code/oracle_datascience/train_eeg_clean.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "confidential-newspaper",
   "metadata": {},
   "outputs": [],
   "source": [
    "###### test connection ######\n",
    "\n",
    "import requests\n",
    "response = requests.get(\"https://oracle.com\")\n",
    "assert response.status_code==200, \"Internet connection failed\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "final-ghost",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ocid1.tenancy.oc1..aaaaaaaazsst3y4ltnibxw2e56dsmdlwkc7zoshzpkst7hkgfgite4csmhoa\n",
      "ocid1.datascienceproject.oc1.eu-zurich-1.amaaaaaa2jwq32iapkcl4klg3qulxmpxlgnyxwhf276mb6ukgtdhms5oc3ua\n",
      "ocid1.saml2idp.oc1..aaaaaaaao4zb7iwgmzbhs3hcvl7qrob453qzrqzs3o2hhl23oczezxmtyz7q/ethlambda@gmail.com\n",
      "ocid1.tenancy.oc1..aaaaaaaazsst3y4ltnibxw2e56dsmdlwkc7zoshzpkst7hkgfgite4csmhoa\n",
      "eu-zurich-1\n"
     ]
    }
   ],
   "source": [
    "###### check environments ######\n",
    "\n",
    "import os\n",
    "print(os.environ[\"NB_SESSION_COMPARTMENT_OCID\"])\n",
    "print(os.environ[\"PROJECT_OCID\"])\n",
    "print(os.environ[\"USER_OCID\"])\n",
    "print(os.environ[\"TENANCY_OCID\"])\n",
    "print(os.environ[\"NB_REGION\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "religious-recommendation",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: scikit-learn in ./conda/classic_cpu/lib/python3.6/site-packages (0.21.3)\n",
      "Requirement already satisfied: joblib>=0.11 in ./conda/classic_cpu/lib/python3.6/site-packages (from scikit-learn) (0.17.0)\n",
      "Requirement already satisfied: scipy>=0.17.0 in ./conda/classic_cpu/lib/python3.6/site-packages (from scikit-learn) (1.3.3)\n",
      "Requirement already satisfied: numpy>=1.11.0 in ./conda/classic_cpu/lib/python3.6/site-packages (from scikit-learn) (1.17.5)\n",
      "Requirement already satisfied: numpy>=1.11.0 in ./conda/classic_cpu/lib/python3.6/site-packages (from scikit-learn) (1.17.5)\n"
     ]
    }
   ],
   "source": [
    "%%bash\n",
    "###### install libs ######\n",
    "#pip install matplotlib\n",
    "#pip install pandas\n",
    "#pip install seaborn\n",
    "#pip list\n",
    "#pip install scikit-learn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "happy-bunny",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "                <div style=\"padding: 7px;\n",
       "                            border-radius: 4px;\n",
       "                            background-color: #d4ecd9;\n",
       "                            margin_bottom: 5px;\">\n",
       "                    <p>You can call <code>ads.environment.ads_inspect.check_ads_env()</code> to check if the version of the libraries in the current environment conflicts with ADS dependencies.</p>\n",
       "                </div>\n",
       "\n",
       "                "
      ],
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       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "###### import libs ######\n",
    "\n",
    "%matplotlib inline\n",
    "%reload_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "import gzip\n",
    "import pickle\n",
    "import logging\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "\n",
    "import ads\n",
    "from ads.dataset.factory import DatasetFactory\n",
    "from ads.automl.provider import OracleAutoMLProvider\n",
    "from ads.automl.driver import AutoML\n",
    "from ads.evaluations.evaluator import ADSEvaluator\n",
    "\n",
    "plt.rcParams['figure.figsize'] = [10, 7]\n",
    "plt.rcParams['font.size'] = 15\n",
    "sns.set(color_codes=True)\n",
    "sns.set(font_scale=1.5)\n",
    "sns.set_palette(\"bright\")\n",
    "sns.set_style(\"whitegrid\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "fresh-plenty",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Warning: Found Libraries inconsistent with ADS Requirement: \n",
      "Library         Version    Required\n",
      "--------------  ---------  ----------\n",
      "arrow           0.14.7     0.10.0\n",
      "configparser    4.0.2      3.5.0\n",
      "ipykernel       5.4.0      5.1.3\n",
      "pyod            0.8.1      0.7.1\n",
      "pyOpenSSL       18.0.0     19.0\n",
      "pytest          4.0.1      5.3.5\n",
      "pytest-asyncio  0.9.0      0.10.0\n",
      "six             1.14.0     1.13\n"
     ]
    }
   ],
   "source": [
    "###### check ads env ######\n",
    "\n",
    "ads.environment.ads_inspect.check_ads_env()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "exceptional-plaza",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       AF3       F7       F3      FC5       T7       P7       O1       O2  \\\n",
      "0  4288.72  4028.72  4240.51  4123.59  4344.62  4608.72  4055.38  4611.79   \n",
      "1  4268.21  4002.56  4232.82  4085.13  4336.41  4622.56  4107.18  4629.23   \n",
      "2  4300.51  4023.08  4278.46  4149.23  4348.72  4614.87  4057.44  4608.21   \n",
      "3  4271.79  3980.51  4251.79  4107.18  4329.23  4609.23  4053.85  4613.33   \n",
      "4  4433.85  4120.51  4313.85  4176.41  4356.92  4615.90  4069.74  4611.28   \n",
      "\n",
      "        P8       T8      FC6       F4       F8      AF4  Eye  \n",
      "0  4187.18  4230.77  4194.87  4265.64  4580.00  4345.64    0  \n",
      "1  4221.03  4230.77  4189.74  4277.44  4617.95  4331.28    1  \n",
      "2  4193.85  4228.21  4215.38  4283.59  4613.33  4367.18    0  \n",
      "3  4192.82  4222.05  4197.95  4258.97  4578.46  4340.51    1  \n",
      "4  4189.23  4226.15  4211.79  4306.15  4664.10  4479.49    0  \n"
     ]
    },
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AF3</th>\n",
       "      <th>F7</th>\n",
       "      <th>F3</th>\n",
       "      <th>FC5</th>\n",
       "      <th>T7</th>\n",
       "      <th>P7</th>\n",
       "      <th>O1</th>\n",
       "      <th>O2</th>\n",
       "      <th>P8</th>\n",
       "      <th>T8</th>\n",
       "      <th>FC6</th>\n",
       "      <th>F4</th>\n",
       "      <th>F8</th>\n",
       "      <th>AF4</th>\n",
       "      <th>Eye</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4343.59</td>\n",
       "      <td>3971.79</td>\n",
       "      <td>4265.13</td>\n",
       "      <td>4092.31</td>\n",
       "      <td>4326.67</td>\n",
       "      <td>4618.97</td>\n",
       "      <td>4100.00</td>\n",
       "      <td>4626.15</td>\n",
       "      <td>4205.64</td>\n",
       "      <td>4227.69</td>\n",
       "      <td>4238.46</td>\n",
       "      <td>4309.74</td>\n",
       "      <td>4654.36</td>\n",
       "      <td>4403.08</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4431.79</td>\n",
       "      <td>4072.31</td>\n",
       "      <td>4304.62</td>\n",
       "      <td>4154.36</td>\n",
       "      <td>4350.77</td>\n",
       "      <td>4621.03</td>\n",
       "      <td>4077.44</td>\n",
       "      <td>4601.54</td>\n",
       "      <td>4211.28</td>\n",
       "      <td>4275.90</td>\n",
       "      <td>4249.74</td>\n",
       "      <td>4321.54</td>\n",
       "      <td>4685.64</td>\n",
       "      <td>4476.41</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4316.41</td>\n",
       "      <td>4037.44</td>\n",
       "      <td>4247.18</td>\n",
       "      <td>4134.87</td>\n",
       "      <td>4327.69</td>\n",
       "      <td>4592.82</td>\n",
       "      <td>4048.21</td>\n",
       "      <td>4598.46</td>\n",
       "      <td>4190.26</td>\n",
       "      <td>4216.92</td>\n",
       "      <td>4177.44</td>\n",
       "      <td>4257.95</td>\n",
       "      <td>4588.21</td>\n",
       "      <td>4365.64</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4285.13</td>\n",
       "      <td>3996.41</td>\n",
       "      <td>4260.51</td>\n",
       "      <td>4108.72</td>\n",
       "      <td>4333.33</td>\n",
       "      <td>4617.95</td>\n",
       "      <td>4064.10</td>\n",
       "      <td>4625.13</td>\n",
       "      <td>4203.08</td>\n",
       "      <td>4229.23</td>\n",
       "      <td>4201.54</td>\n",
       "      <td>4281.03</td>\n",
       "      <td>4606.15</td>\n",
       "      <td>4354.36</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4288.72</td>\n",
       "      <td>4018.46</td>\n",
       "      <td>4267.69</td>\n",
       "      <td>4131.79</td>\n",
       "      <td>4338.97</td>\n",
       "      <td>4629.23</td>\n",
       "      <td>4086.15</td>\n",
       "      <td>4616.41</td>\n",
       "      <td>4191.79</td>\n",
       "      <td>4221.03</td>\n",
       "      <td>4196.41</td>\n",
       "      <td>4270.77</td>\n",
       "      <td>4590.26</td>\n",
       "      <td>4336.92</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       AF3       F7       F3      FC5       T7       P7       O1       O2  \\\n",
       "0  4343.59  3971.79  4265.13  4092.31  4326.67  4618.97  4100.00  4626.15   \n",
       "1  4431.79  4072.31  4304.62  4154.36  4350.77  4621.03  4077.44  4601.54   \n",
       "2  4316.41  4037.44  4247.18  4134.87  4327.69  4592.82  4048.21  4598.46   \n",
       "3  4285.13  3996.41  4260.51  4108.72  4333.33  4617.95  4064.10  4625.13   \n",
       "4  4288.72  4018.46  4267.69  4131.79  4338.97  4629.23  4086.15  4616.41   \n",
       "\n",
       "        P8       T8      FC6       F4       F8      AF4  Eye  \n",
       "0  4205.64  4227.69  4238.46  4309.74  4654.36  4403.08    1  \n",
       "1  4211.28  4275.90  4249.74  4321.54  4685.64  4476.41    0  \n",
       "2  4190.26  4216.92  4177.44  4257.95  4588.21  4365.64    1  \n",
       "3  4203.08  4229.23  4201.54  4281.03  4606.15  4354.36    0  \n",
       "4  4191.79  4221.03  4196.41  4270.77  4590.26  4336.92    1  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "####### load data #######\n",
    "\n",
    "name = 'eeg_clean'\n",
    "id_col = \"\"\n",
    "target_col = \"Eye\"\n",
    "\n",
    "train_name = './{}/train_{}.csv'.format(name, name)\n",
    "test_name = './{}/test_{}.csv'.format(name, name)\n",
    "\n",
    "df = pd.read_csv(train_name)\n",
    "test_df = pd.read_csv(test_name)\n",
    "\n",
    "print(df.head())\n",
    "df.shape\n",
    "\n",
    "test_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "composed-consultancy",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 1]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "### dataset statistics ######\n",
    "sns.countplot(x=target_col, data=df)\n",
    "plt.title(\"train dataset\")\n",
    "print(df[target_col].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "verbal-program",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, description='loop1', max=4, style=ProgressStyle(description_width='initial…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "\n",
       "            <br>\n",
       "\n",
       "            <div style=\"width: calc(100% -20px);\n",
       "                        border-left: 8px solid #588864;\n",
       "                        margin: 10px, 0, 10px, 0px;\n",
       "                        padding: 10px\">\n",
       "\n",
       "                \n",
       "                <p>Sampling (from 10486 rows) to 10000 rows for graphing</p>\n",
       "                \n",
       "            </div>\n",
       "\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "\n",
       "                <div style=\"padding: 7px;\n",
       "                            border-radius: 4px;\n",
       "                            background-color: #d4ecd9;\n",
       "                            margin_bottom: 5px;\">\n",
       "                    <p>Use <code>set_target()</code> to type the dataset for a particular learning task</p>\n",
       "                </div>\n",
       "\n",
       "                "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[BinaryClassificationDataset(target: Eye) 10,486 rows, 15 columns]\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "            <br>\n",
       "\n",
       "            <div style=\"width: calc(100% -20px);\n",
       "                        border-left: 8px solid #588864;\n",
       "                        margin: 10px, 0, 10px, 0px;\n",
       "                        padding: 10px\">\n",
       "\n",
       "                <div style=\"padding: 5px;\n",
       "                            color: #588864;\n",
       "                            border_bottom: 1px solid grey;\n",
       "                            margin_bottom: 5px;\">\n",
       "                    <h2>AUTOML</h2>\n",
       "                </div>\n",
       "                <p>AutoML Training (OracleAutoMLProvider)...</p>\n",
       "                \n",
       "            </div>\n",
       "\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "\n",
       "                <div style=\"padding: 7px;\n",
       "                            border-radius: 4px;\n",
       "                            background-color: #d4ecd9;\n",
       "                            margin_bottom: 5px;\">\n",
       "                    <p>Training complete (88.30 seconds)</p>\n",
       "                </div>\n",
       "\n",
       "                "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>Training Dataset size</td>\n",
       "      <td>(10486, 14)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Validation Dataset size</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>CV</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Target variable</td>\n",
       "      <td>Eye</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Optimization Metric</td>\n",
       "      <td>roc_auc</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Initial number of Features</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Selected number of Features</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Selected Features</td>\n",
       "      <td>[AF3, F7, F3, FC5, T7, P7, O1, O2, P8, T8, FC6, F4, F8, AF4]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Selected Algorithm</td>\n",
       "      <td>ExtraTreesClassifier</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>End-to-end Elapsed Time (seconds)</td>\n",
       "      <td>88.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Selected Hyperparameters</td>\n",
       "      <td>{'class_weight': None, 'criterion': 'entropy', 'max_features': 0.07142857142857142, 'min_samples_leaf': 9.536524890329964e-05, 'min_samples_split': 0.00019073049780659929, 'n_estimators': 253}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Mean Validation Score</td>\n",
       "      <td>0.9652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>AutoML n_jobs</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>AutoML version</td>\n",
       "      <td>0.4.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Python version</td>\n",
       "      <td>3.6.11 | packaged by conda-forge | (default, Aug  5 2020, 20:09:42) \\n[GCC 7.5.0]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
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       "<IPython.core.display.HTML object>"
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     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Rank based on Performance</th>\n",
       "      <th>Algorithm</th>\n",
       "      <th>#Samples</th>\n",
       "      <th>#Features</th>\n",
       "      <th>Mean Validation Score</th>\n",
       "      <th>Hyperparameters</th>\n",
       "      <th>CPU Time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>ExtraTreesClassifier_AS</td>\n",
       "      <td>5000</td>\n",
       "      <td>14</td>\n",
       "      <td>0.9731</td>\n",
       "      <td>{'n_estimators': 100, 'class_weight': 'balanced', 'criterion': 'gini', 'max_features': 0.777777778, 'min_samples_leaf': 0.000625, 'min_samples_split': 0.00125}</td>\n",
       "      <td>10.6847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>ExtraTreesClassifier_HT</td>\n",
       "      <td>10486</td>\n",
       "      <td>14</td>\n",
       "      <td>0.9652</td>\n",
       "      <td>{'class_weight': None, 'criterion': 'gini', 'max_features': 0.07142857142857142, 'min_samples_leaf': 9.536524890329964e-05, 'min_samples_split': 0.00019073049780659929, 'n_estimators': 253}</td>\n",
       "      <td>8.3806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>ExtraTreesClassifier_HT</td>\n",
       "      <td>10486</td>\n",
       "      <td>14</td>\n",
       "      <td>0.9652</td>\n",
       "      <td>{'class_weight': 'balanced', 'criterion': 'entropy', 'max_features': 0.07142857142857142, 'min_samples_leaf': 9.536524890329964e-05, 'min_samples_split': 0.00019073049780659929, 'n_estimators': 253}</td>\n",
       "      <td>8.8793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>ExtraTreesClassifier_HT</td>\n",
       "      <td>10486</td>\n",
       "      <td>14</td>\n",
       "      <td>0.9652</td>\n",
       "      <td>{'class_weight': 'balanced_subsample', 'criterion': 'entropy', 'max_features': 0.07142857142857142, 'min_samples_leaf': 9.536524890329964e-05, 'min_samples_split': 0.00019073049780659929, 'n_estimators': 253}</td>\n",
       "      <td>9.3716</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>ExtraTreesClassifier_HT</td>\n",
       "      <td>10486</td>\n",
       "      <td>14</td>\n",
       "      <td>0.9651</td>\n",
       "      <td>{'class_weight': None, 'criterion': 'entropy', 'max_features': 0.07142857142857142, 'min_samples_leaf': 9.536524890329964e-05, 'min_samples_split': 0.00019073049780659929, 'n_estimators': 252}</td>\n",
       "      <td>9.0848</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>123</td>\n",
       "      <td>ExtraTreesClassifier_AdaBoostClassifier_FS</td>\n",
       "      <td>10486</td>\n",
       "      <td>1</td>\n",
       "      <td>0.6263</td>\n",
       "      <td>{'n_estimators': 100, 'class_weight': 'balanced', 'criterion': 'gini', 'max_features': 0.777777778, 'min_samples_leaf': 0.000625, 'min_samples_split': 0.00125}</td>\n",
       "      <td>1.3697</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>124</td>\n",
       "      <td>ExtraTreesClassifier_HT</td>\n",
       "      <td>10486</td>\n",
       "      <td>14</td>\n",
       "      <td>0.6228</td>\n",
       "      <td>{'class_weight': None, 'criterion': 'entropy', 'max_features': 0.07142857142857142, 'min_samples_leaf': 0.0070839358851845415, 'min_samples_split': 0.00019073049780659929, 'n_estimators': 5}</td>\n",
       "      <td>0.0837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>125</td>\n",
       "      <td>ExtraTreesClassifier_HT</td>\n",
       "      <td>10486</td>\n",
       "      <td>14</td>\n",
       "      <td>0.6228</td>\n",
       "      <td>{'class_weight': None, 'criterion': 'entropy', 'max_features': 0.07142857142857142, 'min_samples_leaf': 0.007088934931532053, 'min_samples_split': 0.00019073049780659929, 'n_estimators': 5}</td>\n",
       "      <td>0.0862</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>126</td>\n",
       "      <td>ExtraTreesClassifier_MutualInfo_FS</td>\n",
       "      <td>10486</td>\n",
       "      <td>1</td>\n",
       "      <td>0.6216</td>\n",
       "      <td>{'n_estimators': 100, 'class_weight': 'balanced', 'criterion': 'gini', 'max_features': 0.777777778, 'min_samples_leaf': 0.000625, 'min_samples_split': 0.00125}</td>\n",
       "      <td>1.8278</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>127</td>\n",
       "      <td>ExtraTreesClassifier_ANOVAF_FS</td>\n",
       "      <td>10486</td>\n",
       "      <td>1</td>\n",
       "      <td>0.5765</td>\n",
       "      <td>{'n_estimators': 100, 'class_weight': 'balanced', 'criterion': 'gini', 'max_features': 0.777777778, 'min_samples_leaf': 0.000625, 'min_samples_split': 0.00125}</td>\n",
       "      <td>1.6723</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
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       "<IPython.core.display.HTML object>"
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   ],
   "source": [
    "###### train model ######\n",
    "\n",
    "train = DatasetFactory.open(df).set_target(target_col)\n",
    "print([train])\n",
    "\n",
    "# create an automl job\n",
    "ml_engine = OracleAutoMLProvider(n_jobs=-1, loglevel=logging.ERROR)\n",
    "oracle_automl = AutoML(training_data = train, provider=ml_engine)\n",
    "\n",
    "# time budget is in seconds\n",
    "best_model, baseline = oracle_automl.train(score_metric='roc_auc', time_budget=600)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "settled-effect",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(4494, 15)\n",
      "(4494,)\n",
      "test accuracy = 0.9457053849577214\n",
      "test auc = 0.9915075217434389\n"
     ]
    }
   ],
   "source": [
    "###### predict test dataset ######\n",
    "\n",
    "print(test_df.shape)\n",
    "\n",
    "y_pred = best_model.predict(test_df)\n",
    "y_pred_prob = best_model.predict_proba(test_df)\n",
    "y_true = test_df[target_col]\n",
    "print(y_true.shape)\n",
    "\n",
    "test_acc = np.mean(y_pred==y_true)\n",
    "print(\"test accuracy = {}\".format(test_acc))\n",
    "\n",
    "import sklearn\n",
    "from sklearn import metrics\n",
    "fpr, tpr, thresholds = metrics.roc_curve(y_true, y_pred_prob[:, 1], pos_label=1)\n",
    "test_auc = metrics.auc(fpr, tpr)\n",
    "print(\"test auc = {}\".format(test_auc))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "demographic-miami",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Algorithm</th>\n",
       "      <th>#Samples</th>\n",
       "      <th>#Features</th>\n",
       "      <th>Mean Validation Score</th>\n",
       "      <th>Hyperparameters</th>\n",
       "      <th>CPU Time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>RandomForestClassifier_HT</td>\n",
       "      <td>29492</td>\n",
       "      <td>9</td>\n",
       "      <td>0.9261</td>\n",
       "      <td>{'class_weight': 'balanced_subsample', 'max_features': 0.1111111111111111, 'min_samples_leaf': 3.3907500339075e-05, 'min_samples_split': 6.781500067815e-05, 'n_estimators': 253}</td>\n",
       "      <td>21.8383</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>RandomForestClassifier_HT</td>\n",
       "      <td>29492</td>\n",
       "      <td>9</td>\n",
       "      <td>0.9259</td>\n",
       "      <td>{'class_weight': None, 'max_features': 0.1111111111111111, 'min_samples_leaf': 3.3907500339075e-05, 'min_samples_split': 6.781500067815e-05, 'n_estimators': 253}</td>\n",
       "      <td>16.8325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>RandomForestClassifier_HT</td>\n",
       "      <td>29492</td>\n",
       "      <td>9</td>\n",
       "      <td>0.9258</td>\n",
       "      <td>{'class_weight': None, 'max_features': 0.1111111111111111, 'min_samples_leaf': 3.3907500339075e-05, 'min_samples_split': 6.781500067815e-05, 'n_estimators': 252}</td>\n",
       "      <td>15.9579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>RandomForestClassifier_HT</td>\n",
       "      <td>29492</td>\n",
       "      <td>9</td>\n",
       "      <td>0.9251</td>\n",
       "      <td>{'class_weight': None, 'max_features': 0.1111111111111111, 'min_samples_leaf': 3.3907500339075e-05, 'min_samples_split': 6.781500067815e-05, 'n_estimators': 101}</td>\n",
       "      <td>6.5867</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>RandomForestClassifier_HT</td>\n",
       "      <td>29492</td>\n",
       "      <td>9</td>\n",
       "      <td>0.9251</td>\n",
       "      <td>{'class_weight': None, 'max_features': 0.1111111111111111, 'min_samples_leaf': 3.3907500339075e-05, 'min_samples_split': 6.781500067815e-05, 'n_estimators': 100}</td>\n",
       "      <td>6.4813</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>RandomForestClassifier_HT</td>\n",
       "      <td>29492</td>\n",
       "      <td>9</td>\n",
       "      <td>0.9249</td>\n",
       "      <td>{'class_weight': 'balanced', 'max_features': 0.1111111111111111, 'min_samples_leaf': 3.3907500339075e-05, 'min_samples_split': 6.781500067815e-05, 'n_estimators': 253}</td>\n",
       "      <td>17.0242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>RandomForestClassifier_HT</td>\n",
       "      <td>29492</td>\n",
       "      <td>9</td>\n",
       "      <td>0.9155</td>\n",
       "      <td>{'class_weight': 'balanced_subsample', 'max_features': 0.777777778, 'min_samples_leaf': 0.00041425204389354867, 'min_samples_split': 0.00125, 'n_estimators': 253}</td>\n",
       "      <td>54.0380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>RandomForestClassifier_HT</td>\n",
       "      <td>29492</td>\n",
       "      <td>9</td>\n",
       "      <td>0.9138</td>\n",
       "      <td>{'class_weight': 'balanced', 'max_features': 0.777777778, 'min_samples_leaf': 0.00041425204389354867, 'min_samples_split': 0.0012599993218499932, 'n_estimators': 100}</td>\n",
       "      <td>19.4435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>RandomForestClassifier_HT</td>\n",
       "      <td>29492</td>\n",
       "      <td>9</td>\n",
       "      <td>0.9138</td>\n",
       "      <td>{'class_weight': 'balanced', 'max_features': 0.777777778, 'min_samples_leaf': 0.00041925170481854526, 'min_samples_split': 0.00125, 'n_estimators': 100}</td>\n",
       "      <td>19.5491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>RandomForestClassifier_HT</td>\n",
       "      <td>29492</td>\n",
       "      <td>9</td>\n",
       "      <td>0.9138</td>\n",
       "      <td>{'class_weight': 'balanced', 'max_features': 0.777777778, 'min_samples_leaf': 0.00041425204389354867, 'min_samples_split': 0.00125, 'n_estimators': 100}</td>\n",
       "      <td>19.4114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>RandomForestClassifier_HT</td>\n",
       "      <td>29492</td>\n",
       "      <td>9</td>\n",
       "      <td>0.9138</td>\n",
       "      <td>{'class_weight': 'balanced', 'max_features': 0.7777866668888889, 'min_samples_leaf': 0.00041425204389354867, 'min_samples_split': 0.00125, 'n_estimators': 100}</td>\n",
       "      <td>19.3147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>RandomForestClassifier_HT</td>\n",
       "      <td>29492</td>\n",
       "      <td>9</td>\n",
       "      <td>0.9137</td>\n",
       "      <td>{'class_weight': 'balanced_subsample', 'max_features': 0.777777778, 'min_samples_leaf': 0.00041425204389354867, 'min_samples_split': 0.00125, 'n_estimators': 100}</td>\n",
       "      <td>21.2479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>RandomForestClassifier_HT</td>\n",
       "      <td>29492</td>\n",
       "      <td>9</td>\n",
       "      <td>0.9134</td>\n",
       "      <td>{'class_weight': 'balanced', 'max_features': 0.777777778, 'min_samples_leaf': 0.00041425204389354867, 'min_samples_split': 0.00125, 'n_estimators': 90}</td>\n",
       "      <td>17.5889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>RandomForestClassifier_HT</td>\n",
       "      <td>29492</td>\n",
       "      <td>9</td>\n",
       "      <td>0.9134</td>\n",
       "      <td>{'class_weight': 'balanced', 'max_features': 0.777777778, 'min_samples_leaf': 0.000625, 'min_samples_split': 0.00125, 'n_estimators': 253}</td>\n",
       "      <td>47.0928</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>RandomForestClassifier_HT</td>\n",
       "      <td>29492</td>\n",
       "      <td>9</td>\n",
       "      <td>0.9133</td>\n",
       "      <td>{'class_weight': 'balanced', 'max_features': 0.777777778, 'min_samples_leaf': 0.000625, 'min_samples_split': 0.00125, 'n_estimators': 252}</td>\n",
       "      <td>47.7916</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>RandomForestClassifier_HT</td>\n",
       "      <td>29492</td>\n",
       "      <td>9</td>\n",
       "      <td>0.9133</td>\n",
       "      <td>{'class_weight': 'balanced', 'max_features': 0.777777778, 'min_samples_leaf': 0.00041425204389354867, 'min_samples_split': 0.00125, 'n_estimators': 81}</td>\n",
       "      <td>15.9106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>RandomForestClassifier_HT</td>\n",
       "      <td>29492</td>\n",
       "      <td>9</td>\n",
       "      <td>0.9132</td>\n",
       "      <td>{'class_weight': 'balanced', 'max_features': 0.777777778, 'min_samples_leaf': 0.00041425204389354867, 'min_samples_split': 0.00125, 'n_estimators': 89}</td>\n",
       "      <td>17.1310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>RandomForestClassifier_HT</td>\n",
       "      <td>29492</td>\n",
       "      <td>9</td>\n",
       "      <td>0.9131</td>\n",
       "      <td>{'class_weight': 'balanced', 'max_features': 0.777777778, 'min_samples_leaf': 0.00041425204389354867, 'min_samples_split': 0.00125, 'n_estimators': 82}</td>\n",
       "      <td>15.9270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>RandomForestClassifier_HT</td>\n",
       "      <td>29492</td>\n",
       "      <td>9</td>\n",
       "      <td>0.9127</td>\n",
       "      <td>{'class_weight': 'balanced', 'max_features': 0.777777778, 'min_samples_leaf': 0.000625, 'min_samples_split': 0.00125, 'n_estimators': 101}</td>\n",
       "      <td>19.3980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>RandomForestClassifier_HT</td>\n",
       "      <td>29492</td>\n",
       "      <td>9</td>\n",
       "      <td>0.9126</td>\n",
       "      <td>{'class_weight': 'balanced', 'max_features': 0.7777866668888889, 'min_samples_leaf': 0.000625, 'min_samples_split': 0.00125, 'n_estimators': 100}</td>\n",
       "      <td>19.2208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>RandomForestClassifier_HT</td>\n",
       "      <td>29492</td>\n",
       "      <td>9</td>\n",
       "      <td>0.9126</td>\n",
       "      <td>{'class_weight': 'balanced', 'max_features': 0.777777778, 'min_samples_leaf': 0.000625, 'min_samples_split': 0.0012599993218499932, 'n_estimators': 100}</td>\n",
       "      <td>19.3105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>RandomForestClassifier_HT</td>\n",
       "      <td>29492</td>\n",
       "      <td>9</td>\n",
       "      <td>0.9126</td>\n",
       "      <td>{'class_weight': 'balanced', 'max_features': 0.777777778, 'min_samples_leaf': 0.0006299996609249966, 'min_samples_split': 0.00125, 'n_estimators': 100}</td>\n",
       "      <td>19.6691</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>RandomForestClassifier_ADS</td>\n",
       "      <td>7827</td>\n",
       "      <td>9</td>\n",
       "      <td>0.9126</td>\n",
       "      <td>{'bootstrap': True, 'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': None, 'max_features': 0.777777778, 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 0.000625, 'min_samples_split': 0.00125, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 100, 'n_jobs': 1, 'oob_score': False, 'random_state': 7, 'verbose': 0, 'warm_start': False}</td>\n",
       "      <td>18.8455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>RandomForestClassifier_HT</td>\n",
       "      <td>29492</td>\n",
       "      <td>9</td>\n",
       "      <td>0.9126</td>\n",
       "      <td>{'class_weight': 'balanced', 'max_features': 0.777777778, 'min_samples_leaf': 0.000625, 'min_samples_split': 0.00125, 'n_estimators': 100}</td>\n",
       "      <td>19.2123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>RandomForestClassifier_HT</td>\n",
       "      <td>29492</td>\n",
       "      <td>9</td>\n",
       "      <td>0.9125</td>\n",
       "      <td>{'class_weight': 'balanced_subsample', 'max_features': 0.777777778, 'min_samples_leaf': 0.000625, 'min_samples_split': 0.00125, 'n_estimators': 100}</td>\n",
       "      <td>20.6074</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>RandomForestClassifier_HT</td>\n",
       "      <td>29492</td>\n",
       "      <td>9</td>\n",
       "      <td>0.9123</td>\n",
       "      <td>{'class_weight': 'balanced', 'max_features': 0.5773618158486156, 'min_samples_leaf': 0.000625, 'min_samples_split': 0.00125, 'n_estimators': 100}</td>\n",
       "      <td>14.7168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>RandomForestClassifier_HT</td>\n",
       "      <td>29492</td>\n",
       "      <td>9</td>\n",
       "      <td>0.9123</td>\n",
       "      <td>{'class_weight': 'balanced', 'max_features': 0.5773707047375044, 'min_samples_leaf': 0.000625, 'min_samples_split': 0.00125, 'n_estimators': 100}</td>\n",
       "      <td>14.8572</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>RandomForestClassifier_ANOVAF_FS</td>\n",
       "      <td>29492</td>\n",
       "      <td>9</td>\n",
       "      <td>0.9115</td>\n",
       "      <td>{'n_estimators': 100, 'class_weight': 'balanced', 'max_features': 0.777777778, 'min_samples_leaf': 0.000625, 'min_samples_split': 0.00125}</td>\n",
       "      <td>18.3015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>RandomForestClassifier_RandomForestClassifier_FS</td>\n",
       "      <td>29492</td>\n",
       "      <td>6</td>\n",
       "      <td>0.9094</td>\n",
       "      <td>{'n_estimators': 100, 'class_weight': 'balanced', 'max_features': 0.777777778, 'min_samples_leaf': 0.000625, 'min_samples_split': 0.00125}</td>\n",
       "      <td>14.5934</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>RandomForestClassifier_MutualInfo_FS</td>\n",
       "      <td>29492</td>\n",
       "      <td>7</td>\n",
       "      <td>0.9090</td>\n",
       "      <td>{'n_estimators': 100, 'class_weight': 'balanced', 'max_features': 0.777777778, 'min_samples_leaf': 0.000625, 'min_samples_split': 0.00125}</td>\n",
       "      <td>16.5526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>RandomForestClassifier_ADS</td>\n",
       "      <td>6643</td>\n",
       "      <td>9</td>\n",
       "      <td>0.9083</td>\n",
       "      <td>{'bootstrap': True, 'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': None, 'max_features': 0.777777778, 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 0.000625, 'min_samples_split': 0.00125, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 100, 'n_jobs': 1, 'oob_score': False, 'random_state': 7, 'verbose': 0, 'warm_start': False}</td>\n",
       "      <td>15.7036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>RandomForestClassifier_AS</td>\n",
       "      <td>5000</td>\n",
       "      <td>9</td>\n",
       "      <td>0.9076</td>\n",
       "      <td>{'n_estimators': 100, 'class_weight': 'balanced', 'max_features': 0.777777778, 'min_samples_leaf': 0.000625, 'min_samples_split': 0.00125}</td>\n",
       "      <td>15.7331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>RandomForestClassifier_HT</td>\n",
       "      <td>29492</td>\n",
       "      <td>9</td>\n",
       "      <td>0.9075</td>\n",
       "      <td>{'class_weight': None, 'max_features': 0.777777778, 'min_samples_leaf': 0.00041425204389354867, 'min_samples_split': 0.00125, 'n_estimators': 100}</td>\n",
       "      <td>20.1207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>RandomForestClassifier_MutualInfo_FS</td>\n",
       "      <td>29492</td>\n",
       "      <td>6</td>\n",
       "      <td>0.9065</td>\n",
       "      <td>{'n_estimators': 100, 'class_weight': 'balanced', 'max_features': 0.777777778, 'min_samples_leaf': 0.000625, 'min_samples_split': 0.00125}</td>\n",
       "      <td>15.0027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>RandomForestClassifier_HT</td>\n",
       "      <td>29492</td>\n",
       "      <td>9</td>\n",
       "      <td>0.9062</td>\n",
       "      <td>{'class_weight': None, 'max_features': 0.777777778, 'min_samples_leaf': 0.000625, 'min_samples_split': 0.00125, 'n_estimators': 100}</td>\n",
       "      <td>20.3249</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>RandomForestClassifier_AVGRanking_FS</td>\n",
       "      <td>29492</td>\n",
       "      <td>7</td>\n",
       "      <td>0.9055</td>\n",
       "      <td>{'n_estimators': 100, 'class_weight': 'balanced', 'max_features': 0.777777778, 'min_samples_leaf': 0.000625, 'min_samples_split': 0.00125}</td>\n",
       "      <td>15.6952</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>RandomForestClassifier_AdaBoostClassifier_FS</td>\n",
       "      <td>29492</td>\n",
       "      <td>6</td>\n",
       "      <td>0.9052</td>\n",
       "      <td>{'n_estimators': 100, 'class_weight': 'balanced', 'max_features': 0.777777778, 'min_samples_leaf': 0.000625, 'min_samples_split': 0.00125}</td>\n",
       "      <td>13.1940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>RandomForestClassifier_ADS</td>\n",
       "      <td>4860</td>\n",
       "      <td>9</td>\n",
       "      <td>0.9049</td>\n",
       "      <td>{'bootstrap': True, 'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': None, 'max_features': 0.777777778, 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 0.000625, 'min_samples_split': 0.00125, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 100, 'n_jobs': 1, 'oob_score': False, 'random_state': 7, 'verbose': 0, 'warm_start': False}</td>\n",
       "      <td>12.2523</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>RandomForestClassifier_AdaBoostClassifier_FS</td>\n",
       "      <td>29492</td>\n",
       "      <td>7</td>\n",
       "      <td>0.9048</td>\n",
       "      <td>{'n_estimators': 100, 'class_weight': 'balanced', 'max_features': 0.777777778, 'min_samples_leaf': 0.000625, 'min_samples_split': 0.00125}</td>\n",
       "      <td>15.0157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>RandomForestClassifier_AdaBoostClassifier_FS</td>\n",
       "      <td>29492</td>\n",
       "      <td>5</td>\n",
       "      <td>0.9039</td>\n",
       "      <td>{'n_estimators': 100, 'class_weight': 'balanced', 'max_features': 0.777777778, 'min_samples_leaf': 0.000625, 'min_samples_split': 0.00125}</td>\n",
       "      <td>11.6480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>RandomForestClassifier_MutualInfo_FS</td>\n",
       "      <td>29492</td>\n",
       "      <td>5</td>\n",
       "      <td>0.9035</td>\n",
       "      <td>{'n_estimators': 100, 'class_weight': 'balanced', 'max_features': 0.777777778, 'min_samples_leaf': 0.000625, 'min_samples_split': 0.00125}</td>\n",
       "      <td>12.2314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>RandomForestClassifier_HT</td>\n",
       "      <td>29492</td>\n",
       "      <td>9</td>\n",
       "      <td>0.8999</td>\n",
       "      <td>{'class_weight': 'balanced', 'max_features': 0.11112888888888889, 'min_samples_leaf': 0.000625, 'min_samples_split': 0.00125, 'n_estimators': 100}</td>\n",
       "      <td>5.6946</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>RandomForestClassifier_HT</td>\n",
       "      <td>29492</td>\n",
       "      <td>9</td>\n",
       "      <td>0.8999</td>\n",
       "      <td>{'class_weight': 'balanced', 'max_features': 0.11112, 'min_samples_leaf': 0.000625, 'min_samples_split': 0.00125, 'n_estimators': 100}</td>\n",
       "      <td>5.5672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>RandomForestClassifier_RandomForestClassifier_FS</td>\n",
       "      <td>29492</td>\n",
       "      <td>5</td>\n",
       "      <td>0.8987</td>\n",
       "      <td>{'n_estimators': 100, 'class_weight': 'balanced', 'max_features': 0.777777778, 'min_samples_leaf': 0.000625, 'min_samples_split': 0.00125}</td>\n",
       "      <td>12.2520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>ExtraTreesClassifier_AS</td>\n",
       "      <td>5000</td>\n",
       "      <td>9</td>\n",
       "      <td>0.8962</td>\n",
       "      <td>{'n_estimators': 100, 'class_weight': 'balanced', 'criterion': 'gini', 'max_features': 0.777777778, 'min_samples_leaf': 0.000625, 'min_samples_split': 0.00125}</td>\n",
       "      <td>7.7089</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>RandomForestClassifier_HT</td>\n",
       "      <td>29492</td>\n",
       "      <td>9</td>\n",
       "      <td>0.8940</td>\n",
       "      <td>{'class_weight': None, 'max_features': 0.1111111111111111, 'min_samples_leaf': 3.3907500339075e-05, 'min_samples_split': 6.781500067815e-05, 'n_estimators': 6}</td>\n",
       "      <td>0.3902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>RandomForestClassifier_AdaBoostClassifier_FS</td>\n",
       "      <td>29492</td>\n",
       "      <td>4</td>\n",
       "      <td>0.8939</td>\n",
       "      <td>{'n_estimators': 100, 'class_weight': 'balanced', 'max_features': 0.777777778, 'min_samples_leaf': 0.000625, 'min_samples_split': 0.00125}</td>\n",
       "      <td>11.0889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>RandomForestClassifier_MutualInfo_FS</td>\n",
       "      <td>29492</td>\n",
       "      <td>4</td>\n",
       "      <td>0.8930</td>\n",
       "      <td>{'n_estimators': 100, 'class_weight': 'balanced', 'max_features': 0.777777778, 'min_samples_leaf': 0.000625, 'min_samples_split': 0.00125}</td>\n",
       "      <td>12.8861</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>RandomForestClassifier_ADS</td>\n",
       "      <td>3264</td>\n",
       "      <td>9</td>\n",
       "      <td>0.8920</td>\n",
       "      <td>{'bootstrap': True, 'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': None, 'max_features': 0.777777778, 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 0.000625, 'min_samples_split': 0.00125, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 100, 'n_jobs': 1, 'oob_score': False, 'random_state': 7, 'verbose': 0, 'warm_start': False}</td>\n",
       "      <td>8.6103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>RandomForestClassifier_AVGRanking_FS</td>\n",
       "      <td>29492</td>\n",
       "      <td>4</td>\n",
       "      <td>0.8905</td>\n",
       "      <td>{'n_estimators': 100, 'class_weight': 'balanced', 'max_features': 0.777777778, 'min_samples_leaf': 0.000625, 'min_samples_split': 0.00125}</td>\n",
       "      <td>12.5645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>RandomForestClassifier_HT</td>\n",
       "      <td>29492</td>\n",
       "      <td>9</td>\n",
       "      <td>0.8867</td>\n",
       "      <td>{'class_weight': None, 'max_features': 0.11112888888888889, 'min_samples_leaf': 3.3907500339075e-05, 'min_samples_split': 6.781500067815e-05, 'n_estimators': 5}</td>\n",
       "      <td>0.3226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>RandomForestClassifier_HT</td>\n",
       "      <td>29492</td>\n",
       "      <td>9</td>\n",
       "      <td>0.8867</td>\n",
       "      <td>{'class_weight': None, 'max_features': 0.11112, 'min_samples_leaf': 3.3907500339075e-05, 'min_samples_split': 6.781500067815e-05, 'n_estimators': 5}</td>\n",
       "      <td>0.3238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>RandomForestClassifier_HT</td>\n",
       "      <td>29492</td>\n",
       "      <td>9</td>\n",
       "      <td>0.8867</td>\n",
       "      <td>{'class_weight': None, 'max_features': 0.1111111111111111, 'min_samples_leaf': 3.3907500339075e-05, 'min_samples_split': 6.781500067815e-05, 'n_estimators': 5}</td>\n",
       "      <td>0.3240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>RandomForestClassifier_HT</td>\n",
       "      <td>29492</td>\n",
       "      <td>9</td>\n",
       "      <td>0.8864</td>\n",
       "      <td>{'class_weight': None, 'max_features': 0.7777866668888889, 'min_samples_leaf': 3.3907500339075e-05, 'min_samples_split': 6.781500067815e-05, 'n_estimators': 5}</td>\n",
       "      <td>0.9708</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>RandomForestClassifier_HT</td>\n",
       "      <td>29492</td>\n",
       "      <td>9</td>\n",
       "      <td>0.8864</td>\n",
       "      <td>{'class_weight': None, 'max_features': 0.777777778, 'min_samples_leaf': 3.3907500339075e-05, 'min_samples_split': 6.781500067815e-05, 'n_estimators': 5}</td>\n",
       "      <td>1.1412</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>RandomForestClassifier_HT</td>\n",
       "      <td>29492</td>\n",
       "      <td>9</td>\n",
       "      <td>0.8861</td>\n",
       "      <td>{'class_weight': 'balanced_subsample', 'max_features': 0.1111111111111111, 'min_samples_leaf': 3.3907500339075e-05, 'min_samples_split': 6.781500067815e-05, 'n_estimators': 5}</td>\n",
       "      <td>0.3842</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>LGBMClassifier_AS</td>\n",
       "      <td>5000</td>\n",
       "      <td>9</td>\n",
       "      <td>0.8840</td>\n",
       "      <td>{'boosting_type': 'gbdt', 'learning_rate': 0.1, 'max_depth': -1, 'min_child_weight': 0.001, 'n_estimators': 100, 'num_leaves': 31, 'reg_alpha': 0, 'reg_lambda': 1, 'class_weight': 'balanced'}</td>\n",
       "      <td>3.2258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>RandomForestClassifier_HT</td>\n",
       "      <td>29492</td>\n",
       "      <td>9</td>\n",
       "      <td>0.8835</td>\n",
       "      <td>{'class_weight': None, 'max_features': 0.5773707047375044, 'min_samples_leaf': 3.3907500339075e-05, 'min_samples_split': 6.781500067815e-05, 'n_estimators': 5}</td>\n",
       "      <td>0.8600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>RandomForestClassifier_HT</td>\n",
       "      <td>29492</td>\n",
       "      <td>9</td>\n",
       "      <td>0.8835</td>\n",
       "      <td>{'class_weight': None, 'max_features': 0.5773618158486156, 'min_samples_leaf': 3.3907500339075e-05, 'min_samples_split': 6.781500067815e-05, 'n_estimators': 5}</td>\n",
       "      <td>0.8961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>RandomForestClassifier_HT</td>\n",
       "      <td>29492</td>\n",
       "      <td>9</td>\n",
       "      <td>0.8829</td>\n",
       "      <td>{'class_weight': 'balanced', 'max_features': 0.777777778, 'min_samples_leaf': 0.000625, 'min_samples_split': 0.00125, 'n_estimators': 6}</td>\n",
       "      <td>1.2328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>RandomForestClassifier_AdaBoostClassifier_FS</td>\n",
       "      <td>29492</td>\n",
       "      <td>3</td>\n",
       "      <td>0.8804</td>\n",
       "      <td>{'n_estimators': 100, 'class_weight': 'balanced', 'max_features': 0.777777778, 'min_samples_leaf': 0.000625, 'min_samples_split': 0.00125}</td>\n",
       "      <td>8.0222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>RandomForestClassifier_HT</td>\n",
       "      <td>29492</td>\n",
       "      <td>9</td>\n",
       "      <td>0.8763</td>\n",
       "      <td>{'class_weight': None, 'max_features': 0.1111111111111111, 'min_samples_leaf': 3.3907500339075e-05, 'min_samples_split': 0.0012599993218499932, 'n_estimators': 5}</td>\n",
       "      <td>0.3593</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>RandomForestClassifier_HT</td>\n",
       "      <td>29492</td>\n",
       "      <td>9</td>\n",
       "      <td>0.8763</td>\n",
       "      <td>{'class_weight': None, 'max_features': 0.1111111111111111, 'min_samples_leaf': 3.3907500339075e-05, 'min_samples_split': 0.00125, 'n_estimators': 5}</td>\n",
       "      <td>0.3566</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>RandomForestClassifier_HT</td>\n",
       "      <td>29492</td>\n",
       "      <td>9</td>\n",
       "      <td>0.8758</td>\n",
       "      <td>{'class_weight': 'balanced', 'max_features': 0.777777778, 'min_samples_leaf': 0.000625, 'min_samples_split': 0.00125, 'n_estimators': 5}</td>\n",
       "      <td>0.9681</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>RandomForestClassifier_ANOVAF_FS</td>\n",
       "      <td>29492</td>\n",
       "      <td>7</td>\n",
       "      <td>0.8755</td>\n",
       "      <td>{'n_estimators': 100, 'class_weight': 'balanced', 'max_features': 0.777777778, 'min_samples_leaf': 0.000625, 'min_samples_split': 0.00125}</td>\n",
       "      <td>12.8374</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>RandomForestClassifier_AVGRanking_FS</td>\n",
       "      <td>29492</td>\n",
       "      <td>3</td>\n",
       "      <td>0.8750</td>\n",
       "      <td>{'n_estimators': 100, 'class_weight': 'balanced', 'max_features': 0.777777778, 'min_samples_leaf': 0.000625, 'min_samples_split': 0.00125}</td>\n",
       "      <td>9.5533</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>RandomForestClassifier_MutualInfo_FS</td>\n",
       "      <td>29492</td>\n",
       "      <td>3</td>\n",
       "      <td>0.8719</td>\n",
       "      <td>{'n_estimators': 100, 'class_weight': 'balanced', 'max_features': 0.777777778, 'min_samples_leaf': 0.000625, 'min_samples_split': 0.00125}</td>\n",
       "      <td>10.6469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>RandomForestClassifier_HT</td>\n",
       "      <td>29492</td>\n",
       "      <td>9</td>\n",
       "      <td>0.8679</td>\n",
       "      <td>{'class_weight': 'balanced', 'max_features': 0.777777778, 'min_samples_leaf': 0.000625, 'min_samples_split': 0.008821242686160672, 'n_estimators': 100}</td>\n",
       "      <td>16.9589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>RandomForestClassifier_HT</td>\n",
       "      <td>29492</td>\n",
       "      <td>9</td>\n",
       "      <td>0.8679</td>\n",
       "      <td>{'class_weight': 'balanced', 'max_features': 0.777777778, 'min_samples_leaf': 0.000625, 'min_samples_split': 0.008831242008010665, 'n_estimators': 100}</td>\n",
       "      <td>17.1430</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>RandomForestClassifier_RandomForestClassifier_FS</td>\n",
       "      <td>29492</td>\n",
       "      <td>3</td>\n",
       "      <td>0.8666</td>\n",
       "      <td>{'n_estimators': 100, 'class_weight': 'balanced', 'max_features': 0.777777778, 'min_samples_leaf': 0.000625, 'min_samples_split': 0.00125}</td>\n",
       "      <td>10.5555</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>RandomForestClassifier_ADS</td>\n",
       "      <td>2000</td>\n",
       "      <td>9</td>\n",
       "      <td>0.8636</td>\n",
       "      <td>{'bootstrap': True, 'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': None, 'max_features': 0.777777778, 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 0.000625, 'min_samples_split': 0.00125, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 100, 'n_jobs': 1, 'oob_score': False, 'random_state': 7, 'verbose': 0, 'warm_start': False}</td>\n",
       "      <td>5.9488</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>RandomForestClassifier_HT</td>\n",
       "      <td>29492</td>\n",
       "      <td>9</td>\n",
       "      <td>0.8547</td>\n",
       "      <td>{'class_weight': None, 'max_features': 0.1111111111111111, 'min_samples_leaf': 0.00041925170481854526, 'min_samples_split': 6.781500067815e-05, 'n_estimators': 5}</td>\n",
       "      <td>0.2882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>RandomForestClassifier_HT</td>\n",
       "      <td>29492</td>\n",
       "      <td>9</td>\n",
       "      <td>0.8547</td>\n",
       "      <td>{'class_weight': None, 'max_features': 0.1111111111111111, 'min_samples_leaf': 0.00041425204389354867, 'min_samples_split': 6.781500067815e-05, 'n_estimators': 5}</td>\n",
       "      <td>0.2884</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>RandomForestClassifier_AdaBoostClassifier_FS</td>\n",
       "      <td>29492</td>\n",
       "      <td>2</td>\n",
       "      <td>0.8459</td>\n",
       "      <td>{'n_estimators': 100, 'class_weight': 'balanced', 'max_features': 0.777777778, 'min_samples_leaf': 0.000625, 'min_samples_split': 0.00125}</td>\n",
       "      <td>5.5303</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>RandomForestClassifier_ANOVAF_FS</td>\n",
       "      <td>29492</td>\n",
       "      <td>6</td>\n",
       "      <td>0.8458</td>\n",
       "      <td>{'n_estimators': 100, 'class_weight': 'balanced', 'max_features': 0.777777778, 'min_samples_leaf': 0.000625, 'min_samples_split': 0.00125}</td>\n",
       "      <td>8.7648</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>RandomForestClassifier_HT</td>\n",
       "      <td>29492</td>\n",
       "      <td>9</td>\n",
       "      <td>0.8451</td>\n",
       "      <td>{'class_weight': None, 'max_features': 0.1111111111111111, 'min_samples_leaf': 0.000625, 'min_samples_split': 6.781500067815e-05, 'n_estimators': 5}</td>\n",
       "      <td>0.2803</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>RandomForestClassifier_HT</td>\n",
       "      <td>29492</td>\n",
       "      <td>9</td>\n",
       "      <td>0.8451</td>\n",
       "      <td>{'class_weight': None, 'max_features': 0.1111111111111111, 'min_samples_leaf': 0.0006299996609249966, 'min_samples_split': 6.781500067815e-05, 'n_estimators': 5}</td>\n",
       "      <td>0.3018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>RandomForestClassifier_HT</td>\n",
       "      <td>29492</td>\n",
       "      <td>9</td>\n",
       "      <td>0.8382</td>\n",
       "      <td>{'class_weight': 'balanced', 'max_features': 0.777777778, 'min_samples_leaf': 0.004410621343080336, 'min_samples_split': 0.00125, 'n_estimators': 100}</td>\n",
       "      <td>15.1576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>RandomForestClassifier_HT</td>\n",
       "      <td>29492</td>\n",
       "      <td>9</td>\n",
       "      <td>0.8382</td>\n",
       "      <td>{'class_weight': 'balanced', 'max_features': 0.777777778, 'min_samples_leaf': 0.004415621004005332, 'min_samples_split': 0.00125, 'n_estimators': 100}</td>\n",
       "      <td>16.1180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>RandomForestClassifier_AdaBoostClassifier_FS</td>\n",
       "      <td>29492</td>\n",
       "      <td>1</td>\n",
       "      <td>0.8368</td>\n",
       "      <td>{'n_estimators': 100, 'class_weight': 'balanced', 'max_features': 0.777777778, 'min_samples_leaf': 0.000625, 'min_samples_split': 0.00125}</td>\n",
       "      <td>7.6485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>DecisionTreeClassifier_AS</td>\n",
       "      <td>5000</td>\n",
       "      <td>9</td>\n",
       "      <td>0.8356</td>\n",
       "      <td>{'class_weight': None, 'max_features': 1.0, 'min_samples_leaf': 0.000625, 'min_samples_split': 0.00125}</td>\n",
       "      <td>0.3387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>RandomForestClassifier_MutualInfo_FS</td>\n",
       "      <td>29492</td>\n",
       "      <td>2</td>\n",
       "      <td>0.8293</td>\n",
       "      <td>{'n_estimators': 100, 'class_weight': 'balanced', 'max_features': 0.777777778, 'min_samples_leaf': 0.000625, 'min_samples_split': 0.00125}</td>\n",
       "      <td>7.7407</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>RandomForestClassifier_HT</td>\n",
       "      <td>29492</td>\n",
       "      <td>9</td>\n",
       "      <td>0.8088</td>\n",
       "      <td>{'class_weight': None, 'max_features': 0.1111111111111111, 'min_samples_leaf': 3.3907500339075e-05, 'min_samples_split': 0.008821242686160672, 'n_estimators': 5}</td>\n",
       "      <td>0.2808</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>RandomForestClassifier_HT</td>\n",
       "      <td>29492</td>\n",
       "      <td>9</td>\n",
       "      <td>0.8088</td>\n",
       "      <td>{'class_weight': None, 'max_features': 0.1111111111111111, 'min_samples_leaf': 3.3907500339075e-05, 'min_samples_split': 0.008831242008010665, 'n_estimators': 5}</td>\n",
       "      <td>0.2720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>RandomForestClassifier_ANOVAF_FS</td>\n",
       "      <td>29492</td>\n",
       "      <td>5</td>\n",
       "      <td>0.8032</td>\n",
       "      <td>{'n_estimators': 100, 'class_weight': 'balanced', 'max_features': 0.777777778, 'min_samples_leaf': 0.000625, 'min_samples_split': 0.00125}</td>\n",
       "      <td>6.2971</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>RandomForestClassifier_ANOVAF_FS</td>\n",
       "      <td>29492</td>\n",
       "      <td>4</td>\n",
       "      <td>0.7997</td>\n",
       "      <td>{'n_estimators': 100, 'class_weight': 'balanced', 'max_features': 0.777777778, 'min_samples_leaf': 0.000625, 'min_samples_split': 0.00125}</td>\n",
       "      <td>6.5642</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>RandomForestClassifier_ANOVAF_FS</td>\n",
       "      <td>29492</td>\n",
       "      <td>3</td>\n",
       "      <td>0.7967</td>\n",
       "      <td>{'n_estimators': 100, 'class_weight': 'balanced', 'max_features': 0.777777778, 'min_samples_leaf': 0.000625, 'min_samples_split': 0.00125}</td>\n",
       "      <td>5.0738</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>XGBClassifier_AS</td>\n",
       "      <td>5000</td>\n",
       "      <td>9</td>\n",
       "      <td>0.7818</td>\n",
       "      <td>{'booster': 'gbtree', 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 1, 'n_estimators': 100, 'reg_alpha': 0, 'reg_lambda': 1, 'class_weight': 'balanced'}</td>\n",
       "      <td>2.8436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>RandomForestClassifier_HT</td>\n",
       "      <td>29492</td>\n",
       "      <td>9</td>\n",
       "      <td>0.7625</td>\n",
       "      <td>{'class_weight': None, 'max_features': 0.1111111111111111, 'min_samples_leaf': 0.004415621004005332, 'min_samples_split': 6.781500067815e-05, 'n_estimators': 5}</td>\n",
       "      <td>0.2715</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>RandomForestClassifier_HT</td>\n",
       "      <td>29492</td>\n",
       "      <td>9</td>\n",
       "      <td>0.7625</td>\n",
       "      <td>{'class_weight': None, 'max_features': 0.1111111111111111, 'min_samples_leaf': 0.004410621343080336, 'min_samples_split': 6.781500067815e-05, 'n_estimators': 5}</td>\n",
       "      <td>0.2810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>RandomForestClassifier_ANOVAF_FS</td>\n",
       "      <td>29492</td>\n",
       "      <td>2</td>\n",
       "      <td>0.7450</td>\n",
       "      <td>{'n_estimators': 100, 'class_weight': 'balanced', 'max_features': 0.777777778, 'min_samples_leaf': 0.000625, 'min_samples_split': 0.00125}</td>\n",
       "      <td>3.8641</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>RandomForestClassifier_RandomForestClassifier_FS</td>\n",
       "      <td>29492</td>\n",
       "      <td>1</td>\n",
       "      <td>0.7182</td>\n",
       "      <td>{'n_estimators': 100, 'class_weight': 'balanced', 'max_features': 0.777777778, 'min_samples_leaf': 0.000625, 'min_samples_split': 0.00125}</td>\n",
       "      <td>8.2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>RandomForestClassifier_HT</td>\n",
       "      <td>29492</td>\n",
       "      <td>9</td>\n",
       "      <td>0.7143</td>\n",
       "      <td>{'class_weight': 'balanced', 'max_features': 0.777777778, 'min_samples_leaf': 0.000625, 'min_samples_split': 0.09392147084751533, 'n_estimators': 100}</td>\n",
       "      <td>10.4127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>RandomForestClassifier_HT</td>\n",
       "      <td>29492</td>\n",
       "      <td>9</td>\n",
       "      <td>0.7143</td>\n",
       "      <td>{'class_weight': 'balanced', 'max_features': 0.777777778, 'min_samples_leaf': 0.000625, 'min_samples_split': 0.09393147016936532, 'n_estimators': 100}</td>\n",
       "      <td>10.0707</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>AdaBoostClassifier_AS</td>\n",
       "      <td>5000</td>\n",
       "      <td>9</td>\n",
       "      <td>0.7118</td>\n",
       "      <td>{'n_estimators': 100, 'learning_rate': 0.667}</td>\n",
       "      <td>5.9844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>RandomForestClassifier_ANOVAF_FS</td>\n",
       "      <td>29492</td>\n",
       "      <td>1</td>\n",
       "      <td>0.6902</td>\n",
       "      <td>{'n_estimators': 100, 'class_weight': 'balanced', 'max_features': 0.777777778, 'min_samples_leaf': 0.000625, 'min_samples_split': 0.00125}</td>\n",
       "      <td>4.5622</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>RandomForestClassifier_HT</td>\n",
       "      <td>29492</td>\n",
       "      <td>9</td>\n",
       "      <td>0.6868</td>\n",
       "      <td>{'class_weight': None, 'max_features': 0.1111111111111111, 'min_samples_leaf': 3.3907500339075e-05, 'min_samples_split': 0.09392147084751533, 'n_estimators': 5}</td>\n",
       "      <td>0.2105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>RandomForestClassifier_HT</td>\n",
       "      <td>29492</td>\n",
       "      <td>9</td>\n",
       "      <td>0.6868</td>\n",
       "      <td>{'class_weight': None, 'max_features': 0.1111111111111111, 'min_samples_leaf': 3.3907500339075e-05, 'min_samples_split': 0.09393147016936532, 'n_estimators': 5}</td>\n",
       "      <td>0.1907</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>LogisticRegression_AS</td>\n",
       "      <td>5000</td>\n",
       "      <td>9</td>\n",
       "      <td>0.5239</td>\n",
       "      <td>{'C': 1.0, 'class_weight': 'balanced', 'solver': 'liblinear'}</td>\n",
       "      <td>0.3317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>LinearSVC_AS</td>\n",
       "      <td>5000</td>\n",
       "      <td>9</td>\n",
       "      <td>0.5203</td>\n",
       "      <td>{'C': 1.0, 'class_weight': 'balanced'}</td>\n",
       "      <td>3.9340</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "oracle_automl.print_trials(max_rows=129, sort_column='Mean Validation Score')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "monthly-pleasure",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "oracle_automl.visualize_algorithm_selection_trials()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "bigger-pixel",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   A_follower_count  A_following_count  A_listed_count  A_mentions_received  \\\n",
      "0            614689                400            5729           199.618296   \n",
      "1            739581              10723            1279           102.026750   \n",
      "2              3638               3341              85             2.793577   \n",
      "3               613               1259              31             1.326041   \n",
      "4           1352348                331           15647         19129.848662   \n",
      "\n",
      "   A_retweets_received  A_mentions_sent  A_retweets_sent    A_posts  \\\n",
      "0            67.812469         9.366192         0.359534   4.094488   \n",
      "1            96.771319         1.537322         1.054358   3.467754   \n",
      "2             1.402703         2.594089         0.598150   3.802143   \n",
      "3             0.356943         6.249678         2.323064   7.488116   \n",
      "4         14552.733991         2.065418         0.580955  16.281502   \n",
      "\n",
      "   A_network_feature_1  A_network_feature_2  ...  B_following_count  \\\n",
      "0                  669            39.193741  ...                563   \n",
      "1                  432            11.191686  ...              12949   \n",
      "2                   11             9.416667  ...                320   \n",
      "3                    4             5.000000  ...               3599   \n",
      "4                52580             3.092448  ...             147521   \n",
      "\n",
      "   B_listed_count  B_mentions_received  B_retweets_received  B_mentions_sent  \\\n",
      "0           20057           967.720642           579.431128         4.473798   \n",
      "1            1311            38.260522            10.697567        12.845793   \n",
      "2            3673          1207.190077           599.721600         0.842022   \n",
      "3            1072            10.300222             3.828561         1.624092   \n",
      "4           15043          9902.070379          5012.055876        10.438460   \n",
      "\n",
      "   B_retweets_sent     B_posts  B_network_feature_1  B_network_feature_2  \\\n",
      "0         0.805680    6.241806                 4027            57.643839   \n",
      "1         1.584284   12.106662                  144           209.717241   \n",
      "2         0.593199    7.044735                 3918             7.859818   \n",
      "3         0.100503    1.614287                   43           113.840909   \n",
      "4         0.100503  193.072418                28564            10.964144   \n",
      "\n",
      "   B_network_feature_3  \n",
      "0          3201.293706  \n",
      "1         21496.565517  \n",
      "2           637.983300  \n",
      "3          2537.772727  \n",
      "4          1035.955493  \n",
      "\n",
      "[5 rows x 22 columns]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(5952, 22)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "###### load kaggle test dataset ######\n",
    "kaggle_test_name = './d_pis/kaggle-test_{}.csv'.format(name)\n",
    "\n",
    "kaggle_test_df = pd.read_csv(kaggle_test_name)\n",
    "kaggle_test_id = np.arange(1, kaggle_test_df.shape[0]  + 1)\n",
    "\n",
    "print(kaggle_test_df.head())\n",
    "kaggle_test_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "intellectual-shore",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(5952,)\n",
      "[0 0 0 0 0 0 1 1 0 1 0 1 1 0 1 1 1 0 1 0 1 0 1 1 0 0 0 0 0 1 0 1 1 1 1 0 0\n",
      " 0 1 1 1 0 0 1 0 1 0 0 0 1 0 0 0 1 1 0 0 1 0 1 0 1 0 1 1 0 1 0 0 1 1 0 0 1\n",
      " 0 1 0 1 0 0 0 0 0 1 0 0 1 0 1 1 0 0 1 1 0 0 1 0 0 0]\n"
     ]
    }
   ],
   "source": [
    "###### predict kaggle test dataset ######\n",
    "\n",
    "kaggle_y_pred = best_model.predict(kaggle_test_df)\n",
    "\n",
    "assert kaggle_y_pred.shape == kaggle_test_id.shape\n",
    "\n",
    "print(kaggle_y_pred.shape)\n",
    "print(kaggle_y_pred[:100])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "humanitarian-deadline",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Id  Choice\n",
      "0   1       0\n",
      "1   2       0\n",
      "2   3       0\n",
      "3   4       0\n",
      "4   5       0\n"
     ]
    }
   ],
   "source": [
    "###### save kaggle prediction ######\n",
    "\n",
    "kaggle_pred_name = './d_pis/kaggle-test_{}_predictions.csv'.format(name)\n",
    "\n",
    "kaggle_pred_df = pd.DataFrame({id_col: kaggle_test_id, target_col: kaggle_y_pred})\n",
    "print(kaggle_pred_df.head())\n",
    "\n",
    "kaggle_pred_df.to_csv(kaggle_pred_name, index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "sexual-bench",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}