synthetic_data_generation.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Synthetic data generation in Python"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Required libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "import random\n",
    "import numpy as np\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Random variables"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### How can we generate random numbers in a computer?\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](img/prng.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5524440934483177"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "random.random()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "random.randint(0,100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[41, 41, 36, 14, 51, 83, 61, 35, 72, 45]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[random.randint(0,100) for _ in range(10)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "random.seed(42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[81, 14, 3, 94, 35, 31, 28, 17, 94, 13]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[random.randint(0,100) for _ in range(10)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[86, 94, 69, 11, 75, 54, 4, 3, 11, 27]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[random.randint(0,100) for _ in range(10)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "random.seed(42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[81, 14, 3, 94, 35, 31, 28, 17, 94, 13]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[random.randint(0,100) for _ in range(10)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[86, 94, 69, 11, 75, 54, 4, 3, 11, 27]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[random.randint(0,100) for _ in range(10)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Probability distributions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Uniform distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "serie = [random.random() for _ in range(10000)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 970., 1033.,  985.,  980., 1049.,  999., 1051., 1016.,  943.,\n",
       "         974.]),\n",
       " array([1.95627856e-04, 1.00173727e-01, 2.00151827e-01, 3.00129926e-01,\n",
       "        4.00108025e-01, 5.00086124e-01, 6.00064224e-01, 7.00042323e-01,\n",
       "        8.00020422e-01, 8.99998522e-01, 9.99976621e-01]),\n",
       " <BarContainer object of 10 artists>)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ = plt.hist(serie)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Normal distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = [random.gauss(0,1) for _ in range(0,10000)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ = plt.hist(a, bins=100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Using numpy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 94.,  96.,  95.,  89.,  97., 114.,  98.,  97., 121.,  99.]),\n",
       " array([5.49521254e-05, 9.99183186e-02, 1.99781685e-01, 2.99645052e-01,\n",
       "        3.99508418e-01, 4.99371785e-01, 5.99235151e-01, 6.99098518e-01,\n",
       "        7.98961884e-01, 8.98825251e-01, 9.98688617e-01]),\n",
       " <BarContainer object of 10 artists>)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(np.random.rand(1000))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([  1.,   6.,  12.,  20.,  36.,  47.,  95., 121., 125., 141., 127.,\n",
       "         90.,  61.,  48.,  28.,  25.,  12.,   3.,   0.,   2.]),\n",
       " array([-3.17492682, -2.83109969, -2.48727256, -2.14344544, -1.79961831,\n",
       "        -1.45579119, -1.11196406, -0.76813694, -0.42430981, -0.08048269,\n",
       "         0.26334444,  0.60717157,  0.95099869,  1.29482582,  1.63865294,\n",
       "         1.98248007,  2.32630719,  2.67013432,  3.01396144,  3.35778857,\n",
       "         3.7016157 ]),\n",
       " <BarContainer object of 20 artists>)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(np.random.randn(1000), bins=20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Using scipy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](img/distributions.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy import stats"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sampling a collection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['f', 'a']"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "random.sample(['a','b','c','d','e','f'],2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Random choices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['F']"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "random.choices(('M','F'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['M', 'F', 'M', 'M', 'M', 'M', 'M', 'F', 'F', 'F']"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "random.choices(('M','F'), k=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "gender = random.choices(('M','F'), weights=(2,1), k=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "from collections import Counter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Counter({'F': 4, 'M': 6})"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Counter(gender)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: ylabel='count'>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x=gender)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "gender = random.choices(('M','F'), weights=(2,1), k=1000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Generating random demographical/social data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "from faker import Faker"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "fake = Faker()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Mackenzie Wilcox'"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fake.name()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'5313 Rodriguez Neck\\nMcfarlandberg, MO 55282'"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fake.address()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "birth_date = fake.date_of_birth(minimum_age=30,maximum_age=65)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.date(1993, 2, 5)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "birth_date"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Rule-based synthetic data generation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "N=1000\n",
    "GENDER_BALANCE = {'M':1,'F':1}\n",
    "AGE_RANGE = (30, 65)\n",
    "HEIGHT = (170, 10)\n",
    "WEIGHT = (70, 20)\n",
    "HIPERTENSION_INCIDENCE = 0.2\n",
    "HIPERTENSION_EFFECT = 2\n",
    "TABACO_INCIDENCE = 0.23\n",
    "TABACO_EFFECT = 10\n",
    "DIABETES_INCIDENCE = 0.06\n",
    "DIABETES_EFFECT = 5\n",
    "BMI = 3 \n",
    "INFARTO = (169 / 10000, 28 /10000) \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "person = {\n",
    " 'gender': random.choices(list(GENDER_BALANCE.keys()), weights=list(GENDER_BALANCE.values()), k=N),\n",
    " 'birth_date': [fake.date_of_birth(minimum_age=30,maximum_age=65) for _ in range(N)],\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "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>birth_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>M</td>\n",
       "      <td>1963-04-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>F</td>\n",
       "      <td>1973-05-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>F</td>\n",
       "      <td>1990-11-29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>M</td>\n",
       "      <td>1973-11-28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>M</td>\n",
       "      <td>1984-03-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>995</th>\n",
       "      <td>F</td>\n",
       "      <td>1968-05-31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>M</td>\n",
       "      <td>1993-06-30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>F</td>\n",
       "      <td>1965-07-20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>M</td>\n",
       "      <td>1978-06-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>M</td>\n",
       "      <td>1983-11-23</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    gender  birth_date\n",
       "0        M  1963-04-01\n",
       "1        F  1973-05-07\n",
       "2        F  1990-11-29\n",
       "3        M  1973-11-28\n",
       "4        M  1984-03-07\n",
       "..     ...         ...\n",
       "995      F  1968-05-31\n",
       "996      M  1993-06-30\n",
       "997      F  1965-07-20\n",
       "998      M  1978-06-08\n",
       "999      M  1983-11-23\n",
       "\n",
       "[1000 rows x 2 columns]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datos = pd.DataFrame(person)\n",
    "datos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "def nombre(gender):\n",
    "    nombre = fake.first_name_male() if gender==\"M\" else fake.first_name_female()\n",
    "    return f'{nombre} {fake.last_name()}'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "from dateutil.relativedelta import relativedelta\n",
    "from datetime import datetime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "def edad(fecha_nac):\n",
    "    return relativedelta(datetime.now(), fecha_nac).years"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "datos['age'] = datos['birth_date'].apply(edad)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "datos['name'] = datos['gender'].apply(nombre)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>birth_date</th>\n",
       "      <th>age</th>\n",
       "      <th>name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>M</td>\n",
       "      <td>1963-04-01</td>\n",
       "      <td>60</td>\n",
       "      <td>Austin Moore</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>F</td>\n",
       "      <td>1973-05-07</td>\n",
       "      <td>50</td>\n",
       "      <td>Yvonne Barnes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>F</td>\n",
       "      <td>1990-11-29</td>\n",
       "      <td>32</td>\n",
       "      <td>Mary Riddle</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>M</td>\n",
       "      <td>1973-11-28</td>\n",
       "      <td>49</td>\n",
       "      <td>Todd Ray</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>M</td>\n",
       "      <td>1984-03-07</td>\n",
       "      <td>39</td>\n",
       "      <td>Travis Holden</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>995</th>\n",
       "      <td>F</td>\n",
       "      <td>1968-05-31</td>\n",
       "      <td>55</td>\n",
       "      <td>Amber Hancock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>M</td>\n",
       "      <td>1993-06-30</td>\n",
       "      <td>30</td>\n",
       "      <td>Andrew Johnson</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>F</td>\n",
       "      <td>1965-07-20</td>\n",
       "      <td>58</td>\n",
       "      <td>Victoria Johnson</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>M</td>\n",
       "      <td>1978-06-08</td>\n",
       "      <td>45</td>\n",
       "      <td>John Davis</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>M</td>\n",
       "      <td>1983-11-23</td>\n",
       "      <td>39</td>\n",
       "      <td>Daniel Galvan</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    gender  birth_date  age              name\n",
       "0        M  1963-04-01   60      Austin Moore\n",
       "1        F  1973-05-07   50     Yvonne Barnes\n",
       "2        F  1990-11-29   32       Mary Riddle\n",
       "3        M  1973-11-28   49          Todd Ray\n",
       "4        M  1984-03-07   39     Travis Holden\n",
       "..     ...         ...  ...               ...\n",
       "995      F  1968-05-31   55     Amber Hancock\n",
       "996      M  1993-06-30   30    Andrew Johnson\n",
       "997      F  1965-07-20   58  Victoria Johnson\n",
       "998      M  1978-06-08   45        John Davis\n",
       "999      M  1983-11-23   39     Daniel Galvan\n",
       "\n",
       "[1000 rows x 4 columns]"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datos"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Probability distributions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "datos['height'] = stats.norm(*HEIGHT).rvs(N)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "datos['weight'] = stats.norm(*WEIGHT).rvs(N)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\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>birth_date</th>\n",
       "      <th>age</th>\n",
       "      <th>name</th>\n",
       "      <th>height</th>\n",
       "      <th>weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>M</td>\n",
       "      <td>1963-04-01</td>\n",
       "      <td>60</td>\n",
       "      <td>Austin Moore</td>\n",
       "      <td>152.061190</td>\n",
       "      <td>34.598333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>F</td>\n",
       "      <td>1973-05-07</td>\n",
       "      <td>50</td>\n",
       "      <td>Yvonne Barnes</td>\n",
       "      <td>174.286050</td>\n",
       "      <td>68.996167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>F</td>\n",
       "      <td>1990-11-29</td>\n",
       "      <td>32</td>\n",
       "      <td>Mary Riddle</td>\n",
       "      <td>181.433625</td>\n",
       "      <td>39.681466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>M</td>\n",
       "      <td>1973-11-28</td>\n",
       "      <td>49</td>\n",
       "      <td>Todd Ray</td>\n",
       "      <td>191.015101</td>\n",
       "      <td>43.410127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>M</td>\n",
       "      <td>1984-03-07</td>\n",
       "      <td>39</td>\n",
       "      <td>Travis Holden</td>\n",
       "      <td>169.232786</td>\n",
       "      <td>40.483333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>995</th>\n",
       "      <td>F</td>\n",
       "      <td>1968-05-31</td>\n",
       "      <td>55</td>\n",
       "      <td>Amber Hancock</td>\n",
       "      <td>169.207138</td>\n",
       "      <td>49.893548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>M</td>\n",
       "      <td>1993-06-30</td>\n",
       "      <td>30</td>\n",
       "      <td>Andrew Johnson</td>\n",
       "      <td>159.801543</td>\n",
       "      <td>57.968145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>F</td>\n",
       "      <td>1965-07-20</td>\n",
       "      <td>58</td>\n",
       "      <td>Victoria Johnson</td>\n",
       "      <td>181.386684</td>\n",
       "      <td>97.551216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>M</td>\n",
       "      <td>1978-06-08</td>\n",
       "      <td>45</td>\n",
       "      <td>John Davis</td>\n",
       "      <td>163.964570</td>\n",
       "      <td>57.230272</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>M</td>\n",
       "      <td>1983-11-23</td>\n",
       "      <td>39</td>\n",
       "      <td>Daniel Galvan</td>\n",
       "      <td>156.345125</td>\n",
       "      <td>51.164004</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    gender  birth_date  age              name      height     weight\n",
       "0        M  1963-04-01   60      Austin Moore  152.061190  34.598333\n",
       "1        F  1973-05-07   50     Yvonne Barnes  174.286050  68.996167\n",
       "2        F  1990-11-29   32       Mary Riddle  181.433625  39.681466\n",
       "3        M  1973-11-28   49          Todd Ray  191.015101  43.410127\n",
       "4        M  1984-03-07   39     Travis Holden  169.232786  40.483333\n",
       "..     ...         ...  ...               ...         ...        ...\n",
       "995      F  1968-05-31   55     Amber Hancock  169.207138  49.893548\n",
       "996      M  1993-06-30   30    Andrew Johnson  159.801543  57.968145\n",
       "997      F  1965-07-20   58  Victoria Johnson  181.386684  97.551216\n",
       "998      M  1978-06-08   45        John Davis  163.964570  57.230272\n",
       "999      M  1983-11-23   39     Daniel Galvan  156.345125  51.164004\n",
       "\n",
       "[1000 rows x 6 columns]"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "def BMI(height,weight):\n",
    "    bmi = weight / ((height/100) ** 2)\n",
    "    return bmi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "datos['bmi'] = datos.apply(lambda r: BMI(r['height'], r['weight']), axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Exploring distributions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7f56a97065f0>"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.displot(data=datos, x='bmi', kind='kde')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "params = stats.norm.fit(datos['bmi'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "mod = stats.norm(*params).rvs(1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='bmi', ylabel='Density'>"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting the KDEs\n",
    "sns.kdeplot(datos['bmi'], fill=True, color=\"r\", label=\"Data1 KDE\")\n",
    "sns.kdeplot(mod, fill=True, color=\"b\", label=\"Data2 KDE\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8282194040312439"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Perform the KS test\n",
    "\n",
    "statistic, p_value = stats.ks_2samp(datos['bmi'], mod)\n",
    "p_value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "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>birth_date</th>\n",
       "      <th>age</th>\n",
       "      <th>name</th>\n",
       "      <th>height</th>\n",
       "      <th>weight</th>\n",
       "      <th>bmi</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>M</td>\n",
       "      <td>1985-10-12</td>\n",
       "      <td>38</td>\n",
       "      <td>Richard Donaldson</td>\n",
       "      <td>158.918951</td>\n",
       "      <td>46.816898</td>\n",
       "      <td>18.537504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>F</td>\n",
       "      <td>1968-10-12</td>\n",
       "      <td>55</td>\n",
       "      <td>Laura Cooley</td>\n",
       "      <td>173.583603</td>\n",
       "      <td>105.342002</td>\n",
       "      <td>34.961026</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>F</td>\n",
       "      <td>1973-12-26</td>\n",
       "      <td>49</td>\n",
       "      <td>Debbie Martin</td>\n",
       "      <td>167.998963</td>\n",
       "      <td>78.530541</td>\n",
       "      <td>27.824373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>M</td>\n",
       "      <td>1966-11-05</td>\n",
       "      <td>57</td>\n",
       "      <td>Christopher Reid</td>\n",
       "      <td>184.077694</td>\n",
       "      <td>23.795228</td>\n",
       "      <td>7.022432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>M</td>\n",
       "      <td>1992-11-10</td>\n",
       "      <td>31</td>\n",
       "      <td>Sean Rivera</td>\n",
       "      <td>164.904076</td>\n",
       "      <td>100.237028</td>\n",
       "      <td>36.860854</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>995</th>\n",
       "      <td>F</td>\n",
       "      <td>1962-12-08</td>\n",
       "      <td>60</td>\n",
       "      <td>Karen Davis</td>\n",
       "      <td>180.288263</td>\n",
       "      <td>107.134759</td>\n",
       "      <td>32.960629</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>M</td>\n",
       "      <td>1970-10-17</td>\n",
       "      <td>53</td>\n",
       "      <td>David Blake</td>\n",
       "      <td>165.696363</td>\n",
       "      <td>31.145822</td>\n",
       "      <td>11.344199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>F</td>\n",
       "      <td>1970-11-17</td>\n",
       "      <td>52</td>\n",
       "      <td>Kristen Hoffman</td>\n",
       "      <td>179.920218</td>\n",
       "      <td>70.082020</td>\n",
       "      <td>21.649440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>M</td>\n",
       "      <td>1992-06-18</td>\n",
       "      <td>31</td>\n",
       "      <td>Jeffrey Haney</td>\n",
       "      <td>198.285461</td>\n",
       "      <td>75.665926</td>\n",
       "      <td>19.245031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>M</td>\n",
       "      <td>1959-03-12</td>\n",
       "      <td>64</td>\n",
       "      <td>Scott Luna</td>\n",
       "      <td>168.628486</td>\n",
       "      <td>74.134171</td>\n",
       "      <td>26.070932</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    gender  birth_date  age               name      height      weight  \\\n",
       "0        M  1985-10-12   38  Richard Donaldson  158.918951   46.816898   \n",
       "1        F  1968-10-12   55       Laura Cooley  173.583603  105.342002   \n",
       "2        F  1973-12-26   49      Debbie Martin  167.998963   78.530541   \n",
       "3        M  1966-11-05   57   Christopher Reid  184.077694   23.795228   \n",
       "4        M  1992-11-10   31        Sean Rivera  164.904076  100.237028   \n",
       "..     ...         ...  ...                ...         ...         ...   \n",
       "995      F  1962-12-08   60        Karen Davis  180.288263  107.134759   \n",
       "996      M  1970-10-17   53        David Blake  165.696363   31.145822   \n",
       "997      F  1970-11-17   52    Kristen Hoffman  179.920218   70.082020   \n",
       "998      M  1992-06-18   31      Jeffrey Haney  198.285461   75.665926   \n",
       "999      M  1959-03-12   64         Scott Luna  168.628486   74.134171   \n",
       "\n",
       "           bmi  \n",
       "0    18.537504  \n",
       "1    34.961026  \n",
       "2    27.824373  \n",
       "3     7.022432  \n",
       "4    36.860854  \n",
       "..         ...  \n",
       "995  32.960629  \n",
       "996  11.344199  \n",
       "997  21.649440  \n",
       "998  19.245031  \n",
       "999  26.070932  \n",
       "\n",
       "[1000 rows x 7 columns]"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "datos['tobacco'] = stats.bernoulli(TABACO_INCIDENCE).rvs(N)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "datos['hypertension'] = stats.bernoulli(HIPERTENSION_INCIDENCE).rvs(N)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "datos['diabetes'] = stats.bernoulli(DIABETES_INCIDENCE).rvs(N)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "def infarct(row):\n",
    "    prob = INFARTO[0] if row['gender'] == 'M' else INFARTO[1]\n",
    "    prob = prob * TABACO_EFFECT if row['tobacco'] else prob\n",
    "    prob = prob * DIABETES_EFFECT if row['diabetes'] else prob\n",
    "    prob = prob * HIPERTENSION_EFFECT if row['hypertension'] else prob\n",
    "    prob = prob * 20\n",
    "    prob = min(prob, 1)\n",
    "    return stats.bernoulli(prob).rvs()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "datos['infarct'] = datos.apply(infarct, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='infarct', ylabel='count'>"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(data=datos, x='infarct')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1000 entries, 0 to 999\n",
      "Data columns (total 11 columns):\n",
      " #   Column        Non-Null Count  Dtype  \n",
      "---  ------        --------------  -----  \n",
      " 0   gender        1000 non-null   object \n",
      " 1   birth_date    1000 non-null   object \n",
      " 2   age           1000 non-null   int64  \n",
      " 3   name          1000 non-null   object \n",
      " 4   height        1000 non-null   float64\n",
      " 5   weight        1000 non-null   float64\n",
      " 6   bmi           1000 non-null   float64\n",
      " 7   tobacco       1000 non-null   int64  \n",
      " 8   hypertension  1000 non-null   int64  \n",
      " 9   diabetes      1000 non-null   int64  \n",
      " 10  infarct       1000 non-null   int64  \n",
      "dtypes: float64(3), int64(5), object(3)\n",
      "memory usage: 86.1+ KB\n"
     ]
    }
   ],
   "source": [
    "datos.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Machine learning: Classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import StratifiedKFold, train_test_split\n",
    "from sklearn.metrics import accuracy_score, classification_report, confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "import xgboost as xgb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = datos[['age','bmi', 'diabetes', 'tobacco', 'hypertension']].copy()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "X['gender'] = LabelEncoder().fit_transform(datos['gender'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = datos['infarct']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.76      0.82      0.79       114\n",
      "           1       0.73      0.66      0.70        86\n",
      "\n",
      "    accuracy                           0.75       200\n",
      "   macro avg       0.75      0.74      0.74       200\n",
      "weighted avg       0.75      0.75      0.75       200\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Split data into training and test sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "\n",
    "# Create an XGBoost classifier\n",
    "clf = xgb.XGBClassifier()\n",
    "\n",
    "# Train the classifier\n",
    "clf.fit(X_train, y_train)\n",
    "\n",
    "# Predict on the validation set\n",
    "y_pred = clf.predict(X_test)\n",
    "\n",
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[93 21]\n",
      " [29 57]]\n"
     ]
    }
   ],
   "source": [
    "print(confusion_matrix(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Generating synthetic data from real data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'1.6.0'"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sdv import __version__ as sdv_version\n",
    "sdv_version"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sdv.single_table.ctgan import CTGANSynthesizer\n",
    "from sdv.single_table.copulagan import CopulaGANSynthesizer\n",
    "from sdv.single_table.copulas import GaussianCopulaSynthesizer\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sdv.metadata import SingleTableMetadata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "metadata = SingleTableMetadata()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "metadata.detect_from_dataframe(data=datos)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "metadata.update_column(\n",
    "    column_name='has_rewards',\n",
    "    sdtype='boolean'\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{\n",
       "    \"columns\": {\n",
       "        \"gender\": {\n",
       "            \"sdtype\": \"categorical\"\n",
       "        },\n",
       "        \"birth_date\": {\n",
       "            \"sdtype\": \"datetime\",\n",
       "            \"datetime_format\": \"%Y-%m-%d\"\n",
       "        },\n",
       "        \"age\": {\n",
       "            \"sdtype\": \"numerical\"\n",
       "        },\n",
       "        \"name\": {\n",
       "            \"sdtype\": \"unknown\",\n",
       "            \"pii\": true\n",
       "        },\n",
       "        \"height\": {\n",
       "            \"sdtype\": \"numerical\"\n",
       "        },\n",
       "        \"weight\": {\n",
       "            \"sdtype\": \"numerical\"\n",
       "        },\n",
       "        \"bmi\": {\n",
       "            \"sdtype\": \"numerical\"\n",
       "        },\n",
       "        \"tobacco\": {\n",
       "            \"sdtype\": \"categorical\"\n",
       "        },\n",
       "        \"hypertension\": {\n",
       "            \"sdtype\": \"categorical\"\n",
       "        },\n",
       "        \"diabetes\": {\n",
       "            \"sdtype\": \"categorical\"\n",
       "        },\n",
       "        \"infarct\": {\n",
       "            \"sdtype\": \"categorical\"\n",
       "        }\n",
       "    },\n",
       "    \"METADATA_SPEC_VERSION\": \"SINGLE_TABLE_V1\"\n",
       "}"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metadata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'columns': {'gender': {'sdtype': 'categorical'},\n",
       "  'birth_date': {'sdtype': 'datetime', 'datetime_format': '%Y-%m-%d'},\n",
       "  'age': {'sdtype': 'numerical'},\n",
       "  'name': {'sdtype': 'unknown', 'pii': True},\n",
       "  'height': {'sdtype': 'numerical'},\n",
       "  'weight': {'sdtype': 'numerical'},\n",
       "  'bmi': {'sdtype': 'numerical'},\n",
       "  'tobacco': {'sdtype': 'categorical'},\n",
       "  'hypertension': {'sdtype': 'categorical'},\n",
       "  'diabetes': {'sdtype': 'categorical'},\n",
       "  'infarct': {'sdtype': 'categorical'}},\n",
       " 'METADATA_SPEC_VERSION': 'SINGLE_TABLE_V1'}"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metadata.to_dict()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](img/ctgan.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = CTGANSynthesizer(metadata)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 59.6 s, sys: 6.45 s, total: 1min 6s\n",
      "Wall time: 17.3 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "model.fit(datos)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "synth_data = model.sample(10000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "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>birth_date</th>\n",
       "      <th>age</th>\n",
       "      <th>name</th>\n",
       "      <th>height</th>\n",
       "      <th>weight</th>\n",
       "      <th>bmi</th>\n",
       "      <th>tobacco</th>\n",
       "      <th>hypertension</th>\n",
       "      <th>diabetes</th>\n",
       "      <th>infarct</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>M</td>\n",
       "      <td>1955-02-11</td>\n",
       "      <td>52</td>\n",
       "      <td>sdv-pii-inuqs</td>\n",
       "      <td>189.994085</td>\n",
       "      <td>74.209039</td>\n",
       "      <td>6.049697</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>M</td>\n",
       "      <td>1967-05-06</td>\n",
       "      <td>58</td>\n",
       "      <td>sdv-pii-vatdi</td>\n",
       "      <td>160.647212</td>\n",
       "      <td>84.129277</td>\n",
       "      <td>21.291555</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>F</td>\n",
       "      <td>1963-08-17</td>\n",
       "      <td>63</td>\n",
       "      <td>sdv-pii-6pw1k</td>\n",
       "      <td>196.121922</td>\n",
       "      <td>64.553854</td>\n",
       "      <td>17.074343</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>F</td>\n",
       "      <td>1972-07-30</td>\n",
       "      <td>54</td>\n",
       "      <td>sdv-pii-h6fyk</td>\n",
       "      <td>165.566769</td>\n",
       "      <td>60.713768</td>\n",
       "      <td>26.227775</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>F</td>\n",
       "      <td>1952-09-15</td>\n",
       "      <td>30</td>\n",
       "      <td>sdv-pii-sh58h</td>\n",
       "      <td>140.759413</td>\n",
       "      <td>101.181993</td>\n",
       "      <td>38.024653</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>M</td>\n",
       "      <td>1955-09-20</td>\n",
       "      <td>60</td>\n",
       "      <td>sdv-pii-0lmu2</td>\n",
       "      <td>148.719158</td>\n",
       "      <td>73.228213</td>\n",
       "      <td>17.437547</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>F</td>\n",
       "      <td>1984-07-30</td>\n",
       "      <td>30</td>\n",
       "      <td>sdv-pii-ro9tz</td>\n",
       "      <td>186.061275</td>\n",
       "      <td>55.230197</td>\n",
       "      <td>35.294049</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>M</td>\n",
       "      <td>1978-07-03</td>\n",
       "      <td>52</td>\n",
       "      <td>sdv-pii-td6d3</td>\n",
       "      <td>196.003628</td>\n",
       "      <td>69.419186</td>\n",
       "      <td>25.832188</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>F</td>\n",
       "      <td>1980-04-17</td>\n",
       "      <td>43</td>\n",
       "      <td>sdv-pii-9zmmo</td>\n",
       "      <td>191.083033</td>\n",
       "      <td>77.689653</td>\n",
       "      <td>29.271255</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>M</td>\n",
       "      <td>1983-05-08</td>\n",
       "      <td>50</td>\n",
       "      <td>sdv-pii-6hzhw</td>\n",
       "      <td>137.187854</td>\n",
       "      <td>22.082619</td>\n",
       "      <td>37.578572</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   gender  birth_date  age           name      height      weight        bmi  \\\n",
       "0       M  1955-02-11   52  sdv-pii-inuqs  189.994085   74.209039   6.049697   \n",
       "1       M  1967-05-06   58  sdv-pii-vatdi  160.647212   84.129277  21.291555   \n",
       "2       F  1963-08-17   63  sdv-pii-6pw1k  196.121922   64.553854  17.074343   \n",
       "3       F  1972-07-30   54  sdv-pii-h6fyk  165.566769   60.713768  26.227775   \n",
       "4       F  1952-09-15   30  sdv-pii-sh58h  140.759413  101.181993  38.024653   \n",
       "..    ...         ...  ...            ...         ...         ...        ...   \n",
       "95      M  1955-09-20   60  sdv-pii-0lmu2  148.719158   73.228213  17.437547   \n",
       "96      F  1984-07-30   30  sdv-pii-ro9tz  186.061275   55.230197  35.294049   \n",
       "97      M  1978-07-03   52  sdv-pii-td6d3  196.003628   69.419186  25.832188   \n",
       "98      F  1980-04-17   43  sdv-pii-9zmmo  191.083033   77.689653  29.271255   \n",
       "99      M  1983-05-08   50  sdv-pii-6hzhw  137.187854   22.082619  37.578572   \n",
       "\n",
       "    tobacco  hypertension  diabetes  infarct  \n",
       "0         0             1         0        0  \n",
       "1         0             0         0        1  \n",
       "2         1             0         0        0  \n",
       "3         0             0         0        1  \n",
       "4         0             0         0        0  \n",
       "..      ...           ...       ...      ...  \n",
       "95        0             1         0        0  \n",
       "96        1             0         0        0  \n",
       "97        1             0         0        1  \n",
       "98        0             0         0        1  \n",
       "99        1             0         0        0  \n",
       "\n",
       "[100 rows x 11 columns]"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "synth_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = synth_data[['age','bmi', 'diabetes', 'tobacco', 'hypertension']].copy()\n",
    "X['gender'] = LabelEncoder().fit_transform(synth_data['gender'])\n",
    "y = synth_data['infarct']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Synthetic data evaluation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](img/evaluation.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fidelity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sdmetrics.reports.single_table import QualityReport"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "report = QualityReport()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generating report ...\n",
      "(1/2) Evaluating Column Shapes: : 100%|██████████| 11/11 [00:00<00:00, 114.08it/s]\n",
      "(2/2) Evaluating Column Pair Trends: : 100%|██████████| 55/55 [00:00<00:00, 105.13it/s]\n",
      "\n",
      "Overall Quality Score: 86.15%\n",
      "\n",
      "Properties:\n",
      "- Column Shapes: 89.19%\n",
      "- Column Pair Trends: 83.12%\n"
     ]
    }
   ],
   "source": [
    "\n",
    "report.generate(datos, synth_data, metadata.to_dict())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\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>Column</th>\n",
       "      <th>Metric</th>\n",
       "      <th>Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>gender</td>\n",
       "      <td>TVComplement</td>\n",
       "      <td>0.9844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>birth_date</td>\n",
       "      <td>KSComplement</td>\n",
       "      <td>0.6391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>age</td>\n",
       "      <td>KSComplement</td>\n",
       "      <td>0.8994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>height</td>\n",
       "      <td>KSComplement</td>\n",
       "      <td>0.8330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>weight</td>\n",
       "      <td>KSComplement</td>\n",
       "      <td>0.8356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>bmi</td>\n",
       "      <td>KSComplement</td>\n",
       "      <td>0.9113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>tobacco</td>\n",
       "      <td>TVComplement</td>\n",
       "      <td>0.9546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>hypertension</td>\n",
       "      <td>TVComplement</td>\n",
       "      <td>0.9745</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>diabetes</td>\n",
       "      <td>TVComplement</td>\n",
       "      <td>0.9338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>infarct</td>\n",
       "      <td>TVComplement</td>\n",
       "      <td>0.9530</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Column        Metric   Score\n",
       "0        gender  TVComplement  0.9844\n",
       "1    birth_date  KSComplement  0.6391\n",
       "2           age  KSComplement  0.8994\n",
       "3        height  KSComplement  0.8330\n",
       "4        weight  KSComplement  0.8356\n",
       "5           bmi  KSComplement  0.9113\n",
       "6       tobacco  TVComplement  0.9546\n",
       "7  hypertension  TVComplement  0.9745\n",
       "8      diabetes  TVComplement  0.9338\n",
       "9       infarct  TVComplement  0.9530"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "report.get_details(property_name='Column Shapes')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "plotlyServerURL": "https://plot.ly"
       },
       "data": [
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         "alignmentgroup": "True",
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           "TVComplement"
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          [
           "TVComplement"
          ]
         ],
         "hovertemplate": "<b>%{hovertext}</b><br><br>Metric=%{customdata[0]}<br>Score=%{y}<extra></extra>",
         "hovertext": [
          "gender",
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         "marker": {
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          "pattern": {
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         "name": "TVComplement",
         "offsetgroup": "TVComplement",
         "orientation": "v",
         "showlegend": true,
         "textposition": "auto",
         "type": "bar",
         "x": [
          "gender",
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         "xaxis": "x",
         "y": [
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         "alignmentgroup": "True",
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           "KSComplement"
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         ],
         "hovertemplate": "<b>%{hovertext}</b><br><br>Metric=%{customdata[0]}<br>Score=%{y}<extra></extra>",
         "hovertext": [
          "birth_date",
          "age",
          "height",
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         "legendgroup": "KSComplement",
         "marker": {
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         "name": "KSComplement",
         "offsetgroup": "KSComplement",
         "orientation": "v",
         "showlegend": true,
         "textposition": "auto",
         "type": "bar",
         "x": [
          "birth_date",
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         "xaxis": "x",
         "y": [
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         ],
         "yaxis": "y"
        }
       ],
       "layout": {
        "barmode": "relative",
        "font": {
         "size": 18
        },
        "legend": {
         "title": {
          "text": "Metric"
         },
         "tracegroupgap": 0
        },
        "margin": {
         "t": 150
        },
        "plot_bgcolor": "#F5F5F8",
        "template": {
         "data": {
          "bar": [
           {
            "error_x": {
             "color": "#2a3f5f"
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            "error_y": {
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            "marker": {
             "line": {
              "color": "#E5ECF6",
              "width": 0.5
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             "pattern": {
              "fillmode": "overlay",
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          "barpolar": [
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        },
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        "yaxis": {
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     "metadata": {},
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   ],
   "source": [
    "report.get_visualization(property_name='Column Shapes')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sdmetrics.visualization import get_column_plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
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        },
        "title": {
         "text": "Real vs. Synthetic Data for column 'age'"
        },
        "xaxis": {
         "anchor": "y2",
         "domain": [
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         "title": {
          "text": "Value"
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     "metadata": {},
     "output_type": "display_data"
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   ],
   "source": [
    "get_column_plot(\n",
    "    real_data=datos,\n",
    "    synthetic_data=synth_data,\n",
    "    column_name='age'\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
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    "get_column_plot(\n",
    "    real_data=datos,\n",
    "    synthetic_data=synth_data,\n",
    "    column_name='height'\n",
    ")"
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            "colorbar": {
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            "type": "histogram2dcontour"
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          ],
          "mesh3d": [
           {
            "colorbar": {
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            "type": "mesh3d"
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          ],
          "parcoords": [
           {
            "line": {
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            "type": "parcoords"
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          ],
          "pie": [
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            "automargin": true,
            "type": "pie"
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          ],
          "scatter": [
           {
            "fillpattern": {
             "fillmode": "overlay",
             "size": 10,
             "solidity": 0.2
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            "type": "scatter"
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          ],
          "scatter3d": [
           {
            "line": {
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          "scattercarpet": [
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            "type": "scattergl"
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            "type": "scattermapbox"
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            "type": "scatterpolar"
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          "scatterpolargl": [
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            "type": "scatterpolargl"
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            "type": "scatterternary"
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          ],
          "surface": [
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            ],
            "type": "surface"
           }
          ],
          "table": [
           {
            "cells": {
             "fill": {
              "color": "#EBF0F8"
             },
             "line": {
              "color": "white"
             }
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            "header": {
             "fill": {
              "color": "#C8D4E3"
             },
             "line": {
              "color": "white"
             }
            },
            "type": "table"
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         "layout": {
          "annotationdefaults": {
           "arrowcolor": "#2a3f5f",
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          },
          "colorway": [
           "#636efa",
           "#EF553B",
           "#00cc96",
           "#ab63fa",
           "#FFA15A",
           "#19d3f3",
           "#FF6692",
           "#B6E880",
           "#FF97FF",
           "#FECB52"
          ],
          "font": {
           "color": "#2a3f5f"
          },
          "geo": {
           "bgcolor": "white",
           "lakecolor": "white",
           "landcolor": "#E5ECF6",
           "showlakes": true,
           "showland": true,
           "subunitcolor": "white"
          },
          "hoverlabel": {
           "align": "left"
          },
          "hovermode": "closest",
          "mapbox": {
           "style": "light"
          },
          "paper_bgcolor": "white",
          "plot_bgcolor": "#E5ECF6",
          "polar": {
           "angularaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "bgcolor": "#E5ECF6",
           "radialaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           }
          },
          "scene": {
           "xaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           },
           "yaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           },
           "zaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           }
          },
          "shapedefaults": {
           "line": {
            "color": "#2a3f5f"
           }
          },
          "ternary": {
           "aaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "baxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "bgcolor": "#E5ECF6",
           "caxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           }
          },
          "title": {
           "x": 0.05
          },
          "xaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "white",
           "zerolinewidth": 2
          },
          "yaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "white",
           "zerolinewidth": 2
          }
         }
        },
        "title": {
         "text": "Data Quality: Column Pair Trends (Average Score=0.83)"
        },
        "width": 900,
        "xaxis": {
         "anchor": "y",
         "domain": [
          0.26,
          0.74
         ],
         "tickangle": 45
        },
        "xaxis2": {
         "anchor": "y2",
         "domain": [
          0,
          0.45
         ],
         "tickangle": 45
        },
        "xaxis3": {
         "anchor": "y3",
         "domain": [
          0.55,
          1
         ],
         "matches": "x2",
         "tickangle": 45
        },
        "yaxis": {
         "anchor": "x",
         "autorange": "reversed",
         "domain": [
          0.625,
          1
         ]
        },
        "yaxis2": {
         "anchor": "x2",
         "autorange": "reversed",
         "domain": [
          0,
          0.375
         ]
        },
        "yaxis3": {
         "anchor": "x3",
         "autorange": "reversed",
         "domain": [
          0,
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         "matches": "y2",
         "visible": false
        }
       }
      }
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "report.get_visualization(property_name='Column Pair Trends')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Utility"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
       "              colsample_bylevel=None, colsample_bynode=None,\n",
       "              colsample_bytree=None, early_stopping_rounds=None,\n",
       "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "              gamma=None, gpu_id=None, grow_policy=None, importance_type=None,\n",
       "              interaction_constraints=None, learning_rate=None, max_bin=None,\n",
       "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "              max_delta_step=None, max_depth=None, max_leaves=None,\n",
       "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "              n_estimators=100, n_jobs=None, num_parallel_tree=None,\n",
       "              predictor=None, random_state=None, ...)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">XGBClassifier</label><div class=\"sk-toggleable__content\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
       "              colsample_bylevel=None, colsample_bynode=None,\n",
       "              colsample_bytree=None, early_stopping_rounds=None,\n",
       "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "              gamma=None, gpu_id=None, grow_policy=None, importance_type=None,\n",
       "              interaction_constraints=None, learning_rate=None, max_bin=None,\n",
       "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "              max_delta_step=None, max_depth=None, max_leaves=None,\n",
       "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "              n_estimators=100, n_jobs=None, num_parallel_tree=None,\n",
       "              predictor=None, random_state=None, ...)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
       "              colsample_bylevel=None, colsample_bynode=None,\n",
       "              colsample_bytree=None, early_stopping_rounds=None,\n",
       "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "              gamma=None, gpu_id=None, grow_policy=None, importance_type=None,\n",
       "              interaction_constraints=None, learning_rate=None, max_bin=None,\n",
       "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "              max_delta_step=None, max_depth=None, max_leaves=None,\n",
       "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "              n_estimators=100, n_jobs=None, num_parallel_tree=None,\n",
       "              predictor=None, random_state=None, ...)"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Split data into training and test sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "\n",
    "# Create an XGBoost classifier\n",
    "clf = xgb.XGBClassifier()\n",
    "\n",
    "# clf = LogisticRegression(max_iter=1000)\n",
    "\n",
    "# Train the classifier\n",
    "clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_eval = datos[['age','bmi', 'diabetes', 'tobacco', 'hypertension']].copy()\n",
    "X_eval['gender'] = LabelEncoder().fit_transform(datos['gender'])\n",
    "y_eval = datos['infarct']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.94      0.96      0.95       598\n",
      "           1       0.94      0.92      0.93       402\n",
      "\n",
      "    accuracy                           0.94      1000\n",
      "   macro avg       0.94      0.94      0.94      1000\n",
      "weighted avg       0.94      0.94      0.94      1000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Predict on the validation set\n",
    "y_pred = clf.predict(X_eval)\n",
    "\n",
    "print(classification_report(y_eval, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[574  24]\n",
      " [ 34 368]]\n"
     ]
    }
   ],
   "source": [
    "print(confusion_matrix(y_eval, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split data into training and test sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "\n",
    "# Create an XGBoost classifier\n",
    "# clf = xgb.XGBClassifier()\n",
    "\n",
    "clf = LogisticRegression()\n",
    "\n",
    "# Set up cross-validation\n",
    "kfold = StratifiedKFold(n_splits=5, shuffle=True, random_state=42)\n",
    "results = []\n",
    "\n",
    "# Perform cross-validation\n",
    "for train_index, valid_index in kfold.split(X_train, y_train):\n",
    "    X_train_fold, X_valid_fold = X_train.iloc[train_index], X_train.iloc[valid_index]\n",
    "    y_train_fold, y_valid_fold = y_train.iloc[train_index], y_train.iloc[valid_index]\n",
    "\n",
    "    # Train the classifier\n",
    "    clf.fit(X_train_fold, y_train_fold)\n",
    "\n",
    "    # Predict on the validation set\n",
    "    y_pred = clf.predict(X_valid_fold)\n",
    "\n",
    "    # Calculate accuracy and append to results\n",
    "    acc = accuracy_score(y_valid_fold, y_pred)\n",
    "    results.append(acc)\n",
    "    print(f\"Fold Accuracy: {acc}\")\n",
    "\n",
    "# Average cross-validation score\n",
    "print(f\"Average Cross-Validation Accuracy: {np.mean(results)}\")\n",
    "\n",
    "# Evaluate on the test set\n",
    "y_pred_test = clf.predict(X_test)\n",
    "print(f\"Test Accuracy: {accuracy_score(y_test, y_pred_test)}\")\n",
    "print(\"\\nClassification Report:\")\n",
    "print(classification_report(y_test, y_pred_test))\n",
    "print(\"\\nConfusion Matrix:\")\n",
    "print(confusion_matrix(y_test, y_pred_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Save results and model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [],
   "source": [
    "datos.to_csv('data/datos_originales.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [],
   "source": [
    "synth_data.to_csv('data/datos_sinteticos.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('model_ctgan.pkl', 'wb') as f:\n",
    "    pickle.dump(model, f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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