fill0.ipynb

{
 "metadata": {
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.9"
  },
  "orig_nbformat": 2,
  "kernelspec": {
   "name": "python369jvsc74a57bd031f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6",
   "display_name": "Python 3.6.9 64-bit"
  },
  "metadata": {
   "interpreter": {
    "hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6"
   }
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 },
 "nbformat": 4,
 "nbformat_minor": 2,
 "cells": [
  {
   "source": [
    "#Importação das bibliotecas"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import sklearn\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn import metrics\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.metrics import mean_absolute_error as mae\n",
    "from sklearn.metrics import mean_squared_error as mse\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import classification_report\n",
    "from matplotlib import pyplot as plt\n",
    "from sklearn.model_selection import learning_curve\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.model_selection import KFold\n"
   ]
  },
  {
   "source": [
    "#Load do nosso conjunto de dados"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Carrega o conjunto de dados\n",
    "pima = pd.read_csv(\"speedDating_trab.csv\")"
   ]
  },
  {
   "source": [
    "#Imprime o nosso conjunto de dados"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "      Unnamed: 0    id  partner   age  age_o  goal  date  go_out  int_corr  \\\n",
       "0              1   1.0        1  21.0   27.0   2.0   7.0     1.0      0.14   \n",
       "1              2   1.0        2  21.0   22.0   2.0   7.0     1.0      0.54   \n",
       "2              3   1.0        3  21.0   22.0   2.0   7.0     1.0      0.16   \n",
       "3              4   1.0        4  21.0   23.0   2.0   7.0     1.0      0.61   \n",
       "4              5   1.0        5  21.0   24.0   2.0   7.0     1.0      0.21   \n",
       "...          ...   ...      ...   ...    ...   ...   ...     ...       ...   \n",
       "8373        8374  22.0       18  25.0   26.0   1.0   2.0     1.0      0.64   \n",
       "8374        8375  22.0       19  25.0   24.0   1.0   2.0     1.0      0.71   \n",
       "8375        8376  22.0       20  25.0   29.0   1.0   2.0     1.0     -0.46   \n",
       "8376        8377  22.0       21  25.0   22.0   1.0   2.0     1.0      0.62   \n",
       "8377        8378   NaN       22  25.0   22.0   1.0   2.0     1.0      0.01   \n",
       "\n",
       "      length  met  like  prob  match  \n",
       "0        2.0  1.0   7.0   6.0      0  \n",
       "1        2.0  1.0   7.0   5.0      0  \n",
       "2        2.0  1.0   7.0   NaN      1  \n",
       "3        2.0  1.0   7.0   6.0      1  \n",
       "4        2.0  1.0   6.0   6.0      1  \n",
       "...      ...  ...   ...   ...    ...  \n",
       "8373     1.0  0.0   2.0   5.0      0  \n",
       "8374     1.0  0.0   4.0   4.0      0  \n",
       "8375     1.0  0.0   6.0   5.0      0  \n",
       "8376     1.0  0.0   5.0   5.0      0  \n",
       "8377     1.0  0.0   4.0   5.0      0  \n",
       "\n",
       "[8378 rows x 14 columns]"
      ],
      "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>Unnamed: 0</th>\n      <th>id</th>\n      <th>partner</th>\n      <th>age</th>\n      <th>age_o</th>\n      <th>goal</th>\n      <th>date</th>\n      <th>go_out</th>\n      <th>int_corr</th>\n      <th>length</th>\n      <th>met</th>\n      <th>like</th>\n      <th>prob</th>\n      <th>match</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>1.0</td>\n      <td>1</td>\n      <td>21.0</td>\n      <td>27.0</td>\n      <td>2.0</td>\n      <td>7.0</td>\n      <td>1.0</td>\n      <td>0.14</td>\n      <td>2.0</td>\n      <td>1.0</td>\n      <td>7.0</td>\n      <td>6.0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2</td>\n      <td>1.0</td>\n      <td>2</td>\n      <td>21.0</td>\n      <td>22.0</td>\n      <td>2.0</td>\n      <td>7.0</td>\n      <td>1.0</td>\n      <td>0.54</td>\n      <td>2.0</td>\n      <td>1.0</td>\n      <td>7.0</td>\n      <td>5.0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3</td>\n      <td>1.0</td>\n      <td>3</td>\n      <td>21.0</td>\n      <td>22.0</td>\n      <td>2.0</td>\n      <td>7.0</td>\n      <td>1.0</td>\n      <td>0.16</td>\n      <td>2.0</td>\n      <td>1.0</td>\n      <td>7.0</td>\n      <td>NaN</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>4</td>\n      <td>1.0</td>\n      <td>4</td>\n      <td>21.0</td>\n      <td>23.0</td>\n      <td>2.0</td>\n      <td>7.0</td>\n      <td>1.0</td>\n      <td>0.61</td>\n      <td>2.0</td>\n      <td>1.0</td>\n      <td>7.0</td>\n      <td>6.0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>5</td>\n      <td>1.0</td>\n      <td>5</td>\n      <td>21.0</td>\n      <td>24.0</td>\n      <td>2.0</td>\n      <td>7.0</td>\n      <td>1.0</td>\n      <td>0.21</td>\n      <td>2.0</td>\n      <td>1.0</td>\n      <td>6.0</td>\n      <td>6.0</td>\n      <td>1</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      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>8373</th>\n      <td>8374</td>\n      <td>22.0</td>\n      <td>18</td>\n      <td>25.0</td>\n      <td>26.0</td>\n      <td>1.0</td>\n      <td>2.0</td>\n      <td>1.0</td>\n      <td>0.64</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>2.0</td>\n      <td>5.0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>8374</th>\n      <td>8375</td>\n      <td>22.0</td>\n      <td>19</td>\n      <td>25.0</td>\n      <td>24.0</td>\n      <td>1.0</td>\n      <td>2.0</td>\n      <td>1.0</td>\n      <td>0.71</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>4.0</td>\n      <td>4.0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>8375</th>\n      <td>8376</td>\n      <td>22.0</td>\n      <td>20</td>\n      <td>25.0</td>\n      <td>29.0</td>\n      <td>1.0</td>\n      <td>2.0</td>\n      <td>1.0</td>\n      <td>-0.46</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>6.0</td>\n      <td>5.0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>8376</th>\n      <td>8377</td>\n      <td>22.0</td>\n      <td>21</td>\n      <td>25.0</td>\n      <td>22.0</td>\n      <td>1.0</td>\n      <td>2.0</td>\n      <td>1.0</td>\n      <td>0.62</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>5.0</td>\n      <td>5.0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>8377</th>\n      <td>8378</td>\n      <td>NaN</td>\n      <td>22</td>\n      <td>25.0</td>\n      <td>22.0</td>\n      <td>1.0</td>\n      <td>2.0</td>\n      <td>1.0</td>\n      <td>0.01</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>4.0</td>\n      <td>5.0</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>8378 rows × 14 columns</p>\n</div>"
     },
     "metadata": {},
     "execution_count": 3
    }
   ],
   "source": [
    "pima"
   ]
  },
  {
   "source": [
    "#Vai contar o número de NaN que existe em cada coluna"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "Unnamed: 0      0\n",
       "id              1\n",
       "partner         0\n",
       "age            95\n",
       "age_o         104\n",
       "goal           79\n",
       "date           97\n",
       "go_out         79\n",
       "int_corr      158\n",
       "length        915\n",
       "met           375\n",
       "like          240\n",
       "prob          309\n",
       "match           0\n",
       "dtype: int64"
      ]
     },
     "metadata": {},
     "execution_count": 4
    }
   ],
   "source": [
    "pima.isnull().sum()"
   ]
  },
  {
   "source": [
    "#A estratégia vai ser preencher todos os NaN a zero"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "pima=pima.fillna(0)"
   ]
  },
  {
   "source": [
    "#Convertemos todas as colunas exceto o partner, o prob, o like e o match para inteiro"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 8378 entries, 0 to 8377\nData columns (total 14 columns):\n #   Column      Non-Null Count  Dtype  \n---  ------      --------------  -----  \n 0   Unnamed: 0  8378 non-null   int64  \n 1   id          8378 non-null   int64  \n 2   partner     8378 non-null   int64  \n 3   age         8378 non-null   int64  \n 4   age_o       8378 non-null   int64  \n 5   goal        8378 non-null   int64  \n 6   date        8378 non-null   int64  \n 7   go_out      8378 non-null   int64  \n 8   int_corr    8378 non-null   float64\n 9   length      8378 non-null   int64  \n 10  met         8378 non-null   int64  \n 11  like        8378 non-null   float64\n 12  prob        8378 non-null   float64\n 13  match       8378 non-null   int64  \ndtypes: float64(3), int64(11)\nmemory usage: 916.5 KB\n"
     ]
    }
   ],
   "source": [
    "#convertemos de float para int\n",
    "pima[['met','length','go_out','date','goal','age_o','age','id']]=  pima[['met','length','go_out','date','goal','age_o','age','id']].astype(int)\n",
    "\n",
    "pima.info()"
   ]
  },
  {
   "source": [
    "#Aqui vamos verificar a quantidade de zeros e uns que existe na coluna match"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "% 1's: 0.16471711625686322\n% 0's: 0.8352828837431368\n"
     ]
    }
   ],
   "source": [
    "\n",
    "print(\"% 1's:\",pima['match'].mean())\n",
    "print(\"% 0's:\", 1-pima['match'].mean())\n"
   ]
  },
  {
   "source": [
    "#Confirma se todos os NaN foram substituídos"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "Unnamed: 0    0\n",
       "id            0\n",
       "partner       0\n",
       "age           0\n",
       "age_o         0\n",
       "goal          0\n",
       "date          0\n",
       "go_out        0\n",
       "int_corr      0\n",
       "length        0\n",
       "met           0\n",
       "like          0\n",
       "prob          0\n",
       "match         0\n",
       "dtype: int64"
      ]
     },
     "metadata": {},
     "execution_count": 8
    }
   ],
   "source": [
    "pima.isnull().sum()\n"
   ]
  },
  {
   "source": [
    "#Confirmar se a tabela foi convertida da maneira que queríamos"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "   Unnamed: 0  id  partner  age  age_o  goal  date  go_out  int_corr  length  \\\n",
       "0           1   1        1   21     27     2     7       1      0.14       2   \n",
       "1           2   1        2   21     22     2     7       1      0.54       2   \n",
       "2           3   1        3   21     22     2     7       1      0.16       2   \n",
       "3           4   1        4   21     23     2     7       1      0.61       2   \n",
       "4           5   1        5   21     24     2     7       1      0.21       2   \n",
       "5           6   1        6   21     25     2     7       1      0.25       2   \n",
       "6           7   1        7   21     30     2     7       1      0.34       2   \n",
       "7           8   1        8   21     27     2     7       1      0.50       2   \n",
       "8           9   1        9   21     28     2     7       1      0.28       2   \n",
       "9          10   1       10   21     24     2     7       1     -0.36       2   \n",
       "\n",
       "   met  like  prob  match  \n",
       "0    1   7.0   6.0      0  \n",
       "1    1   7.0   5.0      0  \n",
       "2    1   7.0   0.0      1  \n",
       "3    1   7.0   6.0      1  \n",
       "4    1   6.0   6.0      1  \n",
       "5    1   6.0   5.0      0  \n",
       "6    1   6.0   5.0      0  \n",
       "7    0   6.0   7.0      0  \n",
       "8    1   7.0   7.0      1  \n",
       "9    1   6.0   6.0      0  "
      ],
      "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>Unnamed: 0</th>\n      <th>id</th>\n      <th>partner</th>\n      <th>age</th>\n      <th>age_o</th>\n      <th>goal</th>\n      <th>date</th>\n      <th>go_out</th>\n      <th>int_corr</th>\n      <th>length</th>\n      <th>met</th>\n      <th>like</th>\n      <th>prob</th>\n      <th>match</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>21</td>\n      <td>27</td>\n      <td>2</td>\n      <td>7</td>\n      <td>1</td>\n      <td>0.14</td>\n      <td>2</td>\n      <td>1</td>\n      <td>7.0</td>\n      <td>6.0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2</td>\n      <td>1</td>\n      <td>2</td>\n      <td>21</td>\n      <td>22</td>\n      <td>2</td>\n      <td>7</td>\n      <td>1</td>\n      <td>0.54</td>\n      <td>2</td>\n      <td>1</td>\n      <td>7.0</td>\n      <td>5.0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3</td>\n      <td>1</td>\n      <td>3</td>\n      <td>21</td>\n      <td>22</td>\n      <td>2</td>\n      <td>7</td>\n      <td>1</td>\n      <td>0.16</td>\n      <td>2</td>\n      <td>1</td>\n      <td>7.0</td>\n      <td>0.0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>4</td>\n      <td>1</td>\n      <td>4</td>\n      <td>21</td>\n      <td>23</td>\n      <td>2</td>\n      <td>7</td>\n      <td>1</td>\n      <td>0.61</td>\n      <td>2</td>\n      <td>1</td>\n      <td>7.0</td>\n      <td>6.0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>5</td>\n      <td>1</td>\n      <td>5</td>\n      <td>21</td>\n      <td>24</td>\n      <td>2</td>\n      <td>7</td>\n      <td>1</td>\n      <td>0.21</td>\n      <td>2</td>\n      <td>1</td>\n      <td>6.0</td>\n      <td>6.0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>6</td>\n      <td>1</td>\n      <td>6</td>\n      <td>21</td>\n      <td>25</td>\n      <td>2</td>\n      <td>7</td>\n      <td>1</td>\n      <td>0.25</td>\n      <td>2</td>\n      <td>1</td>\n      <td>6.0</td>\n      <td>5.0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>7</td>\n      <td>1</td>\n      <td>7</td>\n      <td>21</td>\n      <td>30</td>\n      <td>2</td>\n      <td>7</td>\n      <td>1</td>\n      <td>0.34</td>\n      <td>2</td>\n      <td>1</td>\n      <td>6.0</td>\n      <td>5.0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>8</td>\n      <td>1</td>\n      <td>8</td>\n      <td>21</td>\n      <td>27</td>\n      <td>2</td>\n      <td>7</td>\n      <td>1</td>\n      <td>0.50</td>\n      <td>2</td>\n      <td>0</td>\n      <td>6.0</td>\n      <td>7.0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>9</td>\n      <td>1</td>\n      <td>9</td>\n      <td>21</td>\n      <td>28</td>\n      <td>2</td>\n      <td>7</td>\n      <td>1</td>\n      <td>0.28</td>\n      <td>2</td>\n      <td>1</td>\n      <td>7.0</td>\n      <td>7.0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>10</td>\n      <td>1</td>\n      <td>10</td>\n      <td>21</td>\n      <td>24</td>\n      <td>2</td>\n      <td>7</td>\n      <td>1</td>\n      <td>-0.36</td>\n      <td>2</td>\n      <td>1</td>\n      <td>6.0</td>\n      <td>6.0</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 9
    }
   ],
   "source": [
    "pima.head(10)\n"
   ]
  },
  {
   "source": [
    "#Divisão dos casos variável objetivo com os restantes e o split do conjunto de dados em casos teste e treino"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "#divisao do casos em q tem match e os q n tem match\n",
    "resultados = pima['match']\n",
    "dados = pima.drop(['match'],axis=1)\n",
    "\n",
    "# Separa o nosso conjunto de dados em treino e teste (30%/70%)\n",
    "dados_treino, dados_teste, resultados_treino, resultados_teste= train_test_split( dados, resultados, test_size=0.3,random_state=1)"
   ]
  },
  {
   "source": [
    "#ID3 COM ENTROPIA"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "source": [
    "##Método Holdout"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "source": [
    "###Aplicação, treino e predict do ID3"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "#modelo a aplicar (ID3)\n",
    "clf = DecisionTreeClassifier(criterion=\"entropy\")\n",
    "\n",
    "#treina o nosso modelo\n",
    "clf = clf.fit(dados_treino, resultados_treino)\n",
    "\n",
    "#dá uma previsão \n",
    "predicted_test = clf.predict(dados_teste)"
   ]
  },
  {
   "source": [
    "###Apresentação da Accuracy do modelo, da sua matriz de confusão e o seu classification report"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Accuracy: 0.7848050914876691\nMatriz de confusão:\n [[1834  273]\n [ 268  139]]\nClassification Report:\n               precision    recall  f1-score   support\n\n           0       0.87      0.87      0.87      2107\n           1       0.34      0.34      0.34       407\n\n    accuracy                           0.78      2514\n   macro avg       0.60      0.61      0.61      2514\nweighted avg       0.79      0.78      0.79      2514\n\n"
     ]
    }
   ],
   "source": [
    "\n",
    "print(\"Accuracy:\",metrics.accuracy_score(resultados_teste, predicted_test))\n",
    "\n",
    "print(\"Matriz de confusão:\\n\",confusion_matrix(resultados_teste, clf.predict(dados_teste)))\n",
    "\n",
    "print(\"Classification Report:\\n\",classification_report(resultados_teste, clf.predict(dados_teste)))\n"
   ]
  },
  {
   "source": [
    "##Método Validação Cruzada (Cross-Validation)"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "source": [
    "###Vamos dividir o nosso conjunto de dados em 10 e cada uma das partições vai ser testada como teste e as restantes como treino.\n",
    "###Apresentação da Accuracy de todas as partições, a média dessas Accuracies, matriz de confusão de cada partição e soma total das matrizes de confusão"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Cross Validation:\n",
      " [0.7326969  0.70883055 0.73150358 0.66945107 0.74582339 0.7326969\n",
      " 0.77207637 0.62291169 0.73596177 0.77777778]\n",
      "A média para Cross-Validation do K-fold é: 0.722972999945823 \n",
      "\n",
      "MATRIZ DE CONFUSÃO DE CADA K-FOLD:\n",
      "[[560 149]\n",
      " [ 80  49]]\n",
      "[[572 106]\n",
      " [111  49]]\n",
      "[[559 118]\n",
      " [107  54]]\n",
      "[[563 143]\n",
      " [ 89  43]]\n",
      "[[579 129]\n",
      " [ 87  43]]\n",
      "[[582 146]\n",
      " [ 68  42]]\n",
      "[[588 101]\n",
      " [100  49]]\n",
      "[[551 130]\n",
      " [109  48]]\n",
      "[[604 101]\n",
      " [107  25]]\n",
      "[[614 103]\n",
      " [ 91  29]]\n",
      "\n",
      " SOMA DA MATRIZ DE CONFUSÃO DE TODOS OS K-FOLD:\n",
      " [[5772 1226]\n",
      " [ 949  431]] \n",
      "\n"
     ]
    }
   ],
   "source": [
    "#divide o nosso conjunto de dados em 10\n",
    "kf = KFold(n_splits=10,shuffle=False)\n",
    "kf.split(dados)\n",
    "\n",
    "scores=cross_val_score(DecisionTreeClassifier(criterion=\"entropy\"), dados, resultados, cv=10, scoring='accuracy')\n",
    "print(\"Cross Validation:\\n\", scores)\n",
    "print(\"A média para Cross-Validation do K-fold é: {}\".format(scores.mean()),\"\\n\")\n",
    "\n",
    "# Inicializa o array a zero que vai guardar a nossa matriz de confusão\n",
    "array = [[0,0],[0,0]]\n",
    "print(\"MATRIZ DE CONFUSÃO DE CADA K-FOLD:\")\n",
    "\n",
    "# para cada split train test vai treinar, prever e fazer a matriz de confusão\n",
    "for train_index, test_index in kf.split(dados):\n",
    "    # split train test\n",
    "    dados_treino, dados_teste = dados.iloc[train_index], dados.iloc[test_index]\n",
    "    resultados_treino, resultados_teste = resultados.iloc[train_index], resultados.iloc[test_index]\n",
    "    # treina o modelo\n",
    "    model = clf.fit(dados_treino, resultados_treino)\n",
    "    # calcula a matriz de confusão\n",
    "    score=confusion_matrix(resultados_teste, model.predict(dados_teste))\n",
    "    print(score)\n",
    "    \n",
    "    c = score\n",
    "    # soma as matrizes de confusões\n",
    "    array = array+c\n",
    "print(\"\\n SOMA DA MATRIZ DE CONFUSÃO DE TODOS OS K-FOLD:\\n\",array,\"\\n\")"
   ]
  },
  {
   "source": [
    "#NAIVE BAYES"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "source": [
    "##Método Holdout"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "source": [
    "###Implementação do Gaussian Naive Bayes"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "#Chama o modelo Gaussian\n",
    "gnb = GaussianNB()\n",
    "\n",
    "#treina o modelo\n",
    "gnb.fit(dados_treino,resultados_treino)\n",
    "\n",
    "#dá uma previsão\n",
    "predicted3 = gnb.predict(dados_teste)\n",
    "\n"
   ]
  },
  {
   "source": [
    "###Apresentação da Accuracy, matriz de confusão e classification report"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Accuracy: 0.8649940262843488\nMatriz de confusão:\n [[710   7]\n [106  14]]\nClassification Report:\n               precision    recall  f1-score   support\n\n           0       0.87      0.99      0.93       717\n           1       0.67      0.12      0.20       120\n\n    accuracy                           0.86       837\n   macro avg       0.77      0.55      0.56       837\nweighted avg       0.84      0.86      0.82       837\n\n"
     ]
    }
   ],
   "source": [
    "print(\"Accuracy:\",metrics.accuracy_score(resultados_teste, predicted3))\n",
    "\n",
    "print(\"Matriz de confusão:\\n\",confusion_matrix(resultados_teste, gnb.predict(dados_teste)))\n",
    "\n",
    "print(\"Classification Report:\\n\",classification_report(resultados_teste, gnb.predict(dados_teste)))\n"
   ]
  },
  {
   "source": [
    "##Método Validação Cruzada (Cross-Validation)"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "source": [
    "###Vamos dividir o nosso conjunto de dados em 10 e cada uma das partições vai ser testada como teste e as restantes como treino.\n",
    "###Apresentação da Accuracy de todas as partições, a média dessas Accuracies, matriz de confusão de cada partição e soma total das matrizes de confusão"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Cross Validation:\n",
      " [0.79713604 0.81264916 0.83770883 0.78520286 0.8353222  0.81264916\n",
      " 0.84844869 0.79116945 0.81242533 0.84468339]\n",
      "A média para Cross-Validation do K-fold é: 0.8177395117806234 \n",
      "\n",
      "MATRIZ DE CONFUSÃO DE CADA K-FOLD:\n",
      "[[604 105]\n",
      " [ 70  59]]\n",
      "[[641  37]\n",
      " [121  39]]\n",
      "[[638  39]\n",
      " [113  48]]\n",
      "[[606 100]\n",
      " [ 86  46]]\n",
      "[[691  17]\n",
      " [112  18]]\n",
      "[[686  42]\n",
      " [ 96  14]]\n",
      "[[677  12]\n",
      " [124  25]]\n",
      "[[602  79]\n",
      " [104  53]]\n",
      "[[660  45]\n",
      " [110  22]]\n",
      "[[710   7]\n",
      " [106  14]]\n",
      "\n",
      " SOMA DA MATRIZ DE CONFUSÃO DE TODOS OS K-FOLD:\n",
      " [[6515  483]\n",
      " [1042  338]] \n",
      "\n"
     ]
    }
   ],
   "source": [
    "\n",
    "#divide o nosso conjunto de dados em 10\n",
    "kf = KFold(n_splits=10,shuffle=False)\n",
    "kf.split(dados)\n",
    "\n",
    "scores=cross_val_score(GaussianNB(), dados, resultados, cv=10, scoring='accuracy')\n",
    "print(\"Cross Validation:\\n\", scores)\n",
    "print(\"A média para Cross-Validation do K-fold é: {}\".format(scores.mean()),\"\\n\")\n",
    "\n",
    "# Inicializa o array a zero que vai guardar a nossa matriz de confusão\n",
    "array = [[0,0],[0,0]]\n",
    "print(\"MATRIZ DE CONFUSÃO DE CADA K-FOLD:\")\n",
    "# para cada split train test vai treinar, prever e fazer a matriz de confusão\n",
    "for train_index, test_index in kf.split(dados):\n",
    "    # split train test\n",
    "    dados_treino, dados_teste = dados.iloc[train_index], dados.iloc[test_index]\n",
    "    resultados_treino, resultados_teste = resultados.iloc[train_index], resultados.iloc[test_index]\n",
    "     # treina o modelo\n",
    "    model = gnb.fit(dados_treino, resultados_treino)\n",
    "    # calcula a matriz de confusão\n",
    "    score=confusion_matrix(resultados_teste, model.predict(dados_teste))\n",
    "    print(score )\n",
    "    c = score\n",
    "    # soma as matrizes de confusões\n",
    "    array = array + c\n",
    "print(\"\\n SOMA DA MATRIZ DE CONFUSÃO DE TODOS OS K-FOLD:\\n\",array,\"\\n\")"
   ]
  },
  {
   "source": [
    "##Curva de aprendizagem para o Gaussian Naive Bayes com o método Cross-Validation"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "\n",
    "# cv é o número de folds enquanto faz Cross-Validation\n",
    "sizes, training_scores, testing_scores = learning_curve(GaussianNB(), dados, resultados, cv=10, scoring='accuracy', train_sizes=np.linspace(0.01, 1.0, 50))\n",
    "  \n",
    "# Média e desvio padrão do score do treino\n",
    "mean_training = np.mean(training_scores, axis=1)\n",
    "Standard_Deviation_training = np.std(training_scores, axis=1)\n",
    "  \n",
    "# Média e desvio padrão do score do teste\n",
    "mean_testing = np.mean(testing_scores, axis=1)\n",
    "Standard_Deviation_testing = np.std(testing_scores, axis=1)\n",
    "  \n",
    "# azul trajecado é para os valores de treino e a linha verde para o valor cross.-validation\n",
    "plt.plot(sizes, mean_training, '--', color=\"b\",  label=\"Training score\")\n",
    "plt.plot(sizes, mean_testing, color=\"g\", label=\"Cross-validation score\")\n",
    "  \n",
    "# Desenha gráfico\n",
    "plt.title(\"LEARNING CURVE FOR GAUSSIAN\")\n",
    "plt.xlabel(\"Training Set Size\"), plt.ylabel(\"Accuracy Score\"), plt.legend(loc=\"best\")\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  }
 ]
}