autogen/notebook/flaml_autovw.ipynb

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    "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 flaml with the `notebook` option:\n",
    "```bash\n",
    "pip install flaml[notebook]\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install flaml[notebook];"
   ]
  },
  {
   "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"
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    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "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": 2,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "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]\nvw 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": 3,
   "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",
    "    y_predict_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",
    "        y_predict_list.append([y_pred, y_true])\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": 4,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "Online learning for 10000 steps...\nFinal progressive validation loss of vanilla vw: 15.180878192648041\n"
    }
   ],
   "source": [
    "from vowpalwabbit import pyvw\n",
    "''' create a vanilla vw instance '''\n",
    "vanilla_vw = pyvw.vw()\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": 5,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
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   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "text": "Seed namespaces (singletons and interactions): ['e', 'g', 'b', 'd', 'i', 'h', 'a', 'f', 'c']\nCreated challengers from champion |\nNew challenger size 37, ['|ah', '|ch', '|df', '|ef', '|ag', '|bg', '|be', '|eh', '|hi', '|cd', '|ci', '|eg', '|bh', '|ad', '|bi', '|ab', '|cg', '|bc', '|gi', '|ai', '|cf', '|ei', '|dg', '|ac', '|af', '|ce', '|ae', '|de', '|fi', '|bd', '|gh', '|bf', '|dh', '|di', '|fh', '|fg', '|']\nOnline learning for 10000 steps...\nSeed namespaces (singletons and interactions): ['dh', 'e', 'g', 'b', 'd', 'i', 'h', 'a', 'f', 'c']\nCreated challengers from champion |dh\nNew challenger size 43, ['|dh_ei', '|bd_dh', '|cdh_dh', '|ac_dh', '|bh_dh', '|ab_dh', '|dh_gi', '|cg_dh', '|bf_dh', '|dh_dhi', '|deh_dh', '|dh_fi', '|ad_dh', '|dh_hi', '|dh_eg', '|bdh_dh', '|dh_eh', '|ag_dh', '|dh', '|de_dh', '|dgh_dh', '|bc_dh', '|cd_dh', '|dh_ef', '|cf_dh', '|dh_di', '|bi_dh', '|ah_dh', '|dh_fh', '|ce_dh', '|ae_dh', '|adh_dh', '|df_dh', '|ch_dh', '|dh_fg', '|ai_dh', '|ci_dh', '|dh_gh', '|dfh_dh', '|af_dh', '|dg_dh', '|be_dh', '|bg_dh']\nFinal progressive validation loss of autovw: 10.744201540966063\n"
    }
   ],
   "source": [
    "''' import AutoVW class from flaml package '''\n",
    "from flaml import AutoVW\n",
    "\n",
    "'''create an AutoVW instance for tuning namespace interactions'''\n",
    "autovw_ni = AutoVW(max_live_model_num=5, search_space={'interactions': AutoVW.AUTOMATIC})\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": 6,
   "metadata": {},
   "outputs": [
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232.73891 \nL 149.264865 232.686916 \nL 149.508649 232.386177 \nL 149.630541 231.916478 \nL 149.671171 231.944756 \nL 150.158738 232.426032 \nL 150.199369 232.377573 \nL 150.565044 231.810517 \nL 150.605674 231.856568 \nL 151.093241 232.312066 \nL 151.540178 231.696705 \nL 151.662069 231.810365 \nL 151.987114 232.154961 \nL 152.109006 232.206006 \nL 152.43405 231.881982 \nL 152.515311 231.73118 \nL 152.555942 231.779889 \nL 153.084139 232.274465 \nL 153.206031 232.168813 \nL 153.612337 231.904499 \nL 153.81549 232.09713 \nL 154.099904 232.296425 \nL 154.50621 231.683592 \nL 154.587471 231.740931 \nL 155.359452 232.13885 \nL 155.521974 231.830758 \nL 155.562605 231.878735 \nL 156.050171 232.2797 \nL 156.172063 232.148091 \nL 156.212694 232.193433 \nL 156.253324 232.227496 \nL 156.415847 231.786695 \nL 156.497108 231.838975 \nL 156.619 231.891507 \nL 156.984675 232.213411 \nL 157.147197 232.062639 \nL 157.187828 232.111189 \nL 157.269089 232.05517 \nL 157.39098 231.880992 \nL 157.431611 231.928198 \nL 157.959808 232.341191 \nL 158.122331 232.152473 \nL 158.162961 232.198458 \nL 158.203592 232.201504 \nL 158.366114 231.831131 \nL 158.975573 232.391234 \nL 159.097465 232.267868 \nL 159.138095 232.315206 \nL 159.219356 232.284797 \nL 159.341248 232.144466 \nL 159.381879 232.172698 \nL 159.950707 232.639369 \nL 160.15386 232.508375 \nL 160.235121 232.46209 \nL 160.397643 232.252163 \nL 160.92584 232.682474 \nL 161.332146 232.283481 \nL 161.941605 232.784202 \nL 162.30728 232.505551 \nL 163.079261 232.924978 \nL 163.241783 232.708772 \nL 163.769981 233.113452 \nL 164.216917 233.016052 \nL 164.745114 233.4723 \nL 165.232681 233.253865 \nL 165.801509 233.63396 \nL 166.126554 233.540707 \nL 166.817274 233.977928 \nL 167.101688 233.854246 \nL 167.792408 234.309788 \nL 167.99556 234.253714 \nL 168.239344 234.176945 \nL 168.848803 234.529078 \nL 169.214478 234.464379 \nL 169.823936 234.800788 \nL 169.945828 234.771274 \nL 170.06772 234.76127 \nL 170.636548 235.187314 \nL 170.880331 235.115576 \nL 171.124115 235.171133 \nL 171.814835 235.546944 \nL 172.017987 235.495786 \nL 172.546185 235.909859 \nL 173.074382 235.917826 \nL 173.64321 236.188463 \nL 173.765102 236.179547 \nL 174.130777 236.314484 \nL 174.537083 236.55997 \nL 174.618344 236.507 \nL 174.740236 236.404152 \nL 174.780866 236.442372 \nL 175.105911 236.488357 \nL 175.552847 236.757108 \nL 175.796631 236.673616 \nL 176.040414 236.590832 \nL 176.527981 236.92837 \nL 176.690503 236.875889 \nL 176.771765 236.851513 \nL 176.853026 236.681665 \nL 176.934287 236.750149 \nL 177.503115 237.034533 \nL 177.665637 236.979504 \nL 178.478249 237.34356 \nL 178.762663 237.368596 \nL 179.697166 237.856709 \nL 179.819058 237.794499 \nL 180.347255 238.157349 \nL 180.469147 238.115843 \nL 180.591039 237.9664 \nL 180.631669 237.998985 \nL 181.36302 238.303788 \nL 181.484911 238.199716 \nL 181.566172 238.141404 \nL 181.606803 238.179103 \nL 181.972478 238.267507 \nL 182.378784 238.534855 \nL 182.78509 238.375957 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242.259184 \nL 198.915428 242.528669 \nL 199.281103 242.419069 \nL 199.931192 242.75681 \nL 200.134345 242.707824 \nL 200.296867 242.722209 \nL 200.906326 243.069225 \nL 201.19074 242.994094 \nL 201.434524 243.094578 \nL 201.92209 243.359365 \nL 202.369027 243.195575 \nL 202.937855 243.518431 \nL 203.181638 243.42145 \nL 203.262899 243.369927 \nL 203.30353 243.40058 \nL 203.912989 243.714862 \nL 204.116142 243.636779 \nL 204.238033 243.662333 \nL 204.847492 243.96822 \nL 205.294428 244.003688 \nL 205.781995 244.25411 \nL 206.10704 244.177405 \nL 206.757129 244.541871 \nL 207.122804 244.477634 \nL 207.732263 244.838872 \nL 208.057307 244.740463 \nL 208.748027 245.059 \nL 209.113702 244.871665 \nL 209.723161 245.200798 \nL 210.129467 245.117296 \nL 210.698295 245.371908 \nL 211.023339 245.283439 \nL 211.592167 245.600514 \nL 211.957842 245.489551 \nL 212.607932 245.800851 \nL 212.973607 245.721698 \nL 213.583065 246.01941 \nL 213.867479 245.940994 \nL 214.761352 246.268371 \nL 214.883244 246.19991 \nL 215.533333 246.546767 \nL 215.736486 246.477122 \nL 216.0209 246.441767 \nL 216.549097 246.698968 \nL 216.914773 246.60906 \nL 217.646123 246.968806 \nL 218.17432 246.850119 \nL 218.499365 246.960072 \nL 218.702518 246.847269 \nL 218.86504 246.869364 \nL 219.433868 247.158069 \nL 219.677652 247.073894 \nL 219.799543 247.077156 \nL 220.409002 247.415569 \nL 220.693416 247.348683 \nL 220.896569 247.436692 \nL 221.506028 247.719632 \nL 221.790442 247.704742 \nL 222.3999 248.002291 \nL 222.765575 247.900728 \nL 223.375034 248.203559 \nL 223.78134 248.165263 \nL 224.390798 248.454956 \nL 224.553321 248.412907 \nL 224.715843 248.41669 \nL 225.20341 248.650129 \nL 225.650346 248.586688 \nL 226.178544 248.822376 \nL 226.584849 248.779078 \nL 227.072416 249.02578 \nL 227.275569 249.034927 \nL 227.559983 248.977346 \nL 228.169442 249.252096 \nL 228.900792 249.43209 \nL 229.225837 249.480244 \nL 229.550881 249.410291 \nL 230.119709 249.668878 \nL 230.404123 249.669256 \nL 230.688537 249.722619 \nL 231.419888 249.950891 \nL 231.58241 249.95076 \nL 232.151238 250.18689 \nL 232.557544 250.134098 \nL 232.923219 250.332203 \nL 233.207633 250.403722 \nL 233.532678 250.377071 \nL 233.898353 250.581006 \nL 234.182767 250.651313 \nL 234.548442 250.639302 \nL 235.239162 250.8727 \nL 235.442315 250.89904 \nL 236.051773 251.179384 \nL 236.458079 251.160656 \nL 237.148799 251.425944 \nL 237.717627 251.572954 \nL 238.123933 251.667138 \nL 238.327086 251.569381 \nL 238.895914 251.760207 \nL 239.261589 251.726781 \nL 239.830417 251.968434 \nL 240.317984 251.991901 \nL 241.130595 252.151542 \nL 241.293118 252.201165 \nL 241.943207 252.421019 \nL 242.065098 252.328724 \nL 242.227621 252.332361 \nL 242.796449 252.537141 \nL 243.040232 252.51353 \nL 243.202755 252.411967 \nL 243.812213 252.66198 \nL 244.340411 252.652444 \nL 244.909239 252.815855 \nL 245.274914 252.766917 \nL 245.72185 252.907569 \nL 246.128156 252.89502 \nL 246.737615 253.118097 \nL 246.940767 253.104641 \nL 247.10329 253.12789 \nL 247.915901 253.391252 \nL 248.119054 253.361256 \nL 248.728513 253.616323 \nL 249.053557 253.656244 \nL 249.459863 253.79951 \nL 249.744277 253.81407 \nL 250.231844 253.974287 \nL 250.881933 254.188236 \nL 251.085086 254.228514 \nL 251.775806 254.53233 \nL 252.019589 254.375872 \nL 252.507156 254.551723 \nL 252.913462 254.509839 \nL 253.685443 254.837589 \nL 254.091749 254.737282 \nL 254.701207 254.901731 \nL 254.90436 254.908856 \nL 255.716972 255.173277 \nL 255.920124 255.178175 \nL 257.70787 255.657706 \nL 257.829762 255.594915 \nL 258.642373 255.888309 \nL 258.926787 255.950661 \nL 259.536246 256.193569 \nL 259.901921 256.189969 \nL 260.511379 256.373508 \nL 260.755163 256.371848 \nL 261.24273 256.544554 \nL 261.608405 256.533721 \nL 262.095972 256.750386 \nL 262.502278 256.814218 \nL 262.827322 256.882918 \nL 263.314889 257.024413 \nL 264.899482 257.340645 \nL 266.362182 257.737019 \nL 266.605966 257.649146 \nL 267.337316 257.84364 \nL 267.581099 257.841554 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260.85306 \nL 287.002514 260.739861 \nL 288.749629 260.998244 \nL 289.074674 260.986877 \nL 289.846655 261.071654 \nL 290.131069 261.139866 \nL 290.659266 261.286911 \nL 291.146833 261.311912 \nL 291.796922 261.51738 \nL 292.000075 261.467214 \nL 292.690795 261.670719 \nL 292.934578 261.57441 \nL 295.697457 261.907834 \nL 295.85998 261.915531 \nL 296.469438 262.072365 \nL 297.688355 262.159158 \nL 297.850878 262.172536 \nL 298.500967 262.356539 \nL 298.70412 262.332553 \nL 298.907273 262.392394 \nL 299.516731 262.544009 \nL 299.760515 262.51175 \nL 300.329343 262.663985 \nL 300.938801 262.647506 \nL 301.507629 262.683567 \nL 301.710782 262.68151 \nL 302.401502 262.844908 \nL 302.645286 262.801991 \nL 303.376636 263.004022 \nL 303.620419 262.920598 \nL 304.189247 263.066738 \nL 304.758075 263.081556 \nL 306.220776 263.45805 \nL 306.342668 263.45447 \nL 306.545821 263.305446 \nL 307.19591 263.496518 \nL 308.049152 263.505726 \nL 308.333566 263.545184 \nL 308.496088 263.534166 \nL 309.227439 263.75519 \nL 309.633744 263.786085 \nL 310.243203 263.938353 \nL 311.055815 264.09199 \nL 312.762299 264.515819 \nL 313.127974 264.582831 \nL 313.453019 264.556659 \nL 314.224999 264.69624 \nL 314.468783 264.714114 \nL 316.947248 265.247166 \nL 317.272293 265.126991 \nL 317.841121 265.231617 \nL 318.247426 265.235103 \nL 318.856885 265.378232 \nL 319.750758 265.578946 \nL 320.604 265.662757 \nL 321.457242 265.864357 \nL 322.02607 265.913651 \nL 322.269853 265.965667 \nL 322.960573 266.138007 \nL 323.163726 266.159129 \nL 323.813815 266.309183 \nL 324.951471 266.425546 \nL 325.154624 266.420656 \nL 325.885974 266.613633 \nL 328.730115 267.012589 \nL 332.833803 267.869556 \nL 332.996325 267.888654 \nL 333.687045 268.070105 \nL 333.849567 267.992968 \nL 334.621548 268.189211 \nL 335.190376 268.223086 \nL 335.515421 268.22834 \nL 335.677943 268.158381 \nL 335.962357 268.231427 \nL 336.490554 268.307459 \nL 336.612446 268.255843 \nL 337.018752 268.379149 \nL 337.425058 268.403431 \nL 337.668841 268.259575 \nL 337.953255 268.345016 \nL 338.440822 268.350663 \nL 338.684606 268.373169 \nL 339.294064 268.488679 \nL 341.366223 268.659803 \nL 341.528746 268.573583 \nL 341.731899 268.610296 \nL 342.300727 268.721346 \nL 342.585141 268.671666 \nL 343.27586 268.794078 \nL 343.560274 268.717558 \nL 344.088472 268.808023 \nL 345.022975 268.748066 \nL 345.551173 268.74989 \nL 345.916848 268.777276 \nL 346.648198 268.845666 \nL 347.135765 268.91532 \nL 347.50144 268.619391 \nL 348.110899 268.752923 \nL 348.476574 268.637018 \nL 349.126663 268.715288 \nL 349.289186 268.608959 \nL 349.451708 268.617293 \nL 350.061166 268.716299 \nL 350.34558 268.551362 \nL 351.076931 268.673018 \nL 351.158192 268.521918 \nL 351.198823 268.537515 \nL 353.189721 268.746922 \nL 353.311612 268.674985 \nL 354.002332 268.728419 \nL 354.164855 268.582366 \nL 354.327377 268.541145 \nL 354.977466 268.673216 \nL 355.180619 268.50763 \nL 355.99323 268.557838 \nL 356.155753 268.538116 \nL 356.927734 268.643917 \nL 357.090256 268.541169 \nL 357.902867 268.688964 \nL 358.06539 268.661632 \nL 360.747008 269.116352 \nL 360.828269 269.105808 \nL 360.990791 268.9662 \nL 361.153313 268.953688 \nL 361.803403 269.103191 \nL 361.965925 269.007671 \nL 362.169078 269.011572 \nL 362.778536 269.157172 \nL 362.941059 269.041039 \nL 363.225473 269.077958 \nL 363.75367 269.169714 \nL 363.916192 269.069091 \nL 364.728804 269.204437 \nL 364.810065 269.03325 \nL 364.850696 269.046289 \nL 365.622677 269.183994 \nL 366.069613 269.183015 \nL 366.719702 269.222798 \nL 367.004116 269.27507 \nL 367.654205 269.391313 \nL 367.816728 269.264202 \nL 368.466817 269.332147 \nL 369.807626 269.468198 \nL 369.970148 269.464769 \nL 371.189065 269.728527 \nL 371.595371 269.652553 \nL 371.798524 269.635509 \nL 374.35825 269.979047 \nL 374.480142 269.989926 \nL 374.561403 269.795945 \nL 374.602034 269.809418 \nL 375.374015 269.922043 \nL 376.227257 270.101831 \nL 376.43041 270.127431 \nL 376.511671 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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "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": 7,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "text": "Seed namespaces (singletons and interactions): ['e', 'g', 'b', 'd', 'i', 'h', 'a', 'f', 'c']\nNo low-cost init config given to the search algorithm.For cost-frugal search, consider providing init values for cost-related hps via 'init_config'.\nCreated challengers from champion ||0.5\nNew challenger size 39, ['|dh|0.5', '|ci|0.5', '|bd|0.5', '|bh|0.5', '|ei|0.5', '|ch|0.5', '|bg|0.5', '|bc|0.5', '|cd|0.5', '|ag|0.5', '|eh|0.5', '|hi|0.5', '|dg|0.5', '|fi|0.5', '|ad|0.5', '|cf|0.5', '|ce|0.5', '|be|0.5', '|ab|0.5', '|ah|0.5', '|fh|0.5', '|di|0.5', '|gi|0.5', '|bf|0.5', '|de|0.5', '|ac|0.5', '|ai|0.5', '|df|0.5', '|cg|0.5', '|ae|0.5', '|fg|0.5', '|ef|0.5', '|eg|0.5', '|gh|0.5', '|af|0.5', '|bi|0.5', '||0.05358867312681484', '||1.0', '||0.5']\nOnline learning for 10000 steps...\nSeed namespaces (singletons and interactions): ['e', 'g', 'b', 'd', 'i', 'h', 'a', 'f', 'c']\nNo low-cost init config given to the search algorithm.For cost-frugal search, consider providing init values for cost-related hps via 'init_config'.\nCreated challengers from champion ||1.0\nNew challenger size 38, ['|bf|1.0', '|ab|1.0', '|fg|1.0', '|bg|1.0', '|ad|1.0', '|fi|1.0', '|be|1.0', '|gi|1.0', '|df|1.0', '|de|1.0', '|cg|1.0', '|hi|1.0', '|di|1.0', '|ei|1.0', '|ai|1.0', '|bc|1.0', '|af|1.0', '|ef|1.0', '|ag|1.0', '|dh|1.0', '|fh|1.0', '|cd|1.0', '|dg|1.0', '|gh|1.0', '|ah|1.0', '|eg|1.0', '|ci|1.0', '|ch|1.0', '|eh|1.0', '|ac|1.0', '|ce|1.0', '|bi|1.0', '|bd|1.0', '|ae|1.0', '|cf|1.0', '|bh|1.0', '||0.10717734625362937', '||0.3273795141019504']\nSeed namespaces (singletons and interactions): ['de', 'e', 'g', 'b', 'd', 'i', 'h', 'a', 'f', 'c']\nNo low-cost init config given to the search algorithm.For cost-frugal search, consider providing init values for cost-related hps via 'init_config'.\nCreated challengers from champion |de|1.0\nNew challenger size 45, ['|cf_de|1.0', '|ci_de|1.0', '|cd_de|1.0', '|ac_de|1.0', '|de_dh|1.0', '|ab_de|1.0', '|de_ef|1.0', '|ce_de|1.0', '|de_hi|1.0', '|bg_de|1.0', '|de_fi|1.0', '|ah_de|1.0', '|de_dg|1.0', '|de_fg|1.0', '|ai_de|1.0', '|de_gh|1.0', '|bh_de|1.0', '|ch_de|1.0', '|de|1.0', '|af_de|1.0', '|de_deg|1.0', '|de_eh|1.0', '|de_eg|1.0', '|de_di|1.0', '|de_ei|1.0', '|ag_de|1.0', '|ae_de|1.0', '|de_deh|1.0', '|be_de|1.0', '|de_fh|1.0', '|cg_de|1.0', '|bf_de|1.0', '|bi_de|1.0', '|ad_de|1.0', '|ade_de|1.0', '|de_def|1.0', '|bde_de|1.0', '|cde_de|1.0', '|de_df|1.0', '|bc_de|1.0', '|de_dei|1.0', '|bd_de|1.0', '|de_gi|1.0', '|de|0.10717734625362937', '|de|0.3273795141019504']\nFinal progressive validation loss of autovw_nilr: 6.271218842008241\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)}\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": 8,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "display_data",
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200.879361 \nL 107.537265 200.955335 \nL 107.659157 200.650721 \nL 107.699787 200.734918 \nL 108.227985 201.653378 \nL 108.471768 201.541684 \nL 108.59366 201.226635 \nL 108.634291 201.282182 \nL 109.121857 202.080507 \nL 109.162488 202.110746 \nL 109.203119 202.055226 \nL 109.528163 201.673048 \nL 109.568794 201.733613 \nL 110.056361 202.47592 \nL 110.096991 202.527443 \nL 110.137622 202.478588 \nL 110.340775 201.771988 \nL 110.422036 201.84819 \nL 110.543928 201.907762 \nL 111.072125 202.676778 \nL 111.112756 202.650468 \nL 111.234647 202.476277 \nL 111.39717 202.314348 \nL 111.519061 202.194304 \nL 111.559692 202.266256 \nL 112.047259 203.03345 \nL 112.169151 202.885737 \nL 112.372303 202.563795 \nL 112.494195 202.40081 \nL 112.534826 202.463456 \nL 113.022393 203.10595 \nL 113.144284 203.031038 \nL 113.306807 202.644403 \nL 113.347437 202.717027 \nL 113.428698 202.518483 \nL 113.469329 202.363948 \nL 113.55059 202.445983 \nL 114.119418 203.241628 \nL 114.28194 203.005332 \nL 114.322571 203.081098 \nL 114.363202 203.054096 \nL 114.485093 202.718718 \nL 114.525724 202.751919 \nL 115.053921 203.490019 \nL 115.419597 203.17483 \nL 115.704011 203.575019 \nL 115.988425 203.933161 \nL 116.029055 203.838166 \nL 116.313469 203.487262 \nL 116.435361 203.578577 \nL 116.922928 204.245016 \nL 117.004189 204.163791 \nL 117.207342 203.881577 \nL 117.451125 203.960063 \nL 117.979323 204.565446 \nL 118.060584 204.452844 \nL 118.223106 204.350807 \nL 118.344998 204.25988 \nL 118.385629 204.325992 \nL 118.913826 205.020387 \nL 119.238871 204.849119 \nL 119.320132 204.805772 \nL 119.360762 204.844187 \nL 119.88896 205.514388 \nL 120.010852 205.413189 \nL 120.335896 204.968715 \nL 120.945355 205.812107 \nL 121.067246 205.708273 \nL 121.229769 205.658424 \nL 121.31103 205.494416 \nL 121.351661 205.527172 \nL 121.920489 206.277671 \nL 122.286164 205.974688 \nL 122.773731 206.581591 \nL 122.895622 206.453658 \nL 123.139406 206.258553 \nL 123.261298 206.288486 \nL 123.830126 206.985793 \nL 123.952017 206.954465 \nL 124.073909 207.028599 \nL 124.195801 206.947462 \nL 124.764629 207.614079 \nL 125.211565 207.443749 \nL 125.699132 208.040725 \nL 126.024177 207.853119 \nL 126.146068 207.730842 \nL 126.186699 207.765771 \nL 126.714896 208.391938 \nL 126.836788 208.348865 \nL 126.95868 208.294693 \nL 126.99931 208.344236 \nL 127.161833 207.905546 \nL 127.202463 207.945141 \nL 127.771291 208.597789 \nL 127.811922 208.559469 \nL 128.177597 208.252376 \nL 128.218228 208.305047 \nL 128.746425 208.825259 \nL 128.868317 208.668013 \nL 128.908947 208.707644 \nL 128.990209 208.638705 \nL 129.233992 208.315322 \nL 129.315253 208.35322 \nL 129.680928 208.696414 \nL 130.127865 208.430304 \nL 130.656062 208.986259 \nL 130.696693 208.935033 \nL 130.981107 208.660827 \nL 131.062368 208.710438 \nL 131.631196 209.299173 \nL 131.753088 209.241256 \nL 131.834349 209.180656 \nL 131.874979 209.23411 \nL 131.956241 209.132533 \nL 132.078132 209.096724 \nL 132.64696 209.688713 \nL 132.768852 209.591049 \nL 132.809483 209.646062 \nL 132.890744 209.592091 \nL 133.012636 209.543199 \nL 133.581464 210.132977 \nL 133.703355 210.112358 \nL 134.06903 209.973947 \nL 134.841011 210.528499 \nL 135.003534 210.31606 \nL 135.572362 210.86183 \nL 135.897406 210.593452 \nL 136.506865 211.056153 \nL 136.547496 210.984292 \nL 136.669387 210.866906 \nL 136.710018 210.897597 \nL 136.913171 210.986701 \nL 137.441368 211.478272 \nL 137.685152 211.259804 \nL 137.847674 211.169064 \nL 138.457133 211.738503 \nL 138.904069 211.682931 \nL 139.432266 212.097704 \nL 139.838572 211.987733 \nL 140.4074 212.533559 \nL 140.691814 212.46713 \nL 140.813706 212.296568 \nL 140.854336 212.336463 \nL 141.463795 212.907535 \nL 141.951362 212.786117 \nL 142.438929 213.213758 \nL 142.642082 212.964553 \nL 142.723343 212.650476 \nL 142.804604 212.706805 \nL 143.535954 213.044293 \nL 143.94226 213.070428 \nL 144.307935 213.389302 \nL 144.714241 213.205524 \nL 145.3237 213.750069 \nL 145.567483 213.675912 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214.932265 \nL 157.309719 214.832544 \nL 157.431611 214.832271 \nL 157.959808 215.196907 \nL 158.162961 215.106632 \nL 158.203592 215.108344 \nL 158.366114 214.857262 \nL 158.975573 215.34615 \nL 159.138095 215.303933 \nL 159.219356 215.290319 \nL 159.381879 215.240736 \nL 159.991337 215.615706 \nL 160.397643 215.419741 \nL 160.92584 215.786434 \nL 161.332146 215.542435 \nL 161.941605 215.976541 \nL 162.30728 215.81818 \nL 163.079261 216.209682 \nL 163.241783 216.073714 \nL 163.769981 216.426041 \nL 164.216917 216.408715 \nL 164.785745 216.78541 \nL 165.192051 216.657632 \nL 165.760879 217.029384 \nL 166.289076 217.111899 \nL 166.817274 217.39436 \nL 167.101688 217.333532 \nL 167.792408 217.74537 \nL 168.117452 217.607236 \nL 168.808172 218.029018 \nL 169.255108 218.051349 \nL 169.742675 218.346744 \nL 170.027089 218.332188 \nL 170.839701 218.685905 \nL 171.286637 218.887545 \nL 171.814835 219.150906 \nL 172.058618 219.174222 \nL 172.586815 219.494225 \nL 173.236905 219.67734 \nL 173.846363 219.803336 \nL 174.008886 219.867963 \nL 174.537083 220.175802 \nL 174.658975 220.115078 \nL 174.902758 220.069041 \nL 175.02465 220.128032 \nL 175.593478 220.40177 \nL 176.040414 220.356597 \nL 176.568612 220.624932 \nL 176.934287 220.561198 \nL 177.503115 220.824633 \nL 177.665637 220.800131 \nL 178.518879 221.177336 \nL 178.843924 221.285177 \nL 179.575274 221.664768 \nL 180.550408 221.911449 \nL 180.753561 221.939043 \nL 181.36302 222.222066 \nL 181.647434 222.164014 \nL 181.769325 222.126118 \nL 182.419415 222.49506 \nL 182.78509 222.444952 \nL 183.353918 222.788091 \nL 183.597701 222.749122 \nL 183.760224 222.758673 \nL 184.288421 223.062423 \nL 184.654096 223.009972 \nL 185.304185 223.331175 \nL 185.588599 223.291013 \nL 185.832383 223.428534 \nL 186.279319 223.651123 \nL 186.482472 223.634935 \nL 186.848147 223.834592 \nL 187.416975 224.027464 \nL 187.78265 224.179033 \nL 188.310848 224.433316 \nL 188.676523 224.382788 \nL 189.285982 224.681185 \nL 189.732918 224.738325 \nL 190.261115 224.993848 \nL 190.58616 224.980501 \nL 191.439402 224.738075 \nL 191.805077 224.912301 \nL 192.170753 225.040856 \nL 192.617689 225.07755 \nL 194.283542 225.732521 \nL 194.608587 225.837072 \nL 195.177415 226.108361 \nL 195.299307 226.122449 \nL 195.461829 225.994492 \nL 196.23381 226.372826 \nL 196.355702 226.25982 \nL 196.518224 226.248834 \nL 197.290205 226.509407 \nL 197.61525 226.661879 \nL 198.184078 226.790054 \nL 198.38723 226.855254 \nL 198.915428 227.095645 \nL 199.321734 227.068241 \nL 199.890562 227.336315 \nL 200.784434 227.640934 \nL 201.068848 227.645815 \nL 201.515785 227.792295 \nL 202.003352 227.954569 \nL 202.165874 227.90661 \nL 202.328396 227.869696 \nL 203.100377 228.224037 \nL 203.344161 228.15922 \nL 204.03488 228.447272 \nL 204.238033 228.410104 \nL 204.888122 228.685616 \nL 206.025779 228.962623 \nL 206.188301 229.009485 \nL 206.797759 229.270396 \nL 207.041543 229.239933 \nL 207.447849 229.455707 \nL 207.772893 229.56985 \nL 207.976046 229.539361 \nL 208.21983 229.639849 \nL 208.95118 229.799196 \nL 209.194963 229.7813 \nL 209.723161 230.018664 \nL 210.170097 230.025205 \nL 210.698295 230.233006 \nL 211.06397 230.227177 \nL 211.632798 230.467164 \nL 211.957842 230.43496 \nL 212.607932 230.714519 \nL 213.014237 230.709982 \nL 213.583065 230.952273 \nL 213.90811 230.940407 \nL 214.59883 231.249333 \nL 214.923874 231.220922 \nL 215.533333 231.498657 \nL 215.777117 231.459264 \nL 216.061531 231.493271 \nL 216.71162 231.713405 \nL 217.036664 231.751596 \nL 217.686754 231.99504 \nL 218.052429 231.917888 \nL 218.580626 232.106714 \nL 218.783779 232.005482 \nL 222.643684 233.137191 \nL 222.765575 233.131143 \nL 223.415665 233.38967 \nL 223.943862 233.491449 \nL 224.51269 233.648184 \nL 224.797104 233.706666 \nL 225.284671 233.85417 \nL 225.690977 233.879558 \nL 226.259805 234.067081 \nL 227.478722 234.258139 \nL 227.925658 234.439303 \nL 228.331964 234.540436 \nL 228.7789 234.651095 \nL 229.266467 234.758785 \nL 229.591512 234.766166 \nL 230.200971 234.981319 \nL 230.607276 235.022309 \nL 231.338627 235.315928 \nL 231.704302 235.369542 \nL 232.232499 235.528583 \nL 232.476283 235.470883 \nL 233.207633 235.76353 \nL 233.492047 235.751479 \nL 234.264028 235.989663 \nL 234.507812 236.029006 \nL 236.295557 236.5534 \nL 236.417449 236.557756 \nL 237.189429 236.818825 \nL 237.514474 236.875778 \nL 239.099066 237.198225 \nL 239.383481 237.256951 \nL 240.03357 237.447989 \nL 240.236723 237.438939 \nL 240.846181 237.626547 \nL 241.983837 237.920506 \nL 242.14636 237.826579 \nL 242.999602 238.07918 \nL 243.284016 238.036888 \nL 243.852844 238.218281 \nL 244.502933 238.309255 \nL 244.909239 238.402753 \nL 245.274914 238.400193 \nL 245.762481 238.525575 \nL 246.128156 238.545347 \nL 246.859506 238.78348 \nL 247.225181 238.82799 \nL 247.875271 239.034551 \nL 250.150583 239.629724 \nL 250.922564 239.870546 \nL 251.125717 239.941229 \nL 251.775806 240.18763 \nL 252.019589 240.090383 \nL 252.547787 240.241297 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246.090246 \nL 279.282705 246.064897 \nL 279.973425 246.228956 \nL 280.217208 246.24612 \nL 280.948559 246.443277 \nL 281.151711 246.351418 \nL 281.679909 246.442401 \nL 281.923692 246.436833 \nL 282.126845 246.373105 \nL 282.898826 246.563772 \nL 283.223871 246.585709 \nL 283.752068 246.704384 \nL 284.199005 246.677949 \nL 284.808463 246.814182 \nL 285.377291 246.900207 \nL 285.905489 246.993111 \nL 286.149272 246.992488 \nL 286.880623 247.121579 \nL 287.083775 247.105169 \nL 287.896387 247.257223 \nL 288.018279 247.218554 \nL 288.627737 247.370582 \nL 289.074674 247.380532 \nL 289.846655 247.480605 \nL 290.25296 247.594651 \nL 290.862419 247.672997 \nL 291.146833 247.747427 \nL 291.837553 247.917652 \nL 292.040706 247.93693 \nL 292.731425 248.070456 \nL 292.975209 248.056236 \nL 294.600432 248.408003 \nL 294.844215 248.341837 \nL 295.575566 248.511488 \nL 295.819349 248.467668 \nL 296.550699 248.650375 \nL 296.794483 248.626234 \nL 297.485203 248.787109 \nL 298.257184 248.901366 \nL 298.70412 248.963387 \nL 298.988534 249.044322 \nL 300.451235 249.310839 \nL 300.81691 249.28812 \nL 301.54826 249.358777 \nL 301.954566 249.456399 \nL 302.482763 249.512006 \nL 302.767177 249.574108 \nL 303.376636 249.718586 \nL 303.620419 249.668508 \nL 304.270509 249.805152 \nL 305.205012 249.942332 \nL 306.342668 250.192636 \nL 306.545821 250.102316 \nL 307.317802 250.275792 \nL 307.845999 250.27619 \nL 308.333566 250.374301 \nL 308.536719 250.394066 \nL 309.34933 250.56342 \nL 309.633744 250.622615 \nL 310.283834 250.757571 \nL 310.608878 250.810804 \nL 311.705904 251.037037 \nL 312.518515 251.223669 \nL 313.127974 251.352377 \nL 313.412388 251.342764 \nL 314.224999 251.496147 \nL 314.550044 251.550403 \nL 316.094006 251.866353 \nL 316.459681 251.942095 \nL 317.028509 251.9964 \nL 317.272293 251.927298 \nL 318.044273 252.040857 \nL 319.303821 252.280957 \nL 319.872649 252.329065 \nL 320.360216 252.375857 \nL 321.782286 252.713474 \nL 322.107331 252.688829 \nL 323.773185 253.033142 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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "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": 9,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "text": "Seed namespaces (singletons and interactions): ['e', 'g', 'b', 'd', 'i', 'h', 'a', 'f', 'c']\nCreated challengers from champion |supervised||classic\nNew challenger size 37, ['|supervised|gi|classic', '|supervised|eh|classic', '|supervised|ad|classic', '|supervised|gh|classic', '|supervised|bc|classic', '|supervised|bd|classic', '|supervised|ae|classic', '|supervised|dg|classic', '|supervised|ei|classic', '|supervised|df|classic', '|supervised|fh|classic', '|supervised|ac|classic', '|supervised|ab|classic', '|supervised|cg|classic', '|supervised|hi|classic', '|supervised|fg|classic', '|supervised|bi|classic', '|supervised|be|classic', '|supervised|de|classic', '|supervised|ci|classic', '|supervised|fi|classic', '|supervised|cd|classic', '|supervised|af|classic', '|supervised|ce|classic', '|supervised|di|classic', '|supervised|bf|classic', '|supervised|ai|classic', '|supervised|bh|classic', '|supervised|ag|classic', '|supervised|bg|classic', '|supervised|eg|classic', '|supervised|ah|classic', '|supervised|cf|classic', '|supervised|dh|classic', '|supervised|ef|classic', '|supervised|ch|classic', '|supervised||classic']\nOnline learning for 10000 steps...\nSeed namespaces (singletons and interactions): ['cf', 'e', 'g', 'b', 'd', 'i', 'h', 'a', 'f', 'c']\nCreated challengers from champion |supervised|cf|classic\nNew challenger size 43, ['|supervised|bg_cf|classic', '|supervised|cf_dg|classic', '|supervised|ab_cf|classic', '|supervised|bh_cf|classic', '|supervised|cf_eg|classic', '|supervised|cf_ef|classic', '|supervised|be_cf|classic', '|supervised|cf_di|classic', '|supervised|cf_ci|classic', '|supervised|bd_cf|classic', '|supervised|cf_fi|classic', '|supervised|bf_cf|classic', '|supervised|ah_cf|classic', '|supervised|ac_cf|classic', '|supervised|ce_cf|classic', '|supervised|cf|classic', '|supervised|cf_cfg|classic', '|supervised|cf_gi|classic', '|supervised|ag_cf|classic', '|supervised|ae_cf|classic', '|supervised|cf_fg|classic', '|supervised|cf_hi|classic', '|supervised|cf_df|classic', '|supervised|cef_cf|classic', '|supervised|cdf_cf|classic', '|supervised|cd_cf|classic', '|supervised|bc_cf|classic', '|supervised|cf_gh|classic', '|supervised|cf_cg|classic', '|supervised|cf_ch|classic', '|supervised|bcf_cf|classic', '|supervised|af_cf|classic', '|supervised|cf_ei|classic', '|supervised|ai_cf|classic', '|supervised|cf_dh|classic', '|supervised|ad_cf|classic', '|supervised|cf_de|classic', '|supervised|cf_fh|classic', '|supervised|cf_eh|classic', '|supervised|acf_cf|classic', '|supervised|bi_cf|classic', '|supervised|cf_cfi|classic', '|supervised|cf_cfh|classic']\nAverage final loss of the AutoVW (tuning namespaces) based on customized vw arguments: 9.606119226635231\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'}\n",
    "search_space = fixed_vw_hp_config.copy()\n",
    "search_space.update({'interactions': AutoVW.AUTO_STRING})\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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