code/oracle_datascience/train_blogFeedBack.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "covered-notebook",
   "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": "tracked-surge",
   "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": 3,
   "id": "computational-study",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: matplotlib in ./conda/classic_cpu/lib/python3.6/site-packages (3.1.3)\n",
      "Requirement already satisfied: cycler>=0.10 in ./conda/classic_cpu/lib/python3.6/site-packages (from matplotlib) (0.10.0)\n",
      "Requirement already satisfied: python-dateutil>=2.1 in ./conda/classic_cpu/lib/python3.6/site-packages (from matplotlib) (2.7.3)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in ./conda/classic_cpu/lib/python3.6/site-packages (from matplotlib) (1.3.1)\n",
      "Requirement already satisfied: numpy>=1.11 in ./conda/classic_cpu/lib/python3.6/site-packages (from matplotlib) (1.17.5)\n",
      "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in ./conda/classic_cpu/lib/python3.6/site-packages (from matplotlib) (2.4.7)\n",
      "Requirement already satisfied: six in ./conda/classic_cpu/lib/python3.6/site-packages (from cycler>=0.10->matplotlib) (1.14.0)\n",
      "Requirement already satisfied: six in ./conda/classic_cpu/lib/python3.6/site-packages (from cycler>=0.10->matplotlib) (1.14.0)\n",
      "Requirement already satisfied: pandas in ./conda/classic_cpu/lib/python3.6/site-packages (0.24.2)\n",
      "Requirement already satisfied: pytz>=2011k in ./conda/classic_cpu/lib/python3.6/site-packages (from pandas) (2019.3)\n",
      "Requirement already satisfied: numpy>=1.12.0 in ./conda/classic_cpu/lib/python3.6/site-packages (from pandas) (1.17.5)\n",
      "Requirement already satisfied: python-dateutil>=2.5.0 in ./conda/classic_cpu/lib/python3.6/site-packages (from pandas) (2.7.3)\n",
      "Requirement already satisfied: six>=1.5 in ./conda/classic_cpu/lib/python3.6/site-packages (from python-dateutil>=2.5.0->pandas) (1.14.0)\n",
      "Requirement already satisfied: seaborn in ./conda/classic_cpu/lib/python3.6/site-packages (0.9.0)\n",
      "Requirement already satisfied: numpy>=1.9.3 in ./conda/classic_cpu/lib/python3.6/site-packages (from seaborn) (1.17.5)\n",
      "Requirement already satisfied: pandas>=0.15.2 in ./conda/classic_cpu/lib/python3.6/site-packages (from seaborn) (0.24.2)\n",
      "Requirement already satisfied: scipy>=0.14.0 in ./conda/classic_cpu/lib/python3.6/site-packages (from seaborn) (1.3.3)\n",
      "Requirement already satisfied: matplotlib>=1.4.3 in ./conda/classic_cpu/lib/python3.6/site-packages (from seaborn) (3.1.3)\n",
      "Requirement already satisfied: numpy>=1.9.3 in ./conda/classic_cpu/lib/python3.6/site-packages (from seaborn) (1.17.5)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in ./conda/classic_cpu/lib/python3.6/site-packages (from matplotlib>=1.4.3->seaborn) (1.3.1)\n",
      "Requirement already satisfied: cycler>=0.10 in ./conda/classic_cpu/lib/python3.6/site-packages (from matplotlib>=1.4.3->seaborn) (0.10.0)\n",
      "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in ./conda/classic_cpu/lib/python3.6/site-packages (from matplotlib>=1.4.3->seaborn) (2.4.7)\n",
      "Requirement already satisfied: python-dateutil>=2.1 in ./conda/classic_cpu/lib/python3.6/site-packages (from matplotlib>=1.4.3->seaborn) (2.7.3)\n",
      "Requirement already satisfied: six in ./conda/classic_cpu/lib/python3.6/site-packages (from cycler>=0.10->matplotlib>=1.4.3->seaborn) (1.14.0)\n",
      "Requirement already satisfied: pytz>=2011k in ./conda/classic_cpu/lib/python3.6/site-packages (from pandas>=0.15.2->seaborn) (2019.3)\n",
      "Requirement already satisfied: numpy>=1.9.3 in ./conda/classic_cpu/lib/python3.6/site-packages (from seaborn) (1.17.5)\n",
      "Requirement already satisfied: python-dateutil>=2.1 in ./conda/classic_cpu/lib/python3.6/site-packages (from matplotlib>=1.4.3->seaborn) (2.7.3)\n",
      "Requirement already satisfied: six in ./conda/classic_cpu/lib/python3.6/site-packages (from cycler>=0.10->matplotlib>=1.4.3->seaborn) (1.14.0)\n",
      "Requirement already satisfied: numpy>=1.9.3 in ./conda/classic_cpu/lib/python3.6/site-packages (from seaborn) (1.17.5)\n",
      "Requirement already satisfied: scikit-learn in ./conda/classic_cpu/lib/python3.6/site-packages (0.21.3)\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: joblib>=0.11 in ./conda/classic_cpu/lib/python3.6/site-packages (from scikit-learn) (0.17.0)\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": "compliant-castle",
   "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",
       "                "
      ],
      "text/plain": [
       "<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": "handy-eclipse",
   "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": 7,
   "id": "standing-greensboro",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(59961, 281)\n",
      "          x0         x1   x2     x3   x4         x5         x6   x7     x8  \\\n",
      "0  34.571430  50.082855  0.0  190.0  7.0  14.523809  31.859990  0.0  156.0   \n",
      "1   1.333333   0.992032  0.0    4.0  1.0   0.571429   0.849169  0.0    3.0   \n",
      "2  39.644764  77.917020  0.0  798.0  4.0  16.757700  48.670017  0.0  568.0   \n",
      "3  10.630660  17.882992  1.0  259.0  5.0   4.018276  10.396790  0.0  235.0   \n",
      "4   0.000000   0.000000  0.0    0.0  0.0   0.000000   0.000000  0.0    0.0   \n",
      "\n",
      "    x9  ...  x271  x272  x273  x274  x275  x276  x277  x278  x279    y  \n",
      "0  2.0  ...   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0  1.0  \n",
      "1  0.0  ...   1.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0  0.0  \n",
      "2  1.0  ...   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0  0.0  \n",
      "3  1.0  ...   1.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0  0.0  \n",
      "4  0.0  ...   1.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0  0.0  \n",
      "\n",
      "[5 rows x 281 columns]\n",
      "(47968, 281)\n",
      "              x0         x1   x2     x3   x4        x5         x6   x7     x8  \\\n",
      "47968  18.866241  78.959564  0.0  592.0  0.0  7.898089  44.079716  0.0  397.0   \n",
      "47969  18.866241  78.959564  0.0  592.0  0.0  7.898089  44.079716  0.0  397.0   \n",
      "47970  18.866241  78.959564  0.0  592.0  0.0  7.898089  44.079716  0.0  397.0   \n",
      "47971  18.866241  78.959564  0.0  592.0  0.0  7.898089  44.079716  0.0  397.0   \n",
      "47972  18.866241  78.959564  0.0  592.0  0.0  7.898089  44.079716  0.0  397.0   \n",
      "\n",
      "        x9  ...  x271  x272  x273  x274  x275  x276  x277  x278  x279    y  \n",
      "47968  0.0  ...   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0  0.0  \n",
      "47969  0.0  ...   0.0   0.0   0.0   0.0   1.0   1.0   0.0   0.0   0.0  0.0  \n",
      "47970  0.0  ...   0.0   0.0   0.0   0.0   1.0   1.0   0.0  62.0  62.0  0.0  \n",
      "47971  0.0  ...   0.0   0.0   0.0   0.0   1.0   0.0   0.0   0.0   0.0  0.0  \n",
      "47972  0.0  ...   0.0   0.0   0.0   0.0   1.0   0.0   0.0   0.0   0.0  0.0  \n",
      "\n",
      "[5 rows x 281 columns]\n",
      "(11993, 281)\n"
     ]
    }
   ],
   "source": [
    "####### load data #######\n",
    "\n",
    "name = 'blogFeedBack'\n",
    "id_col = \"\"\n",
    "target_col = \"y\"\n",
    "\n",
    "train_name = './{}/{}.csv'.format(name, name)\n",
    "\n",
    "df = pd.read_csv(train_name)\n",
    "\n",
    "n_data = df.shape[0]\n",
    "n_train = int(n_data * 0.8)\n",
    "n_test = n_data - n_train\n",
    "\n",
    "#splits = np.split(df, [n_train, n_test], axis=0)\n",
    "#train_df = splits[0]\n",
    "#test_df = splits[1]\n",
    "\n",
    "train_df = df.iloc[:n_train, :]\n",
    "test_df = df.iloc[n_train:, :]\n",
    "\n",
    "print(df.shape)\n",
    "print(train_df.head())\n",
    "print(train_df.shape)\n",
    "print(test_df.head())\n",
    "print(test_df.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "precise-paradise",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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     "data": {
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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=train_df)\n",
    "plt.title(\"train dataset\")\n",
    "print(train_df[target_col].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "balanced-delight",
   "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 47968 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": [
      "[RegressionDataset(target: y) 47,968 rows, 281 columns]\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "                <div style=\"padding: 7px;\n",
       "                            border-radius: 4px;\n",
       "                            background-color: #fcc5c5;\n",
       "                            margin_bottom: 5px;\">\n",
       "                    <h3>Warning:</h3>\n",
       "                    <p>Given the size of your dataset (47968 rows with 280 columns), we recommend that you use a larger VM shape, at least a VM.Standard.E2.4 for this task.</p>\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: #fcc5c5;\n",
       "                            margin_bottom: 5px;\">\n",
       "                    <h3>Warning:</h3>\n",
       "                    <p>Given the size of your dataset (47968 rows with 280 columns), we recommend that you use a larger VM shape, at least a VM.Standard.E2.16 for this task.</p>\n",
       "                </div>\n",
       "\n",
       "                "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "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",
       "                <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><h2>NOTE: With 4 cores available for training on 47968 rows with 280 features please consider</h2><br>                - using a larger VM Shape<br>                - sampling the data to be smaller<br>                - using the time_budget feature to reduce training size<br>                - using the model_list to restrict the algorithms<br><br>            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 (3389.07 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>(47968, 280)</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>[([0, 1, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 29, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 59, 60, 61, 62, 64, 65, 66, 69, 71, 72, 73, 74, 75, 76, 77, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 92, 94, 95, 96, 97, 98, 99, 100, 101, 102, 104, 105, 106, 107, 109, 110, 111, 113, 114, 115, ...], [2, 28, 30, 43, 58, 63, 67, 68, 70, 78, 90, 91, 93, 103, 108, 112, 133, 140, 145, 156, 157, 160, 162, 163, 164, 165, 168, 173, 175, 176, 177, 181, 184, 186, 188, 189, 191, 194, 197, 199, 206, 220, 226, 227, 232, 233, 234, 238, 239, 241, 244, 249, 251, 257, 259, 267, 270, 272, 275, 279, 283, 287, 288, 300, 317, 321, 327, 329, 331, 343, 347, 349, 358, 364, 368, 370, 371, 372, 376, 378, 384, 392, 395, 397, 398, 402, 404, 421, 428, 446, 448, 449, 450, 454, 455, 456, 459, 464, 468, 480, ...]), ([0, 1, 2, 3, 4, 5, 6, 7, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 22, 23, 24, 25, 26, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Target variable</td>\n",
       "      <td>y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Optimization Metric</td>\n",
       "      <td>neg_mean_squared_error</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Initial number of Features</td>\n",
       "      <td>280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Selected number of Features</td>\n",
       "      <td>276</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Selected Features</td>\n",
       "      <td>[x0, x1, x2, x3, x4, x5, x6, x7, x8, x9, x10, x11, x13, x14, x15, x16, x17, x18, x19, x20, x21, x22, x23, x24, x25, x26, x27, x28, x29, x30, x31, x33, x34, x35, x36, x38, x39, x40, x41, x42, x43, x44, x45, x46, x47, x48, x49, x50, x51, x52, x53, x54, x55, x56, x57, x58, x59, x60, x61, x62, x63, x64, x65, x66, x67, x68, x69, x70, x71, x72, x73, x74, x75, x76, x77, x78, x79, x80, x81, x82, x83, x84, x85, x86, x87, x88, x89, x90, x91, x92, x93, x94, x95, x96, x97, x98, x99, x100, x101, x102, ...]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Selected Algorithm</td>\n",
       "      <td>RandomForestRegressor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>End-to-end Elapsed Time (seconds)</td>\n",
       "      <td>3389</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Selected Hyperparameters</td>\n",
       "      <td>{'max_features': 0.777777778, 'min_samples_leaf': 0.00015, 'min_samples_split': 0.0003, 'n_estimators': 100}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Mean Validation Score</td>\n",
       "      <td>-618.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>AutoML n_jobs</td>\n",
       "      <td>4</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>RandomForestRegressor_ADS</td>\n",
       "      <td>15950</td>\n",
       "      <td>276</td>\n",
       "      <td>-6.7115e+02</td>\n",
       "      <td>{'bootstrap': True, 'criterion': 'mse', 'max_depth': None, 'max_features': 0.777777778, 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 0.00015, 'min_samples_split': 0.0003, '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>784.3016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>RandomForestRegressor_ADS</td>\n",
       "      <td>8475</td>\n",
       "      <td>276</td>\n",
       "      <td>-6.9857e+02</td>\n",
       "      <td>{'bootstrap': True, 'criterion': 'mse', 'max_depth': None, 'max_features': 0.777777778, 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 0.00015, 'min_samples_split': 0.0003, '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>382.0871</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>RandomForestRegressor_ADS</td>\n",
       "      <td>8921</td>\n",
       "      <td>276</td>\n",
       "      <td>-6.9972e+02</td>\n",
       "      <td>{'bootstrap': True, 'criterion': 'mse', 'max_depth': None, 'max_features': 0.777777778, 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 0.00015, 'min_samples_split': 0.0003, '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>403.3130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>RandomForestRegressor_ADS</td>\n",
       "      <td>4301</td>\n",
       "      <td>276</td>\n",
       "      <td>-7.5805e+02</td>\n",
       "      <td>{'bootstrap': True, 'criterion': 'mse', 'max_depth': None, 'max_features': 0.777777778, 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 0.00015, 'min_samples_split': 0.0003, '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>208.5771</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>RandomForestRegressor_AS</td>\n",
       "      <td>5000</td>\n",
       "      <td>276</td>\n",
       "      <td>-7.8318e+02</td>\n",
       "      <td>{'n_estimators': 100, 'max_features': 0.777777778, 'min_samples_leaf': 0.00015, 'min_samples_split': 0.0003}</td>\n",
       "      <td>238.0606</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>11</td>\n",
       "      <td>XGBRegressor_AS</td>\n",
       "      <td>5000</td>\n",
       "      <td>276</td>\n",
       "      <td>-9.2056e+02</td>\n",
       "      <td>{'booster': 'gbtree', 'learning_rate': 0.1, 'max_depth': 6, 'min_child_weight': 1, 'n_estimators': 100, 'reg_alpha': 0, 'reg_lambda': 1}</td>\n",
       "      <td>124.1897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>RandomForestRegressor_ADS</td>\n",
       "      <td>1000</td>\n",
       "      <td>276</td>\n",
       "      <td>-9.8818e+02</td>\n",
       "      <td>{'bootstrap': True, 'criterion': 'mse', 'max_depth': None, 'max_features': 0.777777778, 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 0.00015, 'min_samples_split': 0.0003, '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>43.8117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>LinearSVR_AS</td>\n",
       "      <td>5000</td>\n",
       "      <td>276</td>\n",
       "      <td>-1.3130e+03</td>\n",
       "      <td>{'C': 1.0}</td>\n",
       "      <td>71.4680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>AdaBoostRegressor_AS</td>\n",
       "      <td>5000</td>\n",
       "      <td>276</td>\n",
       "      <td>-1.9029e+03</td>\n",
       "      <td>{'n_estimators': 50, 'learning_rate': 0.667}</td>\n",
       "      <td>51.3114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>LinearRegression_AS</td>\n",
       "      <td>5000</td>\n",
       "      <td>276</td>\n",
       "      <td>-4.4033e+28</td>\n",
       "      <td>{}</td>\n",
       "      <td>3.4457</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
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   ],
   "source": [
    "###### train model ######\n",
    "\n",
    "train = DatasetFactory.open(train_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='neg_mean_squared_error', time_budget=1800)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "utility-tampa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(11993, 281)\n",
      "(11993,)\n",
      "test mse = 327.8319395627446\n"
     ]
    }
   ],
   "source": [
    "###### predict test dataset ######\n",
    "\n",
    "print(test_df.shape)\n",
    "\n",
    "y_pred = best_model.predict(test_df)\n",
    "y_true = test_df[target_col]\n",
    "print(y_true.shape)\n",
    "\n",
    "from sklearn.metrics import mean_squared_error\n",
    "mse = mean_squared_error(y_true, y_pred)\n",
    "print(\"test mse = {}\".format(mse))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "limited-diving",
   "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>LinearRegression_AdaBoostRegressor_FS</td>\n",
       "      <td>13903</td>\n",
       "      <td>2</td>\n",
       "      <td>-3.2734e-27</td>\n",
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       "      <td>0.1361</td>\n",
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       "    <tr>\n",
       "      <td>LinearRegression_AdaBoostRegressor_FS</td>\n",
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       "      <td>-6.2330e-27</td>\n",
       "      <td>{}</td>\n",
       "      <td>0.0599</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>LinearRegression_UniLinRegression_FS</td>\n",
       "      <td>13903</td>\n",
       "      <td>4</td>\n",
       "      <td>-9.9831e-27</td>\n",
       "      <td>{}</td>\n",
       "      <td>0.2232</td>\n",
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       "    <tr>\n",
       "      <td>LinearRegression_UniLinRegression_FS</td>\n",
       "      <td>13903</td>\n",
       "      <td>3</td>\n",
       "      <td>-1.0651e-26</td>\n",
       "      <td>{}</td>\n",
       "      <td>0.0816</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>LinearRegression_MutualInfo_FS</td>\n",
       "      <td>13903</td>\n",
       "      <td>4</td>\n",
       "      <td>-1.1580e-26</td>\n",
       "      <td>{}</td>\n",
       "      <td>0.1096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>LinearRegression_UniLinRegression_FS</td>\n",
       "      <td>13903</td>\n",
       "      <td>6</td>\n",
       "      <td>-1.1761e-26</td>\n",
       "      <td>{}</td>\n",
       "      <td>0.2344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>LinearRegression_RandomForestRegressor_FS</td>\n",
       "      <td>13903</td>\n",
       "      <td>3</td>\n",
       "      <td>-1.2014e-26</td>\n",
       "      <td>{}</td>\n",
       "      <td>0.1134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>LinearRegression_UniLinRegression_FS</td>\n",
       "      <td>13903</td>\n",
       "      <td>5</td>\n",
       "      <td>-1.3861e-26</td>\n",
       "      <td>{}</td>\n",
       "      <td>0.0968</td>\n",
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       "    <tr>\n",
       "      <td>LinearRegression_AdaBoostRegressor_FS</td>\n",
       "      <td>13903</td>\n",
       "      <td>5</td>\n",
       "      <td>-1.4692e-26</td>\n",
       "      <td>{}</td>\n",
       "      <td>0.0746</td>\n",
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       "    <tr>\n",
       "      <td>LinearRegression_RandomForestRegressor_FS</td>\n",
       "      <td>13903</td>\n",
       "      <td>5</td>\n",
       "      <td>-1.5339e-26</td>\n",
       "      <td>{}</td>\n",
       "      <td>0.1543</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>LinearRegression_HT</td>\n",
       "      <td>13903</td>\n",
       "      <td>2</td>\n",
       "      <td>-1.5777e-26</td>\n",
       "      <td>{}</td>\n",
       "      <td>0.0375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>LinearRegression_RandomForestRegressor_FS</td>\n",
       "      <td>13903</td>\n",
       "      <td>7</td>\n",
       "      <td>-1.7896e-26</td>\n",
       "      <td>{}</td>\n",
       "      <td>0.1242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>LinearRegression_UniLinRegression_FS</td>\n",
       "      <td>13903</td>\n",
       "      <td>13</td>\n",
       "      <td>-1.9446e-26</td>\n",
       "      <td>{}</td>\n",
       "      <td>0.3643</td>\n",
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       "    <tr>\n",
       "      <td>LinearRegression_UniLinRegression_FS</td>\n",
       "      <td>13903</td>\n",
       "      <td>9</td>\n",
       "      <td>-1.9644e-26</td>\n",
       "      <td>{}</td>\n",
       "      <td>0.2294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>LinearRegression_UniLinRegression_FS</td>\n",
       "      <td>13903</td>\n",
       "      <td>16</td>\n",
       "      <td>-1.9991e-26</td>\n",
       "      <td>{}</td>\n",
       "      <td>0.4803</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>LinearRegression_UniLinRegression_FS</td>\n",
       "      <td>13903</td>\n",
       "      <td>11</td>\n",
       "      <td>-2.4503e-26</td>\n",
       "      <td>{}</td>\n",
       "      <td>0.1954</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>LinearRegression_UniLinRegression_FS</td>\n",
       "      <td>13903</td>\n",
       "      <td>19</td>\n",
       "      <td>-2.4871e-26</td>\n",
       "      <td>{}</td>\n",
       "      <td>0.3223</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>LinearRegression_UniLinRegression_FS</td>\n",
       "      <td>13903</td>\n",
       "      <td>7</td>\n",
       "      <td>-2.6242e-26</td>\n",
       "      <td>{}</td>\n",
       "      <td>0.1357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>LinearRegression_MutualInfo_FS</td>\n",
       "      <td>13903</td>\n",
       "      <td>6</td>\n",
       "      <td>-2.8775e-26</td>\n",
       "      <td>{}</td>\n",
       "      <td>0.2841</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>LinearRegression_MutualInfo_FS</td>\n",
       "      <td>13903</td>\n",
       "      <td>11</td>\n",
       "      <td>-3.0179e-26</td>\n",
       "      <td>{}</td>\n",
       "      <td>0.4311</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>LinearRegression_ADS</td>\n",
       "      <td>5049</td>\n",
       "      <td>21</td>\n",
       "      <td>-3.0670e-26</td>\n",
       "      <td>{'copy_X': True, 'fit_intercept': True, 'n_jobs': 1, 'normalize': False}</td>\n",
       "      <td>0.2115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>LinearRegression_ADS</td>\n",
       "      <td>3025</td>\n",
       "      <td>21</td>\n",
       "      <td>-3.1277e-26</td>\n",
       "      <td>{'copy_X': True, 'fit_intercept': True, 'n_jobs': 1, 'normalize': False}</td>\n",
       "      <td>0.3789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>LinearRegression_MutualInfo_FS</td>\n",
       "      <td>13903</td>\n",
       "      <td>16</td>\n",
       "      <td>-3.2854e-26</td>\n",
       "      <td>{}</td>\n",
       "      <td>0.3792</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>LinearRegression_RandomForestRegressor_FS</td>\n",
       "      <td>13903</td>\n",
       "      <td>19</td>\n",
       "      <td>-3.3577e-26</td>\n",
       "      <td>{}</td>\n",
       "      <td>0.3721</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>LinearRegression_ADS</td>\n",
       "      <td>7074</td>\n",
       "      <td>21</td>\n",
       "      <td>-3.4258e-26</td>\n",
       "      <td>{'copy_X': True, 'fit_intercept': True, 'n_jobs': 1, 'normalize': False}</td>\n",
       "      <td>0.3309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>LinearRegression_MutualInfo_FS</td>\n",
       "      <td>13903</td>\n",
       "      <td>13</td>\n",
       "      <td>-3.4879e-26</td>\n",
       "      <td>{}</td>\n",
       "      <td>0.2051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>LinearRegression_ADS</td>\n",
       "      <td>6870</td>\n",
       "      <td>21</td>\n",
       "      <td>-3.5710e-26</td>\n",
       "      <td>{'copy_X': True, 'fit_intercept': True, 'n_jobs': 1, 'normalize': False}</td>\n",
       "      <td>0.4556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>LinearRegression_RandomForestRegressor_FS</td>\n",
       "      <td>13903</td>\n",
       "      <td>9</td>\n",
       "      <td>-3.6622e-26</td>\n",
       "      <td>{}</td>\n",
       "      <td>0.1940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>LinearRegression_AVGRanking_FS</td>\n",
       "      <td>13903</td>\n",
       "      <td>9</td>\n",
       "      <td>-3.7706e-26</td>\n",
       "      <td>{}</td>\n",
       "      <td>0.5260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>LinearRegression_ADS</td>\n",
       "      <td>4244</td>\n",
       "      <td>21</td>\n",
       "      <td>-3.8192e-26</td>\n",
       "      <td>{'copy_X': True, 'fit_intercept': True, 'n_jobs': 1, 'normalize': False}</td>\n",
       "      <td>0.1734</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>LinearRegression_AdaBoostRegressor_FS</td>\n",
       "      <td>13903</td>\n",
       "      <td>21</td>\n",
       "      <td>-3.8286e-26</td>\n",
       "      <td>{}</td>\n",
       "      <td>0.2313</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>LinearRegression_ADS</td>\n",
       "      <td>11122</td>\n",
       "      <td>21</td>\n",
       "      <td>-3.8286e-26</td>\n",
       "      <td>{'copy_X': True, 'fit_intercept': True, 'n_jobs': 1, 'normalize': False}</td>\n",
       "      <td>0.4760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>LinearRegression_ADS</td>\n",
       "      <td>9098</td>\n",
       "      <td>21</td>\n",
       "      <td>-3.8712e-26</td>\n",
       "      <td>{'copy_X': True, 'fit_intercept': True, 'n_jobs': 1, 'normalize': False}</td>\n",
       "      <td>0.3839</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>LinearRegression_MutualInfo_FS</td>\n",
       "      <td>13903</td>\n",
       "      <td>9</td>\n",
       "      <td>-3.9615e-26</td>\n",
       "      <td>{}</td>\n",
       "      <td>0.2306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>LinearRegression_ADS</td>\n",
       "      <td>1000</td>\n",
       "      <td>21</td>\n",
       "      <td>-4.5854e-26</td>\n",
       "      <td>{'copy_X': True, 'fit_intercept': True, 'n_jobs': 1, 'normalize': False}</td>\n",
       "      <td>0.0997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>LinearRegression_AdaBoostRegressor_FS</td>\n",
       "      <td>13903</td>\n",
       "      <td>4</td>\n",
       "      <td>-5.0518e-26</td>\n",
       "      <td>{}</td>\n",
       "      <td>0.1375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>LinearRegression_ADS</td>\n",
       "      <td>2621</td>\n",
       "      <td>21</td>\n",
       "      <td>-5.2578e-26</td>\n",
       "      <td>{'copy_X': True, 'fit_intercept': True, 'n_jobs': 1, 'normalize': False}</td>\n",
       "      <td>0.1511</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>LinearRegression_MutualInfo_FS</td>\n",
       "      <td>13903</td>\n",
       "      <td>19</td>\n",
       "      <td>-5.4701e-26</td>\n",
       "      <td>{}</td>\n",
       "      <td>0.6191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>LinearRegression_AS</td>\n",
       "      <td>5000</td>\n",
       "      <td>21</td>\n",
       "      <td>-5.8598e-26</td>\n",
       "      <td>{}</td>\n",
       "      <td>0.3273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>LinearRegression_ADS</td>\n",
       "      <td>1619</td>\n",
       "      <td>21</td>\n",
       "      <td>-6.1108e-26</td>\n",
       "      <td>{'copy_X': True, 'fit_intercept': True, 'n_jobs': 1, 'normalize': False}</td>\n",
       "      <td>0.1997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>LinearRegression_RandomForestRegressor_FS</td>\n",
       "      <td>13903</td>\n",
       "      <td>11</td>\n",
       "      <td>-6.2106e-26</td>\n",
       "      <td>{}</td>\n",
       "      <td>0.3474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>LinearRegression_RandomForestRegressor_FS</td>\n",
       "      <td>13903</td>\n",
       "      <td>16</td>\n",
       "      <td>-7.1325e-26</td>\n",
       "      <td>{}</td>\n",
       "      <td>0.3886</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>LinearRegression_AdaBoostRegressor_FS</td>\n",
       "      <td>13903</td>\n",
       "      <td>6</td>\n",
       "      <td>-7.1683e-26</td>\n",
       "      <td>{}</td>\n",
       "      <td>0.1656</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>LinearRegression_RandomForestRegressor_FS</td>\n",
       "      <td>13903</td>\n",
       "      <td>6</td>\n",
       "      <td>-1.1080e-25</td>\n",
       "      <td>{}</td>\n",
       "      <td>0.3538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>LinearRegression_RandomForestRegressor_FS</td>\n",
       "      <td>13903</td>\n",
       "      <td>13</td>\n",
       "      <td>-1.4559e-25</td>\n",
       "      <td>{}</td>\n",
       "      <td>0.2282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>LinearRegression_MutualInfo_FS</td>\n",
       "      <td>13903</td>\n",
       "      <td>7</td>\n",
       "      <td>-2.7368e-25</td>\n",
       "      <td>{}</td>\n",
       "      <td>0.1773</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>LinearSVR_AS</td>\n",
       "      <td>5000</td>\n",
       "      <td>21</td>\n",
       "      <td>-7.5403e-08</td>\n",
       "      <td>{'C': 1.0}</td>\n",
       "      <td>0.1391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>XGBRegressor_AS</td>\n",
       "      <td>5000</td>\n",
       "      <td>21</td>\n",
       "      <td>-1.4074e+01</td>\n",
       "      <td>{'booster': 'gbtree', 'learning_rate': 0.1, 'max_depth': 6, 'min_child_weight': 1, 'n_estimators': 100, 'reg_alpha': 0, 'reg_lambda': 1}</td>\n",
       "      <td>22.7032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>RandomForestRegressor_AS</td>\n",
       "      <td>5000</td>\n",
       "      <td>21</td>\n",
       "      <td>-2.1423e+01</td>\n",
       "      <td>{'n_estimators': 100, 'max_features': 0.777777778, 'min_samples_leaf': 0.00015, 'min_samples_split': 0.0003}</td>\n",
       "      <td>62.7026</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>LGBMRegressor_AS</td>\n",
       "      <td>5000</td>\n",
       "      <td>21</td>\n",
       "      <td>-2.3087e+01</td>\n",
       "      <td>{'boosting_type': 'gbdt', 'learning_rate': 0.1, 'max_depth': 63, 'min_child_weight': 0.001, 'n_estimators': 100, 'num_leaves': 31, 'reg_alpha': 0, 'reg_lambda': 0, 'subsample': 1, 'colsample_bytree': 1}</td>\n",
       "      <td>11.1910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>ExtraTreesRegressor_AS</td>\n",
       "      <td>5000</td>\n",
       "      <td>21</td>\n",
       "      <td>-2.8736e+01</td>\n",
       "      <td>{'n_estimators': 100, 'max_features': 0.777777778, 'min_samples_leaf': 0.000625, 'min_samples_split': 0.00125}</td>\n",
       "      <td>30.6317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>DecisionTreeRegressor_AS</td>\n",
       "      <td>5000</td>\n",
       "      <td>21</td>\n",
       "      <td>-9.8100e+01</td>\n",
       "      <td>{'max_features': 1.0, 'min_samples_leaf': 0.002, 'min_samples_split': 0.004}</td>\n",
       "      <td>0.9762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>AdaBoostRegressor_AS</td>\n",
       "      <td>5000</td>\n",
       "      <td>21</td>\n",
       "      <td>-5.5526e+02</td>\n",
       "      <td>{'n_estimators': 50, 'learning_rate': 0.667}</td>\n",
       "      <td>15.0100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>LinearRegression_AdaBoostRegressor_FS</td>\n",
       "      <td>13903</td>\n",
       "      <td>1</td>\n",
       "      <td>-1.6441e+03</td>\n",
       "      <td>{}</td>\n",
       "      <td>0.1790</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": "coordinated-nevada",
   "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": "fundamental-edmonton",
   "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": "integral-sodium",
   "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": "stuffed-syndicate",
   "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": "manufactured-mexican",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:classic_cpu]",
   "language": "python",
   "name": "conda-env-classic_cpu-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}