Baselines.ipynb

{
 "cells": [
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "%env MKL_NUM_THREADS=16\n",
    "%env OMP_NUM_THREADS=16"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Use token from .telepythrc.\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from ipypb import track\n",
    "\n",
    "from polara.evaluation import evaluation_engine as ee\n",
    "from polara.evaluation.pipelines import find_optimal_svd_rank\n",
    "from polara import (SVDModel,\n",
    "                    PopularityModel,\n",
    "                    RandomModel)\n",
    "from polara.recommender.hybrid.models import SimilarityAggregation\n",
    "from polara.recommender.coldstart.models import (SVDModelItemColdStart,\n",
    "                                                 RandomModelItemColdStart,\n",
    "                                                 PopularityModelItemColdStart,\n",
    "                                                 SimilarityAggregationItemColdStart)\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",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from polara.recommender import defaults\n",
    "defaults.memory_hard_limit = 5 # allowed memory usage during recommendations generation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "seed = 42"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "experiment_name = 'baseline'"
   ]
  },
  {
   "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": [
    "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]\n",
    "svd_ranks = {'ML1M': [r for r in ranks_grid if r <= 1000],\n",
    "             'ML10M': [r for r in ranks_grid if r <= 1000],\n",
    "             'BX': [r for r in ranks_grid if r <= 2000]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "topk_values = [1, 3, 10, 20, 30]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "target_metric = 'mrr'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_dict = dict.fromkeys(data_labels)\n",
    "meta_dict = dict.fromkeys(data_labels)\n",
    "similarities = dict.fromkeys(data_labels)\n",
    "sim_indices = dict.fromkeys(data_labels)\n",
    "feature_idx = dict.fromkeys(data_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_data = [data_dict, similarities, sim_indices, meta_dict]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Movielens1M"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "lbl = 'ML1M'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_dict[lbl], meta_dict[lbl] = get_movielens_data('D:/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": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "similarities[lbl], sim_indices[lbl], feature_idx[lbl] = get_similarity_data(meta_dict[lbl])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(meta_dict[lbl].applymap(len).sum(axis=1)==0).mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Movielens10M"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "lbl = 'ML10M'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_dict[lbl], meta_dict[lbl] = get_movielens_data('D:/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": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "similarities[lbl], sim_indices[lbl], feature_idx[lbl] = get_similarity_data(meta_dict[lbl])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 18,
     "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": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "lbl = 'BX'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_dict[lbl], meta_dict[lbl] = get_bookcrossing_data('D:/datasets/recsys/bookcrossing/BX-CSV-Dump.zip',\n",
    "                                                       get_books_meta=True,\n",
    "                                                       implicit=True,\n",
    "                                                       filter_no_meta=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "similarities[lbl], sim_indices[lbl], feature_idx[lbl] = get_similarity_data(meta_dict[lbl])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 22,
     "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": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ML1M\n",
      "{'userid': 6038, 'movieid': 3522}\n",
      "density 2.699052132255699\n",
      "similarity matrix density 51.672185638677114\n",
      "ML10M\n",
      "{'userid': 69797, 'movieid': 10258}\n",
      "density 0.6991397242349022\n",
      "similarity matrix density 51.91219966091452\n",
      "BX\n",
      "{'userid': 7160, 'isbn': 16273}\n",
      "density 0.18925598044812894\n",
      "similarity matrix density 0.4827469267285518\n"
     ]
    }
   ],
   "source": [
    "print_data_stats(data_labels, all_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Standard experiment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "def prepare_recommender_models(data_label, data_models, config):\n",
    "    data_model = data_models[data_label]\n",
    "    models = [SVDModel(data_model),\n",
    "              SimilarityAggregation(data_model),\n",
    "              PopularityModel(data_model),\n",
    "              RandomModel(data_model, seed=seed)]\n",
    "    apply_config(models, config, data_label)\n",
    "    return models\n",
    "\n",
    "\n",
    "def fine_tune_svd(model, ranks, label, record_time=False):\n",
    "    best_svd_rank, svd_scores = find_optimal_svd_rank(model, ranks, target_metric,\n",
    "                                                      return_scores=True,\n",
    "                                                      iterator=lambda x: track(x, label=label))\n",
    "    model_config = {model.method: {'rank': best_svd_rank}}\n",
    "    model_scores = {model.method: svd_scores}\n",
    "    try:\n",
    "        if record_time:\n",
    "            max_rank = max(ranks)\n",
    "            save_training_time(f'{experiment_name}_rank_{max_rank}', model, pd.Index([max_rank], name='rank'), label)\n",
    "    finally:\n",
    "        return model_config, model_scores"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "config = {}\n",
    "scores = {}\n",
    "data_models = {}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
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       "<span class=\"Progress-label\"><strong>100%</strong></span>\n",
       "<span class=\"Iteration-label\">3/3</span>\n",
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       "<span class=\"Progress-label\"><strong>100%</strong></span>\n",
       "<span class=\"Iteration-label\">20/20</span>\n",
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       "\u001b[A\u001b[2K\r",
       "           ML1M [█████████████████████████████████████████████] 20/20 [00:03<00:00, 0.17s/it]"
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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\">ML10M</span>\n",
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       "<span class=\"Progress-label\"><strong>100%</strong></span>\n",
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       "          ML10M [█████████████████████████████████████████████] 20/20 [01:06<00:02, 3.30s/it]"
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       "<span class=\"Progress-label\"><strong>100%</strong></span>\n",
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       "             BX [█████████████████████████████████████████████] 24/24 [00:15<00:00, 0.62s/it]"
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   ],
   "source": [
    "for label in track(data_labels):\n",
    "    data_models[label] = prepare_data_model(label, *all_data, seed)\n",
    "    config[label], scores[label] = fine_tune_svd(SVDModel(data_models[label]),\n",
    "                                                 svd_ranks[label],\n",
    "                                                 label, True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "report_results('rank', scores);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'ML1M': {'PureSVD': {'rank': 30}},\n",
       " 'ML10M': {'PureSVD': {'rank': 30}},\n",
       " 'BX': {'PureSVD': {'rank': 1250}}}"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "config"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### saving data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "save_results(experiment_name, config=config, tuning=scores)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## cross-validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><span class=\"Text-label\" style=\"display:inline-block; overflow:hidden; white-space:nowrap; text-overflow:ellipsis; min-width:0; max-width:15ex; vertical-align:middle; text-align:right\"></span>\n",
       "<progress style=\"width:60ex\" max=\"3\" value=\"3\" class=\"Progress-main\"/></progress>\n",
       "<span class=\"Progress-label\"><strong>100%</strong></span>\n",
       "<span class=\"Iteration-label\">3/3</span>\n",
       "<span class=\"Time-label\">[08:14<05:05, 164.78s/it]</span></div>"
      ],
      "text/plain": [
       "\u001b[A\u001b[A\u001b[2K\r",
       " [████████████████████████████████████████████████████████████] 3/3 [08:14<05:05, 164.78s/it]\u001b[B"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div><span class=\"Text-label\" style=\"display:inline-block; overflow:hidden; white-space:nowrap; text-overflow:ellipsis; min-width:15ex; max-width:15ex; vertical-align:middle; text-align:right\">ML1M folds</span>\n",
       "<progress style=\"width:45ex\" max=\"5\" value=\"5\" class=\"Progress-main\"/></progress>\n",
       "<span class=\"Progress-label\"><strong>100%</strong></span>\n",
       "<span class=\"Iteration-label\">5/5</span>\n",
       "<span class=\"Time-label\">[00:10<00:02, 2.08s/it]</span></div>"
      ],
      "text/plain": [
       "\u001b[A\u001b[2K\r",
       "     ML1M folds [█████████████████████████████████████████████] 5/5 [00:10<00:02, 2.08s/it]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div><span class=\"Text-label\" style=\"display:inline-block; overflow:hidden; white-space:nowrap; text-overflow:ellipsis; min-width:15ex; max-width:15ex; vertical-align:middle; text-align:right\">ML10M folds</span>\n",
       "<progress style=\"width:45ex\" max=\"5\" value=\"5\" class=\"Progress-main\"/></progress>\n",
       "<span class=\"Progress-label\"><strong>100%</strong></span>\n",
       "<span class=\"Iteration-label\">5/5</span>\n",
       "<span class=\"Time-label\">[02:59<00:37, 35.76s/it]</span></div>"
      ],
      "text/plain": [
       "\u001b[A\u001b[2K\r",
       "    ML10M folds [█████████████████████████████████████████████] 5/5 [02:59<00:37, 35.76s/it]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div><span class=\"Text-label\" style=\"display:inline-block; overflow:hidden; white-space:nowrap; text-overflow:ellipsis; min-width:15ex; max-width:15ex; vertical-align:middle; text-align:right\">BX folds</span>\n",
       "<progress style=\"width:45ex\" max=\"5\" value=\"5\" class=\"Progress-main\"/></progress>\n",
       "<span class=\"Progress-label\"><strong>100%</strong></span>\n",
       "<span class=\"Iteration-label\">5/5</span>\n",
       "<span class=\"Time-label\">[05:05<01:01, 61.00s/it]</span></div>"
      ],
      "text/plain": [
       "\u001b[A\u001b[2K\r",
       "       BX folds [█████████████████████████████████████████████] 5/5 [05:05<01:01, 61.00s/it]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "result = {}\n",
    "for label in track(data_labels):\n",
    "    models = prepare_recommender_models(label, data_models, config)\n",
    "    result[label] = ee.run_cv_experiment(models,\n",
    "                                         fold_experiment=ee.topk_test,\n",
    "                                         topk_list=topk_values,\n",
    "                                         ignore_feedback=True,\n",
    "                                         iterator=lambda x: track(x, label=f'{label} folds'))\n",
    "    save_cv_training_time(experiment_name, models, label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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LBRdccMi+X//61xQVFR2y7dZbb+Wee+5h9+7dDB48mL/+9a8cd9xxLVmyiAiQpaGfCbt27Tpk/S9/+Uvt8vr162uXzznnnEOGd2bOnNnstYmIxKW5d0REAqLQFxEJiEJfRCQgGtMPXZbcCk5E0kM9fRGRgCj0RUQCkpXDO2V90vtt1r7vlcVqd9999/HMM8+Qk5NDmzZteOKJJ7j99tt5+OGHKS4uJj8/nx49evDaa6/VHjNgwACqq6sPuaxTRCRTsjL0M+HNN99k4cKFvPPOO7Rv355PP/005fTIO3fuZMuWLfTo0YOysnhvJiIiLUXDOzFt27aNrl270r59ewC6du3Kaaeddli7H/zgB8ybNw+AOXPmUFpa2qJ1iogciUI/pmHDhrFlyxa++c1v8otf/IJly5albDdq1Cief/55IPGt3e9///stWaaIyBEp9GP62te+xqpVq5gxYwa5ubmMHj065RQLnTt35pRTTmHu3Ln07duXjh07tnyxIiL10Jh+I+Tk5DBkyBCGDBlCYWFhvXfKGj16NBMmTNC8OyLS6ij0Y9q0aRNt2rShd+/eAKxZs4aePXumvCrnqquuYtu2bVx22WVs3Zp8kzERkczJytCPe4llOu3atYsbb7yRzz//nLZt29KrVy9mzJjBqFGjDmt74okncvvtt7d4jSIiDcnK0M+E8847j7/97W+HbV+6dGntckVFxWH78/PzdY2+iLQa+iBXRCQgCn0RkYBkTei7e6ZLyEo6byJSV1aEfocOHaiqqlKANZK7U1VVRYcOHTJdioi0ElnxQW5eXh6VlZVs374906VknQ4dOpCXl5fpMkSklciK0G/Xrh0FBQWZLkNEJOtlxfCOiIikh0JfRCQgCn0RkYAo9EVEAqLQFxEJiEJfRCQgCn0RkYAo9EVEAhIr9M1suJltMrNyM5uUYn97M5sX7X/LzPLr7OtvZm+a2QYze9fMNCeAiEiGNBj6ZpYDTAcuB/oBpWbWL6nZeOAzd+8FPAI8FB3bFnga+G/ufhYwBPgqbdWLiEijxOnplwDl7r7Z3fcBc4GRSW1GAjU3jJ0PDDUzA4YB69x9LYC7V7n7/vSULiIijRUn9LsDW+qsV0bbUrZx92rgC6AL8E3AzewlM3vHzG5L9QRmdr2ZrTSzlZpUTUSk+cQJfUuxLXmO4/ratAXOB34Y/XuVmQ09rKH7DHcvdvfi3NzcGCWJiMjRiBP6lUCPOut5wNb62kTj+J2AHdH2Ze7+qbvvBhYB5za1aBEROTpxQn8F0NvMCszsOGAMsCCpzQJgbLQ8CnjVE3c8eQnob2YdozeDi4CN6SldREQaq8H59N292swmkgjwHOApd99gZlOBle6+AHgSmG1m5SR6+GOiYz8zs9+ReONwYJG7v9BMr0VERBoQ6yYq7r6IxNBM3W2T6yzvBa6p59inSVy2KSIiGaZv5IqIBEShLyISEIW+iEhAFPoiIgFR6IuIBEShLyISEIW+iEhAFPoiIgFR6IuIBEShLyISEIW+iEhAFPoiIgFR6IuIBCTWLJvHnCmdYrT5ovnrEBFpYerpi4gERKEvIhIQhb6ISEAU+iIiAVHoi4gERKEvIhIQhb6ISEAU+iIiAVHoi4gERKEvIhIQhb6ISEAU+iIiAVHoi4gERKEvIhIQhb6ISEAU+iIiAVHoi4gERKEvIhKQWKFvZsPNbJOZlZvZpBT725vZvGj/W2aWn7T/dDPbZWa/Sk/ZIiJyNBoMfTPLAaYDlwP9gFIz65fUbDzwmbv3Ah4BHkra/wiwuOnliohIU8Tp6ZcA5e6+2d33AXOBkUltRgKzouX5wFAzMwAzuxLYDGxIT8kiInK04oR+d2BLnfXKaFvKNu5eDXwBdDGzE4DbgbuP9ARmdr2ZrTSzldu3b49bu4iINFLbGG0sxTaP2eZu4BF33xV1/FNy9xnADIDi4uLkxxYRaTb5k1444v6KDi1USAuJE/qVQI8663nA1nraVJpZW6ATsAMYBIwys/8FnAwcMLO97v5YkysXEZFGixP6K4DeZlYAfASMAa5NarMAGAu8CYwCXnV3By6oaWBmU4BdCnwRkcxpMPTdvdrMJgIvATnAU+6+wcymAivdfQHwJDDbzMpJ9PDHNGfRItI0oQ1pyEFxevq4+yJgUdK2yXWW9wLXNPAYU46iPhERSSN9I1dEJCAKfRGRgCj0RUQCotAXEQmIQl9EJCAKfRGRgMS6ZFPkWKBr00XU0xcRCYpCX0QkIAp9EZGAKPRFRAKi0BcRCYhCX0QkIAp9EZGAKPRFRAKi0BcRCYhCX0QkIAp9EZGAKPRFRAKi0BcRCYhCX0QkIAp9EZGAKPRFRAKim6gc43TjEBGpSz19EZGAKPRFRAKi0BcRCYhCX0QkIAp9EZGAKPRFRAKi0BcRCUis0Dez4Wa2yczKzWxSiv3tzWxetP8tM8uPtl9qZqvM7N3o30vSW76IiDRGg6FvZjnAdOByoB9Qamb9kpqNBz5z917AI8BD0fZPge+7eyEwFpidrsJFRKTx4vT0S4Byd9/s7vuAucDIpDYjgVnR8nxgqJmZu692963R9g1ABzNrn47CRUSk8eKEfndgS531ymhbyjbuXg18AXRJanM1sNrdv0x+AjO73sxWmtnK7du3x61dREQaKU7oW4pt3pg2ZnYWiSGfG1I9gbvPcPdidy/Ozc2NUZKIiByNOKFfCfSos54HbK2vjZm1BToBO6L1POBPwI/d/f2mFiwiIkcvTuivAHqbWYGZHQeMARYktVlA4oNagFHAq+7uZnYy8AJwh7u/ka6iRUTk6DQY+tEY/UTgJaAMeNbdN5jZVDMbETV7EuhiZuXAvwA1l3VOBHoB/9PM1kQ/3dL+KkREJJZY8+m7+yJgUdK2yXWW9wLXpDjuXuDeJtYoIiJpom/kiogERKEvIhIQhb6ISEAU+iIiATkmb4yum4GLiKSmnr6ISEAU+iIiAVHoi4gERKEvIhIQhb6ISEAU+iIiAVHoi4gERKEvIhIQhb6ISEAU+iIiAVHoi4gERKEvIhIQhb6ISEAU+iIiAVHoi4gERKEvIhIQhb6ISEAU+iIiATkmb5coItKalPXpe8T9fd8ra6FKFPoi0kxaU9DJQRreEREJiHr6Immk3q20durpi4gERD19abKGeregHq5Ia6GevohIQBT6IiIB0fDOUdKQhohko1ihb2bDgUeBHOD/uPuDSfvbA38AzgOqgNHuXhHtuwMYD+wH/ru7v5S26kUkYwpnFR5x/7MtVEdrkE3nosHQN7McYDpwKVAJrDCzBe6+sU6z8cBn7t7LzMYADwGjzawfMAY4CzgNeMXMvunu+9P9QkRaQjb9coukEqenXwKUu/tmADObC4wE6ob+SGBKtDwfeMzMLNo+192/BP5hZuXR472ZnvKbj365D9K5EDl2xAn97sCWOuuVwKD62rh7tZl9AXSJtv896djuyU9gZtcD10eru8xsU6zqj5LFarW+K/BpfXv7xXqieM+USToXB7XIuciC8wA6F3Vl0bnoGadRnNBPVY3HbBPnWNx9BjAjRi0txsxWuntxputoDXQuDtK5OEjn4qBsOhdxLtmsBHrUWc8DttbXxszaAp2AHTGPFRGRFhIn9FcAvc2swMyOI/HB7IKkNguAsdHyKOBVd/do+xgza29mBUBv4O30lC4iIo3V4PBONEY/EXiJxCWbT7n7BjObCqx09wXAk8Ds6IPaHSTeGIjaPUviQ99qYEIWXbnTqoabMkzn4iCdi4N0Lg7KmnNhiQ65iIiEQNMwiIgERKEvIhIQhX4SM3vKzD4xs/WZriWTzKyDmb1tZmvNbIOZ3Z3pmlpaqv8LZtbZzF42s3+L/j0lkzW2BDPrYWZ/NbOy6P/CTdH2EM9Fyt+L6EKXt6JzMS+66KVVUugfbiYwPNNFtAJfApe4+znAAGC4mQ3OcE0tbSaH/1+YBCxx997Akmj9WFcN3OLufYHBwIRoipUQz0V9vxcPAY9E5+IzElPTtEoK/STuvpzEFUhB84Rd0Wq76CeoT/3r+b8wEpgVLc8CrmzRojLA3be5+zvR8k6gjMQ360M8F/X9XlxCYgoaaOXnQqEv9TKzHDNbA3wCvOzub2W6plbgVHffBokwBLpluJ4WZWb5QBHwFoGei+TfC+B94HN3r46apJxuprVQ6Eu93H2/uw8g8U3qEjM7O9M1SeaY2deA54Bfuvs/M11PpiT/XgCpbq7Rav8qVuhLg9z9c2Ap+qwD4GMz+wZA9O8nGa6nRZhZOxKB/0d3fz7aHOS5qFHn92IwcHI0BQ208ulmFPqSkpnlmtnJ0fLxwH8G3stsVa1C3SlHxgJ/zmAtLSKaJv1JoMzdf1dnV4jnItXvRRnwVxJT0EArPxf6Rm4SM5sDDAG6Ah8Dd7n7kxktKgPMrD+JD6RySHQOnnX3qZmtqmWl+r8A/D8StxA4HfgQuMbdj+kP/s3sfOA14F3gQLT5ThLj+qGdi5S/F2Z2BjAX6AysBn4U3Uek1VHoi4gERMM7IiIBUeiLiAREoS8iEhCFvohIQBT6IiIBUehLsMzsZDP7RabrEGlJCn0J2cmAQl+C0uA9ckWOYQ8CZ0aTZ70cbbucxLwp97r7PDMbAkwFqoBvAcuBX7j7gboPZGbjgBFAR+BM4E/ufltLvAiRxlBPX0I2CXg/mjzr7yTmRz+HxFfrf1MzrwyJSbVuAQpJBPp/qefxBgCjo3ajzaxHM9YuclQU+iIJ5wNzohkUPwaWAQOjfW+7+2Z33w/MidqmssTdv3D3vcBGoGezVy3SSAp9kQQ7wr7kuUrczK4yszXRT3G0ve5cK/vR8Km0Qgp9CdlO4MRoeTmJIZkcM8sFLgTejvaVRPdAbUNi+OZ1d/+Tuw+Ifla2fOkiR0ehL8Fy9yrgjejG598G1gFrgVeB29z936Omb5L40Hc98A/gTxkoVyQtNMumyBFEV+/8yt2vyHQtIumgnr6ISEDU0xcRCYh6+iIiAVHoi4gERKEvIhIQhb6ISEAU+iIiAfn/blWf5z4l21IAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "report_results('topn', result, target_metric);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "report_results('topn', result, 'coverage');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### saving data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "save_results(experiment_name, cv=result)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Cold start"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "import gc\n",
    "del data_models, models\n",
    "gc.collect()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "def prepare_cold_start_recommender_models(data_label, data_models, config):\n",
    "    data_model = data_models[data_label]\n",
    "    models = [SVDModelItemColdStart(data_model, item_features=meta_dict[data_label]),\n",
    "              SimilarityAggregationItemColdStart(data_model),\n",
    "              PopularityModelItemColdStart(data_model),\n",
    "              RandomModelItemColdStart(data_model, seed=seed)]\n",
    "    apply_config(models, config, data_label)\n",
    "    return models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "config_cold = {}\n",
    "scores_cold = {}\n",
    "data_models_cold = {}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "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\">[06:01<02:49, 120.46s/it]</span></div>"
      ],
      "text/plain": [
       "\u001b[A\u001b[A\u001b[2K\r",
       " [████████████████████████████████████████████████████████████] 3/3 [06:01<02:49, 120.46s/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\">[00:04<00:00, 0.22s/it]</span></div>"
      ],
      "text/plain": [
       "\u001b[A\u001b[2K\r",
       "           ML1M [█████████████████████████████████████████████] 20/20 [00:04<00:00, 0.22s/it]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div><span class=\"Text-label\" style=\"display:inline-block; overflow:hidden; white-space:nowrap; text-overflow:ellipsis; min-width:15ex; max-width:15ex; vertical-align:middle; text-align:right\">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\">[00:57<00:03, 2.86s/it]</span></div>"
      ],
      "text/plain": [
       "\u001b[A\u001b[2K\r",
       "          ML10M [█████████████████████████████████████████████] 20/20 [00:57<00:03, 2.86s/it]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div><span class=\"Text-label\" style=\"display:inline-block; overflow:hidden; white-space:nowrap; text-overflow:ellipsis; min-width:15ex; max-width:15ex; vertical-align:middle; text-align:right\">BX</span>\n",
       "<progress style=\"width:45ex\" max=\"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\">[00:27<00:00, 1.14s/it]</span></div>"
      ],
      "text/plain": [
       "\u001b[A\u001b[2K\r",
       "             BX [█████████████████████████████████████████████] 24/24 [00:27<00:00, 1.14s/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 = SVDModelItemColdStart(data_models_cold[label],\n",
    "                                  item_features=meta_dict[label])\n",
    "    model.use_raw_features = True\n",
    "    config_cold[label], scores_cold[label] = fine_tune_svd(model,\n",
    "                                                           svd_ranks[label],\n",
    "                                                           label)\n",
    "    del model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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v55UvNvDFtzvp0rw+zWJLfgHEjQPbMX76Ej5YsZVLKzj0KpJVlt3ScCQmJnpSUlKkyxApFy9+vo6/frCG6/u35aHLuuV9+Gbl5LIu7QCrNu8jees+VqSms2Djbv7w866MObt9udUzae4GHno3mSt7teLvV59OjWOEz+6DmVz+zFwOZebwzviBtDq5fDpAzM11pi/4nsc+XENGTi53ntuJW85uT0xU8Ue/V21J56Ra0bRtHN4J5fyycnK59J9fsmHnQV4Y2YdzOhfdmWRlZGaL3D2x2HYKBJHIm7UolXveXMYlPVowYVivY374ujtjpi7ii2/T+Pius2kX5hUzJTFz4SZ++9ZyLux2Cs+M6E10GB+4KTv2c8Wz82h1ch1m3TaAk4q40qe03J27Zy7j7SWbGdixMY9cflq5rHtRdh/M5LqX55Oy4wBPj+jFBd2aV9iyj1e4gaCTyiIR9snq7fzureWc1bEJ//eaY38Th9Bhmkeu6E7N6Br87q3lZd61wnvLt3Dv28sZ1KkJE4b3CisMADo2q88zI3rz7Y4D3Pn6EnLKuK4n53zL20s2c+e5nXh1dL8KDQOARvVq8vqYM+naMpbbX1vMe8uP/cyTqkiBIBJBCzfu5vbXFtOtZSzPj+xDrejwruc/JbY2f7y4K/M37Gb6gu/LrJ5P12znrhlL6dO2IS+OTAy7nqPOPrUpD17WjU/W7ODWaYvYeyizTOp6a1EqT33yLVf3ieOu8zpF7BLXBnVjeHV0X3q1OZlfvr6EtxadWFceKRBEImTNtn2MnryQVifXYdINZ5T4EMs1ia0Z2LExj364hi17Dx93PbMWpXLrtEV0bRHLKzecQZ2apbvZbOSZbbn/kgQ+W7uDiyd8yeLv9xxXXV+v38W9by+nf/vGPHLFaRG/36F+7Rim3NSX/h0ac/eby5g+v+wCOdIUCCIRsGn3Ia5/ZQF1akYxdXRfGp9Uq8TzMDMevbIHObnO7/+1otT3LeTmOo9/tIZ73lxG3/hGvDq633HfgHXTWfG8ObY/ANc8/xUvf7G+VPWtSzvArdMW0aZRXZ6/rg81oyvHR1bdmtG8MuoMftq5Kb//14oTpr+jyvHbFalGdh7I4PqJC8jIzmXa6H7ENaxb6nm1blSX31zYmc/WpvGvJZtLPP3hzBzGTV/Ms5+tY3jfNky+sS8N6pbN3bi92jTkg18O4mddmvGX91czZmpSiQ4h7T6YyU2TFxJdw5h0Q9nVVVZqx0TxwshEBndrzkPvJvPcZ+siXdJxUyCIVKADGdncOGkhW9MPM/GGRE49pf5xz3PUgHb0bnMyD7+XXKLO5nbsO8LQF7/io1Xb+OPFXfnrFd3DunyzJBrUjeGFkX144NIE/vtNGhdP+JJF3xV/COlIVg63TE1iW/oRXhqVSJvGpQ/N8lQzugZPj+jFZae35LGP1vDE//um0txhXhoKBJEKkpGdw63Tkkjeuo9nr+1Nn7aNymS+UTWMx6/qwaGMHB6cvSqsaVZtSWfIM3NJ2XGAF0cmcvOg9uV2bP7oswVmjR2AGQx94Ste/HxdkVdH5eY6v5m1nKTv9vCPa3r+4Iazyig6qgZPDO3J1X3ieOqTb3n0ozVVNhTCCgQzG2xma80sxczuLWR8LTN7Ixg/38zaBcMbm9l/zOyAmT1dYJo+ZrYimGaCRfpMkUg5ysl1fvXGUuam7OLxX/TgZ11OKdP5d2xWnzvP68T7K7by0cptx2w7J3k7Vz//Fe7w5tj+nJ9QtrUU5fTWJ/P+LwdxXtdT+OsHaxgzNYk9B398COmJOd/w7rIt/HZwZy7u0aJCajteUTWMx37Rg+vObMML/13PQ+8mV8knrRUbCGYWBTwDXAQkAMPNLKFAs9HAHnfvCDwBPBYMPwL8CbinkFk/B9wCdAp+BpdmBUQqO3fn/ndW8sGK0KGZX/Qpn67Cbzm7PQktYvnTOytJP5RVaB0vf7GeMdOS6NjsJN4ZP/CYj5EsDw3qxPDcdb158NIEPv82jYsnfMGi73bnjX8zaRP//DSFoYmtue0nHSq0tuNVo4bx5yHdufmseCbP28jv/7WizO/FKG/h7CH0BVLcfb27ZwIzgCEF2gwBpgSvZwHnmpm5+0F3/5JQMOQxsxZArLt/FTwAeipw+fGsiEhl9eScb3lt/veM/UkHbh5Ufl1NxETV4PGrerD7YCZ/fj/5B+OycnL5/b9W8pf3V3NhQnPeuKU/p5SiT5+yYGbcMDCet24bQFSUcc0LX/PCf9cxL2Unv//XCgZ2bMxfruge8ctLS8PM+MPFXbnjZx2ZsXAT97y5jOygH6qqIJwLn1sBm/K9TwX6FdXG3bPNLB1oDBT1MNJWwXzyz7NVOAWLVCVTv9rIU598yzWJcfxucOdyX173Vg249ez2PPvZOi49vSU/ObUp6YeyuH36Iuam7OL2czpwzwWdi70buiL0iDuZ9+4YxL1vLedvH66hhkH7pifx7LV9yvzkdkUyM+6+oDO1omvw9//5hozsHJ4c2qvSXDJ7LOFUWNj/nIL7QeG0KVV7M7vFzJLMLElPRZOq5N1lW3hg9qrQMfMKvKHql+d2okPTevz+7RWs2pLOlc/NZcGG3fyfq3rw28FdKkUYHNWgTgzPXtubh4d0o3ebhky64Qwa1Klcl5eW1vifdeKPF3flgxXbuP21RRzJyol0ScUKJxBSgdb53scBBTvxyGtjZtFAA2A3RUsN5nOseQLg7i+6e6K7JzZt2jSMckUi74tv0/j1zKWc0bYRT48Ivz+gslA7JorHr+rBlvTDXPLPL9l1MJNXR/fj6sTWxU8cAWbG9f3bMeu2AbRuVDkvLy2tmwe158+Xd2fO6h2MmZrE4czKHQrh/C9dCHQys3gzqwkMA2YXaDMbGBW8vgr41I9x3ZW7bwX2m9mZwdVF1wPvlLh6kUpo2aa93DptER2ansRLoxIj8rzhPm0bccdPO9K9ZQP+fftA+rUv2UNhpOyMPLMtj1/Vgy9TdnLDpAUcyMiOdElFCqv7azP7OfAkEAVMdPdHzOxhIMndZ5tZbWAa0IvQnsEwd18fTLsRiAVqAnuBC9w92cwSgclAHeBD4I5jhQio+2up/NalHeDq57+ibs0o3r5tQKkexiInpneWbubXM5dxelwDJt/Ut0Kfz6znIYhUsG3pR/jFc/PIyM7hzbEDiK/g7pml8vto5TbGT1/MBcFzJirqvJKehyBSgfYeyuT6ifNJP5zF5Bv7KgykUIO7N+eeCzvzwYptZdpteVlRIIgcp8OZOdw0eSEbdx7ixev70L1Vxd7sJVXLLYPaM6hTEx5+N5k12/ZFupwfUCCIHIesnFxuf20RSzbt5alhPRnQoUmkS5JKrkYN4x/X9CS2Tgzjpy/hUGblOcmsQBAppdxc53ezlvOftWn85fLuXHRa1eh3RyKvaf1aPHFNT9alHeCh2cnFT1BBFAgipfS3D1fz9pLN3H3+qVzbr22ky5Eq5qxOTbj9nA68kbSJd5aW/FkW5UGBIFIKr379HS99sYFR/dsy/mcdI12OVFF3nXcqfdo25A//Wsl3uw5GuhwFgkhJzUvZyQOzV/HTzk25/9JuVbITNqkcYqJq8NSwntQwuOP1JWRmR7YjPAWCSAmsTzvAba8tpkPTekwY3ouoStQvkFRNcQ3r8vhVp7M8NZ3HP1oT0VoUCCJhSj+Uxc1TkoiqYbwy6gzqV+CdpnJiG9y9Odf3b8vLX27g0zXbI1aHAkEkDFk5udw+fRGb9hzi+ev6nHCdsEnk/f7nXenSvD73vLmcbelHip+gHCgQRMLw8LvJzE3ZxV+vOI2+8WXzLGSR/GrHRPH0iN4czszhrjeWRORpawoEkWJM/Woj077+jlvPbl9pu5CWE0PHZifx8JBufL1+N09/mlLhy1cgiBzDF9+m8dC7yZzXtRm/Hdwl0uVINXBVnziu6NWKpz75hvnrd1XoshUIIkVI2XGA219bTKdmJ/HkMF1RJBXDzPjz5d1p06gud85Yyu6DmRW2bAWCSCH2Hsrk5ikLqRlVg5euT+SkWuE8flykbJxUK5qnR/Rm18EMfvPmMirqMQUKBJECQh3WLWbL3iO8MFJXFElkdG/VgPsu6sona3Ywae7GClmmAkEkH3fngdmrmLduF3+78jQS2+mKIomcGwe247yuzXj0wzVs3nu43Jen/WCRfKbM28j0+d9z2zkd+EWfuEiXI9WcmfF/rjqd+Rt20+rkOuW+PO0hiAQ+W7uDh99L5vyEU/jNBZ0jXY4IAA3r1WRw9+YVsiwFggiQsmM/d0xfQufmsTw5tCc1dEWRVEMKBKn29hzM5KbJSdSKqcHLoxKppyuKpJrS/3yp1jKzcxn76iK27TvC62POrJDjtCKVlfYQpNoKXVG0kvkbdvP4L3rQp23DSJckElEKBKm2Js7dyOsLNjH+px25vFerSJcjEnEKBKmW/rNmB4+8n8yF3U7h1+efGulyRCoFBYJUO2u37eeO15fQpXksT+iKIpE8OqksJyx3J3XPYZK37mP11n2s2bqf1dv28d2uQzStX4uXRyVSt6b+BESOCuuvwcwGA08BUcDL7v5ogfG1gKlAH2AXMNTdNwbj7gNGAznAL93942D4r4CbAQdWADe6e2QeEyRV3uHMHNZu38/q4MP/aADsz8gGwAzaNa5Ht5axXNU7jktPb0lLXVEk8gPFBoKZRQHPAOcDqcBCM5vt7sn5mo0G9rh7RzMbBjwGDDWzBGAY0A1oCcwxs1OB5sAvgQR3P2xmM4N2k8tu1aQ6eHfZFp6c8w0bdh7k6AOm6tWMokuLWIb0aknXFrF0bRFLl+b1tTcgUoxw/kL6Ainuvh7AzGYAQ4D8gTAEeDB4PQt42swsGD7D3TOADWaWEszv+2DZdcwsC6gLbDn+1ZHq5N1lW7hzxhK6tohl/M86kdCiPl1bxNK6YV2dFxAphXACoRWwKd/7VKBfUW3cPdvM0oHGwfCvC0zbyt2/MrO/EwqGw8D/uPv/FLZwM7sFuAWgTZs2YZQr1cFHK7dx1xtLSWzbiMk3naFv/yJlIJyrjAr7qlXwaQ1FtSl0uJk1JLT3EE/oUFI9M7uusIW7+4vunujuiU2bNg2jXDnRfbJ6O3e8vpgecQ2YeKPCQKSshBMIqUD+J4vH8ePDO3ltzCwaaADsPsa05wEb3D3N3bOAt4EBpVkBqV4+/yaN215dTJfmsUy+sa+eZCZShsIJhIVAJzOLN7OahE7+zi7QZjYwKnh9FfCph575NhsYZma1zCwe6AQsIHSo6EwzqxucazgXWH38qyMnsq/W7WLM1CTaN63HtNF9aVAnJtIliZxQiv16FZwTGA98TOiy04nuvsrMHgaS3H028AowLThpvJtQaBC0m0noBHQ2MM7dc4D5ZjYLWBwMXwK8WParJyeKpI27GT1lIW0a1eW1m/txct2akS5J5IRjFfXw5rKQmJjoSUlJkS5DKtjSTXu57uX5NKtfixm3nkmz+rUjXZJIlWJmi9w9sbh26rpCKrWVm9O5/pX5NKpXk+ljFAYi5UmBIJXWmm37uO6V+dSvHcP0Mf1o3kBhIFKeFAhSKaXs2M+1L82ndnQU08f0I65h3UiXJHLCUyBIpbNh50FGvDQfM+O1Mf1o27hepEsSqRYUCFKpbNp9iBEvfU12rjN9TD86ND0p0iWJVBsKBKk0Nu89zPCXvuZQZg6vju7HqafUj3RJItWKAkEqhe37jnDtS1+TfiiLaaP7ktAyNtIliVQ7CgSJuLT9GYx46WvS9mcwZXRfesSdHOmSRKoldQQjEbX7YCbXvTyfLXuPMOWmvvRu0zDSJYlUW9pDkIhJP5TFyFfms3HXQV4ZlUjf+EaRLkmkWlMgSETsO5LF9ZMW8O32A7wwsg8DOjaJdEki1Z4OGUmF25Z+hBsmLSBlxwGeu64P53RuFumSRAQFglSwb7fvZ9TEBaQfzmLSjWcwqJMeeiRSWSgQpMLMXx96nkGtmChmju1Pt5YNIl2SiOSjQJAK8f7yrfzqjaW0blSHyTf2pXUj9U0kUtkoEKTcvfLlBv7yfjJ92jTk5VGJeriNSCWlQJByk5vr/O3D1bz0xQYGd2vOk8N6UjsmKtJliUgRFAhSLjJlWHr1AAAPcElEQVSyc7jnzeW8u2wLNwxox58uSSCqhkW6LBE5BgWClLn0w1ncOi2Jr9fv5r6LunDL2e0xUxiIVHYKBClTW/Ye5sZJC1m/8wBPDevJkJ6tIl2SiIRJgSBlZs22fdwwcSEHM7KZcmNf3X0sUsUoEKRMzFu3k1unLaJuzdA9Bl1bqPtqkapGgSDHbfayLdwzcxltG9dl8k19aXVynUiXJCKloECQUnN3Xv5iA498sJq+7Rrx0vWJNKgbE+myRKSUFAhSKrm5zp/fT2bS3I1cfFoL/u81p+seA5EqToEgJXYkK4dfz1zKByu2cdPAeP54cVdq6B4DkSovrOchmNlgM1trZilmdm8h42uZ2RvB+Plm1i7fuPuC4WvN7MJ8w082s1lmtsbMVptZ/7JYISlf6YeyuP6VBXywYht/vLgr91+aoDAQOUEUu4dgZlHAM8D5QCqw0Mxmu3tyvmajgT3u3tHMhgGPAUPNLAEYBnQDWgJzzOxUd88BngI+cverzKwmoN7OKrnNew8zauICvt91iAnDe3HZ6S0jXZKIlKFw9hD6Ainuvt7dM4EZwJACbYYAU4LXs4BzLXRr6hBghrtnuPsGIAXoa2axwNnAKwDununue49/daS8JG/Zx5XPzmX7vtCzjxUGIieecAKhFbAp3/vUYFihbdw9G0gHGh9j2vZAGjDJzJaY2ctmVq+whZvZLWaWZGZJaWlpYZQrZW1uyk6ueeErDGPW2AH079A40iWJSDkIJxAKO0DsYbYpang00Bt4zt17AQeBH52bAHD3F9090d0TmzbV07Uq2r+XbOaGSQtodXId/jVuAJ2b1490SSJSTsIJhFSgdb73ccCWotqYWTTQANh9jGlTgVR3nx8Mn0UoIKSScHee+2wdd72xlD5tGzJzbH9aNNANZyInsnACYSHQyczig5O/w4DZBdrMBkYFr68CPnV3D4YPC65Cigc6AQvcfRuwycw6B9OcCyRTTnJzC+7QyLHk5DoPzl7FYx+t4dLTWzLlpr40qKMbzkROdMVeZeTu2WY2HvgYiAImuvsqM3sYSHL32YRODk8zsxRCewbDgmlXmdlMQh/22cC44AojgDuA14KQWQ/cWMbrlufif35J6u5DxNaJoUHwE1snOu/1/w6LKXRYTFRYV+eeEI5k5XDnjCV8vGo7t5zdnnsHd9FlpSLVhIW+yFcNiYmJnpSUVOLpJn65gU17DpF+OIt9h7NIL/BzJCv3mNPXqxmVFxaFhcaPQyU6r12t6Kpz9+6eg5ncPDWJxd/v4U8XJ3DTWfGRLklEyoCZLXL3xOLaVYs7lYv7YMvIzvlRWOw7nP2j4Dj6s2n3IVYGrw9l5hxz3rVjahS7JxJbO3hd94fDK7IriE27DzFq0gJS9xzmmRG9+flpLSps2SJSOVSLQChOregomtWPoln92iWeNjM7l31H8gdJVpF7IumHs9i89wirt+4n/XAWBzKyjznvmtE/DpNQgEQXvreSL1DqxESF/ZSylZvTuXHyQjKycnh1dD/6xjcq8e9BRKo+BcJxqhldgyYn1aLJSbVKPG12Ti77jmQXGR4Fh2/fd4RvtofCZP+RY4dJTJTlhUbeHkghP1m5ufz1/dU0qBPD9NsG0OkUXVYqUl0pECIoOqoGjerVpFG9miWeNifXOXCk6MNaBUNl98FMNuw8GBp2JIv8p466NK/PlJv6ckpsyfeQROTEoUCooqJqWOgQUSmeP5Cb6xzIzCb9UGhPo0OzelXq5LeIlA8FQjVUo4YRWzt0KElE5Kjqc4G9iIgckwJBREQABYKIiAQUCCIiAigQREQkoEAQERFAgSAiIgEFgoiIAAoEEREJKBBERARQIIiISECBICIigAJBREQCCgQREQEUCCIiElAgiIgIoEAQEZGAAkFERAAFgoiIBBQIIiIChBkIZjbYzNaaWYqZ3VvI+Fpm9kYwfr6Ztcs37r5g+Fozu7DAdFFmtsTM3jveFRERkeNTbCCYWRTwDHARkAAMN7OEAs1GA3vcvSPwBPBYMG0CMAzoBgwGng3md9SdwOrjXQkRETl+4ewh9AVS3H29u2cCM4AhBdoMAaYEr2cB55qZBcNnuHuGu28AUoL5YWZxwMXAy8e/GiIicrzCCYRWwKZ871ODYYW2cfdsIB1oXMy0TwK/BXJLXLWIiJS5cALBChnmYbYpdLiZXQLscPdFxS7c7BYzSzKzpLS0tOKrFRGRUgknEFKB1vnexwFbimpjZtFAA2D3MaYdCFxmZhsJHYL6mZm9WtjC3f1Fd09098SmTZuGUa6IiJRGOIGwEOhkZvFmVpPQSeLZBdrMBkYFr68CPnV3D4YPC65Cigc6AQvc/T53j3P3dsH8PnX368pgfUREpJSii2vg7tlmNh74GIgCJrr7KjN7GEhy99nAK8A0M0shtGcwLJh2lZnNBJKBbGCcu+eU07qIiMhxsNAX+aohMTHRk5KSIl2GiEiVYmaL3D2xuHa6U1lERAAFgoiIBBQIIiICKBBERCSgQBAREUCBICIiAQWCiIgACgQREQkoEEREBFAgiIhIQIEgIiKAAkFERAIKBBERARQIIiISUCCIiAigQBARkYACQUREAAWCiIgEFAgiIgIoEEREJKBAEBERQIEgIiIBBYKIiAAKBBERCSgQREQEUCCIiEggrEAws8FmttbMUszs3kLG1zKzN4Lx882sXb5x9wXD15rZhcGw1mb2HzNbbWarzOzOslohEREpnWIDwcyigGeAi4AEYLiZJRRoNhrY4+4dgSeAx4JpE4BhQDdgMPBsML9s4G537wqcCYwrZJ4iIlKBwtlD6AukuPt6d88EZgBDCrQZAkwJXs8CzjUzC4bPcPcMd98ApAB93X2ruy8GcPf9wGqg1fGvjoiIlFY4gdAK2JTvfSo//vDOa+Pu2UA60DicaYPDS72A+eGXLSIiZS2cQLBChnmYbY45rZmdBLwF3OXu+wpduNktZpZkZklpaWlhlCsiIqURTiCkAq3zvY8DthTVxsyigQbA7mNNa2YxhMLgNXd/u6iFu/uL7p7o7olNmzYNo1wRESmNcAJhIdDJzOLNrCahk8SzC7SZDYwKXl8FfOruHgwfFlyFFA90AhYE5xdeAVa7+z/KYkVEROT4RBfXwN2zzWw88DEQBUx091Vm9jCQ5O6zCX24TzOzFEJ7BsOCaVeZ2UwgmdCVRePcPcfMzgJGAivMbGmwqN+7+wdlvYIiIhIeC32RrxoSExM9KSkp0mWIiFQpZrbI3ROLa6c7lUVEBFAgiIhIQIEgIiKAAkFERAIKBBERARQIIiISUCCIiAigQBARkYACQUREAAWCiIgEFAgiIgIoEEREJKBAEBERQIEgIiIBBYKIiAAKBBERCSgQREQEUCCIiEhAgSAiIoACQUREAgoEEREBFAgiIhJQIIiICKBAEBGRgAJBREQABYKIiAQUCCIiAoQZCGY22MzWmlmKmd1byPhaZvZGMH6+mbXLN+6+YPhaM7sw3HmKiEjFKjYQzCwKeAa4CEgAhptZQoFmo4E97t4ReAJ4LJg2ARgGdAMGA8+aWVSY8xQRkQoUzh5CXyDF3de7eyYwAxhSoM0QYErwehZwrplZMHyGu2e4+wYgJZhfOPMUEZEKFB1Gm1bApnzvU4F+RbVx92wzSwcaB8O/LjBtq+B1cfMEwMxuAW4BGgAHzGxtgSYNgPQwhjUBdha2jHJWWC0VNZ9wpymu3bHGFzUunO0SqW1SWC0VNZ/Kvk1AfyvH066k2yXcbXW826RtWK3c/Zg/wNXAy/nejwT+WaDNKiAu3/t1hALhGeC6fMNfAX4RzjwLqePFcIcXMSypuHUtj5+i6q6I+YQ7TXHtjjX+eLZLpLZJJLdLZd8mkdwu1fFvJdxtVVHbJJxDRqlA63zv44AtRbUxs2hC6bb7GNOGM8+C3i3B8KLaRkJZ1VKa+YQ7TXHtjjVe26V82mubVOx8IrVdSrKtyp0F6VN0g9AH/DfAucBmYCEwwt1X5WszDjjN3cea2TDgSne/xsy6AdMJnTNoCXwCdAKsuHmWNTNLcvfE8pq/lJy2SeWk7VL5VNQ2KfYcgofOCYwHPgaigInuvsrMHia0GzOb0KGgaWaWQmjPYFgw7SozmwkkA9nAOHfPAShsnmW/ej/wYjnPX0pO26Ry0napfCpkmxS7hyAiItWD7lQWERFAgSAiIgEFgoiIANU4EMysnplNMbOXzOzaSNcjYGbtzewVM5sV6Vrkf5nZ5cHfyTtmdkGk6xEws65m9ryZzTKz28pqvidUIJjZRDPbYWYrCwwvrCO9K4FZ7j4GuKzCi60mSrJNPNSVyejIVFq9lHC7/Dv4O7kBGBqBcquFEm6T1e4+FrgGKLPLUU+oQAAmE+pEL88xOtKL43+7z8ipwBqrm8mEv02k4kym5Nvlj8F4KR+TKcE2MbPLgC8J3d9VJk6oQHD3zwndB5FfUR3ppRIKBTjBfg+VSQm3iVSQkmwXC3kM+NDdF1d0rdVFSf9W3H22uw8AyuyQd3X4ICysc75WwNvAL8zsOSrX7fvVQaHbxMwam9nzQC8zuy8ypVVrRf2t3AGcB1xlZmMjUVg1VtTfyjlmNsHMXgA+KKuFhdPbaVVnhQxzdz8I3FjRxQhQ9DbZBegDJ3KK2i4TgAkVXYwARW+Tz4DPynph1WEPoTQd6Un50japnLRdKp8K3SbVIRAWAp3MLN7MahLqZ2l2hGuq7rRNKidtl8qnQrfJCRUIZvY68BXQ2cxSzWy0u2cDRzvSWw3MrICO9CSgbVI5abtUPpVhm6hzOxERAU6wPQQRESk9BYKIiAAKBBERCSgQREQEUCCIiEhAgSAiIoACQaRcmdmDZnZPpOsQCYcCQSRMQa+f+puRE5b+c4scg5m1M7PVZvYssBh4xcySzGyVmT2Ur91GM3vIzBab2Qoz61LIvMaY2YdmVqci10EkXAoEkeJ1Bqa6ey/gbndPBHoAPzGzHvna7XT33sBzwA8OE5nZeOBS4HJ3P1xBdYuUiAJBpHjfufvXwetrzGwxsAToRugpVke9Hfy7CGiXb/hIQk+8+oW7Z5RzrSKlpkAQKd5BADOLJ/TN/1x37wG8D9TO1+7oh30OP3zWyEpCARGHSCWmQBAJXyyhcEg3s1MIfesPxxLgVmC2mbUsr+JEjpcCQSRM7r6M0If7KmAiMLcE035JaO/ifTNrUj4VihwfdX8tIiKA9hBERCSgQBAREUCBICIiAQWCiIgACgQREQkoEEREBFAgiIhIQIEgIiIA/H+wRW85mgaRHAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "report_results('rank', scores_cold);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'ML1M': {'PureSVD(cs)': {'rank': 100}},\n",
       " 'ML10M': {'PureSVD(cs)': {'rank': 400}},\n",
       " 'BX': {'PureSVD(cs)': {'rank': 400}}}"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "config_cold"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### saving data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "save_results(experiment_name+'_coldstart', config=config_cold, tuning=scores_cold)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## cross validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "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\">[05:49<01:02, 116.36s/it]</span></div>"
      ],
      "text/plain": [
       "\u001b[A\u001b[A\u001b[2K\r",
       " [████████████████████████████████████████████████████████████] 3/3 [05:49<01:02, 116.36s/it]\u001b[B"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div><span class=\"Text-label\" style=\"display:inline-block; overflow:hidden; white-space:nowrap; text-overflow:ellipsis; min-width:15ex; max-width:15ex; vertical-align:middle; text-align:right\">ML1M folds</span>\n",
       "<progress style=\"width:45ex\" max=\"5\" value=\"5\" class=\"Progress-main\"/></progress>\n",
       "<span class=\"Progress-label\"><strong>100%</strong></span>\n",
       "<span class=\"Iteration-label\">5/5</span>\n",
       "<span class=\"Time-label\">[00:12<00:02, 2.46s/it]</span></div>"
      ],
      "text/plain": [
       "\u001b[A\u001b[2K\r",
       "     ML1M folds [█████████████████████████████████████████████] 5/5 [00:12<00:02, 2.46s/it]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div><span class=\"Text-label\" style=\"display:inline-block; overflow:hidden; white-space:nowrap; text-overflow:ellipsis; min-width:15ex; max-width:15ex; vertical-align:middle; text-align:right\">ML10M folds</span>\n",
       "<progress style=\"width:45ex\" max=\"5\" value=\"5\" class=\"Progress-main\"/></progress>\n",
       "<span class=\"Progress-label\"><strong>100%</strong></span>\n",
       "<span class=\"Iteration-label\">5/5</span>\n",
       "<span class=\"Time-label\">[04:35<00:55, 54.92s/it]</span></div>"
      ],
      "text/plain": [
       "\u001b[A\u001b[2K\r",
       "    ML10M folds [█████████████████████████████████████████████] 5/5 [04:35<00:55, 54.92s/it]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div><span class=\"Text-label\" style=\"display:inline-block; overflow:hidden; white-space:nowrap; text-overflow:ellipsis; min-width:15ex; max-width:15ex; vertical-align:middle; text-align:right\">BX folds</span>\n",
       "<progress style=\"width:45ex\" max=\"5\" value=\"5\" class=\"Progress-main\"/></progress>\n",
       "<span class=\"Progress-label\"><strong>100%</strong></span>\n",
       "<span class=\"Iteration-label\">5/5</span>\n",
       "<span class=\"Time-label\">[01:02<00:12, 12.43s/it]</span></div>"
      ],
      "text/plain": [
       "\u001b[A\u001b[2K\r",
       "       BX folds [█████████████████████████████████████████████] 5/5 [01:02<00:12, 12.43s/it]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "result_cold = {}\n",
    "for label in track(data_labels):\n",
    "    models_cold = prepare_cold_start_recommender_models(label, data_models_cold, config_cold)\n",
    "    result_cold[label] = ee.run_cv_experiment(models_cold,\n",
    "                                              fold_experiment=ee.topk_test,\n",
    "                                              topk_list=topk_values,\n",
    "                                              ignore_feedback=True,\n",
    "                                              iterator=lambda x: track(x, label=f'{label} folds'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "report_results('topn', result_cold, target_metric);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "report_results('topn', result_cold, 'coverage');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### saving data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "save_results(experiment_name+'_coldstart', cv=result_cold)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}