Model_Reducing_Commercial_Aviation_Fatalities.ipynb

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    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/joaopamaral/tcc_puc_cdbd/blob/master/Model_Reducing_Commercial_Aviation_Fatalities.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "A-7YrEb1wzRQ",
        "colab_type": "text"
      },
      "source": [
        "# Treinando Modelo - Reduzindo Fatalidades na Aviação Comercial"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AIrDALMSFqXv",
        "colab_type": "text"
      },
      "source": [
        "## Importando bibliotecas utilizadas"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hlcYeFz-Sgvf",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import sys, os\n",
        "from sklearn.model_selection import train_test_split\n",
        "from tqdm.auto import tqdm\n",
        "\n",
        "IN_COLAB = 'google.colab' in sys.modules\n",
        "\n",
        "%matplotlib inline\n",
        "plt.style.use('ggplot')\n",
        "\n",
        "import warnings\n",
        "warnings.simplefilter(action='ignore', category=FutureWarning)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_hM16Erib2Us",
        "colab_type": "code",
        "outputId": "c88eeda0-6aaa-48dc-cfd6-82cdc6e41acb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 122
        }
      },
      "source": [
        "# Montando google drive caso esteja executando no google colab\n",
        "if IN_COLAB:\n",
        "    from google.colab import drive\n",
        "    drive.mount('/gdrive')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3aietf%3awg%3aoauth%3a2.0%3aoob&response_type=code&scope=email%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdocs.test%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive.photos.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fpeopleapi.readonly\n",
            "\n",
            "Enter your authorization code:\n",
            "··········\n",
            "Mounted at /gdrive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ofImb1TUWmGc",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "gdrive_kaggle_dir = '/gdrive/My Drive/Kaggle'\n",
        "competition_name = 'reducing-commercial-aviation-fatalities'\n",
        "api_gdrive_credential_location = f'{gdrive_kaggle_dir}/kaggle.json'\n",
        "data_gdrive_dir = f'{gdrive_kaggle_dir}/{competition_name}'\n",
        "tcc_dir = '/gdrive/My Drive/Pos Graduação/TCC'\n",
        "mlflow_dir = f'{tcc_dir}/mlruns'\n",
        "h20_models = f'{tcc_dir}/automl'\n",
        "mlflow_models_path = f'{tcc_dir}/models'"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "U283JS6Mx8uV",
        "colab_type": "text"
      },
      "source": [
        "## Carregando os dados"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YjcuNoKQb8hI",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "if IN_COLAB and not os.path.exists(competition_name):\n",
        "    !cp -r '{data_gdrive_dir}' ."
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aYBrsjQdF6hG",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "eeg_columns = ['eeg_fp1', 'eeg_f7', 'eeg_f8', 'eeg_t4', 'eeg_t6', \n",
        "               'eeg_t5', 'eeg_t3', 'eeg_fp2', 'eeg_o1', 'eeg_p3', \n",
        "               'eeg_pz', 'eeg_f3', 'eeg_fz', 'eeg_f4',\t'eeg_c4', \n",
        "               'eeg_p4', 'eeg_poz', 'eeg_c3', 'eeg_cz', 'eeg_o2']\n",
        "\n",
        "numeric_features = [*eeg_columns, 'ecg', 'r', 'gsr']\n",
        "categorical_features = ['event']"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GXUr5K9iXTZW",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Realizando a leitura dos dados salvos após a limpeza\n",
        "train_df = pd.read_parquet(f'{competition_name}/train_balanced.parquet')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZSqV4w45dukf",
        "colab_type": "code",
        "outputId": "df5d3225-b53a-4c1b-de72-01af1b028f47",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 224
        }
      },
      "source": [
        "train_df.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "        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>eeg_fp1</th>\n",
              "      <th>eeg_f7</th>\n",
              "      <th>eeg_f8</th>\n",
              "      <th>eeg_t4</th>\n",
              "      <th>eeg_t6</th>\n",
              "      <th>eeg_t5</th>\n",
              "      <th>eeg_t3</th>\n",
              "      <th>eeg_fp2</th>\n",
              "      <th>eeg_o1</th>\n",
              "      <th>eeg_p3</th>\n",
              "      <th>eeg_pz</th>\n",
              "      <th>eeg_f3</th>\n",
              "      <th>eeg_fz</th>\n",
              "      <th>eeg_f4</th>\n",
              "      <th>eeg_c4</th>\n",
              "      <th>eeg_p4</th>\n",
              "      <th>eeg_poz</th>\n",
              "      <th>eeg_c3</th>\n",
              "      <th>eeg_cz</th>\n",
              "      <th>eeg_o2</th>\n",
              "      <th>ecg</th>\n",
              "      <th>r</th>\n",
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              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>-0.792237</td>\n",
              "      <td>0.831934</td>\n",
              "      <td>-9.429030</td>\n",
              "      <td>-4.17883</td>\n",
              "      <td>-1.58939</td>\n",
              "      <td>5.67909</td>\n",
              "      <td>1.763630</td>\n",
              "      <td>-2.098780</td>\n",
              "      <td>2.96651</td>\n",
              "      <td>1.44328</td>\n",
              "      <td>-3.94931</td>\n",
              "      <td>-2.22939</td>\n",
              "      <td>-4.573080</td>\n",
              "      <td>-8.084340</td>\n",
              "      <td>-8.46484</td>\n",
              "      <td>-4.68844</td>\n",
              "      <td>-4.750770</td>\n",
              "      <td>-2.04203</td>\n",
              "      <td>-4.60896</td>\n",
              "      <td>1.837780</td>\n",
              "      <td>9322.370117</td>\n",
              "      <td>815.594971</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>A</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>5.937200</td>\n",
              "      <td>5.026230</td>\n",
              "      <td>-0.933735</td>\n",
              "      <td>-1.19951</td>\n",
              "      <td>4.79854</td>\n",
              "      <td>-1.96581</td>\n",
              "      <td>1.134890</td>\n",
              "      <td>4.274110</td>\n",
              "      <td>2.21295</td>\n",
              "      <td>4.60106</td>\n",
              "      <td>1.61440</td>\n",
              "      <td>5.25442</td>\n",
              "      <td>4.693770</td>\n",
              "      <td>2.867810</td>\n",
              "      <td>2.75299</td>\n",
              "      <td>8.24299</td>\n",
              "      <td>0.721092</td>\n",
              "      <td>3.08044</td>\n",
              "      <td>4.83091</td>\n",
              "      <td>3.794880</td>\n",
              "      <td>5931.060059</td>\n",
              "      <td>830.568970</td>\n",
              "      <td>1453.670044</td>\n",
              "      <td>A</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>-1.162280</td>\n",
              "      <td>0.260127</td>\n",
              "      <td>-5.989480</td>\n",
              "      <td>-3.46853</td>\n",
              "      <td>-3.87440</td>\n",
              "      <td>3.96828</td>\n",
              "      <td>0.728967</td>\n",
              "      <td>-4.655380</td>\n",
              "      <td>1.64105</td>\n",
              "      <td>-1.29032</td>\n",
              "      <td>-5.34865</td>\n",
              "      <td>-1.59114</td>\n",
              "      <td>-0.350429</td>\n",
              "      <td>-1.959580</td>\n",
              "      <td>-1.04339</td>\n",
              "      <td>-3.01729</td>\n",
              "      <td>-2.019750</td>\n",
              "      <td>-2.43368</td>\n",
              "      <td>-2.89067</td>\n",
              "      <td>-0.602369</td>\n",
              "      <td>9320.519531</td>\n",
              "      <td>815.533997</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>A</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>-10.467600</td>\n",
              "      <td>-8.344960</td>\n",
              "      <td>-4.106620</td>\n",
              "      <td>-3.58671</td>\n",
              "      <td>-5.99199</td>\n",
              "      <td>-6.09401</td>\n",
              "      <td>-8.098010</td>\n",
              "      <td>-13.164800</td>\n",
              "      <td>-8.42648</td>\n",
              "      <td>-8.25574</td>\n",
              "      <td>-8.85742</td>\n",
              "      <td>-8.31783</td>\n",
              "      <td>-10.007400</td>\n",
              "      <td>-4.956020</td>\n",
              "      <td>-6.94528</td>\n",
              "      <td>-10.07500</td>\n",
              "      <td>-9.700760</td>\n",
              "      <td>-6.47439</td>\n",
              "      <td>-5.58930</td>\n",
              "      <td>-10.150300</td>\n",
              "      <td>9293.780273</td>\n",
              "      <td>816.421997</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>A</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>-15.538500</td>\n",
              "      <td>-4.782070</td>\n",
              "      <td>-25.117800</td>\n",
              "      <td>-14.71600</td>\n",
              "      <td>-14.40480</td>\n",
              "      <td>-5.53229</td>\n",
              "      <td>-0.526621</td>\n",
              "      <td>-34.102798</td>\n",
              "      <td>-7.38440</td>\n",
              "      <td>-3.33520</td>\n",
              "      <td>-7.56394</td>\n",
              "      <td>-11.75630</td>\n",
              "      <td>-15.780400</td>\n",
              "      <td>-25.708401</td>\n",
              "      <td>-13.23830</td>\n",
              "      <td>-13.04150</td>\n",
              "      <td>-5.686500</td>\n",
              "      <td>-2.66797</td>\n",
              "      <td>-3.96213</td>\n",
              "      <td>-9.862170</td>\n",
              "      <td>8765.589844</td>\n",
              "      <td>814.518982</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>A</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     eeg_fp1    eeg_f7     eeg_f8  ...           r          gsr  event\n",
              "0  -0.792237  0.831934  -9.429030  ...  815.594971     0.000000      A\n",
              "1   5.937200  5.026230  -0.933735  ...  830.568970  1453.670044      A\n",
              "2  -1.162280  0.260127  -5.989480  ...  815.533997     0.000000      A\n",
              "3 -10.467600 -8.344960  -4.106620  ...  816.421997     0.000000      A\n",
              "4 -15.538500 -4.782070 -25.117800  ...  814.518982     0.000000      A\n",
              "\n",
              "[5 rows x 24 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "B0vAanMBFqYF",
        "colab_type": "code",
        "outputId": "008500a0-cf8f-440d-e060-656688d2d8ee",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "print(f'Quantidade de entradas: {train_df.shape[0]}')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Quantidade de entradas: 522232\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eYJSlhj5ijoT",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "features = numeric_features\n",
        "target = 'event'"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Fm8bTzvoyGs5",
        "colab_type": "text"
      },
      "source": [
        "## Separando os dados de Treinamento/Validação e Testes"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "h-oAIzMfeBEi",
        "colab": {}
      },
      "source": [
        "# Separando os dados balanceados de treinamento/validação e testes\n",
        "balanced_X_train, balanced_X_test, balanced_y_train, balanced_y_test = train_test_split(\n",
        "    train_df[features], train_df[target], test_size=0.20, random_state=42)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "outputId": "fa88442f-ca0c-4d64-952b-6edc4b968e38",
        "id": "Of114ngTeC9a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299
        }
      },
      "source": [
        "# Visualizando a separação dos dados\n",
        "fig, ax = plt.subplots(1, 3, figsize=(15, 4), sharey=True)\n",
        "\n",
        "order = ['A', 'B', 'C', 'D']\n",
        "sns.countplot(train_df[target], order=order, ax=ax[0]).set_title('Dados Completos');\n",
        "sns.countplot(balanced_y_train, order=order, ax=ax[1]).set_title('Dados Trainamento/Validação');\n",
        "sns.countplot(balanced_y_test, order=order, ax=ax[2]).set_title('Dados Teste');"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x288 with 3 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Y4xD7j9KZSxo",
        "colab_type": "text"
      },
      "source": [
        "## Importando bibliotecas dos modelos utilizados"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "j7C8tA7hZXvA",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.pipeline import Pipeline\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "import lightgbm as lgb"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1YygOnC3aQ_2",
        "colab_type": "text"
      },
      "source": [
        "### Importando/Instalando MLFlow"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LdOgByMZaUVW",
        "colab_type": "code",
        "outputId": "b45bdfe4-1e8b-416a-b398-5ed7d1e4d83c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 306
        }
      },
      "source": [
        "# Importando bibliotecas necessárias (instalando no colab, pois esse pacote não é instalado por default)\n",
        "if IN_COLAB:\n",
        "    !pip install -q -U mlflow\n",
        "import mlflow\n",
        "import mlflow.sklearn\n",
        "\n",
        "if IN_COLAB:\n",
        "    mlflow.set_tracking_uri(f'{mlflow_dir}')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\u001b[K     |████████████████████████████████| 16.0MB 131kB/s \n",
            "\u001b[K     |████████████████████████████████| 460kB 49.3MB/s \n",
            "\u001b[K     |████████████████████████████████| 153kB 47.9MB/s \n",
            "\u001b[K     |████████████████████████████████| 6.0MB 34.8MB/s \n",
            "\u001b[K     |████████████████████████████████| 51kB 6.9MB/s \n",
            "\u001b[K     |████████████████████████████████| 1.1MB 34.2MB/s \n",
            "\u001b[K     |████████████████████████████████| 92kB 10.2MB/s \n",
            "\u001b[K     |████████████████████████████████| 81kB 10.0MB/s \n",
            "\u001b[K     |████████████████████████████████| 71kB 9.6MB/s \n",
            "\u001b[K     |████████████████████████████████| 204kB 59.0MB/s \n",
            "\u001b[K     |████████████████████████████████| 81kB 10.8MB/s \n",
            "\u001b[?25h  Building wheel for prometheus-flask-exporter (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for sqlalchemy (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for databricks-cli (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for alembic (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for simplejson (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for querystring-parser (setup.py) ... \u001b[?25l\u001b[?25hdone\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hr9yzzmZFmUK",
        "colab_type": "text"
      },
      "source": [
        "### Importando/Instalando h20"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3W7cix3fFsdh",
        "colab_type": "code",
        "outputId": "78f7be88-522d-4e4c-df6f-7d83c30079a5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "if IN_COLAB:\n",
        "    !pip install -q h2o\n",
        "\n",
        "# Importando bibliotecas h20\n",
        "import h2o\n",
        "from h2o.automl import H2OAutoML, get_leaderboard"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\u001b[K     |████████████████████████████████| 129.5MB 39kB/s \n",
            "\u001b[?25h  Building wheel for h2o (setup.py) ... \u001b[?25l\u001b[?25hdone\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QPuj_8XaGTZu",
        "colab_type": "text"
      },
      "source": [
        "## Avaliando melhores modelos com GridSearch e CrossValidation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zeyjX3Hl6o1E",
        "colab_type": "code",
        "outputId": "72e11a9b-b500-4fd5-90a7-b2dd5c214578",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "# Definição do Pipeline\n",
        "pipe = Pipeline([('classifier', RandomForestClassifier())])\n",
        "\n",
        "# Definição do espaço de busca\n",
        "search_space = [{'classifier': [LogisticRegression()]},\n",
        "                {'classifier': [DecisionTreeClassifier()]},\n",
        "                {'classifier': [RandomForestClassifier()],\n",
        "                 'classifier__n_estimators': [50, 100, 200]},\n",
        "                {'classifier': [lgb.LGBMClassifier()],\n",
        "                 'classifier__n_estimators': [100, 200]}]\n",
        "\n",
        "# Inicialização do GridSearchCV\n",
        "grid_clf = GridSearchCV(pipe, search_space, cv=5, \n",
        "                        verbose=10, n_jobs=-1, \n",
        "                        scoring=['accuracy', \n",
        "                                 'f1_macro', \n",
        "                                 'neg_log_loss'], \n",
        "                        refit='neg_log_loss')\n",
        "\n",
        "# Execução/Treinamento\n",
        "best_model = grid_clf.fit(balanced_X_train, balanced_y_train)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Fitting 5 folds for each of 7 candidates, totalling 35 fits\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 40 concurrent workers.\n",
            "[Parallel(n_jobs=-1)]: Done   4 out of  35 | elapsed:   33.9s remaining:  4.4min\n",
            "[Parallel(n_jobs=-1)]: Done   8 out of  35 | elapsed:   34.7s remaining:  2.0min\n",
            "[Parallel(n_jobs=-1)]: Done  12 out of  35 | elapsed:   56.6s remaining:  1.8min\n",
            "[Parallel(n_jobs=-1)]: Done  16 out of  35 | elapsed:  1.5min remaining:  1.8min\n",
            "[Parallel(n_jobs=-1)]: Done  20 out of  35 | elapsed:  1.6min remaining:  1.2min\n",
            "[Parallel(n_jobs=-1)]: Done  24 out of  35 | elapsed:  3.3min remaining:  1.5min\n",
            "[Parallel(n_jobs=-1)]: Done  28 out of  35 | elapsed:  5.9min remaining:  1.5min\n",
            "[Parallel(n_jobs=-1)]: Done  32 out of  35 | elapsed: 10.7min remaining:  1.0min\n",
            "[Parallel(n_jobs=-1)]: Done  35 out of  35 | elapsed: 11.0min finished\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TfQZ05wdGhkk",
        "colab_type": "code",
        "outputId": "d92124b7-03a3-461c-ca2d-d961cb693eaa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 405
        }
      },
      "source": [
        "grid_search_result_df = pd.DataFrame(\n",
        "    best_model.cv_results_).sort_values('rank_test_neg_log_loss')\n",
        "grid_search_result_df.to_excel('grid_search_result.xlsx')\n",
        "grid_search_result_df"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "    .dataframe tbody tr th {\n",
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              "    }\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_classifier</th>\n",
              "      <th>param_classifier__n_estimators</th>\n",
              "      <th>params</th>\n",
              "      <th>split0_test_accuracy</th>\n",
              "      <th>split1_test_accuracy</th>\n",
              "      <th>split2_test_accuracy</th>\n",
              "      <th>split3_test_accuracy</th>\n",
              "      <th>split4_test_accuracy</th>\n",
              "      <th>mean_test_accuracy</th>\n",
              "      <th>std_test_accuracy</th>\n",
              "      <th>rank_test_accuracy</th>\n",
              "      <th>split0_test_f1_macro</th>\n",
              "      <th>split1_test_f1_macro</th>\n",
              "      <th>split2_test_f1_macro</th>\n",
              "      <th>split3_test_f1_macro</th>\n",
              "      <th>split4_test_f1_macro</th>\n",
              "      <th>mean_test_f1_macro</th>\n",
              "      <th>std_test_f1_macro</th>\n",
              "      <th>rank_test_f1_macro</th>\n",
              "      <th>split0_test_neg_log_loss</th>\n",
              "      <th>split1_test_neg_log_loss</th>\n",
              "      <th>split2_test_neg_log_loss</th>\n",
              "      <th>split3_test_neg_log_loss</th>\n",
              "      <th>split4_test_neg_log_loss</th>\n",
              "      <th>mean_test_neg_log_loss</th>\n",
              "      <th>std_test_neg_log_loss</th>\n",
              "      <th>rank_test_neg_log_loss</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>633.618697</td>\n",
              "      <td>5.795493</td>\n",
              "      <td>11.552373</td>\n",
              "      <td>0.028079</td>\n",
              "      <td>RandomForestClassifier(bootstrap=True, ccp_alp...</td>\n",
              "      <td>200</td>\n",
              "      <td>{'classifier': RandomForestClassifier(bootstra...</td>\n",
              "      <td>0.973742</td>\n",
              "      <td>0.973288</td>\n",
              "      <td>0.974137</td>\n",
              "      <td>0.973264</td>\n",
              "      <td>0.973228</td>\n",
              "      <td>0.973532</td>\n",
              "      <td>0.000356</td>\n",
              "      <td>1</td>\n",
              "      <td>0.973690</td>\n",
              "      <td>0.973249</td>\n",
              "      <td>0.974103</td>\n",
              "      <td>0.973212</td>\n",
              "      <td>0.973177</td>\n",
              "      <td>0.973486</td>\n",
              "      <td>0.000360</td>\n",
              "      <td>1</td>\n",
              "      <td>-0.134448</td>\n",
              "      <td>-0.136491</td>\n",
              "      <td>-0.135835</td>\n",
              "      <td>-0.135104</td>\n",
              "      <td>-0.135105</td>\n",
              "      <td>-0.135396</td>\n",
              "      <td>0.000701</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>341.809483</td>\n",
              "      <td>11.240088</td>\n",
              "      <td>6.379347</td>\n",
              "      <td>0.062299</td>\n",
              "      <td>RandomForestClassifier(bootstrap=True, ccp_alp...</td>\n",
              "      <td>100</td>\n",
              "      <td>{'classifier': RandomForestClassifier(bootstra...</td>\n",
              "      <td>0.973336</td>\n",
              "      <td>0.972677</td>\n",
              "      <td>0.973886</td>\n",
              "      <td>0.973216</td>\n",
              "      <td>0.973156</td>\n",
              "      <td>0.973254</td>\n",
              "      <td>0.000387</td>\n",
              "      <td>3</td>\n",
              "      <td>0.973282</td>\n",
              "      <td>0.972643</td>\n",
              "      <td>0.973856</td>\n",
              "      <td>0.973176</td>\n",
              "      <td>0.973114</td>\n",
              "      <td>0.973214</td>\n",
              "      <td>0.000389</td>\n",
              "      <td>3</td>\n",
              "      <td>-0.135150</td>\n",
              "      <td>-0.137730</td>\n",
              "      <td>-0.133838</td>\n",
              "      <td>-0.134412</td>\n",
              "      <td>-0.136153</td>\n",
              "      <td>-0.135457</td>\n",
              "      <td>0.001375</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>77.845211</td>\n",
              "      <td>2.968557</td>\n",
              "      <td>13.055227</td>\n",
              "      <td>0.115050</td>\n",
              "      <td>LGBMClassifier(boosting_type='gbdt', class_wei...</td>\n",
              "      <td>200</td>\n",
              "      <td>{'classifier': LGBMClassifier(boosting_type='g...</td>\n",
              "      <td>0.951530</td>\n",
              "      <td>0.951518</td>\n",
              "      <td>0.953110</td>\n",
              "      <td>0.951674</td>\n",
              "      <td>0.951434</td>\n",
              "      <td>0.951853</td>\n",
              "      <td>0.000633</td>\n",
              "      <td>5</td>\n",
              "      <td>0.951446</td>\n",
              "      <td>0.951502</td>\n",
              "      <td>0.953108</td>\n",
              "      <td>0.951610</td>\n",
              "      <td>0.951378</td>\n",
              "      <td>0.951809</td>\n",
              "      <td>0.000654</td>\n",
              "      <td>5</td>\n",
              "      <td>-0.134732</td>\n",
              "      <td>-0.137452</td>\n",
              "      <td>-0.135006</td>\n",
              "      <td>-0.135125</td>\n",
              "      <td>-0.136642</td>\n",
              "      <td>-0.135791</td>\n",
              "      <td>0.001064</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>187.488320</td>\n",
              "      <td>5.710254</td>\n",
              "      <td>3.761484</td>\n",
              "      <td>0.081524</td>\n",
              "      <td>RandomForestClassifier(bootstrap=True, ccp_alp...</td>\n",
              "      <td>50</td>\n",
              "      <td>{'classifier': RandomForestClassifier(bootstra...</td>\n",
              "      <td>0.972510</td>\n",
              "      <td>0.972235</td>\n",
              "      <td>0.972450</td>\n",
              "      <td>0.971576</td>\n",
              "      <td>0.972689</td>\n",
              "      <td>0.972292</td>\n",
              "      <td>0.000386</td>\n",
              "      <td>4</td>\n",
              "      <td>0.972470</td>\n",
              "      <td>0.972206</td>\n",
              "      <td>0.972421</td>\n",
              "      <td>0.971535</td>\n",
              "      <td>0.972647</td>\n",
              "      <td>0.972256</td>\n",
              "      <td>0.000387</td>\n",
              "      <td>4</td>\n",
              "      <td>-0.137106</td>\n",
              "      <td>-0.138244</td>\n",
              "      <td>-0.138458</td>\n",
              "      <td>-0.140636</td>\n",
              "      <td>-0.136228</td>\n",
              "      <td>-0.138134</td>\n",
              "      <td>0.001488</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>46.788734</td>\n",
              "      <td>1.281174</td>\n",
              "      <td>6.619330</td>\n",
              "      <td>0.038590</td>\n",
              "      <td>LGBMClassifier(boosting_type='gbdt', class_wei...</td>\n",
              "      <td>100</td>\n",
              "      <td>{'classifier': LGBMClassifier(boosting_type='g...</td>\n",
              "      <td>0.937982</td>\n",
              "      <td>0.936810</td>\n",
              "      <td>0.939036</td>\n",
              "      <td>0.938150</td>\n",
              "      <td>0.938473</td>\n",
              "      <td>0.938090</td>\n",
              "      <td>0.000734</td>\n",
              "      <td>6</td>\n",
              "      <td>0.937879</td>\n",
              "      <td>0.936748</td>\n",
              "      <td>0.939014</td>\n",
              "      <td>0.938051</td>\n",
              "      <td>0.938396</td>\n",
              "      <td>0.938018</td>\n",
              "      <td>0.000744</td>\n",
              "      <td>6</td>\n",
              "      <td>-0.172515</td>\n",
              "      <td>-0.175619</td>\n",
              "      <td>-0.172192</td>\n",
              "      <td>-0.172976</td>\n",
              "      <td>-0.173945</td>\n",
              "      <td>-0.173450</td>\n",
              "      <td>0.001235</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>31.167164</td>\n",
              "      <td>0.691303</td>\n",
              "      <td>1.827406</td>\n",
              "      <td>0.009216</td>\n",
              "      <td>DecisionTreeClassifier(ccp_alpha=0.0, class_we...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>{'classifier': DecisionTreeClassifier(ccp_alph...</td>\n",
              "      <td>0.974963</td>\n",
              "      <td>0.972426</td>\n",
              "      <td>0.973000</td>\n",
              "      <td>0.972881</td>\n",
              "      <td>0.973970</td>\n",
              "      <td>0.973448</td>\n",
              "      <td>0.000909</td>\n",
              "      <td>2</td>\n",
              "      <td>0.974956</td>\n",
              "      <td>0.972428</td>\n",
              "      <td>0.972996</td>\n",
              "      <td>0.972871</td>\n",
              "      <td>0.973956</td>\n",
              "      <td>0.973441</td>\n",
              "      <td>0.000906</td>\n",
              "      <td>2</td>\n",
              "      <td>-0.864740</td>\n",
              "      <td>-0.952372</td>\n",
              "      <td>-0.932531</td>\n",
              "      <td>-0.936664</td>\n",
              "      <td>-0.899049</td>\n",
              "      <td>-0.917071</td>\n",
              "      <td>0.031408</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>28.900620</td>\n",
              "      <td>4.088861</td>\n",
              "      <td>1.678982</td>\n",
              "      <td>0.282991</td>\n",
              "      <td>LogisticRegression(C=1.0, class_weight=None, d...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>{'classifier': LogisticRegression(C=1.0, class...</td>\n",
              "      <td>0.396747</td>\n",
              "      <td>0.398291</td>\n",
              "      <td>0.395646</td>\n",
              "      <td>0.400014</td>\n",
              "      <td>0.400984</td>\n",
              "      <td>0.398336</td>\n",
              "      <td>0.001979</td>\n",
              "      <td>7</td>\n",
              "      <td>0.376654</td>\n",
              "      <td>0.378667</td>\n",
              "      <td>0.376939</td>\n",
              "      <td>0.379422</td>\n",
              "      <td>0.379973</td>\n",
              "      <td>0.378331</td>\n",
              "      <td>0.001323</td>\n",
              "      <td>7</td>\n",
              "      <td>-1.295972</td>\n",
              "      <td>-1.295375</td>\n",
              "      <td>-1.296191</td>\n",
              "      <td>-1.294007</td>\n",
              "      <td>-1.293594</td>\n",
              "      <td>-1.295028</td>\n",
              "      <td>0.001045</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   mean_fit_time  std_fit_time  ...  std_test_neg_log_loss  rank_test_neg_log_loss\n",
              "4     633.618697      5.795493  ...               0.000701                       1\n",
              "3     341.809483     11.240088  ...               0.001375                       2\n",
              "6      77.845211      2.968557  ...               0.001064                       3\n",
              "2     187.488320      5.710254  ...               0.001488                       4\n",
              "5      46.788734      1.281174  ...               0.001235                       5\n",
              "1      31.167164      0.691303  ...               0.031408                       6\n",
              "0      28.900620      4.088861  ...               0.001045                       7\n",
              "\n",
              "[7 rows x 31 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Hy7ZFW6pXkf1",
        "colab_type": "code",
        "outputId": "8b22884a-fbd5-4fc4-e91a-ff8a9ad6d30e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 354
        }
      },
      "source": [
        "grid_search_result_df['model'] = grid_search_result_df.param_classifier.astype(str).str.split('(').apply(lambda x: x[0]) + '[' + grid_search_result_df.param_classifier__n_estimators.astype(str) + ']'\n",
        "\n",
        "plt.figure(figsize=(8, 5))\n",
        "sns.scatterplot(x=\"mean_test_neg_log_loss\", y=\"mean_test_accuracy\",\n",
        "                     hue=\"model\", size=\"mean_test_f1_macro\", alpha=0.8,\n",
        "                     data=grid_search_result_df).set_title('Desempenho dos Modelos gerados pelo Grid Search com Cross Validation');\n",
        "plt.xlabel(\"Média do Negative Log Loss\")\n",
        "plt.ylabel(\"Média da Accurácia\")\n",
        "plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.);"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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LAwODYv9ZSklJEYA4cOCAsu1hneCCc7Rjxw5lf1ZWlrC1tS32u1oI7e1KrVaLJUuWaIQr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\nAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 576x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2Ys-p9C-b1vL",
        "colab_type": "text"
      },
      "source": [
        "## Avaliando modelos separadamente"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "K9pMHeCSb5Rw",
        "colab_type": "text"
      },
      "source": [
        "### Definindo função para exibição dos resultados e métricas\n",
        "\n",
        "Foi utilizado o MLFlow para rastrear todas as rodadas de testes isolados"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OsB0V2poFqYV",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.metrics import plot_confusion_matrix, plot_precision_recall_curve, classification_report, log_loss, f1_score, accuracy_score\n",
        "\n",
        "def show_metrics(model, X_test, y_test, run_name, \n",
        "                 experiment_name='TCC', sklearn_model=True):\n",
        "    \"\"\"Função para mostrar as métricas e salvá-las no ML Flow\"\"\"\n",
        "    # Define o experimento\n",
        "    mlflow.set_experiment(experiment_name)\n",
        "\n",
        "    if sklearn_model: # Avaliação do modelo sklearn\n",
        "        fig, ax = plt.subplots(1, 2, figsize=(10, 4))\n",
        "\n",
        "        # Realiza o plot da matriz confusão\n",
        "        plot_confusion_matrix(model, X_test, y_test, \n",
        "                              cmap=plt.cm.Blues, ax=ax[0])\n",
        "        ax[0].set_title('Matrix Confusão, sem normalização')\n",
        "\n",
        "        # Realiza o plot da matriz confusão Normalizada\n",
        "        plot_confusion_matrix(model, X_test, y_test, \n",
        "                              cmap=plt.cm.Blues, normalize='true', \n",
        "                              ax=ax[1])\n",
        "        ax[1].set_title('Matrix Confusão Normalizada')\n",
        "\n",
        "        plt.tight_layout()\n",
        "        plt.show()\n",
        "\n",
        "        # Obtem as previsões e as probabilidades\n",
        "        y_predict = model.predict(X_test)\n",
        "        y_predict_prob = model.predict_proba(X_test)\n",
        "    else: # Avaliação do modelo h20\n",
        "        # Obtem as previsões e as probabilidades\n",
        "        prediction = model.predict(\n",
        "            h2o.H2OFrame(X_test)).as_data_frame()\n",
        "        y_predict = prediction['predict']\n",
        "        y_predict_prob = prediction.drop('predict', axis=1)\n",
        "\n",
        "    # Calculo das métricas log loss e f1 score\n",
        "    logloss = log_loss(y_test, y_predict_prob)\n",
        "    f1 = f1_score(y_test, y_predict, average='macro')\n",
        "\n",
        "    # Armazena as métricas e modelo no MLFlow para futuro rastreamento\n",
        "    # das execuções\n",
        "    with mlflow.start_run(run_name=run_name):\n",
        "        if sklearn_model:\n",
        "            mlflow.log_param(\"model_description\", str(model))\n",
        "        mlflow.log_metric(\"f1_score\", f1)\n",
        "        mlflow.log_metric(\"log_loss\", logloss)\n",
        "        mlflow.log_metric(\"accuracy\", \n",
        "                          accuracy_score(y_test, y_predict))\n",
        "        if not IN_COLAB:\n",
        "            mlflow.sklearn.log_model(model, \"model\")\n",
        "            mlflow.sklearn.save_model(model, mlflow_models_path)\n",
        "\n",
        "    # Exibe principais métricas de classificação\n",
        "    display(pd.DataFrame(classification_report(\n",
        "        y_test, y_predict, output_dict=True)).T)\n",
        "    print(f'\\nLog loss: {logloss} | F1 Score: {f1}')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JbzZAvIIc3Hj",
        "colab_type": "text"
      },
      "source": [
        "### Analisando desempenho do modelo de Regressão Logística"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zowjRC_EFqYX",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Foi realizada uma normalização das colunas numéricas no pipeline desse modelo,\n",
        "# devido a sua sensibilidade na variação no range das colunas, e essa variação\n",
        "# impacta no desempenho do modelo\n",
        "\n",
        "from sklearn.pipeline import Pipeline\n",
        "from sklearn.compose import ColumnTransformer\n",
        "from sklearn.impute import SimpleImputer\n",
        "from sklearn.preprocessing import OneHotEncoder, StandardScaler\n",
        "\n",
        "numeric_transformer = Pipeline(steps=[\n",
        "    ('imputer', SimpleImputer(strategy='median')),\n",
        "    ('scaler', StandardScaler())])\n",
        "\n",
        "preprocessor = ColumnTransformer(transformers=[('num', numeric_transformer, numeric_features)])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_ua-Vj1_nxz6",
        "colab_type": "code",
        "outputId": "38b54113-c960-4af3-c09b-3924686a23b3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 595
        }
      },
      "source": [
        "LR_clf = Pipeline(steps=[('preprocessor', preprocessor),\n",
        "                         ('classifier', LogisticRegression(n_jobs=-1))])\n",
        "\n",
        "LR_clf.fit(balanced_X_train, balanced_y_train)\n",
        "\n",
        "show_metrics(LR_clf, balanced_X_test, balanced_y_test, 'LogisticRegression')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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lQ8t6BuVRMReJjLnIwi/GsmjWG8TEX+JU9HkTjr5sdDodW9bt4j/DuvP6uwOJ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\nlEolN7JusmX1NoQQCJ2gVr2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6pFIp7t7uBDcK5si+o3j6eOAX6Ef8rTvs+GUH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FS4/iI/P5+YmBjy8/MZPXq0t5vTKIKCgkhPTwegsLCQd955h7lz53q5Ve7ldDpJT0+nX79+vPDC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\niIghqHAVEREREUP4PymiBy/Y9nvgAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 720x288 with 4 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>precision</th>\n",
              "      <th>recall</th>\n",
              "      <th>f1-score</th>\n",
              "      <th>support</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>A</th>\n",
              "      <td>0.386786</td>\n",
              "      <td>0.215416</td>\n",
              "      <td>0.276718</td>\n",
              "      <td>26089.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>B</th>\n",
              "      <td>0.329024</td>\n",
              "      <td>0.311232</td>\n",
              "      <td>0.319881</td>\n",
              "      <td>26167.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>C</th>\n",
              "      <td>0.481164</td>\n",
              "      <td>0.637850</td>\n",
              "      <td>0.548538</td>\n",
              "      <td>25992.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>D</th>\n",
              "      <td>0.334137</td>\n",
              "      <td>0.391656</td>\n",
              "      <td>0.360617</td>\n",
              "      <td>26199.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>accuracy</th>\n",
              "      <td>0.388752</td>\n",
              "      <td>0.388752</td>\n",
              "      <td>0.388752</td>\n",
              "      <td>0.388752</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>macro avg</th>\n",
              "      <td>0.382778</td>\n",
              "      <td>0.389039</td>\n",
              "      <td>0.376438</td>\n",
              "      <td>104447.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>weighted avg</th>\n",
              "      <td>0.382595</td>\n",
              "      <td>0.388752</td>\n",
              "      <td>0.376220</td>\n",
              "      <td>104447.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              precision    recall  f1-score        support\n",
              "A              0.386786  0.215416  0.276718   26089.000000\n",
              "B              0.329024  0.311232  0.319881   26167.000000\n",
              "C              0.481164  0.637850  0.548538   25992.000000\n",
              "D              0.334137  0.391656  0.360617   26199.000000\n",
              "accuracy       0.388752  0.388752  0.388752       0.388752\n",
              "macro avg      0.382778  0.389039  0.376438  104447.000000\n",
              "weighted avg   0.382595  0.388752  0.376220  104447.000000"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Log loss: 1.2794922338334827 | F1 Score: 0.37643827732281887\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "97wjaqbXiMfi",
        "colab_type": "text"
      },
      "source": [
        "### Analisando desempenho do modelo de Árvore de Decisão"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "onFIyNEdhK3g",
        "colab_type": "code",
        "outputId": "a7242d72-694b-4847-a1c3-f0bb49879641",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 595
        }
      },
      "source": [
        "DT_clf = DecisionTreeClassifier()\n",
        "DT_clf.fit(balanced_X_train, balanced_y_train)\n",
        "\n",
        "show_metrics(DT_clf, balanced_X_test, balanced_y_test, 'DecisionTreeClassifier')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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tXw6fikAIgY+3O072VgCUKOLM05Q0UlLT8mQe96Lj2HvsEu2bVs2xXKGAhKSn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\nzR5IXk9nGvQI4p065ShZ+NnYlv90BCfHXJzaFMiGX08QOOs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nWDezv/GOBFv2nGbVtH5m\n77OpbGw0TBnekXaD56DTGejSqhqlinjz9fyfqViqAM3rlqdb6xr0G7uMym0DcXHMzcInf0NKFfGm\nXeNKVOs4ARuNmqDhHdFo1NwNe0D/wOXo9Hr0egNtG1WmWe1y6PV6Pg5cTszDOAwGKFssH1MDUp8/\naAk6nZ7hUzeyYUYfNGo1K38+zsVr9xjxUTPO/PUv2w+ep1blIoz5uAX5PZ1xv3ibz77ZAMBPv52l\njl8xDq34DIPBwJ6jF81WtGbE626/No2ebb9pAcnbD0h1mQB9Ri8lLDIGgwHKFc/PtBGdLT7m5735\nZSuoDObcbU9HREQES5Ys4dq1a9ja2uLu7k7Pnj3x9vZO8z2xCXpLdQ9IPu1kjj11U9loLH9g3k5D\nph9h/S+XmpYv4A8uGWzSkV5zscQR1//Kim35unv0K07eMrlt1yrKuehM6SSfX2Q1+Vz9f5ZdIcnT\nE8x1pNcUph5xNSfJ58xh0TmuWq2W//3P8v9BhBBvDiXs0VsjyWchhBLy+Y27OEsIkb2Zeo9dIYQQ\nlqWEfJbCVQhhUUq4alUIIayREvJZClchhEUp4apVIYSwRkrIZylchRAW9ebHohBCWCcl5LMUrkII\ni1LCHr0QQlgjJeSzFK5CCItSwhwqIYSwRkrIZylchRAWpYQ9eiGEsEZKyGcpXIUQFvXmx6IQQlgn\nJeSzFK5CCItSwA69EEJYJSXksxSuQgiL0ighGYUQwgopIZ+lcBVCWJRKESejhBDC+ighn6VwFUJY\nlAJ26IUQwiopIZ+lcBVCWJRaAXv0QghhjZSQz1K4CiEsSgl79EIIYY2UkM9SuAohLEoJwSiEENZI\nCfkshasQwqKUMPlfCCGskRLyWQpXIYRFqd/8XBRCCKukhHyWwlUIYVFK2KMXQghrpIR8lsJVCGFR\naiVMohJCCCukhHyWwlUIYVFKOBUlhBDWSAn5LIWrEMKilHAqSgghrJES8lkKVyGERSngTJQQQlgl\nJeSzFK5CCItSQC4KIYRVUkI+qwwGgyGrO/EycYmW7V4ODSToLLc+VRbs3thp4LEFx5hVLD1Ol6oD\nLbeyJw6uHE6tLlMsus6407Nf6/1HrkSZ3LZ6UefXWpfIXJLP5if5nDkkn02jhHyWI65CCItSwh69\nEEJYIyXksxSuQgjLUkIyCiGENVJAPkvhKoSwKCVctSqEENZICfkshasQwqKUcJ9AIYSwRkrIZylc\nhRCWpYBgFEIIq6SAfJbCVQhhUUo4FSWEENZICfkshasQwqKUcINrIYSwRkrIZylchRAWpYBcFEII\nq2TufD5z5gyLFy9Gr9fTsGFD2rRpk+L1n3/+mT179qDRaHB0dOTjjz/G3d39pctUm7mPQgjxcqoM\nfAkhhLAcM+azXq9n4cKFfPHFF0yfPp1Dhw5x69atFG18fX2ZNGkS33zzDdWqVWPFihXpLleOuAoh\nLMrcc6gGDBhAzpw5UavVaDQaJk2aRGxsLNOnTyc0NBR3d3eGDh2Kg4MDBoOBxYsXc/r0aXLkyEH/\n/v0pXLgwAPv27WPjxo0A+Pv7U69ePbP2Uwgh3nTmzOcrV67g5eWFp6cnADVq1CA4OJj8+fMb25Qt\nW9b4fbFixfj999/TXa4UrkIIi8qMOVRjx47F0dHR+Hjz5s2UK1eONm3asHnzZjZv3kzXrl05ffo0\nd+/eZebMmVy+fJkffviBr7/+mtjYWNavX8+kSZMACAgIwM/PDwcHB/N3Vggh3lDmzOeIiAhcXV2N\nj11dXbl8+XKa7ffu3UvFihXTXa5MFRBCWJRKZfrXqwoODqZu3boA1K1bl+DgYABOnDhBnTp1UKlU\nFC9enIcPHxIZGcmZM2coX748Dg4OODg4UL58ec6cOWOO4QohhGJkNJ8DAgKMX7t3737l9R44cICr\nV6/SqlWrdNvKEVchhEVlxu1WJkyYAEDjxo1p1KgR0dHRuLi4AODs7Ex0dDSQfATAzc3N+D5XV1ci\nIiJeODKg1WqJiIgwez+FEOJNltF8fnqWKjVarZbw8HDj4/DwcLRa7Qvtzp07x6ZNmwgMDMTW1jbd\ndUrhKoSwqIweSQ0ICDB+36hRIxo1apTi9fHjx6PVaomOjuarr74ib968/1mfCpUS7vEihBBZzJxR\nWaRIEUJCQrh//z5arZbDhw8zePDgFG2uXbvGggUL+OKLL3BycjJpuVK4CiEsKqO5+LI9esC4B+/k\n5ETVqlW5cuUKTk5OREZG4uLiQmRkpHH+q1arJSwszPjep0cAtFotFy5cMD4fERFB6dKlM9hTIYRQ\nNnPu4ms0Gnr16sWECRPQ6/XUr18fHx8f1qxZQ5EiRfDz82PFihXEx8czbdo0ANzc3Pj8889fulwp\nXIUQlmXGZIyPj8dgMJArVy7i4+M5d+4c7du3x8/Pj/3799OmTRv2799P1apVAfDz82PHjh3UrFmT\ny5cvY29vj4uLCxUrVmTVqlXExsYCcPbsWd5//33zdVQIIZTAzCenKleuTOXKlVM816lTJ+P3o0eP\nzvAypXAVQliUOee4RkdH88033wCg0+moVasWFStWpEiRIkyfPp29e/cab4cFUKlSJU6dOsXgwYOx\ns7Ojf//+ADg4ONCuXTtGjBgBQPv27eWOAkIIqyMf+SqEEP9hzjlUnp6eBAUFvfB8njx5GDNmTCrr\nVtG7d+9Ul9WgQQMaNGhgvs4JIYTCKOFyAClchRAWpYBcFEIIq6SEfJbCVQhhWUpIRiGEsEYKyGcp\nXIUQFqVWwrkoIYSwQkrIZylchRAW9ebHohBCWCcl5LMUrkIIy1JCMgohhDVSQD4runAdOH4lvx78\nEzeXPBxe/cULr1+6fpeBX67k3N+3GPlxSwZ1bfja60x4nMjHgcs5e/FfXJxys2jCBxTI68pvxy7y\n5ZwtPE5Mws7WhnGDWlOnaonXXp853bobyceBywiLiAGgR9ua9Huvfhb36vU9HVdoRAwqno3ry7k/\ns3XfOdQqFe7aPMwZ2xVvd+cs62c+T2fmBXbHXZsHA7B00yG+W73vhXY1Kxdj4qftsLHREBEVS8u+\n377Weu1sbZg3rhsVSxYgIvohvb5YxL8hzz7ONL+nC0fWjmLygm3MXrHntdZlCiXcbkVkjt1HLvDF\n1A3o9Hq6ta7OkB5NUryeVr4CTF/yKyu3HEGtVjPx0/Y0rF7K+D6dTk+DHkF4uzuxeno/APqMXsqZ\nv25iY6OhcpmCTB/RGVsbTaaN7fL1e/T6YpHx8Y074Yzo04KP30+ZsQdPXmLE1A0kJenQOjvwy/dD\nMrSeaYt3smLLETRqNZOGtadh9eQPyijfagwO9jnQqNXY2Kj5bdnLb+L+KnYfvsCIqeufbL8aDO2Z\nyvYbu5wzF2+idcrNoq97Gbfff/v9Tq3SL13m/uN/M2bmJvR6A7ntczB3bDcK+7jz790I+gcuJzom\nDp1ez9iBrWlSs4zZx5qehtVLMfHT9mjUapb/dJgZS3eleN3Hy4WC+T04+OMIIh88ou+Ypdy5HwVA\n4MDWNKmV3OeghTvYtOuUxfufFiXks0UK106dOlGgQAF0Oh0ajYY6derQokUL1Gr1ay33/RZv81GH\nOnwcuDzV110cczNpWHu27TuX4WXfvBPOgC9XsHX+JymeX7HlCM557Dm5cSwbfj1J4OyfWPR1L1yd\nc/Pj1L54uztx4Z87dBg8l/O/fPVK48osNjZqvhriT9UyPoQ/iKd+98nUe7skJQt7Z3XXXsvTcVUo\n6UPMw2fjGtK9IQF9WwLw3ep9TPlhO9NHvJdl/UxK0jNqxkbO/X0LB/sc/Lbsc/Ydu8jf1+4a2zg6\n5OKbzzvSYfBcbt2LxM3F9HuJ+nhrmTu2G+/2S1nodmtdnegHcVTxH4d/4yoEDmrNh18sNr7+1VB/\ndh8+//oDNJECplBZlczK5//S6fQMn7KOjbMHkNfDmYY9gmhWu1yK/EkrXy9eDWHjryc5se4LbtyN\npu3AOQSvH41Gk9zH+av3UdzXk5iH8cZldWjmx3dfdgfgo9FLWL75ML3a1zbrmJ5XzNeT338cYRxr\n6eYjaVG/Qoo20TGPGDZ5Letm9sfHS0vok4MIprp4NYSNu05xZM1I7oZG02bAbE5sGGP8OWyd/wmu\nzplz/2GdTs9nU9ayafZA8no606BHEO/USbn9lv90BCfHXJzaFMiGX08QOOsnFk3slWq/m1Qfg05H\nmsv8dPJqVn7TlxKFvPhh3QG+WbiDuYHdmLpwB20aVebD9rW5eDWEjkPmcW7Ll5ky5rSo1SqChnek\n7cDZ3LkXxd6ln7H9wB8psvzLT9oS9eAhtd6fSG2/4owZ0Ip+Y5fRpGYZypf0oXaXSeSwtWHrd5+w\n+/CFFL+7WUkJ+WzeZEqDnZ0dQUFBTJs2jVGjRnHmzBnWr1//2sutUbkoLo72ab7urs1D5dIFsUll\nL3vt9mAa9QyiTpdJDJ24Gp1Ob9I6t+3/g84t3gagdYOKHAi+hMFgoHwJH7zdkz9nt1Rhb+ISEkl4\nnPgKo8o8Xm5OVCjpA0Ce3Dkp7utFSGhUFvfq9aU1LkeHXMY2D+MSsvzz6u+FP+Dc37cAiH2UwKXr\nd184AtyhmR8//3aWW/ciAQiLjDW+1vGdquxeMowDKwOYPqIzarVp43mnTnlW/XIMgJ/2nqbuc2cC\nmtctz8074Vy8ejett5udKgNfIvNlVj7/18nzNyiU3w3ffG7Y2drg36QK2w/8kaJNWvm6/cAf+Dep\nQg47Wwrmc6NQfjdOnr8BwO17kew6dJ5uraunWFbjmmVQqVSoVCoqly5oPNplCfuD/8Y3vzsFvLUp\nnl+34wQt61fAxyv5eXdtHuNra7Ydp2GPIKp1nsiQr1el+jdp2/5z+DeubPw5FPZ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hQ3Ts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\nztB9OAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 720x288 with 4 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>precision</th>\n",
              "      <th>recall</th>\n",
              "      <th>f1-score</th>\n",
              "      <th>support</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>A</th>\n",
              "      <td>0.958407</td>\n",
              "      <td>0.957415</td>\n",
              "      <td>0.957911</td>\n",
              "      <td>26089.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>B</th>\n",
              "      <td>0.977020</td>\n",
              "      <td>0.976497</td>\n",
              "      <td>0.976758</td>\n",
              "      <td>26167.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>C</th>\n",
              "      <td>0.977377</td>\n",
              "      <td>0.978994</td>\n",
              "      <td>0.978184</td>\n",
              "      <td>25992.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>D</th>\n",
              "      <td>0.994885</td>\n",
              "      <td>0.994809</td>\n",
              "      <td>0.994847</td>\n",
              "      <td>26199.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>accuracy</th>\n",
              "      <td>0.976945</td>\n",
              "      <td>0.976945</td>\n",
              "      <td>0.976945</td>\n",
              "      <td>0.976945</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>macro avg</th>\n",
              "      <td>0.976922</td>\n",
              "      <td>0.976929</td>\n",
              "      <td>0.976925</td>\n",
              "      <td>104447.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>weighted avg</th>\n",
              "      <td>0.976941</td>\n",
              "      <td>0.976945</td>\n",
              "      <td>0.976943</td>\n",
              "      <td>104447.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              precision    recall  f1-score        support\n",
              "A              0.958407  0.957415  0.957911   26089.000000\n",
              "B              0.977020  0.976497  0.976758   26167.000000\n",
              "C              0.977377  0.978994  0.978184   25992.000000\n",
              "D              0.994885  0.994809  0.994847   26199.000000\n",
              "accuracy       0.976945  0.976945  0.976945       0.976945\n",
              "macro avg      0.976922  0.976929  0.976925  104447.000000\n",
              "weighted avg   0.976941  0.976945  0.976943  104447.000000"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Log loss: 0.7962830292774825 | F1 Score: 0.9769251078398671\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mgC1UjaFiflD",
        "colab_type": "text"
      },
      "source": [
        "### Analisando desempenho do modelo de Random Forest"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bZ2obE9iU9cX",
        "colab_type": "code",
        "outputId": "2d4ffcfd-5cba-4f5f-d467-b949773280bc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 595
        }
      },
      "source": [
        "RF_clf = RandomForestClassifier(n_jobs=-1, n_estimators=500)\n",
        "RF_clf.fit(balanced_X_train, balanced_y_train)\n",
        "\n",
        "show_metrics(RF_clf, balanced_X_test, balanced_y_test, 'RandomForestClassifier')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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X9x8k8yApORt6lXUNqpXBxiZt0ugq5Yty/eaL20cm0epM/xEWY+58NlXlMoW4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\nZCQpKSkcOHCAwMBAozL58+f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G3i7jV9HP2wVvD2dqv5J2M1a7RgHMXLbD6Dm+Xs54uObF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\n",
            "text/plain": [
              "<Figure size 720x288 with 4 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>precision</th>\n",
              "      <th>recall</th>\n",
              "      <th>f1-score</th>\n",
              "      <th>support</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>A</th>\n",
              "      <td>0.961730</td>\n",
              "      <td>0.952662</td>\n",
              "      <td>0.957175</td>\n",
              "      <td>26089.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>B</th>\n",
              "      <td>0.974994</td>\n",
              "      <td>0.970039</td>\n",
              "      <td>0.972510</td>\n",
              "      <td>26167.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>C</th>\n",
              "      <td>0.973002</td>\n",
              "      <td>0.989997</td>\n",
              "      <td>0.981426</td>\n",
              "      <td>25992.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>D</th>\n",
              "      <td>0.996861</td>\n",
              "      <td>0.994007</td>\n",
              "      <td>0.995432</td>\n",
              "      <td>26199.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>accuracy</th>\n",
              "      <td>0.976677</td>\n",
              "      <td>0.976677</td>\n",
              "      <td>0.976677</td>\n",
              "      <td>0.976677</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>macro avg</th>\n",
              "      <td>0.976647</td>\n",
              "      <td>0.976676</td>\n",
              "      <td>0.976636</td>\n",
              "      <td>104447.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>weighted avg</th>\n",
              "      <td>0.976670</td>\n",
              "      <td>0.976677</td>\n",
              "      <td>0.976648</td>\n",
              "      <td>104447.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              precision    recall  f1-score        support\n",
              "A              0.961730  0.952662  0.957175   26089.000000\n",
              "B              0.974994  0.970039  0.972510   26167.000000\n",
              "C              0.973002  0.989997  0.981426   25992.000000\n",
              "D              0.996861  0.994007  0.995432   26199.000000\n",
              "accuracy       0.976677  0.976677  0.976677       0.976677\n",
              "macro avg      0.976647  0.976676  0.976636  104447.000000\n",
              "weighted avg   0.976670  0.976677  0.976648  104447.000000"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Log loss: 0.1251807246508412 | F1 Score: 0.9766356937346269\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Dz29dcYgl1d5",
        "colab_type": "text"
      },
      "source": [
        "### Analisando desempenho do modelo LightGBM"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FGTg46b3Vq68",
        "colab_type": "code",
        "outputId": "09bf51c3-db00-49cc-f31b-f2ea410338dc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 595
        }
      },
      "source": [
        "lgbm_clf = lgb.LGBMClassifier(n_estimators=500)\n",
        "lgbm_clf.fit(balanced_X_train, balanced_y_train, eval_metric='logloss')\n",
        "\n",
        "show_metrics(lgbm_clf, balanced_X_test, balanced_y_test, 'LGBMClassifier')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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j4sWLBV7/y3gbMrZSpUpA1kGaJ9LT0zl16pT28YMHD7h8+TKjR4+mRYsWVKpUCRMTkxee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\nwlUIIYQQQiiCFK5CCCGEEEIR/g+nSo4qYb9lWgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 720x288 with 4 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>precision</th>\n",
              "      <th>recall</th>\n",
              "      <th>f1-score</th>\n",
              "      <th>support</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>A</th>\n",
              "      <td>0.938779</td>\n",
              "      <td>0.941010</td>\n",
              "      <td>0.939893</td>\n",
              "      <td>26089.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>B</th>\n",
              "      <td>0.971721</td>\n",
              "      <td>0.953376</td>\n",
              "      <td>0.962461</td>\n",
              "      <td>26167.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>C</th>\n",
              "      <td>0.965797</td>\n",
              "      <td>0.984264</td>\n",
              "      <td>0.974943</td>\n",
              "      <td>25992.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>D</th>\n",
              "      <td>0.997589</td>\n",
              "      <td>0.995114</td>\n",
              "      <td>0.996350</td>\n",
              "      <td>26199.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>accuracy</th>\n",
              "      <td>0.968443</td>\n",
              "      <td>0.968443</td>\n",
              "      <td>0.968443</td>\n",
              "      <td>0.968443</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>macro avg</th>\n",
              "      <td>0.968472</td>\n",
              "      <td>0.968441</td>\n",
              "      <td>0.968412</td>\n",
              "      <td>104447.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>weighted avg</th>\n",
              "      <td>0.968507</td>\n",
              "      <td>0.968443</td>\n",
              "      <td>0.968431</td>\n",
              "      <td>104447.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              precision    recall  f1-score        support\n",
              "A              0.938779  0.941010  0.939893   26089.000000\n",
              "B              0.971721  0.953376  0.962461   26167.000000\n",
              "C              0.965797  0.984264  0.974943   25992.000000\n",
              "D              0.997589  0.995114  0.996350   26199.000000\n",
              "accuracy       0.968443  0.968443  0.968443       0.968443\n",
              "macro avg      0.968472  0.968441  0.968412  104447.000000\n",
              "weighted avg   0.968507  0.968443  0.968431  104447.000000"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Log loss: 0.0937984349415199 | F1 Score: 0.9684119575615247\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8hxrW07caune",
        "colab_type": "text"
      },
      "source": [
        "## Gerando modelo final do melhor modelo encontrado"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "N7hxEJ9nm6nj",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Gerando modelo final com todos os dados de treinamento\n",
        "RF_clf = RandomForestClassifier(n_jobs=-1, n_estimators=1000)\n",
        "\n",
        "RF_clf = RF_clf.fit(train_df[features], train_df[target])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LESi-PUgbaSH",
        "colab_type": "text"
      },
      "source": [
        "### Prevendo dados de teste"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5ls-KpcDADVG",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def predict_proba_test(model, nrows=None, sklearn_model=True, chunksize=100_000):\n",
        "    test_df = pd.read_csv(f'{competition_name}/test.csv.zip', \n",
        "                          usecols=features, nrows=nrows, chunksize=chunksize)\n",
        "    predict_results = []\n",
        "    for test_chunk in tqdm(test_df):\n",
        "        if sklearn_model:\n",
        "            predict_results.append(\n",
        "                pd.DataFrame(\n",
        "                    model.predict_proba(test_chunk), columns=model.classes_))\n",
        "        else:\n",
        "            predict_results.append(\n",
        "                model.predict(\n",
        "                    h2o.H2OFrame(test_chunk))\n",
        "                .as_data_frame().drop('predict', axis=1))\n",
        "    return pd.concat(predict_results, ignore_index=True).reset_index().rename(columns={'index': 'id'})"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vhsc1Cn2b2eu",
        "colab_type": "code",
        "outputId": "a4f4cee4-ecac-4116-b8e0-1cd8584d52ae",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 253,
          "referenced_widgets": [
            "5dfd79fa3b2b47609c6a03c7ac3fb299",
            "fd18a2990d1845a18a111a2c5e42a3e4",
            "a41cf73ac4b5428b9dfc31106b5ef5cd",
            "b7c40896c9454d7aa005a5ebf2b2f76a",
            "0569b42da6334258b7415593829cf07b",
            "317b78c6aab34cfe9690cca42cffa83a",
            "9a828487f9824380b8c0675b6f4bb4a6",
            "1aa48a3b14c74c2f91f624d32932b936"
          ]
        }
      },
      "source": [
        "sample_test_result_df = predict_proba_test(RF_clf, 10_000)\n",
        "sample_test_result_df.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "5dfd79fa3b2b47609c6a03c7ac3fb299",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(IntProgress(value=1, bar_style='info', max=1), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>A</th>\n",
              "      <th>B</th>\n",
              "      <th>C</th>\n",
              "      <th>D</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>0.096</td>\n",
              "      <td>0.615</td>\n",
              "      <td>0.047</td>\n",
              "      <td>0.242</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>0.074</td>\n",
              "      <td>0.509</td>\n",
              "      <td>0.092</td>\n",
              "      <td>0.325</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>0.063</td>\n",
              "      <td>0.637</td>\n",
              "      <td>0.052</td>\n",
              "      <td>0.248</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>0.084</td>\n",
              "      <td>0.496</td>\n",
              "      <td>0.099</td>\n",
              "      <td>0.321</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>0.124</td>\n",
              "      <td>0.585</td>\n",
              "      <td>0.068</td>\n",
              "      <td>0.223</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   id      A      B      C      D\n",
              "0   0  0.096  0.615  0.047  0.242\n",
              "1   1  0.074  0.509  0.092  0.325\n",
              "2   2  0.063  0.637  0.052  0.248\n",
              "3   3  0.084  0.496  0.099  0.321\n",
              "4   4  0.124  0.585  0.068  0.223"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5t-wxhHNiQ9k",
        "colab_type": "text"
      },
      "source": [
        "### Submetendo dados para o Kaggle"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GtScnmGFiT6r",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Obtendo as credenciais armazenadas para submissão dos dados\n",
        "def get_kaggle_json(api_location):\n",
        "    api_local_credential_location = os.path.join(os.path.expanduser('~'), '.kaggle/kaggle.json')\n",
        "    if not os.path.exists(api_local_credential_location):\n",
        "        !mkdir -p ~/.kaggle\n",
        "        !cp '{api_location}' '{api_local_credential_location}'\n",
        "\n",
        "if IN_COLAB:\n",
        "    get_kaggle_json(api_gdrive_credential_location)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U7L-pVNGd7wH",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def submit_result_kaggle(model, comment='', sklearn_model=True, chunksize=100_000, only_submit=False):\n",
        "    if not only_submit:\n",
        "        test_result_df = predict_proba_test(model, sklearn_model=sklearn_model, chunksize=chunksize)\n",
        "        test_result_df.to_csv('test_result.csv', index=False)\n",
        "    !kaggle competitions submit -c reducing-commercial-aviation-fatalities -f test_result.csv -m \"{comment}\""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DyzktwYdei5v",
        "colab_type": "code",
        "outputId": "1d820142-dfa9-4af1-b9e9-5e85fe281d23",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 117,
          "referenced_widgets": [
            "598d39f822244a0faf9088ef4344efdf",
            "1fb3f98534c2447d8d40aa0743322578",
            "75e737ff7be549468ecc256aeee2312d",
            "a5532895c5944dc490f8b2467f30d679",
            "cdd469b846d5404cafbe11da7172763b",
            "41308b5452004bfdbbba3196e90309fa",
            "1ba9a1b3387a49a680ba20aecff6280b",
            "5a439411f0624f7a9fb08c8515033bf0"
          ]
        }
      },
      "source": [
        "submit_result_kaggle(RF_clf, \"Submit best random forest model estimators=1000\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "598d39f822244a0faf9088ef4344efdf",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(IntProgress(value=1, bar_style='info', max=1), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Warning: Looks like you're using an outdated API Version, please consider updating (server 1.5.6 / client 1.5.4)\n",
            "100% 547M/547M [00:09<00:00, 58.0MB/s]\n",
            "Successfully submitted to Reducing Commercial Aviation Fatalities"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1AI1Rp8am863",
        "colab_type": "text"
      },
      "source": [
        "## Obtendo modelos utilizando a ferramenta de autoML H2O"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k29CxBV-kJtU",
        "colab_type": "code",
        "outputId": "c3981f7f-d3c8-42ca-cc7f-643d341ee3ed",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 528
        }
      },
      "source": [
        "# Iniciando cluster h20\n",
        "h2o.init()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Checking whether there is an H2O instance running at http://localhost:54321 ..... not found.\n",
            "Attempting to start a local H2O server...\n",
            "  Java Version: openjdk version \"11.0.6\" 2020-01-14; OpenJDK Runtime Environment (build 11.0.6+10-post-Ubuntu-1ubuntu118.04.1); OpenJDK 64-Bit Server VM (build 11.0.6+10-post-Ubuntu-1ubuntu118.04.1, mixed mode, sharing)\n",
            "  Starting server from /usr/local/lib/python3.6/dist-packages/h2o/backend/bin/h2o.jar\n",
            "  Ice root: /tmp/tmpzz499ase\n",
            "  JVM stdout: /tmp/tmpzz499ase/h2o_unknownUser_started_from_python.out\n",
            "  JVM stderr: /tmp/tmpzz499ase/h2o_unknownUser_started_from_python.err\n",
            "  Server is running at http://127.0.0.1:54321\n",
            "Connecting to H2O server at http://127.0.0.1:54321 ... successful.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<div style=\"overflow:auto\"><table style=\"width:50%\"><tr><td>H2O_cluster_uptime:</td>\n",
              "<td>02 secs</td></tr>\n",
              "<tr><td>H2O_cluster_timezone:</td>\n",
              "<td>Etc/UTC</td></tr>\n",
              "<tr><td>H2O_data_parsing_timezone:</td>\n",
              "<td>UTC</td></tr>\n",
              "<tr><td>H2O_cluster_version:</td>\n",
              "<td>3.30.0.1</td></tr>\n",
              "<tr><td>H2O_cluster_version_age:</td>\n",
              "<td>10 days </td></tr>\n",
              "<tr><td>H2O_cluster_name:</td>\n",
              "<td>H2O_from_python_unknownUser_trht79</td></tr>\n",
              "<tr><td>H2O_cluster_total_nodes:</td>\n",
              "<td>1</td></tr>\n",
              "<tr><td>H2O_cluster_free_memory:</td>\n",
              "<td>6.379 Gb</td></tr>\n",
              "<tr><td>H2O_cluster_total_cores:</td>\n",
              "<td>4</td></tr>\n",
              "<tr><td>H2O_cluster_allowed_cores:</td>\n",
              "<td>4</td></tr>\n",
              "<tr><td>H2O_cluster_status:</td>\n",
              "<td>accepting new members, healthy</td></tr>\n",
              "<tr><td>H2O_connection_url:</td>\n",
              "<td>http://127.0.0.1:54321</td></tr>\n",
              "<tr><td>H2O_connection_proxy:</td>\n",
              "<td>{\"http\": null, \"https\": null}</td></tr>\n",
              "<tr><td>H2O_internal_security:</td>\n",
              "<td>False</td></tr>\n",
              "<tr><td>H2O_API_Extensions:</td>\n",
              "<td>Amazon S3, XGBoost, Algos, AutoML, Core V3, TargetEncoder, Core V4</td></tr>\n",
              "<tr><td>Python_version:</td>\n",
              "<td>3.6.9 final</td></tr></table></div>"
            ],
            "text/plain": [
              "--------------------------  ------------------------------------------------------------------\n",
              "H2O_cluster_uptime:         02 secs\n",
              "H2O_cluster_timezone:       Etc/UTC\n",
              "H2O_data_parsing_timezone:  UTC\n",
              "H2O_cluster_version:        3.30.0.1\n",
              "H2O_cluster_version_age:    10 days\n",
              "H2O_cluster_name:           H2O_from_python_unknownUser_trht79\n",
              "H2O_cluster_total_nodes:    1\n",
              "H2O_cluster_free_memory:    6.379 Gb\n",
              "H2O_cluster_total_cores:    4\n",
              "H2O_cluster_allowed_cores:  4\n",
              "H2O_cluster_status:         accepting new members, healthy\n",
              "H2O_connection_url:         http://127.0.0.1:54321\n",
              "H2O_connection_proxy:       {\"http\": null, \"https\": null}\n",
              "H2O_internal_security:      False\n",
              "H2O_API_Extensions:         Amazon S3, XGBoost, Algos, AutoML, Core V3, TargetEncoder, Core V4\n",
              "Python_version:             3.6.9 final\n",
              "--------------------------  ------------------------------------------------------------------"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lS5toiTotfKI",
        "colab_type": "code",
        "outputId": "50497d84-ffcd-4981-8723-688a54bb7346",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "# Executando AutoML para 20 modelos, com limite max de \n",
        "# execução de 6000 s e 600 s por modelo\n",
        "aml = H2OAutoML(max_models=20, seed=10, max_runtime_secs=6000, \n",
        "                max_runtime_secs_per_model=600, sort_metric='logloss')\n",
        "# Iniciando Treinamento\n",
        "aml.train(x=features, y=target, training_frame=h2o.H2OFrame(\n",
        "    pd.concat([balanced_X_train, balanced_y_train], axis=1)))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Parse progress: |█████████████████████████████████████████████████████████| 100%\n",
            "AutoML progress: |████████████████████████████████████████████████████████| 100%\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xJle1aNSuOuy",
        "colab_type": "code",
        "outputId": "1ca15387-a789-40f7-f3b7-0c840ad27728",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 502
        }
      },
      "source": [
        "# Obtem uma tabela com os modelos testados e suas métricas\n",
        "lb = get_leaderboard(aml, extra_columns='ALL')\n",
        "lb.head(rows=lb.nrows)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<table>\n",
              "<thead>\n",
              "<tr><th>model_id                                           </th><th style=\"text-align: right;\">  logloss</th><th style=\"text-align: right;\">  mean_per_class_error</th><th style=\"text-align: right;\">     rmse</th><th style=\"text-align: right;\">       mse</th><th style=\"text-align: right;\">  training_time_ms</th><th style=\"text-align: right;\">  predict_time_per_row_ms</th></tr>\n",
              "</thead>\n",
              "<tbody>\n",
              "<tr><td>GBM_grid__1_AutoML_20200410_150537_model_1         </td><td style=\"text-align: right;\">0.0212255</td><td style=\"text-align: right;\">            0.00705106</td><td style=\"text-align: right;\">0.0744785</td><td style=\"text-align: right;\">0.00554704</td><td style=\"text-align: right;\">            101030</td><td style=\"text-align: right;\">                 0.017959</td></tr>\n",
              "<tr><td>XGBoost_grid__1_AutoML_20200410_150537_model_1     </td><td style=\"text-align: right;\">0.0301429</td><td style=\"text-align: right;\">            0.0100331 </td><td style=\"text-align: right;\">0.0889327</td><td style=\"text-align: right;\">0.00790902</td><td style=\"text-align: right;\">            142137</td><td style=\"text-align: right;\">                 0.002116</td></tr>\n",
              "<tr><td>StackedEnsemble_AllModels_AutoML_20200410_150537   </td><td style=\"text-align: right;\">0.0307664</td><td style=\"text-align: right;\">            0.00785281</td><td style=\"text-align: right;\">0.0815859</td><td style=\"text-align: right;\">0.00665625</td><td style=\"text-align: right;\">            294329</td><td style=\"text-align: right;\">                 0.117313</td></tr>\n",
              "<tr><td>StackedEnsemble_BestOfFamily_AutoML_20200410_150537</td><td style=\"text-align: right;\">0.0309271</td><td style=\"text-align: right;\">            0.0075106 </td><td style=\"text-align: right;\">0.0800303</td><td style=\"text-align: right;\">0.00640484</td><td style=\"text-align: right;\">             87168</td><td style=\"text-align: right;\">                 0.029142</td></tr>\n",
              "<tr><td>XGBoost_2_AutoML_20200410_150537                   </td><td style=\"text-align: right;\">0.0604786</td><td style=\"text-align: right;\">            0.0175883 </td><td style=\"text-align: right;\">0.124791 </td><td style=\"text-align: right;\">0.0155728 </td><td style=\"text-align: right;\">             47715</td><td style=\"text-align: right;\">                 0.00096 </td></tr>\n",
              "<tr><td>GBM_4_AutoML_20200410_150537                       </td><td style=\"text-align: right;\">0.0715908</td><td style=\"text-align: right;\">            0.0210533 </td><td style=\"text-align: right;\">0.137033 </td><td style=\"text-align: right;\">0.018778  </td><td style=\"text-align: right;\">             24462</td><td style=\"text-align: right;\">                 0.012733</td></tr>\n",
              "<tr><td>GBM_5_AutoML_20200410_150537                       </td><td style=\"text-align: right;\">0.0732891</td><td style=\"text-align: right;\">            0.0223669 </td><td style=\"text-align: right;\">0.140079 </td><td style=\"text-align: right;\">0.0196221 </td><td style=\"text-align: right;\">             24719</td><td style=\"text-align: right;\">                 0.007523</td></tr>\n",
              "<tr><td>XGBoost_grid__1_AutoML_20200410_150537_model_3     </td><td style=\"text-align: right;\">0.0867899</td><td style=\"text-align: right;\">            0.0269786 </td><td style=\"text-align: right;\">0.152798 </td><td style=\"text-align: right;\">0.0233472 </td><td style=\"text-align: right;\">            111135</td><td style=\"text-align: right;\">                 0.002437</td></tr>\n",
              "<tr><td>GBM_3_AutoML_20200410_150537                       </td><td style=\"text-align: right;\">0.0946541</td><td style=\"text-align: right;\">            0.0293517 </td><td style=\"text-align: right;\">0.160261 </td><td style=\"text-align: right;\">0.0256835 </td><td style=\"text-align: right;\">             24662</td><td style=\"text-align: right;\">                 0.009836</td></tr>\n",
              "<tr><td>XGBoost_grid__1_AutoML_20200410_150537_model_2     </td><td style=\"text-align: right;\">0.0988431</td><td style=\"text-align: right;\">            0.0319602 </td><td style=\"text-align: right;\">0.16486  </td><td style=\"text-align: right;\">0.0271789 </td><td style=\"text-align: right;\">            127033</td><td style=\"text-align: right;\">                 0.003186</td></tr>\n",
              "<tr><td>XGBoost_1_AutoML_20200410_150537                   </td><td style=\"text-align: right;\">0.104515 </td><td style=\"text-align: right;\">            0.0336456 </td><td style=\"text-align: right;\">0.169856 </td><td style=\"text-align: right;\">0.0288511 </td><td style=\"text-align: right;\">             39713</td><td style=\"text-align: right;\">                 0.001135</td></tr>\n",
              "<tr><td>GBM_2_AutoML_20200410_150537                       </td><td style=\"text-align: right;\">0.108219 </td><td style=\"text-align: right;\">            0.0352499 </td><td style=\"text-align: right;\">0.173117 </td><td style=\"text-align: right;\">0.0299695 </td><td style=\"text-align: right;\">             21541</td><td style=\"text-align: right;\">                 0.009946</td></tr>\n",
              "<tr><td>GBM_grid__1_AutoML_20200410_150537_model_3         </td><td style=\"text-align: right;\">0.116019 </td><td style=\"text-align: right;\">            0.0401033 </td><td style=\"text-align: right;\">0.181985 </td><td style=\"text-align: right;\">0.0331184 </td><td style=\"text-align: right;\">             33718</td><td style=\"text-align: right;\">                 0.020802</td></tr>\n",
              "<tr><td>GBM_1_AutoML_20200410_150537                       </td><td style=\"text-align: right;\">0.128078 </td><td style=\"text-align: right;\">            0.0427253 </td><td style=\"text-align: right;\">0.190441 </td><td style=\"text-align: right;\">0.0362677 </td><td style=\"text-align: right;\">             25118</td><td style=\"text-align: right;\">                 0.010886</td></tr>\n",
              "<tr><td>GBM_grid__1_AutoML_20200410_150537_model_2         </td><td style=\"text-align: right;\">0.150057 </td><td style=\"text-align: right;\">            0.0487602 </td><td style=\"text-align: right;\">0.205576 </td><td style=\"text-align: right;\">0.0422613 </td><td style=\"text-align: right;\">             16427</td><td style=\"text-align: right;\">                 0.005908</td></tr>\n",
              "<tr><td>XGBoost_3_AutoML_20200410_150537                   </td><td style=\"text-align: right;\">0.170503 </td><td style=\"text-align: right;\">            0.057608  </td><td style=\"text-align: right;\">0.223655 </td><td style=\"text-align: right;\">0.0500214 </td><td style=\"text-align: right;\">             39124</td><td style=\"text-align: right;\">                 0.001078</td></tr>\n",
              "<tr><td>DRF_1_AutoML_20200410_150537                       </td><td style=\"text-align: right;\">0.294469 </td><td style=\"text-align: right;\">            0.0703137 </td><td style=\"text-align: right;\">0.289463 </td><td style=\"text-align: right;\">0.0837887 </td><td style=\"text-align: right;\">             12659</td><td style=\"text-align: right;\">                 0.004464</td></tr>\n",
              "<tr><td>XRT_1_AutoML_20200410_150537                       </td><td style=\"text-align: right;\">0.308388 </td><td style=\"text-align: right;\">            0.0707263 </td><td style=\"text-align: right;\">0.297004 </td><td style=\"text-align: right;\">0.0882112 </td><td style=\"text-align: right;\">             14788</td><td style=\"text-align: right;\">                 0.004415</td></tr>\n",
              "<tr><td>DeepLearning_1_AutoML_20200410_150537              </td><td style=\"text-align: right;\">0.421193 </td><td style=\"text-align: right;\">            0.184697  </td><td style=\"text-align: right;\">0.36985  </td><td style=\"text-align: right;\">0.136789  </td><td style=\"text-align: right;\">             11935</td><td style=\"text-align: right;\">                 0.000254</td></tr>\n",
              "<tr><td>DeepLearning_grid__2_AutoML_20200410_150537_model_1</td><td style=\"text-align: right;\">0.445896 </td><td style=\"text-align: right;\">            0.174554  </td><td style=\"text-align: right;\">0.37574  </td><td style=\"text-align: right;\">0.141181  </td><td style=\"text-align: right;\">             71423</td><td style=\"text-align: right;\">                 0.004885</td></tr>\n",
              "<tr><td>DeepLearning_grid__1_AutoML_20200410_150537_model_1</td><td style=\"text-align: right;\">0.557142 </td><td style=\"text-align: right;\">            0.192782  </td><td style=\"text-align: right;\">0.407034 </td><td style=\"text-align: right;\">0.165677  </td><td style=\"text-align: right;\">             75906</td><td style=\"text-align: right;\">                 0.000869</td></tr>\n",
              "<tr><td>GLM_1_AutoML_20200410_150537                       </td><td style=\"text-align: right;\">1.27804  </td><td style=\"text-align: right;\">            0.611315  </td><td style=\"text-align: right;\">0.708707 </td><td style=\"text-align: right;\">0.502265  </td><td style=\"text-align: right;\">              9769</td><td style=\"text-align: right;\">                 6.1e-05 </td></tr>\n",
              "</tbody>\n",
              "</table>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              ""
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 107
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kPdGKaypeEUp",
        "colab_type": "code",
        "outputId": "365e20d3-e4a5-4357-addd-9894b8aba929",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "aml.leader"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model Details\n",
            "=============\n",
            "H2OGradientBoostingEstimator :  Gradient Boosting Machine\n",
            "Model Key:  GBM_grid__1_AutoML_20200410_150537_model_1\n",
            "\n",
            "\n",
            "Model Summary: \n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th>number_of_trees</th>\n",
              "      <th>number_of_internal_trees</th>\n",
              "      <th>model_size_in_bytes</th>\n",
              "      <th>min_depth</th>\n",
              "      <th>max_depth</th>\n",
              "      <th>mean_depth</th>\n",
              "      <th>min_leaves</th>\n",
              "      <th>max_leaves</th>\n",
              "      <th>mean_leaves</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td></td>\n",
              "      <td>136.0</td>\n",
              "      <td>544.0</td>\n",
              "      <td>34539220.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>746.0</td>\n",
              "      <td>8760.0</td>\n",
              "      <td>5058.3916</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     number_of_trees  ...  max_leaves  mean_leaves\n",
              "0              136.0  ...      8760.0    5058.3916\n",
              "\n",
              "[1 rows x 10 columns]"
            ]
          },
          "metadata": {
            "tags": []
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        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\n",
            "ModelMetricsMultinomial: gbm\n",
            "** Reported on train data. **\n",
            "\n",
            "MSE: 2.9175181985310367e-07\n",
            "RMSE: 0.0005401405556455687\n",
            "LogLoss: 0.000182198793627346\n",
            "Mean Per-Class Error: 0.0\n",
            "\n",
            "Confusion Matrix: Row labels: Actual class; Column labels: Predicted class\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>A</th>\n",
              "      <th>B</th>\n",
              "      <th>C</th>\n",
              "      <th>D</th>\n",
              "      <th>Error</th>\n",
              "      <th>Rate</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>104469.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0 / 104,469</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.0</td>\n",
              "      <td>104391.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0 / 104,391</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>104566.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0 / 104,566</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>104359.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0 / 104,359</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>104469.0</td>\n",
              "      <td>104391.0</td>\n",
              "      <td>104566.0</td>\n",
              "      <td>104359.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0 / 417,785</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          A         B         C         D  Error         Rate\n",
              "0  104469.0       0.0       0.0       0.0    0.0  0 / 104,469\n",
              "1       0.0  104391.0       0.0       0.0    0.0  0 / 104,391\n",
              "2       0.0       0.0  104566.0       0.0    0.0  0 / 104,566\n",
              "3       0.0       0.0       0.0  104359.0    0.0  0 / 104,359\n",
              "4  104469.0  104391.0  104566.0  104359.0    0.0  0 / 417,785"
            ]
          },
          "metadata": {
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        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Top-4 Hit Ratios: \n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
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              "  <thead>\n",
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              "      <th></th>\n",
              "      <th>k</th>\n",
              "      <th>hit_ratio</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>1.0</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>1.0</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>1.0</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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            ],
            "text/plain": [
              "   k  hit_ratio\n",
              "0  1        1.0\n",
              "1  2        1.0\n",
              "2  3        1.0\n",
              "3  4        1.0"
            ]
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          "text": [
            "\n",
            "ModelMetricsMultinomial: gbm\n",
            "** Reported on cross-validation data. **\n",
            "\n",
            "MSE: 0.0055470402069107885\n",
            "RMSE: 0.07447845464905128\n",
            "LogLoss: 0.02122546272372881\n",
            "Mean Per-Class Error: 0.007051059136918395\n",
            "\n",
            "Confusion Matrix: Row labels: Actual class; Column labels: Predicted class\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>A</th>\n",
              "      <th>B</th>\n",
              "      <th>C</th>\n",
              "      <th>D</th>\n",
              "      <th>Error</th>\n",
              "      <th>Rate</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>102827.0</td>\n",
              "      <td>543.0</td>\n",
              "      <td>1038.0</td>\n",
              "      <td>61.0</td>\n",
              "      <td>0.015718</td>\n",
              "      <td>1,642 / 104,469</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>607.0</td>\n",
              "      <td>103621.0</td>\n",
              "      <td>163.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.007376</td>\n",
              "      <td>770 / 104,391</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>296.0</td>\n",
              "      <td>27.0</td>\n",
              "      <td>104228.0</td>\n",
              "      <td>15.0</td>\n",
              "      <td>0.003232</td>\n",
              "      <td>338 / 104,566</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>193.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>104163.0</td>\n",
              "      <td>0.001878</td>\n",
              "      <td>196 / 104,359</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>103923.0</td>\n",
              "      <td>104191.0</td>\n",
              "      <td>105432.0</td>\n",
              "      <td>104239.0</td>\n",
              "      <td>0.007051</td>\n",
              "      <td>2,946 / 417,785</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          A         B         C         D     Error             Rate\n",
              "0  102827.0     543.0    1038.0      61.0  0.015718  1,642 / 104,469\n",
              "1     607.0  103621.0     163.0       0.0  0.007376    770 / 104,391\n",
              "2     296.0      27.0  104228.0      15.0  0.003232    338 / 104,566\n",
              "3     193.0       0.0       3.0  104163.0  0.001878    196 / 104,359\n",
              "4  103923.0  104191.0  105432.0  104239.0  0.007051  2,946 / 417,785"
            ]
          },
          "metadata": {
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          }
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        {
          "output_type": "stream",
          "text": [
            "\n",
            "Top-4 Hit Ratios: \n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
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              "\n",
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              "        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>k</th>\n",
              "      <th>hit_ratio</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>0.992949</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>0.999691</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>0.999993</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   k  hit_ratio\n",
              "0  1   0.992949\n",
              "1  2   0.999691\n",
              "2  3   0.999993\n",
              "3  4   1.000000"
            ]
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          "metadata": {
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        {
          "output_type": "stream",
          "text": [
            "\n",
            "Cross-Validation Metrics Summary: \n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th>mean</th>\n",
              "      <th>sd</th>\n",
              "      <th>cv_1_valid</th>\n",
              "      <th>cv_2_valid</th>\n",
              "      <th>cv_3_valid</th>\n",
              "      <th>cv_4_valid</th>\n",
              "      <th>cv_5_valid</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>accuracy</td>\n",
              "      <td>0.99294853</td>\n",
              "      <td>3.7133222E-4</td>\n",
              "      <td>0.992544</td>\n",
              "      <td>0.9931065</td>\n",
              "      <td>0.9928672</td>\n",
              "      <td>0.9927235</td>\n",
              "      <td>0.9935014</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>err</td>\n",
              "      <td>0.007051474</td>\n",
              "      <td>3.7133222E-4</td>\n",
              "      <td>0.0074559883</td>\n",
              "      <td>0.006893498</td>\n",
              "      <td>0.0071328552</td>\n",
              "      <td>0.00727647</td>\n",
              "      <td>0.006498558</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>err_count</td>\n",
              "      <td>589.2</td>\n",
              "      <td>31.027407</td>\n",
              "      <td>623.0</td>\n",
              "      <td>576.0</td>\n",
              "      <td>596.0</td>\n",
              "      <td>608.0</td>\n",
              "      <td>543.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>logloss</td>\n",
              "      <td>0.021225464</td>\n",
              "      <td>4.2718608E-4</td>\n",
              "      <td>0.021581408</td>\n",
              "      <td>0.021050563</td>\n",
              "      <td>0.021197747</td>\n",
              "      <td>0.021678014</td>\n",
              "      <td>0.02061958</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>max_per_class_error</td>\n",
              "      <td>0.015718298</td>\n",
              "      <td>0.0012825482</td>\n",
              "      <td>0.01644768</td>\n",
              "      <td>0.015762499</td>\n",
              "      <td>0.015426759</td>\n",
              "      <td>0.01718675</td>\n",
              "      <td>0.013767805</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>mean_per_class_accuracy</td>\n",
              "      <td>0.9929485</td>\n",
              "      <td>3.6570214E-4</td>\n",
              "      <td>0.9925786</td>\n",
              "      <td>0.99308</td>\n",
              "      <td>0.99285233</td>\n",
              "      <td>0.99271834</td>\n",
              "      <td>0.993513</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>mean_per_class_error</td>\n",
              "      <td>0.007051546</td>\n",
              "      <td>3.6570214E-4</td>\n",
              "      <td>0.007421365</td>\n",
              "      <td>0.006919982</td>\n",
              "      <td>0.0071476847</td>\n",
              "      <td>0.007281668</td>\n",
              "      <td>0.00648703</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>mse</td>\n",
              "      <td>0.00554704</td>\n",
              "      <td>2.0310882E-4</td>\n",
              "      <td>0.0057663093</td>\n",
              "      <td>0.0054638777</td>\n",
              "      <td>0.0056182467</td>\n",
              "      <td>0.0056476607</td>\n",
              "      <td>0.0052391067</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>r2</td>\n",
              "      <td>0.9955613</td>\n",
              "      <td>1.5987207E-4</td>\n",
              "      <td>0.9954006</td>\n",
              "      <td>0.99561906</td>\n",
              "      <td>0.9955055</td>\n",
              "      <td>0.99547124</td>\n",
              "      <td>0.99581003</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>rmse</td>\n",
              "      <td>0.07446837</td>\n",
              "      <td>0.0013706186</td>\n",
              "      <td>0.07593622</td>\n",
              "      <td>0.073918045</td>\n",
              "      <td>0.074954964</td>\n",
              "      <td>0.075150914</td>\n",
              "      <td>0.072381675</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                   mean  ...    cv_4_valid    cv_5_valid\n",
              "0                 accuracy   0.99294853  ...     0.9927235     0.9935014\n",
              "1                      err  0.007051474  ...    0.00727647   0.006498558\n",
              "2                err_count        589.2  ...         608.0         543.0\n",
              "3                  logloss  0.021225464  ...   0.021678014    0.02061958\n",
              "4      max_per_class_error  0.015718298  ...    0.01718675   0.013767805\n",
              "5  mean_per_class_accuracy    0.9929485  ...    0.99271834      0.993513\n",
              "6     mean_per_class_error  0.007051546  ...   0.007281668    0.00648703\n",
              "7                      mse   0.00554704  ...  0.0056476607  0.0052391067\n",
              "8                       r2    0.9955613  ...    0.99547124    0.99581003\n",
              "9                     rmse   0.07446837  ...   0.075150914   0.072381675\n",
              "\n",
              "[10 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Scoring History: \n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th>timestamp</th>\n",
              "      <th>duration</th>\n",
              "      <th>number_of_trees</th>\n",
              "      <th>training_rmse</th>\n",
              "      <th>training_logloss</th>\n",
              "      <th>training_classification_error</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td></td>\n",
              "      <td>2020-04-10 16:11:30</td>\n",
              "      <td>7 min 30.710 sec</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.750000</td>\n",
              "      <td>1.386294</td>\n",
              "      <td>0.749852</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td></td>\n",
              "      <td>2020-04-10 16:11:33</td>\n",
              "      <td>7 min 33.795 sec</td>\n",
              "      <td>5.0</td>\n",
              "      <td>0.461403</td>\n",
              "      <td>0.620243</td>\n",
              "      <td>0.035037</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td></td>\n",
              "      <td>2020-04-10 16:11:37</td>\n",
              "      <td>7 min 37.341 sec</td>\n",
              "      <td>10.0</td>\n",
              "      <td>0.312398</td>\n",
              "      <td>0.364005</td>\n",
              "      <td>0.025745</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td></td>\n",
              "      <td>2020-04-10 16:11:41</td>\n",
              "      <td>7 min 41.065 sec</td>\n",
              "      <td>15.0</td>\n",
              "      <td>0.220699</td>\n",
              "      <td>0.223831</td>\n",
              "      <td>0.020324</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td></td>\n",
              "      <td>2020-04-10 16:11:45</td>\n",
              "      <td>7 min 44.990 sec</td>\n",
              "      <td>20.0</td>\n",
              "      <td>0.171520</td>\n",
              "      <td>0.151190</td>\n",
              "      <td>0.015314</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td></td>\n",
              "      <td>2020-04-10 16:11:48</td>\n",
              "      <td>7 min 48.777 sec</td>\n",
              "      <td>25.0</td>\n",
              "      <td>0.133352</td>\n",
              "      <td>0.100140</td>\n",
              "      <td>0.010379</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td></td>\n",
              "      <td>2020-04-10 16:11:52</td>\n",
              "      <td>7 min 52.746 sec</td>\n",
              "      <td>30.0</td>\n",
              "      <td>0.104721</td>\n",
              "      <td>0.065022</td>\n",
              "      <td>0.006625</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td></td>\n",
              "      <td>2020-04-10 16:11:56</td>\n",
              "      <td>7 min 56.759 sec</td>\n",
              "      <td>35.0</td>\n",
              "      <td>0.084826</td>\n",
              "      <td>0.044570</td>\n",
              "      <td>0.003945</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td></td>\n",
              "      <td>2020-04-10 16:12:00</td>\n",
              "      <td>8 min  0.535 sec</td>\n",
              "      <td>40.0</td>\n",
              "      <td>0.069586</td>\n",
              "      <td>0.033010</td>\n",
              "      <td>0.002090</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td></td>\n",
              "      <td>2020-04-10 16:12:04</td>\n",
              "      <td>8 min  4.502 sec</td>\n",
              "      <td>45.0</td>\n",
              "      <td>0.054873</td>\n",
              "      <td>0.023765</td>\n",
              "      <td>0.000910</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td></td>\n",
              "      <td>2020-04-10 16:12:08</td>\n",
              "      <td>8 min  8.349 sec</td>\n",
              "      <td>50.0</td>\n",
              "      <td>0.044718</td>\n",
              "      <td>0.017475</td>\n",
              "      <td>0.000419</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td></td>\n",
              "      <td>2020-04-10 16:12:12</td>\n",
              "      <td>8 min 12.268 sec</td>\n",
              "      <td>55.0</td>\n",
              "      <td>0.035275</td>\n",
              "      <td>0.012864</td>\n",
              "      <td>0.000139</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td></td>\n",
              "      <td>2020-04-10 16:12:16</td>\n",
              "      <td>8 min 15.964 sec</td>\n",
              "      <td>60.0</td>\n",
              "      <td>0.028401</td>\n",
              "      <td>0.009875</td>\n",
              "      <td>0.000048</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td></td>\n",
              "      <td>2020-04-10 16:12:19</td>\n",
              "      <td>8 min 19.755 sec</td>\n",
              "      <td>65.0</td>\n",
              "      <td>0.021987</td>\n",
              "      <td>0.007383</td>\n",
              "      <td>0.000010</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td></td>\n",
              "      <td>2020-04-10 16:12:23</td>\n",
              "      <td>8 min 23.460 sec</td>\n",
              "      <td>70.0</td>\n",
              "      <td>0.017011</td>\n",
              "      <td>0.005794</td>\n",
              "      <td>0.000002</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td></td>\n",
              "      <td>2020-04-10 16:12:27</td>\n",
              "      <td>8 min 27.410 sec</td>\n",
              "      <td>75.0</td>\n",
              "      <td>0.012866</td>\n",
              "      <td>0.004378</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td></td>\n",
              "      <td>2020-04-10 16:12:31</td>\n",
              "      <td>8 min 31.190 sec</td>\n",
              "      <td>80.0</td>\n",
              "      <td>0.009852</td>\n",
              "      <td>0.003360</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td></td>\n",
              "      <td>2020-04-10 16:12:34</td>\n",
              "      <td>8 min 34.863 sec</td>\n",
              "      <td>85.0</td>\n",
              "      <td>0.007490</td>\n",
              "      <td>0.002516</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td></td>\n",
              "      <td>2020-04-10 16:12:38</td>\n",
              "      <td>8 min 38.523 sec</td>\n",
              "      <td>90.0</td>\n",
              "      <td>0.005823</td>\n",
              "      <td>0.001948</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td></td>\n",
              "      <td>2020-04-10 16:12:42</td>\n",
              "      <td>8 min 42.092 sec</td>\n",
              "      <td>95.0</td>\n",
              "      <td>0.004674</td>\n",
              "      <td>0.001535</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                timestamp  ... training_logloss  training_classification_error\n",
              "0     2020-04-10 16:11:30  ...         1.386294                       0.749852\n",
              "1     2020-04-10 16:11:33  ...         0.620243                       0.035037\n",
              "2     2020-04-10 16:11:37  ...         0.364005                       0.025745\n",
              "3     2020-04-10 16:11:41  ...         0.223831                       0.020324\n",
              "4     2020-04-10 16:11:45  ...         0.151190                       0.015314\n",
              "5     2020-04-10 16:11:48  ...         0.100140                       0.010379\n",
              "6     2020-04-10 16:11:52  ...         0.065022                       0.006625\n",
              "7     2020-04-10 16:11:56  ...         0.044570                       0.003945\n",
              "8     2020-04-10 16:12:00  ...         0.033010                       0.002090\n",
              "9     2020-04-10 16:12:04  ...         0.023765                       0.000910\n",
              "10    2020-04-10 16:12:08  ...         0.017475                       0.000419\n",
              "11    2020-04-10 16:12:12  ...         0.012864                       0.000139\n",
              "12    2020-04-10 16:12:16  ...         0.009875                       0.000048\n",
              "13    2020-04-10 16:12:19  ...         0.007383                       0.000010\n",
              "14    2020-04-10 16:12:23  ...         0.005794                       0.000002\n",
              "15    2020-04-10 16:12:27  ...         0.004378                       0.000000\n",
              "16    2020-04-10 16:12:31  ...         0.003360                       0.000000\n",
              "17    2020-04-10 16:12:34  ...         0.002516                       0.000000\n",
              "18    2020-04-10 16:12:38  ...         0.001948                       0.000000\n",
              "19    2020-04-10 16:12:42  ...         0.001535                       0.000000\n",
              "\n",
              "[20 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "See the whole table with table.as_data_frame()\n",
            "\n",
            "Variable Importances: \n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<div>\n",
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              "\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>variable</th>\n",
              "      <th>relative_importance</th>\n",
              "      <th>scaled_importance</th>\n",
              "      <th>percentage</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>r</td>\n",
              "      <td>503249.343750</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.366682</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>ecg</td>\n",
              "      <td>415689.406250</td>\n",
              "      <td>0.826011</td>\n",
              "      <td>0.302883</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>gsr</td>\n",
              "      <td>259823.296875</td>\n",
              "      <td>0.516291</td>\n",
              "      <td>0.189315</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>eeg_fp1</td>\n",
              "      <td>25491.021484</td>\n",
              "      <td>0.050653</td>\n",
              "      <td>0.018573</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>eeg_fp2</td>\n",
              "      <td>21492.316406</td>\n",
              "      <td>0.042707</td>\n",
              "      <td>0.015660</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>eeg_f8</td>\n",
              "      <td>21220.755859</td>\n",
              "      <td>0.042167</td>\n",
              "      <td>0.015462</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>eeg_f3</td>\n",
              "      <td>20595.017578</td>\n",
              "      <td>0.040924</td>\n",
              "      <td>0.015006</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>eeg_f7</td>\n",
              "      <td>19986.548828</td>\n",
              "      <td>0.039715</td>\n",
              "      <td>0.014563</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>eeg_t3</td>\n",
              "      <td>9712.585938</td>\n",
              "      <td>0.019300</td>\n",
              "      <td>0.007077</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>eeg_c4</td>\n",
              "      <td>8576.305664</td>\n",
              "      <td>0.017042</td>\n",
              "      <td>0.006249</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>eeg_f4</td>\n",
              "      <td>8383.913086</td>\n",
              "      <td>0.016660</td>\n",
              "      <td>0.006109</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>eeg_c3</td>\n",
              "      <td>7642.880371</td>\n",
              "      <td>0.015187</td>\n",
              "      <td>0.005569</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>eeg_fz</td>\n",
              "      <td>7552.700195</td>\n",
              "      <td>0.015008</td>\n",
              "      <td>0.005503</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>eeg_pz</td>\n",
              "      <td>7392.718750</td>\n",
              "      <td>0.014690</td>\n",
              "      <td>0.005387</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>eeg_cz</td>\n",
              "      <td>6245.451660</td>\n",
              "      <td>0.012410</td>\n",
              "      <td>0.004551</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>eeg_t4</td>\n",
              "      <td>4355.973633</td>\n",
              "      <td>0.008656</td>\n",
              "      <td>0.003174</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>eeg_t5</td>\n",
              "      <td>4056.065918</td>\n",
              "      <td>0.008060</td>\n",
              "      <td>0.002955</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>eeg_p4</td>\n",
              "      <td>3939.008789</td>\n",
              "      <td>0.007827</td>\n",
              "      <td>0.002870</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>eeg_poz</td>\n",
              "      <td>3663.713623</td>\n",
              "      <td>0.007280</td>\n",
              "      <td>0.002669</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>eeg_o2</td>\n",
              "      <td>3603.572021</td>\n",
              "      <td>0.007161</td>\n",
              "      <td>0.002626</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   variable  relative_importance  scaled_importance  percentage\n",
              "0         r        503249.343750           1.000000    0.366682\n",
              "1       ecg        415689.406250           0.826011    0.302883\n",
              "2       gsr        259823.296875           0.516291    0.189315\n",
              "3   eeg_fp1         25491.021484           0.050653    0.018573\n",
              "4   eeg_fp2         21492.316406           0.042707    0.015660\n",
              "5    eeg_f8         21220.755859           0.042167    0.015462\n",
              "6    eeg_f3         20595.017578           0.040924    0.015006\n",
              "7    eeg_f7         19986.548828           0.039715    0.014563\n",
              "8    eeg_t3          9712.585938           0.019300    0.007077\n",
              "9    eeg_c4          8576.305664           0.017042    0.006249\n",
              "10   eeg_f4          8383.913086           0.016660    0.006109\n",
              "11   eeg_c3          7642.880371           0.015187    0.005569\n",
              "12   eeg_fz          7552.700195           0.015008    0.005503\n",
              "13   eeg_pz          7392.718750           0.014690    0.005387\n",
              "14   eeg_cz          6245.451660           0.012410    0.004551\n",
              "15   eeg_t4          4355.973633           0.008656    0.003174\n",
              "16   eeg_t5          4056.065918           0.008060    0.002955\n",
              "17   eeg_p4          3939.008789           0.007827    0.002870\n",
              "18  eeg_poz          3663.713623           0.007280    0.002669\n",
              "19   eeg_o2          3603.572021           0.007161    0.002626"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "See the whole table with table.as_data_frame()\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              ""
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 108
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BcqBNd__qReP",
        "colab_type": "code",
        "outputId": "aa29ac7f-75bf-4069-e6d8-676797567e8a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 334
        }
      },
      "source": [
        "# Exibir as métricas do modelo\n",
        "show_metrics(aml.leader, balanced_X_test, balanced_y_test, \n",
        "             'H2O Automl GBM', sklearn_model=False)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Parse progress: |█████████████████████████████████████████████████████████| 100%\n",
            "gbm prediction progress: |████████████████████████████████████████████████| 100%\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>precision</th>\n",
              "      <th>recall</th>\n",
              "      <th>f1-score</th>\n",
              "      <th>support</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>A</th>\n",
              "      <td>0.991991</td>\n",
              "      <td>0.987466</td>\n",
              "      <td>0.989723</td>\n",
              "      <td>26089.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>B</th>\n",
              "      <td>0.995255</td>\n",
              "      <td>0.994000</td>\n",
              "      <td>0.994627</td>\n",
              "      <td>26167.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>C</th>\n",
              "      <td>0.990753</td>\n",
              "      <td>0.997538</td>\n",
              "      <td>0.994134</td>\n",
              "      <td>25992.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>D</th>\n",
              "      <td>0.999618</td>\n",
              "      <td>0.998626</td>\n",
              "      <td>0.999122</td>\n",
              "      <td>26199.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>accuracy</th>\n",
              "      <td>0.994409</td>\n",
              "      <td>0.994409</td>\n",
              "      <td>0.994409</td>\n",
              "      <td>0.994409</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>macro avg</th>\n",
              "      <td>0.994404</td>\n",
              "      <td>0.994407</td>\n",
              "      <td>0.994401</td>\n",
              "      <td>104447.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>weighted avg</th>\n",
              "      <td>0.994414</td>\n",
              "      <td>0.994409</td>\n",
              "      <td>0.994407</td>\n",
              "      <td>104447.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              precision    recall  f1-score        support\n",
              "A              0.991991  0.987466  0.989723   26089.000000\n",
              "B              0.995255  0.994000  0.994627   26167.000000\n",
              "C              0.990753  0.997538  0.994134   25992.000000\n",
              "D              0.999618  0.998626  0.999122   26199.000000\n",
              "accuracy       0.994409  0.994409  0.994409       0.994409\n",
              "macro avg      0.994404  0.994407  0.994401  104447.000000\n",
              "weighted avg   0.994414  0.994409  0.994407  104447.000000"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Log loss: 0.016344507082644198 | F1 Score: 0.9944014452613698\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "W7Or9RgvnVJi",
        "colab_type": "text"
      },
      "source": [
        "### Retreinando melhor modelo h20 com todos os dados disponíveis"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NpPb2rMTBADe",
        "colab_type": "code",
        "outputId": "9e8f50fc-4ba6-49ad-a5e9-2877f6568f3a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "h20_model.train(x=features, y=target, training_frame=h2o.H2OFrame(pd.concat([train_df[features], train_df[target]], axis=1)))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Parse progress: |█████████████████████████████████████████████████████████| 100%\n",
            "gbm Model Build progress: |███████████████████████████████████████████████| 100%\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e5nlb34Kpxrn",
        "colab_type": "code",
        "outputId": "cec3b2ed-ee4f-442c-c29c-60497e68c50a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "# Salvando modelo final\n",
        "h2o.save_model(model=aml.leader, path=h20_models, force=True)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'/gdrive/My Drive/Pos Graduação/TCC/automl/GBM_model_python_1586538302325_1'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AV2-MYQTDTv9",
        "colab_type": "text"
      },
      "source": [
        "### Submetendo dados para o Kaggle"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2zGxUpPim0Oa",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Lendo o modelo salvo\n",
        "h20_model = h2o.load_model('/gdrive/My Drive/Pos Graduação/TCC/automl/GBM_model_python_1586538302325_1')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UWuaPNcdAEol",
        "colab_type": "text"
      },
      "source": [
        "### Submetendo H20 model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BRBMGXRRBAKK",
        "colab_type": "code",
        "outputId": "049e558c-ec4c-4b2f-ff53-208f3b20369f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 729,
          "referenced_widgets": [
            "d732f14ca8114056bfd501dee6ac0310",
            "d18b620379fa4ed2b2cab073db4bb803",
            "97c69eb5653749a8b19537a7ce92dd0b",
            "140183f95c7a4994828845cd832d1730",
            "9985cf8440864d928903e067567b3b06",
            "5b09a31cc70b4d9da4898fa5242ea4f4",
            "b234bc9fb48f4fbdade51decaf1926bd",
            "8096fa525f03439d9ad4791b6ff96b5d"
          ]
        }
      },
      "source": [
        "submit_result_kaggle(h20_model, \"Submit h20 GBM\", sklearn_model=False, chunksize=1_000_000)"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "d732f14ca8114056bfd501dee6ac0310",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(IntProgress(value=1, bar_style='info', max=1), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Parse progress: |█████████████████████████████████████████████████████████| 100%\n",
            "gbm prediction progress: |████████████████████████████████████████████████| 100%\n",
            "Parse progress: |████████████████████████████████████████████████████████████████| 100%\n",
            "gbm prediction progress: |████████████████████████████████████████████████| 100%\n",
            "Parse progress: |█████████████████████████████████████████████████████████| 100%\n",
            "gbm prediction progress: |████████████████████████████████████████████████| 100%\n",
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            "gbm prediction progress: |████████████████████████████████████████████████| 100%\n",
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            "gbm prediction progress: |████████████████████████████████████████████████| 100%\n",
            "Parse progress: |█████████████████████████████████████████████████████████| 100%\n",
            "gbm prediction progress: |████████████████████████████████████████████████| 100%\n",
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            "gbm prediction progress: |████████████████████████████████████████████████| 100%\n",
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            "gbm prediction progress: |████████████████████████████████████████████████| 100%\n",
            "\n",
            "Warning: Looks like you're using an outdated API Version, please consider updating (server 1.5.6 / client 1.5.4)\n",
            "100% 1.53G/1.53G [00:27<00:00, 59.4MB/s]\n",
            "Successfully submitted to Reducing Commercial Aviation Fatalities"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}