{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Copyright (c) 2020-2021 Microsoft Corporation. All rights reserved. \n",
    "\n",
    "Licensed under the MIT License.\n",
    "\n",
    "# AutoVW: ChaCha for Online AutoML with Vowpal Wabbit\n",
    "\n",
    "\n",
    "## 1. Introduction\n",
    "\n",
    "\n",
    "In this notebook, we use one real data example (regression task) to showcase AutoVW, which is an online AutoML solution based on the following work:\n",
    "\n",
    "*ChaCha for online AutoML. Qingyun Wu, Chi Wang, John Langford, Paul Mineiro and Marco Rossi. To appear in ICML 2021.*\n",
    "\n",
    "AutoVW is implemented in FLAML. FLAML requires `Python>=3.6`. To run this notebook example, please install:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install flaml[notebook,vw];"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## 2. Online regression with AutoVW\n",
    "### Load data from openml and preprocess\n",
    "\n",
    "Download [NewFuelCar](https://www.openml.org/d/41506) from OpenML."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(36203, 17) (36203,)\n"
     ]
    }
   ],
   "source": [
    "import openml\n",
    "# did = 42183\n",
    "did = 41506\n",
    "ds = openml.datasets.get_dataset(did)\n",
    "target_attribute = ds.default_target_attribute\n",
    "data = ds.get_data(target=target_attribute, dataset_format='array')\n",
    "X, y = data[0], data[1]\n",
    "print(X.shape, y.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Convert the openml dataset into vowpalwabbit examples:\n",
    "Sequentially group features into up to 10 namespaces and convert the original data examples into vowpal wabbit format."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "openml example: 8.170000076293945 [1.0000e+01 7.0000e+00 3.0000e+00 4.0000e+00        nan 6.3300e+00\n",
      " 1.3600e-01 7.3300e+00 7.0100e+00 6.9800e+00 3.0000e-03 7.0000e+00\n",
      " 9.7000e+00 1.2300e+01 1.0217e+03 0.0000e+00 5.8000e+01]\n",
      "vw example: 8.170000076293945 |a 0:10.000000 1:7.000000|b 2:3.000000 3:4.000000|c 4:nan 5:6.330000|d 6:0.136000 7:7.330000|e 8:7.010000 9:6.980000|f 10:0.003000 11:7.000000|g 12:9.700000 13:12.300000|h 14:1021.700012 15:0.000000|i 16:58.000000\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import string\n",
    "NS_LIST = list(string.ascii_lowercase) + list(string.ascii_uppercase)\n",
    "max_ns_num = 10 # the maximum number of namespaces\n",
    "orginal_dim = X.shape[1]\n",
    "max_size_per_group = int(np.ceil(orginal_dim / float(max_ns_num)))\n",
    "# sequential grouping\n",
    "group_indexes = []\n",
    "for i in range(max_ns_num):\n",
    "    indexes = [ind for ind in range(i * max_size_per_group,\n",
    "                min((i + 1) * max_size_per_group, orginal_dim))]\n",
    "    if len(indexes) > 0:\n",
    "        group_indexes.append(indexes)\n",
    "\n",
    "vw_examples = []\n",
    "for i in range(X.shape[0]):\n",
    "    ns_content = []\n",
    "    for zz in range(len(group_indexes)):\n",
    "        ns_features = ' '.join('{}:{:.6f}'.format(ind, X[i][ind]) for ind in group_indexes[zz])\n",
    "        ns_content.append(ns_features)\n",
    "    ns_line = '{} |{}'.format(str(y[i]), '|'.join('{} {}'.format(NS_LIST[j], ns_content[j]) for j in range(len(group_indexes))))\n",
    "    vw_examples.append(ns_line)\n",
    "print('openml example:', y[0], X[0])\n",
    "print('vw example:', vw_examples[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Set up the online learning loop\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import mean_squared_error\n",
    "def online_learning_loop(iter_num, vw_examples, vw_alg):\n",
    "    \"\"\"Implements the online learning loop.\n",
    "    \"\"\"\n",
    "    print('Online learning for', iter_num, 'steps...')\n",
    "    loss_list = []\n",
    "    for i in range(iter_num):\n",
    "        vw_x = vw_examples[i]\n",
    "        y_true = float(vw_examples[i].split('|')[0])\n",
    "        # predict step\n",
    "        y_pred = vw_alg.predict(vw_x)\n",
    "        # learn step\n",
    "        vw_alg.learn(vw_x)\n",
    "        # calculate one step loss\n",
    "        loss = mean_squared_error([y_pred], [y_true])\n",
    "        loss_list.append(loss)\n",
    "    return loss_list\n",
    "\n",
    "max_iter_num = 10000  # or len(vw_examples)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Vanilla Vowpal Wabbit (VW)\n",
    "Create and run a vanilla vowpal wabbit learner."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Online learning for 10000 steps...\n",
      "Final progressive validation loss of vanilla vw: 15.18087237487917\n"
     ]
    }
   ],
   "source": [
    "from vowpalwabbit import pyvw\n",
    "''' create a vanilla vw instance '''\n",
    "vanilla_vw = pyvw.vw('--quiet')\n",
    "\n",
    "# online learning with vanilla VW\n",
    "loss_list_vanilla = online_learning_loop(max_iter_num, vw_examples, vanilla_vw)\n",
    "print('Final progressive validation loss of vanilla vw:', sum(loss_list_vanilla)/len(loss_list_vanilla))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### AutoVW which tunes namespace interactions  \n",
    "Create and run an AutoVW instance which tunes namespace interactions. Each AutoVW instance allows ```max_live_model_num``` of VW models (each associated with its own hyperaparameter configurations that are tuned online) to run concurrently in each step of the online learning loop."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Seed namespaces (singletons and interactions): ['g', 'a', 'h', 'b', 'c', 'i', 'd', 'e', 'f']\n",
      "Created challengers from champion ||\n",
      "New challenger size 37, ['|ah|', '|eg|', '|gi|', '|ag|', '|de|', '|ei|', '|eh|', '|fg|', '|cf|', '|hi|', '|bf|', '|cd|', '|ai|', '|ef|', '|cg|', '|ch|', '|ad|', '|bc|', '|gh|', '|bh|', '|ci|', '|fh|', '|bg|', '|be|', '|bd|', '|fi|', '|bi|', '|df|', '|ac|', '|ae|', '|dg|', '|af|', '|di|', '|ce|', '|dh|', '|ab|', '||']\n",
      "Online learning for 10000 steps...\n",
      "Seed namespaces (singletons and interactions): ['ce', 'g', 'a', 'h', 'b', 'c', 'i', 'd', 'e', 'f']\n",
      "Created challengers from champion |ce|\n",
      "New challenger size 43, ['|be_ce|', '|bce_ce|', '|ce_ei|', '|ce_ceg|', '|ce_fh|', '|ce_gh|', '|ce_cef|', '|cd_ce|', '|ce_cg|', '|cde_ce|', '|ce_cf|', '|bd_ce|', '|ae_ce|', '|ce_gi|', '|ce_ci|', '|ab_ce|', '|ce_fg|', '|ce_di|', '|bi_ce|', '|ce_de|', '|ce_eg|', '|ce_dg|', '|ce_hi|', '|ai_ce|', '|ag_ce|', '|ac_ce|', '|bh_ce|', '|ce_ch|', '|ce|', '|ace_ce|', '|ah_ce|', '|af_ce|', '|bc_ce|', '|ce_dh|', '|ce_ef|', '|ad_ce|', '|ce_df|', '|ce_cei|', '|ce_eh|', '|bg_ce|', '|ce_ceh|', '|bf_ce|', '|ce_fi|']\n",
      "Final progressive validation loss of autovw: 8.718817421944529\n"
     ]
    }
   ],
   "source": [
    "''' import AutoVW class from flaml package '''\n",
    "from flaml import AutoVW\n",
    "\n",
    "'''create an AutoVW instance for tuning namespace interactions'''\n",
    "# configure both hyperparamters to tune, e.g., 'interactions', and fixed arguments about the online learner,\n",
    "# e.g., 'quiet' in the search_space argument.\n",
    "autovw_ni = AutoVW(max_live_model_num=5, search_space={'interactions': AutoVW.AUTOMATIC, 'quiet': ''})\n",
    "\n",
    "# online learning with AutoVW\n",
    "loss_list_autovw_ni = online_learning_loop(max_iter_num, vw_examples, autovw_ni)\n",
    "print('Final progressive validation loss of autovw:', sum(loss_list_autovw_ni)/len(loss_list_autovw_ni))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Online performance comparison between vanilla VW and AutoVW"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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206.230873 \nL 98.801691 205.453705 \nL 98.842322 205.390101 \nL 98.882953 205.480279 \nL 98.964214 205.692852 \nL 99.004844 205.581585 \nL 99.045475 205.468625 \nL 99.086105 205.53155 \nL 99.695564 206.790481 \nL 99.898717 205.373684 \nL 99.939348 205.412865 \nL 100.061239 205.515049 \nL 100.467545 206.645483 \nL 100.548806 206.600978 \nL 100.670698 206.771637 \nL 100.711328 206.685942 \nL 100.83322 205.687106 \nL 100.914481 205.757178 \nL 101.158265 206.309392 \nL 101.442679 206.860036 \nL 101.645832 206.569547 \nL 101.767723 205.52149 \nL 101.808354 205.530954 \nL 102.336551 206.554105 \nL 102.377182 206.507731 \nL 102.742857 205.719518 \nL 102.824118 205.894716 \nL 103.311685 206.867716 \nL 103.392946 206.804838 \nL 103.555469 206.621929 \nL 103.717991 206.192489 \nL 103.758622 206.271987 \nL 104.246188 207.324221 \nL 104.36808 207.283968 \nL 104.611864 206.772518 \nL 104.652494 206.732994 \nL 104.693125 206.785198 \nL 105.261953 207.878857 \nL 105.343214 207.815453 \nL 105.465106 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112.372303 209.665813 \nL 112.494195 209.448572 \nL 112.534826 209.517453 \nL 112.981762 210.14666 \nL 113.063023 210.202972 \nL 113.103654 210.170969 \nL 113.469329 209.214838 \nL 113.591221 209.372328 \nL 114.119418 210.153064 \nL 114.28194 209.82369 \nL 114.322571 209.907729 \nL 114.363202 209.875544 \nL 114.485093 209.448661 \nL 114.525724 209.483957 \nL 115.013291 210.302385 \nL 115.053921 210.280487 \nL 115.419597 209.829055 \nL 115.460227 209.867973 \nL 115.988425 210.643269 \nL 116.272839 210.106103 \nL 116.3541 210.024346 \nL 116.39473 210.052091 \nL 116.963558 210.827827 \nL 117.288603 210.257778 \nL 117.369864 210.248585 \nL 117.979323 211.026348 \nL 118.019953 210.993651 \nL 118.223106 210.724278 \nL 118.344998 210.585613 \nL 118.385629 210.658072 \nL 118.873195 211.378672 \nL 118.995087 211.362805 \nL 119.076348 211.198673 \nL 119.157609 211.24954 \nL 119.360762 211.09604 \nL 119.401393 211.170626 \nL 119.92959 211.809784 \nL 120.010852 211.665584 \nL 120.335896 211.054545 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213.915881 \nL 129.274623 213.39097 \nL 129.640298 213.849492 \nL 129.80282 213.628701 \nL 129.884081 213.557716 \nL 129.924712 213.613814 \nL 130.005973 213.557425 \nL 130.127865 213.406697 \nL 130.168495 213.454015 \nL 130.53417 213.956613 \nL 130.656062 213.999998 \nL 131.021737 213.528213 \nL 131.590565 214.207732 \nL 131.712457 214.183261 \nL 132.037502 213.866801 \nL 132.159393 213.996033 \nL 132.60633 214.517875 \nL 132.687591 214.454617 \nL 132.768852 214.372269 \nL 132.809483 214.433615 \nL 132.890744 214.355892 \nL 133.012636 214.271087 \nL 133.540833 214.887905 \nL 133.703355 214.851553 \nL 133.987769 214.51606 \nL 134.06903 214.612613 \nL 134.475336 215.124087 \nL 134.597228 215.2249 \nL 134.637858 215.180884 \nL 134.75975 215.098936 \nL 134.800381 215.149827 \nL 134.841011 215.171006 \nL 135.003534 214.866284 \nL 135.572362 215.444465 \nL 135.897406 215.06065 \nL 136.384973 215.563501 \nL 136.466234 215.60065 \nL 136.750648 215.302895 \nL 136.87254 215.314686 \nL 137.441368 215.899035 \nL 137.644521 215.570932 \nL 137.685152 215.593353 \nL 137.847674 215.448891 \nL 137.888304 215.50104 \nL 138.416502 216.064786 \nL 138.538394 215.928303 \nL 138.700916 215.867802 \nL 138.863438 215.845297 \nL 139.351005 216.362591 \nL 139.472897 216.29659 \nL 139.594789 216.127102 \nL 139.635419 216.146758 \nL 140.001094 216.263934 \nL 140.448031 216.695397 \nL 140.813706 216.328793 \nL 140.854336 216.370039 \nL 141.463795 216.978185 \nL 141.951362 216.734267 \nL 142.398298 217.22717 \nL 142.438929 217.186031 \nL 142.642082 216.863471 \nL 142.723343 216.454891 \nL 142.804604 216.510087 \nL 143.21091 216.872829 \nL 143.332802 216.920652 \nL 143.739107 216.662161 \nL 144.267305 217.129497 \nL 144.795502 216.903566 \nL 145.161177 217.288012 \nL 145.3237 217.385933 \nL 145.567483 217.246418 \nL 145.730005 217.278616 \nL 146.298833 217.79051 \nL 146.420725 217.634581 \nL 146.461356 217.682195 \nL 146.7864 217.61002 \nL 146.989553 217.831783 \nL 147.273967 218.080526 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217.067683 \nL 156.212694 217.109771 \nL 156.253324 217.141389 \nL 156.293955 217.038382 \nL 156.415847 216.732225 \nL 156.456477 216.757848 \nL 157.106566 217.009312 \nL 157.512872 216.918842 \nL 157.675394 217.061195 \nL 157.959808 217.246925 \nL 158.122331 217.071751 \nL 158.162961 217.114436 \nL 158.203592 217.117263 \nL 158.366114 216.773472 \nL 158.975573 217.293376 \nL 159.097465 217.178864 \nL 159.138095 217.222804 \nL 159.219356 217.194578 \nL 159.341248 217.064319 \nL 159.381879 217.090524 \nL 159.950707 217.523703 \nL 160.397643 217.164286 \nL 160.92584 217.563713 \nL 161.332146 217.193357 \nL 161.941605 217.658141 \nL 162.347911 217.424761 \nL 162.957369 217.76235 \nL 163.119891 217.680853 \nL 163.241783 217.588124 \nL 163.282414 217.628188 \nL 163.810611 217.947262 \nL 164.135656 217.843289 \nL 164.298178 217.950873 \nL 164.582592 218.17414 \nL 164.785745 218.272497 \nL 165.192051 218.056858 \nL 165.760879 218.452408 \nL 166.289076 218.47611 \nL 166.776643 218.802826 \nL 167.101688 218.651388 \nL 167.792408 219.074236 \nL 168.117452 218.850402 \nL 168.808172 219.291452 \nL 169.255108 219.246767 \nL 169.742675 219.554111 \nL 170.06772 219.493315 \nL 170.636548 219.888782 \nL 170.880331 219.822192 \nL 171.124115 219.873762 \nL 171.855465 220.200903 \nL 172.017987 220.175116 \nL 172.546185 220.559471 \nL 173.115013 220.589233 \nL 173.561949 220.861523 \nL 174.090147 220.902083 \nL 174.577714 221.16593 \nL 174.740236 221.018289 \nL 174.780866 221.053766 \nL 175.105911 221.09645 \nL 175.552847 221.345914 \nL 175.796631 221.268413 \nL 176.040414 221.191571 \nL 176.527981 221.504884 \nL 176.690503 221.456169 \nL 176.771765 221.433543 \nL 176.853026 221.275885 \nL 176.934287 221.339454 \nL 177.503115 221.603428 \nL 177.665637 221.552348 \nL 178.478249 221.890276 \nL 178.803293 221.934355 \nL 179.697166 222.366597 \nL 179.819058 222.308852 \nL 180.347255 222.645661 \nL 180.469147 222.607133 \nL 180.6723 222.52048 \nL 180.956714 222.632283 \nL 181.322389 222.794043 \nL 181.931848 222.714705 \nL 182.419415 222.973428 \nL 182.78509 222.84858 \nL 183.353918 223.212191 \nL 183.51644 223.180591 \nL 183.882115 223.18832 \nL 184.288421 223.434886 \nL 184.654096 223.318389 \nL 185.304185 223.642678 \nL 185.588599 223.558416 \nL 185.791752 223.668302 \nL 186.279319 223.924995 \nL 186.482472 223.88258 \nL 186.766886 224.020511 \nL 187.254453 224.304608 \nL 187.498236 224.265447 \nL 187.701389 224.348419 \nL 188.310848 224.654512 \nL 188.676523 224.538364 \nL 189.245351 224.864282 \nL 189.651657 224.786168 \nL 190.423638 225.087594 \nL 190.708052 224.944752 \nL 191.154988 224.664408 \nL 191.764447 224.773205 \nL 192.130122 224.935151 \nL 192.536428 224.893022 \nL 193.349039 225.172709 \nL 193.511561 225.209413 \nL 194.161651 225.535226 \nL 194.364804 225.525364 \nL 194.567956 225.614411 \nL 195.299307 225.915241 \nL 195.461829 225.727862 \nL 196.23381 226.119036 \nL 196.315071 226.036669 \nL 196.477593 225.899459 \nL 197.290205 226.153616 \nL 197.533988 226.248882 \nL 197.980925 226.445832 \nL 198.184078 226.410104 \nL 198.38723 226.453107 \nL 198.915428 226.703251 \nL 199.281103 226.601516 \nL 199.931192 226.915018 \nL 200.134345 226.869547 \nL 200.296867 226.8829 \nL 200.906326 227.205011 \nL 201.19074 227.135272 \nL 201.434524 227.228545 \nL 201.92209 227.474328 \nL 202.369027 227.322293 \nL 202.978485 227.624123 \nL 203.141008 227.620338 \nL 203.262899 227.484132 \nL 203.30353 227.512585 \nL 203.912989 227.804311 \nL 204.116142 227.731832 \nL 204.238033 227.755553 \nL 204.847492 228.039486 \nL 205.294428 228.072408 \nL 205.781995 228.304857 \nL 206.10704 228.233658 \nL 206.797759 228.555637 \nL 207.041543 228.485125 \nL 207.325957 228.644426 \nL 207.772893 228.823058 \nL 207.976046 228.762343 \nL 208.179199 228.837158 \nL 208.748027 229.051981 \nL 209.113702 228.878091 \nL 209.723161 229.183602 \nL 210.129467 229.106092 \nL 210.698295 229.342431 \nL 211.023339 229.260312 \nL 211.592167 229.554631 \nL 211.957842 229.451631 \nL 212.607932 229.74059 \nL 212.973607 229.667117 \nL 213.583065 229.943462 \nL 213.90811 229.88829 \nL 214.558199 230.202761 \nL 214.761352 230.174555 \nL 214.883244 230.111008 \nL 215.533333 230.432972 \nL 215.736486 230.368324 \nL 216.061531 230.362158 \nL 216.589728 230.553278 \nL 216.955403 230.515644 \nL 217.605492 230.821558 \nL 217.727384 230.778388 \nL 217.889906 230.669841 \nL 218.255582 230.767781 \nL 218.621257 230.787239 \nL 218.86504 230.732415 \nL 219.474499 231.004005 \nL 219.921435 230.997033 \nL 220.449633 231.214186 \nL 220.774677 231.190069 \nL 221.506028 231.521659 \nL 221.790442 231.507838 \nL 222.3999 231.784032 \nL 222.806206 231.715802 \nL 223.375034 231.970856 \nL 223.78134 231.935309 \nL 224.431429 232.218233 \nL 224.715843 232.168691 \nL 225.20341 232.385376 \nL 225.650346 232.326488 \nL 226.178544 232.54526 \nL 226.584849 232.50507 \nL 227.072416 232.734067 \nL 227.275569 232.742557 \nL 227.559983 232.689109 \nL 228.169442 232.94414 \nL 228.941423 233.127572 \nL 229.225837 233.155914 \nL 229.550881 233.090982 \nL 230.16034 233.344475 \nL 230.566646 233.305958 \nL 231.297996 233.629618 \nL 231.460518 233.558095 \nL 231.623041 233.616949 \nL 232.151238 233.811845 \nL 232.557544 233.762842 \nL 235.889251 234.694667 \nL 236.173665 234.734494 \nL 236.295557 234.698882 \nL 236.417449 234.692336 \nL 237.067538 234.940698 \nL 237.555105 235.01224 \nL 238.123933 235.185855 \nL 238.327086 235.095114 \nL 238.895914 235.272245 \nL 239.261589 235.241218 \nL 239.830417 235.465527 \nL 240.317984 235.487311 \nL 241.130595 235.635494 \nL 241.293118 235.681556 \nL 241.943207 235.885631 \nL 242.065098 235.79996 \nL 242.227621 235.803336 \nL 242.796449 235.993419 \nL 243.040232 235.971502 \nL 243.243385 235.90049 \nL 243.812213 236.109298 \nL 244.381041 236.118552 \nL 244.9905 236.177295 \nL 245.153022 236.158801 \nL 245.803111 236.312054 \nL 246.900137 236.555805 \nL 247.062659 236.520116 \nL 247.712748 236.766426 \nL 248.119054 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      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "def plot_progressive_loss(obj_list, alias, result_interval=1):\n",
    "    \"\"\"Show real-time progressive validation loss\n",
    "    \"\"\"\n",
    "    avg_list = [sum(obj_list[:i]) / i for i in range(1, len(obj_list))]\n",
    "    total_obs = len(avg_list)\n",
    "    warm_starting_point = 10 #0\n",
    "    plt.plot(range(warm_starting_point, len(avg_list)), avg_list[warm_starting_point:], label = alias)\n",
    "    plt.xlabel('# of data samples',)\n",
    "    plt.ylabel('Progressive validation loss')\n",
    "    plt.yscale('log')\n",
    "    plt.legend(loc='upper right')\n",
    "plt.figure(figsize=(8, 6))\n",
    "plot_progressive_loss(loss_list_vanilla, 'VanillaVW')\n",
    "plot_progressive_loss(loss_list_autovw_ni, 'AutoVW:NI')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### AutoVW which tunes both namespace interactions and learning rate\n",
    "Create and run an AutoVW instance which tunes both namespace interactions and learning rate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Seed namespaces (singletons and interactions): ['g', 'a', 'h', 'b', 'c', 'i', 'd', 'e', 'f']\n",
      "No low-cost partial config given to the search algorithm. For cost-frugal search, consider providing low-cost values for cost-related hps via 'low_cost_partial_config'.\n",
      "Created challengers from champion ||0.5|\n",
      "New challenger size 39, ['|gi|0.5|', '|af|0.5|', '|df|0.5|', '|gh|0.5|', '|ae|0.5|', '|di|0.5|', '|be|0.5|', '|ac|0.5|', '|hi|0.5|', '|de|0.5|', '|ef|0.5|', '|bc|0.5|', '|cf|0.5|', '|dg|0.5|', '|fg|0.5|', '|bh|0.5|', '|ei|0.5|', '|ce|0.5|', '|bf|0.5|', '|ah|0.5|', '|ad|0.5|', '|bg|0.5|', '|bd|0.5|', '|ab|0.5|', '|bi|0.5|', '|eg|0.5|', '|ai|0.5|', '|eh|0.5|', '|dh|0.5|', '|cd|0.5|', '|fi|0.5|', '|ci|0.5|', '|ag|0.5|', '|fh|0.5|', '|ch|0.5|', '|cg|0.5|', '||0.05358867312681484|', '||1.0|', '||0.5|']\n",
      "Online learning for 10000 steps...\n",
      "Seed namespaces (singletons and interactions): ['g', 'a', 'h', 'b', 'c', 'i', 'd', 'e', 'f']\n",
      "No low-cost partial config given to the search algorithm. For cost-frugal search, consider providing low-cost values for cost-related hps via 'low_cost_partial_config'.\n",
      "Created challengers from champion ||1.0|\n",
      "New challenger size 50, ['|gi|0.5|', '|af|0.5|', '|df|0.5|', '|gh|0.5|', '|ae|0.5|', '|di|0.5|', '|be|0.5|', '|ac|0.5|', '|hi|0.5|', '|de|0.5|', '|ef|0.5|', '|bc|0.5|', '|dh|1.0|', '|ah|1.0|', '|cd|1.0|', '|bh|1.0|', '|bi|1.0|', '|ab|1.0|', '|gi|1.0|', '|bg|1.0|', '|bd|1.0|', '|eh|1.0|', '|af|1.0|', '|hi|1.0|', '|cf|1.0|', '|ei|1.0|', '|ef|1.0|', '|ai|1.0|', '|ch|1.0|', '|gh|1.0|', '|fg|1.0|', '|ad|1.0|', '|ci|1.0|', '|bc|1.0|', '|ag|1.0|', '|df|1.0|', '|dg|1.0|', '|de|1.0|', '|di|1.0|', '|cg|1.0|', '|be|1.0|', '|eg|1.0|', '|ce|1.0|', '|fi|1.0|', '|ae|1.0|', '|bf|1.0|', '|fh|1.0|', '|ac|1.0|', '||0.10717734625362937|', '||0.3273795141019504|']\n",
      "Final progressive validation loss of autovw_nilr: 7.611900319489723\n"
     ]
    }
   ],
   "source": [
    "from flaml.tune import loguniform\n",
    "''' create another AutoVW instance for tuning namespace interactions and learning rate'''\n",
    "# set up the search space and init config\n",
    "search_space_nilr = {'interactions': AutoVW.AUTOMATIC, 'learning_rate': loguniform(lower=2e-10, upper=1.0), 'quiet': ''}\n",
    "init_config_nilr = {'interactions': set(), 'learning_rate': 0.5}\n",
    "# create an AutoVW instance\n",
    "autovw_nilr = AutoVW(max_live_model_num=5, search_space=search_space_nilr, init_config=init_config_nilr)\n",
    "\n",
    "# online learning with AutoVW\n",
    "loss_list_autovw_nilr = online_learning_loop(max_iter_num, vw_examples, autovw_nilr)\n",
    "print('Final progressive validation loss of autovw_nilr:', sum(loss_list_autovw_nilr)/len(loss_list_autovw_nilr))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Online performance comparison between vanilla VW and two AutoVW instances\n",
    "Compare the online progressive validation loss from the vanilla VW and two AutoVW instances."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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196.705188 \nL 96.892054 196.731391 \nL 97.420252 197.761577 \nL 97.460882 197.703339 \nL 97.582774 197.505379 \nL 97.826558 196.881432 \nL 97.907819 196.973786 \nL 98.395386 198.090656 \nL 98.517277 198.022268 \nL 98.801691 197.279731 \nL 98.842322 197.218962 \nL 98.882953 197.305121 \nL 98.964214 197.508222 \nL 99.004844 197.401913 \nL 99.045475 197.293987 \nL 99.086105 197.354107 \nL 99.695564 198.556938 \nL 99.898717 197.203277 \nL 99.939348 197.240712 \nL 100.061239 197.338342 \nL 100.467545 198.418402 \nL 100.548806 198.37588 \nL 100.670698 198.538934 \nL 100.711328 198.457058 \nL 100.83322 197.502732 \nL 100.914481 197.569681 \nL 101.158265 198.097288 \nL 101.442679 198.623394 \nL 101.483309 198.55319 \nL 101.645832 198.345849 \nL 101.767723 197.344496 \nL 101.808354 197.353538 \nL 102.336551 198.331096 \nL 102.377182 198.286788 \nL 102.742857 197.533699 \nL 102.824118 197.70109 \nL 103.311685 198.630731 \nL 103.392946 198.570655 \nL 103.555469 198.395897 \nL 103.717991 197.985594 \nL 103.758622 198.061549 \nL 104.246188 199.066893 \nL 104.36808 199.028434 \nL 104.611864 198.539776 \nL 104.652494 198.502013 \nL 104.693125 198.551891 \nL 105.261953 199.596813 \nL 105.343214 199.536235 \nL 105.465106 199.334769 \nL 105.505736 199.372559 \nL 105.668259 199.141994 \nL 105.708889 199.164163 \nL 106.196456 200.070808 \nL 106.318348 200.152779 \nL 106.684023 199.450327 \nL 106.724654 199.534573 \nL 107.293482 200.548949 \nL 107.456004 200.321911 \nL 107.537265 200.391332 \nL 107.659157 199.997069 \nL 107.699787 200.085117 \nL 108.227985 201.041616 \nL 108.309246 200.927921 \nL 108.349877 200.850421 \nL 108.431138 200.919595 \nL 108.512399 200.642806 \nL 108.59366 200.464044 \nL 108.634291 200.517895 \nL 109.121857 201.350636 \nL 109.162488 201.379368 \nL 109.203119 201.30899 \nL 109.528163 200.7976 \nL 109.568794 200.858209 \nL 110.01573 201.619671 \nL 110.137622 201.61865 \nL 110.340775 200.748029 \nL 110.422036 200.822931 \nL 110.543928 200.864371 \nL 111.072125 201.660065 \nL 111.112756 201.622321 \nL 111.234647 201.396044 \nL 111.39717 201.190629 \nL 111.519061 201.027882 \nL 111.559692 201.103567 \nL 112.047259 201.904146 \nL 112.169151 201.711688 \nL 112.372303 201.304139 \nL 112.494195 201.096579 \nL 112.534826 201.16239 \nL 112.981762 201.763558 \nL 113.063023 201.817361 \nL 113.103654 201.786784 \nL 113.50996 200.8935 \nL 113.591221 201.023732 \nL 114.119418 201.769676 \nL 114.28194 201.45498 \nL 114.322571 201.535274 \nL 114.363202 201.504524 \nL 114.485093 201.096663 \nL 114.525724 201.130386 \nL 115.053921 201.891421 \nL 115.419597 201.460106 \nL 115.460227 201.49729 \nL 115.988425 202.238037 \nL 116.272839 201.724809 \nL 116.3541 201.646694 \nL 116.39473 201.673203 \nL 116.963558 202.414371 \nL 117.288603 201.869725 \nL 117.369864 201.860941 \nL 117.410495 201.913551 \nL 117.898062 202.644064 \nL 118.019953 202.572805 \nL 118.223106 202.315436 \nL 118.344998 202.18295 \nL 118.385629 202.25218 \nL 118.873195 202.940669 \nL 118.995087 202.925509 \nL 119.116979 202.785955 \nL 119.157609 202.817292 \nL 119.360762 202.670631 \nL 119.401393 202.741894 \nL 119.92959 203.352569 \nL 120.010852 203.214795 \nL 120.335896 202.630986 \nL 120.945355 203.509886 \nL 121.067246 203.365245 \nL 121.229769 203.276553 \nL 121.31103 203.060945 \nL 121.351661 203.08987 \nL 121.920489 203.866865 \nL 122.286164 203.438915 \nL 122.773731 204.065893 \nL 122.895622 203.893696 \nL 123.180036 203.577864 \nL 123.261298 203.630801 \nL 123.789495 204.382023 \nL 123.952017 204.284076 \nL 124.073909 204.351025 \nL 124.195801 204.222858 \nL 124.723998 204.939791 \nL 124.764629 204.905696 \nL 125.170935 204.572412 \nL 125.211565 204.622616 \nL 125.699132 205.244176 \nL 125.861654 205.082026 \nL 126.105438 204.787182 \nL 126.227329 204.87901 \nL 126.755527 205.465989 \nL 126.877419 205.327379 \nL 126.918049 205.266645 \nL 126.95868 205.295705 \nL 126.99931 205.35043 \nL 127.161833 204.810711 \nL 127.243094 204.897989 \nL 127.527508 205.273663 \nL 127.771291 205.519133 \nL 128.177597 205.04166 \nL 128.218228 205.095983 \nL 128.746425 205.62146 \nL 128.868317 205.415704 \nL 128.908947 205.460927 \nL 128.990209 205.364814 \nL 129.274623 204.863294 \nL 129.640298 205.301383 \nL 129.80282 205.090431 \nL 129.884081 205.02261 \nL 129.924712 205.076208 \nL 130.005973 205.022331 \nL 130.127865 204.878321 \nL 130.168495 204.92353 \nL 130.656062 205.445183 \nL 130.696693 205.377803 \nL 130.981107 205.009779 \nL 131.062368 205.051402 \nL 131.590565 205.643659 \nL 131.712457 205.620279 \nL 132.037502 205.317921 \nL 132.159393 205.441394 \nL 132.60633 205.939982 \nL 132.687591 205.879543 \nL 132.768852 205.800864 \nL 132.809483 205.859477 \nL 132.890744 205.785218 \nL 133.012636 205.704191 \nL 133.540833 206.293523 \nL 133.703355 206.25879 \nL 133.987769 205.938248 \nL 134.06903 206.030499 \nL 134.475336 206.51918 \nL 134.597228 206.6155 \nL 134.637858 206.573446 \nL 134.75975 206.495149 \nL 134.800381 206.543773 \nL 134.841011 206.564008 \nL 135.003534 206.272865 \nL 135.572362 206.825281 \nL 135.897406 206.458569 \nL 136.384973 206.939013 \nL 136.466234 206.974506 \nL 136.750648 206.690019 \nL 136.87254 206.701285 \nL 137.441368 207.259595 \nL 137.644521 206.946112 \nL 137.685152 206.967535 \nL 137.847674 206.82951 \nL 137.888304 206.879335 \nL 138.416502 207.417959 \nL 138.538394 207.287558 \nL 138.700916 207.229753 \nL 138.863438 207.208251 \nL 139.351005 207.702493 \nL 139.472897 207.639433 \nL 139.635419 207.496279 \nL 140.001094 207.608232 \nL 140.448031 208.020469 \nL 140.813706 207.670202 \nL 140.854336 207.70961 \nL 141.463795 208.290655 \nL 141.951362 208.057606 \nL 142.398298 208.528545 \nL 142.438929 208.489239 \nL 142.642082 208.181053 \nL 142.763973 207.79748 \nL 142.804604 207.843417 \nL 143.21091 208.189994 \nL 143.332802 208.235686 \nL 143.739107 207.988714 \nL 144.267305 208.435225 \nL 144.795502 208.219361 \nL 145.161177 208.586675 \nL 145.3237 208.680233 \nL 145.567483 208.546935 \nL 145.730005 208.577698 \nL 146.298833 209.066781 \nL 146.420725 208.917801 \nL 146.461356 208.963293 \nL 146.7864 208.894334 \nL 146.989553 209.106215 \nL 147.273967 209.343873 \nL 147.599012 208.837805 \nL 147.680273 208.739848 \nL 147.720904 208.77182 \nL 148.452254 209.143495 \nL 148.533515 209.03148 \nL 148.696037 208.379518 \nL 148.736668 208.42673 \nL 149.224235 208.900141 \nL 149.264865 208.85403 \nL 149.508649 208.587315 \nL 149.630541 208.170754 \nL 149.671171 208.195834 \nL 150.199369 208.579684 \nL 150.565044 208.076781 \nL 150.646305 208.157105 \nL 150.768197 208.202661 \nL 151.093241 208.521588 \nL 151.133872 208.480356 \nL 151.540178 207.975845 \nL 151.621439 208.058896 \nL 151.865222 208.267727 \nL 152.109006 208.427528 \nL 152.43405 208.140162 \nL 152.515311 208.006421 \nL 152.555942 208.049619 \nL 153.12477 208.48166 \nL 153.571706 208.130625 \nL 154.099904 208.507717 \nL 154.140534 208.451564 \nL 154.50621 207.964216 \nL 154.54684 207.996991 \nL 155.075038 208.351719 \nL 155.318821 208.406549 \nL 155.643866 208.214644 \nL 156.050171 208.492884 \nL 156.172063 208.376165 \nL 156.212694 208.416377 \nL 156.253324 208.446586 \nL 156.293955 208.34817 \nL 156.415847 208.055656 \nL 156.456477 208.080137 \nL 157.106566 208.320395 \nL 157.512872 208.233956 \nL 157.716025 208.407687 \nL 157.959808 208.547419 \nL 158.122331 208.380051 \nL 158.162961 208.420834 \nL 158.203592 208.423536 \nL 158.366114 208.095064 \nL 158.975573 208.591801 \nL 159.097465 208.482391 \nL 159.138095 208.524374 \nL 159.219356 208.497405 \nL 159.341248 208.37295 \nL 159.381879 208.397988 \nL 159.950707 208.811863 \nL 160.397643 208.468463 \nL 160.92584 208.850091 \nL 161.332146 208.496238 \nL 161.941605 208.940311 \nL 162.347911 208.717331 \nL 162.957369 209.039875 \nL 163.119891 208.96201 \nL 163.282414 208.911693 \nL 163.810611 209.216547 \nL 164.135656 209.117208 \nL 164.298178 209.219998 \nL 164.582592 209.433315 \nL 164.785745 209.52729 \nL 165.192051 209.32126 \nL 165.760879 209.699184 \nL 166.289076 209.721829 \nL 166.776643 210.033986 \nL 167.101688 209.889296 \nL 167.792408 210.293301 \nL 168.117452 210.079442 \nL 168.808172 210.500837 \nL 169.255108 210.458144 \nL 169.742675 210.751792 \nL 170.06772 210.693705 \nL 170.636548 211.07155 \nL 170.880331 211.007927 \nL 171.124115 211.057199 \nL 171.855465 211.369761 \nL 172.017987 211.345124 \nL 172.586815 211.706751 \nL 173.155643 211.775735 \nL 173.561949 212.000943 \nL 174.090147 212.039695 \nL 174.577714 212.291785 \nL 174.740236 212.150723 \nL 174.780866 212.184619 \nL 175.105911 212.225401 \nL 175.552847 212.463748 \nL 175.959153 212.252915 \nL 176.527981 212.615634 \nL 176.731134 212.57011 \nL 177.015548 212.507442 \nL 177.543746 212.671429 \nL 177.665637 212.660983 \nL 178.478249 212.983853 \nL 178.803293 213.025967 \nL 179.697166 213.438948 \nL 179.819058 213.383776 \nL 180.347255 213.705576 \nL 180.469147 213.668765 \nL 180.6723 213.585973 \nL 180.956714 213.692794 \nL 181.322389 213.847345 \nL 181.931848 213.771543 \nL 182.419415 214.018737 \nL 182.78509 213.899452 \nL 183.353918 214.246859 \nL 183.51644 214.216668 \nL 183.882115 214.224052 \nL 184.288421 214.459631 \nL 184.654096 214.348325 \nL 185.304185 214.658163 \nL 185.588599 214.577656 \nL 185.791752 214.682645 \nL 186.279319 214.927899 \nL 186.482472 214.887374 \nL 186.766886 215.019159 \nL 187.254453 215.290596 \nL 187.498236 215.25318 \nL 187.701389 215.332455 \nL 188.310848 215.624908 \nL 188.676523 215.513936 \nL 189.245351 215.82533 \nL 189.651657 215.750697 \nL 190.423638 216.03869 \nL 190.708052 215.902214 \nL 191.195619 215.635111 \nL 191.723816 215.707714 \nL 192.130122 215.893041 \nL 192.536428 215.852789 \nL 193.349039 216.120013 \nL 193.511561 216.155081 \nL 194.202281 216.487348 \nL 194.527326 216.530367 \nL 194.81174 216.711898 \nL 195.136784 216.824826 \nL 195.339937 216.74479 \nL 195.461829 216.650426 \nL 196.23381 217.024169 \nL 196.315071 216.945472 \nL 196.477593 216.814377 \nL 197.980925 217.336402 \nL 198.184078 217.302267 \nL 198.38723 217.343353 \nL 198.915428 217.58235 \nL 199.281103 217.485149 \nL 199.931192 217.78468 \nL 200.134345 217.741236 \nL 200.296867 217.753994 \nL 200.906326 218.061751 \nL 201.19074 217.99512 \nL 201.434524 218.084236 \nL 201.92209 218.319066 \nL 202.409657 218.201458 \nL 202.937855 218.460137 \nL 203.181638 218.374127 \nL 203.262899 218.328434 \nL 203.30353 218.355618 \nL 203.912989 218.634345 \nL 204.116142 218.565095 \nL 204.278664 218.612844 \nL 205.010014 218.882491 \nL 205.253798 218.879939 \nL 205.578842 219.074408 \nL 205.903887 219.085936 \nL 206.432084 219.222023 \nL 206.797759 219.35219 \nL 207.041543 219.28482 \nL 207.325957 219.437022 \nL 207.772893 219.607694 \nL 207.976046 219.549685 \nL 208.179199 219.621165 \nL 208.748027 219.826415 \nL 209.113702 219.660274 \nL 209.723161 219.952171 \nL 210.129467 219.878116 \nL 210.698295 220.103923 \nL 211.023339 220.025463 \nL 211.592167 220.306666 \nL 211.957842 220.208257 \nL 212.607932 220.484338 \nL 212.973607 220.41414 \nL 213.623696 220.656674 \nL 213.90811 220.625457 \nL 214.558199 220.925914 \nL 214.801983 220.842687 \nL 214.964505 220.889715 \nL 215.533333 221.145866 \nL 215.777117 221.069547 \nL 216.0209 221.052745 \nL 216.549097 221.280848 \nL 216.914773 221.201111 \nL 217.646123 221.520157 \nL 218.214951 221.440601 \nL 218.499365 221.512412 \nL 219.149454 221.569635 \nL 219.55576 221.698141 \nL 220.083957 221.768426 \nL 220.449633 221.892268 \nL 220.774677 221.869226 \nL 221.506028 222.18604 \nL 221.831072 222.197472 \nL 222.562423 222.434394 \nL 222.928098 222.439873 \nL 223.456295 222.597842 \nL 223.659448 222.564773 \nL 223.78134 222.581256 \nL 224.431429 222.851573 \nL 224.715843 222.804238 \nL 225.24404 222.998973 \nL 225.812869 223.031685 \nL 226.300435 223.164036 \nL 226.62548 223.135826 \nL 227.275569 223.352531 \nL 227.559983 223.301465 \nL 228.169442 223.545131 \nL 228.941423 223.720389 \nL 229.225837 223.747468 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      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
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     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8, 6))\n",
    "plot_progressive_loss(loss_list_vanilla, 'VanillaVW')\n",
    "plot_progressive_loss(loss_list_autovw_ni, 'AutoVW:NI')\n",
    "plot_progressive_loss(loss_list_autovw_nilr, 'AutoVW:NI+LR')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### AutoVW based on customized VW arguments\n",
    "You can easily create an AutoVW instance based on customized VW arguments (For now only arguments that are compatible with supervised regression task are well supported). The customized arguments can be passed to AutoVW through init_config and search space."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Seed namespaces (singletons and interactions): ['g', 'a', 'h', 'b', 'c', 'i', 'd', 'e', 'f']\n",
      "Created challengers from champion |supervised||classic|\n",
      "New challenger size 37, ['|supervised|fg|classic|', '|supervised|dh|classic|', '|supervised|ef|classic|', '|supervised|ei|classic|', '|supervised|di|classic|', '|supervised|ch|classic|', '|supervised|bh|classic|', '|supervised|cf|classic|', '|supervised|ae|classic|', '|supervised|bc|classic|', '|supervised|ci|classic|', '|supervised|eg|classic|', '|supervised|ag|classic|', '|supervised|be|classic|', '|supervised|bd|classic|', '|supervised|ce|classic|', '|supervised|af|classic|', '|supervised|ad|classic|', '|supervised|ab|classic|', '|supervised|dg|classic|', '|supervised|gh|classic|', '|supervised|bg|classic|', '|supervised|fh|classic|', '|supervised|gi|classic|', '|supervised|cg|classic|', '|supervised|cd|classic|', '|supervised|ai|classic|', '|supervised|ac|classic|', '|supervised|bi|classic|', '|supervised|eh|classic|', '|supervised|fi|classic|', '|supervised|de|classic|', '|supervised|hi|classic|', '|supervised|bf|classic|', '|supervised|df|classic|', '|supervised|ah|classic|', '|supervised||classic|']\n",
      "Online learning for 10000 steps...\n",
      "Seed namespaces (singletons and interactions): ['df', 'g', 'a', 'h', 'b', 'c', 'i', 'd', 'e', 'f']\n",
      "Created challengers from champion |supervised|df|classic|\n",
      "New challenger size 43, ['|supervised|ce_df|classic|', '|supervised|df_gi|classic|', '|supervised|df_fi|classic|', '|supervised|bd_df|classic|', '|supervised|ab_df|classic|', '|supervised|bi_df|classic|', '|supervised|df_ei|classic|', '|supervised|bh_df|classic|', '|supervised|cd_df|classic|', '|supervised|df_dfg|classic|', '|supervised|def_df|classic|', '|supervised|bdf_df|classic|', '|supervised|ag_df|classic|', '|supervised|cg_df|classic|', '|supervised|df_dg|classic|', '|supervised|af_df|classic|', '|supervised|ci_df|classic|', '|supervised|df_dh|classic|', '|supervised|ah_df|classic|', '|supervised|df|classic|', '|supervised|df_di|classic|', '|supervised|ad_df|classic|', '|supervised|df_ef|classic|', '|supervised|ae_df|classic|', '|supervised|ai_df|classic|', '|supervised|be_df|classic|', '|supervised|df_eg|classic|', '|supervised|ch_df|classic|', '|supervised|ac_df|classic|', '|supervised|df_gh|classic|', '|supervised|df_fg|classic|', '|supervised|bc_df|classic|', '|supervised|df_dfh|classic|', '|supervised|df_fh|classic|', '|supervised|df_dfi|classic|', '|supervised|de_df|classic|', '|supervised|bf_df|classic|', '|supervised|bg_df|classic|', '|supervised|df_hi|classic|', '|supervised|cdf_df|classic|', '|supervised|df_eh|classic|', '|supervised|cf_df|classic|', '|supervised|adf_df|classic|']\n",
      "Average final loss of the AutoVW (tuning namespaces) based on customized vw arguments: 8.828759490602918\n"
     ]
    }
   ],
   "source": [
    "''' create an AutoVW instance with ustomized VW arguments'''\n",
    "# parse the customized VW arguments\n",
    "fixed_vw_hp_config = {'alg': 'supervised', 'loss_function': 'classic', 'quiet': ''}\n",
    "search_space = fixed_vw_hp_config.copy()\n",
    "search_space.update({'interactions': AutoVW.AUTOMATIC,})\n",
    "\n",
    "autovw_custom = AutoVW(max_live_model_num=5, search_space=search_space) \n",
    "loss_list_custom = online_learning_loop(max_iter_num, vw_examples, autovw_custom)\n",
    "print('Average final loss of the AutoVW (tuning namespaces) based on customized vw arguments:', sum(loss_list_custom)/len(loss_list_custom))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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