docs/KGFarm_tutorial.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-06-24T22:49:37.821235Z",
     "start_time": "2023-06-24T22:49:29.666184Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KGFarm is running in HUMAN-IN-THE-LOOP mode!\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "os.chdir('../')\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from interface.apis import KGFarm\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "RANDOM_STATE = 7\n",
    "kgfarm = KGFarm()\n",
    "np.random.seed(RANDOM_STATE)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load dataset as a Dataframe\n",
    "(for this tutorial we use the <a href='https://www.kaggle.com/c/titanic'>Titanic</a> dataset from <a href='https://www.kaggle.com/'>Kaggle</a>)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-06-24T22:52:57.142985Z",
     "start_time": "2023-06-24T22:52:57.057697Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Thayer)</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\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",
       "      <th>886</th>\n",
       "      <td>887</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Montvila, Rev. Juozas</td>\n",
       "      <td>male</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>211536</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>888</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Graham, Miss. Margaret Edith</td>\n",
       "      <td>female</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>112053</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>B42</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>889</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Johnston, Miss. Catherine Helen \"Carrie\"</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>W./C. 6607</td>\n",
       "      <td>23.4500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>890</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Behr, Mr. Karl Howell</td>\n",
       "      <td>male</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>111369</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>C148</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>891</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Dooley, Mr. Patrick</td>\n",
       "      <td>male</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>370376</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass  \\\n",
       "0              1         0       3   \n",
       "1              2         1       1   \n",
       "2              3         1       3   \n",
       "3              4         1       1   \n",
       "4              5         0       3   \n",
       "..           ...       ...     ...   \n",
       "886          887         0       2   \n",
       "887          888         1       1   \n",
       "888          889         0       3   \n",
       "889          890         1       1   \n",
       "890          891         0       3   \n",
       "\n",
       "                                                    Name     Sex   Age  SibSp  \\\n",
       "0                                Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1    Cumings, Mrs. John Bradley (Florence Briggs Thayer)  female  38.0      1   \n",
       "2                                 Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3           Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                               Allen, Mr. William Henry    male  35.0      0   \n",
       "..                                                   ...     ...   ...    ...   \n",
       "886                                Montvila, Rev. Juozas    male  27.0      0   \n",
       "887                         Graham, Miss. Margaret Edith  female  19.0      0   \n",
       "888             Johnston, Miss. Catherine Helen \"Carrie\"  female   NaN      1   \n",
       "889                                Behr, Mr. Karl Howell    male  26.0      0   \n",
       "890                                  Dooley, Mr. Patrick    male  32.0      0   \n",
       "\n",
       "     Parch            Ticket     Fare Cabin Embarked  \n",
       "0        0         A/5 21171   7.2500   NaN        S  \n",
       "1        0          PC 17599  71.2833   C85        C  \n",
       "2        0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3        0            113803  53.1000  C123        S  \n",
       "4        0            373450   8.0500   NaN        S  \n",
       "..     ...               ...      ...   ...      ...  \n",
       "886      0            211536  13.0000   NaN        S  \n",
       "887      0            112053  30.0000   B42        S  \n",
       "888      2        W./C. 6607  23.4500   NaN        S  \n",
       "889      0            111369  30.0000  C148        C  \n",
       "890      0            370376   7.7500   NaN        Q  \n",
       "\n",
       "[891 rows x 12 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = kgfarm.load_titanic_dataset()\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Cleaning recommendations "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-06-24T22:53:04.741756Z",
     "start_time": "2023-06-24T22:52:59.496056Z"
    },
    "scrolled": false
   },
   "outputs": [
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",
      "text/plain": [
       "<Figure size 4500x2100 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1800x900 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5/5 [==============================] - 1s 43ms/step\n",
      "1/1 [==============================] - 1s 607ms/step\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Recommended_operation</th>\n",
       "      <th>Confidence</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Fill</td>\n",
       "      <td>0.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Interpolate</td>\n",
       "      <td>0.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Impute</td>\n",
       "      <td>0.30</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Recommended_operation  Confidence\n",
       "0                  Fill        0.69\n",
       "1           Interpolate        0.31\n",
       "2                Impute        0.30"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cleaning_info = kgfarm.recommend_cleaning_operations(df)\n",
    "cleaning_info"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Clean data directly using KGFarm (no explicit code needed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-06-24T22:53:08.519704Z",
     "start_time": "2023-06-24T22:53:08.111221Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cleaning by Fill\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "774eca3d5fe449149c70c70108eb9d8f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/3 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
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       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
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       "      <td>male</td>\n",
       "      <td>22.000000</td>\n",
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       "      <td>0</td>\n",
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       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Thayer)</td>\n",
       "      <td>female</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
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       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <td>53.1000</td>\n",
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       "      <td>Allen, Mr. William Henry</td>\n",
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       "      <td>35.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>8.0500</td>\n",
       "      <td>B96 B98</td>\n",
       "      <td>S</td>\n",
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       "      <th>...</th>\n",
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       "      <th>886</th>\n",
       "      <td>887</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Montvila, Rev. Juozas</td>\n",
       "      <td>male</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>211536</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>B96 B98</td>\n",
       "      <td>S</td>\n",
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       "      <th>887</th>\n",
       "      <td>888</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Graham, Miss. Margaret Edith</td>\n",
       "      <td>female</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>112053</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>B42</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>889</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Johnston, Miss. Catherine Helen \"Carrie\"</td>\n",
       "      <td>female</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>W./C. 6607</td>\n",
       "      <td>23.4500</td>\n",
       "      <td>B96 B98</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>890</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Behr, Mr. Karl Howell</td>\n",
       "      <td>male</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>111369</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>C148</td>\n",
       "      <td>C</td>\n",
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       "      <th>890</th>\n",
       "      <td>891</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Dooley, Mr. Patrick</td>\n",
       "      <td>male</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>370376</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>B96 B98</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass  \\\n",
       "0              1         0       3   \n",
       "1              2         1       1   \n",
       "2              3         1       3   \n",
       "3              4         1       1   \n",
       "4              5         0       3   \n",
       "..           ...       ...     ...   \n",
       "886          887         0       2   \n",
       "887          888         1       1   \n",
       "888          889         0       3   \n",
       "889          890         1       1   \n",
       "890          891         0       3   \n",
       "\n",
       "                                                    Name     Sex        Age  \\\n",
       "0                                Braund, Mr. Owen Harris    male  22.000000   \n",
       "1    Cumings, Mrs. John Bradley (Florence Briggs Thayer)  female  38.000000   \n",
       "2                                 Heikkinen, Miss. Laina  female  26.000000   \n",
       "3           Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.000000   \n",
       "4                               Allen, Mr. William Henry    male  35.000000   \n",
       "..                                                   ...     ...        ...   \n",
       "886                                Montvila, Rev. Juozas    male  27.000000   \n",
       "887                         Graham, Miss. Margaret Edith  female  19.000000   \n",
       "888             Johnston, Miss. Catherine Helen \"Carrie\"  female  29.699118   \n",
       "889                                Behr, Mr. Karl Howell    male  26.000000   \n",
       "890                                  Dooley, Mr. Patrick    male  32.000000   \n",
       "\n",
       "     SibSp  Parch            Ticket     Fare    Cabin Embarked  \n",
       "0        1      0         A/5 21171   7.2500  B96 B98        S  \n",
       "1        1      0          PC 17599  71.2833      C85        C  \n",
       "2        0      0  STON/O2. 3101282   7.9250  B96 B98        S  \n",
       "3        1      0            113803  53.1000     C123        S  \n",
       "4        0      0            373450   8.0500  B96 B98        S  \n",
       "..     ...    ...               ...      ...      ...      ...  \n",
       "886      0      0            211536  13.0000  B96 B98        S  \n",
       "887      0      0            112053  30.0000      B42        S  \n",
       "888      1      2        W./C. 6607  23.4500  B96 B98        S  \n",
       "889      0      0            111369  30.0000     C148        C  \n",
       "890      0      0            370376   7.7500  B96 B98        Q  \n",
       "\n",
       "[891 rows x 12 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = kgfarm.clean(df=df, recommendation=cleaning_info.iloc[0])\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Seperate independent (features or 'X') and dependent variable (target or 'y') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-06-24T22:53:11.547908Z",
     "start_time": "2023-06-24T22:53:11.540071Z"
    }
   },
   "outputs": [
    {
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       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>887</td>\n",
       "      <td>2</td>\n",
       "      <td>Montvila, Rev. Juozas</td>\n",
       "      <td>male</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>211536</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>B96 B98</td>\n",
       "      <td>S</td>\n",
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       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>888</td>\n",
       "      <td>1</td>\n",
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       "      <td>female</td>\n",
       "      <td>19.000000</td>\n",
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       "      <td>0</td>\n",
       "      <td>112053</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>B42</td>\n",
       "      <td>S</td>\n",
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       "      <th>888</th>\n",
       "      <td>889</td>\n",
       "      <td>3</td>\n",
       "      <td>Johnston, Miss. Catherine Helen \"Carrie\"</td>\n",
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       "      <td>29.699118</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>W./C. 6607</td>\n",
       "      <td>23.4500</td>\n",
       "      <td>B96 B98</td>\n",
       "      <td>S</td>\n",
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       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>890</td>\n",
       "      <td>1</td>\n",
       "      <td>Behr, Mr. Karl Howell</td>\n",
       "      <td>male</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>111369</td>\n",
       "      <td>30.0000</td>\n",
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       "      <th>890</th>\n",
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       "      <td>male</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>370376</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>B96 B98</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Pclass                                                 Name  \\\n",
       "0              1       3                              Braund, Mr. Owen Harris   \n",
       "1              2       1  Cumings, Mrs. John Bradley (Florence Briggs Thayer)   \n",
       "2              3       3                               Heikkinen, Miss. Laina   \n",
       "3              4       1         Futrelle, Mrs. Jacques Heath (Lily May Peel)   \n",
       "4              5       3                             Allen, Mr. William Henry   \n",
       "..           ...     ...                                                  ...   \n",
       "886          887       2                                Montvila, Rev. Juozas   \n",
       "887          888       1                         Graham, Miss. Margaret Edith   \n",
       "888          889       3             Johnston, Miss. Catherine Helen \"Carrie\"   \n",
       "889          890       1                                Behr, Mr. Karl Howell   \n",
       "890          891       3                                  Dooley, Mr. Patrick   \n",
       "\n",
       "        Sex        Age  SibSp  Parch            Ticket     Fare    Cabin  \\\n",
       "0      male  22.000000      1      0         A/5 21171   7.2500  B96 B98   \n",
       "1    female  38.000000      1      0          PC 17599  71.2833      C85   \n",
       "2    female  26.000000      0      0  STON/O2. 3101282   7.9250  B96 B98   \n",
       "3    female  35.000000      1      0            113803  53.1000     C123   \n",
       "4      male  35.000000      0      0            373450   8.0500  B96 B98   \n",
       "..      ...        ...    ...    ...               ...      ...      ...   \n",
       "886    male  27.000000      0      0            211536  13.0000  B96 B98   \n",
       "887  female  19.000000      0      0            112053  30.0000      B42   \n",
       "888  female  29.699118      1      2        W./C. 6607  23.4500  B96 B98   \n",
       "889    male  26.000000      0      0            111369  30.0000     C148   \n",
       "890    male  32.000000      0      0            370376   7.7500  B96 B98   \n",
       "\n",
       "    Embarked  \n",
       "0          S  \n",
       "1          C  \n",
       "2          S  \n",
       "3          S  \n",
       "4          S  \n",
       "..       ...  \n",
       "886        S  \n",
       "887        S  \n",
       "888        S  \n",
       "889        C  \n",
       "890        Q  \n",
       "\n",
       "[891 rows x 11 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = df.drop('Survived', axis=1)\n",
    "y = df['Survived']\n",
    "X"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Use KGFarm for Data Transformation recommendations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-06-24T22:53:15.575120Z",
     "start_time": "2023-06-24T22:53:14.389525Z"
    }
   },
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>Feature</th>\n",
       "      <th>Recommended_transformation</th>\n",
       "      <th>Transformation_type</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>[Cabin, Embarked, Name, Sex, Ticket]</td>\n",
       "      <td>OrdinalEncoder</td>\n",
       "      <td>categorical</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>[PassengerId, Pclass, Name, Sex, Age, SibSp, Parch, Ticket, Fare, Cabin, Embarked]</td>\n",
       "      <td>StandardScaler</td>\n",
       "      <td>scaling</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>[Age, Fare, PassengerId, Pclass]</td>\n",
       "      <td>Log</td>\n",
       "      <td>unary</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                              Feature  \\\n",
       "0                                                [Cabin, Embarked, Name, Sex, Ticket]   \n",
       "1  [PassengerId, Pclass, Name, Sex, Age, SibSp, Parch, Ticket, Fare, Cabin, Embarked]   \n",
       "2                                                    [Age, Fare, PassengerId, Pclass]   \n",
       "\n",
       "  Recommended_transformation Transformation_type  \n",
       "0             OrdinalEncoder         categorical  \n",
       "1             StandardScaler             scaling  \n",
       "2                        Log               unary  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transformation_info = kgfarm.recommend_transformations(X=X)\n",
    "transformation_info"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Applying transformations directly using KGFarm (no explicit code needed)\n",
    "It is recommended to apply categorical transformations followed by scaling and unary transformations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-06-24T22:53:15.593601Z",
     "start_time": "2023-06-24T22:53:15.582923Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Applying OrdinalEncoder on ['Cabin', 'Embarked', 'Name', 'Sex', 'Ticket']\n"
     ]
    },
    {
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       "     PassengerId  Pclass   Name  Sex        Age  SibSp  Parch  Ticket  \\\n",
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       "890          891       3  220.0  1.0  32.000000      0      0   466.0   \n",
       "\n",
       "        Fare  Cabin  Embarked  \n",
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       "..       ...    ...       ...  \n",
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       "889  30.0000   60.0       0.0  \n",
       "890   7.7500   47.0       1.0  \n",
       "\n",
       "[891 rows x 11 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X, encoder = kgfarm.apply_transformations(X=X, recommendation=transformation_info.iloc[0])\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
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    "ExecuteTime": {
     "end_time": "2023-06-24T22:53:16.200785Z",
     "start_time": "2023-06-24T22:53:16.182782Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Applying StandardScaler on ['PassengerId', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp', 'Parch', 'Ticket', 'Fare', 'Cabin', 'Embarked']\n"
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       "     PassengerId    Pclass      Name       Sex       Age     SibSp     Parch  \\\n",
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       "4      -1.714556  0.827377 -1.671790  0.737695  0.407926 -0.474545 -0.473674   \n",
       "..           ...       ...       ...       ...       ...       ...       ...   \n",
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       "889     1.726220 -1.566107 -1.415189  0.737695 -0.284663 -0.474545 -0.473674   \n",
       "890     1.730108  0.827377 -0.874774  0.737695  0.177063 -0.474545 -0.473674   \n",
       "\n",
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       "2    1.646283 -0.488854 -0.281881  0.585954  \n",
       "3   -1.442322  0.420730  0.057748  0.585954  \n",
       "4    0.664904 -0.486337 -0.281881  0.585954  \n",
       "..        ...       ...       ...       ...  \n",
       "886 -1.183277 -0.386671 -0.281881  0.585954  \n",
       "887 -1.616678 -0.044381 -1.003594  0.585954  \n",
       "888  1.676173 -0.176263 -0.281881  0.585954  \n",
       "889 -1.646568 -0.044381  0.270017 -1.942303  \n",
       "890  0.635014 -0.492378 -0.281881 -0.678175  \n",
       "\n",
       "[891 rows x 11 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X, scaler = kgfarm.apply_transformations(X=X, recommendation=transformation_info.iloc[1])\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-06-24T22:53:16.708747Z",
     "start_time": "2023-06-24T22:53:16.685084Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Applying Log on ['Age', 'Fare', 'PassengerId', 'Pclass']\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "aeaeb9354f3b4ebda14815b3c6af4474",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/4 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
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       "      <td>1.282625</td>\n",
       "      <td>0.361421</td>\n",
       "      <td>1.161545</td>\n",
       "      <td>-1.942303</td>\n",
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       "      <td>-0.357685</td>\n",
       "      <td>-1.355574</td>\n",
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       "      <td>-0.474545</td>\n",
       "      <td>-0.473674</td>\n",
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       "      <td>-1.834662</td>\n",
       "      <td>-0.281881</td>\n",
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       "      <td>-9.210340</td>\n",
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       "      <td>-1.355574</td>\n",
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       "      <td>-0.473674</td>\n",
       "      <td>-1.442322</td>\n",
       "      <td>0.066959</td>\n",
       "      <td>0.057748</td>\n",
       "      <td>0.585954</td>\n",
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       "      <td>-4.157186</td>\n",
       "      <td>0.872792</td>\n",
       "      <td>-1.671790</td>\n",
       "      <td>0.737695</td>\n",
       "      <td>0.978770</td>\n",
       "      <td>-0.474545</td>\n",
       "      <td>-0.473674</td>\n",
       "      <td>0.664904</td>\n",
       "      <td>-1.819022</td>\n",
       "      <td>-0.281881</td>\n",
       "      <td>0.585954</td>\n",
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       "      <th>...</th>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>1.236856</td>\n",
       "      <td>0.179686</td>\n",
       "      <td>0.400452</td>\n",
       "      <td>0.737695</td>\n",
       "      <td>0.715665</td>\n",
       "      <td>-0.474545</td>\n",
       "      <td>-0.473674</td>\n",
       "      <td>-1.183277</td>\n",
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       "      <td>-0.281881</td>\n",
       "      <td>0.585954</td>\n",
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       "      <td>1.237984</td>\n",
       "      <td>-9.210340</td>\n",
       "      <td>-0.552079</td>\n",
       "      <td>-1.355574</td>\n",
       "      <td>0.357613</td>\n",
       "      <td>-0.474545</td>\n",
       "      <td>-0.473674</td>\n",
       "      <td>-1.616678</td>\n",
       "      <td>-0.503948</td>\n",
       "      <td>-1.003594</td>\n",
       "      <td>0.585954</td>\n",
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       "      <td>0.872792</td>\n",
       "      <td>-0.124412</td>\n",
       "      <td>-1.355574</td>\n",
       "      <td>0.812376</td>\n",
       "      <td>0.432793</td>\n",
       "      <td>2.008933</td>\n",
       "      <td>1.676173</td>\n",
       "      <td>-0.750229</td>\n",
       "      <td>-0.281881</td>\n",
       "      <td>0.585954</td>\n",
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       "      <th>889</th>\n",
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       "      <td>-9.210340</td>\n",
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       "      <td>-0.474545</td>\n",
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       "      <td>-0.503948</td>\n",
       "      <td>0.270017</td>\n",
       "      <td>-1.942303</td>\n",
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       "      <th>890</th>\n",
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       "      <td>0.872792</td>\n",
       "      <td>-0.874774</td>\n",
       "      <td>0.737695</td>\n",
       "      <td>0.888022</td>\n",
       "      <td>-0.474545</td>\n",
       "      <td>-0.473674</td>\n",
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       "<p>891 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId    Pclass      Name       Sex       Age     SibSp     Parch  \\\n",
       "0      -9.210340  0.872792 -1.310217  0.737695  0.507284  0.432793 -0.473674   \n",
       "1      -5.524495 -9.210340 -0.991410 -1.355574  1.061964  0.432793 -0.473674   \n",
       "2      -4.843965  0.872792 -0.357685 -1.355574  0.677319 -0.474545 -0.473674   \n",
       "3      -4.442741 -9.210340 -0.672604 -1.355574  0.978770  0.432793 -0.473674   \n",
       "4      -4.157186  0.872792 -1.671790  0.737695  0.978770 -0.474545 -0.473674   \n",
       "..           ...       ...       ...       ...       ...       ...       ...   \n",
       "886     1.236856  0.179686  0.400452  0.737695  0.715665 -0.474545 -0.473674   \n",
       "887     1.237984 -9.210340 -0.552079 -1.355574  0.357613 -0.474545 -0.473674   \n",
       "888     1.239110  0.872792 -0.124412 -1.355574  0.812376  0.432793  2.008933   \n",
       "889     1.240236 -9.210340 -1.415189  0.737695  0.677319 -0.474545 -0.473674   \n",
       "890     1.241360  0.872792 -0.874774  0.737695  0.888022 -0.474545 -0.473674   \n",
       "\n",
       "       Ticket      Fare     Cabin  Embarked  \n",
       "0    0.918966 -1.923625 -0.281881  0.585954  \n",
       "1    1.282625  0.361421  1.161545 -1.942303  \n",
       "2    1.646283 -1.834662 -0.281881  0.585954  \n",
       "3   -1.442322  0.066959  0.057748  0.585954  \n",
       "4    0.664904 -1.819022 -0.281881  0.585954  \n",
       "..        ...       ...       ...       ...  \n",
       "886 -1.183277 -1.339980 -0.281881  0.585954  \n",
       "887 -1.616678 -0.503948 -1.003594  0.585954  \n",
       "888  1.676173 -0.750229 -0.281881  0.585954  \n",
       "889 -1.646568 -0.503948  0.270017 -1.942303  \n",
       "890  0.635014 -1.856978 -0.281881 -0.678175  \n",
       "\n",
       "[891 rows x 11 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X, u_transformation = kgfarm.apply_transformations(X=X, recommendation=transformation_info.iloc[2])\n",
    "X"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Select subset of features for predicting target (if needed)\n",
    "this is recommended for datasets with > 100 features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-06-24T22:53:17.963544Z",
     "start_time": "2023-06-24T22:53:17.648900Z"
    }
   },
   "outputs": [],
   "source": [
    "features = kgfarm.recommend_features_to_be_selected(task='binary', X=X, y=y)\n",
    "X = X[features['Feature']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Split data into Train and Test sets "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-06-24T22:53:19.024587Z",
     "start_time": "2023-06-24T22:53:19.013212Z"
    }
   },
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=RANDOM_STATE)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train & Evaluate model performance "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-06-24T22:53:20.811206Z",
     "start_time": "2023-06-24T22:53:20.670661Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 81.0%\n"
     ]
    }
   ],
   "source": [
    "model = RandomForestClassifier(random_state=RANDOM_STATE)\n",
    "model.fit(X_train, y_train)\n",
    "print(f'Accuracy: {model.score(X_test, y_test)*100:.1f}%')"
   ]
  }
 ],
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