comparison_openml.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "66eb0613",
   "metadata": {},
   "source": [
    "# Comparison with APL library on OpenML benchmark datasets"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cd6b7fba",
   "metadata": {},
   "source": [
    "In this notebook we will compare our library with official SAP HANA Automated Predictive Library (details here: https://help.sap.com/viewer/cb31bd99d09747089754a0ba75067ed2/2.5.0.0/en-US). In most cases, our library beats it in accuracy. However, there is always room for improvement. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b20df02d",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    Notification.requestPermission(function (permission) {\n",
       "        if(!('permission' in Notification)) {\n",
       "            Notification.permission = permission;\n",
       "        }\n",
       "    })\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%load_ext jupyternotify\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "from benchmarks.perfomance import Benchmark\n",
    "from benchmarks.cleanup import clean\n",
    "from tests.connection import connection_context, schema\n",
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from hana_automl.utils.scripts import setup_user\n",
    "import time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "83d7ccc7",
   "metadata": {
    "tags": [
     "hide-cell"
    ]
   },
   "outputs": [],
   "source": [
    "b = Benchmark(connection_context, schema)\n",
    "GRADIENT = True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ccbdd066",
   "metadata": {
    "tags": [
     "hide-cell"
    ]
   },
   "outputs": [],
   "source": [
    "df = pd.DataFrame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "6b57bcc1",
   "metadata": {
    "tags": [
     "hide-cell"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Solarize_Light2', '_classic_test_patch', 'bmh', 'classic', 'dark_background', 'fast', 'fivethirtyeight', 'ggplot', 'grayscale', 'seaborn', 'seaborn-bright', 'seaborn-colorblind', 'seaborn-dark', 'seaborn-dark-palette', 'seaborn-darkgrid', 'seaborn-deep', 'seaborn-muted', 'seaborn-notebook', 'seaborn-paper', 'seaborn-pastel', 'seaborn-poster', 'seaborn-talk', 'seaborn-ticks', 'seaborn-white', 'seaborn-whitegrid', 'tableau-colorblind10']\n"
     ]
    }
   ],
   "source": [
    "def plot_results(task: str, boosting: bool):\n",
    "    df['APL'] = apl_acc\n",
    "    df['HANA AutoML'] = hana_acc\n",
    "    df['Dataset'] = datasets\n",
    "    x = np.arange(len(datasets))  # the label locations\n",
    "    width = 0.45  # the width of the bars\n",
    "\n",
    "    fig, ax = plt.subplots()\n",
    "    rects1 = ax.bar(x - width/2, apl_acc, width, label='APL')\n",
    "    rects2 = ax.bar(x + width/2, hana_acc, width, label='HANA AutoML')\n",
    "    # Add some text for labels, title and custom x-axis tick labels, etc.\n",
    "    ax.set_ylabel('r2_score')\n",
    "    if boosting:\n",
    "        ax.set_title(f'{task} (APL with Gradient Boosting)')\n",
    "    else:\n",
    "        ax.set_title(f'{task} (APL without Gradient Boosting)')\n",
    "    \n",
    "    ax.set_xticks(x)\n",
    "    ax.set_xticklabels(datasets)\n",
    "    ax.legend()\n",
    "\n",
    "    ax.bar_label(rects1)\n",
    "    ax.bar_label(rects2)\n",
    "\n",
    "    plt.gcf().set_size_inches(25,15)\n",
    "    plt.style.use('default')\n",
    "    plt.show()\n",
    "    \n",
    "def finish(benchmark):\n",
    "    apl_acc.append(benchmark.apl_accuracy)\n",
    "    hana_acc.append(benchmark.automl_accuracy)\n",
    "    clean(connection_context, schema)\n",
    "    time.sleep(180)\n",
    "    \n",
    "print(plt.style.available)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "24ae45da",
   "metadata": {},
   "source": [
    "## Enable Gradient boosting in APL by setting GRADIENT to True\n",
    "Don't know what is Gradient Boosting? Check here: https://machinelearningmastery.com/gentle-introduction-gradient-boosting-algorithm-machine-learning/ . We are using special benchmark datasets from OpenML to compare model accuracy."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4bc9e6ad",
   "metadata": {},
   "source": [
    "## Classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "bd26b810",
   "metadata": {},
   "outputs": [],
   "source": [
    "apl_acc = []\n",
    "hana_acc = []\n",
    "datasets = ['kr-vs-kp.csv', 'australian.csv', 'phoneme.csv', 'adult.csv', 'blood.csv', 'sylvine.csv', 'credit.csv', 'kc1.csv']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "416bab16",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "REMOVED ALL TABLES FROM DEVELOPER\n",
      "100%|██████████| 1/1 [00:00<00:00,  2.10it/s]\n",
      "100%|██████████| 1/1 [00:00<00:00,  4.00it/s]\n",
      "Finished in 6 seconds\n",
      "APL accuracy:  0.9885297184567258\n",
      "REMOVED ALL TABLES FROM DEVELOPER\n",
      "Finished in 167 seconds\n",
      "hana_automl accuracy: 0.99373\n",
      "REMOVED ALL TABLES FROM DEVELOPER\n"
     ]
    }
   ],
   "source": [
    "clean(connection_context, schema)\n",
    "b.run('./data/benchmark/cls/kr-vs-kp.csv', task='cls', label='class', categorical=['class'], grad_boost=GRADIENT)\n",
    "finish(b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "61b9e1a2",
   "metadata": {
    "tags": [
     "hide-cell"
    ]
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1/1 [00:00<00:00,  6.04it/s]\n",
      "100%|██████████| 1/1 [00:00<00:00,  6.45it/s]\n",
      "Finished in 5 seconds\n",
      "APL accuracy:  0.8695652173913043\n",
      "REMOVED ALL TABLES FROM DEVELOPER\n",
      "Finished in 270 seconds\n",
      "hana_automl accuracy: 0.89855\n",
      "REMOVED ALL TABLES FROM DEVELOPER\n"
     ]
    }
   ],
   "source": [
    "b.run('./data/benchmark/cls/australian.csv', task='cls', \n",
    "      label='A15', \n",
    "      categorical=['A15', 'A1', 'A4', 'A5', 'A6', 'A8', 'A9', 'A11', 'A12'],\n",
    "      grad_boost=GRADIENT)\n",
    "finish(b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "25936031",
   "metadata": {
    "scrolled": true,
    "tags": [
     "hide-cell"
    ]
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1/1 [00:00<00:00,  3.28it/s]\n",
      "100%|██████████| 1/1 [00:00<00:00,  5.42it/s]\n",
      "Finished in 6 seconds\n",
      "APL accuracy:  0.8797780517879161\n",
      "REMOVED ALL TABLES FROM DEVELOPER\n",
      "Finished in 281 seconds\n",
      "hana_automl accuracy: 0.897959\n",
      "REMOVED ALL TABLES FROM DEVELOPER\n"
     ]
    }
   ],
   "source": [
    "b.run('./data/benchmark/cls/phoneme.csv', task='cls', label='Class', categorical=['Class'], grad_boost=GRADIENT)\n",
    "finish(b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "0c68d3e3",
   "metadata": {
    "tags": [
     "hide-cell"
    ]
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1/1 [00:03<00:00,  3.96s/it]\n",
      "100%|██████████| 1/1 [00:01<00:00,  1.26s/it]\n",
      "Finished in 12 seconds\n",
      "APL accuracy:  0.868832321026411\n",
      "REMOVED ALL TABLES FROM DEVELOPER\n",
      "Finished in 329 seconds\n",
      "hana_automl accuracy: 0.865751\n",
      "REMOVED ALL TABLES FROM DEVELOPER\n"
     ]
    }
   ],
   "source": [
    "b.run('./data/benchmark/cls/adult.csv', task='cls', label='class', categorical=['workclass', 'education', 'marital-status', 'occupation', 'relationship', 'race', 'sex', 'native-country', 'class'], grad_boost=GRADIENT)\n",
    "finish(b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "0bcb89fd",
   "metadata": {
    "tags": [
     "hide-cell"
    ]
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1/1 [00:00<00:00,  5.48it/s]\n",
      "100%|██████████| 1/1 [00:00<00:00,  6.19it/s]\n",
      "Finished in 5 seconds\n",
      "APL accuracy:  0.7733333333333333\n",
      "REMOVED ALL TABLES FROM DEVELOPER\n",
      "Finished in 275 seconds\n",
      "hana_automl accuracy: 0.81132\n",
      "REMOVED ALL TABLES FROM DEVELOPER\n"
     ]
    }
   ],
   "source": [
    "b.run('./data/benchmark/cls/blood.csv', task='cls', label='Class', categorical=['Class'], grad_boost=GRADIENT)\n",
    "finish(b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "b3737370",
   "metadata": {
    "tags": [
     "hide-cell"
    ]
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1/1 [00:00<00:00,  2.67it/s]\n",
      "100%|██████████| 1/1 [00:00<00:00,  4.57it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Finished in 6 seconds\n",
      "APL accuracy:  0.9401820546163849\n",
      "REMOVED ALL TABLES FROM DEVELOPER\n",
      "Finished in 400 seconds\n",
      "hana_automl accuracy: 0.929687\n",
      "REMOVED ALL TABLES FROM DEVELOPER\n"
     ]
    }
   ],
   "source": [
    "b.run('./data/benchmark/cls/sylvine.csv', task='cls', label='class', categorical=['class'], grad_boost=GRADIENT)\n",
    "finish(b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "812d67ec",
   "metadata": {
    "tags": [
     "hide-cell"
    ]
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1/1 [00:00<00:00,  5.57it/s]\n",
      "100%|██████████| 1/1 [00:00<00:00,  6.48it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Finished in 5 seconds\n",
      "APL accuracy:  0.7366666666666667\n",
      "REMOVED ALL TABLES FROM DEVELOPER\n",
      "Finished in 342 seconds\n",
      "hana_automl accuracy: 0.8\n",
      "REMOVED ALL TABLES FROM DEVELOPER\n"
     ]
    }
   ],
   "source": [
    "b.run('./data/benchmark/cls/credit.csv', task='cls', label='class', categorical=['class', 'checking_status', 'credit_history', 'purpose', 'savings_status', 'employment', 'personal_status', 'other_parties', 'property_magnitude', 'other_payment_plans', 'housing', 'job', 'own_telephone', 'foreign_worker'], grad_boost=GRADIENT)\n",
    "finish(b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "170c4f67",
   "metadata": {
    "tags": [
     "hide-cell"
    ]
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1/1 [00:00<00:00,  2.49it/s]\n",
      "100%|██████████| 1/1 [00:00<00:00,  3.87it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Finished in 7 seconds\n",
      "APL accuracy:  0.8546603475513428\n",
      "REMOVED ALL TABLES FROM DEVELOPER\n",
      "Finished in 615 seconds\n",
      "hana_automl accuracy: 0.776859\n",
      "REMOVED ALL TABLES FROM DEVELOPER\n"
     ]
    }
   ],
   "source": [
    "b.run('./data/benchmark/cls/kc1.csv', task='cls', label='defects', categorical=['defects'], grad_boost=GRADIENT)\n",
    "finish(b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "85cdc6db",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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uu2/uu+++fPCDH8ysWbPSr1+/9O3bN4sWLcrOO++c8847L48++mj23nvv3HvvvW9rnWsTM6YBAAAAAFZBY2NjdtpppzQ2Ntbatthii0ydOjXTpk3L9OnTc/XVV6++At9BBNMAAAAAAG/g+eefz2233ZaLLrooTU1NK+zv0KFDPvShD+XBBx9cDdW98wimAQAAAADewDXXXJM999wzffr0yUYbbZS777671f4XX3wxN9xwQwYOHLiaKnxnEUwDAAAAALyBxsbGjB49OkkyevTo2nIeDz30UIYMGZIPf/jD2WuvvfKJT3xidZb5juHlhwAAAAAAr+Nvf/tbbrzxxtxzzz0ppWTJkiUppeSYY46prTHNm2PGNAAAAADA6/j1r3+dgw8+OI888khmzZqV2bNnZ/PNN8/s2bNXd2nvWGZMAwAAAADvKLNO36uu52tsbMw3vvGNVm37779/TjvttNc85q9//WsaGhpq22effXY+/elPt1mN7zSlqqrVXcObMnTo0Gry5MmruwwAAAAAoE7uu+++9OvXb3WXwetY2Z9RKeXuqqqGrqy/pTwAAAAAAKgrwTQAAAAAAHUlmAYAAAAAoK4E0wAAAAAA1JVgGgAAAACAuhJMAwAAAABQVx1WdwEAAAAAAG/K2Pe9zeM9+4Zd1ltvvTz//PO17fHjx2fy5MkZN25crW3IkCH54Ac/mKamplrbYYcdluuvvz4PP/xw1llnncybNy9Dhw7NrFmzan1++MMf5qSTTsoTTzyR973vta9tVfst8x//8R/5f//v/71hv1JKPvvZz+bnP/95kmTx4sXZdNNNs/322+d//ud/Vnqtb5UZ0wAAAAAAb9F9992XJUuW5NZbb80LL7zQal/79u3z05/+9DWPbWxszLBhw3LllVe+7jlWtd8y//Ef/7FK/d773vfm3nvvzUsvvZQkuf7669OtW7dVOvYfJZgGAAAAAHiLGhsbc/DBB2ePPfbINddc02rfcccdl7PPPjuLFy9e4biHHnoozz//fE499dQ0Nja+5viv1W/8+PH58pe/XNvee++9c/PNN+ekk07KSy+9lCFDhuSzn/1skuSss87KVlttla222io//OEPW40/YsSIXHfddbVrOfDAA9/0PXgzBNMAAAAAAG9gWci77GvMmDGt9l922WUZPXp0DjzwwBUC5h49emSnnXbKpZdeusK4TU1NGT16dD7ykY/kr3/9a5544omVnn9V+y1z+umn5z3veU+mTp2aX/ziF7n77rtz8cUX56677sqdd96ZCy+8MFOmTKn1Hz16dJqamrJw4cJMmzYt22+//aremn+IYBoAAAAA4A0sC3mXfZ1yyim1fZMnT06XLl3So0eP7LbbbpkyZUr+9re/tTr+m9/8Zs4444wsXbq0VXtjY2NGjx6ddu3aZf/998+vfvWrlZ5/Vfu9lttuuy377rtv3vve92a99dbLfvvtl1tvvbW2f9CgQZk1a1YaGxszYsSINzX2P0IwDa8yceLE9O3bN717987pp5++wv5HHnkku+22WwYNGpRddtklc+bMqe37xje+Uft1iMsuu6zWfsQRR2Tw4MEZNGhQDjjggNpC+ccff3ztKVufPn2y4YYb1s6xzTbbZMiQIRkwYEAuuOCCtr1oAAAAAP5hjY2Nuf/++9OzZ89sscUWWbBgQa644opWfbbccssMGTIkl19+ea3tnnvuyYwZM7L77runZ8+eaWpqWulyHq/Xr0OHDq3C7oULF/7D1zFy5MiccMIJbb6MR9KGwXQp5aellCdLKfe+xv5SSvlRKeXBUsq0Uso2bVULrKolS5bkmGOOyW9+85tMnz49jY2NmT59eqs+J5xwQg455JBMmzYtY8aMyTe/+c0kyXXXXZc//elPmTp1au66666ceeaZWbBgQZLk7LPPzp///OdMmzYtPXr0qL3B9Oyzz649ZfvKV76S/fbbL0my6aab5o477qiNdfrpp+exxx6r450AAAAAYFUsXbo0l19+ee65557MmjUrs2bNyjXXXLPSgPnkk0/OmWeeWdtubGzM2LFja8c99thjeeyxx/LII4+0Ou71+vXs2TNTp07N0qVLM3v27EyaNKl2XMeOHfPKK68kST7ykY/k6quvzosvvpgXXnghV111VT7ykY+0Os/hhx+e73znOxk4cODbeYtWqkMbjj0+ybgkP3uN/Z9IsmXL1/ZJzm/5J6w2kyZNSu/evdOrV68kzWvrXHPNNenfv3+tz/Tp03PWWWclSXbdddd86lOfqrXvvPPO6dChQzp06JBBgwZl4sSJGTVqVDbYYIMkSVVVeemll1JKWeHcjY2N+bd/+7ckSadOnWrtL7/88gq/4gEAAADwrjb22dVdQc2tt96abt26pWvXrrW2nXfeOdOnT8/jjz/equ+AAQOyzTbb5E9/+lOS5nWjJ0yY0KrPvvvum6ampnzjG9+otb1evxNPPDGbb755+vfvn379+mWbbf4+//cLX/hCBg0alG222Sa/+MUvcthhh2W77bZLkhx55JHZeuutW43Z0NCQf/3Xf13pdY4fPz5XX311bfvOO+9MQ0PDG92e11SqqvqHD37DwUvpmeR/qqraaiX7fpzk5qqqGlu2/5pkl6qqHn913+UNHTq0mjx5cluUC/n1r3+diRMn5ic/+UmS5NJLL81dd91Vm+GcJAcddFC23377HHvssbnyyiuz//77Z968ebn77rvzb//2b7n++uvz4osvZrvttssxxxyTr33ta0mSz3/+85kwYUL69++f6667Luuuu25tzEceeSQ77LBD5syZk/bt2ydJZs+enb322isPPvhgzjjjjBxzzDF1vBMAAAAAa4777rsv/fr1W91l8DpW9mdUSrm7qqqhK+u/OteY7pZk9nLbc1raVlBK+UIpZXIpZfJTTz1Vl+LWZm9lDeUTTzwxAwYMSL9+/fKv//qvWfZgo7GxMQMHDsygQYOy5557Zt68eUmSsWPHplu3brV1lJc92Zk0aVKtbfDgwbnqqqvqcOVvjzPPPDO33HJLtt5669xyyy3p1q1b2rdvnz322CMjRozIhz70oRx44IHZcccdayFzklx88cV57LHH0q9fv1brTyfNT70OOOCAVv27d++eadOm5cEHH8wll1zyhm9aBQAAAIB3infEyw+rqvqvqqqGVlU1dOONN17d5byjvZU1lP/whz/k9ttvz7Rp03Lvvffmj3/8Y2655ZYsXrw4xx57bG666aZMmzYtgwYNajXD+Pjjj6+to7zsjZ5bbbVVJk+enKlTp2bixIn54he/mMWLF9fvRryGbt26Zfbsvz8vmTNnTrp1a/28pGvXrrnyyiszZcqUfPe7302S2ksLTz755EydOjXXX399qqpKnz59Wh3bvn37jB49eoXF75uaml5zUfmuXbtmq622avWWVAAAAAB4J1udwfTcJN2X225oaaMNLb+GcqdOnWprKC9v+vTp+djHPpakeQ3lZftLKVm4cGEWLVqUl19+Oa+88kr+6Z/+KVVVpaqqvPDCC6mqKgsWLGi1ps7KrLvuuunQoXmJ84ULF650zeXVYdiwYZkxY0ZmzpyZRYsWpampKSNHjmzVZ968ebU1n0877bQcfvjhSZpD//nz5ydJpk2blmnTpmWPPfZIVVV58MEHkzSvMX3ttdfmgx/8YG28+++/P08//XR23HHHWtucOXPy0ksvJUmefvrp3Hbbbenbt2/bXTgAAADAGq4tlyTmrflH/mxWZzB9bZJDSrMdkjz7RutL89bNnTs33bv//XlAQ0ND5s5t/Txg8ODBufLKK5MkV111VZ577rnMnz8/O+64Y3bddddsuumm2XTTTTN8+PD069cvHTt2zPnnn5+BAwema9eumT59eo444ojaeOPGjcugQYNy+OGH5+mnn66133XXXRkwYEAGDhyYCy64oBZUr04dOnTIuHHjatc2atSoDBgwIGPGjMm1116bJLn55pvTt2/f9OnTJ0888UROPvnkJMkrr7ySj3zkI+nfv3++8IUv5Oc//3k6dOiQqqpy6KGHZuDAgRk4cGAef/zxjBkzpnbOpqamjB49ulU4f99992X77bfP4MGD89GPfjQnnHBCXd6GCgAAALAm6ty5c+bPny+cXgNVVZX58+enc+fOb+q4Nnv5YSmlMckuSbokeSLJd5J0TJKqqi4ozSncuCR7JnkxyeerqnrDtxp6+eFbsyov93vsscfy5S9/OTNnzszOO++cK664Ivfee2/mzZuXY489trY+8u67757vf//72WGHHbLnnnvmv/7rv9KrV6985StfyT//8z/nW9/6Vp544ol06dIlpZR8+9vfzuOPP56f/vSnrWq67777cuihh+b3v//9m/4AAwAAALD2e+WVVzJnzpwsXLhwdZfCSnTu3DkNDQ3p2LFjq/bXe/lhm01Rrapq5Qvm/n1/leSYtjo/K/dm1lBOkueffz5XXHFFNtxww1x44YXZYYcdst566yVJPvGJT+SOO+6ohclbbLFFkmTUqFG1lyr+0z/9U23co446KnvvvfcKNfXr1y/rrbde7r333gwdutLP6aob+763dvzabuyzq7sCAAAAgDetY8eO2XzzzVd3GbyN3hEvP+Tt81bWUO7Ro0ftZYevvPJKbrnllvTr1y/dunXL9OnT89RTTyVJrr/++vTr1y9J8vjjf1+d5aqrrspWW22VJJk5c2btZYePPPJI7r///vTs2bNNrx0AAAAAWDOs/kV9qavl11BesmRJDj/88NoaykOHDs3IkSNz880355vf/GZKKdl5551z7rnnJkkOOOCA3HjjjRk4cGBKKdlzzz2zzz77JEm+853vZOedd07Hjh2z2WabZfz48UmSE088MVOnTk0pJT179syPf/zjJMltt92W008/PR07dky7du1y3nnnpUuXLqvlngAAAAAA9dVma0y3FWtM87os5fH6LOUBAAAAQJ2sljWmefv1POm61V3CGm+WdycCAAAAwBrPGtMAAAAAANSVYBoAAAAAgLoSTAMAAAAAUFeCaQAAAAAA6kowDQAAAABAXQmmAQAAAACoK8E0AAAAAAB1JZgGAAAAAKCuBNMAAAAAANSVYBoAAAAAgLoSTAMAAAAAUFeCaQAAAAAA6kowDQAAAABAXQmmAQAAAACoK8E0AAAAAAB1JZgGAAAAAKCuBNMAAAAAANSVYBoAAAAAgLoSTAMAAAAAUFeCaQAAAAAA6kowDQAAAABAXQmmAQAAAACoK8E0AAAAAAB1JZgGAAAAAKCuBNMAAAAAANSVYBoAAAAAgLoSTAMAAAAAUFeCaQAAAAAA6kowDQAAQJuYOHFi+vbtm969e+f0009fYf8jjzyS3XbbLYMGDcouu+ySOXPmtNq/YMGCNDQ05Mtf/nKt7e67787AgQPTu3fv/Ou//muqqkqS/OpXv8qAAQPSrl27TJ48udb/lVdeyaGHHpqBAwemX79+Oe2005Iks2fPzq677pr+/ftnwIAB+c///M+2uAUAwGsQTAMAAPC2W7JkSY455pj85je/yfTp09PY2Jjp06e36nPCCSfkkEMOybRp0zJmzJh885vfbLX/29/+dnbeeedWbUcffXQuvPDCzJgxIzNmzMjEiROTJFtttVWuvPLKFfr/6le/yssvv5x77rknd999d3784x9n1qxZ6dChQ37wgx9k+vTpufPOO3PuueeuUB8A0HYE0wAAALztJk2alN69e6dXr17p1KlTRo8enWuuuaZVn+nTp+djH/tYkmTXXXdttf/uu+/OE088kT322KPW9vjjj2fBggXZYYcdUkrJIYcckquvvjpJ0q9fv/Tt23eFOkopeeGFF7J48eK89NJL6dSpUzbYYINsuumm2WabbZIk66+/fvr165e5c+e+3bcBAHgNgmkAAADednPnzk337t1r2w0NDSsEv4MHD86VV16ZJLnqqqvy3HPPZf78+Vm6dGm+9rWv5cwzz1xhzIaGhtcd89UOOOCAvPe9782mm26aHj165IQTTsgHPvCBVn1mzZqVKVOmZPvtt/+HrhUAePME0wAAAKwWZ555Zm655ZZsvfXWueWWW9KtW7e0b98+5513XkaMGNEqhP5HTZo0Ke3bt89jjz2WmTNn5gc/+EEefvjh2v7nn38++++/f374wx9mgw02eMvnAwBWTYfVXQAAAABrn27dumX27Nm17Tlz5qRbt26t+nTt2rU2Y/r555/PFVdckQ033DB33HFHbr311px33nl5/vnns2jRoqy33no59thjW70gcWVjvtovf/nL7LnnnunYsWM22WSTfPjDH87kyZPTq1evvPLKK9l///3z2c9+Nvvtt9/bePUAwBsxYxoAAIC33bBhwzJjxozMnDkzixYtSlNTU0aOHNmqz7x587J06dIkyWmnnZbDDz88SfKLX/wijz76aGbNmpUzzzwzhxxySE4//fRsuumm2WCDDXLnnXemqqr87Gc/yyc/+cnXraNHjx658cYbkyQvvPBC7rzzznzwgx9MVVU54ogj0q9fv3z1q19tgzsAALwewTQAAABvuw4dOmTcuHEZPnx4+vXrl1GjRmXAgAEZM2ZMrr322iTJzTffnL59+6ZPnz554okncvLJJ7/huOedd16OPPLI9O7dO1tssUU+8YlPJGleo7qhoSF33HFH9tprrwwfPjxJcswxx+T555/PgAEDMmzYsHz+85/PoEGDcvvtt+fSSy/NjTfemCFDhmTIkCGZMGFC290QAKCVUlXV6q7hTRk6dGg1efLk1V3GatHzpOtWdwlrvFmdD1rdJazZxj67uisAAABaTJw4Mccee2yWLFmSI488MieddFKr/Y888kgOP/zwPPXUU/nABz6Qn//852loaMjUqVNz9NFHZ8GCBWnfvn1OPvnkfOYzn0mS3HjjjTnhhBOyaNGibLvttrnooovSoUPzKp4333xzjjvuuLzyyivp0qVLbrnlliTJ2WefnZ/85CcppWTgwIG5+OKL07lz53zkIx/Jc889lyR58skns9122+Xqq6+u3w0C4B2vlHJ3VVVDV7pPMP3OIZh+Y4LpNyCYBgDgH+Bnkdc36/S93vQxS5YsSZ8+fXL99denoaEhw4YNS2NjY/r371/r8+lPfzp77713Dj300Nx44425+OKLc+mll+aBBx5IKSVbbrllHnvssWy77ba57777ssEGG2SzzTbLDTfckD59+mTMmDHZbLPNcsQRR+SZZ57Jhz70oUycODE9evTIk08+mU022SRz587NTjvtlOnTp+c973lPRo0alREjRuSwww5rVe/++++fT37ykznkkEPe6u0C4F3k9YJpS3kAAABAnU2aNCm9e/dOr1690qlTp4wePTrXXHNNqz7Tp0/Pxz72sSTJrrvuWtvfp0+fbLnllkmaXyC5ySab5Kmnnsr8+fPTqVOn9OnTJ0my++6754orrkjS/BLI/fbbLz169EiSbLLJJrXzLF68OC+99FIWL16cF198MV27dm1Vx4IFC3LjjTfmU5/61Nt/IwB41xJMAwAAQJ3NnTs33bt3r203NDRk7ty5rfoMHjw4V155ZZLmNbSfe+65zJ8/v1WfSZMmZdGiRdliiy3SpUuXLF68OMt+y/jXv/51Zs+enSR54IEH8vTTT2eXXXbJtttum5/97GdJkm7duuWEE05Ijx49summm+Z973tf9thjj1bnuPrqq7Pbbrtlgw02eHtvAgDvaoJpAAAAWAOdeeaZueWWW7L11lvnlltuSbdu3dK+ffva/scffzwHH3xwLr744rRr1y6llDQ1NeX444/Pdtttl/XXX7/Wf/Hixbn77rtz3XXX5X//93/z7//+77Ww+pprrsnMmTPz2GOP5YUXXsjPf/7zVnU0NjbmwAMPrOu1A7D267C6CwAAAIB3m27dutVmMyfJnDlz0q1bt1Z9unbtWpsx/fzzz+eKK67IhhtumKR5eY299tor3/3ud7PDDjvUjtlxxx1z6623Jkl++9vf5oEHHkjSPCN7o402ynvf+968973vzc4775w///nPSZLNN988G2+8cZJkv/32yx/+8Id87nOfS5LMmzcvkyZNylVXXdUGdwGAdzMzpgEAAKDOhg0blhkzZmTmzJlZtGhRmpqaMnLkyFZ95s2bl6VLlyZJTjvttBx++OFJkkWLFmXffffNIYcckgMOOKDVMU8++WSS5OWXX873vve9fOlLX0qSfPKTn8xtt91WW0f6rrvuSr9+/dKjR4/ceeedefHFF1NVVW644Yb069evNt6vf/3r7L333uncuXOb3QsA3p0E0wAAAFBnHTp0yLhx4zJ8+PD069cvo0aNyoABAzJmzJhce+21SZKbb745ffv2TZ8+ffLEE0/k5JNPTpJcfvnl+f3vf5/x48dnyJAhGTJkSKZOnZokOeOMM9KvX78MGjQo++yzT+3lif369cuee+6ZQYMGZbvttsuRRx6ZrbbaKttvv30OOOCAbLPNNhk4cGCWLl2aL3zhC7U6m5qaLOMBQJsoVVWt7hrelKFDh1bLXuTwbtPzpOtWdwlrvFmdD1rdJazZxj67uisAAOAdyM8ir2/W6Xut7hIAYI1USrm7qqqhK9tnjWkAAAB4K8a+b3VXsOYzSQaAV7GUBwAAAAAAdSWYBgAAAACgrgTTAAAAAADUlWAaAAAAAIC6EkwDAAAAAFBXgmkAAAAAAOpKMA0AAAAAQF0JpgEAAAAAqCvBNAAAAAAAdSWYBgAAAACgrgTTAAAAAADUlWAaAAAAAIC6EkwDAAAAAFBXgmkAAAAAAOpKMA2wBpk4cWL69u2b3r175/TTT19h/6OPPppdd901W2+9dQYNGpQJEyYkSRYtWpTPf/7zGThwYAYPHpybb765dkxjY2MGDhyYQYMGZc8998y8efOSJGPHjk23bt0yZMiQDBkypDbWrFmz8p73vKfW/qUvfantLxwAAAB4VxFMA6whlixZkmOOOSa/+c1vMn369DQ2Nmb69Omt+px66qkZNWpUpkyZkqampvzLv/xLkuTCCy9Mktxzzz25/vrr87WvfS1Lly7N4sWLc+yxx+amm27KtGnTMmjQoIwbN6423vHHH5+pU6dm6tSpGTFiRK19iy22qLVfcMEFdbh6Vqe3+4HIc889V3uwMWTIkHTp0iXHHXdckuSRRx7JbrvtlkGDBmWXXXbJnDlzaudp37597ZiRI0fW2m+88cZss8022WqrrXLooYdm8eLFbXczAAAAqAvBNMAaYtKkSendu3d69eqVTp06ZfTo0bnmmmta9SmlZMGCBUmSZ599Nl27dk2STJ8+PR/72MeSJJtsskk23HDDTJ48OVVVpaqqvPDCC6mqKgsWLKgdA0nbPBBZf/31aw82pk6dms022yz77bdfkuSEE07IIYcckmnTpmXMmDH55je/WTvPe97zntox1157bZJk6dKlOfTQQ9PU1JR77703m222WS655JJ63BoAAADakGAaYA0xd+7cdO/evbbd0NCQuXPntuozduzY/PznP09DQ0NGjBiRc845J0kyePDgXHvttVm8eHFmzpyZu+++O7Nnz07Hjh1z/vnnZ+DAgenatWumT5+eI444ojbeuHHjMmjQoBx++OF5+umna+0zZ87M1ltvnY9+9KO59dZb2/jKWZ3a4oHI8h544IE8+eST+chHPrLCMbvuuusK53q1+fPnp1OnTunTp0+SZPfdd88VV1zxFq8aAACA1U0wDfAO0tjYmMMOOyxz5szJhAkTcvDBB2fp0qU5/PDD09DQkKFDh+a4447Lhz70obRv3z6vvPJKzj///EyZMiWPPfZYBg0alNNOOy1JcvTRR+ehhx7K1KlTs+mmm+ZrX/takmTTTTfNo48+milTpuSss87KQQcdVAslWfu0xQOR5TU1NeUzn/lMSim1Y6688sokyVVXXZXnnnsu8+fPT5IsXLgwQ4cOzQ477JCrr746SdKlS5csXry4Fnj/+te/XuEcAAAAvPMIpgHWEN26dWsVuM2ZMyfdunVr1eeiiy7KqFGjkiQ77rhjFi5cmHnz5qVDhw45++yzM3Xq1FxzzTV55pln0qdPn0ydOjVJ85rRpZSMGjUqf/jDH5Ik//RP/5T27dunXbt2OeqoozJp0qQkyTrrrJONNtooSbLttttmiy22yAMPPNDWl88a7M0+EFleU1NTDjzwwNr2mWeemVtuuSVbb711brnllnTr1q12zCOPPJLJkyfnl7/8ZY477rg89NBDKaWkqakpxx9/fLbbbrusv/76K5wDAACAdx7BNMAaYtiwYZkxY0ZmzpyZRYsWpampqdUL4JKkR48eueGGG5Ik9913XxYuXJiNN944L774Yl544YUkyfXXX58OHTqkf//+6datW6ZPn56nnnqqtq9fv35Jkscff7w27lVXXZWtttoqSfLUU09lyZIlSZKHH344M2bMSK9evdr24llt2uKByDJ//vOfs3jx4my77ba1tq5du+bKK6/MlClT8t3vfjdJsuGGG9ZqSZJevXpll112yZQpU2rnvPXWWzNp0qTsvPPOrc4BAADAO1OH1V0AAM06dOiQcePGZfjw4VmyZEkOP/zwDBgwIGPGjMnQoUMzcuTI/OAHP8hRRx2Vs88+O6WUjB8/PqWUPPnkkxk+fHjatWuXbt265dJLL03SHAJ+5zvfyc4775yOHTtms802y/jx45MkJ554YqZOnZpSSnr27Jkf//jHSZLf//73GTNmTDp27Jh27drlggsuyAc+8IHVdVtoY8s/EOnWrVuampryy1/+slWfZQ9EDjvssBUeiFRVlfe+972tHogs09jY2Gq2dJLMmzcvH/jAB9KuXbucdtppOfzww5MkTz/9dNZdd92ss846mTdvXm6//faceOKJSZInn3wym2yySV5++eV873vfy8knn9zGdwUAAIC2VqqqWt01vClDhw6tXv1ipXeLniddt7pLWOPN6nzQ6i5hzTb22dVdAbAGmjBhQo477rjaA5GTTz651QOR6dOn56ijjsrzzz+fUkq+//3vZ4899sisWbNaPRC56KKLstlmm9XG7dWrVyZMmJAPfvCDtbZf//rX+eY3v5lSSnbeeeece+65WWeddfKHP/whX/ziF9OuXbssXbo0xx13XO1FnV//+tfzP//zP1m6dGmOPvroHHfccfW+RQDven4WeX1+DlkFfhYBeFcqpdxdVdXQle4TTL9z+J/BN+Z/CN+A/xkEAOAf4GeR1+fnkFXgZxGAd6XXC6Yt5QHwdhn7vtVdwZrNDyNtz2fw9fkMAgAArDG8/BAAAAAAgLoSTAMAAAAAUFeCaQAAAAAA6kowDQAAAABAXQmmAVjrTZw4MX379k3v3r1z+umnr7D/0Ucfza677pqtt946gwYNyoQJE5Ikv/jFLzJkyJDaV7t27TJ16tQkyWWXXZZBgwZlwIAB+cY3vlEb6/jjj6/179OnTzbccMMkyU033dRqrM6dO+fqq69Oktxwww3ZZpttMmTIkOy000558MEH2/R+AAAAwOommAZgrbZkyZIcc8wx+c1vfpPp06ensbEx06dPb9Xn1FNPzahRozJlypQ0NTXlX/7lX5Ikn/3sZzN16tRMnTo1l156aTbffPMMGTIk8+fPz9e//vXccMMN+ctf/pL/+7//yw033JAkOfvss2vHfOUrX8l+++2XJNl1111r7TfeeGPWXXfd7LHHHkmSo48+Or/4xS8yderUHHTQQTn11FPreIcAAACg/gTTAKzVJk2alN69e6dXr17p1KlTRo8enWuuuaZVn1JKFixYkCR59tln07Vr1xXGaWxszOjRo5MkDz/8cLbccstsvPHGSZKPf/zjueKKK1Z6zIEHHrhC+69//et84hOfyLrrrrvK5wcAAIC1SYfVXQAAtKW5c+eme/fute2GhobcddddrfqMHTs2e+yxR84555y88MIL+d3vfrfCOJdddlkt0O7du3f++te/ZtasWWloaMjVV1+dRYsWter/yCOPZObMmfnYxz62wlhNTU356le/Wtv+yU9+khEjRuQ973lPNthgg9x5551v6ZoBAABgTWfGNADveo2NjTnssMMyZ86cTJgwIQcffHCWLl1a23/XXXdl3XXXzVZbbZUkef/735/zzz8/n/nMZ/KRj3wkPXv2TPv27VuN2dTUlAMOOGCF9scffzz33HNPhg8fXms7++yzM2HChMyZMyef//znW4XWAAAAsDYSTAOwVuvWrVtmz55d254zZ066devWqs9FF12UUaNGJUl23HHHLFy4MPPmzavtb2pqWmFJjn322Sd33XVX7rjjjvTt2zd9+vRptX9lxyTJ5Zdfnn333TcdO3ZMkjz11FP585//nO233z5J8pnPfCZ/+MMf3sIVAwAAwJpPMA3AWm3YsGGZMWNGZs6cmUWLFqWpqSkjR45s1adHjx61lxfed999WbhwYW396KVLl+byyy+vrS+9zJNPPpkkefrpp3PeeeflyCOPrO27//778/TTT2fHHXdcoZ5Xrzv9/ve/P88++2weeOCBJMn111+ffv36vQ1XDgAAAGsua0wDsFbr0KFDxo0bl+HDh2fJkiU5/PDDM2DAgIwZMyZDhw7NyJEj84Mf/CBHHXVUzj777JRSMn78+JRSkiS///3v07179/Tq1avVuMcee2z+/Oc/J0nGjBnTasZ0U1NTRo8eXRtjmVmzZmX27Nn56Ec/2qq+Cy+8MPvvv3/atWuX97///fnpT3/aVrcDAAAA1gilqqrVXcObMnTo0Gry5Mmru4zVoudJ163uEtZ4szoftLpLWLONfXa1nHbixIk59thjs2TJkhx55JE56aSTWu1/9NFHc+ihh+aZZ57JkiVLcvrpp2fEiBFJkmnTpuWLX/xiFixYkHbt2uWPf/xjOnfunMsuuyzf/e53s2TJkuy999753ve+lyQZP358vv71r9eWavjyl79cm8n66KOP5sgjj8zs2bNTSsmECRPSs2fPHHbYYbnlllvyvve9rzbGkCFD3vyFjn3fP3iH3iVW0+fvXcVn8PX5DAL8w/ws8vr8HLIK/HcY4F2plHJ3VVVDV7bPjGmgTS1ZsiTHHHNMrr/++jQ0NGTYsGEZOXJk+vfvX+tz6qmnZtSoUTn66KMzffr0jBgxIrNmzcrixYvzuc99LpdeemkGDx6c+fPnp2PHjpk/f36+/vWv5+67787GG2+cQw89NDfccEN22223JM1r9I4bN26FWg455JCcfPLJ2X333fP888+nXbu/r2Z0xhln5IADDmj7G8Jb4ofi1zer8+qugJVpi4dzjY2N+Y//+I+UUtK1a9f8/Oc/T5cuXTJ16tR86UtfysKFC9OhQ4ecd9552W677XLNNdfk29/+dtq1a5cOHTrkhz/8YXbaaadMnTo1Rx99dBYsWJD27dvn5JNPzmc+85nVcZsAAIB3GWtMA21q0qRJ6d27d3r16pVOnTpl9OjRueaaa1r1KaVkwYIFSZJnn302Xbt2TZL89re/zaBBgzJ48OAkyUYbbZT27dvn4YcfzpZbbllbA/jjH/94rrjiitetY/r06Vm8eHF23333JMl6662Xdddd9229VoBXW/Zw7je/+U2mT5+exsbGTJ8+vVWfZQ/npkyZkqampvzLv/xLktQezl1wwQX5y1/+kptvvjkdO3bM4sWLc+yxx+amm27KtGnTMmjQoNrDuBNPPDHf+c53MnXq1Jxyyik58cQTkyS77bZb/vznP2fq1Kn56U9/WvtNknXXXTc/+9nP8pe//CUTJ07Mcccdl2eeeaZ+NwgAAHjXEkwDbWru3Lnp3r17bbuhoSFz585t1Wfs2LH5+c9/noaGhowYMSLnnHNOkuSBBx5IKSXDhw/PNttsk+9///tJkt69e+evf/1rbVb11VdfndmzZ9fGu+KKKzJo0KAccMABtfYHHnggG264Yfbbb79svfXW+frXv54lS5bUjjn55JMzaNCgHH/88Xn55Zfb7H4A7y5t8XCuqqpUVZUXXnghVVVlwYIFtWNea6z11luvtub5Cy+8UPu+T58+2XLLLZMkXbt2zSabbJKnnnqqLW/JCiZOnJi+ffumd+/eOf3001fY/+ijj2bXXXfN1ltvnUGDBmXChAm1fdOmTcuOO+6YAQMGZODAgVm4cGGSZJdddknfvn0zZMiQDBkypPay0uOPP77W1qdPn2y44Ya1sdq3b1/bt/wLUseNG5fevXunlJJ58+a10V0AAIB3H0t5AKtdY2NjDjvssHzta1/LHXfckYMPPjj33ntvFi9enNtuuy1//OMfs+6662a33XbLtttum9122y3nn39+PvOZz6Rdu3b50Ic+lIceeihJss8+++TAAw/MOuuskx//+Mc59NBDc+ONN2bx4sW59dZbM2XKlPTo0SOf+cxnMn78+BxxxBE57bTT8s///M9ZtGhRvvCFL+R73/texowZs5rvCrA2WNnDubvuuqtVn7Fjx2aPPfbIOeeckxdeeCG/+93vkrR+OPfUU09l9OjROfHEE9OxY8ecf/75GThwYN773vdmyy23zLnnnpsk+eEPf5jhw4fnhBNOyNKlS/OHP/yhdp6rrroq3/zmN/Pkk0/muutWXBZn0qRJWbRoUbbYYou2uBUr1RbLPS3zi1/8IkOHtl7K7uyzz659f84552TKlCm17fe85z2ZOnXqCjV++MMfzt57751ddtnl7btwAADAjGmgbXXr1q3VbOY5c+bUXky4zEUXXZRRo0YlSXbccccsXLgw8+bNS0NDQ3beeed06dIl6667bkaMGJE//elPSZoD6Lvuuit33HFH+vbtmz59+iRpnlG4zjrrJEmOPPLI3H333Umaw6AhQ4akV69e6dChQz71qU/Vxtp0001TSsk666yTz3/+85k0aVLb3hSA5Sx7ODdnzpxMmDAhBx98cJYuXVp7OPeLX/wit912W6666qrccMMNeeWVV3L++ednypQpeeyxxzJo0KCcdtppSZLzzz8/Z599dmbPnp2zzz47RxxxRO08++67b+6///5cffXV+fa3v92qhscffzwHH3xwLr744lbr77e1tphRvqoaGxtz4IEHvmG/rbfeOj179lzlcQEAgFUjmAba1LBhwzJjxozMnDkzixYtSlNTU6tfkU6SHj165IYbbkiS3HfffVm4cGE23njjDB8+PPfcc09efPHFLF68OLfcckttFt2yX8t++umnc95559XWS3388cdr41577bXp169frY5nnnmm9ivqN954Y22sZcdUVZWrr746W221VVvdDuBdpi0ezi2b1bvFFluklJJRo0bVZkZfcskl2W+//ZIkn/70p1f6oG3nnXfOww8/XFuWYsGCBdlrr73y3e9+NzvssMPbfg9eT1ss97TM5z//+QwZMiT//u//nqqqWu175JFHMnPmzHzsYx+rtS1cuDBDhw7NDjvskKuvvvptvlIAAODVBNNAm+rQoUPGjRuX4cOHp1+/fhk1alQGDBiQMWPG5Nprr02S/OAHP8iFF16YwYMH58ADD8z48eNTSsn73//+fPWrX82wYcMyZMiQbLPNNtlrr72SJMcee2z69++fD3/4wznppJNqM6Z/9KMfZcCAARk8eHB+9KMfZfz48Uma1w4988wzs9tuu2XgwIGpqipHHXVUkuSzn/1sBg4cmIEDB2bevHn51re+Vf8bBayV2uLhXLdu3TJ9+vTag7brr7++9hCua9euueWWW5I0P4Bbtn70gw8+WAtn//SnP+Xll1/ORhttlEWLFmXffffNIYcckgMOOKAu9+TNerMzypPmZTzuueee3Hrrrbn11ltz6aWXthqzqakpBxxwQKsZ1o888kgmT56cX/7ylznuuONqS0QBAABtwxrTQJsbMWJERowY0artlFNOqX3fv3//3H777Ss99nOf+1w+97nPrdDe2Ni40v6nnXZa7VfaX2333XfPtGnTVmi/8cYbX7N2gLdi+YdzS5YsyeGHH157ODd06NCMHDkyP/jBD3LUUUfl7LPPTillpQ/nSikZMWJE7eHcd77zney8887p2LFjNttss9pDuAsvvDDHHntsFi9enM6dO+e//uu/kjS/FPZnP/tZOnbsmPe85z257LLLUkrJ5Zdfnt///veZP39+bYzx48dnyJAhdbk/qzqjfOLEiUlee0Z5ktqM8t122602xvrrr5+DDjookyZNyiGHHFIbs6mpqbYu9/K1JEmvXr2yyy67ZMqUKXVdbxsAAN5tyqt/tXFNN3To0Gry5Mmru4zVoudJK76oiNZmdT5odZewZhv77Fs63Gfw9fn8vYG3+PlLfAbfiM/gG3gbPoO8vRYvXpw+ffrkhhtuSLdu3TJs2LD88pe/zIABA2p9PvGJT+Qzn/lMDjvssNx3333ZbbfdMnfu3DzzzDPZbbfdctttt6VTp07Zc889c/zxx2f48OF55pln0qVLl7zyyis58MAD8/GPfzxf+tKXkiT3339/9txzz8ycOTOllCTNy0Ktu+66WWeddTJv3rzsuOOOueaaa1q9hLFnz56ZPHlyLQiHdxv/DX59/hu8Cvx3GOBdqZRyd1VVQ1e2z4xpAIBVIJR5fbNO3+tNH9MWM8pfeOGFDB8+PK+88kqWLFmSj3/847Wlm5Lm2dKjR4+uhdJJ8xIqX/ziF9OuXbssXbo0J510Ui2U/tGPfpTvf//7+b//+78MGjQoI0aMyE9+8pO3fsMAAOBdzozpdxA/EL8xMxXegBnTbcrn7w2YMd3mfAbfgH8Htql/JJgG3jn8O/D1+W/wKjBjGuBdyYxpAADa1tj3re4K1nxCGQB415o4cWKOPfbYLFmyJEceeWROOumkVvsfffTRHHrooXnmmWeyZMmSnH766RkxYkRmzZqVfv36pW/fvkmSHXbYIRdccEGrY0eOHJmHH3449957b63tnHPOybnnnpv27dtnr732yve///0kybRp0/LFL34xCxYsSLt27fLHP/4xnTt3buOrh5UTTAMAAABAG1myZEmOOeaYXH/99WloaMiwYcMycuTIVu+zOPXUUzNq1KgcffTRmT59ei2UTpItttgiU6dOXenYV155ZdZbb71WbTfddFOuueaa/PnPf84666yTJ598Mknz+z0+97nP5dJLL83gwYMzf/78dOzYsU2uGVZFu9VdAAAAAACsrSZNmpTevXunV69e6dSpU0aPHp1rrrmmVZ9SShYsWJAkefbZZ9O1a9c3HPf555/PWWedlW9961ut2s8///ycdNJJWWeddZIkm2yySZLkt7/9bQYNGpTBgwcnSTbaaKO0b9/+LV8f/KME0wAAAADQRubOnZvu3bvXthsaGjJ37txWfcaOHZuf//znaWhoyIgRI3LOOefU9s2cOTNbb711PvrRj+bWW2+ttX/729/O1772tay77rqtxnrggQdy6623Zvvtt89HP/rR/PGPf6y1l1IyfPjwbLPNNrXlPWB1EUwDAAAAwGrU2NiYww47LHPmzMmECRNy8MEHZ+nSpdl0003z6KOPZsqUKTnrrLNy0EEHZcGCBZk6dWoeeuih7LvvviuMtXjx4vztb3/LnXfemTPOOCOjRo1KVVVZvHhxbrvttvziF7/Ibbfdlquuuio33HDDarhaaCaYBgAAAIA20q1bt8yePbu2PWfOnHTr1q1Vn4suuiijRo1Kkuy4445ZuHBh5s2bl3XWWScbbbRRkmTbbbfNFltskQceeCB33HFHJk+enJ49e2annXbKAw88kF122SVJ84zs/fbbL6WUbLfddmnXrl3mzZuXhoaG7LzzzunSpUvWXXfdjBgxIn/605/qcxNgJQTTAAAAANBGhg0blhkzZmTmzJlZtGhRmpqaMnLkyFZ9evToUZu9fN9992XhwoXZeOON89RTT2XJkiVJkocffjgzZsxIr169cvTRR+exxx7LrFmzctttt6VPnz65+eabkySf+tSnctNNNyVpXr5j0aJF6dKlS4YPH5577rknL774YhYvXpxbbrml1QsYod46rO4CAAAAAGBt1aFDh4wbNy7Dhw/PkiVLcvjhh2fAgAEZM2ZMhg4dmpEjR+YHP/hBjjrqqJx99tkppWT8+PEppeT3v/99xowZk44dO6Zdu3a54IIL8oEPfOB1z3f44Yfn8MMPz1ZbbZVOnTrlkksuSSkl73//+/PVr341w4YNSyklI0aMyF577VWnuwArEkwDAAAAQBsaMWJERowY0artlFNOqX3fv3//3H777Ssct//++2f//fd/3bF79uyZe++9t7bdqVOn/PznP19p38997nP53Oc+92ZKhzYjmAYAAACA19DzpOtWdwlrtFmnm3XNP8Ya0wAAAAAA1JVgGgAAAACAuhJMAwAAAABQV4JpAAAAAADqSjANAAAAAEBdCaYBAAAAAKgrwTQAAAAAAHUlmAYAAAAAoK4E0wAAAAAA1JVgGgAAAACAuhJMAwAAAABQV4JpAAAAAADqSjANAAAAAEBdCaYBAAAAAKgrwTQAAAAAAHUlmAYAAAAAoK4E0wAAAAAA1JVgGgAAAACAuhJMAwAAAABQV4JpAAAAAADqSjANAAAAAEBdCaYBAAAAAKgrwTQAAAAAAHUlmAYAAAAAoK4E0wAAAEkmTpyYvn37pnfv3jn99NNX2P/oo49m1113zdZbb51BgwZlwoQJSZL58+dn1113zXrrrZcvf/nLrY45+eST071796y33nqt2i+44IIMHDgwQ4YMyU477ZTp06cnSa6//vpsu+22GThwYLbddtvceOONbXS1AACrl2AaAAB411uyZEmOOeaY/OY3v8n06dPT2NhYC4uXOfXUUzNq1KhMmTIlTU1N+Zd/+ZckSefOnfPv//7vOfPMM1cYd5999smkSZNWaD/ooINyzz33ZOrUqTnxxBPz1a9+NUnSpUuX/Pd//3fuueeeXHLJJTn44IPb4GoBAFY/wTQAAPCuN2nSpPTu3Tu9evVKp06dMnr06FxzzTWt+pRSsmDBgiTJs88+m65duyZJ3vve92annXZK586dVxh3hx12yKabbrpC+wYbbFD7/oUXXkgpJUmy9dZb18YdMGBAXnrppbz88stvz0UCAKxBOqzuAgAAAFa3uXPnpnv37rXthoaG3HXXXa36jB07NnvssUfOOeecvPDCC/nd7373ls557rnn5qyzzsqiRYtWumTHFVdckW222SbrrLPOWzoPAMCayIxpAACAVdDY2JjDDjssc+bMyYQJE3LwwQdn6dKl//B4xxxzTB566KF873vfy6mnntpq31/+8pd84xvfyI9//OO3WjYAwBpJMA0AALzrdevWLbNnz65tz5kzJ926dWvV56KLLsqoUaOSJDvuuGMWLlyYefPmveVzjx49OldffXWrc++777752c9+li222OItjw8AsCYSTAMAAO96w4YNy4wZMzJz5swsWrQoTU1NGTlyZKs+PXr0yA033JAkue+++7Jw4cJsvPHG/9D5ZsyYUfv+uuuuy5ZbbpkkeeaZZ7LXXnvl9NNPz4c//OF/8GoAANZ8gmkAAOBdr0OHDhk3blyGDx+efv36ZdSoURkwYEDGjBmTa6+9Nknygx/8IBdeeGEGDx6cAw88MOPHj6+9tLBnz5756le/mvHjx6ehoSHTp09Pkpx44olpaGjIiy++mIaGhowdOzZJMm7cuAwYMCBDhgzJWWedlUsuuaTW/uCDD+aUU07JkCFDMmTIkDz55JP1vyEAAG3Myw8BAACSjBgxIiNGjGjVdsopp9S+79+/f26//faVHjtr1qyVtn//+9/P97///RXa//M//3Ol/b/1rW/lW9/61ipWDLB6TZw4Mccee2yWLFmSI488MieddFKr/Y8++mgOPfTQPPPMM1myZElOP/30Ff49C7x7CaYBAIB3vrHvW90VrNnGPru6KwDWMkuWLMkxxxyT66+/Pg0NDRk2bFhGjhyZ/v371/qceuqpGTVqVI4++uhMnz49I0aMeM0HecC7j6U8AAAAAHhTJk2alN69e6dXr17p1KlTRo8enWuuuaZVn1JKFixYkCR59tln07Vr19VRKrCGMmMaAAAAgDdl7ty56d69e227oaEhd911V6s+Y8eOzR577JFzzjknL7zwQn73u9/Vu0xgDWbGNAAAAABvu8bGxhx22GGZM2dOJkyYkIMPPjhLly5d3WUBawjBNAAAAABvSrdu3TJ79uza9pw5c9KtW7dWfS666KKMGjUqSbLjjjtm4cKFmTdvXl3rBNZcbRpMl1L2LKX8tZTyYCnlpJXs71FKuamUMqWUMq2U4tWsAAAAAGu4YcOGZcaMGZk5c2YWLVqUpqamjBw5slWfHj165IYbbkiS3HfffVm4cGE23njj1VEusAZqs2C6lNI+yblJPpGkf5IDSyn9X9XtW0kur6pq6ySjk5zXVvUAAAAA8Pbo0KFDxo0bl+HDh6dfv34ZNWpUBgwYkDFjxuTaa69NkvzgBz/IhRdemMGDB+fAAw/M+PHjU0pZzZUDa4q2fPnhdkkerKrq4SQppTQl+WSS6cv1qZJs0PL9+5I81ob1AAAAAPA2GTFiREaMaP3L76ecckrt+/79++f222+vd1nAO0RbBtPdksxebntOku1f1Wdskt+WUr6S5L1JPt6G9QAAAAAAsAZoy2B6VRyYZHxVVT8opeyY5NJSylZVVbV6RWsp5QtJvpA0r08EAAAAQIux71vdFazZxj67uisAVqItX344N0n35bYbWtqWd0SSy5Okqqo7knRO0uXVA1VV9V9VVQ2tqmqoRfIBAAAAAN7Z2jKY/mOSLUspm5dSOqX55YbXvqrPo0l2S5JSSr80B9NPtWFNAAAAAACsZm0WTFdVtTjJl5P8b5L7klxeVdVfSimnlFJGtnT7WpKjSil/TtKY5LCqqqq2qgkAAAAAgNWvTdeYrqpqQpIJr2obs9z305N8uC1rAAAAAABgzdKWS3kAAAAAAMAKBNMAAAAAANSVYBoAAAAAgLoSTAMAAAAAUFeCaQAAAACgriZOnJi+ffumd+/eOf3001fYf/zxx2fIkCEZMmRI+vTpkw033DBJctNNN9XahwwZks6dO+fqq69OklRVlZNPPjl9+vRJv3798qMf/ShJ8uyzz2afffbJ4MGDM2DAgFx88cW187Rv37421siRI2vtN954Y7bZZptstdVWOfTQQ7N48eK2uxnvUh1WdwEAAAAAwLvHkiVLcswxx+T6669PQ0NDhg0blpEjR6Z///61PmeffXbt+3POOSdTpkxJkuy6666ZOnVqkuRvf/tbevfunT322CNJMn78+MyePTv3339/2rVrlyeffDJJcu6556Z///757//+7zz11FPp27dvPvvZz6ZTp055z3veUxtvmaVLl+bQQw/NDTfckD59+mTMmDG55JJLcsQRR7ThXXn3MWMaAADWYm0xG+mII47I4MGDM2jQoBxwwAF5/vnnkyQXXHBBBg4cmCFDhmSnnXbK9OnTkySTJk2qjTN48OBcddVVSZKFCxdmu+22q81e+s53vtP2NwQAWO0mTZqU3r17p1evXunUqVNGjx6da6655jX7NzY25sADD1yh/de//nU+8YlPZN11102SnH/++RkzZkzatWuOPDfZZJMkSSklzz33XKqqyvPPP58PfOAD6dDhtefrzp8/P506dUqfPn2SJLvvvnuuuOKKf/h6WTnBNAAArKWWzUb6zW9+k+nTp6exsbEWFi9z9tlnZ+rUqZk6dWq+8pWvZL/99kvy99lIU6dOzY033ph11123Nhvp7LPPzp///OdMmzYtPXr0yLhx45IkBx10UO65555MnTo1J554Yr761a8mSbbaaqtMnjw5U6dOzcSJE/PFL34xixcvzjrrrJMbb7wxf/7zn2v77rzzzjreIQBgdZg7d266d+9e225oaMjcuXNX2veRRx7JzJkz87GPfWyFfU1NTa0C64ceeiiXXXZZhg4dmk984hOZMWNGkuTLX/5y7rvvvnTt2jUDBw7Mf/7nf9bC64ULF2bo0KHZYYcdag/hu3TpksWLF2fy5MlJmgPw2bNnvy3Xzt8JpgEAYC3VVrORNthggyTN6zi+9NJLKaW0ak+SF154oda+7rrr1mYlLVy4sNZeSsl6662XJHnllVfyyiuv1PYBACTN4fMBBxyQ9u3bt2p//PHHc88992T48OG1tpdffjmdO3fO5MmTc9RRR+Xwww9Pkvzv//5vhgwZksceeyxTp07Nl7/85SxYsCBJc/A9efLk/PKXv8xxxx2Xhx56KKWUNDU15fjjj892222X9ddff4Xz89YJpgEAYC3VVrORkuTzn/98/vmf/zn3339/vvKVr9Tazz333GyxxRY58cQTay8cSpK77rorAwYMyMCBA3PBBRfUguolS5ZkyJAh2WSTTbL77rtn++23f0vXDACs+bp169ZqBvKcOXPSrVu3lfZd2f+HJMnll1+efffdNx07dqy1NTQ01H77a9999820adOSJBdffHH222+/lFLSu3fvbL755rn//vtrtSRJr169sssuu9TWst5xxx1z6623ZtKkSdl5551ry3rw9hFMAwAAb2o2UtL8A95jjz2Wfv365bLLLqu1H3PMMXnooYfyve99L6eeemqtffvtt89f/vKX/PGPf8xpp52WhQsXJknat2+fqVOnZs6cOZk0aVLuvffeNrxKAGBNMGzYsMyYMSMzZ87MokWL0tTUlJEjR67Q7/7778/TTz+dHXfccYV9K/tNr0996lO56aabkiS33HJLLUzu0aNHbrjhhiTJE088kb/+9a/p1atXnn766bz88stJknnz5uX222+vvYBx2YsTX3755Xzve9/Ll770pbfp6llGMA0AAGuptpqNtEz79u0zevTolb4MaPTo0bV1GpfXr1+/rLfeeisE0BtuuGF23XXXTJw48Y0uCwB4h+vQoUPGjRuX4cOHp1+/fhk1alQGDBiQMWPG5Nprr631a2pqyujRo1dY6mvWrFmZPXt2PvrRj7ZqP+mkk3LFFVdk4MCB+eY3v5mf/OQnSZJvf/vb+cMf/pCBAwdmt912y/e+97106dIl9913X4YOHZrBgwdn1113zUknnVQLps8444z069cvgwYNyj777LPS3yrjrXnt108CAADvaMvPRurWrVuampryy1/+coV+bzQb6bTTTqttV1WVhx56KL17905VVbn22mvzwQ9+MEkyY8aMbLnllkmS6667rvb9zJkz071793To0CGPPPJI7r///vTs2TNPPfVUOnbsmA033DAvvfRSrr/++nzjG99oi1sBAKxhRowYkREjRrRqO+WUU1ptjx07dqXH9uzZc6XLk2244Ya57rrrVmjv2rVrfvvb367Q/qEPfSj33HPPSs9xxhln5Iwzznit8nkbCKYBAGAttfxspCVLluTwww+vzUYaOnRo7Vdm38xspKqqcuihh2bBggWpqiqDBw/O+eefnyQZN25cfve736Vjx455//vfn0suuSRJctttt+X0009Px44d065du5x33nnp0qVLpk2blkMPPTRLlizJ0qVLM2rUqOy99951ujsAwNti7PtWdwVrvrHPru4K1kiCaQAAWIu93bOR2rVrl9tvv32l/f/zP/9zpe0HH3xwDj744BXaBw0aVHvBEAAA7y6CaQAAeAfoedKKv5bK383qvLorAADgzfDyQwAAAAAA6kowDQAAAABAXQmmAQAAAACoK8E0AAAAAAB1JZgGAAAAAKCuBNMAAAAAANSVYBoAAAAAgLoSTAMAAAAAUFeCaQAAAAAA6kowDQAAAABAXQmmAQAAAACoK8E0AAAAAAB1JZgGAAAAAKCuBNMAAAAAANSVYBoAAAAAgLoSTAMAAAAAUFeCaQAAAAAA6kowDQAAAABAXQmmAQAAAACoK8E0AAAAsFaaOHFi+vbtm969e+f0009fYf/xxx+fIUOGZMiQIenTp0823HDDJMkjjzySbbbZJkOGDMmAAQNywQUX1I5ZtGhRvvCFL6RPnz754Ac/mCuuuKK27/LLL0///v0zYMCAHHTQQbX2Rx99NHvssUf69euX/v37Z9asWUmSj3zkI7Xzd+3aNZ/61Kfa5D4ArIk6rO4CAAAAAN5uS5YsyTHHHJPrr78+DQ0NGTZsWEaOHJn+/fvX+px99tm1788555xMmTIlSbLpppvmjjvuyDrrrJPnn38+W221VUaOHJmuXbvmu9/9bjbZZJM88MADWbp0af72t78lSWbMmJHTTjstt99+e97//vfnySefrI19yCGH5OSTT87uu++e559/Pu3aNc8TvPXWW2t99t9//3zyk59s03sCsCYRTAMAAABrnUmTJqV3797p1atXkmT06NG55pprWgXTy2tsbMy//du/JUk6depUa3/55ZezdOnS2vZPf/rT3H///UmSdu3apUuXLkmSCy+8MMccc0ze//73J0k22WSTJMn06dOzePHi7L777kmS9dZbb4VzL1iwIDfeeGMuvvjit3TNAO8klvIAAAAA1jpz585N9+7da9sNDQ2ZO3fuSvs+8sgjmTlzZj72sY/V2mbPnp1Bgwale/fu+cY3vpGuXbvmmWeeSZJ8+9vfzjbbbJNPf/rTeeKJJ5IkDzzwQB544IF8+MMfzg477JCJEyfW2jfccMPst99+2XrrrfP1r389S5YsaXX+q6++Orvttls22GCDt/MWAKzRBNMAAADAu1pTU1MOOOCAtG/fvtbWvXv3TJs2LQ8++GAuueSSPPHEE1m8eHHmzJmTD33oQ/nTn/6UHXfcMSeccEKSZPHixZkxY0ZuvvnmNDY25qijjsozzzyTxYsX59Zbb82ZZ56ZP/7xj3n44Yczfvz4VudvbGzMgQceWM9LBljtBNMAAADAWqdbt26ZPXt2bXvOnDnp1q3bSvs2NTW9ZjDctWvXbLXVVrn11luz0UYbZd11181+++2XJPn0pz+dP/3pT0maZ2SPHDkyHTt2zOabb54+ffpkxowZaWhoyJAhQ9KrV6906NAhn/rUp2rHJMm8efMyadKk7LXXXm/XpQO8Iwi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\n",
      "text/plain": [
       "<Figure size 1800x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "hana_acc = [0.99373, 0.89855, 0.897959, 0.865751, 0.81132, 0.929687, 0.8, 0.776859]\n",
    "apl_acc = [0.9885297184567258, 0.8695652173913043, 0.8797780517879161, 0.868832321026411, 0.7733333333333333, 0.9401820546163849, 0.7366666666666667, 0.8546603475513428]\n",
    "plot_results(task='Classification', boosting=GRADIENT)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a94ec68",
   "metadata": {},
   "source": [
    "## Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ef213c34",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "REMOVED ALL TABLES FROM DEVELOPER\n"
     ]
    }
   ],
   "source": [
    "datasets = ['elevators.csv', 'wine_quality.csv', 'baseball.csv', 'boston.csv', 'tecator.csv', 'space_ga.csv', 'pol.csv', 'socmob.csv']\n",
    "hana_acc = []\n",
    "apl_acc = []\n",
    "clean(connection_context, schema)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2afb4e2a",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%notify\n",
    "b.run('./data/benchmark/reg/elevators.csv', task='reg', label='Goal', grad_boost=GRADIENT)\n",
    "finish(b) # apl - 0.9294002825634781, hana_automl - 0.9345000820384756 (output was cleared)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "f66296c6",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1/1 [00:02<00:00,  2.20s/it]\n",
      "100%|██████████| 1/1 [00:01<00:00,  1.16s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Finished in 15 seconds\n",
      "APL accuracy:  0.3099873396557854\n",
      "REMOVED ALL TABLES FROM DEVELOPER\n",
      "\u001b[33m \n",
      "\n",
      "Ensemble consists of: HybridGradientBoostingRegressor, GradientBoostingRegressor, GradientBoostingRegressor\n",
      "Ensemble r2_score score: 0.3352677618858584\n",
      "\u001b[0m \n",
      "Finished in 1306 seconds\n",
      "hana_automl accuracy: 0.3352677618858584\n",
      "REMOVED ALL TABLES FROM DEVELOPER\n"
     ]
    },
    {
     "data": {
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       "                    if (Notification.permission === 'granted') {\n",
       "                    var notification = new Notification(\"Jupyter Notebook\", notificationPayload)\n",
       "                    appendUniqueDiv()\n",
       "                    notification.onclick = function () {\n",
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       "                        this.close();\n",
       "                        };\n",
       "                    } \n",
       "                }     \n",
       "            }\n",
       "        }\n",
       "    }\n",
       ")\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%notify\n",
    "b.run('./data/benchmark/reg/wine_quality.csv', task='reg', label='quality', grad_boost=GRADIENT)\n",
    "finish(b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "bbc60e9f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1/1 [00:00<00:00,  2.82it/s]\n",
      "100%|██████████| 1/1 [00:00<00:00,  3.90it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Finished in 7 seconds\n",
      "APL accuracy:  0.9396573145349141\n",
      "REMOVED ALL TABLES FROM DEVELOPER\n",
      "\u001b[32m[I 2021-06-16 17:24:10.107047] \u001b[36mA new bayesian optimization process created in memory\u001b[0m\n",
      "\u001b[33m \n",
      "\n",
      "Ensemble consists of: HybridGradientBoostingRegressor, HybridGradientBoostingRegressor, HybridGradientBoostingRegressor\n",
      "Ensemble r2_score score: 0.9187809116981492\n",
      "\u001b[0m \n",
      "Finished in 964 seconds\n",
      "hana_automl accuracy: 0.9187809116981492\n",
      "REMOVED ALL TABLES FROM DEVELOPER\n"
     ]
    },
    {
     "data": {
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   ],
   "source": [
    "%%notify\n",
    "b.run('./data/benchmark/reg/baseball.csv', task='reg', label='RS', grad_boost=GRADIENT)\n",
    "finish(b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "08073822",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1/1 [00:00<00:00,  3.46it/s]\n",
      "100%|██████████| 1/1 [00:00<00:00,  2.20it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Finished in 8 seconds\n",
      "APL accuracy:  0.8448584138226907\n",
      "REMOVED ALL TABLES FROM DEVELOPER\n",
      "\u001b[33m \n",
      "\n",
      "Ensemble consists of: HybridGradientBoostingRegressor, Random_Decision_Tree_Regressor, Random_Decision_Tree_Regressor\n",
      "Ensemble r2_score score: 0.883268964633891\n",
      "\u001b[0m \n",
      "Finished in 1232 seconds\n",
      "hana_automl accuracy: 0.883268964633891\n",
      "REMOVED ALL TABLES FROM DEVELOPER\n"
     ]
    },
    {
     "data": {
      "application/javascript": [
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       "        function appendUniqueDiv(){\n",
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       "            var notifiedDiv = document.createElement(\"div\")\n",
       "            notifiedDiv.id = \"c0b8d99f-433b-4957-a8a5-0a27e9216448\"\n",
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       "            if (document.getElementById(\"c0b8d99f-433b-4957-a8a5-0a27e9216448\") === null) {\n",
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       "                    if (Notification.permission !== 'granted') { \n",
       "                        Notification.requestPermission(function (permission) {\n",
       "                            if(!('permission' in Notification)) {\n",
       "                                Notification.permission = permission\n",
       "                            }\n",
       "                        })\n",
       "                    }\n",
       "                    if (Notification.permission === 'granted') {\n",
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       "                    appendUniqueDiv()\n",
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       "                    } \n",
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       "    }\n",
       ")\n"
      ],
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   ],
   "source": [
    "%%notify\n",
    "b.run('./data/benchmark/reg/boston.csv', task='reg', label='MEDV', grad_boost=GRADIENT)\n",
    "finish(b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "beb35bae",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%notify\n",
    "b.run('./data/benchmark/reg/tecator.csv', task='reg', label='fat', grad_boost=GRADIENT)\n",
    "finish(b) # hana_automl - 0.9932003960127456, APL - 0.9900203214204571"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "6088a956",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1/1 [00:00<00:00,  1.58it/s]\n",
      "100%|██████████| 1/1 [00:00<00:00,  3.17it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Finished in 8 seconds\n",
      "APL accuracy:  0.7045760787415289\n",
      "REMOVED ALL TABLES FROM DEVELOPER\n",
      "\u001b[32m[I 2021-06-16 17:07:48.965197] \u001b[36mA new bayesian optimization process created in memory\u001b[0m\n",
      "\u001b[33m \n",
      "\n",
      "Ensemble consists of: GradientBoostingRegressor, GradientBoostingRegressor, GradientBoostingRegressor\n",
      "Ensemble r2_score score: 0.6276758240475446\n",
      "\u001b[0m \n",
      "Finished in 783 seconds\n",
      "hana_automl accuracy: 0.6276758240475446\n",
      "REMOVED ALL TABLES FROM DEVELOPER\n"
     ]
    },
    {
     "data": {
      "application/javascript": [
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       "    function() {\n",
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       "\n",
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   ],
   "source": [
    "%%notify\n",
    "b.run('./data/benchmark/reg/space_ga.csv', task='reg', label='ln(VOTES/POP)', grad_boost=GRADIENT)\n",
    "finish(b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "efea68c9",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1/1 [00:11<00:00, 11.61s/it]\n",
      "100%|██████████| 1/1 [00:03<00:00,  3.55s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Finished in 12 seconds\n",
      "APL accuracy:  0.9756879425836702\n",
      "REMOVED ALL TABLES FROM DEVELOPER\n",
      "\u001b[32m[I 2021-06-15 22:18:20.591905] \u001b[36mA new bayesian optimization process created in memory\u001b[0m\n",
      "\u001b[33m \n",
      "\n",
      "Ensemble consists of: HybridGradientBoostingRegressor, HybridGradientBoostingRegressor, GradientBoostingRegressor\n",
      "Ensemble r2_score score: 0.9914697452103169\n",
      "\u001b[0m \n",
      "Finished in 3332 seconds\n",
      "hana_automl accuracy: 0.9914697452103169\n",
      "REMOVED ALL TABLES FROM DEVELOPER\n"
     ]
    },
    {
     "data": {
      "application/javascript": [
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       "    function() {\n",
       "        function appendUniqueDiv(){\n",
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       "            // been sent and avoid duplicates on page reload\n",
       "            var notifiedDiv = document.createElement(\"div\")\n",
       "            notifiedDiv.id = \"47c23be8-b124-483e-8435-d59c188dd436\"\n",
       "            element.append(notifiedDiv)\n",
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       "\n",
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       "        // help stop extra notifications when a saved notebook is loaded,\n",
       "        // which during testing gives us state \"interactive\", not \"complete\"\n",
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       "            // was already sent\n",
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       "                if (Notification.permission !== 'denied') {\n",
       "                    if (Notification.permission !== 'granted') { \n",
       "                        Notification.requestPermission(function (permission) {\n",
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       "                    if (Notification.permission === 'granted') {\n",
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       "                    appendUniqueDiv()\n",
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       "                    } \n",
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   ],
   "source": [
    "%%notify\n",
    "b.run('./data/benchmark/reg/pol.csv', task='reg', label='foo', grad_boost=GRADIENT)\n",
    "finish(b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "d69a8608",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1/1 [00:00<00:00,  2.40it/s]\n",
      "100%|██████████| 1/1 [00:00<00:00,  3.13it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Finished in 10 seconds\n",
      "APL accuracy:  0.8162364498109816\n",
      "REMOVED ALL TABLES FROM DEVELOPER\n",
      "\u001b[32m[I 2021-06-15 17:37:02.855571] \u001b[36mA new bayesian optimization process created in memory\u001b[0m\n",
      "\u001b[33m \n",
      "\n",
      "Ensemble consists of: HybridGradientBoostingRegressor, GradientBoostingRegressor, GradientBoostingRegressor\n",
      "Ensemble r2_score score: 0.8629195161294358\n",
      "\u001b[0m \n",
      "Finished in 764 seconds\n",
      "hana_automl accuracy: 0.8629195161294358\n",
      "REMOVED ALL TABLES FROM DEVELOPER\n"
     ]
    },
    {
     "data": {
      "application/javascript": [
       "$(document).ready(\n",
       "    function() {\n",
       "        function appendUniqueDiv(){\n",
       "            // append a div with our uuid so we can check that it's already\n",
       "            // been sent and avoid duplicates on page reload\n",
       "            var notifiedDiv = document.createElement(\"div\")\n",
       "            notifiedDiv.id = \"ed133717-2cfa-41a2-91a3-1837b779c38c\"\n",
       "            element.append(notifiedDiv)\n",
       "        }\n",
       "\n",
       "        // only send notifications if the pageload is complete; this will\n",
       "        // help stop extra notifications when a saved notebook is loaded,\n",
       "        // which during testing gives us state \"interactive\", not \"complete\"\n",
       "        if (document.readyState === 'complete') {\n",
       "            // check for the div that signifies that the notification\n",
       "            // was already sent\n",
       "            if (document.getElementById(\"ed133717-2cfa-41a2-91a3-1837b779c38c\") === null) {\n",
       "                var notificationPayload = {\"requireInteraction\": false, \"icon\": \"/static/base/images/favicon.ico\", \"body\": \"Cell execution has finished!\"};\n",
       "                if (Notification.permission !== 'denied') {\n",
       "                    if (Notification.permission !== 'granted') { \n",
       "                        Notification.requestPermission(function (permission) {\n",
       "                            if(!('permission' in Notification)) {\n",
       "                                Notification.permission = permission\n",
       "                            }\n",
       "                        })\n",
       "                    }\n",
       "                    if (Notification.permission === 'granted') {\n",
       "                    var notification = new Notification(\"Jupyter Notebook\", notificationPayload)\n",
       "                    appendUniqueDiv()\n",
       "                    notification.onclick = function () {\n",
       "                        window.focus();\n",
       "                        this.close();\n",
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       "                    } \n",
       "                }     \n",
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       "        }\n",
       "    }\n",
       ")\n"
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     },
     "metadata": {},
     "output_type": "display_data"
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   ],
   "source": [
    "%%notify\n",
    "b.run('./data/benchmark/reg/socmob.csv', task='reg', label='counts_for_sons_current_occupation', categorical=['fathers_occupation','sons_occupation', 'family_structure', 'race'],grad_boost=GRADIENT)\n",
    "finish(b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fd68ac92",
   "metadata": {},
   "outputs": [],
   "source": [
    "apl_acc = [0.9294002825634781, 0.3099873396557854, 0.9396573145349141, 0.8448584138226907, 0.9900203214204571, 0.7045760787415289, 0.9756879425836702, 0.8162364498109816]\n",
    "hana_acc = [0.9345000820384756, 0.3352677618858584, 0.9187809116981492, 0.883268964633891, 0.9932003960127456, 0.6276758240475446, 0.9914697452103169, 0.8629195161294358]\n",
    "\n",
    "print('APL ACCURACY', apl_acc)\n",
    "print('AutoML ACCURACY', hana_acc)\n",
    "print(len(datasets), len(hana_acc), len(apl_acc))\n",
    "plot_results(task='Regression', boosting=True)"
   ]
  }
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