plot_generation/alice_plots_paper.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {
    "collapsed": true,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from matplotlib.pylab import *\n",
    "from numpy import random\n",
    "from collections import OrderedDict\n",
    "import os\n",
    "import scipy.io\n",
    "import pdb\n",
    "from glob import glob"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {
    "collapsed": true,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "def figsize(width,height):\n",
    "    rcParams['figure.figsize'] = (width,height)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {
    "collapsed": true,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "#%matplotlib notebook\n",
    "%matplotlib inline\n",
    "import matplotlib.gridspec as gridspec\n",
    "import matplotlib.patches as patches\n",
    "font = {'size'   : 22}\n",
    "matplotlib.rc('font', **font)\n",
    "\n",
    "figsize(15, 8)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Toy Dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To specify a weight hyperparameter value (eg. lambda=1.0) for cycle-consistency regularizer, runs like\n",
    "\n",
    "```\n",
    "python alice_toy.py --lambda 1.0\n",
    "```\n",
    "\n",
    "The result will be saved in a *.mat file, read the file like:\n",
    "```\n",
    "R = scipy.io.loadmat('2noise_ali_0_1.mat')\n",
    "icp = R['icp'][0,:]\n",
    "mse = R['mse'][:,0,0]\n",
    "```   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Style\n",
    "figsize(10, 5)\n",
    "plt.rc('text', usetex=True)\n",
    "\n",
    "# Create directory to save figs\n",
    "fig_dir = 'figures/'\n",
    "directory = fig_dir\n",
    "if not os.path.exists(directory):\n",
    "    os.makedirs(directory)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def plot_hyperpara_ff(dat, logx=False, ylabel_name=None, c=(.7,.7,1), saveas=None, metric=None, pperf=None):\n",
    "    plotfn = semilogx if logx else plot\n",
    "    \n",
    "    x, y, yerr = dat[:,0], dat[:,1], dat[:,2]\n",
    "    if np.isscalar(yerr) or len(yerr) == len(y):\n",
    "        ymin = y - yerr\n",
    "        ymax = y + yerr\n",
    "    elif len(yerr) == 2:\n",
    "        ymin, ymax = yerr \n",
    "        \n",
    "\n",
    "    plotfn(x, y,'o-', lw=2, mfc=(.7,.7,1), color=(.7,.7,1), ms=14)\n",
    "    fill_between(x, ymax, ymin, alpha=0.2, color=(.7,.7,1))             \n",
    "            \n",
    "    if metric=='icp':\n",
    "        idx = np.argmax(y, axis=0)\n",
    "        if pperf:\n",
    "            axhline(pperf, color=(.7,.7,.7), ls='-', label='true samples')\n",
    "        plotfn(x[idx], y[idx], 'o', mec='b', lw=3, mfc=(.7,.7,1), color=(.7,.7,1), ms=14)\n",
    "\n",
    "    elif metric=='mse':\n",
    "        idx = np.argmin(y, axis=0)\n",
    "        if pperf:\n",
    "            axhline(pperf, color=(.7,.7,.7), ls='-', label='denoising auto-encoders')\n",
    "        plotfn(x[idx], y[idx], 'o', mec='b', lw=3, mfc=(.7,.7,1), color=(.7,.7,1), ms=14)    \n",
    "    axhline(y[0], color=(.7,.7,.7), ls='--', label='ALI (w/o CE regularizer)')\n",
    "\n",
    "\n",
    "    legend()\n",
    "    xlabel('Weighting hyperparameter')\n",
    "    if ylabel_name:\n",
    "        ylabel(ylabel_name)\n",
    "    plt.tight_layout()    \n",
    "\n",
    "    if saveas:\n",
    "        savefig('%s.png' % saveas)\n",
    "        savefig('%s.pdf' % saveas)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "collapsed": true
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   "outputs": [],
   "source": [
    "dat_toy_ali = array([\n",
    "    [4.97051, 4.07911157608],[3.97986, 5.08360862732],[1.00001, 3.2225124836],[4.88078, 6.25963449478],[3.65552, 5.47807598114],[1.99106, 4.25255298615],[1.00238, 2.71198272705],[4.7548, 5.06562995911],[3.95075, 6.40726900101],[1.0003, 5.56619977951],[3.7896, 6.3109703064],[4.91474, 6.39080381393],[2.92511, 5.7673459053],[1.48181, 2.52675223351],[1.99445, 7.12504196167],[1.98885, 3.63469362259],[1.99159, 4.48922157288],[3.8702, 3.95899200439],[4.90755, 3.42820215225],[2.93556, 5.93338871002],[2.96732, 2.70947551727],[1.99002, 2.58720541],[2.26631, 9.202044487],[1.99526, 3.72539901733],[1.00001, 6.40787887573],[1.00001, 8.14189720154],[1.00002, 3.31854152679],[3.85498, 2.31136369705],[1.00003, 3.2206196785],[4.83823, 4.78403139114],[2.98278, 5.58848142624],[3.93142, 5.3848285675],[1.00006, 3.25654983521],[4.84386, 3.67101716995],[4.64248, 5.00981521606],[3.90571, 4.68713331223],[1.00002, 3.02960419655],[4.9133, 5.63757991791],[1.02756, 6.53717756271],[1.99618, 4.32610321045],[1.98976, 2.81428337097],[2.04387, 2.00505852699],[2.98102, 5.3882894516],[3.94114, 3.9438624382],[2.4095, 4.9751701355],[0.999995, 7.28145503998],[4.89633, 4.21509790421],[4.11639, 6.60431861877],[4.8438, 5.89116239548],[4.36517, 6.17787456512],[4.00322, 7.110663414],[3.73372, 3.16338086128],[2.32728, 7.23013401031],[3.94701, 3.29042696953],[1.00002, 6.29350996017],[4.95015, 4.65011405945],[2.07254, 2.95656871796],[4.83915, 5.91632938385],[4.88627, 5.16103363037],[0.999997, 3.32765483856],[4.96144, 7.68947839737],[1.00003, 2.57388615608],[4.95166, 4.36495399475],[1.00002, 2.98563861847],[1.983, 4.74464941025],[1.97217, 3.60527944565],[1.00004, 3.01941108704],[1.98006, 2.94733953476],[1.97864, 5.8324213028],[2.98052, 6.982837677],[1.00008, 3.35223221779],[1.00002, 2.92819738388],[2.95375, 10.0],[2.88, 3.5803873539],[1.98517, 2.63706111908],[2.94079, 3.57127952576],[1.99495, 3.7458615303],[2.93355, 3.81901597977],[1.99028, 3.73834848404],[1.99635, 3.65444540977],[1.9937, 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    "])\n",
    "\n",
    "dat_toy_alice_001 = array([\n",
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0.209921],[1.98213, 1.17437],[4.33778, 0.927295],[3.95889, 2.55418],[1.00104, 1.10774],[1.95923, 2.64871],[1.98513, 3.15007],[4.94264, 0.616804],[2.00757, 3.35229],[1.00008, 1.83838],[2.22548, 2.10936],[1.00003, 1.98328],[1.5925, 3.49893]\n",
    "])\n",
    "\n",
    "dat_toy_alice_01 = array([\n",
    "    [4.96424, 0.0327058],[4.96175, 0.0232161],[4.80743, 0.0936535],[4.95224, 0.02517],[4.97361, 0.0079914],[2.21826, 0.248974],[3.95358, 0.0561953],[4.91452, 0.276276],[4.85287, 0.167963],[4.94022, 0.0300485],[4.96918, 0.0389371],[4.94606, 0.0136304],[4.88293, 0.0494305],[3.93338, 0.0826404],[4.08354, 0.0508861],[4.91224, 0.137736],[4.94399, 0.0682719],[4.76955, 0.0744037],[4.92976, 0.0731621],[4.93122, 0.072405],[4.91936, 0.309127],[4.62378, 0.0989824],[3.34545, 0.0838098],[4.23308, 0.108537],[4.94708, 0.0409496],[4.95073, 0.0195505],[4.85501, 0.0396683],[4.82925, 0.215948],[2.11742, 0.142805],[4.94247, 0.00563975],[4.97432, 0.0362159],[4.95205, 0.0741989],[4.86523, 0.27529],[4.90881, 0.0361267],[4.90065, 0.145859],[4.94992, 0.0117832],[2.30488, 0.167594],[4.8733, 0.0187096],[4.96486, 0.00633677],[4.94142, 0.0429774],[4.92629, 0.0662474],[4.94891, 0.102393],[4.89354, 0.0822884],[4.91294, 0.041839],[4.88963, 0.0263189],[4.92151, 0.0176219],[4.94194, 0.080042],[4.97343, 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0.0118405],[4.93607, 0.0214048],[4.96035, 0.130911],[4.86325, 0.0907282],[2.15611, 0.788512],[4.94094, 0.0519572],[4.94386, 0.0337698],[2.92385, 0.197635],[4.0784, 0.147173],[4.92664, 0.0581702],[4.93161, 0.0345267],[4.90725, 0.15102],[4.92233, 0.0669093],[2.78205, 0.0734744],[3.04398, 0.254739],[4.97399, 0.0136111],[4.8986, 0.234739],[2.07415, 1.3355],[3.05936, 0.4176],[4.79099, 0.397492],[4.87417, 0.185619],[4.93066, 0.045971],[4.89438, 0.125151],[4.82385, 0.0801073],[4.84904, 0.0384804],[4.82345, 0.0115996],[4.43478, 0.0543388],[4.82602, 0.0666664],[4.96806, 0.0977293],[4.87556, 0.0681002],[3.73422, 0.279496],[3.14193, 0.0850914],[4.92586, 0.0220139],[4.92877, 0.0249923],[4.71873, 0.127035],[4.02601, 0.210933],[4.9614, 0.129286],[4.69101, 0.0924577],[4.84759, 0.234214],[4.81081, 0.214087],[4.95212, 0.0877008],[4.94652, 0.225158],[4.94647, 0.0415255],[4.52315, 0.192538],[4.96384, 0.0362189],[4.92611, 0.0355608],[4.87624, 0.155712],[4.96643, 0.0313811],[4.96545, 0.025829],[4.96705, 0.00567007],[4.82605, 0.214151],[4.93334, 0.129204],[4.73816, 0.10945],[2.8379, 0.394234],[4.93997, 0.0208555],[4.91181, 0.128706],[4.8926, 0.0485463],[4.88147, 0.0210038],[4.85323, 0.0145062],[4.87437, 0.077551],[4.95456, 0.0510875],[4.93308, 0.0576327],[4.91435, 0.122894],[4.92944, 0.0509664],[3.76659, 0.0826073],[4.91797, 0.0702747],[4.93787, 0.196675],[4.87072, 0.0225846],[4.95561, 0.0053444],[2.98734, 0.214298],[4.96872, 0.0218604],[2.53141, 0.057249],[3.94186, 0.321956],[4.94637, 0.011607],[4.94499, 0.254893],[4.03328, 0.134461],[4.93795, 0.0862237],[2.34471, 0.185718],[4.87691, 0.0703887],[2.94732, 0.0560412],[4.9342, 0.00809639],[4.96041, 0.0364404],[4.92221, 0.0385462],[3.95613, 0.15562],[2.06679, 0.166966],[4.90443, 0.0281913],[2.96064, 0.497515],[4.87665, 0.0567084],[4.92516, 0.327205],[3.41469, 0.147907],[4.56772, 0.324816],[4.8341, 0.205606],[4.81106, 0.11484],[4.96302, 0.0843993],[4.96672, 0.0172353],[4.91896, 0.0470291],[4.88215, 0.0901128],[4.95448, 0.0157621],[4.91024, 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0.171988],[4.89093, 0.0341777],[3.01093, 0.138668],[3.30342, 0.118291],[4.93856, 0.0278598],[3.9044, 0.216012],[4.93234, 1.01371],[4.88123, 0.0435288],[2.19072, 0.396723],[4.9364, 0.00887914],[4.82972, 0.0968993],[2.749, 0.133891],[4.54777, 0.22363],[4.90286, 0.0723115],[4.91219, 0.025436],[4.95261, 0.033699],[4.91152, 0.0981715],[4.94745, 0.0433243],[2.04034, 0.108967],[4.85089, 0.0709363],[4.57139, 0.0449154],[4.86771, 0.127984],[2.61208, 0.350196],[4.9655, 0.0562485],[3.16341, 0.135725],[4.12302, 0.081751],[3.81252, 0.0403925],[3.97746, 0.0588117],[4.76972, 0.138842],[4.84843, 0.0686487],[4.96106, 0.0120758],[4.91559, 0.18985],[2.52765, 0.189685],[4.3008, 0.0318995],[4.97082, 0.0148206],[4.94504, 0.0799551],[4.91679, 0.0222354],[4.96219, 0.125217],[4.95615, 0.0210219],[4.9642, 0.026617],[4.83843, 0.123961],[4.77894, 0.0488416],[4.80635, 0.173713],[3.19347, 0.0533819],[2.72844, 0.0895268],[4.90145, 0.0859718],[4.93741, 0.105438],[4.33627, 0.0284103],[4.86249, 0.383271],[4.87749, 0.0391108],[3.14781, 0.0931037],[4.89847, 0.169685],[4.96585, 0.073611],[4.97094, 0.00899741],[4.91572, 0.0417463],[4.95845, 0.0133323],[4.89676, 0.240142],[2.90466, 0.153138],[4.94331, 0.067113],[4.90475, 0.146085],[4.15726, 0.0684004],[4.86749, 0.0800095],[3.56681, 0.140962],[4.92572, 0.0470897],[4.96335, 0.119808],[4.92176, 0.98849],[2.95474, 0.783167],[4.95592, 0.0587504],[4.75712, 0.105687],[4.94377, 0.0521474],[4.95464, 0.0174787],[4.96099, 0.0514016],[4.7638, 0.172213],[4.89923, 0.0601796],[2.78284, 0.545444],[2.34743, 0.827719],[4.91527, 0.0121924],[2.98407, 0.12134],[4.93308, 0.129568],[4.87589, 0.12561],[3.92453, 0.159694],[2.14161, 0.900815],[4.87839, 0.200648],[4.96074, 0.0224523],[4.86487, 0.430577],[4.82048, 0.190158],[4.83722, 0.0328335],[3.32737, 0.200484],[4.66916, 0.139219]\n",
    "])\n",
    "\n",
    "dat_toy_alice_1 = array([\n",
    "    [4.915, 0.0228054],[4.94397, 0.0114046],[4.72653, 0.00347172],[4.89296, 0.00818895],[4.8699, 0.0422828],[4.48569, 0.031812],[3.8725, 0.0160966],[4.84382, 0.0321291],[3.89951, 0.0189693],[4.96415, 0.0044189],[4.94354, 0.00428854],[4.96076, 0.0048655],[4.88529, 0.0100628],[4.18644, 0.0224861],[3.86944, 0.0145094],[4.12478, 0.0186733],[4.94832, 0.0177923],[4.86059, 0.0123844],[4.90427, 0.0239073],[4.83962, 0.0357389],[4.473, 0.0390213],[3.50969, 0.0509686],[2.98604, 0.0424711],[3.22374, 0.0182444],[4.92164, 0.0126164],[4.94817, 0.00866056],[4.83132, 0.0122676],[4.75434, 0.0415332],[3.08807, 0.0352785],[4.92368, 0.00324212],[4.23975, 0.00922528],[4.972, 0.00243155],[4.77789, 0.0275154],[4.89682, 0.0172998],[4.8541, 0.018432],[4.92583, 0.00367383],[4.03363, 0.0260234],[4.91971, 0.00997944],[4.93691, 0.00411399],[4.04324, 0.0133985],[4.83622, 0.00833764],[4.89618, 0.039369],[4.43601, 0.0170101],[4.96655, 0.0122973],[4.92538, 0.00293223],[4.89515, 0.0053912],[4.83705, 0.0569989],[4.98617, 0.00670499],[4.92246, 0.00860984],[4.16927, 0.019864],[4.90888, 0.0166972],[3.85017, 0.0296273],[2.38438, 0.0165511],[4.96932, 0.0163934],[4.96528, 0.00726656],[4.90733, 0.0101889],[4.78022, 0.0135065],[4.86377, 0.00541745],[4.51954, 0.0101578],[4.79751, 0.00370534],[4.9617, 0.00897095],[4.12456, 0.0282894],[4.96907, 0.00248462],[4.12392, 0.0302504],[4.90937, 0.0109345],[3.04865, 0.0432951],[4.35613, 0.0321755],[4.82945, 0.0348235],[4.84561, 0.0436255],[4.6665, 0.0233883],[4.94774, 0.00622504],[3.92498, 0.0365131],[4.9209, 0.0210227],[4.11947, 0.00955071],[4.04868, 0.0247777],[4.8398, 0.0338421],[4.90756, 0.0580037],[4.84514, 0.0161735],[4.83624, 0.0824458],[4.88907, 0.00587788],[4.87389, 0.0270939],[4.72097, 0.0226993],[2.81872, 0.0321599],[4.06171, 0.0178952],[4.9402, 0.00473237],[4.73252, 0.00600224],[2.00994, 0.0183275],[4.94987, 0.00700734],[4.93357, 0.00619087],[4.59974, 0.0375816],[4.96949, 0.0155914],[2.29274, 0.014522],[4.91619, 0.0127185],[3.86907, 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0.0445942],[4.88879, 0.0075283],[4.93308, 0.0111765],[4.77092, 0.0127049],[3.89887, 0.0248856],[4.88372, 0.0128805],[4.97499, 0.00236108],[4.80949, 0.028131],[4.74306, 0.0118199],[4.92995, 0.0115816],[4.95428, 0.00418361],[4.95219, 0.00421557],[4.86837, 0.0136003],[4.93815, 0.0327395],[4.90381, 0.00649196],[2.69946, 0.0253324],[3.87618, 0.0247983],[4.90834, 0.0075197],[3.52578, 0.00907153],[4.76954, 0.0068574],[4.91117, 0.0602378],[4.85666, 0.00647022],[4.62958, 0.0078473],[4.86422, 0.0557878],[4.8235, 0.0126218],[4.96511, 0.0142414],[4.95458, 0.00769437],[4.96081, 0.00956461],[4.81839, 0.0255081],[3.92348, 0.0195623],[4.43611, 0.017619],[4.82659, 0.0194094],[4.65975, 0.0119756],[4.84737, 0.0141959],[3.74876, 0.0270185],[4.88826, 0.00368482],[4.6756, 0.0133547],[4.91832, 0.0154459],[4.24522, 0.0272818],[4.91312, 0.00691839],[4.81493, 0.030262],[4.80079, 0.236425],[4.95666, 0.0120954],[4.07541, 0.005207],[4.92018, 0.0488698],[4.86487, 0.016434],[3.14487, 0.0310669],[4.75838, 0.0326312],[4.95172, 0.016729],[4.80341, 0.0331387],[4.9235, 0.00525561],[4.82509, 0.0268302],[4.84196, 0.0199192],[3.87147, 0.0258416],[4.84694, 0.0327325],[4.51808, 0.00693768],[4.64767, 0.0433329],[4.71796, 0.0487307],[4.84191, 0.00310803],[4.72958, 0.0460843],[4.79067, 0.0215029]\n",
    "])\n",
    "\n",
    "dat_toy_alice_10 = array([\n",
    "    [2.48441, 0.000252772],[3.40883, 0.000631335],[2.54153, 0.00219016],[2.37646, 0.000319466],[3.77949, 0.000118929],[3.89852, 0.00223084],[2.9462, 0.00608388],[4.13535, 0.00218628],[4.17478, 0.013884],[3.67269, 0.000405312],[3.30948, 0.000139854],[3.21844, 0.000165064],[3.58708, 0.00136694],[4.21612, 0.00148963],[3.20748, 0.000385397],[3.68778, 0.000459607],[3.13524, 0.000265476],[3.57862, 0.00157444],[4.02772, 0.00681521],[4.08727, 0.000525059],[4.23234, 0.0107003],[3.66318, 0.00210534],[3.55835, 0.00212044],[1.43428, 0.00333057],[3.87462, 0.000287306],[2.26296, 0.000757119],[3.385, 0.000264701],[4.32293, 0.000644797],[3.17375, 0.0160303],[3.76108, 0.00331938],[3.71648, 0.000218443],[3.8876, 0.000139868],[3.44692, 0.00127475],[3.03668, 0.00121591],[3.80335, 0.000465579],[3.20883, 0.000404725],[3.62363, 0.000380053],[1.29837, 0.000769085],[3.35821, 0.000158623],[3.71154, 0.000385019],[2.70152, 0.00135447],[3.39213, 0.00325859],[1.61259, 0.000359957],[3.34063, 0.0001639],[2.6549, 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0.00419038]\n",
    "])\n",
    "\n",
    "lambda_toy = [0,1E-2,1E-1,1,10]\n",
    "dat_toy = hstack((dat_toy_ali, dat_toy_alice_001, dat_toy_alice_01, dat_toy_alice_1, dat_toy_alice_10))\n",
    "dat_toy_mean = np.mean(dat_toy,0)\n",
    "dat_toy_std  = np.std(dat_toy,0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dat_toy_mean_icp = [dat_toy_mean[2*i+0] for i in range(5)]\n",
    "dat_toy_mean_mse = [dat_toy_mean[2*i+1] for i in range(5)]\n",
    "dat_toy_std_icp = [dat_toy_std[2*i+0] for i in range(5)]\n",
    "dat_toy_std_mse = [dat_toy_std[2*i+1] for i in range(5)]\n",
    "\n",
    "dat_toy_icp = vstack((lambda_toy, dat_toy_mean_icp, dat_toy_std_icp)).T\n",
    "dat_toy_mse = vstack((lambda_toy, dat_toy_mean_mse, dat_toy_std_mse)).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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RlWdMJo3g+XOmETSbnWXz88+1rJaNM+wMXlNTwLlz7dn/nRy4MruthNFR4Lvf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\nRESe8P8DGRkKwyoBUWsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x110f45bd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_hyperpara_ff(dat_toy_icp,logx=True, ylabel_name=\"Inception Score\" ,saveas=fig_dir+'toy_icp_weighting', metric='icp', pperf=4.977)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Z2Qndu+x/2PBDfTKZ1MbGRl/jB/slApIbJSD4gcF/T8Mcg7D8oD2sXunQrLUd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\n8XGSgc/wSQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10f9ef090>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_hyperpara_ff(dat_toy_mse,logx=True, ylabel_name=\"Mean Square Error\" ,saveas=fig_dir+'toy_mse_weighting', metric='mse', pperf=0.003 )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## MNIST"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To specify a weight hyperparameter value (eg. lambda=1.0) for cycle-consistency regularizer, runs like\n",
    "\n",
    "```\n",
    "python alice_mnist.py --lambda 1.0\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dat_MNIST_ALICE_ICP = array([\n",
    "    [0, 8.749, 0.09],\n",
    "    [0.00001, 8.851, 0.08],\n",
    "    [0.0001, 8.869, 0.10],\n",
    "    [0.001, 8.901, 0.09],\n",
    "    [0.01, 9.1045, 0.07],\n",
    "    [0.1, 9.2618, 0.04],\n",
    "    [1, 9.279, 0.07],\n",
    "    [10, 8.918, 0.11],\n",
    "    [100, 8.533, 0.09],\n",
    "    [1000, 7.776, 0.10],\n",
    "    [10000, 1.334, 0.01]\n",
    "])\n",
    "\n",
    "dat_MNIST_ALICE_MSE = array([\n",
    "    [0, 0.4803, 0.100],\n",
    "    [0.00001, 0.5012, 0.039],\n",
    "    [0.0001, 0.5272, 0.037],\n",
    "    [0.001, 0.4567, 0.024],\n",
    "    [0.01, 0.1721, 0.012],\n",
    "    [0.1, 0.0982, 0.008],\n",
    "    [1, 0.0803, 0.007],\n",
    "    [10, 0.0856, 0.006],\n",
    "    [100, 0.2228, 0.014],\n",
    "    [1000, 0.2389, 0.026],\n",
    "    [10000, 0.1107, 0.005]\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {
    "collapsed": true,
    "scrolled": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {
    "collapsed": true,
    "scrolled": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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PHwPr68BLL/ldos5RMce1xcQBXDJBqyVmTSciIiLyXU8PcOKEfn3rlh6UgOqj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\nIiIiagsMXImIiIioLTBwJSIiIqK28P8Do2aZBGmgF2QAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10fc05910>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_hyperpara_ff(dat_MNIST_ALICE_ICP,logx=True, ylabel_name=\"Inception Score\" ,saveas=fig_dir+'mnist_icp_weighting', metric='icp', pperf=9.8793)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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4UkN48KDwADKR0Nvl0QJvC8fREoK1NT21t2s5AcsHiCjdxx9/vO3I0XPnzmF8fDxjuYBd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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11017df10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_hyperpara_ff(dat_MNIST_ALICE_MSE,logx=True, ylabel_name=\"Mean Square Error\" ,saveas=fig_dir+'mnist_mse_weighting', metric='mse')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## CIFAR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dat_CIFAR_ALICE_ICP = array([\n",
    "    [0, 5.93, 0.0437],\n",
    "    [1.00E-06, 6.015, 0.0284],\n",
    "    [1.00E-04, 5.994, 0.0305],\n",
    "    [1.00E-02, 5.83, 0.0221],\n",
    "    [1.00E-01, 4.42, 0.0247],\n",
    "    [5.00E-01, 3.02, 0.0222],\n",
    "    [1.00E+00, 2.18, 0.0204],\n",
    "    [1.00E+01, 1.942, 0.0156],\n",
    "    [5.00E+01, 1.32, 0.0122],\n",
    "    [1.00E+02, 1.22, .0172],\n",
    "    [1.00E+03, 1.08, 0.0211]\n",
    "])\n",
    "\n",
    "dat_CIFAR_ALICE_MSE = array([\n",
    "    [0, 0.672, 0.1129],\n",
    "    [1.00E-06, 0.4155, 0.2015],\n",
    "    [1.00E-04, 0.2902, 0.1071],\n",
    "    [1.00E-02, 0.1553, 0.0566],\n",
    "    [1.00E-01, 0.0991, 0.0349],\n",
    "    [5.00E-01, 0.0786, 0.0198],\n",
    "    [1.00E+00, 0.0561, 0.0281],\n",
    "    [1.00E+01, 0.0321, 0.00198],\n",
    "    [5.00E+01, 0.0323, 0.003108],\n",
    "    [1.00E+02, 0.0295, 0.00221],\n",
    "    [1.00E+03, 0.0217, 0.00176]\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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J+MustYuSXpf018VoIAAgM2NcveuDD7rfP/lE+vJLwiuArSdoj2uPpBFr7c+stXMpln8u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\nMmNx4gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1106e3cd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_hyperpara_ff(dat_CIFAR_ALICE_ICP,logx=True, ylabel_name=\"Inception Score\" ,saveas=fig_dir+'cifar_icp_weighting', metric='icp')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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yMoLl5eWy+gfbEgFAuwR43zDYcxrkOQjKBu1qjUoHFbZUYAVAobEjgS4qUJBT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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10f130250>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_hyperpara_ff(dat_CIFAR_ALICE_MSE,logx=True, ylabel_name=\"Mean Square Error\" ,saveas=fig_dir+'cifar_mse_weighting', metric='mse')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "scrolled": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}