ScaledSVD.ipynb

{
 "cells": [
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "%env MKL_NUM_THREADS=20\n",
    "%env OMP_NUM_THREADS=20"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from ipypb import track\n",
    "\n",
    "from polara.evaluation import evaluation_engine as ee\n",
    "from polara.evaluation.pipelines import (find_optimal_svd_rank,\n",
    "                                         find_optimal_config,\n",
    "                                         set_config)\n",
    "\n",
    "from scaledsvd import ScaledSVD, ScaledSVDItemColdStart\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": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "from polara.recommender import defaults\n",
    "defaults.memory_hard_limit = 3 # 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 = 'scaledsvd'"
   ]
  },
  {
   "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,\n",
    "              350, 400, 500, 750, 1000, 1250, 1500, 1750, 2000, 2500, 3000]\n",
    "svd_ranks = {'ML1M': [r for r in ranks_grid if r <= 1000],\n",
    "             'ML10M': [r for r in ranks_grid if r <= 1000],\n",
    "             'BX': [r for r in ranks_grid if r <= 2000]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaling_params = [0.2, 0.4, 0.6]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "topk_values = [1, 3, 10, 20, 30]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "target_metric = 'mrr'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_dict = dict.fromkeys(data_labels)\n",
    "meta_dict = dict.fromkeys(data_labels)\n",
    "similarities = dict.fromkeys(data_labels)\n",
    "sim_indices = dict.fromkeys(data_labels)\n",
    "feature_idx = dict.fromkeys(data_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_data = [data_dict, similarities, sim_indices, meta_dict]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Movielens1M"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "lbl = 'ML1M'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_dict[lbl], meta_dict[lbl] = get_movielens_data('C:/Users/evfro/Downloads/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": [
    "similarities[lbl], sim_indices[lbl], feature_idx[lbl] = get_similarity_data(meta_dict[lbl])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(meta_dict[lbl].applymap(len).sum(axis=1)==0).mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Movielens10M"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "lbl = 'ML10M'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_dict[lbl], meta_dict[lbl] = get_movielens_data('C:/Users/evfro/Downloads/ml-10m.zip',\n",
    "                                                    meta_path='data/meta_info_ml10m.csv',\n",
    "                                                    implicit=True,\n",
    "                                                    filter_no_meta=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "similarities[lbl], sim_indices[lbl], feature_idx[lbl] = get_similarity_data(meta_dict[lbl])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(meta_dict[lbl].applymap(len).sum(axis=1)==0).mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## BookCrossing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "lbl = 'BX'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_dict[lbl], meta_dict[lbl] = get_bookcrossing_data('C:/Users/evfro/Downloads/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": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "similarities[lbl], sim_indices[lbl], feature_idx[lbl] = get_similarity_data(meta_dict[lbl])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 25,
     "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": 26,
   "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": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "def prepare_recommender_models(data_label, data_models, config):\n",
    "    data_model = data_models[data_label]\n",
    "    models = [ScaledSVD(data_model)]\n",
    "    apply_config(models, config, data_label)\n",
    "    return models\n",
    "\n",
    "def fine_tune_scaledsvd(model, ranks, scale_params, label, record_time=False):\n",
    "    param_grid = [(s, r) for s in scale_params for r in reversed(list(sorted(ranks)))]\n",
    "    param_names = ('col_scaling', 'rank')\n",
    "    best_svd_config, svd_scores = find_optimal_config(model, param_grid, param_names,\n",
    "                                                      target_metric,\n",
    "                                                      return_scores=True,\n",
    "                                                      force_build=False,\n",
    "                                                      iterator=lambda x: track(x, label=label))\n",
    "    model_config = {model.method: dict(zip(param_names, best_svd_config))}\n",
    "    model_scores = {model.method: svd_scores}\n",
    "    try:\n",
    "        if record_time:\n",
    "            max_rank = max(ranks)\n",
    "            save_training_time(f'{experiment_name}_rank_{max_rank}', model, svd_scores.xs(max_rank, level='rank').index, label)\n",
    "    finally:\n",
    "        return model_config, model_scores"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "config = {}\n",
    "scores = {}\n",
    "data_models = {}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
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       "           ML1M [█████████████████████████████████████████████] 60/60 [02:01<00:00, 2.01s/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_scaledsvd(ScaledSVD(data_models[label]),\n",
    "                                                       svd_ranks[label],\n",
    "                                                       scaling_params,\n",
    "                                                       label, record_time=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\evfro\\Anaconda\\envs\\lab\\lib\\site-packages\\pandas\\plotting\\_core.py:1001: UserWarning: Attempted to set non-positive left xlim on a log-scaled axis.\n",
      "Invalid limit will be ignored.\n",
      "  ax.set_xlim(left, right)\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "report_results('rank', scores);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'ML1M': {'PureSVDs': {'col_scaling': 0.4, 'rank': 75}},\n",
       " 'ML10M': {'PureSVDs': {'col_scaling': 0.2, 'rank': 1000}},\n",
       " 'BX': {'PureSVDs': {'col_scaling': 0.4, 'rank': 1000}}}"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "config"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### saving data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "save_results(experiment_name, config=config, tuning=scores)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## cross-validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><span class=\"Text-label\" style=\"display:inline-block; overflow:hidden; white-space:nowrap; text-overflow:ellipsis; min-width:0; max-width:15ex; vertical-align:middle; text-align:right\"></span>\n",
       "<progress style=\"width:60ex\" max=\"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\">[22:04<06:13, 441.47s/it]</span></div>"
      ],
      "text/plain": [
       "\u001b[A\u001b[A\u001b[2K\r",
       " [████████████████████████████████████████████████████████████] 3/3 [22:04<06:13, 441.47s/it]\u001b[B"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div><span class=\"Text-label\" style=\"display:inline-block; overflow:hidden; white-space:nowrap; text-overflow:ellipsis; min-width:15ex; max-width:15ex; vertical-align:middle; text-align:right\">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\">[00:08<00:02, 1.56s/it]</span></div>"
      ],
      "text/plain": [
       "\u001b[A\u001b[2K\r",
       "     ML1M folds [█████████████████████████████████████████████] 5/5 [00:08<00:02, 1.56s/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\">[15:44<03:13, 188.76s/it]</span></div>"
      ],
      "text/plain": [
       "\u001b[A\u001b[2K\r",
       "    ML10M folds [█████████████████████████████████████████████] 5/5 [15:44<03:13, 188.76s/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\">[06:13<01:14, 74.55s/it]</span></div>"
      ],
      "text/plain": [
       "\u001b[A\u001b[2K\r",
       "       BX folds [█████████████████████████████████████████████] 5/5 [06:13<01:14, 74.55s/it]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "result = {}\n",
    "\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": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "report_results('topn', result, target_metric);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### saving data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "save_results(experiment_name, cv=result)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Cold start"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7580"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import gc\n",
    "gc.collect()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "def prepare_cold_start_recommender_models(data_label, data_models, config):\n",
    "    data_model = data_models[data_label]\n",
    "    models = [ScaledSVDItemColdStart(data_model, item_features=meta_dict[data_label])]\n",
    "    apply_config(models, config, data_label)\n",
    "    return models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "config_cold = {}\n",
    "scores_cold = {}\n",
    "data_models_cold = {}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "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\">[26:17<12:17, 525.55s/it]</span></div>"
      ],
      "text/plain": [
       "\u001b[A\u001b[A\u001b[2K\r",
       " [████████████████████████████████████████████████████████████] 3/3 [26:17<12:17, 525.55s/it]\u001b[B"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div><span class=\"Text-label\" style=\"display:inline-block; overflow:hidden; white-space:nowrap; text-overflow:ellipsis; min-width:15ex; max-width:15ex; vertical-align:middle; text-align:right\">ML1M</span>\n",
       "<progress style=\"width:45ex\" max=\"60\" value=\"60\" class=\"Progress-main\"/></progress>\n",
       "<span class=\"Progress-label\"><strong>100%</strong></span>\n",
       "<span class=\"Iteration-label\">60/60</span>\n",
       "<span class=\"Time-label\">[02:00<00:00, 2.00s/it]</span></div>"
      ],
      "text/plain": [
       "\u001b[A\u001b[2K\r",
       "           ML1M [█████████████████████████████████████████████] 60/60 [02:00<00:00, 2.00s/it]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div><span class=\"Text-label\" style=\"display:inline-block; overflow:hidden; white-space:nowrap; text-overflow:ellipsis; min-width:15ex; max-width:15ex; vertical-align:middle; text-align:right\">ML10M</span>\n",
       "<progress style=\"width:45ex\" max=\"60\" value=\"60\" class=\"Progress-main\"/></progress>\n",
       "<span class=\"Progress-label\"><strong>100%</strong></span>\n",
       "<span class=\"Iteration-label\">60/60</span>\n",
       "<span class=\"Time-label\">[11:54<00:02, 11.90s/it]</span></div>"
      ],
      "text/plain": [
       "\u001b[A\u001b[2K\r",
       "          ML10M [█████████████████████████████████████████████] 60/60 [11:54<00:02, 11.90s/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=\"72\" value=\"72\" class=\"Progress-main\"/></progress>\n",
       "<span class=\"Progress-label\"><strong>100%</strong></span>\n",
       "<span class=\"Iteration-label\">72/72</span>\n",
       "<span class=\"Time-label\">[12:16<00:01, 10.22s/it]</span></div>"
      ],
      "text/plain": [
       "\u001b[A\u001b[2K\r",
       "             BX [█████████████████████████████████████████████] 72/72 [12:16<00:01, 10.22s/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 = ScaledSVDItemColdStart(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_scaledsvd(model,\n",
    "                                                                 svd_ranks[label],\n",
    "                                                                 scaling_params,\n",
    "                                                                 label, record_time=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\evfro\\Anaconda\\envs\\lab\\lib\\site-packages\\pandas\\plotting\\_core.py:1001: UserWarning: Attempted to set non-positive left xlim on a log-scaled axis.\n",
      "Invalid limit will be ignored.\n",
      "  ax.set_xlim(left, right)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "report_results('rank', scores_cold);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'ML1M': {'ScaledSVD(cs)': {'col_scaling': 0.6, 'rank': 30}},\n",
       " 'ML10M': {'ScaledSVD(cs)': {'col_scaling': 0.2, 'rank': 60}},\n",
       " 'BX': {'ScaledSVD(cs)': {'col_scaling': 0.2, 'rank': 400}}}"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "config_cold"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### saving data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "save_results(experiment_name+'_coldstart', config=config_cold, tuning=scores_cold)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## cross validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><span class=\"Text-label\" style=\"display:inline-block; overflow:hidden; white-space:nowrap; text-overflow:ellipsis; min-width:0; max-width:15ex; vertical-align:middle; text-align:right\"></span>\n",
       "<progress style=\"width:60ex\" max=\"3\" value=\"3\" class=\"Progress-main\"/></progress>\n",
       "<span class=\"Progress-label\"><strong>100%</strong></span>\n",
       "<span class=\"Iteration-label\">3/3</span>\n",
       "<span class=\"Time-label\">[02:06<01:10, 41.91s/it]</span></div>"
      ],
      "text/plain": [
       "\u001b[A\u001b[A\u001b[2K\r",
       " [████████████████████████████████████████████████████████████] 3/3 [02:06<01:10, 41.91s/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\">[00:05<00:01, 0.95s/it]</span></div>"
      ],
      "text/plain": [
       "\u001b[A\u001b[2K\r",
       "     ML1M folds [█████████████████████████████████████████████] 5/5 [00:05<00:01, 0.95s/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\">[00:51<00:11, 10.28s/it]</span></div>"
      ],
      "text/plain": [
       "\u001b[A\u001b[2K\r",
       "    ML10M folds [█████████████████████████████████████████████] 5/5 [00:51<00:11, 10.28s/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\">[01:10<00:14, 13.91s/it]</span></div>"
      ],
      "text/plain": [
       "\u001b[A\u001b[2K\r",
       "       BX folds [█████████████████████████████████████████████] 5/5 [01:10<00:14, 13.91s/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": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "report_results('topn', result_cold, target_metric);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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7JK37MIs2M7P2SfXlrIioA+oK2q7Pe1xPbtjng/YxF5jb5grNzKzDeBoGM7MMceibmWWIQ9/MLEMc+mZmGeLQNzPLEIe+mVmGOPTNzDLEoW9mliEOfTOzDHHom5lliEPfzCxDHPpmZhni0DczyxCHvplZhjj0zcwyxKFvZpYhqUJf0gRJGyQ1SDrodoeSRkt6TlKzpEl57UMlPSNpnaQ1ki7ryOLNzKxtWr1zlqQyYBbwOXI3Sa+XtDAifpPXbRMwFfhmwebvAFMi4iVJpwArJS2KiLc6pHoza5fKGQ+XugQAGm++oNQlZE6a2yWOBBoiYiOApPnARGB/6EdEY7Jub/6GEfFi3uPXJL0OVAAOfTOzEkgzvNMH2Jy33JS0tYmkkUB34OUi66ZJWiFpxdatW9u6azMzSylN6KtIW7TlSSSdDNwDXBURewvXR8TsiKiJiJqKioq27NrMzNogTeg3AafmLfcFXkv7BJKOBR4G/jYiftW28szMrCOlCf16oL+kKkndgcnAwjQ7T/o/BNwdEQ+0v0wzM+sIrYZ+RDQD04FFwHrg/ohYJ2mmpAsBJI2Q1ARcAtwhaV2y+aXAaGCqpFXJz9AP5ZWYmVmr0ly9Q0TUAXUFbdfnPa4nN+xTuN29wL2HWKOZmXUQfyPXzCxDHPpmZhni0DczyxCHvplZhjj0zcwyxKFvZpYhDn0zswxx6JuZZYhD38wsQxz6ZmYZ4tA3M8sQh76ZWYY49M3MMsShb2aWIQ59M7MMceibmWWIQ9/MLENShb6kCZI2SGqQNKPI+tGSnpPULGlSwborJb2U/FzZUYWbmVnbtRr6ksqAWcD5wACgVtKAgm6bgKnATwq2PQG4ATgHGAncIOn4Qy/bzMzaI82Z/kigISI2RsQeYD4wMb9DRDRGxBpgb8G25wGLI2J7RLwJLAYmdEDdZmbWDmlCvw+wOW+5KWlLI9W2kqZJWiFpxdatW1Pu2szM2ipN6KtIW6Tcf6ptI2J2RNRERE1FRUXKXZuZWVulCf0m4NS85b7Aayn3fyjbmplZB0sT+vVAf0lVkroDk4GFKfe/CDhX0vHJB7jnJm1mZlYCrYZ+RDQD08mF9Xrg/ohYJ2mmpAsBJI2Q1ARcAtwhaV2y7Xbgu+TeOOqBmUmbmZmVQNc0nSKiDqgraLs+73E9uaGbYtveBdx1CDWamVkH8TdyzcwyxKFvZpYhDn0zswxx6JuZZYhD38wsQxz6ZmYZ4tA3M8sQh76ZWYY49M3MMsShb2aWIQ59M7MMceibmWWIQ9/MLEMc+mZmGeLQNzPLEIe+mVmGpAp9SRMkbZDUIGlGkfU9JC1I1i+XVJm0d5M0T9JaSesl/U3Hlm9mZm3RauhLKgNmAecDA4BaSQMKun0ZeDMi+gG3Abck7ZcAPSKiGjgbuHrfG4KZmR1+ac70RwINEbExIvYA84GJBX0mAvOSxw8C4yUJCOBoSV2BXsAe4HcdUrmZmbVZmtDvA2zOW25K2or2SW6kvgMoJ/cG8HtgC7AJuLXYjdElTZO0QtKKrVu3tvlFmJlZOmlCX0XaImWfkcD7wClAFfANSX98UMeI2RFRExE1FRUVKUoyM7P2SBP6TcCpect9gdda6pMM5RwHbAcuB34ZEe9FxOvA00DNoRZtZmbtkyb064H+kqokdQcmAwsL+iwErkweTwKWRkSQG9IZp5yjgVHACx1TupmZtVWroZ+M0U8HFgHrgfsjYp2kmZIuTLrNAcolNQB/Dey7rHMW8DHg1+TePP5fRKzp4NdgZmYpdU3TKSLqgLqCtuvzHu8id3lm4XY7i7WbmVlp+Bu5ZmYZ4tA3M8sQh76ZWYY49M3MMsShb2aWIamu3jH7KKic8XCpSwCg8eYLSl2CZZjP9M3MMsShb2aWIQ59M7MMceibmWWIQ9/MLEMc+mZmGeLQNzPLEIe+mVmGOPTNzDLEoW9mliGpQl/SBEkbJDVImlFkfQ9JC5L1yyVV5q0bLOkZSeskrZXUs+PKNzOztmg19CWVkbvt4fnAAKBW0oCCbl8G3oyIfsBtwC3Jtl2Be4H/EREDgbHAex1WvZmZtUmaM/2RQENEbIyIPcB8YGJBn4nAvOTxg8B4SQLOBdZExGqAiNgWEe93TOlmZtZWaUK/D7A5b7kpaSvaJ7mR+g6gHDgDCEmLJD0n6VuHXrKZmbVXmqmVVaQtUvbpCnwaGAG8AzwqaWVEPPoHG0vTgGkAp512WoqSzMysPdKc6TcBp+Yt9wVea6lPMo5/HLA9aX88It6IiHeAOmB44RNExOyIqImImoqKira/CjMzSyVN6NcD/SVVSeoOTAYWFvRZCFyZPJ4ELI2IABYBgyUdlbwZjAF+0zGlm5lZW7U6vBMRzZKmkwvwMuCuiFgnaSawIiIWAnOAeyQ1kDvDn5xs+6akvyf3xhFAXUR0jtsXmZllUKrbJUZEHbmhmfy26/Me7wIuaWHbe8ldtmlmZiXmb+SamWWIQ9/MLEMc+mZmGeLQNzPLEIe+mVmGOPTNzDLEoW9mliEOfTOzDHHom5lliEPfzCxDHPpmZhni0DczyxCHvplZhjj0zcwyxKFvZpYhDn0zswxx6JuZZUiq0Jc0QdIGSQ2SZhRZ30PSgmT9ckmVBetPk7RT0jc7pmwzM2uPVkNfUhkwCzgfGADUShpQ0O3LwJsR0Q+4DbilYP1twCOHXq6ZmR2KNGf6I4GGiNgYEXuA+cDEgj4TgXnJ4weB8ZIEIOkLwEZgXceUbGZm7ZUm9PsAm/OWm5K2on0iohnYAZRLOhr4X8B3PugJJE2TtELSiq1bt6at3czM2ihN6KtIW6Ts8x3gtojY+UFPEBGzI6ImImoqKipSlGRmZu3RNUWfJuDUvOW+wGst9GmS1BU4DtgOnANMkvQD4OPAXkm7IuIfD7lyMzNrszShXw/0l1QFvApMBi4v6LMQuBJ4BpgELI2IAD6zr4OkbwM7HfhmZqXTauhHRLOk6cAioAy4KyLWSZoJrIiIhcAc4B5JDeTO8Cd/mEWbmVn7pDnTJyLqgLqCtuvzHu8CLmllH99uR31mZtaB/I1cM7MMceibmWWIQ9/MLEMc+mZmGeLQNzPLEIe+mVmGOPTNzDLEoW9mliEOfTOzDHHom5lliEPfzCxDHPpmZhmSasI1O3JVzni41CUA0HjzBaUuwczwmb6ZWaY49M3MMsShb2aWIalCX9IESRskNUiaUWR9D0kLkvXLJVUm7Z+TtFLS2uTPcR1bvpmZtUWroS+pDJgFnA8MAGolDSjo9mXgzYjoB9wG3JK0vwF8PiKqyd1D956OKtzMzNouzZn+SKAhIjZGxB5gPjCxoM9EYF7y+EFgvCRFxPMR8VrSvg7oKalHRxRuZmZtlyb0+wCb85abkraifSKiGdgBlBf0uRh4PiJ2Fz6BpGmSVkhasXXr1rS1m5lZG6UJfRVpi7b0kTSQ3JDP1cWeICJmR0RNRNRUVFSkKMnMzNojTeg3AafmLfcFXmupj6SuwHHA9mS5L/AQMCUiXj7Ugs3MrP3ShH490F9SlaTuwGRgYUGfheQ+qAWYBCyNiJD0ceBh4G8i4umOKtrMzNqn1dBPxuinA4uA9cD9EbFO0kxJFybd5gDlkhqAvwb2XdY5HegH/B9Jq5KfEzv8VZiZWSqp5t6JiDqgrqDt+rzHu4BLimx3I3DjIdZoZmYdxN/INTPLEIe+mVmGOPTNzDLEoW9mliEOfTOzDHHom5llyEfydom+RaCZWXE+0zczyxCHvplZhjj0zcwyxKFvZpYhDn0zswxx6JuZZYhD38wsQxz6ZmYZ4tA3M8sQh76ZWYakCn1JEyRtkNQgaUaR9T0kLUjWL5dUmbfub5L2DZLO67jSzcysrVoNfUllwCzgfGAAUCtpQEG3LwNvRkQ/4DbglmTbAeRupD4QmAD832R/ZmZWAmnO9EcCDRGxMSL2APOBiQV9JgLzkscPAuMlKWmfHxG7I+K3QEOyPzMzK4E0s2z2ATbnLTcB57TUJyKaJe0AypP2XxVs26fwCSRNA6YlizslbUhV/YerN/DGoexAt3RQJaXnY3GAj8UBPhYHdIZjcXqaTmlCX0XaImWfNNsSEbOB2SlqOWwkrYiImlLX0Rn4WBzgY3GAj8UBR9KxSDO80wScmrfcF3itpT6SugLHAdtTbmtmZodJmtCvB/pLqpLUndwHswsL+iwErkweTwKWRkQk7ZOTq3uqgP7Asx1TupmZtVWrwzvJGP10YBFQBtwVEeskzQRWRMRCYA5wj6QGcmf4k5Nt10m6H/gN0Az8ZUS8/yG9lo7WqYabSszH4gAfiwN8LA44Yo6FcifkZmaWBf5GrplZhjj0zcwyxKFfQNJdkl6X9OtS11JKknpKelbSaknrJH2n1DUdbsX+LUg6QdJiSS8lfx5fyhoPB0mnSlomaX3yb+FrSXsWj0XR34vkQpflybFYkFz00ik59A82l9yUEVm3GxgXEUOAocAESaNKXNPhNpeD/y3MAB6NiP7Ao8nyR10z8I2IOAsYBfxlMsVKFo9FS78XtwC3JcfiTXJT03RKDv0CEfEEuSuQMi1ydiaL3ZKfTH3q38K/hfwpR+YBXzisRZVARGyJiOeSx28D68l9sz6Lx6Kl34tx5KaggU5+LBz61iJJZZJWAa8DiyNiealr6gROiogtkAtD4MQS13NYJTPoDgOWk9FjUfh7AbwMvBURzUmXotPNdBYOfWtRRLwfEUPJfZN6pKRBpa7JSkfSx4CfAv8zIn5X6npKpfD3AjirWLfDW1V6Dn1rVUS8BTyGP+sA+C9JJwMkf75e4noOC0ndyAX+fRHxs6Q5k8din7zfi1HAx5MpaKCTTzfj0LeiJFVI+njyuBfwZ8ALpa2qU8ifcuRK4F9LWMthkUyTPgdYHxF/n7cqi8ei2O/FemAZuSlooJMfC38jt4CkfwHGkpsq9b+AGyJiTkmLKgFJg8l9IFVG7uTg/oiYWdqqDq9i/xaAnwP3A6cBm4BLIuIj/cG/pE8DTwJrgb1J8/8mN66ftWNR9PdC0h+Tu9fICcDzwH+PiN2lq7RlDn0zswzx8I6ZWYY49M3MMsShb2aWIQ59M7MMceibmWWIQ98yS9LHJV1T6jrMDieHvmXZxwGHvmVKq/fINfsIuxn4k2TyrMVJ2/nk5k25MSIWSBoLzAS2AWcCTwDXRMTe/B1JmgpcCBwF/AnwUER863C8CLO28Jm+ZdkM4OVk8qxfkZsffQi5r9b/cN+8MuQm1foGUE0u0L/Ywv6GApcl/S6TdOqHWLtZuzj0zXI+DfxLMoPifwGPAyOSdc9GxMaIeB/4l6RvMY9GxI6I2AX8Bjj9Q6/arI0c+mY5+oB1hXOVhKSLJK1KfmqS9vy5Vt7Hw6fWCTn0LcveBo5JHj9BbkimTFIFMBp4Nlk3MrkHahdywzdPRcRDETE0+Vlx+Es3ax+HvmVWRGwDnk5ufP4JYA2wGlgKfCsi/jPp+gy5D31/DfwWeKgE5Zp1CM+yafYBkqt3vhkR/63UtZh1BJ/pm5lliM/0zcwyxGf6ZmYZ4tA3M8sQh76ZWYY49M3MMsShb2aWIf8fIYiBY2XY2ToAAAAASUVORK5CYII=\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": 47,
   "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": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}