cie4_backend.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "8de86f7c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "a7419ca2",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('dataset/kidney_stone_data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "aa2efeef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "treatment     0\n",
       "stone_size    0\n",
       "success       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "38d9e444",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 700 entries, 0 to 699\n",
      "Data columns (total 3 columns):\n",
      " #   Column      Non-Null Count  Dtype \n",
      "---  ------      --------------  ----- \n",
      " 0   treatment   700 non-null    object\n",
      " 1   stone_size  700 non-null    object\n",
      " 2   success     700 non-null    int64 \n",
      "dtypes: int64(1), object(2)\n",
      "memory usage: 16.5+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "8732f73f",
   "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>success</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>700.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.802857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.398126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          success\n",
       "count  700.000000\n",
       "mean     0.802857\n",
       "std      0.398126\n",
       "min      0.000000\n",
       "25%      1.000000\n",
       "50%      1.000000\n",
       "75%      1.000000\n",
       "max      1.000000"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "7c7cd324",
   "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>treatment</th>\n",
       "      <th>stone_size</th>\n",
       "      <th>success</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>B</td>\n",
       "      <td>large</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A</td>\n",
       "      <td>large</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A</td>\n",
       "      <td>large</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A</td>\n",
       "      <td>large</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A</td>\n",
       "      <td>large</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  treatment stone_size  success\n",
       "0         B      large        1\n",
       "1         A      large        1\n",
       "2         A      large        0\n",
       "3         A      large        1\n",
       "4         A      large        1"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "084a69a1",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = pd.DataFrame(df.iloc[:,[0,1]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "5fa3ce48",
   "metadata": {},
   "outputs": [],
   "source": [
    "y = df['success']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "82ad8447",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "x_train,x_test,y_train,y_test = train_test_split(x,y,test_size=0.2,random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "9b84bee4",
   "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>treatment</th>\n",
       "      <th>stone_size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>B</td>\n",
       "      <td>small</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>B</td>\n",
       "      <td>small</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>220</th>\n",
       "      <td>B</td>\n",
       "      <td>large</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>669</th>\n",
       "      <td>A</td>\n",
       "      <td>large</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>545</th>\n",
       "      <td>A</td>\n",
       "      <td>small</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>B</td>\n",
       "      <td>large</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>B</td>\n",
       "      <td>small</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>270</th>\n",
       "      <td>A</td>\n",
       "      <td>small</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>435</th>\n",
       "      <td>B</td>\n",
       "      <td>small</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>B</td>\n",
       "      <td>small</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>560 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    treatment stone_size\n",
       "82          B      small\n",
       "51          B      small\n",
       "220         B      large\n",
       "669         A      large\n",
       "545         A      small\n",
       "..        ...        ...\n",
       "71          B      large\n",
       "106         B      small\n",
       "270         A      small\n",
       "435         B      small\n",
       "102         B      small\n",
       "\n",
       "[560 rows x 2 columns]"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "cf61c54f",
   "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>treatment</th>\n",
       "      <th>stone_size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>158</th>\n",
       "      <td>A</td>\n",
       "      <td>large</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>500</th>\n",
       "      <td>B</td>\n",
       "      <td>small</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>396</th>\n",
       "      <td>B</td>\n",
       "      <td>small</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>155</th>\n",
       "      <td>B</td>\n",
       "      <td>large</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>321</th>\n",
       "      <td>B</td>\n",
       "      <td>small</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>A</td>\n",
       "      <td>small</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>218</th>\n",
       "      <td>A</td>\n",
       "      <td>small</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>431</th>\n",
       "      <td>A</td>\n",
       "      <td>large</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>281</th>\n",
       "      <td>A</td>\n",
       "      <td>large</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110</th>\n",
       "      <td>B</td>\n",
       "      <td>small</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>140 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    treatment stone_size\n",
       "158         A      large\n",
       "500         B      small\n",
       "396         B      small\n",
       "155         B      large\n",
       "321         B      small\n",
       "..        ...        ...\n",
       "24          A      small\n",
       "218         A      small\n",
       "431         A      large\n",
       "281         A      large\n",
       "110         B      small\n",
       "\n",
       "[140 rows x 2 columns]"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "f52ff10e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.compose import ColumnTransformer\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "trf1 = ColumnTransformer( transformers=[\n",
    "    ('a',OneHotEncoder(drop='first',sparse=False,handle_unknown='ignore'),[0])\n",
    "],remainder='passthrough')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "8028b575",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import OrdinalEncoder\n",
    "trf2 = ColumnTransformer( transformers=[\n",
    "    ('b',OrdinalEncoder(categories=[['small','large']] ,unknown_value=-1, handle_unknown='use_encoded_value'),[1])\n",
    "],remainder='passthrough')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "ceb5af4a",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_selection import SelectKBest,chi2\n",
    "trf3 = SelectKBest(score_func=chi2,k=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "4850fa0e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "trf4 = LogisticRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "98297b7d",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.pipeline import Pipeline,make_pipeline\n",
    "pipe = Pipeline([\n",
    "    ('trf1',trf1),\n",
    "    ('trf2',trf2),\n",
    "    ('trf3',trf3),\n",
    "    ('trf4',trf4)\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "ee2abab1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-c3cb2c8b-e855-4bca-81b8-4b0956e4e42c {color: black;background-color: white;}#sk-c3cb2c8b-e855-4bca-81b8-4b0956e4e42c pre{padding: 0;}#sk-c3cb2c8b-e855-4bca-81b8-4b0956e4e42c div.sk-toggleable {background-color: white;}#sk-c3cb2c8b-e855-4bca-81b8-4b0956e4e42c label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-c3cb2c8b-e855-4bca-81b8-4b0956e4e42c label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-c3cb2c8b-e855-4bca-81b8-4b0956e4e42c label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-c3cb2c8b-e855-4bca-81b8-4b0956e4e42c div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-c3cb2c8b-e855-4bca-81b8-4b0956e4e42c div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-c3cb2c8b-e855-4bca-81b8-4b0956e4e42c div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-c3cb2c8b-e855-4bca-81b8-4b0956e4e42c input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-c3cb2c8b-e855-4bca-81b8-4b0956e4e42c input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-c3cb2c8b-e855-4bca-81b8-4b0956e4e42c div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-c3cb2c8b-e855-4bca-81b8-4b0956e4e42c div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-c3cb2c8b-e855-4bca-81b8-4b0956e4e42c input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-c3cb2c8b-e855-4bca-81b8-4b0956e4e42c div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-c3cb2c8b-e855-4bca-81b8-4b0956e4e42c div.sk-estimator:hover {background-color: #d4ebff;}#sk-c3cb2c8b-e855-4bca-81b8-4b0956e4e42c div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-c3cb2c8b-e855-4bca-81b8-4b0956e4e42c div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-c3cb2c8b-e855-4bca-81b8-4b0956e4e42c div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 2em;bottom: 0;left: 50%;}#sk-c3cb2c8b-e855-4bca-81b8-4b0956e4e42c div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;}#sk-c3cb2c8b-e855-4bca-81b8-4b0956e4e42c div.sk-item {z-index: 1;}#sk-c3cb2c8b-e855-4bca-81b8-4b0956e4e42c div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;}#sk-c3cb2c8b-e855-4bca-81b8-4b0956e4e42c div.sk-parallel::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 2em;bottom: 0;left: 50%;}#sk-c3cb2c8b-e855-4bca-81b8-4b0956e4e42c div.sk-parallel-item {display: flex;flex-direction: column;position: relative;background-color: white;}#sk-c3cb2c8b-e855-4bca-81b8-4b0956e4e42c div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-c3cb2c8b-e855-4bca-81b8-4b0956e4e42c div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-c3cb2c8b-e855-4bca-81b8-4b0956e4e42c div.sk-parallel-item:only-child::after {width: 0;}#sk-c3cb2c8b-e855-4bca-81b8-4b0956e4e42c div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;position: relative;}#sk-c3cb2c8b-e855-4bca-81b8-4b0956e4e42c div.sk-label label {font-family: monospace;font-weight: bold;background-color: white;display: inline-block;line-height: 1.2em;}#sk-c3cb2c8b-e855-4bca-81b8-4b0956e4e42c div.sk-label-container {position: relative;z-index: 2;text-align: center;}#sk-c3cb2c8b-e855-4bca-81b8-4b0956e4e42c div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-c3cb2c8b-e855-4bca-81b8-4b0956e4e42c div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-c3cb2c8b-e855-4bca-81b8-4b0956e4e42c\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;trf1&#x27;,\n",
       "                 ColumnTransformer(remainder=&#x27;passthrough&#x27;,\n",
       "                                   transformers=[(&#x27;a&#x27;,\n",
       "                                                  OneHotEncoder(drop=&#x27;first&#x27;,\n",
       "                                                                handle_unknown=&#x27;ignore&#x27;,\n",
       "                                                                sparse=False),\n",
       "                                                  [0])])),\n",
       "                (&#x27;trf2&#x27;,\n",
       "                 ColumnTransformer(remainder=&#x27;passthrough&#x27;,\n",
       "                                   transformers=[(&#x27;b&#x27;,\n",
       "                                                  OrdinalEncoder(categories=[[&#x27;small&#x27;,\n",
       "                                                                              &#x27;large&#x27;]],\n",
       "                                                                 handle_unknown=&#x27;use_encoded_value&#x27;,\n",
       "                                                                 unknown_value=-1),\n",
       "                                                  [1])])),\n",
       "                (&#x27;trf3&#x27;,\n",
       "                 SelectKBest(k=2,\n",
       "                             score_func=&lt;function chi2 at 0x000001AAF6FB9C10&gt;)),\n",
       "                (&#x27;trf4&#x27;, LogisticRegression())])</pre><b>Please rerun this cell to show the HTML repr or trust the notebook.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"02e1a343-346f-4097-9927-9bd858d51a5a\" type=\"checkbox\" ><label for=\"02e1a343-346f-4097-9927-9bd858d51a5a\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;trf1&#x27;,\n",
       "                 ColumnTransformer(remainder=&#x27;passthrough&#x27;,\n",
       "                                   transformers=[(&#x27;a&#x27;,\n",
       "                                                  OneHotEncoder(drop=&#x27;first&#x27;,\n",
       "                                                                handle_unknown=&#x27;ignore&#x27;,\n",
       "                                                                sparse=False),\n",
       "                                                  [0])])),\n",
       "                (&#x27;trf2&#x27;,\n",
       "                 ColumnTransformer(remainder=&#x27;passthrough&#x27;,\n",
       "                                   transformers=[(&#x27;b&#x27;,\n",
       "                                                  OrdinalEncoder(categories=[[&#x27;small&#x27;,\n",
       "                                                                              &#x27;large&#x27;]],\n",
       "                                                                 handle_unknown=&#x27;use_encoded_value&#x27;,\n",
       "                                                                 unknown_value=-1),\n",
       "                                                  [1])])),\n",
       "                (&#x27;trf3&#x27;,\n",
       "                 SelectKBest(k=2,\n",
       "                             score_func=&lt;function chi2 at 0x000001AAF6FB9C10&gt;)),\n",
       "                (&#x27;trf4&#x27;, LogisticRegression())])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"6ccd5a16-fe82-4e9c-bad1-b4dbb76b10db\" type=\"checkbox\" ><label for=\"6ccd5a16-fe82-4e9c-bad1-b4dbb76b10db\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">trf1: ColumnTransformer</label><div class=\"sk-toggleable__content\"><pre>ColumnTransformer(remainder=&#x27;passthrough&#x27;,\n",
       "                  transformers=[(&#x27;a&#x27;,\n",
       "                                 OneHotEncoder(drop=&#x27;first&#x27;,\n",
       "                                               handle_unknown=&#x27;ignore&#x27;,\n",
       "                                               sparse=False),\n",
       "                                 [0])])</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"d5296a29-8a7a-49b4-acf8-b9e6d90df4cf\" type=\"checkbox\" ><label for=\"d5296a29-8a7a-49b4-acf8-b9e6d90df4cf\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">a</label><div class=\"sk-toggleable__content\"><pre>[0]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"dfe3cccd-f82f-420b-b0eb-57222bf45094\" type=\"checkbox\" ><label for=\"dfe3cccd-f82f-420b-b0eb-57222bf45094\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">OneHotEncoder</label><div class=\"sk-toggleable__content\"><pre>OneHotEncoder(drop=&#x27;first&#x27;, handle_unknown=&#x27;ignore&#x27;, sparse=False)</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"18c8d169-b149-4114-8851-47b8f0c3be75\" type=\"checkbox\" ><label for=\"18c8d169-b149-4114-8851-47b8f0c3be75\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">remainder</label><div class=\"sk-toggleable__content\"><pre>[&#x27;stone_size&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"940e22f1-412a-4559-b9c7-3a05f0144ff3\" type=\"checkbox\" ><label for=\"940e22f1-412a-4559-b9c7-3a05f0144ff3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">passthrough</label><div class=\"sk-toggleable__content\"><pre>passthrough</pre></div></div></div></div></div></div></div></div><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"0d878aef-8b4f-4935-bcdf-e51d09582059\" type=\"checkbox\" ><label for=\"0d878aef-8b4f-4935-bcdf-e51d09582059\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">trf2: ColumnTransformer</label><div class=\"sk-toggleable__content\"><pre>ColumnTransformer(remainder=&#x27;passthrough&#x27;,\n",
       "                  transformers=[(&#x27;b&#x27;,\n",
       "                                 OrdinalEncoder(categories=[[&#x27;small&#x27;, &#x27;large&#x27;]],\n",
       "                                                handle_unknown=&#x27;use_encoded_value&#x27;,\n",
       "                                                unknown_value=-1),\n",
       "                                 [1])])</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"42053168-d24b-4c3a-a161-8ebc90a3c49a\" type=\"checkbox\" ><label for=\"42053168-d24b-4c3a-a161-8ebc90a3c49a\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">b</label><div class=\"sk-toggleable__content\"><pre>[1]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"cf3ae860-e0b9-4452-9b53-6741f2981fb1\" type=\"checkbox\" ><label for=\"cf3ae860-e0b9-4452-9b53-6741f2981fb1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">OrdinalEncoder</label><div class=\"sk-toggleable__content\"><pre>OrdinalEncoder(categories=[[&#x27;small&#x27;, &#x27;large&#x27;]],\n",
       "               handle_unknown=&#x27;use_encoded_value&#x27;, unknown_value=-1)</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"74f98d21-0897-45e8-be5c-470913f94782\" type=\"checkbox\" ><label for=\"74f98d21-0897-45e8-be5c-470913f94782\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">remainder</label><div class=\"sk-toggleable__content\"><pre>[0]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"d8f1f089-148f-4f62-a229-0eb848595d5e\" type=\"checkbox\" ><label for=\"d8f1f089-148f-4f62-a229-0eb848595d5e\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">passthrough</label><div class=\"sk-toggleable__content\"><pre>passthrough</pre></div></div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"55cab1e7-9206-47e6-9eb9-de160fdb2486\" type=\"checkbox\" ><label for=\"55cab1e7-9206-47e6-9eb9-de160fdb2486\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SelectKBest</label><div class=\"sk-toggleable__content\"><pre>SelectKBest(k=2, score_func=&lt;function chi2 at 0x000001AAF6FB9C10&gt;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"d95c029b-ea8e-410f-b546-7446feebfb2d\" type=\"checkbox\" ><label for=\"d95c029b-ea8e-410f-b546-7446feebfb2d\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression()</pre></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "Pipeline(steps=[('trf1',\n",
       "                 ColumnTransformer(remainder='passthrough',\n",
       "                                   transformers=[('a',\n",
       "                                                  OneHotEncoder(drop='first',\n",
       "                                                                handle_unknown='ignore',\n",
       "                                                                sparse=False),\n",
       "                                                  [0])])),\n",
       "                ('trf2',\n",
       "                 ColumnTransformer(remainder='passthrough',\n",
       "                                   transformers=[('b',\n",
       "                                                  OrdinalEncoder(categories=[['small',\n",
       "                                                                              'large']],\n",
       "                                                                 handle_unknown='use_encoded_value',\n",
       "                                                                 unknown_value=-1),\n",
       "                                                  [1])])),\n",
       "                ('trf3',\n",
       "                 SelectKBest(k=2,\n",
       "                             score_func=<function chi2 at 0x000001AAF6FB9C10>)),\n",
       "                ('trf4', LogisticRegression())])"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipe.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "72a84588",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import set_config\n",
    "set_config(display='diagram')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "39bc24f3",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = pipe.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "51b2d33c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7571428571428571"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "accuracy_score(y_test,y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "82bc2df4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8142857142857143"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import  cross_val_score\n",
    "cross_val_score(pipe , x_train,y_train,cv=5,scoring='accuracy').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "de11face",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "pickle.dump(pipe,open('ksd.pkl','wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "812a0f3f",
   "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>treatment</th>\n",
       "      <th>stone_size</th>\n",
       "      <th>success</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>B</td>\n",
       "      <td>large</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A</td>\n",
       "      <td>large</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A</td>\n",
       "      <td>large</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A</td>\n",
       "      <td>large</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A</td>\n",
       "      <td>large</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>695</th>\n",
       "      <td>B</td>\n",
       "      <td>small</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>696</th>\n",
       "      <td>B</td>\n",
       "      <td>small</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>697</th>\n",
       "      <td>B</td>\n",
       "      <td>small</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>698</th>\n",
       "      <td>A</td>\n",
       "      <td>large</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>699</th>\n",
       "      <td>A</td>\n",
       "      <td>small</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>700 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    treatment stone_size  success\n",
       "0           B      large        1\n",
       "1           A      large        1\n",
       "2           A      large        0\n",
       "3           A      large        1\n",
       "4           A      large        1\n",
       "..        ...        ...      ...\n",
       "695         B      small        0\n",
       "696         B      small        1\n",
       "697         B      small        1\n",
       "698         A      large        1\n",
       "699         A      small        1\n",
       "\n",
       "[700 rows x 3 columns]"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "631052eb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([  3,   4,  10,  18,  21,  22,  24,  26,  31,  32,  38,  40,  46,\n",
       "         51,  58,  60,  64,  68,  71,  75,  90,  93, 101, 105, 106, 107,\n",
       "        114, 120, 121, 122, 124, 126, 128, 138], dtype=int64),)"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.where(.values==0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "2492c1e3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['A', 'large'], dtype=object)"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_test.loc[2].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "ba1d8048",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1], dtype=int64)"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "c962c1da",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "158    1\n",
       "500    1\n",
       "396    1\n",
       "155    0\n",
       "321    0\n",
       "      ..\n",
       "24     1\n",
       "218    1\n",
       "431    1\n",
       "281    0\n",
       "110    1\n",
       "Name: success, Length: 140, dtype: int64"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "6177b069",
   "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>treatment</th>\n",
       "      <th>stone_size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>158</th>\n",
       "      <td>A</td>\n",
       "      <td>large</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>500</th>\n",
       "      <td>B</td>\n",
       "      <td>small</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>396</th>\n",
       "      <td>B</td>\n",
       "      <td>small</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>155</th>\n",
       "      <td>B</td>\n",
       "      <td>large</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>321</th>\n",
       "      <td>B</td>\n",
       "      <td>small</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>A</td>\n",
       "      <td>small</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>218</th>\n",
       "      <td>A</td>\n",
       "      <td>small</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>431</th>\n",
       "      <td>A</td>\n",
       "      <td>large</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>281</th>\n",
       "      <td>A</td>\n",
       "      <td>large</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110</th>\n",
       "      <td>B</td>\n",
       "      <td>small</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>140 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    treatment stone_size\n",
       "158         A      large\n",
       "500         B      small\n",
       "396         B      small\n",
       "155         B      large\n",
       "321         B      small\n",
       "..        ...        ...\n",
       "24          A      small\n",
       "218         A      small\n",
       "431         A      large\n",
       "281         A      large\n",
       "110         B      small\n",
       "\n",
       "[140 rows x 2 columns]"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "aed45db1",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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