meetings/mlc01/mlc01.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Machine Learning Clássico 01"
      ],
      "metadata": {
        "id": "aAvRIjyFqqoI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "\n",
        "from sklearn import datasets, linear_model, model_selection, metrics"
      ],
      "metadata": {
        "id": "62xMeYEcqeNu"
      },
      "execution_count": 97,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Instanciando um Modelo Linear"
      ],
      "metadata": {
        "id": "JpscofdQXpAL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "linear_regression = linear_model.LinearRegression()"
      ],
      "metadata": {
        "id": "PP1TPMtp2ivs"
      },
      "execution_count": 98,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Carregando o Dataset"
      ],
      "metadata": {
        "id": "tja9WlBc2tKq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "diabetes_X, diabetes_y = datasets.load_diabetes(return_X_y=True)\n",
        "\n",
        "# Utiliza apenas uma feature\n",
        "diabetes_X = diabetes_X[:, np.newaxis, 2]"
      ],
      "metadata": {
        "id": "Rt_k5B2zWwHM"
      },
      "execution_count": 99,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Divisão Treino/Teste"
      ],
      "metadata": {
        "id": "QXkhoyFF3nnK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_test, y_train, y_test = model_selection.train_test_split(diabetes_X, diabetes_y, test_size=0.5)"
      ],
      "metadata": {
        "id": "R43aPfs53buB"
      },
      "execution_count": 100,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Treinamento do Modelo"
      ],
      "metadata": {
        "id": "FpdrCxyOXv1g"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "linear_regression.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "vDWIZtD130vt",
        "outputId": "194c4c3e-1148-4af5-e596-b9436d3e45dd"
      },
      "execution_count": 101,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression()"
            ],
            "text/html": [
              "<style>#sk-container-id-12 {color: black;background-color: white;}#sk-container-id-12 pre{padding: 0;}#sk-container-id-12 div.sk-toggleable {background-color: white;}#sk-container-id-12 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-12 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-12 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-12 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-12 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-12 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-12 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-12 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-12 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-12 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-12 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-12 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-12 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-12 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-12 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-12 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-12 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-12 div.sk-item {position: relative;z-index: 1;}#sk-container-id-12 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-12 div.sk-item::before, #sk-container-id-12 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-12 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-12 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-12 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-12 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-12 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-12 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-12 div.sk-label-container {text-align: center;}#sk-container-id-12 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-12 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-12\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-12\" type=\"checkbox\" checked><label for=\"sk-estimator-id-12\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 101
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = linear_regression.predict(X_test)"
      ],
      "metadata": {
        "id": "iqbBbLs93-Js"
      },
      "execution_count": 102,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Erro Quadrático Médio: %.2f\" % metrics.mean_squared_error(y_pred, y_test))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "79Rx2iOY4PIz",
        "outputId": "8d2ca08d-5390-46dc-d525-a77b34fbfd54"
      },
      "execution_count": 103,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Erro Quadrático Médio: 3950.86\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.scatter(X_test, y_pred)\n",
        "plt.scatter(X_test, y_test)\n",
        "plt.plot()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "B1Wq6O2hX9c8",
        "outputId": "6a78936c-ef57-4c5c-d7ac-2bad8210d083"
      },
      "execution_count": 104,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[]"
            ]
          },
          "metadata": {},
          "execution_count": 104
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Validação Cruzada"
      ],
      "metadata": {
        "id": "9ie4jZaMxHyN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import cross_val_predict\n",
        "\n",
        "cvy_pred = model_selection.cross_val_score(linear_regression, diabetes_X, diabetes_y, cv=10, scoring='neg_mean_squared_error')"
      ],
      "metadata": {
        "id": "qoj5yUe2pkuD"
      },
      "execution_count": 105,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Erro Quadrático Médio: %.2f\" % abs(np.mean(cvy_pred)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IAmi2DVR105N",
        "outputId": "a205792e-056c-43bf-b50b-bb9fc8d81f09"
      },
      "execution_count": 106,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Erro Quadrático Médio: 3906.92\n"
          ]
        }
      ]
    }
  ],
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      "display_name": "Python 3",
      "language": "python",
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    },
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