notebooks/dataprocessing/module_tests/scaler.ipynb

{
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  {
   "cell_type": "code",
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   "id": "193967f5",
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   "source": [
    "import random\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import scipy.stats as stat\n",
    "from sklearn.preprocessing import (\n",
    "    MinMaxScaler,\n",
    "    RobustScaler,\n",
    "    QuantileTransformer\n",
    ")\n",
    "\n",
    "from raimitigations.utils import create_dummy_dataset\n",
    "from raimitigations.dataprocessing import (\n",
    "    BasicImputer,\n",
    "    EncoderOHE,\n",
    "    DataRobustScaler,\n",
    "    DataPowerTransformer,\n",
    "    DataNormalizer,\n",
    "    DataStandardScaler,\n",
    "    DataMinMaxScaler,\n",
    "    DataQuantileTransformer\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a7278259",
   "metadata": {},
   "source": [
    "# Data Scaling\n",
    "\n",
    "In this notebook we will show how to use data transformation classes. Each class applies a different data transformation over the numerical data, and it was designed to allow several transformations to be applied using a simple and easy to use interface. \n",
    "\n",
    "Let's start with the basic scenarios where we use only default values. But before jumping to the different classes, let's first create a dummy dataset with numerical and categorical features, and Nan values scattered along different columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a7fb191f",
   "metadata": {},
   "outputs": [
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       "      <th>CC_1_num_1</th>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.838682</td>\n",
       "      <td>-1.408271</td>\n",
       "      <td>2.758909</td>\n",
       "      <td>-1.558914</td>\n",
       "      <td>0.606338</td>\n",
       "      <td>2.828664</td>\n",
       "      <td>1</td>\n",
       "      <td>1.796571</td>\n",
       "      <td>-1.397257</td>\n",
       "      <td>val0_2</td>\n",
       "      <td>val1_2</td>\n",
       "      <td>val0_1</td>\n",
       "      <td>val1_1</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3.249825</td>\n",
       "      <td>-3.925450</td>\n",
       "      <td>2.953185</td>\n",
       "      <td>3.540991</td>\n",
       "      <td>-2.340552</td>\n",
       "      <td>3.398367</td>\n",
       "      <td>1</td>\n",
       "      <td>3.257607</td>\n",
       "      <td>-3.925562</td>\n",
       "      <td>val0_1</td>\n",
       "      <td>val1_1</td>\n",
       "      <td>val0_1</td>\n",
       "      <td>val1_0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.978148</td>\n",
       "      <td>0.330699</td>\n",
       "      <td>1.483723</td>\n",
       "      <td>3.198539</td>\n",
       "      <td>-4.134112</td>\n",
       "      <td>2.312435</td>\n",
       "      <td>1</td>\n",
       "      <td>1.041164</td>\n",
       "      <td>0.338390</td>\n",
       "      <td>val0_2</td>\n",
       "      <td>val1_3</td>\n",
       "      <td>val0_0</td>\n",
       "      <td>val1_1</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.847425</td>\n",
       "      <td>1.008353</td>\n",
       "      <td>1.192225</td>\n",
       "      <td>3.496178</td>\n",
       "      <td>-1.120895</td>\n",
       "      <td>1.910650</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.875685</td>\n",
       "      <td>1.020226</td>\n",
       "      <td>val0_1</td>\n",
       "      <td>val1_0</td>\n",
       "      <td>val0_0</td>\n",
       "      <td>val1_2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.972314</td>\n",
       "      <td>-2.305080</td>\n",
       "      <td>2.136697</td>\n",
       "      <td>2.224677</td>\n",
       "      <td>-2.409424</td>\n",
       "      <td>2.504838</td>\n",
       "      <td>1</td>\n",
       "      <td>1.012618</td>\n",
       "      <td>-2.295010</td>\n",
       "      <td>val0_2</td>\n",
       "      <td>val1_0</td>\n",
       "      <td>val0_0</td>\n",
       "      <td>val1_1</td>\n",
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       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "      <th>2995</th>\n",
       "      <td>0.740834</td>\n",
       "      <td>-1.171719</td>\n",
       "      <td>0.357037</td>\n",
       "      <td>1.863302</td>\n",
       "      <td>-2.869701</td>\n",
       "      <td>0.658475</td>\n",
       "      <td>1</td>\n",
       "      <td>0.776097</td>\n",
       "      <td>-1.167798</td>\n",
       "      <td>val0_2</td>\n",
       "      <td>val1_2</td>\n",
       "      <td>val0_0</td>\n",
       "      <td>val1_1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2996</th>\n",
       "      <td>1.865602</td>\n",
       "      <td>-1.226943</td>\n",
       "      <td>3.212207</td>\n",
       "      <td>0.961388</td>\n",
       "      <td>-0.925646</td>\n",
       "      <td>2.316213</td>\n",
       "      <td>1</td>\n",
       "      <td>1.848407</td>\n",
       "      <td>-1.231393</td>\n",
       "      <td>val0_2</td>\n",
       "      <td>val1_2</td>\n",
       "      <td>val0_1</td>\n",
       "      <td>val1_1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2997</th>\n",
       "      <td>-0.854474</td>\n",
       "      <td>-2.369469</td>\n",
       "      <td>0.457335</td>\n",
       "      <td>3.216465</td>\n",
       "      <td>-4.680820</td>\n",
       "      <td>3.322093</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.876535</td>\n",
       "      <td>-2.366553</td>\n",
       "      <td>val0_1</td>\n",
       "      <td>val1_1</td>\n",
       "      <td>val0_0</td>\n",
       "      <td>val1_1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2998</th>\n",
       "      <td>0.892994</td>\n",
       "      <td>-2.115929</td>\n",
       "      <td>2.741942</td>\n",
       "      <td>-0.218843</td>\n",
       "      <td>-0.097098</td>\n",
       "      <td>1.893662</td>\n",
       "      <td>1</td>\n",
       "      <td>0.890398</td>\n",
       "      <td>-2.143039</td>\n",
       "      <td>val0_2</td>\n",
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       "    <tr>\n",
       "      <th>2999</th>\n",
       "      <td>1.664427</td>\n",
       "      <td>-1.075205</td>\n",
       "      <td>3.487387</td>\n",
       "      <td>-0.182207</td>\n",
       "      <td>0.033374</td>\n",
       "      <td>2.417933</td>\n",
       "      <td>1</td>\n",
       "      <td>1.654759</td>\n",
       "      <td>-1.052976</td>\n",
       "      <td>val0_2</td>\n",
       "      <td>val1_2</td>\n",
       "      <td>val0_1</td>\n",
       "      <td>val1_1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3000 rows × 13 columns</p>\n",
       "</div>"
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       "         num_0     num_1     num_2     num_3     num_4     num_5  label  \\\n",
       "0     1.838682 -1.408271  2.758909 -1.558914  0.606338  2.828664      1   \n",
       "1     3.249825 -3.925450  2.953185  3.540991 -2.340552  3.398367      1   \n",
       "2     0.978148  0.330699  1.483723  3.198539 -4.134112  2.312435      1   \n",
       "3    -0.847425  1.008353  1.192225  3.496178 -1.120895  1.910650      0   \n",
       "4     0.972314 -2.305080  2.136697  2.224677 -2.409424  2.504838      1   \n",
       "...        ...       ...       ...       ...       ...       ...    ...   \n",
       "2995  0.740834 -1.171719  0.357037  1.863302 -2.869701  0.658475      1   \n",
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       "2998  0.892994 -2.115929  2.741942 -0.218843 -0.097098  1.893662      1   \n",
       "2999  1.664427 -1.075205  3.487387 -0.182207  0.033374  2.417933      1   \n",
       "\n",
       "      num_c0_num_0  num_c1_num_1 CN_0_num_0 CN_1_num_1 CC_0_num_0 CC_1_num_1  \n",
       "0         1.796571     -1.397257     val0_2     val1_2     val0_1     val1_1  \n",
       "1         3.257607     -3.925562     val0_1     val1_1     val0_1     val1_0  \n",
       "2         1.041164      0.338390     val0_2     val1_3     val0_0     val1_1  \n",
       "3        -0.875685      1.020226     val0_1     val1_0     val0_0     val1_2  \n",
       "4         1.012618     -2.295010     val0_2     val1_0     val0_0     val1_1  \n",
       "...            ...           ...        ...        ...        ...        ...  \n",
       "2995      0.776097     -1.167798     val0_2     val1_2     val0_0     val1_1  \n",
       "2996      1.848407     -1.231393     val0_2     val1_2     val0_1     val1_1  \n",
       "2997     -0.876535     -2.366553     val0_1     val1_1     val0_0     val1_1  \n",
       "2998      0.890398     -2.143039     val0_2     val1_1     val0_0     val1_1  \n",
       "2999      1.654759     -1.052976     val0_2     val1_2     val0_1     val1_1  \n",
       "\n",
       "[3000 rows x 13 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# -----------------------------------\n",
    "def add_nan(vec, pct):\n",
    "\tvec = list(vec)\n",
    "\tnan_index = random.sample(range(len(vec)), int(pct*len(vec)))\n",
    "\tfor index in nan_index:\n",
    "\t\tvec[index] = np.nan\n",
    "\treturn vec\n",
    "\n",
    "# -----------------------------------\n",
    "def build_dummy_dataset():\n",
    "\tdf = create_dummy_dataset(\n",
    "\t\t\t\t\tsamples=3000, \n",
    "\t\t\t\t\tn_features=6, \n",
    "\t\t\t\t\tn_num_num=2, \n",
    "\t\t\t\t\tn_cat_num=2,\n",
    "\t\t\t\t\tn_cat_cat=2,\n",
    "\t\t\t\t\tnum_num_noise=[0.01, 0.05],\n",
    "\t\t\t\t\tpct_change=[0.05, 0.1]\n",
    "\t\t\t\t)\n",
    "\tlabel_col = \"label\"\n",
    "\n",
    "\treturn df, label_col\n",
    "\n",
    "df, label_col = build_dummy_dataset()\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
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    {
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     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 3000 entries, 0 to 2999\n",
      "Data columns (total 13 columns):\n",
      " #   Column        Non-Null Count  Dtype  \n",
      "---  ------        --------------  -----  \n",
      " 0   num_0         3000 non-null   float64\n",
      " 1   num_1         3000 non-null   float64\n",
      " 2   num_2         3000 non-null   float64\n",
      " 3   num_3         3000 non-null   float64\n",
      " 4   num_4         3000 non-null   float64\n",
      " 5   num_5         3000 non-null   float64\n",
      " 6   label         3000 non-null   int64  \n",
      " 7   num_c0_num_0  3000 non-null   float64\n",
      " 8   num_c1_num_1  3000 non-null   float64\n",
      " 9   CN_0_num_0    3000 non-null   object \n",
      " 10  CN_1_num_1    3000 non-null   object \n",
      " 11  CC_0_num_0    3000 non-null   object \n",
      " 12  CC_1_num_1    3000 non-null   object \n",
      "dtypes: float64(8), int64(1), object(4)\n",
      "memory usage: 304.8+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1ca494e7",
   "metadata": {},
   "source": [
    "## Basic Use Case: Robust Scaler\n",
    "\n",
    "As we can see, the dataset we are working with has several numerical and several categorical data. Also, all columns have a few missing values. When using data scalers or data transformers using Sklearn, we usually need to provide a dataset with only the numerical columns. This requires the user to separate these columns from the original dataset, and then they need to merge the scaled columns with the categorical columns. We encapsulate all of these processes inside the main scaler class, making it easier to use.\n",
    "\n",
    "Let's see an example of how to use the Robust scaler through the **DataRobustScaler** class implemented in this library. For now, we will use only the default values, with exception of the *exclude_cols* parameter. This parameter is a list of column names or column indexes that should be ignored when doing the scaling process, that is, all columns that should not be scaled. By default, this parameter is set as being the list of all column names associated to categorical columns. But since our label is numerical and we don't want to transform the values in the label column, we will force the label column to be ignored as well (along with the categorical columns). This way, we create the object by passing \"exclude_cols=['label']\". The categorical columns are added internally to the exclude_cols list if they are not already specified.\n",
    "\n",
    "After creating the object of **DataRobustScaler**, we must call the fit() and transform() methods, following the pattern established in this library (which is a pattern followed by several well established machine learning libraries). Here we will fit and transform the data using the same dataset, but remember that we should use the training dataset when calling the fit method and then call the transform method for both training and testing datasets."
   ]
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  {
   "cell_type": "code",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.601353</td>\n",
       "      <td>-0.795141</td>\n",
       "      <td>0.621367</td>\n",
       "      <td>0.807144</td>\n",
       "      <td>-0.197305</td>\n",
       "      <td>0.629804</td>\n",
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       "      <td>-0.796681</td>\n",
       "      <td>val0_1</td>\n",
       "      <td>val1_1</td>\n",
       "      <td>val0_1</td>\n",
       "      <td>val1_0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.175575</td>\n",
       "      <td>0.951508</td>\n",
       "      <td>-0.351287</td>\n",
       "      <td>0.628771</td>\n",
       "      <td>-1.147206</td>\n",
       "      <td>0.140249</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.152420</td>\n",
       "      <td>0.957595</td>\n",
       "      <td>val0_2</td>\n",
       "      <td>val1_3</td>\n",
       "      <td>val0_0</td>\n",
       "      <td>val1_1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.799932</td>\n",
       "      <td>1.229606</td>\n",
       "      <td>-0.544233</td>\n",
       "      <td>0.783802</td>\n",
       "      <td>0.448647</td>\n",
       "      <td>-0.040882</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.807285</td>\n",
       "      <td>1.238116</td>\n",
       "      <td>val0_1</td>\n",
       "      <td>val1_0</td>\n",
       "      <td>val0_0</td>\n",
       "      <td>val1_2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.177570</td>\n",
       "      <td>-0.130169</td>\n",
       "      <td>0.080924</td>\n",
       "      <td>0.121516</td>\n",
       "      <td>-0.233781</td>\n",
       "      <td>0.226987</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.162172</td>\n",
       "      <td>-0.125839</td>\n",
       "      <td>val0_2</td>\n",
       "      <td>val1_0</td>\n",
       "      <td>val0_0</td>\n",
       "      <td>val1_1</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2995</th>\n",
       "      <td>-0.256738</td>\n",
       "      <td>0.334942</td>\n",
       "      <td>-1.097054</td>\n",
       "      <td>-0.066713</td>\n",
       "      <td>-0.477552</td>\n",
       "      <td>-0.605382</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.242977</td>\n",
       "      <td>0.337919</td>\n",
       "      <td>val0_2</td>\n",
       "      <td>val1_2</td>\n",
       "      <td>val0_0</td>\n",
       "      <td>val1_1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2996</th>\n",
       "      <td>0.127940</td>\n",
       "      <td>0.312279</td>\n",
       "      <td>0.792817</td>\n",
       "      <td>-0.536493</td>\n",
       "      <td>0.552054</td>\n",
       "      <td>0.141952</td>\n",
       "      <td>1</td>\n",
       "      <td>0.123363</td>\n",
       "      <td>0.311755</td>\n",
       "      <td>val0_2</td>\n",
       "      <td>val1_2</td>\n",
       "      <td>val0_1</td>\n",
       "      <td>val1_1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2997</th>\n",
       "      <td>-0.802343</td>\n",
       "      <td>-0.156593</td>\n",
       "      <td>-1.030666</td>\n",
       "      <td>0.638108</td>\n",
       "      <td>-1.436752</td>\n",
       "      <td>0.595418</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.807575</td>\n",
       "      <td>-0.155273</td>\n",
       "      <td>val0_1</td>\n",
       "      <td>val1_1</td>\n",
       "      <td>val0_0</td>\n",
       "      <td>val1_1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2998</th>\n",
       "      <td>-0.204698</td>\n",
       "      <td>-0.052545</td>\n",
       "      <td>0.481543</td>\n",
       "      <td>-1.151239</td>\n",
       "      <td>0.990868</td>\n",
       "      <td>-0.048541</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.203927</td>\n",
       "      <td>-0.063315</td>\n",
       "      <td>val0_2</td>\n",
       "      <td>val1_1</td>\n",
       "      <td>val0_0</td>\n",
       "      <td>val1_1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2999</th>\n",
       "      <td>0.059137</td>\n",
       "      <td>0.374550</td>\n",
       "      <td>0.974962</td>\n",
       "      <td>-1.132156</td>\n",
       "      <td>1.059968</td>\n",
       "      <td>0.187809</td>\n",
       "      <td>1</td>\n",
       "      <td>0.057206</td>\n",
       "      <td>0.385159</td>\n",
       "      <td>val0_2</td>\n",
       "      <td>val1_2</td>\n",
       "      <td>val0_1</td>\n",
       "      <td>val1_1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3000 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         num_0     num_1     num_2     num_3     num_4     num_5  label  \\\n",
       "0     0.118733  0.237866  0.492773 -1.849241  1.363420  0.372973      1   \n",
       "1     0.601353 -0.795141  0.621367  0.807144 -0.197305  0.629804      1   \n",
       "2    -0.175575  0.951508 -0.351287  0.628771 -1.147206  0.140249      1   \n",
       "3    -0.799932  1.229606 -0.544233  0.783802  0.448647 -0.040882      0   \n",
       "4    -0.177570 -0.130169  0.080924  0.121516 -0.233781  0.226987      1   \n",
       "...        ...       ...       ...       ...       ...       ...    ...   \n",
       "2995 -0.256738  0.334942 -1.097054 -0.066713 -0.477552 -0.605382      1   \n",
       "2996  0.127940  0.312279  0.792817 -0.536493  0.552054  0.141952      1   \n",
       "2997 -0.802343 -0.156593 -1.030666  0.638108 -1.436752  0.595418      1   \n",
       "2998 -0.204698 -0.052545  0.481543 -1.151239  0.990868 -0.048541      1   \n",
       "2999  0.059137  0.374550  0.974962 -1.132156  1.059968  0.187809      1   \n",
       "\n",
       "      num_c0_num_0  num_c1_num_1 CN_0_num_0 CN_1_num_1 CC_0_num_0 CC_1_num_1  \n",
       "0         0.105654      0.243515     val0_2     val1_2     val0_1     val1_1  \n",
       "1         0.604797     -0.796681     val0_1     val1_1     val0_1     val1_0  \n",
       "2        -0.152420      0.957595     val0_2     val1_3     val0_0     val1_1  \n",
       "3        -0.807285      1.238116     val0_1     val1_0     val0_0     val1_2  \n",
       "4        -0.162172     -0.125839     val0_2     val1_0     val0_0     val1_1  \n",
       "...            ...           ...        ...        ...        ...        ...  \n",
       "2995     -0.242977      0.337919     val0_2     val1_2     val0_0     val1_1  \n",
       "2996      0.123363      0.311755     val0_2     val1_2     val0_1     val1_1  \n",
       "2997     -0.807575     -0.155273     val0_1     val1_1     val0_0     val1_1  \n",
       "2998     -0.203927     -0.063315     val0_2     val1_1     val0_0     val1_1  \n",
       "2999      0.057206      0.385159     val0_2     val1_2     val0_1     val1_1  \n",
       "\n",
       "[3000 rows x 13 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaler = DataRobustScaler(exclude_cols=['label'])\n",
    "scaler.fit(df)\n",
    "scaled_df = scaler.transform(df)\n",
    "scaled_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "35fd3a91",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 3000 entries, 0 to 2999\n",
      "Data columns (total 13 columns):\n",
      " #   Column        Non-Null Count  Dtype  \n",
      "---  ------        --------------  -----  \n",
      " 0   num_0         3000 non-null   float64\n",
      " 1   num_1         3000 non-null   float64\n",
      " 2   num_2         3000 non-null   float64\n",
      " 3   num_3         3000 non-null   float64\n",
      " 4   num_4         3000 non-null   float64\n",
      " 5   num_5         3000 non-null   float64\n",
      " 6   label         3000 non-null   int64  \n",
      " 7   num_c0_num_0  3000 non-null   float64\n",
      " 8   num_c1_num_1  3000 non-null   float64\n",
      " 9   CN_0_num_0    3000 non-null   object \n",
      " 10  CN_1_num_1    3000 non-null   object \n",
      " 11  CC_0_num_0    3000 non-null   object \n",
      " 12  CC_1_num_1    3000 non-null   object \n",
      "dtypes: float64(8), int64(1), object(4)\n",
      "memory usage: 304.8+ KB\n"
     ]
    }
   ],
   "source": [
    "scaled_df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bd6448a6",
   "metadata": {},
   "source": [
    "## Inverse Transform\n",
    "\n",
    "The scalers implemented in Sklearn all have an inverse transform method, which reverts the scaling performed over the numerical data, returning the dataset to its original state. We encapsulate this behavior in a method with the same name as the one used by sklearn: **inverse_transform()**. Note that in our case, the inverse_transform method receives a dataset with scaled numerical columns, as well as categorical columns, and returns a new dataframe with only the scaled numerical columns reversed (the label column is kept the same, since it was set to be ignored)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "19e73f9c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>num_0</th>\n",
       "      <th>num_1</th>\n",
       "      <th>num_2</th>\n",
       "      <th>num_3</th>\n",
       "      <th>num_4</th>\n",
       "      <th>num_5</th>\n",
       "      <th>label</th>\n",
       "      <th>num_c0_num_0</th>\n",
       "      <th>num_c1_num_1</th>\n",
       "      <th>CN_0_num_0</th>\n",
       "      <th>CN_1_num_1</th>\n",
       "      <th>CC_0_num_0</th>\n",
       "      <th>CC_1_num_1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.838682</td>\n",
       "      <td>-1.408271</td>\n",
       "      <td>2.758909</td>\n",
       "      <td>-1.558914</td>\n",
       "      <td>0.606338</td>\n",
       "      <td>2.828664</td>\n",
       "      <td>1</td>\n",
       "      <td>1.796571</td>\n",
       "      <td>-1.397257</td>\n",
       "      <td>val0_2</td>\n",
       "      <td>val1_2</td>\n",
       "      <td>val0_1</td>\n",
       "      <td>val1_1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3.249825</td>\n",
       "      <td>-3.925450</td>\n",
       "      <td>2.953185</td>\n",
       "      <td>3.540991</td>\n",
       "      <td>-2.340552</td>\n",
       "      <td>3.398367</td>\n",
       "      <td>1</td>\n",
       "      <td>3.257607</td>\n",
       "      <td>-3.925562</td>\n",
       "      <td>val0_1</td>\n",
       "      <td>val1_1</td>\n",
       "      <td>val0_1</td>\n",
       "      <td>val1_0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.978148</td>\n",
       "      <td>0.330699</td>\n",
       "      <td>1.483723</td>\n",
       "      <td>3.198539</td>\n",
       "      <td>-4.134112</td>\n",
       "      <td>2.312435</td>\n",
       "      <td>1</td>\n",
       "      <td>1.041164</td>\n",
       "      <td>0.338390</td>\n",
       "      <td>val0_2</td>\n",
       "      <td>val1_3</td>\n",
       "      <td>val0_0</td>\n",
       "      <td>val1_1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.847425</td>\n",
       "      <td>1.008353</td>\n",
       "      <td>1.192225</td>\n",
       "      <td>3.496178</td>\n",
       "      <td>-1.120895</td>\n",
       "      <td>1.910650</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.875685</td>\n",
       "      <td>1.020226</td>\n",
       "      <td>val0_1</td>\n",
       "      <td>val1_0</td>\n",
       "      <td>val0_0</td>\n",
       "      <td>val1_2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.972314</td>\n",
       "      <td>-2.305080</td>\n",
       "      <td>2.136697</td>\n",
       "      <td>2.224677</td>\n",
       "      <td>-2.409424</td>\n",
       "      <td>2.504838</td>\n",
       "      <td>1</td>\n",
       "      <td>1.012618</td>\n",
       "      <td>-2.295010</td>\n",
       "      <td>val0_2</td>\n",
       "      <td>val1_0</td>\n",
       "      <td>val0_0</td>\n",
       "      <td>val1_1</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2995</th>\n",
       "      <td>0.740834</td>\n",
       "      <td>-1.171719</td>\n",
       "      <td>0.357037</td>\n",
       "      <td>1.863302</td>\n",
       "      <td>-2.869701</td>\n",
       "      <td>0.658475</td>\n",
       "      <td>1</td>\n",
       "      <td>0.776097</td>\n",
       "      <td>-1.167798</td>\n",
       "      <td>val0_2</td>\n",
       "      <td>val1_2</td>\n",
       "      <td>val0_0</td>\n",
       "      <td>val1_1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2996</th>\n",
       "      <td>1.865602</td>\n",
       "      <td>-1.226943</td>\n",
       "      <td>3.212207</td>\n",
       "      <td>0.961388</td>\n",
       "      <td>-0.925646</td>\n",
       "      <td>2.316213</td>\n",
       "      <td>1</td>\n",
       "      <td>1.848407</td>\n",
       "      <td>-1.231393</td>\n",
       "      <td>val0_2</td>\n",
       "      <td>val1_2</td>\n",
       "      <td>val0_1</td>\n",
       "      <td>val1_1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2997</th>\n",
       "      <td>-0.854474</td>\n",
       "      <td>-2.369469</td>\n",
       "      <td>0.457335</td>\n",
       "      <td>3.216465</td>\n",
       "      <td>-4.680820</td>\n",
       "      <td>3.322093</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.876535</td>\n",
       "      <td>-2.366553</td>\n",
       "      <td>val0_1</td>\n",
       "      <td>val1_1</td>\n",
       "      <td>val0_0</td>\n",
       "      <td>val1_1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2998</th>\n",
       "      <td>0.892994</td>\n",
       "      <td>-2.115929</td>\n",
       "      <td>2.741942</td>\n",
       "      <td>-0.218843</td>\n",
       "      <td>-0.097098</td>\n",
       "      <td>1.893662</td>\n",
       "      <td>1</td>\n",
       "      <td>0.890398</td>\n",
       "      <td>-2.143039</td>\n",
       "      <td>val0_2</td>\n",
       "      <td>val1_1</td>\n",
       "      <td>val0_0</td>\n",
       "      <td>val1_1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2999</th>\n",
       "      <td>1.664427</td>\n",
       "      <td>-1.075205</td>\n",
       "      <td>3.487387</td>\n",
       "      <td>-0.182207</td>\n",
       "      <td>0.033374</td>\n",
       "      <td>2.417933</td>\n",
       "      <td>1</td>\n",
       "      <td>1.654759</td>\n",
       "      <td>-1.052976</td>\n",
       "      <td>val0_2</td>\n",
       "      <td>val1_2</td>\n",
       "      <td>val0_1</td>\n",
       "      <td>val1_1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3000 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         num_0     num_1     num_2     num_3     num_4     num_5  label  \\\n",
       "0     1.838682 -1.408271  2.758909 -1.558914  0.606338  2.828664      1   \n",
       "1     3.249825 -3.925450  2.953185  3.540991 -2.340552  3.398367      1   \n",
       "2     0.978148  0.330699  1.483723  3.198539 -4.134112  2.312435      1   \n",
       "3    -0.847425  1.008353  1.192225  3.496178 -1.120895  1.910650      0   \n",
       "4     0.972314 -2.305080  2.136697  2.224677 -2.409424  2.504838      1   \n",
       "...        ...       ...       ...       ...       ...       ...    ...   \n",
       "2995  0.740834 -1.171719  0.357037  1.863302 -2.869701  0.658475      1   \n",
       "2996  1.865602 -1.226943  3.212207  0.961388 -0.925646  2.316213      1   \n",
       "2997 -0.854474 -2.369469  0.457335  3.216465 -4.680820  3.322093      1   \n",
       "2998  0.892994 -2.115929  2.741942 -0.218843 -0.097098  1.893662      1   \n",
       "2999  1.664427 -1.075205  3.487387 -0.182207  0.033374  2.417933      1   \n",
       "\n",
       "      num_c0_num_0  num_c1_num_1 CN_0_num_0 CN_1_num_1 CC_0_num_0 CC_1_num_1  \n",
       "0         1.796571     -1.397257     val0_2     val1_2     val0_1     val1_1  \n",
       "1         3.257607     -3.925562     val0_1     val1_1     val0_1     val1_0  \n",
       "2         1.041164      0.338390     val0_2     val1_3     val0_0     val1_1  \n",
       "3        -0.875685      1.020226     val0_1     val1_0     val0_0     val1_2  \n",
       "4         1.012618     -2.295010     val0_2     val1_0     val0_0     val1_1  \n",
       "...            ...           ...        ...        ...        ...        ...  \n",
       "2995      0.776097     -1.167798     val0_2     val1_2     val0_0     val1_1  \n",
       "2996      1.848407     -1.231393     val0_2     val1_2     val0_1     val1_1  \n",
       "2997     -0.876535     -2.366553     val0_1     val1_1     val0_0     val1_1  \n",
       "2998      0.890398     -2.143039     val0_2     val1_1     val0_0     val1_1  \n",
       "2999      1.654759     -1.052976     val0_2     val1_2     val0_1     val1_1  \n",
       "\n",
       "[3000 rows x 13 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "org_df = scaler.inverse_transform(scaled_df)\n",
    "org_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "995b8faf",
   "metadata": {},
   "source": [
    "## Power Transform"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "526e0806",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>num_0</th>\n",
       "      <th>num_1</th>\n",
       "      <th>num_2</th>\n",
       "      <th>num_3</th>\n",
       "      <th>num_4</th>\n",
       "      <th>num_5</th>\n",
       "      <th>label</th>\n",
       "      <th>num_c0_num_0</th>\n",
       "      <th>num_c1_num_1</th>\n",
       "      <th>CN_0_num_0</th>\n",
       "      <th>CN_1_num_1</th>\n",
       "      <th>CC_0_num_0</th>\n",
       "      <th>CC_1_num_1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.204668</td>\n",
       "      <td>0.340865</td>\n",
       "      <td>0.624318</td>\n",
       "      <td>-2.355844</td>\n",
       "      <td>1.892091</td>\n",
       "      <td>0.497892</td>\n",
       "      <td>1</td>\n",
       "      <td>0.184078</td>\n",
       "      <td>0.347054</td>\n",
       "      <td>val0_2</td>\n",
       "      <td>val1_2</td>\n",
       "      <td>val0_1</td>\n",
       "      <td>val1_1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.938741</td>\n",
       "      <td>-1.046138</td>\n",
       "      <td>0.791909</td>\n",
       "      <td>1.060248</td>\n",
       "      <td>-0.260243</td>\n",
       "      <td>0.853418</td>\n",
       "      <td>1</td>\n",
       "      <td>0.943084</td>\n",
       "      <td>-1.046297</td>\n",
       "      <td>val0_1</td>\n",
       "      <td>val1_1</td>\n",
       "      <td>val0_1</td>\n",
       "      <td>val1_0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.211606</td>\n",
       "      <td>1.280236</td>\n",
       "      <td>-0.462634</td>\n",
       "      <td>0.821744</td>\n",
       "      <td>-1.544629</td>\n",
       "      <td>0.177042</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.181683</td>\n",
       "      <td>1.284699</td>\n",
       "      <td>val0_2</td>\n",
       "      <td>val1_3</td>\n",
       "      <td>val0_0</td>\n",
       "      <td>val1_1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.969191</td>\n",
       "      <td>1.639213</td>\n",
       "      <td>-0.707276</td>\n",
       "      <td>1.028996</td>\n",
       "      <td>0.621462</td>\n",
       "      <td>-0.071696</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.979450</td>\n",
       "      <td>1.645924</td>\n",
       "      <td>val0_1</td>\n",
       "      <td>val1_0</td>\n",
       "      <td>val0_0</td>\n",
       "      <td>val1_2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.214327</td>\n",
       "      <td>-0.150747</td>\n",
       "      <td>0.090894</td>\n",
       "      <td>0.147964</td>\n",
       "      <td>-0.309796</td>\n",
       "      <td>0.296469</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.195061</td>\n",
       "      <td>-0.145133</td>\n",
       "      <td>val0_2</td>\n",
       "      <td>val1_0</td>\n",
       "      <td>val0_0</td>\n",
       "      <td>val1_1</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2995</th>\n",
       "      <td>-0.321017</td>\n",
       "      <td>0.469917</td>\n",
       "      <td>-1.397383</td>\n",
       "      <td>-0.100105</td>\n",
       "      <td>-0.640434</td>\n",
       "      <td>-0.839805</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.304500</td>\n",
       "      <td>0.472252</td>\n",
       "      <td>val0_2</td>\n",
       "      <td>val1_2</td>\n",
       "      <td>val0_0</td>\n",
       "      <td>val1_1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2996</th>\n",
       "      <td>0.218123</td>\n",
       "      <td>0.439816</td>\n",
       "      <td>1.016051</td>\n",
       "      <td>-0.713380</td>\n",
       "      <td>0.763497</td>\n",
       "      <td>0.179385</td>\n",
       "      <td>1</td>\n",
       "      <td>0.209931</td>\n",
       "      <td>0.437581</td>\n",
       "      <td>val0_2</td>\n",
       "      <td>val1_2</td>\n",
       "      <td>val0_1</td>\n",
       "      <td>val1_1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2997</th>\n",
       "      <td>-0.971766</td>\n",
       "      <td>-0.186168</td>\n",
       "      <td>-1.315517</td>\n",
       "      <td>0.834209</td>\n",
       "      <td>-1.934027</td>\n",
       "      <td>0.805738</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.979760</td>\n",
       "      <td>-0.184495</td>\n",
       "      <td>val0_1</td>\n",
       "      <td>val1_1</td>\n",
       "      <td>val0_0</td>\n",
       "      <td>val1_1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2998</th>\n",
       "      <td>-0.251167</td>\n",
       "      <td>-0.046782</td>\n",
       "      <td>0.609704</td>\n",
       "      <td>-1.497267</td>\n",
       "      <td>1.370180</td>\n",
       "      <td>-0.082193</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.251934</td>\n",
       "      <td>-0.061582</td>\n",
       "      <td>val0_2</td>\n",
       "      <td>val1_1</td>\n",
       "      <td>val0_0</td>\n",
       "      <td>val1_1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2999</th>\n",
       "      <td>0.118160</td>\n",
       "      <td>0.522483</td>\n",
       "      <td>1.255006</td>\n",
       "      <td>-1.473387</td>\n",
       "      <td>1.466475</td>\n",
       "      <td>0.242502</td>\n",
       "      <td>1</td>\n",
       "      <td>0.113814</td>\n",
       "      <td>0.534794</td>\n",
       "      <td>val0_2</td>\n",
       "      <td>val1_2</td>\n",
       "      <td>val0_1</td>\n",
       "      <td>val1_1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3000 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         num_0     num_1     num_2     num_3     num_4     num_5  label  \\\n",
       "0     0.204668  0.340865  0.624318 -2.355844  1.892091  0.497892      1   \n",
       "1     0.938741 -1.046138  0.791909  1.060248 -0.260243  0.853418      1   \n",
       "2    -0.211606  1.280236 -0.462634  0.821744 -1.544629  0.177042      1   \n",
       "3    -0.969191  1.639213 -0.707276  1.028996  0.621462 -0.071696      0   \n",
       "4    -0.214327 -0.150747  0.090894  0.147964 -0.309796  0.296469      1   \n",
       "...        ...       ...       ...       ...       ...       ...    ...   \n",
       "2995 -0.321017  0.469917 -1.397383 -0.100105 -0.640434 -0.839805      1   \n",
       "2996  0.218123  0.439816  1.016051 -0.713380  0.763497  0.179385      1   \n",
       "2997 -0.971766 -0.186168 -1.315517  0.834209 -1.934027  0.805738      1   \n",
       "2998 -0.251167 -0.046782  0.609704 -1.497267  1.370180 -0.082193      1   \n",
       "2999  0.118160  0.522483  1.255006 -1.473387  1.466475  0.242502      1   \n",
       "\n",
       "      num_c0_num_0  num_c1_num_1 CN_0_num_0 CN_1_num_1 CC_0_num_0 CC_1_num_1  \n",
       "0         0.184078      0.347054     val0_2     val1_2     val0_1     val1_1  \n",
       "1         0.943084     -1.046297     val0_1     val1_1     val0_1     val1_0  \n",
       "2        -0.181683      1.284699     val0_2     val1_3     val0_0     val1_1  \n",
       "3        -0.979450      1.645924     val0_1     val1_0     val0_0     val1_2  \n",
       "4        -0.195061     -0.145133     val0_2     val1_0     val0_0     val1_1  \n",
       "...            ...           ...        ...        ...        ...        ...  \n",
       "2995     -0.304500      0.472252     val0_2     val1_2     val0_0     val1_1  \n",
       "2996      0.209931      0.437581     val0_2     val1_2     val0_1     val1_1  \n",
       "2997     -0.979760     -0.184495     val0_1     val1_1     val0_0     val1_1  \n",
       "2998     -0.251934     -0.061582     val0_2     val1_1     val0_0     val1_1  \n",
       "2999      0.113814      0.534794     val0_2     val1_2     val0_1     val1_1  \n",
       "\n",
       "[3000 rows x 13 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaler = DataPowerTransformer(exclude_cols=['label'])\n",
    "scaler.fit(df)\n",
    "scaled_df = scaler.transform(df)\n",
    "scaled_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6e57f4f9",
   "metadata": {},
   "source": [
    "## Normalizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "9afd5fba",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No columns specified for imputation. These columns have been automatically identified:\n",
      "[]\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>num_0</th>\n",
       "      <th>num_1</th>\n",
       "      <th>num_2</th>\n",
       "      <th>num_3</th>\n",
       "      <th>num_4</th>\n",
       "      <th>num_5</th>\n",
       "      <th>label</th>\n",
       "      <th>num_c0_num_0</th>\n",
       "      <th>num_c1_num_1</th>\n",
       "      <th>CN_0_num_0</th>\n",
       "      <th>CN_1_num_1</th>\n",
       "      <th>CC_0_num_0</th>\n",
       "      <th>CC_1_num_1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.341701</td>\n",
       "      <td>-0.261714</td>\n",
       "      <td>0.512717</td>\n",
       "      <td>-0.289709</td>\n",
       "      <td>0.112682</td>\n",
       "      <td>0.525680</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.333876</td>\n",
       "      <td>-0.259667</td>\n",
       "      <td>val0_2</td>\n",
       "      <td>val1_2</td>\n",
       "      <td>val0_1</td>\n",
       "      <td>val1_1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.342031</td>\n",
       "      <td>-0.413138</td>\n",
       "      <td>0.310811</td>\n",
       "      <td>0.372675</td>\n",
       "      <td>-0.246334</td>\n",
       "      <td>0.357664</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.342850</td>\n",
       "      <td>-0.413149</td>\n",
       "      <td>val0_1</td>\n",
       "      <td>val1_1</td>\n",
       "      <td>val0_1</td>\n",
       "      <td>val1_0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.160514</td>\n",
       "      <td>0.054268</td>\n",
       "      <td>0.243479</td>\n",
       "      <td>0.524880</td>\n",
       "      <td>-0.678407</td>\n",
       "      <td>0.379470</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.170855</td>\n",
       "      <td>0.055530</td>\n",
       "      <td>val0_2</td>\n",
       "      <td>val1_3</td>\n",
       "      <td>val0_0</td>\n",
       "      <td>val1_1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.180286</td>\n",
       "      <td>0.214523</td>\n",
       "      <td>0.253640</td>\n",
       "      <td>0.743796</td>\n",
       "      <td>-0.238466</td>\n",
       "      <td>0.406482</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.186298</td>\n",
       "      <td>0.217048</td>\n",
       "      <td>val0_1</td>\n",
       "      <td>val1_0</td>\n",
       "      <td>val0_0</td>\n",
       "      <td>val1_2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.166395</td>\n",
       "      <td>-0.394475</td>\n",
       "      <td>0.365659</td>\n",
       "      <td>0.380715</td>\n",
       "      <td>-0.412332</td>\n",
       "      <td>0.428660</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.173292</td>\n",
       "      <td>-0.392752</td>\n",
       "      <td>val0_2</td>\n",
       "      <td>val1_0</td>\n",
       "      <td>val0_0</td>\n",
       "      <td>val1_1</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2995</th>\n",
       "      <td>0.184312</td>\n",
       "      <td>-0.291512</td>\n",
       "      <td>0.088827</td>\n",
       "      <td>0.463571</td>\n",
       "      <td>-0.713954</td>\n",
       "      <td>0.163822</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.193085</td>\n",
       "      <td>-0.290537</td>\n",
       "      <td>val0_2</td>\n",
       "      <td>val1_2</td>\n",
       "      <td>val0_0</td>\n",
       "      <td>val1_1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2996</th>\n",
       "      <td>0.356516</td>\n",
       "      <td>-0.234468</td>\n",
       "      <td>0.613851</td>\n",
       "      <td>0.183721</td>\n",
       "      <td>-0.176890</td>\n",
       "      <td>0.442627</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.353230</td>\n",
       "      <td>-0.235319</td>\n",
       "      <td>val0_2</td>\n",
       "      <td>val1_2</td>\n",
       "      <td>val0_1</td>\n",
       "      <td>val1_1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2997</th>\n",
       "      <td>-0.113966</td>\n",
       "      <td>-0.316029</td>\n",
       "      <td>0.060997</td>\n",
       "      <td>0.428997</td>\n",
       "      <td>-0.624306</td>\n",
       "      <td>0.443085</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.116908</td>\n",
       "      <td>-0.315640</td>\n",
       "      <td>val0_1</td>\n",
       "      <td>val1_1</td>\n",
       "      <td>val0_0</td>\n",
       "      <td>val1_1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2998</th>\n",
       "      <td>0.191164</td>\n",
       "      <td>-0.452958</td>\n",
       "      <td>0.586970</td>\n",
       "      <td>-0.046848</td>\n",
       "      <td>-0.020786</td>\n",
       "      <td>0.405378</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.190608</td>\n",
       "      <td>-0.458762</td>\n",
       "      <td>val0_2</td>\n",
       "      <td>val1_1</td>\n",
       "      <td>val0_0</td>\n",
       "      <td>val1_1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2999</th>\n",
       "      <td>0.327582</td>\n",
       "      <td>-0.211615</td>\n",
       "      <td>0.686367</td>\n",
       "      <td>-0.035861</td>\n",
       "      <td>0.006568</td>\n",
       "      <td>0.475883</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.325680</td>\n",
       "      <td>-0.207240</td>\n",
       "      <td>val0_2</td>\n",
       "      <td>val1_2</td>\n",
       "      <td>val0_1</td>\n",
       "      <td>val1_1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3000 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         num_0     num_1     num_2     num_3     num_4     num_5  label  \\\n",
       "0     0.341701 -0.261714  0.512717 -0.289709  0.112682  0.525680    1.0   \n",
       "1     0.342031 -0.413138  0.310811  0.372675 -0.246334  0.357664    1.0   \n",
       "2     0.160514  0.054268  0.243479  0.524880 -0.678407  0.379470    1.0   \n",
       "3    -0.180286  0.214523  0.253640  0.743796 -0.238466  0.406482    0.0   \n",
       "4     0.166395 -0.394475  0.365659  0.380715 -0.412332  0.428660    1.0   \n",
       "...        ...       ...       ...       ...       ...       ...    ...   \n",
       "2995  0.184312 -0.291512  0.088827  0.463571 -0.713954  0.163822    1.0   \n",
       "2996  0.356516 -0.234468  0.613851  0.183721 -0.176890  0.442627    1.0   \n",
       "2997 -0.113966 -0.316029  0.060997  0.428997 -0.624306  0.443085    1.0   \n",
       "2998  0.191164 -0.452958  0.586970 -0.046848 -0.020786  0.405378    1.0   \n",
       "2999  0.327582 -0.211615  0.686367 -0.035861  0.006568  0.475883    1.0   \n",
       "\n",
       "      num_c0_num_0  num_c1_num_1 CN_0_num_0 CN_1_num_1 CC_0_num_0 CC_1_num_1  \n",
       "0         0.333876     -0.259667     val0_2     val1_2     val0_1     val1_1  \n",
       "1         0.342850     -0.413149     val0_1     val1_1     val0_1     val1_0  \n",
       "2         0.170855      0.055530     val0_2     val1_3     val0_0     val1_1  \n",
       "3        -0.186298      0.217048     val0_1     val1_0     val0_0     val1_2  \n",
       "4         0.173292     -0.392752     val0_2     val1_0     val0_0     val1_1  \n",
       "...            ...           ...        ...        ...        ...        ...  \n",
       "2995      0.193085     -0.290537     val0_2     val1_2     val0_0     val1_1  \n",
       "2996      0.353230     -0.235319     val0_2     val1_2     val0_1     val1_1  \n",
       "2997     -0.116908     -0.315640     val0_1     val1_1     val0_0     val1_1  \n",
       "2998      0.190608     -0.458762     val0_2     val1_1     val0_0     val1_1  \n",
       "2999      0.325680     -0.207240     val0_2     val1_2     val0_1     val1_1  \n",
       "\n",
       "[3000 rows x 13 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaler = DataNormalizer(exclude_cols=['label'])\n",
    "scaler.fit(df)\n",
    "scaled_df = scaler.transform(df)\n",
    "scaled_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dafc75c9",
   "metadata": {},
   "source": [
    "## Standard Scaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "d1327eb3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>num_0</th>\n",
       "      <th>num_1</th>\n",
       "      <th>num_2</th>\n",
       "      <th>num_3</th>\n",
       "      <th>num_4</th>\n",
       "      <th>num_5</th>\n",
       "      <th>label</th>\n",
       "      <th>num_c0_num_0</th>\n",
       "      <th>num_c1_num_1</th>\n",
       "      <th>CN_0_num_0</th>\n",
       "      <th>CN_1_num_1</th>\n",
       "      <th>CC_0_num_0</th>\n",
       "      <th>CC_1_num_1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.279543</td>\n",
       "      <td>0.332628</td>\n",
       "      <td>0.628637</td>\n",
       "      <td>-2.422861</td>\n",
       "      <td>1.875546</td>\n",
       "      <td>0.502397</td>\n",
       "      <td>1</td>\n",
       "      <td>0.260887</td>\n",
       "      <td>0.338829</td>\n",
       "      <td>val0_2</td>\n",
       "      <td>val1_2</td>\n",
       "      <td>val0_1</td>\n",
       "      <td>val1_1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.908693</td>\n",
       "      <td>-1.043038</td>\n",
       "      <td>0.793104</td>\n",
       "      <td>1.055566</td>\n",
       "      <td>-0.255713</td>\n",
       "      <td>0.853082</td>\n",
       "      <td>1</td>\n",
       "      <td>0.912494</td>\n",
       "      <td>-1.043198</td>\n",
       "      <td>val0_1</td>\n",
       "      <td>val1_1</td>\n",
       "      <td>val0_1</td>\n",
       "      <td>val1_0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.104122</td>\n",
       "      <td>1.282994</td>\n",
       "      <td>-0.450880</td>\n",
       "      <td>0.821995</td>\n",
       "      <td>-1.552858</td>\n",
       "      <td>0.184628</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.076018</td>\n",
       "      <td>1.287573</td>\n",
       "      <td>val0_2</td>\n",
       "      <td>val1_3</td>\n",
       "      <td>val0_0</td>\n",
       "      <td>val1_1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.918043</td>\n",
       "      <td>1.653340</td>\n",
       "      <td>-0.697650</td>\n",
       "      <td>1.025001</td>\n",
       "      <td>0.626371</td>\n",
       "      <td>-0.062694</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.930914</td>\n",
       "      <td>1.660279</td>\n",
       "      <td>val0_1</td>\n",
       "      <td>val1_0</td>\n",
       "      <td>val0_0</td>\n",
       "      <td>val1_2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.106723</td>\n",
       "      <td>-0.157488</td>\n",
       "      <td>0.101900</td>\n",
       "      <td>0.157765</td>\n",
       "      <td>-0.305523</td>\n",
       "      <td>0.303063</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.088749</td>\n",
       "      <td>-0.151902</td>\n",
       "      <td>val0_2</td>\n",
       "      <td>val1_0</td>\n",
       "      <td>val0_0</td>\n",
       "      <td>val1_1</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2995</th>\n",
       "      <td>-0.209927</td>\n",
       "      <td>0.461906</td>\n",
       "      <td>-1.404684</td>\n",
       "      <td>-0.088714</td>\n",
       "      <td>-0.638406</td>\n",
       "      <td>-0.833480</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.194235</td>\n",
       "      <td>0.464257</td>\n",
       "      <td>val0_2</td>\n",
       "      <td>val1_2</td>\n",
       "      <td>val0_0</td>\n",
       "      <td>val1_1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2996</th>\n",
       "      <td>0.291545</td>\n",
       "      <td>0.431726</td>\n",
       "      <td>1.012380</td>\n",
       "      <td>-0.703871</td>\n",
       "      <td>0.767580</td>\n",
       "      <td>0.186954</td>\n",
       "      <td>1</td>\n",
       "      <td>0.284005</td>\n",
       "      <td>0.429494</td>\n",
       "      <td>val0_2</td>\n",
       "      <td>val1_2</td>\n",
       "      <td>val0_1</td>\n",
       "      <td>val1_1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2997</th>\n",
       "      <td>-0.921186</td>\n",
       "      <td>-0.192677</td>\n",
       "      <td>-1.319776</td>\n",
       "      <td>0.834221</td>\n",
       "      <td>-1.948250</td>\n",
       "      <td>0.806131</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.931293</td>\n",
       "      <td>-0.191009</td>\n",
       "      <td>val0_1</td>\n",
       "      <td>val1_1</td>\n",
       "      <td>val0_0</td>\n",
       "      <td>val1_1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2998</th>\n",
       "      <td>-0.142087</td>\n",
       "      <td>-0.054115</td>\n",
       "      <td>0.614274</td>\n",
       "      <td>-1.508856</td>\n",
       "      <td>1.366805</td>\n",
       "      <td>-0.073151</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.143258</td>\n",
       "      <td>-0.068831</td>\n",
       "      <td>val0_2</td>\n",
       "      <td>val1_1</td>\n",
       "      <td>val0_0</td>\n",
       "      <td>val1_1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2999</th>\n",
       "      <td>0.201852</td>\n",
       "      <td>0.514652</td>\n",
       "      <td>1.245336</td>\n",
       "      <td>-1.483868</td>\n",
       "      <td>1.461165</td>\n",
       "      <td>0.249568</td>\n",
       "      <td>1</td>\n",
       "      <td>0.197640</td>\n",
       "      <td>0.527021</td>\n",
       "      <td>val0_2</td>\n",
       "      <td>val1_2</td>\n",
       "      <td>val0_1</td>\n",
       "      <td>val1_1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3000 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         num_0     num_1     num_2     num_3     num_4     num_5  label  \\\n",
       "0     0.279543  0.332628  0.628637 -2.422861  1.875546  0.502397      1   \n",
       "1     0.908693 -1.043038  0.793104  1.055566 -0.255713  0.853082      1   \n",
       "2    -0.104122  1.282994 -0.450880  0.821995 -1.552858  0.184628      1   \n",
       "3    -0.918043  1.653340 -0.697650  1.025001  0.626371 -0.062694      0   \n",
       "4    -0.106723 -0.157488  0.101900  0.157765 -0.305523  0.303063      1   \n",
       "...        ...       ...       ...       ...       ...       ...    ...   \n",
       "2995 -0.209927  0.461906 -1.404684 -0.088714 -0.638406 -0.833480      1   \n",
       "2996  0.291545  0.431726  1.012380 -0.703871  0.767580  0.186954      1   \n",
       "2997 -0.921186 -0.192677 -1.319776  0.834221 -1.948250  0.806131      1   \n",
       "2998 -0.142087 -0.054115  0.614274 -1.508856  1.366805 -0.073151      1   \n",
       "2999  0.201852  0.514652  1.245336 -1.483868  1.461165  0.249568      1   \n",
       "\n",
       "      num_c0_num_0  num_c1_num_1 CN_0_num_0 CN_1_num_1 CC_0_num_0 CC_1_num_1  \n",
       "0         0.260887      0.338829     val0_2     val1_2     val0_1     val1_1  \n",
       "1         0.912494     -1.043198     val0_1     val1_1     val0_1     val1_0  \n",
       "2        -0.076018      1.287573     val0_2     val1_3     val0_0     val1_1  \n",
       "3        -0.930914      1.660279     val0_1     val1_0     val0_0     val1_2  \n",
       "4        -0.088749     -0.151902     val0_2     val1_0     val0_0     val1_1  \n",
       "...            ...           ...        ...        ...        ...        ...  \n",
       "2995     -0.194235      0.464257     val0_2     val1_2     val0_0     val1_1  \n",
       "2996      0.284005      0.429494     val0_2     val1_2     val0_1     val1_1  \n",
       "2997     -0.931293     -0.191009     val0_1     val1_1     val0_0     val1_1  \n",
       "2998     -0.143258     -0.068831     val0_2     val1_1     val0_0     val1_1  \n",
       "2999      0.197640      0.527021     val0_2     val1_2     val0_1     val1_1  \n",
       "\n",
       "[3000 rows x 13 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaler = DataStandardScaler(exclude_cols=['label'])\n",
    "scaler.fit(df)\n",
    "scaled_df = scaler.transform(df)\n",
    "scaled_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "f8b51b2e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 3000 entries, 0 to 2999\n",
      "Data columns (total 13 columns):\n",
      " #   Column        Non-Null Count  Dtype  \n",
      "---  ------        --------------  -----  \n",
      " 0   num_0         3000 non-null   float64\n",
      " 1   num_1         3000 non-null   float64\n",
      " 2   num_2         3000 non-null   float64\n",
      " 3   num_3         3000 non-null   float64\n",
      " 4   num_4         3000 non-null   float64\n",
      " 5   num_5         3000 non-null   float64\n",
      " 6   label         3000 non-null   int64  \n",
      " 7   num_c0_num_0  3000 non-null   float64\n",
      " 8   num_c1_num_1  3000 non-null   float64\n",
      " 9   CN_0_num_0    3000 non-null   object \n",
      " 10  CN_1_num_1    3000 non-null   object \n",
      " 11  CC_0_num_0    3000 non-null   object \n",
      " 12  CC_1_num_1    3000 non-null   object \n",
      "dtypes: float64(8), int64(1), object(4)\n",
      "memory usage: 304.8+ KB\n"
     ]
    }
   ],
   "source": [
    "scaled_df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "122b0076",
   "metadata": {},
   "source": [
    "## Min Max Scaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "e54018aa",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.538771</td>\n",
       "      <td>0.495719</td>\n",
       "      <td>0.588105</td>\n",
       "      <td>0.161065</td>\n",
       "      <td>0.773957</td>\n",
       "      <td>0.542666</td>\n",
       "      <td>1</td>\n",
       "      <td>0.537741</td>\n",
       "      <td>0.496233</td>\n",
       "      <td>val0_2</td>\n",
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       "      <td>val1_1</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.634397</td>\n",
       "      <td>0.317108</td>\n",
       "      <td>0.608658</td>\n",
       "      <td>0.621755</td>\n",
       "      <td>0.492937</td>\n",
       "      <td>0.590060</td>\n",
       "      <td>1</td>\n",
       "      <td>0.637324</td>\n",
       "      <td>0.317234</td>\n",
       "      <td>val0_1</td>\n",
       "      <td>val1_1</td>\n",
       "      <td>val0_1</td>\n",
       "      <td>val1_0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.480457</td>\n",
       "      <td>0.619110</td>\n",
       "      <td>0.453200</td>\n",
       "      <td>0.590821</td>\n",
       "      <td>0.321901</td>\n",
       "      <td>0.499721</td>\n",
       "      <td>1</td>\n",
       "      <td>0.486254</td>\n",
       "      <td>0.619113</td>\n",
       "      <td>val0_2</td>\n",
       "      <td>val1_3</td>\n",
       "      <td>val0_0</td>\n",
       "      <td>val1_1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.356747</td>\n",
       "      <td>0.667194</td>\n",
       "      <td>0.422362</td>\n",
       "      <td>0.617707</td>\n",
       "      <td>0.609246</td>\n",
       "      <td>0.466296</td>\n",
       "      <td>0</td>\n",
       "      <td>0.355604</td>\n",
       "      <td>0.667385</td>\n",
       "      <td>val0_1</td>\n",
       "      <td>val1_0</td>\n",
       "      <td>val0_0</td>\n",
       "      <td>val1_2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.480061</td>\n",
       "      <td>0.432084</td>\n",
       "      <td>0.522280</td>\n",
       "      <td>0.502849</td>\n",
       "      <td>0.486370</td>\n",
       "      <td>0.515727</td>\n",
       "      <td>1</td>\n",
       "      <td>0.484308</td>\n",
       "      <td>0.432674</td>\n",
       "      <td>val0_2</td>\n",
       "      <td>val1_0</td>\n",
       "      <td>val0_0</td>\n",
       "      <td>val1_1</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2995</th>\n",
       "      <td>0.464375</td>\n",
       "      <td>0.512504</td>\n",
       "      <td>0.334005</td>\n",
       "      <td>0.470205</td>\n",
       "      <td>0.442477</td>\n",
       "      <td>0.362127</td>\n",
       "      <td>1</td>\n",
       "      <td>0.468187</td>\n",
       "      <td>0.512478</td>\n",
       "      <td>val0_2</td>\n",
       "      <td>val1_2</td>\n",
       "      <td>val0_0</td>\n",
       "      <td>val1_1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2996</th>\n",
       "      <td>0.540595</td>\n",
       "      <td>0.508585</td>\n",
       "      <td>0.636061</td>\n",
       "      <td>0.388732</td>\n",
       "      <td>0.627865</td>\n",
       "      <td>0.500035</td>\n",
       "      <td>1</td>\n",
       "      <td>0.541275</td>\n",
       "      <td>0.507975</td>\n",
       "      <td>val0_2</td>\n",
       "      <td>val1_2</td>\n",
       "      <td>val0_1</td>\n",
       "      <td>val1_1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2997</th>\n",
       "      <td>0.356269</td>\n",
       "      <td>0.427515</td>\n",
       "      <td>0.344616</td>\n",
       "      <td>0.592440</td>\n",
       "      <td>0.269766</td>\n",
       "      <td>0.583715</td>\n",
       "      <td>1</td>\n",
       "      <td>0.355546</td>\n",
       "      <td>0.427609</td>\n",
       "      <td>val0_1</td>\n",
       "      <td>val1_1</td>\n",
       "      <td>val0_0</td>\n",
       "      <td>val1_1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2998</th>\n",
       "      <td>0.474686</td>\n",
       "      <td>0.445506</td>\n",
       "      <td>0.586310</td>\n",
       "      <td>0.282118</td>\n",
       "      <td>0.706876</td>\n",
       "      <td>0.464883</td>\n",
       "      <td>1</td>\n",
       "      <td>0.475978</td>\n",
       "      <td>0.443433</td>\n",
       "      <td>val0_2</td>\n",
       "      <td>val1_1</td>\n",
       "      <td>val0_0</td>\n",
       "      <td>val1_1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2999</th>\n",
       "      <td>0.526962</td>\n",
       "      <td>0.519352</td>\n",
       "      <td>0.665173</td>\n",
       "      <td>0.285427</td>\n",
       "      <td>0.719318</td>\n",
       "      <td>0.508497</td>\n",
       "      <td>1</td>\n",
       "      <td>0.528076</td>\n",
       "      <td>0.520607</td>\n",
       "      <td>val0_2</td>\n",
       "      <td>val1_2</td>\n",
       "      <td>val0_1</td>\n",
       "      <td>val1_1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3000 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         num_0     num_1     num_2     num_3     num_4     num_5  label  \\\n",
       "0     0.538771  0.495719  0.588105  0.161065  0.773957  0.542666      1   \n",
       "1     0.634397  0.317108  0.608658  0.621755  0.492937  0.590060      1   \n",
       "2     0.480457  0.619110  0.453200  0.590821  0.321901  0.499721      1   \n",
       "3     0.356747  0.667194  0.422362  0.617707  0.609246  0.466296      0   \n",
       "4     0.480061  0.432084  0.522280  0.502849  0.486370  0.515727      1   \n",
       "...        ...       ...       ...       ...       ...       ...    ...   \n",
       "2995  0.464375  0.512504  0.334005  0.470205  0.442477  0.362127      1   \n",
       "2996  0.540595  0.508585  0.636061  0.388732  0.627865  0.500035      1   \n",
       "2997  0.356269  0.427515  0.344616  0.592440  0.269766  0.583715      1   \n",
       "2998  0.474686  0.445506  0.586310  0.282118  0.706876  0.464883      1   \n",
       "2999  0.526962  0.519352  0.665173  0.285427  0.719318  0.508497      1   \n",
       "\n",
       "      num_c0_num_0  num_c1_num_1 CN_0_num_0 CN_1_num_1 CC_0_num_0 CC_1_num_1  \n",
       "0         0.537741      0.496233     val0_2     val1_2     val0_1     val1_1  \n",
       "1         0.637324      0.317234     val0_1     val1_1     val0_1     val1_0  \n",
       "2         0.486254      0.619113     val0_2     val1_3     val0_0     val1_1  \n",
       "3         0.355604      0.667385     val0_1     val1_0     val0_0     val1_2  \n",
       "4         0.484308      0.432674     val0_2     val1_0     val0_0     val1_1  \n",
       "...            ...           ...        ...        ...        ...        ...  \n",
       "2995      0.468187      0.512478     val0_2     val1_2     val0_0     val1_1  \n",
       "2996      0.541275      0.507975     val0_2     val1_2     val0_1     val1_1  \n",
       "2997      0.355546      0.427609     val0_1     val1_1     val0_0     val1_1  \n",
       "2998      0.475978      0.443433     val0_2     val1_1     val0_0     val1_1  \n",
       "2999      0.528076      0.520607     val0_2     val1_2     val0_1     val1_1  \n",
       "\n",
       "[3000 rows x 13 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaler = DataMinMaxScaler(exclude_cols=['label'])\n",
    "scaler.fit(df)\n",
    "scaled_df = scaler.transform(df)\n",
    "scaled_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "82c8d2df",
   "metadata": {},
   "source": [
    "## Quantile Transform"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "cd18097e",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>num_0</th>\n",
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       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>0.563929</td>\n",
       "      <td>0.629615</td>\n",
       "      <td>0.748641</td>\n",
       "      <td>0.010215</td>\n",
       "      <td>0.970001</td>\n",
       "      <td>0.687788</td>\n",
       "      <td>1</td>\n",
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       "      <td>val0_2</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.823907</td>\n",
       "      <td>0.149258</td>\n",
       "      <td>0.792533</td>\n",
       "      <td>0.857954</td>\n",
       "      <td>0.400110</td>\n",
       "      <td>0.800766</td>\n",
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       "      <td>0.825264</td>\n",
       "      <td>0.148166</td>\n",
       "      <td>val0_1</td>\n",
       "      <td>val1_1</td>\n",
       "      <td>val0_1</td>\n",
       "      <td>val1_0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.406645</td>\n",
       "      <td>0.906626</td>\n",
       "      <td>0.326266</td>\n",
       "      <td>0.799757</td>\n",
       "      <td>0.057018</td>\n",
       "      <td>0.564204</td>\n",
       "      <td>1</td>\n",
       "      <td>0.412858</td>\n",
       "      <td>0.907607</td>\n",
       "      <td>val0_2</td>\n",
       "      <td>val1_3</td>\n",
       "      <td>val0_0</td>\n",
       "      <td>val1_1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.182652</td>\n",
       "      <td>0.951966</td>\n",
       "      <td>0.232610</td>\n",
       "      <td>0.852475</td>\n",
       "      <td>0.735349</td>\n",
       "      <td>0.478572</td>\n",
       "      <td>0</td>\n",
       "      <td>0.180338</td>\n",
       "      <td>0.952968</td>\n",
       "      <td>val0_1</td>\n",
       "      <td>val1_0</td>\n",
       "      <td>val0_0</td>\n",
       "      <td>val1_2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.405598</td>\n",
       "      <td>0.430716</td>\n",
       "      <td>0.542525</td>\n",
       "      <td>0.566506</td>\n",
       "      <td>0.379286</td>\n",
       "      <td>0.606670</td>\n",
       "      <td>1</td>\n",
       "      <td>0.409656</td>\n",
       "      <td>0.434478</td>\n",
       "      <td>val0_2</td>\n",
       "      <td>val1_0</td>\n",
       "      <td>val0_0</td>\n",
       "      <td>val1_1</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2995</th>\n",
       "      <td>0.364130</td>\n",
       "      <td>0.678050</td>\n",
       "      <td>0.075035</td>\n",
       "      <td>0.462473</td>\n",
       "      <td>0.266227</td>\n",
       "      <td>0.194694</td>\n",
       "      <td>1</td>\n",
       "      <td>0.372719</td>\n",
       "      <td>0.678672</td>\n",
       "      <td>val0_2</td>\n",
       "      <td>val1_2</td>\n",
       "      <td>val0_0</td>\n",
       "      <td>val1_1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2996</th>\n",
       "      <td>0.568323</td>\n",
       "      <td>0.666334</td>\n",
       "      <td>0.857290</td>\n",
       "      <td>0.229171</td>\n",
       "      <td>0.778167</td>\n",
       "      <td>0.564785</td>\n",
       "      <td>1</td>\n",
       "      <td>0.564750</td>\n",
       "      <td>0.665816</td>\n",
       "      <td>val0_2</td>\n",
       "      <td>val1_2</td>\n",
       "      <td>val0_1</td>\n",
       "      <td>val1_1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2997</th>\n",
       "      <td>0.182363</td>\n",
       "      <td>0.415749</td>\n",
       "      <td>0.088817</td>\n",
       "      <td>0.804872</td>\n",
       "      <td>0.024023</td>\n",
       "      <td>0.786688</td>\n",
       "      <td>1</td>\n",
       "      <td>0.180254</td>\n",
       "      <td>0.417115</td>\n",
       "      <td>val0_1</td>\n",
       "      <td>val1_1</td>\n",
       "      <td>val0_0</td>\n",
       "      <td>val1_1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2998</th>\n",
       "      <td>0.391255</td>\n",
       "      <td>0.475301</td>\n",
       "      <td>0.744740</td>\n",
       "      <td>0.065804</td>\n",
       "      <td>0.913498</td>\n",
       "      <td>0.473249</td>\n",
       "      <td>1</td>\n",
       "      <td>0.392185</td>\n",
       "      <td>0.470084</td>\n",
       "      <td>val0_2</td>\n",
       "      <td>val1_1</td>\n",
       "      <td>val0_0</td>\n",
       "      <td>val1_1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2999</th>\n",
       "      <td>0.531529</td>\n",
       "      <td>0.698122</td>\n",
       "      <td>0.897845</td>\n",
       "      <td>0.069955</td>\n",
       "      <td>0.927006</td>\n",
       "      <td>0.584906</td>\n",
       "      <td>1</td>\n",
       "      <td>0.529527</td>\n",
       "      <td>0.702879</td>\n",
       "      <td>val0_2</td>\n",
       "      <td>val1_2</td>\n",
       "      <td>val0_1</td>\n",
       "      <td>val1_1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3000 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         num_0     num_1     num_2     num_3     num_4     num_5  label  \\\n",
       "0     0.563929  0.629615  0.748641  0.010215  0.970001  0.687788      1   \n",
       "1     0.823907  0.149258  0.792533  0.857954  0.400110  0.800766      1   \n",
       "2     0.406645  0.906626  0.326266  0.799757  0.057018  0.564204      1   \n",
       "3     0.182652  0.951966  0.232610  0.852475  0.735349  0.478572      0   \n",
       "4     0.405598  0.430716  0.542525  0.566506  0.379286  0.606670      1   \n",
       "...        ...       ...       ...       ...       ...       ...    ...   \n",
       "2995  0.364130  0.678050  0.075035  0.462473  0.266227  0.194694      1   \n",
       "2996  0.568323  0.666334  0.857290  0.229171  0.778167  0.564785      1   \n",
       "2997  0.182363  0.415749  0.088817  0.804872  0.024023  0.786688      1   \n",
       "2998  0.391255  0.475301  0.744740  0.065804  0.913498  0.473249      1   \n",
       "2999  0.531529  0.698122  0.897845  0.069955  0.927006  0.584906      1   \n",
       "\n",
       "      num_c0_num_0  num_c1_num_1 CN_0_num_0 CN_1_num_1 CC_0_num_0 CC_1_num_1  \n",
       "0         0.554528      0.632123     val0_2     val1_2     val0_1     val1_1  \n",
       "1         0.825264      0.148166     val0_1     val1_1     val0_1     val1_0  \n",
       "2         0.412858      0.907607     val0_2     val1_3     val0_0     val1_1  \n",
       "3         0.180338      0.952968     val0_1     val1_0     val0_0     val1_2  \n",
       "4         0.409656      0.434478     val0_2     val1_0     val0_0     val1_1  \n",
       "...            ...           ...        ...        ...        ...        ...  \n",
       "2995      0.372719      0.678672     val0_2     val1_2     val0_0     val1_1  \n",
       "2996      0.564750      0.665816     val0_2     val1_2     val0_1     val1_1  \n",
       "2997      0.180254      0.417115     val0_1     val1_1     val0_0     val1_1  \n",
       "2998      0.392185      0.470084     val0_2     val1_1     val0_0     val1_1  \n",
       "2999      0.529527      0.702879     val0_2     val1_2     val0_1     val1_1  \n",
       "\n",
       "[3000 rows x 13 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaler = DataQuantileTransformer(exclude_cols=['label'])\n",
    "scaler.fit(df)\n",
    "scaled_df = scaler.transform(df)\n",
    "scaled_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "005ffd81",
   "metadata": {},
   "source": [
    "## Using Multiple Scalers: Fit and Transform\n",
    "\n",
    "In case the user wants to use multiple scalers in succession, they can use the **transform_pipe** parameter. This parameter is used by many other classes in this library, and it allows users to provide other class objects that has a fit and transform methods from the current library to be used internally. To do this, instantiate different scaler classes implemented in the **dataprocessing** module, create a list of these objects, and provide this list to the **transform_pipe** parameter. In the example below, we created a list with the following scalers: DataRobustScaler, DataMinMaxScaler, and DataQuantileTransformer. These scalers are passed as a parameter when creating an object of the DataRobustScaler class. Note however that we also pass an object from the class BasicImputer() into this list. The order of the list is important: it dictates the order by which the fit() and transform() methods will be called. In this case for example, when the fit method is called, we call the fit method of all transforms in the transform_pipe before calling the fit method of the main scaler (DataRobustScaler in this case). Therefore, we first call the fit and transform for the BasicImputer class, the resulting data frame is used to call the fit and transform methods of the DataRobustScaler class, and so on. This same process is used for the transform method as well.\n",
    "\n",
    "Note that this time, instead of using the default values for each scaler, we are also providing a specific sklearn scaler to each of the scalers created. For instance, when creating the DataPowerTransformer object, we specify that instead of creating a default PowerTransformer (from sklearn) internally, we want to use a specific object of this PowerTransformer class. This allows the users to customize their sklearn instances that should be used inside each scaler class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "4304b234",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No columns specified for imputation. These columns have been automatically identified:\n",
      "[]\n"
     ]
    },
    {
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       "      <th>CN_0_num_0</th>\n",
       "      <th>CN_1_num_1</th>\n",
       "      <th>CC_0_num_0</th>\n",
       "      <th>CC_1_num_1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.118733</td>\n",
       "      <td>0.237866</td>\n",
       "      <td>0.492773</td>\n",
       "      <td>0.161065</td>\n",
       "      <td>0.970001</td>\n",
       "      <td>0.372973</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.108853</td>\n",
       "      <td>0.264084</td>\n",
       "      <td>val0_2</td>\n",
       "      <td>val1_2</td>\n",
       "      <td>val0_1</td>\n",
       "      <td>val1_1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.601353</td>\n",
       "      <td>-0.795141</td>\n",
       "      <td>0.621367</td>\n",
       "      <td>0.621755</td>\n",
       "      <td>0.400110</td>\n",
       "      <td>0.629804</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.650448</td>\n",
       "      <td>-0.703694</td>\n",
       "      <td>val0_1</td>\n",
       "      <td>val1_1</td>\n",
       "      <td>val0_1</td>\n",
       "      <td>val1_0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.175575</td>\n",
       "      <td>0.951508</td>\n",
       "      <td>-0.351287</td>\n",
       "      <td>0.590821</td>\n",
       "      <td>0.057018</td>\n",
       "      <td>0.140249</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.174553</td>\n",
       "      <td>0.814974</td>\n",
       "      <td>val0_2</td>\n",
       "      <td>val1_3</td>\n",
       "      <td>val0_0</td>\n",
       "      <td>val1_1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.799932</td>\n",
       "      <td>1.229606</td>\n",
       "      <td>-0.544233</td>\n",
       "      <td>0.617707</td>\n",
       "      <td>0.735349</td>\n",
       "      <td>-0.040882</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.639699</td>\n",
       "      <td>0.905684</td>\n",
       "      <td>val0_1</td>\n",
       "      <td>val1_0</td>\n",
       "      <td>val0_0</td>\n",
       "      <td>val1_2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.177570</td>\n",
       "      <td>-0.130169</td>\n",
       "      <td>0.080924</td>\n",
       "      <td>0.502849</td>\n",
       "      <td>0.379286</td>\n",
       "      <td>0.226987</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.180957</td>\n",
       "      <td>-0.131151</td>\n",
       "      <td>val0_2</td>\n",
       "      <td>val1_0</td>\n",
       "      <td>val0_0</td>\n",
       "      <td>val1_1</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2995</th>\n",
       "      <td>-0.256738</td>\n",
       "      <td>0.334942</td>\n",
       "      <td>-1.097054</td>\n",
       "      <td>0.470205</td>\n",
       "      <td>0.266227</td>\n",
       "      <td>-0.605382</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.254850</td>\n",
       "      <td>0.357168</td>\n",
       "      <td>val0_2</td>\n",
       "      <td>val1_2</td>\n",
       "      <td>val0_0</td>\n",
       "      <td>val1_1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2996</th>\n",
       "      <td>0.127940</td>\n",
       "      <td>0.312279</td>\n",
       "      <td>0.792817</td>\n",
       "      <td>0.388732</td>\n",
       "      <td>0.778167</td>\n",
       "      <td>0.141952</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.129300</td>\n",
       "      <td>0.331460</td>\n",
       "      <td>val0_2</td>\n",
       "      <td>val1_2</td>\n",
       "      <td>val0_1</td>\n",
       "      <td>val1_1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2997</th>\n",
       "      <td>-0.802343</td>\n",
       "      <td>-0.156593</td>\n",
       "      <td>-1.030666</td>\n",
       "      <td>0.592440</td>\n",
       "      <td>0.024023</td>\n",
       "      <td>0.595418</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.639868</td>\n",
       "      <td>-0.165871</td>\n",
       "      <td>val0_1</td>\n",
       "      <td>val1_1</td>\n",
       "      <td>val0_0</td>\n",
       "      <td>val1_1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2998</th>\n",
       "      <td>-0.204698</td>\n",
       "      <td>-0.052545</td>\n",
       "      <td>0.481543</td>\n",
       "      <td>0.282118</td>\n",
       "      <td>0.913498</td>\n",
       "      <td>-0.048541</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.215909</td>\n",
       "      <td>-0.059949</td>\n",
       "      <td>val0_2</td>\n",
       "      <td>val1_1</td>\n",
       "      <td>val0_0</td>\n",
       "      <td>val1_1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2999</th>\n",
       "      <td>0.059137</td>\n",
       "      <td>0.374550</td>\n",
       "      <td>0.974962</td>\n",
       "      <td>0.285427</td>\n",
       "      <td>0.927006</td>\n",
       "      <td>0.187809</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.058840</td>\n",
       "      <td>0.405575</td>\n",
       "      <td>val0_2</td>\n",
       "      <td>val1_2</td>\n",
       "      <td>val0_1</td>\n",
       "      <td>val1_1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3000 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         num_0     num_1     num_2     num_3     num_4     num_5  label  \\\n",
       "0     0.118733  0.237866  0.492773  0.161065  0.970001  0.372973    1.0   \n",
       "1     0.601353 -0.795141  0.621367  0.621755  0.400110  0.629804    1.0   \n",
       "2    -0.175575  0.951508 -0.351287  0.590821  0.057018  0.140249    1.0   \n",
       "3    -0.799932  1.229606 -0.544233  0.617707  0.735349 -0.040882    0.0   \n",
       "4    -0.177570 -0.130169  0.080924  0.502849  0.379286  0.226987    1.0   \n",
       "...        ...       ...       ...       ...       ...       ...    ...   \n",
       "2995 -0.256738  0.334942 -1.097054  0.470205  0.266227 -0.605382    1.0   \n",
       "2996  0.127940  0.312279  0.792817  0.388732  0.778167  0.141952    1.0   \n",
       "2997 -0.802343 -0.156593 -1.030666  0.592440  0.024023  0.595418    1.0   \n",
       "2998 -0.204698 -0.052545  0.481543  0.282118  0.913498 -0.048541    1.0   \n",
       "2999  0.059137  0.374550  0.974962  0.285427  0.927006  0.187809    1.0   \n",
       "\n",
       "      num_c0_num_0  num_c1_num_1 CN_0_num_0 CN_1_num_1 CC_0_num_0 CC_1_num_1  \n",
       "0         0.108853      0.264084     val0_2     val1_2     val0_1     val1_1  \n",
       "1         0.650448     -0.703694     val0_1     val1_1     val0_1     val1_0  \n",
       "2        -0.174553      0.814974     val0_2     val1_3     val0_0     val1_1  \n",
       "3        -0.639699      0.905684     val0_1     val1_0     val0_0     val1_2  \n",
       "4        -0.180957     -0.131151     val0_2     val1_0     val0_0     val1_1  \n",
       "...            ...           ...        ...        ...        ...        ...  \n",
       "2995     -0.254850      0.357168     val0_2     val1_2     val0_0     val1_1  \n",
       "2996      0.129300      0.331460     val0_2     val1_2     val0_1     val1_1  \n",
       "2997     -0.639868     -0.165871     val0_1     val1_1     val0_0     val1_1  \n",
       "2998     -0.215909     -0.059949     val0_2     val1_1     val0_0     val1_1  \n",
       "2999      0.058840      0.405575     val0_2     val1_2     val0_1     val1_1  \n",
       "\n",
       "[3000 rows x 13 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "imputer = BasicImputer()\n",
    "s1 = DataRobustScaler(\n",
    "\t\t\tscaler_obj=RobustScaler(),\n",
    "\t\t\texclude_cols=['num_3', 'num_4', 'num_5', 'label', 'CN_0_num_0', 'CN_1_num_1', 'CC_0_num_0', 'CC_1_num_1']\n",
    "\t\t\t)\n",
    "s2 = DataMinMaxScaler(\n",
    "\t\t\tscaler_obj=MinMaxScaler(),\n",
    "\t\t\texclude_cols=['num_0', 'num_1', 'num_2', 'num_4', 'num_5', 'label', 'CN_0_num_0', 'CN_1_num_1', 'CC_0_num_0', 'CC_1_num_1']\n",
    "\t\t\t)\n",
    "s3 = DataQuantileTransformer(\n",
    "\t\t\tscaler_obj=QuantileTransformer(),\n",
    "\t\t\texclude_cols=['num_0', 'num_1', 'num_2', 'num_3', 'num_5', 'label', 'CN_0_num_0', 'CN_1_num_1', 'CC_0_num_0', 'CC_1_num_1']\n",
    "\t\t\t)\n",
    "scaler = DataRobustScaler(\n",
    "\t\t\tscaler_obj=RobustScaler(),\n",
    "\t\t\texclude_cols=['num_0', 'num_1', 'num_2', 'num_3', 'num_4', 'label', 'CN_0_num_0', 'CN_1_num_1', 'CC_0_num_0', 'CC_1_num_1'],\n",
    "\t\t\ttransform_pipe=[imputer, s1, s2, s3]\n",
    "\t\t\t)\n",
    "\n",
    "\n",
    "scaler.fit(df)\n",
    "scaled_df = scaler.transform(df)\n",
    "scaled_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6bf63eb5",
   "metadata": {},
   "source": [
    "## Using Multiple Scalers: Inverse Transform\n",
    "\n",
    "When calling the **inverse_transform**, we should use the reverse logic used for the fit and transform methods when using the transform_pipe parameter: we call the inverse_transform of each scaler in the reverse order that they were called during the transform method. This way, the order of the inverse_transform calls are the following: DataRobustScaler, DataQuantileTransformer, DataMinMaxScaler, DataPowerTransformer. By doing this in the reverse order, we can recover the original dataset, as shown below. \n",
    "\n",
    "Note however that the **inverse_transform()** method is only called for the scalers that apear after the last transform object that doesn't inherit from the DataScaler() class. For example, if we create a DataRobustScaler() object using **transform_pipe = [BasicImputer(), DataQuantileTransformer(), EncoderOHE(), DataMinMaxScaler()]**, then when calling the fit or transform method for the base scaler (**DataRobustScaler**), we would execute the fit or transform for the objects in the transform_pipe in the order they appear. But when the **inverse_transform()** of the base scaler is called, the  **inverse_transform** would be called in the following order: **DataRobustScaler**, followed by **DataMinMaxScaler**. The inverse transform of the DataQuantileTransformer wouldn't be called because it appears between two non-scaler transforms (**BasicImputer** and **EncoderOHE**). Since the **EncoderOHE** doesn't have an inverse transform, we stop the chain of inverse transformations when we reach the first non-scaler transform object moving from the last object to the first, which in this case is the **EncoderOHE**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "bd39a646",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>num_0</th>\n",
       "      <th>num_1</th>\n",
       "      <th>num_2</th>\n",
       "      <th>num_3</th>\n",
       "      <th>num_4</th>\n",
       "      <th>num_5</th>\n",
       "      <th>label</th>\n",
       "      <th>num_c0_num_0</th>\n",
       "      <th>num_c1_num_1</th>\n",
       "      <th>CN_0_num_0</th>\n",
       "      <th>CN_1_num_1</th>\n",
       "      <th>CC_0_num_0</th>\n",
       "      <th>CC_1_num_1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.838682</td>\n",
       "      <td>-1.408271</td>\n",
       "      <td>2.758909</td>\n",
       "      <td>-1.558914</td>\n",
       "      <td>0.606338</td>\n",
       "      <td>2.828664</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.796571</td>\n",
       "      <td>-1.397257</td>\n",
       "      <td>val0_2</td>\n",
       "      <td>val1_2</td>\n",
       "      <td>val0_1</td>\n",
       "      <td>val1_1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3.249825</td>\n",
       "      <td>-3.925450</td>\n",
       "      <td>2.953185</td>\n",
       "      <td>3.540991</td>\n",
       "      <td>-2.340552</td>\n",
       "      <td>3.398367</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.257607</td>\n",
       "      <td>-3.925562</td>\n",
       "      <td>val0_1</td>\n",
       "      <td>val1_1</td>\n",
       "      <td>val0_1</td>\n",
       "      <td>val1_0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.978148</td>\n",
       "      <td>0.330699</td>\n",
       "      <td>1.483723</td>\n",
       "      <td>3.198539</td>\n",
       "      <td>-4.134112</td>\n",
       "      <td>2.312435</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.041164</td>\n",
       "      <td>0.338390</td>\n",
       "      <td>val0_2</td>\n",
       "      <td>val1_3</td>\n",
       "      <td>val0_0</td>\n",
       "      <td>val1_1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.847425</td>\n",
       "      <td>1.008353</td>\n",
       "      <td>1.192225</td>\n",
       "      <td>3.496178</td>\n",
       "      <td>-1.120895</td>\n",
       "      <td>1.910650</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.875685</td>\n",
       "      <td>1.020226</td>\n",
       "      <td>val0_1</td>\n",
       "      <td>val1_0</td>\n",
       "      <td>val0_0</td>\n",
       "      <td>val1_2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.972314</td>\n",
       "      <td>-2.305080</td>\n",
       "      <td>2.136697</td>\n",
       "      <td>2.224677</td>\n",
       "      <td>-2.409424</td>\n",
       "      <td>2.504838</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.012618</td>\n",
       "      <td>-2.295010</td>\n",
       "      <td>val0_2</td>\n",
       "      <td>val1_0</td>\n",
       "      <td>val0_0</td>\n",
       "      <td>val1_1</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2995</th>\n",
       "      <td>0.740834</td>\n",
       "      <td>-1.171719</td>\n",
       "      <td>0.357037</td>\n",
       "      <td>1.863302</td>\n",
       "      <td>-2.869701</td>\n",
       "      <td>0.658475</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.776097</td>\n",
       "      <td>-1.167798</td>\n",
       "      <td>val0_2</td>\n",
       "      <td>val1_2</td>\n",
       "      <td>val0_0</td>\n",
       "      <td>val1_1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2996</th>\n",
       "      <td>1.865602</td>\n",
       "      <td>-1.226943</td>\n",
       "      <td>3.212207</td>\n",
       "      <td>0.961388</td>\n",
       "      <td>-0.925646</td>\n",
       "      <td>2.316213</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.848407</td>\n",
       "      <td>-1.231393</td>\n",
       "      <td>val0_2</td>\n",
       "      <td>val1_2</td>\n",
       "      <td>val0_1</td>\n",
       "      <td>val1_1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2997</th>\n",
       "      <td>-0.854474</td>\n",
       "      <td>-2.369469</td>\n",
       "      <td>0.457335</td>\n",
       "      <td>3.216465</td>\n",
       "      <td>-4.680820</td>\n",
       "      <td>3.322093</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.876535</td>\n",
       "      <td>-2.366553</td>\n",
       "      <td>val0_1</td>\n",
       "      <td>val1_1</td>\n",
       "      <td>val0_0</td>\n",
       "      <td>val1_1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2998</th>\n",
       "      <td>0.892994</td>\n",
       "      <td>-2.115929</td>\n",
       "      <td>2.741942</td>\n",
       "      <td>-0.218843</td>\n",
       "      <td>-0.097098</td>\n",
       "      <td>1.893662</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.890398</td>\n",
       "      <td>-2.143039</td>\n",
       "      <td>val0_2</td>\n",
       "      <td>val1_1</td>\n",
       "      <td>val0_0</td>\n",
       "      <td>val1_1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2999</th>\n",
       "      <td>1.664427</td>\n",
       "      <td>-1.075205</td>\n",
       "      <td>3.487387</td>\n",
       "      <td>-0.182207</td>\n",
       "      <td>0.033374</td>\n",
       "      <td>2.417933</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.654759</td>\n",
       "      <td>-1.052976</td>\n",
       "      <td>val0_2</td>\n",
       "      <td>val1_2</td>\n",
       "      <td>val0_1</td>\n",
       "      <td>val1_1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3000 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         num_0     num_1     num_2     num_3     num_4     num_5  label  \\\n",
       "0     1.838682 -1.408271  2.758909 -1.558914  0.606338  2.828664    1.0   \n",
       "1     3.249825 -3.925450  2.953185  3.540991 -2.340552  3.398367    1.0   \n",
       "2     0.978148  0.330699  1.483723  3.198539 -4.134112  2.312435    1.0   \n",
       "3    -0.847425  1.008353  1.192225  3.496178 -1.120895  1.910650    0.0   \n",
       "4     0.972314 -2.305080  2.136697  2.224677 -2.409424  2.504838    1.0   \n",
       "...        ...       ...       ...       ...       ...       ...    ...   \n",
       "2995  0.740834 -1.171719  0.357037  1.863302 -2.869701  0.658475    1.0   \n",
       "2996  1.865602 -1.226943  3.212207  0.961388 -0.925646  2.316213    1.0   \n",
       "2997 -0.854474 -2.369469  0.457335  3.216465 -4.680820  3.322093    1.0   \n",
       "2998  0.892994 -2.115929  2.741942 -0.218843 -0.097098  1.893662    1.0   \n",
       "2999  1.664427 -1.075205  3.487387 -0.182207  0.033374  2.417933    1.0   \n",
       "\n",
       "      num_c0_num_0  num_c1_num_1 CN_0_num_0 CN_1_num_1 CC_0_num_0 CC_1_num_1  \n",
       "0         1.796571     -1.397257     val0_2     val1_2     val0_1     val1_1  \n",
       "1         3.257607     -3.925562     val0_1     val1_1     val0_1     val1_0  \n",
       "2         1.041164      0.338390     val0_2     val1_3     val0_0     val1_1  \n",
       "3        -0.875685      1.020226     val0_1     val1_0     val0_0     val1_2  \n",
       "4         1.012618     -2.295010     val0_2     val1_0     val0_0     val1_1  \n",
       "...            ...           ...        ...        ...        ...        ...  \n",
       "2995      0.776097     -1.167798     val0_2     val1_2     val0_0     val1_1  \n",
       "2996      1.848407     -1.231393     val0_2     val1_2     val0_1     val1_1  \n",
       "2997     -0.876535     -2.366553     val0_1     val1_1     val0_0     val1_1  \n",
       "2998      0.890398     -2.143039     val0_2     val1_1     val0_0     val1_1  \n",
       "2999      1.654759     -1.052976     val0_2     val1_2     val0_1     val1_1  \n",
       "\n",
       "[3000 rows x 13 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "org_df = scaler.inverse_transform(scaled_df)\n",
    "org_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "79caaed3",
   "metadata": {},
   "source": [
    "## Using the include_cols instead of the exclude_cols parameter\n",
    "\n",
    "The exclude_cols parameter is useful when we want to leave only a few features out of the scaling process. However, in some cases (such as the one presented above), we might want to create scalers that are applied to only a few columns. Using the exclude_cols can become tedious and error prone in these situations, since we need to pass a long list of columns that should be ignored. In these scenarios, we can instead use the include_cols parameter, which works as the inverse of the exclude_cols parameter: the data transformations will be applied only to the columns in the include_cols parameter. We replicated the experiment presented two cells above, but this time using the include_cols parameter instead of the exclude_cols parameter:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "52e4c83b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No columns specified for imputation. These columns have been automatically identified:\n",
      "[]\n",
      "No columns specified for encoding. These columns have been automatically identfied as the following:\n",
      "['CN_0_num_0', 'CN_1_num_1', 'CC_0_num_0', 'CC_1_num_1']\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>num_0</th>\n",
       "      <th>num_1</th>\n",
       "      <th>num_2</th>\n",
       "      <th>num_3</th>\n",
       "      <th>num_4</th>\n",
       "      <th>num_5</th>\n",
       "      <th>label</th>\n",
       "      <th>num_c0_num_0</th>\n",
       "      <th>num_c1_num_1</th>\n",
       "      <th>CN_0_num_0_val0_1</th>\n",
       "      <th>CN_0_num_0_val0_2</th>\n",
       "      <th>CN_0_num_0_val0_3</th>\n",
       "      <th>CN_0_num_0_val0_4</th>\n",
       "      <th>CN_1_num_1_val1_1</th>\n",
       "      <th>CN_1_num_1_val1_2</th>\n",
       "      <th>CN_1_num_1_val1_3</th>\n",
       "      <th>CC_0_num_0_val0_1</th>\n",
       "      <th>CC_1_num_1_val1_1</th>\n",
       "      <th>CC_1_num_1_val1_2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.118733</td>\n",
       "      <td>0.237866</td>\n",
       "      <td>0.492773</td>\n",
       "      <td>0.161065</td>\n",
       "      <td>0.970001</td>\n",
       "      <td>0.372973</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.796571</td>\n",
       "      <td>-1.397257</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.601353</td>\n",
       "      <td>-0.795141</td>\n",
       "      <td>0.621367</td>\n",
       "      <td>0.621755</td>\n",
       "      <td>0.400110</td>\n",
       "      <td>0.629804</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.257607</td>\n",
       "      <td>-3.925562</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.175575</td>\n",
       "      <td>0.951508</td>\n",
       "      <td>-0.351287</td>\n",
       "      <td>0.590821</td>\n",
       "      <td>0.057018</td>\n",
       "      <td>0.140249</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.041164</td>\n",
       "      <td>0.338390</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.799932</td>\n",
       "      <td>1.229606</td>\n",
       "      <td>-0.544233</td>\n",
       "      <td>0.617707</td>\n",
       "      <td>0.735349</td>\n",
       "      <td>-0.040882</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.875685</td>\n",
       "      <td>1.020226</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.177570</td>\n",
       "      <td>-0.130169</td>\n",
       "      <td>0.080924</td>\n",
       "      <td>0.502849</td>\n",
       "      <td>0.379286</td>\n",
       "      <td>0.226987</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.012618</td>\n",
       "      <td>-2.295010</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2995</th>\n",
       "      <td>-0.256738</td>\n",
       "      <td>0.334942</td>\n",
       "      <td>-1.097054</td>\n",
       "      <td>0.470205</td>\n",
       "      <td>0.266227</td>\n",
       "      <td>-0.605382</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.776097</td>\n",
       "      <td>-1.167798</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2996</th>\n",
       "      <td>0.127940</td>\n",
       "      <td>0.312279</td>\n",
       "      <td>0.792817</td>\n",
       "      <td>0.388732</td>\n",
       "      <td>0.778167</td>\n",
       "      <td>0.141952</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.848407</td>\n",
       "      <td>-1.231393</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2997</th>\n",
       "      <td>-0.802343</td>\n",
       "      <td>-0.156593</td>\n",
       "      <td>-1.030666</td>\n",
       "      <td>0.592440</td>\n",
       "      <td>0.024023</td>\n",
       "      <td>0.595418</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.876535</td>\n",
       "      <td>-2.366553</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2998</th>\n",
       "      <td>-0.204698</td>\n",
       "      <td>-0.052545</td>\n",
       "      <td>0.481543</td>\n",
       "      <td>0.282118</td>\n",
       "      <td>0.913498</td>\n",
       "      <td>-0.048541</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.890398</td>\n",
       "      <td>-2.143039</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2999</th>\n",
       "      <td>0.059137</td>\n",
       "      <td>0.374550</td>\n",
       "      <td>0.974962</td>\n",
       "      <td>0.285427</td>\n",
       "      <td>0.927006</td>\n",
       "      <td>0.187809</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.654759</td>\n",
       "      <td>-1.052976</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3000 rows × 19 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         num_0     num_1     num_2     num_3     num_4     num_5  label  \\\n",
       "0     0.118733  0.237866  0.492773  0.161065  0.970001  0.372973    1.0   \n",
       "1     0.601353 -0.795141  0.621367  0.621755  0.400110  0.629804    1.0   \n",
       "2    -0.175575  0.951508 -0.351287  0.590821  0.057018  0.140249    1.0   \n",
       "3    -0.799932  1.229606 -0.544233  0.617707  0.735349 -0.040882    0.0   \n",
       "4    -0.177570 -0.130169  0.080924  0.502849  0.379286  0.226987    1.0   \n",
       "...        ...       ...       ...       ...       ...       ...    ...   \n",
       "2995 -0.256738  0.334942 -1.097054  0.470205  0.266227 -0.605382    1.0   \n",
       "2996  0.127940  0.312279  0.792817  0.388732  0.778167  0.141952    1.0   \n",
       "2997 -0.802343 -0.156593 -1.030666  0.592440  0.024023  0.595418    1.0   \n",
       "2998 -0.204698 -0.052545  0.481543  0.282118  0.913498 -0.048541    1.0   \n",
       "2999  0.059137  0.374550  0.974962  0.285427  0.927006  0.187809    1.0   \n",
       "\n",
       "      num_c0_num_0  num_c1_num_1  CN_0_num_0_val0_1  CN_0_num_0_val0_2  \\\n",
       "0         1.796571     -1.397257                  0                  1   \n",
       "1         3.257607     -3.925562                  1                  0   \n",
       "2         1.041164      0.338390                  0                  1   \n",
       "3        -0.875685      1.020226                  1                  0   \n",
       "4         1.012618     -2.295010                  0                  1   \n",
       "...            ...           ...                ...                ...   \n",
       "2995      0.776097     -1.167798                  0                  1   \n",
       "2996      1.848407     -1.231393                  0                  1   \n",
       "2997     -0.876535     -2.366553                  1                  0   \n",
       "2998      0.890398     -2.143039                  0                  1   \n",
       "2999      1.654759     -1.052976                  0                  1   \n",
       "\n",
       "      CN_0_num_0_val0_3  CN_0_num_0_val0_4  CN_1_num_1_val1_1  \\\n",
       "0                     0                  0                  0   \n",
       "1                     0                  0                  1   \n",
       "2                     0                  0                  0   \n",
       "3                     0                  0                  0   \n",
       "4                     0                  0                  0   \n",
       "...                 ...                ...                ...   \n",
       "2995                  0                  0                  0   \n",
       "2996                  0                  0                  0   \n",
       "2997                  0                  0                  1   \n",
       "2998                  0                  0                  1   \n",
       "2999                  0                  0                  0   \n",
       "\n",
       "      CN_1_num_1_val1_2  CN_1_num_1_val1_3  CC_0_num_0_val0_1  \\\n",
       "0                     1                  0                  1   \n",
       "1                     0                  0                  1   \n",
       "2                     0                  1                  0   \n",
       "3                     0                  0                  0   \n",
       "4                     0                  0                  0   \n",
       "...                 ...                ...                ...   \n",
       "2995                  1                  0                  0   \n",
       "2996                  1                  0                  1   \n",
       "2997                  0                  0                  0   \n",
       "2998                  0                  0                  0   \n",
       "2999                  1                  0                  1   \n",
       "\n",
       "      CC_1_num_1_val1_1  CC_1_num_1_val1_2  \n",
       "0                     1                  0  \n",
       "1                     0                  0  \n",
       "2                     1                  0  \n",
       "3                     0                  1  \n",
       "4                     1                  0  \n",
       "...                 ...                ...  \n",
       "2995                  1                  0  \n",
       "2996                  1                  0  \n",
       "2997                  1                  0  \n",
       "2998                  1                  0  \n",
       "2999                  1                  0  \n",
       "\n",
       "[3000 rows x 19 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "imputer = BasicImputer()\n",
    "encoder = EncoderOHE()\n",
    "s1 = DataRobustScaler(\n",
    "\t\t\tscaler_obj=RobustScaler(),\n",
    "\t\t\tinclude_cols=['num_0', 'num_1', 'num_2']\n",
    "\t\t\t)\n",
    "s2 = DataMinMaxScaler(\n",
    "\t\t\tscaler_obj=MinMaxScaler(),\n",
    "\t\t\tinclude_cols=['num_3']\n",
    "\t\t\t)\n",
    "s3 = DataQuantileTransformer(\n",
    "\t\t\tscaler_obj=QuantileTransformer(),\n",
    "\t\t\tinclude_cols=['num_4']\n",
    "\t\t\t)\n",
    "scaler = DataRobustScaler(\n",
    "\t\t\tscaler_obj=RobustScaler(),\n",
    "\t\t\tinclude_cols=['num_5'],\n",
    "\t\t\ttransform_pipe=[imputer, encoder, s1, s2, s3]\n",
    "\t\t\t)\n",
    "\n",
    "\n",
    "scaler.fit(df)\n",
    "scaled_df = scaler.transform(df)\n",
    "scaled_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "52270d43",
   "metadata": {},
   "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>CN_0_num_0</th>\n",
       "      <th>CN_1_num_1</th>\n",
       "      <th>CC_0_num_0</th>\n",
       "      <th>CC_1_num_1</th>\n",
       "      <th>num_0</th>\n",
       "      <th>num_1</th>\n",
       "      <th>num_2</th>\n",
       "      <th>num_3</th>\n",
       "      <th>num_4</th>\n",
       "      <th>num_5</th>\n",
       "      <th>label</th>\n",
       "      <th>num_c0_num_0</th>\n",
       "      <th>num_c1_num_1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>val0_2</td>\n",
       "      <td>val1_2</td>\n",
       "      <td>val0_1</td>\n",
       "      <td>val1_1</td>\n",
       "      <td>1.838682</td>\n",
       "      <td>-1.408271</td>\n",
       "      <td>2.758909</td>\n",
       "      <td>-1.558914</td>\n",
       "      <td>0.606338</td>\n",
       "      <td>2.828664</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.796571</td>\n",
       "      <td>-1.397257</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>val0_1</td>\n",
       "      <td>val1_1</td>\n",
       "      <td>val0_1</td>\n",
       "      <td>val1_0</td>\n",
       "      <td>3.249825</td>\n",
       "      <td>-3.925450</td>\n",
       "      <td>2.953185</td>\n",
       "      <td>3.540991</td>\n",
       "      <td>-2.340552</td>\n",
       "      <td>3.398367</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.257607</td>\n",
       "      <td>-3.925562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>val0_2</td>\n",
       "      <td>val1_3</td>\n",
       "      <td>val0_0</td>\n",
       "      <td>val1_1</td>\n",
       "      <td>0.978148</td>\n",
       "      <td>0.330699</td>\n",
       "      <td>1.483723</td>\n",
       "      <td>3.198539</td>\n",
       "      <td>-4.134112</td>\n",
       "      <td>2.312435</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.041164</td>\n",
       "      <td>0.338390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>val0_1</td>\n",
       "      <td>val1_0</td>\n",
       "      <td>val0_0</td>\n",
       "      <td>val1_2</td>\n",
       "      <td>-0.847425</td>\n",
       "      <td>1.008353</td>\n",
       "      <td>1.192225</td>\n",
       "      <td>3.496178</td>\n",
       "      <td>-1.120895</td>\n",
       "      <td>1.910650</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.875685</td>\n",
       "      <td>1.020226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>val0_2</td>\n",
       "      <td>val1_0</td>\n",
       "      <td>val0_0</td>\n",
       "      <td>val1_1</td>\n",
       "      <td>0.972314</td>\n",
       "      <td>-2.305080</td>\n",
       "      <td>2.136697</td>\n",
       "      <td>2.224677</td>\n",
       "      <td>-2.409424</td>\n",
       "      <td>2.504838</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.012618</td>\n",
       "      <td>-2.295010</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2995</th>\n",
       "      <td>val0_2</td>\n",
       "      <td>val1_2</td>\n",
       "      <td>val0_0</td>\n",
       "      <td>val1_1</td>\n",
       "      <td>0.740834</td>\n",
       "      <td>-1.171719</td>\n",
       "      <td>0.357037</td>\n",
       "      <td>1.863302</td>\n",
       "      <td>-2.869701</td>\n",
       "      <td>0.658475</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.776097</td>\n",
       "      <td>-1.167798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2996</th>\n",
       "      <td>val0_2</td>\n",
       "      <td>val1_2</td>\n",
       "      <td>val0_1</td>\n",
       "      <td>val1_1</td>\n",
       "      <td>1.865602</td>\n",
       "      <td>-1.226943</td>\n",
       "      <td>3.212207</td>\n",
       "      <td>0.961388</td>\n",
       "      <td>-0.925646</td>\n",
       "      <td>2.316213</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.848407</td>\n",
       "      <td>-1.231393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2997</th>\n",
       "      <td>val0_1</td>\n",
       "      <td>val1_1</td>\n",
       "      <td>val0_0</td>\n",
       "      <td>val1_1</td>\n",
       "      <td>-0.854474</td>\n",
       "      <td>-2.369469</td>\n",
       "      <td>0.457335</td>\n",
       "      <td>3.216465</td>\n",
       "      <td>-4.680820</td>\n",
       "      <td>3.322093</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.876535</td>\n",
       "      <td>-2.366553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2998</th>\n",
       "      <td>val0_2</td>\n",
       "      <td>val1_1</td>\n",
       "      <td>val0_0</td>\n",
       "      <td>val1_1</td>\n",
       "      <td>0.892994</td>\n",
       "      <td>-2.115929</td>\n",
       "      <td>2.741942</td>\n",
       "      <td>-0.218843</td>\n",
       "      <td>-0.097098</td>\n",
       "      <td>1.893662</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.890398</td>\n",
       "      <td>-2.143039</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2999</th>\n",
       "      <td>val0_2</td>\n",
       "      <td>val1_2</td>\n",
       "      <td>val0_1</td>\n",
       "      <td>val1_1</td>\n",
       "      <td>1.664427</td>\n",
       "      <td>-1.075205</td>\n",
       "      <td>3.487387</td>\n",
       "      <td>-0.182207</td>\n",
       "      <td>0.033374</td>\n",
       "      <td>2.417933</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.654759</td>\n",
       "      <td>-1.052976</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3000 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     CN_0_num_0 CN_1_num_1 CC_0_num_0 CC_1_num_1     num_0     num_1  \\\n",
       "0        val0_2     val1_2     val0_1     val1_1  1.838682 -1.408271   \n",
       "1        val0_1     val1_1     val0_1     val1_0  3.249825 -3.925450   \n",
       "2        val0_2     val1_3     val0_0     val1_1  0.978148  0.330699   \n",
       "3        val0_1     val1_0     val0_0     val1_2 -0.847425  1.008353   \n",
       "4        val0_2     val1_0     val0_0     val1_1  0.972314 -2.305080   \n",
       "...         ...        ...        ...        ...       ...       ...   \n",
       "2995     val0_2     val1_2     val0_0     val1_1  0.740834 -1.171719   \n",
       "2996     val0_2     val1_2     val0_1     val1_1  1.865602 -1.226943   \n",
       "2997     val0_1     val1_1     val0_0     val1_1 -0.854474 -2.369469   \n",
       "2998     val0_2     val1_1     val0_0     val1_1  0.892994 -2.115929   \n",
       "2999     val0_2     val1_2     val0_1     val1_1  1.664427 -1.075205   \n",
       "\n",
       "         num_2     num_3     num_4     num_5  label  num_c0_num_0  \\\n",
       "0     2.758909 -1.558914  0.606338  2.828664    1.0      1.796571   \n",
       "1     2.953185  3.540991 -2.340552  3.398367    1.0      3.257607   \n",
       "2     1.483723  3.198539 -4.134112  2.312435    1.0      1.041164   \n",
       "3     1.192225  3.496178 -1.120895  1.910650    0.0     -0.875685   \n",
       "4     2.136697  2.224677 -2.409424  2.504838    1.0      1.012618   \n",
       "...        ...       ...       ...       ...    ...           ...   \n",
       "2995  0.357037  1.863302 -2.869701  0.658475    1.0      0.776097   \n",
       "2996  3.212207  0.961388 -0.925646  2.316213    1.0      1.848407   \n",
       "2997  0.457335  3.216465 -4.680820  3.322093    1.0     -0.876535   \n",
       "2998  2.741942 -0.218843 -0.097098  1.893662    1.0      0.890398   \n",
       "2999  3.487387 -0.182207  0.033374  2.417933    1.0      1.654759   \n",
       "\n",
       "      num_c1_num_1  \n",
       "0        -1.397257  \n",
       "1        -3.925562  \n",
       "2         0.338390  \n",
       "3         1.020226  \n",
       "4        -2.295010  \n",
       "...            ...  \n",
       "2995     -1.167798  \n",
       "2996     -1.231393  \n",
       "2997     -2.366553  \n",
       "2998     -2.143039  \n",
       "2999     -1.052976  \n",
       "\n",
       "[3000 rows x 13 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "org_df = scaler.inverse_transform(scaled_df)\n",
    "org_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "55d1b4a9",
   "metadata": {},
   "source": [
    "## Other non-scaler Transforms\n",
    "\n",
    "As a final note, the scaler classes presented here also accept a list of other transformations that are not scalers, such as **DataImputer**, **EncoderOHE**, **CatBoostSelection**, among other classes from the dataprocessing module. Just create a list with these objects and pass it to the **transform_pipe** parameter. These transforms will be applied before scaling the numerical columns."
   ]
  }
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