Sesion-04/Ejemplo-02/matriz_de_correlaciones.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Ejemplo 2: Matriz de correlaciones y mapas de calor\n",
    "\n",
    "### 1. Objetivos:\n",
    "    - Aprender a generar matrices de correlaciones y mapas de calor para cuantificar el coeficiente de correlación de Pearson de múltiples variables al mismo tiempo.\n",
    " \n",
    "---\n",
    "    \n",
    "### 2. Desarrollo:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Analicemos un poco el siguiente dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('../../Datasets/diabetes-clean.csv', index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pregnancies</th>\n",
       "      <th>glucose</th>\n",
       "      <th>blood_pressure</th>\n",
       "      <th>skin_thickness</th>\n",
       "      <th>insulin</th>\n",
       "      <th>bmi</th>\n",
       "      <th>diabetes_pedigree_function</th>\n",
       "      <th>age</th>\n",
       "      <th>outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>148</td>\n",
       "      <td>72</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>33.6</td>\n",
       "      <td>0.627</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
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       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>85</td>\n",
       "      <td>66</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>26.6</td>\n",
       "      <td>0.351</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>183</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23.3</td>\n",
       "      <td>0.672</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>89</td>\n",
       "      <td>66</td>\n",
       "      <td>23</td>\n",
       "      <td>94</td>\n",
       "      <td>28.1</td>\n",
       "      <td>0.167</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>137</td>\n",
       "      <td>40</td>\n",
       "      <td>35</td>\n",
       "      <td>168</td>\n",
       "      <td>43.1</td>\n",
       "      <td>2.288</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "   pregnancies  glucose  blood_pressure  skin_thickness  insulin   bmi  \\\n",
       "0            6      148              72              35        0  33.6   \n",
       "1            1       85              66              29        0  26.6   \n",
       "2            8      183              64               0        0  23.3   \n",
       "3            1       89              66              23       94  28.1   \n",
       "4            0      137              40              35      168  43.1   \n",
       "\n",
       "   diabetes_pedigree_function  age  outcome  \n",
       "0                       0.627   50        1  \n",
       "1                       0.351   31        0  \n",
       "2                       0.672   32        1  \n",
       "3                       0.167   21        0  \n",
       "4                       2.288   33        1  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Este dataset tiene muchas variables numéricas que se prestan a la aplicación de nuestro coeficiente de correlación de Pearson.\n",
    "\n",
    "Antes de aplicarlo, removeré la variable dependiente ('outcome'), dado que es una variable binaria no apta para este tipo de análisis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_filtered = df.drop(columns=['outcome'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pregnancies</th>\n",
       "      <th>glucose</th>\n",
       "      <th>blood_pressure</th>\n",
       "      <th>skin_thickness</th>\n",
       "      <th>insulin</th>\n",
       "      <th>bmi</th>\n",
       "      <th>diabetes_pedigree_function</th>\n",
       "      <th>age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>pregnancies</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.129459</td>\n",
       "      <td>0.141282</td>\n",
       "      <td>-0.081672</td>\n",
       "      <td>-0.073535</td>\n",
       "      <td>0.017683</td>\n",
       "      <td>-0.033523</td>\n",
       "      <td>0.544341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>glucose</th>\n",
       "      <td>0.129459</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.152590</td>\n",
       "      <td>0.057328</td>\n",
       "      <td>0.331357</td>\n",
       "      <td>0.221071</td>\n",
       "      <td>0.137337</td>\n",
       "      <td>0.263514</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>blood_pressure</th>\n",
       "      <td>0.141282</td>\n",
       "      <td>0.152590</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.207371</td>\n",
       "      <td>0.088933</td>\n",
       "      <td>0.281805</td>\n",
       "      <td>0.041265</td>\n",
       "      <td>0.239528</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>skin_thickness</th>\n",
       "      <td>-0.081672</td>\n",
       "      <td>0.057328</td>\n",
       "      <td>0.207371</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.436783</td>\n",
       "      <td>0.392573</td>\n",
       "      <td>0.183928</td>\n",
       "      <td>-0.113970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>insulin</th>\n",
       "      <td>-0.073535</td>\n",
       "      <td>0.331357</td>\n",
       "      <td>0.088933</td>\n",
       "      <td>0.436783</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.197859</td>\n",
       "      <td>0.185071</td>\n",
       "      <td>-0.042163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bmi</th>\n",
       "      <td>0.017683</td>\n",
       "      <td>0.221071</td>\n",
       "      <td>0.281805</td>\n",
       "      <td>0.392573</td>\n",
       "      <td>0.197859</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.140647</td>\n",
       "      <td>0.036242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>diabetes_pedigree_function</th>\n",
       "      <td>-0.033523</td>\n",
       "      <td>0.137337</td>\n",
       "      <td>0.041265</td>\n",
       "      <td>0.183928</td>\n",
       "      <td>0.185071</td>\n",
       "      <td>0.140647</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.033561</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>age</th>\n",
       "      <td>0.544341</td>\n",
       "      <td>0.263514</td>\n",
       "      <td>0.239528</td>\n",
       "      <td>-0.113970</td>\n",
       "      <td>-0.042163</td>\n",
       "      <td>0.036242</td>\n",
       "      <td>0.033561</td>\n",
       "      <td>1.000000</td>\n",
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      ],
      "text/plain": [
       "                            pregnancies   glucose  blood_pressure  \\\n",
       "pregnancies                    1.000000  0.129459        0.141282   \n",
       "glucose                        0.129459  1.000000        0.152590   \n",
       "blood_pressure                 0.141282  0.152590        1.000000   \n",
       "skin_thickness                -0.081672  0.057328        0.207371   \n",
       "insulin                       -0.073535  0.331357        0.088933   \n",
       "bmi                            0.017683  0.221071        0.281805   \n",
       "diabetes_pedigree_function    -0.033523  0.137337        0.041265   \n",
       "age                            0.544341  0.263514        0.239528   \n",
       "\n",
       "                            skin_thickness   insulin       bmi  \\\n",
       "pregnancies                      -0.081672 -0.073535  0.017683   \n",
       "glucose                           0.057328  0.331357  0.221071   \n",
       "blood_pressure                    0.207371  0.088933  0.281805   \n",
       "skin_thickness                    1.000000  0.436783  0.392573   \n",
       "insulin                           0.436783  1.000000  0.197859   \n",
       "bmi                               0.392573  0.197859  1.000000   \n",
       "diabetes_pedigree_function        0.183928  0.185071  0.140647   \n",
       "age                              -0.113970 -0.042163  0.036242   \n",
       "\n",
       "                            diabetes_pedigree_function       age  \n",
       "pregnancies                                  -0.033523  0.544341  \n",
       "glucose                                       0.137337  0.263514  \n",
       "blood_pressure                                0.041265  0.239528  \n",
       "skin_thickness                                0.183928 -0.113970  \n",
       "insulin                                       0.185071 -0.042163  \n",
       "bmi                                           0.140647  0.036242  \n",
       "diabetes_pedigree_function                    1.000000  0.033561  \n",
       "age                                           0.033561  1.000000  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_filtered.corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Tan fácil como esto. Como puedes observar, es un poco difícil discernir las distintas correlaciones de esta manera. Digamos que no 'saltan' a la vista.\n",
    "\n",
    "Un par de cosas que podemos observar es que:\n",
    "\n",
    "1. Hay una diagonal de 1s a la mitad de la matriz, donde cada variable se relaciona consigo misma.\n",
    "2. Hay redundancia de datos debajo y arriba de la diagonal."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Usemos ahora un mapa de calor para visualizar esta matriz de una manera más fácil de interpretar:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8, 6))\n",
    "ax = sns.heatmap(df_filtered.corr(), vmin=-1, vmax=1, annot=True, cmap=\"YlGnBu\", linewidths=.5);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Resulta mucho más sencillo visualizar las relaciones entre variables, ¿no es así?\n",
    "\n",
    "¿Qué variables tienen correlaciones negativas? ¿Qué variables tienen correlaciones positivas? ¿Qué variables no tienen correlación?\n",
    "\n",
    "Recuerda que estamos cuantificando la relación lineal entre dos variables. Esto significa que podría existir algún tipo de relación no lineal que no estamos visualizando con este mapa."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python (data_science)",
   "language": "python",
   "name": "data_science"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}