외주.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "외주.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyON+f207vQkqAHtVq52N/oL",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Templar137/AI_project/blob/main/%EC%99%B8%EC%A3%BC.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OS3R_s2Ctd2s",
        "outputId": "fe5f3687-9e57-4fe1-a737-7e7cb233a7fa"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Task 1 (한길테크)"
      ],
      "metadata": {
        "id": "qvS-8lCuSTpF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import tensorflow as tf\n",
        "from tensorflow.keras.layers import Dense, Dropout, Input, Concatenate, BatchNormalization, LayerNormalization, Add\n",
        "from keras.layers.advanced_activations import PReLU\n",
        "from tensorflow.keras.models import Model\n",
        "from tensorflow.keras.optimizers import Adam\n",
        "from tensorflow.keras.callbacks import EarlyStopping\n",
        "from sklearn.preprocessing import MinMaxScaler, LabelEncoder, OneHotEncoder\n",
        "from sklearn.model_selection import train_test_split, StratifiedKFold\n",
        "from sklearn.metrics import accuracy_score, f1_score, precision_score, recall_score, confusion_matrix\n",
        "from sklearn.cluster import KMeans\n",
        "import random\n",
        "import xgboost\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ],
      "metadata": {
        "id": "NIoyQX8SSWxN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv('/content/drive/MyDrive/Dataset/Industry/한길테크/20211207.csv')"
      ],
      "metadata": {
        "id": "17cUoudRSWuP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = df[['Sec', 'Ch1', 'Ch2', 'Ch3']]"
      ],
      "metadata": {
        "id": "PzCKRwnHSWoY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "Hi13t5dATn0S",
        "outputId": "06a5af19-206c-4359-b8eb-04f4d13e5d37"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       Sec    Ch1    Ch2   Ch3\n",
              "0      0.0   24.9   24.9  26.1\n",
              "1      0.3   24.9   25.5  26.1\n",
              "2      0.6   24.9   24.9  26.1\n",
              "3      0.9   25.5   24.9  25.5\n",
              "4      1.2   25.5   24.9  25.5\n",
              "..     ...    ...    ...   ...\n",
              "930  279.0  137.5  144.3  62.5\n",
              "931  279.3  137.5  143.7  62.5\n",
              "932  279.6  137.5  143.4  61.3\n",
              "933  279.9  136.4  143.4  61.3\n",
              "934  280.2  136.4  143.4  61.3\n",
              "\n",
              "[935 rows x 4 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-6cea206b-6cdd-46af-b98e-090ea8a2ac20\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Sec</th>\n",
              "      <th>Ch1</th>\n",
              "      <th>Ch2</th>\n",
              "      <th>Ch3</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.0</td>\n",
              "      <td>24.9</td>\n",
              "      <td>24.9</td>\n",
              "      <td>26.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.3</td>\n",
              "      <td>24.9</td>\n",
              "      <td>25.5</td>\n",
              "      <td>26.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.6</td>\n",
              "      <td>24.9</td>\n",
              "      <td>24.9</td>\n",
              "      <td>26.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.9</td>\n",
              "      <td>25.5</td>\n",
              "      <td>24.9</td>\n",
              "      <td>25.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1.2</td>\n",
              "      <td>25.5</td>\n",
              "      <td>24.9</td>\n",
              "      <td>25.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>930</th>\n",
              "      <td>279.0</td>\n",
              "      <td>137.5</td>\n",
              "      <td>144.3</td>\n",
              "      <td>62.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>931</th>\n",
              "      <td>279.3</td>\n",
              "      <td>137.5</td>\n",
              "      <td>143.7</td>\n",
              "      <td>62.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>932</th>\n",
              "      <td>279.6</td>\n",
              "      <td>137.5</td>\n",
              "      <td>143.4</td>\n",
              "      <td>61.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>933</th>\n",
              "      <td>279.9</td>\n",
              "      <td>136.4</td>\n",
              "      <td>143.4</td>\n",
              "      <td>61.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>934</th>\n",
              "      <td>280.2</td>\n",
              "      <td>136.4</td>\n",
              "      <td>143.4</td>\n",
              "      <td>61.3</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>935 rows × 4 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-6cea206b-6cdd-46af-b98e-090ea8a2ac20')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-6cea206b-6cdd-46af-b98e-090ea8a2ac20 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-6cea206b-6cdd-46af-b98e-090ea8a2ac20');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 71
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cluster = KMeans(init=\"k-means++\", n_clusters=2, n_init=15)\n",
        "cluster.fit(df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sG3oyoHDSWFq",
        "outputId": "930dd4a8-b0dc-46a4-ce65-e51078c254a9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KMeans(n_clusters=2, n_init=15)"
            ]
          },
          "metadata": {},
          "execution_count": 72
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "labels = cluster.labels_\n",
        "k_means_cluster_centers = cluster.cluster_centers_"
      ],
      "metadata": {
        "id": "IJwSduzSTmwu"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "labels"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "krVxxLDuTmtP",
        "outputId": "992552c4-bbe7-4b68-b977-bf57f7ea8ef5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], dtype=int32)"
            ]
          },
          "metadata": {},
          "execution_count": 74
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.iloc[my_members, 0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dkOMyNpNUj8Q",
        "outputId": "747d0a35-e1da-4b12-90d4-661eb4b71dfe"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "299     89.7\n",
              "300     90.0\n",
              "301     90.3\n",
              "302     90.6\n",
              "303     90.9\n",
              "       ...  \n",
              "930    279.0\n",
              "931    279.3\n",
              "932    279.6\n",
              "933    279.9\n",
              "934    280.2\n",
              "Name: Sec, Length: 636, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 75
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 지정된 크기로 초기화\n",
        "fig = plt.figure(figsize=(6, 4))\n",
        "\n",
        "# 레이블 수에 따라 색상 배열 생성, 고유한 색상을 얻기 위해 set(k_means_labels) 설정\n",
        "colors = plt.cm.Spectral(np.linspace(0, 1, len(set(labels))))\n",
        "\n",
        "# plot 생성\n",
        "ax = fig.add_subplot(1, 1, 1)\n",
        "\n",
        "for k, col in zip(range(2), colors):\n",
        "    my_members = (labels == k)\n",
        "\n",
        "    # 중심 정의\n",
        "    cluster_center = k_means_cluster_centers[k]\n",
        "\n",
        "    # 중심 그리기\n",
        "    ax.plot(df.iloc[my_members, 1], df.iloc[my_members, 3], 'w', markerfacecolor=col, marker='.')\n",
        "    ax.plot(cluster_center[0], cluster_center[1], 'o', markerfacecolor=col, markeredgecolor='k', markersize=6)\n",
        "\n",
        "ax.set_title('K-Means')\n",
        "ax.set_xticks(())\n",
        "ax.set_yticks(())\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 268
        },
        "id": "0PsJK3cqTmoo",
        "outputId": "18bd11e8-ca28-4b6e-8f60-5eb00de0deb6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['label'] = labels\n",
        "x_train = df.drop(columns = 'label')\n",
        "y_train = df['label']"
      ],
      "metadata": {
        "id": "5fnMKoeuU7pj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "models_xgb = []\n",
        "count = 0\n",
        "n_fold = 5\n",
        "total_nmae = 0\n",
        "\n",
        "skt = StratifiedKFold(n_splits=n_fold, shuffle = True, random_state = 137)\n",
        "folds=[]\n",
        "for train_idx, valid_idx in skt.split(x_train, y_train):\n",
        "    folds.append((train_idx, valid_idx))\n",
        "\n",
        "count = 0\n",
        "for i in range(n_fold):\n",
        "    count += 1\n",
        "    \n",
        "    train_idx, valid_idx = folds[i]\n",
        "    train_x = x_train.iloc[train_idx,:]\n",
        "    train_y = y_train[train_idx]\n",
        "\n",
        "    val_x = x_train.iloc[valid_idx,:]\n",
        "    val_y = y_train[valid_idx]\n",
        "    print(f'XGB {count}th fold')\n",
        "\n",
        "    xgb = xgboost.XGBClassifier()\n",
        "    xgb.fit(train_x, train_y,\n",
        "            eval_set = [(val_x, val_y)],\n",
        "            eval_metric = 'logloss',\n",
        "            early_stopping_rounds = 500,\n",
        "            verbose = 10\n",
        "            )\n",
        "    prediction = xgb.predict(val_x)\n",
        "\n",
        "    print(\"\\n정확도:\", accuracy_score(prediction, val_y))\n",
        "    print(\"정밀도:\", precision_score(prediction, val_y, average = 'macro'))\n",
        "    print(\"재현율:\", recall_score(prediction, val_y, average = 'macro'))\n",
        "    print(\"F1:\",f1_score(prediction, val_y, average = 'macro'))\n",
        "\n",
        "    print(confusion_matrix(prediction, val_y))\n",
        "\n",
        "    models_xgb.append(xgb)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-9ECu_MRVAE5",
        "outputId": "ebb20598-9a03-4f9b-cb6d-522e51a88eaf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "XGB 1th fold\n",
            "[0]\tvalidation_0-logloss:0.599091\n",
            "Will train until validation_0-logloss hasn't improved in 500 rounds.\n",
            "[10]\tvalidation_0-logloss:0.179705\n",
            "[20]\tvalidation_0-logloss:0.063892\n",
            "[30]\tvalidation_0-logloss:0.024575\n",
            "[40]\tvalidation_0-logloss:0.010355\n",
            "[50]\tvalidation_0-logloss:0.004963\n",
            "[60]\tvalidation_0-logloss:0.002808\n",
            "[70]\tvalidation_0-logloss:0.002574\n",
            "[80]\tvalidation_0-logloss:0.002564\n",
            "[90]\tvalidation_0-logloss:0.002561\n",
            "[99]\tvalidation_0-logloss:0.002561\n",
            "\n",
            "정확도: 1.0\n",
            "정밀도: 1.0\n",
            "재현율: 1.0\n",
            "F1: 1.0\n",
            "[[ 60   0]\n",
            " [  0 127]]\n",
            "XGB 2th fold\n",
            "[0]\tvalidation_0-logloss:0.599091\n",
            "Will train until validation_0-logloss hasn't improved in 500 rounds.\n",
            "[10]\tvalidation_0-logloss:0.179705\n",
            "[20]\tvalidation_0-logloss:0.063892\n",
            "[30]\tvalidation_0-logloss:0.024575\n",
            "[40]\tvalidation_0-logloss:0.010355\n",
            "[50]\tvalidation_0-logloss:0.004963\n",
            "[60]\tvalidation_0-logloss:0.002806\n",
            "[70]\tvalidation_0-logloss:0.002585\n",
            "[80]\tvalidation_0-logloss:0.002575\n",
            "[90]\tvalidation_0-logloss:0.002572\n",
            "[99]\tvalidation_0-logloss:0.002571\n",
            "\n",
            "정확도: 1.0\n",
            "정밀도: 1.0\n",
            "재현율: 1.0\n",
            "F1: 1.0\n",
            "[[ 60   0]\n",
            " [  0 127]]\n",
            "XGB 3th fold\n",
            "[0]\tvalidation_0-logloss:0.599091\n",
            "Will train until validation_0-logloss hasn't improved in 500 rounds.\n",
            "[10]\tvalidation_0-logloss:0.179705\n",
            "[20]\tvalidation_0-logloss:0.063892\n",
            "[30]\tvalidation_0-logloss:0.024575\n",
            "[40]\tvalidation_0-logloss:0.010355\n",
            "[50]\tvalidation_0-logloss:0.004963\n",
            "[60]\tvalidation_0-logloss:0.002827\n",
            "[70]\tvalidation_0-logloss:0.0026\n",
            "[80]\tvalidation_0-logloss:0.002588\n",
            "[90]\tvalidation_0-logloss:0.002585\n",
            "[99]\tvalidation_0-logloss:0.002584\n",
            "\n",
            "정확도: 1.0\n",
            "정밀도: 1.0\n",
            "재현율: 1.0\n",
            "F1: 1.0\n",
            "[[ 60   0]\n",
            " [  0 127]]\n",
            "XGB 4th fold\n",
            "[0]\tvalidation_0-logloss:0.600148\n",
            "Will train until validation_0-logloss hasn't improved in 500 rounds.\n",
            "[10]\tvalidation_0-logloss:0.188409\n",
            "[20]\tvalidation_0-logloss:0.078481\n",
            "[30]\tvalidation_0-logloss:0.044462\n",
            "[40]\tvalidation_0-logloss:0.035056\n",
            "[50]\tvalidation_0-logloss:0.033826\n",
            "[60]\tvalidation_0-logloss:0.033442\n",
            "[70]\tvalidation_0-logloss:0.033266\n",
            "[80]\tvalidation_0-logloss:0.03354\n",
            "[90]\tvalidation_0-logloss:0.033682\n",
            "[99]\tvalidation_0-logloss:0.03375\n",
            "\n",
            "정확도: 0.9946524064171123\n",
            "정밀도: 0.9916666666666667\n",
            "재현율: 0.99609375\n",
            "F1: 0.9938375350140056\n",
            "[[ 59   0]\n",
            " [  1 127]]\n",
            "XGB 5th fold\n",
            "[0]\tvalidation_0-logloss:0.599085\n",
            "Will train until validation_0-logloss hasn't improved in 500 rounds.\n",
            "[10]\tvalidation_0-logloss:0.179689\n",
            "[20]\tvalidation_0-logloss:0.063878\n",
            "[30]\tvalidation_0-logloss:0.024562\n",
            "[40]\tvalidation_0-logloss:0.010344\n",
            "[50]\tvalidation_0-logloss:0.004954\n",
            "[60]\tvalidation_0-logloss:0.00282\n",
            "[70]\tvalidation_0-logloss:0.002591\n",
            "[80]\tvalidation_0-logloss:0.002577\n",
            "[90]\tvalidation_0-logloss:0.002573\n",
            "[99]\tvalidation_0-logloss:0.002572\n",
            "\n",
            "정확도: 1.0\n",
            "정밀도: 1.0\n",
            "재현율: 1.0\n",
            "F1: 1.0\n",
            "[[ 59   0]\n",
            " [  0 128]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "5번의 Test 결과, 고장 진단에 상당히 높은 정확도를 보여주고 있음"
      ],
      "metadata": {
        "id": "n8o-CJIgVbXx"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Task 2 (동연스틸)"
      ],
      "metadata": {
        "id": "UUpF_WvjVnt7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import tensorflow as tf\n",
        "from tensorflow.keras.layers import Dense, Dropout, Input, Concatenate, BatchNormalization, LayerNormalization, Add\n",
        "from keras.layers.advanced_activations import PReLU\n",
        "from tensorflow.keras.models import Model\n",
        "from tensorflow.keras.optimizers import Adam\n",
        "from tensorflow.keras.callbacks import EarlyStopping\n",
        "from sklearn.preprocessing import MinMaxScaler, LabelEncoder, OneHotEncoder\n",
        "from sklearn.model_selection import train_test_split, StratifiedKFold\n",
        "from sklearn.metrics import accuracy_score, f1_score, precision_score, recall_score, confusion_matrix\n",
        "from sklearn.cluster import KMeans\n",
        "import random\n",
        "import xgboost\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ],
      "metadata": {
        "id": "98-VAnLhZgTC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv('/content/drive/MyDrive/Dataset/Industry/동연스틸/동연스틸 교정1호기 공정측정데이타.csv')"
      ],
      "metadata": {
        "id": "ryNLjQH2Vpzi"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 485
        },
        "id": "UUoXkJFKVpw3",
        "outputId": "5cf3531d-552e-4de7-f368-746ca8782832"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    area_id   lot_no     od_item_seq is_prc_chk    lot_dt   c_tag  lot_tag  \\\n",
              "0       사업장   LOT NO          수주세부번호     작업지시여부     로트생성일  작업취소여부  LOT채번구분   \n",
              "1       A01  IK04017  20201104006002          Y  20201104     NaN      NaN   \n",
              "2       A01  IK04017  20201104006002          Y  20201104     NaN      NaN   \n",
              "3       A01  IK04017  20201104006002          Y  20201104     NaN      NaN   \n",
              "4       A01  IK04017  20201104006002          Y  20201104     NaN      NaN   \n",
              "..      ...      ...             ...        ...       ...     ...      ...   \n",
              "625     A01  JK04030  20211104006001          Y  20211104     NaN      NaN   \n",
              "626     A01  JK24120  20211124002003          Y  20211124     NaN      NaN   \n",
              "627     A01  JK24125  20211124003004          Y  20211124     NaN      NaN   \n",
              "628     A01  JK24125  20211124003004          Y  20211124     NaN      NaN   \n",
              "629     A01  JK24125  20211124003004          Y  20211124     NaN      NaN   \n",
              "\n",
              "     etc c_user_id    p01  ...    p05    p06 chk_datetime p01.1 p02.1 p03.1  \\\n",
              "0     비고   작업취소자ID  탐침1기준  ...  탐침5기준  탐침6기준         작업시간   탐침1   탐침2   탐침3   \n",
              "1    NaN       NaN      6  ...      6      6      52:12.4  0.17  0.42  0.14   \n",
              "2    NaN       NaN      6  ...      6      6      52:48.5  0.15  0.42  0.15   \n",
              "3    NaN       NaN      6  ...      6      6      57:53.6  0.18  0.42  0.16   \n",
              "4    NaN       NaN      6  ...      6      6      58:03.2  0.18  0.47   0.2   \n",
              "..   ...       ...    ...  ...    ...    ...          ...   ...   ...   ...   \n",
              "625  NaN       NaN      6  ...      6      6      09:16.9   0.4  0.01  0.24   \n",
              "626  NaN       NaN      6  ...      6      6      39:17.2     0     0     0   \n",
              "627  NaN       NaN      6  ...      6      6      38:08.0  0.38  0.48  0.23   \n",
              "628  NaN       NaN      6  ...      6      6      55:59.4  0.06   0.2  0.52   \n",
              "629  NaN       NaN      6  ...      6      6      45:35.3  0.26  0.18  0.23   \n",
              "\n",
              "    p04.1 p05.1 p06.1  ok_ng  \n",
              "0     탐침4   탐침5   탐침6  OK,NG  \n",
              "1    0.19   0.5  0.32     OK  \n",
              "2     0.2  0.53  0.39     OK  \n",
              "3    0.22  0.47  0.29     OK  \n",
              "4    0.22   0.5  0.34     OK  \n",
              "..    ...   ...   ...    ...  \n",
              "625  0.11  2.12  0.34     OK  \n",
              "626     0     0  2.17     OK  \n",
              "627   0.2  0.47  0.69     OK  \n",
              "628  0.27  0.17  0.35     OK  \n",
              "629  0.12  0.15   0.3     OK  \n",
              "\n",
              "[630 rows x 23 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-12d4581d-8b43-4f58-8f7b-a18357b3c3a1\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>area_id</th>\n",
              "      <th>lot_no</th>\n",
              "      <th>od_item_seq</th>\n",
              "      <th>is_prc_chk</th>\n",
              "      <th>lot_dt</th>\n",
              "      <th>c_tag</th>\n",
              "      <th>lot_tag</th>\n",
              "      <th>etc</th>\n",
              "      <th>c_user_id</th>\n",
              "      <th>p01</th>\n",
              "      <th>...</th>\n",
              "      <th>p05</th>\n",
              "      <th>p06</th>\n",
              "      <th>chk_datetime</th>\n",
              "      <th>p01.1</th>\n",
              "      <th>p02.1</th>\n",
              "      <th>p03.1</th>\n",
              "      <th>p04.1</th>\n",
              "      <th>p05.1</th>\n",
              "      <th>p06.1</th>\n",
              "      <th>ok_ng</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>사업장</td>\n",
              "      <td>LOT NO</td>\n",
              "      <td>수주세부번호</td>\n",
              "      <td>작업지시여부</td>\n",
              "      <td>로트생성일</td>\n",
              "      <td>작업취소여부</td>\n",
              "      <td>LOT채번구분</td>\n",
              "      <td>비고</td>\n",
              "      <td>작업취소자ID</td>\n",
              "      <td>탐침1기준</td>\n",
              "      <td>...</td>\n",
              "      <td>탐침5기준</td>\n",
              "      <td>탐침6기준</td>\n",
              "      <td>작업시간</td>\n",
              "      <td>탐침1</td>\n",
              "      <td>탐침2</td>\n",
              "      <td>탐침3</td>\n",
              "      <td>탐침4</td>\n",
              "      <td>탐침5</td>\n",
              "      <td>탐침6</td>\n",
              "      <td>OK,NG</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>A01</td>\n",
              "      <td>IK04017</td>\n",
              "      <td>20201104006002</td>\n",
              "      <td>Y</td>\n",
              "      <td>20201104</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>6</td>\n",
              "      <td>...</td>\n",
              "      <td>6</td>\n",
              "      <td>6</td>\n",
              "      <td>52:12.4</td>\n",
              "      <td>0.17</td>\n",
              "      <td>0.42</td>\n",
              "      <td>0.14</td>\n",
              "      <td>0.19</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.32</td>\n",
              "      <td>OK</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>A01</td>\n",
              "      <td>IK04017</td>\n",
              "      <td>20201104006002</td>\n",
              "      <td>Y</td>\n",
              "      <td>20201104</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>6</td>\n",
              "      <td>...</td>\n",
              "      <td>6</td>\n",
              "      <td>6</td>\n",
              "      <td>52:48.5</td>\n",
              "      <td>0.15</td>\n",
              "      <td>0.42</td>\n",
              "      <td>0.15</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.53</td>\n",
              "      <td>0.39</td>\n",
              "      <td>OK</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>A01</td>\n",
              "      <td>IK04017</td>\n",
              "      <td>20201104006002</td>\n",
              "      <td>Y</td>\n",
              "      <td>20201104</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>6</td>\n",
              "      <td>...</td>\n",
              "      <td>6</td>\n",
              "      <td>6</td>\n",
              "      <td>57:53.6</td>\n",
              "      <td>0.18</td>\n",
              "      <td>0.42</td>\n",
              "      <td>0.16</td>\n",
              "      <td>0.22</td>\n",
              "      <td>0.47</td>\n",
              "      <td>0.29</td>\n",
              "      <td>OK</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>A01</td>\n",
              "      <td>IK04017</td>\n",
              "      <td>20201104006002</td>\n",
              "      <td>Y</td>\n",
              "      <td>20201104</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>6</td>\n",
              "      <td>...</td>\n",
              "      <td>6</td>\n",
              "      <td>6</td>\n",
              "      <td>58:03.2</td>\n",
              "      <td>0.18</td>\n",
              "      <td>0.47</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.22</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.34</td>\n",
              "      <td>OK</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",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>625</th>\n",
              "      <td>A01</td>\n",
              "      <td>JK04030</td>\n",
              "      <td>20211104006001</td>\n",
              "      <td>Y</td>\n",
              "      <td>20211104</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>6</td>\n",
              "      <td>...</td>\n",
              "      <td>6</td>\n",
              "      <td>6</td>\n",
              "      <td>09:16.9</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.24</td>\n",
              "      <td>0.11</td>\n",
              "      <td>2.12</td>\n",
              "      <td>0.34</td>\n",
              "      <td>OK</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>626</th>\n",
              "      <td>A01</td>\n",
              "      <td>JK24120</td>\n",
              "      <td>20211124002003</td>\n",
              "      <td>Y</td>\n",
              "      <td>20211124</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>6</td>\n",
              "      <td>...</td>\n",
              "      <td>6</td>\n",
              "      <td>6</td>\n",
              "      <td>39:17.2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2.17</td>\n",
              "      <td>OK</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>627</th>\n",
              "      <td>A01</td>\n",
              "      <td>JK24125</td>\n",
              "      <td>20211124003004</td>\n",
              "      <td>Y</td>\n",
              "      <td>20211124</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>6</td>\n",
              "      <td>...</td>\n",
              "      <td>6</td>\n",
              "      <td>6</td>\n",
              "      <td>38:08.0</td>\n",
              "      <td>0.38</td>\n",
              "      <td>0.48</td>\n",
              "      <td>0.23</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.47</td>\n",
              "      <td>0.69</td>\n",
              "      <td>OK</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>628</th>\n",
              "      <td>A01</td>\n",
              "      <td>JK24125</td>\n",
              "      <td>20211124003004</td>\n",
              "      <td>Y</td>\n",
              "      <td>20211124</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>6</td>\n",
              "      <td>...</td>\n",
              "      <td>6</td>\n",
              "      <td>6</td>\n",
              "      <td>55:59.4</td>\n",
              "      <td>0.06</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.52</td>\n",
              "      <td>0.27</td>\n",
              "      <td>0.17</td>\n",
              "      <td>0.35</td>\n",
              "      <td>OK</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>629</th>\n",
              "      <td>A01</td>\n",
              "      <td>JK24125</td>\n",
              "      <td>20211124003004</td>\n",
              "      <td>Y</td>\n",
              "      <td>20211124</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>6</td>\n",
              "      <td>...</td>\n",
              "      <td>6</td>\n",
              "      <td>6</td>\n",
              "      <td>45:35.3</td>\n",
              "      <td>0.26</td>\n",
              "      <td>0.18</td>\n",
              "      <td>0.23</td>\n",
              "      <td>0.12</td>\n",
              "      <td>0.15</td>\n",
              "      <td>0.3</td>\n",
              "      <td>OK</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>630 rows × 23 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-12d4581d-8b43-4f58-8f7b-a18357b3c3a1')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-12d4581d-8b43-4f58-8f7b-a18357b3c3a1 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-12d4581d-8b43-4f58-8f7b-a18357b3c3a1');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 80
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = df.iloc[1:,:]\n",
        "df = df.reset_index()"
      ],
      "metadata": {
        "id": "d-kjJOCnXT9B"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x_train = df[['chk_datetime', 'p01.1', 'p02.1', 'p03.1', 'p04.1', 'p05.1', 'p06.1']]\n",
        "y_train = df['c_tag']"
      ],
      "metadata": {
        "id": "YDOWALrKVptP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x_train['chk_datetime'] = x_train['chk_datetime'].str[0:2].astype(int) * 3600 + x_train['chk_datetime'].str[3:5].astype(int) * 60 + x_train['chk_datetime'].str[6:8].astype(int)"
      ],
      "metadata": {
        "id": "hDsZsZUGVpnL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cluster = KMeans(init=\"k-means++\", n_clusters=2, n_init=100)\n",
        "cluster.fit(x_train)\n",
        "y_train = cluster.labels_"
      ],
      "metadata": {
        "id": "dmMmt0-3YFF3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x_train = x_train.astype(float)"
      ],
      "metadata": {
        "id": "9V4EkKeWYtBo"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "models_xgb = []\n",
        "count = 0\n",
        "n_fold = 5\n",
        "total_nmae = 0\n",
        "\n",
        "skt = StratifiedKFold(n_splits=n_fold, shuffle = True, random_state = 137)\n",
        "folds=[]\n",
        "for train_idx, valid_idx in skt.split(x_train, y_train):\n",
        "    folds.append((train_idx, valid_idx))\n",
        "\n",
        "count = 0\n",
        "for i in range(n_fold):\n",
        "    count += 1\n",
        "    \n",
        "    train_idx, valid_idx = folds[i]\n",
        "    train_x = x_train.iloc[train_idx,:]\n",
        "    train_y = y_train[train_idx]\n",
        "\n",
        "    val_x = x_train.iloc[valid_idx,:]\n",
        "    val_y = y_train[valid_idx]\n",
        "    print(f'XGB {count}th fold')\n",
        "\n",
        "    xgb = xgboost.XGBClassifier()\n",
        "    xgb.fit(train_x, train_y,\n",
        "            eval_set = [(val_x, val_y)],\n",
        "            eval_metric = 'logloss',\n",
        "            early_stopping_rounds = 500,\n",
        "            verbose = 10\n",
        "            )\n",
        "    prediction = xgb.predict(val_x)\n",
        "\n",
        "    print(\"\\n정확도:\", accuracy_score(prediction, val_y))\n",
        "    print(\"정밀도:\", precision_score(prediction, val_y, average = 'macro'))\n",
        "    print(\"재현율:\", recall_score(prediction, val_y, average = 'macro'))\n",
        "    print(\"F1:\",f1_score(prediction, val_y, average = 'macro'))\n",
        "\n",
        "    print(confusion_matrix(prediction, val_y))\n",
        "\n",
        "    models_xgb.append(xgb)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hlmtPL6fYE3G",
        "outputId": "b2ce3de0-1c08-41fc-bca3-99d1fc1034df"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "XGB 1th fold\n",
            "[0]\tvalidation_0-logloss:0.599552\n",
            "Will train until validation_0-logloss hasn't improved in 500 rounds.\n",
            "[10]\tvalidation_0-logloss:0.181118\n",
            "[20]\tvalidation_0-logloss:0.065234\n",
            "[30]\tvalidation_0-logloss:0.025776\n",
            "[40]\tvalidation_0-logloss:0.011385\n",
            "[50]\tvalidation_0-logloss:0.005798\n",
            "[60]\tvalidation_0-logloss:0.003947\n",
            "[70]\tvalidation_0-logloss:0.003946\n",
            "[80]\tvalidation_0-logloss:0.003945\n",
            "[90]\tvalidation_0-logloss:0.003945\n",
            "[99]\tvalidation_0-logloss:0.003945\n",
            "\n",
            "정확도: 1.0\n",
            "정밀도: 1.0\n",
            "재현율: 1.0\n",
            "F1: 1.0\n",
            "[[68  0]\n",
            " [ 0 58]]\n",
            "XGB 2th fold\n",
            "[0]\tvalidation_0-logloss:0.599552\n",
            "Will train until validation_0-logloss hasn't improved in 500 rounds.\n",
            "[10]\tvalidation_0-logloss:0.181118\n",
            "[20]\tvalidation_0-logloss:0.065234\n",
            "[30]\tvalidation_0-logloss:0.025776\n",
            "[40]\tvalidation_0-logloss:0.011385\n",
            "[50]\tvalidation_0-logloss:0.005798\n",
            "[60]\tvalidation_0-logloss:0.003944\n",
            "[70]\tvalidation_0-logloss:0.003943\n",
            "[80]\tvalidation_0-logloss:0.003942\n",
            "[90]\tvalidation_0-logloss:0.003942\n",
            "[99]\tvalidation_0-logloss:0.003942\n",
            "\n",
            "정확도: 1.0\n",
            "정밀도: 1.0\n",
            "재현율: 1.0\n",
            "F1: 1.0\n",
            "[[68  0]\n",
            " [ 0 58]]\n",
            "XGB 3th fold\n",
            "[0]\tvalidation_0-logloss:0.599552\n",
            "Will train until validation_0-logloss hasn't improved in 500 rounds.\n",
            "[10]\tvalidation_0-logloss:0.181118\n",
            "[20]\tvalidation_0-logloss:0.065234\n",
            "[30]\tvalidation_0-logloss:0.025776\n",
            "[40]\tvalidation_0-logloss:0.011385\n",
            "[50]\tvalidation_0-logloss:0.005798\n",
            "[60]\tvalidation_0-logloss:0.003944\n",
            "[70]\tvalidation_0-logloss:0.003943\n",
            "[80]\tvalidation_0-logloss:0.003942\n",
            "[90]\tvalidation_0-logloss:0.003942\n",
            "[99]\tvalidation_0-logloss:0.003942\n",
            "\n",
            "정확도: 1.0\n",
            "정밀도: 1.0\n",
            "재현율: 1.0\n",
            "F1: 1.0\n",
            "[[68  0]\n",
            " [ 0 58]]\n",
            "XGB 4th fold\n",
            "[0]\tvalidation_0-logloss:0.601124\n",
            "Will train until validation_0-logloss hasn't improved in 500 rounds.\n",
            "[10]\tvalidation_0-logloss:0.194034\n",
            "[20]\tvalidation_0-logloss:0.086768\n",
            "[30]\tvalidation_0-logloss:0.054848\n",
            "[40]\tvalidation_0-logloss:0.046972\n",
            "[50]\tvalidation_0-logloss:0.046711\n",
            "[60]\tvalidation_0-logloss:0.047826\n",
            "[70]\tvalidation_0-logloss:0.0477\n",
            "[80]\tvalidation_0-logloss:0.047637\n",
            "[90]\tvalidation_0-logloss:0.047605\n",
            "[99]\tvalidation_0-logloss:0.047591\n",
            "\n",
            "정확도: 0.992\n",
            "정밀도: 0.9925373134328358\n",
            "재현율: 0.9915254237288136\n",
            "F1: 0.9919670972302551\n",
            "[[66  0]\n",
            " [ 1 58]]\n",
            "XGB 5th fold\n",
            "[0]\tvalidation_0-logloss:0.59955\n",
            "Will train until validation_0-logloss hasn't improved in 500 rounds.\n",
            "[10]\tvalidation_0-logloss:0.181112\n",
            "[20]\tvalidation_0-logloss:0.065229\n",
            "[30]\tvalidation_0-logloss:0.025771\n",
            "[40]\tvalidation_0-logloss:0.011381\n",
            "[50]\tvalidation_0-logloss:0.005795\n",
            "[60]\tvalidation_0-logloss:0.003942\n",
            "[70]\tvalidation_0-logloss:0.003941\n",
            "[80]\tvalidation_0-logloss:0.003941\n",
            "[90]\tvalidation_0-logloss:0.003941\n",
            "[99]\tvalidation_0-logloss:0.003941\n",
            "\n",
            "정확도: 1.0\n",
            "정밀도: 1.0\n",
            "재현율: 1.0\n",
            "F1: 1.0\n",
            "[[67  0]\n",
            " [ 0 58]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Task3 (로보스타비에스디)"
      ],
      "metadata": {
        "id": "0C02tgLfa-Iw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import tensorflow as tf\n",
        "from tensorflow.keras.layers import Dense, Dropout, Input, Concatenate, BatchNormalization, LayerNormalization, Add\n",
        "from keras.layers.advanced_activations import PReLU\n",
        "from tensorflow.keras.models import Model\n",
        "from tensorflow.keras.optimizers import Adam\n",
        "from tensorflow.keras.callbacks import EarlyStopping\n",
        "from sklearn.preprocessing import MinMaxScaler, LabelEncoder, OneHotEncoder\n",
        "from sklearn.model_selection import train_test_split, StratifiedKFold\n",
        "from sklearn.metrics import accuracy_score, f1_score, precision_score, recall_score, confusion_matrix\n",
        "from sklearn.cluster import KMeans\n",
        "import random\n",
        "import xgboost\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ],
      "metadata": {
        "id": "pdODOjCDbCEg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_excel('/content/drive/MyDrive/Dataset/Industry/로보스타비에스디/로보스타비에스디_노후공정 제조데이터 활용 공급기술 고도화 사업_시스템 사용데이터(화면).xlsx')"
      ],
      "metadata": {
        "id": "fPd9d1fabCBT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = df[['품명', '검사항목', '측정값', '기준값', '하한값', '상한값', '합/불']]"
      ],
      "metadata": {
        "id": "-yENb_45bB_H"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Le = LabelEncoder()\n",
        "# Le.fit(df['품명'])\n",
        "# label = Le.transform(df['품명'])\n",
        "Ohe = OneHotEncoder(sparse=False)\n",
        "Ohe.fit(df[['품명']])\n",
        "df_name = pd.DataFrame(Ohe.transform(df[['품명']]))\n",
        "df_name = df_name.set_axis(['name_' + str(i) for i in range(41)], axis='columns')\n",
        "Ohe = OneHotEncoder(sparse=False)\n",
        "Ohe.fit(df[['검사항목']])\n",
        "df_check = pd.DataFrame(Ohe.transform(df[['검사항목']]))\n",
        "df_check = df_check.set_axis(['check_' + str(i) for i in range(7)], axis='columns')"
      ],
      "metadata": {
        "id": "ALrdp_G6cLQx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.concat([df.drop(columns = ['품명', '검사항목']), df_name, df_check], axis=1)"
      ],
      "metadata": {
        "id": "NiQKbhpzeH4v"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x_train = df.drop(columns = '합/불')\n",
        "y_train = df['합/불']"
      ],
      "metadata": {
        "id": "5l85J9zqfhaT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "models_xgb = []\n",
        "count = 0\n",
        "n_fold = 5\n",
        "total_nmae = 0\n",
        "\n",
        "skt = StratifiedKFold(n_splits=n_fold, shuffle = True, random_state = 137)\n",
        "folds=[]\n",
        "for train_idx, valid_idx in skt.split(x_train, y_train):\n",
        "    folds.append((train_idx, valid_idx))\n",
        "\n",
        "count = 0\n",
        "for i in range(n_fold):\n",
        "    count += 1\n",
        "    \n",
        "    train_idx, valid_idx = folds[i]\n",
        "    train_x = x_train.iloc[train_idx,:]\n",
        "    train_y = y_train[train_idx]\n",
        "\n",
        "    val_x = x_train.iloc[valid_idx,:]\n",
        "    val_y = y_train[valid_idx]\n",
        "    print(f'XGB {count}th fold')\n",
        "\n",
        "    xgb = xgboost.XGBClassifier()\n",
        "    xgb.fit(train_x, train_y,\n",
        "            eval_set = [(val_x, val_y)],\n",
        "            eval_metric = 'logloss',\n",
        "            early_stopping_rounds = 500,\n",
        "            verbose = 10\n",
        "            )\n",
        "    prediction = xgb.predict(val_x)\n",
        "\n",
        "    print(\"\\n정확도:\", accuracy_score(prediction, val_y))\n",
        "    print(\"정밀도:\", precision_score(prediction, val_y, average = 'macro'))\n",
        "    print(\"재현율:\", recall_score(prediction, val_y, average = 'macro'))\n",
        "    print(\"F1:\",f1_score(prediction, val_y, average = 'macro'))\n",
        "\n",
        "    print(confusion_matrix(prediction, val_y))\n",
        "\n",
        "    models_xgb.append(xgb)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OigR5tIFfiLt",
        "outputId": "8cc2a71a-b90b-4a71-874c-2f0c5fa8a52a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "XGB 1th fold\n",
            "[0]\tvalidation_0-logloss:0.600726\n",
            "Will train until validation_0-logloss hasn't improved in 500 rounds.\n",
            "[10]\tvalidation_0-logloss:0.193041\n",
            "[20]\tvalidation_0-logloss:0.085294\n",
            "[30]\tvalidation_0-logloss:0.052371\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_split.py:680: UserWarning: The least populated class in y has only 4 members, which is less than n_splits=5.\n",
            "  UserWarning,\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[40]\tvalidation_0-logloss:0.039471\n",
            "[50]\tvalidation_0-logloss:0.036124\n",
            "[60]\tvalidation_0-logloss:0.037364\n",
            "[70]\tvalidation_0-logloss:0.03741\n",
            "[80]\tvalidation_0-logloss:0.037587\n",
            "[90]\tvalidation_0-logloss:0.037639\n",
            "[99]\tvalidation_0-logloss:0.037779\n",
            "\n",
            "정확도: 0.9938650306748467\n",
            "정밀도: 0.5\n",
            "재현율: 0.49693251533742333\n",
            "F1: 0.49846153846153846\n",
            "[[  0   0]\n",
            " [  1 162]]\n",
            "XGB 2th fold\n",
            "[0]\tvalidation_0-logloss:0.600726\n",
            "Will train until validation_0-logloss hasn't improved in 500 rounds.\n",
            "[10]\tvalidation_0-logloss:0.191922\n",
            "[20]\tvalidation_0-logloss:0.083594\n",
            "[30]\tvalidation_0-logloss:0.049694\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Recall is ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[40]\tvalidation_0-logloss:0.038253\n",
            "[50]\tvalidation_0-logloss:0.037104\n",
            "[60]\tvalidation_0-logloss:0.039248\n",
            "[70]\tvalidation_0-logloss:0.040013\n",
            "[80]\tvalidation_0-logloss:0.040001\n",
            "[90]\tvalidation_0-logloss:0.040035\n",
            "[99]\tvalidation_0-logloss:0.040184\n",
            "\n",
            "정확도: 0.9938650306748467\n",
            "정밀도: 0.5\n",
            "재현율: 0.49693251533742333\n",
            "F1: 0.49846153846153846\n",
            "[[  0   0]\n",
            " [  1 162]]\n",
            "XGB 3th fold\n",
            "[0]\tvalidation_0-logloss:0.600726\n",
            "Will train until validation_0-logloss hasn't improved in 500 rounds.\n",
            "[10]\tvalidation_0-logloss:0.193041\n",
            "[20]\tvalidation_0-logloss:0.084623\n",
            "[30]\tvalidation_0-logloss:0.052525\n",
            "[40]\tvalidation_0-logloss:0.04433\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Recall is ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[50]\tvalidation_0-logloss:0.043742\n",
            "[60]\tvalidation_0-logloss:0.044487\n",
            "[70]\tvalidation_0-logloss:0.045688\n",
            "[80]\tvalidation_0-logloss:0.047206\n",
            "[90]\tvalidation_0-logloss:0.048208\n",
            "[99]\tvalidation_0-logloss:0.048364\n",
            "\n",
            "정확도: 0.9938650306748467\n",
            "정밀도: 0.5\n",
            "재현율: 0.49693251533742333\n",
            "F1: 0.49846153846153846\n",
            "[[  0   0]\n",
            " [  1 162]]\n",
            "XGB 4th fold\n",
            "[0]\tvalidation_0-logloss:0.59979\n",
            "Will train until validation_0-logloss hasn't improved in 500 rounds.\n",
            "[10]\tvalidation_0-logloss:0.183159\n",
            "[20]\tvalidation_0-logloss:0.068903\n",
            "[30]\tvalidation_0-logloss:0.029796\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Recall is ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[40]\tvalidation_0-logloss:0.015881\n",
            "[50]\tvalidation_0-logloss:0.010819\n",
            "[60]\tvalidation_0-logloss:0.008714\n",
            "[70]\tvalidation_0-logloss:0.007489\n",
            "[80]\tvalidation_0-logloss:0.006781\n",
            "[90]\tvalidation_0-logloss:0.006437\n",
            "[99]\tvalidation_0-logloss:0.006247\n",
            "\n",
            "정확도: 1.0\n",
            "정밀도: 1.0\n",
            "재현율: 1.0\n",
            "F1: 1.0\n",
            "[[162]]\n",
            "XGB 5th fold\n",
            "[0]\tvalidation_0-logloss:0.600731\n",
            "Will train until validation_0-logloss hasn't improved in 500 rounds.\n",
            "[10]\tvalidation_0-logloss:0.191942\n",
            "[20]\tvalidation_0-logloss:0.083464\n",
            "[30]\tvalidation_0-logloss:0.050944\n",
            "[40]\tvalidation_0-logloss:0.042089\n",
            "[50]\tvalidation_0-logloss:0.041691\n",
            "[60]\tvalidation_0-logloss:0.042825\n",
            "[70]\tvalidation_0-logloss:0.043049\n",
            "[80]\tvalidation_0-logloss:0.043957\n",
            "[90]\tvalidation_0-logloss:0.044092\n",
            "[99]\tvalidation_0-logloss:0.04428\n",
            "\n",
            "정확도: 0.9938271604938271\n",
            "정밀도: 0.5\n",
            "재현율: 0.49691358024691357\n",
            "F1: 0.4984520123839009\n",
            "[[  0   0]\n",
            " [  1 161]]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Recall is ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n"
          ]
        }
      ]
    }
  ]
}