02-Model-Optimization.ipynb

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      "cell_type": "markdown",
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        "<a href=\"https://colab.research.google.com/github/devdebdeb/ML-Classificacao/blob/main/Otimiza%C3%A7%C3%A3o_de_modelos.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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          "text": [
            "\u001b[?25l   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/107.8 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K   \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[91m╸\u001b[0m\u001b[90m━━\u001b[0m \u001b[32m102.4/107.8 kB\u001b[0m \u001b[31m3.5 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m107.8/107.8 kB\u001b[0m \u001b[31m1.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hversão do sklearn = 1.3.2\n",
            "versão do pandas = 2.1.4\n",
            "versão do matplotlib = 3.7.1\n",
            "versão do skopt = 0.10.2\n",
            "versão do plotly = 5.15.0\n",
            "versão do numpy = 1.26.4\n"
          ]
        }
      ],
      "source": [
        "!pip install scikit-optimize -q\n",
        "\n",
        "import sklearn\n",
        "import pandas\n",
        "import matplotlib\n",
        "import skopt\n",
        "import plotly\n",
        "import numpy\n",
        "\n",
        "print(f'versão do sklearn = {sklearn.__version__}')\n",
        "print(f'versão do pandas = {pandas.__version__}')\n",
        "print(f'versão do matplotlib = {matplotlib.__version__}')\n",
        "print(f'versão do skopt = {skopt.__version__}')\n",
        "print(f'versão do plotly = {plotly.__version__}')\n",
        "print(f'versão do numpy = {numpy.__version__}')"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Conhecendo os dados"
      ],
      "metadata": {
        "id": "3d6kCbpeLSMe"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "rs = 42"
      ],
      "metadata": {
        "id": "Mz1KY3mBNchQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd"
      ],
      "metadata": {
        "id": "00TjHSRALhey"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data = pd.read_csv('/content/dados_inadimplencia.csv')\n",
        "data.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eIuYGyHkLbSH",
        "outputId": "7c969e2c-f63d-4306-a4dd-32472c081195"
      },
      "execution_count": null,
      "outputs": [
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          "data": {
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              "   receita_cliente  anuidade_emprestimo  anos_casa_propria  telefone_trab  \\\n",
              "0     16855.246324          2997.000000          12.157324              0   \n",
              "1     13500.000000          2776.050000          12.157324              0   \n",
              "2     11250.000000          2722.188351          12.157324              0   \n",
              "3     27000.000000          6750.000000           3.000000              0   \n",
              "4     22500.000000          3097.800000          12.157324              0   \n",
              "\n",
              "   avaliacao_cidade   score_1   score_2   score_3  score_social  \\\n",
              "0               2.0  0.501213  0.003109  0.513171      0.117428   \n",
              "1               2.0  0.501213  0.269730  0.513171      0.097900   \n",
              "2               3.0  0.701396  0.518625  0.700184      0.118600   \n",
              "3               2.0  0.501213  0.649571  0.513171      0.047400   \n",
              "4               2.0  0.440744  0.509677  0.513171      0.014400   \n",
              "\n",
              "   troca_telefone  inadimplente  \n",
              "0           243.0             1  \n",
              "1           617.0             0  \n",
              "2             9.0             0  \n",
              "3           300.0             0  \n",
              "4          2913.0             1  "
            ],
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              "      <td>300.0</td>\n",
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              "      <td>0.509677</td>\n",
              "      <td>0.513171</td>\n",
              "      <td>0.014400</td>\n",
              "      <td>2913.0</td>\n",
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              "type": "dataframe",
              "variable_name": "data",
              "summary": "{\n  \"name\": \"data\",\n  \"rows\": 14578,\n  \"fields\": [\n    {\n      \"column\": \"receita_cliente\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 9474.981329756309,\n        \"min\": 2700.0,\n        \"max\": 450000.0,\n        \"num_unique_values\": 292,\n        \"samples\": [\n          17550.0,\n          22511.25,\n          7200.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"anuidade_emprestimo\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1267.5133873316158,\n        \"min\": 332.1,\n        \"max\": 22500.0,\n        \"num_unique_values\": 4235,\n        \"samples\": [\n          865.8,\n          2185.2,\n          5041.35\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"anos_casa_propria\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7.713729595778532,\n        \"min\": 0.0,\n        \"max\": 69.0,\n        \"num_unique_values\": 49,\n        \"samples\": [\n          5.0,\n          69.0,\n          43.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"telefone_trab\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"avaliacao_cidade\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.4993021355726368,\n        \"min\": 1.0,\n        \"max\": 3.0,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          3.0,\n          2.030038427084816\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"score_1\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.14360807313442903,\n        \"min\": 0.017176539,\n        \"max\": 0.931286683,\n        \"num_unique_values\": 6483,\n        \"samples\": [\n          0.278799993,\n          0.568276523\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"score_2\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.19743470949256733,\n        \"min\": 5e-06,\n        \"max\": 0.820487147,\n        \"num_unique_values\": 12466,\n        \"samples\": [\n          0.501620079,\n          0.205865238\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"score_3\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0673654952870157,\n        \"min\": 0.000527265,\n        \"max\": 0.866731234,\n        \"num_unique_values\": 505,\n        \"samples\": [\n          0.289611238,\n          0.69562193\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"score_social\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.07496586517930975,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 842,\n        \"samples\": [\n          0.3433,\n          0.1057\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"troca_telefone\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 797.4999514927747,\n        \"min\": 0.0,\n        \"max\": 4128.0,\n        \"num_unique_values\": 2843,\n        \"samples\": [\n          2986.0,\n          247.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"inadimplente\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-YaBTq0NL115",
        "outputId": "181a061c-d595-48b1-9b12-3c46434fb1a5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 14578 entries, 0 to 14577\n",
            "Data columns (total 11 columns):\n",
            " #   Column               Non-Null Count  Dtype  \n",
            "---  ------               --------------  -----  \n",
            " 0   receita_cliente      14578 non-null  float64\n",
            " 1   anuidade_emprestimo  14578 non-null  float64\n",
            " 2   anos_casa_propria    14578 non-null  float64\n",
            " 3   telefone_trab        14578 non-null  int64  \n",
            " 4   avaliacao_cidade     14578 non-null  float64\n",
            " 5   score_1              14578 non-null  float64\n",
            " 6   score_2              14578 non-null  float64\n",
            " 7   score_3              14578 non-null  float64\n",
            " 8   score_social         14578 non-null  float64\n",
            " 9   troca_telefone       14578 non-null  float64\n",
            " 10  inadimplente         14578 non-null  int64  \n",
            "dtypes: float64(9), int64(2)\n",
            "memory usage: 1.2 MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "round(data['inadimplente'].value_counts(normalize=True)*100, 2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "N9zabCDzL_Du",
        "outputId": "ad7b867b-b028-498f-c409-70fd9d7a06e2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "inadimplente\n",
              "0    67.65\n",
              "1    32.35\n",
              "Name: proportion, dtype: float64"
            ],
            "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>proportion</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>inadimplente</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>67.65</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>32.35</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> float64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X = data.drop('inadimplente', axis=1)\n",
        "y = data['inadimplente']"
      ],
      "metadata": {
        "id": "IMZAVQRTMabm"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=rs, stratify=y)"
      ],
      "metadata": {
        "id": "6tVDwJ9yMjAa"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Construindo os modelos"
      ],
      "metadata": {
        "id": "8cCGjnVINIWf"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##DecisionTreeClassifier"
      ],
      "metadata": {
        "id": "ImS5iOEpPLCP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.tree import DecisionTreeClassifier\n"
      ],
      "metadata": {
        "id": "YUDbhnuvM4E1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model_dt = DecisionTreeClassifier(max_depth=3, random_state=rs)\n",
        "model_dt.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "X_Se8ttCNQOl",
        "outputId": "08d14141-3a8a-4fe0-9681-329cd921d1d5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DecisionTreeClassifier(max_depth=3, random_state=42)"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {color: black;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 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-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 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-1 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-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 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-1 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-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 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-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 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-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 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-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier(max_depth=3, random_state=42)</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-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier(max_depth=3, random_state=42)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import recall_score"
      ],
      "metadata": {
        "id": "3CVAXLAENjqn"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "recall_dt = recall_score(y_test, model_dt.predict(X_test))\n",
        "print(f\"recall dt: {recall_dt:.3f}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LoWYYwhiNtj4",
        "outputId": "f8367385-d15e-4018-9c2a-6f2e521333b0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "recall dt: 0.143\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##LogisticRegressor"
      ],
      "metadata": {
        "id": "Y4DvPX8MPMx0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.pipeline import make_pipeline\n",
        "from sklearn.preprocessing import StandardScaler"
      ],
      "metadata": {
        "id": "uf0sNbFUPRAm"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "logistic_pipeline = make_pipeline(StandardScaler(), LogisticRegression())\n",
        "logistic_pipeline.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 126
        },
        "id": "Funh21ChPoAD",
        "outputId": "cf16cacf-6dcb-4227-b83f-bb7f39e18d15"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Pipeline(steps=[('standardscaler', StandardScaler()),\n",
              "                ('logisticregression', LogisticRegression())])"
            ],
            "text/html": [
              "<style>#sk-container-id-2 {color: black;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 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-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 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-2 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-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 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-2 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-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 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-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 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-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 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-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;standardscaler&#x27;, StandardScaler()),\n",
              "                (&#x27;logisticregression&#x27;, LogisticRegression())])</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 sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;standardscaler&#x27;, StandardScaler()),\n",
              "                (&#x27;logisticregression&#x27;, LogisticRegression())])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression()</pre></div></div></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "recall_lr = recall_score(y_test, logistic_pipeline.predict(X_test))\n",
        "print(f'recall do lr: {recall_lr:.3f}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ooXXHQI6QB26",
        "outputId": "d7850254-981d-43c3-9e27-0460049bf94a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "recall do lr: 0.253\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Busca em grade - Otimização de hiper parâmetros"
      ],
      "metadata": {
        "id": "vT5xdukJUyfC"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## DecisionTreeClassifier"
      ],
      "metadata": {
        "id": "_3a0FvdxUam8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import GridSearchCV"
      ],
      "metadata": {
        "id": "o8vChfczUd7r"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "\n",
        "param_grid_dt = {\n",
        "    'criterion':  ['gini', 'entropy'],\n",
        "    'max_depth': np.linspace(6, 12, 4, dtype=int),\n",
        "    'min_samples_split': np.linspace(5, 20, 4, dtype=int),\n",
        "    'min_samples_leaf': np.linspace(5, 20, 4, dtype=int),\n",
        "    'max_features': ['sqrt', 'log2'],\n",
        "    'splitter': ['best', 'random']\n",
        "}"
      ],
      "metadata": {
        "id": "7O9VDBEOU6XU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import StratifiedKFold"
      ],
      "metadata": {
        "id": "kUBGoUOJV3_E"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cv = StratifiedKFold(shuffle=True, random_state=rs)\n",
        "\n",
        "dt_grid_search = GridSearchCV(estimator=DecisionTreeClassifier(random_state=rs),\n",
        "             param_grid=param_grid_dt,\n",
        "             scoring=\"recall\",\n",
        "             n_jobs=-1,\n",
        "             cv=cv\n",
        "             )\n",
        "\n",
        "dt_grid_search.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 117
        },
        "id": "WDQ1nRFpVSDD",
        "outputId": "3abff54e-0fb7-487c-95a2-967b5b6aabe5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GridSearchCV(cv=StratifiedKFold(n_splits=5, random_state=42, shuffle=True),\n",
              "             estimator=DecisionTreeClassifier(random_state=42), n_jobs=-1,\n",
              "             param_grid={'criterion': ['gini', 'entropy'],\n",
              "                         'max_depth': array([ 6,  8, 10, 12]),\n",
              "                         'max_features': ['sqrt', 'log2'],\n",
              "                         'min_samples_leaf': array([ 5, 10, 15, 20]),\n",
              "                         'min_samples_split': array([ 5, 10, 15, 20]),\n",
              "                         'splitter': ['best', 'random']},\n",
              "             scoring='recall')"
            ],
            "text/html": [
              "<style>#sk-container-id-3 {color: black;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 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-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 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-3 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-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 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-3 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-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 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-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 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-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 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-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=StratifiedKFold(n_splits=5, random_state=42, shuffle=True),\n",
              "             estimator=DecisionTreeClassifier(random_state=42), n_jobs=-1,\n",
              "             param_grid={&#x27;criterion&#x27;: [&#x27;gini&#x27;, &#x27;entropy&#x27;],\n",
              "                         &#x27;max_depth&#x27;: array([ 6,  8, 10, 12]),\n",
              "                         &#x27;max_features&#x27;: [&#x27;sqrt&#x27;, &#x27;log2&#x27;],\n",
              "                         &#x27;min_samples_leaf&#x27;: array([ 5, 10, 15, 20]),\n",
              "                         &#x27;min_samples_split&#x27;: array([ 5, 10, 15, 20]),\n",
              "                         &#x27;splitter&#x27;: [&#x27;best&#x27;, &#x27;random&#x27;]},\n",
              "             scoring=&#x27;recall&#x27;)</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 sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=StratifiedKFold(n_splits=5, random_state=42, shuffle=True),\n",
              "             estimator=DecisionTreeClassifier(random_state=42), n_jobs=-1,\n",
              "             param_grid={&#x27;criterion&#x27;: [&#x27;gini&#x27;, &#x27;entropy&#x27;],\n",
              "                         &#x27;max_depth&#x27;: array([ 6,  8, 10, 12]),\n",
              "                         &#x27;max_features&#x27;: [&#x27;sqrt&#x27;, &#x27;log2&#x27;],\n",
              "                         &#x27;min_samples_leaf&#x27;: array([ 5, 10, 15, 20]),\n",
              "                         &#x27;min_samples_split&#x27;: array([ 5, 10, 15, 20]),\n",
              "                         &#x27;splitter&#x27;: [&#x27;best&#x27;, &#x27;random&#x27;]},\n",
              "             scoring=&#x27;recall&#x27;)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier(random_state=42)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" ><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier(random_state=42)</pre></div></div></div></div></div></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Melhores parâmetros de acordo com GridSearch"
      ],
      "metadata": {
        "id": "bUHoV_6odkjN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dt_grid_search.best_params_"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BtqDJuMvddxN",
        "outputId": "78410270-0702-4f28-d48f-9b80fa57e7ec"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'criterion': 'gini',\n",
              " 'max_depth': 12,\n",
              " 'max_features': 'sqrt',\n",
              " 'min_samples_leaf': 15,\n",
              " 'min_samples_split': 5,\n",
              " 'splitter': 'best'}"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_cv_results_dt = pd.DataFrame(dt_grid_search.cv_results_)\n",
        "df_cv_results_dt.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 573
        },
        "id": "EYjcUvSwdyKH",
        "outputId": "946a20f5-0cc1-4c08-cd37-a8d6ef23d7d4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   mean_fit_time  std_fit_time  mean_score_time  std_score_time  \\\n",
              "0       0.022165      0.003719         0.008516        0.001120   \n",
              "1       0.008017      0.000373         0.007055        0.000391   \n",
              "2       0.017334      0.000933         0.007538        0.000477   \n",
              "3       0.007825      0.000516         0.007069        0.000362   \n",
              "4       0.017837      0.001206         0.008172        0.001411   \n",
              "\n",
              "  param_criterion param_max_depth param_max_features param_min_samples_leaf  \\\n",
              "0            gini               6               sqrt                      5   \n",
              "1            gini               6               sqrt                      5   \n",
              "2            gini               6               sqrt                      5   \n",
              "3            gini               6               sqrt                      5   \n",
              "4            gini               6               sqrt                      5   \n",
              "\n",
              "  param_min_samples_split param_splitter  \\\n",
              "0                       5           best   \n",
              "1                       5         random   \n",
              "2                      10           best   \n",
              "3                      10         random   \n",
              "4                      15           best   \n",
              "\n",
              "                                              params  split0_test_score  \\\n",
              "0  {'criterion': 'gini', 'max_depth': 6, 'max_fea...           0.213608   \n",
              "1  {'criterion': 'gini', 'max_depth': 6, 'max_fea...           0.193038   \n",
              "2  {'criterion': 'gini', 'max_depth': 6, 'max_fea...           0.213608   \n",
              "3  {'criterion': 'gini', 'max_depth': 6, 'max_fea...           0.193038   \n",
              "4  {'criterion': 'gini', 'max_depth': 6, 'max_fea...           0.200949   \n",
              "\n",
              "   split1_test_score  split2_test_score  split3_test_score  split4_test_score  \\\n",
              "0           0.145570           0.205696           0.164557           0.200949   \n",
              "1           0.132911           0.132911           0.112342           0.161392   \n",
              "2           0.145570           0.205696           0.164557           0.200949   \n",
              "3           0.132911           0.132911           0.112342           0.161392   \n",
              "4           0.145570           0.180380           0.164557           0.204114   \n",
              "\n",
              "   mean_test_score  std_test_score  rank_test_score  \n",
              "0         0.186076        0.026336              251  \n",
              "1         0.146519        0.028013              493  \n",
              "2         0.186076        0.026336              251  \n",
              "3         0.146519        0.028013              493  \n",
              "4         0.179114        0.022093              277  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-668ed73b-687f-4f89-9694-4d81ff1874e2\" class=\"colab-df-container\">\n",
              "    <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>mean_fit_time</th>\n",
              "      <th>std_fit_time</th>\n",
              "      <th>mean_score_time</th>\n",
              "      <th>std_score_time</th>\n",
              "      <th>param_criterion</th>\n",
              "      <th>param_max_depth</th>\n",
              "      <th>param_max_features</th>\n",
              "      <th>param_min_samples_leaf</th>\n",
              "      <th>param_min_samples_split</th>\n",
              "      <th>param_splitter</th>\n",
              "      <th>params</th>\n",
              "      <th>split0_test_score</th>\n",
              "      <th>split1_test_score</th>\n",
              "      <th>split2_test_score</th>\n",
              "      <th>split3_test_score</th>\n",
              "      <th>split4_test_score</th>\n",
              "      <th>mean_test_score</th>\n",
              "      <th>std_test_score</th>\n",
              "      <th>rank_test_score</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.022165</td>\n",
              "      <td>0.003719</td>\n",
              "      <td>0.008516</td>\n",
              "      <td>0.001120</td>\n",
              "      <td>gini</td>\n",
              "      <td>6</td>\n",
              "      <td>sqrt</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>best</td>\n",
              "      <td>{'criterion': 'gini', 'max_depth': 6, 'max_fea...</td>\n",
              "      <td>0.213608</td>\n",
              "      <td>0.145570</td>\n",
              "      <td>0.205696</td>\n",
              "      <td>0.164557</td>\n",
              "      <td>0.200949</td>\n",
              "      <td>0.186076</td>\n",
              "      <td>0.026336</td>\n",
              "      <td>251</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.008017</td>\n",
              "      <td>0.000373</td>\n",
              "      <td>0.007055</td>\n",
              "      <td>0.000391</td>\n",
              "      <td>gini</td>\n",
              "      <td>6</td>\n",
              "      <td>sqrt</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>random</td>\n",
              "      <td>{'criterion': 'gini', 'max_depth': 6, 'max_fea...</td>\n",
              "      <td>0.193038</td>\n",
              "      <td>0.132911</td>\n",
              "      <td>0.132911</td>\n",
              "      <td>0.112342</td>\n",
              "      <td>0.161392</td>\n",
              "      <td>0.146519</td>\n",
              "      <td>0.028013</td>\n",
              "      <td>493</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.017334</td>\n",
              "      <td>0.000933</td>\n",
              "      <td>0.007538</td>\n",
              "      <td>0.000477</td>\n",
              "      <td>gini</td>\n",
              "      <td>6</td>\n",
              "      <td>sqrt</td>\n",
              "      <td>5</td>\n",
              "      <td>10</td>\n",
              "      <td>best</td>\n",
              "      <td>{'criterion': 'gini', 'max_depth': 6, 'max_fea...</td>\n",
              "      <td>0.213608</td>\n",
              "      <td>0.145570</td>\n",
              "      <td>0.205696</td>\n",
              "      <td>0.164557</td>\n",
              "      <td>0.200949</td>\n",
              "      <td>0.186076</td>\n",
              "      <td>0.026336</td>\n",
              "      <td>251</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.007825</td>\n",
              "      <td>0.000516</td>\n",
              "      <td>0.007069</td>\n",
              "      <td>0.000362</td>\n",
              "      <td>gini</td>\n",
              "      <td>6</td>\n",
              "      <td>sqrt</td>\n",
              "      <td>5</td>\n",
              "      <td>10</td>\n",
              "      <td>random</td>\n",
              "      <td>{'criterion': 'gini', 'max_depth': 6, 'max_fea...</td>\n",
              "      <td>0.193038</td>\n",
              "      <td>0.132911</td>\n",
              "      <td>0.132911</td>\n",
              "      <td>0.112342</td>\n",
              "      <td>0.161392</td>\n",
              "      <td>0.146519</td>\n",
              "      <td>0.028013</td>\n",
              "      <td>493</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.017837</td>\n",
              "      <td>0.001206</td>\n",
              "      <td>0.008172</td>\n",
              "      <td>0.001411</td>\n",
              "      <td>gini</td>\n",
              "      <td>6</td>\n",
              "      <td>sqrt</td>\n",
              "      <td>5</td>\n",
              "      <td>15</td>\n",
              "      <td>best</td>\n",
              "      <td>{'criterion': 'gini', 'max_depth': 6, 'max_fea...</td>\n",
              "      <td>0.200949</td>\n",
              "      <td>0.145570</td>\n",
              "      <td>0.180380</td>\n",
              "      <td>0.164557</td>\n",
              "      <td>0.204114</td>\n",
              "      <td>0.179114</td>\n",
              "      <td>0.022093</td>\n",
              "      <td>277</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-668ed73b-687f-4f89-9694-4d81ff1874e2')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
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          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_cv_results_dt.loc[[dt_grid_search.best_index_]]"
      ],
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          "height": 170
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        "id": "v2-r3PCCekYs",
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      "execution_count": null,
      "outputs": [
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          "data": {
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              "     mean_fit_time  std_fit_time  mean_score_time  std_score_time  \\\n",
              "208       0.026698      0.001745         0.007677        0.000423   \n",
              "\n",
              "    param_criterion param_max_depth param_max_features param_min_samples_leaf  \\\n",
              "208            gini              12               sqrt                     15   \n",
              "\n",
              "    param_min_samples_split param_splitter  \\\n",
              "208                       5           best   \n",
              "\n",
              "                                                params  split0_test_score  \\\n",
              "208  {'criterion': 'gini', 'max_depth': 12, 'max_fe...           0.242089   \n",
              "\n",
              "     split1_test_score  split2_test_score  split3_test_score  \\\n",
              "208           0.324367           0.272152           0.245253   \n",
              "\n",
              "     split4_test_score  mean_test_score  std_test_score  rank_test_score  \n",
              "208           0.232595         0.263291        0.033244                1  "
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              "      <td>0.026698</td>\n",
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              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-02d733bb-166e-41df-a77e-443d9f9b1040 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-02d733bb-166e-41df-a77e-443d9f9b1040');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "repr_error": "0"
            }
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Analisar informações obtidas"
      ],
      "metadata": {
        "id": "ZFESo-DPfcwv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt"
      ],
      "metadata": {
        "id": "iCN4uP_vezU3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10, 6))\n",
        "plt.scatter(\n",
        "    df_cv_results_dt['param_max_depth'],\n",
        "    df_cv_results_dt['mean_test_score'],\n",
        "    )\n",
        "\n",
        "plt.title('max_depth vs. mean_test_score')\n",
        "plt.xlabel('max_depth')\n",
        "plt.ylabel('recall')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "id": "djOTid8aezT-",
        "outputId": "8f3b9cc6-2323-47ed-c3c1-ab7b436baba2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10, 6))\n",
        "plt.scatter(\n",
        "    df_cv_results_dt['param_min_samples_leaf'],\n",
        "    df_cv_results_dt['mean_test_score'],\n",
        "    )\n",
        "\n",
        "plt.title('min_samples_leaf vs. mean_test_score')\n",
        "plt.xlabel('min_samples_leaf')\n",
        "plt.ylabel('recall')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "id": "pAtjm1r8fLNI",
        "outputId": "65c04d12-a087-414f-86d0-44b8bcc4174e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## LogisticRegression"
      ],
      "metadata": {
        "id": "Pvkg-yWygOIq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "max_iter = np.linspace(100, 300, 5, dtype=int)\n",
        "c = [0.001, 0.01, 0.1, 1, 10]\n",
        "\n",
        "param_grid_lr = [\n",
        "    {'logisticregression__solver' : ['newton-cg', 'lbfgs'],\n",
        "      'logisticregression__penalty' : ['l2'],\n",
        "      'logisticregression__max_iter' : max_iter,\n",
        "       'logisticregression__C' : c},\n",
        "    {'logisticregression__solver' : ['liblinear'],\n",
        "      'logisticregression__penalty' : ['l1', 'l2'],\n",
        "      'logisticregression__max_iter' : max_iter,\n",
        "      'logisticregression__C' : c},\n",
        "]"
      ],
      "metadata": {
        "id": "RWWEWXTbgkzn"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "lr_grid_search = GridSearchCV(estimator=make_pipeline(StandardScaler(), LogisticRegression()),\n",
        "             param_grid=param_grid_lr,\n",
        "             scoring=\"recall\",\n",
        "             n_jobs=-1,\n",
        "             cv=cv\n",
        "             )\n",
        "\n",
        "lr_grid_search.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 152
        },
        "id": "BPk95D-OgRXL",
        "outputId": "fe441c8f-fccd-46a4-fe5b-71386848313d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GridSearchCV(cv=StratifiedKFold(n_splits=5, random_state=42, shuffle=True),\n",
              "             estimator=Pipeline(steps=[('standardscaler', StandardScaler()),\n",
              "                                       ('logisticregression',\n",
              "                                        LogisticRegression())]),\n",
              "             n_jobs=-1,\n",
              "             param_grid=[{'logisticregression__C': [0.001, 0.01, 0.1, 1, 10],\n",
              "                          'logisticregression__max_iter': array([100, 150, 200, 250, 300]),\n",
              "                          'logisticregression__penalty': ['l2'],\n",
              "                          'logisticregression__solver': ['newton-cg', 'lbfgs']},\n",
              "                         {'logisticregression__C': [0.001, 0.01, 0.1, 1, 10],\n",
              "                          'logisticregression__max_iter': array([100, 150, 200, 250, 300]),\n",
              "                          'logisticregression__penalty': ['l1', 'l2'],\n",
              "                          'logisticregression__solver': ['liblinear']}],\n",
              "             scoring='recall')"
            ],
            "text/html": [
              "<style>#sk-container-id-4 {color: black;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 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-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 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-4 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-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 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-4 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-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 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-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 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-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 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-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=StratifiedKFold(n_splits=5, random_state=42, shuffle=True),\n",
              "             estimator=Pipeline(steps=[(&#x27;standardscaler&#x27;, StandardScaler()),\n",
              "                                       (&#x27;logisticregression&#x27;,\n",
              "                                        LogisticRegression())]),\n",
              "             n_jobs=-1,\n",
              "             param_grid=[{&#x27;logisticregression__C&#x27;: [0.001, 0.01, 0.1, 1, 10],\n",
              "                          &#x27;logisticregression__max_iter&#x27;: array([100, 150, 200, 250, 300]),\n",
              "                          &#x27;logisticregression__penalty&#x27;: [&#x27;l2&#x27;],\n",
              "                          &#x27;logisticregression__solver&#x27;: [&#x27;newton-cg&#x27;, &#x27;lbfgs&#x27;]},\n",
              "                         {&#x27;logisticregression__C&#x27;: [0.001, 0.01, 0.1, 1, 10],\n",
              "                          &#x27;logisticregression__max_iter&#x27;: array([100, 150, 200, 250, 300]),\n",
              "                          &#x27;logisticregression__penalty&#x27;: [&#x27;l1&#x27;, &#x27;l2&#x27;],\n",
              "                          &#x27;logisticregression__solver&#x27;: [&#x27;liblinear&#x27;]}],\n",
              "             scoring=&#x27;recall&#x27;)</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 sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" ><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=StratifiedKFold(n_splits=5, random_state=42, shuffle=True),\n",
              "             estimator=Pipeline(steps=[(&#x27;standardscaler&#x27;, StandardScaler()),\n",
              "                                       (&#x27;logisticregression&#x27;,\n",
              "                                        LogisticRegression())]),\n",
              "             n_jobs=-1,\n",
              "             param_grid=[{&#x27;logisticregression__C&#x27;: [0.001, 0.01, 0.1, 1, 10],\n",
              "                          &#x27;logisticregression__max_iter&#x27;: array([100, 150, 200, 250, 300]),\n",
              "                          &#x27;logisticregression__penalty&#x27;: [&#x27;l2&#x27;],\n",
              "                          &#x27;logisticregression__solver&#x27;: [&#x27;newton-cg&#x27;, &#x27;lbfgs&#x27;]},\n",
              "                         {&#x27;logisticregression__C&#x27;: [0.001, 0.01, 0.1, 1, 10],\n",
              "                          &#x27;logisticregression__max_iter&#x27;: array([100, 150, 200, 250, 300]),\n",
              "                          &#x27;logisticregression__penalty&#x27;: [&#x27;l1&#x27;, &#x27;l2&#x27;],\n",
              "                          &#x27;logisticregression__solver&#x27;: [&#x27;liblinear&#x27;]}],\n",
              "             scoring=&#x27;recall&#x27;)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-9\" type=\"checkbox\" ><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;standardscaler&#x27;, StandardScaler()),\n",
              "                (&#x27;logisticregression&#x27;, LogisticRegression())])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-10\" type=\"checkbox\" ><label for=\"sk-estimator-id-10\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-11\" type=\"checkbox\" ><label for=\"sk-estimator-id-11\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression()</pre></div></div></div></div></div></div></div></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
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    {
      "cell_type": "code",
      "source": [
        "lr_grid_search.best_params_"
      ],
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        "outputId": "93cda115-9649-4de3-91ba-26f1de4d8667"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'logisticregression__C': 0.001,\n",
              " 'logisticregression__max_iter': 100,\n",
              " 'logisticregression__penalty': 'l2',\n",
              " 'logisticregression__solver': 'liblinear'}"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_cv_results_lr = pd.DataFrame(lr_grid_search.cv_results_)\n",
        "df_cv_results_lr.head(3)"
      ],
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          "height": 215
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        "id": "XjZKQ1pFiepJ",
        "outputId": "e76bd8dc-4c5a-48d2-e25d-bf670ea589bb"
      },
      "execution_count": null,
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              "   mean_fit_time  std_fit_time  mean_score_time  std_score_time  \\\n",
              "0       0.062857      0.018153         0.011159        0.004534   \n",
              "1       0.021667      0.003888         0.008746        0.000832   \n",
              "2       0.038744      0.002093         0.008612        0.000568   \n",
              "\n",
              "  param_logisticregression__C param_logisticregression__max_iter  \\\n",
              "0                       0.001                                100   \n",
              "1                       0.001                                100   \n",
              "2                       0.001                                150   \n",
              "\n",
              "  param_logisticregression__penalty param_logisticregression__solver  \\\n",
              "0                                l2                        newton-cg   \n",
              "1                                l2                            lbfgs   \n",
              "2                                l2                        newton-cg   \n",
              "\n",
              "                                              params  split0_test_score  \\\n",
              "0  {'logisticregression__C': 0.001, 'logisticregr...           0.102848   \n",
              "1  {'logisticregression__C': 0.001, 'logisticregr...           0.102848   \n",
              "2  {'logisticregression__C': 0.001, 'logisticregr...           0.102848   \n",
              "\n",
              "   split1_test_score  split2_test_score  split3_test_score  split4_test_score  \\\n",
              "0           0.101266           0.115506           0.088608           0.079114   \n",
              "1           0.101266           0.115506           0.088608           0.079114   \n",
              "2           0.101266           0.115506           0.088608           0.079114   \n",
              "\n",
              "   mean_test_score  std_test_score  rank_test_score  \n",
              "0         0.097468        0.012523               86  \n",
              "1         0.097468        0.012523               86  \n",
              "2         0.097468        0.012523               86  "
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{\n      \"column\": \"split1_test_score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.06194481909160606,\n        \"min\": 0.0,\n        \"max\": 0.254746835443038,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          0.10126582278481013,\n          0.2231012658227848\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"split2_test_score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0642795377069776,\n        \"min\": 0.0,\n        \"max\": 0.26582278481012656,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          0.23259493670886075,\n          0.26582278481012656\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"split3_test_score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.06351470073604921,\n        \"min\": 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0.21772151898734177\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"std_test_score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0027433007862798756,\n        \"min\": 0.0,\n        \"max\": 0.015740291285374543,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          0.010027202226427128,\n          0.010476547694460032\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"rank_test_score\",\n      \"properties\": {\n        \"dtype\": \"int32\",\n        \"num_unique_values\": 10,\n        \"samples\": [\n          66,\n          71\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_cv_results_lr.loc[[lr_grid_search.best_index_]]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 118
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        "id": "tQ3jupjsi_l4",
        "outputId": "6fc1c74a-1a2f-4fe8-e84b-0a772ebb769e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    mean_fit_time  std_fit_time  mean_score_time  std_score_time  \\\n",
              "51       0.022362      0.001587         0.008093        0.000148   \n",
              "\n",
              "   param_logisticregression__C param_logisticregression__max_iter  \\\n",
              "51                       0.001                                100   \n",
              "\n",
              "   param_logisticregression__penalty param_logisticregression__solver  \\\n",
              "51                                l2                        liblinear   \n",
              "\n",
              "                                               params  split0_test_score  \\\n",
              "51  {'logisticregression__C': 0.001, 'logisticregr...           0.251582   \n",
              "\n",
              "    split1_test_score  split2_test_score  split3_test_score  \\\n",
              "51           0.254747           0.265823           0.248418   \n",
              "\n",
              "    split4_test_score  mean_test_score  std_test_score  rank_test_score  \n",
              "51           0.235759         0.251266        0.009723                1  "
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              "      <th></th>\n",
              "      <th>mean_fit_time</th>\n",
              "      <th>std_fit_time</th>\n",
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              "      <th>split4_test_score</th>\n",
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              "      <th>51</th>\n",
              "      <td>0.022362</td>\n",
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    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10, 6))\n",
        "plt.scatter(\n",
        "    df_cv_results_lr['param_logisticregression__max_iter'],\n",
        "    df_cv_results_lr['mean_test_score'],\n",
        "    )\n",
        "\n",
        "plt.title('max_iter vs. mean_test_score')\n",
        "plt.xlabel('max_iter')\n",
        "plt.ylabel('recall')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "id": "UyBd9fMwjPox",
        "outputId": "6ce64387-8736-4284-a505-4403c8fe9e13"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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m+++/1/jx4zV48GD16dNH0q/38WnTpo1GjhypxYsXV3gOjRo1Uu3atTVnzhwFBQWpVq1aSkhIUGxsrObNm6devXrpuuuu05AhQ3T11Vdr//79WrlypYKDg/XBBx9c0v6npKRowoQJCggI0NChQ+Xj899/6zt+/Ljq16+v22+/XXFxcQoMDNRnn32mb775xu1+YTNnztSkSZO0cuVK1/27LodGjRrpqaee0rhx47Rnzx717dtXQUFByszM1Lvvvqvhw4dr7NixatasmRo1aqSxY8dq//79Cg4O1jvvvGPZd6hKEx8fr0WLFmnMmDFq3769AgMD1adPH/3pT3/S4sWLdf/992vlypW66aabVFhYqB07dmjx4sX69NNP1a5dOz3xxBP6/PPP1bt3bzVo0EDZ2dl66aWXVL9+fdc9yy6UlbK0aNFCXbt2VXx8vEJDQ7V+/XrXZfaLvPDCC+rYsaPatm2r4cOHKzY2Vnv27NFHH32kzZs3S5Keeuop1/24Ro4cqWrVqunll19Wfn6+pk6dWuY8goODNXv2bP3pT39S27Zt1b9/f4WFhWnv3r366KOPdNNNN2nmzJkV+yUAQBGPXKMQAGyu6BLk51++etCgQaZWrVrFxnfp0sVcd911rp+dTqd55plnTIMGDYy/v7+5/vrrzYcffmgGDRrkuqz2uXPnTPv27U39+vXNsWPH3Lb3j3/8w0gyixYtKvecz78UuzHGvP/++6ZFixamWrVqxS61vWnTJtOvXz9z1VVXGX9/f9OgQQNz5513mvT09DKPQ1l27txpJBlJ5ssvv3Rbl5+fbx5++GETFxdngoKCTK1atUxcXJx56aWX3MYVvfbKlSsv+FpFl2JfsmSJ2/KiS3Z/8803JW73/H165513TMeOHU2tWrVMrVq1TLNmzcwDDzxgfvjhB9eY7du3m6SkJBMYGGjq1q1rhg0bZr799ttix7a0nBS99sU4ceKEueuuu0zt2rWNJLfLshcUFJgpU6aY6667zvj7+5s6deqY+Ph4M2nSJJObm2uMMSY9Pd3ceuutJioqyvj5+ZmoqCgzYMAA8+OPP7q9zoWyciFPPfWU6dChg6ldu7apUaOGadasmXn66afdLotvjDFbt241f/zjH03t2rVNQECAadq0qRk/frzbmI0bN5rk5GQTGBhoatasaX73u9+ZNWvWuI0p7fdaZOXKlSY5OdmEhISYgIAA06hRIzN48GCzfv36cu0PAFyIw5hS7vYIAAAAACg3vnMFAAAAABbgO1cAYHOHDx9WYWFhqev9/PwUGhpaiTOCFXJyclRQUFDqel9f32L3yapMp0+fLvFCGr8VGhrqulk2AFwJ+FggANhcTEyMfvrpp1LXd+nSRatWraq8CcESXbt21erVq0td36BBA7cr51W2BQsWaMiQIRccc7kvNgIA3oZyBQA299VXX+n06dOlrq9Tp47i4+MrcUawwoYNGy54pcEaNWropptuqsQZuTt48KC2bdt2wTHx8fGqU6dOJc0IADyPcgUAAAAAFuCCFgAAAABgAS5oUQKn06kDBw4oKChIDofD09MBAAAA4CHGGB0/flxRUVFuN70vCeWqBAcOHFB0dLSnpwEAAADAS+zbt0/169e/4BjKVQmCgoIk/XoAg4ODPTwbAAAAAJ6Sl5en6OhoV0e4EMpVCYo+ChgcHEy5AgAAAFCurwtxQQsAAAAAsADlCgAAAAAsQLkCAAAAAAtQrgAAAADAApQrAAAAALAA5QoAAAAALEC5AgAAAAALUK4AAAAAwAKUKwAAAACwAOUKAAAAACxAuQIAAAAAC1CuAAAAAMAClCsAAAAAsEA1T08ApSt0Gq3LzFH28TOqFxSgDrGh8vVxeHpaQLmQX9gdGYadkV/YmZ3zS7nyUsu2HtSkD7brYO4Z17LIkACl9mmhni0jPTgzoGzkF3ZHhmFn5Bd2Zvf8OowxxtOT8DZ5eXkKCQlRbm6ugoODK/31l209qBGvbdT5v5iivj77nra2CBeuTOQXdkeGYWfkF3bmrfm9mG7gFd+5mjVrlmJiYhQQEKCEhAStW7eu1LFz585Vp06dVKdOHdWpU0dJSUnFxg8ePFgOh8Pt0bNnz8u9G5YodBpN+mB7sVBJci2b9MF2FTrpxPA+5Bd2R4ZhZ+QXdlZV8uvxcrVo0SKNGTNGqamp2rhxo+Li4pScnKzs7OwSx69atUoDBgzQypUrlZGRoejoaPXo0UP79+93G9ezZ08dPHjQ9XjzzTcrY3cu2brMHLfToOczkg7mntG6zJzKmxRQTuQXdkeGYWfkF3ZWVfLr8XI1Y8YMDRs2TEOGDFGLFi00Z84c1axZU/Pnzy9x/Ouvv66RI0eqTZs2atasmebNmyen06n09HS3cf7+/oqIiHA96tSpUxm7c8myj5ceqoqMAyoT+YXdkWHYGfmFnVWV/Hq0XBUUFGjDhg1KSkpyLfPx8VFSUpIyMjLKtY1Tp07p7NmzCg0NdVu+atUq1atXT02bNtWIESN09OjRUreRn5+vvLw8t4en1AsKsHQcUJnIL+yODMPOyC/srKrk16Pl6siRIyosLFR4eLjb8vDwcGVlZZVrG48++qiioqLcClrPnj31z3/+U+np6ZoyZYpWr16tXr16qbCwsMRtpKWlKSQkxPWIjo6u+E5dog6xoYoMCVBpF5t06NcrpnSIDS1lBOA55Bd2R4ZhZ+QXdlZV8uvxjwVeismTJ+utt97Su+++q4CA/7bY/v3765ZbblGrVq3Ut29fffjhh/rmm2+0atWqErczbtw45ebmuh779u2rpD0oztfHodQ+LSSpWLiKfk7t08I21/rHlYX8wu7IMOyM/MLOqkp+PVqu6tatK19fXx06dMht+aFDhxQREXHB506fPl2TJ0/W8uXL1bp16wuObdiwoerWratdu3aVuN7f31/BwcFuD0/q2TJSs+9pq4gQ99OeESEBXEIVXo/8wu7IMOyM/MLOqkJ+PX6fq4SEBHXo0EEvvviiJMnpdOqaa67Rgw8+qMcee6zE50ydOlVPP/20Pv30U91www1lvsbPP/+sa665Ru+9955uueWWMsd7+j5XRex8d2qA/MLuyDDsjPzCzrwtvxfTDTxerhYtWqRBgwbp5ZdfVocOHfT8889r8eLF2rFjh8LDwzVw4EBdffXVSktLkyRNmTJFEyZM0BtvvKGbbrrJtZ3AwEAFBgbqxIkTmjRpkm677TZFRERo9+7deuSRR3T8+HFt2bJF/v7+Zc7JW8oVAAAAAM+6mG5QrZLmVKqUlBQdPnxYEyZMUFZWltq0aaNly5a5LnKxd+9e+fj899OLs2fPVkFBgW6//Xa37aSmpmrixIny9fXVd999p4ULF+rYsWOKiopSjx499OSTT5arWAEAAABARXj8zJU34swVAAAAAOniuoGtrxYIAAAAAN6CcgUAAAAAFqBcAQAAAIAFKFcAAAAAYAHKFQAAAABYgHIFAAAAABagXAEAAACABShXAAAAAGAByhUAAAAAWIByBQAAAAAWoFwBAAAAgAUoVwAAAABgAcoVAAAAAFiAcgUAAAAAFqBcAQAAAIAFKFcAAAAAYIFqnp4ASlfoNFqXmaPs42dULyhAHWJD5evj8PS0gHIhv7A7Mgw7I7+wMzvnl3LlpZZtPahJH2zXwdwzrmWRIQFK7dNCPVtGenBmQNnIL+yODMPOyC/szO75dRhjjKcn4W3y8vIUEhKi3NxcBQcHV/rrL9t6UCNe26jzfzFFfX32PW1tES5cmcgv7I4Mw87IL+zMW/N7Md2A71x5mUKn0aQPthcLlSTXskkfbFehk04M70N+YXdkGHZGfmFnVSW/lCsvsy4zx+006PmMpIO5Z7QuM6fyJgWUE/mF3ZFh2Bn5hZ1VlfxSrrxM9vHSQ1WRcUBlIr+wOzIMOyO/sLOqkl/KlZepbsp3JZTyjgMqE/mF3ZFh2Bn5hZ1VlfxSrrzM+A+2WDoOqEzkF3ZHhmFn5Bd2VlXyS7nyMjmnzlk6DqhM5Bd2R4ZhZ+QXdlZV8ku58jK+5fyNlHccUJnIL+yODMPOyC/srKrk18und+W5Iba2peOAykR+YXdkGHZGfmFnVSW/lCsv0/f6aywdB1Qm8gu7I8OwM/ILO6sq+aVceZmr69S0dBxQmcgv7I4Mw87IL+ysquSXcuVlOsSGKjIk4IJjIkMC1CE2tJJmBJQf+YXdkWHYGfmFnVWV/FKuvIyvj0OpfVqotCv4OySl9mkhXx/vvsY/rkzkF3ZHhmFn5Bd2VlXyS7nyQj1bRmr2PW2LtffIkADNvqeteraM9NDMgLKRX9gdGYadkV/YWVXIr8MYYzw9CW+Tl5enkJAQ5ebmKjg42GPzKHQarcvMUfbxM6oX9OtpUG9v60AR8gu7I8OwM/ILO/O2/F5MN6BclcBbyhUAAAAAz7qYbsDHAgEAAADAApQrAAAAALAA5QoAAAAALEC5AgAAAAALUK4AAAAAwAKUKwAAAACwAOUKAAAAACxAuQIAAAAAC1Tz9ARQuoJzTv0rY49+yjmlBqE19afEGPlVow/DHsgv7I4Mw87IL+zMzvl1GGOMpyfhbS7mLsyXS9rH2zX3i0w5f/Pb8XFIwzrFatzvW3hkTkB5kV/YHRmGnZFf2Jk35vdiugFnrrxQ2sfb9fLnmcWWO41cy3lzhLciv7A7Mgw7I7+ws6qQX3ucX7uCFJxzau4XxUP1W3O/yFTBOWclzQgoP/ILuyPDsDPyCzurKvmlXHmZf2XscTsNWhKn+XUc4G3IL+yODMPOyC/srKrkl3LlZX7KOWXpOKAykV/YHRmGnZFf2FlVyS/lyss0CK1p6TigMpFf2B0Zhp2RX9hZVckv5crL/CkxRj6OC4/xcfw6DvA25Bd2R4ZhZ+QXdlZV8ku58jJ+1Xw0rFPsBccM6xRrm2v948pCfmF3ZBh2Rn5hZ1Ulv1yK3QsVXWLS267xD5QH+YXdkWHYGfmFnVWF/HIT4RJ4w02EJXvfnRogv7A7Mgw7I7+wM2/L78V0A8pVCbylXAEAAADwrIvpBvwTBgAAAABYgHIFAAAAABagXAEAAACABbhaoBcrdBqty8xR9vEzqhcUoA6xofIt6wYAgJcgv7A7Mgw7I7+wMzvnl3LlpZZtPahJH2zXwdwzrmWRIQFK7dNCPVtGenBmQNnIL+yODMPOyC/szO755WqBJfD01QKXbT2oEa9t1Pm/mKK+PvuetrYIF65M5Bd2R4ZhZ+QXduat+eVqgTZW6DSa9MH2YqGS5Fo26YPtKnTSieF9yC/sjgzDzsgv7Kyq5Jdy5WXWZea4nQY9n5F0MPeM1mXmVN6kgHIiv7A7Mgw7I7+ws6qSX8qVl8k+XnqoKjIOqEzkF3ZHhmFn5Bd2VlXyS7nyMvWCAiwdB1Qm8gu7I8OwM/ILO6sq+aVceZkOsaGKDAlQaRebdOjXK6Z0iA2tzGkB5UJ+YXdkGHZGfmFnVSW/lCsv4+vjUGqfFpJULFxFP6f2aWGba/3jykJ+YXdkGHZGfmFnVSW/lCsv1LNlpGbf01YRIe6nPSNCAriEKrwe+YXdkWHYGfmFnVWF/HKfqxJ4+j5XRex8d2qA/MLuyDDsjPzCzrwtvxfTDShXJfCWcgUAAADAs7iJMAAAAABUMsoVAAAAAFiAcgUAAAAAFqBcAQAAAIAFvKJczZo1SzExMQoICFBCQoLWrVtX6ti5c+eqU6dOqlOnjurUqaOkpKRi440xmjBhgiIjI1WjRg0lJSVp586dl3s3AAAAAFzBPF6uFi1apDFjxig1NVUbN25UXFyckpOTlZ2dXeL4VatWacCAAVq5cqUyMjIUHR2tHj16aP/+/a4xU6dO1QsvvKA5c+Zo7dq1qlWrlpKTk3XmzJnK2i0AAAAAVxiPX4o9ISFB7du318yZMyVJTqdT0dHRGjVqlB577LEyn19YWKg6depo5syZGjhwoIwxioqK0kMPPaSxY8dKknJzcxUeHq4FCxaof//+xbaRn5+v/Px81895eXmKjo7mUuwAAADAFc42l2IvKCjQhg0blJSU5Frm4+OjpKQkZWRklGsbp06d0tmzZxUaGipJyszMVFZWlts2Q0JClJCQUOo209LSFBIS4npER0dfwl4BAAAAuBJ5tFwdOXJEhYWFCg8Pd1seHh6urKyscm3j0UcfVVRUlKtMFT3vYrY5btw45ebmuh779u272F0BAAAAcIWr5ukJXIrJkyfrrbfe0qpVqxQQEFDh7fj7+8vf39/CmQEAAAC40nj0zFXdunXl6+urQ4cOuS0/dOiQIiIiLvjc6dOna/LkyVq+fLlat27tWl70vIpsEwAAAAAqyqPlys/PT/Hx8UpPT3ctczqdSk9PV2JiYqnPmzp1qp588kktW7ZM7dq1c1sXGxuriIgIt23m5eVp7dq1F9wmAAAAAFwKj38scMyYMRo0aJDatWunDh066Pnnn9fJkyc1ZMgQSdLAgQN19dVXKy0tTZI0ZcoUTZgwQW+88YZiYmJc36MKDAxUYGCgHA6HRo8eraeeekpNmjRRbGysxo8fr6ioKPXt29dTuwkAAACgivN4uUpJSdHhw4c1YcIEZWVlqU2bNlq2bJnrghR79+6Vj89/T7DNnj1bBQUFuv322922k5qaqokTJ0qSHnnkEZ08eVLDhw/XsWPH1LFjRy1btuySvpcFAAAAABfi8ftceaOLuZY9AAAAgKrLNve5AgAAAICqgnIFAAAAABagXAEAAACABShXAAAAAGAByhUAAAAAWIByBQAAAAAWoFwBAAAAgAUoVwAAAABgAcoVAAAAAFiAcgUAAAAAFqBcAQAAAIAFKFcAAAAAYAHKFQAAAABYgHIFAAAAABagXAEAAACABShXAAAAAGAByhUAAAAAWIByBQAAAAAWoFwBAAAAgAUoVwAAAABgAcoVAAAAAFiAcgUAAAAAFqBcAQAAAIAFKFcAAAAAYAHKFQAAAABYgHIFAAAAABagXAEAAACABShXAAAAAGAByhUAAAAAWIByBQAAAAAWoFwBAAAAgAUoVwAAAABgAcoVAAAAAFiAcgUAAAAAFqBcAQAAAIAFKFcAAAAAYAHKFQAAAABYgHIFAAAAABagXAEAAACABShXAAAAAGAByhUAAAAAWIByBQAAAAAWoFwBAAAAgAUoVwAAAABgAcoVAAAAAFiAcgUAAAAAFqBcAQAAAIAFKFcAAAAAYAHKFQAAAABYgHIFAAAAABagXAEAAACABShXAAAAAGAByhUAAAAAWIByBQAAAAAWoFwBAAAAgAUoVwAAAABgAcoVAAAAAFiAcgUAAAAAFqBcAQAAAIAFKFcAAAAAYAHKFQAAAABYgHIFAAAAABagXAEAAACABShXAAAAAGAByhUAAAAAWIByBQAAAAAWoFwBAAAAgAUoVwAAAABgAcoVAAAAAFiAcgUAAAAAFqBcAQAAAIAFKFcAAAAAYAGPl6tZs2YpJiZGAQEBSkhI0Lp160odu23bNt12222KiYmRw+HQ888/X2zMxIkT5XA43B7NmjW7jHsAAAAAAB4uV4sWLdKYMWOUmpqqjRs3Ki4uTsnJycrOzi5x/KlTp9SwYUNNnjxZERERpW73uuuu08GDB12PL7/88nLtAgAAAABI8nC5mjFjhoYNG6YhQ4aoRYsWmjNnjmrWrKn58+eXOL59+/aaNm2a+vfvL39//1K3W61aNUVERLgedevWvVy7AAAAAACSPFiuCgoKtGHDBiUlJf13Mj4+SkpKUkZGxiVte+fOnYqKilLDhg119913a+/evRccn5+fr7y8PLcHAAAAAFwMj5WrI0eOqLCwUOHh4W7Lw8PDlZWVVeHtJiQkaMGCBVq2bJlmz56tzMxMderUScePHy/1OWlpaQoJCXE9oqOjK/z6AAAAAK5MHr+ghdV69eqlO+64Q61bt1ZycrI+/vhjHTt2TIsXLy71OePGjVNubq7rsW/fvkqcMQAAAICqoJqnXrhu3bry9fXVoUOH3JYfOnTogheruFi1a9fWtddeq127dpU6xt/f/4Lf4QIAAACAsnjszJWfn5/i4+OVnp7uWuZ0OpWenq7ExETLXufEiRPavXu3IiMjLdsmAAAAAJzPY2euJGnMmDEaNGiQ2rVrpw4dOuj555/XyZMnNWTIEEnSwIEDdfXVVystLU3SrxfB2L59u+vP+/fv1+bNmxUYGKjGjRtLksaOHas+ffqoQYMGOnDggFJTU+Xr66sBAwZ4ZicBAAAAXBE8Wq5SUlJ0+PBhTZgwQVlZWWrTpo2WLVvmusjF3r175ePz35NrBw4c0PXXX+/6efr06Zo+fbq6dOmiVatWSZJ+/vlnDRgwQEePHlVYWJg6duyor7/+WmFhYZW6bwAAAACuLA5jjPH0JLxNXl6eQkJClJubq+DgYE9PBwAAAICHXEw3qHJXCwQAAAAAT6BcAQAAAIAFKFcAAAAAYAHKFQAAAABYgHIFAAAAABagXAEAAACABShXAAAAAGAByhUAAAAAWIByBQAAAAAWoFwBAAAAgAUoVwAAAABgAcoVAAAAAFiAcgUAAAAAFqBcAQAAAIAFKFcAAAAAYAHKFQAAAABYgHIFAAAAABagXAEAAACABShXAAAAAGCBauUd2K9fv3JvdOnSpRWaDAAAAADYVbnLVUhIyOWcBwAAAADYWrnL1auvvno55wEAAAAAtsZ3rgAAAADAAuU+c3X99dfL4XCUa+zGjRsrPCEAAAAAsKNyl6u+fftexmkAAAAAgL05jDHG05PwNnl5eQoJCVFubq6Cg4M9PR0AAAAAHnIx3YDvXAEAAACABcr9scDfKiws1HPPPafFixdr7969KigocFufk5NjyeQAAAAAwC4qdOZq0qRJmjFjhlJSUpSbm6sxY8aoX79+8vHx0cSJEy2eIgAAAAB4vwqVq9dff11z587VQw89pGrVqmnAgAGaN2+eJkyYoK+//trqOQIAAACA16tQucrKylKrVq0kSYGBgcrNzZUk/eEPf9BHH31k3ewAAAAAwCYqVK7q16+vgwcPSpIaNWqk5cuXS5K++eYb+fv7Wzc7AAAAALCJCpWrP/7xj0pPT5ckjRo1SuPHj1eTJk00cOBA3XvvvZZOEAAAAADswJL7XH399ddas2aNmjRpoj59+lgxL4/iPlcAAAAApIvrBhW6FPv5brjhBt1www1WbAoAAAAAbKlCHwtMS0vT/Pnziy2fP3++pkyZcsmTAgAAAAC7qVC5evnll9WsWbNiy6+77jrNmTPnkicFAAAAAHZT4UuxR0ZGFlseFhbmuoogAAAAAFxJKlSuoqOj9dVXXxVb/tVXXykqKuqSJwUAAAAAdlOhC1oMGzZMo0eP1tmzZ9WtWzdJUnp6uh555BE99NBDlk4QAAAAAOygQuXq4Ycf1tGjRzVy5EgVFBRIkgICAvToo49q3Lhxlk4QAAAAAOzgku5zdeLECX3//feqUaOGmjRpIn9/fyvn5jHc5woAAACAdHHdoELfuSqSlZWlnJwcNWrUSP7+/rLgfsQAAAAAYEsVKldHjx5V9+7dde211+r3v/+96wqBQ4cO5TtXAAAAAK5IFSpXf/3rX1W9enXt3btXNWvWdC1PSUnRsmXLLJscAAAAANhFhS5osXz5cn366aeqX7++2/ImTZrop59+smRiAAAAAGAnFTpzdfLkSbczVkVycnKqzEUtAAAAAOBiVKhcderUSf/85z9dPzscDjmdTk2dOlW/+93vLJscAAAAANhFhT4WOG3aNHXr1k3r169XQUGBHnnkEW3btk05OTn66quvrJ4jAAAAAHi9iy5XZ8+e1Z///Gd98MEHWrFihYKCgnTixAn169dPDzzwgCIjIy/HPAEAAADAq110uapevbq+++471alTR48//vjlmBMAAAAA2E6FvnN1zz336JVXXrF6LgAAAABgWxX6ztW5c+c0f/58ffbZZ4qPj1etWrXc1s+YMcOSyQEAAACAXVSoXG3dulVt27aVJP34449u6xwOx6XPCgAAAABspkLlauXKlVbPAwAAAABsrULfuQIAAAAAuKNcAQAAAIAFKFcAAAAAYAHKFQAAAABYgHIFAAAAABagXAEAAACABShXAAAAAGAByhUAAAAAWIByBQAAAAAWoFwBAAAAgAUoVwAAAABgAcoVAAAAAFiAcgUAAAAAFqBcAQAAAIAFKFcAAAAAYAHKFQAAAABYgHIFAAAAABagXAEAAACABTxermbNmqWYmBgFBAQoISFB69atK3Xstm3bdNtttykmJkYOh0PPP//8JW8TAAAAAKzg0XK1aNEijRkzRqmpqdq4caPi4uKUnJys7OzsEsefOnVKDRs21OTJkxUREWHJNgEAAADACg5jjPHUiyckJKh9+/aaOXOmJMnpdCo6OlqjRo3SY489dsHnxsTEaPTo0Ro9evQlbzM/P1/5+fmun/Py8hQdHa3c3FwFBwdfwh4CAAAAsLO8vDyFhISUqxtUq6Q5FVNQUKANGzZo3LhxrmU+Pj5KSkpSRkZGpW4zLS1NkyZNqtBrXk4F55z6V8Ye/ZRzSg1Ca+pPiTHyq+bxT3IC5UJ+YXdkGHZGfmFnds6vx8rVkSNHVFhYqPDwcLfl4eHh2rFjR6Vuc9y4cRozZozr56IzV56U9vF2zf0iU87fnFd8+uPvNaxTrMb9voXnJgaUA/mF3ZFh2Bn5hZ3ZPb8eK1fexN/fX/7+/p6ehkvax9v18ueZxZY7jVzL7RAuXJnIL+yODMPOyC/srCrk12Pn1+rWrStfX18dOnTIbfmhQ4dKvViFJ7ZZ2QrOOTX3i+Kh+q25X2Sq4JyzkmYElB/5hd2RYdgZ+YWdVZX8eqxc+fn5KT4+Xunp6a5lTqdT6enpSkxM9JptVrZ/ZexxOw1aEqf5dRzgbcgv7I4Mw87IL+ysquTXox8LHDNmjAYNGqR27dqpQ4cOev7553Xy5EkNGTJEkjRw4EBdffXVSktLk/TrBSu2b9/u+vP+/fu1efNmBQYGqnHjxuXaprf7KeeUpeOAykR+YXdkGHZGfmFnVSW/Hi1XKSkpOnz4sCZMmKCsrCy1adNGy5Ytc12QYu/evfLx+e/JtQMHDuj66693/Tx9+nRNnz5dXbp00apVq8q1TW/XILSmpeOAykR+YXdkGHZGfmFnVSW/Hr3Plbe6mGvZW63gnFPNxn9ywdOiPg5px5O9bHNJSlw5yC/sjgzDzsgv7Myb83sx3YD/sryMXzUfDesUe8ExwzrF8qYIr0R+YXdkGHZGfmFnVSW/XIrdCxVdYvL8a/z7OGSba/zjykV+YXdkGHZGfmFnVSG/fCywBJ78WOBv2fnu1AD5hd2RYdgZ+YWdeVt+L6YbUK5K4C3lCgAAAIBn8Z0rAAAAAKhklCsAAAAAsADlCgAAAAAsQLkCAAAAAAtQrgAAAADAApQrAAAAALAA5QoAAAAALEC5AgAAAAALUK4AAAAAwAKUKwAAAACwAOUKAAAAACxAuQIAAAAAC1CuAAAAAMAClCsAAAAAsADlCgAAAAAsQLkCAAAAAAtQrgAAAADAApQrAAAAALAA5QoAAAAALEC5AgAAAAALUK4AAAAAwAKUKwAAAACwAOUKAAAAACxAuQIAAAAAC1CuAAAAAMAClCsAAAAAsADlCgAAAAAsQLkCAAAAAAtQrgAAAADAApQrAAAAALAA5QoAAAAALEC5AgAAAAALUK4AAAAAwAKUKwAAAACwAOUKAAAAACxAuQIAAAAAC1CuAAAAAMAClCsAAAAAsADlCgAAAAAsQLkCAAAAAAtQrgAAAADAApQrAAAAALAA5QoAAAAALEC5AgAAAAALUK4AAAAAwAKUKwAAAACwAOUKAAAAACxAuQIAAAAAC1CuAAAAAMAClCsAAAAAsADlCgAAAAAsQLkCAAAAAAtQrgAAAADAApQrAAAAALAA5QoAAAAALEC5AgAAAAALUK4AAAAAwAKUKwAAAACwAOUKAAAAACxAuQIAAAAAC1CuAAAAAMAClCsAAAAAsADlCgAAAAAsQLkCAAAAAAtQrgAAAADAApQrAAAAALAA5QoAAAAALEC5AgAAAAALeEW5mjVrlmJiYhQQEKCEhAStW7fuguOXLFmiZs2aKSAgQK1atdLHH3/stn7w4MFyOBxuj549e17OXQAAAABwhfN4uVq0aJHGjBmj1NRUbdy4UXFxcUpOTlZ2dnaJ49esWaMBAwZo6NCh2rRpk/r27au+fftq69atbuN69uypgwcPuh5vvvlmZewOAAAAgCuUwxhjPDmBhIQEtW/fXjNnzpQkOZ1ORUdHa9SoUXrssceKjU9JSdHJkyf14YcfupbdcMMNatOmjebMmSPp1zNXx44d03vvvVehOeXl5SkkJES5ubkKDg6u0DYAAAAA2N/FdAOPnrkqKCjQhg0blJSU5Frm4+OjpKQkZWRklPicjIwMt/GSlJycXGz8qlWrVK9ePTVt2lQjRozQ0aNHS51Hfn6+8vLy3B4AAAAAcDE8Wq6OHDmiwsJChYeHuy0PDw9XVlZWic/Jysoqc3zPnj31z3/+U+np6ZoyZYpWr16tXr16qbCwsMRtpqWlKSQkxPWIjo6+xD0DAAAAcKWp5ukJXA79+/d3/blVq1Zq3bq1GjVqpFWrVql79+7Fxo8bN05jxoxx/ZyXl0fBAgAAAHBRPHrmqm7duvL19dWhQ4fclh86dEgRERElPiciIuKixktSw4YNVbduXe3atavE9f7+/goODnZ7AAAAAMDF8Gi58vPzU3x8vNLT013LnE6n0tPTlZiYWOJzEhMT3cZL0ooVK0odL0k///yzjh49qsjISGsmDgAAAADn8fil2MeMGaO5c+dq4cKF+v777zVixAidPHlSQ4YMkSQNHDhQ48aNc43/y1/+omXLlunZZ5/Vjh07NHHiRK1fv14PPvigJOnEiRN6+OGH9fXXX2vPnj1KT0/XrbfeqsaNGys5Odkj+wgAAACg6vP4d65SUlJ0+PBhTZgwQVlZWWrTpo2WLVvmumjF3r175ePz3w5444036o033tDf//53/e1vf1OTJk303nvvqWXLlpIkX19ffffdd1q4cKGOHTumqKgo9ejRQ08++aT8/f09so8AAAAAqj6P3+fKG3GfKwAAAACSje5zBQAAAABVBeUKAAAAACxAuQIAAAAAC1CuAAAAAMAClCsAAAAAsADlCgAAAAAsQLkCAAAAAAtQrgAAAADAApQrAAAAALAA5QoAAAAALEC5AgAAAAALUK4AAAAAwAKUKwAAAACwAOUKAAAAACxAuQIAAAAAC1CuAAAAAMAClCsAAAAAsADlCgAAAAAsQLkCAAAAAAtQrgAAAADAApQrAAAAALAA5QoAAAAALEC5AgAAAAALUK4AAAAAwAKUKwAAAACwAOUKAAAAACxAuQIAAAAAC1CuAAAAAMAClCsAAAAAsADlCgAAAAAsQLkCAAAAAAtQrgAAAADAApQrAAAAALAA5QoAAAAALEC5AgAAAAALUK4AAAAAwAKUKwAAAACwAOUKAAAAACxAuQIAAAAAC1CuAAAAAMAClCsAAAAAsADlCgAAAAAsQLkCAAAAAAtQrgAAAADAApQrAAAAALAA5QoAAAAALEC5AgAAAAALUK4AAAAAwAKUKwAAAACwAOUKAAAAACxAuQIAAAAAC1CuAAAAAMAClCsAAAAAsADlCgAAAAAsQLkCAAAAAAtQrgAAAADAApQrAAAAALAA5QoAAAAALEC5AgAAAAALUK4AAAAAwAKUKwAAAACwAOUKAAAAACxAuQIAAAAAC1CuAAAAAMAClCsAAAAAsADlCgAAAAAsQLkCAAAAAAtQrgAAAADAApQrAAAAALAA5QoAAAAALEC5AgAAAAALUK4AAAAAwALVPD0BlK7QabQuM0fZx8+oXlCAOsSGytfH4elpAeVCfmF3ZBh2Rn5hZ3bOr1eUq1mzZmnatGnKyspSXFycXnzxRXXo0KHU8UuWLNH48eO1Z88eNWnSRFOmTNHvf/9713pjjFJTUzV37lwdO3ZMN910k2bPnq0mTZpUxu5YYtnWg5r0wXYdzD3jWhYZEqDUPi3Us2WkB2cGlI38wu7IMOyM/MLO7J5fj38scNGiRRozZoxSU1O1ceNGxcXFKTk5WdnZ2SWOX7NmjQYMGKChQ4dq06ZN6tu3r/r27autW7e6xkydOlUvvPCC5syZo7Vr16pWrVpKTk7WmTNnStymt1m29aBGvLbRLVSSlJV7RiNe26hlWw96aGZA2cgv7I4Mw87IL+ysKuTXYYwxnpxAQkKC2rdvr5kzZ0qSnE6noqOjNWrUKD322GPFxqekpOjkyZP68MMPXctuuOEGtWnTRnPmzJExRlFRUXrooYc0duxYSVJubq7Cw8O1YMEC9e/fv8w55eXlKSQkRLm5uQoODrZoT8un0GnUccp/ioWqiENSREiAvny0m21Oj+LKQX5hd2QYdkZ+YWfenN+L6QYePXNVUFCgDRs2KCkpybXMx8dHSUlJysjIKPE5GRkZbuMlKTk52TU+MzNTWVlZbmNCQkKUkJBQ6jbz8/OVl5fn9vCUdZk5pYZKkoykg7lntC4zp/ImBZQT+YXdkWHYGfmFnVWV/Hq0XB05ckSFhYUKDw93Wx4eHq6srKwSn5OVlXXB8UX/ezHbTEtLU0hIiOsRHR1dof2xQvbx8n10sbzjgMpEfmF3ZBh2Rn5hZ1Ulvx7/zpU3GDdunHJzc12Pffv2eWwu9YICLB0HVCbyC7sjw7Az8gs7qyr59Wi5qlu3rnx9fXXo0CG35YcOHVJERESJz4mIiLjg+KL/vZht+vv7Kzg42O3hKR1iQxUZEqDSPknq0K9XTOkQG1qZ0wLKhfzC7sgw7Iz8ws6qSn49Wq78/PwUHx+v9PR01zKn06n09HQlJiaW+JzExES38ZK0YsUK1/jY2FhFRES4jcnLy9PatWtL3aY38fVxKLVPC0kqFq6in1P7tOCLqPBK5Bd2R4ZhZ+QXdlZV8uvxjwWOGTNGc+fO1cKFC/X9999rxIgROnnypIYMGSJJGjhwoMaNG+ca/5e//EXLli3Ts88+qx07dmjixIlav369HnzwQUmSw+HQ6NGj9dRTT+nf//63tmzZooEDByoqKkp9+/b1xC5etJ4tIzX7nraKCHE/7RkREqDZ97S1xTX+ceUiv7A7Mgw7I7+ws6qQX49fil2SZs6c6bqJcJs2bfTCCy8oISFBktS1a1fFxMRowYIFrvFLlizR3//+d9dNhKdOnVriTYT/93//V8eOHVPHjh310ksv6dprry3XfDx5KfbfsvPdqQHyC7sjw7Az8gs787b8Xkw38Ipy5W28pVwBAAAA8Czb3OcKAAAAAKoKyhUAAAAAWIByBQAAAAAWoFwBAAAAgAUoVwAAAABgAcoVAAAAAFiAcgUAAAAAFqBcAQAAAIAFKFcAAAAAYAHKFQAAAABYgHIFAAAAABagXAEAAACABShXAAAAAGCBap6egDcyxkiS8vLyPDwTAAAAAJ5U1AmKOsKFUK5KcPz4cUlSdHS0h2cCAAAAwBscP35cISEhFxzjMOWpYFcYp9OpAwcOKCgoSA6Hw6NzycvLU3R0tPbt26fg4GCPzqUq4vheXhzfy4vje/lxjC8vju/lxfG9vDi+l5c3HV9jjI4fP66oqCj5+Fz4W1WcuSqBj4+P6tev7+lpuAkODvZ4sKoyju/lxfG9vDi+lx/H+PLi+F5eHN/Li+N7eXnL8S3rjFURLmgBAAAAABagXAEAAACABShXXs7f31+pqany9/f39FSqJI7v5cXxvbw4vpcfx/jy4vheXhzfy4vje3nZ9fhyQQsAAAAAsABnrgAAAADAApQrAAAAALAA5QoAAAAALEC5AgAAAAALUK485PPPP1efPn0UFRUlh8Oh9957z229MUYTJkxQZGSkatSooaSkJO3cudNtTE5Oju6++24FBwerdu3aGjp0qE6cOFGJe+G9LnR8z549q0cffVStWrVSrVq1FBUVpYEDB+rAgQNu24iJiZHD4XB7TJ48uZL3xDuVld/BgwcXO3Y9e/Z0G0N+S1fW8T3/2BY9pk2b5hpDfkuWlpam9u3bKygoSPXq1VPfvn31ww8/uI05c+aMHnjgAV111VUKDAzUbbfdpkOHDrmN2bt3r3r37q2aNWuqXr16evjhh3Xu3LnK3BWvVNbxzcnJ0ahRo9S0aVPVqFFD11xzjf785z8rNzfXbTsl5futt96q7N3xSuXJcNeuXYsdv/vvv99tDBkuWVnHd8+ePaW+By9ZssQ1jgyXbPbs2WrdurXrxsCJiYn65JNPXOurwvsv5cpDTp48qbi4OM2aNavE9VOnTtULL7ygOXPmaO3atapVq5aSk5N15swZ15i7775b27Zt04oVK/Thhx/q888/1/DhwytrF7zahY7vqVOntHHjRo0fP14bN27U0qVL9cMPP+iWW24pNvaJJ57QwYMHXY9Ro0ZVxvS9Xln5laSePXu6Hbs333zTbT35LV1Zx/e3x/XgwYOaP3++HA6HbrvtNrdx5Le41atX64EHHtDXX3+tFStW6OzZs+rRo4dOnjzpGvPXv/5VH3zwgZYsWaLVq1frwIED6tevn2t9YWGhevfurYKCAq1Zs0YLFy7UggULNGHCBE/sklcp6/geOHBABw4c0PTp07V161YtWLBAy5Yt09ChQ4tt69VXX3XLb9++fSt5b7xTeTIsScOGDXM7flOnTnWtI8OlK+v4RkdHF3sPnjRpkgIDA9WrVy+3bZHh4urXr6/Jkydrw4YNWr9+vbp166Zbb71V27Ztk1RF3n8NPE6Seffdd10/O51OExERYaZNm+ZaduzYMePv72/efPNNY4wx27dvN5LMN9984xrzySefGIfDYfbv319pc7eD849vSdatW2ckmZ9++sm1rEGDBua55567vJOrAko6voMGDTK33nprqc8hv+VXnvzeeuutplu3bm7LyG/5ZGdnG0lm9erVxphf32urV69ulixZ4hrz/fffG0kmIyPDGGPMxx9/bHx8fExWVpZrzOzZs01wcLDJz8+v3B3wcucf35IsXrzY+Pn5mbNnz7qWlSf3+FVJx7hLly7mL3/5S6nPIcPlV54Mt2nTxtx7771uy8hw+dWpU8fMmzevyrz/cubKC2VmZiorK0tJSUmuZSEhIUpISFBGRoYkKSMjQ7Vr11a7du1cY5KSkuTj46O1a9dW+pztLjc3Vw6HQ7Vr13ZbPnnyZF111VW6/vrrNW3aNK867eztVq1apXr16qlp06YaMWKEjh496lpHfq1z6NAhffTRRyX+yz/5LVvRx9FCQ0MlSRs2bNDZs2fd3n+bNWuma665xu39t1WrVgoPD3eNSU5OVl5enutfX/Gr849vaWOCg4NVrVo1t+UPPPCA6tatqw4dOmj+/Pky3JazRKUd49dff11169ZVy5YtNW7cOJ06dcq1jgyXX1kZ3rBhgzZv3lziezAZvrDCwkK99dZbOnnypBITE6vM+2+1soegsmVlZUmSW3CKfi5al5WVpXr16rmtr1atmkJDQ11jUD5nzpzRo48+qgEDBig4ONi1/M9//rPatm2r0NBQrVmzRuPGjdPBgwc1Y8YMD87WHnr27Kl+/fopNjZWu3fv1t/+9jf16tVLGRkZ8vX1Jb8WWrhwoYKCgtw+NiGR3/JwOp0aPXq0brrpJrVs2VLSr++tfn5+xf6h5fz335Len4vW4VclHd/zHTlyRE8++WSxjwQ/8cQT6tatm2rWrKnly5dr5MiROnHihP785z9XxtRto7RjfNddd6lBgwaKiorSd999p0cffVQ//PCDli5dKokMl1d5MvzKK6+oefPmuvHGG92Wk+HSbdmyRYmJiTpz5owCAwP17rvvqkWLFtq8eXOVeP+lXOGKdvbsWd15550yxmj27Nlu68aMGeP6c+vWreXn56f/+Z//UVpamvz9/St7qrbSv39/159btWql1q1bq1GjRlq1apW6d+/uwZlVPfPnz9fdd9+tgIAAt+Xkt2wPPPCAtm7dqi+//NLTU6mSyjq+eXl56t27t1q0aKGJEye6rRs/frzrz9dff71OnjypadOm8RfT85R2jH9bVlu1aqXIyEh1795du3fvVqNGjSp7mrZVVoZPnz6tN954wy2vRchw6Zo2barNmzcrNzdXb7/9tgYNGqTVq1d7elqW4WOBXigiIkKSil0d5dChQ651ERERys7Odlt/7tw55eTkuMbgwoqK1U8//aQVK1a4nbUqSUJCgs6dO6c9e/ZUzgSrkIYNG6pu3bratWuXJPJrlS+++EI//PCD7rvvvjLHkl93Dz74oD788EOtXLlS9evXdy2PiIhQQUGBjh075jb+/Pffkt6fi9ah9ONb5Pjx4+rZs6eCgoL07rvvqnr16hfcXkJCgn7++Wfl5+dfrinbTlnH+LcSEhIkye09mAxfWHmO79tvv61Tp05p4MCBZW6PDP+Xn5+fGjdurPj4eKWlpSkuLk7/+Mc/qsz7L+XKC8XGxioiIkLp6emuZXl5eVq7dq0SExMlSYmJiTp27Jg2bNjgGvOf//xHTqfT9SaK0hUVq507d+qzzz7TVVddVeZzNm/eLB8fn2IfZ0PZfv75Zx09elSRkZGSyK9VXnnlFcXHxysuLq7MseT3V8YYPfjgg3r33Xf1n//8R7GxsW7r4+PjVb16dbf33x9++EF79+51e//dsmWL2z8QFP0DTYsWLSpnR7xUWcdX+vX/z3r06CE/Pz/9+9//LnbWtSSbN29WnTp1OOuq8h3j823evFmS3N6DyXDJLub4vvLKK7rlllsUFhZW5nbJcOmcTqfy8/OrzvuvBy+mcUU7fvy42bRpk9m0aZORZGbMmGE2bdrkulrd5MmTTe3atc37779vvvvuO3Prrbea2NhYc/r0adc2evbsaa6//nqzdu1a8+WXX5omTZqYAQMGeGqXvMqFjm9BQYG55ZZbTP369c3mzZvNwYMHXY+iK82sWbPGPPfcc2bz5s1m9+7d5rXXXjNhYWFm4MCBHt4z73Ch43v8+HEzduxYk5GRYTIzM81nn31m2rZta5o0aWLOnDnj2gb5LV1Z7w/GGJObm2tq1qxpZs+eXez55Ld0I0aMMCEhIWbVqlVu/+2fOnXKNeb+++8311xzjfnPf/5j1q9fbxITE01iYqJr/blz50zLli1Njx49zObNm82yZctMWFiYGTdunCd2yauUdXxzc3NNQkKCadWqldm1a5fbmHPnzhljjPn3v/9t5s6da7Zs2WJ27txpXnrpJVOzZk0zYcIET+6a1yjrGO/atcs88cQTZv369SYzM9O8//77pmHDhqZz586ubZDh0pXnPcIYY3bu3GkcDof55JNPim2DDJfuscceM6tXrzaZmZnmu+++M4899phxOBxm+fLlxpiq8f5LufKQlStXGknFHoMGDTLG/Ho59vHjx5vw8HDj7+9vunfvbn744Qe3bRw9etQMGDDABAYGmuDgYDNkyBBz/PhxD+yN97nQ8c3MzCxxnSSzcuVKY4wxGzZsMAkJCSYkJMQEBASY5s2bm2eeecatHFzJLnR8T506ZXr06GHCwsJM9erVTYMGDcywYcPcLptqDPm9kLLeH4wx5uWXXzY1atQwx44dK/Z88lu60v7bf/XVV11jTp8+bUaOHGnq1Kljatasaf74xz+agwcPum1nz549plevXqZGjRqmbt265qGHHnK7lPiVqqzjW1q2JZnMzExjzK+3ZWjTpo0JDAw0tWrVMnFxcWbOnDmmsLDQczvmRco6xnv37jWdO3c2oaGhxt/f3zRu3Ng8/PDDJjc31207ZLhk5XmPMMaYcePGmejo6BJzSYZLd++995oGDRoYPz8/ExYWZrp37+4qVsZUjfdfhzFcFxIAAAAALhXfuQIAAAAAC1CuAAAAAMAClCsAAAAAsADlCgAAAAAsQLkCAAAAAAtQrgAAAADAApQrAAAAALAA5QoAAAAALEC5AgCgAhYsWKDatWt7ehoAAC9CuQIAoAJSUlL0448/un6eOHGi2rRp47kJAQA8rpqnJwAAgB3VqFFDNWrUsHy7BQUF8vPzs3y7AIDLjzNXAABb6Nq1q0aNGqXRo0erTp06Cg8P19y5c3Xy5EkNGTJEQUFBaty4sT755BNJUmFhoYYOHarY2FjVqFFDTZs21T/+8Q/X9s6cOaPrrrtOw4cPdy3bvXu3goKCNH/+/DLn89uPBS5YsECTJk3St99+K4fDIYfDoQULFkiSjh07pvvuu09hYWEKDg5Wt27d9O2337q2U3TGa968eYqNjVVAQIAFRwsA4AmUKwCAbSxcuFB169bVunXrNGrUKI0YMUJ33HGHbrzxRm3cuFE9evTQn/70J506dUpOp1P169fXkiVLtH37dk2YMEF/+9vftHjxYklSQECAXn/9dS1cuFDvv/++CgsLdc899+jmm2/Wvffee1HzSklJ0UMPPaTrrrtOBw8e1MGDB5WSkiJJuuOOO5Sdna1PPvlEGzZsUNu2bdW9e3fl5OS4nr9r1y698847Wrp0qTZv3mzZ8QIAVC6HMcZ4ehIAAJSla9euKiws1BdffCHp1zNTISEh6tevn/75z39KkrKyshQZGamMjAzdcMMNxbbx4IMPKisrS2+//bZr2bRp0zR16lT1799f77zzjrZs2aKrrrqqzPksWLBAo0eP1rFjxyT9egbqvffecytHX375pXr37q3s7Gz5+/u7ljdu3FiPPPKIhg8frokTJ+qZZ57R/v37FRYWVpFDAwDwEnznCgBgG61bt3b92dfXV1dddZVatWrlWhYeHi5Jys7OliTNmjVL8+fP1969e3X69GkVFBQUu+jEQw89pPfee08zZ87UJ598Uq5iVV7ffvutTpw4UWybp0+f1u7du10/N2jQgGIFAFUA5QoAYBvVq1d3+9nhcLgtczgckiSn06m33npLY8eO1bPPPqvExEQFBQVp2rRpWrt2rds2srOz9eOPP8rX11c7d+5Uz549LZvviRMnFBkZqVWrVhVb99vLuNeqVcuy1wQAeA7lCgBQJX311Ve68cYbNXLkSNey354tKnLvvfeqVatWGjp0qIYNG6akpCQ1b978ol/Pz89PhYWFbsvatm2rrKwsVatWTTExMRe9TQCAvXBBCwBAldSkSROtX79en376qX788UeNHz9e33zzjduYWbNmKSMjQwsXLtTdd9+tvn376u6771ZBQcFFv15MTIwyMzO1efNmHTlyRPn5+UpKSlJiYqL69u2r5cuXa8+ePVqzZo0ef/xxrV+/3qpdBQB4CcoVAKBK+p//+R/169dPKSkpSkhI0NGjR93OYu3YsUMPP/ywXnrpJUVHR0uSXnrpJR05ckTjx4+/6Ne77bbb1LNnT/3ud79TWFiY3nzzTTkcDn388cfq3LmzhgwZomuvvVb9+/fXTz/95Pp+GACg6uBqgQAAAABgAc5cAQAAAIAFKFcAAJSgV69eCgwMLPHxzDPPeHp6AAAvxMcCAQAowf79+3X69OkS14WGhio0NLSSZwQA8HaUKwAAAACwAB8LBAAAAAALUK4AAAAAwAKUKwAAAACwAOUKAAAAACxAuQIAAAAAC1CuAAAAAMAClCsAAAAAsMD/AzuYLaR/qML5AAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## KNN"
      ],
      "metadata": {
        "id": "qDtShhPqk18a"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from numpy import linspace\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.model_selection import GridSearchCV, StratifiedKFold\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.pipeline import Pipeline\n",
        "\n",
        "\n",
        "# Definindo a grade de hiperparâmetros\n",
        "param_grid_knn = {\n",
        "    'knn__n_neighbors': linspace(5, 25, 10, dtype=int),\n",
        "    'knn__weights': ['uniform', 'distance'],\n",
        "    'knn__metric': ['euclidean', 'manhattan']\n",
        "}\n",
        "\n",
        "pipe_knn = Pipeline([\n",
        "    ('scaler', StandardScaler()),\n",
        "    ('knn', KNeighborsClassifier())\n",
        "])\n",
        "\n",
        "\n",
        "cv = StratifiedKFold(n_splits=5, shuffle=True, random_state=42)\n",
        "\n",
        "knn_grid_search = GridSearchCV(estimator = pipe_knn,\n",
        "                               param_grid = param_grid_knn,\n",
        "                               scoring = \"recall\",\n",
        "                               n_jobs = -1,\n",
        "                               cv = cv)\n",
        "\n",
        "knn_grid_search.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 152
        },
        "id": "JlZ6oywCl9WL",
        "outputId": "73aec25a-88e3-4db8-b52b-dea5de82c375"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GridSearchCV(cv=StratifiedKFold(n_splits=5, random_state=42, shuffle=True),\n",
              "             estimator=Pipeline(steps=[('scaler', StandardScaler()),\n",
              "                                       ('knn', KNeighborsClassifier())]),\n",
              "             n_jobs=-1,\n",
              "             param_grid={'knn__metric': ['euclidean', 'manhattan'],\n",
              "                         'knn__n_neighbors': array([ 5,  7,  9, 11, 13, 16, 18, 20, 22, 25]),\n",
              "                         'knn__weights': ['uniform', 'distance']},\n",
              "             scoring='recall')"
            ],
            "text/html": [
              "<style>#sk-container-id-5 {color: black;}#sk-container-id-5 pre{padding: 0;}#sk-container-id-5 div.sk-toggleable {background-color: white;}#sk-container-id-5 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-5 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-5 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-5 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-5 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 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-5 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-5 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-5 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 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-5 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-5 div.sk-item {position: relative;z-index: 1;}#sk-container-id-5 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-5 div.sk-item::before, #sk-container-id-5 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-5 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-5 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-5 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-5 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-5 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-5 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-5 div.sk-label-container {text-align: center;}#sk-container-id-5 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-5 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=StratifiedKFold(n_splits=5, random_state=42, shuffle=True),\n",
              "             estimator=Pipeline(steps=[(&#x27;scaler&#x27;, StandardScaler()),\n",
              "                                       (&#x27;knn&#x27;, KNeighborsClassifier())]),\n",
              "             n_jobs=-1,\n",
              "             param_grid={&#x27;knn__metric&#x27;: [&#x27;euclidean&#x27;, &#x27;manhattan&#x27;],\n",
              "                         &#x27;knn__n_neighbors&#x27;: array([ 5,  7,  9, 11, 13, 16, 18, 20, 22, 25]),\n",
              "                         &#x27;knn__weights&#x27;: [&#x27;uniform&#x27;, &#x27;distance&#x27;]},\n",
              "             scoring=&#x27;recall&#x27;)</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 sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-12\" type=\"checkbox\" ><label for=\"sk-estimator-id-12\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=StratifiedKFold(n_splits=5, random_state=42, shuffle=True),\n",
              "             estimator=Pipeline(steps=[(&#x27;scaler&#x27;, StandardScaler()),\n",
              "                                       (&#x27;knn&#x27;, KNeighborsClassifier())]),\n",
              "             n_jobs=-1,\n",
              "             param_grid={&#x27;knn__metric&#x27;: [&#x27;euclidean&#x27;, &#x27;manhattan&#x27;],\n",
              "                         &#x27;knn__n_neighbors&#x27;: array([ 5,  7,  9, 11, 13, 16, 18, 20, 22, 25]),\n",
              "                         &#x27;knn__weights&#x27;: [&#x27;uniform&#x27;, &#x27;distance&#x27;]},\n",
              "             scoring=&#x27;recall&#x27;)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-13\" type=\"checkbox\" ><label for=\"sk-estimator-id-13\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;scaler&#x27;, StandardScaler()), (&#x27;knn&#x27;, KNeighborsClassifier())])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-14\" type=\"checkbox\" ><label for=\"sk-estimator-id-14\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-15\" type=\"checkbox\" ><label for=\"sk-estimator-id-15\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier()</pre></div></div></div></div></div></div></div></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### KNN Metrics"
      ],
      "metadata": {
        "id": "m3ixk6F_nhhF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "knn_grid_search.best_params_"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "k-o3wrCjni--",
        "outputId": "0f843b0e-fbfc-4b2a-ac65-25c4e3fcef00"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'knn__metric': 'manhattan', 'knn__n_neighbors': 5, 'knn__weights': 'distance'}"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_cv_results_knn = pd.DataFrame(knn_grid_search.cv_results_)\n",
        "df_cv_results_knn.head(3)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 267
        },
        "id": "DSFOReo5nn1F",
        "outputId": "72e0f41c-e5d6-4fdc-9e60-d9c39989637a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   mean_fit_time  std_fit_time  mean_score_time  std_score_time  \\\n",
              "0       0.028654      0.001976         0.601962        0.020597   \n",
              "1       0.025841      0.000749         0.407426        0.017975   \n",
              "2       0.025802      0.000505         0.577963        0.017165   \n",
              "\n",
              "  param_knn__metric param_knn__n_neighbors param_knn__weights  \\\n",
              "0         euclidean                      5            uniform   \n",
              "1         euclidean                      5           distance   \n",
              "2         euclidean                      7            uniform   \n",
              "\n",
              "                                              params  split0_test_score  \\\n",
              "0  {'knn__metric': 'euclidean', 'knn__n_neighbors...           0.327532   \n",
              "1  {'knn__metric': 'euclidean', 'knn__n_neighbors...           0.371835   \n",
              "2  {'knn__metric': 'euclidean', 'knn__n_neighbors...           0.310127   \n",
              "\n",
              "   split1_test_score  split2_test_score  split3_test_score  split4_test_score  \\\n",
              "0           0.324367           0.363924           0.324367           0.294304   \n",
              "1           0.362342           0.379747           0.351266           0.318038   \n",
              "2           0.306962           0.337025           0.316456           0.319620   \n",
              "\n",
              "   mean_test_score  std_test_score  rank_test_score  \n",
              "0         0.326899        0.022120               10  \n",
              "1         0.356646        0.021524                2  \n",
              "2         0.318038        0.010496               15  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-271f6c47-08a5-422c-bff7-8057541a2668\" class=\"colab-df-container\">\n",
              "    <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>mean_fit_time</th>\n",
              "      <th>std_fit_time</th>\n",
              "      <th>mean_score_time</th>\n",
              "      <th>std_score_time</th>\n",
              "      <th>param_knn__metric</th>\n",
              "      <th>param_knn__n_neighbors</th>\n",
              "      <th>param_knn__weights</th>\n",
              "      <th>params</th>\n",
              "      <th>split0_test_score</th>\n",
              "      <th>split1_test_score</th>\n",
              "      <th>split2_test_score</th>\n",
              "      <th>split3_test_score</th>\n",
              "      <th>split4_test_score</th>\n",
              "      <th>mean_test_score</th>\n",
              "      <th>std_test_score</th>\n",
              "      <th>rank_test_score</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.028654</td>\n",
              "      <td>0.001976</td>\n",
              "      <td>0.601962</td>\n",
              "      <td>0.020597</td>\n",
              "      <td>euclidean</td>\n",
              "      <td>5</td>\n",
              "      <td>uniform</td>\n",
              "      <td>{'knn__metric': 'euclidean', 'knn__n_neighbors...</td>\n",
              "      <td>0.327532</td>\n",
              "      <td>0.324367</td>\n",
              "      <td>0.363924</td>\n",
              "      <td>0.324367</td>\n",
              "      <td>0.294304</td>\n",
              "      <td>0.326899</td>\n",
              "      <td>0.022120</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.025841</td>\n",
              "      <td>0.000749</td>\n",
              "      <td>0.407426</td>\n",
              "      <td>0.017975</td>\n",
              "      <td>euclidean</td>\n",
              "      <td>5</td>\n",
              "      <td>distance</td>\n",
              "      <td>{'knn__metric': 'euclidean', 'knn__n_neighbors...</td>\n",
              "      <td>0.371835</td>\n",
              "      <td>0.362342</td>\n",
              "      <td>0.379747</td>\n",
              "      <td>0.351266</td>\n",
              "      <td>0.318038</td>\n",
              "      <td>0.356646</td>\n",
              "      <td>0.021524</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.025802</td>\n",
              "      <td>0.000505</td>\n",
              "      <td>0.577963</td>\n",
              "      <td>0.017165</td>\n",
              "      <td>euclidean</td>\n",
              "      <td>7</td>\n",
              "      <td>uniform</td>\n",
              "      <td>{'knn__metric': 'euclidean', 'knn__n_neighbors...</td>\n",
              "      <td>0.310127</td>\n",
              "      <td>0.306962</td>\n",
              "      <td>0.337025</td>\n",
              "      <td>0.316456</td>\n",
              "      <td>0.319620</td>\n",
              "      <td>0.318038</td>\n",
              "      <td>0.010496</td>\n",
              "      <td>15</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-271f6c47-08a5-422c-bff7-8057541a2668')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
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              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-271f6c47-08a5-422c-bff7-8057541a2668 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-271f6c47-08a5-422c-bff7-8057541a2668');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-a407df93-8408-4796-95d1-beedb2924a9e\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-a407df93-8408-4796-95d1-beedb2924a9e')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
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              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
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              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
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              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-a407df93-8408-4796-95d1-beedb2924a9e button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
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              "type": "dataframe",
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              "summary": "{\n  \"name\": \"df_cv_results_knn\",\n  \"rows\": 40,\n  \"fields\": [\n    {\n      \"column\": \"mean_fit_time\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.008614788123020361,\n        \"min\": 0.025012731552124023,\n        \"max\": 0.05291762351989746,\n        \"num_unique_values\": 40,\n        \"samples\": [\n          0.02578744888305664,\n          0.04338960647583008,\n          0.04298768043518066\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"std_fit_time\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.00508499824397401,\n        \"min\": 0.0001773059198819237,\n        \"max\": 0.016454993984488332,\n        \"num_unique_values\": 40,\n        \"samples\": [\n          0.00046918352382353617,\n          0.015587662380369583,\n          0.006103126466161377\n        ],\n        \"semantic_type\": \"\",\n     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\"param_knn__metric\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"manhattan\",\n          \"euclidean\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"param_knn__n_neighbors\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"5\",\n        \"max\": \"25\",\n        \"num_unique_values\": 10,\n        \"samples\": [\n          \"22\",\n          \"7\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"param_knn__weights\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"distance\",\n          \"uniform\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"params\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"split0_test_score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.04900670468597879,\n        \"min\": 0.21835443037974683,\n        \"max\": 0.375,\n        \"num_unique_values\": 30,\n        \"samples\": [\n          0.30854430379746833,\n          0.22626582278481014\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"split1_test_score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.05006359926944642,\n        \"min\": 0.20411392405063292,\n        \"max\": 0.37341772151898733,\n        \"num_unique_values\": 32,\n        \"samples\": [\n          0.31962025316455694,\n          0.20727848101265822\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"split2_test_score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.04448159194345732,\n        \"min\": 0.22626582278481014,\n        \"max\": 0.379746835443038,\n        \"num_unique_values\": 33,\n        \"samples\": [\n          0.3259493670886076,\n          0.30854430379746833\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"split3_test_score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.052585533690221585,\n        \"min\": 0.17405063291139242,\n        \"max\": 0.3560126582278481,\n        \"num_unique_values\": 35,\n        \"samples\": [\n          0.3291139240506329,\n          0.26582278481012656\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"split4_test_score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 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            }
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_cv_results_knn.loc[[knn_grid_search.best_index_]]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 135
        },
        "id": "u6gR83MQn2Xu",
        "outputId": "a52aadeb-d1a0-4f3a-f555-29db918158ff"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    mean_fit_time  std_fit_time  mean_score_time  std_score_time  \\\n",
              "21       0.028573       0.00177         0.650409        0.034161   \n",
              "\n",
              "   param_knn__metric param_knn__n_neighbors param_knn__weights  \\\n",
              "21         manhattan                      5           distance   \n",
              "\n",
              "                                               params  split0_test_score  \\\n",
              "21  {'knn__metric': 'manhattan', 'knn__n_neighbors...              0.375   \n",
              "\n",
              "    split1_test_score  split2_test_score  split3_test_score  \\\n",
              "21           0.370253           0.373418           0.356013   \n",
              "\n",
              "    split4_test_score  mean_test_score  std_test_score  rank_test_score  \n",
              "21           0.357595         0.366456        0.008043                1  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-ce5cf955-f855-4877-9f2d-a6aaa6f0bf54\" class=\"colab-df-container\">\n",
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              "<style scoped>\n",
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              "        vertical-align: middle;\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>mean_fit_time</th>\n",
              "      <th>std_fit_time</th>\n",
              "      <th>mean_score_time</th>\n",
              "      <th>std_score_time</th>\n",
              "      <th>param_knn__metric</th>\n",
              "      <th>param_knn__n_neighbors</th>\n",
              "      <th>param_knn__weights</th>\n",
              "      <th>params</th>\n",
              "      <th>split0_test_score</th>\n",
              "      <th>split1_test_score</th>\n",
              "      <th>split2_test_score</th>\n",
              "      <th>split3_test_score</th>\n",
              "      <th>split4_test_score</th>\n",
              "      <th>mean_test_score</th>\n",
              "      <th>std_test_score</th>\n",
              "      <th>rank_test_score</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>0.028573</td>\n",
              "      <td>0.00177</td>\n",
              "      <td>0.650409</td>\n",
              "      <td>0.034161</td>\n",
              "      <td>manhattan</td>\n",
              "      <td>5</td>\n",
              "      <td>distance</td>\n",
              "      <td>{'knn__metric': 'manhattan', 'knn__n_neighbors...</td>\n",
              "      <td>0.375</td>\n",
              "      <td>0.370253</td>\n",
              "      <td>0.373418</td>\n",
              "      <td>0.356013</td>\n",
              "      <td>0.357595</td>\n",
              "      <td>0.366456</td>\n",
              "      <td>0.008043</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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              "\n",
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              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ce5cf955-f855-4877-9f2d-a6aaa6f0bf54')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-ce5cf955-f855-4877-9f2d-a6aaa6f0bf54 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-ce5cf955-f855-4877-9f2d-a6aaa6f0bf54');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
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              "\n",
              "\n",
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              "type": "dataframe",
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            }
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "plt.figure(figsize=(10, 6))\n",
        "plt.scatter(\n",
        "    df_cv_results_knn['param_knn__n_neighbors'],\n",
        "    df_cv_results_knn['mean_test_score'],\n",
        "    )\n",
        "\n",
        "plt.title('param_knn__n_neighbors vs. mean_test_score')\n",
        "plt.xlabel('param_knn__n_neighbors')\n",
        "plt.ylabel('recall')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 450
        },
        "id": "uI1hcK6ooBiZ",
        "outputId": "d5ddf997-4a2b-41bb-e09e-a6f1f758de9a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### KNN model"
      ],
      "metadata": {
        "id": "YwcqFSysoGSh"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "{'knn__metric': 'manhattan', 'knn__n_neighbors': 5, 'knn__weights': 'distance'}"
      ],
      "metadata": {
        "id": "00RCpA8UoO_L"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "knn_model = KNeighborsClassifier(metric='manhattan', n_neighbors=5, weights='distance')"
      ],
      "metadata": {
        "id": "ERQwOcaAoHoU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train = StandardScaler().fit_transform(X_train)\n",
        "X_test = StandardScaler().fit_transform(X_test)"
      ],
      "metadata": {
        "id": "SxdZHse8phlx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "knn_model.fit(X_train, y_train)\n",
        "y_pred = knn_model.predict(X_test)"
      ],
      "metadata": {
        "id": "VfE0kfaooj-R"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(f'KNN, recall score: {recall_score(y_test, y_pred):.3f}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PK2NHONop2ck",
        "outputId": "5d378070-a06b-4540-b682-57e4c6b947a6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "KNN, recall score: 0.371\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Nested CV - Validação cruzada aninhada"
      ],
      "metadata": {
        "id": "VFWVbXbwmzon"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##DecisionTreeClassifier"
      ],
      "metadata": {
        "id": "svbO17H6oI5j"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "inner_cv = StratifiedKFold(shuffle=True, random_state=rs)\n",
        "outer_cv = StratifiedKFold(n_splits=3, shuffle=True, random_state=rs)"
      ],
      "metadata": {
        "id": "pJqfJHOkoOmF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import cross_val_score\n",
        "\n",
        "dt_nested_gs = GridSearchCV(estimator=DecisionTreeClassifier(random_state=rs),\n",
        "             param_grid=param_grid_dt,\n",
        "             scoring=\"recall\",\n",
        "             n_jobs=-1,\n",
        "             cv=inner_cv\n",
        "             )\n",
        "\n",
        "dt_nested_scores = cross_val_score(dt_nested_gs, X_train, y_train, cv=outer_cv)"
      ],
      "metadata": {
        "id": "b0Lw871RoklH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(f'Resultado de cada iteração do cv externo: {dt_nested_scores}')\n",
        "print(f'Média: {dt_nested_scores.mean()}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JQYU3qCFqLyv",
        "outputId": "e4984bca-122d-403b-b6c9-91008193feca"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Resultado de cada iteração do cv externo: [0.23646724 0.24762808 0.20512821]\n",
            "Média: 0.2297411750289676\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## LogisticRegression"
      ],
      "metadata": {
        "id": "IEabm0Wjsjua"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "inner_cv = StratifiedKFold(n_splits=5, shuffle=True, random_state=42)\n",
        "outer_cv = StratifiedKFold(n_splits=3, shuffle=True, random_state=42)\n",
        "\n",
        "max_iter = np.linspace(100, 300, 5, dtype=int)\n",
        "c = [0.001, 0.01, 0.1, 1, 10]\n",
        "\n",
        "param_grid_lr = [\n",
        "    {'logisticregression__solver' : ['newton-cg', 'lbfgs'],\n",
        "      'logisticregression__penalty' : ['l2'],\n",
        "      'logisticregression__max_iter' : max_iter,\n",
        "       'logisticregression__C' : c},\n",
        "    {'logisticregression__solver' : ['liblinear'],\n",
        "      'logisticregression__penalty' : ['l1', 'l2'],\n",
        "      'logisticregression__max_iter' : max_iter,\n",
        "      'logisticregression__C' : c},\n",
        "]"
      ],
      "metadata": {
        "id": "T5SqjUQRqXf9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "lr_nested_gs = GridSearchCV(estimator=make_pipeline(StandardScaler(), LogisticRegression()),\n",
        "                            param_grid=param_grid_lr,\n",
        "                            scoring=\"recall\",\n",
        "                            n_jobs=-1,\n",
        "                            cv=inner_cv\n",
        "                            )\n",
        "\n",
        "lr_nested_scores=cross_val_score(lr_nested_gs, X_train, y_train, cv=outer_cv)"
      ],
      "metadata": {
        "id": "e03YJsFutZik"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print('Resultado de cada iteração do cv externo: ', lr_nested_scores)\n",
        "print('Média: ', lr_nested_scores.mean())\n",
        "\n",
        "lr_nested_gs.fit(X_train, y_train)\n",
        "best_params = lr_nested_gs.best_params_\n",
        "\n",
        "print('Melhores hiperparâmetros: ', best_params)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NO9V2ovCugf-",
        "outputId": "f7d6f64a-be86-4f28-eeca-d1ae1fe6b0f7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Resultado de cada iteração do cv externo:  [0.24691358 0.27703985 0.2374169 ]\n",
            "Média:  0.2537901108426093\n",
            "Melhores hiperparâmetros:  {'logisticregression__C': 0.001, 'logisticregression__max_iter': 100, 'logisticregression__penalty': 'l2', 'logisticregression__solver': 'liblinear'}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Randomized Search CV - Busca em grade aleatória"
      ],
      "metadata": {
        "id": "7OYaKFoEwxxr"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## DecisionTreeClassifier"
      ],
      "metadata": {
        "id": "qWfH3EsjxMbW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import RandomizedSearchCV"
      ],
      "metadata": {
        "id": "KtojcSX0wz6_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "rs_dt = RandomizedSearchCV(estimator=DecisionTreeClassifier(random_state=rs),\n",
        "                   param_distributions=param_grid_dt,\n",
        "                   n_iter=100,\n",
        "                   scoring=\"recall\",\n",
        "                   n_jobs=-1,\n",
        "                   cv=inner_cv,\n",
        "                   random_state=rs\n",
        "                   )\n",
        "\n",
        "rs_dt_nested_score = cross_val_score(rs_dt, X_train, y_train, cv=outer_cv)"
      ],
      "metadata": {
        "id": "cBWAZmqUxRu4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print('Resultado de cada iteração do cv externo: ', rs_dt_nested_score)\n",
        "print('Média: ', rs_dt_nested_score.mean())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9MStLxjuyVVH",
        "outputId": "7ae9cf62-cfa3-4dcc-9a3b-fd5bc750c744"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Resultado de cada iteração do cv externo:  [0.23646724 0.18975332 0.21272555]\n",
            "Média:  0.21298203440307595\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "rs_dt.fit(X_train, y_train)\n",
        "rs_dt.best_params_"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0P1xw3xkyzoc",
        "outputId": "188a8210-8060-4d1d-cbe1-3922e93674f6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'splitter': 'best',\n",
              " 'min_samples_split': 5,\n",
              " 'min_samples_leaf': 15,\n",
              " 'max_features': 'sqrt',\n",
              " 'max_depth': 12,\n",
              " 'criterion': 'gini'}"
            ]
          },
          "metadata": {},
          "execution_count": 76
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Logistic Regression"
      ],
      "metadata": {
        "id": "FAVyTjut2DjR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "rs_lr = RandomizedSearchCV(estimator=make_pipeline(StandardScaler(), LogisticRegression()),\n",
        "                   param_distributions=param_grid_lr,\n",
        "                   n_iter=50,\n",
        "                   scoring=\"recall\",\n",
        "                   n_jobs=-1,\n",
        "                   cv=inner_cv,\n",
        "                   random_state=rs\n",
        "                   )\n",
        "\n",
        "rs_lr_nested_score = cross_val_score(rs_lr, X_train, y_train, cv=outer_cv)\n",
        "\n",
        "print('Resultado de cada iteração do cv externo: ', rs_lr_nested_score)\n",
        "print('Média: ', rs_lr_nested_score.mean())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uVP-sqxK2GEG",
        "outputId": "a2565459-eb1c-4292-e7cc-79e3dc792f16"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Resultado de cada iteração do cv externo:  [0.24691358 0.27703985 0.2374169 ]\n",
            "Média:  0.2537901108426093\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "rs_lr.fit(X_train,y_train)\n",
        "rs_lr.best_params_"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dJYB1lR02-rd",
        "outputId": "47bb79ff-edf0-4949-91d7-cc54cf44866a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'logisticregression__solver': 'liblinear',\n",
              " 'logisticregression__penalty': 'l2',\n",
              " 'logisticregression__max_iter': 150,\n",
              " 'logisticregression__C': 0.001}"
            ]
          },
          "metadata": {},
          "execution_count": 78
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Otimização Bayesiana"
      ],
      "metadata": {
        "id": "pDTdQ5Bx51lR"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## DecisionTreeClassifier"
      ],
      "metadata": {
        "id": "9hqB-g447Y8Q"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install scikit-optimize"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "t4524CjH53tY",
        "outputId": "a4a90098-aade-4efb-cbae-3e9e27aa89da"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting scikit-optimize\n",
            "  Downloading scikit_optimize-0.10.2-py2.py3-none-any.whl.metadata (9.7 kB)\n",
            "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.10/dist-packages (from scikit-optimize) (1.4.2)\n",
            "Collecting pyaml>=16.9 (from scikit-optimize)\n",
            "  Downloading pyaml-24.7.0-py3-none-any.whl.metadata (11 kB)\n",
            "Requirement already satisfied: numpy>=1.20.3 in /usr/local/lib/python3.10/dist-packages (from scikit-optimize) (1.26.4)\n",
            "Requirement already satisfied: scipy>=1.1.0 in /usr/local/lib/python3.10/dist-packages (from scikit-optimize) (1.13.1)\n",
            "Requirement already satisfied: scikit-learn>=1.0.0 in /usr/local/lib/python3.10/dist-packages (from scikit-optimize) (1.3.2)\n",
            "Requirement already satisfied: packaging>=21.3 in /usr/local/lib/python3.10/dist-packages (from scikit-optimize) (24.1)\n",
            "Requirement already satisfied: PyYAML in /usr/local/lib/python3.10/dist-packages (from pyaml>=16.9->scikit-optimize) (6.0.2)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn>=1.0.0->scikit-optimize) (3.5.0)\n",
            "Downloading scikit_optimize-0.10.2-py2.py3-none-any.whl (107 kB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m107.8/107.8 kB\u001b[0m \u001b[31m4.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading pyaml-24.7.0-py3-none-any.whl (24 kB)\n",
            "Installing collected packages: pyaml, scikit-optimize\n",
            "Successfully installed pyaml-24.7.0 scikit-optimize-0.10.2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from skopt.space import Real, Integer, Categorical\n",
        "\n",
        "space_dt = {\n",
        "    'criterion': Categorical(['gini', 'entropy']),\n",
        "    'max_depth': Integer(6, 12),\n",
        "    'min_samples_split': Integer(5, 20),\n",
        "    'min_samples_leaf': Integer(5, 20),\n",
        "    'max_features': Categorical(['sqrt', 'log2']),\n",
        "    'splitter': Categorical(['best', 'random'])\n",
        "}"
      ],
      "metadata": {
        "id": "AoTa0Uv37wix"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from skopt import BayesSearchCV"
      ],
      "metadata": {
        "id": "roizXO2W8ny3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "opt_dt = BayesSearchCV(estimator=DecisionTreeClassifier(random_state=rs),\n",
        "              search_spaces=space_dt,\n",
        "              n_iter=50,\n",
        "              scoring=\"recall\",\n",
        "              n_jobs=-1,\n",
        "              cv=inner_cv,\n",
        "              random_state=rs\n",
        "              )\n",
        "\n",
        "bs_dt_nested_score = cross_val_score(opt_dt, X_train,y_train, cv=outer_cv)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "X9onZGRA8qz6",
        "outputId": "9af8923f-b025-468a-9f5f-530a8a5d793b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/skopt/optimizer/optimizer.py:517: UserWarning: The objective has been evaluated at point ['entropy', 11, 'log2', 20, 5, 'best'] before, using random point ['entropy', 7, 'log2', 14, 15, 'random']\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/skopt/optimizer/optimizer.py:517: UserWarning: The objective has been evaluated at point ['gini', 11, 'log2', 20, 5, 'best'] before, using random point ['entropy', 11, 'log2', 16, 16, 'best']\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/skopt/optimizer/optimizer.py:517: UserWarning: The objective has been evaluated at point ['entropy', 12, 'log2', 20, 5, 'best'] before, using random point ['gini', 6, 'log2', 8, 15, 'best']\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/skopt/optimizer/optimizer.py:517: UserWarning: The objective has been evaluated at point ['gini', 12, 'log2', 20, 5, 'best'] before, using random point ['entropy', 11, 'log2', 16, 17, 'best']\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/skopt/optimizer/optimizer.py:517: UserWarning: The objective has been evaluated at point ['gini', 12, 'log2', 5, 20, 'best'] before, using random point ['gini', 10, 'log2', 17, 18, 'best']\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/skopt/optimizer/optimizer.py:517: UserWarning: The objective has been evaluated at point ['gini', 12, 'log2', 5, 6, 'best'] before, using random point ['gini', 11, 'log2', 12, 13, 'best']\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/skopt/optimizer/optimizer.py:517: UserWarning: The objective has been evaluated at point ['entropy', 12, 'sqrt', 5, 20, 'best'] before, using random point ['entropy', 10, 'sqrt', 11, 6, 'best']\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/skopt/optimizer/optimizer.py:517: UserWarning: The objective has been evaluated at point ['entropy', 12, 'sqrt', 5, 19, 'best'] before, using random point ['gini', 12, 'log2', 9, 5, 'best']\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/skopt/optimizer/optimizer.py:517: UserWarning: The objective has been evaluated at point ['gini', 12, 'sqrt', 20, 20, 'best'] before, using random point ['entropy', 7, 'log2', 14, 15, 'random']\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print('Resultado de cada iteração do cv externo: ', bs_dt_nested_score)\n",
        "print('Média: ', bs_dt_nested_score.mean())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eeb8szKX-_f-",
        "outputId": "9b02404a-6082-4171-9d20-bad1b1e620fa"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Resultado de cada iteração do cv externo:  [0.23931624 0.31973435 0.20512821]\n",
            "Média:  0.25472626326516273\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "opt_dt.fit(X_train, y_train)\n",
        "opt_dt.best_params_"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qSmwOyENAC6F",
        "outputId": "f7c91afc-24f5-4cbd-9c6a-df034cff7aa9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/skopt/optimizer/optimizer.py:517: UserWarning: The objective has been evaluated at point ['gini', 12, 'sqrt', 20, 20, 'best'] before, using random point ['entropy', 11, 'log2', 16, 16, 'best']\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/skopt/optimizer/optimizer.py:517: UserWarning: The objective has been evaluated at point ['gini', 12, 'sqrt', 20, 5, 'best'] before, using random point ['entropy', 7, 'log2', 12, 9, 'best']\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "OrderedDict([('criterion', 'gini'),\n",
              "             ('max_depth', 12),\n",
              "             ('max_features', 'log2'),\n",
              "             ('min_samples_leaf', 15),\n",
              "             ('min_samples_split', 5),\n",
              "             ('splitter', 'best')])"
            ]
          },
          "metadata": {},
          "execution_count": 86
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Logistic Regression"
      ],
      "metadata": {
        "id": "jkRsVDbkBdpM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "max_iter = Integer(100, 300)\n",
        "c = Categorical([0.001, 0.01, 0.1, 1, 10])\n",
        "\n",
        "space_lr = [\n",
        "    {\n",
        "        'logisticregression__solver': Categorical(['newton-cg', 'lbfgs']),\n",
        "        'logisticregression__penalty': Categorical(['l2']),\n",
        "        'logisticregression__max_iter': max_iter,\n",
        "        'logisticregression__C': c\n",
        "    },\n",
        "    {\n",
        "        'logisticregression__solver': Categorical(['liblinear']),\n",
        "        'logisticregression__penalty': Categorical(['l1', 'l2']),\n",
        "        'logisticregression__max_iter': max_iter,\n",
        "        'logisticregression__C': c\n",
        "    },\n",
        "]"
      ],
      "metadata": {
        "id": "_3CS9PA-Bkrl"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "opt_lr = BayesSearchCV(estimator=make_pipeline(StandardScaler(), LogisticRegression()),\n",
        "            search_spaces = space_lr,\n",
        "            n_iter=50,\n",
        "            scoring='recall',\n",
        "            n_jobs=-1,\n",
        "            cv=inner_cv,\n",
        "            random_state=rs)"
      ],
      "metadata": {
        "id": "8hRPvDerBfaQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "bs_lr_nested_scores=cross_val_score(opt_lr, X_train, y_train, cv=outer_cv)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mLIVsXOBDdTl",
        "outputId": "bb0ac7db-53dc-4868-eccb-162a40d165d1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/skopt/optimizer/optimizer.py:517: UserWarning: The objective has been evaluated at point [0.1, 148, 'l2', 'newton-cg'] before, using random point [0.01, 220, 'l2', 'lbfgs']\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/skopt/optimizer/optimizer.py:517: UserWarning: The objective has been evaluated at point [0.001, 300, 'l2', 'liblinear'] before, using random point [0.001, 224, 'l1', 'liblinear']\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/skopt/optimizer/optimizer.py:517: UserWarning: The objective has been evaluated at point [0.1, 148, 'l2', 'newton-cg'] before, using random point [0.01, 220, 'l2', 'lbfgs']\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/skopt/optimizer/optimizer.py:517: UserWarning: The objective has been evaluated at point [0.1, 148, 'l2', 'newton-cg'] before, using random point [0.01, 220, 'l2', 'lbfgs']\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/skopt/optimizer/optimizer.py:517: UserWarning: The objective has been evaluated at point [0.001, 100, 'l2', 'liblinear'] before, using random point [0.001, 224, 'l1', 'liblinear']\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print('Resultado de cada iteração do cv externo: ', bs_lr_nested_scores)\n",
        "print('Média: ', bs_lr_nested_scores.mean())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "70t2HpLcInAz",
        "outputId": "40473154-41c5-4fa6-b162-912a373d43b1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Resultado de cada iteração do cv externo:  [0.24691358 0.27703985 0.2374169 ]\n",
            "Média:  0.2537901108426093\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "opt_lr.fit(X_train, y_train)\n",
        "opt_lr.best_params_"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wISPxL-HI04L",
        "outputId": "f5a0c695-07f5-48b2-f1ec-d5826c5e13e6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/skopt/optimizer/optimizer.py:517: UserWarning: The objective has been evaluated at point [0.1, 148, 'l2', 'newton-cg'] before, using random point [0.01, 220, 'l2', 'lbfgs']\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/skopt/optimizer/optimizer.py:517: UserWarning: The objective has been evaluated at point [0.001, 300, 'l2', 'liblinear'] before, using random point [0.001, 289, 'l2', 'liblinear']\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/skopt/optimizer/optimizer.py:517: UserWarning: The objective has been evaluated at point [0.001, 300, 'l2', 'liblinear'] before, using random point [0.001, 224, 'l1', 'liblinear']\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "OrderedDict([('logisticregression__C', 0.001),\n",
              "             ('logisticregression__max_iter', 150),\n",
              "             ('logisticregression__penalty', 'l2'),\n",
              "             ('logisticregression__solver', 'liblinear')])"
            ]
          },
          "metadata": {},
          "execution_count": 104
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = opt_lr.predict(X_test)\n",
        "y_pred"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "s2E1s60sJ5X8",
        "outputId": "acb0aedc-5d8d-4600-9f68-9153aa75d1a3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 1, 0, ..., 0, 0, 0])"
            ]
          },
          "metadata": {},
          "execution_count": 107
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## KNN"
      ],
      "metadata": {
        "id": "cjK_pUBZLSRr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "param_space_knn = {\n",
        "    'knn__n_neighbors': (5, 25),\n",
        "    'knn__weights': ['uniform', 'distance'],\n",
        "    'knn__metric': ['euclidean', 'manhattan']\n",
        "}"
      ],
      "metadata": {
        "id": "d4-02gRbLTeF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "bs_pipe_knn = Pipeline([\n",
        "    ('scaler', StandardScaler()),\n",
        "    ('knn', KNeighborsClassifier())\n",
        "])\n",
        "\n",
        "\n",
        "cv = StratifiedKFold(n_splits=5, shuffle=True, random_state=42)\n",
        "\n",
        "knn_bayes_search = BayesSearchCV(estimator = bs_pipe_knn,\n",
        "                               search_spaces = param_space_knn,\n",
        "                               scoring = \"recall\",\n",
        "                               n_jobs = -1,\n",
        "                               cv = cv)\n",
        "\n",
        "knn_bayes_search.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 755
        },
        "id": "iIj2YG_kLY9F",
        "outputId": "2fc8ab39-a520-4b6f-fea6-c5dc49bbb79b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/skopt/optimizer/optimizer.py:517: UserWarning: The objective has been evaluated at point ['manhattan', 5, 'distance'] before, using random point ['euclidean', 21, 'uniform']\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/skopt/optimizer/optimizer.py:517: UserWarning: The objective has been evaluated at point ['manhattan', 5, 'distance'] before, using random point ['manhattan', 6, 'uniform']\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/skopt/optimizer/optimizer.py:517: UserWarning: The objective has been evaluated at point ['manhattan', 5, 'distance'] before, using random point ['manhattan', 9, 'uniform']\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/skopt/optimizer/optimizer.py:517: UserWarning: The objective has been evaluated at point ['manhattan', 5, 'distance'] before, using random point ['manhattan', 8, 'uniform']\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/skopt/optimizer/optimizer.py:517: UserWarning: The objective has been evaluated at point ['manhattan', 5, 'distance'] before, using random point ['manhattan', 14, 'uniform']\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/skopt/optimizer/optimizer.py:517: UserWarning: The objective has been evaluated at point ['manhattan', 5, 'distance'] before, using random point ['euclidean', 20, 'distance']\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/skopt/optimizer/optimizer.py:517: UserWarning: The objective has been evaluated at point ['manhattan', 5, 'distance'] before, using random point ['manhattan', 10, 'distance']\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/skopt/optimizer/optimizer.py:517: UserWarning: The objective has been evaluated at point ['manhattan', 5, 'distance'] before, using random point ['manhattan', 17, 'distance']\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/skopt/optimizer/optimizer.py:517: UserWarning: The objective has been evaluated at point ['manhattan', 5, 'distance'] before, using random point ['manhattan', 14, 'uniform']\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/skopt/optimizer/optimizer.py:517: UserWarning: The objective has been evaluated at point ['manhattan', 5, 'distance'] before, using random point ['euclidean', 22, 'uniform']\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/skopt/optimizer/optimizer.py:517: UserWarning: The objective has been evaluated at point ['manhattan', 5, 'distance'] before, using random point ['euclidean', 23, 'distance']\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/skopt/optimizer/optimizer.py:517: UserWarning: The objective has been evaluated at point ['manhattan', 5, 'distance'] before, using random point ['manhattan', 22, 'distance']\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/skopt/optimizer/optimizer.py:517: UserWarning: The objective has been evaluated at point ['manhattan', 5, 'distance'] before, using random point ['euclidean', 22, 'uniform']\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/skopt/optimizer/optimizer.py:517: UserWarning: The objective has been evaluated at point ['manhattan', 5, 'distance'] before, using random point ['manhattan', 13, 'distance']\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/skopt/optimizer/optimizer.py:517: UserWarning: The objective has been evaluated at point ['manhattan', 5, 'distance'] before, using random point ['euclidean', 8, 'distance']\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/skopt/optimizer/optimizer.py:517: UserWarning: The objective has been evaluated at point ['manhattan', 5, 'distance'] before, using random point ['manhattan', 22, 'uniform']\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "BayesSearchCV(cv=StratifiedKFold(n_splits=5, random_state=42, shuffle=True),\n",
              "              estimator=Pipeline(steps=[('scaler', StandardScaler()),\n",
              "                                        ('knn', KNeighborsClassifier())]),\n",
              "              n_jobs=-1, scoring='recall',\n",
              "              search_spaces={'knn__metric': ['euclidean', 'manhattan'],\n",
              "                             'knn__n_neighbors': (5, 25),\n",
              "                             'knn__weights': ['uniform', 'distance']})"
            ],
            "text/html": [
              "<style>#sk-container-id-7 {color: black;}#sk-container-id-7 pre{padding: 0;}#sk-container-id-7 div.sk-toggleable {background-color: white;}#sk-container-id-7 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-7 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-7 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-7 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-7 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-7 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-7 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-7 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-7 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 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-7 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-7 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-7 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-7 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 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-7 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-7 div.sk-item {position: relative;z-index: 1;}#sk-container-id-7 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-7 div.sk-item::before, #sk-container-id-7 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-7 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-7 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-7 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-7 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-7 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-7 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-7 div.sk-label-container {text-align: center;}#sk-container-id-7 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-7 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-7\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>BayesSearchCV(cv=StratifiedKFold(n_splits=5, random_state=42, shuffle=True),\n",
              "              estimator=Pipeline(steps=[(&#x27;scaler&#x27;, StandardScaler()),\n",
              "                                        (&#x27;knn&#x27;, KNeighborsClassifier())]),\n",
              "              n_jobs=-1, scoring=&#x27;recall&#x27;,\n",
              "              search_spaces={&#x27;knn__metric&#x27;: [&#x27;euclidean&#x27;, &#x27;manhattan&#x27;],\n",
              "                             &#x27;knn__n_neighbors&#x27;: (5, 25),\n",
              "                             &#x27;knn__weights&#x27;: [&#x27;uniform&#x27;, &#x27;distance&#x27;]})</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 sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-19\" type=\"checkbox\" ><label for=\"sk-estimator-id-19\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">BayesSearchCV</label><div class=\"sk-toggleable__content\"><pre>BayesSearchCV(cv=StratifiedKFold(n_splits=5, random_state=42, shuffle=True),\n",
              "              estimator=Pipeline(steps=[(&#x27;scaler&#x27;, StandardScaler()),\n",
              "                                        (&#x27;knn&#x27;, KNeighborsClassifier())]),\n",
              "              n_jobs=-1, scoring=&#x27;recall&#x27;,\n",
              "              search_spaces={&#x27;knn__metric&#x27;: [&#x27;euclidean&#x27;, &#x27;manhattan&#x27;],\n",
              "                             &#x27;knn__n_neighbors&#x27;: (5, 25),\n",
              "                             &#x27;knn__weights&#x27;: [&#x27;uniform&#x27;, &#x27;distance&#x27;]})</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-20\" type=\"checkbox\" ><label for=\"sk-estimator-id-20\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;scaler&#x27;, StandardScaler()), (&#x27;knn&#x27;, KNeighborsClassifier())])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-21\" type=\"checkbox\" ><label for=\"sk-estimator-id-21\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-22\" type=\"checkbox\" ><label for=\"sk-estimator-id-22\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier()</pre></div></div></div></div></div></div></div></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 118
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "bs_knn_nested_scores=cross_val_score(knn_bayes_search, X_train, y_train, cv=outer_cv)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-QhrH-HmN2jb",
        "outputId": "a58f805a-c8e8-4da9-fd4e-c5bc94a0e6e4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/skopt/optimizer/optimizer.py:517: UserWarning: The objective has been evaluated at point ['manhattan', 5, 'distance'] before, using random point ['euclidean', 19, 'distance']\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/skopt/optimizer/optimizer.py:517: UserWarning: The objective has been evaluated at point ['manhattan', 5, 'distance'] before, using random point ['manhattan', 20, 'uniform']\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/skopt/optimizer/optimizer.py:517: UserWarning: The objective has been evaluated at point ['manhattan', 5, 'distance'] before, using random point ['manhattan', 17, 'uniform']\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/skopt/optimizer/optimizer.py:517: UserWarning: The objective has been evaluated at point ['manhattan', 5, 'distance'] before, using random point ['euclidean', 25, 'distance']\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/skopt/optimizer/optimizer.py:517: UserWarning: The objective has been evaluated at point ['manhattan', 5, 'distance'] before, using random point ['manhattan', 24, 'distance']\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/skopt/optimizer/optimizer.py:517: UserWarning: The objective has been evaluated at point ['manhattan', 5, 'distance'] before, using random point ['manhattan', 6, 'distance']\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/skopt/optimizer/optimizer.py:517: UserWarning: The objective has been evaluated at point ['manhattan', 5, 'distance'] before, using random point ['manhattan', 12, 'uniform']\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/skopt/optimizer/optimizer.py:517: UserWarning: The objective has been evaluated at point ['manhattan', 5, 'distance'] before, using random point ['euclidean', 12, 'distance']\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/skopt/optimizer/optimizer.py:517: UserWarning: The objective has been evaluated at point ['manhattan', 5, 'distance'] before, using random point ['manhattan', 16, 'distance']\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/skopt/optimizer/optimizer.py:517: UserWarning: The objective has been evaluated at point ['manhattan', 5, 'distance'] before, using random point ['manhattan', 5, 'uniform']\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/skopt/optimizer/optimizer.py:517: UserWarning: The objective has been evaluated at point ['manhattan', 5, 'distance'] before, using random point ['euclidean', 9, 'uniform']\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/skopt/optimizer/optimizer.py:517: UserWarning: The objective has been evaluated at point ['manhattan', 5, 'distance'] before, using random point ['euclidean', 21, 'uniform']\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/skopt/optimizer/optimizer.py:517: UserWarning: The objective has been evaluated at point ['manhattan', 5, 'distance'] before, using random point ['manhattan', 22, 'distance']\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/skopt/optimizer/optimizer.py:517: UserWarning: The objective has been evaluated at point ['manhattan', 5, 'distance'] before, using random point ['manhattan', 19, 'distance']\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/skopt/optimizer/optimizer.py:517: UserWarning: The objective has been evaluated at point ['manhattan', 5, 'distance'] before, using random point ['manhattan', 18, 'distance']\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/skopt/optimizer/optimizer.py:517: UserWarning: The objective has been evaluated at point ['manhattan', 5, 'distance'] before, using random point ['euclidean', 5, 'uniform']\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/skopt/optimizer/optimizer.py:517: UserWarning: The objective has been evaluated at point ['manhattan', 5, 'distance'] before, using random point ['euclidean', 9, 'uniform']\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/skopt/optimizer/optimizer.py:517: UserWarning: The objective has been evaluated at point ['manhattan', 5, 'distance'] before, using random point ['euclidean', 20, 'uniform']\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/skopt/optimizer/optimizer.py:517: UserWarning: The objective has been evaluated at point ['manhattan', 5, 'distance'] before, using random point ['manhattan', 17, 'distance']\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/skopt/optimizer/optimizer.py:517: UserWarning: The objective has been evaluated at point ['manhattan', 5, 'distance'] before, using random point ['manhattan', 24, 'uniform']\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/skopt/optimizer/optimizer.py:517: UserWarning: The objective has been evaluated at point ['manhattan', 5, 'distance'] before, using random point ['euclidean', 14, 'uniform']\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/skopt/optimizer/optimizer.py:517: UserWarning: The objective has been evaluated at point ['manhattan', 5, 'distance'] before, using random point ['manhattan', 12, 'uniform']\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/skopt/optimizer/optimizer.py:517: UserWarning: The objective has been evaluated at point ['manhattan', 5, 'distance'] before, using random point ['euclidean', 11, 'uniform']\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/skopt/optimizer/optimizer.py:517: UserWarning: The objective has been evaluated at point ['manhattan', 5, 'distance'] before, using random point ['manhattan', 15, 'distance']\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/skopt/optimizer/optimizer.py:517: UserWarning: The objective has been evaluated at point ['manhattan', 5, 'distance'] before, using random point ['euclidean', 17, 'distance']\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/skopt/optimizer/optimizer.py:517: UserWarning: The objective has been evaluated at point ['manhattan', 5, 'distance'] before, using random point ['euclidean', 12, 'uniform']\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/skopt/optimizer/optimizer.py:517: UserWarning: The objective has been evaluated at point ['manhattan', 5, 'distance'] before, using random point ['euclidean', 24, 'uniform']\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/skopt/optimizer/optimizer.py:517: UserWarning: The objective has been evaluated at point ['manhattan', 5, 'distance'] before, using random point ['euclidean', 15, 'distance']\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/skopt/optimizer/optimizer.py:517: UserWarning: The objective has been evaluated at point ['manhattan', 6, 'distance'] before, using random point ['manhattan', 13, 'distance']\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/skopt/optimizer/optimizer.py:517: UserWarning: The objective has been evaluated at point ['manhattan', 5, 'distance'] before, using random point ['euclidean', 11, 'distance']\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/skopt/optimizer/optimizer.py:517: UserWarning: The objective has been evaluated at point ['manhattan', 5, 'distance'] before, using random point ['euclidean', 17, 'distance']\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/skopt/optimizer/optimizer.py:517: UserWarning: The objective has been evaluated at point ['manhattan', 5, 'distance'] before, using random point ['manhattan', 19, 'distance']\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/skopt/optimizer/optimizer.py:517: UserWarning: The objective has been evaluated at point ['manhattan', 5, 'distance'] before, using random point ['manhattan', 12, 'uniform']\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/skopt/optimizer/optimizer.py:517: UserWarning: The objective has been evaluated at point ['manhattan', 5, 'distance'] before, using random point ['euclidean', 7, 'uniform']\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/skopt/optimizer/optimizer.py:517: UserWarning: The objective has been evaluated at point ['manhattan', 5, 'distance'] before, using random point ['manhattan', 15, 'distance']\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/skopt/optimizer/optimizer.py:517: UserWarning: The objective has been evaluated at point ['manhattan', 5, 'distance'] before, using random point ['euclidean', 14, 'uniform']\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/skopt/optimizer/optimizer.py:517: UserWarning: The objective has been evaluated at point ['manhattan', 5, 'distance'] before, using random point ['euclidean', 22, 'distance']\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/skopt/optimizer/optimizer.py:517: UserWarning: The objective has been evaluated at point ['manhattan', 5, 'distance'] before, using random point ['manhattan', 18, 'uniform']\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/skopt/optimizer/optimizer.py:517: UserWarning: The objective has been evaluated at point ['manhattan', 5, 'distance'] before, using random point ['euclidean', 10, 'distance']\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print('Resultado de cada iteração do cv externo: ', bs_knn_nested_scores)\n",
        "print('Média: ', bs_knn_nested_scores.mean())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "V26DPVCKOCvs",
        "outputId": "ef2c0376-07f8-4374-c116-071e6e31634f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Resultado de cada iteração do cv externo:  [0.3637227  0.38519924 0.35327635]\n",
            "Média:  0.3673994304397003\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "knn_bayes_search.best_params_"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Z9QjiyHUOV-F",
        "outputId": "7a09a69a-d371-4a64-8927-69d99da87cd5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "OrderedDict([('knn__metric', 'manhattan'),\n",
              "             ('knn__n_neighbors', 6),\n",
              "             ('knn__weights', 'distance')])"
            ]
          },
          "metadata": {},
          "execution_count": 121
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = knn_bayes_search.predict(X_test)\n",
        "y_pred"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rSxt7jktO4ZE",
        "outputId": "bc4afd0a-97b1-4ad9-aa10-fc5333d071b5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 0, 0, ..., 1, 0, 0])"
            ]
          },
          "metadata": {},
          "execution_count": 122
        }
      ]
    }
  ]
}