Census_MLP_Atencao.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Census-MLP-Atencao.ipynb",
      "provenance": [],
      "toc_visible": true,
      "mount_file_id": "1cv_DqIcH7Rm241CjOj7JgxHzZuDZ9OAh",
      "authorship_tag": "ABX9TyMRCLBD+BhxFoaxQPXYxts7",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/finardi/tutos/blob/master/Census_MLP_Atencao.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ljhWsSxBCW1F",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 357
        },
        "outputId": "428ff5d5-4c40-43a8-d27a-747bae4138d2"
      },
      "source": [
        "! nvidia-smi"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Wed Aug  5 23:04:40 2020       \n",
            "+-----------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 450.57       Driver Version: 418.67       CUDA Version: 10.1     |\n",
            "|-------------------------------+----------------------+----------------------+\n",
            "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
            "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
            "|                               |                      |               MIG M. |\n",
            "|===============================+======================+======================|\n",
            "|   0  Tesla T4            Off  | 00000000:00:04.0 Off |                    0 |\n",
            "| N/A   61C    P8    10W /  70W |      0MiB / 15079MiB |      0%      Default |\n",
            "|                               |                      |                 ERR! |\n",
            "+-------------------------------+----------------------+----------------------+\n",
            "                                                                               \n",
            "+-----------------------------------------------------------------------------+\n",
            "| Processes:                                                                  |\n",
            "|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\n",
            "|        ID   ID                                                   Usage      |\n",
            "|=============================================================================|\n",
            "|  No running processes found                                                 |\n",
            "+-----------------------------------------------------------------------------+\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NDnZKE82XmkJ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!pip install -q torch_optimizer\n",
        "import torch_optimizer as optim_\n",
        "\n",
        "!pip install -q einops\n",
        "from einops import rearrange, reduce, asnumpy, parse_shape\n",
        "from einops.layers.torch import Rearrange, Reduce"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DGGzrVrhDkwK",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 71
        },
        "outputId": "6cf872c7-7f24-442e-c781-861df0ee4c4e"
      },
      "source": [
        "# Basics\n",
        "import os\n",
        "import pandas as pd\n",
        "import math\n",
        "import copy\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "# PyTorch\n",
        "import torch \n",
        "from torch import nn\n",
        "import torch.nn.functional as F\n",
        "from torch.utils.data import Dataset, DataLoader\n",
        "\n",
        "# Sklearn\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import roc_auc_score\n",
        "from sklearn.metrics import roc_curve\n",
        "from sklearn.metrics import auc\n",
        "from sklearn.metrics import confusion_matrix\n",
        "from sklearn.metrics import precision_recall_curve\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "from sklearn.preprocessing import quantile_transform\n",
        "\n",
        "# Scipy\n",
        "from scipy import stats"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
            "  import pandas.util.testing as tm\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ExCYVPc1CZlH",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "2a4bdca1-3cb2-4d40-f96d-ae6b6205ca02"
      },
      "source": [
        "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
        "manual_seed = 2357\n",
        "\n",
        "def deterministic(rep=True):\n",
        "    if rep:\n",
        "        np.random.seed(manual_seed)\n",
        "        torch.manual_seed(manual_seed)\n",
        "        if torch.cuda.is_available():\n",
        "            torch.cuda.manual_seed(manual_seed)\n",
        "            torch.cuda.manual_seed_all(manual_seed)\n",
        "        torch.backends.cudnn.enabled = False \n",
        "        torch.backends.cudnn.benchmark = False\n",
        "        torch.backends.cudnn.deterministic = True\n",
        "        print(f'Deterministic experiment, seed: {manual_seed}')\n",
        "    else:\n",
        "        print('Random experiment')\n",
        "\n",
        "deterministic()"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Deterministic experiment, seed: 2357\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gwVVI3JADtbh",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "73fe0154-b778-4c27-dcfe-101bc340ec7a"
      },
      "source": [
        "try:\n",
        "    df = pd.read_csv('/content/CensusData.csv');\n",
        "except:\n",
        "    !unzip '/content/drive/My Drive/Colab Notebooks/Tabular/36093_54253_bundle_archive.zip'\n",
        "    df = pd.read_csv('/content/CensusData.csv');"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Archive:  /content/drive/My Drive/Colab Notebooks/Tabular/36093_54253_bundle_archive.zip\n",
            "  inflating: CensusData.csv          \n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Cc_N4JIrD1UN",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 555
        },
        "outputId": "0628e5b4-8521-4323-db53-d2f116827b03"
      },
      "source": [
        "df"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Age</th>\n",
              "      <th>workclass</th>\n",
              "      <th>fnlwgt</th>\n",
              "      <th>education</th>\n",
              "      <th>education-num</th>\n",
              "      <th>Marital-status</th>\n",
              "      <th>Occupation</th>\n",
              "      <th>Relationship</th>\n",
              "      <th>Race</th>\n",
              "      <th>Sex</th>\n",
              "      <th>capital-gain</th>\n",
              "      <th>capital-loss</th>\n",
              "      <th>hours-per-week</th>\n",
              "      <th>native-country</th>\n",
              "      <th>Income</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>39</td>\n",
              "      <td>State-gov</td>\n",
              "      <td>77516</td>\n",
              "      <td>Bachelors</td>\n",
              "      <td>13</td>\n",
              "      <td>Never-married</td>\n",
              "      <td>Adm-clerical</td>\n",
              "      <td>Not-in-family</td>\n",
              "      <td>White</td>\n",
              "      <td>Male</td>\n",
              "      <td>2174</td>\n",
              "      <td>0</td>\n",
              "      <td>40</td>\n",
              "      <td>United-States</td>\n",
              "      <td>&lt;=50K</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>50</td>\n",
              "      <td>Self-emp-not-inc</td>\n",
              "      <td>83311</td>\n",
              "      <td>Bachelors</td>\n",
              "      <td>13</td>\n",
              "      <td>Married-civ-spouse</td>\n",
              "      <td>Exec-managerial</td>\n",
              "      <td>Husband</td>\n",
              "      <td>White</td>\n",
              "      <td>Male</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>13</td>\n",
              "      <td>United-States</td>\n",
              "      <td>&lt;=50K</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>38</td>\n",
              "      <td>Private</td>\n",
              "      <td>215646</td>\n",
              "      <td>HS-grad</td>\n",
              "      <td>9</td>\n",
              "      <td>Divorced</td>\n",
              "      <td>Handlers-cleaners</td>\n",
              "      <td>Not-in-family</td>\n",
              "      <td>White</td>\n",
              "      <td>Male</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>40</td>\n",
              "      <td>United-States</td>\n",
              "      <td>&lt;=50K</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>53</td>\n",
              "      <td>Private</td>\n",
              "      <td>234721</td>\n",
              "      <td>11th</td>\n",
              "      <td>7</td>\n",
              "      <td>Married-civ-spouse</td>\n",
              "      <td>Handlers-cleaners</td>\n",
              "      <td>Husband</td>\n",
              "      <td>Black</td>\n",
              "      <td>Male</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>40</td>\n",
              "      <td>United-States</td>\n",
              "      <td>&lt;=50K</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>28</td>\n",
              "      <td>Private</td>\n",
              "      <td>338409</td>\n",
              "      <td>Bachelors</td>\n",
              "      <td>13</td>\n",
              "      <td>Married-civ-spouse</td>\n",
              "      <td>Prof-specialty</td>\n",
              "      <td>Wife</td>\n",
              "      <td>Black</td>\n",
              "      <td>Female</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>40</td>\n",
              "      <td>Cuba</td>\n",
              "      <td>&lt;=50K</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32556</th>\n",
              "      <td>27</td>\n",
              "      <td>Private</td>\n",
              "      <td>257302</td>\n",
              "      <td>Assoc-acdm</td>\n",
              "      <td>12</td>\n",
              "      <td>Married-civ-spouse</td>\n",
              "      <td>Tech-support</td>\n",
              "      <td>Wife</td>\n",
              "      <td>White</td>\n",
              "      <td>Female</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>38</td>\n",
              "      <td>United-States</td>\n",
              "      <td>&lt;=50K</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32557</th>\n",
              "      <td>40</td>\n",
              "      <td>Private</td>\n",
              "      <td>154374</td>\n",
              "      <td>HS-grad</td>\n",
              "      <td>9</td>\n",
              "      <td>Married-civ-spouse</td>\n",
              "      <td>Machine-op-inspct</td>\n",
              "      <td>Husband</td>\n",
              "      <td>White</td>\n",
              "      <td>Male</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>40</td>\n",
              "      <td>United-States</td>\n",
              "      <td>&gt;50K</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32558</th>\n",
              "      <td>58</td>\n",
              "      <td>Private</td>\n",
              "      <td>151910</td>\n",
              "      <td>HS-grad</td>\n",
              "      <td>9</td>\n",
              "      <td>Widowed</td>\n",
              "      <td>Adm-clerical</td>\n",
              "      <td>Unmarried</td>\n",
              "      <td>White</td>\n",
              "      <td>Female</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>40</td>\n",
              "      <td>United-States</td>\n",
              "      <td>&lt;=50K</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32559</th>\n",
              "      <td>22</td>\n",
              "      <td>Private</td>\n",
              "      <td>201490</td>\n",
              "      <td>HS-grad</td>\n",
              "      <td>9</td>\n",
              "      <td>Never-married</td>\n",
              "      <td>Adm-clerical</td>\n",
              "      <td>Own-child</td>\n",
              "      <td>White</td>\n",
              "      <td>Male</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>20</td>\n",
              "      <td>United-States</td>\n",
              "      <td>&lt;=50K</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32560</th>\n",
              "      <td>52</td>\n",
              "      <td>Self-emp-inc</td>\n",
              "      <td>287927</td>\n",
              "      <td>HS-grad</td>\n",
              "      <td>9</td>\n",
              "      <td>Married-civ-spouse</td>\n",
              "      <td>Exec-managerial</td>\n",
              "      <td>Wife</td>\n",
              "      <td>White</td>\n",
              "      <td>Female</td>\n",
              "      <td>15024</td>\n",
              "      <td>0</td>\n",
              "      <td>40</td>\n",
              "      <td>United-States</td>\n",
              "      <td>&gt;50K</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>32561 rows × 15 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "       Age          workclass  fnlwgt  ... hours-per-week  native-country  Income\n",
              "0       39          State-gov   77516  ...             40   United-States   <=50K\n",
              "1       50   Self-emp-not-inc   83311  ...             13   United-States   <=50K\n",
              "2       38            Private  215646  ...             40   United-States   <=50K\n",
              "3       53            Private  234721  ...             40   United-States   <=50K\n",
              "4       28            Private  338409  ...             40            Cuba   <=50K\n",
              "...    ...                ...     ...  ...            ...             ...     ...\n",
              "32556   27            Private  257302  ...             38   United-States   <=50K\n",
              "32557   40            Private  154374  ...             40   United-States    >50K\n",
              "32558   58            Private  151910  ...             40   United-States   <=50K\n",
              "32559   22            Private  201490  ...             20   United-States   <=50K\n",
              "32560   52       Self-emp-inc  287927  ...             40   United-States    >50K\n",
              "\n",
              "[32561 rows x 15 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IPCQqzxT39xB",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 391
        },
        "outputId": "5c0cf7c6-5c9f-4aa7-a925-8682cde41e40"
      },
      "source": [
        "df.info()"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 32561 entries, 0 to 32560\n",
            "Data columns (total 15 columns):\n",
            " #   Column          Non-Null Count  Dtype \n",
            "---  ------          --------------  ----- \n",
            " 0   Age             32561 non-null  int64 \n",
            " 1   workclass       32561 non-null  object\n",
            " 2   fnlwgt          32561 non-null  int64 \n",
            " 3   education       32561 non-null  object\n",
            " 4   education-num   32561 non-null  int64 \n",
            " 5   Marital-status  32561 non-null  object\n",
            " 6   Occupation      32561 non-null  object\n",
            " 7   Relationship    32561 non-null  object\n",
            " 8   Race            32561 non-null  object\n",
            " 9   Sex             32561 non-null  object\n",
            " 10  capital-gain    32561 non-null  int64 \n",
            " 11  capital-loss    32561 non-null  int64 \n",
            " 12  hours-per-week  32561 non-null  int64 \n",
            " 13  native-country  32561 non-null  object\n",
            " 14  Income          32561 non-null  object\n",
            "dtypes: int64(6), object(9)\n",
            "memory usage: 3.7+ MB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "h_4qDj3w7tB5",
        "colab_type": "text"
      },
      "source": [
        "### Troca do Label para numérico"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5X2KIjyy7whu",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        },
        "outputId": "b3df9be0-9786-4c37-c42a-f9286a7d230b"
      },
      "source": [
        "# Verificar a contagem antes\n",
        "df.Income.value_counts()"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              " <=50K    24720\n",
              " >50K      7841\n",
              "Name: Income, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1TZ22KPWgBC5",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        },
        "outputId": "bd271840-99e1-483f-b9d7-11569f3116e7"
      },
      "source": [
        "# Verificar a contagem depois\n",
        "df['Income'] = np.where(df['Income'] == ' <=50K', 0, 1)\n",
        "df.Income.value_counts()"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    24720\n",
              "1     7841\n",
              "Name: Income, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "W5FFvnYe79jx",
        "colab_type": "text"
      },
      "source": [
        "### Fazer split entre train e test"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gz1e4bvIZDNs",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "414bf96f-1d01-41d7-98f3-9d357f8e68b8"
      },
      "source": [
        "df_train, df_test, y_train, y_test = train_test_split(\n",
        "    df.iloc[:, :-1], \n",
        "    df.Income, \n",
        "    test_size=0.3)\n",
        "\n",
        "# retorna para dataframe\n",
        "df_train = pd.DataFrame(df_train)\n",
        "df_test = pd.DataFrame(df_test)\n",
        "df_train.shape, y_train.shape, df_test.shape, y_test.shape"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((22792, 14), (22792,), (9769, 14), (9769,))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IrdPFgURgLaL",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 572
        },
        "outputId": "d3fc53b3-e12f-4ba0-c3f1-1be5f1c91b4a"
      },
      "source": [
        "df_train"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Age</th>\n",
              "      <th>workclass</th>\n",
              "      <th>fnlwgt</th>\n",
              "      <th>education</th>\n",
              "      <th>education-num</th>\n",
              "      <th>Marital-status</th>\n",
              "      <th>Occupation</th>\n",
              "      <th>Relationship</th>\n",
              "      <th>Race</th>\n",
              "      <th>Sex</th>\n",
              "      <th>capital-gain</th>\n",
              "      <th>capital-loss</th>\n",
              "      <th>hours-per-week</th>\n",
              "      <th>native-country</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>18991</th>\n",
              "      <td>24</td>\n",
              "      <td>Private</td>\n",
              "      <td>276851</td>\n",
              "      <td>HS-grad</td>\n",
              "      <td>9</td>\n",
              "      <td>Divorced</td>\n",
              "      <td>Protective-serv</td>\n",
              "      <td>Own-child</td>\n",
              "      <td>White</td>\n",
              "      <td>Female</td>\n",
              "      <td>0</td>\n",
              "      <td>1762</td>\n",
              "      <td>40</td>\n",
              "      <td>United-States</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9743</th>\n",
              "      <td>33</td>\n",
              "      <td>Self-emp-not-inc</td>\n",
              "      <td>193246</td>\n",
              "      <td>HS-grad</td>\n",
              "      <td>9</td>\n",
              "      <td>Never-married</td>\n",
              "      <td>Exec-managerial</td>\n",
              "      <td>Not-in-family</td>\n",
              "      <td>White</td>\n",
              "      <td>Male</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>55</td>\n",
              "      <td>France</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23417</th>\n",
              "      <td>50</td>\n",
              "      <td>Private</td>\n",
              "      <td>197322</td>\n",
              "      <td>11th</td>\n",
              "      <td>7</td>\n",
              "      <td>Married-civ-spouse</td>\n",
              "      <td>Exec-managerial</td>\n",
              "      <td>Husband</td>\n",
              "      <td>White</td>\n",
              "      <td>Male</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>50</td>\n",
              "      <td>United-States</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25248</th>\n",
              "      <td>45</td>\n",
              "      <td>Private</td>\n",
              "      <td>75673</td>\n",
              "      <td>Assoc-voc</td>\n",
              "      <td>11</td>\n",
              "      <td>Widowed</td>\n",
              "      <td>Adm-clerical</td>\n",
              "      <td>Not-in-family</td>\n",
              "      <td>Asian-Pac-Islander</td>\n",
              "      <td>Female</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>40</td>\n",
              "      <td>United-States</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12892</th>\n",
              "      <td>27</td>\n",
              "      <td>Private</td>\n",
              "      <td>208725</td>\n",
              "      <td>Assoc-acdm</td>\n",
              "      <td>12</td>\n",
              "      <td>Never-married</td>\n",
              "      <td>Sales</td>\n",
              "      <td>Not-in-family</td>\n",
              "      <td>White</td>\n",
              "      <td>Male</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>42</td>\n",
              "      <td>United-States</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16432</th>\n",
              "      <td>18</td>\n",
              "      <td>Private</td>\n",
              "      <td>267965</td>\n",
              "      <td>11th</td>\n",
              "      <td>7</td>\n",
              "      <td>Never-married</td>\n",
              "      <td>Sales</td>\n",
              "      <td>Not-in-family</td>\n",
              "      <td>White</td>\n",
              "      <td>Female</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>15</td>\n",
              "      <td>United-States</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25934</th>\n",
              "      <td>47</td>\n",
              "      <td>Private</td>\n",
              "      <td>503454</td>\n",
              "      <td>12th</td>\n",
              "      <td>8</td>\n",
              "      <td>Never-married</td>\n",
              "      <td>Adm-clerical</td>\n",
              "      <td>Other-relative</td>\n",
              "      <td>Black</td>\n",
              "      <td>Female</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>40</td>\n",
              "      <td>United-States</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3235</th>\n",
              "      <td>36</td>\n",
              "      <td>Self-emp-not-inc</td>\n",
              "      <td>367020</td>\n",
              "      <td>7th-8th</td>\n",
              "      <td>4</td>\n",
              "      <td>Married-civ-spouse</td>\n",
              "      <td>Craft-repair</td>\n",
              "      <td>Husband</td>\n",
              "      <td>White</td>\n",
              "      <td>Male</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>35</td>\n",
              "      <td>United-States</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16377</th>\n",
              "      <td>54</td>\n",
              "      <td>Self-emp-not-inc</td>\n",
              "      <td>154785</td>\n",
              "      <td>Bachelors</td>\n",
              "      <td>13</td>\n",
              "      <td>Divorced</td>\n",
              "      <td>Exec-managerial</td>\n",
              "      <td>Not-in-family</td>\n",
              "      <td>White</td>\n",
              "      <td>Male</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>50</td>\n",
              "      <td>United-States</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20271</th>\n",
              "      <td>41</td>\n",
              "      <td>?</td>\n",
              "      <td>206916</td>\n",
              "      <td>Bachelors</td>\n",
              "      <td>13</td>\n",
              "      <td>Married-spouse-absent</td>\n",
              "      <td>?</td>\n",
              "      <td>Not-in-family</td>\n",
              "      <td>White</td>\n",
              "      <td>Male</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>30</td>\n",
              "      <td>United-States</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>22792 rows × 14 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "       Age          workclass  ...  hours-per-week  native-country\n",
              "18991   24            Private  ...              40   United-States\n",
              "9743    33   Self-emp-not-inc  ...              55          France\n",
              "23417   50            Private  ...              50   United-States\n",
              "25248   45            Private  ...              40   United-States\n",
              "12892   27            Private  ...              42   United-States\n",
              "...    ...                ...  ...             ...             ...\n",
              "16432   18            Private  ...              15   United-States\n",
              "25934   47            Private  ...              40   United-States\n",
              "3235    36   Self-emp-not-inc  ...              35   United-States\n",
              "16377   54   Self-emp-not-inc  ...              50   United-States\n",
              "20271   41                  ?  ...              30   United-States\n",
              "\n",
              "[22792 rows x 14 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cRcDi2618Rus",
        "colab_type": "text"
      },
      "source": [
        "### Tratar colunas categóricas"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fhkgXstZEJpf",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 289
        },
        "outputId": "21cee73b-25d6-40ab-d1cf-8f9d32fad5ee"
      },
      "source": [
        "df.dtypes"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Age                int64\n",
              "workclass         object\n",
              "fnlwgt             int64\n",
              "education         object\n",
              "education-num      int64\n",
              "Marital-status    object\n",
              "Occupation        object\n",
              "Relationship      object\n",
              "Race              object\n",
              "Sex               object\n",
              "capital-gain       int64\n",
              "capital-loss       int64\n",
              "hours-per-week     int64\n",
              "native-country    object\n",
              "Income             int64\n",
              "dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OjDb3NOhJJxU",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 272
        },
        "outputId": "fe43089a-063e-4383-b4bb-ba930d4ec7b9"
      },
      "source": [
        "columns   = df.columns.to_list() # todas as colunas\n",
        "col_types = df.dtypes.to_list()  # todos os tipos de colunas\n",
        "col_types"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[dtype('int64'),\n",
              " dtype('O'),\n",
              " dtype('int64'),\n",
              " dtype('O'),\n",
              " dtype('int64'),\n",
              " dtype('O'),\n",
              " dtype('O'),\n",
              " dtype('O'),\n",
              " dtype('O'),\n",
              " dtype('O'),\n",
              " dtype('int64'),\n",
              " dtype('int64'),\n",
              " dtype('int64'),\n",
              " dtype('O'),\n",
              " dtype('int64')]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iI1ekW8qLXGK",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        },
        "outputId": "20c243de-46a7-4b8e-e207-5347f7e1e99b"
      },
      "source": [
        "num_cols, cat_cols = [], []\n",
        "for e in zip(columns, col_types):\n",
        "    if e[1] == 'object':\n",
        "        cat_cols.append(e[0])\n",
        "    else:\n",
        "        num_cols.append(e[0])\n",
        "\n",
        "# excluit o target Income da num_cols\n",
        "num_cols.pop(-1)\n",
        "\n",
        "print(cat_cols, '\\n')\n",
        "print(num_cols)"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['workclass', 'education', 'Marital-status', 'Occupation', 'Relationship', 'Race', 'Sex', 'native-country'] \n",
            "\n",
            "['Age', 'fnlwgt', 'education-num', 'capital-gain', 'capital-loss', 'hours-per-week']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iioFiC42TAG1",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def map_categorical_train(df_train, cat_cols):\n",
        "    df = df_train.copy()\n",
        "    cat_dims, list_of_dicts = [], []\n",
        "    \n",
        "    for col in cat_cols:\n",
        "        j = 1\n",
        "        my_dict = {}\n",
        "        my_dict.update({' ?':0})\n",
        "        my_dict.update({'UNK':1})\n",
        "\n",
        "        for e in df[col].value_counts().index.to_list():\n",
        "            if e not in my_dict.keys():\n",
        "                j +=1\n",
        "                my_dict.update({e:j})\n",
        "        list_of_dicts.append(my_dict)\n",
        "        cat_dims.append(len(my_dict))\n",
        "\n",
        "        df[col] = df[col].apply(lambda x: my_dict[x] if x in \n",
        "                                my_dict.keys() else my_dict['UNK'])\n",
        "        \n",
        "        emb_szs = [(c, min(100, (c+10)//2)) for c in cat_dims]\n",
        "        cat_emb_dims = [e[0] for e in emb_szs]\n",
        "\n",
        "    return df, list_of_dicts, emb_szs, cat_dims\n",
        "\n",
        "#----------------------------------------------------------------------------------\n",
        "def map_categorical_test(df_test, list_of_dicts):\n",
        "    df = df_test.copy()\n",
        "    for i, col in enumerate(cat_cols):\n",
        "        df[col] = df[col].apply(lambda x: list_of_dicts[i][x] if x in \n",
        "                                list_of_dicts[i].keys() else list_of_dicts[i]['UNK'])\n",
        "    return df"
      ],
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OjSvRFo6W8G_",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "d12f2a94-dd71-4c3a-d9a0-ba584e0f8200"
      },
      "source": [
        "df_train_, list_of_dicts, emb_szs, cat_dims = map_categorical_train(df_train, cat_cols)   \n",
        "df_test_ = map_categorical_test(df_test, list_of_dicts)   \n",
        "print(df_train_.shape, df_test_.shape)"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(22792, 14) (9769, 14)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OTVPO_c79GZi",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "76ed9a93-a2f0-4955-bf62-fb19c7a58b40"
      },
      "source": [
        "# Conferindo\n",
        "df_train_.head(5)"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Age</th>\n",
              "      <th>workclass</th>\n",
              "      <th>fnlwgt</th>\n",
              "      <th>education</th>\n",
              "      <th>education-num</th>\n",
              "      <th>Marital-status</th>\n",
              "      <th>Occupation</th>\n",
              "      <th>Relationship</th>\n",
              "      <th>Race</th>\n",
              "      <th>Sex</th>\n",
              "      <th>capital-gain</th>\n",
              "      <th>capital-loss</th>\n",
              "      <th>hours-per-week</th>\n",
              "      <th>native-country</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>18991</th>\n",
              "      <td>24</td>\n",
              "      <td>2</td>\n",
              "      <td>276851</td>\n",
              "      <td>2</td>\n",
              "      <td>9</td>\n",
              "      <td>4</td>\n",
              "      <td>13</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>1762</td>\n",
              "      <td>40</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9743</th>\n",
              "      <td>33</td>\n",
              "      <td>3</td>\n",
              "      <td>193246</td>\n",
              "      <td>2</td>\n",
              "      <td>9</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>55</td>\n",
              "      <td>30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23417</th>\n",
              "      <td>50</td>\n",
              "      <td>2</td>\n",
              "      <td>197322</td>\n",
              "      <td>7</td>\n",
              "      <td>7</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>50</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25248</th>\n",
              "      <td>45</td>\n",
              "      <td>2</td>\n",
              "      <td>75673</td>\n",
              "      <td>6</td>\n",
              "      <td>11</td>\n",
              "      <td>6</td>\n",
              "      <td>5</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>40</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12892</th>\n",
              "      <td>27</td>\n",
              "      <td>2</td>\n",
              "      <td>208725</td>\n",
              "      <td>8</td>\n",
              "      <td>12</td>\n",
              "      <td>3</td>\n",
              "      <td>6</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>42</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       Age  workclass  fnlwgt  ...  capital-loss  hours-per-week  native-country\n",
              "18991   24          2  276851  ...          1762              40               2\n",
              "9743    33          3  193246  ...             0              55              30\n",
              "23417   50          2  197322  ...             0              50               2\n",
              "25248   45          2   75673  ...             0              40               2\n",
              "12892   27          2  208725  ...             0              42               2\n",
              "\n",
              "[5 rows x 14 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "muLbz4Xa9KpR",
        "colab_type": "text"
      },
      "source": [
        "### Tratamento colunas numéricas"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RKAZcUE59Nau",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def normalize(df, num_cols, cat_cols, norm='minmax'):\n",
        "    data = df.copy()\n",
        "    df_num = df[num_cols]\n",
        "    \n",
        "    if norm == 'minmax':\n",
        "        m_m_s = MinMaxScaler()\n",
        "        mim_max_scal = m_m_s.fit(df_num.values)\n",
        "        mim_max_scal = m_m_s.transform(df_num.values)\n",
        "        df_norm = pd.DataFrame(data=mim_max_scal, \n",
        "                        columns=df_num.columns, \n",
        "                        index=df_num.index)\n",
        "\n",
        "    if norm == 'quantile':\n",
        "        quantile = quantile_transform(df_num.values, \n",
        "                        random_state=manual_seed, \n",
        "                        copy=True, \n",
        "                        output_distribution='uniform')\n",
        "        df_norm  = pd.DataFrame(data=quantile, \n",
        "                        columns=df_num.columns, \n",
        "                        index=df_num.index)\n",
        "\n",
        "    df= pd.concat((\n",
        "        df_norm, \n",
        "        df[cat_cols]), \n",
        "        axis=1)\n",
        "    return df"
      ],
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BtS4IlOY-HJB",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        },
        "outputId": "3c1c923c-ab06-4415-eadc-c5c3b8ed6401"
      },
      "source": [
        "# df_train[num_cols]\n",
        "num_cols"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['Age',\n",
              " 'fnlwgt',\n",
              " 'education-num',\n",
              " 'capital-gain',\n",
              " 'capital-loss',\n",
              " 'hours-per-week']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-20zO9BI9p32",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df_train = normalize(df_train_, num_cols, cat_cols, norm='quantile')\n",
        "df_test  = normalize(df_test_, num_cols, cat_cols, norm='quantile') "
      ],
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9rGqkpVp-Dcs",
        "colab_type": "text"
      },
      "source": [
        "# Conferindo"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gxmnnwld-nUw",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 436
        },
        "outputId": "13b5fad3-1101-4302-df26-bec5a300b379"
      },
      "source": [
        "df_train"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Age</th>\n",
              "      <th>fnlwgt</th>\n",
              "      <th>education-num</th>\n",
              "      <th>capital-gain</th>\n",
              "      <th>capital-loss</th>\n",
              "      <th>hours-per-week</th>\n",
              "      <th>workclass</th>\n",
              "      <th>education</th>\n",
              "      <th>Marital-status</th>\n",
              "      <th>Occupation</th>\n",
              "      <th>Relationship</th>\n",
              "      <th>Race</th>\n",
              "      <th>Sex</th>\n",
              "      <th>native-country</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>18991</th>\n",
              "      <td>0.157157</td>\n",
              "      <td>0.828224</td>\n",
              "      <td>0.292292</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.968969</td>\n",
              "      <td>0.472973</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>13</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9743</th>\n",
              "      <td>0.391391</td>\n",
              "      <td>0.580776</td>\n",
              "      <td>0.292292</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.906907</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23417</th>\n",
              "      <td>0.791792</td>\n",
              "      <td>0.601957</td>\n",
              "      <td>0.098098</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.845345</td>\n",
              "      <td>2</td>\n",
              "      <td>7</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25248</th>\n",
              "      <td>0.692693</td>\n",
              "      <td>0.116928</td>\n",
              "      <td>0.699199</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.472973</td>\n",
              "      <td>2</td>\n",
              "      <td>6</td>\n",
              "      <td>6</td>\n",
              "      <td>5</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12892</th>\n",
              "      <td>0.233233</td>\n",
              "      <td>0.657658</td>\n",
              "      <td>0.736737</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.711211</td>\n",
              "      <td>2</td>\n",
              "      <td>8</td>\n",
              "      <td>3</td>\n",
              "      <td>6</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16432</th>\n",
              "      <td>0.020521</td>\n",
              "      <td>0.813811</td>\n",
              "      <td>0.098098</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.035536</td>\n",
              "      <td>2</td>\n",
              "      <td>7</td>\n",
              "      <td>3</td>\n",
              "      <td>6</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25934</th>\n",
              "      <td>0.737738</td>\n",
              "      <td>0.988794</td>\n",
              "      <td>0.123123</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.472973</td>\n",
              "      <td>2</td>\n",
              "      <td>13</td>\n",
              "      <td>3</td>\n",
              "      <td>5</td>\n",
              "      <td>7</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3235</th>\n",
              "      <td>0.471972</td>\n",
              "      <td>0.940512</td>\n",
              "      <td>0.026527</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.191191</td>\n",
              "      <td>3</td>\n",
              "      <td>10</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16377</th>\n",
              "      <td>0.855856</td>\n",
              "      <td>0.382639</td>\n",
              "      <td>0.835335</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.845345</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20271</th>\n",
              "      <td>0.599600</td>\n",
              "      <td>0.650760</td>\n",
              "      <td>0.835335</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.143143</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>7</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>22792 rows × 14 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "            Age    fnlwgt  education-num  ...  Race  Sex  native-country\n",
              "18991  0.157157  0.828224       0.292292  ...     2    3               2\n",
              "9743   0.391391  0.580776       0.292292  ...     2    2              30\n",
              "23417  0.791792  0.601957       0.098098  ...     2    2               2\n",
              "25248  0.692693  0.116928       0.699199  ...     4    3               2\n",
              "12892  0.233233  0.657658       0.736737  ...     2    2               2\n",
              "...         ...       ...            ...  ...   ...  ...             ...\n",
              "16432  0.020521  0.813811       0.098098  ...     2    3               2\n",
              "25934  0.737738  0.988794       0.123123  ...     3    3               2\n",
              "3235   0.471972  0.940512       0.026527  ...     2    2               2\n",
              "16377  0.855856  0.382639       0.835335  ...     2    2               2\n",
              "20271  0.599600  0.650760       0.835335  ...     2    2               2\n",
              "\n",
              "[22792 rows x 14 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WA2Dcphh-o5H",
        "colab_type": "text"
      },
      "source": [
        "## Criar classe do dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2JXfxrmD-sqg",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "ca9777a0-c075-489e-82e9-2bdf595178ea"
      },
      "source": [
        "class CensusDataset(Dataset):\n",
        "    def __init__(self, df, num_cols, cat_cols, target):\n",
        "        super(CensusDataset, self).__init__()\n",
        "\n",
        "        self.x_cont = df[num_cols].values.copy().astype(np.float32)\n",
        "        self.x_cat  = df[cat_cols].values.copy().astype(np.int64)\n",
        "        \n",
        "        self.y = target.values.reshape(-1).copy().astype(np.int64)\n",
        "        \n",
        "    def __len__(self):\n",
        "        return self.x_cat.shape[0]\n",
        "    \n",
        "    def __getitem__(self, index):\n",
        "        x_cont = self.x_cont[index]\n",
        "        x_cat  = self.x_cat[index]\n",
        "        \n",
        "        y = self.y[index]\n",
        "        \n",
        "        return x_cont, x_cat, y\n",
        "\n",
        "# testando\n",
        "ds = CensusDataset(df_train, \n",
        "                     num_cols, \n",
        "                     cat_cols, \n",
        "                     y_train)\n",
        "x_cont, x_cat, y = ds[0]\n",
        "x_cont.shape, x_cat.shape, y"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((6,), (8,), 0)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XzX-03rt_Te-",
        "colab_type": "text"
      },
      "source": [
        "# Dataloaders"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "t6bE6o7W3EgQ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "a9466b0e-f4dd-4ccf-8abc-b2dafb48648d"
      },
      "source": [
        "BATCH_SZ = 32\n",
        "\n",
        "# datasets\n",
        "ds_train = CensusDataset(df_train, \n",
        "                     num_cols, \n",
        "                     cat_cols, \n",
        "                     y_train)\n",
        "\n",
        "ds_debug = CensusDataset(df_train[:BATCH_SZ], \n",
        "                     num_cols, \n",
        "                     cat_cols, \n",
        "                     y_train[:BATCH_SZ])\n",
        "\n",
        "ds_test  = CensusDataset(df_test, \n",
        "                     num_cols, \n",
        "                     cat_cols, \n",
        "                     y_test)\n",
        "# dataloaders\n",
        "dataloaders = {\n",
        "     'train': DataLoader(ds_train,\n",
        "                         batch_size=BATCH_SZ,\n",
        "                         shuffle = True, \n",
        "                         num_workers=4,\n",
        "                         pin_memory=True),\n",
        "     'test' : DataLoader(ds_test,\n",
        "                         batch_size=BATCH_SZ,\n",
        "                         shuffle=False,\n",
        "                         num_workers=4,\n",
        "                         pin_memory=True),\n",
        "     'debug': DataLoader(ds_debug,\n",
        "                         batch_size=BATCH_SZ,\n",
        "                         shuffle=False,\n",
        "                         num_workers=4,\n",
        "                         pin_memory=True)\n",
        "     }\n",
        "\n",
        "# sanity check\n",
        "dl_sizes = {x: len(dataloaders[x]) for x in dataloaders.keys()}\n",
        "dl_sizes "
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'debug': 1, 'test': 306, 'train': 713}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "G2ZAQNy2QeFR",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "e0379f49-d30e-429f-d312-91fec3108d5c"
      },
      "source": [
        "x_cont, x_cat, y= next(iter(dataloaders['train']))\n",
        "torch.mean(y.type(torch.float))"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor(0.2500)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vz12PBQxAi_C",
        "colab_type": "text"
      },
      "source": [
        "# Modelo"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NnLEd-Xk59fZ",
        "colab_type": "text"
      },
      "source": [
        "##  <span style=\"color:orange\"> Atenção MultiHead\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gzM2sdoy9Ede",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def clones(module, N):\n",
        "    return nn.ModuleList([copy.deepcopy(module) for _ in range(N)])"
      ],
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "1k7JUjXRt3Dq",
        "colab": {}
      },
      "source": [
        "einsum = torch.einsum\n",
        "sqrt = math.sqrt\n",
        "\n",
        "class MultiHead(nn.Module):\n",
        "  def __init__(self, n_heads, d_model):\n",
        "    super(MultiHead, self).__init__()\n",
        "    assert d_model % n_heads == 0\n",
        "\n",
        "    self.n_heads = n_heads\n",
        "    self.d_k = d_model // n_heads; self.d_v = d_model // n_heads \n",
        "    self.W   = clones(nn.Linear(d_model, self.d_k * n_heads), 4)\n",
        "      \n",
        "  def forward(self, emb_x): \n",
        "    N, L,_= emb_x.size()\n",
        "\n",
        "    q = self.W[0](emb_x).view(N, L, self.n_heads, self.d_k).transpose(1,2) \n",
        "    k = self.W[1](emb_x).view(N, L, self.n_heads, self.d_k).transpose(1,2) \n",
        "    v = self.W[2](emb_x).view(N, L, self.n_heads, self.d_v).transpose(1,2) \n",
        "    \n",
        "    #------------------------------- scores  ---------------------------------\n",
        "    scores = einsum('bhld, bhsd -> bhls', q, k) / sqrt(self.d_k) # (N,H,L,dq)x(N,H,L,dk)=(N,H,L,L)\n",
        "    scores = F.softmax(scores, dim=-1)\n",
        "    \n",
        "    #------------------------------ value e contexto -------------------------\n",
        "    att = einsum('bhli, bhiv -> bhlv', scores, v)     # (N,H,L,L)x(N,H,L,dv)=(N,H,L,dv)\n",
        "    # att = att.transpose(1,2).contiguous()           #  (N,L,H,L)\n",
        "    # att = att.view(N, L, -1)  # N, L, (H*dv)  \n",
        "    att = rearrange(att, 'n h l dv -> n l (h dv)')\n",
        "    \n",
        "    return self.W[-1](att)  "
      ],
      "execution_count": 45,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "q648boUFAssI",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "d1f4694a-e286-4bc6-8719-ff915928cfea"
      },
      "source": [
        "class CatModel_ATT(nn.Module):\n",
        "    def __init__(\n",
        "        self, \n",
        "        cat_dims=cat_dims, \n",
        "        n_cont=len(num_cols), \n",
        "        d_model=128, \n",
        "        n_head=4, \n",
        "        out=2):\n",
        "        super().__init__()\n",
        "\n",
        "        self.d_model    = d_model\n",
        "        self.cont_feats = n_cont\n",
        "\n",
        "        self.mha  = MultiHead(n_head, d_model)\n",
        "\n",
        "        self.cat_emb = nn.ModuleList([nn.Embedding(cat_dim, d_model) \n",
        "                    for cat_dim in cat_dims])\n",
        "        self.cont_emb   = nn.Linear(1, d_model)\n",
        "\n",
        "        self.actv = nn.ReLU()\n",
        "\n",
        "        self.fc0  = nn.Linear(d_model * (self.cont_feats + len(cat_dims)), d_model)\n",
        "        self.fc1  = nn.Linear(d_model, out)\n",
        "\n",
        "    def forward(self, x_cont, x_cat):\n",
        "        xcat  = torch.zeros(x_cat.shape[0], len(cat_dims), self.d_model).to(x_cat.device)\n",
        "        for i, emb in enumerate(self.cat_emb):\n",
        "            xi = x_cat[:, i].long()\n",
        "            xemb = emb(xi)\n",
        "            xcat[:, i,:] = xemb\n",
        "\n",
        "        xcont = self.cont_emb(x_cont.unsqueeze(-1))\n",
        "        \n",
        "        o = torch.cat((xcont, xcat), dim=1)\n",
        "        mha = self.mha(torch.cat((xcont, xcat), dim=1))\n",
        "        o = mha.view(o.shape[0], -1) \n",
        "\n",
        "        o = self.fc0(o)\n",
        "        o = self.actv(o)\n",
        "        return self.fc1(o)\n",
        "#--------------------------------------------------------------------------------\n",
        "model = CatModel_ATT(\n",
        "    cat_dims=cat_dims, \n",
        "    n_cont=len(num_cols),\n",
        "    d_model=128,\n",
        "    n_head=4,\n",
        "    )\n",
        "with torch.no_grad():\n",
        "    print(model(x_cont, x_cat).shape)   "
      ],
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "torch.Size([32, 2])\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sRLtcXLpA_Kx",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 510
        },
        "outputId": "e0a2fadb-b76a-4274-e5ca-25115228bff4"
      },
      "source": [
        "def count_parameters(model):\n",
        "    return sum(p.numel() for p in model.parameters() if p.requires_grad)\n",
        "\n",
        "print('\\n','#' * 45,f'\\n # The model has {count_parameters(model):,}' \\\n",
        "       ' trainable parameters #\\n', '#' * 45,'\\n' )  \n",
        "model"
      ],
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            " ############################################# \n",
            " # The model has 310,786 trainable parameters #\n",
            " ############################################# \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "CatModel_ATT(\n",
              "  (mha): MultiHead(\n",
              "    (W): ModuleList(\n",
              "      (0): Linear(in_features=128, out_features=128, bias=True)\n",
              "      (1): Linear(in_features=128, out_features=128, bias=True)\n",
              "      (2): Linear(in_features=128, out_features=128, bias=True)\n",
              "      (3): Linear(in_features=128, out_features=128, bias=True)\n",
              "    )\n",
              "  )\n",
              "  (cat_emb): ModuleList(\n",
              "    (0): Embedding(10, 128)\n",
              "    (1): Embedding(18, 128)\n",
              "    (2): Embedding(9, 128)\n",
              "    (3): Embedding(16, 128)\n",
              "    (4): Embedding(8, 128)\n",
              "    (5): Embedding(7, 128)\n",
              "    (6): Embedding(4, 128)\n",
              "    (7): Embedding(43, 128)\n",
              "  )\n",
              "  (cont_emb): Linear(in_features=1, out_features=128, bias=True)\n",
              "  (actv): ReLU()\n",
              "  (fc0): Linear(in_features=1792, out_features=128, bias=True)\n",
              "  (fc1): Linear(in_features=128, out_features=2, bias=True)\n",
              ")"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8D2pwp6fBGhx",
        "colab_type": "text"
      },
      "source": [
        "# Treino"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gye0TYAYBViY",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def train(model, device, train_loader, loss_fn, optimizer):\n",
        "    model.train()\n",
        "    loss_train = []\n",
        "    for x_cont, x_cat, y in train_loader:\n",
        "        optimizer.zero_grad()\n",
        "        y_pred = model(\n",
        "            x_cont.to(device),\n",
        "            x_cat.to(device),\n",
        "            )\n",
        "        loss = loss_fn(y_pred, y.to(device))\n",
        "        loss_train.append(loss.item())\n",
        "    \n",
        "        loss.backward()\n",
        "        optimizer.step()\n",
        "  \n",
        "    ave_train_loss = sum(loss_train) / len(loss_train)\n",
        "    return ave_train_loss\n",
        "\n",
        "def test(model, device, valid_loader, loss_fn):\n",
        "    model.eval()\n",
        "    preds, trues, loss_test = [],[],[]\n",
        "    for x_cont, x_cat, y in valid_loader:\n",
        "        with torch.no_grad():\n",
        "            y_pred = model(\n",
        "            x_cont.to(device),\n",
        "            x_cat.to(device),\n",
        "            )\n",
        "\n",
        "        loss = loss_fn(y_pred, y.to(device))\n",
        "        loss_test.append(loss.item())\n",
        "      \n",
        "        preds += y_pred.argmax(-1).cpu().numpy().tolist()\n",
        "        trues += y.cpu().numpy().tolist()\n",
        "\n",
        "    ave_test_loss = sum(loss_test) / len(loss_test)\n",
        "    \n",
        "    preds = np.array(preds)\n",
        "    trues = np.array(trues)\n",
        "    acc = (1.* (preds==trues)).mean() \n",
        "    return ave_test_loss, f1_score(trues, preds), acc"
      ],
      "execution_count": 48,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MmEdJmcOraR5",
        "colab_type": "text"
      },
      "source": [
        "### LabelSmoothing"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f6YOw8sxrZnp",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "class LabelSmoothing(nn.Module):\n",
        "    def __init__(self, smoothing = 0.1):\n",
        "        assert 0.0 <= smoothing < 1.0, f\"got smoothing={smoothing}\"\n",
        "        super(LabelSmoothing, self).__init__()\n",
        "        self.smoothing = smoothing\n",
        "\n",
        "    def forward(self, input, target):\n",
        "        target = target.detach()\n",
        "        log = F.log_softmax(input, dim=1)\n",
        "        oh = torch.ones_like(input).detach_()*(self.smoothing/input.size(1))\n",
        "        oh = oh.scatter_(1, target.unsqueeze(1),\n",
        "                        (1.0 - self.smoothing + self.smoothing/input.size(1)))\n",
        "        return ((-oh*log).sum(dim=1).mean())"
      ],
      "execution_count": 49,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8-ZfIleCESXH",
        "colab_type": "text"
      },
      "source": [
        "# <span style=\"color:orange\"> Treino completo"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IJr14D5p_H74",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 680
        },
        "outputId": "53cc9044-4619-4c5d-b14e-c1816ed0ee78"
      },
      "source": [
        "from sklearn.metrics import f1_score\n",
        "\n",
        "start = torch.cuda.Event(enable_timing=True)\n",
        "end = torch.cuda.Event(enable_timing=True)\n",
        "#---------------------------------------------------------------------------------\n",
        "deterministic()\n",
        "N_EPOCHS = 30\n",
        "SMTH     = 0.08\n",
        "D        = 128\n",
        "N_HEAD   = 8\n",
        "\n",
        "model = CatModel_ATT(\n",
        "    cat_dims=cat_dims, \n",
        "    n_cont=len(num_cols),\n",
        "    d_model=D,\n",
        "    n_head=N_HEAD,\n",
        "    ).to(device)\n",
        "\n",
        "criterion = LabelSmoothing(SMTH)\n",
        "optimizer = optim_.AdaBound(model.parameters())\n",
        "#---------------------------------------------------------------------------------\n",
        "start.record()\n",
        "for step in range(1, N_EPOCHS+1):\n",
        "    l_train    = train(model, device, dataloaders['train'], criterion, optimizer)\n",
        "    l_test, f1,acc = test( model, device, dataloaders['test'],  criterion)\n",
        "    torch.save(model.state_dict(), os.path.join('', 'Epoca-{}.pth'.format(step)))\n",
        "    print(f'Epoch [{step}/{N_EPOCHS}] |', end=' ')\n",
        "    print(f'Train Loss: {l_train:.3} -- Test Loss: {l_test:.3} -- F1: {f1:.3} -- ACC: {acc:.3}' )\n",
        "\n",
        "end.record()\n",
        "torch.cuda.synchronize()    \n",
        "#---------------------------------------------------------------------------------\n",
        "print(f'\\nEnd of training')\n",
        "print(f'Tempo: {start.elapsed_time(end)/1000/60 :.3f} min.')"
      ],
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Deterministic experiment, seed: 2357\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/torch_optimizer/adabound.py:142: UserWarning: This overload of add_ is deprecated:\n",
            "\tadd_(Number alpha, Tensor other)\n",
            "Consider using one of the following signatures instead:\n",
            "\tadd_(Tensor other, *, Number alpha) (Triggered internally at  /pytorch/torch/csrc/utils/python_arg_parser.cpp:766.)\n",
            "  exp_avg.mul_(beta1).add_(1 - beta1, grad)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Epoch [1/30] | Train Loss: 0.418 -- Test Loss: 0.425 -- F1: 0.581 -- ACC: 0.834\n",
            "Epoch [2/30] | Train Loss: 0.405 -- Test Loss: 0.416 -- F1: 0.611 -- ACC: 0.838\n",
            "Epoch [3/30] | Train Loss: 0.402 -- Test Loss: 0.413 -- F1: 0.607 -- ACC: 0.84\n",
            "Epoch [4/30] | Train Loss: 0.401 -- Test Loss: 0.415 -- F1: 0.676 -- ACC: 0.843\n",
            "Epoch [5/30] | Train Loss: 0.399 -- Test Loss: 0.409 -- F1: 0.663 -- ACC: 0.844\n",
            "Epoch [6/30] | Train Loss: 0.397 -- Test Loss: 0.408 -- F1: 0.663 -- ACC: 0.846\n",
            "Epoch [7/30] | Train Loss: 0.397 -- Test Loss: 0.409 -- F1: 0.68 -- ACC: 0.845\n",
            "Epoch [8/30] | Train Loss: 0.396 -- Test Loss: 0.408 -- F1: 0.669 -- ACC: 0.846\n",
            "Epoch [9/30] | Train Loss: 0.394 -- Test Loss: 0.405 -- F1: 0.653 -- ACC: 0.846\n",
            "Epoch [10/30] | Train Loss: 0.392 -- Test Loss: 0.406 -- F1: 0.673 -- ACC: 0.847\n",
            "Epoch [11/30] | Train Loss: 0.391 -- Test Loss: 0.408 -- F1: 0.633 -- ACC: 0.843\n",
            "Epoch [12/30] | Train Loss: 0.391 -- Test Loss: 0.405 -- F1: 0.664 -- ACC: 0.849\n",
            "Epoch [13/30] | Train Loss: 0.39 -- Test Loss: 0.407 -- F1: 0.635 -- ACC: 0.843\n",
            "Epoch [14/30] | Train Loss: 0.389 -- Test Loss: 0.406 -- F1: 0.652 -- ACC: 0.846\n",
            "Epoch [15/30] | Train Loss: 0.387 -- Test Loss: 0.406 -- F1: 0.672 -- ACC: 0.85\n",
            "Epoch [16/30] | Train Loss: 0.386 -- Test Loss: 0.41 -- F1: 0.625 -- ACC: 0.844\n",
            "Epoch [17/30] | Train Loss: 0.384 -- Test Loss: 0.405 -- F1: 0.673 -- ACC: 0.848\n",
            "Epoch [18/30] | Train Loss: 0.383 -- Test Loss: 0.409 -- F1: 0.653 -- ACC: 0.845\n",
            "Epoch [19/30] | Train Loss: 0.383 -- Test Loss: 0.407 -- F1: 0.649 -- ACC: 0.846\n",
            "Epoch [20/30] | Train Loss: 0.38 -- Test Loss: 0.406 -- F1: 0.671 -- ACC: 0.848\n",
            "Epoch [21/30] | Train Loss: 0.378 -- Test Loss: 0.404 -- F1: 0.653 -- ACC: 0.848\n",
            "Epoch [22/30] | Train Loss: 0.378 -- Test Loss: 0.407 -- F1: 0.644 -- ACC: 0.847\n",
            "Epoch [23/30] | Train Loss: 0.376 -- Test Loss: 0.408 -- F1: 0.627 -- ACC: 0.844\n",
            "Epoch [24/30] | Train Loss: 0.375 -- Test Loss: 0.407 -- F1: 0.666 -- ACC: 0.847\n",
            "Epoch [25/30] | Train Loss: 0.372 -- Test Loss: 0.407 -- F1: 0.678 -- ACC: 0.85\n",
            "Epoch [26/30] | Train Loss: 0.371 -- Test Loss: 0.404 -- F1: 0.672 -- ACC: 0.852\n",
            "Epoch [27/30] | Train Loss: 0.369 -- Test Loss: 0.41 -- F1: 0.664 -- ACC: 0.848\n",
            "Epoch [28/30] | Train Loss: 0.369 -- Test Loss: 0.422 -- F1: 0.631 -- ACC: 0.846\n",
            "Epoch [29/30] | Train Loss: 0.367 -- Test Loss: 0.416 -- F1: 0.643 -- ACC: 0.843\n",
            "Epoch [30/30] | Train Loss: 0.365 -- Test Loss: 0.417 -- F1: 0.616 -- ACC: 0.842\n",
            "\n",
            "End of training\n",
            "Tempo: 4.313 min.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "039Qjhr6LUG_",
        "colab_type": "text"
      },
      "source": [
        "#  <span style=\"color:orange\"> Validação no conj. de teste\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HlIw4vY5drmf",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def logits_and_trues(model, dloader, device=device):\n",
        "    model.eval()\n",
        "    logits, trues = [],[]\n",
        "    for x_cont, x_cat, y in dloader:\n",
        "        with torch.no_grad():\n",
        "            y_pred = model(\n",
        "            x_cont.to(device),\n",
        "            x_cat.to(device),\n",
        "            )\n",
        "\n",
        "        logits.extend(y_pred.to('cpu').numpy().tolist())\n",
        "        trues.extend(y.to('cpu').numpy().tolist())\n",
        "    \n",
        "    prob  = F.softmax(torch.tensor(logits), dim=1).detach().numpy()\n",
        "    probs = prob[:,1]\n",
        "    probs = np.array(probs)\n",
        "    trues = np.array(trues)\n",
        "    err_ =  far_vs_frr(trues, probs, fig=False)\n",
        "  \n",
        "    return probs, trues, err_"
      ],
      "execution_count": 51,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2LETRF7MpApO",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def far_vs_frr(labels, outs, steps=1000, fig=False):\n",
        "    lives = outs[labels == 1]\n",
        "    spoofs = outs[labels == 0]\n",
        "    max_live = max(lives)\n",
        "    min_spoof = min(spoofs)\n",
        "    num_lives = len(lives)\n",
        "    num_spoofs = len(spoofs)\n",
        "    histogram = np.linspace(0,1,num=steps)\n",
        "    FRR = np.array([100*np.mean(spoofs >= hist) for hist in histogram])\n",
        "    FAR = np.array([100*np.mean(lives < hist) for hist in histogram])\n",
        "    a = FRR[FAR - FRR<= 0]\n",
        "    s_a = len(a)\n",
        "    if (FAR[s_a] - FRR[s_a]) <= (FRR[s_a+1]-FAR[s_a+1]):\n",
        "        ERR = (FAR[s_a] + FRR[s_a])/2\n",
        "        ERR_index = s_a\n",
        "    else:\n",
        "        ERR = (FAR[s_a+1] + FRR[s_a+1])/2\n",
        "        ERR_index = s_a+1\n",
        "    \n",
        "    if fig:\n",
        "        print(f'Negados: {num_lives} Aprovados: {num_spoofs}')\n",
        "        figure = plt.figure(num=1, figsize=(10, 8), dpi=80, facecolor='w', edgecolor='k')\n",
        "        ax = figure.add_subplot(111)\n",
        "        plt.plot(histogram, FRR, color='r', label='FRR', lw=2)\n",
        "        plt.plot(histogram, FAR, color='navy', label='FAR', lw=2)\n",
        "        plt.plot([histogram[ERR_index]], [ERR], marker='o', markersize=12, color=\"lime\", label=f'ERR: {ERR :.3}')\n",
        "        axes = plt.gca()\n",
        "        axes.set_ylim([0, 4*ERR])\n",
        "        plt.title('FAR (erro tipo I) vs FRR (erro tipo II)')\n",
        "        plt.legend()\n",
        "        plt.show()\n",
        "        return ERR, histogram[ERR_index]\n",
        "    else:\n",
        "        return ERR"
      ],
      "execution_count": 52,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gGKPONUrnTge",
        "colab_type": "text"
      },
      "source": [
        "# Carrega todas as épocas salvas e verifica o melhor F$_1$ \n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "M-YjtTMcih9y",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "b00709cb-d125-4f59-afa1-4b0ef57bba70"
      },
      "source": [
        "step0,step1,step2,best_recall, best,low_err = 0,0,0,0,0,100\n",
        "for i in range(1,N_EPOCHS+1):\n",
        "    model = CatModel_ATT(\n",
        "    cat_dims=cat_dims, \n",
        "    n_cont=len(num_cols),\n",
        "    d_model=D,\n",
        "    n_head=N_HEAD,\n",
        "    ).to(device)\n",
        "    \n",
        "    string = '/content/Epoca-'+str(i)+'.pth'\n",
        "    model.load_state_dict(torch.load(string, map_location=device))\n",
        "    \n",
        "    probs, trues, _err = logits_and_trues(\n",
        "        model, \n",
        "        dataloaders['test'], \n",
        "        device=device\n",
        "        )\n",
        "    \n",
        "    fpr, tpr, thresholds = roc_curve(trues, probs)\n",
        "    precision, recall, thresh = precision_recall_curve(trues, probs)\n",
        "    f1_score = 2*precision*recall/(precision + recall)\n",
        "    f1_score = f1_score[:-1]\n",
        "    precision = precision[:-1]\n",
        "    recall = recall[:-1]\n",
        "    f1_score[np.isnan(f1_score)] = -1.0\n",
        "    bestF1 = np.argmax(f1_score)\n",
        "    \n",
        "    if f1_score[bestF1] > best:\n",
        "        best = f1_score[bestF1]\n",
        "        step0 = i\n",
        "\n",
        "    if recall[bestF1] > best_recall:\n",
        "        best_recall = recall[bestF1]\n",
        "        step2 = i\n",
        "\n",
        "\n",
        "    if _err < low_err:\n",
        "        low_err = _err\n",
        "        step1 = i\n",
        "    \n",
        "    print(f'Epoca: {i}')\n",
        "    print(f'   O threshold do melhor F1 score: >>> {thresh[bestF1] :.3} <<<')\n",
        "    print(f'   F1: {f1_score[bestF1] :.3} -- ', end='')\n",
        "    print(f'Precision {thresh[bestF1] :.3}: {precision[bestF1] :.3} -- ', end='')\n",
        "    print(f'Recall {thresh[bestF1] :.3}: {recall[bestF1] :.3}')\n",
        "    print(f'   ERR: {_err :.3}\\n')        "
      ],
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoca: 1\n",
            "   O threshold do melhor F1 score: >>> 0.227 <<<\n",
            "   F1: 0.681 -- Precision 0.227: 0.596 -- Recall 0.227: 0.794\n",
            "   ERR: 19.0\n",
            "\n",
            "Epoca: 2\n",
            "   O threshold do melhor F1 score: >>> 0.267 <<<\n",
            "   F1: 0.683 -- Precision 0.267: 0.625 -- Recall 0.267: 0.754\n",
            "   ERR: 18.8\n",
            "\n",
            "Epoca: 3\n",
            "   O threshold do melhor F1 score: >>> 0.297 <<<\n",
            "   F1: 0.686 -- Precision 0.297: 0.641 -- Recall 0.297: 0.739\n",
            "   ERR: 18.6\n",
            "\n",
            "Epoca: 4\n",
            "   O threshold do melhor F1 score: >>> 0.349 <<<\n",
            "   F1: 0.688 -- Precision 0.349: 0.586 -- Recall 0.349: 0.834\n",
            "   ERR: 18.4\n",
            "\n",
            "Epoca: 5\n",
            "   O threshold do melhor F1 score: >>> 0.327 <<<\n",
            "   F1: 0.688 -- Precision 0.327: 0.611 -- Recall 0.327: 0.788\n",
            "   ERR: 18.4\n",
            "\n",
            "Epoca: 6\n",
            "   O threshold do melhor F1 score: >>> 0.301 <<<\n",
            "   F1: 0.69 -- Precision 0.301: 0.604 -- Recall 0.301: 0.805\n",
            "   ERR: 18.2\n",
            "\n",
            "Epoca: 7\n",
            "   O threshold do melhor F1 score: >>> 0.368 <<<\n",
            "   F1: 0.692 -- Precision 0.368: 0.604 -- Recall 0.368: 0.81\n",
            "   ERR: 18.0\n",
            "\n",
            "Epoca: 8\n",
            "   O threshold do melhor F1 score: >>> 0.402 <<<\n",
            "   F1: 0.688 -- Precision 0.402: 0.645 -- Recall 0.402: 0.737\n",
            "   ERR: 18.4\n",
            "\n",
            "Epoca: 9\n",
            "   O threshold do melhor F1 score: >>> 0.309 <<<\n",
            "   F1: 0.694 -- Precision 0.309: 0.623 -- Recall 0.309: 0.785\n",
            "   ERR: 17.9\n",
            "\n",
            "Epoca: 10\n",
            "   O threshold do melhor F1 score: >>> 0.38 <<<\n",
            "   F1: 0.695 -- Precision 0.38: 0.633 -- Recall 0.38: 0.769\n",
            "   ERR: 18.3\n",
            "\n",
            "Epoca: 11\n",
            "   O threshold do melhor F1 score: >>> 0.329 <<<\n",
            "   F1: 0.695 -- Precision 0.329: 0.637 -- Recall 0.329: 0.765\n",
            "   ERR: 18.3\n",
            "\n",
            "Epoca: 12\n",
            "   O threshold do melhor F1 score: >>> 0.335 <<<\n",
            "   F1: 0.696 -- Precision 0.335: 0.631 -- Recall 0.335: 0.775\n",
            "   ERR: 18.0\n",
            "\n",
            "Epoca: 13\n",
            "   O threshold do melhor F1 score: >>> 0.345 <<<\n",
            "   F1: 0.696 -- Precision 0.345: 0.645 -- Recall 0.345: 0.756\n",
            "   ERR: 18.0\n",
            "\n",
            "Epoca: 14\n",
            "   O threshold do melhor F1 score: >>> 0.284 <<<\n",
            "   F1: 0.695 -- Precision 0.284: 0.601 -- Recall 0.284: 0.826\n",
            "   ERR: 17.8\n",
            "\n",
            "Epoca: 15\n",
            "   O threshold do melhor F1 score: >>> 0.354 <<<\n",
            "   F1: 0.699 -- Precision 0.354: 0.635 -- Recall 0.354: 0.777\n",
            "   ERR: 17.8\n",
            "\n",
            "Epoca: 16\n",
            "   O threshold do melhor F1 score: >>> 0.31 <<<\n",
            "   F1: 0.694 -- Precision 0.31: 0.642 -- Recall 0.31: 0.754\n",
            "   ERR: 17.8\n",
            "\n",
            "Epoca: 17\n",
            "   O threshold do melhor F1 score: >>> 0.388 <<<\n",
            "   F1: 0.698 -- Precision 0.388: 0.645 -- Recall 0.388: 0.761\n",
            "   ERR: 17.8\n",
            "\n",
            "Epoca: 18\n",
            "   O threshold do melhor F1 score: >>> 0.351 <<<\n",
            "   F1: 0.697 -- Precision 0.351: 0.636 -- Recall 0.351: 0.77\n",
            "   ERR: 18.1\n",
            "\n",
            "Epoca: 19\n",
            "   O threshold do melhor F1 score: >>> 0.301 <<<\n",
            "   F1: 0.696 -- Precision 0.301: 0.619 -- Recall 0.301: 0.794\n",
            "   ERR: 18.0\n",
            "\n",
            "Epoca: 20\n",
            "   O threshold do melhor F1 score: >>> 0.355 <<<\n",
            "   F1: 0.698 -- Precision 0.355: 0.627 -- Recall 0.355: 0.788\n",
            "   ERR: 18.0\n",
            "\n",
            "Epoca: 21\n",
            "   O threshold do melhor F1 score: >>> 0.319 <<<\n",
            "   F1: 0.698 -- Precision 0.319: 0.627 -- Recall 0.319: 0.786\n",
            "   ERR: 17.7\n",
            "\n",
            "Epoca: 22\n",
            "   O threshold do melhor F1 score: >>> 0.294 <<<\n",
            "   F1: 0.694 -- Precision 0.294: 0.613 -- Recall 0.294: 0.801\n",
            "   ERR: 18.1\n",
            "\n",
            "Epoca: 23\n",
            "   O threshold do melhor F1 score: >>> 0.268 <<<\n",
            "   F1: 0.696 -- Precision 0.268: 0.61 -- Recall 0.268: 0.81\n",
            "   ERR: 18.0\n",
            "\n",
            "Epoca: 24\n",
            "   O threshold do melhor F1 score: >>> 0.333 <<<\n",
            "   F1: 0.696 -- Precision 0.333: 0.623 -- Recall 0.333: 0.788\n",
            "   ERR: 18.0\n",
            "\n",
            "Epoca: 25\n",
            "   O threshold do melhor F1 score: >>> 0.375 <<<\n",
            "   F1: 0.702 -- Precision 0.375: 0.642 -- Recall 0.375: 0.773\n",
            "   ERR: 17.9\n",
            "\n",
            "Epoca: 26\n",
            "   O threshold do melhor F1 score: >>> 0.363 <<<\n",
            "   F1: 0.696 -- Precision 0.363: 0.639 -- Recall 0.363: 0.764\n",
            "   ERR: 18.0\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:21: RuntimeWarning: invalid value encountered in true_divide\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Epoca: 27\n",
            "   O threshold do melhor F1 score: >>> 0.331 <<<\n",
            "   F1: 0.691 -- Precision 0.331: 0.624 -- Recall 0.331: 0.775\n",
            "   ERR: 18.1\n",
            "\n",
            "Epoca: 28\n",
            "   O threshold do melhor F1 score: >>> 0.219 <<<\n",
            "   F1: 0.693 -- Precision 0.219: 0.609 -- Recall 0.219: 0.805\n",
            "   ERR: 18.0\n",
            "\n",
            "Epoca: 29\n",
            "   O threshold do melhor F1 score: >>> 0.285 <<<\n",
            "   F1: 0.688 -- Precision 0.285: 0.599 -- Recall 0.285: 0.807\n",
            "   ERR: 18.3\n",
            "\n",
            "Epoca: 30\n",
            "   O threshold do melhor F1 score: >>> 0.306 <<<\n",
            "   F1: 0.687 -- Precision 0.306: 0.628 -- Recall 0.306: 0.757\n",
            "   ERR: 18.9\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U-R4KlZJfIhO",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        },
        "outputId": "7564b5ea-65f4-4d35-cfd7-8f98323faa97"
      },
      "source": [
        "print(f'Melhor F1: {best:.3} na época: {step0}') \n",
        "print(f'Melhor Err: {low_err:.3} na época: {step1}') \n",
        "print(f'Melhor Recall: {best_recall:.3} na época: {step2}') "
      ],
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Melhor F1: 0.702 na época: 25\n",
            "Melhor Err: 17.7 na época: 21\n",
            "Melhor Recall: 0.834 na época: 4\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "fY_wyc-UiC4x",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 359
        },
        "outputId": "1c9ed9b0-b406-4e1c-a7a6-2e70f201d959"
      },
      "source": [
        "model = CatModel_ATT(\n",
        "    cat_dims=cat_dims, \n",
        "    n_cont=len(num_cols),\n",
        "    d_model=D,\n",
        "    n_head=N_HEAD,\n",
        "    ).to(device)\n",
        "\n",
        "string = '/content/Epoca-'+str(step1)+'.pth'\n",
        "model.load_state_dict(torch.load(string, map_location=device))\n",
        "\n",
        "probs, trues, err_ = logits_and_trues(model, dataloaders['test'], device=device)\n",
        "\n",
        "data = pd.DataFrame({'Target':trues, 'Prob':probs})\n",
        "data.head(10)"
      ],
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .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>Target</th>\n",
              "      <th>Prob</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>0.783570</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0</td>\n",
              "      <td>0.122499</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>0.254624</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>0.033375</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>0.875701</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>1</td>\n",
              "      <td>0.827191</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>1</td>\n",
              "      <td>0.772593</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>1</td>\n",
              "      <td>0.846923</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>0</td>\n",
              "      <td>0.083764</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>1</td>\n",
              "      <td>0.156010</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Target      Prob\n",
              "0       1  0.783570\n",
              "1       0  0.122499\n",
              "2       1  0.254624\n",
              "3       0  0.033375\n",
              "4       1  0.875701\n",
              "5       1  0.827191\n",
              "6       1  0.772593\n",
              "7       1  0.846923\n",
              "8       0  0.083764\n",
              "9       1  0.156010"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kk5CcCL_dAeZ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "FP = data[data.Target==1].sort_values(by='Prob')\n",
        "FN = data[data.Target==0].sort_values(by='Prob', ascending=False)"
      ],
      "execution_count": 56,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2aoeypaPw5MO",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "outputId": "da883ff3-012c-4ee9-b4d6-4cedf8440300"
      },
      "source": [
        "FP"
      ],
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "execute_result",
          "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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              "  <thead>\n",
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              "      <th></th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>5555</th>\n",
              "      <td>1</td>\n",
              "      <td>0.009399</td>\n",
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              "    <tr>\n",
              "      <th>7109</th>\n",
              "      <td>1</td>\n",
              "      <td>0.036097</td>\n",
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              "    <tr>\n",
              "      <th>3956</th>\n",
              "      <td>1</td>\n",
              "      <td>0.040303</td>\n",
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              "    <tr>\n",
              "      <th>3733</th>\n",
              "      <td>1</td>\n",
              "      <td>0.042188</td>\n",
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              "    <tr>\n",
              "      <th>119</th>\n",
              "      <td>1</td>\n",
              "      <td>0.045396</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>623</th>\n",
              "      <td>1</td>\n",
              "      <td>0.976683</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6466</th>\n",
              "      <td>1</td>\n",
              "      <td>0.978230</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2132</th>\n",
              "      <td>1</td>\n",
              "      <td>0.979672</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>651</th>\n",
              "      <td>1</td>\n",
              "      <td>0.980928</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3321</th>\n",
              "      <td>1</td>\n",
              "      <td>0.982058</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2391 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "      Target      Prob\n",
              "5555       1  0.009399\n",
              "7109       1  0.036097\n",
              "3956       1  0.040303\n",
              "3733       1  0.042188\n",
              "119        1  0.045396\n",
              "...      ...       ...\n",
              "623        1  0.976683\n",
              "6466       1  0.978230\n",
              "2132       1  0.979672\n",
              "651        1  0.980928\n",
              "3321       1  0.982058\n",
              "\n",
              "[2391 rows x 2 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z747rR01Z7Gy",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "outputId": "5c2f9db3-d9ad-418a-922c-6119e0d95f33"
      },
      "source": [
        "FN"
      ],
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Target</th>\n",
              "      <th>Prob</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1027</th>\n",
              "      <td>0</td>\n",
              "      <td>0.963097</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6095</th>\n",
              "      <td>0</td>\n",
              "      <td>0.920068</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4847</th>\n",
              "      <td>0</td>\n",
              "      <td>0.919384</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1026</th>\n",
              "      <td>0</td>\n",
              "      <td>0.917375</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5530</th>\n",
              "      <td>0</td>\n",
              "      <td>0.913357</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2802</th>\n",
              "      <td>0</td>\n",
              "      <td>0.014133</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5599</th>\n",
              "      <td>0</td>\n",
              "      <td>0.013197</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6550</th>\n",
              "      <td>0</td>\n",
              "      <td>0.011677</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9454</th>\n",
              "      <td>0</td>\n",
              "      <td>0.010873</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>539</th>\n",
              "      <td>0</td>\n",
              "      <td>0.008925</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>7378 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "      Target      Prob\n",
              "1027       0  0.963097\n",
              "6095       0  0.920068\n",
              "4847       0  0.919384\n",
              "1026       0  0.917375\n",
              "5530       0  0.913357\n",
              "...      ...       ...\n",
              "2802       0  0.014133\n",
              "5599       0  0.013197\n",
              "6550       0  0.011677\n",
              "9454       0  0.010873\n",
              "539        0  0.008925\n",
              "\n",
              "[7378 rows x 2 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "81gX6QFOiC5M",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 369
        },
        "outputId": "07c39350-680b-4af0-cb1f-c9569318f570"
      },
      "source": [
        "def plot_roc_curve(y, y_pred, gini, ks):\n",
        "    fpr, tpr, thresholds = roc_curve(y, y_pred)\n",
        "    roc_auc = auc(fpr, tpr)\n",
        "    fig = plt.figure()\n",
        "    plt.rcParams['figure.figsize'] = (15, 8)\n",
        "\n",
        "    plt.plot(fpr, tpr, 'b', lw=3, \n",
        "             label='%s KS = %0.3f,  AUC = %0.3f, GINI = %0.3f' \n",
        "             % ('Model: ',ks, roc_auc, gini))\n",
        "\n",
        "    plt.plot([0, 1], [0, 1], 'k--')\n",
        "    plt.xlim([0.0, 1.0])\n",
        "    plt.xticks(fontsize=16)\n",
        "    plt.yticks(fontsize=16)\n",
        "    plt.ylim([0.0, 1.0])\n",
        "    plt.xlabel('False Positive Rate', size=16)\n",
        "    plt.ylabel('True Positive Rate', size=16)\n",
        "    plt.legend(loc=0, fontsize=16)\n",
        "    plt.show()\n",
        "\n",
        "fpr, tpr, thresholds = roc_curve(data['Target'], data['Prob'])\n",
        "roc_auc = auc(fpr, tpr)\n",
        "gini_ = 2 * roc_auc_score(data.Target.values, data.Prob.values) - 1\n",
        "\n",
        "ks = stats.ks_2samp(data[data['Target'] == 0]['Prob'], \n",
        "                    data[data['Target'] == 1]['Prob']).statistic\n",
        "\n",
        "print(f'KS = {ks :.3} -- AUC = {roc_auc :.3} -- GINI = {gini_ :.3}')\n",
        "\n",
        "plot_roc_curve(data.Target.values, data.Prob.values, gini_, ks)"
      ],
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "KS = 0.648 -- AUC = 0.904 -- GINI = 0.809\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 648x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "B5_wt2KBphXv",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "131d9b28-9249-41a1-f0a6-b96237cdb728"
      },
      "source": [
        "fpr, tpr, thresholds = roc_curve(data.Target.values, data.Prob.values)\n",
        "\n",
        "precision,recall,pr_thresh = precision_recall_curve(data.Target.values, \n",
        "                                                    data.Prob.values)\n",
        "\n",
        "f1_score = 2*precision*recall/(precision+recall)\n",
        "f1_score = f1_score[:-1]\n",
        "precision = precision[:-1]\n",
        "recall = recall[:-1]\n",
        "f1_score[np.isnan(f1_score)] = -1.0\n",
        "bestF1 = np.argmax(f1_score)\n",
        "\n",
        "print(f'O threshold do melhor F1: >>> {pr_thresh[bestF1] :.3} <<<')\n",
        "print(f'   F1: {f1_score[bestF1] :.3} -- ', end='')\n",
        "print(f'Precision: {precision[bestF1] :.3} -- ', end='')\n",
        "print(f'Recall: {recall[bestF1] :.3}')"
      ],
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "O threshold do melhor F1: >>> 0.319 <<<\n",
            "   F1: 0.698 -- Precision: 0.627 -- Recall: 0.786\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ce3gNHYWq3od",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 102
        },
        "outputId": "df4f78db-2ed0-47b2-930b-0b0f4ca7c532"
      },
      "source": [
        "opt_idx = np.argmin(np.sqrt(np.square(1.0-tpr) + np.square(fpr)))\n",
        "opt_threshold = thresholds[opt_idx]\n",
        "print(f'O threshold do melhor F1: {pr_thresh[bestF1] :.3}')\n",
        "\n",
        "y_hat_best_thres = (data.Prob.values >= pr_thresh[bestF1]).astype(int)\n",
        "# y_hat_best_thres = (data.Prob.values >= 0.5).astype(int)\n",
        "\n",
        "print(f'\\nMatriz de confusão no thrs {pr_thresh[bestF1] :.3}:')\n",
        "confusion_matrix(trues, y_hat_best_thres)"
      ],
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "O threshold do melhor F1: 0.319\n",
            "\n",
            "Matriz de confusão no thrs 0.319:\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[6261, 1117],\n",
              "       [ 512, 1879]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vpWUGgmcpqfU",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 577
        },
        "outputId": "438fe316-b934-4ccf-f812-adf18af66ec8"
      },
      "source": [
        "plt.rcParams.update({'font.size': 16})\n",
        "plt.plot(pr_thresh,f1_score, lw=2)\n",
        "plt.plot(pr_thresh,precision,  lw=2)\n",
        "plt.plot(pr_thresh,recall, lw=2)\n",
        "plt.xlabel('Threshold')\n",
        "plt.ylabel('Score')\n",
        "plt.xticks(fontsize=16)\n",
        "plt.xlim(-0.01, 1.01)\n",
        "plt.ylim(-0.02, 1.02)\n",
        "plt.yticks(fontsize=16)\n",
        "plt.grid()\n",
        "plt.legend(['F$_1$-score', 'Precision', 'Recall'],prop={'size': 16})\n",
        "plt.tight_layout(2)\n",
        "plt.savefig('precision-recall-thres', bbox_inches='tight')\n",
        "print(f'O threshold do melhor F1: >>> {pr_thresh[bestF1] :.3} <<<')\n",
        "print(f'   F1: {f1_score[bestF1] :.3} -- ', end='')\n",
        "print(f'Precision: {precision[bestF1] :.3} -- ', end='')\n",
        "print(f'Recall: {recall[bestF1] :.3}')"
      ],
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "O threshold do melhor F1: >>> 0.319 <<<\n",
            "   F1: 0.698 -- Precision: 0.627 -- Recall: 0.786\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "avk39os_fC2l",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 598
        },
        "outputId": "a299fa13-42a8-475e-dc90-d3043a64b82f"
      },
      "source": [
        "_,Err_point = far_vs_frr(data.Target.values, data.Prob.values, steps=2000, fig=True)\n",
        "Err_point"
      ],
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Negados: 2391 Aprovados: 7378\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 800x640 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.2816408204102051"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MUmiK6RNYXuc",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        },
        "outputId": "64e04501-52b2-4781-8575-d9fb29ce59df"
      },
      "source": [
        "y_err = (data.Prob.values >= Err_point).astype(int)\n",
        "\n",
        "print(f'\\nMatriz de confusão no ponto do ERR {Err_point :.3}:')\n",
        "confusion_matrix(trues, y_err)"
      ],
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Matriz de confusão no ponto do ERR 0.282:\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[6080, 1298],\n",
              "       [ 425, 1966]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-ODZHUY1ZHYm",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        },
        "outputId": "3c5a2ea8-2a32-4f22-c465-40ea7aed4545"
      },
      "source": [
        "y_50 = (data.Prob.values >= 0.5).astype(int)\n",
        "\n",
        "print(f'\\nMatriz de confusão no threshold {0.50}:')\n",
        "confusion_matrix(trues, y_50)"
      ],
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Matriz de confusão no threshold 0.5:\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[6879,  499],\n",
              "       [ 990, 1401]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "y9ixWS0q85th",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 368
        },
        "outputId": "0fd54da7-6069-4e14-9fe0-7291e02468e9"
      },
      "source": [
        "plt.rcParams['figure.figsize'] = (9, 5)\n",
        "\n",
        "sns.set_context(\"notebook\", font_scale=1.3)\n",
        "\n",
        "confusao = confusion_matrix(trues, y_hat_best_thres)\n",
        "group_names = ['TN\\nAprovados corretos',\n",
        "               'FN - erro tipo II (FRR)\\nNegados errados', \n",
        "               'FP - erro tipo I  (FAR)\\nAprovados errados',\n",
        "               'TP\\nNegados corretos']\n",
        "\n",
        "group_counts = ['({0:0.0f})'.format(value) for value in\n",
        "                confusao.flatten()]\n",
        "\n",
        "group_percentages = ['{0:.1%}'.format(value) for value in\n",
        "                     confusao.flatten()/np.sum(confusao)]\n",
        "\n",
        "labels = [f'{v1}\\n{v2}' for v1, v2 in\n",
        "          zip(group_names,group_counts)]\n",
        "\n",
        "labels = np.asarray(labels).reshape(2,2)\n",
        "\n",
        "cmap = 'Reds'#'binary'#'PuRd'#'jet'#'YlOrRd'#\n",
        "\n",
        "ax = sns.heatmap(confusao, annot=labels, fmt='', square=False,\n",
        "                 cmap=cmap, cbar=True, linewidths=0.7,\n",
        "                )\n",
        "\n",
        "ax.set_ylabel('Target - Real')\n",
        "ax.set_xlabel('Predição - Modelo')\n",
        "ax.set_xticklabels(['Aprovados','Negados'], va='center', \n",
        "                   rotation = 0, position=(0,-0.03), \n",
        "                   fontsize=18)\n",
        "ax.set_yticklabels(['Aprovados','Negados'], \n",
        "                   va='center', rotation = 90, position=(0,0.28),\n",
        "                   fontsize=18)\n",
        "ax.set_title(f'limiar = {pr_thresh[bestF1] :.3}', fontsize=18)\n",
        "\n",
        "#######\n",
        "# Save\n",
        "#######\n",
        "plt.savefig('confusao.png', bbox_inches='tight')\n",
        "plt.show()"
      ],
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 648x360 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Q61ej_qQKRcl",
        "colab_type": "text"
      },
      "source": [
        "# FIM\n"
      ]
    }
  ]
}