KGLiDS_DataPrep_notebook.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## <b>KGLiDS APIs</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from gnn_applications.OnDemandDataPrep.api import KGLiDSDataPrep\n",
    "kglids_dataprep = KGLiDSDataPrep(endpoint='http://localhost:7200', db='kaggle_demo')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('experiments/OnDemandDataPrep_experiments/Cleaning_datasets/hepatitis.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
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       "     Class  AGE  SEX  STEROID  ANTIVIRALS  FATIGUE  MALAISE  ANOREXIA  \\\n",
       "0        0   30    2      1.0           2      2.0      2.0       2.0   \n",
       "1        0   50    1      1.0           2      1.0      2.0       2.0   \n",
       "2        0   78    1      2.0           2      1.0      2.0       2.0   \n",
       "3        0   31    1      NaN           1      2.0      2.0       2.0   \n",
       "4        0   34    1      2.0           2      2.0      2.0       2.0   \n",
       "..     ...  ...  ...      ...         ...      ...      ...       ...   \n",
       "150      1   46    1      2.0           2      1.0      1.0       1.0   \n",
       "151      0   44    1      2.0           2      1.0      2.0       2.0   \n",
       "152      0   61    1      1.0           2      1.0      1.0       2.0   \n",
       "153      0   53    2      1.0           2      1.0      2.0       2.0   \n",
       "154      1   43    1      2.0           2      1.0      2.0       2.0   \n",
       "\n",
       "     LIVER BIG  LIVER FIRM  SPLEEN PALPABLE  SPIDERS  ASCITES  VARICES  \\\n",
       "0          1.0         2.0              2.0      2.0      2.0      2.0   \n",
       "1          1.0         2.0              2.0      2.0      2.0      2.0   \n",
       "2          2.0         2.0              2.0      2.0      2.0      2.0   \n",
       "3          2.0         2.0              2.0      2.0      2.0      2.0   \n",
       "4          2.0         2.0              2.0      2.0      2.0      2.0   \n",
       "..         ...         ...              ...      ...      ...      ...   \n",
       "150        2.0         2.0              2.0      1.0      1.0      1.0   \n",
       "151        2.0         1.0              2.0      2.0      2.0      2.0   \n",
       "152        1.0         1.0              2.0      1.0      2.0      2.0   \n",
       "153        2.0         2.0              1.0      1.0      2.0      1.0   \n",
       "154        2.0         2.0              1.0      1.0      1.0      2.0   \n",
       "\n",
       "     BILIRUBIN  ALK PHOSPHATE   SGOT  ALBUMIN  PROTIME  HISTOLOGY  \n",
       "0          1.0           85.0   18.0      4.0      NaN          1  \n",
       "1          0.9          135.0   42.0      3.5      NaN          1  \n",
       "2          0.7           96.0   32.0      4.0      NaN          1  \n",
       "3          0.7           46.0   52.0      4.0     80.0          1  \n",
       "4          1.0            NaN  200.0      4.0      NaN          1  \n",
       "..         ...            ...    ...      ...      ...        ...  \n",
       "150        7.6            NaN  242.0      3.3     50.0          2  \n",
       "151        0.9          126.0  142.0      4.3      NaN          2  \n",
       "152        0.8           75.0   20.0      4.1      NaN          2  \n",
       "153        1.5           81.0   19.0      4.1     48.0          2  \n",
       "154        1.2          100.0   19.0      3.1     42.0          2  \n",
       "\n",
       "[155 rows x 20 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b> Cleaning Recommendation</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Encoding Completed!\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Downloading registry model 'cleaning', version None, stage None from workspace 'nikimonjazeb'...\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Unzipping model to 'C:\\\\Users\\\\niki_\\\\OneDrive\\\\Documents\\\\GitHub\\\\kglids\\\\gnn_applications\\\\OnDemandDataPrep\\\\Models' ...\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m done!\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The recommended cleaning operation is:  Interpolate\n"
     ]
    }
   ],
   "source": [
    "cl_op=kglids_dataprep.recommend_cleaning_operations(df,'hepatitis')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "all features look clean\n"
     ]
    }
   ],
   "source": [
    "clean_df = kglids_dataprep.apply_cleaning_operations(cl_op,df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
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       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.7</td>\n",
       "      <td>96.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.50</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.7</td>\n",
       "      <td>46.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>80.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>100.5</td>\n",
       "      <td>200.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.50</td>\n",
       "      <td>1.0</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",
       "      <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>150</th>\n",
       "      <td>1.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.6</td>\n",
       "      <td>124.8</td>\n",
       "      <td>242.0</td>\n",
       "      <td>3.3</td>\n",
       "      <td>50.00</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151</th>\n",
       "      <td>0.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.9</td>\n",
       "      <td>126.0</td>\n",
       "      <td>142.0</td>\n",
       "      <td>4.3</td>\n",
       "      <td>3.15</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>0.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>75.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>4.1</td>\n",
       "      <td>3.05</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153</th>\n",
       "      <td>0.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>81.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>4.1</td>\n",
       "      <td>48.00</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154</th>\n",
       "      <td>1.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>100.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>3.1</td>\n",
       "      <td>42.00</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>155 rows × 20 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Class   AGE  SEX  STEROID  ANTIVIRALS  FATIGUE  MALAISE  ANOREXIA  \\\n",
       "0      0.0  30.0  2.0      1.0         2.0      2.0      2.0       2.0   \n",
       "1      0.0  50.0  1.0      1.0         2.0      1.0      2.0       2.0   \n",
       "2      0.0  78.0  1.0      2.0         2.0      1.0      2.0       2.0   \n",
       "3      0.0  31.0  1.0      1.0         1.0      2.0      2.0       2.0   \n",
       "4      0.0  34.0  1.0      2.0         2.0      2.0      2.0       2.0   \n",
       "..     ...   ...  ...      ...         ...      ...      ...       ...   \n",
       "150    1.0  46.0  1.0      2.0         2.0      1.0      1.0       1.0   \n",
       "151    0.0  44.0  1.0      2.0         2.0      1.0      2.0       2.0   \n",
       "152    0.0  61.0  1.0      1.0         2.0      1.0      1.0       2.0   \n",
       "153    0.0  53.0  2.0      1.0         2.0      1.0      2.0       2.0   \n",
       "154    1.0  43.0  1.0      2.0         2.0      1.0      2.0       2.0   \n",
       "\n",
       "     LIVER BIG  LIVER FIRM  SPLEEN PALPABLE  SPIDERS  ASCITES  VARICES  \\\n",
       "0          1.0         2.0              2.0      2.0      2.0      2.0   \n",
       "1          1.0         2.0              2.0      2.0      2.0      2.0   \n",
       "2          2.0         2.0              2.0      2.0      2.0      2.0   \n",
       "3          2.0         2.0              2.0      2.0      2.0      2.0   \n",
       "4          2.0         2.0              2.0      2.0      2.0      2.0   \n",
       "..         ...         ...              ...      ...      ...      ...   \n",
       "150        2.0         2.0              2.0      1.0      1.0      1.0   \n",
       "151        2.0         1.0              2.0      2.0      2.0      2.0   \n",
       "152        1.0         1.0              2.0      1.0      2.0      2.0   \n",
       "153        2.0         2.0              1.0      1.0      2.0      1.0   \n",
       "154        2.0         2.0              1.0      1.0      1.0      2.0   \n",
       "\n",
       "     BILIRUBIN  ALK PHOSPHATE   SGOT  ALBUMIN  PROTIME  HISTOLOGY  \n",
       "0          1.0           85.0   18.0      4.0     2.50        1.0  \n",
       "1          0.9          135.0   42.0      3.5     2.25        1.0  \n",
       "2          0.7           96.0   32.0      4.0     2.50        1.0  \n",
       "3          0.7           46.0   52.0      4.0    80.00        1.0  \n",
       "4          1.0          100.5  200.0      4.0     2.50        1.0  \n",
       "..         ...            ...    ...      ...      ...        ...  \n",
       "150        7.6          124.8  242.0      3.3    50.00        2.0  \n",
       "151        0.9          126.0  142.0      4.3     3.15        2.0  \n",
       "152        0.8           75.0   20.0      4.1     3.05        2.0  \n",
       "153        1.5           81.0   19.0      4.1    48.00        2.0  \n",
       "154        1.2          100.0   19.0      3.1    42.00        2.0  \n",
       "\n",
       "[155 rows x 20 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clean_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b> Transformation Recommendation</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Encoding Completed!\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Downloading registry model 'scaling-trans', version None, stage None from workspace 'nikimonjazeb'...\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Unzipping model to 'C:\\\\Users\\\\niki_\\\\OneDrive\\\\Documents\\\\GitHub\\\\kglids\\\\gnn_applications\\\\OnDemandDataPrep\\\\Models' ...\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m done!\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Encoding Completed!\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Downloading registry model 'unary-trans', version None, stage None from workspace 'nikimonjazeb'...\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Unzipping model to 'C:\\\\Users\\\\niki_\\\\OneDrive\\\\Documents\\\\GitHub\\\\kglids\\\\gnn_applications\\\\OnDemandDataPrep\\\\Models' ...\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m done!\n"
     ]
    }
   ],
   "source": [
    "trans = kglids_dataprep.recommend_transformation_operations(df,'hepatitis')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
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       "  Recommended_transformation  \\\n",
       "0               RobustScaler   \n",
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   "source": [
    "trans"
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  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Applying RobustScaler\n"
     ]
    }
   ],
   "source": [
    "df_t = kglids_dataprep.apply_transformation_operations(df, trans)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Class</th>\n",
       "      <th>AGE</th>\n",
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       "      <th>STEROID</th>\n",
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       "      <td>0.0</td>\n",
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       "      <td>-0.566112</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.375</td>\n",
       "      <td>0.173228</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>-0.562154</td>\n",
       "      <td>0.0</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",
       "      <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>150</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.388889</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>8.250</td>\n",
       "      <td>0.626772</td>\n",
       "      <td>2.729927</td>\n",
       "      <td>-0.777778</td>\n",
       "      <td>0.190024</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.277778</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.125</td>\n",
       "      <td>0.645669</td>\n",
       "      <td>1.270073</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>-0.551861</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.222222</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.250</td>\n",
       "      <td>-0.157480</td>\n",
       "      <td>-0.510949</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>-0.553444</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.777778</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.625</td>\n",
       "      <td>-0.062992</td>\n",
       "      <td>-0.525547</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.158353</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.222222</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.250</td>\n",
       "      <td>0.236220</td>\n",
       "      <td>-0.525547</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.063341</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>155 rows × 20 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Class       AGE  SEX  STEROID  ANTIVIRALS  FATIGUE  MALAISE  ANOREXIA  \\\n",
       "0      0.0 -0.500000  1.0     -1.0         0.0      1.0      0.0       0.0   \n",
       "1      0.0  0.611111  0.0     -1.0         0.0      0.0      0.0       0.0   \n",
       "2      0.0  2.166667  0.0      0.0         0.0      0.0      0.0       0.0   \n",
       "3      0.0 -0.444444  0.0     -1.0        -1.0      1.0      0.0       0.0   \n",
       "4      0.0 -0.277778  0.0      0.0         0.0      1.0      0.0       0.0   \n",
       "..     ...       ...  ...      ...         ...      ...      ...       ...   \n",
       "150    1.0  0.388889  0.0      0.0         0.0      0.0     -1.0      -1.0   \n",
       "151    0.0  0.277778  0.0      0.0         0.0      0.0      0.0       0.0   \n",
       "152    0.0  1.222222  0.0     -1.0         0.0      0.0     -1.0       0.0   \n",
       "153    0.0  0.777778  1.0     -1.0         0.0      0.0      0.0       0.0   \n",
       "154    1.0  0.222222  0.0      0.0         0.0      0.0      0.0       0.0   \n",
       "\n",
       "     LIVER BIG  LIVER FIRM  SPLEEN PALPABLE  SPIDERS  ASCITES  VARICES  \\\n",
       "0         -1.0         0.0              0.0      0.0      0.0      0.0   \n",
       "1         -1.0         0.0              0.0      0.0      0.0      0.0   \n",
       "2          0.0         0.0              0.0      0.0      0.0      0.0   \n",
       "3          0.0         0.0              0.0      0.0      0.0      0.0   \n",
       "4          0.0         0.0              0.0      0.0      0.0      0.0   \n",
       "..         ...         ...              ...      ...      ...      ...   \n",
       "150        0.0         0.0              0.0     -1.0     -1.0     -1.0   \n",
       "151        0.0        -1.0              0.0      0.0      0.0      0.0   \n",
       "152       -1.0        -1.0              0.0     -1.0      0.0      0.0   \n",
       "153        0.0         0.0             -1.0     -1.0      0.0     -1.0   \n",
       "154        0.0         0.0             -1.0     -1.0     -1.0      0.0   \n",
       "\n",
       "     BILIRUBIN  ALK PHOSPHATE      SGOT   ALBUMIN   PROTIME  HISTOLOGY  \n",
       "0        0.000       0.000000 -0.540146  0.000000 -0.562154        0.0  \n",
       "1       -0.125       0.787402 -0.189781 -0.555556 -0.566112        0.0  \n",
       "2       -0.375       0.173228 -0.335766  0.000000 -0.562154        0.0  \n",
       "3       -0.375      -0.614173 -0.043796  0.000000  0.665083        0.0  \n",
       "4        0.000       0.244094  2.116788  0.000000 -0.562154        0.0  \n",
       "..         ...            ...       ...       ...       ...        ...  \n",
       "150      8.250       0.626772  2.729927 -0.777778  0.190024        1.0  \n",
       "151     -0.125       0.645669  1.270073  0.333333 -0.551861        1.0  \n",
       "152     -0.250      -0.157480 -0.510949  0.111111 -0.553444        1.0  \n",
       "153      0.625      -0.062992 -0.525547  0.111111  0.158353        1.0  \n",
       "154      0.250       0.236220 -0.525547 -1.000000  0.063341        1.0  \n",
       "\n",
       "[155 rows x 20 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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