aula-10/Aula_10.ipynb

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      "name": "Aula_10.ipynb",
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KlcibMKA_a-j",
        "colab_type": "text"
      },
      "source": [
        "# Aula 10 - Aprendizagem de máquina"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9aXkSGB3qPN9",
        "colab_type": "text"
      },
      "source": [
        "Neste notebook, começaremos nossos estudos sobre aprendizagem de máquina. Começaremos com duas técnicas de aprendizado supervisionado: árvores de decisão e random forests. Para isto, utilizaremos a biblioteca [scikit-learn](http://scikit-learn.org) do Python."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TqWhkCoq_jyp",
        "colab_type": "text"
      },
      "source": [
        "## Parte 1 - Árvores de decisão"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BAFF0wpoqcKt",
        "colab_type": "text"
      },
      "source": [
        "Primeiramente, precisamos importar algumas bibliotecas."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PnzYoE-K_qfP",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.datasets import load_iris\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import confusion_matrix\n",
        "from sklearn.tree import export_graphviz\n",
        "from sklearn.externals.six import StringIO \n",
        "from sklearn.metrics import accuracy_score, precision_score, recall_score\n",
        "from IPython.display import Image \n",
        "from pydot import graph_from_dot_data\n",
        "import pandas as pd\n",
        "import numpy as np"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ffMwKq4kqwFR",
        "colab_type": "text"
      },
      "source": [
        "Feito isto, agora iremos importar um dataset clássico da literatura, o [Iris dataset](https://archive.ics.uci.edu/ml/datasets/iris). Felizmente, este dataset já está incluído na biblioteca scikit-learn. Desta forma, basta executar o comando abaixo para importá-lo."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "O_VPUwgcrWJU",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "iris = load_iris()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "B89SX6UeT25G",
        "colab_type": "code",
        "outputId": "17cd7a51-e62f-472b-d7eb-bb5465a01749",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "iris.DESCR.splitlines()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['.. _iris_dataset:',\n",
              " '',\n",
              " 'Iris plants dataset',\n",
              " '--------------------',\n",
              " '',\n",
              " '**Data Set Characteristics:**',\n",
              " '',\n",
              " '    :Number of Instances: 150 (50 in each of three classes)',\n",
              " '    :Number of Attributes: 4 numeric, predictive attributes and the class',\n",
              " '    :Attribute Information:',\n",
              " '        - sepal length in cm',\n",
              " '        - sepal width in cm',\n",
              " '        - petal length in cm',\n",
              " '        - petal width in cm',\n",
              " '        - class:',\n",
              " '                - Iris-Setosa',\n",
              " '                - Iris-Versicolour',\n",
              " '                - Iris-Virginica',\n",
              " '                ',\n",
              " '    :Summary Statistics:',\n",
              " '',\n",
              " '    ============== ==== ==== ======= ===== ====================',\n",
              " '                    Min  Max   Mean    SD   Class Correlation',\n",
              " '    ============== ==== ==== ======= ===== ====================',\n",
              " '    sepal length:   4.3  7.9   5.84   0.83    0.7826',\n",
              " '    sepal width:    2.0  4.4   3.05   0.43   -0.4194',\n",
              " '    petal length:   1.0  6.9   3.76   1.76    0.9490  (high!)',\n",
              " '    petal width:    0.1  2.5   1.20   0.76    0.9565  (high!)',\n",
              " '    ============== ==== ==== ======= ===== ====================',\n",
              " '',\n",
              " '    :Missing Attribute Values: None',\n",
              " '    :Class Distribution: 33.3% for each of 3 classes.',\n",
              " '    :Creator: R.A. Fisher',\n",
              " '    :Donor: Michael Marshall (MARSHALL%PLU@io.arc.nasa.gov)',\n",
              " '    :Date: July, 1988',\n",
              " '',\n",
              " 'The famous Iris database, first used by Sir R.A. Fisher. The dataset is taken',\n",
              " \"from Fisher's paper. Note that it's the same as in R, but not as in the UCI\",\n",
              " 'Machine Learning Repository, which has two wrong data points.',\n",
              " '',\n",
              " 'This is perhaps the best known database to be found in the',\n",
              " \"pattern recognition literature.  Fisher's paper is a classic in the field and\",\n",
              " 'is referenced frequently to this day.  (See Duda & Hart, for example.)  The',\n",
              " 'data set contains 3 classes of 50 instances each, where each class refers to a',\n",
              " 'type of iris plant.  One class is linearly separable from the other 2; the',\n",
              " 'latter are NOT linearly separable from each other.',\n",
              " '',\n",
              " '.. topic:: References',\n",
              " '',\n",
              " '   - Fisher, R.A. \"The use of multiple measurements in taxonomic problems\"',\n",
              " '     Annual Eugenics, 7, Part II, 179-188 (1936); also in \"Contributions to',\n",
              " '     Mathematical Statistics\" (John Wiley, NY, 1950).',\n",
              " '   - Duda, R.O., & Hart, P.E. (1973) Pattern Classification and Scene Analysis.',\n",
              " '     (Q327.D83) John Wiley & Sons.  ISBN 0-471-22361-1.  See page 218.',\n",
              " '   - Dasarathy, B.V. (1980) \"Nosing Around the Neighborhood: A New System',\n",
              " '     Structure and Classification Rule for Recognition in Partially Exposed',\n",
              " '     Environments\".  IEEE Transactions on Pattern Analysis and Machine',\n",
              " '     Intelligence, Vol. PAMI-2, No. 1, 67-71.',\n",
              " '   - Gates, G.W. (1972) \"The Reduced Nearest Neighbor Rule\".  IEEE Transactions',\n",
              " '     on Information Theory, May 1972, 431-433.',\n",
              " '   - See also: 1988 MLC Proceedings, 54-64.  Cheeseman et al\"s AUTOCLASS II',\n",
              " '     conceptual clustering system finds 3 classes in the data.',\n",
              " '   - Many, many more ...']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Be1H39tprWgB",
        "colab_type": "text"
      },
      "source": [
        "O dataset acima possui diversas instâncias de exemplo. Nosso objetivo é utilizar aprendizado supervisionado para aprender a distinguir espécies de iris com base na largura e comprimento de suas sépalas e pétalas. Para isto, precisamos separar as colunas representando as features de entrada (variável `X` abaixo) das classes de saída (variável `y` abaixo)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7WS6aJZTibsW",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X = pd.DataFrame(iris.data, columns=iris.feature_names)\n",
        "y = pd.Categorical.from_codes(iris.target, iris.target_names)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kgHTfzXCr9_5",
        "colab_type": "text"
      },
      "source": [
        "Para entender melhor os dados de entrada, podemos listar os primeiros 10 registros como segue."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MJotRo01ibx-",
        "colab_type": "code",
        "outputId": "624a69d8-e0f7-439c-9abe-a4171901f28e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 343
        }
      },
      "source": [
        "X.head(10)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>sepal length (cm)</th>\n",
              "      <th>sepal width (cm)</th>\n",
              "      <th>petal length (cm)</th>\n",
              "      <th>petal width (cm)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>5.1</td>\n",
              "      <td>3.5</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>4.9</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>4.7</td>\n",
              "      <td>3.2</td>\n",
              "      <td>1.3</td>\n",
              "      <td>0.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4.6</td>\n",
              "      <td>3.1</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5.0</td>\n",
              "      <td>3.6</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>5.4</td>\n",
              "      <td>3.9</td>\n",
              "      <td>1.7</td>\n",
              "      <td>0.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>4.6</td>\n",
              "      <td>3.4</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>5.0</td>\n",
              "      <td>3.4</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>4.4</td>\n",
              "      <td>2.9</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>4.9</td>\n",
              "      <td>3.1</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0.1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)\n",
              "0                5.1               3.5                1.4               0.2\n",
              "1                4.9               3.0                1.4               0.2\n",
              "2                4.7               3.2                1.3               0.2\n",
              "3                4.6               3.1                1.5               0.2\n",
              "4                5.0               3.6                1.4               0.2\n",
              "5                5.4               3.9                1.7               0.4\n",
              "6                4.6               3.4                1.4               0.3\n",
              "7                5.0               3.4                1.5               0.2\n",
              "8                4.4               2.9                1.4               0.2\n",
              "9                4.9               3.1                1.5               0.1"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OlMVz1JvsFAc",
        "colab_type": "text"
      },
      "source": [
        "Da mesma forma, podemos verificar os dados de saída como segue."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HAW5JQqKT5I-",
        "colab_type": "code",
        "outputId": "b47ce9ad-f34a-4986-f55a-cfd5969c63cb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 67
        }
      },
      "source": [
        "y"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[setosa, setosa, setosa, setosa, setosa, ..., virginica, virginica, virginica, virginica, virginica]\n",
              "Length: 150\n",
              "Categories (3, object): [setosa, versicolor, virginica]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "71niLZqrsK7-",
        "colab_type": "text"
      },
      "source": [
        "Agora, precisamos converter os dados de saída para um formato categórico. Isto vai facilitar o processo de aprendizagem. Para isto, podemos executar o comando abaixo."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Cdvl-OYBib0r",
        "colab_type": "code",
        "outputId": "b2118ec4-9537-4ce9-f98c-cf338010ed82",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 402
        }
      },
      "source": [
        "y = pd.get_dummies(y)\n",
        "y"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>setosa</th>\n",
              "      <th>versicolor</th>\n",
              "      <th>virginica</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>145</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>146</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>147</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>148</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>149</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>150 rows × 3 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     setosa  versicolor  virginica\n",
              "0         1           0          0\n",
              "1         1           0          0\n",
              "2         1           0          0\n",
              "3         1           0          0\n",
              "4         1           0          0\n",
              "..      ...         ...        ...\n",
              "145       0           0          1\n",
              "146       0           0          1\n",
              "147       0           0          1\n",
              "148       0           0          1\n",
              "149       0           0          1\n",
              "\n",
              "[150 rows x 3 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "c_zAIcc3saYN",
        "colab_type": "text"
      },
      "source": [
        "Com os comandos acima, agora temos um conjunto de exemplos de entrada (`X`) e saída (`y`). No entanto, lembre-se que, para poder validar os modelos aprendidos, é preciso não apenas treiná-los, mas também testá-los. Para isto, podemos dividir aleatoriamente o conjunto de dados em conjunto de treino e de teste como segue."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3y4LdwOoib3j",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nixhvcIys12K",
        "colab_type": "text"
      },
      "source": [
        "Até aqui, processamos os dados. Agora podemos nos preocupar com os algoritmos em si.\n",
        "\n",
        "O código abaixo cria uma instância do algoritmo [Decision Tree](https://scikit-learn.org/stable/modules/generated/sklearn.tree.DecisionTreeClassifier.html) para a tarefa de classificação. Em sua chamada padrão, o algoritmo utiliza o índice Gini para escolher as features."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "J-fvHdActHVu",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "dt = DecisionTreeClassifier()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EUAYaH_TtGw9",
        "colab_type": "text"
      },
      "source": [
        "Criado o algoritmo, agora podemos finalmente treinar o modelo. Para isto, basta chamar a função `fit`, que recebe um conjunto de dados de treino (separados em entradas e saídas) e encontra a melhor árvore de decisão para representar estes dados."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WyhWG49zi3LY",
        "colab_type": "code",
        "outputId": "869debdf-93df-4e40-d00c-d1cedc3e4c63",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 118
        }
      },
      "source": [
        "dt.fit(X_train, y_train)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "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')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NEUQVn6hulR_",
        "colab_type": "text"
      },
      "source": [
        "Após treinar o algoritmo, agora podemos analisar os resultados. Primeiramente, vamos visualizar a árvore resultante."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BBsAn1QQi3fp",
        "colab_type": "code",
        "outputId": "cb630ef3-fe25-4de1-8370-41ceeb3c4c14",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "dot_data = StringIO()\n",
        "export_graphviz(dt, out_file=dot_data, feature_names=iris.feature_names)\n",
        "(graph, ) = graph_from_dot_data(dot_data.getvalue())\n",
        "Image(graph.create_png())"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Im0KY2QtuyW7",
        "colab_type": "text"
      },
      "source": [
        "Para avaliar nosso algoritmo, podemos verificar o quão bem ele se sai na classificação de dados novos. Para isto, podemos utilizar os dados de teste. \n",
        "\n",
        "Em particular, a função `predict` recebe um conjunto de dados de entrada (`X_test`) e retorna a classificação obtida através do algoritmo."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Bim_SZAHi3iz",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "y_pred = dt.predict(X_test)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fozeaiHQvKKb",
        "colab_type": "text"
      },
      "source": [
        "Para medir o desempenho do nosso método, podemos calcular algumas métricas como acurácia, precisão e recall."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U5bBht9qwi6S",
        "colab_type": "code",
        "outputId": "f121ed85-698d-4db7-bfc6-ad661ed9021f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 67
        }
      },
      "source": [
        "a = accuracy_score(y_test, y_pred)\n",
        "p = precision_score(y_test, y_pred, average='micro')\n",
        "r = recall_score(y_test, y_pred, average='micro')\n",
        "\n",
        "print('Accuracy:\\t%f' % a)\n",
        "print('Precision:\\t%f' % p)\n",
        "print('Recall: \\t%f' % r)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy:\t0.973684\n",
            "Precision:\t0.973684\n",
            "Recall: \t0.973684\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WoVQWhEiwjBy",
        "colab_type": "text"
      },
      "source": [
        "Os resultados acima mostram que o algoritmo obteve um bom desempenho. De fato, ao gerar a matriz de confusão abaixo, podemos observar que os apenas uma instância foi classificada incorretamente."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "55D50oz7i3mh",
        "colab_type": "code",
        "outputId": "dca993dc-1eaf-449a-843c-5326b11e3f73",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 67
        }
      },
      "source": [
        "species = np.array(y_test).argmax(axis=1)\n",
        "predictions = np.array(y_pred).argmax(axis=1)\n",
        "confusion_matrix(species, predictions)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[13,  0,  0],\n",
              "       [ 0, 15,  1],\n",
              "       [ 0,  0,  9]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GARK4PZu_tve",
        "colab_type": "text"
      },
      "source": [
        "## Exercício 1\n",
        "\n",
        "Crie uma árvore de decisão para resolver um dos outros datasets de classificação disponíveis no scikit-learn. A lista de datasets pode ser encontrada [aqui](https://scikit-learn.org/stable/datasets/index.html#real-world-datasets). A tarefa consiste em importar os dados, treinar o algoritmo e avaliar os resultados."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6tGoLkDksSW2",
        "colab_type": "text"
      },
      "source": [
        "### Wine Data Set\n",
        "\n",
        "*   Número de instâncias: 178\n",
        "*   Número de atributos: 13 preditivos\n",
        "    1.   Alcohol\n",
        "    2.   Malic acid\n",
        "    3.   Ash\n",
        "    4.   Alcalinity of ash\n",
        "    5.   Magnesium\n",
        "    6.   Total phenols\n",
        "    7.   Flavanoids\n",
        "    8.   Nonflavanoid phenols\n",
        "    9.   Proanthocvanins\n",
        "    10.  Color intensity\n",
        "    11.  Hue\n",
        "    12.  OD280/OD315 of diluted wines\n",
        "    13.  Proline\n",
        "\n",
        "*   Classes:\n",
        "    1.   class_0\n",
        "    2.   class_1\n",
        "    3.   class_2\n",
        "\n",
        "Distribuição nas classes: class_0 (59), class_1 (71), class_2 (48)'.\n",
        "\n",
        "Data Set link: [sklearn.datasets.load_wine](https://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_wine.html#sklearn.datasets.load_wine)\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RtqDrRUB7BcY",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.datasets import load_wine\n",
        "\n",
        "wine = load_wine()\n",
        "\n",
        "x = pd.DataFrame(wine.data, columns=wine.feature_names)\n",
        "y = pd.Categorical.from_codes(wine.target, wine.target_names)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tBbnpQyO_tfA",
        "colab_type": "code",
        "outputId": "c79cfb59-de76-4e12-c2f3-20f156c291cb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 215
        }
      },
      "source": [
        "x.head()"
      ],
      "execution_count": 34,
      "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>alcohol</th>\n",
              "      <th>malic_acid</th>\n",
              "      <th>ash</th>\n",
              "      <th>alcalinity_of_ash</th>\n",
              "      <th>magnesium</th>\n",
              "      <th>total_phenols</th>\n",
              "      <th>flavanoids</th>\n",
              "      <th>nonflavanoid_phenols</th>\n",
              "      <th>proanthocyanins</th>\n",
              "      <th>color_intensity</th>\n",
              "      <th>hue</th>\n",
              "      <th>od280/od315_of_diluted_wines</th>\n",
              "      <th>proline</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>14.23</td>\n",
              "      <td>1.71</td>\n",
              "      <td>2.43</td>\n",
              "      <td>15.6</td>\n",
              "      <td>127.0</td>\n",
              "      <td>2.80</td>\n",
              "      <td>3.06</td>\n",
              "      <td>0.28</td>\n",
              "      <td>2.29</td>\n",
              "      <td>5.64</td>\n",
              "      <td>1.04</td>\n",
              "      <td>3.92</td>\n",
              "      <td>1065.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>13.20</td>\n",
              "      <td>1.78</td>\n",
              "      <td>2.14</td>\n",
              "      <td>11.2</td>\n",
              "      <td>100.0</td>\n",
              "      <td>2.65</td>\n",
              "      <td>2.76</td>\n",
              "      <td>0.26</td>\n",
              "      <td>1.28</td>\n",
              "      <td>4.38</td>\n",
              "      <td>1.05</td>\n",
              "      <td>3.40</td>\n",
              "      <td>1050.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>13.16</td>\n",
              "      <td>2.36</td>\n",
              "      <td>2.67</td>\n",
              "      <td>18.6</td>\n",
              "      <td>101.0</td>\n",
              "      <td>2.80</td>\n",
              "      <td>3.24</td>\n",
              "      <td>0.30</td>\n",
              "      <td>2.81</td>\n",
              "      <td>5.68</td>\n",
              "      <td>1.03</td>\n",
              "      <td>3.17</td>\n",
              "      <td>1185.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>14.37</td>\n",
              "      <td>1.95</td>\n",
              "      <td>2.50</td>\n",
              "      <td>16.8</td>\n",
              "      <td>113.0</td>\n",
              "      <td>3.85</td>\n",
              "      <td>3.49</td>\n",
              "      <td>0.24</td>\n",
              "      <td>2.18</td>\n",
              "      <td>7.80</td>\n",
              "      <td>0.86</td>\n",
              "      <td>3.45</td>\n",
              "      <td>1480.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>13.24</td>\n",
              "      <td>2.59</td>\n",
              "      <td>2.87</td>\n",
              "      <td>21.0</td>\n",
              "      <td>118.0</td>\n",
              "      <td>2.80</td>\n",
              "      <td>2.69</td>\n",
              "      <td>0.39</td>\n",
              "      <td>1.82</td>\n",
              "      <td>4.32</td>\n",
              "      <td>1.04</td>\n",
              "      <td>2.93</td>\n",
              "      <td>735.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   alcohol  malic_acid   ash  ...   hue  od280/od315_of_diluted_wines  proline\n",
              "0    14.23        1.71  2.43  ...  1.04                          3.92   1065.0\n",
              "1    13.20        1.78  2.14  ...  1.05                          3.40   1050.0\n",
              "2    13.16        2.36  2.67  ...  1.03                          3.17   1185.0\n",
              "3    14.37        1.95  2.50  ...  0.86                          3.45   1480.0\n",
              "4    13.24        2.59  2.87  ...  1.04                          2.93    735.0\n",
              "\n",
              "[5 rows x 13 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "agpyjjll_3hr",
        "colab_type": "code",
        "outputId": "f4218ecc-446b-47e1-ed04-1a3a07cd0db1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 402
        }
      },
      "source": [
        "y = pd.get_dummies(y)\n",
        "y"
      ],
      "execution_count": 35,
      "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>class_0</th>\n",
              "      <th>class_1</th>\n",
              "      <th>class_2</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>173</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>174</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>175</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>176</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>177</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>178 rows × 3 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     class_0  class_1  class_2\n",
              "0          1        0        0\n",
              "1          1        0        0\n",
              "2          1        0        0\n",
              "3          1        0        0\n",
              "4          1        0        0\n",
              "..       ...      ...      ...\n",
              "173        0        0        1\n",
              "174        0        0        1\n",
              "175        0        0        1\n",
              "176        0        0        1\n",
              "177        0        0        1\n",
              "\n",
              "[178 rows x 3 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ci-c-W9EAR6a",
        "colab_type": "code",
        "outputId": "d8f5e3fe-fb65-4a15-8a16-eeee0dd4670c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 118
        }
      },
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(x, y, random_state=1)\n",
        "dt = DecisionTreeClassifier()\n",
        "dt.fit(X_train, y_train)"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "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')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dZzG_bfQAsgd",
        "colab_type": "code",
        "outputId": "56ecfdc3-c02a-44a5-a8ac-3cae5ba360f2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 674
        }
      },
      "source": [
        "dot_data = StringIO()\n",
        "export_graphviz(dt, out_file=dot_data, feature_names=wine.feature_names)\n",
        "(graph, ) = graph_from_dot_data(dot_data.getvalue())\n",
        "Image(graph.create_png())"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pO3K_ZBxBZ53",
        "colab_type": "code",
        "outputId": "589e95cd-2cfa-43fc-b2fa-fb1fe7b2d7d1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 67
        }
      },
      "source": [
        "y_pred = dt.predict(X_test)\n",
        "\n",
        "a = accuracy_score(y_test, y_pred)\n",
        "p = precision_score(y_test, y_pred, average='micro')\n",
        "r = recall_score(y_test, y_pred, average='micro')\n",
        "\n",
        "print('Accuracy:\\t%f' % a)\n",
        "print('Precision:\\t%f' % p)\n",
        "print('Recall: \\t%f' % r)"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy:\t0.955556\n",
            "Precision:\t0.955556\n",
            "Recall: \t0.955556\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "21HdyZ3wBmy3",
        "colab_type": "code",
        "outputId": "d2a84535-a4fd-4c75-fa26-7fbe1656d9c7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 67
        }
      },
      "source": [
        "species = np.array(y_test).argmax(axis=1)\n",
        "predictions = np.array(y_pred).argmax(axis=1)\n",
        "confusion_matrix(species, predictions)"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[18,  0,  0],\n",
              "       [ 1, 16,  0],\n",
              "       [ 0,  1,  9]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KJ6T53LM_j1t",
        "colab_type": "text"
      },
      "source": [
        "## Parte 2 - Random forests"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TB4GSNaF31r1",
        "colab_type": "text"
      },
      "source": [
        "Nesta segunda parte do notebook, veremos como utilizar random forests.\n",
        "\n",
        "Primeiramente, precisamos importar os dados e tratá-los novamente. Utilizaremos novamente o dataset de plantas iris. O código é basicamente o mesmo, então basta executar o trecho abaixo."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ff4T3u5O_fx6",
        "colab_type": "code",
        "cellView": "both",
        "colab": {}
      },
      "source": [
        "#@title Código-base de importação do dataset (basta executar)\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.datasets import load_iris\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import confusion_matrix\n",
        "import sklearn.metrics\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "from sklearn.tree import export_graphviz\n",
        "from sklearn.externals.six import StringIO \n",
        "from IPython.display import Image \n",
        "from pydot import graph_from_dot_data\n",
        "\n",
        "iris = load_iris()\n",
        "X = pd.DataFrame(iris.data, columns=iris.feature_names)\n",
        "y = pd.Categorical.from_codes(iris.target, iris.target_names)\n",
        "\n",
        "y = pd.get_dummies(y)\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fOLz_Ga_4Zxd",
        "colab_type": "text"
      },
      "source": [
        "Agora podemos criar uma instância de `RandomForestClassifier` ([documentação](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html)). Esta instância cria um algoritmo random forest para a tarefa de classificação. No exemplo abaixo, utilizamos a entropia para escolher as features. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NbFlovrljp74",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "rf = RandomForestClassifier(criterion='entropy', oob_score=True, random_state=1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tGSzwg3B5P_u",
        "colab_type": "text"
      },
      "source": [
        "Criado o algoritmo, agora podemos treiná-lo."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3zVwAE0JjqBf",
        "colab_type": "code",
        "outputId": "ee19985c-8414-4023-b0b3-59bd3e08eb91",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 151
        }
      },
      "source": [
        "rf.fit(X_train, y_train)"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,\n",
              "                       criterion='entropy', max_depth=None, max_features='auto',\n",
              "                       max_leaf_nodes=None, max_samples=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, n_estimators=100,\n",
              "                       n_jobs=None, oob_score=True, random_state=1, verbose=0,\n",
              "                       warm_start=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qKPdfn4f5WWn",
        "colab_type": "text"
      },
      "source": [
        "Agora, podemos gerar a árvore de decisão resultante."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Vqf9dT31jqIq",
        "colab_type": "code",
        "outputId": "a833f1b2-c3fa-444d-ca72-b0e94224a118",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "dt = rf.estimators_[0]\n",
        "dot_data = StringIO()\n",
        "export_graphviz(dt, out_file=dot_data, feature_names=iris.feature_names)\n",
        "(graph, ) = graph_from_dot_data(dot_data.getvalue())\n",
        "Image(graph.create_png())"
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "k8DdRJd05v7E",
        "colab_type": "text"
      },
      "source": [
        "Para analisar o desempenho do algoritmo, podemos calcular algumas métricas."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Sa84ZBeMqRw_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "y_pred = rf.predict(X_test)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vZKSrcMn531R",
        "colab_type": "code",
        "outputId": "c731fe0f-6956-4fa2-8ee2-3a7a93feec2c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 67
        }
      },
      "source": [
        "a = accuracy_score(y_test, y_pred)\n",
        "p = precision_score(y_test, y_pred, average='micro')\n",
        "r = recall_score(y_test, y_pred, average='micro')\n",
        "\n",
        "print('Accuracy:\\t%f' % a)\n",
        "print('Precision:\\t%f' % p)\n",
        "print('Recall: \\t%f' % r)"
      ],
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy:\t0.973684\n",
            "Precision:\t0.973684\n",
            "Recall: \t0.973684\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4-5bR8Hn58oQ",
        "colab_type": "text"
      },
      "source": [
        "Além do mais, podemos analisar a matriz de confusão. Novamente, note que o resultado ficou bastante bom e que apenas uma instância foi classificada incorretamente."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eJD4T2l6jqSV",
        "colab_type": "code",
        "outputId": "6379645b-7a37-44c9-8720-c434f532e06a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 67
        }
      },
      "source": [
        "species = np.array(y_test).argmax(axis=1)\n",
        "predictions = np.array(y_pred).argmax(axis=1)\n",
        "confusion_matrix(species, predictions)"
      ],
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[13,  0,  0],\n",
              "       [ 0, 15,  1],\n",
              "       [ 0,  0,  9]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NEWTPfem3zKi",
        "colab_type": "text"
      },
      "source": [
        ""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FHRr4bOL_-Nz",
        "colab_type": "text"
      },
      "source": [
        "### Exercício 2\n",
        "\n",
        "Crie uma random forest para resolver um dos outros datasets de classificação disponíveis no scikit-learn. A lista de datasets pode ser encontrada [aqui](https://scikit-learn.org/stable/datasets/index.html#real-world-datasets). Utilize um dataset diferente do utilizado no exercício 1. A tarefa consiste em importar os dados, treinar o algoritmo e avaliar os resultados."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iqAOuIMvwuNb",
        "colab_type": "text"
      },
      "source": [
        "### Data set Breast Cancer (diagnóstico)\n",
        "\n",
        "Características:\n",
        "\n",
        "*   Número de instâncias: 569\n",
        "*   Número de atributos: 30\n",
        " \n",
        " - radius (mean of distances from center to points on the perimeter)\n",
        " - texture (standard deviation of gray-scale values)\n",
        " - perimeter\n",
        " - area\n",
        " - smoothness (local variation in radius lengths)\n",
        " - compactness (perimeter^2 / area - 1.0)\n",
        " - concavity (severity of concave portions of the contour)\n",
        " - concave points (number of concave portions of the contour)\n",
        " - symmetry \n",
        " - fractal dimension (\"coastline approximation\" - 1)\n",
        " - ...\n",
        "\n",
        "Distribuição da classe: 212 - Maligno, 357 - Benigno\n",
        "\n",
        "Data Set link: [sklearn.datasets.load_breast_cancer](https://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_breast_cancer.html#sklearn.datasets.load_breast_cancer)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1IJrFAeUAKdJ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.datasets import load_breast_cancer\n",
        "\n",
        "breast_cancer = load_breast_cancer()\n",
        "\n",
        "X = pd.DataFrame(breast_cancer.data, columns=breast_cancer.feature_names)\n",
        "y = pd.Categorical.from_codes(breast_cancer.target, breast_cancer.target_names)\n",
        "\n",
        "y = pd.get_dummies(y)\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lgOBrBiXq0pN",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "rf = RandomForestClassifier(criterion='entropy', oob_score=True, random_state=1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Jv5ebLjcq5Gl",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 151
        },
        "outputId": "094574b9-591c-4541-fe0c-7036943b2ca6"
      },
      "source": [
        "rf.fit(X_train, y_train)"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,\n",
              "                       criterion='entropy', max_depth=None, max_features='auto',\n",
              "                       max_leaf_nodes=None, max_samples=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, n_estimators=100,\n",
              "                       n_jobs=None, oob_score=True, random_state=1, verbose=0,\n",
              "                       warm_start=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kHGIodpHq-Oe",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 927
        },
        "outputId": "7b66dac0-b6f0-4a7e-8f74-8c79653ea013"
      },
      "source": [
        "dt = rf.estimators_[0]\n",
        "dot_data = StringIO()\n",
        "export_graphviz(dt, out_file=dot_data, feature_names=breast_cancer.feature_names)\n",
        "(graph, ) = graph_from_dot_data(dot_data.getvalue())\n",
        "Image(graph.create_png())"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mUXD8K0arm6n",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 67
        },
        "outputId": "010cc0eb-efe1-4d5d-991a-6b1497b8a09e"
      },
      "source": [
        "y_pred = rf.predict(X_test)\n",
        "\n",
        "a = accuracy_score(y_test, y_pred)\n",
        "p = precision_score(y_test, y_pred, average='micro')\n",
        "r = recall_score(y_test, y_pred, average='micro')\n",
        "\n",
        "print('Accuracy:\\t%f' % a)\n",
        "print('Precision:\\t%f' % p)\n",
        "print('Recall: \\t%f' % r)"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy:\t0.958042\n",
            "Precision:\t0.958042\n",
            "Recall: \t0.958042\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uQc0OHakryTi",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        },
        "outputId": "4c6c073a-846c-4ec7-8f4c-5171c13b42a6"
      },
      "source": [
        "species = np.array(y_test).argmax(axis=1)\n",
        "predictions = np.array(y_pred).argmax(axis=1)\n",
        "confusion_matrix(species, predictions)"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[51,  4],\n",
              "       [ 2, 86]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        }
      ]
    }
  ]
}