykjg6VUFHKhvIDyke3+TDY2OB9/eTFZSLKeNG9atY3WWP4GQDBS2eF0ETG+vjHOu3szKgXhv+wet9k12zi03s3vxBcN+4E3n3JttndzMrgeuB0hNTfWjuv1fTV0DG0sqWdtisHf99goO1PsGBiNDg5mYFMuFxyczKSmOrORYMhNjCAvp3Ulh09KH8srNs3hqZQH3LN7Aufe9y9UzR/OdMzKJ6WIrpD95Y+0OvvP0hyQNjuTxa6a1O3DY5NuzM3jtk+08uiyP7515VC/Vsn3Pry6ktqGRy6cf/Lu4fHoaf313K8+sKuTbczL7sHadk1/a8ZTTJpnDvYHlXft6PBCcc+SXVjNz7OFbW7MyEnjs/Tw+LNjLjG528bz26Xa27q7iz/OP7/VWuT+B0FaNWt82116ZNreb2RB8rYd0YC/wnJnNd849eUhh5x4CHgKYOnXqgFt/tupAPZ9tr/C6fSrIKa5gU0kl9d5SujERIUxKiuNrJ6aRlRTHpORY0hOiCe6F5rA/QoKDuPLE0Zw3eST3vrmBh5dt5eWPirnz3PF85bjkXmm294Qnlufxs0U5HJMymEeuPoGhUYe/4h8/IpazJg7n0WVbue7k9D4NxcZGx8IVBUwbPZSjWrTa0hOiOCkjwZsVldFv/h11xDlHflkVJ2Ue/os3Y5gXCCX7OL2Nrr1A2lV5gP11DX61EKaPGUqQ+cYRuhMIjY2O+5fkkpkYzVkTR3T5OF3lTyAUAaNavE7h0O6dpjJFZhYCxAFlHex7BrDVObcLwMxeBGYChwTCQFJeXedd8Zez1uv62bq7qnmaZ0J0GFlJccweP6x5pk/KkMgB0TcfHx3Ory88msumpfLTf+Rw23Mfs3BlAb/4clZAZ1b0tMZGx92LN/DndzZzxoTh/PGy4zrV7fbt2Zm8ua6EJ5bn99rc8La8v7mUvNLqNlsq82ekcsOTa1i6fidnTBzeB7XrnJ2VB6ipa2S0H1+8Q6LCSIgO65WB5YNrKx2+XrERoRwzajDLcndz21njOixbdaCe6x7PZmhUGCdnJnBSZgIpQ3zn+PdnJWwoqeT3lx7bJxdb/gTCKiDTzNKBbfgGfy9vVWYRcBWwHLgIWOKcc2a2CFhoZv8P36ByJrASaARmmNkgfF1Gc4DsAHyeXrOzssbX3+/N9llbXE7RnoN3syYPjmRiUixzj0lmUnIsk5LjSIwJHxBf/h05OmUwL35rJi+sKeK3b6znS396j8unpXL7WeMY4sdVdl+qrW/kBy98wksfbuPy6anc9eUsQoI71w03OSWO08cN42/vbuHqmaOJCu+bmdsLVuQzNCqMcyYdehU5Z8JwhseG8+SK/AERCE0zjFL9nOackRjdK1NPD8588q9es8Ym8OA7m6msqeuw9fjap9tZvqWU+KgwXvt0O+Br2c3KiGfV1j2kxQ/igqNHdv8DdMFh/zV7YwI3A4vxTTt9xDmXY2Z3AdnOuUXAw8DfvUHjMnyhgVfuWXwD0PXATc65BmCFmT0PrPG2f4jXLdTfOOco2rPfN8WzuLw5AHZWHpzmmZ4QxbGjBnPF9DQmJceSlRTnVxfEQBUUZFw8dRRnZY3g9//eyBPL83nt0+3cftY4LpuW2i+7KSpr6vjWk2t4L3c3d5w9jhtPG9vlcP72nEwufOB9nvwgP2CzSjqjpKKGN9eVcN1J6W0OYIYGB3HpCan8cckmCsuqe2XwtTua7kHwp4UAvplGL3+0Dedcj15gFZRWEWS+izt/zMpI4E9Lc1mxpazDIH52VSFjhkXx1q2nkrtzH+9u2s17ubt5cc02qmsbuPurR3f6QiVQ/Lq8cc79E/hnq20/bfFzDXBxO/v+D/A/bWz/GfCzzlS2pzU2OraWVn1ufv/abRWU7z84zTMzMYaTMhN8g71JsUxMiv1CDLB2RVxkKD/7UhbzTkjlZ4vW8t8vr+WplQXcNTeLKWlD+7p6zUq8eww2lVRyz0VHc/HUUYffqQPHpw7hpIwE/vruFr524uhev0/j2VWFNDQ6LpvW/iSLy6aN4v6luSxcWcAP+tE02bYUlFYTHGQk+fnFm5EYTWVNPTsrDzC8B2dS5ZVWkzQ40u/JHMenDSYiNIhlm3e3Gwibd+0jO38PPzx3PGZG5vAYMofH8PWT0qmtbySvtIpM747svnDE3qlc19BI7s59B2/w8ub5V9X61vNpmuZ53uQR3mBvHONHxPTKAlMDzbgRMTz1jRm8+sl2/vefn/HVB5dz4fHJ/PDc8X2+1EPuzkquemSV7x6Dq0/g1KMCM43v27MzuPShD3hqZQFfPyk9IMf0R0Oj46mVBZycmcDohPa7MkbGRTJnfCLPrirku2dkdnsqZHte+2Q7U9KGMCKu6/8/55dVkzIkklA/r4ozWyxh0ZOBkF9W3eYaRu0JDwnmhNFDWZbb/pLYz2YXEhxkXHh88iHvhYUEfW6CQF8YUIHQ6ByVNXVd2M93g0nTFX9OcTnrd1RS603zHBQWzISRsVw0JcWb4x9HRmJ0r0/zHMjMjC8dk8Ts8YncvzSXv727lTdzSvjuGZlcNXO033/sgbQqr4zrHs8mNDiow3sMumL6mHimpQ/lL//ZzOXTU3vtQuHtDTspLq/hp186/BIaV8xI4811Jbyxdgdzjz30C6i7Vm4t46aFa7h06ih+e9HRXT5OfmkVqZ3o1srwpp5uKqlkVg/egFdQWsW5kzvXlz8rI4HfvL6enZU1h1wM1TU08sLqbZw+LrHPL5TaM6ACIae4gsk/b/N2Bb/FRoQwKTmOq05MY1JyHFlJcaQnRPXLfu+BKCo8hO+fM56Lp47iF6/k8KvXPuOZVYX8/MtZPfrH29oba7dzy9MfkTI4kse/Pq1H+tFvmZ3J/IdX8NzqIq6ckRbw47dl4YoChsWEM2fC4QeLT85IIC1+EAtWFAQ8EJxz/Ob1zwBYvG4Hv2qY1OXQzy+t5svHJPldflh0OLERIT369LTy/XXsqa7rcPXVtjQtY/F+bin/ddznf+dvb9jF7n0HuPSE7nVZ9qQBFQgj4yL47/MndGnf5MGRA2qa50CXnhDFo1efwFuf7eSuV9dxxd9WcN7kEfz4/Il+D9J11ePv5/HzV3I4btRg/naVf/cYdMWsjHiOSx3Mn9/ezKVTR/V4i7JoTzVLNuzk5tMz/PryDQoyLp+Wyq9fX8/GksqAdke8ua6ENQV7OXPicP61roTlm0s5pQvdcXuraynfX+fX1M4mTX3vPfl85QI/11ZqbWJSLIMHhbIsd/chgfBsdiEJ0eG9fvdxZwyoQEiIDh+QS/seqcyMMyYO56TMBP76ny3c/3YuS9b7vtCuO3lMwLtZGhsdv128nr+8s4UzJw7nD/M6d49BZ5kZt8zO5JrHVvHSh0VcekLP3kn/zKpCDJjXwWByaxdNSeH/3tzIgg/y+cXcSQGpR31DI3e/sZ6xw6L4/aXHMv1/3+Kfn27vUiA0Tznt5JV4ZmI0/1pX0unz+atp2evOrvgbHGScOCaeZbm7PzcLamdlDUvW7+S6k9P7pPvUX/23ZvKFEREazLfnZPLWbacxe3wi9765kbN+9x/e+ixwf9C19Y3c+uxH/OWdLcyfkcqf50/pldk/p40bxuTkOO5furlHH+1Y19DI06sKOX1cYqdaWPHR4Zw3eYQ3pbE+IHV5YU0Rm3dVccfZ44kKD2HOhEQW5+ygrgufv3nKaQcD5G3JSIymtKqWsqqeeSBQV4MKYGZGAsXlNeSVHlx+/qU122hodFw8pf92F4ECQXpR8uBIHrhiCguum05YSBDXPp7NNY+uZOvuqm4dt6KmjmseW8nLHxVzx9nj+OXcSb02JmRm3Dw7g4KyahZ93HPrM/57XQm7Kg9wxYzOt0Lmz0ij8kA9iz7qfv1q6hr43b82cVzqYM7O8o1jnDd5JHuq6/hgS2mnj1fQxS/elg/L6QkFpdUkRId36cbDkzIOLocNvvGWZ7ILmZo2pLne/ZUCQXrdrIwEXv/Oyfz3+RNYlbeHs3/3H+5ZvL5LV7AlFTVc8uflrNhSxr0XH8NNp2f0+hjRmROGM35EDH9amktDY88st7VgRQHJgyM59ajOr98zJW0I44bH8OSKfJzrXv0eez+PHRU1/PCc8c2/51OPGkZUWDD/9O667Yy80mpGxEZ0uvvw4OM0e2YJi7zSqk6PHzQZHT+IpLiI5kBYU7CHLbuqPveMjf5KgSB9IjQ4iOtOHsOS20/lgmNGcv/Szcz5v3d49ZNiv7+0NpVUcuED71NYVs0jV5/ARVNSerjWbQsK8rUStuyq6tKX4uFs3V3Fe7m7uWzaqC61fMyM+TNSWbutgk+Kyrtcj73VtTywNJfZ4xOZ3mIBt4jQYOZMGM7inJJOd5sVlFUddpXZtiTFRTIoLLjnWgh+LnvdFjNjVkYCy7eU0tDoeGZVIYPCgjm/j5aj6AwFgvSpxJgI/t8lx/LCt05kaFQYNy/8kMv/uoINOzq+8lu5tYyvPvg+tQ2NPPPNE7s0oBlI504aydhhUfxpSS6NAW4lPLWygJAg69YV5n8dl8ygsGCe/CC/y8d48O3NVB6o5/vnHLp423mTR1BWVcsHW8o6dcy80mq/l6xoKSjIemxNo5q6BnZU1JA2tOuPkJ2VkcDe6jpW5ZXx6ifbueDokX227lVnKBCkX5iSNpRFN5/Er/5rEp/tqOC8P7zLXa+so6KNGxFf/3Q78x9eQUJMOC9+a2a/WG012GslbCip5M0Azn6pqWvguexCzpw4vFsPvImJCGXuscm88kkx5dWdv7mzeO9+Hn0/j68cl8z4EbGHvH/auEQGhQU3L9bmj+raenZVHujys7szhvVMIBTtqca5zk85bWnmWF8L6ueLcqiubRgQ3UWgQJB+JDjImD8jjaW3nca8E0bx6PtbmX3v2zyXXdh81f3osq3cuHANk5JieeGGmf1q4bYvHZ1EWvwg/rhkU7f76pu8sXYHe6rruGJ69298u2J6KjV1jbywpqjT+/7+3xvBwa3tPBgoIjSY2eN9s4387Tby9znK7ckYHs328pourV7QkbzdTauvdv3fVmJsBEcNj2b9jkrGDItiStqQQFWvRykQpN8ZEhXG/3xlMq/cfBKpQwdxx/Of8NU/v8+PXvqUX7yyjjMnDGfhN2b0u+W2Q4KDuOn0DHKKK1i6YWdAjrlwRQGj4wc1X3F2x6TkOI4dNZgFnRxc3lRSyfOri7jyxLTmdfvbcv7kkZRV1bJiq3/dRs2B0MWumcxE3412gW4lND8HoZsXG01PWrtk6qgBczOsAkH6rUnJcTx/w0z+7+JjKCzbz8IVBVw5I40H50/pt4sMfuW4ZFKGRPKHt3K73UrYWFLJyrwyLp+eGrCHpcyfkcbmXVWd6uu/e/EGosJCDvtAoNPGJRIZ6n+3Ub53D0JXr8R7auppQWkVMeEh3b7Dfe6xSc1rpA0UCgTp14KCjK9OSWHJ7afy9PUzuGtuVr9edyo0OIhvnTaWjwr38l4Hq176Y+GKAsKCg7gogDczXXD0SOIiQ3lyhX+Dy9l5ZfxrXQnfPHXMYb8gI8OCmT0hkcVr/es2yi+rZsigUOIiu7Z8/KghvqWpe6KFkBo/qNtX9celDuH175xMQnR4gGrW8xQIMiDERoQyY0z8gGh6XzQlhZFxEfzxrdwuH6O6tp4X1hRx3uQRAV2LKSI0mIumpLB47Q52VtZ0WNY5x2/fWM+wmHC/l/g+f/JISqtqWelHt1FBaXWXB5TB10U3JiGKTYEOhNKuTzkd6BQIIgEWHhLMN08Zw8q8si7dvQvw6sfbqayp54oeWEX1iump1Dc6nsvueHD5rc92sipvD9+Zk8mgMP+mTJ7eiW6j7tz81STQU08bGh1Fe6pJ7caU04FMgSDSA+ZNSyUhOpw/LtnUpf0XrMjnqOHRTO2B2SljhkUzKyOehSsK2r2zuqHRcffi9aQnRHVquebIsIOzjTq6a7u2vpHivfu71UIA38By4Z5qauoaunWcJsV791PX4Lp0b8QXgQJBpAdEhPpaCctyS1md37mbtT4tKufjonIun5baY12FEa2eAAASs0lEQVRkV0xPY9ve/bzdzmyoF9cUsbFkH3ecPa7Tq3OeN3kku/fVsmJr+62joj3VNLruz+TJHB6Nc75HUwZCQVn3p5wOZAoEkR5yxYxUhkaF8YdOjiUsXJlPRGgQXzm+52annDlxOIkx4SxYUXDIe74F7DZyTEoc504a0eljnz5+GBGhQR0u49E8tTMAXUYAn20PzJpGTauvdrflMlApEER6yKCwEK49KZ13Nu7i48K9fu1TWVPHPz4q5svHJHV59o0/QoODmHfCKJZu2ElhWfXn3vv78nyKy2v4wbnju9RCGRQWwuzxibyxtqTdbqP83YH54h2TEEXy4EgWBmDhPvANdIcFBzGiB5/V3J8pEER60NdOTCMuMpQ/LvGvlfDyR8VU1zYE5M7kw5k3LRXDt1ZSk/L9dfxpaS6nHDWs+caqrvB1Gx1od7ZRflk1g8KCSYju3gyqkOAgbjh1DGsK9vp9Q1xH8kurGTU0sl9Pbe5JCgSRHhQTEco1s0bz789KWFdc0WFZ5xwLPshnUnIsR6f0/PpMSYMjmT1+OM9mF1Jb77tv4C/vbKZ8fx0/aGMBu86YPT6xw26jfG/KaSDGSC6eOoqE6HDuX9r1ab7N9Srr3lTYgU6BINLDrpmZTnR4CH9a2vGMozUFe1m/o5Irpqf12v0W82eksntfLYtzdrCjvIZHlm1l7rFJZCV1L5AGhYVw+rhEXl/b9myj/NKqbg8oN4kIDebak9J5d9NuPinyr2uuLc458kuruvSUtC8KBYJID4sbFMpVM9N4fe0ONpW0P/i5YEU+0eEhfPmYpF6r2ymZwxg1NJIFK/K5762NNDQ6bjuze62DJk3dRqvyPt+V09DoKCzbT1pC4L54589IJSYihAeWbu7yMXbvq6W6tuGIvSkNFAgiveLak8YQGRrMn9rp1thbXcurn2znK8cl9+q6+UFBxuXT0vhgSxnPrCrkiulpAZtyOXt8IuEhh3Yb7aioobahsVvPG2gtJiKUq2eO5o2cHeR28SlqBWXe853VZSQiPWloVBjzZ6TxysfFbT5D+vnVRdTWN3L59M4/M7m7LpmaQmiwERkazM2zO17ArjOiwtvuNmqaYRTom7+umZVOZGgwD769pUv7N62+eqTegwAKBJFec93J6YQGBx0y+OmcY+GKAo5PHcyEkYc+fKanxUeH89MLJvLrrx4d8IXYzjt6JLsqD5Ddotsov4du/hoaFcZl01J5+aNth0yl9UdeaTVmkDIkMqD1GkgUCCK9JDEmgsumpfLSh5//wlq+pZQtu6t6Zappe648cXSPjF3MaaPbKK+0irDgIEbGBf6L9xunpBNk8Nd3O99KKCitIikukvCQ/rm0em9QIIj0ohtOHUuwGQ+8fXDwc8GKAuIiQwfEQ9g7Kyo8hNPGDeP1tTuan3pXUFpNSg/N9R8ZF8lXj0/h6VWFh13NtTXflNMjt7sIFAgivWpEXAQXT03h+dWFFO/dz67KA7yZs4OLpqT024f+dNd5k0eys/IA2fl7AF9ffU8O3H7z1LHUNzTyyHt5ndrvSF72uokCQaSXfeu0sTjnuwnsudWF1DW4PhlM7i1zJgwnzOs26o25/ukJUZw3eSRPfpBPebV/z1uurKmjrKr2iF32uokCQaSXpQwZxIXHJ/PUqkKeeD+fE8fEM3ZYdF9Xq8dEh4dw2lHDeH3tdnbtO0BVbUOPLy9942kZ7DtQzxPL8/wq3/x8Z7UQRKS33XhaBvUNjeyoqOGKGV/c1kGT848eSUnFAV5asw3o+dVEJybFMnt8Io8s20p1bf1hyxcEaPXVgU6BINIHRidEcdGUFJLiIjhrYueXmB5omrqN/vbeVqB35vrfdPpY9lTX8dTKwsOWPdKXvW7iVyCY2TlmtsHMcs3sh228H25mz3jvrzCz0S3eu9PbvsHMzm6xfbCZPW9m683sMzM7MRAfSGSg+J+vTGbx904hLOSLf10WHR7CqUcNY1flAYJ6aa7/lLShTE8fyl//s4UD9R0/Ua2gtJr4qDCie/Eu8f7osP8SzSwYuB84F5gIXGZmE1sVuxbY45zLAH4H/NbbdyIwD8gCzgEe8I4HcB/whnNuPHAM8Fn3P47IwBEaHERMRM8986C/OX+yb1rtyF6c63/T6RnsqKjh5Q+3HfKec47Csmr++el2VuaVHdF3KDfxJw6nAbnOuS0AZvY0MBdY16LMXODn3s/PA38y33KNc4GnnXMHgK1mlgtMM7Mc4BTgagDnXC1Q2+1PIyL91pwJiYSFBDE6gIvaHc7JmQlMSo7lwbc3Mz09npziCj7dVs7abeV8uq2c8v2+WUghQcb3zjyq1+rVX/kTCMlAy064ImB6e2Wcc/VmVg7Ee9s/aLVvMrAf2AU8ambHAKuB7zjnDlnkxcyuB64HSE394g++iXxRxUSE8pMLJpIU13tPIzMzbjotg28tWMNp974NQGiwMW5EDOdNHsGk5DgmJ8cxbkTMEX2HchN/AqGt2wlbL3DeXpn2tocAxwPfds6tMLP7gB8CPzmksHMPAQ8BTJ06tfvPyBORPnPljN5fnuPsrBH8+LwJRIWHMDk5jqNGROvLvx3+BEIRMKrF6xSguJ0yRWYWAsQBZR3sWwQUOedWeNufxxcIIiIBFRRkfOOUMX1djQHBn+kNq4BMM0s3szB8g8SLWpVZBFzl/XwRsMT5nni9CJjnzUJKBzKBlc65HUChmTU9iWMOnx+TEBGRXnbYFoI3JnAzsBgIBh5xzuWY2V1AtnNuEfAw8Hdv0LgMX2jglXsW35d9PXCTc65p/te3gQVeyGwBrgnwZxMRkU4w34X8wDB16lSXnZ3d19UQERkwzGy1c26qP2W/+HfEiIiIXxQIIiICKBBERMSjQBAREUCBICIiHgWCiIgACgQREfEoEEREBFAgiIiIR4EgIiKAAkFERDwKBBERARQIIiLiUSCIiAigQBAREY8CQUREAAWCiIh4FAgiIgIoEERExKNAEBERQIEgIiIeBYKIiAAKBBER8SgQREQEUCCIiIhHgSAiIoACQUREPAoEEREBFAgiIuJRIIiICKBAEBERjwJBREQABYKIiHgUCCIiAigQRETEo0AQERHAz0Aws3PMbIOZ5ZrZD9t4P9zMnvHeX2Fmo1u8d6e3fYOZnd1qv2Az+9DMXu3uBxERke45bCCYWTBwP3AuMBG4zMwmtip2LbDHOZcB/A74rbfvRGAekAWcAzzgHa/Jd4DPuvshRESk+/xpIUwDcp1zW5xztcDTwNxWZeYCj3s/Pw/MMTPztj/tnDvgnNsK5HrHw8xSgPOBv3X/Y4iISHf5EwjJQGGL10XetjbLOOfqgXIg/jD7/h74PtDY0cnN7Hozyzaz7F27dvlRXRER6Qp/AsHa2Ob8LNPmdjO7ANjpnFt9uJM75x5yzk11zk0dNmzY4WsrIiJd4k8gFAGjWrxOAYrbK2NmIUAcUNbBvrOAL5tZHr4uqNlm9mQX6i8iIgHiTyCsAjLNLN3MwvANEi9qVWYRcJX380XAEuec87bP82YhpQOZwErn3J3OuRTn3GjveEucc/MD8HlERKSLQg5XwDlXb2Y3A4uBYOAR51yOmd0FZDvnFgEPA383s1x8LYN53r45ZvYssA6oB25yzjX00GcREZFuMN+F/MAwdepUl52d3dfVEBEZMMxstXNuqj9ldaeyiIgACgQREfEoEEREBFAgiIiIR4EgIiKAAkFERDwKBBERARQIIiLiUSCIiAigQBAREY8CQUREAAWCiIh4FAgiIgIoEERExKNAEBERQIEgIiIeBYKIiAAKBBER8SgQREQEUCCIiIhHgSAiIoACQUREPAoEEREBFAgiIuJRIIiICKBAEBERjwJBREQABYKIiHgUCCIiAigQRETEo0AQERFAgSAiIh4FgoiIAAoEERHxKBBERARQIIiIiMevQDCzc8xsg5nlmtkP23g/3Mye8d5fYWajW7x3p7d9g5md7W0bZWZLzewzM8sxs+8E6gOJiEjXHDYQzCwYuB84F5gIXGZmE1sVuxbY45zLAH4H/NbbdyIwD8gCzgEe8I5XD9zmnJsAzABuauOYIiLSi/xpIUwDcp1zW5xztcDTwNxWZeYCj3s/Pw/MMTPztj/tnDvgnNsK5ALTnHPbnXNrAJxzlcBnQHL3P46IiHSVP4GQDBS2eF3EoV/ezWWcc/VAORDvz75e99JxwIq2Tm5m15tZtpll79q1y4/qiohIV/gTCNbGNudnmQ73NbNo4AXgu865irZO7px7yDk31Tk3ddiwYX5UV0REusKfQCgCRrV4nQIUt1fGzEKAOKCso33NLBRfGCxwzr3YlcqLiEjg+BMIq4BMM0s3szB8g8SLWpVZBFzl/XwRsMQ557zt87xZSOlAJrDSG194GPjMOff/AvFBRESke0IOV8A5V29mNwOLgWDgEedcjpndBWQ75xbh+3L/u5nl4msZzPP2zTGzZ4F1+GYW3eScazCzk4ArgU/N7CPvVD9yzv0z0B9QRET8Y74L+YFh6tSpLjs7u6+rISIyYJjZaufcVH/K6k5lEREBFAgiIuJRIIiICKBAEBERjwJBREQABYKIiHgUCCIiAigQRETEo0AQERFAgSAiIh4FgoiIAAoEERHxKBBERARQIIiIiEeBICIigAJBREQ8CgQREQEUCCIi4lEgiIgIoEAQERGPAkFERAAFgoiIeBQIIiICKBBERMSjQBAREUCBICIiHgWCiIgACgQREfEoEEREBFAgiIiIR4EgIiKAAkFERDwKBBERARQIIiLiUSCIiAigQBAREY9fgWBm55jZBjPLNbMftvF+uJk9472/wsxGt3jvTm/7BjM7299jiohI7zpsIJhZMHA/cC4wEbjMzCa2KnYtsMc5lwH8Dvitt+9EYB6QBZwDPGBmwX4eU0REepE/LYRpQK5zbotzrhZ4Gpjbqsxc4HHv5+eBOWZm3vannXMHnHNbgVzveP4cU0REelGIH2WSgcIWr4uA6e2Vcc7Vm1k5EO9t/6DVvsnez4c7JgBmdj1wvfdyn5ltaPF2HFDux2cASAB2+1n2SNOZ32Nf6qt69uR5A3ns7h6rq/t3dj/93QaGv7/HNH8P6E8gWBvbnJ9l2tveVsuk9TF9G517CHiozYqZPeScu76t99oom+2cm+pP2SNNZ36Pfamv6tmT5w3ksbt7rK7u39n99HcbGD3x79KfLqMiYFSL1ylAcXtlzCwEX3KVdbCvP8f0xytd2EcONVB+j31Vz548byCP3d1jdXX/zu43UP699XcB/z2ac21emB8s4PuC3wjMAbYBq4DLnXM5LcrcBEx2zt1gZvOAC51zl5hZFrAQ35hBEvAWkImv5dDhMQNNVxoiA4/+bnvXYbuMvDGBm4HFQDDwiHMux8zuArKdc4uAh4G/m1kuvpbBPG/fHDN7FlgH1AM3OecaANo6ZuA/3ue02e0kIv2a/m570WFbCCIicmTQncoiIgIoEERExKNAEBERQIEgIiIeBYKIiAAKBBER8SgQ5IhkZnlmltDdMh3se5qZvdrJfb5rZoO6cj6RQFAgiPQf3wUUCNJnFAjyhWdmL5vZajPL8VbPbfneaDNbb2aPm9knZvZ8q6v0b5vZGjP71MzGe/tMM7P3zexD77/j2jl1rJm9ZGbrzOzPZhbk7X+WmS33jvucmUWb2S34lndZamZLvXIPmlm2V+9fBP43I/J5CgQ5EnzdOTcFmArcYmbxrd4fBzzknDsaqABubPHebufc8cCDwO3etvXAKc6544CfAv/bznmnAbcBk4GxwIVeF9R/A2d4x80GbnXO/QHfAo+nO+dO9/b/sbeOz9HAqWZ2dBc/v4hf/Fn+WmSgu8XMvuL9PArfAostFTrnlnk/PwncAtzrvX7R++9q4ELv5zjgcTPLxLdse2g7513pnNsCYGZPAScBNfieErjM9wwpwoDl7ex/ideiCQFGevt90vFHFek6BYJ8oZnZacAZwInOuWozexuIaFWs9YJeLV8f8P7bwMG/l18CS51zX/GeH/52O6dv67gG/Ms5d9lh6p2Or0VygnNuj5k91ka9RQJKXUbyRReH73nf1d4YwIw2yqSa2Ynez5cB7/lxzG3ez1c3bTSzZDN7q0W5aWaW7o0dXOod9wNglpllePsMMrOjvPKVQIz3cyxQBZSb2XB8zx8X6VEKBPmiewMIMbNP8F3Zf9BGmc+Aq7wyQ/GNF3TkbuDXZrYM3/LtTUbiW+a9yXLgN8BaYCvwknNuF74Qeco73wfAeK/8Q8DrZrbUOfcx8CGQAzwCLEOkh2n5azmieV0+rzrnJgXgWDcDBd4zQkQGHI0hiASIc+5PfV0Hke5QC0FERACNIYiIiEeBICIigAJBREQ8CgQREQEUCCIi4vn/RLA8V2AoqVQAAAAASUVORK5CYII=\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": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'ML1M': {'LCE(cs)': {'alpha': 0.9,\n",
       "   'beta': 0.0,\n",
       "   'regularization': 30,\n",
       "   'rank': 40}},\n",
       " 'ML10M': {'LCE(cs)': {'alpha': 0.3,\n",
       "   'beta': 0.0,\n",
       "   'regularization': 30,\n",
       "   'rank': 40}},\n",
       " 'BX': {'LCE(cs)': {'alpha': 0.1,\n",
       "   'beta': 0.0,\n",
       "   'regularization': 10,\n",
       "   'rank': 40}}}"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "config_cold"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### saving data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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": [],
   "source": [
    "for label in track(data_labels):\n",
    "    model, = prepare_cold_start_recommender_models(label, data_models_cold,\n",
    "                                                   [lce_init_config, config_cold]) # initiate with optimal config\n",
    "    rank_config_cold[label], rank_scores_cold[label] = fine_tune_lce(model, {'rank': lce_ranks[label]},\n",
    "                                                                     label, ntrials=0)\n",
    "del model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "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": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'ML1M': {'LCE(cs)': {'rank': 1}},\n",
       " 'ML10M': {'LCE(cs)': {'rank': 15}},\n",
       " 'BX': {'LCE(cs)': {'rank': 75}}}"
      ]
     },
     "execution_count": 66,
     "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": [],
   "source": [
    "result_cold = {}\n",
    "for label in track(data_labels):\n",
    "    models_cold = prepare_cold_start_recommender_models(label, data_models_cold,\n",
    "                                                        [lce_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": 67,
   "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": "iVBORw0KGgoAAAANSUhEUgAAAYQAAAEWCAYAAABmE+CbAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvOIA7rQAAFuZJREFUeJzt3XuQVeWZ7/HvQ3N1VFRAo6I2ZyQnchFUwvEkRmeEIJYzYma0xDHRGBMoL0dTGgymyphYjrc4J8lMHCrO4PEWIylvoeItQeJ4S8BGgYBEQwzGHh1EYIyaoAGf88d+gbbppnc30Lsbvp+qXaz1rnetftaq3v1jXfa7IzORJKlHrQuQJHUNBoIkCTAQJEmFgSBJAgwESVJhIEiSAANBklQYCFKVImJFRPwpIt6JiLUR8WBEHFSW3RsRNzfr/0BEfK821UrtZyBI7fO3mbk7sD+wEviX0n4B8PcR8dcAEXE6cAQwvSZVSh1gIEgdkJnrgHuAYWX+v4BLgX+LiIOBfwamZuY7tatSah8DQeqAiNgNOB345ca2zLwV+C3wHPBIZj5Sm+qkjulZ6wKkbuaBiFgP7A68AZzQbPmTwATgzs4uTNpWniFI7XNKZu4F9AEuBP4jIj4CEBFDga8A/wr8U0T0ql2ZUvsZCFIHZOaGzLwP2AAcExEB/DvwHeD/AO8CX61hiVK7GQhSB0TFJGBvYBlwHjAQuCYzPwDOBS6LiI/VsEypXcLvQ5CqExErgP2onBUk8ApwLfAE8CtgYmb+skn/K4HxwLHpG03dgIEgSQK8ZCRJKgwESRJgIEiSCgNBkgR0s08qDxw4MOvr62tdhiR1KwsWLHgzMwe11a9bBUJ9fT0NDQ21LkOSupWIeKWafl4ykiQBBoIkqTAQJElAN7uH0JI///nPNDY2sm7dulqX0iX17duXwYMH06uXA29K2rpuHwiNjY3sscce1NfXUxlwUhtlJqtXr6axsZEhQ4bUuhxJXVy3v2S0bt06BgwYYBi0ICIYMGCAZ0+SqtLtAwEwDLbCYyOpWjtFIEiStp2B0Anq6+t58803t7mPJO1I3f6msiTtCPXTH6x1CQCsuO6kTvtZniG0YsWKFXzsYx/ji1/8IiNGjODMM89kzpw5fPKTn2To0KHMnz+fNWvWcMopp3D44Ydz9NFHs3jxYgBWr17NhAkTOOKII5g6dSpNv4TozjvvZOzYsYwePZqpU6eyYcOGWu2iJH2IgbAVy5cv5+KLL2bx4sX8+te/5q677uKpp57ixhtv5JprruHKK6/kiCOOYPHixVxzzTWcddZZAHzzm9/kmGOO4fnnn+fkk0/m97//PQDLli1j1qxZPP300yxcuJC6ujp+8IMf1HIXJWkTLxltxZAhQxg5ciQAw4cPZ9y4cUQEI0eOZMWKFbzyyivce++9ABx//PGsXr2at956iyeeeIL77rsPgJNOOom9994bgMcee4wFCxbw8Y9/HIA//elP7LvvvjXYM0nakoGwFX369Nk03aNHj03zPXr0YP369fTsueXh2/iYZ0uPe2YmZ599Ntdee+0OqliSOs5LRtvg2GOP3XTJ5/HHH2fgwIHsueeeH2p/+OGHWbt2LQDjxo3jnnvu4Y033gBgzZo1vPJKVaPSStIO5xnCNvjGN77BOeecw+GHH85uu+3GbbfdBsCVV17JGWecwZFHHslxxx3HwQcfDMCwYcO4+uqrmTBhAh988AG9evXipptu4pBDDqnlbkgSANH0CZiubsyYMdn8C3KWLVvGYYcdVqOKugePkdR+O9NjpxGxIDPHtNXPMwRJm+xMfwTVft5DkCQBBoIkqTAQJEmAgSBJKgwESRJQ5VNGETER+C5QB/x7Zl7XbHkf4HbgKGA1cHpmroiITwPXAb2B94FpmTm3rHMUcCvQD3gIuDi3wzOw2/spCZ92kLSraPMMISLqgJuAE4FhwBkRMaxZt3OBtZl5KPBt4PrS/ibwt5k5EjgbuKPJOjOAKcDQ8pq4DftRU7vvvnuL7bfffjsjRoxg+PDhDBs2jBtvvBGAz3/+8wwZMoTRo0czevRoPvGJT2xa54EHHuCqq65qdw2TJ0/mN7/5Tcd2QJKo7pLRWGB5Zr6cme8DdwOTmvWZBNxWpu8BxkVEZObzmflaaV8K9I2IPhGxP7BnZv6inBXcDpyyzXvThTz88MN85zvf4ac//SlLly7lueeeo3///puWf+tb32LhwoUsXLiQZ555ZlP7DTfcwPnnn9/un3feeedxww03bJfaJe2aqgmEA4FXm8w3lrYW+2TmeuAtYECzPn8PPJ+Z75X+jW1sE4CImBIRDRHRsGrVqirK7RquvfZabrzxRg444AAA+vbty5e+9KWtrvPSSy/Rp08fBg4cCMDKlSv5zGc+w6hRoxg1ahTPPPMM7777LieddBKjRo1ixIgRzJo1C4BPfepTzJkzh/Xr1+/YHZO006omEFr6lvbm1/q32icihlO5jDS1HdusNGbenJljMnPMoEGDqii3a1iyZAlHHXVUq8unTZu26ZLRmWeeCcDTTz/NkUceuanPRRddxHHHHceiRYt47rnnGD58OI888ggHHHAAixYtYsmSJUycWLnS1qNHDw499FAWLVq0Y3dM0k6rmkBoBA5qMj8YeK21PhHRE+gPrCnzg4H7gbMy87dN+g9uY5s7taaXjDaOjPr666/TNPTmzp3LeeedB0BdXR39+/dn5MiRzJkzh69+9as8+eSTH7oMte+++/Laa7vUYZS0HVUTCM8CQyNiSET0BiYDs5v1mU3lpjHAqcDczMyI2At4ELg8M5/e2DkzXwfejoijo/LFAWcBP97GfelShg8fzoIFC9q1Tr9+/Vi3bt1W+3z0ox9lwYIFjBw5kssvv/xDN6DXrVtHv379OlSvJLX52Glmro+IC4FHqTx2ektmLo2Iq4CGzJwNzATuiIjlVM4MJpfVLwQOBa6IiCtK24TMfAM4j82PnT5cXtusqzwmevnll3PZZZfxk5/8hI985CO89957fP/73+eiiy5qdZ3DDjuMO++8c9P8uHHjmDFjBl/+8pfZsGED7777Lu+88w777LMPn/3sZ9l999259dZbN/V/6aWXGD58+I7cLUk7sao+h5CZD1H5rEDTtq83mV4HnNbCelcDV7eyzQZgRHuK7ar++Mc/Mnjw5itgl1xyCZdccgkrV65k/PjxZCYRwRe+8IVNfaZNm8bVV28+NPPnz+fYY4/l0ksv3dT/u9/9LlOmTGHmzJnU1dUxY8YM/vCHPzBt2jR69OhBr169mDFjBlC5Ad2vXz/233//zttxSTsVh7/eDj744IMW28855xzOOeecLdqb/q++qd69ezN+/Hgee+wxxo8fz3777cePf7zllbQTTjhhi7a77rqLqVOnbtEuSdUyELqYr33ta8ybN6/d6+2111587nOf2wEV7fz8DgCpYqcIhI2XWHYG++23HyeffHK712vpTAQqx0aSqtHtB7fr27cvq1ev9g9fCzKT1atX07dv31qXIqkb6PZnCIMHD6axsZHu9CnmztS3b98P3fCWpNZ0+0Do1asXQ4YMqXUZktTtdftLRpKk7cNAkCQBBoIkqTAQJEmAgSBJKgwESRJgIEiSCgNBkgQYCJKkwkCQJAEGgiSpMBAkSYCBIEkqDARJEmAgSJIKA0GSBBgIkqTCQJAkAQaCJKkwECRJgIEgSSoMBEkSYCBIkgoDQZIEGAiSpMJAkCQBBoIkqTAQJEmAgSBJKgwESRJgIEiSCgNBkgRUGQgRMTEiXoyI5RExvYXlfSJiVlk+LyLqS/uAiPh5RLwTEd9rts7jZZsLy2vf7bFDkqSO6dlWh4ioA24CPg00As9GxOzMfKFJt3OBtZl5aERMBq4HTgfWAVcAI8qruTMzs2Eb90GStB1Uc4YwFliemS9n5vvA3cCkZn0mAbeV6XuAcRERmfluZj5FJRgkSV1YNYFwIPBqk/nG0tZin8xcD7wFDKhi2/+vXC66IiKiiv6SpB2kmkBo6Q91dqBPc2dm5kjgU+X1uRZ/eMSUiGiIiIZVq1a1WawkqWOqCYRG4KAm84OB11rrExE9gf7Amq1tNDP/s/z7NnAXlUtTLfW7OTPHZOaYQYMGVVGuJKkjqgmEZ4GhETEkInoDk4HZzfrMBs4u06cCczOz1TOEiOgZEQPLdC/gb4Al7S1ekrT9tPmUUWauj4gLgUeBOuCWzFwaEVcBDZk5G5gJ3BERy6mcGUzeuH5ErAD2BHpHxCnABOAV4NESBnXAHODftuueSZLapc1AAMjMh4CHmrV9vcn0OuC0Vtatb2WzR1VXoiSpM1QVCNr51E9/sNYlALDiupNqXYKkwqErJEmAgSBJKgwESRJgIEiSCgNBkgQYCJKkwkCQJAEGgiSpMBAkSYCBIEkqDARJEmAgSJIKA0GSBBgIkqTCQJAkAQaCJKkwECRJgIEgSSoMBEkSYCBIkgoDQZIEGAiSpMJAkCQBBoIkqTAQJEmAgSBJKgwESRJgIEiSCgNBkgQYCJKkwkCQJAEGgiSpMBAkSYCBIEkqDARJEmAgSJIKA0GSBBgIkqSiqkCIiIkR8WJELI+I6S0s7xMRs8ryeRFRX9oHRMTPI+KdiPhes3WOiohflXX+OSJie+yQJKlj2gyEiKgDbgJOBIYBZ0TEsGbdzgXWZuahwLeB60v7OuAK4CstbHoGMAUYWl4TO7IDkqTto5ozhLHA8sx8OTPfB+4GJjXrMwm4rUzfA4yLiMjMdzPzKSrBsElE7A/smZm/yMwEbgdO2ZYdkSRtm2oC4UDg1SbzjaWtxT6ZuR54CxjQxjYb29gmABExJSIaIqJh1apVVZQrSeqIagKhpWv72YE+HeqfmTdn5pjMHDNo0KCtbFKStC2qCYRG4KAm84OB11rrExE9gf7Amja2ObiNbUqSOlHPKvo8CwyNiCHAfwKTgX9o1mc2cDbwC+BUYG65N9CizHw9It6OiKOBecBZwL90oP52qZ/+4I7+EVVZcd1JtS5BkrbQZiBk5vqIuBB4FKgDbsnMpRFxFdCQmbOBmcAdEbGcypnB5I3rR8QKYE+gd0ScAkzIzBeA84BbgX7Aw+UlSaqRas4QyMyHgIeatX29yfQ64LRW1q1vpb0BGFFtoZKkHctPKkuSAANBklQYCJIkwECQJBUGgiQJMBAkSYWBIEkCDARJUmEgSJIAA0GSVBgIkiTAQJAkFQaCJAkwECRJhYEgSQIMBElSYSBIkgADQZJUGAiSJMBAkCQVBoIkCTAQJEmFgSBJAgwESVJhIEiSAANBklQYCJIkwECQJBUGgiQJMBAkSYWBIEkCDARJUmEgSJIAA0GSVBgIkiTAQJAkFQaCJAkwECRJRVWBEBETI+LFiFgeEdNbWN4nImaV5fMior7JsstL+4sRcUKT9hUR8auIWBgRDdtjZyRJHdezrQ4RUQfcBHwaaASejYjZmflCk27nAmsz89CImAxcD5weEcOAycBw4ABgTkR8NDM3lPX+OjPf3I77I0nqoGrOEMYCyzPz5cx8H7gbmNSszyTgtjJ9DzAuIqK0352Z72Xm74DlZXuSpC6mmkA4EHi1yXxjaWuxT2auB94CBrSxbgI/jYgFETGltR8eEVMioiEiGlatWlVFuZKkjqgmEKKFtqyyz9bW/WRmHgmcCFwQEce29MMz8+bMHJOZYwYNGlRFuZKkjqgmEBqBg5rMDwZea61PRPQE+gNrtrZuZm789w3gfryUJEk1VU0gPAsMjYghEdGbyk3i2c36zAbOLtOnAnMzM0v75PIU0hBgKDA/Iv4iIvYAiIi/ACYAS7Z9dyRJHdXmU0aZuT4iLgQeBeqAWzJzaURcBTRk5mxgJnBHRCyncmYwuay7NCJ+BLwArAcuyMwNEbEfcH/lvjM9gbsy85EdsH+SpCq1GQgAmfkQ8FCztq83mV4HnNbKuv8I/GOztpeBUe0tVpK04/hJZUkSYCBIkgoDQZIEGAiSpMJAkCQBBoIkqTAQJEmAgSBJKgwESRJgIEiSCgNBkgQYCJKkwkCQJAEGgiSpMBAkSYCBIEkqDARJEmAgSJIKA0GSBBgIkqTCQJAkAQaCJKkwECRJgIEgSSoMBEkSYCBIkgoDQZIEGAiSpMJAkCQBBoIkqTAQJEmAgSBJKgwESRJgIEiSCgNBkgQYCJKkwkCQJAEGgiSpMBAkSUCVgRAREyPixYhYHhHTW1jeJyJmleXzIqK+ybLLS/uLEXFCtduUJHWuNgMhIuqAm4ATgWHAGRExrFm3c4G1mXko8G3g+rLuMGAyMByYCPxrRNRVuU1JUieq5gxhLLA8M1/OzPeBu4FJzfpMAm4r0/cA4yIiSvvdmfleZv4OWF62V802JUmdqGcVfQ4EXm0y3wj8r9b6ZOb6iHgLGFDaf9ls3QPLdFvbBCAipgBTyuw7EfFiFTXvSAOBN7dlA3H9dqqk9jwWm3ksNvNYbNZVjsUh1XSqJhCihbassk9r7S2dmTTfZqUx82bg5q0V2JkioiEzx9S6jq7AY7GZx2Izj8Vm3e1YVHPJqBE4qMn8YOC11vpERE+gP7BmK+tWs01JUieqJhCeBYZGxJCI6E3lJvHsZn1mA2eX6VOBuZmZpX1yeQppCDAUmF/lNiVJnajNS0blnsCFwKNAHXBLZi6NiKuAhsycDcwE7oiI5VTODCaXdZdGxI+AF4D1wAWZuQGgpW1u/93bIbrM5asuwGOxmcdiM4/FZt3qWETlP/KSpF2dn1SWJAEGgiSpMBCqFBG3RMQbEbGk1rXUWkT0jYj5EbEoIpZGxDdrXVNnaul3ISL2iYifRcRvyr9717LGzhARB0XEzyNiWfk9uLi073LHAlp/X5SHZ+aV4zGrPEjTJRkI1buVyvAbgveA4zNzFDAamBgRR9e4ps50K1v+LkwHHsvMocBjZX5ntx64NDMPA44GLihD0OyKxwJaf19cD3y7HI+1VIb66ZIMhCpl5hNUnqDa5WXFO2W2V3ntMk8ntPK70HT4ltuAUzq1qBrIzNcz87ky/TawjMpIBLvcsYCtvi+OpzKkD3Tx42EgqEPKIIULgTeAn2XmvFrXVGP7ZebrUPlDCexb43o6VRnh+AhgHrvwsWj+vgB+C/x3Zq4vXZoO39PlGAjqkMzckJmjqXzKfGxEjKh1TaqNiNgduBf4cmb+odb11FLz9wVwWEvdOreq6hkI2iaZ+d/A43h/ZWVE7A9Q/n2jxvV0iojoRSUMfpCZ95XmXfJYNNXkfXE0sFcZ0ge6+DA9BoLaLSIGRcReZbofMB74dW2rqrmmw7ecDfy4hrV0ijLE/UxgWWb+3yaLdrljAa2+L5YBP6cypA908ePhJ5WrFBE/BP6KynC2K4ErM3NmTYuqkYg4nMrNsToq/6n4UWZeVduqOk9LvwvAA8CPgIOB3wOnZeZO/RBCRBwDPAn8CvigNH+Nyn2EXepYQOvvi4j4H1S+82Uf4Hngs5n5Xu0qbZ2BIEkCvGQkSSoMBEkSYCBIkgoDQZIEGAiSpMJAkJqJiL0i4vxa1yF1NgNB2tJegIGgXU6b36ks7YKuA/6yDFL2s9J2IpUxaK7OzFkR8VfAVcBq4H8CTwDnZ+YHTTcUEZ8HTgZ2A/4SuD8zL+uMnZDayzMEaUvTgd+WQcp+SWVs+1FUhiL41sZxeqgMXnYpMJLKH/u/a2V7o4HTS7/TI+KgHVi71GEGgrR1xwA/LKNYrgT+A/h4WTY/M1/OzA3AD0vfljyWmW9l5jrgBeCQHV611AEGgrR1sZVlzcd9yYj4TEQsLK8xpb3puDUb8FKtuigDQdrS28AeZfoJKpd56iJiEHAsML8sG1u+L7cHlUtCT2Xm/Zk5urwaOr90qeMMBKmZzFwNPB0RS4D/DSwGFgFzgcsy879K119QuQG9BPgdcH8NypW2G0c7lTqgPGX0lcz8m1rXIm0vniFIkgDPECRJhWcIkiTAQJAkFQaCJAkwECRJhYEgSQLg/wMVKczsppbdAgAAAABJRU5ErkJggg==\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": 68,
   "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": "3.6.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}