View_all_results.ipynb

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   "cell_type": "markdown",
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   "source": [
    "<h1>Table of Contents<span class=\"tocSkip\"></span></h1>\n",
    "<div class=\"toc\"><ul class=\"toc-item\"><li><span><a href=\"#Load-data\" data-toc-modified-id=\"Load-data-1\"><span class=\"toc-item-num\">1&nbsp;&nbsp;</span>Load data</a></span></li><li><span><a href=\"#Standard-settings\" data-toc-modified-id=\"Standard-settings-2\"><span class=\"toc-item-num\">2&nbsp;&nbsp;</span>Standard settings</a></span><ul class=\"toc-item\"><li><span><a href=\"#Movielens---HR\" data-toc-modified-id=\"Movielens---HR-2.1\"><span class=\"toc-item-num\">2.1&nbsp;&nbsp;</span>Movielens - HR</a></span></li><li><span><a href=\"#Movielens---MRR\" data-toc-modified-id=\"Movielens---MRR-2.2\"><span class=\"toc-item-num\">2.2&nbsp;&nbsp;</span>Movielens - MRR</a></span></li><li><span><a href=\"#BookCrossing---HR\" data-toc-modified-id=\"BookCrossing---HR-2.3\"><span class=\"toc-item-num\">2.3&nbsp;&nbsp;</span>BookCrossing - HR</a></span></li><li><span><a href=\"#BookCrossing---MRR\" data-toc-modified-id=\"BookCrossing---MRR-2.4\"><span class=\"toc-item-num\">2.4&nbsp;&nbsp;</span>BookCrossing - MRR</a></span></li></ul></li><li><span><a href=\"#Cold-start-settings\" data-toc-modified-id=\"Cold-start-settings-3\"><span class=\"toc-item-num\">3&nbsp;&nbsp;</span>Cold-start settings</a></span><ul class=\"toc-item\"><li><span><a href=\"#Movielens---HR\" data-toc-modified-id=\"Movielens---HR-3.1\"><span class=\"toc-item-num\">3.1&nbsp;&nbsp;</span>Movielens - HR</a></span></li><li><span><a href=\"#Movielens---MRR\" data-toc-modified-id=\"Movielens---MRR-3.2\"><span class=\"toc-item-num\">3.2&nbsp;&nbsp;</span>Movielens - MRR</a></span></li><li><span><a href=\"#BookCrossing---HR\" data-toc-modified-id=\"BookCrossing---HR-3.3\"><span class=\"toc-item-num\">3.3&nbsp;&nbsp;</span>BookCrossing - HR</a></span></li><li><span><a href=\"#BookCrossing---MRR\" data-toc-modified-id=\"BookCrossing---MRR-3.4\"><span class=\"toc-item-num\">3.4&nbsp;&nbsp;</span>BookCrossing - MRR</a></span></li></ul></li><li><span><a href=\"#Combined\" data-toc-modified-id=\"Combined-4\"><span class=\"toc-item-num\">4&nbsp;&nbsp;</span>Combined</a></span></li></ul></div>"
   ]
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  {
   "cell_type": "code",
   "execution_count": 1,
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   "source": [
    "# imports\n",
    "from collections import defaultdict\n",
    "from collections import OrderedDict\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.patches as mpatches\n",
    "import matplotlib.lines as mlines\n",
    "\n",
    "from polara.evaluation.evaluation_engine import sample_ci\n",
    "\n",
    "from utils import read_cv_data\n",
    "\n",
    "%matplotlib inline\n",
    "%config InlineBackend.figure_format = 'retina'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "lgndsize = 'x-large'\n",
    "fontsize = 'xx-large'\n",
    "params = {'text.usetex': True,\n",
    "          'text.latex.preamble': [ # to conform with acmart style\n",
    "              r'\\usepackage[T1]{fontenc}',\n",
    "              r'\\usepackage[libertine]{newtxmath}',\n",
    "              r'\\usepackage{libertine}'\n",
    "          ],\n",
    "          'font.weight': 'regular',\n",
    "          'legend.fontsize': lgndsize,         \n",
    "          'axes.labelsize': fontsize,\n",
    "          'axes.titlesize': fontsize,\n",
    "          'xtick.labelsize': fontsize,\n",
    "          'ytick.labelsize': fontsize,\n",
    "         }\n",
    "\n",
    "plt.style.use(['seaborn-paper']) # see plt.style.available\n",
    "plt.rcParams.update(params)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_labels = ['ML1M', 'ML10M', 'BX', 'AMZe', 'AMZvg', 'YaMus']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## standard"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "baselines = {lbl: read_cv_data('baseline', lbl) for lbl in data_labels}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaledsvd = {lbl: read_cv_data('scaledsvd', lbl) for lbl in data_labels}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "fm = {lbl: read_cv_data('fm', lbl) for lbl in data_labels if lbl!='YaMus'} # were unable to get anything meaningful for Yahoo data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "mf = {lbl: read_cv_data('sgd', lbl) for lbl in data_labels}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "lce = {lbl: read_cv_data('lce', lbl) for lbl in data_labels}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "hybridsvd = {lbl: read_cv_data('hybridsvd', lbl) for lbl in data_labels}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaledhybridsvd = {lbl: read_cv_data('scaledhybridsvd', lbl) for lbl in data_labels}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_standard = [baselines, scaledsvd, mf, fm, lce, hybridsvd, scaledhybridsvd]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## cold-start"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "baselines_cs = {lbl: read_cv_data('baseline_coldstart', lbl) for lbl in data_labels}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaledsvd_cs = {lbl: read_cv_data('scaledsvd_coldstart', lbl) for lbl in data_labels}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "fm_cs = {lbl: read_cv_data('fm_coldstart', lbl) for lbl in data_labels if lbl!='YaMus'}  # were unable to get anything meaningful for Yahoo data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "lce_cs = {lbl: read_cv_data('lce_coldstart', lbl) for lbl in data_labels}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "hybridsvd_cs = {lbl: read_cv_data('hybridsvd_coldstart', lbl) for lbl in data_labels}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaledhybridsvd_cs = {lbl: read_cv_data('scaledhybridsvd_coldstart', lbl) for lbl in data_labels}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_coldstart = [baselines_cs, scaledsvd_cs, fm_cs, lce_cs, hybridsvd_cs, scaledhybridsvd_cs]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Postprocess"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "def postprocess(data):\n",
    "    for models in data:\n",
    "        for label, res in models.items():\n",
    "            # filter out n=20 from top-n results for brevity\n",
    "            res.drop(20, level='top-n', axis=0, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "postprocess(all_standard + all_coldstart)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Vizualization settings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_properties(main_methods, compare_methods, baselines):\n",
    "    prop_cycle = plt.rcParams['axes.prop_cycle']\n",
    "    assert len(main_methods) + len(compare_methods) + len(baselines) <= len(prop_cycle)\n",
    "    types = ['main', 'compare', 'baseline']\n",
    "    lines = dict(zip(types, ['-', '-.', ':']))\n",
    "    \n",
    "    method_viz_properties = {}\n",
    "    properties = iter(prop_cycle)\n",
    "    \n",
    "    for methods, typ in zip([main_methods, compare_methods, baselines], types):\n",
    "        for method in methods:\n",
    "            method_viz_properties[method] = next(properties)\n",
    "            method_viz_properties[method]['linestyle'] = lines[typ]\n",
    "    return method_viz_properties"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "main_methods = [r'HybridSVD', r'HybridSVDs']\n",
    "compare_methods = [ u'PureSVD', u'FM', u'PureSVDs', 'LCE']\n",
    "base_methods = [u'RND', u'MP', u'SIM', u'SGD']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "line_styles = get_properties(main_methods, compare_methods, base_methods)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "alpha = 0.1\n",
    "bars_width = 0.95\n",
    "\n",
    "def plot_rank_results(methods, baselines=None, metric='HR', ax=None, xlim=None, ylim=None, logx=False, err_alpha=None, styles={}):\n",
    "    draw_legend = ax is None\n",
    "    \n",
    "    if isinstance(methods, pd.DataFrame):\n",
    "        methods = [methods]\n",
    "        \n",
    "    for data in methods:\n",
    "        data_sample = data.xs(metric, level=1, axis=1)\n",
    "        data_mean = data_sample.mean(level=1, axis=0)\n",
    "        data_yerr = sample_ci(data_sample, level=1)\n",
    "        for label, result in data_mean.iteritems():\n",
    "            props = styles.get(label, {})\n",
    "            ax = result.plot(label=label, ax=ax, xlim=xlim, ylim=ylim, logx=logx, **props)\n",
    "            if err_alpha: # plot CI region\n",
    "                x = result.index\n",
    "                y = result\n",
    "                err = data_yerr.loc[x, label]\n",
    "                ax.fill_between(x, y-err, y+err, alpha=err_alpha)\n",
    "            \n",
    "    \n",
    "    if baselines is not None:\n",
    "        baseline_sample = baselines.xs(metric, level=1, axis=1)\n",
    "        baseline_mean = baseline_sample.mean(axis=0)\n",
    "        baseline_yerr = sample_ci(baseline_sample)\n",
    "        for label, result in baseline_mean.iteritems():\n",
    "            props = styles.get(label, {})\n",
    "            ax.axhline(y=result, label=label, **props)\n",
    "            if err_alpha: # plot CI region\n",
    "                err = baseline_yerr[label]\n",
    "                ax.axhspan(result-err, result+err, alpha=err_alpha, **props)\n",
    "    \n",
    "    if draw_legend:\n",
    "        ax.legend(bbox_to_anchor=(1.05, 1), loc=2,)\n",
    "    return ax\n",
    "\n",
    "    \n",
    "def plot_topn_results(methods, metric='HR', ax=None, error_bars=True, styles=None, **kwds):\n",
    "    draw_legend = ax is None\n",
    "    styles = styles or line_styles\n",
    "    \n",
    "    if not isinstance(methods, (list, tuple)):\n",
    "        methods = [methods]\n",
    "    \n",
    "    scores = []\n",
    "    errors = []\n",
    "    for data in methods:\n",
    "        data_sample = (data.xs(metric.lower(), level='metric', axis='columns')\n",
    "                       .squeeze().unstack('model')\n",
    "                       .rename(columns=lambda x: x.split('(cs)')[0]))\n",
    "        data_mean = data_sample.mean(level='top-n', axis='index')\n",
    "        scores.append(data_mean)\n",
    "        \n",
    "        if error_bars: # plot CI region\n",
    "            data_yerr = sample_ci(data_sample, level='top-n')\n",
    "            errors.append(data_yerr)\n",
    "        else:\n",
    "            data_yerr = None\n",
    "        \n",
    "    all_scores = pd.concat(scores, axis=1)\n",
    "    all_scores = all_scores.sort_values(10, axis=1) # sort based on top-10 result\n",
    "    all_errors = pd.concat(errors, axis=1) if errors else None\n",
    "    colors = [styles[col]['color'] for col in all_scores.columns]\n",
    "    \n",
    "    ax = all_scores.plot.bar(ax=ax, yerr=all_errors, color=colors, legend=draw_legend, rot=0, width=bars_width, **kwds)\n",
    "    if draw_legend:\n",
    "        ax.legend(bbox_to_anchor=(1.05, 1), loc=2)\n",
    "    return ax"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# \"Bird eye\" view"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1807dee4978>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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      "text/plain": [
       "<Figure size 1152x2880 with 36 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 2905,
       "width": 1155
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "metrics = ['MRR', 'HR', 'COVERAGE']\n",
    "fig, ax = plt.subplots(2*len(data_labels), len(metrics), figsize=(16, 40))\n",
    "\n",
    "for i, lbl in enumerate(data_labels):\n",
    "    for j, metric in enumerate(metrics):\n",
    "        plot_topn_results([model[lbl] for model in all_standard if lbl in model],\n",
    "                          metric, title=f'{metric}', ax=ax[i, j])\n",
    "        plot_topn_results([model[lbl] for model in all_coldstart if lbl in model],\n",
    "                          metric, title=f'{metric} (cold start)', ax=ax[i+6, j])\n",
    "\n",
    "for i, lbl in enumerate(data_labels):\n",
    "    ax[i, 0].set_ylabel(lbl)\n",
    "    ax[i+6, 0].set_ylabel(lbl)\n",
    "        \n",
    "plt.tight_layout()\n",
    "ax[0, 0].legend(bbox_to_anchor=(3.41, 1.35), loc='upper right', ncol=10)\n",
    "# plt.savefig(\"all_experiments.pdf\", bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Paper figures"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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E1dLd3X28UsYeN+CYiIxvlNeKfskDDzzQkb1H7e3tl/T09Cxau3btR/RhaAZjIQXGQk5xzRkZQTxMG1/4whcuyB7fc889B2ptv379+qFssGvnzp1zq138m5nmbdWqVe9kj7NUZjBS+U73Lbfc0rVixYquZs+g2bt37+zFixcfyUf2Pvjgg+dX2n7Tpk3zu7u7j65cuXI4t7SdWT00RavPx/zn66qrrqqYGhtGq55zrNH2yNDQ0Ixrr732ooGBgVkREVdfffXh/GvLli37+F133XXBOeecc+I73/nO61/96lcH9+7dO3vVqlUXletQNvpdtH79+rciCjcdKw2kZTNK6mnb1UtbjGY7dOhQ2YGcUu+++27F7bLUxsPDwzMqfY527949a3BwcGa5tcwz3//+99svvfTS7gsvvPBY/nN8yy23dN10000XlNsnU+0akU//XG7wZ9u2bXO7uro+8fjjj8/7oz/6o4PPPvvsa8Vjz8rKK8d1BEav1e1jaMTu3btnZRPxTJJgMmnkptuOHTs6IiI6OzuPdXd3Hx+L+tUiGJnxYHxjYvRLVqxY0XXXXXdd8Id/+IeHd+3a9bMHH3zwzcOHD8945JFHzl+3bl3nB0uDkTEWYiwk/5przsgJ4mFKGxoamrFt27a5PT09i/r6+trb2tpOPPXUU69V6giVRno+99xz+7JU0nfdddcF49FxWbhw4cm69ff3z662LVTS3d19/KmnnnotG3zduXPn3Msvv3xRT0/PokYy82SyzFYf+9jHjuUjewcHB2eWaxQMDQ3N2LFjR0fWeFiwYMGxbPtG6wIT4XzcuHFjZ0TE0qVLD9eT8hpGqto5lrVfKmUGzNLSZt8J5V6LiPjc5z53wc033/z2rl27frZly5Y38+vU//t//+8v6uvra1+9evXQc889t2/lypXD69evH/rOd77zekShQ1ladqPfRWvWrDmY7bt169ayM0cef/zxeUuXLj3czEFubTGabfPmzZ3ZzKxqP319fRVnbOdTG3/rW9+q9HmouJZ53rPPPvvaww8//Mvsc7xv376fRhRuHJVOXKj3GlHN008/3XbLLbd0DQ8Pz9i3b99P169fP7R8+fKj69evH/rxj3/8s3LXpozrCBTcddddF5S7bixYsODicttPhPYxNOL//t//e/ImRVdX13vNLHuknycYidHedMtfm2tlkB9rgpEZa8Y3Wt8v2b1796ydO3fOXbp06eGHH374l8uXLz+6Zs2ag/v379/b3d199IorrjBBkYYZCzEWkj12zRkdQTxMOfnO+Lx58y7JGl0bN24c3Ldv309Xrlw5oo7Q888//1r2+Oqrr76o3DbNXLNx7ty5J6PMq6VTg1pWrlw5vG/fvp+uXr36ZPTxwMDArGwQoVJ6v3r8zd/8zeyIQmq+iIjNmzefHKS4//77PzC784477ujs7Ow8lnXMPvrRjx7LXmukHhAxvufjnj17Zg8MDJw1NDQ0Y2Bg4Kynn366raenZ9HAwMCszs7OY5XWtod6jeYcy2aoVZqpls0KKfd69trAwMCsuXPnnsg6UuvXrz+ZSnXbtm1zs9kh27dvfzO/f362frmshY1+F2Ud8J07d84t3Xbbtm1zh4eHZ2zYsOED6zY3QluMZlu9evXQrl27flbrJ7vJXk5HR8eJLEvoM888U3bAJnv+S1/6UsWBq8suu+xw6YBTR0fHidtvv/2tiIg/+7M/O232Uz3XiIjqM2W/9KUvXRBR6I+Vzkzr6Og4cdlll1UdrHEdgZNZGY52dnYe6+zsPNbW1nai2qCv/hqTXb0zt0djpJ8nGInR3nT7h3/4h5PthWYHro2UYGSaxfjG6SZSvyQfLFvq1Vdf/VmlbCYwEsZCjIVkj11zRkcQD1PO7bff/tarr77601dfffWn2cU9ImLTpk2/qrWu3+HDhz9w0evu7j6+ZcuWNyMKs9BWrFjxgUjsZrrooouO1d4K6tPR0XFi+/btb7799ts/yRokEYUv7s9+9rOjPpd/+MMfzsrKjyg0KLKsVX19fe35WUbZrM4777zz5PEvvPDCk+f53/3d37mxQUPG83zcsWNHR09PzyfmzZt3SU9PzydWrVp10eHDh2ds3LhxcP/+/XubsX4s09tozrFawcT1pqF+6KGHBss9/+1vf7sjojBLrlr5L730UtmZM418F+U74H/5l395Wmf9gQceOL+zs/PYSAO0a9EWo9kWL158ZPny5Udr/eRvmpeTLYlTLh1ytpb5Nddcc3A0s6iydNDVUlRXukZERJx33nn/EvHBGbFZWuuIiGuuuWbUn1XXEaa7O++8861XX331Z/v379+7f//+vYcPH/7J4cOHf7J///695bbXX2Mq2bdv39nNLG+knycYqdHcdBtt4NrQ0NCMZgdbCkamWYxvnG4i9Us+9rGPnWwX3nPPPb8raJuxYCzEWEjGNWd0BPEw5SxcuPBYcf3e43/913/9ZkSho1Querpe69evH8o6Xjt37pzbSFm1vPPOO2dmj2t9eUG9Ojo6Tjz88MO/fPXVV3+aH7y95557fnc05b3yyiuzSiOk84O+mzdvPjm78ytf+cr5peuRLly48OS5XS0KF+oxnufjli1b3kzTtD8LFn377bd/sn///r2bNm36VXP+Gqa7Rs6xWoNd5dJRZwNUbW1tJyoFoe3du3d2ROF7o1za26xTWm0N64jRfRd1d3cfzwbXtm3bdjL96+7du2cNDAzMyn/Wm0VbjIkqvyROaTrkbC3zbHBrpPIDLfnvwnquEXmlg+o7d+48OcD2B3/wBw1/nlxHoD76a0x2c+bMOfmd88orrzgHmXQauek2ksC1z33ucxfMmzfvkiRJltSzvEY9BCPTLMY3TjeR+iUrV64czuqyefPmznnz5l2yYsWKrmZdR6CZjIW45kxXgniYcvIzFzo6Ok5s3LhxMKKwzNbAwMBZ1fZ99913z6z02vbt29/MLo6lZWWp1ZohH4GYn/0GzdDd3X18x44dJ1OVPvXUU+eOppy9e/fOLr0hkR/0zVL0DQ0NzXjkkUfOv+OOOw7kt80ibyMiXn/9dbN6aEgrzscsWFTmHcbKSM6xWu2QbFZINQsWLKjY5hgeHp4RUUiDmw2+lfupdzm5kX4XffGLXxyKKARlZ+2vb33rW/NKbzg2i7YYzdbMJUHWrFlTNh3y448/Pq+zs/NYvWuzl/rwhz9c9TpS7RoRcfrN1rxf//rXJ/tXzfzOdB2B6vTXmOyuvfbakxkSsmVPYDKq96ZbfgmrN954o+r4dV6+L5jPoNMIwcg0m/GNUyZSv+QHP/jBzzdu3DiYX/7vlltu6erp6VkkSwbNYCykwFhIgWvOyAniYcrbtGnTr7JO0he+8IULqm2bNeIqef7551/LHl999dUXRVQP/BmN559//mSqxptvvvlgtW1hNJYvX340+6Lcv39/1QHZcg2toaGhGcPDwzOWLFlypPS1/Oyir3zlK+dnszpLZ1jkGy5vvPGGQWFGbbzPx2Z2PqCciXiO5VPCZoNv5X5G0ikdyXfRmjVrDmbbbtq0aX627Ee2tnOzaYvRKtlsz3JL/GbygyxZOuRsLfNGZlHll8s577zzmjbAlM/m0exBGdcRppORtA/015gKuru7j2djeRERo80iXM5EbG8z9dW66fYf/+N/PBm4lmUKqUd+5n+zbhIKRqZZJuL11vjG6TZt2vSrw4cP/+Spp5567ZprrjkYUbjZXytjGDSTsZCRcc2ZPgTxMCXkL4L5CMfMN7/5zTcjCjMdai2FVS1VYnd39/HHHntsX0TE4ODgzGXLln282kVyNBfnZ555Zm5ERCPRo1Ar61QWQVxuHeFan6dsbdD8LKHM17/+9ZMNpkceeeT8Rx555PxyDYJ8CkHra9OI8TgfRYIz1sb6HHv55ZfbI6p3hqu9ln1njGRGaERj30Wlss/uM888M/cb3/hGR8Tpn/FKtMVolVrtqUZky/w++OCD50dEfPvb3+5odBZVvr75m0iZateIiFMD9KUTHPLZPP7pn/6pahkHDx78wOuuI0xno20f6K8xkTTyffjHf/zHJ8/Nhx56aP5I9i39/hjL72WoV7Wbbh0dHSeyZSaGh4dn1Lu8RD4rybnnntuUTDyCkWmE8Y3J2S9ZuXLl8HPPPbcvW9Wir6+vvdK2UI2xkAJjIa45zSCIhymh1kUwv97eXXfddUG1i0+tzDpr1qw5mM1e6+vrax8eHp5R6UJZq16l7rnnnt/N1l3Nz86AkRgaGppx6aWXdlc7z7N01Nddd90HOuO1ztsf/ehHsyIiPv7xj39gNk5HR8eJLII2U65BkJ/NIDU2jRiP8zH/mTDgy1ho9BzL2iHlbrKtXbv2I1lHqFwbp56Mgn/4h394OKLQ7qnWicx/7zT6XVTqS1/60tsRhQHtzZs3d15zzTUH65kZpy1Gq4z03Is49Xms9bm85557DkQUJhVs27Ztbl9fX3ujs6i+973vnRsRsXTp0sP5z1a9WUeza1dpZtOVK1cOZzersrXqS+3du3dW8Vin7es6wnQ32vaB/hoT1UgzWeczaw8PD89YsWJFzRm6Q0NDM3p6ehZt3779tACI/Oep2Rm1Ia+Rm24bNmw4eT1+4IEHzq/nePllPMotCSIYmfFmfGNy9Esq/S033XSTwD0aYiykwFiIa04zCOJhSsiv1Vvp4nr//ff/MntcLjXXSDo1Dz/88C+7u7trdmLy9aqVPnLt2rUf2bx5c2dExGOPPbZPJ4nR+p//83+2Dw8Pz/jUpz61qFxnZO3atR+JKHTGyw3Y1vo89ff3z44ozCAqd/yNGzeeLHP16tVDlRoE+dTYMp0wWuNxPtbzHQONaPQc+7f/9t8ejTh9feOIQufpkUceOT8/o7N032y2R7Xj5r8rrr766ouyGf6Z3bt3z+rp6Vn0la985eRAc6PfRaW6u7uPZ39HxOmf7Wq0xWiV0XyusxletZb4LaZ4PxoRccstt3RF1DeLKqIwSFT6PTc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      "text/plain": [
       "<Figure size 1152x432 with 6 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 462,
       "width": 1144
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "metrics = ['MRR', 'HR', 'COVERAGE']\n",
    "data_labels_std = ['AMZe', 'YaMus']\n",
    "fig, ax = plt.subplots(len(data_labels_std), len(metrics), figsize=(16, 6))\n",
    "\n",
    "for i, lbl in enumerate(data_labels_std):\n",
    "    for j, metric in enumerate(metrics):\n",
    "        plot_topn_results([model[lbl] for model in all_standard if lbl in model],\n",
    "                          metric, title=f'{metric}', ax=ax[i, j])\n",
    "\n",
    "for i, lbl in enumerate(data_labels_std):\n",
    "    ax[i, -1].set_ylabel(lbl, rotation=-90, labelpad=20)\n",
    "    ax[i, -1].yaxis.set_label_position(\"right\")\n",
    "    if i != 0:\n",
    "        for axi in ax[i, :]:\n",
    "            axi.set_title('')\n",
    "    \n",
    "    for k, axi in enumerate(ax[i, :]):\n",
    "        if i != len(data_labels_std)-1:\n",
    "            axi.set_xlabel('')\n",
    "        else:\n",
    "            axi.set_xlabel('top-$n$')\n",
    "\n",
    "plt.tight_layout()\n",
    "ax[0, 1].set_ylim(None, 0.1)\n",
    "ax[0, 0].legend(bbox_to_anchor=(3.32, 1.4), loc='upper right', ncol=10)\n",
    "# plt.savefig(\"std_ML10_AMZe.pdf\", bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1152x432 with 6 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 462,
       "width": 1144
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "metrics = ['MRR', 'HR', 'COVERAGE']\n",
    "data_labels_cls = ['BX', 'YaMus']\n",
    "fig, ax = plt.subplots(len(data_labels_cls), len(metrics), figsize=(16, 6))\n",
    "\n",
    "for i, lbl in enumerate(data_labels_cls):\n",
    "    for j, metric in enumerate(metrics):\n",
    "        plot_topn_results([model[lbl] for model in all_coldstart if lbl in model],\n",
    "                          metric, title=f'{metric} (cold start)', ax=ax[i, j])\n",
    "\n",
    "for i, lbl in enumerate(data_labels_cls):\n",
    "    ax[i, -1].set_ylabel(lbl, rotation=-90, labelpad=20)\n",
    "    ax[i, -1].yaxis.set_label_position(\"right\")\n",
    "    if i != 0:\n",
    "        for axi in ax[i, :]:\n",
    "            axi.set_title('')\n",
    "    \n",
    "    for axi in ax[i, :]:\n",
    "        if i != len(data_labels_std)-1:\n",
    "            axi.set_xlabel('')\n",
    "        else:\n",
    "            axi.set_xlabel('top-$n$')\n",
    "        \n",
    "plt.tight_layout()\n",
    "ax[0, 1].set_ylim(None, 0.2)\n",
    "ax[0, 0].legend(bbox_to_anchor=(3.1, 1.4), loc='upper right', ncol=10)\n",
    "# plt.savefig(\"cls_BX_YM.pdf\", bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Standard scenario"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "## Movielens-1M"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "lbl = 'ML1M'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "models = [model[lbl] for model in all_standard]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "target_metric = 'MRR'\n",
    "plot_topn_results(models, target_metric, title=f'{lbl}-{target_metric}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "target_metric = 'HR'\n",
    "plot_topn_results(models, target_metric, title=f'{lbl}-{target_metric}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "target_metric = 'COVERAGE'\n",
    "plot_topn_results(models, target_metric, title=f'{lbl}-{target_metric}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "## Movielens-10M"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "lbl = 'ML10M'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "models = [model[lbl] for model in all_standard]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "target_metric = 'MRR'\n",
    "plot_topn_results(models, target_metric, title=f'{lbl}-{target_metric}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "target_metric = 'HR'\n",
    "plot_topn_results(models, target_metric)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "target_metric = 'COVERAGE'\n",
    "plot_topn_results(models, target_metric, title=f'{lbl}-{target_metric}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "## BookCrossing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "lbl = 'BX'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "models = [model[lbl] for model in all_standard]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "target_metric = 'MRR'\n",
    "plot_topn_results(models, target_metric, title=f'{lbl}-{target_metric}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "target_metric = 'HR'\n",
    "plot_topn_results(models, target_metric, title=f'{lbl}-{target_metric}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "target_metric = 'COVERAGE'\n",
    "plot_topn_results(models, target_metric, title=f'{lbl}-{target_metric}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Amazon Electronics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "lbl = 'AMZe'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "models = [model[lbl] for model in all_standard]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "target_metric = 'MRR'\n",
    "plot_topn_results(models, target_metric, title=f'{lbl}-{target_metric}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "target_metric = 'HR'\n",
    "plot_topn_results(models, target_metric, title=f'{lbl}-{target_metric}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "target_metric = 'COVERAGE'\n",
    "plot_topn_results(models, target_metric, title=f'{lbl}-{target_metric}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Amazon Video Games"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "lbl = 'AMZvg'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "models = [model[lbl] for model in all_standard]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "target_metric = 'MRR'\n",
    "plot_topn_results(models, target_metric, title=f'{lbl}-{target_metric}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "target_metric = 'HR'\n",
    "plot_topn_results(models, target_metric, title=f'{lbl}-{target_metric}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "target_metric = 'COVERAGE'\n",
    "plot_topn_results(models, target_metric, title=f'{lbl}-{target_metric}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Yahoo Music"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "lbl = 'YaMus'"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "models = [model[lbl] for model in all_standard]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "target_metric = 'MRR'\n",
    "plot_topn_results(models, target_metric, title=f'{lbl}-{target_metric}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "target_metric = 'HR'\n",
    "plot_topn_results(models, target_metric, title=f'{lbl}-{target_metric}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "target_metric = 'COVERAGE'\n",
    "plot_topn_results(models, target_metric, title=f'{lbl}-{target_metric}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Cold-start scenario"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Movielens-1M"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "lbl = 'ML1M'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "models = [model[lbl] for model in all_coldstart]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "target_metric = 'MRR'\n",
    "plot_topn_results(models, target_metric, title=f'{lbl}-{target_metric}(cs)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "target_metric = 'HR'\n",
    "plot_topn_results(models, target_metric, title=f'{lbl}-{target_metric}(cs)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "target_metric = 'COVERAGE'\n",
    "plot_topn_results(models, target_metric, title=f'{lbl}-{target_metric}(cs)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Movielens-10M"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "lbl = 'ML10M'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "models = [model[lbl] for model in all_coldstart]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "target_metric = 'MRR'\n",
    "plot_topn_results(models, target_metric, title=f'{lbl}-{target_metric}(cs)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "target_metric = 'HR'\n",
    "plot_topn_results(models, target_metric, title=f'{lbl}-{target_metric}(cs)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "target_metric = 'COVERAGE'\n",
    "plot_topn_results(models, target_metric, title=f'{lbl}-{target_metric}(cs)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## BookCrossing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "lbl = 'BX'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "models = [model[lbl] for model in all_coldstart]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "target_metric = 'MRR'\n",
    "plot_topn_results(models, target_metric, title=f'{lbl}-{target_metric}(cs)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "target_metric = 'HR'\n",
    "plot_topn_results(models, target_metric, title=f'{lbl}-{target_metric}(cs)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "target_metric = 'COVERAGE'\n",
    "plot_topn_results(models, target_metric, title=f'{lbl}-{target_metric}(cs)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Amazon Electronics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "lbl = 'AMZe'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "models = [model[lbl] for model in all_coldstart if lbl in model]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "target_metric = 'MRR'\n",
    "plot_topn_results(models, target_metric, title=f'{lbl}-{target_metric}(cs)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "target_metric = 'HR'\n",
    "plot_topn_results(models, target_metric, title=f'{lbl}-{target_metric}(cs)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "target_metric = 'COVERAGE'\n",
    "plot_topn_results(models, target_metric, title=f'{lbl}-{target_metric}(cs)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Amazon Video Games"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "lbl = 'AMZvg'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "models = [model[lbl] for model in all_coldstart if lbl in model]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "target_metric = 'MRR'\n",
    "plot_topn_results(models, target_metric, title=f'{lbl}-{target_metric}(cs)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "target_metric = 'HR'\n",
    "plot_topn_results(models, target_metric, title=f'{lbl}-{target_metric}(cs)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "target_metric = 'COVERAGE'\n",
    "plot_topn_results(models, target_metric, title=f'{lbl}-{target_metric}(cs)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Yahoo Music"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "lbl = 'YaMus'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "models = [model[lbl] for model in all_coldstart if lbl in model]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "target_metric = 'MRR'\n",
    "plot_topn_results(models, target_metric, title=f'{lbl}-{target_metric}(cs)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "target_metric = 'HR'\n",
    "plot_topn_results(models, target_metric, title=f'{lbl}-{target_metric}(cs)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "target_metric = 'COVERAGE'\n",
    "plot_topn_results(models, target_metric, title=f'{lbl}-{target_metric}(cs)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Combined"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## standard"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig, ax = plt.subplots(1, 2, figsize=(15, 4))\n",
    "\n",
    "ax_std_mrr_ml = plot_rank_results([std_svd_ml, std_fm_ml], std_base_ml, ax=ax[0], metric='MRR', xlim=(0, 300), styles=line_styles)\n",
    "ax_std_mrr_bx = plot_rank_results([std_svd_bx, std_fm_bx], std_base_bx, ax=ax[1], metric='MRR', xlim=(0, 3000), ylim=(-0.0009, 0.0175), styles=line_styles)\n",
    "\n",
    "ax_std_mrr_ml.set_title('Movielens')\n",
    "ax_std_mrr_bx.set_title('BookCrossing')\n",
    "ax_std_mrr_ml.set_ylabel('MRR')\n",
    "\n",
    "\n",
    "ax_std_mrr_bx.legend(loc=1, bbox_to_anchor=(1.6, 0.95))\n",
    "\n",
    "# plt.tight_layout()\n",
    "# plt.savefig(\"std_rank_mrr.pdf\", bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig, ax = plt.subplots(1, 2, figsize=(15, 3.5))\n",
    "\n",
    "ax_std_topn_mrr_ml = plot_topn_results(std_topn_ml.drop(r'HybridSVD ($\\alpha=0.5$)', axis=1, level=0), metric='MRR', ax=ax[0], styles=line_styles, width=0.85, rot=0)\n",
    "ax_std_topn_mrr_bx = plot_topn_results(std_topn_bx, metric='MRR', ax=ax[1], styles=line_styles, width=0.85, rot=0)\n",
    "\n",
    "ax_std_topn_mrr_ml.set_title('Movielens')\n",
    "ax_std_topn_mrr_ml.set_ylabel('MRR')\n",
    "ax_std_topn_mrr_bx.set_title('BookCrossing')\n",
    "\n",
    "axh1, axl1 = ax_std_topn_mrr_ml.get_legend_handles_labels()\n",
    "axh2, axl2 = ax_std_topn_mrr_bx.get_legend_handles_labels()\n",
    "axl = r'HybridSVD ($\\alpha=0.5$)'\n",
    "axh = axh2[axl2.index(axl)]\n",
    "\n",
    "plt.legend(handles=axh1+[axh], labels=axl1+[axl], loc=1, bbox_to_anchor=(1.6, 0.95))\n",
    "# plt.savefig(\"std_topn_mrr.pdf\", bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig, (ax1, ax2) = plt.subplots(2, 2, figsize=(17, 8))\n",
    "\n",
    "ax_std_mrr_ml = plot_rank_results([std_svd_ml, std_fm_ml], std_base_ml, ax=ax1[0], metric='MRR', xlim=(1, 1000), logx=True, ylim=(None, 0.115), styles=line_styles)\n",
    "ax_std_mrr_bx = plot_rank_results([std_svd_bx, std_fm_bx], std_base_bx, ax=ax1[1], metric='MRR', xlim=(1, 3000), logx=True, ylim=(-0.0009, 0.0185), styles=line_styles)\n",
    "\n",
    "ax_std_mrr_ml.set_title('Movielens')\n",
    "ax_std_mrr_bx.set_title('BookCrossing')\n",
    "ax_std_mrr_ml.set_ylabel('MRR@10')\n",
    "\n",
    "\n",
    "ax_std_mrr_bx.legend(loc=2, bbox_to_anchor=(-0.85, 0.98))\n",
    "\n",
    "\n",
    "ax_std_topn_mrr_ml = plot_topn_results(std_topn_ml.drop(r'HybridSVD ($\\alpha=0.5$)', axis=1, level=0), metric='MRR', ax=ax2[0], styles=line_styles, width=0.85, rot=0, ylim=(0, None))\n",
    "ax_std_topn_mrr_bx = plot_topn_results(std_topn_bx, metric='MRR', ax=ax2[1], styles=line_styles, ylim=(0, None), width=0.85, rot=0)\n",
    "\n",
    "ax_std_topn_mrr_ml.set_ylabel('MRR@$n$')\n",
    "ax_std_topn_mrr_ml.set_xlabel('top-$n$')\n",
    "ax_std_topn_mrr_bx.set_xlabel('top-$n$')\n",
    "\n",
    "axh1, axl1 = ax_std_topn_mrr_ml.get_legend_handles_labels()\n",
    "axh2, axl2 = ax_std_topn_mrr_bx.get_legend_handles_labels()\n",
    "axl = r'HybridSVD ($\\alpha=0.5$)'\n",
    "axh = axh2[axl2.index(axl)]\n",
    "\n",
    "plt.legend(handles=reversed(axh1+[axh]), labels=reversed(axl1+[axl]), loc=2, bbox_to_anchor=(-0.85, 0.93))\n",
    "fig.subplots_adjust(wspace=0.9, hspace=0.28)\n",
    "\n",
    "# plt.savefig(\"std_mrr.pdf\", bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## cold-start"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig, ax = plt.subplots(1, 2, figsize=(15, 4))\n",
    "\n",
    "ax_cls_mrr_ml = plot_rank_results([cls_svd_ml, cls_fm_ml], cls_base_ml, metric='MRR', ax=ax[0], xlim=(1, 1000), logx=True, styles=line_styles)\n",
    "ax_cls_mrr_bx = plot_rank_results([cls_svd_bx, cls_fm_bx], cls_base_bx, metric='MRR', ax=ax[1], xlim=(1, 3000), logx=True, styles=line_styles)\n",
    "\n",
    "ax_cls_mrr_ml.set_title('Movielens')\n",
    "ax_cls_mrr_bx.set_title('BookCrossing')\n",
    "ax_cls_mrr_ml.set_ylabel('MRR')\n",
    "\n",
    "\n",
    "ax_cls_mrr_bx.legend(loc=1, bbox_to_anchor=(1.6, 0.95))\n",
    "# plt.tight_layout()\n",
    "# plt.savefig(\"cls_rank_mrr.pdf\", bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig, ax = plt.subplots(1, 2, figsize=(15, 3.5))\n",
    "\n",
    "ax_cls_topn_mrr_ml = plot_topn_results(cls_topn_ml.drop(r'HybridSVD ($\\alpha=0.5$)', axis=1, level=0), metric='MRR', ax=ax[0], styles=line_styles, width=0.85, rot=0)\n",
    "ax_cls_topn_mrr_bx = plot_topn_results(cls_topn_bx.drop(r'HybridSVD ($\\alpha=0.5$)', axis=1, level=0), metric='MRR', ax=ax[1], styles=line_styles, width=0.85, rot=0)\n",
    "\n",
    "ax_cls_topn_mrr_ml.set_title('Movielens')\n",
    "ax_cls_topn_mrr_ml.set_ylabel('MRR')\n",
    "ax_cls_topn_mrr_bx.set_title('BookCrossing')\n",
    "\n",
    "ax_cls_topn_mrr_bx.legend(bbox_to_anchor=(1.6, 0.95))\n",
    "# plt.savefig(\"cls_topn_mrr.pdf\", bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig, (ax1, ax2) = plt.subplots(2, 2, figsize=(17, 8))\n",
    "\n",
    "\n",
    "ax_cls_mrr_ml = plot_rank_results([cls_svd_ml, cls_fm_ml], cls_base_ml, metric='MRR', ax=ax1[0], xlim=(1, 1000), ylim=(-0.001, 0.033), logx=True, styles=line_styles)\n",
    "ax_cls_mrr_bx = plot_rank_results([cls_svd_bx, cls_fm_bx], cls_base_bx, metric='MRR', ax=ax1[1], xlim=(1, 3200), ylim=(-0.001, 0.047), logx=True, styles=line_styles)\n",
    "\n",
    "ax_cls_mrr_ml.set_title('Movielens')\n",
    "ax_cls_mrr_bx.set_title('BookCrossing')\n",
    "ax_cls_mrr_ml.set_ylabel('MRR@10')\n",
    "\n",
    "ax_cls_mrr_bx.legend(loc=2, bbox_to_anchor=(-0.85, 0.98))\n",
    "\n",
    "ax_cls_topn_mrr_ml = plot_topn_results(cls_topn_ml.drop(r'HybridSVD ($\\alpha=0.5$)', axis=1, level=0), metric='MRR', ax=ax2[0], styles=line_styles, ylim=(0, None), width=0.85, rot=0)\n",
    "ax_cls_topn_mrr_bx = plot_topn_results(cls_topn_bx.drop(r'HybridSVD ($\\alpha=0.5$)', axis=1, level=0), metric='MRR', ax=ax2[1], styles=line_styles, width=0.85, rot=0, ylim=(-0.0001, None))\n",
    "\n",
    "ax_cls_topn_mrr_ml.set_ylabel('MRR@$n$')\n",
    "ax_cls_topn_mrr_ml.set_xlabel('top-$n$')\n",
    "ax_cls_topn_mrr_bx.set_xlabel('top-$n$')\n",
    "\n",
    "handles, labels = ax_cls_topn_mrr_bx.get_legend_handles_labels()\n",
    "ax_cls_topn_mrr_bx.legend(reversed(handles), reversed(labels), loc=2, bbox_to_anchor=(-0.85, 0.85))\n",
    "\n",
    "fig.subplots_adjust(wspace=0.9, hspace=0.28)\n",
    "\n",
    "plt.savefig(\"cls_mrr.pdf\", bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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