01.11-regressão-rede-neural-regressão.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "# importação de bibliotecas\n",
    "\n",
    "import pandas as pd                                             # importação de biblioteca para manipulação de dados\n",
    "import numpy as np                                              # importação de biblioteca para manipulação de numeros\n",
    "import seaborn as sns                                           # importação de biblioteca para visualização de dados graficamente\n",
    "import plotly.express as px                                     # importação de biblioteca para visualização de dados graficamente\n",
    "from sklearn.neural_network import MLPRegressor                 # importação de biblioteca para regressão com redes neurais\n",
    "from sklearn.preprocessing import StandardScaler                # importação de biblioteca sklearn para normalização de dados\n",
    "from sklearn.metrics import mean_squared_error                  # importação de biblioteca sklearn para calculo de erro"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "# carregamento de dados\n",
    "\n",
    "plano_saude = pd.read_csv('./data/plano_saude2.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "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>idade</th>\n",
       "      <th>custo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>18</td>\n",
       "      <td>470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>23</td>\n",
       "      <td>520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>28</td>\n",
       "      <td>630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>33</td>\n",
       "      <td>830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>38</td>\n",
       "      <td>1150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>43</td>\n",
       "      <td>1530</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>48</td>\n",
       "      <td>2040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>53</td>\n",
       "      <td>3080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>58</td>\n",
       "      <td>5100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>63</td>\n",
       "      <td>10100</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   idade  custo\n",
       "0     18    470\n",
       "1     23    520\n",
       "2     28    630\n",
       "3     33    830\n",
       "4     38   1150\n",
       "5     43   1530\n",
       "6     48   2040\n",
       "7     53   3080\n",
       "8     58   5100\n",
       "9     63  10100"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# exploração da base de dados visualizando os registros\n",
    "\n",
    "plano_saude"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "# separação de dados\n",
    "\n",
    "X_plano_saude = plano_saude.values[ :, 0:1]\n",
    "y_plano_saude = plano_saude.values[ :, 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[18],\n",
       "       [23],\n",
       "       [28],\n",
       "       [33],\n",
       "       [38],\n",
       "       [43],\n",
       "       [48],\n",
       "       [53],\n",
       "       [58],\n",
       "       [63]], dtype=int64)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# visualização das bases de dados\n",
    "\n",
    "X_plano_saude"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  470,   520,   630,   830,  1150,  1530,  2040,  3080,  5100,\n",
       "       10100], dtype=int64)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# visualização das bases de dados\n",
    "\n",
    "y_plano_saude"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([18. , 18.1, 18.2, 18.3, 18.4, 18.5, 18.6, 18.7, 18.8, 18.9, 19. ,\n",
       "       19.1, 19.2, 19.3, 19.4, 19.5, 19.6, 19.7, 19.8, 19.9, 20. , 20.1,\n",
       "       20.2, 20.3, 20.4, 20.5, 20.6, 20.7, 20.8, 20.9, 21. , 21.1, 21.2,\n",
       "       21.3, 21.4, 21.5, 21.6, 21.7, 21.8, 21.9, 22. , 22.1, 22.2, 22.3,\n",
       "       22.4, 22.5, 22.6, 22.7, 22.8, 22.9, 23. , 23.1, 23.2, 23.3, 23.4,\n",
       "       23.5, 23.6, 23.7, 23.8, 23.9, 24. , 24.1, 24.2, 24.3, 24.4, 24.5,\n",
       "       24.6, 24.7, 24.8, 24.9, 25. , 25.1, 25.2, 25.3, 25.4, 25.5, 25.6,\n",
       "       25.7, 25.8, 25.9, 26. , 26.1, 26.2, 26.3, 26.4, 26.5, 26.6, 26.7,\n",
       "       26.8, 26.9, 27. , 27.1, 27.2, 27.3, 27.4, 27.5, 27.6, 27.7, 27.8,\n",
       "       27.9, 28. , 28.1, 28.2, 28.3, 28.4, 28.5, 28.6, 28.7, 28.8, 28.9,\n",
       "       29. , 29.1, 29.2, 29.3, 29.4, 29.5, 29.6, 29.7, 29.8, 29.9, 30. ,\n",
       "       30.1, 30.2, 30.3, 30.4, 30.5, 30.6, 30.7, 30.8, 30.9, 31. , 31.1,\n",
       "       31.2, 31.3, 31.4, 31.5, 31.6, 31.7, 31.8, 31.9, 32. , 32.1, 32.2,\n",
       "       32.3, 32.4, 32.5, 32.6, 32.7, 32.8, 32.9, 33. , 33.1, 33.2, 33.3,\n",
       "       33.4, 33.5, 33.6, 33.7, 33.8, 33.9, 34. , 34.1, 34.2, 34.3, 34.4,\n",
       "       34.5, 34.6, 34.7, 34.8, 34.9, 35. , 35.1, 35.2, 35.3, 35.4, 35.5,\n",
       "       35.6, 35.7, 35.8, 35.9, 36. , 36.1, 36.2, 36.3, 36.4, 36.5, 36.6,\n",
       "       36.7, 36.8, 36.9, 37. , 37.1, 37.2, 37.3, 37.4, 37.5, 37.6, 37.7,\n",
       "       37.8, 37.9, 38. , 38.1, 38.2, 38.3, 38.4, 38.5, 38.6, 38.7, 38.8,\n",
       "       38.9, 39. , 39.1, 39.2, 39.3, 39.4, 39.5, 39.6, 39.7, 39.8, 39.9,\n",
       "       40. , 40.1, 40.2, 40.3, 40.4, 40.5, 40.6, 40.7, 40.8, 40.9, 41. ,\n",
       "       41.1, 41.2, 41.3, 41.4, 41.5, 41.6, 41.7, 41.8, 41.9, 42. , 42.1,\n",
       "       42.2, 42.3, 42.4, 42.5, 42.6, 42.7, 42.8, 42.9, 43. , 43.1, 43.2,\n",
       "       43.3, 43.4, 43.5, 43.6, 43.7, 43.8, 43.9, 44. , 44.1, 44.2, 44.3,\n",
       "       44.4, 44.5, 44.6, 44.7, 44.8, 44.9, 45. , 45.1, 45.2, 45.3, 45.4,\n",
       "       45.5, 45.6, 45.7, 45.8, 45.9, 46. , 46.1, 46.2, 46.3, 46.4, 46.5,\n",
       "       46.6, 46.7, 46.8, 46.9, 47. , 47.1, 47.2, 47.3, 47.4, 47.5, 47.6,\n",
       "       47.7, 47.8, 47.9, 48. , 48.1, 48.2, 48.3, 48.4, 48.5, 48.6, 48.7,\n",
       "       48.8, 48.9, 49. , 49.1, 49.2, 49.3, 49.4, 49.5, 49.6, 49.7, 49.8,\n",
       "       49.9, 50. , 50.1, 50.2, 50.3, 50.4, 50.5, 50.6, 50.7, 50.8, 50.9,\n",
       "       51. , 51.1, 51.2, 51.3, 51.4, 51.5, 51.6, 51.7, 51.8, 51.9, 52. ,\n",
       "       52.1, 52.2, 52.3, 52.4, 52.5, 52.6, 52.7, 52.8, 52.9, 53. , 53.1,\n",
       "       53.2, 53.3, 53.4, 53.5, 53.6, 53.7, 53.8, 53.9, 54. , 54.1, 54.2,\n",
       "       54.3, 54.4, 54.5, 54.6, 54.7, 54.8, 54.9, 55. , 55.1, 55.2, 55.3,\n",
       "       55.4, 55.5, 55.6, 55.7, 55.8, 55.9, 56. , 56.1, 56.2, 56.3, 56.4,\n",
       "       56.5, 56.6, 56.7, 56.8, 56.9, 57. , 57.1, 57.2, 57.3, 57.4, 57.5,\n",
       "       57.6, 57.7, 57.8, 57.9, 58. , 58.1, 58.2, 58.3, 58.4, 58.5, 58.6,\n",
       "       58.7, 58.8, 58.9, 59. , 59.1, 59.2, 59.3, 59.4, 59.5, 59.6, 59.7,\n",
       "       59.8, 59.9, 60. , 60.1, 60.2, 60.3, 60.4, 60.5, 60.6, 60.7, 60.8,\n",
       "       60.9, 61. , 61.1, 61.2, 61.3, 61.4, 61.5, 61.6, 61.7, 61.8, 61.9,\n",
       "       62. , 62.1, 62.2, 62.3, 62.4, 62.5, 62.6, 62.7, 62.8, 62.9])"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# criando um range entre o valor minimo e o maximo da base de treino\n",
    "# 18 - 63 anos\n",
    "\n",
    "X_teste_arvore = np.arange(min(X_plano_saude), max(X_plano_saude), 0.1)\n",
    "X_teste_arvore"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(450,)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# tamanho da amostra\n",
    "\n",
    "X_teste_arvore.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
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     },
     "execution_count": 49,
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   ],
   "source": [
    "# criando uma base de dados com os valores do range\n",
    "# transformando a base de dados em uma matriz\n",
    "\n",
    "X_teste_arvore = X_teste_arvore.reshape(-1, 1)\n",
    "X_teste_arvore"
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  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "# implementação de redes neurais"
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   "execution_count": 51,
   "metadata": {},
   "outputs": [],
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    "# normalização dos dados\n",
    "\n",
    "scaler_x = StandardScaler()\n",
    "X_plano_scaled = scaler_x.fit_transform(X_plano_saude.reshape(-1, 1))\n",
    "\n",
    "scaler_y = StandardScaler()\n",
    "y_plano_scaled = scaler_y.fit_transform(y_plano_saude.reshape(-1, 1))"
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   "cell_type": "code",
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   "metadata": {},
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   "source": [
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  {
   "cell_type": "code",
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   "metadata": {},
   "outputs": [
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     "execution_count": 53,
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  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
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     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
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    "# criando um modelo de regressão e treinando\n",
    "\n",
    "regressor_nn = MLPRegressor(max_iter=1000)\n",
    "regressor_nn.fit(X_plano_scaled, y_plano_scaled.ravel())"
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   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
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    "regressor_nn.score(X_plano_scaled, y_plano_scaled.ravel())"
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          "font": {
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          "geo": {
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           "showlakes": true,
           "showland": true,
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          "polar": {
           "angularaxis": {
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           "bgcolor": "#E5ECF6",
           "radialaxis": {
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          },
          "scene": {
           "xaxis": {
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            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
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           "yaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
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           "bgcolor": "#E5ECF6",
           "caxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
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          },
          "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
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           "zerolinecolor": "white",
           "zerolinewidth": 2
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        },
        "xaxis": {
         "anchor": "y",
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         "title": {
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        "yaxis": {
         "anchor": "x",
         "domain": [
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         "title": {
          "text": "y"
         }
        }
       }
      }
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# grafico de dispersão com o treinamento do kernel = 'rbf' e dados normalizados\n",
    "\n",
    "grafico = px.scatter(x = X_plano_scaled.ravel(), y = y_plano_scaled.ravel())\n",
    "grafico.add_scatter(x = X_plano_scaled.ravel(), y = regressor_nn.predict(X_plano_scaled), name = 'Previsões')\n",
    "grafico.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2681.4651862]])"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# erro medio quadratico\n",
    "\n",
    "mean_error = scaler_y.inverse_transform(mean_squared_error(y_plano_scaled, regressor_nn.predict(X_plano_scaled)).reshape(-1, 1))\n",
    "mean_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "# novo valor para o plano de saude\n",
    "novo = [[40]]\n",
    "novo = scaler_x.transform(novo)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-0.52622144])"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# previsão do algoritmo\n",
    "\n",
    "regressor_nn.predict(novo)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1036.86137709]])"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# previsão do algoritmo\n",
    "\n",
    "novo = scaler_y.inverse_transform(regressor_nn.predict(novo).reshape(-1, 1))\n",
    "novo"
   ]
  }
 ],
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