show_results.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "import numpy as np\n",
    "import ast\n",
    "import re\n",
    "from tabulate import tabulate\n",
    "import lightgbm\n",
    "import category_encoders"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from category_encoders.ordinal import OrdinalEncoder\n",
    "from category_encoders.woe import WOEEncoder\n",
    "from category_encoders.target_encoder import TargetEncoder\n",
    "from category_encoders.sum_coding import SumEncoder\n",
    "from category_encoders.m_estimate import MEstimateEncoder\n",
    "from category_encoders.backward_difference import BackwardDifferenceEncoder\n",
    "from category_encoders.leave_one_out import LeaveOneOutEncoder\n",
    "from category_encoders.helmert import HelmertEncoder\n",
    "from category_encoders.cat_boost import CatBoostEncoder\n",
    "from category_encoders.james_stein import JamesSteinEncoder\n",
    "from category_encoders.one_hot import OneHotEncoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset_list = [\n",
    "    \"telecom\", \"adult\", \n",
    "    \"employee\", \"credit\", \"mortgages\", \n",
    "    \"promotion\", \"kick\", \"kdd_upselling\", \"taxi\", \n",
    "    \"poverty_A\",\"poverty_B\", \"poverty_C\",\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def read_info(path):\n",
    "    with open(path, \"r\") as f:\n",
    "        a = f.read()\n",
    "    dic = ast.literal_eval(a)[\"info\"]\n",
    "    print(path, dic[\"train_shape\"][0]+dic[\"test_shape\"][0], dic[\"train_shape\"], dic[\"test_shape\"], dic[\"num_cat_cols\"])\n",
    "    return dic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "./results/exp_1_telecom_r.txt 7043 (4225, 20) (2818, 20) 16\n",
      "./results/exp_1_adult_r.txt 48842 (29305, 15) (19537, 15) 8\n",
      "./results/exp_1_employee_r.txt 32769 (19661, 10) (13108, 10) 9\n",
      "./results/exp_1_credit_r.txt 307511 (184506, 121) (123005, 121) 18\n",
      "./results/exp_1_mortgages_r.txt 45642 (27385, 20) (18257, 20) 9\n",
      "./results/exp_1_promotion_r.txt 54808 (32884, 13) (21924, 13) 5\n",
      "./results/exp_1_kick_r.txt 72983 (43789, 32) (29194, 32) 19\n",
      "./results/exp_1_kdd_upselling_r.txt 50000 (30000, 231) (20000, 231) 40\n",
      "./results/exp_1_taxi_r.txt 892557 (535534, 8) (357023, 8) 5\n",
      "./results/exp_1_poverty_A_r.txt 37560 (22536, 41) (15024, 41) 38\n",
      "./results/exp_1_poverty_B_r.txt 20252 (12151, 224) (8101, 224) 191\n",
      "./results/exp_1_poverty_C_r.txt 29913 (17947, 41) (11966, 41) 35\n"
     ]
    }
   ],
   "source": [
    "for ds_name in dataset_list:\n",
    "    _ = read_info(f\"./results/exp_1_{ds_name}_r.txt\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def read_dict(path, val_type=\"None\"):\n",
    "    with open(path, \"r\") as f:\n",
    "        a = f.read()\n",
    "\n",
    "    dic = ast.literal_eval(a)\n",
    "    res = {}\n",
    "    for k in dic.keys():\n",
    "        if k != \"info\":\n",
    "            res[k] = dic[k][\"test_score\"]\n",
    "    dataframe = pd.DataFrame.from_dict(res, orient='index', columns=[val_type])\n",
    "    return dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\denis\\Anaconda3\\envs\\py36\\lib\\site-packages\\ipykernel_launcher.py:10: FutureWarning: Sorting because non-concatenation axis is not aligned. A future version\n",
      "of pandas will change to not sort by default.\n",
      "\n",
      "To accept the future behavior, pass 'sort=False'.\n",
      "\n",
      "To retain the current behavior and silence the warning, pass 'sort=True'.\n",
      "\n",
      "  # Remove the CWD from sys.path while we load stuff.\n"
     ]
    }
   ],
   "source": [
    "def _f(exp=\"exp_1\", dec=3):\n",
    "    res = []\n",
    "    cols = []\n",
    "    for ds_name in dataset_list:\n",
    "        cols.append(ds_name)\n",
    "        d1 = read_dict(f\"./results/{exp}_{ds_name}_r.txt\")\n",
    "    \n",
    "        res.append(d1)\n",
    "\n",
    "    res = pd.concat(res, axis=1)\n",
    "    res.columns = cols\n",
    "    if dec != -1:\n",
    "        res = np.round(res, dec)\n",
    "    res = res.sort_index()\n",
    "    \n",
    "    indx = []\n",
    "    for d in res.index:\n",
    "        indx.append(re.sub(r'[^\\w\\s]', '', d)) \n",
    "    res.index = indx\n",
    "    return res\n",
    "\n",
    "r_None = _f(exp=\"exp_1\", dec=-1)\n",
    "r_Single = _f(exp=\"exp_2\", dec=-1)\n",
    "r_Double = _f(exp=\"exp_3\", dec=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "telecom          0.840515\n",
       "adult            0.929890\n",
       "employee         0.853749\n",
       "credit           0.760054\n",
       "mortgages        0.702734\n",
       "promotion        0.908301\n",
       "kick             0.790890\n",
       "kdd_upselling    0.865361\n",
       "taxi             0.603382\n",
       "poverty_A        0.742915\n",
       "poverty_B        0.693057\n",
       "poverty_C        0.742004\n",
       "dtype: float64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max_scores = pd.concat([r_None, r_Single, r_Double], axis=0)\n",
    "max_scores = np.max(max_scores)\n",
    "max_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "telecom          0.500000\n",
       "adult            0.518181\n",
       "employee         0.498967\n",
       "credit           0.495732\n",
       "mortgages        0.499965\n",
       "promotion        0.540260\n",
       "kick             0.468199\n",
       "kdd_upselling    0.500000\n",
       "taxi             0.499984\n",
       "poverty_A        0.472994\n",
       "poverty_B        0.434025\n",
       "poverty_C        0.452673\n",
       "dtype: float64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "min_scores = pd.concat([r_None, r_Single, r_Double], axis=0)\n",
    "min_scores = np.min(min_scores)\n",
    "min_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "r_None_scaled = (r_None - min_scores) / (max_scores - min_scores)\n",
    "r_Single_scaled = (r_Single - min_scores) / (max_scores - min_scores)\n",
    "r_Double_scaled = (r_Double - min_scores) / (max_scores - min_scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "telecom          0.840471\n",
       "adult            0.929659\n",
       "employee         0.830000\n",
       "credit           0.760054\n",
       "mortgages        0.700131\n",
       "promotion        0.907945\n",
       "kick             0.790748\n",
       "kdd_upselling    0.864319\n",
       "taxi             0.565624\n",
       "poverty_A        0.735490\n",
       "poverty_B        0.645296\n",
       "poverty_C        0.737249\n",
       "dtype: float64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.max(r_None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "telecom          0.840427\n",
       "adult            0.929890\n",
       "employee         0.849834\n",
       "credit           0.759940\n",
       "mortgages        0.702734\n",
       "promotion        0.908301\n",
       "kick             0.790249\n",
       "kdd_upselling    0.865361\n",
       "taxi             0.603382\n",
       "poverty_A        0.742915\n",
       "poverty_B        0.690204\n",
       "poverty_C        0.742004\n",
       "dtype: float64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.max(r_Single)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "telecom          0.840515\n",
       "adult            0.929587\n",
       "employee         0.853749\n",
       "credit           0.759915\n",
       "mortgages        0.698062\n",
       "promotion        0.906287\n",
       "kick             0.790890\n",
       "kdd_upselling    0.864302\n",
       "taxi             0.603292\n",
       "poverty_A        0.741768\n",
       "poverty_B        0.693057\n",
       "poverty_C        0.736608\n",
       "dtype: float64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.max(r_Double)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Best encoder "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "|                           |      0 |\n",
      "|:--------------------------|-------:|\n",
      "| HelmertEncoder            | 0.9517 |\n",
      "| SumEncoder                | 0.9434 |\n",
      "| FrequencyEncoder          | 0.9176 |\n",
      "| CatBoostEncoderShuffle    | 0.6815 |\n",
      "| CatBoostEncoder           | 0.573  |\n",
      "| TargetEncoder             | 0.5176 |\n",
      "| JamesSteinEncoder         | 0.5164 |\n",
      "| OrdinalEncoder            | 0.4966 |\n",
      "| WOEEncoder                | 0.4907 |\n",
      "| MEstimateEncoder          | 0.4503 |\n",
      "| BackwardDifferenceEncoder | 0.413  |\n",
      "| LeaveOneOutEncoder        | 0.0698 |\n"
     ]
    }
   ],
   "source": [
    "a = r_None_scaled.mean(axis=1).sort_values(ascending=False).round(4)\n",
    "print(tabulate(pd.DataFrame(a), tablefmt=\"pipe\", headers=\"keys\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "|                           |      0 |\n",
      "|:--------------------------|-------:|\n",
      "| CatBoostEncoder           | 0.9726 |\n",
      "| OrdinalEncoder            | 0.9694 |\n",
      "| HelmertEncoder            | 0.9558 |\n",
      "| SumEncoder                | 0.9434 |\n",
      "| WOEEncoder                | 0.9326 |\n",
      "| FrequencyEncoder          | 0.9315 |\n",
      "| BackwardDifferenceEncoder | 0.9109 |\n",
      "| TargetEncoder             | 0.8915 |\n",
      "| JamesSteinEncoder         | 0.8556 |\n",
      "| MEstimateEncoder          | 0.8189 |\n",
      "| CatBoostEncoderShuffle    | 0.6961 |\n",
      "| LeaveOneOutEncoder        | 0.073  |\n"
     ]
    }
   ],
   "source": [
    "a = r_Single_scaled.mean(axis=1).sort_values(ascending=False).round(4)\n",
    "print(tabulate(pd.DataFrame(a), tablefmt=\"pipe\", headers=\"keys\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "|                        |      0 |\n",
      "|:-----------------------|-------:|\n",
      "| JamesSteinEncoder      | 0.9918 |\n",
      "| CatBoostEncoder        | 0.9917 |\n",
      "| TargetEncoder          | 0.9916 |\n",
      "| LeaveOneOutEncoder     | 0.9909 |\n",
      "| WOEEncoder             | 0.9838 |\n",
      "| MEstimateEncoder       | 0.9686 |\n",
      "| FrequencyEncoder       | 0.8019 |\n",
      "| CatBoostEncoderShuffle | 0.6656 |\n"
     ]
    }
   ],
   "source": [
    "a = r_Double_scaled.mean(axis=1).sort_values(ascending=False).round(4)\n",
    "print(tabulate(pd.DataFrame(a), tablefmt=\"pipe\", headers=\"keys\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Influence of validation "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "|                           |   telecom |   adult |   employee |   credit |   mortgages |   promotion | kick   | kdd_upselling   | taxi   |   poverty_A |   poverty_B |   poverty_C |\n",
      "|:--------------------------|----------:|--------:|-----------:|---------:|------------:|------------:|:-------|:----------------|:-------|------------:|------------:|------------:|\n",
      "| BackwardDifferenceEncoder |    0.6454 |  0.8555 |     0.5006 |   0.7442 |      0.5997 |      0.6482 |        |                 |        |      0.5149 |      0.5484 |      0.4945 |\n",
      "| CatBoostEncoder           |    0.7666 |  0.868  |     0.5004 |   0.7478 |      0.6279 |      0.7811 | 0.6583 | 0.8549          | 0.5477 |      0.5179 |      0.5638 |      0.5427 |\n",
      "| CatBoostEncoderShuffle    |    0.8177 |  0.878  |     0.5004 |   0.7527 |      0.6595 |      0.8387 | 0.6708 | 0.8558          | 0.5524 |      0.5788 |      0.6453 |      0.5916 |\n",
      "| FrequencyEncoder          |    0.8405 |  0.9291 |     0.807  |   0.7593 |      0.6949 |      0.9052 | 0.7907 | 0.8643          | 0.5656 |      0.7276 |      0.6164 |      0.7177 |\n",
      "| HelmertEncoder            |    0.8404 |  0.9297 |     0.83   |   0.7601 |      0.7001 |      0.9079 |        |                 |        |      0.7325 |      0.6343 |      0.7168 |\n",
      "| JamesSteinEncoder         |    0.7195 |  0.8688 |     0.5003 |   0.7485 |      0.6049 |      0.7984 | 0.6592 | 0.8516          | 0.5432 |      0.4918 |      0.5304 |      0.4836 |\n",
      "| LeaveOneOutEncoder        |    0.5    |  0.5214 |     0.6233 |   0.4957 |      0.5    |      0.5457 | 0.5027 | 0.5             | 0.5    |      0.5006 |      0.5002 |      0.4527 |\n",
      "| MEstimateEncoder          |    0.6944 |  0.8617 |     0.4998 |   0.7368 |      0.6086 |      0.8156 | 0.653  | 0.8448          | 0.5091 |      0.5254 |      0.434  |      0.4528 |\n",
      "| OrdinalEncoder            |    0.7409 |  0.8616 |     0.501  |   0.7445 |      0.6008 |      0.7124 | 0.6531 | 0.8448          | 0.5498 |      0.473  |      0.4683 |      0.5611 |\n",
      "| SumEncoder                |    0.8404 |  0.929  |     0.8053 |   0.7593 |      0.6944 |      0.9073 |        |                 |        |      0.7355 |      0.6206 |      0.7372 |\n",
      "| TargetEncoder             |    0.7195 |  0.8696 |     0.5003 |   0.7483 |      0.6064 |      0.7971 | 0.6594 | 0.8483          | 0.5428 |      0.4955 |      0.5401 |      0.4751 |\n",
      "| WOEEncoder                |    0.7056 |  0.8645 |     0.5012 |   0.7439 |      0.615  |      0.7345 | 0.6398 | 0.844           | 0.5485 |      0.478  |      0.5356 |      0.4671 |\n"
     ]
    }
   ],
   "source": [
    "r_None.round(4).fillna(\"\")\n",
    "print(tabulate(r_None.round(4).fillna(\"\"), tablefmt=\"pipe\", headers=\"keys\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "|                           |   telecom |   adult |   employee |   credit |   mortgages |   promotion | kick   | kdd_upselling   | taxi   |   poverty_A |   poverty_B |   poverty_C |\n",
      "|:--------------------------|----------:|--------:|-----------:|---------:|------------:|------------:|:-------|:----------------|:-------|------------:|------------:|------------:|\n",
      "| BackwardDifferenceEncoder |    0.8382 |  0.9293 |     0.7569 |   0.7595 |      0.6894 |      0.9064 |        |                 |        |      0.7323 |      0.6151 |      0.7108 |\n",
      "| CatBoostEncoder           |    0.8392 |  0.9292 |     0.8498 |   0.7594 |      0.6951 |      0.8918 | 0.7901 | 0.8654          | 0.5844 |      0.7429 |      0.6902 |      0.7333 |\n",
      "| CatBoostEncoderShuffle    |    0.8203 |  0.8781 |     0.499  |   0.753  |      0.6607 |      0.8394 | 0.6746 | 0.8552          | 0.5553 |      0.6007 |      0.6449 |      0.6046 |\n",
      "| FrequencyEncoder          |    0.8392 |  0.9293 |     0.8138 |   0.7592 |      0.6937 |      0.9055 | 0.7902 | 0.8634          | 0.582  |      0.7302 |      0.6128 |      0.7195 |\n",
      "| HelmertEncoder            |    0.8404 |  0.9297 |     0.8344 |   0.7597 |      0.7027 |      0.9083 |        |                 |        |      0.7297 |      0.6374 |      0.7196 |\n",
      "| JamesSteinEncoder         |    0.8388 |  0.9292 |     0.7817 |   0.7597 |      0.667  |      0.9053 | 0.5835 | 0.726           | 0.5898 |      0.7303 |      0.6764 |      0.7217 |\n",
      "| LeaveOneOutEncoder        |    0.5    |  0.5182 |     0.6121 |   0.4997 |      0.5    |      0.5403 | 0.4682 | 0.5             | 0.5    |      0.5103 |      0.5    |      0.4959 |\n",
      "| MEstimateEncoder          |    0.8394 |  0.929  |     0.7353 |   0.7593 |      0.6957 |      0.9054 | 0.5877 | 0.5953          | 0.5946 |      0.7302 |      0.6493 |      0.7076 |\n",
      "| OrdinalEncoder            |    0.8404 |  0.9299 |     0.8274 |   0.7585 |      0.6917 |      0.9078 | 0.7809 | 0.8465          | 0.6034 |      0.7337 |      0.6635 |      0.742  |\n",
      "| SumEncoder                |    0.8404 |  0.929  |     0.8053 |   0.7593 |      0.6944 |      0.9073 |        |                 |        |      0.7355 |      0.6206 |      0.7372 |\n",
      "| TargetEncoder             |    0.8388 |  0.9293 |     0.815  |   0.7599 |      0.6702 |      0.9057 | 0.7042 | 0.713           | 0.5894 |      0.7292 |      0.6742 |      0.7207 |\n",
      "| WOEEncoder                |    0.8393 |  0.9294 |     0.8325 |   0.7599 |      0.6801 |      0.9056 | 0.7172 | 0.8391          | 0.5903 |      0.7279 |      0.6737 |      0.7224 |\n"
     ]
    }
   ],
   "source": [
    "r_Single.round(4).fillna(\"\")\n",
    "print(tabulate(r_Single.round(4).fillna(\"\"), tablefmt=\"pipe\", headers=\"keys\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "|                        |   telecom |   adult |   employee |   credit |   mortgages |   promotion | kick   | kdd_upselling   | taxi   |   poverty_A |   poverty_B |   poverty_C |\n",
      "|:-----------------------|----------:|--------:|-----------:|---------:|------------:|------------:|:-------|:----------------|:-------|------------:|------------:|------------:|\n",
      "| CatBoostEncoder        |    0.8394 |  0.9293 |     0.8529 |   0.7592 |      0.6967 |      0.9056 | 0.7899 | 0.8633          | 0.6031 |      0.7418 |      0.6902 |      0.7343 |\n",
      "| CatBoostEncoderShuffle |    0.8233 |  0.8859 |     0.5321 |   0.7525 |      0.6236 |      0.8398 |        |                 |        |      0.6066 |      0.5815 |      0.6248 |\n",
      "| FrequencyEncoder       |    0.8371 |  0.9221 |     0.5563 |   0.755  |      0.6582 |      0.8749 | 0.7655 | 0.8551          | 0.5657 |      0.6873 |      0.6037 |      0.6961 |\n",
      "| JamesSteinEncoder      |    0.8398 |  0.9296 |     0.8489 |   0.7598 |      0.6981 |      0.905  | 0.7901 | 0.8628          | 0.6033 |      0.7412 |      0.6895 |      0.7366 |\n",
      "| LeaveOneOutEncoder     |    0.8393 |  0.9295 |     0.8496 |   0.7595 |      0.6963 |      0.9055 | 0.7902 | 0.8635          | 0.602  |      0.7416 |      0.6931 |      0.7345 |\n",
      "| MEstimateEncoder       |    0.8405 |  0.9292 |     0.8125 |   0.7597 |      0.6939 |      0.9063 | 0.7881 | 0.863           | 0.5984 |      0.7375 |      0.6801 |      0.7204 |\n",
      "| TargetEncoder          |    0.8393 |  0.9294 |     0.8537 |   0.7596 |      0.6954 |      0.9057 | 0.7909 | 0.8643          | 0.6025 |      0.7415 |      0.6903 |      0.7352 |\n",
      "| WOEEncoder             |    0.8401 |  0.9294 |     0.824  |   0.7599 |      0.6977 |      0.9041 | 0.7905 | 0.8631          | 0.6011 |      0.7407 |      0.6911 |      0.7345 |\n"
     ]
    }
   ],
   "source": [
    "r_Double.round(4).fillna(\"\")\n",
    "print(tabulate(r_Double.round(4).fillna(\"\"), tablefmt=\"pipe\", headers=\"keys\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>telecom</th>\n",
       "      <th>adult</th>\n",
       "      <th>employee</th>\n",
       "      <th>credit</th>\n",
       "      <th>mortgages</th>\n",
       "      <th>promotion</th>\n",
       "      <th>kick</th>\n",
       "      <th>kdd_upselling</th>\n",
       "      <th>taxi</th>\n",
       "      <th>poverty_A</th>\n",
       "      <th>poverty_B</th>\n",
       "      <th>poverty_C</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>BackwardDifferenceEncoder</th>\n",
       "      <td>29.9</td>\n",
       "      <td>8.6</td>\n",
       "      <td>51.2</td>\n",
       "      <td>2.1</td>\n",
       "      <td>15.0</td>\n",
       "      <td>39.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>42.2</td>\n",
       "      <td>12.2</td>\n",
       "      <td>43.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CatBoostEncoder</th>\n",
       "      <td>9.5</td>\n",
       "      <td>7.0</td>\n",
       "      <td>69.8</td>\n",
       "      <td>1.6</td>\n",
       "      <td>10.7</td>\n",
       "      <td>14.2</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>6.7</td>\n",
       "      <td>43.4</td>\n",
       "      <td>22.4</td>\n",
       "      <td>35.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CatBoostEncoderShuffle</th>\n",
       "      <td>0.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.6</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>0.5</td>\n",
       "      <td>3.8</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>2.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FrequencyEncoder</th>\n",
       "      <td>-0.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>2.9</td>\n",
       "      <td>0.4</td>\n",
       "      <td>-0.6</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HelmertEncoder</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.4</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JamesSteinEncoder</th>\n",
       "      <td>16.6</td>\n",
       "      <td>6.9</td>\n",
       "      <td>56.2</td>\n",
       "      <td>1.5</td>\n",
       "      <td>10.3</td>\n",
       "      <td>13.4</td>\n",
       "      <td>-11.5</td>\n",
       "      <td>-14.8</td>\n",
       "      <td>8.6</td>\n",
       "      <td>48.5</td>\n",
       "      <td>27.5</td>\n",
       "      <td>49.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LeaveOneOutEncoder</th>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.6</td>\n",
       "      <td>-1.8</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-6.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>9.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MEstimateEncoder</th>\n",
       "      <td>20.9</td>\n",
       "      <td>7.8</td>\n",
       "      <td>47.1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>14.3</td>\n",
       "      <td>11.0</td>\n",
       "      <td>-10.0</td>\n",
       "      <td>-29.5</td>\n",
       "      <td>16.8</td>\n",
       "      <td>39.0</td>\n",
       "      <td>49.6</td>\n",
       "      <td>56.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OrdinalEncoder</th>\n",
       "      <td>13.4</td>\n",
       "      <td>7.9</td>\n",
       "      <td>65.2</td>\n",
       "      <td>1.9</td>\n",
       "      <td>15.1</td>\n",
       "      <td>27.4</td>\n",
       "      <td>19.6</td>\n",
       "      <td>0.2</td>\n",
       "      <td>9.7</td>\n",
       "      <td>55.1</td>\n",
       "      <td>41.7</td>\n",
       "      <td>32.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SumEncoder</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TargetEncoder</th>\n",
       "      <td>16.6</td>\n",
       "      <td>6.9</td>\n",
       "      <td>62.9</td>\n",
       "      <td>1.5</td>\n",
       "      <td>10.5</td>\n",
       "      <td>13.6</td>\n",
       "      <td>6.8</td>\n",
       "      <td>-15.9</td>\n",
       "      <td>8.6</td>\n",
       "      <td>47.2</td>\n",
       "      <td>24.8</td>\n",
       "      <td>51.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WOEEncoder</th>\n",
       "      <td>19.0</td>\n",
       "      <td>7.5</td>\n",
       "      <td>66.1</td>\n",
       "      <td>2.2</td>\n",
       "      <td>10.6</td>\n",
       "      <td>23.3</td>\n",
       "      <td>12.1</td>\n",
       "      <td>-0.6</td>\n",
       "      <td>7.6</td>\n",
       "      <td>52.3</td>\n",
       "      <td>25.8</td>\n",
       "      <td>54.6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           telecom  adult  employee  credit  mortgages  \\\n",
       "BackwardDifferenceEncoder     29.9    8.6      51.2     2.1       15.0   \n",
       "CatBoostEncoder                9.5    7.0      69.8     1.6       10.7   \n",
       "CatBoostEncoderShuffle         0.3    0.0      -0.3     0.0        0.2   \n",
       "FrequencyEncoder              -0.2    0.0       0.8    -0.0       -0.2   \n",
       "HelmertEncoder                 0.0    0.0       0.5    -0.0        0.4   \n",
       "JamesSteinEncoder             16.6    6.9      56.2     1.5       10.3   \n",
       "LeaveOneOutEncoder             0.0   -0.6      -1.8     0.8        0.0   \n",
       "MEstimateEncoder              20.9    7.8      47.1     3.0       14.3   \n",
       "OrdinalEncoder                13.4    7.9      65.2     1.9       15.1   \n",
       "SumEncoder                     0.0    0.0       0.0    -0.0        0.0   \n",
       "TargetEncoder                 16.6    6.9      62.9     1.5       10.5   \n",
       "WOEEncoder                    19.0    7.5      66.1     2.2       10.6   \n",
       "\n",
       "                           promotion  kick  kdd_upselling  taxi  poverty_A  \\\n",
       "BackwardDifferenceEncoder       39.8   NaN            NaN   NaN       42.2   \n",
       "CatBoostEncoder                 14.2  20.0            1.2   6.7       43.4   \n",
       "CatBoostEncoderShuffle           0.1   0.6           -0.1   0.5        3.8   \n",
       "FrequencyEncoder                 0.0  -0.1           -0.1   2.9        0.4   \n",
       "HelmertEncoder                   0.0   NaN            NaN   NaN       -0.4   \n",
       "JamesSteinEncoder               13.4 -11.5          -14.8   8.6       48.5   \n",
       "LeaveOneOutEncoder              -1.0  -6.9            0.0  -0.0        1.9   \n",
       "MEstimateEncoder                11.0 -10.0          -29.5  16.8       39.0   \n",
       "OrdinalEncoder                  27.4  19.6            0.2   9.7       55.1   \n",
       "SumEncoder                       0.0   NaN            NaN   NaN       -0.0   \n",
       "TargetEncoder                   13.6   6.8          -15.9   8.6       47.2   \n",
       "WOEEncoder                      23.3  12.1           -0.6   7.6       52.3   \n",
       "\n",
       "                           poverty_B  poverty_C  \n",
       "BackwardDifferenceEncoder       12.2       43.7  \n",
       "CatBoostEncoder                 22.4       35.1  \n",
       "CatBoostEncoderShuffle          -0.1        2.2  \n",
       "FrequencyEncoder                -0.6        0.3  \n",
       "HelmertEncoder                   0.5        0.4  \n",
       "JamesSteinEncoder               27.5       49.2  \n",
       "LeaveOneOutEncoder              -0.0        9.5  \n",
       "MEstimateEncoder                49.6       56.3  \n",
       "OrdinalEncoder                  41.7       32.2  \n",
       "SumEncoder                       0.0       -0.0  \n",
       "TargetEncoder                   24.8       51.7  \n",
       "WOEEncoder                      25.8       54.6  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "single_none = np.round(((r_Single - r_None) / r_None * 100), 1)\n",
    "single_none\n",
    "# print(tabulate(single_none, tablefmt=\"pipe\", headers=\"keys\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "|                           |   None -> Single |\n",
      "|:--------------------------|-----------------:|\n",
      "| BackwardDifferenceEncoder |        27.1889   |\n",
      "| CatBoostEncoder           |        20.1333   |\n",
      "| CatBoostEncoderShuffle    |         0.6      |\n",
      "| FrequencyEncoder          |         0.266667 |\n",
      "| HelmertEncoder            |         0.155556 |\n",
      "| JamesSteinEncoder         |        17.7      |\n",
      "| LeaveOneOutEncoder        |         0.158333 |\n",
      "| MEstimateEncoder          |        18.8583   |\n",
      "| OrdinalEncoder            |        24.1167   |\n",
      "| SumEncoder                |         0        |\n",
      "| TargetEncoder             |        19.6      |\n",
      "| WOEEncoder                |        23.375    |\n"
     ]
    }
   ],
   "source": [
    "a = pd.DataFrame(np.mean(single_none, axis=1))\n",
    "a.columns = [\"None -> Single\"]\n",
    "print(tabulate(a, tablefmt=\"pipe\", headers=\"keys\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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>telecom</th>\n",
       "      <th>adult</th>\n",
       "      <th>employee</th>\n",
       "      <th>credit</th>\n",
       "      <th>mortgages</th>\n",
       "      <th>promotion</th>\n",
       "      <th>kick</th>\n",
       "      <th>kdd_upselling</th>\n",
       "      <th>taxi</th>\n",
       "      <th>poverty_A</th>\n",
       "      <th>poverty_B</th>\n",
       "      <th>poverty_C</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>BackwardDifferenceEncoder</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CatBoostEncoder</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1.6</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.2</td>\n",
       "      <td>3.2</td>\n",
       "      <td>-0.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CatBoostEncoderShuffle</th>\n",
       "      <td>0.4</td>\n",
       "      <td>0.9</td>\n",
       "      <td>6.6</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>-5.6</td>\n",
       "      <td>0.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-9.8</td>\n",
       "      <td>3.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FrequencyEncoder</th>\n",
       "      <td>-0.2</td>\n",
       "      <td>-0.8</td>\n",
       "      <td>-31.6</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>-5.1</td>\n",
       "      <td>-3.4</td>\n",
       "      <td>-3.1</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-2.8</td>\n",
       "      <td>-5.9</td>\n",
       "      <td>-1.5</td>\n",
       "      <td>-3.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HelmertEncoder</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JamesSteinEncoder</th>\n",
       "      <td>0.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.7</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>35.4</td>\n",
       "      <td>18.9</td>\n",
       "      <td>2.3</td>\n",
       "      <td>1.5</td>\n",
       "      <td>1.9</td>\n",
       "      <td>2.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LeaveOneOutEncoder</th>\n",
       "      <td>67.9</td>\n",
       "      <td>79.4</td>\n",
       "      <td>38.8</td>\n",
       "      <td>52.0</td>\n",
       "      <td>39.3</td>\n",
       "      <td>67.6</td>\n",
       "      <td>68.8</td>\n",
       "      <td>72.7</td>\n",
       "      <td>20.4</td>\n",
       "      <td>45.3</td>\n",
       "      <td>38.6</td>\n",
       "      <td>48.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MEstimateEncoder</th>\n",
       "      <td>0.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.3</td>\n",
       "      <td>0.1</td>\n",
       "      <td>34.1</td>\n",
       "      <td>45.0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.7</td>\n",
       "      <td>1.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OrdinalEncoder</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SumEncoder</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TargetEncoder</th>\n",
       "      <td>0.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.8</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>3.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12.3</td>\n",
       "      <td>21.2</td>\n",
       "      <td>2.2</td>\n",
       "      <td>1.7</td>\n",
       "      <td>2.4</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WOEEncoder</th>\n",
       "      <td>0.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>2.6</td>\n",
       "      <td>-0.2</td>\n",
       "      <td>10.2</td>\n",
       "      <td>2.9</td>\n",
       "      <td>1.8</td>\n",
       "      <td>1.8</td>\n",
       "      <td>2.6</td>\n",
       "      <td>1.7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           telecom  adult  employee  credit  mortgages  \\\n",
       "BackwardDifferenceEncoder      NaN    NaN       NaN     NaN        NaN   \n",
       "CatBoostEncoder                0.0    0.0       0.4    -0.0        0.2   \n",
       "CatBoostEncoderShuffle         0.4    0.9       6.6    -0.1       -5.6   \n",
       "FrequencyEncoder              -0.2   -0.8     -31.6    -0.5       -5.1   \n",
       "HelmertEncoder                 NaN    NaN       NaN     NaN        NaN   \n",
       "JamesSteinEncoder              0.1    0.0       8.6     0.0        4.7   \n",
       "LeaveOneOutEncoder            67.9   79.4      38.8    52.0       39.3   \n",
       "MEstimateEncoder               0.1    0.0      10.5     0.0       -0.3   \n",
       "OrdinalEncoder                 NaN    NaN       NaN     NaN        NaN   \n",
       "SumEncoder                     NaN    NaN       NaN     NaN        NaN   \n",
       "TargetEncoder                  0.1    0.0       4.8    -0.0        3.8   \n",
       "WOEEncoder                     0.1    0.0      -1.0    -0.0        2.6   \n",
       "\n",
       "                           promotion  kick  kdd_upselling  taxi  poverty_A  \\\n",
       "BackwardDifferenceEncoder        NaN   NaN            NaN   NaN        NaN   \n",
       "CatBoostEncoder                  1.6  -0.0           -0.2   3.2       -0.2   \n",
       "CatBoostEncoderShuffle           0.1   NaN            NaN   NaN        1.0   \n",
       "FrequencyEncoder                -3.4  -3.1           -1.0  -2.8       -5.9   \n",
       "HelmertEncoder                   NaN   NaN            NaN   NaN        NaN   \n",
       "JamesSteinEncoder               -0.0  35.4           18.9   2.3        1.5   \n",
       "LeaveOneOutEncoder              67.6  68.8           72.7  20.4       45.3   \n",
       "MEstimateEncoder                 0.1  34.1           45.0   0.6        1.0   \n",
       "OrdinalEncoder                   NaN   NaN            NaN   NaN        NaN   \n",
       "SumEncoder                       NaN   NaN            NaN   NaN        NaN   \n",
       "TargetEncoder                    0.0  12.3           21.2   2.2        1.7   \n",
       "WOEEncoder                      -0.2  10.2            2.9   1.8        1.8   \n",
       "\n",
       "                           poverty_B  poverty_C  \n",
       "BackwardDifferenceEncoder        NaN        NaN  \n",
       "CatBoostEncoder                  0.0        0.1  \n",
       "CatBoostEncoderShuffle          -9.8        3.3  \n",
       "FrequencyEncoder                -1.5       -3.3  \n",
       "HelmertEncoder                   NaN        NaN  \n",
       "JamesSteinEncoder                1.9        2.1  \n",
       "LeaveOneOutEncoder              38.6       48.1  \n",
       "MEstimateEncoder                 4.7        1.8  \n",
       "OrdinalEncoder                   NaN        NaN  \n",
       "SumEncoder                       NaN        NaN  \n",
       "TargetEncoder                    2.4        2.0  \n",
       "WOEEncoder                       2.6        1.7  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "double_single = np.round(((r_Double - r_Single) / r_Single * 100), 1)\n",
    "double_single\n",
    "# print(tabulate(double_single, tablefmt=\"pipe\", headers=\"keys\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "|                           |   Single -> Double |\n",
      "|:--------------------------|-------------------:|\n",
      "| BackwardDifferenceEncoder |         nan        |\n",
      "| CatBoostEncoder           |           0.425    |\n",
      "| CatBoostEncoderShuffle    |          -0.355556 |\n",
      "| FrequencyEncoder          |          -4.93333  |\n",
      "| HelmertEncoder            |         nan        |\n",
      "| JamesSteinEncoder         |           6.29167  |\n",
      "| LeaveOneOutEncoder        |          53.2417   |\n",
      "| MEstimateEncoder          |           8.13333  |\n",
      "| OrdinalEncoder            |         nan        |\n",
      "| SumEncoder                |         nan        |\n",
      "| TargetEncoder             |           4.20833  |\n",
      "| WOEEncoder                |           1.875    |\n"
     ]
    }
   ],
   "source": [
    "b = pd.DataFrame(np.mean(double_single, axis=1))\n",
    "b.columns = [\"Single -> Double\"]\n",
    "print(tabulate(b, tablefmt=\"pipe\", headers=\"keys\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "|                           |   None -> Single | Single -> Double   |\n",
      "|:--------------------------|-----------------:|:-------------------|\n",
      "| BackwardDifferenceEncoder |             27.2 |                    |\n",
      "| CatBoostEncoder           |             20.1 | 0.4                |\n",
      "| CatBoostEncoderShuffle    |              0.6 | -0.4               |\n",
      "| FrequencyEncoder          |              0.3 | -4.9               |\n",
      "| HelmertEncoder            |              0.2 |                    |\n",
      "| JamesSteinEncoder         |             17.7 | 6.3                |\n",
      "| LeaveOneOutEncoder        |              0.2 | 53.2               |\n",
      "| MEstimateEncoder          |             18.9 | 8.1                |\n",
      "| OrdinalEncoder            |             24.1 |                    |\n",
      "| SumEncoder                |              0   |                    |\n",
      "| TargetEncoder             |             19.6 | 4.2                |\n",
      "| WOEEncoder                |             23.4 | 1.9                |\n"
     ]
    }
   ],
   "source": [
    "res = pd.concat([a,b],axis=1)\n",
    "print(tabulate(res.round(1).fillna(\"\"), tablefmt=\"pipe\", headers=\"keys\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "None -> Single      12.679398\n",
       "Single -> Double     8.610764\n",
       "dtype: float64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(res)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Top scores improvement "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "|               |   None -> Single |\n",
      "|:--------------|-----------------:|\n",
      "| telecom       |      -0.00440061 |\n",
      "| adult         |       0.0230934  |\n",
      "| employee      |       1.98344    |\n",
      "| credit        |      -0.0113489  |\n",
      "| mortgages     |       0.260293   |\n",
      "| promotion     |       0.0355479  |\n",
      "| kick          |      -0.0498629  |\n",
      "| kdd_upselling |       0.104221   |\n",
      "| taxi          |       3.77584    |\n",
      "| poverty_A     |       0.742493   |\n",
      "| poverty_B     |       4.49078    |\n",
      "| poverty_C     |       0.475514   |\n"
     ]
    }
   ],
   "source": [
    "a = np.max(r_Single) - np.max(r_None)\n",
    "a = pd.DataFrame(a) * 100\n",
    "a.columns = [\"None -> Single\"]\n",
    "print(tabulate(a, tablefmt=\"pipe\", headers=\"keys\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "|               |   Single -> Double |\n",
      "|:--------------|-------------------:|\n",
      "| telecom       |         0.00880122 |\n",
      "| adult         |        -0.0303435  |\n",
      "| employee      |         0.391465   |\n",
      "| credit        |        -0.00252879 |\n",
      "| mortgages     |        -0.467158   |\n",
      "| promotion     |        -0.201342   |\n",
      "| kick          |         0.064122   |\n",
      "| kdd_upselling |        -0.105947   |\n",
      "| taxi          |        -0.0089831  |\n",
      "| poverty_A     |        -0.114748   |\n",
      "| poverty_B     |         0.285318   |\n",
      "| poverty_C     |        -0.539663   |\n"
     ]
    }
   ],
   "source": [
    "b = np.max(r_Double) - np.max(r_Single)\n",
    "b = pd.DataFrame(b) * 100\n",
    "b.columns = [\"Single -> Double\"]\n",
    "print(tabulate(b, tablefmt=\"pipe\", headers=\"keys\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "|               |   None -> Single |   Single -> Double |\n",
      "|:--------------|-----------------:|-------------------:|\n",
      "| telecom       |            -0    |               0.01 |\n",
      "| adult         |             0.02 |              -0.03 |\n",
      "| employee      |             1.98 |               0.39 |\n",
      "| credit        |            -0.01 |              -0    |\n",
      "| mortgages     |             0.26 |              -0.47 |\n",
      "| promotion     |             0.04 |              -0.2  |\n",
      "| kick          |            -0.05 |               0.06 |\n",
      "| kdd_upselling |             0.1  |              -0.11 |\n",
      "| taxi          |             3.78 |              -0.01 |\n",
      "| poverty_A     |             0.74 |              -0.11 |\n",
      "| poverty_B     |             4.49 |               0.29 |\n",
      "| poverty_C     |             0.48 |              -0.54 |\n"
     ]
    }
   ],
   "source": [
    "res = pd.concat([a,b],axis=1)\n",
    "print(tabulate(res.round(2).fillna(\"\"), tablefmt=\"pipe\", headers=\"keys\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "None -> Single      0.985467\n",
       "Single -> Double   -0.060084\n",
       "dtype: float64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(res)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Encoders examples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# http://latex2png.com/ 200"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train = pd.DataFrame({})\n",
    "df_train[\"category\"] = [\"A\", \"A\", \"A\", \"A\", \"B\", \"B\", \"B\", \"C\", \"C\", \"D\"]\n",
    "df_train[\"category_representation\"] = df_train[\"category\"]\n",
    "df_train[\"target\"] = [0, 1, 0, 1, 0, 1, 0, 1, 0, 1]\n",
    "\n",
    "df_test = pd.DataFrame({})\n",
    "df_test[\"category\"] = [\"A\", \"B\", \"C\", \"D\", \"NewCategory\"]\n",
    "df_test[\"category_representation\"] = df_test[\"category\"]\n",
    "df_test[\"target\"] = None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>C</td>\n",
       "      <td>1</td>\n",
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       "      <th>8</th>\n",
       "      <td>C</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>D</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  category  target\n",
       "0        A       0\n",
       "1        A       1\n",
       "2        A       0\n",
       "3        A       1\n",
       "4        B       0\n",
       "5        B       1\n",
       "6        B       0\n",
       "7        C       1\n",
       "8        C       0\n",
       "9        D       1"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.drop(\"category_representation\", axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
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       "      <th>3</th>\n",
       "      <td>D</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NewCategory</td>\n",
       "      <td>None</td>\n",
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       "  </tbody>\n",
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      "text/plain": [
       "      category target\n",
       "0            A   None\n",
       "1            B   None\n",
       "2            C   None\n",
       "3            D   None\n",
       "4  NewCategory   None"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df_test.drop(\"category_representation\", axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "scrolled": false
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   "outputs": [
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       "      <th>6</th>\n",
       "      <td>B</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>C</td>\n",
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       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
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       "      <td>3</td>\n",
       "      <td>0</td>\n",
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       "      <th>9</th>\n",
       "      <td>D</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "  category  category_representation  target\n",
       "0        A                        1       0\n",
       "1        A                        1       1\n",
       "2        A                        1       0\n",
       "3        A                        1       1\n",
       "4        B                        2       0\n",
       "5        B                        2       1\n",
       "6        B                        2       0\n",
       "7        C                        3       1\n",
       "8        C                        3       0\n",
       "9        D                        4       1"
      ]
     },
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A</td>\n",
       "      <td>1.0</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>B</td>\n",
       "      <td>2.0</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>C</td>\n",
       "      <td>3.0</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>D</td>\n",
       "      <td>4.0</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NewCategory</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      category  category_representation target\n",
       "0            A                      1.0   None\n",
       "1            B                      2.0   None\n",
       "2            C                      3.0   None\n",
       "3            D                      4.0   None\n",
       "4  NewCategory                     -1.0   None"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "encoder = OrdinalEncoder(cols=[\"category_representation\"])\n",
    "temp = encoder.fit_transform(df_train, df_train[\"target\"])\n",
    "display(temp)\n",
    "temp = encoder.transform(df_test)\n",
    "display(temp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
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       "      <td>0</td>\n",
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       "      <th>9</th>\n",
       "      <td>D</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  category  category_representation_1  category_representation_2  \\\n",
       "0        A                          1                          0   \n",
       "1        A                          1                          0   \n",
       "2        A                          1                          0   \n",
       "3        A                          1                          0   \n",
       "4        B                          0                          1   \n",
       "5        B                          0                          1   \n",
       "6        B                          0                          1   \n",
       "7        C                          0                          0   \n",
       "8        C                          0                          0   \n",
       "9        D                          0                          0   \n",
       "\n",
       "   category_representation_3  category_representation_4  target  \n",
       "0                          0                          0       0  \n",
       "1                          0                          0       1  \n",
       "2                          0                          0       0  \n",
       "3                          0                          0       1  \n",
       "4                          0                          0       0  \n",
       "5                          0                          0       1  \n",
       "6                          0                          0       0  \n",
       "7                          1                          0       1  \n",
       "8                          1                          0       0  \n",
       "9                          0                          1       1  "
      ]
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     "metadata": {},
     "output_type": "display_data"
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     "data": {
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
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      "text/plain": [
       "      category  category_representation_1  category_representation_2  \\\n",
       "0            A                          1                          0   \n",
       "1            B                          0                          1   \n",
       "2            C                          0                          0   \n",
       "3            D                          0                          0   \n",
       "4  NewCategory                          0                          0   \n",
       "\n",
       "   category_representation_3  category_representation_4 target  \n",
       "0                          0                          0   None  \n",
       "1                          0                          0   None  \n",
       "2                          1                          0   None  \n",
       "3                          0                          1   None  \n",
       "4                          0                          0   None  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "encoder = OneHotEncoder(cols=[\"category_representation\"])\n",
    "temp = encoder.fit_transform(df_train, df_train[\"target\"])\n",
    "display(temp)\n",
    "temp = encoder.transform(df_test)\n",
    "display(temp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "class FrequencyEncoder:\n",
    "    def __init__(self, cols):\n",
    "        self.cols = cols\n",
    "        self.counts_dict = None\n",
    "\n",
    "    def fit(self, X: pd.DataFrame, y=None) -> pd.DataFrame:\n",
    "        counts_dict = {}\n",
    "        for col in self.cols:\n",
    "            values, counts = np.unique(X[col], return_counts=True)\n",
    "            counts_dict[col] = dict(zip(values, counts))\n",
    "        self.counts_dict = counts_dict\n",
    "\n",
    "    def transform(self, X: pd.DataFrame) -> pd.DataFrame:\n",
    "        counts_dict_test = {}\n",
    "        res = []\n",
    "        for col in self.cols:\n",
    "            values, counts = np.unique(X[col], return_counts=True)\n",
    "            counts_dict_test[col] = dict(zip(values, counts))\n",
    "\n",
    "            # if value is in \"train\" keys - replace \"test\" counts with \"train\" counts\n",
    "            for k in [key for key in counts_dict_test[col].keys() if key in self.counts_dict[col].keys()]:\n",
    "                counts_dict_test[col][k] = self.counts_dict[col][k]\n",
    "\n",
    "            res.append(X[col].map(counts_dict_test[col]).values.reshape(-1, 1))\n",
    "        res = np.hstack(res)\n",
    "\n",
    "        X[self.cols] = res\n",
    "        return X\n",
    "\n",
    "    def fit_transform(self, X: pd.DataFrame, y=None) -> pd.DataFrame:\n",
    "        self.fit(X, y)\n",
    "        X = self.transform(X)\n",
    "        return X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>B</td>\n",
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       "    <tr>\n",
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       "      <td>2</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>D</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  category  category_representation  target\n",
       "0        A                        4       0\n",
       "1        A                        4       1\n",
       "2        A                        4       0\n",
       "3        A                        4       1\n",
       "4        B                        3       0\n",
       "5        B                        3       1\n",
       "6        B                        3       0\n",
       "7        C                        2       1\n",
       "8        C                        2       0\n",
       "9        D                        1       1"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      ],
      "text/plain": [
       "      category category_representation target\n",
       "0            A                       4   None\n",
       "1            B                       3   None\n",
       "2            C                       2   None\n",
       "3            D                       1   None\n",
       "4  NewCategory                       1   None"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "encoder = FrequencyEncoder(cols=[\"category_representation\"])\n",
    "temp = encoder.fit_transform(df_train, df_train[\"target\"])\n",
    "display(temp)\n",
    "temp = encoder.transform(df_test)\n",
    "display(temp)"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "s = \\frac{1}{1 + exp(-\\frac{n - mdl}{a})}\n",
    "\n",
    "\n",
    "\\hat{x}^{k} = prior * (1 - s) + s * \\frac{n^{+}}{n}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>D</td>\n",
       "      <td>0.5000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  category  category_representation  target\n",
       "0        A                   0.5000       0\n",
       "1        A                   0.5000       1\n",
       "2        A                   0.5000       0\n",
       "3        A                   0.5000       1\n",
       "4        B                   0.3532       0\n",
       "5        B                   0.3532       1\n",
       "6        B                   0.3532       0\n",
       "7        C                   0.5000       1\n",
       "8        C                   0.5000       0\n",
       "9        D                   0.5000       1"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\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>category</th>\n",
       "      <th>category_representation</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A</td>\n",
       "      <td>0.5000</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>B</td>\n",
       "      <td>0.3532</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>C</td>\n",
       "      <td>0.5000</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>D</td>\n",
       "      <td>0.5000</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NewCategory</td>\n",
       "      <td>0.5000</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      category  category_representation target\n",
       "0            A                   0.5000   None\n",
       "1            B                   0.3532   None\n",
       "2            C                   0.5000   None\n",
       "3            D                   0.5000   None\n",
       "4  NewCategory                   0.5000   None"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "encoder = TargetEncoder(cols=[\"category_representation\"])\n",
    "temp = encoder.fit_transform(df_train, df_train[\"target\"])\n",
    "display(temp)\n",
    "temp = encoder.transform(df_test)\n",
    "display(temp)"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "# woe \n",
    "nominator = \\frac{n^{+} + a}{y^{+} + 2*a}\n",
    "denominator = \\frac{n - n^{+} + a}{y - y^{+} + 2*a}\n",
    "\\hat{x}^{k} = ln(\\frac{nominator}{denominator})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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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>category</th>\n",
       "      <th>category_representation</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
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       "      <th>1</th>\n",
       "      <td>A</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>B</td>\n",
       "      <td>-0.405465</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>B</td>\n",
       "      <td>-0.405465</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>B</td>\n",
       "      <td>-0.405465</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>C</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>C</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>D</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  category  category_representation  target\n",
       "0        A                 0.000000       0\n",
       "1        A                 0.000000       1\n",
       "2        A                 0.000000       0\n",
       "3        A                 0.000000       1\n",
       "4        B                -0.405465       0\n",
       "5        B                -0.405465       1\n",
       "6        B                -0.405465       0\n",
       "7        C                 0.000000       1\n",
       "8        C                 0.000000       0\n",
       "9        D                 0.000000       1"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
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    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>category</th>\n",
       "      <th>category_representation</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A</td>\n",
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       "      <th>1</th>\n",
       "      <td>B</td>\n",
       "      <td>-0.405465</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>C</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>D</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NewCategory</td>\n",
       "      <td>0.000000</td>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      category  category_representation target\n",
       "0            A                 0.000000   None\n",
       "1            B                -0.405465   None\n",
       "2            C                 0.000000   None\n",
       "3            D                 0.000000   None\n",
       "4  NewCategory                 0.000000   None"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "encoder = WOEEncoder(cols=[\"category_representation\"])\n",
    "temp = encoder.fit_transform(df_train, df_train[\"target\"])\n",
    "display(temp)\n",
    "temp = encoder.transform(df_test)\n",
    "display(temp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>category</th>\n",
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       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>1</th>\n",
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       "      <td>0.500000</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>B</td>\n",
       "      <td>0.363636</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>B</td>\n",
       "      <td>0.363636</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>B</td>\n",
       "      <td>0.363636</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>C</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>C</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>D</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  category  category_representation  target\n",
       "0        A                 0.500000       0\n",
       "1        A                 0.500000       1\n",
       "2        A                 0.500000       0\n",
       "3        A                 0.500000       1\n",
       "4        B                 0.363636       0\n",
       "5        B                 0.363636       1\n",
       "6        B                 0.363636       0\n",
       "7        C                 0.500000       1\n",
       "8        C                 0.500000       0\n",
       "9        D                 1.000000       1"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "  <tbody>\n",
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       "      <th>2</th>\n",
       "      <td>C</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>D</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NewCategory</td>\n",
       "      <td>1.000000</td>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      category  category_representation target\n",
       "0            A                 0.500000   None\n",
       "1            B                 0.363636   None\n",
       "2            C                 0.500000   None\n",
       "3            D                 1.000000   None\n",
       "4  NewCategory                 1.000000   None"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "encoder = JamesSteinEncoder(cols=[\"category_representation\"])\n",
    "temp = encoder.fit_transform(df_train, df_train[\"target\"])\n",
    "display(temp)\n",
    "temp = encoder.transform(df_test)\n",
    "display(temp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "# \\hat{x}^{k} = \\frac{n^{+} + prior * m}{y^{+} + m}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train = pd.DataFrame({})\n",
    "df_train[\"category\"] = [\"A\", \"A\", \"A\", \"A\", \"B\", \"B\", \"B\", \"C\", \"C\"]\n",
    "df_train[\"category_representation\"] = df_train[\"category\"]\n",
    "df_train[\"target\"] = [1, 0, 1, 1, 1, 1, 0, 1, 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
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       "      <td>1</td>\n",
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       "      <th>6</th>\n",
       "      <td>B</td>\n",
       "      <td>0.347222</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>C</td>\n",
       "      <td>0.347222</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>C</td>\n",
       "      <td>0.347222</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  category  category_representation  target\n",
       "0        A                 0.472222       1\n",
       "1        A                 0.472222       0\n",
       "2        A                 0.472222       1\n",
       "3        A                 0.472222       1\n",
       "4        B                 0.347222       1\n",
       "5        B                 0.347222       1\n",
       "6        B                 0.347222       0\n",
       "7        C                 0.347222       1\n",
       "8        C                 0.347222       1"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>D</td>\n",
       "      <td>0.777778</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NewCategory</td>\n",
       "      <td>0.777778</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      category  category_representation target\n",
       "0            A                 0.777778   None\n",
       "1            B                 0.777778   None\n",
       "2            C                 0.777778   None\n",
       "3            D                 0.777778   None\n",
       "4  NewCategory                 0.777778   None"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "encoder = MEstimateEncoder(cols=[\"category_representation\"])\n",
    "temp = encoder.fit_transform(df_train, df_train[\"target\"])\n",
    "display(temp)\n",
    "temp = encoder.transform(df_test)\n",
    "display(temp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>category</th>\n",
       "      <th>category_representation</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <td>0.888889</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A</td>\n",
       "      <td>0.592593</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A</td>\n",
       "      <td>0.694444</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>B</td>\n",
       "      <td>0.777778</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>B</td>\n",
       "      <td>0.888889</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>B</td>\n",
       "      <td>0.925926</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>C</td>\n",
       "      <td>0.777778</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>C</td>\n",
       "      <td>0.888889</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  category  category_representation  target\n",
       "0        A                 0.777778       1\n",
       "1        A                 0.888889       0\n",
       "2        A                 0.592593       1\n",
       "3        A                 0.694444       1\n",
       "4        B                 0.777778       1\n",
       "5        B                 0.888889       1\n",
       "6        B                 0.925926       0\n",
       "7        C                 0.777778       1\n",
       "8        C                 0.888889       1"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "      <th></th>\n",
       "      <th>category</th>\n",
       "      <th>category_representation</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>B</td>\n",
       "      <td>0.777778</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>C</td>\n",
       "      <td>0.777778</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>D</td>\n",
       "      <td>0.777778</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NewCategory</td>\n",
       "      <td>0.777778</td>\n",
       "      <td>None</td>\n",
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       "</table>\n",
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      ],
      "text/plain": [
       "      category  category_representation target\n",
       "0            A                 0.777778   None\n",
       "1            B                 0.777778   None\n",
       "2            C                 0.777778   None\n",
       "3            D                 0.777778   None\n",
       "4  NewCategory                 0.777778   None"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "encoder = CatBoostEncoder(cols=[\"category_representation\"])\n",
    "temp = encoder.fit_transform(df_train, df_train[\"target\"])\n",
    "display(temp)\n",
    "temp = encoder.transform(df_test)\n",
    "display(temp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "### cb\n",
    "# train:\n",
    "# \\hat{x}^{k}_{i} = \\frac{\\sum_{j=0}^{j \\leqslant i}(y_{j} * (x_{j}==k)) - y_{i} + prior}{\\sum_{j=0}^{j \\leqslant i}x_{j}==k}\n",
    "# test: \n",
    "# \\hat{x}^{k} = \\frac{\\sum (y_{j} * (x_{j}==k)) + prior}{\\sum x_{j}==k}\n",
    "\n",
    "### loo\n",
    "# train: \n",
    "# \\hat{x}^{k}_{i} = \\frac{\\sum_{j \\neq i}(y_{j} * (x_{j}==k)) - y_{i}}{\\sum_{j \\neq i}x_{j}==k}\n",
    "# test:\n",
    "# \\hat{x}^{k} = \\frac{\\sum (y_{j} * (x_{j}==k))}{\\sum x_{j}==k}\n",
    "# opt th:\n",
    "# t^{k} = \\frac{\\sum (y_{j} * (x_{j}==k)) - 0.5}{\\sum x_{j}==k}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>category_representation_0</th>\n",
       "      <th>category_representation_1</th>\n",
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       "      <th>category_representation_3</th>\n",
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       "      <th>3</th>\n",
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       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>B</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>B</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>B</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>C</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>C</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  category  category_representation_0  category_representation_1  \\\n",
       "0        A                       -1.0                       -1.0   \n",
       "1        A                       -1.0                       -1.0   \n",
       "2        A                       -1.0                       -1.0   \n",
       "3        A                       -1.0                       -1.0   \n",
       "4        B                        1.0                       -1.0   \n",
       "5        B                        1.0                       -1.0   \n",
       "6        B                        1.0                       -1.0   \n",
       "7        C                        0.0                        2.0   \n",
       "8        C                        0.0                        2.0   \n",
       "\n",
       "   category_representation_2  category_representation_3  target  \n",
       "0                       -1.0                       -1.0       1  \n",
       "1                       -1.0                       -1.0       0  \n",
       "2                       -1.0                       -1.0       1  \n",
       "3                       -1.0                       -1.0       1  \n",
       "4                       -1.0                       -1.0       1  \n",
       "5                       -1.0                       -1.0       1  \n",
       "6                       -1.0                       -1.0       0  \n",
       "7                       -1.0                       -1.0       1  \n",
       "8                       -1.0                       -1.0       1  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>B</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>None</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>C</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>None</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>D</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>None</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NewCategory</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>None</td>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      category  category_representation_0  category_representation_1  \\\n",
       "0            A                        0.0                        0.0   \n",
       "1            B                        0.0                        0.0   \n",
       "2            C                        0.0                        0.0   \n",
       "3            D                        0.0                        0.0   \n",
       "4  NewCategory                        0.0                        0.0   \n",
       "\n",
       "   category_representation_2  category_representation_3 target  \n",
       "0                        0.0                        4.0   None  \n",
       "1                        0.0                        4.0   None  \n",
       "2                        0.0                        4.0   None  \n",
       "3                        0.0                        4.0   None  \n",
       "4                        0.0                        4.0   None  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "encoder = HelmertEncoder(cols=[\"category_representation\"])\n",
    "temp = encoder.fit_transform(df_train, df_train[\"target\"])\n",
    "display(temp.drop(\"intercept\", axis=1))\n",
    "temp = encoder.transform(df_test)\n",
    "display(temp.drop(\"intercept\", axis=1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "# js\n",
    "# \\hat{x}^{k} = (1-B) * \\frac{n^{+}}{n} + B * \\frac{y^{+}}{y}\n",
    "\n",
    "# $$\\newcommand{\\Var}{\\operatorname{Var}}\n",
    "# B = \\frac{\\Var[y^{k}]}{\\Var[y^{k}] + \\Var[y]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>B</td>\n",
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       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>B</td>\n",
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       "      <td>0</td>\n",
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       "      <td>C</td>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  category  category_representation  target\n",
       "0        A                 0.666667       1\n",
       "1        A                 1.000000       0\n",
       "2        A                 0.666667       1\n",
       "3        A                 0.666667       1\n",
       "4        B                 0.500000       1\n",
       "5        B                 0.500000       1\n",
       "6        B                 1.000000       0\n",
       "7        C                 1.000000       1\n",
       "8        C                 1.000000       1"
      ]
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>category</th>\n",
       "      <th>category_representation</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A</td>\n",
       "      <td>0.777778</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>B</td>\n",
       "      <td>0.777778</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>C</td>\n",
       "      <td>0.777778</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>D</td>\n",
       "      <td>0.777778</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NewCategory</td>\n",
       "      <td>0.777778</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      category  category_representation target\n",
       "0            A                 0.777778   None\n",
       "1            B                 0.777778   None\n",
       "2            C                 0.777778   None\n",
       "3            D                 0.777778   None\n",
       "4  NewCategory                 0.777778   None"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "encoder = LeaveOneOutEncoder(cols=[\"category_representation\"])\n",
    "temp = encoder.fit_transform(df_train, df_train[\"target\"])\n",
    "display(temp)\n",
    "temp = encoder.transform(df_test)\n",
    "display(temp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr>\n",
       "      <th>3</th>\n",
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       "    <tr>\n",
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       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>B</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>B</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>C</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>C</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  category  category_representation_0  category_representation_1  target\n",
       "0        A                        1.0                        0.0       1\n",
       "1        A                        1.0                        0.0       0\n",
       "2        A                        1.0                        0.0       1\n",
       "3        A                        1.0                        0.0       1\n",
       "4        B                        0.0                        1.0       1\n",
       "5        B                        0.0                        1.0       1\n",
       "6        B                        0.0                        1.0       0\n",
       "7        C                       -1.0                       -1.0       1\n",
       "8        C                       -1.0                       -1.0       1"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
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       "      <th></th>\n",
       "      <th>category</th>\n",
       "      <th>category_representation_0</th>\n",
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       "  <tbody>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>B</td>\n",
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       "      <td>0.0</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>D</td>\n",
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       "      <td>0.0</td>\n",
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       "    </tr>\n",
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       "      <th>4</th>\n",
       "      <td>NewCategory</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      category  category_representation_0  category_representation_1 target\n",
       "0            A                        0.0                        0.0   None\n",
       "1            B                        0.0                        0.0   None\n",
       "2            C                        0.0                        0.0   None\n",
       "3            D                        0.0                        0.0   None\n",
       "4  NewCategory                        0.0                        0.0   None"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "encoder = SumEncoder(cols=[\"category_representation\"])\n",
    "temp = encoder.fit_transform(df_train, df_train[\"target\"])\n",
    "display(temp.drop(\"intercept\", axis=1))\n",
    "temp = encoder.transform(df_test)\n",
    "display(temp.drop(\"intercept\", axis=1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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