Teste_07.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Bibliotecas Instaladas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd #Pandas para carregamento do conjunto de dados\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Loading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Aqui vamos ler uma arquivo de Excel para começarmos a tratar os dados e as variáveis\n",
    "data = pd.read_excel(r\"D:\\OCEAN\\Dataset\\Rubelmar\\treinamento_cordoes.xlsx\" , index_col = 0)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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>Et</th>\n",
       "      <th>Tipo</th>\n",
       "      <th>Furo</th>\n",
       "      <th>Gas</th>\n",
       "      <th>Diam</th>\n",
       "      <th>u</th>\n",
       "      <th>v</th>\n",
       "      <th>t_arco</th>\n",
       "      <th>u.1</th>\n",
       "      <th>i</th>\n",
       "      <th>v.1</th>\n",
       "      <th>tempo_t</th>\n",
       "      <th>ciclo_trabalho</th>\n",
       "      <th>interrupções</th>\n",
       "      <th>energia_total</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cp</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>fc</td>\n",
       "      <td>1°</td>\n",
       "      <td>Não</td>\n",
       "      <td>Ar</td>\n",
       "      <td>Maior</td>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>7.7786</td>\n",
       "      <td>15.529282</td>\n",
       "      <td>256.051236</td>\n",
       "      <td>7.722713</td>\n",
       "      <td>8.7230</td>\n",
       "      <td>0.891734</td>\n",
       "      <td>27</td>\n",
       "      <td>27562.852404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>fc</td>\n",
       "      <td>2°</td>\n",
       "      <td>Sim</td>\n",
       "      <td>Ar</td>\n",
       "      <td>Maior</td>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>6.7134</td>\n",
       "      <td>16.111797</td>\n",
       "      <td>237.984484</td>\n",
       "      <td>7.426446</td>\n",
       "      <td>7.1168</td>\n",
       "      <td>0.943317</td>\n",
       "      <td>5</td>\n",
       "      <td>23767.347607</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>fc</td>\n",
       "      <td>1°</td>\n",
       "      <td>Não</td>\n",
       "      <td>Ar</td>\n",
       "      <td>Maior</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>7.4672</td>\n",
       "      <td>23.069799</td>\n",
       "      <td>135.090920</td>\n",
       "      <td>1.412267</td>\n",
       "      <td>17.6674</td>\n",
       "      <td>0.422654</td>\n",
       "      <td>34</td>\n",
       "      <td>23359.194766</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>fc</td>\n",
       "      <td>2°</td>\n",
       "      <td>Não</td>\n",
       "      <td>Ar</td>\n",
       "      <td>Maior</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>7.3786</td>\n",
       "      <td>23.322624</td>\n",
       "      <td>127.537741</td>\n",
       "      <td>1.406391</td>\n",
       "      <td>18.1418</td>\n",
       "      <td>0.406718</td>\n",
       "      <td>29</td>\n",
       "      <td>22239.599199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>fc</td>\n",
       "      <td>1°</td>\n",
       "      <td>Sim</td>\n",
       "      <td>Ar</td>\n",
       "      <td>Maior</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>5.4818</td>\n",
       "      <td>20.072319</td>\n",
       "      <td>238.922962</td>\n",
       "      <td>7.576412</td>\n",
       "      <td>7.2680</td>\n",
       "      <td>0.754238</td>\n",
       "      <td>16</td>\n",
       "      <td>25026.993814</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Et Tipo Furo Gas   Diam  u  v  t_arco        u.1           i       v.1  \\\n",
       "cp                                                                           \n",
       "2   fc   1°  Não  Ar  Maior  4  9  7.7786  15.529282  256.051236  7.722713   \n",
       "2   fc   2°  Sim  Ar  Maior  4  9  6.7134  16.111797  237.984484  7.426446   \n",
       "3   fc   1°  Não  Ar  Maior  5  5  7.4672  23.069799  135.090920  1.412267   \n",
       "3   fc   2°  Não  Ar  Maior  5  5  7.3786  23.322624  127.537741  1.406391   \n",
       "4   fc   1°  Sim  Ar  Maior  5  9  5.4818  20.072319  238.922962  7.576412   \n",
       "\n",
       "    tempo_t  ciclo_trabalho  interrupções  energia_total  \n",
       "cp                                                        \n",
       "2    8.7230        0.891734            27   27562.852404  \n",
       "2    7.1168        0.943317             5   23767.347607  \n",
       "3   17.6674        0.422654            34   23359.194766  \n",
       "3   18.1418        0.406718            29   22239.599199  \n",
       "4    7.2680        0.754238            16   25026.993814  "
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Mostrar primerias 5 colunas\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "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>Et</th>\n",
       "      <th>Tipo</th>\n",
       "      <th>Furo</th>\n",
       "      <th>Gas</th>\n",
       "      <th>Diam</th>\n",
       "      <th>u</th>\n",
       "      <th>v</th>\n",
       "      <th>t_arco</th>\n",
       "      <th>u.1</th>\n",
       "      <th>i</th>\n",
       "      <th>v.1</th>\n",
       "      <th>tempo_t</th>\n",
       "      <th>ciclo_trabalho</th>\n",
       "      <th>interrupções</th>\n",
       "      <th>energia_total</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cp</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>ex</td>\n",
       "      <td>2°</td>\n",
       "      <td>Sim</td>\n",
       "      <td>Ar</td>\n",
       "      <td>Maior</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>5.0824</td>\n",
       "      <td>21.263082</td>\n",
       "      <td>181.566372</td>\n",
       "      <td>4.762359</td>\n",
       "      <td>5.4470</td>\n",
       "      <td>0.933064</td>\n",
       "      <td>2</td>\n",
       "      <td>19697.616418</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>ex</td>\n",
       "      <td>1°</td>\n",
       "      <td>Não</td>\n",
       "      <td>Ar</td>\n",
       "      <td>Menor</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>9.8296</td>\n",
       "      <td>19.333369</td>\n",
       "      <td>105.046360</td>\n",
       "      <td>4.500873</td>\n",
       "      <td>14.1524</td>\n",
       "      <td>0.694554</td>\n",
       "      <td>173</td>\n",
       "      <td>19891.163571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>ex</td>\n",
       "      <td>2°</td>\n",
       "      <td>Não</td>\n",
       "      <td>Ar</td>\n",
       "      <td>Menor</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>9.0706</td>\n",
       "      <td>19.717527</td>\n",
       "      <td>98.726511</td>\n",
       "      <td>4.384289</td>\n",
       "      <td>16.5522</td>\n",
       "      <td>0.548000</td>\n",
       "      <td>78</td>\n",
       "      <td>17852.863558</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>ex</td>\n",
       "      <td>1°</td>\n",
       "      <td>Não</td>\n",
       "      <td>Ar</td>\n",
       "      <td>Menor</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>6.0450</td>\n",
       "      <td>23.022218</td>\n",
       "      <td>115.534215</td>\n",
       "      <td>4.375145</td>\n",
       "      <td>12.5830</td>\n",
       "      <td>0.480410</td>\n",
       "      <td>77</td>\n",
       "      <td>16591.749844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>ex</td>\n",
       "      <td>2°</td>\n",
       "      <td>Não</td>\n",
       "      <td>Ar</td>\n",
       "      <td>Menor</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>5.7988</td>\n",
       "      <td>23.171592</td>\n",
       "      <td>109.538104</td>\n",
       "      <td>4.487649</td>\n",
       "      <td>12.5018</td>\n",
       "      <td>0.463837</td>\n",
       "      <td>86</td>\n",
       "      <td>15371.833738</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Et Tipo Furo Gas   Diam  u  v  t_arco        u.1           i       v.1  \\\n",
       "cp                                                                           \n",
       "22  ex   2°  Sim  Ar  Maior  5  7  5.0824  21.263082  181.566372  4.762359   \n",
       "23  ex   1°  Não  Ar  Menor  4  7  9.8296  19.333369  105.046360  4.500873   \n",
       "23  ex   2°  Não  Ar  Menor  4  7  9.0706  19.717527   98.726511  4.384289   \n",
       "24  ex   1°  Não  Ar  Menor  5  7  6.0450  23.022218  115.534215  4.375145   \n",
       "24  ex   2°  Não  Ar  Menor  5  7  5.7988  23.171592  109.538104  4.487649   \n",
       "\n",
       "    tempo_t  ciclo_trabalho  interrupções  energia_total  \n",
       "cp                                                        \n",
       "22   5.4470        0.933064             2   19697.616418  \n",
       "23  14.1524        0.694554           173   19891.163571  \n",
       "23  16.5522        0.548000            78   17852.863558  \n",
       "24  12.5830        0.480410            77   16591.749844  \n",
       "24  12.5018        0.463837            86   15371.833738  "
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#mostrar últimas 5 linhas\n",
    "data.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(42, 15)"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# verificar o formato do dataset\n",
    "data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plotar um Gráfico do comportamento de certas variáveis em funções de outras variáveis\n",
    "https://seaborn.pydata.org/tutorial/regression.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x1cf93104f10>"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1058.4x504 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Aqui temos uma regressão kind = reg\n",
    "#Bug - os dados do primeiro gráfico não estão funcionando como deveriam\n",
    "sns.pairplot(data, x_vars=['energia_total', 'v.1','u.1'], y_vars='t_arco', height=7, aspect=0.7, kind='reg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x1cf931140d0>"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1058.4x504 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(data, x_vars=[ 'v.1','energia_total','u.1'], y_vars='t_arco', height=7, aspect=0.7, kind='reg')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Escolhendo Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "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>v.1</th>\n",
       "      <th>u.1</th>\n",
       "      <th>u</th>\n",
       "      <th>v</th>\n",
       "      <th>energia_total</th>\n",
       "      <th>t_arco</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cp</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7.722713</td>\n",
       "      <td>15.529282</td>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>27562.852404</td>\n",
       "      <td>7.7786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7.426446</td>\n",
       "      <td>16.111797</td>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>23767.347607</td>\n",
       "      <td>6.7134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.412267</td>\n",
       "      <td>23.069799</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>23359.194766</td>\n",
       "      <td>7.4672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.406391</td>\n",
       "      <td>23.322624</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>22239.599199</td>\n",
       "      <td>7.3786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7.576412</td>\n",
       "      <td>20.072319</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>25026.993814</td>\n",
       "      <td>5.4818</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         v.1        u.1  u  v  energia_total  t_arco\n",
       "cp                                                  \n",
       "2   7.722713  15.529282  4  9   27562.852404  7.7786\n",
       "2   7.426446  16.111797  4  9   23767.347607  6.7134\n",
       "3   1.412267  23.069799  5  5   23359.194766  7.4672\n",
       "3   1.406391  23.322624  5  5   22239.599199  7.3786\n",
       "4   7.576412  20.072319  5  9   25026.993814  5.4818"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Selecionar as features\n",
    "feature_cols = ['v.1','u.1', 'u', 'v', 'energia_total', 't_arco']\n",
    "\n",
    "# Selecionar essas features Dataset\n",
    "X = data[feature_cols]\n",
    "\n",
    "# Equivalente\n",
    "X = data[['v.1','u.1', 'u', 'v', 'energia_total', 't_arco']]\n",
    "\n",
    "# Verificar o dataset\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "(42, 6)\n"
     ]
    }
   ],
   "source": [
    "# verificar o tipo e a forma de X\n",
    "print(type(X))\n",
    "print(X.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "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>v.1</th>\n",
       "      <th>u.1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cp</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7.722713</td>\n",
       "      <td>15.529282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7.426446</td>\n",
       "      <td>16.111797</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.412267</td>\n",
       "      <td>23.069799</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.406391</td>\n",
       "      <td>23.322624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7.576412</td>\n",
       "      <td>20.072319</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         v.1        u.1\n",
       "cp                     \n",
       "2   7.722713  15.529282\n",
       "2   7.426446  16.111797\n",
       "3   1.412267  23.069799\n",
       "3   1.406391  23.322624\n",
       "4   7.576412  20.072319"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Selecionar uma coluna do Dataframe\n",
    "y = data[['v.1','u.1']]\n",
    "\n",
    "# print the first 5 values\n",
    "y.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Divisão do Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(31, 6)\n",
      "(31, 2)\n",
      "(11, 6)\n",
      "(11, 2)\n"
     ]
    }
   ],
   "source": [
    "#A DIUVISÃO PADRÃSO É 3/4 para treinamento e 1/4 para teste\n",
    "print(X_train.shape)\n",
    "print(y_train.shape)\n",
    "print(X_test.shape)\n",
    "print(y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Preparando o Algoritmo das Redes Neurais"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.inspection import plot_partial_dependence\n",
    "from sklearn.datasets import make_friedman1\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "X, y = make_friedman1()\n",
    "est1 = LinearRegression().fit(X, y)\n",
    "est2 = RandomForestRegressor().fit(X, y)\n",
    "disp1 = plot_partial_dependence(est1, X,\n",
    "                                [1, 2])  \n",
    "disp2 = plot_partial_dependence(est2, X, [1, 2],\n",
    "                                ax=disp1.axes_) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Modelo Instantâneo\n",
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "# Iniciação Instantânea\n",
    "linreg = LinearRegression()\n",
    "\n",
    "# Fit model\n",
    "linreg.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 4.44089210e-14 -1.84741111e-12]\n",
      "[[ 1.00000000e+00 -4.82396268e-18 -3.69407435e-16  4.72001533e-16\n",
      "  -2.27988114e-18 -1.11742268e-16]\n",
      " [-1.12176671e-13  1.00000000e+00 -7.20299398e-13 -7.77927125e-15\n",
      "   1.21554351e-16 -1.30889196e-16]]\n"
     ]
    }
   ],
   "source": [
    "# Printar os Coeficientes\n",
    "print(linreg.intercept_)\n",
    "print(linreg.coef_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('v.1',\n",
       "  array([ 1.00000000e+00, -4.82396268e-18, -3.69407435e-16,  4.72001533e-16,\n",
       "         -2.27988114e-18, -1.11742268e-16])),\n",
       " ('u.1',\n",
       "  array([-1.12176671e-13,  1.00000000e+00, -7.20299398e-13, -7.77927125e-15,\n",
       "          1.21554351e-16, -1.30889196e-16]))]"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Juntar os nomes com os coeficientes\n",
    "list(zip(feature_cols, linreg.coef_))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 1.40639123 23.32262377]\n",
      " [ 1.41226719 23.06979938]\n",
      " [ 1.61244929 23.17600943]\n",
      " [ 4.38428893 19.71752693]\n",
      " [ 8.51751996 23.2419648 ]\n",
      " [ 4.20840268 22.96822123]\n",
      " [ 1.77357508 19.2093568 ]\n",
      " [ 5.0061771  16.9205817 ]\n",
      " [ 4.48820166 16.89765322]\n",
      " [ 1.65113031 23.37411573]\n",
      " [ 7.56642567 15.07127756]]\n"
     ]
    }
   ],
   "source": [
    "# Fazer as predições de acordo com o dataset\n",
    "y_pred = linreg.predict(X_test)\n",
    "print(y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         v.1        u.1\n",
      "cp                     \n",
      "3   1.406391  23.322624\n",
      "3   1.412267  23.069799\n",
      "15  1.612449  23.176009\n",
      "23  4.384289  19.717527\n",
      "12  8.517520  23.241965\n",
      "18  4.208403  22.968221\n",
      "13  1.773575  19.209357\n",
      "21  5.006177  16.920582\n",
      "19  4.488202  16.897653\n",
      "15  1.651130  23.374116\n",
      "14  7.566426  15.071278\n"
     ]
    }
   ],
   "source": [
    "print(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "# define true and predicted response values\n",
    "true = y_test\n",
    "pred = y_pred"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Mean Absolute Error (MAE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.0027414070579075e-13\n"
     ]
    }
   ],
   "source": [
    "# calculate MAE using scikit-learn\n",
    "from sklearn import metrics\n",
    "print(metrics.mean_absolute_error(true, pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Mean Square Erro (MSE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.346680026823503e-25\n"
     ]
    }
   ],
   "source": [
    "# calculate MSE using scikit-learn\n",
    "print(metrics.mean_squared_error(true, pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Root Mean Squared Error (RMSE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.669713921852088e-13\n"
     ]
    }
   ],
   "source": [
    "# calculate RMSE using scikit-learn\n",
    "print(np.sqrt(metrics.mean_squared_error(true, pred)))"
   ]
  }
 ],
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