01. Arvores_Decisao/2020_02_AP_04_ArvDecisão_Completo.ipynb

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  "metadata": {
    "colab": {
      "name": "2020.02_AP_04_ArvDecisão_Eduardo_Thiago_Wendel.ipynb",
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      "collapsed_sections": [
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mLKPZajBKe3L"
      },
      "source": [
        "**Instituto de Informática - UFG**\n",
        "\n",
        "Disciplina: Inteligência Artificial\n",
        "\n",
        "Prof. Cedric Luiz de Carvalho\n",
        "\n",
        "\n",
        "Grupo (A)\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "Aluna(o): Eduardo Barros Pimenta - Gerente\n",
        "\n",
        "---\n",
        "\n",
        "Aluna(o): Thiago Emanuell Vieira Moura - Engenheiro de Conhecimento\n",
        "\n",
        "---\n",
        "\n",
        "Aluna(o): Wendel Marques de Jesus Souza - Analista de Dados\n",
        "\n",
        "---"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "o_bZhd8FMiSa"
      },
      "source": [
        "#Especificação do problema: Árvore de Decisão"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YTE0x5IrTiwN"
      },
      "source": [
        "A área financeira é uma área muito interessante para a aplicação de técnicas de Inteligência Artificial. Por exemplo, pode-se desenvolver aplicações que dêem suporte aos agentes financeiros na concessão ou não de crédito aos seus clientes. Pode-se também utilizar-se das técnicas de Inteligência Artificial para a identificação de atividades fraudulentas.\n",
        "\n",
        "Utilizando a base de dados [Paysim](https://www.kaggle.com/ntnu-testimon/paysim1) projete e implemente uma árvore de decisão para a identificação de fraudes. Utilize a referência a seguir para a visualização da árvore projetada.\n",
        "\n",
        "[Para visualização de uma AD.](https://towardsdatascience.com/interactive-visualization-of-decision-trees-with-jupyter-widgets-ca15dd312084)\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OVTBkY7fIklu"
      },
      "source": [
        "### Recomendações"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vgmMVeMmGQWa"
      },
      "source": [
        "**Recomendações:**\n",
        "\n",
        "   O primeiro passo para resolver um problema é entendê-lo muito bem, considerar o contexto e fazer as abstrações necessárias. A seguir, para não \"reinventar a roda\" é preciso identificar como a comunidade resolveu o problema em questão, ou problemas parecidos, cuja solução possa ser mapeada para situação em análise. Desta forma, deve ser feito um estudo, o mais amplo possível sobre esse assunto. O próximo passo é o grupo se reunir e instanciar o modelo em estudo para o caso específico do problema a ser resolvido. Em paralelo, uma análise rigorosa deve ser feita na base de dados a fim de prepará-la para seu uso no modelo. Finalmente, com o modelo bem compreendido e instanciado e os dados devidamente preparados, a implementação do modelo pode ser feita.\n",
        "   \n",
        "    "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BmiqZcXTJPdm"
      },
      "source": [
        "**Observação**: O trabalho deve ser feito EM GRUPO, mas a avaliação será feita individualmente. Para isso, o grupo deverá fazer um planejamento/cronograma de trabalho."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7LGIwGmXIn1Z"
      },
      "source": [
        "### Produtos a serem entregues"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6i831PNuJiPG"
      },
      "source": [
        "**Produtos a serem entregues:**\n",
        "\n",
        "\n",
        "\n",
        "1.   Relatório técnico, no formato especificado (gerente)\n",
        "2.   Notebook com a AED (analista de dados). A base de dados original não deve ser alterada. Todo o processamento e seleção dos dados a serem utilizados deve ser feita na aplicação.\n",
        "3.   Notebook com a implementação (engenheiro de conhecimento)\n",
        "4.   Video de apresentação do trabalho (gerente) - no grupo com 4 alunos, essa tarefa deve ser feita pelo quarto aluno, e não pelo gerente.\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oevJAeN1H_Md"
      },
      "source": [
        "### Bibliotecas utilizadas"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SGC3zn7Om3QL",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "ea0cf986-c375-438b-e1cb-fcd285ddd9a2"
      },
      "source": [
        "#implementacoes\n",
        "!pip install scikit-plot \n",
        "!pip install ipywidgets #Instalando biblioteca para interatividade\n",
        "!pip install --upgrade ipython jupyter  #Instalando biblioteca para imprimir, graficamente, a árvore, em SVG, na tela\n",
        "!pip install --upgrade pip"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
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            "\u001b[31mERROR: jupyter-console 5.2.0 has requirement prompt-toolkit<2.0.0,>=1.0.0, but you'll have prompt-toolkit 3.0.18 which is incompatible.\u001b[0m\n",
            "\u001b[31mERROR: google-colab 1.0.0 has requirement ipython~=5.5.0, but you'll have ipython 7.22.0 which is incompatible.\u001b[0m\n",
            "Installing collected packages: prompt-toolkit, ipython\n",
            "  Found existing installation: prompt-toolkit 1.0.18\n",
            "    Uninstalling prompt-toolkit-1.0.18:\n",
            "      Successfully uninstalled prompt-toolkit-1.0.18\n",
            "  Found existing installation: ipython 5.5.0\n",
            "    Uninstalling ipython-5.5.0:\n",
            "      Successfully uninstalled ipython-5.5.0\n",
            "Successfully installed ipython-7.22.0 prompt-toolkit-3.0.18\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.colab-display-data+json": {
              "pip_warning": {
                "packages": [
                  "IPython",
                  "prompt_toolkit"
                ]
              }
            }
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Collecting pip\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/fe/ef/60d7ba03b5c442309ef42e7d69959f73aacccd0d86008362a681c4698e83/pip-21.0.1-py3-none-any.whl (1.5MB)\n",
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            "\u001b[?25hInstalling collected packages: pip\n",
            "  Found existing installation: pip 19.3.1\n",
            "    Uninstalling pip-19.3.1:\n",
            "      Successfully uninstalled pip-19.3.1\n",
            "Successfully installed pip-21.0.1\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9u2qKEXkfSkd"
      },
      "source": [
        "#Pré-processamento\n",
        "import pandas as pd # processamento de dados, entrada e saída de arquivos CSV(e.g. pd.read_csv)\n",
        "import numpy as np # algebra linear\n",
        "import seaborn as sns\n",
        "from imblearn.over_sampling import SMOTE\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import Normalizer\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "\n",
        "#Implementações\n",
        "from sklearn.tree import DecisionTreeClassifier, export_graphviz\n",
        "from sklearn import tree\n",
        "from IPython.display import SVG\n",
        "from graphviz import Source\n",
        "from IPython.display import display \n",
        "from ipywidgets import interactive\n",
        "from sklearn.metrics import classification_report, confusion_matrix\n",
        "import scikitplot as skplt"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zScKwJu8ICn9"
      },
      "source": [
        "### Leitura dos dados"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jArwYNGvVIzr",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a0fe549e-01fe-41a1-fce0-9bc55f94a600"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2ryVzajXikSt"
      },
      "source": [
        "#Lendo os dados\n",
        "#O arquivo precisa estar baixado no \"MyDrive\"\n",
        "data = pd.read_csv('/content/drive/MyDrive/PS_20174392719_1491204439457_log.csv') "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cghpLNKqjZxS"
      },
      "source": [
        "# Pré-processamento  \n",
        "Por: Wendel"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vdYrb_u-ODig",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "8b5ee141-449b-40b5-d5f9-061a6093bbdc"
      },
      "source": [
        "data.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>PAYMENT</td>\n",
              "      <td>1864.28</td>\n",
              "      <td>C1666544295</td>\n",
              "      <td>21249.0</td>\n",
              "      <td>19384.72</td>\n",
              "      <td>M2044282225</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>TRANSFER</td>\n",
              "      <td>181.00</td>\n",
              "      <td>C1305486145</td>\n",
              "      <td>181.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>C553264065</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>CASH_OUT</td>\n",
              "      <td>181.00</td>\n",
              "      <td>C840083671</td>\n",
              "      <td>181.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>C38997010</td>\n",
              "      <td>21182.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>PAYMENT</td>\n",
              "      <td>11668.14</td>\n",
              "      <td>C2048537720</td>\n",
              "      <td>41554.0</td>\n",
              "      <td>29885.86</td>\n",
              "      <td>M1230701703</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   step      type    amount  ... newbalanceDest  isFraud  isFlaggedFraud\n",
              "0     1   PAYMENT   9839.64  ...            0.0        0               0\n",
              "1     1   PAYMENT   1864.28  ...            0.0        0               0\n",
              "2     1  TRANSFER    181.00  ...            0.0        1               0\n",
              "3     1  CASH_OUT    181.00  ...            0.0        1               0\n",
              "4     1   PAYMENT  11668.14  ...            0.0        0               0\n",
              "\n",
              "[5 rows x 11 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NqbSLiYDd8Dx"
      },
      "source": [
        "### Limpeza e transformação"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "phsvo2Thd94G"
      },
      "source": [
        "Descartamos do conjunto de dados as variáveis *isFlaggedFraud*, *nameOrig* e *nameDest* porque são do tipo ID  e não contribuem para a máquina preditiva.\n",
        "\n",
        "Não precisamos ensinar para a máquina preditiva que se *nameDest* for um comerciante, então não haverá fraude. Isso porque, cumprimos essa tarefa ao utiliza *PAYMENT*. Como mostrado na análise e exploração dos dados, só há fraudes apenas quando as transações são do tipo *TRANSFER* e *CASH_IN*.\n",
        "\n",
        "Obs.: Em uma aplicação mais sofisticada, é provável que o uso de *nameOrig* e *nameDest* se torne necessário, uma vez que ao descartar esses atributos, descartamos também as conexões entre as transações. Isto é, ao fazer isto, deixamos de explorar esquemas de fraude mais aperfeiçoados. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "g1yJeyy8lkwr",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 111
        },
        "outputId": "d4f83e1e-9f65-4a9e-9a72-c174d38a8744"
      },
      "source": [
        "data = data.drop([\"isFlaggedFraud\",\"nameOrig\",\"nameDest\"],axis=1)\n",
        "data.head(2)"
      ],
      "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>step</th>\n",
              "      <th>type</th>\n",
              "      <th>amount</th>\n",
              "      <th>oldbalanceOrg</th>\n",
              "      <th>newbalanceOrig</th>\n",
              "      <th>oldbalanceDest</th>\n",
              "      <th>newbalanceDest</th>\n",
              "      <th>isFraud</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>PAYMENT</td>\n",
              "      <td>9839.64</td>\n",
              "      <td>170136.0</td>\n",
              "      <td>160296.36</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>PAYMENT</td>\n",
              "      <td>1864.28</td>\n",
              "      <td>21249.0</td>\n",
              "      <td>19384.72</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   step     type   amount  ...  oldbalanceDest  newbalanceDest  isFraud\n",
              "0     1  PAYMENT  9839.64  ...             0.0             0.0        0\n",
              "1     1  PAYMENT  1864.28  ...             0.0             0.0        0\n",
              "\n",
              "[2 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MHyn57iypCyo"
      },
      "source": [
        "Para que possamos utilizar a técnica de balanceamento escolhida, devemos conveter o atributo categórico (*type*) em numérico."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1L6VXEgsXHRV",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "63d74bb3-3603-4fde-a483-0f75edbfccb3"
      },
      "source": [
        "#transformação do dataFrame com uma coluna de variáveis categóricas em colunas que representam cada tipo de transação\n",
        "data_type = pd.get_dummies(data.type)\n",
        "# data_type = data_type.drop([\"CASH_IN\",\"DEBIT\",\"PAYMENT\"],axis=1)\n",
        "data_type.head()"
      ],
      "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>CASH_IN</th>\n",
              "      <th>CASH_OUT</th>\n",
              "      <th>DEBIT</th>\n",
              "      <th>PAYMENT</th>\n",
              "      <th>TRANSFER</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   CASH_IN  CASH_OUT  DEBIT  PAYMENT  TRANSFER\n",
              "0        0         0      0        1         0\n",
              "1        0         0      0        1         0\n",
              "2        0         0      0        0         1\n",
              "3        0         1      0        0         0\n",
              "4        0         0      0        1         0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fbj3g8FdahiA",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "cde14b44-941c-47ae-bdae-3f28aed2ef52"
      },
      "source": [
        "#mesclagem com o dataframe original\n",
        "data = data.drop([\"type\"],axis=1)\n",
        "data = data.join(data_type)\n",
        "data.head()"
      ],
      "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>step</th>\n",
              "      <th>amount</th>\n",
              "      <th>oldbalanceOrg</th>\n",
              "      <th>newbalanceOrig</th>\n",
              "      <th>oldbalanceDest</th>\n",
              "      <th>newbalanceDest</th>\n",
              "      <th>isFraud</th>\n",
              "      <th>CASH_IN</th>\n",
              "      <th>CASH_OUT</th>\n",
              "      <th>DEBIT</th>\n",
              "      <th>PAYMENT</th>\n",
              "      <th>TRANSFER</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>9839.64</td>\n",
              "      <td>170136.0</td>\n",
              "      <td>160296.36</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>1864.28</td>\n",
              "      <td>21249.0</td>\n",
              "      <td>19384.72</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>181.00</td>\n",
              "      <td>181.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>181.00</td>\n",
              "      <td>181.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>21182.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>11668.14</td>\n",
              "      <td>41554.0</td>\n",
              "      <td>29885.86</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   step    amount  oldbalanceOrg  ...  DEBIT  PAYMENT  TRANSFER\n",
              "0     1   9839.64       170136.0  ...      0        1         0\n",
              "1     1   1864.28        21249.0  ...      0        1         0\n",
              "2     1    181.00          181.0  ...      0        0         1\n",
              "3     1    181.00          181.0  ...      0        0         0\n",
              "4     1  11668.14        41554.0  ...      0        1         0\n",
              "\n",
              "[5 rows x 12 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "I4s3HWI8Ui-4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "0171b911-bb22-49ca-a10a-8439748cfc64"
      },
      "source": [
        "#reescala os dados\n",
        "data_minMax = data.copy()\n",
        "data_minMax[['amount', 'newbalanceOrig', 'newbalanceDest', 'oldbalanceDest', 'oldbalanceOrg']] = MinMaxScaler().fit_transform(data[['amount', 'newbalanceOrig', 'newbalanceDest', 'oldbalanceDest', 'oldbalanceOrg']])\n",
        "\n",
        "data = data_minMax.copy()\n",
        "data.head()"
      ],
      "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>step</th>\n",
              "      <th>amount</th>\n",
              "      <th>oldbalanceOrg</th>\n",
              "      <th>newbalanceOrig</th>\n",
              "      <th>oldbalanceDest</th>\n",
              "      <th>newbalanceDest</th>\n",
              "      <th>isFraud</th>\n",
              "      <th>CASH_IN</th>\n",
              "      <th>CASH_OUT</th>\n",
              "      <th>DEBIT</th>\n",
              "      <th>PAYMENT</th>\n",
              "      <th>TRANSFER</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>0.000106</td>\n",
              "      <td>0.002855</td>\n",
              "      <td>0.003233</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>0.000020</td>\n",
              "      <td>0.000357</td>\n",
              "      <td>0.000391</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>0.000002</td>\n",
              "      <td>0.000003</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>0.000002</td>\n",
              "      <td>0.000003</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000059</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>0.000126</td>\n",
              "      <td>0.000697</td>\n",
              "      <td>0.000603</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   step    amount  oldbalanceOrg  ...  DEBIT  PAYMENT  TRANSFER\n",
              "0     1  0.000106       0.002855  ...      0        1         0\n",
              "1     1  0.000020       0.000357  ...      0        1         0\n",
              "2     1  0.000002       0.000003  ...      0        0         1\n",
              "3     1  0.000002       0.000003  ...      0        0         0\n",
              "4     1  0.000126       0.000697  ...      0        1         0\n",
              "\n",
              "[5 rows x 12 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ozh02hPIk6O3"
      },
      "source": [
        "### Divisão: dados de treino e de teste"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jOqCWiQ5k9V0"
      },
      "source": [
        "#Separando os dados para treinamento e teste \n",
        "X = data.drop(['isFraud'], axis=1)\n",
        "y = data['isFraud']\n",
        "\n",
        "#Separação de dados treino e dados de teste\n",
        "#Proporção de 30% para teste\n",
        "X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.3, random_state=7, stratify=y)\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yRpQgp-odep1"
      },
      "source": [
        "### Balanceamento"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "q6HkObradh21"
      },
      "source": [
        "Como visto na etapa AED, do número total de transações (6.362.620) apenas 0,13% são transações frauldulentas. Nesse sentido, é necessário realizar o balanceamento da classe *target* (isFraud).\n",
        "\n",
        "A técnica escolhida é a Oversampling que é o processo de criar observações sintéticas da classe minoritária - nesse caso, isFraud.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Whoi-NWciQ7v",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 387
        },
        "outputId": "62dd1537-eecd-40cf-9326-dc07e178fbb3"
      },
      "source": [
        "from imblearn.under_sampling import RandomUnderSampler\n",
        "\n",
        "# usar técnica under-sampling\n",
        "rus = RandomUnderSampler()\n",
        "X_res, y_res = rus.fit_sample(X_train, y_train)\n",
        " \n",
        "# ver o balanceamento das classes\n",
        "print(pd.Series(y_res).value_counts())\n",
        " \n",
        "# plotar a nova distribuição de classes\n",
        "sns.countplot(y_res);"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.\n",
            "  warnings.warn(msg, category=FutureWarning)\n",
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "1    5749\n",
            "0    5749\n",
            "dtype: int64\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5Q7LdofdjgPY"
      },
      "source": [
        "# Implementação\n",
        "Por: Thiago"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Xbbsy0nlBjnw",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "abc0a3ed-6a29-4efe-9ada-d2a1486188ce"
      },
      "source": [
        "#Classificador da árvore de decisão\n",
        "estimator = DecisionTreeClassifier(max_depth=3,criterion='entropy') \n",
        "#Construindo um classificador de árvore de decisão a partir do conjunto de treinamento \n",
        "estimator.fit(X_res,y_res)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='entropy',\n",
              "                       max_depth=3, 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')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "x0cwrVTj3HLe"
      },
      "source": [
        "#Armazenando os nomes das colunas em um objeto\n",
        "colunas = data.drop(['isFraud'], axis=1).columns\n",
        "#Transformando o objeto em um vetor\n",
        "colunas = np.array(colunas) "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6cTbY_zh3OHd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 518
        },
        "outputId": "70cff8fd-3a29-48f5-e0ad-88858f49e097"
      },
      "source": [
        "#Imprimindo a árvore\n",
        "graph = Source(tree.export_graphviz(estimator, out_file=None, feature_names=colunas, filled = True))\n",
        "display(SVG(graph.pipe(format='svg')))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.SVG object>"
            ],
            "image/svg+xml": "<svg height=\"373pt\" viewBox=\"0.00 0.00 1094.50 373.00\" width=\"1095pt\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n<g class=\"graph\" id=\"graph0\" transform=\"scale(1 1) rotate(0) translate(4 369)\">\n<title>Tree</title>\n<polygon fill=\"#ffffff\" points=\"-4,4 -4,-369 1090.5,-369 1090.5,4 -4,4\" stroke=\"transparent\"/>\n<!-- 0 -->\n<g class=\"node\" id=\"node1\">\n<title>0</title>\n<polygon fill=\"#ffffff\" points=\"613.5,-365 455.5,-365 455.5,-297 613.5,-297 613.5,-365\" stroke=\"#000000\"/>\n<text fill=\"#000000\" font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"534.5\" y=\"-349.8\">oldbalanceOrg &lt;= 0.001</text>\n<text fill=\"#000000\" font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"534.5\" y=\"-334.8\">entropy = 1.0</text>\n<text fill=\"#000000\" font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"534.5\" y=\"-319.8\">samples = 11498</text>\n<text fill=\"#000000\" font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"534.5\" y=\"-304.8\">value = [5749, 5749]</text>\n</g>\n<!-- 1 -->\n<g class=\"node\" id=\"node2\">\n<title>1</title>\n<polygon fill=\"#ea9a60\" points=\"467.5,-261 335.5,-261 335.5,-193 467.5,-193 467.5,-261\" stroke=\"#000000\"/>\n<text fill=\"#000000\" font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"401.5\" y=\"-245.8\">amount &lt;= 0.001</text>\n<text fill=\"#000000\" font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"401.5\" y=\"-230.8\">entropy = 0.649</text>\n<text fill=\"#000000\" font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"401.5\" y=\"-215.8\">samples = 4666</text>\n<text fill=\"#000000\" font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"401.5\" y=\"-200.8\">value = [3891, 775]</text>\n</g>\n<!-- 0&#45;&gt;1 -->\n<g class=\"edge\" id=\"edge1\">\n<title>0-&gt;1</title>\n<path d=\"M490.9508,-296.9465C478.8946,-287.519 465.6911,-277.1946 453.2184,-267.4415\" fill=\"none\" stroke=\"#000000\"/>\n<polygon fill=\"#000000\" points=\"455.1806,-264.5328 445.147,-261.13 450.8686,-270.0471 455.1806,-264.5328\" stroke=\"#000000\"/>\n<text fill=\"#000000\" font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"448.1834\" y=\"-282.2449\">True</text>\n</g>\n<!-- 8 -->\n<g class=\"node\" id=\"node9\">\n<title>8</title>\n<polygon fill=\"#83c2ef\" points=\"751.5,-261 599.5,-261 599.5,-193 751.5,-193 751.5,-261\" stroke=\"#000000\"/>\n<text fill=\"#000000\" font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"675.5\" y=\"-245.8\">newbalanceOrig &lt;= 0.0</text>\n<text fill=\"#000000\" font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"675.5\" y=\"-230.8\">entropy = 0.844</text>\n<text fill=\"#000000\" font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"675.5\" y=\"-215.8\">samples = 6832</text>\n<text fill=\"#000000\" font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"675.5\" y=\"-200.8\">value = [1858, 4974]</text>\n</g>\n<!-- 0&#45;&gt;8 -->\n<g class=\"edge\" id=\"edge8\">\n<title>0-&gt;8</title>\n<path d=\"M580.6687,-296.9465C593.5719,-287.4293 607.7145,-276.9978 621.0483,-267.163\" fill=\"none\" stroke=\"#000000\"/>\n<polygon fill=\"#000000\" points=\"623.2575,-269.8826 629.2276,-261.13 619.1023,-264.2492 623.2575,-269.8826\" stroke=\"#000000\"/>\n<text fill=\"#000000\" font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"625.4944\" y=\"-282.1497\">False</text>\n</g>\n<!-- 2 -->\n<g class=\"node\" id=\"node3\">\n<title>2</title>\n<polygon fill=\"#f0b58a\" points=\"267.5,-157 135.5,-157 135.5,-89 267.5,-89 267.5,-157\" stroke=\"#000000\"/>\n<text fill=\"#000000\" font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"201.5\" 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          "metadata": {
            "tags": []
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    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dZUcVXk6uPcf"
      },
      "source": [
        "# Implementação Interativa\n",
        "Por: Thiago"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "j0qprfv1TIKd"
      },
      "source": [
        "#Definindo a função de plotagem da árvore\n",
        "def plot_tree(crit, split, depth, min_split, min_leaf=0.2): \n",
        "  #Classificador da árvore de decisão\n",
        "  estimator = DecisionTreeClassifier(random_state = 0 \n",
        "                                    , criterion = crit\n",
        "                                    , splitter = split\n",
        "                                    , max_depth = depth\n",
        "                                    , min_samples_split=min_split\n",
        "                                    , min_samples_leaf=min_leaf)\n",
        "  \n",
        "  #Construindo um classificador de árvore de decisão a partir do conjunto de treinamento \n",
        "  estimator.fit(X_train, y_train)\n",
        "\n",
        "  #Definindo processo de impressão da árvore\n",
        "  graph = Source(tree.export_graphviz(estimator\n",
        "                                      , out_file=None\n",
        "                                      , feature_names=colunas\n",
        "                                      , filled = True))\n",
        "   \n",
        "  display(SVG(graph.pipe(format='svg'))) \n",
        "\n",
        "  #Retorna o classificador\n",
        "  return estimator\n",
        "\n",
        "#Definição da função Interativa\n",
        "inter = interactive(plot_tree \n",
        "   , crit = [\"gini\", \"entropy\"]\n",
        "   , split = [\"best\", \"random\"]\n",
        "   , depth=[1,2,3,4,5,6,7,8,9,10]\n",
        "   , min_split=(0.1,1)\n",
        "   , min_leaf=(0.1,0.5))\n",
        "\n",
        "#Plotagem da função Interativa\n",
        "display(inter)\n",
        "\n",
        "#(OBS : A árvore demora atualizar à cada interação)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QW7Id1XAQwrv",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 581
        },
        "outputId": "94e15736-c4cc-4732-def7-e8b3a5f5612f"
      },
      "source": [
        "# Previsão da classe de teste \n",
        "predictions = estimator.predict(X_val)\n",
        "\n",
        "# Acurárica das previsões\n",
        "print(\"Acurácia média: \", estimator.score(X_val, y_val), \"\\n\")\n",
        "print(\"Matriz de confusão: \")\n",
        "print(confusion_matrix(y_val, predictions))\n",
        "print(\"\\n\")\n",
        "print(classification_report(y_val, predictions))\n",
        "skplt.metrics.plot_confusion_matrix(y_val, predictions, normalize=True)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Acurácia média:  0.8854916161371678 \n",
            "\n",
            "Matriz de confusão: \n",
            "[[1687757  218565]\n",
            " [      7    2457]]\n",
            "\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      0.89      0.94   1906322\n",
            "           1       0.01      1.00      0.02      2464\n",
            "\n",
            "    accuracy                           0.89   1908786\n",
            "   macro avg       0.51      0.94      0.48   1908786\n",
            "weighted avg       1.00      0.89      0.94   1908786\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f05861f7d10>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        },
        {
          "output_type": "display_data",
          "data": {
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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}