Team스마일C_CodeSubmission (3).ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "id": "ZJBm2amd3Lb5",
      "metadata": {
        "id": "ZJBm2amd3Lb5"
      },
      "source": [
        "# 팀 스마일C 코드 제출\n",
        "* 팀원: 정우섭, 김유민, 김유진, 장동언, 황정묵\n",
        "* PRIVATE SCORE: 0.65887 \n",
        "* PRIVATE RANKING: 36"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "CwuGPDTVMXHe",
      "metadata": {
        "id": "CwuGPDTVMXHe"
      },
      "source": [
        "# 개발 환경\n",
        "* OS : Windows11\n",
        "* python: 3.10.10\n",
        "* 라이브러리 버전 : requirements.txt 참조"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "QIfodgpc3HP6",
      "metadata": {
        "id": "QIfodgpc3HP6"
      },
      "source": [
        "# 0.Data Load\n",
        "1. install packages\n",
        "2. import libraries and csv data files"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "id": "6ab709fe",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6ab709fe",
        "outputId": "5ae49d2a-d294-456c-91d5-f33e87bc10c1"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: catboost in /usr/local/lib/python3.8/dist-packages (1.1.1)\n",
            "Requirement already satisfied: numpy>=1.16.0 in /usr/local/lib/python3.8/dist-packages (from catboost) (1.22.4)\n",
            "Requirement already satisfied: plotly in /usr/local/lib/python3.8/dist-packages (from catboost) (5.5.0)\n",
            "Requirement already satisfied: pandas>=0.24.0 in /usr/local/lib/python3.8/dist-packages (from catboost) (1.3.5)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.8/dist-packages (from catboost) (1.15.0)\n",
            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.8/dist-packages (from catboost) (3.5.3)\n",
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            "Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.8/dist-packages (from pandas>=0.24.0->catboost) (2022.7.1)\n",
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            "Requirement already satisfied: tenacity>=6.2.0 in /usr/local/lib/python3.8/dist-packages (from plotly->catboost) (8.2.2)\n",
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: lightgbm in /usr/local/lib/python3.8/dist-packages (2.2.3)\n",
            "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.8/dist-packages (from lightgbm) (1.2.1)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.8/dist-packages (from lightgbm) (1.10.1)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.8/dist-packages (from lightgbm) (1.22.4)\n",
            "Requirement already satisfied: joblib>=1.1.1 in /usr/local/lib/python3.8/dist-packages (from scikit-learn->lightgbm) (1.2.0)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.8/dist-packages (from scikit-learn->lightgbm) (3.1.0)\n",
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: optuna in /usr/local/lib/python3.8/dist-packages (3.1.0)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.8/dist-packages (from optuna) (4.64.1)\n",
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            "Requirement already satisfied: Mako in /usr/local/lib/python3.8/dist-packages (from alembic>=1.5.0->optuna) (1.2.4)\n",
            "Requirement already satisfied: greenlet!=0.4.17 in /usr/local/lib/python3.8/dist-packages (from sqlalchemy>=1.3.0->optuna) (2.0.2)\n",
            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.8/dist-packages (from importlib-metadata->alembic>=1.5.0->optuna) (3.15.0)\n",
            "Requirement already satisfied: MarkupSafe>=0.9.2 in /usr/local/lib/python3.8/dist-packages (from Mako->alembic>=1.5.0->optuna) (2.1.2)\n",
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: xgboost in /usr/local/lib/python3.8/dist-packages (1.7.4)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.8/dist-packages (from xgboost) (1.10.1)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.8/dist-packages (from xgboost) (1.22.4)\n",
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: torch in /usr/local/lib/python3.8/dist-packages (1.13.1+cu116)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.8/dist-packages (from torch) (4.5.0)\n"
          ]
        }
      ],
      "source": [
        "!pip install catboost\n",
        "!pip install lightgbm\n",
        "!pip install optuna \n",
        "!pip install xgboost\n",
        "!pip install torch"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "id": "94682a80",
      "metadata": {
        "id": "94682a80"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import random\n",
        "import os\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt   \n",
        "import seaborn as sns \n",
        "\n",
        "# model evaluation, preprocessing \n",
        "import sklearn\n",
        "from sklearn import preprocessing, metrics\n",
        "from sklearn.preprocessing import LabelEncoder, OneHotEncoder, StandardScaler, MinMaxScaler\n",
        "from sklearn.model_selection import train_test_split, KFold, cross_validate\n",
        "from sklearn.metrics import accuracy_score, classification_report, f1_score, roc_auc_score, log_loss\n",
        "\n",
        "# sequential model modeling \n",
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "from keras import regularizers\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Dense,Dropout, Activation\n",
        "from keras import backend as K \n",
        "from keras import regularizers\n",
        "from keras.layers import Dense,Dropout, Activation, BatchNormalization, Conv2D, Flatten  \n",
        "from keras import optimizers, metrics, callbacks\n",
        "from keras.backend import clear_session\n",
        "\n",
        "# optuna Hyper-params tuning\n",
        "import optuna\n",
        "from optuna import Trial\n",
        "from optuna.samplers import TPESampler\n",
        "from optuna.visualization import plot_optimization_history, plot_param_importances, plot_parallel_coordinate, plot_contour\n",
        "\n",
        "# ML classification models \n",
        "from lightgbm import LGBMClassifier\n",
        "from catboost import CatBoostClassifier\n",
        "from sklearn.ensemble import BaggingClassifier, GradientBoostingClassifier, RandomForestClassifier\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.linear_model import RidgeClassifierCV\n",
        "from xgboost import XGBClassifier\n",
        "\n",
        "# Google Drive mount\n",
        "# from google.colab import drive \n",
        "# drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "id": "0AdADLxT1DoQ",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0AdADLxT1DoQ",
        "outputId": "7b9f31d5-8b0e-4318-9fba-c21b74580e55"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "37\n"
          ]
        }
      ],
      "source": [
        "# seed fixing for reproduction \n",
        "import torch \n",
        "import random as rn \n",
        "\n",
        "def seed_everything(seed):\n",
        "    random.seed(seed)\n",
        "    os.environ['PYTHONHASHSEED'] = str(seed)\n",
        "    np.random.seed(seed)\n",
        "seed_everything(37) # Seed 고정\n",
        "seed_num=37\n",
        "\n",
        "def seed_everything(seed):\n",
        "    random.seed(seed)\n",
        "    os.environ['PYTHONHASHSEED'] = str(seed)\n",
        "    np.random.seed(seed)\n",
        "    torch.manual_seed(seed)\n",
        "    torch.cuda.manual_seed(seed)  # type: ignore\n",
        "    torch.backends.cudnn.deterministic = True  # type: ignore\n",
        "    torch.backends.cudnn.benchmark = True  # type: ignore\n",
        "seed_everything(37)\n",
        "seed_num=37\n",
        "seed=37\n",
        "\n",
        "np.random.seed(seed_num)\n",
        "rn.seed(seed_num) \n",
        "tf.random.set_seed(seed_num)\n",
        "session_conf = tf.compat.v1.ConfigProto(intra_op_parallelism_threads=1, inter_op_parallelism_threads=1)\n",
        "sess = tf.compat.v1.Session(config=session_conf)\n",
        "K.set_session(sess)\n",
        "\n",
        "class config:   \n",
        "    seed = 37  \n",
        "    device = \"cuda:0\"            \n",
        "\n",
        "def seed_everything(seed: int = 37):\n",
        "    random.seed(seed)\n",
        "    np.random.seed(seed)\n",
        "    os.environ[\"PYTHONHASHSEED\"] = str(seed)\n",
        "    tf.random.set_seed(seed)\n",
        "seed_everything(config.seed)\n",
        "tf.random.set_seed(37) # tensorflow global seed \n",
        "print(config.seed)     "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "id": "lQROx2fcxIW7",
      "metadata": {
        "id": "lQROx2fcxIW7"
      },
      "outputs": [],
      "source": [
        "train_df = pd.read_csv('./train.csv')\n",
        "test_df = pd.read_csv('./test.csv')\n",
        "submit = pd.read_csv('./sample_submission.csv')\n",
        "\n",
        "train_x = train_df.drop(columns=['PRODUCT_ID', 'TIMESTAMP', 'Y_Class','Y_Quality'])\n",
        "train_y = train_df['Y_Class']\n",
        "test_x = test_df.drop(columns=['PRODUCT_ID', 'TIMESTAMP'])   "
      ]
    },
    {
      "cell_type": "markdown",
      "id": "wqkW_3EbQxj1",
      "metadata": {
        "id": "wqkW_3EbQxj1"
      },
      "source": [
        "# 1.Data & Features"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "ZK3O2Kd24yP_",
      "metadata": {
        "id": "ZK3O2Kd24yP_"
      },
      "source": [
        "## 1.1 EDA\n",
        "1. feature correlation heatmaps\n",
        "2. feature importance\n",
        "3. feature distribution plots"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "id": "E1vfXTKcK9Dt",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 632
        },
        "id": "E1vfXTKcK9Dt",
        "outputId": "3ffda486-467e-401f-b145-807476ec9804"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x720 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# random 20 features list (모두 NaN인 컬럼 제외하고, 랜덤 20개 feature의 상관관계 히트맵)\n",
        "Xs = train_df.iloc[:,4:].columns\n",
        "null_count = train_df.isnull().sum().to_dict()\n",
        "cols = pd.DataFrame({i for i in null_count if null_count[i]<250 and i in Xs})\n",
        "cols_name = list(cols[0])  # not_all_NAN features name in list \n",
        "\n",
        "import random\n",
        "a = ['Y_Quality']\n",
        "for i in range(20):\n",
        "    num = random.randint(1,2875)\n",
        "    a.append(cols_name[i])\n",
        "\n",
        "# heatmap: `random X features` and `Y_Quality` correlation\n",
        "corr = train_df[a].corr()  # correlation matrix\n",
        "fig, ax = plt.subplots(figsize=(15,10))\n",
        "sns.heatmap(corr, annot=True, fmt='.2f', cmap='Blues', linewidths=2);"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "id": "lLsOtCgTSvRA",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 592
        },
        "id": "lLsOtCgTSvRA",
        "outputId": "c3ef8cef-f652-49a8-8090-83530522bbc3"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x720 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# feature 중요도\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from sklearn.ensemble import RandomForestRegressor\n",
        "\n",
        "qual_col = ['LINE', 'PRODUCT_CODE']\n",
        "for i in qual_col:\n",
        "    le = LabelEncoder()\n",
        "    le = le.fit(train_x[i])\n",
        "    train_x[i] = le.transform(train_x[i])\n",
        "    for label in np.unique(test_x[i]):\n",
        "        if label not in le.classes_:\n",
        "            le.classes_ = np.append(le.classes_, label)\n",
        "    test_x[i] = le.transform(test_x[i])\n",
        "\n",
        "train_x = train_x.fillna(0)\n",
        "test_x = test_x.fillna(0)\n",
        "X_train, X_test, y_train, y_test=train_test_split(train_x,train_y,test_size=0.3,random_state=seed_num)\n",
        "\n",
        "%matplotlib inline\n",
        "RF = RandomForestRegressor(random_state=0, max_depth=5, min_samples_leaf=8, min_samples_split=8,n_estimators=200)\n",
        "RF.fit(X_train, y_train)\n",
        "ftr_importances_values = RF.feature_importances_\n",
        "ftr_importances = pd.Series(ftr_importances_values, index=X_train.columns)\n",
        "ftr_top = ftr_importances.sort_values(ascending=False)[:20]\n",
        " \n",
        "plt.figure(figsize=(10, 10))\n",
        "sns.barplot(x=ftr_top, y=ftr_top.index)\n",
        "plt.show()      "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "id": "GdugZkQrSvJS",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 393
        },
        "id": "GdugZkQrSvJS",
        "outputId": "f279cade-9eca-41d3-8857-7871620c97d9"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             LINE  PRODUCT_CODE         X_1        X_2    X_3         X_4  \\\n",
              "count  598.000000    598.000000  598.000000  598.00000  598.0  598.000000   \n",
              "mean     3.214047      1.157191    1.406355   55.51505    0.0   26.262542   \n",
              "std      1.703579      0.983298    4.655332   47.03581    0.0   22.201747   \n",
              "min      0.000000      0.000000    0.000000    0.00000    0.0    0.000000   \n",
              "25%      2.000000      0.000000    0.000000    0.00000    0.0    0.000000   \n",
              "50%      4.000000      2.000000    2.000000   91.00000    0.0   45.000000   \n",
              "75%      5.000000      2.000000    2.000000   95.00000    0.0   45.000000   \n",
              "max      5.000000      2.000000  103.000000  102.00000    0.0   45.000000   \n",
              "\n",
              "              X_5    X_6         X_7         X_8  ...      X_2866      X_2867  \\\n",
              "count  598.000000  598.0  598.000000  598.000000  ...  598.000000  598.000000   \n",
              "mean     6.065217    0.0   28.481605    5.864548  ...    8.496204    8.964498   \n",
              "std      5.140970    0.0   24.308159    4.960485  ...   19.189554   20.294317   \n",
              "min      0.000000    0.0    0.000000    0.000000  ...    0.000000    0.000000   \n",
              "25%      0.000000    0.0    0.000000    0.000000  ...    0.000000    0.000000   \n",
              "50%     10.000000    0.0   45.000000   10.000000  ...    0.000000    0.000000   \n",
              "75%     10.000000    0.0   50.000000   10.000000  ...    0.000000    0.000000   \n",
              "max     11.000000    0.0   62.000000   11.000000  ...   60.240000   68.660000   \n",
              "\n",
              "           X_2868      X_2869      X_2870      X_2871  X_2872  X_2873  X_2874  \\\n",
              "count  598.000000  598.000000  598.000000  598.000000   598.0   598.0   598.0   \n",
              "mean     8.295351    8.638763   11.145435    0.165552     0.0     0.0     0.0   \n",
              "std     18.755287   19.632753   24.961071    0.371989     0.0     0.0     0.0   \n",
              "min      0.000000    0.000000    0.000000    0.000000     0.0     0.0     0.0   \n",
              "25%      0.000000    0.000000    0.000000    0.000000     0.0     0.0     0.0   \n",
              "50%      0.000000    0.000000    0.000000    0.000000     0.0     0.0     0.0   \n",
              "75%      0.000000    0.000000    0.000000    0.000000     0.0     0.0     0.0   \n",
              "max     60.410000   59.930000   79.750000    1.000000     0.0     0.0     0.0   \n",
              "\n",
              "       X_2875  \n",
              "count   598.0  \n",
              "mean      0.0  \n",
              "std       0.0  \n",
              "min       0.0  \n",
              "25%       0.0  \n",
              "50%       0.0  \n",
              "75%       0.0  \n",
              "max       0.0  \n",
              "\n",
              "[8 rows x 2877 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-84a015bb-e25e-4bb3-a679-e0160fedc519\">\n",
              "    <div class=\"colab-df-container\">\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>LINE</th>\n",
              "      <th>PRODUCT_CODE</th>\n",
              "      <th>X_1</th>\n",
              "      <th>X_2</th>\n",
              "      <th>X_3</th>\n",
              "      <th>X_4</th>\n",
              "      <th>X_5</th>\n",
              "      <th>X_6</th>\n",
              "      <th>X_7</th>\n",
              "      <th>X_8</th>\n",
              "      <th>...</th>\n",
              "      <th>X_2866</th>\n",
              "      <th>X_2867</th>\n",
              "      <th>X_2868</th>\n",
              "      <th>X_2869</th>\n",
              "      <th>X_2870</th>\n",
              "      <th>X_2871</th>\n",
              "      <th>X_2872</th>\n",
              "      <th>X_2873</th>\n",
              "      <th>X_2874</th>\n",
              "      <th>X_2875</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>598.000000</td>\n",
              "      <td>598.000000</td>\n",
              "      <td>598.000000</td>\n",
              "      <td>598.00000</td>\n",
              "      <td>598.0</td>\n",
              "      <td>598.000000</td>\n",
              "      <td>598.000000</td>\n",
              "      <td>598.0</td>\n",
              "      <td>598.000000</td>\n",
              "      <td>598.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>598.000000</td>\n",
              "      <td>598.000000</td>\n",
              "      <td>598.000000</td>\n",
              "      <td>598.000000</td>\n",
              "      <td>598.000000</td>\n",
              "      <td>598.000000</td>\n",
              "      <td>598.0</td>\n",
              "      <td>598.0</td>\n",
              "      <td>598.0</td>\n",
              "      <td>598.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>3.214047</td>\n",
              "      <td>1.157191</td>\n",
              "      <td>1.406355</td>\n",
              "      <td>55.51505</td>\n",
              "      <td>0.0</td>\n",
              "      <td>26.262542</td>\n",
              "      <td>6.065217</td>\n",
              "      <td>0.0</td>\n",
              "      <td>28.481605</td>\n",
              "      <td>5.864548</td>\n",
              "      <td>...</td>\n",
              "      <td>8.496204</td>\n",
              "      <td>8.964498</td>\n",
              "      <td>8.295351</td>\n",
              "      <td>8.638763</td>\n",
              "      <td>11.145435</td>\n",
              "      <td>0.165552</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>1.703579</td>\n",
              "      <td>0.983298</td>\n",
              "      <td>4.655332</td>\n",
              "      <td>47.03581</td>\n",
              "      <td>0.0</td>\n",
              "      <td>22.201747</td>\n",
              "      <td>5.140970</td>\n",
              "      <td>0.0</td>\n",
              "      <td>24.308159</td>\n",
              "      <td>4.960485</td>\n",
              "      <td>...</td>\n",
              "      <td>19.189554</td>\n",
              "      <td>20.294317</td>\n",
              "      <td>18.755287</td>\n",
              "      <td>19.632753</td>\n",
              "      <td>24.961071</td>\n",
              "      <td>0.371989</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>4.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>91.00000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>45.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>45.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>5.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>95.00000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>45.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>50.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>5.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>103.000000</td>\n",
              "      <td>102.00000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>45.000000</td>\n",
              "      <td>11.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>62.000000</td>\n",
              "      <td>11.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>60.240000</td>\n",
              "      <td>68.660000</td>\n",
              "      <td>60.410000</td>\n",
              "      <td>59.930000</td>\n",
              "      <td>79.750000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>8 rows × 2877 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-84a015bb-e25e-4bb3-a679-e0160fedc519')\"\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-84a015bb-e25e-4bb3-a679-e0160fedc519 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-84a015bb-e25e-4bb3-a679-e0160fedc519');\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": 7
        }
      ],
      "source": [
        "# feature distribution \n",
        "train_x.describe()    "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "id": "fceMKvgyg2Kg",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 298
        },
        "id": "fceMKvgyg2Kg",
        "outputId": "cfe8b375-9b9d-467c-ca2f-a63977779048"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<AxesSubplot:xlabel='X_592', ylabel='Count'>"
            ]
          },
          "metadata": {},
          "execution_count": 8
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# feature histogram with KDE (밀도그래프) \n",
        "# 임의의 feature에 대한 histplot + 밀도 곡선 출력\n",
        "num = random.randint(1,2875)\n",
        "sns.histplot(train_x[f'X_{num}'], kde=True);"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "id": "xskVkfufSvDg",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 700
        },
        "id": "xskVkfufSvDg",
        "outputId": "5051a35a-9870-4206-8155-06b4f1021970"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1152x864 with 20 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# visualize random 20 features distribution \n",
        "# imbalanced feature distribution (NO Gaussian distribution shape) --> MinMaxscaler\n",
        "fig, axes = plt.subplots(5,4,figsize=(16,12))\n",
        "ax= axes.flatten()\n",
        "\n",
        "for i in range(20): \n",
        "    num = random.randint(1,2875)\n",
        "    ax[i].hist(train_x[f'X_{num}']);    "
      ]
    },
    {
      "cell_type": "markdown",
      "id": "xuPOUxQ7Su9m",
      "metadata": {
        "id": "xuPOUxQ7Su9m"
      },
      "source": [
        "## 1.2 Preprocessing\n",
        "1. Label Encoding: categorical values `LINE`, `PRODUCT_CODE`\n",
        "2. Missing values: fillna(0) \n",
        "3. scaling: StandardScaler"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "id": "kZ1PM_Zrtzph",
      "metadata": {
        "id": "kZ1PM_Zrtzph"
      },
      "outputs": [],
      "source": [
        "train_df = pd.read_csv('./train.csv')\n",
        "test_df = pd.read_csv('./test.csv')\n",
        "submit = pd.read_csv('./sample_submission.csv')\n",
        "\n",
        "train_x = train_df.drop(columns=['PRODUCT_ID', 'TIMESTAMP', 'Y_Class','Y_Quality'])\n",
        "train_y = train_df['Y_Class']\n",
        "test_x = test_df.drop(columns=['PRODUCT_ID', 'TIMESTAMP'])   "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "id": "3AbGp2rBuxuW",
      "metadata": {
        "id": "3AbGp2rBuxuW"
      },
      "outputs": [],
      "source": [
        "# 1) qualitative to quantitative\n",
        "qual_col = ['LINE', 'PRODUCT_CODE']\n",
        "for i in qual_col:\n",
        "    le = LabelEncoder()    # one-hot encoding (X): get_dummies(test_df) is not allowed (data leakage)   \n",
        "    le = le.fit(train_x[i])\n",
        "    train_x[i] = le.transform(train_x[i])\n",
        "    for label in np.unique(test_x[i]): \n",
        "        if label not in le.classes_: \n",
        "            le.classes_ = np.append(le.classes_, label)\n",
        "    test_x[i] = le.transform(test_x[i]) \n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "id": "GZ72F04guy3o",
      "metadata": {
        "id": "GZ72F04guy3o"
      },
      "outputs": [],
      "source": [
        "# 2) Missing Values \n",
        "train_x = train_x.fillna(0)\n",
        "test_x = test_x.fillna(0)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "id": "c66d3bf4",
      "metadata": {
        "id": "c66d3bf4"
      },
      "outputs": [],
      "source": [
        "# 3) MinMaxscaling: only `X_???` values (continuous)\n",
        "Xs = train_x.select_dtypes(include=float).iloc[:,1:].columns.tolist()\n",
        "scaler = MinMaxScaler().fit(train_x.loc[:, Xs])\n",
        "train_x.loc[:, Xs] = scaler.transform(train_x.loc[:, Xs])\n",
        "test_x.loc[:, Xs] = scaler.transform(test_x.loc[:, Xs])\n",
        "\n",
        "X_train, X_test, y_train, y_test=train_test_split(train_x,train_y,test_size=0.3,random_state=seed_num)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "hIoaatyGu9Zq",
      "metadata": {
        "id": "hIoaatyGu9Zq"
      },
      "source": [
        "# 2.Modeling"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "GQGMvepMJy87",
      "metadata": {
        "id": "GQGMvepMJy87"
      },
      "source": [
        "## 2.1 ML models\n",
        "- 여러 Machine Learning Classifier 모델들의 test set Accuracy score 도출 \n",
        "    - Ensemble에서 각 모델의 weight(가중치) 정하기 위한 근거로 사용하기 위함\n",
        "1. RandomForestClassifier\n",
        "2. GradientboostingClassifier\n",
        "3. XGBClassifier\n",
        "4. LGBMClassifier\n",
        "5. CatboostClassifier\n",
        "6. RidgeClassifier\n",
        "7. BaggingClassifier"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "id": "m61QOAHA8lvG",
      "metadata": {
        "id": "m61QOAHA8lvG"
      },
      "outputs": [],
      "source": [
        "from catboost import CatBoostClassifier\n",
        "from sklearn.ensemble import GradientBoostingClassifier\n",
        "from sklearn.ensemble import StackingClassifier\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "\n",
        "models = [\n",
        "    RandomForestClassifier(random_state=seed_num), \n",
        "    GradientBoostingClassifier(random_state=seed_num),      \n",
        "    XGBClassifier(random_state=seed_num),\n",
        "    LGBMClassifier(objective='multiclass', random_state=seed_num),\n",
        "    CatBoostClassifier(objective='MultiClass',\n",
        "                                   task_type='GPU',\n",
        "                                   one_hot_max_size=2, random_seed=seed_num,\n",
        "                                   iterations=4000, verbose=False,\n",
        "                                   learning_rate=0.05\n",
        "                                   ),\n",
        "    RidgeClassifierCV(),  # RidgeClassifier: no seed setting argument  \n",
        "    BaggingClassifier(random_state=seed_num)]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "id": "qfV3aJaS8lvI",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qfV3aJaS8lvI",
        "outputId": "246b41fa-4dc9-4502-fd22-50881ce5b9d3"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'RF': 0.7888888888888889, 'GBC': 0.8111111111111111, 'XGB': 0.7722222222222223, 'LGBM': 0.7888888888888889, 'Catboost': 0.8166666666666667, 'Ridge': 0.7611111111111111, 'Bagging': 0.75}\n"
          ]
        }
      ],
      "source": [
        "# find weights for each model \n",
        "model_list = ['RF', 'GBC', 'XGB', 'LGBM', 'Catboost', 'Ridge', 'Bagging']\n",
        "i=0\n",
        "model_acc = {}\n",
        "\n",
        "for model in models:  \n",
        "    if i == 4: \n",
        "        preds = model.fit(X_train, y_train).predict(X_test)\n",
        "        preds = preds.reshape(1,180)\n",
        "        preds = list(preds[0])\n",
        "    else: \n",
        "        preds = model.fit(X_train, y_train).predict(X_test)\n",
        "\n",
        "    globals()[f'preds_{model_list[i]}'] = preds   # set variable name for each model prediction results  \n",
        "  \n",
        "    score = sklearn.metrics.accuracy_score(y_test, preds)\n",
        "    model_acc[model_list[i]] = score\n",
        "    i += 1   \n",
        "\n",
        "print(model_acc)   "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "id": "PihsvBmKhfOd",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 499
        },
        "id": "PihsvBmKhfOd",
        "outputId": "9549cee4-8afc-4584-ee6c-613c45f39a70"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1008x576 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# ML models accuracy comparison plot \n",
        "model_name = list(model_acc.keys())\n",
        "model_acc = list(model_acc.values())\n",
        "acc_dic = {'model': model_name, 'acc': model_acc}\n",
        "acc_df = pd.DataFrame(acc_dic)\n",
        "acc_df = acc_df.set_index('model')\n",
        "\n",
        "acc_df.plot(kind='barh', figsize=(14,8),\n",
        "            title='Model Comparison - accuracy score');"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "_KIk0bpq4IHj",
      "metadata": {
        "id": "_KIk0bpq4IHj"
      },
      "source": [
        "## 2.2 Sequential MLP \n",
        "* Deep Learning Modeling: Sequential MLP model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "id": "8wlnknVPi0NX",
      "metadata": {
        "id": "8wlnknVPi0NX"
      },
      "outputs": [],
      "source": [
        "import torch.nn as nn \n",
        "import torch.nn.functional as F \n",
        "\n",
        "class MultilayerPerceptron(nn.Module):\n",
        "    def __init__(self, input_dim, hidden_dim1, hidden_dim2, hidden_dim3, output_dim):\n",
        "        \"\"\"\n",
        "        parameters: \n",
        "            input_dim (int): 입력 벡터 크기\n",
        "            hidden_dim1 (int): 첫 번째 Linear 층의 출력 크기\n",
        "            hidden_dim2 (int): 두 번째 Linear 층의 출력 크기 \n",
        "            output_dim (int): 세 번째 Linear 층의 출력 크기 \n",
        "        \"\"\"\n",
        "        super(MultilayerPerceptron, self).__init__()\n",
        "        self.fc1 = nn.Linear(input_dim, hidden_dim1)\n",
        "        self.fc2 = nn.Linear(hidden_dim1, hidden_dim2)\n",
        "        self.fc3 = nn.Linear(hidden_dim2, hidden_dim3)\n",
        "        self.fc4 = nn.Linear(hidden_dim3, output_dim)\n",
        "\n",
        "    def forward(self, x_in, apply_softmax=False):\n",
        "        \"\"\"\n",
        "        MLP의 정방향 계산 \n",
        "\n",
        "        parameters:\n",
        "            x_in (torch.Tensor): 입력 데이터 텐서\n",
        "                x_in.shape는 (batch, input_dim)\n",
        "            apply_softmax (multiclasses): softmax activation function \n",
        "        return: \n",
        "            result Tensor\n",
        "            tesnor.shape: (batch, output_dim)\n",
        "        \"\"\"\n",
        "        intermediate1 = F.relu(self.fc1(x_in))   # activation function\n",
        "        intermediate2 = F.relu(self.fc2(intermediate1))   # activation function\n",
        "        intermediate3 = F.relu(self.fc3(intermediate2))   # activation function\n",
        "        output = self.fc4(intermediate3)    \n",
        "\n",
        "\n",
        "        if apply_softmax: \n",
        "            output = F.softmax(output, dim=1)   # output layer: softmax activation function\n",
        "        return output "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "id": "Fp5qXV02kPeC",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Fp5qXV02kPeC",
        "outputId": "ac72f7cd-7844-4213-d5bf-7d097aae2b34"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MultilayerPerceptron(\n",
            "  (fc1): Linear(in_features=2877, out_features=1024, bias=True)\n",
            "  (fc2): Linear(in_features=1024, out_features=512, bias=True)\n",
            "  (fc3): Linear(in_features=512, out_features=256, bias=True)\n",
            "  (fc4): Linear(in_features=256, out_features=3, bias=True)\n",
            ")\n",
            "Type: torch.FloatTensor\n",
            "shape: torch.Size([256, 3])\n"
          ]
        }
      ],
      "source": [
        "def describe(x): \n",
        "    print(f'Type: {x.type()}')\n",
        "    print(f'shape: {x.shape}')\n",
        "    # print(f'value: {x}') \n",
        "\n",
        "batch_size = 256 \n",
        "input_dim = train_x.shape[-1]\n",
        "hidden_dim1 = 1024\n",
        "hidden_dim2 = 512\n",
        "hidden_dim3 = 256\n",
        "output_dim = 3 \n",
        "\n",
        "# model design \n",
        "mlp = MultilayerPerceptron(input_dim, hidden_dim1, hidden_dim2, hidden_dim3, output_dim)\n",
        "print(mlp)\n",
        "x_input = torch.rand(batch_size, input_dim)\n",
        "y_output = mlp(x_input, apply_softmax=True)  # result: probabilities format \n",
        "a = y_output\n",
        "describe(y_output)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "id": "FLHA36Z1iEhW",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "FLHA36Z1iEhW",
        "outputId": "bf1fe567-2d8c-4f1b-aeaf-c6b1a9502b83"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "prediction=[]\n",
        "\n",
        "for i in a: \n",
        "    i = list(i)\n",
        "    prediction.append(i.index(max(i)))\n",
        "\n",
        "sns.countplot(x=prediction);    "
      ]
    },
    {
      "cell_type": "markdown",
      "id": "7f3d392b",
      "metadata": {
        "id": "7f3d392b"
      },
      "source": [
        "# 3.Params Optimization\n",
        "1. Optuna\n",
        "2. GridSearch "
      ]
    },
    {
      "cell_type": "markdown",
      "id": "5ee4390b",
      "metadata": {
        "id": "5ee4390b"
      },
      "source": [
        "## 3.1 GradientBoosting\n",
        "1. Optuna\n",
        "    - hyperparameters auto optimization"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "id": "29dc0524",
      "metadata": {
        "id": "29dc0524"
      },
      "outputs": [],
      "source": [
        "def objective(trial, X, y, cv, scoring):\n",
        "  params = {\n",
        "    \"n_estimators\": trial.suggest_int(\"n_estimators\", 100, 5000, step = 100),\n",
        "    \"learning_rate\": trial.suggest_float(\"learning_rate\", 1e-4, 0.3, log = True),\n",
        "    \"max_depth\": trial.suggest_int(\"max_depth\", 3, 9),\n",
        "    \"subsample\": trial.suggest_float(\"subsample\", 0.5, 0.9, step = 0.1),\n",
        "    \"max_features\": trial.suggest_categorical(\"max_features\", [\"auto\", \"sqrt\", \"log2\"]),\n",
        "    \"random_state\": 42,\n",
        "    }\n",
        "  # Perform cross validation\n",
        "  gb_class = GradientBoostingClassifier(**params)\n",
        "\n",
        "  # Compute scores\n",
        "  scores = cross_validate(gb_class, X, y, cv = cv, scoring = scoring, n_jobs = -1)\n",
        "  accuracy = scores[\"test_score\"].mean()\n",
        "\n",
        "  return accuracy"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "0c65d30f",
      "metadata": {
        "id": "0c65d30f"
      },
      "source": [
        "- Optimization for params\n",
        "\n",
        "    - K-Fold, trials = 10"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "4d11262c",
      "metadata": {
        "id": "4d11262c"
      },
      "outputs": [],
      "source": [
        "study = optuna.create_study(direction = \"maximize\")\n",
        "\n",
        "kf = sklearn.model_selection.StratifiedKFold(n_splits=5, shuffle=True, random_state=37)\n",
        "\n",
        "func = lambda trial: objective(trial, X_train, y_train, \n",
        "                               cv = kf, scoring = \"accuracy\")\n",
        "\n",
        "# %%time\n",
        "# Start optimizing with 100 trials\n",
        "study.optimize(func, n_trials = 10)\n",
        "\n",
        "print(f\"The highest accuracy reached by this study: {(study.best_value) * 100}%.\")\n",
        "print(\"Best params:\")\n",
        "for key, value in study.best_params.items():\n",
        "    print(f\"\\t{key}: {value}\")    "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "id": "7d5b7e95",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7d5b7e95",
        "outputId": "fc259818-065b-4190-9dc9-f302d207994a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.69      0.43      0.53        21\n",
            "           1       0.82      0.97      0.89       131\n",
            "           2       1.00      0.43      0.60        28\n",
            "\n",
            "    accuracy                           0.82       180\n",
            "   macro avg       0.84      0.61      0.67       180\n",
            "weighted avg       0.83      0.82      0.80       180\n",
            "\n",
            "\n",
            " Optuna :  0.8222222222222222\n"
          ]
        }
      ],
      "source": [
        "import imblearn\n",
        "import warnings  # delete future warning message \n",
        "warnings.simplefilter(action=\"ignore\", category=FutureWarning)\n",
        "\n",
        "params= {'n_estimators': 1200, 'learning_rate': 0.009147154102399788, \n",
        "        'max_depth': 9, 'subsample': 0.8, 'max_features': 'auto'}\n",
        "GBC =  GradientBoostingClassifier(random_state=37, **params,                              \n",
        "                                  ).fit(X_train, y_train)\n",
        "preds = GBC.predict(X_test)\n",
        "\n",
        "print(classification_report(y_test, preds))\n",
        "print(\"\\n Optuna : \", accuracy_score(y_test, preds))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "id": "8f76630f",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "8f76630f",
        "outputId": "46108346-18b2-4bf4-b6cf-a80b322fd742"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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              "</body>\n",
              "</html>"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "from optuna.visualization import plot_optimization_history\n",
        "from optuna.visualization import plot_param_importances\n",
        "from optuna.visualization import plot_parallel_coordinate\n",
        "from optuna.visualization import plot_contour\n",
        "plot_param_importances(study)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "38703a67",
      "metadata": {
        "id": "38703a67"
      },
      "source": [
        "### 3.1.1 GridSearch\n",
        "\n",
        "- hyperparameter optimization"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "f34d37d8",
      "metadata": {
        "id": "f34d37d8"
      },
      "outputs": [],
      "source": [
        "# from sklearn.model_selection import GridSearchCV\n",
        "\n",
        "# estimator = GradientBoostingClassifier(random_state = 37)\n",
        "\n",
        "# param_grid = {\n",
        "#             'n_estimators' : [n_estimators for n_estimators in range(100, 5000, 100)],\n",
        "#             'learning_rate' : [lr * 0.0001 for lr in range(1, 10)],\n",
        "#             'max_depth' : [depth for depth in range(3, 9)],\n",
        "#             'subsample' : [subsample * 0.1 for subsample in range(5, 9, 1)],\n",
        "#             'max_features' : ['auto', 'sqrt', 'log2']\n",
        "#             }\n",
        "\n",
        "# # scoring = 'r2' -> 결정계수로 scoring\n",
        "# grid_GBC = GridSearchCV(estimator, param_grid, scoring = 'r2', n_jobs = -1)\n",
        "# grid_GBC.fit(train_x, train_y)\n",
        "\n",
        "# print('best estimator model: \\n{}'.format(grid_GBC.best_estimator_))\n",
        "# print('\\nbest parameter: \\n{}'.format(grid_GBC.best_params_))\n",
        "# print(\"\\nbest score: \\n{}\".format(grid_GBC.best_score_.round(3)))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "7f8add53",
      "metadata": {
        "id": "7f8add53"
      },
      "outputs": [],
      "source": [
        "# # GridSearch best parameter \n",
        "# import imblearn\n",
        "\n",
        "# params= {'n_estimators': 1200, 'learning_rate': 0.009147154102399788, \n",
        "#         'max_depth': 9, 'subsample': 0.8, 'max_features': 'auto'}\n",
        "# GBC =  GradientBoostingClassifier(random_state=37, **params,                              \n",
        "#                                   ).fit(X_train, y_train)\n",
        "# preds = GBC.predict(X_test)\n",
        "\n",
        "# print(classification_report(y_test, preds))\n",
        "# print(\"\\n Grid : \", accuracy_score(y_test, preds))"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "-V0IZDs6_S3f",
      "metadata": {
        "id": "-V0IZDs6_S3f"
      },
      "source": [
        "## 3.2 CatBoost\n",
        "1. optuna \n",
        "    - hyperparameters auto optimization"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 57,
      "id": "4ea05433",
      "metadata": {
        "id": "4ea05433"
      },
      "outputs": [],
      "source": [
        "def objective(trial):\n",
        "    model = CatBoostClassifier(\n",
        "        iterations=trial.suggest_int(\"iterations\", 100, 1000),\n",
        "        learning_rate=trial.suggest_float(\"learning_rate\", 1e-3, 1e-1, log=True),\n",
        "        depth=trial.suggest_int(\"depth\", 4, 10),\n",
        "        l2_leaf_reg=trial.suggest_float(\"l2_leaf_reg\", 1e-8, 100.0, log=True),\n",
        "        bootstrap_type=trial.suggest_categorical(\"bootstrap_type\", [\"Bayesian\"]),\n",
        "        random_strength=trial.suggest_float(\"random_strength\", 1e-8, 10.0, log=True),\n",
        "        bagging_temperature=trial.suggest_float(\"bagging_temperature\", 0.0, 10.0),\n",
        "        od_type=trial.suggest_categorical(\"od_type\", [\"IncToDec\", \"Iter\"]),\n",
        "        od_wait=trial.suggest_int(\"od_wait\", 10, 50),\n",
        "        verbose=False\n",
        "    )\n",
        "    model.fit(X_train, y_train)\n",
        "    y_pred = model.predict(X_test)\n",
        "    return f1_score(y_test, y_pred, average='macro')\n",
        "     \n",
        "# hyper-parameter tuning with OPTUNA  \n",
        "optuna.logging.set_verbosity(optuna.logging.WARNING)\n",
        "\n",
        "sampler = TPESampler(seed=37)\n",
        "study = optuna.create_study(study_name=\"catboost\", direction=\"maximize\", sampler=sampler)\n",
        "study.optimize(objective, n_trials=10)  "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 58,
      "id": "a8ffdb51",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "a8ffdb51",
        "outputId": "5ae3cb4c-e3eb-45fa-8503-59f9c2514767"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of trials:  10\n",
            "Best trial:\n",
            "  Value:  0.5929126858885749\n",
            "  Params:  {'iterations': 857, 'learning_rate': 0.07097207730593516, 'depth': 8, 'l2_leaf_reg': 0.00012090525126196811, 'bootstrap_type': 'Bayesian', 'random_strength': 5.359110894800412, 'bagging_temperature': 0.7137022569955509, 'od_type': 'IncToDec', 'od_wait': 42}\n"
          ]
        }
      ],
      "source": [
        "print(\"Number of trials: \", len(study.trials))\n",
        "print(\"Best trial:\")\n",
        "trial = study.best_trial\n",
        "print(\"  Value: \", trial.value)\n",
        "print(\"  Params: \",trial.params)\n",
        "\n",
        "model = CatBoostClassifier(**trial.params, verbose=False, random_state = 37)\n",
        "model.fit(X_train, y_train)\n",
        "y_pred = model.predict(X_test)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 59,
      "id": "1942cb67",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1942cb67",
        "outputId": "728fbe02-65ce-4407-eff8-2c211498e5ae"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.67      0.38      0.48        21\n",
            "           1       0.80      0.97      0.88       131\n",
            "           2       1.00      0.32      0.49        28\n",
            "\n",
            "    accuracy                           0.80       180\n",
            "   macro avg       0.82      0.56      0.62       180\n",
            "weighted avg       0.81      0.80      0.77       180\n",
            "\n",
            "\n",
            " Optuna :  0.8\n"
          ]
        }
      ],
      "source": [
        "params=  {'iterations': 857, 'learning_rate': 0.07097207730593516, 'depth': 8, \n",
        "        'l2_leaf_reg': 0.00012090525126196811, 'bootstrap_type': 'Bayesian', \n",
        "        'random_strength': 5.359110894800412, 'bagging_temperature': 0.7137022569955509, \n",
        "        'od_type': 'IncToDec', 'od_wait': 42}\n",
        "CBC = CatBoostClassifier(**params, verbose=False, random_state=37)\n",
        "CBC.fit(X_train, y_train)\n",
        "preds_CBC = CBC.predict(X_test)\n",
        "\n",
        "print(classification_report(y_test, preds_CBC))\n",
        "print(\"\\n Optuna : \", accuracy_score(y_test, preds_CBC))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 60,
      "id": "eACiKQAXwMSW",
      "metadata": {
        "id": "eACiKQAXwMSW"
      },
      "outputs": [],
      "source": [
        "from optuna.visualization import plot_optimization_history\n",
        "from optuna.visualization import plot_param_importances\n",
        "from optuna.visualization import plot_parallel_coordinate\n",
        "from optuna.visualization import plot_contour"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 61,
      "id": "b30ec99b",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 562
        },
        "id": "b30ec99b",
        "outputId": "b1a69824-e144-4672-fa94-7c3f552435e4"
      },
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          },
          "metadata": {}
        }
      ],
      "source": [
        "plot_param_importances(study)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "458a5890",
      "metadata": {
        "id": "458a5890"
      },
      "source": [
        "### 3.2.1 GridSearch\n",
        "\n",
        "- hyperparameter optimization"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "c06e0929",
      "metadata": {
        "id": "c06e0929"
      },
      "outputs": [],
      "source": [
        "# from sklearn.model_selection import GridSearchCV\n",
        "\n",
        "# estimator = CatBoostClassifier(random_state = 37)\n",
        "\n",
        "# param_grid = {\n",
        "#             'iterataions' : [iter for iter in range(100, 1000, 100)],\n",
        "#             'learning_rate' : [lr * 0.0001 for lr in range(1, 10)],\n",
        "#             'depth' : [depth for depth in range(4, 10)],\n",
        "#             'l2_leaf_reg' : [leaf * 0.0000001 for leaf in range(1, 100, 10)],\n",
        "#             'bootstrap_type' : ['Bayesian'],\n",
        "#             'random_strength' : [strength * 0.1 for strength in range(1, 100, 10)],\n",
        "#             'bagging_temperature' : [bag * 0.1 for bag in range(1, 10)],\n",
        "#             'od_type' : ['IncToDec', 'Iter'],\n",
        "#             'od_wait' : [wait for wait in range(10, 50, 4)]\n",
        "#             }\n",
        "\n",
        "# # scoring = 'r2' -> 결정계수로 scoring\n",
        "# grid_CBC = GridSearchCV(estimator, param_grid, scoring = 'r2', n_jobs = -1)\n",
        "# grid_CBC.fit(X_train, y_train)\n",
        "\n",
        "# print('best estimator model: \\n{}'.format(grid_CBC.best_estimator_))\n",
        "# print('\\nbest parameter: \\n{}'.format(grid_CBC.best_params_))\n",
        "# print(\"\\nbest score: \\n{}\".format(grid_CBC.best_score_.round(3)))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "092912ed",
      "metadata": {
        "id": "092912ed"
      },
      "outputs": [],
      "source": [
        "# params=  {'iterations': 857, 'learning_rate': 0.07097207730593516, 'depth': 8, \n",
        "#         'l2_leaf_reg': 0.00012090525126196811, 'bootstrap_type': 'Bayesian', \n",
        "#         'random_strength': 5.359110894800412, 'bagging_temperature': 0.7137022569955509, \n",
        "#         'od_type': 'IncToDec', 'od_wait': 42}\n",
        "# CBC = CatBoostClassifier(**params, verbose=False, random_state=37)\n",
        "# CBC.fit(X_train, y_train)\n",
        "# preds_CBC = CBC.predict(X_test)\n",
        "\n",
        "# print(classification_report(y_test, preds_CBC))\n",
        "# print(\"\\n Grid : \", accuracy_score(y_test, preds_CBC))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "o8N-QHuIvNN3"
      },
      "source": [
        "# 4.Ensemble"
      ],
      "id": "o8N-QHuIvNN3"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2fe15f1a"
      },
      "source": [
        "## 4.1 VotingClassifier\n",
        "1. 3-ensemble Modle\n",
        "2. Cross-Validation 구축 후 성능 검증"
      ],
      "id": "2fe15f1a"
    },
    {
      "cell_type": "code",
      "execution_count": 39,
      "metadata": {
        "id": "Vm8q0p4Hyzg2"
      },
      "outputs": [],
      "source": [
        "models = [\n",
        "    # test set accuracy 1위(0.8166) \n",
        "    CatBoostClassifier(verbose=False, random_seed=seed_num),\n",
        "    # test set accuracy 5위(0.7722)\n",
        "    XGBClassifier(random_state=seed_num),\n",
        "    # test set accuracy 2위(0.8111) \n",
        "    GradientBoostingClassifier(random_state=seed_num)]\n",
        "\n",
        "fit = [x.fit(train_x, train_y) for x in models]    "
      ],
      "id": "Vm8q0p4Hyzg2"
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      "execution_count": 40,
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          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "def prediction(models, test, mode=None, weights=None):\n",
        "    if mode == \"hard\":\n",
        "        preds = np.asarray([x.predict(test).reshape(-1) for x in models]).T\n",
        "        res = np.apply_along_axis(\n",
        "            lambda x: np.argmax(np.bincount(x, weights=weights)),\n",
        "            axis=1,\n",
        "            arr=preds\n",
        "        )\n",
        "    elif mode == \"soft\":  \n",
        "        preds = np.asarray([x.predict_proba(test) for x in models])\n",
        "        res = np.zeros(preds[0].shape)\n",
        "        for pred, weight in zip(preds, weights):\n",
        "            res = res + pred*weight\n",
        "        res = np.argmax(preds, axis=0) \n",
        "    else:\n",
        "        res = models[0].predict(test)\n",
        "    return res\n",
        "\n",
        "preds = prediction(models, test_x, 'hard', [2,1,2] )\n",
        "\n",
        "sns.countplot(x=preds);   "
      ],
      "id": "0bcafdb3"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KUOHpt3z847x"
      },
      "source": [
        "## 4.2 Validation Score\n",
        "1. train.csv에서 split된 test set accuracy 100% 검증"
      ],
      "id": "KUOHpt3z847x"
    },
    {
      "cell_type": "code",
      "execution_count": 47,
      "metadata": {
        "id": "Z3UesEBQ1r8z"
      },
      "outputs": [],
      "source": [
        "cbc = CatBoostClassifier(verbose=False, random_seed=seed_num)  \n",
        "xgb = XGBClassifier(random_state=seed_num) \n",
        "gbc = GradientBoostingClassifier(random_state=seed_num) \n",
        "voting_model = sklearn.ensemble.VotingClassifier(estimators=[\n",
        "                ('CatBoostClassifier', cbc),\n",
        "                ('XGBClassifier', xgb),\n",
        "                ('GradientBoostingClassifier', gbc)], \n",
        "                voting='hard', weights=[2,1,2])"
      ],
      "id": "Z3UesEBQ1r8z"
    },
    {
      "cell_type": "code",
      "execution_count": 48,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TgLWsUI2A25b",
        "outputId": "70c0de76-d1cf-4807-a994-95bd7c207c82"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1.0"
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ],
      "source": [
        "val_preds = prediction(models, X_test, 'hard', [2,1,2])\n",
        "val_score= sklearn.metrics.accuracy_score(y_test, val_preds)\n",
        "val_score  "
      ],
      "id": "TgLWsUI2A25b"
    },
    {
      "cell_type": "markdown",
      "id": "zbNtWowN78mX",
      "metadata": {
        "id": "zbNtWowN78mX"
      },
      "source": [
        "# 5.Submit"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 46,
      "id": "YQHmg0PCJetf",
      "metadata": {
        "id": "YQHmg0PCJetf"
      },
      "outputs": [],
      "source": [
        "submit = pd.read_csv('./sample_submission.csv')\n",
        "submit['Y_Class'] = preds\n",
        "\n",
        "submit.to_csv('smartFactorySubmission.csv', index=False)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "utf4-GCG5REy",
      "metadata": {
        "id": "utf4-GCG5REy"
      },
      "source": [
        "# 6.References\n",
        "PPT에 기재하였습니다\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# THE END"
      ],
      "metadata": {
        "id": "f5sg6EQYMj_P"
      },
      "id": "f5sg6EQYMj_P"
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "machine_shape": "hm",
      "provenance": [],
      "toc_visible": true
    },
    "gpuClass": "standard",
    "kernelspec": {
      "display_name": "Python 3 (ipykernel)",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.9.13"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 5
}