Training_bayesianopt.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "%config Application.log_level='INFO'\n",
    "import logging\n",
    "logging.getLogger().setLevel(logging.INFO)\n",
    "log = logging.getLogger()\n",
    "#for hdlr in log.handlers[:]:  # remove all old handlers\n",
    "#    log.removeHandler(hdlr)\n",
    "fileh = logging.FileHandler('/storage/vbsjjlnu/log.txt', 'a')\n",
    "formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')\n",
    "fileh.setFormatter(formatter)\n",
    "log.addHandler(fileh)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-01-27T10:17:08.744141Z",
     "start_time": "2020-01-27T10:17:07.742537Z"
    }
   },
   "outputs": [],
   "source": [
    "%load_ext autoreload"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import model_opt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.11684126844902726\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "config = {\n",
    "        \"base_dir\":        \"/storage/vbsjjlnu\",\n",
    "        \"plot_config\":     \"Full2018v6s5\",\n",
    "        \"cut\":             \"boost\",\n",
    "        \"samples_version\": \"v10\",\n",
    "        \"cols\": ['mjj_vbs', 'vbs_0_pt', 'vbs_1_pt', 'deltaeta_vbs', 'deltaphi_vbs', \n",
    "            'mjj_vjet', 'vjet_0_pt', 'vjet_1_pt', 'vjet_0_eta', 'vjet_1_eta', \n",
    "            'Lepton_pt', 'Lepton_eta', 'Lepton_flavour', \n",
    "            'PuppiMET', \n",
    "            'Zvjets_0', 'Zlep', \n",
    "            'Asym_vbs', 'Asym_vjet', 'A_ww', \n",
    "            'Mtw_lep', 'w_lep_pt', 'Mww', 'R_ww', 'R_mw', \n",
    "            'Centr_vbs', 'Centr_ww'\n",
    "            ],\n",
    "    \"verbose\": 0\n",
    "    }\n",
    "ev = model_opt.test_vbsdnn_model(config)\n",
    "print(ev)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## GPyOpt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# !pip3 install --user gpyopt\n",
    "\n",
    "import GPyOpt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "config = {\n",
    "    \"base_dir\":        \"/storage/vbsjjlnu\",\n",
    "    \"plot_config\":     \"Full2018v6s5\",\n",
    "    \"cut\":             \"boost\",\n",
    "    \"samples_version\": \"v10\",\n",
    "    \"cols\": ['mjj_vbs','deltaeta_vbs','Centr_ww','Asym_vbs','A_ww',\n",
    "             'Lepton_eta','mjj_vjet','vbs_0_pt','vbs_1_pt','vjet_0_pt',\n",
    "             'Lepton_pt','Centr_vbs','w_lep_pt','Mtw_lep','deltaphi_vbs',\n",
    "             'Mww','PuppiMET','Lepton_flavour','R_mw','vjet_0_eta','Zlep',\n",
    "             'Zvjets_0','R_ww',\n",
    "        ],\n",
    "    \"verbose\": 0\n",
    "}\n",
    "    \n",
    "bounds = [{'name': 'batch_size', 'type': 'discrete',  'domain': (1024, 2048, 4096)},\n",
    "          {'name': 'n_layers', 'type': 'discrete',  'domain': (2,3,4,5,6)},\n",
    "          {'name': 'n_nodes', 'type': 'discrete',  'domain': (10,30,50,80,100,150,200)},\n",
    "          {'name': 'dropout', 'type': 'discrete',  'domain': (0,0.05,0.1,0.2,0.3)},\n",
    "          {'name': 'batch_norm', 'type': 'discrete',  'domain': (0,1)},\n",
    "          {'name': 'input_dim', 'type': 'discrete',  'domain': tuple(list(range(4,len(config[\"cols\"])+1) ))},\n",
    "         ]\n",
    "\n",
    "\n",
    "fixed_params={\n",
    "    \"epochs\": 200,\n",
    "    \"val_ratio\": 0.25,\n",
    "    \"test_ratio\": 0.1,\n",
    "    \"patience\": (0.001, 10)\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "## optimizer function\n",
    "def f(x):\n",
    "    ev = model_opt.evaluate_vbsdnn_model(config, fixed_params, x)\n",
    "    return ev"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:root:> L:2 , N:80, BS:4096, D:0.30, BN:True, I:14\n",
      "INFO:root:1581783129\n",
      "INFO:root:Start Training\n",
      "INFO:root:Saving training metadata\n",
      "INFO:root:Reporting Significance: 0.15219973645591836\n",
      "INFO:root:> L:5 , N:50, BS:2048, D:0.10, BN:False, I:6\n",
      "INFO:root:1581783155\n",
      "INFO:root:Start Training\n",
      "INFO:root:Saving training metadata\n",
      "INFO:root:Reporting Significance: 0.14645825803345874\n",
      "INFO:root:> L:3 , N:30, BS:1024, D:0.00, BN:False, I:9\n",
      "INFO:root:1581783190\n",
      "INFO:root:Start Training\n",
      "INFO:root:Saving training metadata\n",
      "INFO:root:Reporting Significance: 0.15099210700999735\n",
      "INFO:root:> L:3 , N:50, BS:2048, D:0.00, BN:False, I:13\n",
      "INFO:root:1581783220\n",
      "INFO:root:Start Training\n",
      "INFO:root:Saving training metadata\n",
      "INFO:root:Reporting Significance: 0.15031770276769127\n",
      "INFO:root:> L:3 , N:30, BS:4096, D:0.30, BN:False, I:23\n",
      "INFO:root:1581783246\n",
      "INFO:root:Start Training\n",
      "INFO:root:Saving training metadata\n",
      "INFO:root:Reporting Significance: 0.15713873751453747\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "opt_mnist = GPyOpt.methods.BayesianOptimization(f=f, domain=bounds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "ename": "error",
     "evalue": "failed in converting 2nd argument `b' of _flapack.dpotrs to C/Fortran array",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31merror\u001b[0m                                     Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-76-51092e5c3f41>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mopt_mnist\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun_optimization\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmax_iter\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/GPyOpt/core/bo.py\u001b[0m in \u001b[0;36mrun_optimization\u001b[0;34m(self, max_iter, max_time, eps, context, verbosity, save_models_parameters, report_file, evaluations_file, models_file)\u001b[0m\n\u001b[1;32m    133\u001b[0m             \u001b[0;31m# --- Update model\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    134\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 135\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_update_model\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnormalization_type\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    136\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlinalg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlinalg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mLinAlgError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    137\u001b[0m                 \u001b[0;32mbreak\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/GPyOpt/core/bo.py\u001b[0m in \u001b[0;36m_update_model\u001b[0;34m(self, normalization_type)\u001b[0m\n\u001b[1;32m    249\u001b[0m                 \u001b[0mY_inmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mY\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    250\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 251\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdateModel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_inmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mY_inmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    252\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    253\u001b[0m         \u001b[0;31m# Save parameters of the model\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/GPyOpt/models/gpmodel.py\u001b[0m in \u001b[0;36mupdateModel\u001b[0;34m(self, X_all, Y_all, X_new, Y_new)\u001b[0m\n\u001b[1;32m     81\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_create_model\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_all\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mY_all\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     82\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 83\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_XY\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_all\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mY_all\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     84\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     85\u001b[0m         \u001b[0;31m# WARNING: Even if self.max_iters=0, the hyperparameters are bit modified...\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/GPy/core/gp.py\u001b[0m in \u001b[0;36mset_XY\u001b[0;34m(self, X, Y)\u001b[0m\n\u001b[1;32m    236\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    237\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mX\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mObsAr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 238\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate_model\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    239\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    240\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mset_X\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/paramz/core/updateable.py\u001b[0m in \u001b[0;36mupdate_model\u001b[0;34m(self, updates)\u001b[0m\n\u001b[1;32m     58\u001b[0m             \u001b[0ms\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_update_on\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mupdates\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     59\u001b[0m         \u001b[0mp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtraverse\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mturn_updates\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 60\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrigger_update\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     61\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     62\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mtoggle_update\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/paramz/core/updateable.py\u001b[0m in \u001b[0;36mtrigger_update\u001b[0;34m(self, trigger_parent)\u001b[0m\n\u001b[1;32m     77\u001b[0m             \u001b[0;31m#print \"Warning: updates are off, updating the model will do nothing\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     78\u001b[0m             \u001b[0;32mreturn\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 79\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_trigger_params_changed\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrigger_parent\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/paramz/core/parameter_core.py\u001b[0m in \u001b[0;36m_trigger_params_changed\u001b[0;34m(self, trigger_parent)\u001b[0m\n\u001b[1;32m    132\u001b[0m         \"\"\"\n\u001b[1;32m    133\u001b[0m         \u001b[0;34m[\u001b[0m\u001b[0mp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_trigger_params_changed\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrigger_parent\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mp\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparameters\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_fixed\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 134\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnotify_observers\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mtrigger_parent\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minf\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    135\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    136\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_size_transformed\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/paramz/core/observable.py\u001b[0m in \u001b[0;36mnotify_observers\u001b[0;34m(self, which, min_priority)\u001b[0m\n\u001b[1;32m     89\u001b[0m                 \u001b[0mwhich\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     90\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mmin_priority\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 91\u001b[0;31m                 \u001b[0;34m[\u001b[0m\u001b[0mcallble\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwhich\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mwhich\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0m_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallble\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mobservers\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     92\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     93\u001b[0m                 \u001b[0;32mfor\u001b[0m \u001b[0mp\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallble\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mobservers\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/paramz/core/observable.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m     89\u001b[0m                 \u001b[0mwhich\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     90\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mmin_priority\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 91\u001b[0;31m                 \u001b[0;34m[\u001b[0m\u001b[0mcallble\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwhich\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mwhich\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0m_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallble\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mobservers\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     92\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     93\u001b[0m                 \u001b[0;32mfor\u001b[0m \u001b[0mp\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallble\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mobservers\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/paramz/core/parameter_core.py\u001b[0m in \u001b[0;36m_parameters_changed_notification\u001b[0;34m(self, me, which)\u001b[0m\n\u001b[1;32m    506\u001b[0m         \"\"\"\n\u001b[1;32m    507\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_optimizer_copy_transformed\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m \u001b[0;31m# tells the optimizer array to update on next request\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 508\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparameters_changed\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    509\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_pass_through_notify_observers\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mme\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwhich\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    510\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnotify_observers\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mwhich\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mwhich\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/GPy/core/gp.py\u001b[0m in \u001b[0;36mparameters_changed\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    265\u001b[0m             \u001b[0mthis\u001b[0m \u001b[0mmethod\u001b[0m \u001b[0myourself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mthere\u001b[0m \u001b[0mmay\u001b[0m \u001b[0mbe\u001b[0m \u001b[0munexpected\u001b[0m \u001b[0mconsequences\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    266\u001b[0m         \"\"\"\n\u001b[0;32m--> 267\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mposterior\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_log_marginal_likelihood\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgrad_dict\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minference_method\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minference\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkern\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlikelihood\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mY_normalized\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean_function\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mY_metadata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    268\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlikelihood\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate_gradients\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgrad_dict\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'dL_dthetaL'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    269\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkern\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate_gradients_full\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgrad_dict\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'dL_dK'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/GPy/inference/latent_function_inference/exact_gaussian_inference.py\u001b[0m in \u001b[0;36minference\u001b[0;34m(self, kern, X, likelihood, Y, mean_function, Y_metadata, K, variance, Z_tilde)\u001b[0m\n\u001b[1;32m     58\u001b[0m         \u001b[0mWi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mLW\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mLWi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mW_logdet\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpdinv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mKy\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     59\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 60\u001b[0;31m         \u001b[0malpha\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdpotrs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mLW\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mYYT_factor\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlower\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     61\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     62\u001b[0m         \u001b[0mlog_marginal\u001b[0m \u001b[0;34m=\u001b[0m  \u001b[0;36m0.5\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0mY\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msize\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mlog_2_pi\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mY\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mW_logdet\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0malpha\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mYYT_factor\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/GPy/util/linalg.py\u001b[0m in \u001b[0;36mdpotrs\u001b[0;34m(A, B, lower)\u001b[0m\n\u001b[1;32m    123\u001b[0m     \"\"\"\n\u001b[1;32m    124\u001b[0m     \u001b[0mA\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mforce_F_ordered\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mA\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 125\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mlapack\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdpotrs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mA\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mB\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlower\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlower\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    126\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    127\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mdpotri\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mA\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlower\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31merror\u001b[0m: failed in converting 2nd argument `b' of _flapack.dpotrs to C/Fortran array"
     ]
    }
   ],
   "source": [
    "opt_mnist.run_optimization(max_iter=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Optimized Parameters:\n",
      "\ttest_ratio:\t0.1\n",
      "\tbatch_size:\t256.0\n",
      "\n",
      "optimized loss: 0.5144698499900197\n"
     ]
    }
   ],
   "source": [
    "print(\"\"\"\n",
    "Optimized Parameters:\n",
    "\\t{0}:\\t{1}\n",
    "\\t{2}:\\t{3}\n",
    "\"\"\".format(\n",
    "    bounds[0][\"name\"],opt_mnist.x_opt[0],\n",
    "    bounds[1][\"name\"],opt_mnist.x_opt[1],\n",
    "))\n",
    "print(\"optimized loss: {0}\".format(opt_mnist.fx_opt))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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