sensitivity.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import scipy\n",
    "import robust_mean_estimate as rl\n",
    "import importlib as il\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 = 'Arial'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"loss vs sensitivity -- Fisk -- dense biased -- set data -- inf\"\n",
    "\n",
    "il.reload(rl)\n",
    "\n",
    "trials = 10\n",
    "\n",
    "d, eps, param, m = 100, 0.1, 2, 600\n",
    "xs = list(np.arange(1, 41, 3))\n",
    "\n",
    "keys = [rl.Oracle, rl.RME_sp, rl.GDAlgs_npre, rl.Top_K, rl.Full, rl.Full_Filter]\n",
    "\n",
    "model = rl.FiskModel()\n",
    "noise_model = rl.DenseNoise(-2)\n",
    "model_params = rl.Params(d = d, eps = eps, param = param, m = m)\n",
    "\n",
    "inputfilename = 'data/error-sen-Fisk-inf.pkl'\n",
    "plot_k = rl.load_data(model = model, noise_model = noise_model, params = model_params, loss = rl.err, keys = keys)\n",
    "plot_k.setdata_tofile(filename = inputfilename, xvar_name = 'sen', trials = trials, xs = xs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"loss vs sensitivity -- Fisk -- dense biased -- set data\"\n",
    "\n",
    "il.reload(rl)\n",
    "\n",
    "trials = 10\n",
    "\n",
    "d, eps, param, m = 100, 0.1, 3.1, 600\n",
    "xs = list(np.arange(1, 41, 3))\n",
    "\n",
    "keys = [rl.Oracle, rl.RME_sp, rl.GDAlgs_npre, rl.Top_K, rl.Full, rl.Full_Filter]\n",
    "\n",
    "model = rl.FiskModel()\n",
    "noise_model = rl.DenseNoise(-2)\n",
    "model_params = rl.Params(d = d, eps = eps, param = param, m = m)\n",
    "\n",
    "inputfilename = 'data/error-sen-Fisk.pkl'\n",
    "plot_k = rl.load_data(model = model, noise_model = noise_model, params = model_params, loss = rl.err, keys = keys)\n",
    "plot_k.setdata_tofile(filename = inputfilename, xvar_name = 'sen', trials = trials, xs = xs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"loss vs sensitivity -- Fisk -- dense biased -- plot -- inf\"\n",
    "\n",
    "il.reload(rl)\n",
    "\n",
    "trials = 10\n",
    "\n",
    "d, eps, param, m = 100, 0.1, 2, 600\n",
    "xs = list(np.arange(1, 41, 3))\n",
    "\n",
    "keys = [rl.Oracle, rl.RME_sp, rl.GDAlgs_npre, rl.Top_K, rl.Full, rl.Full_Filter]\n",
    "\n",
    "model = rl.FiskModel()\n",
    "noise_model = rl.DenseNoise(-2)\n",
    "model_params = rl.Params(d = d, eps = eps, param = param)\n",
    "inputfilename = 'data/error-sen-Fisk-inf.pkl'\n",
    "outputfilename = 'figs/error-sen-Fisk-inf.pdf'\n",
    "\n",
    "title = 'Fisk'\n",
    "xlabel = '$k\\'$'\n",
    "ylabel = '$\\\\ell_2$ error'\n",
    "\n",
    "plot_k_loss = rl.plot_data(model = model, noise_model = noise_model, params = model_params, loss = rl.err, keys = keys)\n",
    "\n",
    "plot_k_loss.plotxy_fromfile(outputfilename = outputfilename, filename = inputfilename, title = title, xlabel = xlabel, ylabel = ylabel, figsize = figsize, fsize = fsize, fpad = fpad, xs = xs, fontname = fontname)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"loss vs sensitivity -- Fisk -- dense biased -- plot\"\n",
    "\n",
    "il.reload(rl)\n",
    "\n",
    "trials = 10\n",
    "\n",
    "d, eps, param, m = 100, 0.1, 3.1, 600\n",
    "xs = list(np.arange(1, 41, 3))\n",
    "\n",
    "keys = [rl.Oracle, rl.RME_sp, rl.GDAlgs_npre, rl.Top_K, rl.Full, rl.Full_Filter]\n",
    "\n",
    "model = rl.FiskModel()\n",
    "noise_model = rl.DenseNoise(-2)\n",
    "model_params = rl.Params(d = d, eps = eps, param = param)\n",
    "inputfilename = 'data/error-sen-Fisk.pkl'\n",
    "outputfilename = 'figs/error-sen-Fisk.pdf'\n",
    "\n",
    "title = 'Fisk'\n",
    "xlabel = '$k\\'$'\n",
    "ylabel = '$\\\\ell_2$ error'\n",
    "\n",
    "plot_k_loss = rl.plot_data(model = model, noise_model = noise_model, params = model_params, loss = rl.err, keys = keys)\n",
    "\n",
    "plot_k_loss.plotxy_fromfile(outputfilename = outputfilename, filename = inputfilename, title = title, xlabel = xlabel, ylabel = ylabel, figsize = figsize, fsize = fsize, fpad = fpad, xs = xs, fontname = fontname)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"loss vs sensitivity -- Pareto -- dense biased -- set data -- inf\"\n",
    "\n",
    "il.reload(rl)\n",
    "\n",
    "trials = 10\n",
    "\n",
    "d, eps, param, m = 100, 0.1, 2, 600\n",
    "xs = list(np.arange(1, 41, 3))\n",
    "\n",
    "keys = [rl.Oracle, rl.RME_sp, rl.GDAlgs_npre, rl.Top_K, rl.Full, rl.Full_Filter]\n",
    "\n",
    "model = rl.ParetoModel()\n",
    "noise_model = rl.DenseNoise(-2)\n",
    "model_params = rl.Params(d = d, eps = eps, param = param, m = m)\n",
    "\n",
    "inputfilename = 'data/error-sen-Pareto-inf.pkl'\n",
    "plot_k = rl.load_data(model = model, noise_model = noise_model, params = model_params, loss = rl.err, keys = keys)\n",
    "plot_k.setdata_tofile(filename = inputfilename, xvar_name = 'sen', trials = trials, xs = xs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"loss vs sensitivity -- Pareto -- dense biased -- set data\"\n",
    "\n",
    "il.reload(rl)\n",
    "\n",
    "trials = 10\n",
    "\n",
    "d, eps, param, m = 100, 0.1, 3.1, 600\n",
    "xs = list(np.arange(1, 41, 3))\n",
    "\n",
    "keys = [rl.Oracle, rl.RME_sp, rl.GDAlgs_npre, rl.Top_K, rl.Full, rl.Full_Filter]\n",
    "\n",
    "model = rl.ParetoModel()\n",
    "noise_model = rl.DenseNoise(-2)\n",
    "model_params = rl.Params(d = d, eps = eps, param = param, m = m)\n",
    "\n",
    "inputfilename = 'data/error-sen-Pareto.pkl'\n",
    "plot_k = rl.load_data(model = model, noise_model = noise_model, params = model_params, loss = rl.err, keys = keys)\n",
    "plot_k.setdata_tofile(filename = inputfilename, xvar_name = 'sen', trials = trials, xs = xs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"loss vs sensitivity -- Pareto -- dense biased -- plot -- inf\"\n",
    "\n",
    "il.reload(rl)\n",
    "\n",
    "trials = 10\n",
    "\n",
    "d, eps, param, m = 100, 0.1, 2, 600\n",
    "xs = list(np.arange(1, 41, 3))\n",
    "\n",
    "keys = [rl.Oracle, rl.RME_sp, rl.GDAlgs_npre, rl.Top_K, rl.Full, rl.Full_Filter]\n",
    "\n",
    "model = rl.ParetoModel()\n",
    "noise_model = rl.DenseNoise(-2)\n",
    "model_params = rl.Params(d = d, eps = eps, param = param)\n",
    "inputfilename = 'data/error-sen-Pareto-inf.pkl'\n",
    "outputfilename = 'figs/error-sen-Pareto-inf.pdf'\n",
    "\n",
    "title = 'Pareto'\n",
    "xlabel = '$k\\'$'\n",
    "ylabel = '$\\\\ell_2$ error'\n",
    "\n",
    "plot_k_loss = rl.plot_data(model = model, noise_model = noise_model, params = model_params, loss = rl.err, keys = keys)\n",
    "\n",
    "plot_k_loss.plotxy_fromfile(outputfilename = outputfilename, filename = inputfilename, title = title, xlabel = xlabel, ylabel = ylabel, figsize = figsize, fsize = fsize, fpad = fpad, xs = xs, fontname = fontname)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"loss vs sensitivity -- Pareto -- dense biased -- plot\"\n",
    "\n",
    "il.reload(rl)\n",
    "\n",
    "trials = 10\n",
    "\n",
    "d, eps, param, m = 100, 0.1, 3.1, 600\n",
    "xs = list(np.arange(1, 41, 3))\n",
    "\n",
    "keys = [rl.Oracle, rl.RME_sp, rl.GDAlgs_npre, rl.Top_K, rl.Full, rl.Full_Filter]\n",
    "\n",
    "model = rl.ParetoModel()\n",
    "noise_model = rl.DenseNoise(-2)\n",
    "model_params = rl.Params(d = d, eps = eps, param = param)\n",
    "inputfilename = 'data/error-sen-Pareto.pkl'\n",
    "outputfilename = 'figs/error-sen-Pareto.pdf'\n",
    "\n",
    "title = 'Pareto'\n",
    "xlabel = '$k\\'$'\n",
    "ylabel = '$\\\\ell_2$ error'\n",
    "\n",
    "plot_k_loss = rl.plot_data(model = model, noise_model = noise_model, params = model_params, loss = rl.err, keys = keys)\n",
    "\n",
    "plot_k_loss.plotxy_fromfile(outputfilename = outputfilename, filename = inputfilename, title = title, xlabel = xlabel, ylabel = ylabel, figsize = figsize, fsize = fsize, fpad = fpad, xs = xs, fontname = fontname)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"loss vs sensitivity -- T -- dense biased -- set data -- inf\"\n",
    "\n",
    "il.reload(rl)\n",
    "\n",
    "trials = 10\n",
    "\n",
    "d, eps, param, m = 100, 0.1, 2, 600\n",
    "xs = list(np.arange(1, 41, 3))\n",
    "\n",
    "keys = [rl.Oracle, rl.RME_sp, rl.GDAlgs_npre, rl.Top_K, rl.Full, rl.Full_Filter]\n",
    "\n",
    "model = rl.TModel()\n",
    "noise_model = rl.DenseNoise(-2)\n",
    "model_params = rl.Params(d = d, eps = eps, param = param, m = m)\n",
    "\n",
    "inputfilename = 'data/error-sen-T-inf.pkl'\n",
    "plot_k = rl.load_data(model = model, noise_model = noise_model, params = model_params, loss = rl.err, keys = keys)\n",
    "plot_k.setdata_tofile(filename = inputfilename, xvar_name = 'sen', trials = trials, xs = xs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"loss vs sensitivity -- T -- dense biased -- set data\"\n",
    "\n",
    "il.reload(rl)\n",
    "\n",
    "trials = 10\n",
    "\n",
    "d, eps, param, m = 100, 0.1, 3.1, 600\n",
    "xs = list(np.arange(1, 41, 3))\n",
    "\n",
    "keys = [rl.Oracle, rl.RME_sp, rl.GDAlgs_npre, rl.Top_K, rl.Full, rl.Full_Filter]\n",
    "\n",
    "model = rl.TModel()\n",
    "noise_model = rl.DenseNoise(-2)\n",
    "model_params = rl.Params(d = d, eps = eps, param = param, m = m)\n",
    "\n",
    "inputfilename = 'data/error-sen-T.pkl'\n",
    "plot_k = rl.load_data(model = model, noise_model = noise_model, params = model_params, loss = rl.err, keys = keys)\n",
    "plot_k.setdata_tofile(filename = inputfilename, xvar_name = 'sen', trials = trials, xs = xs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"loss vs sensitivity -- T -- dense biased -- plot -- inf\"\n",
    "\n",
    "il.reload(rl)\n",
    "\n",
    "trials = 10\n",
    "\n",
    "d, eps, param, m = 100, 0.1, 2, 600\n",
    "xs = list(np.arange(1, 41, 3))\n",
    "\n",
    "keys = [rl.Oracle, rl.RME_sp, rl.GDAlgs_npre, rl.Top_K, rl.Full, rl.Full_Filter]\n",
    "\n",
    "model = rl.TModel()\n",
    "noise_model = rl.DenseNoise(-2)\n",
    "model_params = rl.Params(d = d, eps = eps, param = param)\n",
    "inputfilename = 'data/error-sen-T-inf.pkl'\n",
    "outputfilename = 'figs/error-sen-T-inf.pdf'\n",
    "\n",
    "title = 'T'\n",
    "xlabel = '$k\\'$'\n",
    "ylabel = '$\\\\ell_2$ error'\n",
    "\n",
    "plot_k_loss = rl.plot_data(model = model, noise_model = noise_model, params = model_params, loss = rl.err, keys = keys)\n",
    "\n",
    "plot_k_loss.plotxy_fromfile(outputfilename = outputfilename, filename = inputfilename, title = title, xlabel = xlabel, ylabel = ylabel, figsize = figsize, fsize = fsize, fpad = fpad, xs = xs, fontname = fontname)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"loss vs sensitivity -- T -- dense biased -- plot\"\n",
    "\n",
    "il.reload(rl)\n",
    "\n",
    "trials = 10\n",
    "\n",
    "d, eps, param, m = 100, 0.1, 3.1, 600\n",
    "xs = list(np.arange(1, 41, 3))\n",
    "\n",
    "keys = [rl.Oracle, rl.RME_sp, rl.GDAlgs_npre, rl.Top_K, rl.Full, rl.Full_Filter]\n",
    "\n",
    "model = rl.TModel()\n",
    "noise_model = rl.DenseNoise(-2)\n",
    "model_params = rl.Params(d = d, eps = eps, param = param)\n",
    "inputfilename = 'data/error-sen-T.pkl'\n",
    "outputfilename = 'figs/error-sen-T.pdf'\n",
    "\n",
    "title = 'T'\n",
    "xlabel = '$k\\'$'\n",
    "ylabel = '$\\\\ell_2$ error'\n",
    "\n",
    "plot_k_loss = rl.plot_data(model = model, noise_model = noise_model, params = model_params, loss = rl.err, keys = keys)\n",
    "\n",
    "plot_k_loss.plotxy_fromfile(outputfilename = outputfilename, filename = inputfilename, title = title, xlabel = xlabel, ylabel = ylabel, figsize = figsize, fsize = fsize, fpad = fpad, xs = xs, fontname = fontname)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"loss vs sensitivity -- Lognormal -- dense biased -- set data\"\n",
    "\n",
    "il.reload(rl)\n",
    "\n",
    "trials = 10\n",
    "\n",
    "d, eps, param, m = 100, 0.1, 2, 600\n",
    "xs = list(np.arange(1, 41, 3))\n",
    "\n",
    "keys = [rl.Oracle, rl.RME_sp, rl.GDAlgs_npre, rl.Top_K, rl.Full, rl.Full_Filter]\n",
    "\n",
    "model = rl.LognormalModel()\n",
    "noise_model = rl.DenseNoise(-2)\n",
    "model_params = rl.Params(d = d, eps = eps, param = param, m = m)\n",
    "\n",
    "inputfilename = 'data/error-sen-Lognormal.pkl'\n",
    "plot_k = rl.load_data(model = model, noise_model = noise_model, params = model_params, loss = rl.err, keys = keys)\n",
    "plot_k.setdata_tofile(filename = inputfilename, xvar_name = 'sen', trials = trials, xs = xs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"loss vs sensitivity -- Lognormal -- dense biased -- plot\"\n",
    "\n",
    "il.reload(rl)\n",
    "\n",
    "trials = 10\n",
    "\n",
    "d, eps, param, m = 100, 0.1, 2, 600\n",
    "xs = list(np.arange(1, 41, 3))\n",
    "\n",
    "keys = [rl.Oracle, rl.RME_sp, rl.GDAlgs_npre, rl.Top_K, rl.Full, rl.Full_Filter]\n",
    "\n",
    "model = rl.LognormalModel()\n",
    "noise_model = rl.DenseNoise(-2)\n",
    "model_params = rl.Params(d = d, eps = eps, param = param)\n",
    "inputfilename = 'data/error-sen-Lognormal.pkl'\n",
    "outputfilename = 'figs/error-sen-Lognormal.pdf'\n",
    "\n",
    "title = 'Lognormal'\n",
    "xlabel = '$k\\'$'\n",
    "ylabel = '$\\\\ell_2$ error'\n",
    "\n",
    "plot_k_loss = rl.plot_data(model = model, noise_model = noise_model, params = model_params, loss = rl.err, keys = keys)\n",
    "\n",
    "plot_k_loss.plotxy_fromfile(outputfilename = outputfilename, filename = inputfilename, title = title, xlabel = xlabel, ylabel = ylabel, figsize = figsize, fsize = fsize, fpad = fpad, xs = xs, fontname = fontname)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"loss vs sensitivity -- Gaussian -- dense biased -- set data\"\n",
    "\n",
    "il.reload(rl)\n",
    "\n",
    "trials = 10\n",
    "\n",
    "d, eps, param, m = 100, 0.1, 2, 600\n",
    "xs = list(np.arange(1, 41, 3))\n",
    "\n",
    "keys = [rl.Oracle, rl.RME_sp, rl.GDAlgs_npre, rl.Top_K, rl.Full, rl.Full_Filter]\n",
    "\n",
    "model = rl.GaussianModel()\n",
    "noise_model = rl.DenseNoise(-2)\n",
    "model_params = rl.Params(d = d, eps = eps, param = param, m = m)\n",
    "\n",
    "inputfilename = 'data/error-sen-Gaussian.pkl'\n",
    "plot_k = rl.load_data(model = model, noise_model = noise_model, params = model_params, loss = rl.err, keys = keys)\n",
    "plot_k.setdata_tofile(filename = inputfilename, xvar_name = 'sen', trials = trials, xs = xs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"loss vs sensitivity -- Gaussian -- dense biased -- plot\"\n",
    "\n",
    "il.reload(rl)\n",
    "\n",
    "trials = 20\n",
    "\n",
    "d, eps, param, m = 100, 0.1, 2, 600\n",
    "xs = list(np.arange(1, 41, 3))\n",
    "\n",
    "keys = [rl.Oracle, rl.RME_sp, rl.GDAlgs, rl.Top_K, rl.Top_K_Filtered, rl.Topk_GD]\n",
    "\n",
    "model = rl.GaussianModel()\n",
    "noise_model = rl.DenseNoise(-2)\n",
    "model_params = rl.Params(d = d, eps = eps, param = param)\n",
    "inputfilename = 'data/error-sen-Gaussian.pkl'\n",
    "outputfilename = 'figs/error-sen-Gaussian.pdf'\n",
    "\n",
    "title = 'Gaussian'\n",
    "xlabel = '$k\\'$'\n",
    "ylabel = '$\\\\ell_2$ error'\n",
    "\n",
    "plot_k_loss = rl.plot_data(model = model, noise_model = noise_model, params = model_params, loss = rl.err, keys = keys)\n",
    "\n",
    "plot_k_loss.plotxy_fromfile(outputfilename = outputfilename, filename = inputfilename, title = title, xlabel = xlabel, ylabel = ylabel, figsize = figsize, fsize = fsize, fpad = fpad, xs = xs, fontname = fontname)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"loss vs sensitivity -- all three -- plot\"\n",
    "\n",
    "inputfilename1 = 'data/error-sen-Fisk-inf.pkl'\n",
    "inputfilename2 = 'data/error-sen-Pareto-inf.pkl'\n",
    "inputfilename3 = 'data/error-sen-T-inf.pkl'\n",
    "outputfilename = 'figs/appendix/error-sen-3-inf.pdf'\n",
    "\n",
    "il.reload(rl)\n",
    "\n",
    "trials = 10\n",
    "\n",
    "d, eps, param, m = 100, 0.1, 2, 600\n",
    "xs = list(np.arange(1, 41, 3))\n",
    "\n",
    "keys = [rl.Oracle, rl.RME_sp, rl.GDAlgs_npre, rl.Top_K, rl.Full, rl.Full_Filter]\n",
    "\n",
    "model = rl.TModel()\n",
    "noise_model = rl.DenseNoise(-2)\n",
    "model_params = rl.Params(d = d, eps = eps, param = param)\n",
    "title = ['Fisk ($c=2$)', 'Pareto ($b=2$)', \"Student $t$ ($\\\\nu=2$)\"]\n",
    "xlabel = ['$k\\'$', '$k\\'$', '$k\\'$']\n",
    "ylabel = '$\\\\ell_2$ error'\n",
    "xlim = (10, 40)\n",
    "ylim = (0, 4)\n",
    "\n",
    "plot_k_loss = rl.plot_data(model = model, noise_model = noise_model, params = model_params, loss = rl.err, keys = keys)\n",
    "\n",
    "plot_k_loss.plotxy_3_fromfile(outputfilename = outputfilename, filename1 = inputfilename1, filename2 = inputfilename2, filename3 = inputfilename3, title = title, xlabel = xlabel, ylabel = ylabel, figsize = figsize, fsize = fsize, fpad = fpad, xs = xs, fontname = fontname, xlim=xlim, ylim=ylim)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
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       "\n",
       "\n",
       "<style>\n",
       "\n",
       "</style>\n",
       "\n",
       "<div id=\"fig_el286061403788708771523863783698\"></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_el286061403788708771523863783698\", {\"width\": 1800.0, \"height\": 375.0, \"axes\": [{\"bbox\": [0.05025462962962965, 0.2914444444444445, 0.2689120370370369, 0.5005555555555555], \"xlim\": [10.0, 40.0], \"ylim\": [0.0, 1.5], \"xdomain\": [10.0, 40.0], \"ydomain\": [0.0, 1.5], \"xscale\": \"linear\", \"yscale\": \"linear\", \"axes\": [{\"position\": \"bottom\", \"nticks\": 7, \"tickvalues\": null, \"tickformat_formatter\": \"\", \"tickformat\": null, \"scale\": \"linear\", \"fontsize\": 8.0, \"grid\": {\"gridOn\": false}, \"visible\": true}, {\"position\": \"left\", \"nticks\": 4, \"tickvalues\": null, \"tickformat_formatter\": \"\", \"tickformat\": null, \"scale\": \"linear\", \"fontsize\": 8.0, \"grid\": {\"gridOn\": false}, \"visible\": true}], \"axesbg\": \"#FFFFFF\", \"axesbgalpha\": null, \"zoomable\": true, \"id\": \"el28606140378870953776\", \"lines\": [{\"data\": \"data01\", \"xindex\": 0, \"yindex\": 1, \"coordinates\": \"data\", \"id\": 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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",
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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",
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   "source": [
    "\"loss vs sensitivity -- all three -- plot\"\n",
    "\n",
    "inputfilename1 = 'data/error-sen-Fisk.pkl'\n",
    "inputfilename2 = 'data/error-sen-Pareto.pkl'\n",
    "inputfilename3 = 'data/error-sen-T.pkl'\n",
    "outputfilename = 'figs/appendix/error-sen-3.pdf'\n",
    "\n",
    "il.reload(rl)\n",
    "\n",
    "trials = 10\n",
    "\n",
    "d, eps, param, m = 100, 0.1, 3.1, 600\n",
    "xs = list(np.arange(1, 41, 3))\n",
    "\n",
    "keys = [rl.Oracle, rl.RME_sp, rl.GDAlgs_npre, rl.Top_K, rl.Full, rl.Full_Filter]\n",
    "\n",
    "model = rl.TModel()\n",
    "noise_model = rl.DenseNoise(-2)\n",
    "model_params = rl.Params(d = d, eps = eps, param = param)\n",
    "title = ['Fisk ($c=3$)', 'Pareto ($b=3$)', \"Student $t$ ($\\\\nu=3$)\"]\n",
    "xlabel = ['$k\\'$', '$k\\'$', '$k\\'$']\n",
    "ylabel = '$\\\\ell_2$ error'\n",
    "xlim = (10, 40)\n",
    "ylim = (0, 1.5)\n",
    "\n",
    "plot_k_loss = rl.plot_data(model = model, noise_model = noise_model, params = model_params, loss = rl.err, keys = keys)\n",
    "\n",
    "plot_k_loss.plotxy_3_fromfile(outputfilename = outputfilename, filename1 = inputfilename1, filename2 = inputfilename2, filename3 = inputfilename3, title = title, xlabel = xlabel, ylabel = ylabel, figsize = figsize, fsize = fsize, fpad = fpad, xs = xs, fontname = fontname, xlim=xlim, ylim=ylim)"
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   "metadata": {},
   "outputs": [],
   "source": []
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