3_[Redes_Bayesianas].ipynb

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        "<a href=\"https://colab.research.google.com/github/yvesemmanuel/machine_learning/blob/main/3_%5BRedes_Bayesianas%5D.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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            "--2022-04-08 13:41:08--  https://archive.ics.uci.edu/ml/machine-learning-databases/glass/glass.data\n",
            "Resolving archive.ics.uci.edu (archive.ics.uci.edu)... 128.195.10.252\n",
            "Connecting to archive.ics.uci.edu (archive.ics.uci.edu)|128.195.10.252|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 11903 (12K) [application/x-httpd-php]\n",
            "Saving to: ‘glass.data’\n",
            "\n",
            "\rglass.data            0%[                    ]       0  --.-KB/s               \rglass.data          100%[===================>]  11.62K  --.-KB/s    in 0s      \n",
            "\n",
            "2022-04-08 13:41:08 (91.0 MB/s) - ‘glass.data’ saved [11903/11903]\n",
            "\n"
          ]
        }
      ],
      "source": [
        "!wget https://archive.ics.uci.edu/ml/machine-learning-databases/glass/glass.data\n",
        "!pip -qqq install tqdm"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "S3IfvBUsE1l8"
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    {
      "cell_type": "markdown",
      "source": [
        "# Objetivo do exercício:\n",
        "\n",
        "* Implementar do zero o pipeline de treinamento em ML.\n",
        "  * Carregamento dos dados\n",
        "  * Pré-processamento dos dados\n",
        "  * Instanciação do modelo  \n",
        "  * Setup de parâmetros\n",
        "  * Treinamento \n",
        "  * Avaliação do modelo treinado\n",
        "\n",
        "* Plotar o dataset usando a Análise de componentes Principais (PCA) para redução de dimensionalidade. Responda por escrito em seu notebook:\n",
        "  * As instâncias de classes diferentes estão visualmente bem separadas?\n",
        "  * O que essa configuração da distribuição sugere sobre a dificuldade ou facilidade de um classificador qualquer em categorizar corretamente as instâncias?\n",
        "\n",
        "* O dataset utilizado será o Glass. Seu link está logo abaixo.\n",
        "\n",
        "* Treinar com duas versões diferentes de Modelo Bayesiano, a seu critério, considerando as implementações do scikit. Justificar o uso dos modelos;\n",
        "\n",
        "* Utilizar a medida de precisão para avaliar o modelo."
      ],
      "metadata": {
        "id": "8E8EWrFURVnb"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Lendo a base de dados do glass:\n",
        "\n",
        "*   Descrição da base: https://archive.ics.uci.edu/ml/datasets/glass+identification\n",
        "\n",
        "* Colunas:\n",
        "\n",
        "1. Id number: 1 to 214\n",
        "2. RI: refractive index\n",
        "3. Na: Sodium (unit measurement: weight percent in corresponding oxide, as are attributes 4-10)\n",
        "4. Mg: Magnesium\n",
        "5. Al: Aluminum\n",
        "6. Si: Silicon\n",
        "7. K: Potassium\n",
        "8. Ca: Calcium\n",
        "9. Ba: Barium\n",
        "10. Fe: Iron\n",
        "11. Type of glass: (class attribute)\n",
        "-- 1 building_windows_float_processed\n",
        "\n",
        "-- 2 building_windows_non_float_processed\n",
        "\n",
        "-- 3 vehicle_windows_float_processed\n",
        "\n",
        "-- 4 vehicle_windows_non_float_processed (none in this database)\n",
        "\n",
        "-- 5 containers\n",
        "\n",
        "-- 6 tableware\n",
        "\n",
        "-- 7 headlamps"
      ],
      "metadata": {
        "id": "Mcg807LR65OV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "from google.colab import data_table\n",
        "\n",
        "columns = \"id,ri,na,mg,ai,si,k,ca,ba,fe,class\".split(\",\")\n",
        "df = pd.read_csv(\"glass.data\", header=None)\n",
        "df.columns = columns\n",
        "\n",
        "data_table.DataTable(df, include_index=False, num_rows_per_page=10)"
      ],
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  'v': 15.01,\n            'f': \"15.01\",\n        },\n{\n            'v': 0.0,\n            'f': \"0.0\",\n        },\n{\n            'v': 2.51,\n            'f': \"2.51\",\n        },\n{\n            'v': 73.05,\n            'f': \"73.05\",\n        },\n{\n            'v': 0.05,\n            'f': \"0.05\",\n        },\n{\n            'v': 8.83,\n            'f': \"8.83\",\n        },\n{\n            'v': 0.53,\n            'f': \"0.53\",\n        },\n{\n            'v': 0.0,\n            'f': \"0.0\",\n        },\n{\n            'v': 7,\n            'f': \"7\",\n        }],\n [{\n            'v': 201,\n            'f': \"201\",\n        },\n{\n            'v': 1.51508,\n            'f': \"1.51508\",\n        },\n{\n            'v': 15.15,\n            'f': \"15.15\",\n        },\n{\n            'v': 0.0,\n            'f': \"0.0\",\n        },\n{\n            'v': 2.25,\n            'f': \"2.25\",\n        },\n{\n            'v': 73.5,\n            'f': \"73.5\",\n        },\n{\n            'v': 0.0,\n            'f': \"0.0\",\n        },\n{\n            'v': 8.34,\n            'f': \"8.34\",\n        },\n{\n            'v': 0.63,\n            'f': \"0.63\",\n        },\n{\n            'v': 0.0,\n            'f': \"0.0\",\n        },\n{\n            'v': 7,\n            'f': \"7\",\n        }],\n [{\n            'v': 202,\n            'f': \"202\",\n        },\n{\n            'v': 1.51653,\n            'f': \"1.51653\",\n        },\n{\n            'v': 11.95,\n            'f': \"11.95\",\n        },\n{\n            'v': 0.0,\n            'f': \"0.0\",\n        },\n{\n            'v': 1.19,\n            'f': \"1.19\",\n        },\n{\n            'v': 75.18,\n            'f': \"75.18\",\n        },\n{\n            'v': 2.7,\n            'f': \"2.7\",\n        },\n{\n            'v': 8.93,\n            'f': \"8.93\",\n        },\n{\n            'v': 0.0,\n            'f': \"0.0\",\n        },\n{\n            'v': 0.0,\n            'f': \"0.0\",\n        },\n{\n            'v': 7,\n            'f': \"7\",\n        }],\n [{\n            'v': 203,\n            'f': \"203\",\n        },\n{\n            'v': 1.51514,\n            'f': \"1.51514\",\n        },\n{\n            'v': 14.85,\n            'f': \"14.85\",\n        },\n{\n            'v': 0.0,\n            'f': \"0.0\",\n        },\n{\n            'v': 2.42,\n            'f': \"2.42\",\n        },\n{\n            'v': 73.72,\n            'f': \"73.72\",\n        },\n{\n            'v': 0.0,\n            'f': \"0.0\",\n        },\n{\n            'v': 8.39,\n            'f': \"8.39\",\n        },\n{\n            'v': 0.56,\n            'f': \"0.56\",\n        },\n{\n            'v': 0.0,\n            'f': \"0.0\",\n        },\n{\n            'v': 7,\n            'f': \"7\",\n        }],\n [{\n            'v': 204,\n            'f': \"204\",\n        },\n{\n            'v': 1.51658,\n            'f': \"1.51658\",\n        },\n{\n            'v': 14.8,\n            'f': \"14.8\",\n        },\n{\n            'v': 0.0,\n            'f': \"0.0\",\n        },\n{\n            'v': 1.99,\n            'f': \"1.99\",\n        },\n{\n            'v': 73.11,\n            'f': \"73.11\",\n        },\n{\n            'v': 0.0,\n            'f': \"0.0\",\n        },\n{\n            'v': 8.28,\n            'f': \"8.28\",\n        },\n{\n            'v': 1.71,\n            'f': \"1.71\",\n        },\n{\n            'v': 0.0,\n            'f': \"0.0\",\n        },\n{\n            'v': 7,\n            'f': \"7\",\n        }],\n [{\n            'v': 205,\n            'f': \"205\",\n        },\n{\n            'v': 1.51617,\n            'f': \"1.51617\",\n        },\n{\n            'v': 14.95,\n            'f': \"14.95\",\n        },\n{\n            'v': 0.0,\n            'f': \"0.0\",\n        },\n{\n            'v': 2.27,\n            'f': \"2.27\",\n        },\n{\n            'v': 73.3,\n            'f': \"73.3\",\n        },\n{\n            'v': 0.0,\n            'f': \"0.0\",\n        },\n{\n            'v': 8.71,\n            'f': \"8.71\",\n        },\n{\n            'v': 0.67,\n            'f': \"0.67\",\n        },\n{\n            'v': 0.0,\n            'f': \"0.0\",\n        },\n{\n            'v': 7,\n            'f': \"7\",\n        }],\n [{\n            'v': 206,\n            'f': \"206\",\n        },\n{\n            'v': 1.51732,\n            'f': \"1.51732\",\n        },\n{\n            'v': 14.95,\n            'f': \"14.95\",\n        },\n{\n            'v': 0.0,\n            'f': \"0.0\",\n        },\n{\n            'v': 1.8,\n            'f': \"1.8\",\n        },\n{\n            'v': 72.99,\n            'f': \"72.99\",\n        },\n{\n            'v': 0.0,\n            'f': \"0.0\",\n        },\n{\n            'v': 8.61,\n            'f': \"8.61\",\n        },\n{\n            'v': 1.55,\n            'f': \"1.55\",\n        },\n{\n            'v': 0.0,\n            'f': \"0.0\",\n        },\n{\n            'v': 7,\n            'f': \"7\",\n        }],\n [{\n            'v': 207,\n            'f': \"207\",\n        },\n{\n            'v': 1.51645,\n            'f': \"1.51645\",\n        },\n{\n            'v': 14.94,\n            'f': \"14.94\",\n        },\n{\n            'v': 0.0,\n            'f': \"0.0\",\n        },\n{\n            'v': 1.87,\n            'f': \"1.87\",\n        },\n{\n            'v': 73.11,\n            'f': \"73.11\",\n        },\n{\n            'v': 0.0,\n            'f': \"0.0\",\n        },\n{\n            'v': 8.67,\n            'f': \"8.67\",\n        },\n{\n            'v': 1.38,\n            'f': \"1.38\",\n        },\n{\n            'v': 0.0,\n            'f': \"0.0\",\n        },\n{\n            'v': 7,\n            'f': \"7\",\n        }],\n [{\n            'v': 208,\n            'f': \"208\",\n        },\n{\n            'v': 1.51831,\n            'f': \"1.51831\",\n        },\n{\n            'v': 14.39,\n            'f': \"14.39\",\n        },\n{\n            'v': 0.0,\n            'f': \"0.0\",\n        },\n{\n            'v': 1.82,\n            'f': \"1.82\",\n        },\n{\n            'v': 72.86,\n            'f': \"72.86\",\n        },\n{\n            'v': 1.41,\n            'f': \"1.41\",\n        },\n{\n            'v': 6.47,\n            'f': \"6.47\",\n        },\n{\n            'v': 2.88,\n            'f': \"2.88\",\n        },\n{\n            'v': 0.0,\n            'f': \"0.0\",\n        },\n{\n            'v': 7,\n            'f': \"7\",\n        }],\n [{\n            'v': 209,\n            'f': \"209\",\n        },\n{\n            'v': 1.5164,\n            'f': \"1.5164\",\n        },\n{\n            'v': 14.37,\n            'f': \"14.37\",\n        },\n{\n            'v': 0.0,\n            'f': \"0.0\",\n        },\n{\n            'v': 2.74,\n            'f': \"2.74\",\n        },\n{\n            'v': 72.85,\n            'f': \"72.85\",\n        },\n{\n            'v': 0.0,\n            'f': \"0.0\",\n        },\n{\n            'v': 9.45,\n            'f': \"9.45\",\n        },\n{\n            'v': 0.54,\n            'f': \"0.54\",\n        },\n{\n            'v': 0.0,\n            'f': \"0.0\",\n        },\n{\n            'v': 7,\n            'f': \"7\",\n        }],\n [{\n            'v': 210,\n            'f': \"210\",\n        },\n{\n            'v': 1.51623,\n            'f': \"1.51623\",\n        },\n{\n            'v': 14.14,\n            'f': \"14.14\",\n        },\n{\n            'v': 0.0,\n            'f': \"0.0\",\n        },\n{\n            'v': 2.88,\n            'f': \"2.88\",\n        },\n{\n            'v': 72.61,\n            'f': \"72.61\",\n        },\n{\n            'v': 0.08,\n            'f': \"0.08\",\n        },\n{\n            'v': 9.18,\n            'f': \"9.18\",\n        },\n{\n            'v': 1.06,\n            'f': \"1.06\",\n        },\n{\n            'v': 0.0,\n            'f': \"0.0\",\n        },\n{\n            'v': 7,\n            'f': \"7\",\n        }],\n [{\n            'v': 211,\n            'f': \"211\",\n        },\n{\n            'v': 1.51685,\n            'f': \"1.51685\",\n        },\n{\n            'v': 14.92,\n            'f': \"14.92\",\n        },\n{\n            'v': 0.0,\n            'f': \"0.0\",\n        },\n{\n            'v': 1.99,\n            'f': \"1.99\",\n        },\n{\n            'v': 73.06,\n            'f': \"73.06\",\n        },\n{\n            'v': 0.0,\n            'f': \"0.0\",\n        },\n{\n            'v': 8.4,\n            'f': \"8.4\",\n        },\n{\n            'v': 1.59,\n            'f': \"1.59\",\n        },\n{\n            'v': 0.0,\n            'f': \"0.0\",\n        },\n{\n            'v': 7,\n            'f': \"7\",\n        }],\n [{\n            'v': 212,\n            'f': \"212\",\n        },\n{\n            'v': 1.52065,\n            'f': \"1.52065\",\n        },\n{\n            'v': 14.36,\n            'f': \"14.36\",\n        },\n{\n            'v': 0.0,\n            'f': \"0.0\",\n        },\n{\n            'v': 2.02,\n            'f': \"2.02\",\n        },\n{\n            'v': 73.42,\n            'f': \"73.42\",\n        },\n{\n            'v': 0.0,\n            'f': \"0.0\",\n        },\n{\n            'v': 8.44,\n            'f': \"8.44\",\n        },\n{\n            'v': 1.64,\n            'f': \"1.64\",\n        },\n{\n            'v': 0.0,\n            'f': \"0.0\",\n        },\n{\n            'v': 7,\n            'f': \"7\",\n        }],\n [{\n            'v': 213,\n            'f': \"213\",\n        },\n{\n            'v': 1.51651,\n            'f': \"1.51651\",\n        },\n{\n            'v': 14.38,\n            'f': \"14.38\",\n        },\n{\n            'v': 0.0,\n            'f': \"0.0\",\n        },\n{\n            'v': 1.94,\n            'f': \"1.94\",\n        },\n{\n            'v': 73.61,\n            'f': \"73.61\",\n        },\n{\n            'v': 0.0,\n            'f': \"0.0\",\n        },\n{\n            'v': 8.48,\n            'f': \"8.48\",\n        },\n{\n            'v': 1.57,\n            'f': \"1.57\",\n        },\n{\n            'v': 0.0,\n            'f': \"0.0\",\n        },\n{\n            'v': 7,\n            'f': \"7\",\n        }],\n [{\n            'v': 214,\n            'f': \"214\",\n        },\n{\n            'v': 1.51711,\n            'f': \"1.51711\",\n        },\n{\n            'v': 14.23,\n            'f': \"14.23\",\n        },\n{\n            'v': 0.0,\n            'f': \"0.0\",\n        },\n{\n            'v': 2.08,\n            'f': \"2.08\",\n        },\n{\n            'v': 73.36,\n            'f': \"73.36\",\n        },\n{\n            'v': 0.0,\n            'f': \"0.0\",\n        },\n{\n            'v': 8.62,\n            'f': \"8.62\",\n        },\n{\n            'v': 1.67,\n            'f': \"1.67\",\n        },\n{\n            'v': 0.0,\n            'f': \"0.0\",\n        },\n{\n            'v': 7,\n            'f': \"7\",\n        }]],\n        columns: [[\"number\", \"id\"], [\"number\", \"ri\"], [\"number\", \"na\"], [\"number\", \"mg\"], [\"number\", \"ai\"], [\"number\", \"si\"], [\"number\", \"k\"], [\"number\", \"ca\"], [\"number\", \"ba\"], [\"number\", \"fe\"], [\"number\", \"class\"]],\n        columnOptions: [],\n        rowsPerPage: 10,\n        helpUrl: \"https://colab.research.google.com/notebooks/data_table.ipynb\",\n        suppressOutputScrolling: true,\n        minimumWidth: undefined,\n      });\n    ",
            "text/plain": [
              "<google.colab.data_table.DataTable object>"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Separando os atributos (features) e a classe\n",
        "\n",
        "* O campo id (identificador único) deve ser descartado."
      ],
      "metadata": {
        "id": "f7eFbC8p8AP6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X = df.drop([\"id\", \"class\"], axis = 1).to_numpy()\n",
        "y = df[\"class\"].values"
      ],
      "metadata": {
        "id": "V3i1qHL18PVq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Pré-processando "
      ],
      "metadata": {
        "id": "_Nw6R3oY8ifL"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Recodificando os rótulos (classes)\n",
        "\n",
        "* De maneira geral precisamos mapear as classes de valores textuais para valores numérico-categóricos.\n",
        "* Para este dataset, entretanto, não é necessário, uma vez que as classes estão apresentadas como números.\n",
        "* Ou seja, você não precisa fazer nada nesta etapa :)"
      ],
      "metadata": {
        "id": "sFa06dM881pZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "qeW-ynbx8hig"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Plotando o dataset\n",
        "\n",
        "* As classes dos elementas deve ser destacadas por cores\n",
        "* Usamos a técnica de Análise de Componentes Principais para reduzir as dimensões"
      ],
      "metadata": {
        "id": "1V1DDqcFb0f1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "oKDmUGoMcTMe"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Dividindo a base em treino, teste e validação"
      ],
      "metadata": {
        "id": "ThkZII6H9dHo"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "SEED = 0\n"
      ],
      "metadata": {
        "id": "Va1Ld_oY9iRM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Padronizando separadamente as bases"
      ],
      "metadata": {
        "id": "L2x1NDOu9-z8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "-8T_eAWE-D5h"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Instanciando o modelo\n",
        "\n"
      ],
      "metadata": {
        "id": "D5Y6o3P39v2R"
      }
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "Okox8Y_K9yK5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Validação do modelo\n",
        "\n",
        "* Validar testando diferentes valores do *var_smoothing*.\n",
        "* Considerar o espaço de busca para o *var_smoothing* considerado abaixo. "
      ],
      "metadata": {
        "id": "DPDz2KY9CwTr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "\n",
        "search_space = np.linspace(1e-12,1, 10)\n",
        "search_space"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rqjsBRFsCx6C",
        "outputId": "493b49e2-ae78-4c82-9d52-cd6c5ea4bd4f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1.00000000e-12, 1.11111111e-01, 2.22222222e-01, 3.33333333e-01,\n",
              "       4.44444444e-01, 5.55555556e-01, 6.66666667e-01, 7.77777778e-01,\n",
              "       8.88888889e-01, 1.00000000e+00])"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "9ySS7IDIGh0O"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Avaliando o modelo final com os parâmetros otimizados"
      ],
      "metadata": {
        "id": "Tc9ds1FSHLgY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "31pWGgnKHOcX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Quantidade de exemplos por classes\n",
        "\n",
        "* Buscar na referência das classes scikit como ter acesso às contagens das classes e exibí-las aqui para cada classificador usado\n",
        "*GaussianNB"
      ],
      "metadata": {
        "id": "NEnYI8tiRmB2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "GNhZX-lgmTXA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Probabilidade à priori das classes\n",
        "\n",
        "* A probabilidade à priori equivale à frequência relativa das classes quando não informada através do parâmetro *prios*.\n",
        "\n",
        "* Buscar na referência das classes scikit como ter acesso às probabilidades à priori calculadas para cada classificar usado."
      ],
      "metadata": {
        "id": "yhBcYnsORuTz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "H_h2QEI1mezg"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}