Aula3.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Aula3",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 379
        },
        "id": "_1ZKpvvcIE3H",
        "outputId": "8980afa1-22e5-4e96-fbeb-52099f8504ab"
      },
      "source": [
        "import pandas as pd\n",
        "\n",
        "dados = pd.read_csv(\"drive/MyDrive/creditcard.csv\")\n",
        "dados.head(10)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Time</th>\n",
              "      <th>V1</th>\n",
              "      <th>V2</th>\n",
              "      <th>V3</th>\n",
              "      <th>V4</th>\n",
              "      <th>V5</th>\n",
              "      <th>V6</th>\n",
              "      <th>V7</th>\n",
              "      <th>V8</th>\n",
              "      <th>V9</th>\n",
              "      <th>V10</th>\n",
              "      <th>V11</th>\n",
              "      <th>V12</th>\n",
              "      <th>V13</th>\n",
              "      <th>V14</th>\n",
              "      <th>V15</th>\n",
              "      <th>V16</th>\n",
              "      <th>V17</th>\n",
              "      <th>V18</th>\n",
              "      <th>V19</th>\n",
              "      <th>V20</th>\n",
              "      <th>V21</th>\n",
              "      <th>V22</th>\n",
              "      <th>V23</th>\n",
              "      <th>V24</th>\n",
              "      <th>V25</th>\n",
              "      <th>V26</th>\n",
              "      <th>V27</th>\n",
              "      <th>V28</th>\n",
              "      <th>Amount</th>\n",
              "      <th>Class</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.0</td>\n",
              "      <td>-1.359807</td>\n",
              "      <td>-0.072781</td>\n",
              "      <td>2.536347</td>\n",
              "      <td>1.378155</td>\n",
              "      <td>-0.338321</td>\n",
              "      <td>0.462388</td>\n",
              "      <td>0.239599</td>\n",
              "      <td>0.098698</td>\n",
              "      <td>0.363787</td>\n",
              "      <td>0.090794</td>\n",
              "      <td>-0.551600</td>\n",
              "      <td>-0.617801</td>\n",
              "      <td>-0.991390</td>\n",
              "      <td>-0.311169</td>\n",
              "      <td>1.468177</td>\n",
              "      <td>-0.470401</td>\n",
              "      <td>0.207971</td>\n",
              "      <td>0.025791</td>\n",
              "      <td>0.403993</td>\n",
              "      <td>0.251412</td>\n",
              "      <td>-0.018307</td>\n",
              "      <td>0.277838</td>\n",
              "      <td>-0.110474</td>\n",
              "      <td>0.066928</td>\n",
              "      <td>0.128539</td>\n",
              "      <td>-0.189115</td>\n",
              "      <td>0.133558</td>\n",
              "      <td>-0.021053</td>\n",
              "      <td>149.62</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.0</td>\n",
              "      <td>1.191857</td>\n",
              "      <td>0.266151</td>\n",
              "      <td>0.166480</td>\n",
              "      <td>0.448154</td>\n",
              "      <td>0.060018</td>\n",
              "      <td>-0.082361</td>\n",
              "      <td>-0.078803</td>\n",
              "      <td>0.085102</td>\n",
              "      <td>-0.255425</td>\n",
              "      <td>-0.166974</td>\n",
              "      <td>1.612727</td>\n",
              "      <td>1.065235</td>\n",
              "      <td>0.489095</td>\n",
              "      <td>-0.143772</td>\n",
              "      <td>0.635558</td>\n",
              "      <td>0.463917</td>\n",
              "      <td>-0.114805</td>\n",
              "      <td>-0.183361</td>\n",
              "      <td>-0.145783</td>\n",
              "      <td>-0.069083</td>\n",
              "      <td>-0.225775</td>\n",
              "      <td>-0.638672</td>\n",
              "      <td>0.101288</td>\n",
              "      <td>-0.339846</td>\n",
              "      <td>0.167170</td>\n",
              "      <td>0.125895</td>\n",
              "      <td>-0.008983</td>\n",
              "      <td>0.014724</td>\n",
              "      <td>2.69</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1.0</td>\n",
              "      <td>-1.358354</td>\n",
              "      <td>-1.340163</td>\n",
              "      <td>1.773209</td>\n",
              "      <td>0.379780</td>\n",
              "      <td>-0.503198</td>\n",
              "      <td>1.800499</td>\n",
              "      <td>0.791461</td>\n",
              "      <td>0.247676</td>\n",
              "      <td>-1.514654</td>\n",
              "      <td>0.207643</td>\n",
              "      <td>0.624501</td>\n",
              "      <td>0.066084</td>\n",
              "      <td>0.717293</td>\n",
              "      <td>-0.165946</td>\n",
              "      <td>2.345865</td>\n",
              "      <td>-2.890083</td>\n",
              "      <td>1.109969</td>\n",
              "      <td>-0.121359</td>\n",
              "      <td>-2.261857</td>\n",
              "      <td>0.524980</td>\n",
              "      <td>0.247998</td>\n",
              "      <td>0.771679</td>\n",
              "      <td>0.909412</td>\n",
              "      <td>-0.689281</td>\n",
              "      <td>-0.327642</td>\n",
              "      <td>-0.139097</td>\n",
              "      <td>-0.055353</td>\n",
              "      <td>-0.059752</td>\n",
              "      <td>378.66</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1.0</td>\n",
              "      <td>-0.966272</td>\n",
              "      <td>-0.185226</td>\n",
              "      <td>1.792993</td>\n",
              "      <td>-0.863291</td>\n",
              "      <td>-0.010309</td>\n",
              "      <td>1.247203</td>\n",
              "      <td>0.237609</td>\n",
              "      <td>0.377436</td>\n",
              "      <td>-1.387024</td>\n",
              "      <td>-0.054952</td>\n",
              "      <td>-0.226487</td>\n",
              "      <td>0.178228</td>\n",
              "      <td>0.507757</td>\n",
              "      <td>-0.287924</td>\n",
              "      <td>-0.631418</td>\n",
              "      <td>-1.059647</td>\n",
              "      <td>-0.684093</td>\n",
              "      <td>1.965775</td>\n",
              "      <td>-1.232622</td>\n",
              "      <td>-0.208038</td>\n",
              "      <td>-0.108300</td>\n",
              "      <td>0.005274</td>\n",
              "      <td>-0.190321</td>\n",
              "      <td>-1.175575</td>\n",
              "      <td>0.647376</td>\n",
              "      <td>-0.221929</td>\n",
              "      <td>0.062723</td>\n",
              "      <td>0.061458</td>\n",
              "      <td>123.50</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2.0</td>\n",
              "      <td>-1.158233</td>\n",
              "      <td>0.877737</td>\n",
              "      <td>1.548718</td>\n",
              "      <td>0.403034</td>\n",
              "      <td>-0.407193</td>\n",
              "      <td>0.095921</td>\n",
              "      <td>0.592941</td>\n",
              "      <td>-0.270533</td>\n",
              "      <td>0.817739</td>\n",
              "      <td>0.753074</td>\n",
              "      <td>-0.822843</td>\n",
              "      <td>0.538196</td>\n",
              "      <td>1.345852</td>\n",
              "      <td>-1.119670</td>\n",
              "      <td>0.175121</td>\n",
              "      <td>-0.451449</td>\n",
              "      <td>-0.237033</td>\n",
              "      <td>-0.038195</td>\n",
              "      <td>0.803487</td>\n",
              "      <td>0.408542</td>\n",
              "      <td>-0.009431</td>\n",
              "      <td>0.798278</td>\n",
              "      <td>-0.137458</td>\n",
              "      <td>0.141267</td>\n",
              "      <td>-0.206010</td>\n",
              "      <td>0.502292</td>\n",
              "      <td>0.219422</td>\n",
              "      <td>0.215153</td>\n",
              "      <td>69.99</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>2.0</td>\n",
              "      <td>-0.425966</td>\n",
              "      <td>0.960523</td>\n",
              "      <td>1.141109</td>\n",
              "      <td>-0.168252</td>\n",
              "      <td>0.420987</td>\n",
              "      <td>-0.029728</td>\n",
              "      <td>0.476201</td>\n",
              "      <td>0.260314</td>\n",
              "      <td>-0.568671</td>\n",
              "      <td>-0.371407</td>\n",
              "      <td>1.341262</td>\n",
              "      <td>0.359894</td>\n",
              "      <td>-0.358091</td>\n",
              "      <td>-0.137134</td>\n",
              "      <td>0.517617</td>\n",
              "      <td>0.401726</td>\n",
              "      <td>-0.058133</td>\n",
              "      <td>0.068653</td>\n",
              "      <td>-0.033194</td>\n",
              "      <td>0.084968</td>\n",
              "      <td>-0.208254</td>\n",
              "      <td>-0.559825</td>\n",
              "      <td>-0.026398</td>\n",
              "      <td>-0.371427</td>\n",
              "      <td>-0.232794</td>\n",
              "      <td>0.105915</td>\n",
              "      <td>0.253844</td>\n",
              "      <td>0.081080</td>\n",
              "      <td>3.67</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>4.0</td>\n",
              "      <td>1.229658</td>\n",
              "      <td>0.141004</td>\n",
              "      <td>0.045371</td>\n",
              "      <td>1.202613</td>\n",
              "      <td>0.191881</td>\n",
              "      <td>0.272708</td>\n",
              "      <td>-0.005159</td>\n",
              "      <td>0.081213</td>\n",
              "      <td>0.464960</td>\n",
              "      <td>-0.099254</td>\n",
              "      <td>-1.416907</td>\n",
              "      <td>-0.153826</td>\n",
              "      <td>-0.751063</td>\n",
              "      <td>0.167372</td>\n",
              "      <td>0.050144</td>\n",
              "      <td>-0.443587</td>\n",
              "      <td>0.002821</td>\n",
              "      <td>-0.611987</td>\n",
              "      <td>-0.045575</td>\n",
              "      <td>-0.219633</td>\n",
              "      <td>-0.167716</td>\n",
              "      <td>-0.270710</td>\n",
              "      <td>-0.154104</td>\n",
              "      <td>-0.780055</td>\n",
              "      <td>0.750137</td>\n",
              "      <td>-0.257237</td>\n",
              "      <td>0.034507</td>\n",
              "      <td>0.005168</td>\n",
              "      <td>4.99</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>7.0</td>\n",
              "      <td>-0.644269</td>\n",
              "      <td>1.417964</td>\n",
              "      <td>1.074380</td>\n",
              "      <td>-0.492199</td>\n",
              "      <td>0.948934</td>\n",
              "      <td>0.428118</td>\n",
              "      <td>1.120631</td>\n",
              "      <td>-3.807864</td>\n",
              "      <td>0.615375</td>\n",
              "      <td>1.249376</td>\n",
              "      <td>-0.619468</td>\n",
              "      <td>0.291474</td>\n",
              "      <td>1.757964</td>\n",
              "      <td>-1.323865</td>\n",
              "      <td>0.686133</td>\n",
              "      <td>-0.076127</td>\n",
              "      <td>-1.222127</td>\n",
              "      <td>-0.358222</td>\n",
              "      <td>0.324505</td>\n",
              "      <td>-0.156742</td>\n",
              "      <td>1.943465</td>\n",
              "      <td>-1.015455</td>\n",
              "      <td>0.057504</td>\n",
              "      <td>-0.649709</td>\n",
              "      <td>-0.415267</td>\n",
              "      <td>-0.051634</td>\n",
              "      <td>-1.206921</td>\n",
              "      <td>-1.085339</td>\n",
              "      <td>40.80</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>7.0</td>\n",
              "      <td>-0.894286</td>\n",
              "      <td>0.286157</td>\n",
              "      <td>-0.113192</td>\n",
              "      <td>-0.271526</td>\n",
              "      <td>2.669599</td>\n",
              "      <td>3.721818</td>\n",
              "      <td>0.370145</td>\n",
              "      <td>0.851084</td>\n",
              "      <td>-0.392048</td>\n",
              "      <td>-0.410430</td>\n",
              "      <td>-0.705117</td>\n",
              "      <td>-0.110452</td>\n",
              "      <td>-0.286254</td>\n",
              "      <td>0.074355</td>\n",
              "      <td>-0.328783</td>\n",
              "      <td>-0.210077</td>\n",
              "      <td>-0.499768</td>\n",
              "      <td>0.118765</td>\n",
              "      <td>0.570328</td>\n",
              "      <td>0.052736</td>\n",
              "      <td>-0.073425</td>\n",
              "      <td>-0.268092</td>\n",
              "      <td>-0.204233</td>\n",
              "      <td>1.011592</td>\n",
              "      <td>0.373205</td>\n",
              "      <td>-0.384157</td>\n",
              "      <td>0.011747</td>\n",
              "      <td>0.142404</td>\n",
              "      <td>93.20</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>9.0</td>\n",
              "      <td>-0.338262</td>\n",
              "      <td>1.119593</td>\n",
              "      <td>1.044367</td>\n",
              "      <td>-0.222187</td>\n",
              "      <td>0.499361</td>\n",
              "      <td>-0.246761</td>\n",
              "      <td>0.651583</td>\n",
              "      <td>0.069539</td>\n",
              "      <td>-0.736727</td>\n",
              "      <td>-0.366846</td>\n",
              "      <td>1.017614</td>\n",
              "      <td>0.836390</td>\n",
              "      <td>1.006844</td>\n",
              "      <td>-0.443523</td>\n",
              "      <td>0.150219</td>\n",
              "      <td>0.739453</td>\n",
              "      <td>-0.540980</td>\n",
              "      <td>0.476677</td>\n",
              "      <td>0.451773</td>\n",
              "      <td>0.203711</td>\n",
              "      <td>-0.246914</td>\n",
              "      <td>-0.633753</td>\n",
              "      <td>-0.120794</td>\n",
              "      <td>-0.385050</td>\n",
              "      <td>-0.069733</td>\n",
              "      <td>0.094199</td>\n",
              "      <td>0.246219</td>\n",
              "      <td>0.083076</td>\n",
              "      <td>3.68</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Time        V1        V2        V3  ...       V27       V28  Amount  Class\n",
              "0   0.0 -1.359807 -0.072781  2.536347  ...  0.133558 -0.021053  149.62      0\n",
              "1   0.0  1.191857  0.266151  0.166480  ... -0.008983  0.014724    2.69      0\n",
              "2   1.0 -1.358354 -1.340163  1.773209  ... -0.055353 -0.059752  378.66      0\n",
              "3   1.0 -0.966272 -0.185226  1.792993  ...  0.062723  0.061458  123.50      0\n",
              "4   2.0 -1.158233  0.877737  1.548718  ...  0.219422  0.215153   69.99      0\n",
              "5   2.0 -0.425966  0.960523  1.141109  ...  0.253844  0.081080    3.67      0\n",
              "6   4.0  1.229658  0.141004  0.045371  ...  0.034507  0.005168    4.99      0\n",
              "7   7.0 -0.644269  1.417964  1.074380  ... -1.206921 -1.085339   40.80      0\n",
              "8   7.0 -0.894286  0.286157 -0.113192  ...  0.011747  0.142404   93.20      0\n",
              "9   9.0 -0.338262  1.119593  1.044367  ...  0.246219  0.083076    3.68      0\n",
              "\n",
              "[10 rows x 31 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sM_zj61oMuuz",
        "outputId": "f953c92f-e70b-4cef-f1b0-47d2be33d143"
      },
      "source": [
        "print(dados.isna().sum())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Time      0\n",
            "V1        0\n",
            "V2        0\n",
            "V3        0\n",
            "V4        0\n",
            "V5        0\n",
            "V6        0\n",
            "V7        0\n",
            "V8        0\n",
            "V9        0\n",
            "V10       0\n",
            "V11       0\n",
            "V12       0\n",
            "V13       0\n",
            "V14       0\n",
            "V15       0\n",
            "V16       0\n",
            "V17       0\n",
            "V18       0\n",
            "V19       0\n",
            "V20       0\n",
            "V21       0\n",
            "V22       0\n",
            "V23       0\n",
            "V24       0\n",
            "V25       0\n",
            "V26       0\n",
            "V27       0\n",
            "V28       0\n",
            "Amount    0\n",
            "Class     0\n",
            "dtype: int64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zgXwJk2REei7",
        "outputId": "364c5b6d-414e-42f3-b171-18dcb6c2381f"
      },
      "source": [
        "n_transacoes = dados['Class'].count()\n",
        "n_fraudes = dados['Class'].sum()\n",
        "n_normais = n_transacoes - n_fraudes\n",
        "fraudes_porc = n_fraudes / n_transacoes\n",
        "normais_porc = n_normais / n_transacoes\n",
        "\n",
        "print(\"Número de transações: \", n_transacoes)\n",
        "print(\"Número de fraudes: \", n_fraudes, \"%.2f\" %(fraudes_porc*100))\n",
        "print(\"Número de transações normais: \", n_normais, \"%.2f\" %(normais_porc*100))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Número de transações:  284807\n",
            "Número de fraudes:  492 0.17\n",
            "Número de transações normais:  284315 99.83\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fBChtnzKFgPx"
      },
      "source": [
        "from sklearn.model_selection import StratifiedShuffleSplit\n",
        "\n",
        "def executar_validador(X, y):\n",
        "  validador = StratifiedShuffleSplit(n_splits=1, test_size=0.1, random_state=0)\n",
        "  for treino_id, teste_id in validador.split(X, y):\n",
        "    X_train, X_test = X[treino_id], X[teste_id]\n",
        "    y_train, y_test = y[treino_id], y[teste_id]\n",
        "  return X_train, X_test, y_train, y_test\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "82mowqxcJpQ3",
        "outputId": "96d852ca-7b7a-4b4b-8aae-be0b12428f77"
      },
      "source": [
        "%%time\n",
        "from sklearn import tree\n",
        "\n",
        "def executar_classificador(classificador, X_train, X_test, y_train):\n",
        "  arvore = classificador.fit(X_train, y_train)\n",
        "  y_pred = arvore.predict(X_test)\n",
        "  return y_pred"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "CPU times: user 35.9 ms, sys: 13.6 ms, total: 49.6 ms\n",
            "Wall time: 197 ms\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Mr1aVQnWOOrW"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "def salvar_arvore(classificador, nome):\n",
        "  plt.figure(figsize=(200,100))\n",
        "  tree.plot_tree(classificador, filled=True, fontsize=14)\n",
        "  plt.savefig(nome)\n",
        "  plt.close()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "49-Kg-s2qkGw"
      },
      "source": [
        "from sklearn.metrics import accuracy_score\n",
        "from sklearn.metrics import confusion_matrix\n",
        "from sklearn.metrics import precision_score\n",
        "from sklearn.metrics import recall_score\n",
        "\n",
        "def validar_arvore(y_test, y_pred):\n",
        "  print(accuracy_score(y_test, y_pred))\n",
        "  print(precision_score(y_test, y_pred))\n",
        "  print(recall_score(y_test, y_pred))\n",
        "  print(confusion_matrix(y_test, y_pred))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ErGjdLatLMyy"
      },
      "source": [
        "#execucao do validador\n",
        "X = dados.drop('Class', axis=1).values\n",
        "y = dados['Class'].values\n",
        "X_train, X_test, y_train, y_test = executar_validador(X, y)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Fh_M-NVrO2lc"
      },
      "source": [
        "#execucao do classificador DecisionTreeClassifier\n",
        "classificador_arvore_decisao = tree.DecisionTreeClassifier()\n",
        "y_pred_arvore_decisao = executar_classificador(classificador_arvore_decisao, X_train, X_test, y_train)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jzzeD7fePCD-"
      },
      "source": [
        "#criacao da figura da arvore de decisao\n",
        "salvar_arvore(classificador_arvore_decisao, \"arvore_decisao1.png\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FNBBsD9Vqf_2",
        "outputId": "a850e05d-122c-4967-f7ed-9e21c9b8b8b4"
      },
      "source": [
        "#validacao arvore de decisao\n",
        "validar_arvore(y_test, y_pred_arvore_decisao)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.9991222218320986\n",
            "0.7608695652173914\n",
            "0.7142857142857143\n",
            "[[28421    11]\n",
            " [   14    35]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QGY4IhuQ6I1I",
        "outputId": "bdfcdd97-f90f-40a5-ae5b-8dc50a4ec574"
      },
      "source": [
        "print(classificador_arvore_decisao)\n",
        "print(classificador_arvore_decisao.get_depth())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
            "                       max_depth=None, max_features=None, max_leaf_nodes=None,\n",
            "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
            "                       min_samples_leaf=1, min_samples_split=2,\n",
            "                       min_weight_fraction_leaf=0.0, presort='deprecated',\n",
            "                       random_state=None, splitter='best')\n",
            "21\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "35qhelAb6uC8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5ae7857b-6e9a-407e-88da-c906cf8237b0"
      },
      "source": [
        "%%time\n",
        "#execucao do classificador DecisionTreeClassifier\n",
        "classificador_arvore_decisao = tree.DecisionTreeClassifier(max_depth=10, random_state=0)\n",
        "y_pred_arvore_decisao = executar_classificador(classificador_arvore_decisao, X_train, X_test, y_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "CPU times: user 16.7 s, sys: 3.93 ms, total: 16.7 s\n",
            "Wall time: 16.7 s\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mNFTMFjJ65O_",
        "outputId": "be775125-2f57-4a1c-f641-c072f7924b2a"
      },
      "source": [
        "validar_arvore(y_test, y_pred_arvore_decisao)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.9994733330992591\n",
            "0.9473684210526315\n",
            "0.7346938775510204\n",
            "[[28430     2]\n",
            " [   13    36]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8-6271ld7HzJ"
      },
      "source": [
        "#execucao do classificador DecisionTreeClassifier\n",
        "classificador_arvore_decisao = tree.DecisionTreeClassifier(max_depth=10, random_state=0, min_samples_leaf=10)\n",
        "y_pred_arvore_decisao = executar_classificador(classificador_arvore_decisao, X_train, X_test, y_train)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4AD1ip2T7QzQ",
        "outputId": "a202c5fa-74fb-4648-f3bf-23c6dc6c81c7"
      },
      "source": [
        "validar_arvore(y_test, y_pred_arvore_decisao)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.9993679997191109\n",
            "0.8604651162790697\n",
            "0.7551020408163265\n",
            "[[28426     6]\n",
            " [   12    37]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8thhDKUUQrRS"
      },
      "source": [
        "classificador_arvore_decisao = tree.DecisionTreeClassifier(max_depth=5, random_state=0)\n",
        "y_pred_arvore_decisao = executar_classificador(classificador_arvore_decisao, X_train, X_test, y_train)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QWLKQ5FIQwhx",
        "outputId": "c9f39e08-62c3-4eb8-b0ab-33f0b1c22b49"
      },
      "source": [
        "validar_arvore(y_test, y_pred_arvore_decisao)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.999403110845827\n",
            "0.9210526315789473\n",
            "0.7142857142857143\n",
            "[[28429     3]\n",
            " [   14    35]]\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}