ch_03_classification.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "91a2b85b",
   "metadata": {},
   "source": [
    "# Importing Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "2da5cb30",
   "metadata": {},
   "outputs": [],
   "source": [
    "from mimic.mimic import MIMIC, MIMICManipulations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "6985474c",
   "metadata": {},
   "outputs": [],
   "source": [
    "from utils.util import generic_utils"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "4c29e327",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import os"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c854dfd3",
   "metadata": {},
   "source": [
    "# Load Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "fd2c05b8",
   "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>admission_type</th>\n",
       "      <th>admission_location</th>\n",
       "      <th>discharge_location</th>\n",
       "      <th>insurance</th>\n",
       "      <th>language</th>\n",
       "      <th>marital_status</th>\n",
       "      <th>ethnicity</th>\n",
       "      <th>hospital_expire_flag</th>\n",
       "      <th>los_admission</th>\n",
       "      <th>gender</th>\n",
       "      <th>...</th>\n",
       "      <th>flag</th>\n",
       "      <th>priority</th>\n",
       "      <th>drg_type</th>\n",
       "      <th>drg_code</th>\n",
       "      <th>drug</th>\n",
       "      <th>route</th>\n",
       "      <th>org_name</th>\n",
       "      <th>test_name</th>\n",
       "      <th>ab_name</th>\n",
       "      <th>curr_service</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ELECTIVE</td>\n",
       "      <td>Other</td>\n",
       "      <td>HOME</td>\n",
       "      <td>Other</td>\n",
       "      <td>ENGLISH</td>\n",
       "      <td>Other</td>\n",
       "      <td>WHITE</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.968056</td>\n",
       "      <td>M</td>\n",
       "      <td>...</td>\n",
       "      <td>normal</td>\n",
       "      <td>STAT</td>\n",
       "      <td>HCFA</td>\n",
       "      <td>795.0</td>\n",
       "      <td>Other</td>\n",
       "      <td>IM</td>\n",
       "      <td>None</td>\n",
       "      <td>Blood Culture, Routine</td>\n",
       "      <td>None</td>\n",
       "      <td>NB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>EW EMER.</td>\n",
       "      <td>EMERGENCY ROOM</td>\n",
       "      <td>HOME</td>\n",
       "      <td>Medicaid</td>\n",
       "      <td>ENGLISH</td>\n",
       "      <td>WIDOWED</td>\n",
       "      <td>WHITE</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.222222</td>\n",
       "      <td>F</td>\n",
       "      <td>...</td>\n",
       "      <td>abnormal</td>\n",
       "      <td>ROUTINE</td>\n",
       "      <td>HCFA</td>\n",
       "      <td>442.0</td>\n",
       "      <td>Influenza Vaccine Quadrivalent</td>\n",
       "      <td>IM</td>\n",
       "      <td>None</td>\n",
       "      <td>Other</td>\n",
       "      <td>None</td>\n",
       "      <td>MED</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ELECTIVE</td>\n",
       "      <td>Other</td>\n",
       "      <td>HOME</td>\n",
       "      <td>Other</td>\n",
       "      <td>ENGLISH</td>\n",
       "      <td>Other</td>\n",
       "      <td>ASIAN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.865278</td>\n",
       "      <td>F</td>\n",
       "      <td>...</td>\n",
       "      <td>normal</td>\n",
       "      <td>STAT</td>\n",
       "      <td>HCFA</td>\n",
       "      <td>795.0</td>\n",
       "      <td>Erythromycin 0.5% Ophth Oint</td>\n",
       "      <td>BOTH EYES</td>\n",
       "      <td>None</td>\n",
       "      <td>Other</td>\n",
       "      <td>None</td>\n",
       "      <td>NB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>EU OBSERVATION</td>\n",
       "      <td>EMERGENCY ROOM</td>\n",
       "      <td>Other</td>\n",
       "      <td>Other</td>\n",
       "      <td>ENGLISH</td>\n",
       "      <td>SINGLE</td>\n",
       "      <td>WHITE</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.298611</td>\n",
       "      <td>F</td>\n",
       "      <td>...</td>\n",
       "      <td>normal</td>\n",
       "      <td>OTHER</td>\n",
       "      <td>OTHER</td>\n",
       "      <td>0.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>MED</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ELECTIVE</td>\n",
       "      <td>Other</td>\n",
       "      <td>HOME</td>\n",
       "      <td>Other</td>\n",
       "      <td>ENGLISH</td>\n",
       "      <td>Other</td>\n",
       "      <td>WHITE</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.109722</td>\n",
       "      <td>M</td>\n",
       "      <td>...</td>\n",
       "      <td>normal</td>\n",
       "      <td>STAT</td>\n",
       "      <td>HCFA</td>\n",
       "      <td>795.0</td>\n",
       "      <td>Erythromycin 0.5% Ophth Oint</td>\n",
       "      <td>BOTH EYES</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>NB</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   admission_type admission_location discharge_location insurance language  \\\n",
       "0        ELECTIVE              Other               HOME     Other  ENGLISH   \n",
       "1        EW EMER.     EMERGENCY ROOM               HOME  Medicaid  ENGLISH   \n",
       "2        ELECTIVE              Other               HOME     Other  ENGLISH   \n",
       "3  EU OBSERVATION     EMERGENCY ROOM              Other     Other  ENGLISH   \n",
       "4        ELECTIVE              Other               HOME     Other  ENGLISH   \n",
       "\n",
       "  marital_status ethnicity  hospital_expire_flag  los_admission gender  ...  \\\n",
       "0          Other     WHITE                   0.0       1.968056      M  ...   \n",
       "1        WIDOWED     WHITE                   0.0       2.222222      F  ...   \n",
       "2          Other     ASIAN                   0.0       1.865278      F  ...   \n",
       "3         SINGLE     WHITE                   0.0       0.298611      F  ...   \n",
       "4          Other     WHITE                   0.0       4.109722      M  ...   \n",
       "\n",
       "       flag priority drg_type drg_code                            drug  \\\n",
       "0    normal     STAT     HCFA    795.0                           Other   \n",
       "1  abnormal  ROUTINE     HCFA    442.0  Influenza Vaccine Quadrivalent   \n",
       "2    normal     STAT     HCFA    795.0    Erythromycin 0.5% Ophth Oint   \n",
       "3    normal    OTHER    OTHER      0.0                            None   \n",
       "4    normal     STAT     HCFA    795.0    Erythromycin 0.5% Ophth Oint   \n",
       "\n",
       "       route org_name               test_name ab_name curr_service  \n",
       "0         IM     None  Blood Culture, Routine    None           NB  \n",
       "1         IM     None                   Other    None          MED  \n",
       "2  BOTH EYES     None                   Other    None           NB  \n",
       "3       None     None                    None    None          MED  \n",
       "4  BOTH EYES     None                    None    None           NB  \n",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('./dataset/ch_03_dataset.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "8976c817",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "normal      227037\n",
       "abnormal     29759\n",
       "Name: flag, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.flag.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "f2bd0746",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "None                        83079\n",
       "URINE CULTURE               58901\n",
       "Blood Culture, Routine      34109\n",
       "Other                       27219\n",
       "MRSA SCREEN                 23028\n",
       "Blood Culture, Neonate      11207\n",
       "WOUND CULTURE                6335\n",
       "Staph aureus Screen          4438\n",
       "RAPID PLASMA REAGIN TEST     3576\n",
       "GRAM STAIN                   2815\n",
       "FLUID CULTURE                2089\n",
       "Name: test_name, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.test_name.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "3b08e004",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MED      88405\n",
       "NB       59972\n",
       "SURG     17386\n",
       "CMED     15344\n",
       "ORTHO    13681\n",
       "OBS      10748\n",
       "NMED     10447\n",
       "NSURG     7448\n",
       "CSURG     5621\n",
       "TRAUM     5071\n",
       "OMED      4948\n",
       "VSURG     3452\n",
       "GYN       3336\n",
       "PSYCH     2545\n",
       "PSURG     2534\n",
       "GU        2415\n",
       "TSURG     2019\n",
       "Other     1424\n",
       "Name: curr_service, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.curr_service.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "0f2b5dfe",
   "metadata": {},
   "outputs": [],
   "source": [
    "#For binary classification, use flag and for multi class classification use current service"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "118c831a",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.loc[df['flag'] == 'normal', 'flag'] = '1'\n",
    "df.loc[df['flag'] == 'abnormal', 'flag'] = '0'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "525dacaf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    227037\n",
       "0     29759\n",
       "Name: flag, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.flag.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "1c45e7cd",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.flag = df.flag.astype('int64')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "5a9bbc8a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('int64')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.flag.dtype"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8a7a11a3",
   "metadata": {},
   "source": [
    "# Transformation Pipelines"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "a0d1667f",
   "metadata": {},
   "outputs": [],
   "source": [
    "for column in df.columns:\n",
    "    if column in ['hospital_expire_flag', 'los_admission', 'anchor_age', 'drg_code']:\n",
    "        try:\n",
    "            df[column] = df[column].astype('float64')\n",
    "        except Exception as ex:\n",
    "            pass\n",
    "    else:\n",
    "        df[column] = df[column].astype('string')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "a70dc94a",
   "metadata": {},
   "outputs": [],
   "source": [
    "categorical_columns = df.select_dtypes(include=['string']).columns.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "a0a4fbd9",
   "metadata": {},
   "outputs": [],
   "source": [
    "categorical_columns.remove('flag')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "44195729",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['anchor_age']"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numerical_columns = [df.select_dtypes(exclude=['string']).columns.tolist()[2]]\n",
    "numerical_columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "322cdb71",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.preprocessing import StandardScaler, OrdinalEncoder\n",
    "from sklearn.compose import ColumnTransformer\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "6a609052",
   "metadata": {},
   "outputs": [],
   "source": [
    "num_pipeline = Pipeline([\n",
    "        ('std_scaler', StandardScaler())\n",
    "    ])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "2431f224",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "num_attribs = numerical_columns\n",
    "cat_attribs = categorical_columns\n",
    "\n",
    "full_pipeline = ColumnTransformer([\n",
    "        (\"num\", num_pipeline, num_attribs),\n",
    "        (\"cat\", OrdinalEncoder(), cat_attribs),\n",
    "    ])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "52a337e8",
   "metadata": {},
   "source": [
    "# Create Test Set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "728236d6",
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "759a1a1c",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import StratifiedShuffleSplit\n",
    "\n",
    "split = StratifiedShuffleSplit(n_splits=1, test_size=0.2, random_state=42)\n",
    "for train_index, test_index in split.split(df, df[\"flag\"]):\n",
    "    strat_train_set = df.loc[train_index]\n",
    "    strat_test_set = df.loc[test_index]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "46feacd1",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_set_X = strat_train_set.drop(\"flag\", axis = 1)\n",
    "train_set_y = strat_train_set[\"flag\"]\n",
    "\n",
    "test_set_X = strat_test_set.drop(\"flag\", axis = 1)\n",
    "test_set_y = strat_test_set[\"flag\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "7d73f508",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_set_X_prepared = full_pipeline.fit_transform(train_set_X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "a338467a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(205436, 18)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_set_X_prepared.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "2fa3f246",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(train_set_X_prepared)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "e2e812ce",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.39909861,  2.        ,  4.        , ...,  6.        ,\n",
       "         4.        ,  4.        ],\n",
       "       [-0.11151772,  7.        ,  4.        , ...,  9.        ,\n",
       "         0.        ,  6.        ],\n",
       "       [-1.43912018,  3.        ,  2.        , ...,  5.        ,\n",
       "         4.        ,  5.        ],\n",
       "       ...,\n",
       "       [-1.43912018,  3.        ,  2.        , ...,  5.        ,\n",
       "         4.        ,  5.        ],\n",
       "       [ 0.50122187,  5.        ,  1.        , ...,  6.        ,\n",
       "         4.        ,  0.        ],\n",
       "       [ 1.35224909,  4.        ,  1.        , ...,  1.        ,\n",
       "         4.        ,  4.        ]])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_set_X_prepared"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "cf59d6c2",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test_prepared = full_pipeline.transform(test_set_X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "cb27999f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['1', '1', '1', ..., '1', '1', '1'], dtype=object)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_set_y = train_set_y.to_numpy()\n",
    "train_set_y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "f0eb108c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['1', '1', '1', ..., '1', '1', '1'], dtype=object)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_set_y = test_set_y.to_numpy()\n",
    "test_set_y"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "892b956b",
   "metadata": {},
   "source": [
    "# Training a Binary Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "c81a328b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SGDClassifier(random_state=42)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import SGDClassifier\n",
    "\n",
    "sgd_clf = SGDClassifier(max_iter=1000, tol=1e-3, random_state=42)\n",
    "sgd_clf.fit(train_set_X_prepared, train_set_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "03eb1abf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['1'], dtype='<U1')"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sgd_clf.predict([train_set_X_prepared[2]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "fbf24ffd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.88411046, 0.88411046, 0.88412337])"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "cross_val_score(sgd_clf, train_set_X_prepared, train_set_y, cv=3, scoring=\"accuracy\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3361c136",
   "metadata": {},
   "source": [
    "# Performance Measures"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f89b7269",
   "metadata": {},
   "source": [
    "## Measuring Accuracy Using Cross-Validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "c44bafae",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8841104572204618\n",
      "0.8841104572204618\n",
      "0.8841233680890213\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import StratifiedKFold\n",
    "from sklearn.base import clone\n",
    "\n",
    "skfolds = StratifiedKFold(n_splits=3, shuffle=True, random_state=42)\n",
    "\n",
    "for train_index, test_index in skfolds.split(train_set_X_prepared, train_set_y):\n",
    "    clone_clf = clone(sgd_clf)\n",
    "    X_train_folds = train_set_X_prepared[train_index]\n",
    "    y_train_folds = train_set_y[train_index]\n",
    "    X_test_fold = train_set_X_prepared[test_index]\n",
    "    y_test_fold = train_set_y[test_index]\n",
    "\n",
    "    clone_clf.fit(X_train_folds, y_train_folds)\n",
    "    y_pred = clone_clf.predict(X_test_fold)\n",
    "    n_correct = sum(y_pred == y_test_fold)\n",
    "    print(n_correct / len(y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1dd1a4d5",
   "metadata": {},
   "source": [
    "## Confusion Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "44ff17b7",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import cross_val_predict\n",
    "\n",
    "y_train_pred = cross_val_predict(sgd_clf, train_set_X_prepared, train_set_y, cv=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "ff465683",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[     1,  23806],\n",
       "       [     1, 181628]])"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "confusion_matrix(train_set_y, y_train_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c856341b",
   "metadata": {},
   "source": [
    "## Precision and Recall"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "cf97dd8f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8841185003456098"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import precision_score, recall_score\n",
    "\n",
    "precision_score(train_set_y, y_train_pred, pos_label=\"1\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "0e205a70",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8841185003456098"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cm = confusion_matrix(train_set_y, y_train_pred)\n",
    "cm[1, 1] / (cm[0, 1] + cm[1, 1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "72f0ca0f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9999944942712893"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recall_score(train_set_y, y_train_pred, pos_label=\"1\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "d9902923",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9999944942712893"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cm[1, 1] / (cm[1, 0] + cm[1, 1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "f430488d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.938493216866505"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import f1_score\n",
    "\n",
    "f1_score(train_set_y, y_train_pred, pos_label=\"1\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "caa1de50",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9384932168665049"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cm[1, 1] / (cm[1, 1] + (cm[1, 0] + cm[0, 1]) / 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1f17eb92",
   "metadata": {},
   "source": [
    "## Precision/Recall Trade-off"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "805e6992",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.97163006])"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_scores = sgd_clf.decision_function([train_set_X_prepared[546]])\n",
    "y_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "cffe1d64",
   "metadata": {},
   "outputs": [],
   "source": [
    "threshold = 0\n",
    "y_some_digit_pred = (y_scores > threshold)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "8cbbd438",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ True])"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_some_digit_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "af62b76e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([False])"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "threshold = 8000\n",
    "y_some_digit_pred = (y_scores > threshold)\n",
    "y_some_digit_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "6b6e26c0",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_scores = cross_val_predict(sgd_clf, train_set_X_prepared, train_set_y, cv=3,\n",
    "                             method=\"decision_function\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "6cbdbb9e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import precision_recall_curve\n",
    "\n",
    "precisions, recalls, thresholds = precision_recall_curve(train_set_y, y_scores, pos_label='1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "dc51b99f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_precision_recall_vs_threshold(precisions, recalls, thresholds):\n",
    "    plt.plot(thresholds, precisions[:-1], \"b--\", label=\"Precision\", linewidth=2)\n",
    "    plt.plot(thresholds, recalls[:-1], \"g-\", label=\"Recall\", linewidth=2)\n",
    "    plt.legend(loc=\"center right\", fontsize=16) # Not shown in the book\n",
    "    plt.xlabel(\"Threshold\", fontsize=16)        # Not shown\n",
    "    plt.grid(True)                              # Not shown\n",
    "    plt.axis([-50000, 50000, 0, 1])             # Not shown\n",
    "\n",
    "\n",
    "\n",
    "recall_90_precision = recalls[np.argmax(precisions >= 0.90)]\n",
    "threshold_90_precision = thresholds[np.argmax(precisions >= 0.90)]\n",
    "\n",
    "\n",
    "plt.figure(figsize=(8, 4))                                                                  # Not shown\n",
    "plot_precision_recall_vs_threshold(precisions, recalls, thresholds)\n",
    "plt.plot([threshold_90_precision, threshold_90_precision], [0., 0.9], \"r:\")                 # Not shown\n",
    "plt.plot([-50000, threshold_90_precision], [0.9, 0.9], \"r:\")                                # Not shown\n",
    "plt.plot([-50000, threshold_90_precision], [recall_90_precision, recall_90_precision], \"r:\")# Not shown\n",
    "plt.plot([threshold_90_precision], [0.9], \"ro\")                                             # Not shown\n",
    "plt.plot([threshold_90_precision], [recall_90_precision], \"ro\")                             # Not shown\n",
    "#save_fig(\"precision_recall_vs_threshold_plot\")                                              # Not shown\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "786e317d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_1674/1146860681.py:1: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  (y_train_pred == (y_scores > 0))\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(y_train_pred == (y_scores > 0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "8b17808b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_precision_vs_recall(precisions, recalls):\n",
    "    plt.plot(recalls, precisions, \"b-\", linewidth=2)\n",
    "    plt.xlabel(\"Recall\", fontsize=16)\n",
    "    plt.ylabel(\"Precision\", fontsize=16)\n",
    "    plt.axis([0, 1, 0, 1])\n",
    "    plt.grid(True)\n",
    "\n",
    "plt.figure(figsize=(8, 6))\n",
    "plot_precision_vs_recall(precisions, recalls)\n",
    "plt.plot([recall_90_precision, recall_90_precision], [0., 0.9], \"r:\")\n",
    "plt.plot([0.0, recall_90_precision], [0.9, 0.9], \"r:\")\n",
    "plt.plot([recall_90_precision], [0.9], \"ro\")\n",
    "#save_fig(\"precision_vs_recall_plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "692a31ba",
   "metadata": {},
   "outputs": [],
   "source": [
    "threshold_90_precision = thresholds[np.argmax(precisions >= 0.90)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "843646ce",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.4165053991977394"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "threshold_90_precision"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "b6c5e56f",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train_pred_90 = (y_scores >= threshold_90_precision)\n",
    "y_train_pred_90 = y_train_pred_90.astype('int')\n",
    "y_train_90 = []\n",
    "for item in y_train_pred_90:\n",
    "    if item == 1:\n",
    "        y_train_90.append('1')\n",
    "    elif item == 0:\n",
    "        y_train_90.append('0')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "58611139",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "precision_score(train_set_y, y_train_90, pos_label='1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "2cf3c041",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.2563797631435509"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recall_score(train_set_y, y_train_90, pos_label='1')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e5b8ed05",
   "metadata": {},
   "source": [
    "## The ROC Curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "2741ad98",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import roc_curve\n",
    "\n",
    "fpr, tpr, thresholds = roc_curve(train_set_y, y_scores, pos_label='1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "9b9687bc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_roc_curve(fpr, tpr, label=None):\n",
    "    plt.plot(fpr, tpr, linewidth=2, label=label)\n",
    "    plt.plot([0, 1], [0, 1], 'k--') # dashed diagonal\n",
    "    plt.axis([0, 1, 0, 1])                                    # Not shown in the book\n",
    "    plt.xlabel('False Positive Rate (Fall-Out)', fontsize=16) # Not shown\n",
    "    plt.ylabel('True Positive Rate (Recall)', fontsize=16)    # Not shown\n",
    "    plt.grid(True)                                            # Not shown\n",
    "\n",
    "plt.figure(figsize=(8, 6))                                    # Not shown\n",
    "plot_roc_curve(fpr, tpr)\n",
    "fpr_90 = fpr[np.argmax(tpr >= recall_90_precision)]           # Not shown\n",
    "plt.plot([fpr_90, fpr_90], [0., recall_90_precision], \"r:\")   # Not shown\n",
    "plt.plot([0.0, fpr_90], [recall_90_precision, recall_90_precision], \"r:\")  # Not shown\n",
    "plt.plot([fpr_90], [recall_90_precision], \"ro\")               # Not shown\n",
    "#save_fig(\"roc_curve_plot\")                                    # Not shown\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "40501d6e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.550572925419654"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import roc_auc_score\n",
    "\n",
    "roc_auc_score(train_set_y, y_scores)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "26b105ba",
   "metadata": {},
   "source": [
    "**Note**: we set `n_estimators=100` to be future-proof since this will be the default value in Scikit-Learn 0.22."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "9585e1e0",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "forest_clf = RandomForestClassifier(n_estimators=100, random_state=42)\n",
    "y_probas_forest = cross_val_predict(forest_clf, train_set_X_prepared, train_set_y, cv=3,\n",
    "                                    method=\"predict_proba\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "8e74fad1",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_scores_forest = y_probas_forest[:, 1] # score = proba of positive class\n",
    "fpr_forest, tpr_forest, thresholds_forest = roc_curve(train_set_y,y_scores_forest, pos_label='1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "ca78249f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "recall_for_forest = tpr_forest[np.argmax(fpr_forest >= fpr_90)]\n",
    "\n",
    "plt.figure(figsize=(8, 6))\n",
    "plt.plot(fpr, tpr, \"b:\", linewidth=2, label=\"SGD\")\n",
    "plot_roc_curve(fpr_forest, tpr_forest, \"Random Forest\")\n",
    "plt.plot([fpr_90, fpr_90], [0., recall_90_precision], \"r:\")\n",
    "plt.plot([0.0, fpr_90], [recall_90_precision, recall_90_precision], \"r:\")\n",
    "plt.plot([fpr_90], [recall_90_precision], \"ro\")\n",
    "plt.plot([fpr_90, fpr_90], [0., recall_for_forest], \"r:\")\n",
    "plt.plot([fpr_90], [recall_for_forest], \"ro\")\n",
    "plt.grid(True)\n",
    "plt.legend(loc=\"lower right\", fontsize=16)\n",
    "#save_fig(\"roc_curve_comparison_plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "450b7426",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6513689581630975"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "roc_auc_score(train_set_y, y_scores_forest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "58078050",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8871488385658426"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train_pred_forest = cross_val_predict(forest_clf, train_set_X_prepared, train_set_y, cv=3)\n",
    "precision_score(train_set_y, y_train_pred_forest, pos_label='1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "f7f6a086",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9880800973412837"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recall_score(train_set_y, y_train_pred_forest, pos_label='1')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5ea0990b",
   "metadata": {},
   "source": [
    "# Multiclass Classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "6c6303a5",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "210ca6db",
   "metadata": {},
   "outputs": [],
   "source": [
    "le = LabelEncoder()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "101fdd17",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.curr_service = le.fit_transform(df.curr_service)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "c0819501",
   "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>admission_type</th>\n",
       "      <th>admission_location</th>\n",
       "      <th>discharge_location</th>\n",
       "      <th>insurance</th>\n",
       "      <th>language</th>\n",
       "      <th>marital_status</th>\n",
       "      <th>ethnicity</th>\n",
       "      <th>hospital_expire_flag</th>\n",
       "      <th>los_admission</th>\n",
       "      <th>gender</th>\n",
       "      <th>...</th>\n",
       "      <th>flag</th>\n",
       "      <th>priority</th>\n",
       "      <th>drg_type</th>\n",
       "      <th>drg_code</th>\n",
       "      <th>drug</th>\n",
       "      <th>route</th>\n",
       "      <th>org_name</th>\n",
       "      <th>test_name</th>\n",
       "      <th>ab_name</th>\n",
       "      <th>curr_service</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ELECTIVE</td>\n",
       "      <td>Other</td>\n",
       "      <td>HOME</td>\n",
       "      <td>Other</td>\n",
       "      <td>ENGLISH</td>\n",
       "      <td>Other</td>\n",
       "      <td>WHITE</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.968056</td>\n",
       "      <td>M</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>STAT</td>\n",
       "      <td>HCFA</td>\n",
       "      <td>795.0</td>\n",
       "      <td>Other</td>\n",
       "      <td>IM</td>\n",
       "      <td>None</td>\n",
       "      <td>Blood Culture, Routine</td>\n",
       "      <td>None</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>EW EMER.</td>\n",
       "      <td>EMERGENCY ROOM</td>\n",
       "      <td>HOME</td>\n",
       "      <td>Medicaid</td>\n",
       "      <td>ENGLISH</td>\n",
       "      <td>WIDOWED</td>\n",
       "      <td>WHITE</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.222222</td>\n",
       "      <td>F</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>ROUTINE</td>\n",
       "      <td>HCFA</td>\n",
       "      <td>442.0</td>\n",
       "      <td>Influenza Vaccine Quadrivalent</td>\n",
       "      <td>IM</td>\n",
       "      <td>None</td>\n",
       "      <td>Other</td>\n",
       "      <td>None</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ELECTIVE</td>\n",
       "      <td>Other</td>\n",
       "      <td>HOME</td>\n",
       "      <td>Other</td>\n",
       "      <td>ENGLISH</td>\n",
       "      <td>Other</td>\n",
       "      <td>ASIAN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.865278</td>\n",
       "      <td>F</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>STAT</td>\n",
       "      <td>HCFA</td>\n",
       "      <td>795.0</td>\n",
       "      <td>Erythromycin 0.5% Ophth Oint</td>\n",
       "      <td>BOTH EYES</td>\n",
       "      <td>None</td>\n",
       "      <td>Other</td>\n",
       "      <td>None</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>EU OBSERVATION</td>\n",
       "      <td>EMERGENCY ROOM</td>\n",
       "      <td>Other</td>\n",
       "      <td>Other</td>\n",
       "      <td>ENGLISH</td>\n",
       "      <td>SINGLE</td>\n",
       "      <td>WHITE</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.298611</td>\n",
       "      <td>F</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>OTHER</td>\n",
       "      <td>OTHER</td>\n",
       "      <td>0.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ELECTIVE</td>\n",
       "      <td>Other</td>\n",
       "      <td>HOME</td>\n",
       "      <td>Other</td>\n",
       "      <td>ENGLISH</td>\n",
       "      <td>Other</td>\n",
       "      <td>WHITE</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.109722</td>\n",
       "      <td>M</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>STAT</td>\n",
       "      <td>HCFA</td>\n",
       "      <td>795.0</td>\n",
       "      <td>Erythromycin 0.5% Ophth Oint</td>\n",
       "      <td>BOTH EYES</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   admission_type admission_location discharge_location insurance language  \\\n",
       "0        ELECTIVE              Other               HOME     Other  ENGLISH   \n",
       "1        EW EMER.     EMERGENCY ROOM               HOME  Medicaid  ENGLISH   \n",
       "2        ELECTIVE              Other               HOME     Other  ENGLISH   \n",
       "3  EU OBSERVATION     EMERGENCY ROOM              Other     Other  ENGLISH   \n",
       "4        ELECTIVE              Other               HOME     Other  ENGLISH   \n",
       "\n",
       "  marital_status ethnicity  hospital_expire_flag  los_admission gender  ...  \\\n",
       "0          Other     WHITE                   0.0       1.968056      M  ...   \n",
       "1        WIDOWED     WHITE                   0.0       2.222222      F  ...   \n",
       "2          Other     ASIAN                   0.0       1.865278      F  ...   \n",
       "3         SINGLE     WHITE                   0.0       0.298611      F  ...   \n",
       "4          Other     WHITE                   0.0       4.109722      M  ...   \n",
       "\n",
       "   flag priority drg_type drg_code                            drug      route  \\\n",
       "0     1     STAT     HCFA    795.0                           Other         IM   \n",
       "1     0  ROUTINE     HCFA    442.0  Influenza Vaccine Quadrivalent         IM   \n",
       "2     1     STAT     HCFA    795.0    Erythromycin 0.5% Ophth Oint  BOTH EYES   \n",
       "3     1    OTHER    OTHER      0.0                            None       None   \n",
       "4     1     STAT     HCFA    795.0    Erythromycin 0.5% Ophth Oint  BOTH EYES   \n",
       "\n",
       "  org_name               test_name ab_name curr_service  \n",
       "0     None  Blood Culture, Routine    None            5  \n",
       "1     None                   Other    None            4  \n",
       "2     None                   Other    None            5  \n",
       "3     None                    None    None            4  \n",
       "4     None                    None    None            5  \n",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "3e52f82b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4     88405\n",
       "5     59972\n",
       "14    17386\n",
       "0     15344\n",
       "10    13681\n",
       "8     10748\n",
       "6     10447\n",
       "7      7448\n",
       "1      5621\n",
       "15     5071\n",
       "9      4948\n",
       "17     3452\n",
       "3      3336\n",
       "13     2545\n",
       "12     2534\n",
       "2      2415\n",
       "16     2019\n",
       "11     1424\n",
       "Name: curr_service, dtype: int64"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.curr_service.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "f7015cfa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['admission_type',\n",
       " 'admission_location',\n",
       " 'discharge_location',\n",
       " 'insurance',\n",
       " 'language',\n",
       " 'marital_status',\n",
       " 'ethnicity',\n",
       " 'gender',\n",
       " 'eventtype',\n",
       " 'flag',\n",
       " 'priority',\n",
       " 'drg_type',\n",
       " 'drug',\n",
       " 'route',\n",
       " 'org_name',\n",
       " 'test_name',\n",
       " 'ab_name']"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "categorical_columns = df.select_dtypes(include=['string']).columns.tolist()\n",
    "#categorical_columns.remove('curr_service')\n",
    "categorical_columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "66ed6c87",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['los_admission', 'anchor_age']"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numerical_columns = df.select_dtypes(exclude=['string']).columns.tolist()[1:3]\n",
    "numerical_columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "ae039176",
   "metadata": {},
   "outputs": [],
   "source": [
    "num_attribs = numerical_columns\n",
    "cat_attribs = categorical_columns\n",
    "\n",
    "full_pipeline = ColumnTransformer([\n",
    "        (\"num\", num_pipeline, num_attribs),\n",
    "        (\"cat\", OrdinalEncoder(), cat_attribs),\n",
    "    ])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "64e26450",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import StratifiedShuffleSplit\n",
    "\n",
    "split = StratifiedShuffleSplit(n_splits=1, test_size=0.2, random_state=42)\n",
    "for train_index, test_index in split.split(df, df[\"flag\"]):\n",
    "    strat_train_set = df.loc[train_index]\n",
    "    strat_test_set = df.loc[test_index]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "eadb93a7",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_set_X = strat_train_set.drop(\"curr_service\", axis = 1)\n",
    "train_set_y = strat_train_set[\"curr_service\"].to_numpy()\n",
    "\n",
    "test_set_X = strat_test_set.drop(\"curr_service\", axis = 1)\n",
    "test_set_y = strat_test_set[\"curr_service\"].to_numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "04749269",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_set_X_prepared = full_pipeline.fit_transform(train_set_X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "2532d8bb",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_set_X_prepared = full_pipeline.fit_transform(test_set_X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "fd3dab48",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import SVC\n",
    "from sklearn.multiclass import OneVsRestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "569123e7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "OneVsRestClassifier(estimator=SVC(gamma='auto', random_state=42))"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ovr_clf = OneVsRestClassifier(SVC(gamma=\"auto\", random_state=42))\n",
    "ovr_clf.fit(train_set_X_prepared[:1000], train_set_y[:1000])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "62780eb0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([14])"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ovr_clf.predict([train_set_X_prepared[52]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "8ffcbdb3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "18"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(ovr_clf.estimators_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "d40c6b57",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([10])"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sgd_clf.fit(train_set_X_prepared, train_set_y)\n",
    "sgd_clf.predict([train_set_X_prepared[52]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "2f4a2730",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "14"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_set_y[52]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "d972df64",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ -1.32779146,  -1.08781789,  -1.63157524,  -3.18839774,\n",
       "         -1.75153898, -27.01120603,  -1.35871746,  -1.137276  ,\n",
       "        -23.87295587,  -1.58222721,  -1.06029787,  -4.51988364,\n",
       "         -1.76432238,  -5.67973405,  -1.14035372,  -1.67443212,\n",
       "         -1.3781951 ,  -1.70979585]])"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sgd_clf.decision_function([train_set_X_prepared[52]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "06f7c8f4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.60465252, 0.54422524, 0.58027396])"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cross_val_score(sgd_clf, train_set_X_prepared, train_set_y, cv=3, scoring=\"accuracy\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b1151b32",
   "metadata": {},
   "source": [
    "# Error Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "f6dd6eb2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1369,   186,     0,    20,  7914,    14,   670,     0,   116,\n",
       "           11,   263,     0,    18,    35,  1590,     6,     6,   114],\n",
       "       [  425,   337,     0,     8,  1352,     6,   198,     0,    66,\n",
       "            7,   616,     2,     8,     5,  1364,     3,     2,    47],\n",
       "       [   90,    37,     1,     8,   838,    11,    78,     0,    12,\n",
       "            1,   267,     1,     8,     8,   540,     1,     5,    12],\n",
       "       [   50,     1,     0,    70,  1110,    57,   100,     0,   157,\n",
       "            8,   268,     1,    20,     9,   837,     0,     4,     6],\n",
       "       [ 2453,   273,     4,    88, 55802,   211,  2964,     0,  1254,\n",
       "          114,   869,    23,    51,   728,  5619,    81,    11,   187],\n",
       "       [    0,     0,     0,     0,     0, 47898,     0,     0,    23,\n",
       "            0,     0,     3,     0,    10,     0,     1,     0,     0],\n",
       "       [  397,    64,     0,    11,  5782,    29,   711,     0,   230,\n",
       "           14,    93,     2,     6,    56,   917,    14,     0,    18],\n",
       "       [  221,    59,     0,    20,  2845,    33,   383,     0,   106,\n",
       "            9,   629,     1,    59,    30,  1476,    23,     6,    28],\n",
       "       [   48,     0,     0,    38,   961,    74,   177,     0,  6529,\n",
       "           10,   110,     0,    22,    65,   577,     3,     1,     1],\n",
       "       [  280,    18,     0,    23,  2367,    54,   352,     1,    29,\n",
       "           21,   127,     0,     6,    15,   654,     6,     1,    30],\n",
       "       [  294,   177,     0,    26,  4494,    61,   510,     0,   209,\n",
       "            9,  1805,     3,   104,    71,  3120,    27,    11,    32],\n",
       "       [   30,     6,     0,     4,   194,   386,    17,     0,   161,\n",
       "            0,    66,    78,     6,     3,   183,     1,     0,     3],\n",
       "       [   45,     6,     0,    10,   724,    20,    52,     0,    40,\n",
       "            2,   376,     0,    84,    13,   674,     4,     1,     6],\n",
       "       [   66,     3,     0,     2,  1070,     8,   158,     0,   166,\n",
       "            6,    24,     0,     3,   354,   161,    14,     0,     3],\n",
       "       [  606,    96,     0,    63,  7252,   118,   700,     0,   337,\n",
       "           21,  1166,     3,   102,    61,  3261,    31,    18,    67],\n",
       "       [  112,    16,     0,     4,  2853,    31,   304,     0,    81,\n",
       "            4,    54,     4,     2,    31,   502,    41,     0,     8],\n",
       "       [   61,    18,     0,     8,   612,     5,    63,     0,    22,\n",
       "            0,   232,     0,    21,     7,   549,     3,     5,    19],\n",
       "       [  253,    35,     0,     6,  1524,     5,   149,     0,    16,\n",
       "            6,   196,     0,    16,     8,   482,     2,     1,    44]])"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train_pred = cross_val_predict(sgd_clf, train_set_X_prepared, train_set_y, cv=3)\n",
    "conf_mx = confusion_matrix(train_set_y, y_train_pred)\n",
    "conf_mx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "e2ecfabe",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import plot_confusion_matrix\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "f7648938",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/pri2si17/Development/virtual_envs/homl_mimic/lib/python3.8/site-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function plot_confusion_matrix is deprecated; Function `plot_confusion_matrix` is deprecated in 1.0 and will be removed in 1.2. Use one of the class methods: ConfusionMatrixDisplay.from_predictions or ConfusionMatrixDisplay.from_estimator.\n",
      "  warnings.warn(msg, category=FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x7f569ebacaf0>"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(10, 10))\n",
    "plot_confusion_matrix(sgd_clf, train_set_X_prepared, train_set_y, ax = ax)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b9182528",
   "metadata": {},
   "source": [
    "# Multilabel Multioutput Classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "647cd980",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Include flag and current_service as multilabel classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "939a19cb",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.flag = le.fit_transform(df.flag)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "2cbf64f3",
   "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>admission_type</th>\n",
       "      <th>admission_location</th>\n",
       "      <th>discharge_location</th>\n",
       "      <th>insurance</th>\n",
       "      <th>language</th>\n",
       "      <th>marital_status</th>\n",
       "      <th>ethnicity</th>\n",
       "      <th>hospital_expire_flag</th>\n",
       "      <th>los_admission</th>\n",
       "      <th>gender</th>\n",
       "      <th>...</th>\n",
       "      <th>flag</th>\n",
       "      <th>priority</th>\n",
       "      <th>drg_type</th>\n",
       "      <th>drg_code</th>\n",
       "      <th>drug</th>\n",
       "      <th>route</th>\n",
       "      <th>org_name</th>\n",
       "      <th>test_name</th>\n",
       "      <th>ab_name</th>\n",
       "      <th>curr_service</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ELECTIVE</td>\n",
       "      <td>Other</td>\n",
       "      <td>HOME</td>\n",
       "      <td>Other</td>\n",
       "      <td>ENGLISH</td>\n",
       "      <td>Other</td>\n",
       "      <td>WHITE</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.968056</td>\n",
       "      <td>M</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>STAT</td>\n",
       "      <td>HCFA</td>\n",
       "      <td>795.0</td>\n",
       "      <td>Other</td>\n",
       "      <td>IM</td>\n",
       "      <td>None</td>\n",
       "      <td>Blood Culture, Routine</td>\n",
       "      <td>None</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>EW EMER.</td>\n",
       "      <td>EMERGENCY ROOM</td>\n",
       "      <td>HOME</td>\n",
       "      <td>Medicaid</td>\n",
       "      <td>ENGLISH</td>\n",
       "      <td>WIDOWED</td>\n",
       "      <td>WHITE</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.222222</td>\n",
       "      <td>F</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>ROUTINE</td>\n",
       "      <td>HCFA</td>\n",
       "      <td>442.0</td>\n",
       "      <td>Influenza Vaccine Quadrivalent</td>\n",
       "      <td>IM</td>\n",
       "      <td>None</td>\n",
       "      <td>Other</td>\n",
       "      <td>None</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ELECTIVE</td>\n",
       "      <td>Other</td>\n",
       "      <td>HOME</td>\n",
       "      <td>Other</td>\n",
       "      <td>ENGLISH</td>\n",
       "      <td>Other</td>\n",
       "      <td>ASIAN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.865278</td>\n",
       "      <td>F</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>STAT</td>\n",
       "      <td>HCFA</td>\n",
       "      <td>795.0</td>\n",
       "      <td>Erythromycin 0.5% Ophth Oint</td>\n",
       "      <td>BOTH EYES</td>\n",
       "      <td>None</td>\n",
       "      <td>Other</td>\n",
       "      <td>None</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>EU OBSERVATION</td>\n",
       "      <td>EMERGENCY ROOM</td>\n",
       "      <td>Other</td>\n",
       "      <td>Other</td>\n",
       "      <td>ENGLISH</td>\n",
       "      <td>SINGLE</td>\n",
       "      <td>WHITE</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.298611</td>\n",
       "      <td>F</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>OTHER</td>\n",
       "      <td>OTHER</td>\n",
       "      <td>0.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ELECTIVE</td>\n",
       "      <td>Other</td>\n",
       "      <td>HOME</td>\n",
       "      <td>Other</td>\n",
       "      <td>ENGLISH</td>\n",
       "      <td>Other</td>\n",
       "      <td>WHITE</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.109722</td>\n",
       "      <td>M</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>STAT</td>\n",
       "      <td>HCFA</td>\n",
       "      <td>795.0</td>\n",
       "      <td>Erythromycin 0.5% Ophth Oint</td>\n",
       "      <td>BOTH EYES</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   admission_type admission_location discharge_location insurance language  \\\n",
       "0        ELECTIVE              Other               HOME     Other  ENGLISH   \n",
       "1        EW EMER.     EMERGENCY ROOM               HOME  Medicaid  ENGLISH   \n",
       "2        ELECTIVE              Other               HOME     Other  ENGLISH   \n",
       "3  EU OBSERVATION     EMERGENCY ROOM              Other     Other  ENGLISH   \n",
       "4        ELECTIVE              Other               HOME     Other  ENGLISH   \n",
       "\n",
       "  marital_status ethnicity  hospital_expire_flag  los_admission gender  ...  \\\n",
       "0          Other     WHITE                   0.0       1.968056      M  ...   \n",
       "1        WIDOWED     WHITE                   0.0       2.222222      F  ...   \n",
       "2          Other     ASIAN                   0.0       1.865278      F  ...   \n",
       "3         SINGLE     WHITE                   0.0       0.298611      F  ...   \n",
       "4          Other     WHITE                   0.0       4.109722      M  ...   \n",
       "\n",
       "   flag priority  drg_type drg_code                            drug  \\\n",
       "0     1     STAT      HCFA    795.0                           Other   \n",
       "1     0  ROUTINE      HCFA    442.0  Influenza Vaccine Quadrivalent   \n",
       "2     1     STAT      HCFA    795.0    Erythromycin 0.5% Ophth Oint   \n",
       "3     1    OTHER     OTHER      0.0                            None   \n",
       "4     1     STAT      HCFA    795.0    Erythromycin 0.5% Ophth Oint   \n",
       "\n",
       "       route org_name               test_name ab_name curr_service  \n",
       "0         IM     None  Blood Culture, Routine    None            5  \n",
       "1         IM     None                   Other    None            4  \n",
       "2  BOTH EYES     None                   Other    None            5  \n",
       "3       None     None                    None    None            4  \n",
       "4  BOTH EYES     None                    None    None            5  \n",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "8823b7a5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['admission_type',\n",
       " 'admission_location',\n",
       " 'discharge_location',\n",
       " 'insurance',\n",
       " 'language',\n",
       " 'marital_status',\n",
       " 'ethnicity',\n",
       " 'gender',\n",
       " 'eventtype',\n",
       " 'priority',\n",
       " 'drg_type',\n",
       " 'drug',\n",
       " 'route',\n",
       " 'org_name',\n",
       " 'test_name',\n",
       " 'ab_name']"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "categorical_columns = df.select_dtypes(include=['string']).columns.tolist()\n",
    "#categorical_columns.remove('curr_service')\n",
    "categorical_columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "1991ec4a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['los_admission', 'anchor_age']"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numerical_columns = df.select_dtypes(exclude=['string']).columns.tolist()[1:3]\n",
    "numerical_columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "07ed8c98",
   "metadata": {},
   "outputs": [],
   "source": [
    "num_attribs = numerical_columns\n",
    "cat_attribs = categorical_columns\n",
    "\n",
    "full_pipeline = ColumnTransformer([\n",
    "        (\"num\", num_pipeline, num_attribs),\n",
    "        (\"cat\", OrdinalEncoder(), cat_attribs),\n",
    "    ])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "17d8bf51",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import StratifiedShuffleSplit\n",
    "\n",
    "split = StratifiedShuffleSplit(n_splits=1, test_size=0.2, random_state=42)\n",
    "for train_index, test_index in split.split(df, df[\"flag\"]):\n",
    "    strat_train_set = df.loc[train_index]\n",
    "    strat_test_set = df.loc[test_index]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "f60cd72e",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_set_X = strat_train_set.drop([\"flag\", \"curr_service\"], axis = 1)\n",
    "train_set_y = strat_train_set[[\"flag\", \"curr_service\"]].to_numpy()\n",
    "\n",
    "test_set_X = strat_test_set.drop([\"flag\", \"curr_service\"], axis = 1)\n",
    "test_set_y = strat_test_set[[\"flag\", \"curr_service\"]].to_numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "e1d74817",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_set_X_prepared = full_pipeline.fit_transform(train_set_X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "56a396e3",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_set_X_prepared = full_pipeline.fit_transform(test_set_X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "2913ecc1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 4],\n",
       "       [1, 6],\n",
       "       [1, 5],\n",
       "       ...,\n",
       "       [1, 5],\n",
       "       [1, 0],\n",
       "       [1, 4]])"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_set_y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "5c4ab221",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier()"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "knn_clf = KNeighborsClassifier()\n",
    "knn_clf.fit(train_set_X_prepared, train_set_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "a5147a51",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1, 10]])"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn_clf.predict([train_set_X_prepared[52]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "b85d271b",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train_knn_pred = cross_val_predict(knn_clf, train_set_X_prepared, train_set_y, cv=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "c515eb16",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 4],\n",
       "       [1, 2],\n",
       "       [1, 5],\n",
       "       ...,\n",
       "       [1, 5],\n",
       "       [1, 4],\n",
       "       [1, 4]])"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train_knn_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "1d2ae7b3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 4],\n",
       "       [1, 6],\n",
       "       [1, 5],\n",
       "       ...,\n",
       "       [1, 5],\n",
       "       [1, 0],\n",
       "       [1, 4]])"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_set_y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4dcd4965",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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