diabetes_prediction_pytorch.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "158a99a4-695c-41f6-a194-e2468e64ac3b",
   "metadata": {},
   "outputs": [],
   "source": [
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "2654036a-46b6-42e1-9d00-cd2a841b7e66",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Objective: To explore the relationship between various medical and demographic factors among patients\n",
    "#          : To predict the likelihood of developing diabetes based on patients' medical records"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "f2219079-4382-4bce-93ca-f88aabe4e86b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# to import relevant libraries\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.utils import shuffle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "90ebfffa-2aa6-47e1-af08-27e85c033af0",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "937b467a-15da-4875-a43e-a656c9476e48",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"C:/Users/miche/Desktop/machine_learning/diabetes_prediction_dataset.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "cfbda75e-12da-43a5-830f-7d5982a6576d",
   "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>gender</th>\n",
       "      <th>age</th>\n",
       "      <th>hypertension</th>\n",
       "      <th>heart_disease</th>\n",
       "      <th>smoking_history</th>\n",
       "      <th>bmi</th>\n",
       "      <th>HbA1c_level</th>\n",
       "      <th>blood_glucose_level</th>\n",
       "      <th>diabetes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Female</td>\n",
       "      <td>80.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>never</td>\n",
       "      <td>25.19</td>\n",
       "      <td>6.6</td>\n",
       "      <td>140</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Female</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No Info</td>\n",
       "      <td>27.32</td>\n",
       "      <td>6.6</td>\n",
       "      <td>80</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Male</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>never</td>\n",
       "      <td>27.32</td>\n",
       "      <td>5.7</td>\n",
       "      <td>158</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Female</td>\n",
       "      <td>36.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>current</td>\n",
       "      <td>23.45</td>\n",
       "      <td>5.0</td>\n",
       "      <td>155</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Male</td>\n",
       "      <td>76.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>current</td>\n",
       "      <td>20.14</td>\n",
       "      <td>4.8</td>\n",
       "      <td>155</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   gender   age  hypertension  heart_disease smoking_history    bmi  \\\n",
       "0  Female  80.0             0              1           never  25.19   \n",
       "1  Female  54.0             0              0         No Info  27.32   \n",
       "2    Male  28.0             0              0           never  27.32   \n",
       "3  Female  36.0             0              0         current  23.45   \n",
       "4    Male  76.0             1              1         current  20.14   \n",
       "\n",
       "   HbA1c_level  blood_glucose_level  diabetes  \n",
       "0          6.6                  140         0  \n",
       "1          6.6                   80         0  \n",
       "2          5.7                  158         0  \n",
       "3          5.0                  155         0  \n",
       "4          4.8                  155         0  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# to display variables in dataset\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "563422ea-02e8-460d-b21f-7794e1480e04",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "gender                 0\n",
       "age                    0\n",
       "hypertension           0\n",
       "heart_disease          0\n",
       "smoking_history        0\n",
       "bmi                    0\n",
       "HbA1c_level            0\n",
       "blood_glucose_level    0\n",
       "diabetes               0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# to check for missing values\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "5c346aa1-e6c8-43dc-90b6-f44e7456b541",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "100000"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Dataset contains 100,000 patients' data\n",
    "len(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "fe1a74e8-8066-4ab6-9fcd-c49eccef7779",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# to see distribution of target variable = 'diabetes'\n",
    "sns.countplot(data= df, x='diabetes')\n",
    "plt.title('Distribution of diabetic population');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "08eb3a20-c2b2-44e1-842e-031463670476",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "diabetes\n",
       "0    91500\n",
       "1     8500\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['diabetes'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "08c36d36-6eb5-43d3-bebe-27ef1fb911f2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "diabetes\n",
       "0    91.5\n",
       "1     8.5\n",
       "Name: count, dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 8.5% of patients have diabetes\n",
    "100*(df['diabetes'].value_counts())/len(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "c157b432-8f0c-42f7-890f-de92ea3a695d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['gender', 'age', 'hypertension', 'heart_disease', 'smoking_history',\n",
       "       'bmi', 'HbA1c_level', 'blood_glucose_level', 'diabetes'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# to print out all variable names\n",
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "cbfdbd9d-9bd3-4c71-aa5b-155c1f0e482b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 100000 entries, 0 to 99999\n",
      "Data columns (total 9 columns):\n",
      " #   Column               Non-Null Count   Dtype  \n",
      "---  ------               --------------   -----  \n",
      " 0   gender               100000 non-null  object \n",
      " 1   age                  100000 non-null  float64\n",
      " 2   hypertension         100000 non-null  int64  \n",
      " 3   heart_disease        100000 non-null  int64  \n",
      " 4   smoking_history      100000 non-null  object \n",
      " 5   bmi                  100000 non-null  float64\n",
      " 6   HbA1c_level          100000 non-null  float64\n",
      " 7   blood_glucose_level  100000 non-null  int64  \n",
      " 8   diabetes             100000 non-null  int64  \n",
      "dtypes: float64(3), int64(4), object(2)\n",
      "memory usage: 6.9+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "de05d03f-993b-43fd-aed7-3b36b40a451a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# to separate continuous, categorical and label columns\n",
    "cat_cols = ['gender','smoking_history']\n",
    "cont_cols = ['age','hypertension','heart_disease','bmi','HbA1c_level','blood_glucose_level']\n",
    "y_col = ['diabetes']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "3a1cf4c2-7595-4a47-8fb0-26afdf9fb7f6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cat_cols  has 2 columns\n",
      "cont_cols has 6 columns\n",
      "y_col     has 1 column\n"
     ]
    }
   ],
   "source": [
    "# RUN THIS CODE TO COMPARE RESULTS:\n",
    "print(f'cat_cols  has {len(cat_cols)} columns')\n",
    "print(f'cont_cols has {len(cont_cols)} columns')\n",
    "print(f'y_col     has {len(y_col)} column')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "39a835cc-5cd1-4d23-801f-e1bad374a39b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# to convert categorical columns to category datatypes\n",
    "for cat in cat_cols:\n",
    "    df[cat] = df[cat].astype('category')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "b2e837c9-90b3-4f20-ae37-92c6e2a9d8e5",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "125239a3-8ffd-4274-929c-c141c8a68a25",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "gender                 category\n",
       "age                     float64\n",
       "hypertension              int64\n",
       "heart_disease             int64\n",
       "smoking_history        category\n",
       "bmi                     float64\n",
       "HbA1c_level             float64\n",
       "blood_glucose_level       int64\n",
       "diabetes                  int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "fd816a33-6d1b-46d0-9d50-ada50270826f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# to shuffle the dataset\n",
    "df = shuffle(df, random_state =101)\n",
    "df.reset_index(drop=True, inplace= True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "c8beca84-8f8a-477b-ad95-e5a6243a4982",
   "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>gender</th>\n",
       "      <th>age</th>\n",
       "      <th>hypertension</th>\n",
       "      <th>heart_disease</th>\n",
       "      <th>smoking_history</th>\n",
       "      <th>bmi</th>\n",
       "      <th>HbA1c_level</th>\n",
       "      <th>blood_glucose_level</th>\n",
       "      <th>diabetes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Female</td>\n",
       "      <td>37.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>former</td>\n",
       "      <td>36.31</td>\n",
       "      <td>5.7</td>\n",
       "      <td>100</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Female</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No Info</td>\n",
       "      <td>27.32</td>\n",
       "      <td>6.2</td>\n",
       "      <td>145</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Female</td>\n",
       "      <td>23.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>former</td>\n",
       "      <td>37.25</td>\n",
       "      <td>6.0</td>\n",
       "      <td>90</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Male</td>\n",
       "      <td>23.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>never</td>\n",
       "      <td>27.32</td>\n",
       "      <td>6.0</td>\n",
       "      <td>80</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Female</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>former</td>\n",
       "      <td>22.73</td>\n",
       "      <td>6.0</td>\n",
       "      <td>159</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   gender   age  hypertension  heart_disease smoking_history    bmi  \\\n",
       "0  Female  37.0             0              0          former  36.31   \n",
       "1  Female  12.0             0              0         No Info  27.32   \n",
       "2  Female  23.0             0              0          former  37.25   \n",
       "3    Male  23.0             0              0           never  27.32   \n",
       "4  Female  32.0             0              0          former  22.73   \n",
       "\n",
       "   HbA1c_level  blood_glucose_level  diabetes  \n",
       "0          5.7                  100         0  \n",
       "1          6.2                  145         0  \n",
       "2          6.0                   90         0  \n",
       "3          6.0                   80         0  \n",
       "4          6.0                  159         0  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "8fe8911f-153c-4f57-a527-b68b004373eb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# to Set the embedding size\n",
    "# To create a variable \"cat_szs\" to hold the number of categories in each variable.\n",
    "# Then create a variable \"emb_szs\" to hold the list of (category size, embedding size) tuples."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "0433b45d-dba3-4eb7-b353-d29bed38bb6b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(3, 2), (6, 3)]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cat_szs = [len(df[col].cat.categories) for col in cat_cols]\n",
    "emb_szs = [(size, min(50, (size+1)//2)) for size in cat_szs]\n",
    "emb_szs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "18d1ee03-9d64-4ab4-b92b-c775c96b8d00",
   "metadata": {},
   "outputs": [],
   "source": [
    "# To create an array of categorical values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "b1ffa26c-ff17-4c22-9bbc-ec0bb1343485",
   "metadata": {},
   "outputs": [],
   "source": [
    "sx = df['gender'].cat.codes.values\n",
    "sh = df['smoking_history'].cat.codes.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "3fbb5f86-f5d3-4c7c-b8dd-4bc87a7ac0b4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# To create a NumPy array called \"cats\" that contains a stack of each categorical column .cat.codes.values\n",
    "cats = np.stack([sx,sh], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "f7b8ae3f-ba0b-45ed-8d62-c84e1b2d80f7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 3],\n",
       "       [0, 0],\n",
       "       [0, 3],\n",
       "       [1, 4],\n",
       "       [0, 3]], dtype=int8)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# to print 1st 5 rows of categorical data\n",
    "cats[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "f8a79bf2-d5bd-4438-a5fd-bfa22fa59c9b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# to convert all categorical variables to a tensor of dtype int64\n",
    "cats = torch.tensor(cats, dtype=torch.int64)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "c43932df-a9e0-4511-8416-f5bdac381111",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 37.  ,   0.  ,   0.  ,  36.31,   5.7 , 100.  ],\n",
       "       [ 12.  ,   0.  ,   0.  ,  27.32,   6.2 , 145.  ],\n",
       "       [ 23.  ,   0.  ,   0.  ,  37.25,   6.  ,  90.  ],\n",
       "       [ 23.  ,   0.  ,   0.  ,  27.32,   6.  ,  80.  ],\n",
       "       [ 32.  ,   0.  ,   0.  ,  22.73,   6.  , 159.  ]])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# to create an array of continuous values\n",
    "conts = np.stack([df[col].values for col in cont_cols], 1)\n",
    "conts[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "2bd18d04-1a45-4c5a-9694-de07f9a23cfa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.float32"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# to convert all continuous data into a tensor of dtype float32\n",
    "conts = torch.tensor(conts, dtype=torch.float)\n",
    "conts.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "f03d5347-349d-412a-9961-a4f7ab0d203d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# To create a tensor called \"y\" from the values in the label column.\n",
    "# to flatten the tensor so that it can be passed into the CE Loss function.\n",
    "y = torch.tensor(df[y_col].values).flatten()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "6a73ccc5-5222-4fe8-bba4-94ed82a1321c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([0, 0, 0,  ..., 0, 1, 0])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "ba040cf8-6d71-4089-93e1-e024891850e1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# To create train and test sets from 'cats', 'conts' and y\n",
    "# We use the entire batch of 30,000 records, but a smaller batch size will save time during training.\n",
    "# We used a test size of 5,000 records, but you can choose another fixed value or a percentage of the batch size.\n",
    "# To assign batch and test sizes to simple variables like \"b\" and \"t\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "60cada42-f257-44a0-ad3d-1293b40b7bbb",
   "metadata": {},
   "outputs": [],
   "source": [
    "b = 100000 # suggested batch size\n",
    "t = 10000  # suggested test size\n",
    "\n",
    "cat_train = cats[:b-t]\n",
    "cat_test  = cats[b-t:b]\n",
    "cont_train = conts[:b-t]\n",
    "cont_test  = conts[b-t:b]\n",
    "y_train   = y[:b-t]\n",
    "y_test    = y[b-t:b]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "67f63da7-113d-4a25-be75-604108b0db1b",
   "metadata": {},
   "outputs": [],
   "source": [
    "class TabularModel(nn.Module):\n",
    "\n",
    "    def __init__(self, emb_szs, n_cont, out_sz, layers, p=0.5):\n",
    "        # Call the parent __init__\n",
    "        super().__init__()\n",
    "        \n",
    "        # Set up the embedding, dropout, and batch normalization layer attributes\n",
    "        self.embeds = nn.ModuleList([nn.Embedding(ni, nf) for ni,nf in emb_szs])\n",
    "        self.emb_drop = nn.Dropout(p)\n",
    "        self.bn_cont = nn.BatchNorm1d(n_cont)\n",
    "        \n",
    "        # Assign a variable to hold a list of layers\n",
    "        layerlist = []\n",
    "        \n",
    "        # Assign a variable to store the number of embedding and continuous layers\n",
    "        n_emb = sum((nf for ni,nf in emb_szs))\n",
    "        n_in = n_emb + n_cont\n",
    "        \n",
    "        # Iterate through the passed-in \"layers\" parameter (ie, [200,100]) to build a list of layers\n",
    "        for i in layers:\n",
    "            layerlist.append(nn.Linear(n_in,i)) \n",
    "            layerlist.append(nn.ReLU(inplace=True))\n",
    "            layerlist.append(nn.BatchNorm1d(i))\n",
    "            layerlist.append(nn.Dropout(p))\n",
    "            n_in = i\n",
    "        layerlist.append(nn.Linear(layers[-1],out_sz))\n",
    "        \n",
    "        # Convert the list of layers into an attribute\n",
    "        self.layers = nn.Sequential(*layerlist)\n",
    "    \n",
    "    def forward(self, x_cat, x_cont):\n",
    "        # Extract embedding values from the incoming categorical data\n",
    "        embeddings = []\n",
    "        for i,e in enumerate(self.embeds):\n",
    "            embeddings.append(e(x_cat[:,i]))\n",
    "        x = torch.cat(embeddings, 1)\n",
    "        # Perform an initial dropout on the embeddings\n",
    "        x = self.emb_drop(x)\n",
    "        \n",
    "        # Normalize the incoming continuous data\n",
    "        x_cont = self.bn_cont(x_cont)\n",
    "        x = torch.cat([x, x_cont], 1)\n",
    "        \n",
    "        # Set up model layers\n",
    "        x = self.layers(x)\n",
    "        return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "194cc12a-59b2-453d-a81e-5d67edd6f6a9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<torch._C.Generator at 0x20490c9c410>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# to set the random seed\n",
    "torch.manual_seed(33)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "a109a192-26bc-43fa-81b7-994149f80fcb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TabularModel(\n",
       "  (embeds): ModuleList(\n",
       "    (0): Embedding(3, 2)\n",
       "    (1): Embedding(6, 3)\n",
       "  )\n",
       "  (emb_drop): Dropout(p=0.4, inplace=False)\n",
       "  (bn_cont): BatchNorm1d(6, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (layers): Sequential(\n",
       "    (0): Linear(in_features=11, out_features=50, bias=True)\n",
       "    (1): ReLU(inplace=True)\n",
       "    (2): BatchNorm1d(50, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    (3): Dropout(p=0.4, inplace=False)\n",
       "    (4): Linear(in_features=50, out_features=2, bias=True)\n",
       "  )\n",
       ")"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# To create a TabularModel instance\n",
    "# the model with one hidden layer containing 50 neurons and a dropout layer p-value of 0.4\n",
    "model = TabularModel(emb_szs, conts.shape[1], 2, [50], p=0.4)\n",
    "model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "d8da1b9a-7053-4eab-aaed-c857ef167784",
   "metadata": {},
   "outputs": [],
   "source": [
    "# To define the loss and optimization functions\n",
    "# Create a loss function called \"criterion\" using CrossEntropyLoss\n",
    "# Create an optimization function called \"optimizer\" using Adam, with a learning rate of 0.001"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "69ae9d98-c0ea-4c16-97a8-2539efb58d16",
   "metadata": {},
   "outputs": [],
   "source": [
    "criterion = nn.CrossEntropyLoss()\n",
    "optimizer = torch.optim.Adam(model.parameters(), lr=0.001)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "050ed650-cb57-43ca-afb3-e1f4a01dff07",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch:   1  loss: 0.96803540\n",
      "epoch:  26  loss: 0.68100637\n",
      "epoch:  51  loss: 0.57014394\n",
      "epoch:  76  loss: 0.47924387\n",
      "epoch: 101  loss: 0.38769075\n",
      "epoch: 126  loss: 0.30173814\n",
      "epoch: 151  loss: 0.23612170\n",
      "epoch: 176  loss: 0.19526161\n",
      "epoch: 201  loss: 0.16984563\n",
      "epoch: 226  loss: 0.15348917\n",
      "epoch: 251  loss: 0.14104924\n",
      "epoch: 276  loss: 0.13195595\n",
      "epoch: 300  loss: 0.12622231\n",
      "\n",
      "Duration: 41 seconds\n"
     ]
    }
   ],
   "source": [
    "# To train the model through 300 epochs\n",
    "import time\n",
    "start_time = time.time()\n",
    "\n",
    "epochs = 300\n",
    "losses = []\n",
    "\n",
    "for i in range(epochs):\n",
    "    i+=1\n",
    "    y_pred = model(cat_train, cont_train)\n",
    "    loss = criterion(y_pred, y_train)\n",
    "    losses.append(loss.detach().numpy())\n",
    "    \n",
    "    # a neat trick to save screen space:\n",
    "    if i%25 == 1:\n",
    "        print(f'epoch: {i:3}  loss: {loss.item():10.8f}')\n",
    "\n",
    "    optimizer.zero_grad()\n",
    "    loss.backward()\n",
    "    optimizer.step()\n",
    "\n",
    "print(f'epoch: {i:3}  loss: {loss.item():10.8f}') # print the last line\n",
    "print(f'\\nDuration: {time.time() - start_time:.0f} seconds') # print the time elapsed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "7a746676-03fc-4ab3-aec8-4319977c572a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# to plot the cross entropy loss against epochs\n",
    "plt.plot(range(epochs), losses)\n",
    "plt.ylabel('Cross Entropy Loss')\n",
    "plt.xlabel('epoch');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "d25270bb-f39a-4ad3-90c6-b2cd0cb18c9b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# To evaluate the testing set\n",
    "# With torch set to no_grad, pass cat_test and con_test through the trained model.\n",
    "# Create a validation set called \"y_val\". Compare the output to y_test using the loss function defined above"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "e4b622eb-43f3-4f96-97a6-1d170562adec",
   "metadata": {},
   "outputs": [],
   "source": [
    "with torch.no_grad():\n",
    "    y_val = model(cat_test, cont_test)\n",
    "    loss = criterion(y_val, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "2f399410-4a8f-4ad3-96b8-919785800197",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CE Loss: 0.12647468\n"
     ]
    }
   ],
   "source": [
    "# the loss function is 0.12, same as epoch = 300\n",
    "print(f'CE Loss: {loss:.8f}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "b8435feb-4011-4074-94cd-904f732937dc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# to calculate the overall accuracy\n",
    "# Using a for loop, compare the argmax values of the y_val validation set to the y_test set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "5ecf3d2e-2115-43c4-aab2-944d2daaadd1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "9618 out of 10000 = 96.18% correct\n"
     ]
    }
   ],
   "source": [
    "rows = len(y_test)\n",
    "correct = 0\n",
    "\n",
    "# print(f'{\"MODEL OUTPUT\":26} ARGMAX  Y_TEST')\n",
    "\n",
    "for i in range(rows):\n",
    "    # print(f'{str(y_val[i]):26} {y_val[i].argmax().item():^7}{y_test[i]:^7}')\n",
    "\n",
    "    if y_val[i].argmax().item() == y_test[i]:\n",
    "        correct += 1\n",
    "\n",
    "print(f'\\n{correct} out of {rows} = {100*correct/rows:.2f}% correct')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "4163eba4-6ffc-426f-98a5-cd9ff88eb54d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "gender                 category\n",
       "age                     float64\n",
       "hypertension              int64\n",
       "heart_disease             int64\n",
       "smoking_history        category\n",
       "bmi                     float64\n",
       "HbA1c_level             float64\n",
       "blood_glucose_level       int64\n",
       "diabetes                  int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "d1c0448a-3a85-4e14-903f-a5482a2b9623",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    100000.000000\n",
       "mean         41.885856\n",
       "std          22.516840\n",
       "min           0.080000\n",
       "25%          24.000000\n",
       "50%          43.000000\n",
       "75%          60.000000\n",
       "max          80.000000\n",
       "Name: age, dtype: float64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['age'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "b0675337-ce9e-49a3-9111-e6b46183bcd5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Female', 'Male', 'Other']\n",
       "Categories (3, object): ['Female', 'Male', 'Other']"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['gender'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "b74cd78c-20ec-49e8-8019-89c1e034299b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['former', 'No Info', 'never', 'not current', 'ever', 'current']\n",
       "Categories (6, object): ['No Info', 'current', 'ever', 'former', 'never', 'not current']"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['smoking_history'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "3e3b3ba3-1547-4cc5-871d-16f33e399463",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1], dtype=int64)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['hypertension'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "7a2dc341-2363-4306-8b7f-3db40f6be5c8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1], dtype=int64)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['heart_disease'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "f9f9ee3f-ac86-4d87-bf98-d65749e34d01",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    100000.000000\n",
       "mean         27.320767\n",
       "std           6.636783\n",
       "min          10.010000\n",
       "25%          23.630000\n",
       "50%          27.320000\n",
       "75%          29.580000\n",
       "max          95.690000\n",
       "Name: bmi, dtype: float64"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['bmi'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "0e195704-cb51-4e20-a0dd-457d8e305fc1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    100000.000000\n",
       "mean          5.527507\n",
       "std           1.070672\n",
       "min           3.500000\n",
       "25%           4.800000\n",
       "50%           5.800000\n",
       "75%           6.200000\n",
       "max           9.000000\n",
       "Name: HbA1c_level, dtype: float64"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['HbA1c_level'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "cfffe1da-843f-4758-b6be-46e1b416239a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    100000.000000\n",
       "mean        138.058060\n",
       "std          40.708136\n",
       "min          80.000000\n",
       "25%         100.000000\n",
       "50%         140.000000\n",
       "75%         159.000000\n",
       "max         300.000000\n",
       "Name: blood_glucose_level, dtype: float64"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['blood_glucose_level'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "4391c86e-821d-4638-9d5f-1bca03f9efb0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# To feed new data through the trained model\n",
    "# To write a function that allows the user to input their own values, and generates a prediction."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "9305fafd-d42d-4faf-b772-637c67a6732e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "What is the person's age? (0-80)   40\n",
      "What is the person's sex? (Female/Male/Other)  Female\n",
      "What is the person's smoking history?  ever\n",
      "Is the patient hypertensive? (0-1)  0\n",
      "Does the patient have heart disease? (0-1)  1\n",
      "What is patient's bmi? (10-96)  27\n",
      "What is the patients' HbA1c_reading? (3-9)  6\n",
      "What is patient's blood glucose reading? (80-300)  200\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "The predicted label is 1\n"
     ]
    }
   ],
   "source": [
    "def test_data(mdl): # pass in the name of the model\n",
    "    # INPUT NEW DATA\n",
    "    age = float(input(\"What is the person's age? (0-80)  \")) # input means its a string variable\n",
    "    sex = input(\"What is the person's sex? (Female/Male/Other) \").capitalize()\n",
    "    sh = input(\"What is the person's smoking history? \")\n",
    "    hp = float(input(\"Is the patient hypertensive? (0-1) \")) # float means it has decimal place or integer\n",
    "    hd = float(input(\"Does the patient have heart disease? (0-1) \"))\n",
    "    bmi = float(input(\"What is patient's bmi? (10-96) \"))\n",
    "    A1c = float(input(\"What is the patients' HbA1c_reading? (3-9) \"))\n",
    "    bg = float(input(\"What is patient's blood glucose reading? (80-300) \"))\n",
    "\n",
    "    # PREPROCESS THE DATA\n",
    "    sex_d = {'Female':0, 'Male':1, 'Other':2}\n",
    "    sh_d = {'No Info':0, 'never':1, 'former':2, 'not current':3, 'ever':4, 'current':5}\n",
    "   \n",
    "    sex = sex_d[sex]\n",
    "    sh = sh_d[sh]\n",
    "\n",
    "    # CREATE CAT AND CONT TENSORS\n",
    "    cats = torch.tensor([sex,sh], dtype=torch.int64).reshape(1,-1)\n",
    "    conts = torch.tensor([age,hp,hd,A1c,bg,bmi], dtype=torch.float).reshape(1,-1)\n",
    "    \n",
    "    # SET MODEL TO EVAL (in case this hasn't been done)\n",
    "    mdl.eval()\n",
    "\n",
    "    # PASS NEW DATA THROUGH THE MODEL WITHOUT PERFORMING A BACKPROP\n",
    "    with torch.no_grad():\n",
    "        z = mdl(cats, conts).argmax().item()\n",
    "\n",
    "    print(f'\\nThe predicted label is {z}')\n",
    "    \n",
    "test_data(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "539a250d-274c-4ed8-82c9-06e2ce636395",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Patients with the above characters is predicted to be a Diabetic"
   ]
  }
 ],
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