HybridSVD_YaMus.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": [],
   "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_config,\n",
    "                                         set_config)\n",
    "\n",
    "from hybrids import HybridSVD, HybridSVDColdStart\n",
    "from data_preprocessing import (get_yahoo_music_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 = 15 # allowed memory usage during recommendations generationa\n",
    "max_test_workers = 6 # use this manyparallel thread for evaluation each using up to {memory_hard_limit} Gb of RAM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "seed = 42"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "experiment_name = 'hybridsvd'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Experiment setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_labels = ['YaMus']"
   ]
  },
  {
   "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 = {'YaMus': ranks_grid}\n",
    "\n",
    "hybridsvd_weights = [0.1, 0.3, 0.5, 0.7, 0.9]"
   ]
  },
  {
   "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": [
    "## Yahoo Music"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "lbl = 'YaMus'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_dict[lbl], meta_dict[lbl] = get_yahoo_music_data('../datasets/yahoo_music/yamus_train0_rating5.gz',\n",
    "                                                      meta_path='../datasets/yahoo_music/yamus_attrs.gz',\n",
    "                                                      implicit=True,\n",
    "                                                      pcore=5,\n",
    "                                                      filter_data={'genreid': [0]}, # filter unknown genre\n",
    "                                                      filter_no_meta=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>albumid</th>\n",
       "      <th>artistid</th>\n",
       "      <th>genreid</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>songid</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>[12070]</td>\n",
       "      <td>[8490]</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>[19512]</td>\n",
       "      <td>[7975]</td>\n",
       "      <td>[134]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>[18953]</td>\n",
       "      <td>[3492]</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>[695]</td>\n",
       "      <td>[2653]</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>[243]</td>\n",
       "      <td>[2282]</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        albumid artistid genreid\n",
       "songid                          \n",
       "0       [12070]   [8490]      []\n",
       "1       [19512]   [7975]   [134]\n",
       "2       [18953]   [3492]      []\n",
       "3         [695]   [2653]      []\n",
       "4         [243]   [2282]      []"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "meta_dict[lbl].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "similarities[lbl], sim_indices[lbl], feature_idx[lbl] = get_similarity_data(meta_dict[lbl])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 15,
     "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": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "YaMus\n",
      "{'userid': 183003, 'songid': 134059}\n",
      "density 0.09740952587383789\n",
      "similarity matrix density 0.4576464914574314\n"
     ]
    }
   ],
   "source": [
    "print_data_stats(data_labels, all_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Standard experiment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def prepare_recommender_models(data_label, data_models, config):\n",
    "    data_model = data_models[data_label]\n",
    "    svd = HybridSVD(data_model)\n",
    "    svd.max_test_workers = max_test_workers\n",
    "    models = [svd]\n",
    "    apply_config(models, config, data_label)\n",
    "    return models\n",
    "\n",
    "\n",
    "def fine_tune_hybridsvd(model, ranks, weights, label, record_time=False):\n",
    "    param_grid = [(w, r) for w in weights for r in reversed(list(sorted(ranks)))]\n",
    "    param_names = ('features_weight', 'rank')\n",
    "    model.max_test_workers = max_test_workers\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": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "config = {}\n",
    "scores = {}\n",
    "data_models = {}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<span class=\"Progress-label\"><strong>100%</strong></span>\n",
       "<span class=\"Iteration-label\">1/1</span>\n",
       "<span class=\"Time-label\">[20:14:37<20:14:37, 72876.77s/it]</span></div>"
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       "\u001b[A\u001b[A\u001b[2K\r",
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     "metadata": {},
     "output_type": "display_data"
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     "data": {
      "text/html": [
       "<div><span class=\"Text-label\" style=\"display:inline-block; overflow:hidden; white-space:nowrap; text-overflow:ellipsis; min-width:15ex; max-width:15ex; vertical-align:middle; text-align:right\">YaMus</span>\n",
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       "<span class=\"Progress-label\"><strong>100%</strong></span>\n",
       "<span class=\"Iteration-label\">130/130</span>\n",
       "<span class=\"Time-label\">[20:14:07<00:51, 560.36s/it]</span></div>"
      ],
      "text/plain": [
       "\u001b[A\u001b[2K\r",
       "          YaMus [█████████████████████████████████████████████] 130/130 [20:14:07<00:51, 560.36s/it]"
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     "metadata": {},
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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_hybridsvd(HybridSVD(data_models[label]),\n",
    "                                                       svd_ranks[label],\n",
    "                                                       hybridsvd_weights,\n",
    "                                                       label, record_time=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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"
    }
   ],
   "source": [
    "report_results('rank', scores);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'YaMus': {'HybridSVD': {'features_weight': 0.7, 'rank': 3000}}}"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "config"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### saving data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "save_results(experiment_name, config=config, tuning=scores)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## cross-validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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=\"1\" value=\"1\" class=\"Progress-main\"/></progress>\n",
       "<span class=\"Progress-label\"><strong>100%</strong></span>\n",
       "<span class=\"Iteration-label\">1/1</span>\n",
       "<span class=\"Time-label\">[16:06:29<16:06:29, 57989.24s/it]</span></div>"
      ],
      "text/plain": [
       "\u001b[A\u001b[A\u001b[2K\r",
       " [████████████████████████████████████████████████████████████] 1/1 [16:06:29<16:06:29, 57989.24s/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\">YaMus</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\">[16:06:29<04:18:04, 11597.84s/it]</span></div>"
      ],
      "text/plain": [
       "\u001b[A\u001b[2K\r",
       "          YaMus [█████████████████████████████████████████████] 5/5 [16:06:29<04:18:04, 11597.84s/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=label))\n",
    "    save_cv_training_time(experiment_name, models, label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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"
    }
   ],
   "source": [
    "report_results('topn', result, target_metric);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### saving data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "save_results(experiment_name, cv=result)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Cold start"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "import gc\n",
    "gc.collect()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "def prepare_cold_start_recommender_models(data_label, data_models, config):\n",
    "    data_model = data_models[data_label]\n",
    "    svd = HybridSVDColdStart(data_model, item_features=meta_dict[data_label])\n",
    "    svd.max_test_workers = max_test_workers\n",
    "    models = [svd]\n",
    "    apply_config(models, config, data_label)\n",
    "    return models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "config_cold = {}\n",
    "scores_cold = {}\n",
    "data_models_cold = {}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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=\"1\" value=\"1\" class=\"Progress-main\"/></progress>\n",
       "<span class=\"Progress-label\"><strong>100%</strong></span>\n",
       "<span class=\"Iteration-label\">1/1</span>\n",
       "<span class=\"Time-label\">[23:58:16<23:58:16, 86295.77s/it]</span></div>"
      ],
      "text/plain": [
       "\u001b[A\u001b[A\u001b[2K\r",
       " [████████████████████████████████████████████████████████████] 1/1 [23:58:16<23:58:16, 86295.77s/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\">YaMus</span>\n",
       "<progress style=\"width:45ex\" max=\"130\" value=\"130\" class=\"Progress-main\"/></progress>\n",
       "<span class=\"Progress-label\"><strong>100%</strong></span>\n",
       "<span class=\"Iteration-label\">130/130</span>\n",
       "<span class=\"Time-label\">[23:57:44<01:32, 663.57s/it]</span></div>"
      ],
      "text/plain": [
       "\u001b[A\u001b[2K\r",
       "          YaMus [█████████████████████████████████████████████] 130/130 [23:57:44<01:32, 663.57s/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 = HybridSVDColdStart(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_hybridsvd(model,\n",
    "                                                                 svd_ranks[label],\n",
    "                                                                 hybridsvd_weights,\n",
    "                                                                 label)\n",
    "    del model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "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"
    }
   ],
   "source": [
    "report_results('rank', scores_cold);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'YaMus': {'HybridSVD(cs)': {'features_weight': 0.9, 'rank': 2500}}}"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "config_cold"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### saving data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "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": 37,
   "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=\"1\" value=\"1\" class=\"Progress-main\"/></progress>\n",
       "<span class=\"Progress-label\"><strong>100%</strong></span>\n",
       "<span class=\"Iteration-label\">1/1</span>\n",
       "<span class=\"Time-label\">[13:07:28<13:07:28, 47248.16s/it]</span></div>"
      ],
      "text/plain": [
       "\u001b[A\u001b[A\u001b[2K\r",
       " [████████████████████████████████████████████████████████████] 1/1 [13:07:28<13:07:28, 47248.16s/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\">YaMus 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\">[13:07:28<02:20:41, 9449.63s/it]</span></div>"
      ],
      "text/plain": [
       "\u001b[A\u001b[2K\r",
       "    YaMus folds [█████████████████████████████████████████████] 5/5 [13:07:28<02:20:41, 9449.63s/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": 38,
   "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"
    }
   ],
   "source": [
    "report_results('topn', result_cold, target_metric);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "report_results('topn', result_cold, 'coverage');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### saving data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "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
}