simulation_heatmap.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import scipy\n",
    "import robust_mean_estimate as rl\n",
    "import importlib as il\n",
    "import matplotlib as mpl\n",
    "mpl.rcParams.update(mpl.rcParamsDefault)\n",
    "from scipy import special\n",
    "from numpy import linalg as LA\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import mpld3\n",
    "mpld3.enable_notebook()\n",
    "\n",
    "fsize = 20\n",
    "fpad = 10\n",
    "figsize = (8,4)\n",
    "fontname = 'fantasy'\n",
    "# plt.rcParams.update({\n",
    "#     \"text.usetex\": True,\n",
    "#     \"font.family\": \"Helvetica\"\n",
    "# })\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"heatmap -- Lognormal -- dense biased -- set data -- TopK_GD\"\n",
    "\n",
    "il.reload(rl)\n",
    "\n",
    "trials = 50\n",
    "\n",
    "eps = 0.1\n",
    "d = 100\n",
    "\n",
    "xs = list(np.arange(200, 1000, 100))\n",
    "ys = list(np.arange(4, 9, 1))\n",
    "\n",
    "keys = rl.Topk_GD\n",
    "\n",
    "model = rl.LognormalModel()\n",
    "noise_model = rl.DenseNoise(-2)\n",
    "model_params = rl.Params(eps = eps, d = d)\n",
    "\n",
    "inputfilename = 'data/heatmap-Lognormal-TopK_GD.pkl'\n",
    "plot_heatmap = rl.load_data(model = model, noise_model = noise_model, params = model_params, loss = rl.err, keys = keys)\n",
    "plot_heatmap.setdata_tofile_heatmap(filename = inputfilename, trials = trials, xs = xs, ys = ys)\n",
    "print(plot_heatmap.Run.runs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"heatmap -- Lognormal -- dense biased -- plot -- TopK_GD\"\n",
    "\n",
    "il.reload(rl)\n",
    "\n",
    "trials = 50\n",
    "\n",
    "eps = 0.1\n",
    "d = 100\n",
    "\n",
    "xs = list(np.arange(200, 1000, 100))\n",
    "ys = list(np.arange(4, 9, 1))\n",
    "\n",
    "keys = rl.Topk_GD\n",
    "\n",
    "model = rl.LognormalModel()\n",
    "noise_model = rl.DenseNoise(-2)\n",
    "model_params = rl.Params(eps = eps, d = d)\n",
    "\n",
    "inputfilename = 'data/heatmap-Lognormal-TopK_GD.pkl'\n",
    "outputfilename = 'figs/heatmap-Lognormal-TopK_GD.pdf'\n",
    "\n",
    "# title = 'eps = {eps}, d = {d}. Model is Lognormal. Noise is constant -2 biased.'.format(eps = eps, d = d)\n",
    "title = 'Lognormal'\n",
    "xlabel = 'n'\n",
    "ylabel = 'k'\n",
    "\n",
    "heatmap = rl.plot_data(model = model, noise_model = noise_model, params = model_params, loss = rl.err, keys = keys)\n",
    "\n",
    "heatmap.plotheatmap_fromfile(yscale = 'log', outputfilename = outputfilename, filename = inputfilename, title = title, xlabel = xlabel, ylabel = ylabel, figsize = figsize, fsize = fsize, fpad = fpad, xs = xs, ys = ys, fontname = fontname)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"heatmap -- Pareto -- dense biased -- set data -- TopK_GD\"\n",
    "\n",
    "il.reload(rl)\n",
    "\n",
    "trials = 50\n",
    "\n",
    "eps = 0.1\n",
    "d = 100\n",
    "\n",
    "xs = list(np.arange(200, 1000, 100))\n",
    "ys = list(np.arange(4, 9, 1))\n",
    "\n",
    "keys = rl.Topk_GD\n",
    "\n",
    "model = rl.ParetoModel()\n",
    "noise_model = rl.DenseNoise(-2)\n",
    "model_params = rl.Params(eps = eps, d = d, param=2)\n",
    "\n",
    "inputfilename = 'data/heatmap-Pareto-TopK_GD.pkl'\n",
    "plot_heatmap = rl.load_data(model = model, noise_model = noise_model, params = model_params, loss = rl.err, keys = keys)\n",
    "plot_heatmap.setdata_tofile_heatmap(filename = inputfilename, trials = trials, xs = xs, ys = ys)\n",
    "print(plot_heatmap.Run.runs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"heatmap -- Pareto -- dense biased -- plot -- TopK_GD\"\n",
    "\n",
    "il.reload(rl)\n",
    "\n",
    "trials = 10\n",
    "\n",
    "eps = 0.1\n",
    "d = 100\n",
    "\n",
    "xs = list(np.arange(200, 1000, 100))\n",
    "ys = list(np.arange(4, 9, 1))\n",
    "\n",
    "keys = rl.Topk_GD\n",
    "\n",
    "model = rl.ParetoModel()\n",
    "noise_model = rl.DenseNoise(-2)\n",
    "model_params = rl.Params(eps = eps, d = d)\n",
    "\n",
    "inputfilename = 'data/heatmap-Pareto-TopK_GD.pkl'\n",
    "outputfilename = 'figs/heatmap-Pareto-TopK_GD.pdf'\n",
    "\n",
    "# title = 'eps = {eps}, d = {d}. Model is Pareto. Noise is constant -2 biased.'.format(eps = eps, d = d)\n",
    "title = 'Pareto'\n",
    "xlabel = 'n'\n",
    "ylabel = 'k'\n",
    "\n",
    "heatmap = rl.plot_data(model = model, noise_model = noise_model, params = model_params, loss = rl.err, keys = keys)\n",
    "\n",
    "heatmap.plotheatmap_fromfile(yscale = 'log', outputfilename = outputfilename, filename = inputfilename, title = title, xlabel = xlabel, ylabel = ylabel, figsize = figsize, fsize = fsize, fpad = fpad, xs = xs, ys = ys, fontname = fontname)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"heatmap -- StudentT -- dense biased -- set data -- TopK_GD\"\n",
    "\n",
    "il.reload(rl)\n",
    "\n",
    "trials = 50\n",
    "\n",
    "eps = 0.1\n",
    "d = 100\n",
    "\n",
    "xs = list(np.arange(200, 1000, 100))\n",
    "ys = list(np.arange(4, 9, 1))\n",
    "\n",
    "keys = rl.Topk_GD\n",
    "\n",
    "model = rl.TModel()\n",
    "noise_model = rl.DenseNoise(-2)\n",
    "model_params = rl.Params(eps = eps, d = d, param=2)\n",
    "\n",
    "inputfilename = 'data/heatmap-StudentT-TopK_GD.pkl'\n",
    "plot_heatmap = rl.load_data(model = model, noise_model = noise_model, params = model_params, loss = rl.err, keys = keys)\n",
    "plot_heatmap.setdata_tofile_heatmap(filename = inputfilename, trials = trials, xs = xs, ys = ys)\n",
    "print(plot_heatmap.Run.runs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"heatmap -- 3 -- dense biased -- set data -- TopK_Filtered\"\n",
    "\n",
    "il.reload(rl)\n",
    "\n",
    "trials = 50\n",
    "\n",
    "eps = 0.1\n",
    "d = 100\n",
    "\n",
    "xs = list(np.arange(200, 1000, 100))\n",
    "ys = list(np.arange(4, 9, 1))\n",
    "\n",
    "keys = rl.Top_K_Filtered\n",
    "\n",
    "# Lognormal\n",
    "model = rl.LognormalModel()\n",
    "noise_model = rl.DenseNoise(-2)\n",
    "model_params = rl.Params(eps = eps, d = d, param=2)\n",
    "\n",
    "inputfilename = 'data/heatmap-Lognormal-Full.pkl'\n",
    "plot_heatmap = rl.load_data(model = model, noise_model = noise_model, params = model_params, loss = rl.err, keys = keys)\n",
    "plot_heatmap.setdata_tofile_heatmap(filename = inputfilename, trials = trials, xs = xs, ys = ys)\n",
    "print('Lognormal Finished')\n",
    "\n",
    "# Student t\n",
    "model = rl.TModel()\n",
    "noise_model = rl.DenseNoise(-2)\n",
    "model_params = rl.Params(eps = eps, d = d, param=2)\n",
    "\n",
    "inputfilename = 'data/heatmap-StudentT-Full.pkl'\n",
    "plot_heatmap = rl.load_data(model = model, noise_model = noise_model, params = model_params, loss = rl.err, keys = keys)\n",
    "plot_heatmap.setdata_tofile_heatmap(filename = inputfilename, trials = trials, xs = xs, ys = ys)\n",
    "# print(plot_heatmap.Run.runs)\n",
    "print('T Finished')\n",
    "\n",
    "# Pareto\n",
    "model = rl.ParetoModel()\n",
    "noise_model = rl.DenseNoise(-2)\n",
    "model_params = rl.Params(eps = eps, d = d, param=2)\n",
    "\n",
    "inputfilename = 'data/heatmap-Pareto-Full.pkl'\n",
    "plot_heatmap = rl.load_data(model = model, noise_model = noise_model, params = model_params, loss = rl.err, keys = keys)\n",
    "plot_heatmap.setdata_tofile_heatmap(filename = inputfilename, trials = trials, xs = xs, ys = ys)\n",
    "print('Pareto Finished')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"heatmap -- StudentT -- dense biased -- plot -- TopK_GD\"\n",
    "\n",
    "il.reload(rl)\n",
    "\n",
    "trials = 50\n",
    "\n",
    "eps = 0.1\n",
    "d = 100\n",
    "\n",
    "xs = list(np.arange(200, 1000, 100))\n",
    "ys = list(np.arange(4, 9, 1))\n",
    "\n",
    "keys = rl.Topk_GD\n",
    "\n",
    "model = rl.TModel()\n",
    "noise_model = rl.DenseNoise(-2)\n",
    "model_params = rl.Params(eps = eps, d = d, param=2.74)\n",
    "\n",
    "inputfilename = 'data/heatmap-StudentT-TopK_GD.pkl'\n",
    "outputfilename = 'figs/heatmap-StudentT-TopK_GD.pdf'\n",
    "\n",
    "# title = 'eps = {eps}, d = {d}. Model is StudentT. Noise is constant -2 biased.'.format(eps = eps, d = d)\n",
    "title = 'Student T'\n",
    "xlabel = \"n\"\n",
    "ylabel = \"k\"\n",
    "\n",
    "heatmap = rl.plot_data(model = model, noise_model = noise_model, params = model_params, loss = rl.err, keys = keys)\n",
    "\n",
    "heatmap.plotheatmap_fromfile(yscale = 'log', outputfilename = outputfilename, filename = inputfilename, title = title, xlabel = xlabel, ylabel = ylabel, figsize = figsize, fsize = fsize, fpad = fpad, xs = xs, ys = ys, fontname = fontname)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "heatmap input: [[0.68671656 0.62803278 0.64761809 0.59799646 0.62571547 0.57774644\n",
      "  0.60421733 0.60102032]\n",
      " [0.66049945 0.58759226 0.59600333 0.58297299 0.56818522 0.52777871\n",
      "  0.54031213 0.57729876]\n",
      " [0.5861269  0.54153175 0.53727321 0.5349556  0.52472291 0.50167381\n",
      "  0.51442528 0.52987619]\n",
      " [0.51846577 0.48783972 0.48998306 0.46317839 0.4873691  0.50077144\n",
      "  0.45406755 0.48083716]\n",
      " [0.4684073  0.44108996 0.40348487 0.42208229 0.39901522 0.42935511\n",
      "  0.41716692 0.42869517]]\n",
      "heatmap input: [[0.67965493 0.62236488 0.60479941 0.58754809 0.59339136 0.60112983\n",
      "  0.6040159  0.60332251]\n",
      " [0.57706939 0.60028797 0.59180253 0.58675952 0.56794268 0.56510515\n",
      "  0.57166204 0.56552194]\n",
      " [0.53788055 0.53460411 0.52549355 0.53640825 0.51606462 0.51783836\n",
      "  0.54764008 0.5450976 ]\n",
      " [0.52685163 0.51655633 0.47407895 0.46000295 0.47137035 0.47971456\n",
      "  0.46803305 0.48984218]\n",
      " [0.43465013 0.46294087 0.40094989 0.40673228 0.42644471 0.41156633\n",
      "  0.44644736 0.44732213]]\n",
      "heatmap input: [[0.61642941 0.62678319 0.59074206 0.60416256 0.59838386 0.57295308\n",
      "  0.61639597 0.59202631]\n",
      " [0.62700761 0.57898063 0.54184891 0.56293332 0.57139701 0.52057159\n",
      "  0.54244618 0.53297912]\n",
      " [0.55991595 0.52130589 0.53572462 0.49360385 0.50069603 0.49673013\n",
      "  0.5075918  0.51320975]\n",
      " [0.51076244 0.51093688 0.47904699 0.4560642  0.46685916 0.46629027\n",
      "  0.45967626 0.44259349]\n",
      " [0.42212534 0.40214578 0.38232127 0.42439451 0.40759251 0.4263763\n",
      "  0.42483043 0.40353412]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/html": [
       "\n",
       "\n",
       "<style>\n",
       "\n",
       "</style>\n",
       "\n",
       "<div id=\"fig_el309491401560573168969456733540\"></div>\n",
       "<script>\n",
       "function mpld3_load_lib(url, callback){\n",
       "  var s = document.createElement('script');\n",
       "  s.src = url;\n",
       "  s.async = true;\n",
       "  s.onreadystatechange = s.onload = callback;\n",
       "  s.onerror = function(){console.warn(\"failed to load library \" + url);};\n",
       "  document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
       "}\n",
       "\n",
       "if(typeof(mpld3) !== \"undefined\" && mpld3._mpld3IsLoaded){\n",
       "   // already loaded: just create the figure\n",
       "   !function(mpld3){\n",
       "       \n",
       "       mpld3.draw_figure(\"fig_el309491401560573168969456733540\", {\"width\": 1800.0, \"height\": 600.0, \"axes\": [{\"bbox\": [0.125, 0.32404411764705887, 0.18235294117647055, 0.34191176470588236], \"xlim\": [-0.5, 7.5], \"ylim\": [4.5, -0.5], \"xdomain\": [-0.5, 7.5], \"ydomain\": [4.5, -0.5], \"xscale\": \"linear\", \"yscale\": \"linear\", \"axes\": [{\"position\": \"bottom\", \"nticks\": 8, \"tickvalues\": [0, 1, 2, 3, 4, 5, 6, 7], \"tickformat_formatter\": \"\", \"tickformat\": null, \"scale\": \"linear\", \"fontsize\": 8.0, \"grid\": {\"gridOn\": false}, \"visible\": true}, {\"position\": \"left\", \"nticks\": 5, \"tickvalues\": [0, 1, 2, 3, 4], \"tickformat_formatter\": \"\", \"tickformat\": null, \"scale\": \"linear\", \"fontsize\": 8.0, \"grid\": {\"gridOn\": false}, \"visible\": true}], \"axesbg\": \"#FFFFFF\", \"axesbgalpha\": null, \"zoomable\": true, \"id\": \"el30949140157938703328\", \"lines\": [], \"paths\": [], \"markers\": [], \"texts\": [{\"text\": \"$n$\", 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       "   }(mpld3);\n",
       "}else if(typeof define === \"function\" && define.amd){\n",
       "   // require.js is available: use it to load d3/mpld3\n",
       "   require.config({paths: {d3: \"https://d3js.org/d3.v5\"}});\n",
       "   require([\"d3\"], function(d3){\n",
       "      window.d3 = d3;\n",
       "      mpld3_load_lib(\"https://mpld3.github.io/js/mpld3.v0.5.9.js\", function(){\n",
       "         \n",
       "         mpld3.draw_figure(\"fig_el309491401560573168969456733540\", {\"width\": 1800.0, \"height\": 600.0, \"axes\": [{\"bbox\": [0.125, 0.32404411764705887, 0.18235294117647055, 0.34191176470588236], \"xlim\": [-0.5, 7.5], \"ylim\": [4.5, -0.5], \"xdomain\": [-0.5, 7.5], \"ydomain\": [4.5, -0.5], \"xscale\": \"linear\", \"yscale\": \"linear\", \"axes\": [{\"position\": \"bottom\", \"nticks\": 8, \"tickvalues\": [0, 1, 2, 3, 4, 5, 6, 7], \"tickformat_formatter\": \"\", \"tickformat\": null, \"scale\": \"linear\", \"fontsize\": 8.0, \"grid\": {\"gridOn\": false}, \"visible\": true}, {\"position\": \"left\", \"nticks\": 5, \"tickvalues\": [0, 1, 2, 3, 4], \"tickformat_formatter\": \"\", \"tickformat\": null, \"scale\": \"linear\", \"fontsize\": 8.0, \"grid\": {\"gridOn\": false}, \"visible\": true}], \"axesbg\": \"#FFFFFF\", \"axesbgalpha\": null, \"zoomable\": true, \"id\": \"el30949140157938703328\", \"lines\": [], \"paths\": [], \"markers\": [], \"texts\": [{\"text\": \"$n$\", \"position\": [0.5, -0.22077658303464764], \"coordinates\": \"axes\", \"h_anchor\": \"middle\", \"v_baseline\": \"hanging\", \"rotation\": -0.0, \"fontsize\": 8.8, \"color\": \"#000000\", \"alpha\": 1, \"zorder\": 3, \"id\": \"el30949140157938675280\"}, {\"text\": \"Lognormal ($\\\\alpha=2$)\", \"position\": [0.5, 1.060931899641577], \"coordinates\": \"axes\", \"h_anchor\": \"middle\", \"v_baseline\": \"auto\", \"rotation\": -0.0, \"fontsize\": 12.0, \"color\": \"#000000\", \"alpha\": 1, \"zorder\": 3, \"id\": \"el30949140156329239696\"}], \"collections\": [], \"images\": [{\"data\": \"iVBORw0KGgoAAAANSUhEUgAAAAgAAAAFCAYAAAB4ka1VAAAAnUlEQVR4nAXB3wrBUADA4d/OZjJs7YJRO/K3LJLiSikX9iDezbt4CC5ZCBsiI47v0wgDVR6XOCUpg77HProSDGsct2d0XWBQtWh1K/jPN3nHoiE9ptLn4ktWuy3GZN5m1mmSEYKMEDimSd22idMXTtbEWIyGdF0XBciCQ/S4EacvQtkmeiZo6+tSeTmXgtHjow6Ywif9bhBakZ+68wffMS7YSkMjpAAAAABJRU5ErkJggg==\", \"extent\": [-0.5, 7.5, 4.5, -0.5], \"coordinates\": \"data\", \"alpha\": null, \"zorder\": 0, \"id\": 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\"coordinates\": \"axes\", \"h_anchor\": \"middle\", \"v_baseline\": \"hanging\", \"rotation\": -0.0, \"fontsize\": 8.8, \"color\": \"#000000\", \"alpha\": 1, \"zorder\": 3, \"id\": \"el30949140156059633888\"}, {\"text\": \"Pareto ($b=2$)\", \"position\": [0.5, 1.0609318996415769], \"coordinates\": \"axes\", \"h_anchor\": \"middle\", \"v_baseline\": \"auto\", \"rotation\": -0.0, \"fontsize\": 12.0, \"color\": \"#000000\", \"alpha\": 1, \"zorder\": 3, \"id\": \"el30949140156059671184\"}], \"collections\": [], \"images\": [{\"data\": \"iVBORw0KGgoAAAANSUhEUgAAAAgAAAAFCAYAAAB4ka1VAAAAmUlEQVR4nAXByw7BQABA0TvTmT4SLdKQkCiWtmJjIWLhT/ylv7C0R4SSeHc67ThHsJ647qxDuxXSTGNKYyl+hsrWCCFQy82Uy+FGGAUIKej3U3zp8S4NkdaoRTZEj8bEvuZeGEZJg6p27K45SkpUKwxo+j7zXo9ER3xswduWrAYZx9cT8bVbd/7kDOMFtSuw9QlPpjzMHgf8AXK4Nh9jyRQxAAAAAElFTkSuQmCC\", \"extent\": [-0.5, 7.5, 4.5, -0.5], \"coordinates\": \"data\", \"alpha\": null, \"zorder\": 0, \"id\": \"el30949140156059247712\"}], \"sharex\": [], \"sharey\": []}, {\"bbox\": 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\"rotation\": -0.0, \"fontsize\": 8.8, \"color\": \"#000000\", \"alpha\": 1, \"zorder\": 3, \"id\": \"el30949140156059092352\"}, {\"text\": \"Student $t$ ($\\\\nu=2$)\", \"position\": [0.5, 1.0609318996415769], \"coordinates\": \"axes\", \"h_anchor\": \"middle\", \"v_baseline\": \"auto\", \"rotation\": -0.0, \"fontsize\": 12.0, \"color\": \"#000000\", \"alpha\": 1, \"zorder\": 3, \"id\": \"el30949140157939284096\"}], \"collections\": [], \"images\": [{\"data\": \"iVBORw0KGgoAAAANSUhEUgAAAAgAAAAFCAYAAAB4ka1VAAAAnklEQVR4nAXBvQ7BUACA0e+291a4fkOIxkKaVPwMJBaLxSAxeS5v4zWsZhsG1aTaaLlN6hzBYV6Q5Ci/gXkbcCzcvuZ+eSFdjSTJaS87+FOX4BbySQ2b9YSrF1CtlhHj465YzIY84pi955EYQ8m2eaYpmcmR29UUgP1oyKjRJPp+EQIqUtGraESYnQqtejjWgCA7U1Mtol+IJQR11eUPDgsxqNvNbMoAAAAASUVORK5CYII=\", \"extent\": [-0.5, 7.5, 4.5, -0.5], \"coordinates\": \"data\", \"alpha\": null, \"zorder\": 0, \"id\": \"el30949140156059248624\"}], \"sharex\": [], \"sharey\": []}, {\"bbox\": [0.78375, 0.264, 0.00770000000000004, 0.46199999999999997], 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       "      });\n",
       "    });\n",
       "}else{\n",
       "    // require.js not available: dynamically load d3 & mpld3\n",
       "    mpld3_load_lib(\"https://d3js.org/d3.v5.js\", function(){\n",
       "         mpld3_load_lib(\"https://mpld3.github.io/js/mpld3.v0.5.9.js\", function(){\n",
       "                 \n",
       "                 mpld3.draw_figure(\"fig_el309491401560573168969456733540\", {\"width\": 1800.0, \"height\": 600.0, \"axes\": [{\"bbox\": [0.125, 0.32404411764705887, 0.18235294117647055, 0.34191176470588236], \"xlim\": [-0.5, 7.5], \"ylim\": [4.5, -0.5], \"xdomain\": [-0.5, 7.5], \"ydomain\": [4.5, -0.5], \"xscale\": \"linear\", \"yscale\": \"linear\", \"axes\": [{\"position\": \"bottom\", \"nticks\": 8, \"tickvalues\": [0, 1, 2, 3, 4, 5, 6, 7], \"tickformat_formatter\": \"\", \"tickformat\": null, \"scale\": \"linear\", \"fontsize\": 8.0, \"grid\": {\"gridOn\": false}, \"visible\": true}, {\"position\": \"left\", \"nticks\": 5, \"tickvalues\": [0, 1, 2, 3, 4], \"tickformat_formatter\": \"\", \"tickformat\": null, \"scale\": \"linear\", \"fontsize\": 8.0, \"grid\": {\"gridOn\": false}, \"visible\": true}], \"axesbg\": \"#FFFFFF\", \"axesbgalpha\": null, \"zoomable\": true, \"id\": \"el30949140157938703328\", \"lines\": [], \"paths\": [], \"markers\": [], \"texts\": [{\"text\": \"$n$\", \"position\": [0.5, -0.22077658303464764], \"coordinates\": \"axes\", \"h_anchor\": \"middle\", \"v_baseline\": \"hanging\", \"rotation\": -0.0, \"fontsize\": 8.8, \"color\": \"#000000\", \"alpha\": 1, \"zorder\": 3, \"id\": \"el30949140157938675280\"}, {\"text\": \"Lognormal ($\\\\alpha=2$)\", \"position\": [0.5, 1.060931899641577], \"coordinates\": \"axes\", \"h_anchor\": \"middle\", \"v_baseline\": \"auto\", \"rotation\": -0.0, \"fontsize\": 12.0, \"color\": \"#000000\", \"alpha\": 1, \"zorder\": 3, \"id\": \"el30949140156329239696\"}], \"collections\": [], \"images\": [{\"data\": \"iVBORw0KGgoAAAANSUhEUgAAAAgAAAAFCAYAAAB4ka1VAAAAnUlEQVR4nAXB3wrBUADA4d/OZjJs7YJRO/K3LJLiSikX9iDezbt4CC5ZCBsiI47v0wgDVR6XOCUpg77HProSDGsct2d0XWBQtWh1K/jPN3nHoiE9ptLn4ktWuy3GZN5m1mmSEYKMEDimSd22idMXTtbEWIyGdF0XBciCQ/S4EacvQtkmeiZo6+tSeTmXgtHjow6Ywif9bhBakZ+68wffMS7YSkMjpAAAAABJRU5ErkJggg==\", \"extent\": [-0.5, 7.5, 4.5, -0.5], \"coordinates\": \"data\", \"alpha\": null, \"zorder\": 0, \"id\": 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\"coordinates\": \"axes\", \"h_anchor\": \"middle\", \"v_baseline\": \"hanging\", \"rotation\": -0.0, \"fontsize\": 8.8, \"color\": \"#000000\", \"alpha\": 1, \"zorder\": 3, \"id\": \"el30949140156059633888\"}, {\"text\": \"Pareto ($b=2$)\", \"position\": [0.5, 1.0609318996415769], \"coordinates\": \"axes\", \"h_anchor\": \"middle\", \"v_baseline\": \"auto\", \"rotation\": -0.0, \"fontsize\": 12.0, \"color\": \"#000000\", \"alpha\": 1, \"zorder\": 3, \"id\": \"el30949140156059671184\"}], \"collections\": [], \"images\": [{\"data\": \"iVBORw0KGgoAAAANSUhEUgAAAAgAAAAFCAYAAAB4ka1VAAAAmUlEQVR4nAXByw7BQABA0TvTmT4SLdKQkCiWtmJjIWLhT/ylv7C0R4SSeHc67ThHsJ647qxDuxXSTGNKYyl+hsrWCCFQy82Uy+FGGAUIKej3U3zp8S4NkdaoRTZEj8bEvuZeGEZJg6p27K45SkpUKwxo+j7zXo9ER3xswduWrAYZx9cT8bVbd/7kDOMFtSuw9QlPpjzMHgf8AXK4Nh9jyRQxAAAAAElFTkSuQmCC\", \"extent\": [-0.5, 7.5, 4.5, -0.5], \"coordinates\": \"data\", \"alpha\": null, \"zorder\": 0, \"id\": \"el30949140156059247712\"}], \"sharex\": [], \"sharey\": []}, {\"bbox\": [0.5626470588235294, 0.32404411764705876, 0.1823529411764706, 0.34191176470588247], \"xlim\": [-0.5, 7.5], \"ylim\": [4.5, -0.5], \"xdomain\": [-0.5, 7.5], \"ydomain\": [4.5, -0.5], \"xscale\": \"linear\", \"yscale\": \"linear\", \"axes\": [{\"position\": \"bottom\", \"nticks\": 8, \"tickvalues\": [0, 1, 2, 3, 4, 5, 6, 7], \"tickformat_formatter\": \"\", \"tickformat\": null, \"scale\": \"linear\", \"fontsize\": 8.0, \"grid\": {\"gridOn\": false}, \"visible\": true}, {\"position\": \"left\", \"nticks\": 5, \"tickvalues\": [0, 1, 2, 3, 4], \"tickformat_formatter\": \"\", \"tickformat\": null, \"scale\": \"linear\", \"fontsize\": 8.0, \"grid\": {\"gridOn\": false}, \"visible\": true}], \"axesbg\": \"#FFFFFF\", \"axesbgalpha\": null, \"zoomable\": true, \"id\": \"el30949140156059632544\", \"lines\": [], \"paths\": [], \"markers\": [], \"texts\": [{\"text\": \"$n$\", \"position\": [0.5, -0.22077658303464753], \"coordinates\": \"axes\", \"h_anchor\": \"middle\", \"v_baseline\": \"hanging\", \"rotation\": -0.0, \"fontsize\": 8.8, \"color\": \"#000000\", \"alpha\": 1, \"zorder\": 3, \"id\": \"el30949140156059092352\"}, {\"text\": \"Student $t$ ($\\\\nu=2$)\", \"position\": [0.5, 1.0609318996415769], \"coordinates\": \"axes\", \"h_anchor\": \"middle\", \"v_baseline\": \"auto\", \"rotation\": -0.0, \"fontsize\": 12.0, \"color\": \"#000000\", \"alpha\": 1, \"zorder\": 3, \"id\": \"el30949140157939284096\"}], \"collections\": [], \"images\": [{\"data\": \"iVBORw0KGgoAAAANSUhEUgAAAAgAAAAFCAYAAAB4ka1VAAAAnklEQVR4nAXBvQ7BUACA0e+291a4fkOIxkKaVPwMJBaLxSAxeS5v4zWsZhsG1aTaaLlN6hzBYV6Q5Ci/gXkbcCzcvuZ+eSFdjSTJaS87+FOX4BbySQ2b9YSrF1CtlhHj465YzIY84pi955EYQ8m2eaYpmcmR29UUgP1oyKjRJPp+EQIqUtGraESYnQqtejjWgCA7U1Mtol+IJQR11eUPDgsxqNvNbMoAAAAASUVORK5CYII=\", \"extent\": [-0.5, 7.5, 4.5, -0.5], \"coordinates\": \"data\", \"alpha\": null, \"zorder\": 0, \"id\": \"el30949140156059248624\"}], \"sharex\": [], \"sharey\": []}, {\"bbox\": [0.78375, 0.264, 0.00770000000000004, 0.46199999999999997], 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       "            })\n",
       "         });\n",
       "}\n",
       "</script>"
      ],
      "text/plain": [
       "<Figure size 1800x600 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\"\"\"Plot 3 heatmaps.\"\"\"\n",
    "il.reload(rl)\n",
    "\n",
    "trials = 50\n",
    "\n",
    "eps = 0.1\n",
    "d = 100\n",
    "\n",
    "xs = list(np.arange(200, 1000, 100))\n",
    "ys = list(np.arange(4, 9, 1))\n",
    "\n",
    "keys = rl.Top_K_Filtered\n",
    "\n",
    "model = rl.TModel()\n",
    "noise_model = rl.DenseNoise(-2)\n",
    "model_params = rl.Params(eps = eps, d = d, param=2.74)\n",
    "\n",
    "inputfilename1 = 'data/heatmap-Lognormal-Full.pkl'\n",
    "inputfilename2 = 'data/heatmap-Pareto-Full.pkl'\n",
    "inputfilename3 = 'data/heatmap-StudentT-Full.pkl'\n",
    "\n",
    "outputfilename = 'figs/heatmap-3-Full.pdf'\n",
    "\n",
    "# title = 'eps = {eps}, d = {d}. Model is StudentT. Noise is constant -2 biased.'.format(eps = eps, d = d)\n",
    "title = '3 heatmaps'\n",
    "xlabel = \"sample size (n)\"\n",
    "ylabel = \"sparsity (k)\"\n",
    "\n",
    "heatmap = rl.plot_data(model = model, noise_model = noise_model, params = model_params, loss = rl.err, keys = keys)\n",
    "\n",
    "heatmap.plotheatmap_3_fromfile(yscale = 'log', outputfilename = outputfilename, filename1 = inputfilename1, filename2 = inputfilename2, filename3 = inputfilename3, title = title, xlabel = xlabel, ylabel = ylabel, figsize = figsize, fsize = fsize, fpad = fpad, xs = xs, ys = ys, fontname = fontname)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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