smartFactory_TabNet_Yumin (1).ipynb

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      "cell_type": "markdown",
      "source": [
        "# Data Description\n",
        "\n",
        "**1. train.csv [파일]**\n",
        "- PRODUCT_ID : 제품의 고유 ID\n",
        "- Y_Class : 제품 품질 상태(Target) \n",
        "    - 0 : 적정 기준 미달 (부적합)\n",
        "    - 1 : 적합\n",
        "    - 2 : 적정 기준 초과 (부적합)\n",
        "- Y_Quality : 제품 품질 관련 정량적 수치\n",
        "- TIMESTAMP : 제품이 공정에 들어간 시각\n",
        "- LINE : 제품이 들어간 공정 LINE 종류 ('T050304', 'T050307', 'T100304', 'T100306', 'T010306', 'T010305' 존재)\n",
        "- PRODUCT_CODE : 제품의 CODE 번호 ('A_31', 'T_31', 'O_31' 존재)\n",
        "- X_1 ~ X_2875 : 공정 과정에서 추출되어 비식별화된 변수\n",
        "- 06.13.2022~09.08.2022 DATA\n",
        "\n",
        "\n",
        "\n",
        "**2. test.csv [파일]**\n",
        "- PRODUCT_ID : 제품의 고유 ID\n",
        "- TIMESTAMP : 제품이 공정에 들어간 시각\n",
        "- LINE : 제품이 들어간 공정 LINE 종류 ('T050304', 'T050307', 'T100304', 'T100306', 'T010306', 'T010305' 존재)\n",
        "- PRODUCT_CODE : 제품의 CODE 번호 ('A_31', 'T_31', 'O_31' 존재)\n",
        "- X_1 ~ X_2875 : 공정 과정에서 추출되어 비식별화된 변수\n",
        "- 09.09.2022~11.05.2022 DATA \n",
        "\n",
        "**3. sample_submission.csv [파일] - 제출 양식**\n",
        "- PRODUCT_ID : 제품의 고유 ID\n",
        "- Y_Class : 예측한 제품 품질 상태\n",
        "    - 0 : 적정 기준 미달 (부적합)\n",
        "    - 1 : 적합\n",
        "    - 2 : 적정 기준 초과 (부적합)\n",
        "\n",
        "- 실제 공정 과정에서의 데이터로, 보안상의 이유로 일부 변수가 비식별화 처리 되었습니다. (X변수)\n",
        "- 'LINE', 'PRODUCT_CODE'는 Train / Test 모두 동일한 종류가 존재합니다."
      ],
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    },
    {
      "cell_type": "markdown",
      "source": [
        "## DATA LEAKAGE - RULE \n",
        "1. 대표적인 Data Leakage에 해당하는 사례\n",
        "    - Label Encoding 시 Test 데이터로부터 Encoder를 fit 시키는 경우\n",
        "    - Scaler 사용 시 Test 데이터로부터 Scaler를 fit 시키는 경우\n",
        "    Test 데이터에 대한 결측치 보간 시 Test 데이터의 통계 정보(평균, 최빈값 등)를 사용하는 경우\n",
        "    - 파생변수를 생성할 시 2개 이상의 Test 데이터들의 정보를 활용할 경우\n",
        "    - Test 데이터들의 이동 평균을 활용하는 경우\n",
        "    - Test 데이터들을 모델 학습에 활용하는 경우 (이번 해커톤에서는 Pseudo-Labeling도 허용하지 않습니다)\n",
        "    - Test 데이터에 pandas의 get_dummies() 함수를 적용하는 경우\n",
        "        - (=) Test DATA로 Encoder를 fitting 한 것과 같으므로\n",
        "        - ==> Encoder를 Train data로 fitting 한 후, Test data에 Transform (GOOD)  \n",
        "    - Test 데이터의 index 정보 혹은 row 개수 정보를 활용하는 경우\n",
        "    - Test 데이터로부터 EDA를 진행한 결과(인사이트)를 전처리, 모델 학습에 적용하는 경우\n",
        "    - 이 밖의 Test 데이터들의 정보를 모델 학습에 활용하는 모든 경우\n",
        "\n",
        "\n",
        "2. 대표적인 Data Leakage에 해당하지 않는 사례\n",
        "    - 독립적인 샘플(행(row)) 내에서의 모든 연산 혹은 파생 변수 생성\n",
        "        - 예를 들어, Test['C'] = Test['A'] + Test['B']와 같이 독립적인 샘플 내에서 A Column과 B Column을 더한 새로운 파생 변수 C Column을 생성하는 경우\n",
        "    - Train 데이터의 통계 정보를 Test 데이터에 적용하여 전처리하는 경우 혹은 파생 변수를 생성하는 경우\n",
        "    - Test 데이터의 결측치를 Train 데이터의 통계 정보를 바탕으로 보간하는 경우 \n"
      ],
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      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# MODULE IMPORT"
      ],
      "metadata": {
        "id": "7c9x-uVynqst"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install tabnet \n",
        "!pip install pytorch_tabnet\n",
        "!pip install optuna"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3kxzrmiq7l5m",
        "outputId": "df7df5be-88e1-4674-9ea8-e1ed5acf5664"
      },
      "execution_count": 1,
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        {
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          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: tabnet in /usr/local/lib/python3.8/dist-packages (0.1.6)\n",
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: pytorch_tabnet in /usr/local/lib/python3.8/dist-packages (4.0)\n",
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            "Requirement already satisfied: scikit_learn>0.21 in /usr/local/lib/python3.8/dist-packages (from pytorch_tabnet) (1.0.2)\n",
            "Requirement already satisfied: torch<2.0,>=1.2 in /usr/local/lib/python3.8/dist-packages (from pytorch_tabnet) (1.13.1+cu116)\n",
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            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.8/dist-packages (from scikit_learn>0.21->pytorch_tabnet) (3.1.0)\n",
            "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.8/dist-packages (from scikit_learn>0.21->pytorch_tabnet) (1.2.0)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.8/dist-packages (from torch<2.0,>=1.2->pytorch_tabnet) (4.5.0)\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",
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            "Requirement already satisfied: importlib-resources in /usr/local/lib/python3.8/dist-packages (from alembic>=1.5.0->optuna) (5.10.2)\n",
            "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: importlib-metadata in /usr/local/lib/python3.8/dist-packages (from alembic>=1.5.0->optuna) (6.0.0)\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) (3.13.0)\n",
            "Requirement already satisfied: MarkupSafe>=0.9.2 in /usr/local/lib/python3.8/dist-packages (from Mako->alembic>=1.5.0->optuna) (2.0.1)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "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",
        "import tensorflow as tf\n",
        "import optuna   # auto hyper-parameter tuning  \n",
        "from tqdm.notebook import tqdm\n",
        "\n",
        "import sklearn.metrics\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "from sklearn.model_selection import train_test_split \n",
        "from sklearn.metrics import accuracy_score, classification_report, f1_score, roc_auc_score, log_loss\n",
        "\n",
        "# Modeling\n",
        "from sklearn.ensemble import RandomForestRegressor\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.ensemble import GradientBoostingRegressor   \n",
        "from sklearn.ensemble import GradientBoostingClassifier \n",
        "from xgboost import XGBRegressor\n",
        "from xgboost import XGBClassifier \n",
        "from lightgbm import LGBMRegressor\n",
        "from lightgbm import LGBMClassifier \n",
        "from sklearn.svm import SVC\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.optim as optim\n",
        "from torch.utils.data import Dataset, DataLoader\n",
        "\n",
        "# TabNet \n",
        "from tabnet import TabNet, TabNetClassifier\n",
        "from tabnet import StackedTabNetClassifier        \n",
        "from pytorch_tabnet.tab_model import TabNetRegressor\n",
        "# from pytorch_tabnet.tab_model import TabNetClassification\n",
        "# from pytorch_tabnet.tab_model import TabNetModel\n",
        "from pytorch_tabnet.metrics import Metric\n",
        "from pytorch_tabnet.pretraining import TabNetPretrainer\n",
        "\n",
        "# Oversampling\n",
        "from imblearn.over_sampling import BorderlineSMOTE\n",
        "\n",
        "# google drive mount\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive')\n",
        "\n",
        "# random_state seed 37 fixed \n",
        "def seed_everything(seed):\n",
        "    random.seed(seed)\n",
        "    os.environ['PYTHONHASHSEED'] = str(seed)\n",
        "    np.random.seed(seed)\n",
        "seed_everything(37)\n",
        "\n",
        "# warning message delete \n",
        "import warnings\n",
        "warnings.simplefilter(action=\"ignore\", category=FutureWarning)   \n"
      ],
      "metadata": {
        "id": "N524BAv7ohSr",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6cc0cbca-cf6a-485f-8a9e-62ac364a27ec"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 0.Data Load"
      ],
      "metadata": {
        "id": "RYf_ZaTJ9vgg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "train_df = pd.read_csv('/content/drive/MyDrive/train.csv')\n",
        "train_df = train_df.drop(columns=['PRODUCT_ID', 'TIMESTAMP'])"
      ],
      "metadata": {
        "id": "kLBoulM6u2Fb"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "test_df = pd.read_csv('/content/drive/MyDrive/test.csv')\n",
        "test_df = test_df.drop(columns=['PRODUCT_ID','TIMESTAMP'])"
      ],
      "metadata": {
        "id": "vOExaTnZCvnk"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 1.Pre-processing\n",
        "1. Missing Values:\n",
        "    - All NaN column: `fillna(0)`\n",
        "    - fill grouped mean \n",
        "    - fill feature(column)'s mean "
      ],
      "metadata": {
        "id": "f5Kb__mmD7CN"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 1.1 EDA\n",
        "1. `Y_Quality` and `X features`: \n",
        "    - NOT high correlation, yet  "
      ],
      "metadata": {
        "id": "iw0f-7TDF9wW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# random 20 features list \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);"
      ],
      "metadata": {
        "id": "E1vfXTKcK9Dt"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 1.2 Merge & Encoding\n",
        "1. Merge `LINE` & `PRODUCT_CODE` & `Y_Class`columns by JOIN function\n",
        "    - set the joined column name as `line_pcode_class`\n",
        "2. Label encoding \n",
        "    - encode qualitative into quantitative"
      ],
      "metadata": {
        "id": "E9UrfEIwMJ7z"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Merge columns \n",
        "col = ['LINE', 'PRODUCT_CODE']\n",
        "train_df['line_pcode'] = train_df[col].apply(lambda row: ':'.join(row.values), axis=1)\n",
        "train_df = train_df.drop(columns=['LINE', 'PRODUCT_CODE'])  \n",
        "print(train_df['line_pcode'].value_counts())  \n",
        "\n",
        "# Label Encoding \n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "qual_col = ['line_pcode']\n",
        "for i in qual_col:\n",
        "    le = LabelEncoder()\n",
        "    le = le.fit(train_df[i])\n",
        "    train_df[i] = le.transform(train_df[i])\n",
        "print(train_df.line_pcode.unique())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 467
        },
        "id": "GqgXqn8c12dV",
        "outputId": "878137a9-91d3-481c-f8a7-296632d1045f"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "error",
          "ename": "KeyError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-17-481a822ddb21>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# Merge columns\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mcol\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m'LINE'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'PRODUCT_CODE'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mtrain_df\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'line_pcode'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtrain_df\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcol\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mlambda\u001b[0m \u001b[0mrow\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m':'\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrow\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m \u001b[0mtrain_df\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtrain_df\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'LINE'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'PRODUCT_CODE'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain_df\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'line_pcode'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalue_counts\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   3462\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mis_iterator\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3463\u001b[0m                 \u001b[0mkey\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3464\u001b[0;31m             \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_listlike_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3465\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3466\u001b[0m         \u001b[0;31m# take() does not accept boolean indexers\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m_get_listlike_indexer\u001b[0;34m(self, key, axis)\u001b[0m\n\u001b[1;32m   1312\u001b[0m             \u001b[0mkeyarr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindexer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnew_indexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_reindex_non_unique\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkeyarr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1313\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1314\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_validate_read_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkeyarr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindexer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1315\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1316\u001b[0m         if needs_i8_conversion(ax.dtype) or isinstance(\n",
            "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m_validate_read_indexer\u001b[0;34m(self, key, indexer, axis)\u001b[0m\n\u001b[1;32m   1372\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0muse_interval_msg\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1373\u001b[0m                     \u001b[0mkey\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1374\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"None of [{key}] are in the [{axis_name}]\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1375\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1376\u001b[0m             \u001b[0mnot_found\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mensure_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mmissing_mask\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnonzero\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munique\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyError\u001b[0m: \"None of [Index(['LINE', 'PRODUCT_CODE'], dtype='object')] are in the [columns]\""
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 1.3 Missing Values\n",
        "- [fill out NaN with Column's && Group's mean](https://rfriend.tistory.com/402) "
      ],
      "metadata": {
        "id": "qfIq2KqeFZdt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# ONLY 0 \n",
        "\n",
        "train_df.fillna(0, inplace=True)\n",
        "test_df.fillna(0, inplace=True)"
      ],
      "metadata": {
        "id": "DfBC_b8LE1wD"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 1) features that have only 'NaN' \n",
        "## Total 81 features only have 'NaN' \n",
        "miss_dic = train_df.isnull().sum().to_dict()\n",
        "all_NaN = pd.DataFrame({i for i in miss_dic if miss_dic[i]==598})\n",
        "all_NaN_name = list(all_NaN[0])  # all_NaN features name in list \n",
        "print(len(all_NaN_name))  "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8e62abc2-3b05-45e7-8a25-4d09ee0ad82a",
        "id": "tmQ4DYKrFZdu"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "82\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "all_NaN2 = pd.DataFrame(np.zeros((82,82)), columns=list(all_NaN[0]))\n",
        "\n",
        "for i in all_NaN_name: \n",
        "    train_df[i] = train_df[i].apply(lambda x: 0)\n",
        "train_df.X_2686.isnull().sum()  # e.g. check whether or not it's perfectly fill with ZERO "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a84d0d09-d8ba-4a48-b63b-072cd49b2687",
        "id": "aE5B1BfPFZdu"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 531
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_df.isnull().sum().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Seh70wI5oyz3",
        "outputId": "f095be86-fe07-450f-8bcb-64d2ff049b28"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1123798"
            ]
          },
          "metadata": {},
          "execution_count": 532
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 2) fill NaN with the feature's `line_pcde` grouped mean \n",
        "fill_group_mean = lambda g: g.fillna(g.mean())\n",
        "train_df.groupby('line_pcode').apply(fill_group_mean)\n",
        "train_df.isnull().sum().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5C_CETzulQ2E",
        "outputId": "9fece46a-6184-47e9-e4d9-7dc69c9800d1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1123798"
            ]
          },
          "metadata": {},
          "execution_count": 533
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 3) fillout remaining NaN with the feature's MEAN \n",
        "cols = train_df.iloc[:,2:].columns\n",
        "for i in cols: \n",
        "    train_df[i].fillna(train_df[i].mean(), inplace=True)\n",
        "train_df.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HzrSWDpYFzU8",
        "outputId": "ef7aa98f-30ed-4276-f7fb-d6228091ba9e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          Y_Class   Y_Quality         X_1         X_2    X_3    X_4  \\\n",
              "count  598.000000  598.000000  598.000000  598.000000  598.0  598.0   \n",
              "mean     1.025084    0.530896    2.409742   95.123209    0.0   45.0   \n",
              "std      0.565069    0.007401    4.500960    3.136136    0.0    0.0   \n",
              "min      0.000000    0.500856    1.000000   87.000000    0.0   45.0   \n",
              "25%      1.000000    0.527535    2.000000   94.000000    0.0   45.0   \n",
              "50%      1.000000    0.530436    2.000000   95.123209    0.0   45.0   \n",
              "75%      1.000000    0.533433    2.409742   95.123209    0.0   45.0   \n",
              "max      2.000000    0.578841  103.000000  102.000000    0.0   45.0   \n",
              "\n",
              "             X_5    X_6         X_7         X_8  ...     X_2867      X_2868  \\\n",
              "count  598.00000  598.0  598.000000  598.000000  ...  598.00000  598.000000   \n",
              "mean    10.39255    0.0   48.802292   10.048711  ...   53.60770   49.606200   \n",
              "std      0.37336    0.0    3.339364    0.164586  ...    3.31437    2.915263   \n",
              "min     10.00000    0.0   45.000000   10.000000  ...   31.70000   32.560000   \n",
              "25%     10.00000    0.0   45.000000   10.000000  ...   53.60770   49.606200   \n",
              "50%     10.39255    0.0   48.802292   10.000000  ...   53.60770   49.606200   \n",
              "75%     10.39255    0.0   50.000000   10.048711  ...   53.60770   49.606200   \n",
              "max     11.00000    0.0   62.000000   11.000000  ...   68.66000   60.410000   \n",
              "\n",
              "          X_2869     X_2870  X_2871  X_2872  X_2873  X_2874  X_2875  \\\n",
              "count  598.00000  598.00000   598.0   598.0   598.0   598.0   598.0   \n",
              "mean    51.65980   66.64970     1.0     0.0     0.0     0.0     0.0   \n",
              "std      3.62959    1.84382     0.0     0.0     0.0     0.0     0.0   \n",
              "min     30.49000   61.67000     1.0     0.0     0.0     0.0     0.0   \n",
              "25%     51.65980   66.64970     1.0     0.0     0.0     0.0     0.0   \n",
              "50%     51.65980   66.64970     1.0     0.0     0.0     0.0     0.0   \n",
              "75%     51.65980   66.64970     1.0     0.0     0.0     0.0     0.0   \n",
              "max     59.93000   79.75000     1.0     0.0     0.0     0.0     0.0   \n",
              "\n",
              "       line_pcode  \n",
              "count  598.000000  \n",
              "mean     4.078595  \n",
              "std      2.471718  \n",
              "min      0.000000  \n",
              "25%      2.000000  \n",
              "50%      5.000000  \n",
              "75%      7.000000  \n",
              "max      7.000000  \n",
              "\n",
              "[8 rows x 2878 columns]"
            ],
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Y_Class</th>\n",
              "      <th>Y_Quality</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_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",
              "      <th>line_pcode</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>598.000000</td>\n",
              "      <td>598.0</td>\n",
              "      <td>598.0</td>\n",
              "      <td>598.00000</td>\n",
              "      <td>598.0</td>\n",
              "      <td>598.000000</td>\n",
              "      <td>598.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>598.00000</td>\n",
              "      <td>598.000000</td>\n",
              "      <td>598.00000</td>\n",
              "      <td>598.00000</td>\n",
              "      <td>598.0</td>\n",
              "      <td>598.0</td>\n",
              "      <td>598.0</td>\n",
              "      <td>598.0</td>\n",
              "      <td>598.0</td>\n",
              "      <td>598.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>1.025084</td>\n",
              "      <td>0.530896</td>\n",
              "      <td>2.409742</td>\n",
              "      <td>95.123209</td>\n",
              "      <td>0.0</td>\n",
              "      <td>45.0</td>\n",
              "      <td>10.39255</td>\n",
              "      <td>0.0</td>\n",
              "      <td>48.802292</td>\n",
              "      <td>10.048711</td>\n",
              "      <td>...</td>\n",
              "      <td>53.60770</td>\n",
              "      <td>49.606200</td>\n",
              "      <td>51.65980</td>\n",
              "      <td>66.64970</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.078595</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.565069</td>\n",
              "      <td>0.007401</td>\n",
              "      <td>4.500960</td>\n",
              "      <td>3.136136</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.37336</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.339364</td>\n",
              "      <td>0.164586</td>\n",
              "      <td>...</td>\n",
              "      <td>3.31437</td>\n",
              "      <td>2.915263</td>\n",
              "      <td>3.62959</td>\n",
              "      <td>1.84382</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.471718</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.500856</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>87.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>45.0</td>\n",
              "      <td>10.00000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>45.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>31.70000</td>\n",
              "      <td>32.560000</td>\n",
              "      <td>30.49000</td>\n",
              "      <td>61.67000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.527535</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>94.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>45.0</td>\n",
              "      <td>10.00000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>45.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>53.60770</td>\n",
              "      <td>49.606200</td>\n",
              "      <td>51.65980</td>\n",
              "      <td>66.64970</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.530436</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>95.123209</td>\n",
              "      <td>0.0</td>\n",
              "      <td>45.0</td>\n",
              "      <td>10.39255</td>\n",
              "      <td>0.0</td>\n",
              "      <td>48.802292</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>53.60770</td>\n",
              "      <td>49.606200</td>\n",
              "      <td>51.65980</td>\n",
              "      <td>66.64970</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.533433</td>\n",
              "      <td>2.409742</td>\n",
              "      <td>95.123209</td>\n",
              "      <td>0.0</td>\n",
              "      <td>45.0</td>\n",
              "      <td>10.39255</td>\n",
              "      <td>0.0</td>\n",
              "      <td>50.000000</td>\n",
              "      <td>10.048711</td>\n",
              "      <td>...</td>\n",
              "      <td>53.60770</td>\n",
              "      <td>49.606200</td>\n",
              "      <td>51.65980</td>\n",
              "      <td>66.64970</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.578841</td>\n",
              "      <td>103.000000</td>\n",
              "      <td>102.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>45.0</td>\n",
              "      <td>11.00000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>62.000000</td>\n",
              "      <td>11.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>68.66000</td>\n",
              "      <td>60.410000</td>\n",
              "      <td>59.93000</td>\n",
              "      <td>79.75000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>8 rows × 2878 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e49d3c0c-9b85-4cf7-b7a2-d772855b843f')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "\n",
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            ]
          },
          "metadata": {},
          "execution_count": 534
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 4) check the remaining NULL \n",
        "miss_dic = train_df.isnull().sum().to_dict()\n",
        "check = {i for i in miss_dic if miss_dic[i]>0}\n",
        "check  # all_NaN features name in list   "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YthctslpG5cd",
        "outputId": "355e0ce8-5c17-4aa7-9555-ce4aa0674956"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "set()"
            ]
          },
          "metadata": {},
          "execution_count": 535
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "9PAmM2lyzU0f"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 1.5 Outlier \n",
        "\n",
        "[outlier Dection: IsolationForest](https://scikit-learn.org/stable/auto_examples/miscellaneous/plot_anomaly_comparison.html)\n"
      ],
      "metadata": {
        "id": "4038bXsY3ngy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# outlier (replace 1Q, 3Q with MEDIAN)\n",
        "# xs = list(train_df.iloc[:, 6:].columns)\n",
        "# def outlier(df):\n",
        "#     for col in xs: \n",
        "#         q1 = train_df[col].quantile(0.25)\n",
        "#         q3 = train_df[col].quantile(0.75)\n",
        "#         median = train_df[col].quantile(0.50)\n",
        "\n",
        "#         train_df[col] = np.where(train_df[col]<q1, median, train_df[col])\n",
        "#         train_df[col] = np.where(train_df[col]>q3, median, train_df[col])\n",
        "\n",
        "# train_df "
      ],
      "metadata": {
        "id": "nJuv_OqajZze"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 1-1. Test_df Preprocessing"
      ],
      "metadata": {
        "id": "AQf-saI4BQyu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Merge \n",
        "col = ['LINE', 'PRODUCT_CODE']\n",
        "test_df['line_pcode'] = test_df[col].apply(lambda row: ':'.join(row.values), axis=1)\n",
        "test_df = test_df.drop(columns=['LINE', 'PRODUCT_CODE'])\n",
        "\n",
        "# Label Encoding \n",
        "for i in qual_col:\n",
        "    for label in np.unique(test_df[i]): \n",
        "        if label not in le.classes_: \n",
        "            le.classes_ = np.append(le.classes_, label)\n",
        "    test_df[i] = le.transform(test_df[i])  # CAUTION: DO NOT fit test_df (only fit train_df) \n",
        "print('Done.')"
      ],
      "metadata": {
        "id": "nT3nx4o7B085",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 467
        },
        "outputId": "4fd140dd-8f41-4c51-c966-e6a5f369789c"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "error",
          "ename": "KeyError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-18-ac8bcfa68a87>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# Merge\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mcol\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m'LINE'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'PRODUCT_CODE'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mtest_df\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'line_pcode'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtest_df\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcol\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mlambda\u001b[0m \u001b[0mrow\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m':'\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrow\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m \u001b[0mtest_df\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtest_df\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'LINE'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'PRODUCT_CODE'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   3462\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mis_iterator\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3463\u001b[0m                 \u001b[0mkey\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3464\u001b[0;31m             \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_listlike_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3465\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3466\u001b[0m         \u001b[0;31m# take() does not accept boolean indexers\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m_get_listlike_indexer\u001b[0;34m(self, key, axis)\u001b[0m\n\u001b[1;32m   1312\u001b[0m             \u001b[0mkeyarr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindexer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnew_indexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_reindex_non_unique\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkeyarr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1313\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1314\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_validate_read_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkeyarr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindexer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1315\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1316\u001b[0m         if needs_i8_conversion(ax.dtype) or isinstance(\n",
            "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m_validate_read_indexer\u001b[0;34m(self, key, indexer, axis)\u001b[0m\n\u001b[1;32m   1372\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0muse_interval_msg\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1373\u001b[0m                     \u001b[0mkey\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1374\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"None of [{key}] are in the [{axis_name}]\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1375\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1376\u001b[0m             \u001b[0mnot_found\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mensure_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mmissing_mask\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnonzero\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munique\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyError\u001b[0m: \"None of [Index(['LINE', 'PRODUCT_CODE'], dtype='object')] are in the [columns]\""
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Missing Values\n",
        "# 1) features that have only 'NaN' without any recorded values \n",
        "## Total 506 features only have 'NaN' \n",
        "miss_dic = test_df.isnull().sum().to_dict()\n",
        "all_nan = pd.DataFrame({i for i in miss_dic if miss_dic[i]==310})\n",
        "all_nan_name = list(all_nan[0])  # all_NaN features name in list \n",
        "\n",
        "for i in all_nan_name: \n",
        "    test_df[i].fillna(0, inplace=True)\n",
        "test_df.X_2277.isnull().sum()  # e.g. check whether or not it's perfectly fill with ZERO "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a6fa78c7-398b-414f-ac53-12e0b52b9a95",
        "id": "9vLY-L_IBQy5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 538
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(len(test_df['line_pcode'].unique()))\n",
        "print(test_df.isnull().sum().sum())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_7VrI1t3C5tK",
        "outputId": "4b4d10bd-0acb-4830-c90a-503e94b583c6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "8\n",
            "0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 2) fill NaN with the feature's `line_pcde` grouped mean \n",
        "fill_group_mean = lambda g: g.fillna(g.mean())\n",
        "test_df.groupby('line_pcode').apply(fill_group_mean)\n",
        "test_df.isnull().sum().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6808cdb3-7a0b-448e-c281-1580ce21e086",
        "id": "se7WLEdQrokm"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "491347"
            ]
          },
          "metadata": {},
          "execution_count": 540
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 3) fillout remaining NaN with the feature's MEAN \n",
        "cols = test_df.iloc[:,:].columns\n",
        "for i in cols: \n",
        "    test_df[i].fillna(test_df[i].mean(), inplace=True)\n",
        "test_df.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5512b2c0-5123-425a-d079-c52c3446f4ae",
        "id": "pNDoPbW5rokn"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              X_1         X_2    X_3    X_4         X_5    X_6         X_7  \\\n",
              "count  310.000000  310.000000  310.0  310.0  310.000000  310.0  310.000000   \n",
              "mean     4.337449   94.448560    0.0   45.0   10.271605    0.0   48.485597   \n",
              "std     14.711941    3.595263    0.0    0.0    0.394436    0.0    3.303768   \n",
              "min      1.000000   87.000000    0.0   45.0   10.000000    0.0   45.000000   \n",
              "25%      2.000000   93.000000    0.0   45.0   10.000000    0.0   45.000000   \n",
              "50%      2.000000   94.448560    0.0   45.0   10.000000    0.0   48.485597   \n",
              "75%      2.000000   96.000000    0.0   45.0   10.271605    0.0   50.000000   \n",
              "max    154.000000  102.000000    0.0   45.0   11.000000    0.0   67.000000   \n",
              "\n",
              "              X_8         X_9   X_10  ...      X_2867      X_2868      X_2869  \\\n",
              "count  310.000000  310.000000  310.0  ...  310.000000  310.000000  310.000000   \n",
              "mean    10.012346   42.407407    2.0  ...   53.014706   49.994412   54.202353   \n",
              "std      0.097923    9.276526    0.0  ...    1.448128    1.998771    1.117486   \n",
              "min     10.000000   31.000000    2.0  ...   41.630000   39.850000   47.820000   \n",
              "25%     10.000000   31.000000    2.0  ...   53.014706   49.994412   54.202353   \n",
              "50%     10.000000   42.407407    2.0  ...   53.014706   49.994412   54.202353   \n",
              "75%     10.000000   52.000000    2.0  ...   53.014706   49.994412   54.202353   \n",
              "max     11.000000   52.000000    2.0  ...   60.490000   60.310000   58.290000   \n",
              "\n",
              "           X_2870      X_2871      X_2872      X_2873      X_2874      X_2875  \\\n",
              "count  310.000000  310.000000  310.000000  310.000000  310.000000  310.000000   \n",
              "mean    63.806176    0.974359  247.988889  251.888889  243.300000  228.844444   \n",
              "std      0.628799    0.056154    4.097781    3.230763    5.715889    3.139910   \n",
              "min     60.720000    0.000000  199.000000  214.600000  169.700000  191.900000   \n",
              "25%     63.806176    0.974359  247.988889  251.888889  243.300000  228.844444   \n",
              "50%     63.806176    0.974359  247.988889  251.888889  243.300000  228.844444   \n",
              "75%     63.806176    0.974359  247.988889  251.888889  243.300000  228.844444   \n",
              "max     67.240000    1.000000  268.000000  278.600000  271.400000  245.600000   \n",
              "\n",
              "       line_pcode  \n",
              "count  310.000000  \n",
              "mean     5.138710  \n",
              "std      2.064522  \n",
              "min      0.000000  \n",
              "25%      5.000000  \n",
              "50%      5.000000  \n",
              "75%      7.000000  \n",
              "max      7.000000  \n",
              "\n",
              "[8 rows x 2876 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-ba6fa947-60d8-4d11-aa08-ffc07011c212\">\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>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>X_9</th>\n",
              "      <th>X_10</th>\n",
              "      <th>...</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",
              "      <th>line_pcode</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>310.000000</td>\n",
              "      <td>310.000000</td>\n",
              "      <td>310.0</td>\n",
              "      <td>310.0</td>\n",
              "      <td>310.000000</td>\n",
              "      <td>310.0</td>\n",
              "      <td>310.000000</td>\n",
              "      <td>310.000000</td>\n",
              "      <td>310.000000</td>\n",
              "      <td>310.0</td>\n",
              "      <td>...</td>\n",
              "      <td>310.000000</td>\n",
              "      <td>310.000000</td>\n",
              "      <td>310.000000</td>\n",
              "      <td>310.000000</td>\n",
              "      <td>310.000000</td>\n",
              "      <td>310.000000</td>\n",
              "      <td>310.000000</td>\n",
              "      <td>310.000000</td>\n",
              "      <td>310.000000</td>\n",
              "      <td>310.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>4.337449</td>\n",
              "      <td>94.448560</td>\n",
              "      <td>0.0</td>\n",
              "      <td>45.0</td>\n",
              "      <td>10.271605</td>\n",
              "      <td>0.0</td>\n",
              "      <td>48.485597</td>\n",
              "      <td>10.012346</td>\n",
              "      <td>42.407407</td>\n",
              "      <td>2.0</td>\n",
              "      <td>...</td>\n",
              "      <td>53.014706</td>\n",
              "      <td>49.994412</td>\n",
              "      <td>54.202353</td>\n",
              "      <td>63.806176</td>\n",
              "      <td>0.974359</td>\n",
              "      <td>247.988889</td>\n",
              "      <td>251.888889</td>\n",
              "      <td>243.300000</td>\n",
              "      <td>228.844444</td>\n",
              "      <td>5.138710</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>14.711941</td>\n",
              "      <td>3.595263</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.394436</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.303768</td>\n",
              "      <td>0.097923</td>\n",
              "      <td>9.276526</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>1.448128</td>\n",
              "      <td>1.998771</td>\n",
              "      <td>1.117486</td>\n",
              "      <td>0.628799</td>\n",
              "      <td>0.056154</td>\n",
              "      <td>4.097781</td>\n",
              "      <td>3.230763</td>\n",
              "      <td>5.715889</td>\n",
              "      <td>3.139910</td>\n",
              "      <td>2.064522</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>87.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>45.0</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>45.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>31.000000</td>\n",
              "      <td>2.0</td>\n",
              "      <td>...</td>\n",
              "      <td>41.630000</td>\n",
              "      <td>39.850000</td>\n",
              "      <td>47.820000</td>\n",
              "      <td>60.720000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>199.000000</td>\n",
              "      <td>214.600000</td>\n",
              "      <td>169.700000</td>\n",
              "      <td>191.900000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>2.000000</td>\n",
              "      <td>93.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>45.0</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>45.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>31.000000</td>\n",
              "      <td>2.0</td>\n",
              "      <td>...</td>\n",
              "      <td>53.014706</td>\n",
              "      <td>49.994412</td>\n",
              "      <td>54.202353</td>\n",
              "      <td>63.806176</td>\n",
              "      <td>0.974359</td>\n",
              "      <td>247.988889</td>\n",
              "      <td>251.888889</td>\n",
              "      <td>243.300000</td>\n",
              "      <td>228.844444</td>\n",
              "      <td>5.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>2.000000</td>\n",
              "      <td>94.448560</td>\n",
              "      <td>0.0</td>\n",
              "      <td>45.0</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>48.485597</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>42.407407</td>\n",
              "      <td>2.0</td>\n",
              "      <td>...</td>\n",
              "      <td>53.014706</td>\n",
              "      <td>49.994412</td>\n",
              "      <td>54.202353</td>\n",
              "      <td>63.806176</td>\n",
              "      <td>0.974359</td>\n",
              "      <td>247.988889</td>\n",
              "      <td>251.888889</td>\n",
              "      <td>243.300000</td>\n",
              "      <td>228.844444</td>\n",
              "      <td>5.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>2.000000</td>\n",
              "      <td>96.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>45.0</td>\n",
              "      <td>10.271605</td>\n",
              "      <td>0.0</td>\n",
              "      <td>50.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>52.000000</td>\n",
              "      <td>2.0</td>\n",
              "      <td>...</td>\n",
              "      <td>53.014706</td>\n",
              "      <td>49.994412</td>\n",
              "      <td>54.202353</td>\n",
              "      <td>63.806176</td>\n",
              "      <td>0.974359</td>\n",
              "      <td>247.988889</td>\n",
              "      <td>251.888889</td>\n",
              "      <td>243.300000</td>\n",
              "      <td>228.844444</td>\n",
              "      <td>7.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>154.000000</td>\n",
              "      <td>102.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>45.0</td>\n",
              "      <td>11.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>67.000000</td>\n",
              "      <td>11.000000</td>\n",
              "      <td>52.000000</td>\n",
              "      <td>2.0</td>\n",
              "      <td>...</td>\n",
              "      <td>60.490000</td>\n",
              "      <td>60.310000</td>\n",
              "      <td>58.290000</td>\n",
              "      <td>67.240000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>268.000000</td>\n",
              "      <td>278.600000</td>\n",
              "      <td>271.400000</td>\n",
              "      <td>245.600000</td>\n",
              "      <td>7.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>8 rows × 2876 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ba6fa947-60d8-4d11-aa08-ffc07011c212')\"\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",
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              "  </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",
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              "      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",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-ba6fa947-60d8-4d11-aa08-ffc07011c212 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-ba6fa947-60d8-4d11-aa08-ffc07011c212');\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",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 541
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 4) check the remaining NULL \n",
        "miss_dic = test_df.isnull().sum().to_dict()\n",
        "check = {i for i in miss_dic if miss_dic[i]>0}\n",
        "check  # all_NaN features name in list   "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d0a62154-996c-4c45-f5a3-36cce1ff1513",
        "id": "P-vVEt6EHe7p"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "set()"
            ]
          },
          "metadata": {},
          "execution_count": 542
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# outlier (replace 1Q, 3Q with MEDIAN)\n",
        "# xs = list(test_df.iloc[:, 6:].columns)\n",
        "# def outlier(df):\n",
        "#     for col in xs: \n",
        "#         q1 = test_df[col].quantile(0.25)\n",
        "#         q3 = test_df[col].quantile(0.75)\n",
        "#         median = test_df[col].quantile(0.50)\n",
        "\n",
        "#         test_df[col] = np.where(test_df[col]>q3, median, test_df[col])\n",
        "\n",
        "# test_df.describe()"
      ],
      "metadata": {
        "id": "skEOybzqBQy6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 2.Modeling Prep"
      ],
      "metadata": {
        "id": "7GodRp6O-5TM"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 2.1 OverSampling\n",
        "1. SMOTE\n",
        "2. SMOTE-NC\n",
        "3. Borderline-SMOTE\n",
        "4. SVM-SMOTE\n",
        "5. ADASYN \n",
        "- All methods' test set f1_score is same \n",
        "- Finally, selected oversampling technique = Borderline-SMOTE\n"
      ],
      "metadata": {
        "id": "yKYVY4DwWSts"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X = train_df.drop(columns=['Y_Quality','Y_Class'])\n",
        "y = train_df['Y_Quality']\n",
        "# X, y = BorderlineSMOTE(random_state=37).fit_resample(X,y)\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3,\n",
        "                                                    random_state=37,\n",
        "                                                    shuffle=True)\n",
        "print(X_train.shape, X_test.shape)\n",
        "print(y_train.shape, y_test.shape)\n",
        "\n",
        "# model = XGBClassifier()\n",
        "# fitted = model.fit(X_train, y_train)\n",
        "# preds = fitted.predict(X_test)    \n",
        "# # print(classification_report(y_test, model.predict(X_test))) \n",
        "# print(f1_score(y_test, preds, average='macro'))\n",
        "# pd.DataFrame(y).value_counts()       "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f018f636-2136-47c3-df10-714a2fb218e7",
        "id": "kQswVm3B7Kz4"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(418, 2876) (180, 2876)\n",
            "(418,) (180,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 2.2 train_val_test"
      ],
      "metadata": {
        "id": "DbDJsvfNx1VH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X = train_df.drop(columns=['Y_Class'])\n",
        "y = train_df['Y_Class']\n",
        "X, y = BorderlineSMOTE(random_state=37).fit_resample(X,y)\n",
        "\n",
        "test_x = test_df\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=37, shuffle=True)\n",
        "print(X_train.shape, X_test.shape)   \n",
        "print(y_train.shape, y_test.shape)\n",
        "X_train, X_validation, y_train, y_validation = train_test_split(X_train, y_train, \n",
        "                                                                test_size=0.3, random_state=37)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rphmsOtYx7QD",
        "outputId": "7e05da63-2799-472f-97b7-5786853703d3"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(854, 2877) (367, 2877)\n",
            "(854,) (367,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "GB_model = GradientBoostingRegressor(random_state=37).fit(X_train, y_train) \n",
        "preds=GB_model.predict(X_test)  \n",
        "# print(accuracy_score(y_test, preds))\n",
        "from sklearn.metrics import mean_squared_error\n",
        "import math\n",
        "print(mean_squared_error(y_test, preds))\n",
        "print(math.sqrt(mean_squared_error(y_test, preds)))\n",
        "\n",
        "train_df.drop(columns=['Y_Class'])\n",
        "# X_train = train_df.drop(columns=['Y_Class'])\n",
        "# X = pd.DataFrame(train_df['Y_Quality'])\n",
        "X_train = pd.DataFrame(y_train)\n",
        "X_test = pd.DataFrame(preds )\n",
        "y = train_df['Y_Class']\n",
        "y_train, y_test = train_test_split(y, test_size=0.3, random_state=37, shuffle=True)\n",
        "\n",
        "\n",
        "GB_model =  GradientBoostingClassifier(random_state=37).fit(X_train, y_train)\n",
        "# test_x = pd.DataFrame(test_x, columns=['Y_Quality'])\n",
        "# test_x = pd.DataFrame(test_x)\n",
        "\n",
        "preds = GB_model.predict(X_test)   \n",
        "print(f1_score(y_test, preds, average='macro'))   \n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "q1pAk-EfJbab",
        "outputId": "62231ece-4f3e-4007-9f4f-620d8a521175"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2.0364228522075375e-05\n",
            "0.004512674209609572\n",
            "0.6486772486772486\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GradientBoostingClassifier was fitted with feature names\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 3.ML_models\n",
        "1. Test Set f1_score [@without oversampling]\n",
        "    - 0.9511763871934015 RF\n",
        "    - 0.987653850550623 XGB  \n",
        "    - 0.987653850550623 GB \n",
        "    - 0.9959720958447074 LGBM (overfitting)\n"
      ],
      "metadata": {
        "id": "rpo-UWaiyLNW"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 3.1 GradientBoosting [0.63]"
      ],
      "metadata": {
        "id": "yXJSNKPRYsvb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import xgboost as xgb\n",
        "import psutil\n",
        "import time \n",
        "\n",
        "def memory_usage(message: str = 'debug'):\n",
        "    # current process RAM usage\n",
        "    p = psutil.Process()\n",
        "    rss = p.memory_info().rss / 2 ** 20 # Bytes to MB\n",
        "    print(f\"[{message}] memory usage: {rss: 10.5f} MB\")\n",
        "\n",
        "\n",
        "def objective(trial):\n",
        "    params = {\n",
        "        \"objective\": \"multi:softprob\",\n",
        "        \"eval_metric\":'mlogloss',\n",
        "        \"booster\": 'gbtree',\n",
        "        'tree_method':'gpu_hist', 'predictor':'gpu_predictor', 'gpu_id': 0, # GPU 사용시\n",
        "        # \"tree_method\": 'exact', 'gpu_id': -1,  # CPU 사용시\n",
        "        \"verbosity\": 0,\n",
        "        'num_class':3,\n",
        "        \"max_depth\": trial.suggest_int(\"max_depth\", 4, 10),\n",
        "        \"learning_rate\": trial.suggest_uniform('learning_rate', 0.0001, 0.99),\n",
        "        'n_estimators': trial.suggest_int(\"n_estimators\", 1000, 10000, step=100),\n",
        "        \"colsample_bytree\": trial.suggest_float(\"colsample_bytree\", 0.5, 1.0),\n",
        "        \"colsample_bylevel\": trial.suggest_float(\"colsample_bylevel\", 0.5, 1.0),\n",
        "        \"colsample_bynode\": trial.suggest_float(\"colsample_bynode\", 0.5, 1.0),\n",
        "        \"reg_lambda\": trial.suggest_loguniform(\"reg_lambda\", 1e-2, 1),\n",
        "        \"reg_alpha\": trial.suggest_loguniform(\"reg_alpha\", 1e-2, 1),\n",
        "        'subsample': trial.suggest_discrete_uniform('subsample', 0.6, 1.0, 0.05),     \n",
        "        'min_child_weight': trial.suggest_int('min_child_weight', 2, 15),\n",
        "        \"gamma\": trial.suggest_float(\"gamma\", 0.1, 1.0, log=True),\n",
        "        # 'num_parallel_tree': trial.suggest_int(\"num_parallel_tree\", 1, 500) 추가하면 느려짐.\n",
        "    }\n",
        "\n",
        "\n",
        "    model = GradientBoostingRegressor(**params, \n",
        "                                      random_state = 37, use_label_encoder = False)\n",
        "\n",
        "    bst = model.fit(X_train, y_train,\n",
        "                    eval_set=[(X_validation,y_validation)], \n",
        "                    early_stopping_rounds=50, \n",
        "                    verbose=False)\n",
        "    preds = bst.predict(X_validation)\n",
        "    pred_labels = np.rint(preds)\n",
        "    accuracy = sklearn.metrics.accuracy_score(y_validation, pred_labels)\n",
        "    # print(f1_score(y_test, preds, average='macro'))\n",
        "    return f1_score\n",
        "\n",
        "\n",
        "if __name__ == \"__main__\":\n",
        "\n",
        "    train_start = time.time()\n",
        "\n",
        "    study = optuna.create_study(direction=\"maximize\")\n",
        "    study.optimize(objective, n_trials=50, show_progress_bar=True)\n",
        "\n",
        "    print(\"Number of finished trials: \", len(study.trials))\n",
        "    print(\"Best trial:\")\n",
        "\n",
        "\n",
        "    trial = study.best_trial\n",
        "\n",
        "    print(\"  Accuracy: {}\".format(trial.value))\n",
        "    print(\"  Best hyperparameters: \")\n",
        "    for key, value in trial.params.items():\n",
        "        print(\"    {}: {}\".format(key, value))\n",
        "\n",
        "   \n",
        "    clf = GradientBoostingRegressor(**study.best_params, random_state = 1234, use_label_encoder = False)\n",
        "    clf.fit(X_train, y_train)\n",
        "\n",
        "    preds = clf.predict(X_test)\n",
        "    # pred_labels = np.rint(preds)\n",
        "    accuracy = sklearn.metrics.accuracy_score(y_test, preds)\n",
        "\n",
        "    print(\"Accuracy: {}\".format(accuracy))\n",
        "\n",
        "    # memory_usage(\"학습하는데 걸린 시간  {:.2f} 분\\n\".format( (time.time() - train_start)/60))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 641,
          "referenced_widgets": [
            "4c9efcf6fc904594abeb7cf6eeb650aa",
            "290cfd0cc67c4971aac0dc54d3f6641a",
            "2da480c0dea24371b67ec386bec2d6c7",
            "a50a5ff90bbd4ceba5b194bb1cc2da84",
            "451fe2f87dbc45db9f4d762d1bebb059",
            "1a91ef4ea81c44ddb1b3e7c10267d8c8",
            "b7b6771c2b9f45faa3ff7032d91cda7a",
            "65e1e778c971423baa65889d0ec4bd80",
            "353143dad7ed41ac96516e435328a7dc",
            "6aed88203b7a4737805cb85a7703914d",
            "ce7c567282384397be8505faf8d04d91"
          ]
        },
        "outputId": "b5a8473e-0d21-467c-8a24-1db6f62bebea",
        "id": "Eiyq_o3JH1bY"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\u001b[32m[I 2023-02-21 13:19:57,200]\u001b[0m A new study created in memory with name: no-name-02aa9ef8-46a0-40f9-9913-31b065cd018d\u001b[0m\n",
            "/usr/local/lib/python3.8/dist-packages/optuna/progress_bar.py:56: ExperimentalWarning: Progress bar is experimental (supported from v1.2.0). The interface can change in the future.\n",
            "  self._init_valid()\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/50 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "4c9efcf6fc904594abeb7cf6eeb650aa"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[33m[W 2023-02-21 13:19:57,248]\u001b[0m Trial 0 failed with parameters: {'max_depth': 7, 'learning_rate': 0.47424752609903553, 'n_estimators': 3200, 'colsample_bytree': 0.5695402218192154, 'colsample_bylevel': 0.502090257313133, 'colsample_bynode': 0.9797156763677304, 'reg_lambda': 0.041567295366939254, 'reg_alpha': 0.018103836160769358, 'subsample': 0.65, 'min_child_weight': 10, 'gamma': 0.29705661747732764} because of the following error: TypeError(\"__init__() got an unexpected keyword argument 'objective'\").\u001b[0m\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.8/dist-packages/optuna/study/_optimize.py\", line 200, in _run_trial\n",
            "    value_or_values = func(trial)\n",
            "  File \"<ipython-input-35-81c73c93eb72>\", line 37, in objective\n",
            "    model = GradientBoostingRegressor(**params, random_state = 37, use_label_encoder = False)\n",
            "TypeError: __init__() got an unexpected keyword argument 'objective'\n",
            "\u001b[33m[W 2023-02-21 13:19:57,250]\u001b[0m Trial 0 failed with value None.\u001b[0m\n"
          ]
        },
        {
          "output_type": "error",
          "ename": "TypeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-35-81c73c93eb72>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     53\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     54\u001b[0m     \u001b[0mstudy\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0moptuna\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcreate_study\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdirection\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"maximize\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 55\u001b[0;31m     \u001b[0mstudy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptimize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobjective\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_trials\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m50\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mshow_progress_bar\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     56\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     57\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Number of finished trials: \"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstudy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrials\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/optuna/study/study.py\u001b[0m in \u001b[0;36moptimize\u001b[0;34m(self, func, n_trials, timeout, n_jobs, catch, callbacks, gc_after_trial, show_progress_bar)\u001b[0m\n\u001b[1;32m    423\u001b[0m         \"\"\"\n\u001b[1;32m    424\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 425\u001b[0;31m         _optimize(\n\u001b[0m\u001b[1;32m    426\u001b[0m             \u001b[0mstudy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    427\u001b[0m             \u001b[0mfunc\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/optuna/study/_optimize.py\u001b[0m in \u001b[0;36m_optimize\u001b[0;34m(study, func, n_trials, timeout, n_jobs, catch, callbacks, gc_after_trial, show_progress_bar)\u001b[0m\n\u001b[1;32m     64\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     65\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mn_jobs\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 66\u001b[0;31m             _optimize_sequential(\n\u001b[0m\u001b[1;32m     67\u001b[0m                 \u001b[0mstudy\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     68\u001b[0m                 \u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/optuna/study/_optimize.py\u001b[0m in \u001b[0;36m_optimize_sequential\u001b[0;34m(study, func, n_trials, timeout, catch, callbacks, gc_after_trial, reseed_sampler_rng, time_start, progress_bar)\u001b[0m\n\u001b[1;32m    161\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    162\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 163\u001b[0;31m             \u001b[0mfrozen_trial\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_run_trial\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstudy\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcatch\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    164\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    165\u001b[0m             \u001b[0;31m# The following line mitigates memory problems that can be occurred in some\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/optuna/study/_optimize.py\u001b[0m in \u001b[0;36m_run_trial\u001b[0;34m(study, func, catch)\u001b[0m\n\u001b[1;32m    249\u001b[0m         \u001b[0;32mand\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfunc_err\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcatch\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    250\u001b[0m     ):\n\u001b[0;32m--> 251\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mfunc_err\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    252\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mfrozen_trial\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    253\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/optuna/study/_optimize.py\u001b[0m in \u001b[0;36m_run_trial\u001b[0;34m(study, func, catch)\u001b[0m\n\u001b[1;32m    198\u001b[0m     \u001b[0;32mwith\u001b[0m \u001b[0mget_heartbeat_thread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrial\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_trial_id\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstudy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_storage\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    199\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 200\u001b[0;31m             \u001b[0mvalue_or_values\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrial\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    201\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mexceptions\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTrialPruned\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    202\u001b[0m             \u001b[0;31m# TODO(mamu): Handle multi-objective cases.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m<ipython-input-35-81c73c93eb72>\u001b[0m in \u001b[0;36mobjective\u001b[0;34m(trial)\u001b[0m\n\u001b[1;32m     35\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     36\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 37\u001b[0;31m     \u001b[0mmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mGradientBoostingRegressor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mparams\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrandom_state\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m37\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0muse_label_encoder\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     38\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     39\u001b[0m     bst = model.fit(X_train, y_train,\n",
            "\u001b[0;31mTypeError\u001b[0m: __init__() got an unexpected keyword argument 'objective'"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "zHCvGdc9H067"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import GradientBoostingRegressor   \n",
        "\n",
        "GB_model = GradientBoostingRegressor(random_state=37).fit(X_train, y_train) \n",
        "print('Done.') "
      ],
      "metadata": {
        "id": "U7pCY5HPsv4Q",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d4b29a22-fbda-43f3-b37b-d3d56d0e4f70"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Done.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "preds=GB_model.predict(test_x)  \n",
        "print('Done.')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "359fc337-05f0-45d4-d44b-81903d19d18d",
        "id": "Hpn3cF6CVVgM"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Done.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_x['Y_Quality'] = preds\n",
        "test_x  "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 488
        },
        "outputId": "779efa31-eb0a-4b82-89a6-82db1155da56",
        "id": "xPXPRUoYVVgM"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          X_1       X_2  X_3   X_4        X_5  X_6        X_7        X_8  \\\n",
              "0    2.000000  94.00000  0.0  45.0  10.000000  0.0  51.000000  10.000000   \n",
              "1    2.000000  93.00000  0.0  45.0  11.000000  0.0  45.000000  10.000000   \n",
              "2    2.000000  95.00000  0.0  45.0  11.000000  0.0  45.000000  10.000000   \n",
              "3    4.337449  94.44856  0.0  45.0  10.271605  0.0  48.485597  10.012346   \n",
              "4    4.337449  94.44856  0.0  45.0  10.271605  0.0  48.485597  10.012346   \n",
              "..        ...       ...  ...   ...        ...  ...        ...        ...   \n",
              "305  2.000000  91.00000  0.0  45.0  10.000000  0.0  51.000000  10.000000   \n",
              "306  2.000000  96.00000  0.0  45.0  11.000000  0.0  45.000000  10.000000   \n",
              "307  2.000000  91.00000  0.0  45.0  10.000000  0.0  50.000000  10.000000   \n",
              "308  2.000000  95.00000  0.0  45.0  10.000000  0.0  51.000000  10.000000   \n",
              "309  2.000000  87.00000  0.0  45.0  10.000000  0.0  51.000000  10.000000   \n",
              "\n",
              "           X_9  X_10  ...     X_2868     X_2869     X_2870    X_2871  \\\n",
              "0    52.000000   2.0  ...  49.994412  54.202353  63.806176  0.974359   \n",
              "1    31.000000   2.0  ...  49.994412  54.202353  63.806176  0.974359   \n",
              "2    31.000000   2.0  ...  49.994412  54.202353  63.806176  0.974359   \n",
              "3    42.407407   2.0  ...  49.994412  54.202353  63.806176  0.974359   \n",
              "4    42.407407   2.0  ...  49.994412  54.202353  63.806176  0.974359   \n",
              "..         ...   ...  ...        ...        ...        ...       ...   \n",
              "305  52.000000   2.0  ...  49.994412  54.202353  63.806176  0.974359   \n",
              "306  31.000000   2.0  ...  49.994412  54.202353  63.806176  0.974359   \n",
              "307  52.000000   2.0  ...  49.994412  54.202353  63.806176  0.974359   \n",
              "308  52.000000   2.0  ...  49.994412  54.202353  63.806176  0.974359   \n",
              "309  52.000000   2.0  ...  49.994412  54.202353  63.806176  0.974359   \n",
              "\n",
              "         X_2872      X_2873  X_2874      X_2875  line_pcode  Y_Quality  \n",
              "0    247.988889  251.888889   243.3  228.844444           7   0.532668  \n",
              "1    247.988889  251.888889   243.3  228.844444           5   0.534932  \n",
              "2    247.988889  251.888889   243.3  228.844444           5   0.534789  \n",
              "3    247.988889  251.888889   243.3  228.844444           0   0.533128  \n",
              "4    247.988889  251.888889   243.3  228.844444           1   0.537065  \n",
              "..          ...         ...     ...         ...         ...        ...  \n",
              "305  247.988889  251.888889   243.3  228.844444           7   0.531996  \n",
              "306  247.988889  251.888889   243.3  228.844444           5   0.535282  \n",
              "307  247.988889  251.888889   243.3  228.844444           7   0.532321  \n",
              "308  247.988889  251.888889   243.3  228.844444           7   0.532803  \n",
              "309  247.988889  251.888889   243.3  228.844444           7   0.531677  \n",
              "\n",
              "[310 rows x 2877 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-0d8709cc-cf56-420f-8aea-5a8af3a8d2be\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
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              "        vertical-align: middle;\n",
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              "\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>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>X_9</th>\n",
              "      <th>X_10</th>\n",
              "      <th>...</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",
              "      <th>line_pcode</th>\n",
              "      <th>Y_Quality</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2.000000</td>\n",
              "      <td>94.00000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>45.0</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>51.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>52.000000</td>\n",
              "      <td>2.0</td>\n",
              "      <td>...</td>\n",
              "      <td>49.994412</td>\n",
              "      <td>54.202353</td>\n",
              "      <td>63.806176</td>\n",
              "      <td>0.974359</td>\n",
              "      <td>247.988889</td>\n",
              "      <td>251.888889</td>\n",
              "      <td>243.3</td>\n",
              "      <td>228.844444</td>\n",
              "      <td>7</td>\n",
              "      <td>0.532668</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2.000000</td>\n",
              "      <td>93.00000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>45.0</td>\n",
              "      <td>11.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>45.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>31.000000</td>\n",
              "      <td>2.0</td>\n",
              "      <td>...</td>\n",
              "      <td>49.994412</td>\n",
              "      <td>54.202353</td>\n",
              "      <td>63.806176</td>\n",
              "      <td>0.974359</td>\n",
              "      <td>247.988889</td>\n",
              "      <td>251.888889</td>\n",
              "      <td>243.3</td>\n",
              "      <td>228.844444</td>\n",
              "      <td>5</td>\n",
              "      <td>0.534932</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2.000000</td>\n",
              "      <td>95.00000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>45.0</td>\n",
              "      <td>11.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>45.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>31.000000</td>\n",
              "      <td>2.0</td>\n",
              "      <td>...</td>\n",
              "      <td>49.994412</td>\n",
              "      <td>54.202353</td>\n",
              "      <td>63.806176</td>\n",
              "      <td>0.974359</td>\n",
              "      <td>247.988889</td>\n",
              "      <td>251.888889</td>\n",
              "      <td>243.3</td>\n",
              "      <td>228.844444</td>\n",
              "      <td>5</td>\n",
              "      <td>0.534789</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4.337449</td>\n",
              "      <td>94.44856</td>\n",
              "      <td>0.0</td>\n",
              "      <td>45.0</td>\n",
              "      <td>10.271605</td>\n",
              "      <td>0.0</td>\n",
              "      <td>48.485597</td>\n",
              "      <td>10.012346</td>\n",
              "      <td>42.407407</td>\n",
              "      <td>2.0</td>\n",
              "      <td>...</td>\n",
              "      <td>49.994412</td>\n",
              "      <td>54.202353</td>\n",
              "      <td>63.806176</td>\n",
              "      <td>0.974359</td>\n",
              "      <td>247.988889</td>\n",
              "      <td>251.888889</td>\n",
              "      <td>243.3</td>\n",
              "      <td>228.844444</td>\n",
              "      <td>0</td>\n",
              "      <td>0.533128</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4.337449</td>\n",
              "      <td>94.44856</td>\n",
              "      <td>0.0</td>\n",
              "      <td>45.0</td>\n",
              "      <td>10.271605</td>\n",
              "      <td>0.0</td>\n",
              "      <td>48.485597</td>\n",
              "      <td>10.012346</td>\n",
              "      <td>42.407407</td>\n",
              "      <td>2.0</td>\n",
              "      <td>...</td>\n",
              "      <td>49.994412</td>\n",
              "      <td>54.202353</td>\n",
              "      <td>63.806176</td>\n",
              "      <td>0.974359</td>\n",
              "      <td>247.988889</td>\n",
              "      <td>251.888889</td>\n",
              "      <td>243.3</td>\n",
              "      <td>228.844444</td>\n",
              "      <td>1</td>\n",
              "      <td>0.537065</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>305</th>\n",
              "      <td>2.000000</td>\n",
              "      <td>91.00000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>45.0</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>51.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>52.000000</td>\n",
              "      <td>2.0</td>\n",
              "      <td>...</td>\n",
              "      <td>49.994412</td>\n",
              "      <td>54.202353</td>\n",
              "      <td>63.806176</td>\n",
              "      <td>0.974359</td>\n",
              "      <td>247.988889</td>\n",
              "      <td>251.888889</td>\n",
              "      <td>243.3</td>\n",
              "      <td>228.844444</td>\n",
              "      <td>7</td>\n",
              "      <td>0.531996</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>306</th>\n",
              "      <td>2.000000</td>\n",
              "      <td>96.00000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>45.0</td>\n",
              "      <td>11.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>45.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>31.000000</td>\n",
              "      <td>2.0</td>\n",
              "      <td>...</td>\n",
              "      <td>49.994412</td>\n",
              "      <td>54.202353</td>\n",
              "      <td>63.806176</td>\n",
              "      <td>0.974359</td>\n",
              "      <td>247.988889</td>\n",
              "      <td>251.888889</td>\n",
              "      <td>243.3</td>\n",
              "      <td>228.844444</td>\n",
              "      <td>5</td>\n",
              "      <td>0.535282</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>307</th>\n",
              "      <td>2.000000</td>\n",
              "      <td>91.00000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>45.0</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>50.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>52.000000</td>\n",
              "      <td>2.0</td>\n",
              "      <td>...</td>\n",
              "      <td>49.994412</td>\n",
              "      <td>54.202353</td>\n",
              "      <td>63.806176</td>\n",
              "      <td>0.974359</td>\n",
              "      <td>247.988889</td>\n",
              "      <td>251.888889</td>\n",
              "      <td>243.3</td>\n",
              "      <td>228.844444</td>\n",
              "      <td>7</td>\n",
              "      <td>0.532321</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>308</th>\n",
              "      <td>2.000000</td>\n",
              "      <td>95.00000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>45.0</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>51.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>52.000000</td>\n",
              "      <td>2.0</td>\n",
              "      <td>...</td>\n",
              "      <td>49.994412</td>\n",
              "      <td>54.202353</td>\n",
              "      <td>63.806176</td>\n",
              "      <td>0.974359</td>\n",
              "      <td>247.988889</td>\n",
              "      <td>251.888889</td>\n",
              "      <td>243.3</td>\n",
              "      <td>228.844444</td>\n",
              "      <td>7</td>\n",
              "      <td>0.532803</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>309</th>\n",
              "      <td>2.000000</td>\n",
              "      <td>87.00000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>45.0</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>51.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>52.000000</td>\n",
              "      <td>2.0</td>\n",
              "      <td>...</td>\n",
              "      <td>49.994412</td>\n",
              "      <td>54.202353</td>\n",
              "      <td>63.806176</td>\n",
              "      <td>0.974359</td>\n",
              "      <td>247.988889</td>\n",
              "      <td>251.888889</td>\n",
              "      <td>243.3</td>\n",
              "      <td>228.844444</td>\n",
              "      <td>7</td>\n",
              "      <td>0.531677</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>310 rows × 2877 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0d8709cc-cf56-420f-8aea-5a8af3a8d2be')\"\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",
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              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
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              "      gap: 12px;\n",
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              "\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",
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              "\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",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "\n",
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              "          document.querySelector('#df-0d8709cc-cf56-420f-8aea-5a8af3a8d2be 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-0d8709cc-cf56-420f-8aea-5a8af3a8d2be');\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",
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              "          element.appendChild(docLink);\n",
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              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 549
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.countplot(x=preds);"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "bP9e3szwGvDU",
        "outputId": "6eadc6e8-809c-4f20-bbf0-97c5b8521764"
      },
      "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": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "sjXjfwJCWCYF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# X_train = train_df\n",
        "X_train = train_df.drop(columns=['Y_Class'])\n",
        "X_train = pd.DataFrame(train_df['Y_Quality'])\n",
        "y_train = train_df['Y_Class']"
      ],
      "metadata": {
        "id": "XyiKQJUeuHOu"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "52c88101-fe85-4f38-8b13-5ee25a48b322",
        "id": "Plefo87K5yqD"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Y_Quality    0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 552
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import GradientBoostingClassifier\n",
        "\n",
        "GB_model =  GradientBoostingClassifier(random_state=37).fit(X_train, y_train)\n",
        "print('Done.')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ov8uVwu2uHfF",
        "outputId": "9cf2b26f-7bac-42fe-c910-5af94a5baa30"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Done.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_x\n",
        "# test_x = test_x.drop(columns=['LINE//','PRODUCT_CODE'])\n",
        "# test_x = test_x['Y_Quality']  \n",
        "test_x = pd.DataFrame(test_x, columns=['Y_Quality'])\n",
        "# test_x = test_x['Y_Quality'] "
      ],
      "metadata": {
        "id": "T0ovkbQa57OV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "test_x = pd.DataFrame(test_x)\n",
        "\n",
        "preds = GB_model.predict(test_x)   \n",
        "print('Done.')   "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mncvR7m1wE-3",
        "outputId": "4b72844d-f2a6-46d1-fde0-a1edfb3daf02"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Done.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "preds"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GVailz2p8_VT",
        "outputId": "d92258bf-7b56-4277-942b-cf9d96d1ff8c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 2, 1, 1, 2, 2, 2, 1, 1, 2, 2, 2, 2, 1, 1, 1, 2, 1, 1, 2, 1, 2,\n",
              "       1, 2, 1, 2, 2, 1, 1, 1, 1, 2, 1, 1, 2, 2, 2, 1, 2, 1, 2, 2, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 2,\n",
              "       1, 1, 2, 1, 1, 1, 1, 1, 2, 2, 1, 1, 2, 2, 1, 2, 2, 2, 2, 2, 1, 1,\n",
              "       1, 1, 2, 2, 2, 2, 1, 2, 2, 2, 1, 2, 1, 2, 1, 2, 2, 2, 2, 1, 2, 1,\n",
              "       2, 1, 1, 2, 2, 2, 2, 1, 1, 2, 1, 1, 2, 2, 1, 1, 2, 2, 1, 1, 2, 1,\n",
              "       1, 1, 2, 2, 2, 2, 2, 1, 2, 1, 1, 2, 2, 1, 2, 2, 1, 1, 1, 2, 1, 2,\n",
              "       1, 1, 1, 1, 2, 1, 2, 2, 1, 2, 1, 2, 1, 1, 1, 2, 2, 1, 2, 1, 1, 1,\n",
              "       2, 2, 1, 2, 2, 1, 1, 2, 1, 1, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 1,\n",
              "       2, 1, 2, 1, 2, 1, 2, 2, 2, 1, 1, 2, 1, 1, 1, 2, 2, 1, 1, 2, 1, 2,\n",
              "       1, 2, 2, 1, 1, 1, 1, 1, 2, 1, 1, 1, 2, 2, 2, 1, 1, 1, 2, 1, 1, 2,\n",
              "       1, 2, 2, 2, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1, 2, 1, 2, 1, 1, 2,\n",
              "       2, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2,\n",
              "       1, 2, 1, 1, 1, 1, 2, 2, 1, 1, 1, 2, 2, 2, 2, 1, 1, 1, 1, 1, 2, 1,\n",
              "       1, 1])"
            ]
          },
          "metadata": {},
          "execution_count": 557
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.countplot(x=preds);"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "5ajeXCsP9Awa",
        "outputId": "15105ab3-69d0-40d9-8677-7255ba7216c8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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BNwAvS7Kzm84ZOtRq5i2pkqTGIwVJUmMpSJIaS0GS1FgKkqTGUpAkNZaCJKmxFCRJzf8DZkiRYhRSyCIAAAAASUVORK5CYII=\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "q5miG4FKZgP8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "l9KnrCyaZgWD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 3.2 LGBM [0.5]"
      ],
      "metadata": {
        "id": "E4reyRMdiyQA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from lightgbm import LGBMRegressor\n",
        "\n",
        "LGBM_model = LGBMRegressor(random_state=37).fit(X_train, y_train) \n",
        "print('Done.') "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b383ec96-e0c5-4034-f56d-31225c565ad2",
        "id": "JHtmoDYXiyQB"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Done.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "preds=LGBM_model.predict(test_x)  \n",
        "print('Done.')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e1e463a2-501b-472f-90ad-be4628631993",
        "id": "IkvFzh12iyQB"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Done.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_x['Y_Quality'] = preds\n",
        "test_x  "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "04689c6d-48ce-4d25-9677-02ef6b852fbd",
        "id": "cFIbunxpiyQB"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     X_1   X_2  X_3   X_4   X_5  X_6   X_7   X_8   X_9  X_10  ...  X_2868  \\\n",
              "0    2.0  94.0  0.0  45.0  10.0  0.0  51.0  10.0  52.0   2.0  ...     0.0   \n",
              "1    2.0  93.0  0.0  45.0  11.0  0.0  45.0  10.0  31.0   2.0  ...     0.0   \n",
              "2    2.0  95.0  0.0  45.0  11.0  0.0  45.0  10.0  31.0   2.0  ...     0.0   \n",
              "3    0.0   0.0  0.0   0.0   0.0  0.0   0.0   0.0   0.0   0.0  ...     0.0   \n",
              "4    0.0   0.0  0.0   0.0   0.0  0.0   0.0   0.0   0.0   0.0  ...     0.0   \n",
              "..   ...   ...  ...   ...   ...  ...   ...   ...   ...   ...  ...     ...   \n",
              "305  2.0  91.0  0.0  45.0  10.0  0.0  51.0  10.0  52.0   2.0  ...     0.0   \n",
              "306  2.0  96.0  0.0  45.0  11.0  0.0  45.0  10.0  31.0   2.0  ...     0.0   \n",
              "307  2.0  91.0  0.0  45.0  10.0  0.0  50.0  10.0  52.0   2.0  ...     0.0   \n",
              "308  2.0  95.0  0.0  45.0  10.0  0.0  51.0  10.0  52.0   2.0  ...     0.0   \n",
              "309  2.0  87.0  0.0  45.0  10.0  0.0  51.0  10.0  52.0   2.0  ...     0.0   \n",
              "\n",
              "     X_2869  X_2870  X_2871  X_2872  X_2873  X_2874  X_2875  line_pcode  \\\n",
              "0       0.0     0.0     0.0     0.0     0.0     0.0     0.0           7   \n",
              "1       0.0     0.0     0.0     0.0     0.0     0.0     0.0           5   \n",
              "2       0.0     0.0     0.0     0.0     0.0     0.0     0.0           5   \n",
              "3       0.0     0.0     0.0     0.0     0.0     0.0     0.0           0   \n",
              "4       0.0     0.0     0.0     0.0     0.0     0.0     0.0           1   \n",
              "..      ...     ...     ...     ...     ...     ...     ...         ...   \n",
              "305     0.0     0.0     0.0     0.0     0.0     0.0     0.0           7   \n",
              "306     0.0     0.0     0.0     0.0     0.0     0.0     0.0           5   \n",
              "307     0.0     0.0     0.0     0.0     0.0     0.0     0.0           7   \n",
              "308     0.0     0.0     0.0     0.0     0.0     0.0     0.0           7   \n",
              "309     0.0     0.0     0.0     0.0     0.0     0.0     0.0           7   \n",
              "\n",
              "     Y_Quality  \n",
              "0     0.531415  \n",
              "1     0.530844  \n",
              "2     0.531456  \n",
              "3     0.524097  \n",
              "4     0.528226  \n",
              "..         ...  \n",
              "305   0.527747  \n",
              "306   0.528078  \n",
              "307   0.527309  \n",
              "308   0.527817  \n",
              "309   0.527303  \n",
              "\n",
              "[310 rows x 2877 columns]"
            ],
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              "\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></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>X_9</th>\n",
              "      <th>X_10</th>\n",
              "      <th>...</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",
              "      <th>line_pcode</th>\n",
              "      <th>Y_Quality</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2.0</td>\n",
              "      <td>94.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>45.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>51.0</td>\n",
              "      <td>10.0</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2.0</td>\n",
              "      <td>93.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>45.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>45.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>31.0</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2.0</td>\n",
              "      <td>95.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>45.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>45.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>31.0</td>\n",
              "      <td>2.0</td>\n",
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              "      <td>5</td>\n",
              "      <td>0.531456</td>\n",
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              "    <tr>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.528226</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>305</th>\n",
              "      <td>2.0</td>\n",
              "      <td>91.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>45.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>51.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>52.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7</td>\n",
              "      <td>0.527747</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>306</th>\n",
              "      <td>2.0</td>\n",
              "      <td>96.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>45.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>45.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>31.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5</td>\n",
              "      <td>0.528078</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>307</th>\n",
              "      <td>2.0</td>\n",
              "      <td>91.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>45.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>50.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>52.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7</td>\n",
              "      <td>0.527309</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>308</th>\n",
              "      <td>2.0</td>\n",
              "      <td>95.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>45.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>51.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>52.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7</td>\n",
              "      <td>0.527817</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>309</th>\n",
              "      <td>2.0</td>\n",
              "      <td>87.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>45.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>51.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>52.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7</td>\n",
              "      <td>0.527303</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>310 rows × 2877 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e4edc22a-2a8b-4efc-8ee5-d20270484753')\"\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-e4edc22a-2a8b-4efc-8ee5-d20270484753 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-e4edc22a-2a8b-4efc-8ee5-d20270484753');\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": 226
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.countplot(x=preds);"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ab473a9c-4837-4c41-fb06-4edf6f6a7f6e",
        "id": "2l1zCM2FiyQB"
      },
      "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": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# X_train = train_df\n",
        "X_train = train_df.drop(columns=['Y_Class'])\n",
        "X_train = pd.DataFrame(train_df['Y_Quality'])\n",
        "y_train = train_df['Y_Class']"
      ],
      "metadata": {
        "id": "Bkd-DzIniyQC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "be39bf31-e27d-4609-9616-04ff88e0d0e9",
        "id": "RZH1smGFiyQC"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Y_Quality    0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 229
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from lightgbm import LGBMClassifier \n",
        "\n",
        "LGBM_model =  LGBMClassifier(random_state=37).fit(X_train, y_train)\n",
        "print('Done.')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "208a91f4-262e-44ff-ade2-9346fd12830d",
        "id": "8bo1XHm1iyQC"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Done.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_x\n",
        "# test_x = test_x.drop(columns=['LINE//','PRODUCT_CODE'])\n",
        "# test_x = test_x['Y_Quality']  \n",
        "test_x = pd.DataFrame(test_x, columns=['Y_Quality'])\n",
        "# test_x = test_x['Y_Quality'] "
      ],
      "metadata": {
        "id": "Xfk0Bff6iyQC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "test_x = pd.DataFrame(test_x)\n",
        "\n",
        "preds = RF.predict(test_x)   \n",
        "print('Done.')   "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "beaddb3d-fe70-4f57-ff33-6470aa0bc049",
        "id": "mg33TKrciyQD"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Done.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "preds"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3b87eaa6-105d-4937-e107-571f44be5a6a",
        "id": "nXoNAT8IiyQD"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1., 1., 1., 0., 1., 1., 2., 1., 0., 2., 1., 2., 2., 1., 0., 0., 1.,\n",
              "       1., 1., 1., 0., 1., 0., 0., 0., 0., 1., 0., 0., 1., 0., 1., 0., 1.,\n",
              "       1., 0., 1., 0., 2., 0., 1., 0., 0., 1., 1., 1., 1., 0., 1., 0., 1.,\n",
              "       0., 0., 0., 0., 0., 1., 1., 0., 0., 0., 0., 1., 1., 0., 0., 0., 1.,\n",
              "       1., 1., 1., 0., 1., 1., 1., 1., 2., 1., 2., 1., 1., 1., 1., 1., 1.,\n",
              "       1., 0., 1., 0., 0., 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., 2., 2., 1., 0., 1.,\n",
              "       1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
              "       1., 1., 1., 1., 1., 0., 1., 2., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
              "       1., 1., 1., 1., 1., 1., 1., 2., 2., 1., 2., 1., 1., 1., 1., 1., 2.,\n",
              "       1., 1., 2., 1., 2., 1., 2., 2., 1., 2., 2., 1., 1., 2., 1., 1., 1.,\n",
              "       2., 1., 2., 1., 2., 1., 2., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
              "       2., 2., 1., 1., 1., 2., 1., 1., 1., 1., 1., 1., 1., 2., 1., 2., 1.,\n",
              "       1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 2., 1., 1., 1., 1., 1.,\n",
              "       1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
              "       1., 1., 1., 1., 1., 2., 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.,\n",
              "       1., 1., 1., 1., 1., 0., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
              "       1., 1., 1., 1.])"
            ]
          },
          "metadata": {},
          "execution_count": 233
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.countplot(x=preds);"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8c89c23f-2d93-45ff-9827-3825e48cba0a",
        "id": "6pqwzfvGiyQD"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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a/wcjao1z7egnlQAAAABJRU5ErkJggg==\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "p6NCz8FkmGAX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 3.3 XGBoost [0.56]\n",
        "1. fillna(0)\n",
        "2. SMOTE oversampling\n",
        "3. params: optuna\n",
        "4. directly predict `Y_Class`"
      ],
      "metadata": {
        "id": "37DAV5FriyQD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import xgboost as xgb\n",
        "import psutil\n",
        "import time \n",
        "\n",
        "def memory_usage(message: str = 'debug'):\n",
        "    # current process RAM usage\n",
        "    p = psutil.Process()\n",
        "    rss = p.memory_info().rss / 2 ** 20 # Bytes to MB\n",
        "    print(f\"[{message}] memory usage: {rss: 10.5f} MB\")\n",
        "\n",
        "\n",
        "def objective(trial):\n",
        "\n",
        "    params = {\n",
        "        \"objective\": \"multi:softprob\",\n",
        "        \"eval_metric\":'mlogloss',\n",
        "        \"booster\": 'gbtree',\n",
        "        'tree_method':'gpu_hist', 'predictor':'gpu_predictor', 'gpu_id': 0, # GPU 사용시\n",
        "        # \"tree_method\": 'exact', 'gpu_id': -1,  # CPU 사용시\n",
        "        \"verbosity\": 0,\n",
        "        'num_class':3,\n",
        "        \"max_depth\": trial.suggest_int(\"max_depth\", 4, 10),\n",
        "        \"learning_rate\": trial.suggest_uniform('learning_rate', 0.0001, 0.99),\n",
        "        'n_estimators': trial.suggest_int(\"n_estimators\", 1000, 10000, step=100),\n",
        "        \"colsample_bytree\": trial.suggest_float(\"colsample_bytree\", 0.5, 1.0),\n",
        "        \"colsample_bylevel\": trial.suggest_float(\"colsample_bylevel\", 0.5, 1.0),\n",
        "        \"colsample_bynode\": trial.suggest_float(\"colsample_bynode\", 0.5, 1.0),\n",
        "        \"reg_lambda\": trial.suggest_loguniform(\"reg_lambda\", 1e-2, 1),\n",
        "        \"reg_alpha\": trial.suggest_loguniform(\"reg_alpha\", 1e-2, 1),\n",
        "        'subsample': trial.suggest_discrete_uniform('subsample', 0.6, 1.0, 0.05),     \n",
        "        'min_child_weight': trial.suggest_int('min_child_weight', 2, 15),\n",
        "        \"gamma\": trial.suggest_float(\"gamma\", 0.1, 1.0, log=True),\n",
        "        # 'num_parallel_tree': trial.suggest_int(\"num_parallel_tree\", 1, 500) 추가하면 느려짐.\n",
        "    }\n",
        "\n",
        "\n",
        "    model = xgb.XGBClassifier(**params, random_state = 37, use_label_encoder = False)\n",
        "\n",
        "    bst = model.fit(X_train, y_train,\n",
        "                    eval_set=[(X_validation,y_validation)], \n",
        "                    early_stopping_rounds=50, \n",
        "                    verbose=False)\n",
        "    preds = bst.predict(X_validation)\n",
        "    pred_labels = np.rint(preds)\n",
        "    accuracy = sklearn.metrics.accuracy_score(y_validation, pred_labels)\n",
        "    # f1_score = f1_score(y_validation, pred_labels)\n",
        "    # print(f1_score(y_test, preds, average='macro'))\n",
        "    return f1_score\n",
        "\n",
        "\n",
        "if __name__ == \"__main__\":\n",
        "\n",
        "    train_start = time.time()\n",
        "\n",
        "    study = optuna.create_study(direction=\"maximize\")\n",
        "    study.optimize(objective, n_trials=50, show_progress_bar=True)\n",
        "\n",
        "    print(\"Number of finished trials: \", len(study.trials))\n",
        "    print(\"Best trial:\")\n",
        "\n",
        "\n",
        "    trial = study.best_trial\n",
        "\n",
        "    print(\"  Accuracy: {}\".format(trial.value))\n",
        "    print(\"  Best hyperparameters: \")\n",
        "    for key, value in trial.params.items():\n",
        "        print(\"    {}: {}\".format(key, value))\n",
        "\n",
        "   \n",
        "    clf = xgb.XGBClassifier(**study.best_params, random_state = 1234, use_label_encoder = False)\n",
        "    clf.fit(X_train, y_train)\n",
        "\n",
        "    preds = clf.predict(X_test)\n",
        "    # pred_labels = np.rint(preds)\n",
        "    accuracy = sklearn.metrics.accuracy_score(y_test, preds)\n",
        "\n",
        "    print(\"Accuracy: {}\".format(accuracy))\n",
        "\n",
        "    # memory_usage(\"학습하는데 걸린 시간  {:.2f} 분\\n\".format( (time.time() - train_start)/60))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
          "referenced_widgets": [
            "da1fd9190bec41cf8f6efdac42f8fe3a",
            "8ee8abbd080e4e9884b52e375f77fa60",
            "ab7c60b97f1d4128a25c051770cda905",
            "832bd91e6d514b578fddc1efd21744b3",
            "017fa302fd574eabb29e43100832535c",
            "ec44661c7b5443afbd9ac71fc682ed5c",
            "576d0d62399946079e084fc52a1b7359",
            "4fcbf10867c54a6baf258316cfd4d6e2",
            "a798576b172e43569fcad90aab4136fd",
            "770279dd122a4a669d918667583170fc",
            "15044f71d99b44749def93d843b602e1"
          ]
        },
        "id": "QhuyVg7r44Kk",
        "outputId": "cad64d18-b13b-435b-8ed7-5ccc3619cbe2"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\u001b[32m[I 2023-02-21 12:31:02,652]\u001b[0m A new study created in memory with name: no-name-b87725a7-c3f9-4336-b543-0bb13d1b38b5\u001b[0m\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/50 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "da1fd9190bec41cf8f6efdac42f8fe3a"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[33m[W 2023-02-21 12:31:04,356]\u001b[0m Trial 0 failed with parameters: {'max_depth': 8, 'learning_rate': 0.9744527889163418, 'n_estimators': 9000, 'colsample_bytree': 0.6976232878412735, 'colsample_bylevel': 0.8653572638148754, 'colsample_bynode': 0.6828791668027459, 'reg_lambda': 0.20424681542609288, 'reg_alpha': 0.07363968859366331, 'subsample': 0.95, 'min_child_weight': 2, 'gamma': 0.26920276598286536} because of the following error: The value <function f1_score at 0x7f78c3a288b0> could not be cast to float..\u001b[0m\n",
            "\u001b[33m[W 2023-02-21 12:31:04,359]\u001b[0m Trial 0 failed with value <function f1_score at 0x7f78c3a288b0>.\u001b[0m\n",
            "\u001b[33m[W 2023-02-21 12:31:05,717]\u001b[0m Trial 1 failed with parameters: {'max_depth': 5, 'learning_rate': 0.6956267332668656, 'n_estimators': 10000, 'colsample_bytree': 0.7628289025706083, 'colsample_bylevel': 0.7095354321434431, 'colsample_bynode': 0.82895394357054, 'reg_lambda': 0.7578141893503155, 'reg_alpha': 0.010727805627030643, 'subsample': 0.6, 'min_child_weight': 13, 'gamma': 0.43679149006940493} because of the following error: The value <function f1_score at 0x7f78c3a288b0> could not be cast to float..\u001b[0m\n",
            "\u001b[33m[W 2023-02-21 12:31:05,720]\u001b[0m Trial 1 failed with value <function f1_score at 0x7f78c3a288b0>.\u001b[0m\n",
            "\u001b[33m[W 2023-02-21 12:31:06,902]\u001b[0m Trial 2 failed with parameters: {'max_depth': 6, 'learning_rate': 0.8018496557731744, 'n_estimators': 8400, 'colsample_bytree': 0.5882255618993344, 'colsample_bylevel': 0.7180356666377743, 'colsample_bynode': 0.8234316001830535, 'reg_lambda': 0.04555869654915273, 'reg_alpha': 0.07535989827899513, 'subsample': 1.0, 'min_child_weight': 9, 'gamma': 0.1329602934408789} because of the following error: The value <function f1_score at 0x7f78c3a288b0> could not be cast to float..\u001b[0m\n",
            "\u001b[33m[W 2023-02-21 12:31:06,906]\u001b[0m Trial 2 failed with value <function f1_score at 0x7f78c3a288b0>.\u001b[0m\n",
            "\u001b[33m[W 2023-02-21 12:31:08,147]\u001b[0m Trial 3 failed with parameters: {'max_depth': 8, 'learning_rate': 0.8597138671061849, 'n_estimators': 7000, 'colsample_bytree': 0.8609426638780477, 'colsample_bylevel': 0.777554044881426, 'colsample_bynode': 0.502359240543764, 'reg_lambda': 0.07512525435462467, 'reg_alpha': 0.03477008546437197, 'subsample': 0.8, 'min_child_weight': 11, 'gamma': 0.4010713648342959} because of the following error: The value <function f1_score at 0x7f78c3a288b0> could not be cast to float..\u001b[0m\n",
            "\u001b[33m[W 2023-02-21 12:31:08,150]\u001b[0m Trial 3 failed with value <function f1_score at 0x7f78c3a288b0>.\u001b[0m\n",
            "\u001b[33m[W 2023-02-21 12:31:09,932]\u001b[0m Trial 4 failed with parameters: {'max_depth': 5, 'learning_rate': 0.4438366961170764, 'n_estimators': 8700, 'colsample_bytree': 0.7274573586482735, 'colsample_bylevel': 0.7514109636077452, 'colsample_bynode': 0.7668810923486719, 'reg_lambda': 0.023877148013891316, 'reg_alpha': 0.09204744886479402, 'subsample': 0.65, 'min_child_weight': 15, 'gamma': 0.4567406225893368} because of the following error: The value <function f1_score at 0x7f78c3a288b0> could not be cast to float..\u001b[0m\n",
            "\u001b[33m[W 2023-02-21 12:31:09,935]\u001b[0m Trial 4 failed with value <function f1_score at 0x7f78c3a288b0>.\u001b[0m\n",
            "\u001b[33m[W 2023-02-21 12:31:11,522]\u001b[0m Trial 5 failed with parameters: {'max_depth': 6, 'learning_rate': 0.41319351497963214, 'n_estimators': 8400, 'colsample_bytree': 0.5087718820109712, 'colsample_bylevel': 0.5629891104971242, 'colsample_bynode': 0.7109189164101557, 'reg_lambda': 0.03800674096070691, 'reg_alpha': 0.014084310421285625, 'subsample': 0.95, 'min_child_weight': 3, 'gamma': 0.3971629879240017} because of the following error: The value <function f1_score at 0x7f78c3a288b0> could not be cast to float..\u001b[0m\n",
            "\u001b[33m[W 2023-02-21 12:31:11,524]\u001b[0m Trial 5 failed with value <function f1_score at 0x7f78c3a288b0>.\u001b[0m\n",
            "\u001b[33m[W 2023-02-21 12:31:12,767]\u001b[0m Trial 6 failed with parameters: {'max_depth': 6, 'learning_rate': 0.9343344530138333, 'n_estimators': 7500, 'colsample_bytree': 0.8371481539689851, 'colsample_bylevel': 0.8721839149950892, 'colsample_bynode': 0.5179562332263039, 'reg_lambda': 0.01467556936653254, 'reg_alpha': 0.6211558151031638, 'subsample': 0.7, 'min_child_weight': 15, 'gamma': 0.3848559399303251} because of the following error: The value <function f1_score at 0x7f78c3a288b0> could not be cast to float..\u001b[0m\n",
            "\u001b[33m[W 2023-02-21 12:31:12,770]\u001b[0m Trial 6 failed with value <function f1_score at 0x7f78c3a288b0>.\u001b[0m\n",
            "\u001b[33m[W 2023-02-21 12:31:13,990]\u001b[0m Trial 7 failed with parameters: {'max_depth': 7, 'learning_rate': 0.861159073302561, 'n_estimators': 2200, 'colsample_bytree': 0.5958067402966689, 'colsample_bylevel': 0.9361487847968504, 'colsample_bynode': 0.6864871319350165, 'reg_lambda': 0.06221554379800746, 'reg_alpha': 0.03501802483998028, 'subsample': 0.6, 'min_child_weight': 13, 'gamma': 0.5903741178431089} because of the following error: The value <function f1_score at 0x7f78c3a288b0> could not be cast to float..\u001b[0m\n",
            "\u001b[33m[W 2023-02-21 12:31:13,993]\u001b[0m Trial 7 failed with value <function f1_score at 0x7f78c3a288b0>.\u001b[0m\n",
            "\u001b[33m[W 2023-02-21 12:31:15,289]\u001b[0m Trial 8 failed with parameters: {'max_depth': 10, 'learning_rate': 0.679218883448387, 'n_estimators': 5400, 'colsample_bytree': 0.6942436808807488, 'colsample_bylevel': 0.6071083866048004, 'colsample_bynode': 0.5552433809938203, 'reg_lambda': 0.10793581002382381, 'reg_alpha': 0.12050693077508416, 'subsample': 0.8, 'min_child_weight': 11, 'gamma': 0.4290667537785515} because of the following error: The value <function f1_score at 0x7f78c3a288b0> could not be cast to float..\u001b[0m\n",
            "\u001b[33m[W 2023-02-21 12:31:15,292]\u001b[0m Trial 8 failed with value <function f1_score at 0x7f78c3a288b0>.\u001b[0m\n",
            "\u001b[33m[W 2023-02-21 12:31:16,739]\u001b[0m Trial 9 failed with parameters: {'max_depth': 4, 'learning_rate': 0.7583280586542971, 'n_estimators': 2500, 'colsample_bytree': 0.5061985529833427, 'colsample_bylevel': 0.5618649496810297, 'colsample_bynode': 0.8417077420623366, 'reg_lambda': 0.022917582585200193, 'reg_alpha': 0.10399061371546604, 'subsample': 1.0, 'min_child_weight': 8, 'gamma': 0.7514066066121992} because of the following error: The value <function f1_score at 0x7f78c3a288b0> could not be cast to float..\u001b[0m\n",
            "\u001b[33m[W 2023-02-21 12:31:16,741]\u001b[0m Trial 9 failed with value <function f1_score at 0x7f78c3a288b0>.\u001b[0m\n",
            "\u001b[33m[W 2023-02-21 12:31:17,987]\u001b[0m Trial 10 failed with parameters: {'max_depth': 7, 'learning_rate': 0.554839655523739, 'n_estimators': 7400, 'colsample_bytree': 0.9393809295138205, 'colsample_bylevel': 0.567496025684294, 'colsample_bynode': 0.918063656300822, 'reg_lambda': 0.46147013935909265, 'reg_alpha': 0.6032268317726889, 'subsample': 0.75, 'min_child_weight': 14, 'gamma': 0.5258194117437208} because of the following error: The value <function f1_score at 0x7f78c3a288b0> could not be cast to float..\u001b[0m\n",
            "\u001b[33m[W 2023-02-21 12:31:17,990]\u001b[0m Trial 10 failed with value <function f1_score at 0x7f78c3a288b0>.\u001b[0m\n",
            "\u001b[33m[W 2023-02-21 12:31:19,467]\u001b[0m Trial 11 failed with parameters: {'max_depth': 8, 'learning_rate': 0.45036343459100175, 'n_estimators': 2400, 'colsample_bytree': 0.8705802382348109, 'colsample_bylevel': 0.9011028916247924, 'colsample_bynode': 0.6821392367528006, 'reg_lambda': 0.019634286416833922, 'reg_alpha': 0.014942346861787689, 'subsample': 0.65, 'min_child_weight': 9, 'gamma': 0.7134795678131195} because of the following error: The value <function f1_score at 0x7f78c3a288b0> could not be cast to float..\u001b[0m\n",
            "\u001b[33m[W 2023-02-21 12:31:19,470]\u001b[0m Trial 11 failed with value <function f1_score at 0x7f78c3a288b0>.\u001b[0m\n",
            "\u001b[33m[W 2023-02-21 12:31:28,723]\u001b[0m Trial 12 failed with parameters: {'max_depth': 8, 'learning_rate': 0.016248038534160906, 'n_estimators': 3100, 'colsample_bytree': 0.8480782635925144, 'colsample_bylevel': 0.5456402627751993, 'colsample_bynode': 0.8613668796218942, 'reg_lambda': 0.7569242543778325, 'reg_alpha': 0.31776619551696816, 'subsample': 1.0, 'min_child_weight': 8, 'gamma': 0.2050114797784309} because of the following error: The value <function f1_score at 0x7f78c3a288b0> could not be cast to float..\u001b[0m\n",
            "\u001b[33m[W 2023-02-21 12:31:28,725]\u001b[0m Trial 12 failed with value <function f1_score at 0x7f78c3a288b0>.\u001b[0m\n",
            "\u001b[33m[W 2023-02-21 12:31:29,826]\u001b[0m Trial 13 failed with parameters: {'max_depth': 7, 'learning_rate': 0.8704931615284431, 'n_estimators': 5800, 'colsample_bytree': 0.7764607290738178, 'colsample_bylevel': 0.90941153593516, 'colsample_bynode': 0.5002872784011432, 'reg_lambda': 0.012939633698179565, 'reg_alpha': 0.17187051661014613, 'subsample': 0.8, 'min_child_weight': 13, 'gamma': 0.1962921700638965} because of the following error: The value <function f1_score at 0x7f78c3a288b0> could not be cast to float..\u001b[0m\n",
            "\u001b[33m[W 2023-02-21 12:31:29,829]\u001b[0m Trial 13 failed with value <function f1_score at 0x7f78c3a288b0>.\u001b[0m\n",
            "\u001b[33m[W 2023-02-21 12:31:31,622]\u001b[0m Trial 14 failed with parameters: {'max_depth': 5, 'learning_rate': 0.5868534805486142, 'n_estimators': 7900, 'colsample_bytree': 0.8971555286275121, 'colsample_bylevel': 0.5466669359634028, 'colsample_bynode': 0.6862169826394515, 'reg_lambda': 0.022305327642114216, 'reg_alpha': 0.03232554176900355, 'subsample': 0.9, 'min_child_weight': 12, 'gamma': 0.24249460726672173} because of the following error: The value <function f1_score at 0x7f78c3a288b0> could not be cast to float..\u001b[0m\n",
            "\u001b[33m[W 2023-02-21 12:31:31,625]\u001b[0m Trial 14 failed with value <function f1_score at 0x7f78c3a288b0>.\u001b[0m\n",
            "\u001b[33m[W 2023-02-21 12:31:33,032]\u001b[0m Trial 15 failed with parameters: {'max_depth': 6, 'learning_rate': 0.6887347302189849, 'n_estimators': 3600, 'colsample_bytree': 0.6277615674147223, 'colsample_bylevel': 0.8349958315595034, 'colsample_bynode': 0.6334449452346165, 'reg_lambda': 0.05565603980529343, 'reg_alpha': 0.022774773503472102, 'subsample': 0.7, 'min_child_weight': 12, 'gamma': 0.11754936948063507} because of the following error: The value <function f1_score at 0x7f78c3a288b0> could not be cast to float..\u001b[0m\n",
            "\u001b[33m[W 2023-02-21 12:31:33,036]\u001b[0m Trial 15 failed with value <function f1_score at 0x7f78c3a288b0>.\u001b[0m\n",
            "\u001b[33m[W 2023-02-21 12:31:34,279]\u001b[0m Trial 16 failed with parameters: {'max_depth': 5, 'learning_rate': 0.889526885989192, 'n_estimators': 5300, 'colsample_bytree': 0.543904975042386, 'colsample_bylevel': 0.8545458063616433, 'colsample_bynode': 0.7144588246476165, 'reg_lambda': 0.10711331276827134, 'reg_alpha': 0.818052599818898, 'subsample': 0.7, 'min_child_weight': 13, 'gamma': 0.2261053611957724} because of the following error: The value <function f1_score at 0x7f78c3a288b0> could not be cast to float..\u001b[0m\n",
            "\u001b[33m[W 2023-02-21 12:31:34,281]\u001b[0m Trial 16 failed with value <function f1_score at 0x7f78c3a288b0>.\u001b[0m\n",
            "\u001b[33m[W 2023-02-21 12:31:35,683]\u001b[0m Trial 17 failed with parameters: {'max_depth': 6, 'learning_rate': 0.6212807920180239, 'n_estimators': 4400, 'colsample_bytree': 0.6510891457133698, 'colsample_bylevel': 0.6410668870333631, 'colsample_bynode': 0.9002664997378552, 'reg_lambda': 0.24131231436107092, 'reg_alpha': 0.7596158604349724, 'subsample': 0.75, 'min_child_weight': 11, 'gamma': 0.6462605123827732} because of the following error: The value <function f1_score at 0x7f78c3a288b0> could not be cast to float..\u001b[0m\n",
            "\u001b[33m[W 2023-02-21 12:31:35,688]\u001b[0m Trial 17 failed with value <function f1_score at 0x7f78c3a288b0>.\u001b[0m\n",
            "\u001b[33m[W 2023-02-21 12:31:44,968]\u001b[0m Trial 18 failed with parameters: {'max_depth': 10, 'learning_rate': 0.026938240433947568, 'n_estimators': 3000, 'colsample_bytree': 0.9538267906052214, 'colsample_bylevel': 0.6364621806165631, 'colsample_bynode': 0.9243408743035344, 'reg_lambda': 0.3070801982740798, 'reg_alpha': 0.01101462594353066, 'subsample': 0.65, 'min_child_weight': 9, 'gamma': 0.16084489434080135} because of the following error: The value <function f1_score at 0x7f78c3a288b0> could not be cast to float..\u001b[0m\n",
            "\u001b[33m[W 2023-02-21 12:31:44,971]\u001b[0m Trial 18 failed with value <function f1_score at 0x7f78c3a288b0>.\u001b[0m\n",
            "\u001b[33m[W 2023-02-21 12:31:46,424]\u001b[0m Trial 19 failed with parameters: {'max_depth': 5, 'learning_rate': 0.48770588095744294, 'n_estimators': 5800, 'colsample_bytree': 0.526082832686614, 'colsample_bylevel': 0.776083144098888, 'colsample_bynode': 0.5573299120018191, 'reg_lambda': 0.43345486223280305, 'reg_alpha': 0.025210071005969618, 'subsample': 0.85, 'min_child_weight': 6, 'gamma': 0.5373500396292654} because of the following error: The value <function f1_score at 0x7f78c3a288b0> could not be cast to float..\u001b[0m\n",
            "\u001b[33m[W 2023-02-21 12:31:46,427]\u001b[0m Trial 19 failed with value <function f1_score at 0x7f78c3a288b0>.\u001b[0m\n",
            "\u001b[33m[W 2023-02-21 12:31:48,010]\u001b[0m Trial 20 failed with parameters: {'max_depth': 6, 'learning_rate': 0.3526599540567725, 'n_estimators': 1600, 'colsample_bytree': 0.9921103907687853, 'colsample_bylevel': 0.9496035300675053, 'colsample_bynode': 0.858335328213389, 'reg_lambda': 0.01807833089848586, 'reg_alpha': 0.2552836955337673, 'subsample': 0.75, 'min_child_weight': 2, 'gamma': 0.15443496641129548} because of the following error: The value <function f1_score at 0x7f78c3a288b0> could not be cast to float..\u001b[0m\n",
            "\u001b[33m[W 2023-02-21 12:31:48,017]\u001b[0m Trial 20 failed with value <function f1_score at 0x7f78c3a288b0>.\u001b[0m\n",
            "\u001b[33m[W 2023-02-21 12:31:50,306]\u001b[0m Trial 21 failed with parameters: {'max_depth': 7, 'learning_rate': 0.7917271046739697, 'n_estimators': 7800, 'colsample_bytree': 0.5695027869561231, 'colsample_bylevel': 0.732686014714703, 'colsample_bynode': 0.8325614071438977, 'reg_lambda': 0.027470352569550577, 'reg_alpha': 0.03577436536820426, 'subsample': 0.85, 'min_child_weight': 3, 'gamma': 0.24859014453629802} because of the following error: The value <function f1_score at 0x7f78c3a288b0> could not be cast to float..\u001b[0m\n",
            "\u001b[33m[W 2023-02-21 12:31:50,312]\u001b[0m Trial 21 failed with value <function f1_score at 0x7f78c3a288b0>.\u001b[0m\n",
            "\u001b[33m[W 2023-02-21 12:31:53,451]\u001b[0m Trial 22 failed with parameters: {'max_depth': 10, 'learning_rate': 0.17028262645048564, 'n_estimators': 9000, 'colsample_bytree': 0.5926954292710336, 'colsample_bylevel': 0.9065643693759604, 'colsample_bynode': 0.9012894174665974, 'reg_lambda': 0.04839417610128667, 'reg_alpha': 0.07507573640460613, 'subsample': 0.9, 'min_child_weight': 13, 'gamma': 0.17163100173724022} because of the following error: The value <function f1_score at 0x7f78c3a288b0> could not be cast to float..\u001b[0m\n",
            "\u001b[33m[W 2023-02-21 12:31:53,454]\u001b[0m Trial 22 failed with value <function f1_score at 0x7f78c3a288b0>.\u001b[0m\n",
            "\u001b[33m[W 2023-02-21 12:31:55,068]\u001b[0m Trial 23 failed with parameters: {'max_depth': 5, 'learning_rate': 0.3429947056454994, 'n_estimators': 8700, 'colsample_bytree': 0.6471665283522061, 'colsample_bylevel': 0.5400163488714229, 'colsample_bynode': 0.9079289531318738, 'reg_lambda': 0.9192230766368539, 'reg_alpha': 0.09153692303910628, 'subsample': 0.8, 'min_child_weight': 14, 'gamma': 0.7651640992320805} because of the following error: The value <function f1_score at 0x7f78c3a288b0> could not be cast to float..\u001b[0m\n",
            "\u001b[33m[W 2023-02-21 12:31:55,071]\u001b[0m Trial 23 failed with value <function f1_score at 0x7f78c3a288b0>.\u001b[0m\n",
            "\u001b[33m[W 2023-02-21 12:31:56,679]\u001b[0m Trial 24 failed with parameters: {'max_depth': 5, 'learning_rate': 0.41366405424095654, 'n_estimators': 8800, 'colsample_bytree': 0.9141387492462036, 'colsample_bylevel': 0.9429558830056058, 'colsample_bynode': 0.6766213144165023, 'reg_lambda': 0.08442102955079106, 'reg_alpha': 0.10324368650168211, 'subsample': 0.6, 'min_child_weight': 10, 'gamma': 0.10652089378862276} because of the following error: The value <function f1_score at 0x7f78c3a288b0> could not be cast to float..\u001b[0m\n",
            "\u001b[33m[W 2023-02-21 12:31:56,682]\u001b[0m Trial 24 failed with value <function f1_score at 0x7f78c3a288b0>.\u001b[0m\n",
            "\u001b[33m[W 2023-02-21 12:31:59,997]\u001b[0m Trial 25 failed with parameters: {'max_depth': 6, 'learning_rate': 0.1017889624492967, 'n_estimators': 7500, 'colsample_bytree': 0.9907329627676797, 'colsample_bylevel': 0.680905901472548, 'colsample_bynode': 0.706418045536273, 'reg_lambda': 0.7991493558839369, 'reg_alpha': 0.7127612618735262, 'subsample': 0.95, 'min_child_weight': 6, 'gamma': 0.12565725564465322} because of the following error: The value <function f1_score at 0x7f78c3a288b0> could not be cast to float..\u001b[0m\n",
            "\u001b[33m[W 2023-02-21 12:32:00,000]\u001b[0m Trial 25 failed with value <function f1_score at 0x7f78c3a288b0>.\u001b[0m\n",
            "\u001b[33m[W 2023-02-21 12:32:01,326]\u001b[0m Trial 26 failed with parameters: {'max_depth': 4, 'learning_rate': 0.8434125753845371, 'n_estimators': 2700, 'colsample_bytree': 0.6554598973507106, 'colsample_bylevel': 0.556832086709518, 'colsample_bynode': 0.8694436562274217, 'reg_lambda': 0.04273736578882208, 'reg_alpha': 0.013138766155438247, 'subsample': 1.0, 'min_child_weight': 13, 'gamma': 0.7112241357471418} because of the following error: The value <function f1_score at 0x7f78c3a288b0> could not be cast to float..\u001b[0m\n",
            "\u001b[33m[W 2023-02-21 12:32:01,329]\u001b[0m Trial 26 failed with value <function f1_score at 0x7f78c3a288b0>.\u001b[0m\n",
            "\u001b[33m[W 2023-02-21 12:32:02,871]\u001b[0m Trial 27 failed with parameters: {'max_depth': 7, 'learning_rate': 0.6164013520506885, 'n_estimators': 3100, 'colsample_bytree': 0.7815169345632111, 'colsample_bylevel': 0.5270378295188897, 'colsample_bynode': 0.5780920114654327, 'reg_lambda': 0.04574127630962273, 'reg_alpha': 0.04966405473864255, 'subsample': 0.9, 'min_child_weight': 5, 'gamma': 0.20886631624291582} because of the following error: The value <function f1_score at 0x7f78c3a288b0> could not be cast to float..\u001b[0m\n",
            "\u001b[33m[W 2023-02-21 12:32:02,875]\u001b[0m Trial 27 failed with value <function f1_score at 0x7f78c3a288b0>.\u001b[0m\n",
            "\u001b[33m[W 2023-02-21 12:32:04,319]\u001b[0m Trial 28 failed with parameters: {'max_depth': 7, 'learning_rate': 0.40064050700443105, 'n_estimators': 6900, 'colsample_bytree': 0.8538248142480791, 'colsample_bylevel': 0.8993116487929393, 'colsample_bynode': 0.7981184496278464, 'reg_lambda': 0.09865831621126539, 'reg_alpha': 0.49066043186451214, 'subsample': 1.0, 'min_child_weight': 12, 'gamma': 0.6707593796190151} because of the following error: The value <function f1_score at 0x7f78c3a288b0> could not be cast to float..\u001b[0m\n",
            "\u001b[33m[W 2023-02-21 12:32:04,324]\u001b[0m Trial 28 failed with value <function f1_score at 0x7f78c3a288b0>.\u001b[0m\n",
            "\u001b[33m[W 2023-02-21 12:32:15,122]\u001b[0m Trial 29 failed with parameters: {'max_depth': 7, 'learning_rate': 0.027324610529556207, 'n_estimators': 10000, 'colsample_bytree': 0.8596611787890714, 'colsample_bylevel': 0.8428004278344334, 'colsample_bynode': 0.5528181222118537, 'reg_lambda': 0.9002513171452284, 'reg_alpha': 0.18939938879600016, 'subsample': 0.8, 'min_child_weight': 10, 'gamma': 0.26484465874711854} because of the following error: The value <function f1_score at 0x7f78c3a288b0> could not be cast to float..\u001b[0m\n",
            "\u001b[33m[W 2023-02-21 12:32:15,124]\u001b[0m Trial 29 failed with value <function f1_score at 0x7f78c3a288b0>.\u001b[0m\n",
            "\u001b[33m[W 2023-02-21 12:32:16,893]\u001b[0m Trial 30 failed with parameters: {'max_depth': 9, 'learning_rate': 0.4334514951274626, 'n_estimators': 1600, 'colsample_bytree': 0.8486433918142862, 'colsample_bylevel': 0.6817899828051761, 'colsample_bynode': 0.7455867496126738, 'reg_lambda': 0.034297131180070245, 'reg_alpha': 0.021961296715971614, 'subsample': 0.8, 'min_child_weight': 8, 'gamma': 0.9628237530731472} because of the following error: The value <function f1_score at 0x7f78c3a288b0> could not be cast to float..\u001b[0m\n",
            "\u001b[33m[W 2023-02-21 12:32:16,896]\u001b[0m Trial 30 failed with value <function f1_score at 0x7f78c3a288b0>.\u001b[0m\n",
            "\u001b[33m[W 2023-02-21 12:32:23,256]\u001b[0m Trial 31 failed with parameters: {'max_depth': 10, 'learning_rate': 0.09078298957807321, 'n_estimators': 3000, 'colsample_bytree': 0.8344908752617058, 'colsample_bylevel': 0.5925999952337078, 'colsample_bynode': 0.5267030728525022, 'reg_lambda': 0.32554737212461615, 'reg_alpha': 0.016894641725465457, 'subsample': 0.6, 'min_child_weight': 4, 'gamma': 0.9057982998585516} because of the following error: The value <function f1_score at 0x7f78c3a288b0> could not be cast to float..\u001b[0m\n",
            "\u001b[33m[W 2023-02-21 12:32:23,258]\u001b[0m Trial 31 failed with value <function f1_score at 0x7f78c3a288b0>.\u001b[0m\n",
            "\u001b[33m[W 2023-02-21 12:32:24,642]\u001b[0m Trial 32 failed with parameters: {'max_depth': 6, 'learning_rate': 0.6991494272624481, 'n_estimators': 6100, 'colsample_bytree': 0.8829618508513872, 'colsample_bylevel': 0.574681575824423, 'colsample_bynode': 0.880497811869865, 'reg_lambda': 0.05573067043148484, 'reg_alpha': 0.2777348767317687, 'subsample': 0.6, 'min_child_weight': 10, 'gamma': 0.2332739208652669} because of the following error: The value <function f1_score at 0x7f78c3a288b0> could not be cast to float..\u001b[0m\n",
            "\u001b[33m[W 2023-02-21 12:32:24,645]\u001b[0m Trial 32 failed with value <function f1_score at 0x7f78c3a288b0>.\u001b[0m\n",
            "\u001b[33m[W 2023-02-21 12:34:32,575]\u001b[0m Trial 33 failed with parameters: {'max_depth': 4, 'learning_rate': 0.00023373536879290393, 'n_estimators': 5700, 'colsample_bytree': 0.6300207413241347, 'colsample_bylevel': 0.5748113574791262, 'colsample_bynode': 0.8078530706364444, 'reg_lambda': 0.013533637629053215, 'reg_alpha': 0.3834462414089086, 'subsample': 0.9, 'min_child_weight': 6, 'gamma': 0.15676255220656715} because of the following error: The value <function f1_score at 0x7f78c3a288b0> could not be cast to float..\u001b[0m\n",
            "\u001b[33m[W 2023-02-21 12:34:32,581]\u001b[0m Trial 33 failed with value <function f1_score at 0x7f78c3a288b0>.\u001b[0m\n",
            "\u001b[33m[W 2023-02-21 12:34:34,249]\u001b[0m Trial 34 failed with parameters: {'max_depth': 4, 'learning_rate': 0.47208475985098514, 'n_estimators': 9100, 'colsample_bytree': 0.6518462367151636, 'colsample_bylevel': 0.7971651840243994, 'colsample_bynode': 0.9386564942438927, 'reg_lambda': 0.0685767357792435, 'reg_alpha': 0.2366726597062271, 'subsample': 0.6, 'min_child_weight': 10, 'gamma': 0.3044410460333096} because of the following error: The value <function f1_score at 0x7f78c3a288b0> could not be cast to float..\u001b[0m\n",
            "\u001b[33m[W 2023-02-21 12:34:34,253]\u001b[0m Trial 34 failed with value <function f1_score at 0x7f78c3a288b0>.\u001b[0m\n",
            "\u001b[33m[W 2023-02-21 12:34:36,367]\u001b[0m Trial 35 failed with parameters: {'max_depth': 4, 'learning_rate': 0.29888208602044736, 'n_estimators': 3900, 'colsample_bytree': 0.672910793522348, 'colsample_bylevel': 0.6258390354665574, 'colsample_bynode': 0.8330498031218576, 'reg_lambda': 0.49066118217196125, 'reg_alpha': 0.020716248657220163, 'subsample': 1.0, 'min_child_weight': 7, 'gamma': 0.5916835300117091} because of the following error: The value <function f1_score at 0x7f78c3a288b0> could not be cast to float..\u001b[0m\n",
            "\u001b[33m[W 2023-02-21 12:34:36,370]\u001b[0m Trial 35 failed with value <function f1_score at 0x7f78c3a288b0>.\u001b[0m\n",
            "\u001b[33m[W 2023-02-21 12:34:37,902]\u001b[0m Trial 36 failed with parameters: {'max_depth': 9, 'learning_rate': 0.47253418244493656, 'n_estimators': 2500, 'colsample_bytree': 0.9913680223281565, 'colsample_bylevel': 0.7800675907765278, 'colsample_bynode': 0.9313031812096446, 'reg_lambda': 0.3022135680048, 'reg_alpha': 0.11257304720734519, 'subsample': 0.6, 'min_child_weight': 5, 'gamma': 0.3884375593241267} because of the following error: The value <function f1_score at 0x7f78c3a288b0> could not be cast to float..\u001b[0m\n",
            "\u001b[33m[W 2023-02-21 12:34:37,905]\u001b[0m Trial 36 failed with value <function f1_score at 0x7f78c3a288b0>.\u001b[0m\n",
            "\u001b[33m[W 2023-02-21 12:34:39,208]\u001b[0m Trial 37 failed with parameters: {'max_depth': 9, 'learning_rate': 0.666757039543617, 'n_estimators': 5200, 'colsample_bytree': 0.918995790810134, 'colsample_bylevel': 0.7134573922447028, 'colsample_bynode': 0.7520597769243023, 'reg_lambda': 0.012228495348268286, 'reg_alpha': 0.013028203830761566, 'subsample': 1.0, 'min_child_weight': 9, 'gamma': 0.2241158831617855} because of the following error: The value <function f1_score at 0x7f78c3a288b0> could not be cast to float..\u001b[0m\n",
            "\u001b[33m[W 2023-02-21 12:34:39,211]\u001b[0m Trial 37 failed with value <function f1_score at 0x7f78c3a288b0>.\u001b[0m\n",
            "\u001b[33m[W 2023-02-21 12:34:42,749]\u001b[0m Trial 38 failed with parameters: {'max_depth': 10, 'learning_rate': 0.10652941608661884, 'n_estimators': 6600, 'colsample_bytree': 0.909647642682782, 'colsample_bylevel': 0.7319017331971162, 'colsample_bynode': 0.7949608221368373, 'reg_lambda': 0.023316558417111402, 'reg_alpha': 0.03433602144565088, 'subsample': 0.6, 'min_child_weight': 8, 'gamma': 0.11999946176518274} because of the following error: The value <function f1_score at 0x7f78c3a288b0> could not be cast to float..\u001b[0m\n",
            "\u001b[33m[W 2023-02-21 12:34:42,751]\u001b[0m Trial 38 failed with value <function f1_score at 0x7f78c3a288b0>.\u001b[0m\n",
            "\u001b[33m[W 2023-02-21 12:34:44,899]\u001b[0m Trial 39 failed with parameters: {'max_depth': 6, 'learning_rate': 0.6278401094309873, 'n_estimators': 5100, 'colsample_bytree': 0.6793638208181276, 'colsample_bylevel': 0.5285653704305084, 'colsample_bynode': 0.7712004612204975, 'reg_lambda': 0.02654738916692019, 'reg_alpha': 0.6187095874166512, 'subsample': 0.85, 'min_child_weight': 3, 'gamma': 0.18051122028678016} because of the following error: The value <function f1_score at 0x7f78c3a288b0> could not be cast to float..\u001b[0m\n",
            "\u001b[33m[W 2023-02-21 12:34:44,902]\u001b[0m Trial 39 failed with value <function f1_score at 0x7f78c3a288b0>.\u001b[0m\n",
            "\u001b[33m[W 2023-02-21 12:34:48,268]\u001b[0m Trial 40 failed with parameters: {'max_depth': 9, 'learning_rate': 0.30779250151521464, 'n_estimators': 9300, 'colsample_bytree': 0.5419881480632311, 'colsample_bylevel': 0.6487554154228203, 'colsample_bynode': 0.7383424232647124, 'reg_lambda': 0.6081882523069224, 'reg_alpha': 0.27755805174896253, 'subsample': 1.0, 'min_child_weight': 8, 'gamma': 0.7149121368002345} because of the following error: The value <function f1_score at 0x7f78c3a288b0> could not be cast to float..\u001b[0m\n",
            "\u001b[33m[W 2023-02-21 12:34:48,270]\u001b[0m Trial 40 failed with value <function f1_score at 0x7f78c3a288b0>.\u001b[0m\n",
            "\u001b[33m[W 2023-02-21 12:34:49,981]\u001b[0m Trial 41 failed with parameters: {'max_depth': 10, 'learning_rate': 0.4040713373596055, 'n_estimators': 1800, 'colsample_bytree': 0.8304815220554431, 'colsample_bylevel': 0.9550010888526322, 'colsample_bynode': 0.8055945008636071, 'reg_lambda': 0.9870022146233344, 'reg_alpha': 0.25841041459243813, 'subsample': 0.85, 'min_child_weight': 2, 'gamma': 0.16030324548653285} because of the following error: The value <function f1_score at 0x7f78c3a288b0> could not be cast to float..\u001b[0m\n",
            "\u001b[33m[W 2023-02-21 12:34:49,984]\u001b[0m Trial 41 failed with value <function f1_score at 0x7f78c3a288b0>.\u001b[0m\n",
            "\u001b[33m[W 2023-02-21 12:34:51,430]\u001b[0m Trial 42 failed with parameters: {'max_depth': 4, 'learning_rate': 0.571828898058506, 'n_estimators': 9500, 'colsample_bytree': 0.7523710917686719, 'colsample_bylevel': 0.5459626310894421, 'colsample_bynode': 0.5634011703868769, 'reg_lambda': 0.017430057529467226, 'reg_alpha': 0.013960241309235294, 'subsample': 0.9, 'min_child_weight': 3, 'gamma': 0.21265315825492806} because of the following error: The value <function f1_score at 0x7f78c3a288b0> could not be cast to float..\u001b[0m\n",
            "\u001b[33m[W 2023-02-21 12:34:51,433]\u001b[0m Trial 42 failed with value <function f1_score at 0x7f78c3a288b0>.\u001b[0m\n",
            "\u001b[33m[W 2023-02-21 12:35:19,937]\u001b[0m Trial 43 failed with parameters: {'max_depth': 8, 'learning_rate': 0.013256485341841207, 'n_estimators': 8000, 'colsample_bytree': 0.6824481900052806, 'colsample_bylevel': 0.7890731460507873, 'colsample_bynode': 0.5959313352510358, 'reg_lambda': 0.86250929426378, 'reg_alpha': 0.033680461500625127, 'subsample': 0.65, 'min_child_weight': 6, 'gamma': 0.2436835570600415} because of the following error: The value <function f1_score at 0x7f78c3a288b0> could not be cast to float..\u001b[0m\n",
            "\u001b[33m[W 2023-02-21 12:35:19,940]\u001b[0m Trial 43 failed with value <function f1_score at 0x7f78c3a288b0>.\u001b[0m\n",
            "\u001b[33m[W 2023-02-21 12:35:21,408]\u001b[0m Trial 44 failed with parameters: {'max_depth': 6, 'learning_rate': 0.5114030602334503, 'n_estimators': 8800, 'colsample_bytree': 0.8274947491015969, 'colsample_bylevel': 0.6775190820417485, 'colsample_bynode': 0.8029026690073162, 'reg_lambda': 0.4749214303566005, 'reg_alpha': 0.14249084214485108, 'subsample': 0.65, 'min_child_weight': 4, 'gamma': 0.1879295654560137} because of the following error: The value <function f1_score at 0x7f78c3a288b0> could not be cast to float..\u001b[0m\n",
            "\u001b[33m[W 2023-02-21 12:35:21,412]\u001b[0m Trial 44 failed with value <function f1_score at 0x7f78c3a288b0>.\u001b[0m\n",
            "\u001b[33m[W 2023-02-21 12:35:23,475]\u001b[0m Trial 45 failed with parameters: {'max_depth': 7, 'learning_rate': 0.29258254244134974, 'n_estimators': 4100, 'colsample_bytree': 0.8891466682347235, 'colsample_bylevel': 0.6441227783837528, 'colsample_bynode': 0.8055548438475894, 'reg_lambda': 0.014933898899163802, 'reg_alpha': 0.3009904078032061, 'subsample': 0.75, 'min_child_weight': 3, 'gamma': 0.15048880143037036} because of the following error: The value <function f1_score at 0x7f78c3a288b0> could not be cast to float..\u001b[0m\n",
            "\u001b[33m[W 2023-02-21 12:35:23,478]\u001b[0m Trial 45 failed with value <function f1_score at 0x7f78c3a288b0>.\u001b[0m\n",
            "\u001b[33m[W 2023-02-21 12:35:24,899]\u001b[0m Trial 46 failed with parameters: {'max_depth': 6, 'learning_rate': 0.7335580689628526, 'n_estimators': 3400, 'colsample_bytree': 0.902741090531735, 'colsample_bylevel': 0.6887205677827046, 'colsample_bynode': 0.9218551124741223, 'reg_lambda': 0.7870679246550999, 'reg_alpha': 0.02636616801030039, 'subsample': 0.7, 'min_child_weight': 14, 'gamma': 0.4239977578840262} because of the following error: The value <function f1_score at 0x7f78c3a288b0> could not be cast to float..\u001b[0m\n",
            "\u001b[33m[W 2023-02-21 12:35:24,902]\u001b[0m Trial 46 failed with value <function f1_score at 0x7f78c3a288b0>.\u001b[0m\n",
            "\u001b[33m[W 2023-02-21 12:35:29,687]\u001b[0m Trial 47 failed with parameters: {'max_depth': 10, 'learning_rate': 0.3265137597636028, 'n_estimators': 3700, 'colsample_bytree': 0.5068479984881358, 'colsample_bylevel': 0.798138291874278, 'colsample_bynode': 0.6402633364312622, 'reg_lambda': 0.016128543569826348, 'reg_alpha': 0.7620334651950171, 'subsample': 0.6, 'min_child_weight': 5, 'gamma': 0.8690166888079419} because of the following error: The value <function f1_score at 0x7f78c3a288b0> could not be cast to float..\u001b[0m\n",
            "\u001b[33m[W 2023-02-21 12:35:29,691]\u001b[0m Trial 47 failed with value <function f1_score at 0x7f78c3a288b0>.\u001b[0m\n",
            "\u001b[33m[W 2023-02-21 12:35:30,963]\u001b[0m Trial 48 failed with parameters: {'max_depth': 9, 'learning_rate': 0.609817087521609, 'n_estimators': 4200, 'colsample_bytree': 0.702808907061784, 'colsample_bylevel': 0.953743567612326, 'colsample_bynode': 0.6040676492993331, 'reg_lambda': 0.9186261841081503, 'reg_alpha': 0.038455848148123285, 'subsample': 0.7, 'min_child_weight': 11, 'gamma': 0.42926904795327164} because of the following error: The value <function f1_score at 0x7f78c3a288b0> could not be cast to float..\u001b[0m\n",
            "\u001b[33m[W 2023-02-21 12:35:30,965]\u001b[0m Trial 48 failed with value <function f1_score at 0x7f78c3a288b0>.\u001b[0m\n",
            "\u001b[33m[W 2023-02-21 12:35:32,597]\u001b[0m Trial 49 failed with parameters: {'max_depth': 6, 'learning_rate': 0.2923202233439832, 'n_estimators': 2300, 'colsample_bytree': 0.8654291598002488, 'colsample_bylevel': 0.9591350503275962, 'colsample_bynode': 0.5481143564929227, 'reg_lambda': 0.1157593630879168, 'reg_alpha': 0.01771776065250554, 'subsample': 0.7, 'min_child_weight': 12, 'gamma': 0.9845126264172028} because of the following error: The value <function f1_score at 0x7f78c3a288b0> could not be cast to float..\u001b[0m\n",
            "\u001b[33m[W 2023-02-21 12:35:32,600]\u001b[0m Trial 49 failed with value <function f1_score at 0x7f78c3a288b0>.\u001b[0m\n",
            "Number of finished trials:  50\n",
            "Best trial:\n"
          ]
        },
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-21-5e5af8b056e7>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     56\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     57\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 58\u001b[0;31m     \u001b[0mtrial\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mstudy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbest_trial\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     59\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     60\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"  Accuracy: {}\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrial\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/optuna/study/study.py\u001b[0m in \u001b[0;36mbest_trial\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    157\u001b[0m             )\n\u001b[1;32m    158\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 159\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdeepcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_storage\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_best_trial\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_study_id\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    160\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    161\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mproperty\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/optuna/storages/_in_memory.py\u001b[0m in \u001b[0;36mget_best_trial\u001b[0;34m(self, study_id)\u001b[0m\n\u001b[1;32m    248\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    249\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mbest_trial_id\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 250\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"No trials are completed yet.\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    251\u001b[0m             \u001b[0;32melif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_studies\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mstudy_id\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdirections\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    252\u001b[0m                 raise RuntimeError(\n",
            "\u001b[0;31mValueError\u001b[0m: No trials are completed yet."
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Best trial:\n",
        "#   Accuracy: 1.0\n",
        "#   Best hyperparameters: \n",
        "#     max_depth: 9\n",
        "#     learning_rate: 0.2743942545025299\n",
        "#     n_estimators: 7100\n",
        "#     colsample_bytree: 0.976037633839835\n",
        "#     colsample_bylevel: 0.8208068730328048\n",
        "#     colsample_bynode: 0.7028939363080522\n",
        "#     reg_lambda: 0.12197001975058792\n",
        "#     reg_alpha: 0.5492373681818031\n",
        "#     subsample: 0.8\n",
        "#     min_child_weight: 11\n",
        "#     gamma: 0.38692872540988676"
      ],
      "metadata": {
        "id": "9GAA0E_R7Ysk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "clf = xgb.XGBClassifier( max_depth= 9,\n",
        "    learning_rate= 0.2743942545025299,\n",
        "    n_estimators= 7100,\n",
        "    colsample_bytree= 0.976037633839835,\n",
        "    colsample_bylevel= 0.8208068730328048,\n",
        "    colsample_bynode= 0.7028939363080522,\n",
        "    reg_lambda=0.12197001975058792,\n",
        "    reg_alpha= 0.5492373681818031,\n",
        "    subsample= 0.8,\n",
        "    min_child_weight= 11,\n",
        "    gamma= 0.38692872540988676,\n",
        "    random_state = 37, use_label_encoder = False)\n",
        "clf.fit(X_train, y_train)\n",
        "\n",
        "preds = clf.predict(X_test)\n",
        "# pred_labels = np.rint(preds)\n",
        "accuracy = sklearn.metrics.accuracy_score(y_test, preds)\n",
        "\n",
        "print(\"Accuracy: {}\".format(accuracy))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GeR03KDq8Wl7",
        "outputId": "15dfbd2d-f775-43c9-8032-f3687b13807e"
      },
      "execution_count": 24,
      "outputs": [
        {
          "metadata": {
            "tags": null
          },
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy: 0.9291553133514986\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "preds = clf.predict(test_x)"
      ],
      "metadata": {
        "id": "6hYC3qnjB8ur"
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sns.countplot(x=preds)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "8aJeWPh-FNj_",
        "outputId": "a0699e66-bca0-4944-9ca4-551ed64187b7"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f0e61dbaee0>"
            ]
          },
          "metadata": {},
          "execution_count": 29
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from xgboost import XGBRegressor\n",
        "\n",
        "XGB_model = XGBRegressor(random_state=37).fit(X_train, y_train) \n",
        "print('Done.') "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f3b57215-7ac9-4d7d-f15e-57afb615aae8",
        "id": "loABUoxqlPnx"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[12:12:21] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "Done.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "preds=XGB_model.predict(test_x)  \n",
        "print('Done.')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3bfaca71-3d3f-482c-ddd4-c4dacfa1b5fe",
        "id": "Mi39v4bWlPnx"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Done.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_x['Y_Quality'] = preds\n",
        "test_x  "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 488
        },
        "outputId": "bb6ca2a6-e0b1-4f08-a32e-d71f26669e84",
        "id": "H7XKB3_RlPnx"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          X_1       X_2  X_3   X_4        X_5  X_6        X_7        X_8  \\\n",
              "0    2.000000  94.00000  0.0  45.0  10.000000  0.0  51.000000  10.000000   \n",
              "1    2.000000  93.00000  0.0  45.0  11.000000  0.0  45.000000  10.000000   \n",
              "2    2.000000  95.00000  0.0  45.0  11.000000  0.0  45.000000  10.000000   \n",
              "3    4.337449  94.44856  0.0  45.0  10.271605  0.0  48.485597  10.012346   \n",
              "4    4.337449  94.44856  0.0  45.0  10.271605  0.0  48.485597  10.012346   \n",
              "..        ...       ...  ...   ...        ...  ...        ...        ...   \n",
              "305  2.000000  91.00000  0.0  45.0  10.000000  0.0  51.000000  10.000000   \n",
              "306  2.000000  96.00000  0.0  45.0  11.000000  0.0  45.000000  10.000000   \n",
              "307  2.000000  91.00000  0.0  45.0  10.000000  0.0  50.000000  10.000000   \n",
              "308  2.000000  95.00000  0.0  45.0  10.000000  0.0  51.000000  10.000000   \n",
              "309  2.000000  87.00000  0.0  45.0  10.000000  0.0  51.000000  10.000000   \n",
              "\n",
              "           X_9  X_10  ...     X_2868     X_2869     X_2870    X_2871  \\\n",
              "0    52.000000   2.0  ...  49.994412  54.202353  63.806176  0.974359   \n",
              "1    31.000000   2.0  ...  49.994412  54.202353  63.806176  0.974359   \n",
              "2    31.000000   2.0  ...  49.994412  54.202353  63.806176  0.974359   \n",
              "3    42.407407   2.0  ...  49.994412  54.202353  63.806176  0.974359   \n",
              "4    42.407407   2.0  ...  49.994412  54.202353  63.806176  0.974359   \n",
              "..         ...   ...  ...        ...        ...        ...       ...   \n",
              "305  52.000000   2.0  ...  49.994412  54.202353  63.806176  0.974359   \n",
              "306  31.000000   2.0  ...  49.994412  54.202353  63.806176  0.974359   \n",
              "307  52.000000   2.0  ...  49.994412  54.202353  63.806176  0.974359   \n",
              "308  52.000000   2.0  ...  49.994412  54.202353  63.806176  0.974359   \n",
              "309  52.000000   2.0  ...  49.994412  54.202353  63.806176  0.974359   \n",
              "\n",
              "         X_2872      X_2873  X_2874      X_2875  line_pcode  Y_Quality  \n",
              "0    247.988889  251.888889   243.3  228.844444           7   0.524482  \n",
              "1    247.988889  251.888889   243.3  228.844444           5   0.527027  \n",
              "2    247.988889  251.888889   243.3  228.844444           5   0.528316  \n",
              "3    247.988889  251.888889   243.3  228.844444           0   0.524518  \n",
              "4    247.988889  251.888889   243.3  228.844444           1   0.525997  \n",
              "..          ...         ...     ...         ...         ...        ...  \n",
              "305  247.988889  251.888889   243.3  228.844444           7   0.523075  \n",
              "306  247.988889  251.888889   243.3  228.844444           5   0.522320  \n",
              "307  247.988889  251.888889   243.3  228.844444           7   0.523158  \n",
              "308  247.988889  251.888889   243.3  228.844444           7   0.521964  \n",
              "309  247.988889  251.888889   243.3  228.844444           7   0.522379  \n",
              "\n",
              "[310 rows x 2877 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-94bf40b6-f6ce-4227-b6b4-12d9e5921df4\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>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>X_9</th>\n",
              "      <th>X_10</th>\n",
              "      <th>...</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",
              "      <th>line_pcode</th>\n",
              "      <th>Y_Quality</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2.000000</td>\n",
              "      <td>94.00000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>45.0</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>51.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>52.000000</td>\n",
              "      <td>2.0</td>\n",
              "      <td>...</td>\n",
              "      <td>49.994412</td>\n",
              "      <td>54.202353</td>\n",
              "      <td>63.806176</td>\n",
              "      <td>0.974359</td>\n",
              "      <td>247.988889</td>\n",
              "      <td>251.888889</td>\n",
              "      <td>243.3</td>\n",
              "      <td>228.844444</td>\n",
              "      <td>7</td>\n",
              "      <td>0.524482</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2.000000</td>\n",
              "      <td>93.00000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>45.0</td>\n",
              "      <td>11.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>45.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>31.000000</td>\n",
              "      <td>2.0</td>\n",
              "      <td>...</td>\n",
              "      <td>49.994412</td>\n",
              "      <td>54.202353</td>\n",
              "      <td>63.806176</td>\n",
              "      <td>0.974359</td>\n",
              "      <td>247.988889</td>\n",
              "      <td>251.888889</td>\n",
              "      <td>243.3</td>\n",
              "      <td>228.844444</td>\n",
              "      <td>5</td>\n",
              "      <td>0.527027</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2.000000</td>\n",
              "      <td>95.00000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>45.0</td>\n",
              "      <td>11.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>45.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>31.000000</td>\n",
              "      <td>2.0</td>\n",
              "      <td>...</td>\n",
              "      <td>49.994412</td>\n",
              "      <td>54.202353</td>\n",
              "      <td>63.806176</td>\n",
              "      <td>0.974359</td>\n",
              "      <td>247.988889</td>\n",
              "      <td>251.888889</td>\n",
              "      <td>243.3</td>\n",
              "      <td>228.844444</td>\n",
              "      <td>5</td>\n",
              "      <td>0.528316</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4.337449</td>\n",
              "      <td>94.44856</td>\n",
              "      <td>0.0</td>\n",
              "      <td>45.0</td>\n",
              "      <td>10.271605</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>10.012346</td>\n",
              "      <td>42.407407</td>\n",
              "      <td>2.0</td>\n",
              "      <td>...</td>\n",
              "      <td>49.994412</td>\n",
              "      <td>54.202353</td>\n",
              "      <td>63.806176</td>\n",
              "      <td>0.974359</td>\n",
              "      <td>247.988889</td>\n",
              "      <td>251.888889</td>\n",
              "      <td>243.3</td>\n",
              "      <td>228.844444</td>\n",
              "      <td>0</td>\n",
              "      <td>0.524518</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4.337449</td>\n",
              "      <td>94.44856</td>\n",
              "      <td>0.0</td>\n",
              "      <td>45.0</td>\n",
              "      <td>10.271605</td>\n",
              "      <td>0.0</td>\n",
              "      <td>48.485597</td>\n",
              "      <td>10.012346</td>\n",
              "      <td>42.407407</td>\n",
              "      <td>2.0</td>\n",
              "      <td>...</td>\n",
              "      <td>49.994412</td>\n",
              "      <td>54.202353</td>\n",
              "      <td>63.806176</td>\n",
              "      <td>0.974359</td>\n",
              "      <td>247.988889</td>\n",
              "      <td>251.888889</td>\n",
              "      <td>243.3</td>\n",
              "      <td>228.844444</td>\n",
              "      <td>1</td>\n",
              "      <td>0.525997</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>305</th>\n",
              "      <td>2.000000</td>\n",
              "      <td>91.00000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>45.0</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>51.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>52.000000</td>\n",
              "      <td>2.0</td>\n",
              "      <td>...</td>\n",
              "      <td>49.994412</td>\n",
              "      <td>54.202353</td>\n",
              "      <td>63.806176</td>\n",
              "      <td>0.974359</td>\n",
              "      <td>247.988889</td>\n",
              "      <td>251.888889</td>\n",
              "      <td>243.3</td>\n",
              "      <td>228.844444</td>\n",
              "      <td>7</td>\n",
              "      <td>0.523075</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>306</th>\n",
              "      <td>2.000000</td>\n",
              "      <td>96.00000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>45.0</td>\n",
              "      <td>11.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>45.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>31.000000</td>\n",
              "      <td>2.0</td>\n",
              "      <td>...</td>\n",
              "      <td>49.994412</td>\n",
              "      <td>54.202353</td>\n",
              "      <td>63.806176</td>\n",
              "      <td>0.974359</td>\n",
              "      <td>247.988889</td>\n",
              "      <td>251.888889</td>\n",
              "      <td>243.3</td>\n",
              "      <td>228.844444</td>\n",
              "      <td>5</td>\n",
              "      <td>0.522320</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>307</th>\n",
              "      <td>2.000000</td>\n",
              "      <td>91.00000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>45.0</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>50.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>52.000000</td>\n",
              "      <td>2.0</td>\n",
              "      <td>...</td>\n",
              "      <td>49.994412</td>\n",
              "      <td>54.202353</td>\n",
              "      <td>63.806176</td>\n",
              "      <td>0.974359</td>\n",
              "      <td>247.988889</td>\n",
              "      <td>251.888889</td>\n",
              "      <td>243.3</td>\n",
              "      <td>228.844444</td>\n",
              "      <td>7</td>\n",
              "      <td>0.523158</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>308</th>\n",
              "      <td>2.000000</td>\n",
              "      <td>95.00000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>45.0</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>51.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>52.000000</td>\n",
              "      <td>2.0</td>\n",
              "      <td>...</td>\n",
              "      <td>49.994412</td>\n",
              "      <td>54.202353</td>\n",
              "      <td>63.806176</td>\n",
              "      <td>0.974359</td>\n",
              "      <td>247.988889</td>\n",
              "      <td>251.888889</td>\n",
              "      <td>243.3</td>\n",
              "      <td>228.844444</td>\n",
              "      <td>7</td>\n",
              "      <td>0.521964</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>309</th>\n",
              "      <td>2.000000</td>\n",
              "      <td>87.00000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>45.0</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>51.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>52.000000</td>\n",
              "      <td>2.0</td>\n",
              "      <td>...</td>\n",
              "      <td>49.994412</td>\n",
              "      <td>54.202353</td>\n",
              "      <td>63.806176</td>\n",
              "      <td>0.974359</td>\n",
              "      <td>247.988889</td>\n",
              "      <td>251.888889</td>\n",
              "      <td>243.3</td>\n",
              "      <td>228.844444</td>\n",
              "      <td>7</td>\n",
              "      <td>0.522379</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>310 rows × 2877 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-94bf40b6-f6ce-4227-b6b4-12d9e5921df4')\"\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",
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              "  </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",
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              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-94bf40b6-f6ce-4227-b6b4-12d9e5921df4 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-94bf40b6-f6ce-4227-b6b4-12d9e5921df4');\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",
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              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 268
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.countplot(x=preds);"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "outputId": "6cf5d4d5-7907-473e-ba44-12d8eec19c6d",
        "id": "pFy9yZDSlPnx"
      },
      "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": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# X_train = train_df\n",
        "X_train = train_df.drop(columns=['Y_Class'])\n",
        "X_train = pd.DataFrame(train_df['Y_Quality'])\n",
        "y_train = train_df['Y_Class']"
      ],
      "metadata": {
        "id": "_pdf8dM-lPny"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "158738d0-7834-4d9b-cebd-6d54c7f39ffe",
        "id": "F469MpDQlPny"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Y_Quality    0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 271
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from xgboost import XGBClassifier\n",
        "\n",
        "XGB_model =  XGBClassifier(random_state=37).fit(X_train, y_train)\n",
        "print('Done.')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0fa3dd75-1312-445b-d8a0-98ac1a23fcc0",
        "id": "-UlR05R1lPny"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Done.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_x\n",
        "# test_x = test_x.drop(columns=['LINE//','PRODUCT_CODE'])\n",
        "# test_x = test_x['Y_Quality']  \n",
        "test_x = pd.DataFrame(test_x, columns=['Y_Quality'])\n",
        "# test_x = test_x['Y_Quality'] "
      ],
      "metadata": {
        "id": "nVsSiMD6lPnz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "test_x = pd.DataFrame(test_x)\n",
        "\n",
        "preds = RF.predict(test_x)   \n",
        "print('Done.')   "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c3d83fbc-15f5-4088-a615-b5a76960d67e",
        "id": "SYNJBRSzlPnz"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Done.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "preds"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "31724fe0-6e7c-4000-d2f4-0410f75495db",
        "id": "oxJn-HFKlPnz"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0., 1., 1., 0., 1., 1., 1., 0., 0., 1., 1., 1., 1., 1., 0., 0., 1.,\n",
              "       0., 0., 1., 0., 1., 0., 0., 0., 0., 1., 0., 0., 1., 0., 1., 0., 0.,\n",
              "       1., 0., 1., 0., 2., 0., 1., 0., 0., 0., 0., 1., 1., 0., 0., 0., 1.,\n",
              "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 0., 0., 0., 1.,\n",
              "       0., 0., 0., 0., 1., 0., 1., 0., 1., 0., 2., 1., 1., 1., 1., 1., 1.,\n",
              "       1., 0., 1., 0., 0., 1., 1., 1., 1., 0., 1., 1., 1., 0., 1., 0., 1.,\n",
              "       1., 1., 1., 1., 1., 0., 1., 1., 1., 0., 0., 1., 1., 1., 0., 0., 0.,\n",
              "       1., 1., 1., 1., 1., 0., 0., 1., 1., 0., 0., 1., 1., 1., 0., 1., 1.,\n",
              "       1., 1., 1., 0., 1., 0., 0., 1., 1., 1., 1., 1., 0., 0., 0., 1., 1.,\n",
              "       1., 0., 0., 0., 0., 1., 0., 2., 1., 1., 1., 0., 1., 0., 1., 0., 1.,\n",
              "       0., 0., 1., 0., 0., 0., 1., 1., 0., 1., 1., 0., 0., 1., 1., 0., 0.,\n",
              "       1., 0., 1., 0., 1., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
              "       1., 0., 1., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0.,\n",
              "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0.,\n",
              "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1., 1., 1., 1.,\n",
              "       1., 0., 0., 0., 0., 2., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
              "       0., 0., 0., 0., 0., 0., 0., 0., 1., 2., 1., 2., 1., 1., 1., 0., 0.,\n",
              "       0., 0., 0., 1., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
              "       0., 0., 0., 0.])"
            ]
          },
          "metadata": {},
          "execution_count": 275
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.countplot(x=preds);"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "outputId": "c0b4abf7-a623-40e0-ca36-e79cc3053a28",
        "id": "C9YYggQ9lPnz"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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K7DvEJucB19ec24BjkywbT3c6lKraVVXf6uYfAO4ETjhos4k7foZC/04Avj+wvINH/sNs21TVfuA+4Flj6U6P1TDHVz3rhmRPBb5+0KqJO36GgqSJluRpwAbg7VV1f9/99M1Q6N8wr/Zo2yQ5BvhlYO9YutNj5atbFrEkT2YuED5VVZ+fZ5OJO36GQv+GebXHRuCibv61wL+XTx0eLTYCr+/uYjkduK+qdvXdlObuLAKuAe6sqg8usNnEHb9F9ZqLSbTQqz2SXAHMVNVG5v7hfjLJNuYual7QX8calGQ9cCawNMkO4HLgyQBV9THgJuBcYBvwY+CN/XSqeZwBXAh8J8nmrvYuYDlM7vHzNReSpMbhI0lSYyhIkhpDQZLUGAqSpMZQkCQ1hoIkqTEUJEnN/wNvZANtKPoy1AAAAABJRU5ErkJggg==\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 3.4 RandomForest\n",
        "- Output: class 0 too small"
      ],
      "metadata": {
        "id": "_JA7HNFnmHcw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import RandomForestRegressor\n",
        "\n",
        "RF_model = RandomForestRegressor(random_state=37).fit(X_train, y_train) \n",
        "print('Done.') "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0d4000e7-1509-42c1-9890-5a4be7bb5eee",
        "id": "6bXpl80hmHcw"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Done.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "preds=RF_model.predict(test_x)  \n",
        "print('Done.')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "88b84dc4-596f-45b7-8fef-9a7718793a6e",
        "id": "qek-NdR0mHcx"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Done.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_x['Y_Quality'] = preds\n",
        "test_x  "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 488
        },
        "outputId": "58c6f33a-0225-41fa-dfa7-715e4f3022b8",
        "id": "Gbatqqe8mHcx"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          X_1       X_2  X_3   X_4        X_5  X_6        X_7        X_8  \\\n",
              "0    2.000000  94.00000  0.0  45.0  10.000000  0.0  51.000000  10.000000   \n",
              "1    2.000000  93.00000  0.0  45.0  11.000000  0.0  45.000000  10.000000   \n",
              "2    2.000000  95.00000  0.0  45.0  11.000000  0.0  45.000000  10.000000   \n",
              "3    4.337449  94.44856  0.0  45.0  10.271605  0.0  48.485597  10.012346   \n",
              "4    4.337449  94.44856  0.0  45.0  10.271605  0.0  48.485597  10.012346   \n",
              "..        ...       ...  ...   ...        ...  ...        ...        ...   \n",
              "305  2.000000  91.00000  0.0  45.0  10.000000  0.0  51.000000  10.000000   \n",
              "306  2.000000  96.00000  0.0  45.0  11.000000  0.0  45.000000  10.000000   \n",
              "307  2.000000  91.00000  0.0  45.0  10.000000  0.0  50.000000  10.000000   \n",
              "308  2.000000  95.00000  0.0  45.0  10.000000  0.0  51.000000  10.000000   \n",
              "309  2.000000  87.00000  0.0  45.0  10.000000  0.0  51.000000  10.000000   \n",
              "\n",
              "           X_9  X_10  ...     X_2868     X_2869     X_2870    X_2871  \\\n",
              "0    52.000000   2.0  ...  49.994412  54.202353  63.806176  0.974359   \n",
              "1    31.000000   2.0  ...  49.994412  54.202353  63.806176  0.974359   \n",
              "2    31.000000   2.0  ...  49.994412  54.202353  63.806176  0.974359   \n",
              "3    42.407407   2.0  ...  49.994412  54.202353  63.806176  0.974359   \n",
              "4    42.407407   2.0  ...  49.994412  54.202353  63.806176  0.974359   \n",
              "..         ...   ...  ...        ...        ...        ...       ...   \n",
              "305  52.000000   2.0  ...  49.994412  54.202353  63.806176  0.974359   \n",
              "306  31.000000   2.0  ...  49.994412  54.202353  63.806176  0.974359   \n",
              "307  52.000000   2.0  ...  49.994412  54.202353  63.806176  0.974359   \n",
              "308  52.000000   2.0  ...  49.994412  54.202353  63.806176  0.974359   \n",
              "309  52.000000   2.0  ...  49.994412  54.202353  63.806176  0.974359   \n",
              "\n",
              "         X_2872      X_2873  X_2874      X_2875  line_pcode  Y_Quality  \n",
              "0    247.988889  251.888889   243.3  228.844444           7   0.533170  \n",
              "1    247.988889  251.888889   243.3  228.844444           5   0.535017  \n",
              "2    247.988889  251.888889   243.3  228.844444           5   0.534964  \n",
              "3    247.988889  251.888889   243.3  228.844444           0   0.532341  \n",
              "4    247.988889  251.888889   243.3  228.844444           1   0.532555  \n",
              "..          ...         ...     ...         ...         ...        ...  \n",
              "305  247.988889  251.888889   243.3  228.844444           7   0.532656  \n",
              "306  247.988889  251.888889   243.3  228.844444           5   0.532410  \n",
              "307  247.988889  251.888889   243.3  228.844444           7   0.532898  \n",
              "308  247.988889  251.888889   243.3  228.844444           7   0.532914  \n",
              "309  247.988889  251.888889   243.3  228.844444           7   0.533151  \n",
              "\n",
              "[310 rows x 2877 columns]"
            ],
            "text/html": [
              "\n",
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              "    <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>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>X_9</th>\n",
              "      <th>X_10</th>\n",
              "      <th>...</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",
              "      <th>line_pcode</th>\n",
              "      <th>Y_Quality</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2.000000</td>\n",
              "      <td>94.00000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>45.0</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>51.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>52.000000</td>\n",
              "      <td>2.0</td>\n",
              "      <td>...</td>\n",
              "      <td>49.994412</td>\n",
              "      <td>54.202353</td>\n",
              "      <td>63.806176</td>\n",
              "      <td>0.974359</td>\n",
              "      <td>247.988889</td>\n",
              "      <td>251.888889</td>\n",
              "      <td>243.3</td>\n",
              "      <td>228.844444</td>\n",
              "      <td>7</td>\n",
              "      <td>0.533170</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2.000000</td>\n",
              "      <td>93.00000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>45.0</td>\n",
              "      <td>11.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>45.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>31.000000</td>\n",
              "      <td>2.0</td>\n",
              "      <td>...</td>\n",
              "      <td>49.994412</td>\n",
              "      <td>54.202353</td>\n",
              "      <td>63.806176</td>\n",
              "      <td>0.974359</td>\n",
              "      <td>247.988889</td>\n",
              "      <td>251.888889</td>\n",
              "      <td>243.3</td>\n",
              "      <td>228.844444</td>\n",
              "      <td>5</td>\n",
              "      <td>0.535017</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2.000000</td>\n",
              "      <td>95.00000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>45.0</td>\n",
              "      <td>11.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>45.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>31.000000</td>\n",
              "      <td>2.0</td>\n",
              "      <td>...</td>\n",
              "      <td>49.994412</td>\n",
              "      <td>54.202353</td>\n",
              "      <td>63.806176</td>\n",
              "      <td>0.974359</td>\n",
              "      <td>247.988889</td>\n",
              "      <td>251.888889</td>\n",
              "      <td>243.3</td>\n",
              "      <td>228.844444</td>\n",
              "      <td>5</td>\n",
              "      <td>0.534964</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4.337449</td>\n",
              "      <td>94.44856</td>\n",
              "      <td>0.0</td>\n",
              "      <td>45.0</td>\n",
              "      <td>10.271605</td>\n",
              "      <td>0.0</td>\n",
              "      <td>48.485597</td>\n",
              "      <td>10.012346</td>\n",
              "      <td>42.407407</td>\n",
              "      <td>2.0</td>\n",
              "      <td>...</td>\n",
              "      <td>49.994412</td>\n",
              "      <td>54.202353</td>\n",
              "      <td>63.806176</td>\n",
              "      <td>0.974359</td>\n",
              "      <td>247.988889</td>\n",
              "      <td>251.888889</td>\n",
              "      <td>243.3</td>\n",
              "      <td>228.844444</td>\n",
              "      <td>0</td>\n",
              "      <td>0.532341</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4.337449</td>\n",
              "      <td>94.44856</td>\n",
              "      <td>0.0</td>\n",
              "      <td>45.0</td>\n",
              "      <td>10.271605</td>\n",
              "      <td>0.0</td>\n",
              "      <td>48.485597</td>\n",
              "      <td>10.012346</td>\n",
              "      <td>42.407407</td>\n",
              "      <td>2.0</td>\n",
              "      <td>...</td>\n",
              "      <td>49.994412</td>\n",
              "      <td>54.202353</td>\n",
              "      <td>63.806176</td>\n",
              "      <td>0.974359</td>\n",
              "      <td>247.988889</td>\n",
              "      <td>251.888889</td>\n",
              "      <td>243.3</td>\n",
              "      <td>228.844444</td>\n",
              "      <td>1</td>\n",
              "      <td>0.532555</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>305</th>\n",
              "      <td>2.000000</td>\n",
              "      <td>91.00000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>45.0</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>51.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>52.000000</td>\n",
              "      <td>2.0</td>\n",
              "      <td>...</td>\n",
              "      <td>49.994412</td>\n",
              "      <td>54.202353</td>\n",
              "      <td>63.806176</td>\n",
              "      <td>0.974359</td>\n",
              "      <td>247.988889</td>\n",
              "      <td>251.888889</td>\n",
              "      <td>243.3</td>\n",
              "      <td>228.844444</td>\n",
              "      <td>7</td>\n",
              "      <td>0.532656</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>306</th>\n",
              "      <td>2.000000</td>\n",
              "      <td>96.00000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>45.0</td>\n",
              "      <td>11.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>45.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>31.000000</td>\n",
              "      <td>2.0</td>\n",
              "      <td>...</td>\n",
              "      <td>49.994412</td>\n",
              "      <td>54.202353</td>\n",
              "      <td>63.806176</td>\n",
              "      <td>0.974359</td>\n",
              "      <td>247.988889</td>\n",
              "      <td>251.888889</td>\n",
              "      <td>243.3</td>\n",
              "      <td>228.844444</td>\n",
              "      <td>5</td>\n",
              "      <td>0.532410</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>307</th>\n",
              "      <td>2.000000</td>\n",
              "      <td>91.00000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>45.0</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>50.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>52.000000</td>\n",
              "      <td>2.0</td>\n",
              "      <td>...</td>\n",
              "      <td>49.994412</td>\n",
              "      <td>54.202353</td>\n",
              "      <td>63.806176</td>\n",
              "      <td>0.974359</td>\n",
              "      <td>247.988889</td>\n",
              "      <td>251.888889</td>\n",
              "      <td>243.3</td>\n",
              "      <td>228.844444</td>\n",
              "      <td>7</td>\n",
              "      <td>0.532898</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>308</th>\n",
              "      <td>2.000000</td>\n",
              "      <td>95.00000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>45.0</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>51.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>52.000000</td>\n",
              "      <td>2.0</td>\n",
              "      <td>...</td>\n",
              "      <td>49.994412</td>\n",
              "      <td>54.202353</td>\n",
              "      <td>63.806176</td>\n",
              "      <td>0.974359</td>\n",
              "      <td>247.988889</td>\n",
              "      <td>251.888889</td>\n",
              "      <td>243.3</td>\n",
              "      <td>228.844444</td>\n",
              "      <td>7</td>\n",
              "      <td>0.532914</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>309</th>\n",
              "      <td>2.000000</td>\n",
              "      <td>87.00000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>45.0</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>51.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>52.000000</td>\n",
              "      <td>2.0</td>\n",
              "      <td>...</td>\n",
              "      <td>49.994412</td>\n",
              "      <td>54.202353</td>\n",
              "      <td>63.806176</td>\n",
              "      <td>0.974359</td>\n",
              "      <td>247.988889</td>\n",
              "      <td>251.888889</td>\n",
              "      <td>243.3</td>\n",
              "      <td>228.844444</td>\n",
              "      <td>7</td>\n",
              "      <td>0.533151</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>310 rows × 2877 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a6af05ce-aa69-458d-880c-774bd76921ea')\"\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",
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              "  </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",
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              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "\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",
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              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-a6af05ce-aa69-458d-880c-774bd76921ea 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-a6af05ce-aa69-458d-880c-774bd76921ea');\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",
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              "          element.appendChild(docLink);\n",
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              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 308
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.countplot(x=preds);"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "outputId": "d7ca2855-bbc0-4d77-9bc2-d72ee5af711b",
        "id": "BdTa6wmvmHcx"
      },
      "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": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# X_train = train_df\n",
        "X_train = train_df.drop(columns=['Y_Class'])\n",
        "X_train = pd.DataFrame(train_df['Y_Quality'])\n",
        "y_train = train_df['Y_Class']"
      ],
      "metadata": {
        "id": "3CLUxMwnmHcx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7dcfd452-d247-4e78-abdd-030623e4e73a",
        "id": "I4oUj_iNmHcy"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Y_Quality    0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 311
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "\n",
        "RF_model =  RandomForestClassifier(random_state=37).fit(X_train, y_train)\n",
        "print('Done.')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "cedda7bc-6949-4d00-ad36-96ba7be2afb4",
        "id": "QNxn6BzmmHcy"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Done.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_x\n",
        "# test_x = test_x.drop(columns=['LINE//','PRODUCT_CODE'])\n",
        "# test_x = test_x['Y_Quality']  \n",
        "test_x = pd.DataFrame(test_x, columns=['Y_Quality'])\n",
        "# test_x = test_x['Y_Quality'] "
      ],
      "metadata": {
        "id": "ZitUPecemHcy"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "test_x = pd.DataFrame(test_x)\n",
        "\n",
        "preds = RF.predict(test_x)   \n",
        "print('Done.')   "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "827518b1-e2e0-44db-c633-92ff0928ea83",
        "id": "BwTORrAdmHcz"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Done.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "preds"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "22d6d442-d5c8-4088-e544-13c04a665534",
        "id": "DbNwsGI2mHcz"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1., 2., 2., 1., 1., 1., 2., 1., 1., 1., 2., 2., 1., 1., 1., 1., 1.,\n",
              "       1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
              "       1., 1., 1., 0., 2., 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.,\n",
              "       1., 1., 1., 0., 1., 1., 1., 1., 1., 1., 2., 1., 1., 1., 1., 1., 2.,\n",
              "       1., 0., 1., 0., 1., 2., 1., 2., 2., 1., 2., 1., 2., 1., 2., 1., 2.,\n",
              "       1., 1., 1., 1., 2., 1., 2., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
              "       2., 1., 1., 1., 2., 1., 1., 2., 1., 1., 1., 1., 1., 1., 1., 2., 2.,\n",
              "       1., 2., 1., 1., 1., 1., 1., 1., 2., 2., 1., 1., 1., 1., 1., 1., 1.,\n",
              "       1., 1., 1., 1., 1., 2., 1., 2., 2., 1., 2., 1., 1., 1., 1., 1., 2.,\n",
              "       1., 1., 1., 1., 1., 1., 2., 1., 1., 2., 1., 1., 1., 2., 1., 1., 1.,\n",
              "       2., 1., 2., 1., 2., 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.,\n",
              "       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.,\n",
              "       1., 1., 1., 1., 1., 2., 1., 1., 2., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
              "       1., 1., 1., 1., 1., 1., 1., 1., 1., 2., 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.,\n",
              "       1., 1., 1., 1.])"
            ]
          },
          "metadata": {},
          "execution_count": 315
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.countplot(x=preds);"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "outputId": "244a9392-2175-472b-d964-27313685c7bd",
        "id": "xOUunqOEmHcz"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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JjaEgSWoMBUlS8//Bz5bRawJlewAAAABJRU5ErkJggg==\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 3-1.DL_models"
      ],
      "metadata": {
        "id": "Vl05J9tpExbR"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 3.1 TabNet"
      ],
      "metadata": {
        "id": "IFIeaDruExgK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "train_x = train_df.drop(columns=['Y_Class'], axis=1)\n",
        "train_y = train_df[['Y_Class']]\n",
        "test_x = test_df\n",
        "if \"Set\" not in train_df.columns:\n",
        "    train_df[\"Set\"] = np.random.choice([\"train\", \"valid\"], p =[.8, .2], size=(train_df.shape[0],))\n",
        "\n",
        "train_indices = train_x[train_df.Set==\"train\"].index\n",
        "valid_indices = train_x[train_df.Set==\"valid\"].index\n",
        "\n",
        "\n",
        "nunique = train_df.nunique()\n",
        "types = train_df.dtypes\n",
        "\n",
        "categorical_columns = []\n",
        "categorical_dims =  {}\n",
        "for col in tqdm(train_x.columns):   \n",
        "    if types[col] == 'object' or nunique[col] < 200:\n",
        "        print(col, train_x[col].nunique())\n",
        "        l_enc = LabelEncoder()\n",
        "        train_x[col] = train_x[col].fillna(\"NaN\")\n",
        "        train_x[col] = l_enc.fit_transform(train_x[col].values)\n",
        "        try:\n",
        "            test_x[col] = test_x[col].fillna(\"NaN\")\n",
        "            test_x[col] = l_enc.transform(test_x[col].values)\n",
        "        except:\n",
        "            print(f\"Column {col} does not exist in test set\")\n",
        "        categorical_columns.append(col)\n",
        "        categorical_dims[col] = len(l_enc.classes_)\n",
        "    else:\n",
        "        training_mean = train_x.loc[train_indices, col].mean()\n",
        "        train_df.fillna(training_mean, inplace=True)\n",
        "        test_df.fillna(training_mean, inplace=True)\n",
        "\n",
        "features = [ col for col in train_x.columns] \n",
        "cat_idxs = [ i for i, f in enumerate(features) if f in categorical_columns]\n",
        "cat_dims = [ categorical_dims[f] for i, f in enumerate(features) if f in categorical_columns]\n",
        "\n",
        "X_train = train_x[features].values[train_indices]\n",
        "y_train = train_y.values[train_indices]\n",
        "\n",
        "X_valid = train_x[features].values[valid_indices]\n",
        "y_valid = train_y.values[valid_indices]\n",
        "\n",
        "X_test = test_x.values\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
          "referenced_widgets": [
            "2cd7ff40dc3246648d6f40f5f418accb",
            "a6fa4df60bc04f9887cb397411ae0a90",
            "0bd0cd38921548bdbc947a798bb8142f",
            "a9403d9b3d444842b33147c5f479b020",
            "593b83454f0c4ee8960917a9f8ef1690",
            "edb1e61cc5404f38874a457d46b93800",
            "2f67e7dd5d2f4dda9dd99ef2be3272e2",
            "548b37cd7b7f42dfaf0b59cba9e46e9c",
            "b008ed350d8d48e2b7a83e107ed2ff40",
            "57b03bba163c47b6b1994e69355b5afa",
            "63657a930cc4415382b65ee42cae6620"
          ]
        },
        "id": "jUzaH5zH4A48",
        "outputId": "7b2595e3-781b-4cfe-9422-6b7200f74534"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/2878 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "2cd7ff40dc3246648d6f40f5f418accb"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "X_1 11\n",
            "Column X_1 does not exist in test set\n",
            "X_2 17\n",
            "Column X_2 does not exist in test set\n",
            "X_3 1\n",
            "X_4 2\n",
            "Column X_4 does not exist in test set\n",
            "X_5 3\n",
            "Column X_5 does not exist in test set\n",
            "X_6 1\n",
            "X_7 14\n",
            "Column X_7 does not exist in test set\n",
            "X_8 3\n",
            "Column X_8 does not exist in test set\n",
            "X_9 3\n",
            "Column X_9 does not exist in test set\n",
            "X_10 2\n",
            "Column X_10 does not exist in test set\n",
            "X_12 179\n",
            "Column X_12 does not exist in test set\n",
            "X_13 170\n",
            "Column X_13 does not exist in test set\n",
            "X_14 1\n",
            "X_15 2\n",
            "Column X_15 does not exist in test set\n",
            "X_16 5\n",
            "Column X_16 does not exist in test set\n",
            "X_17 9\n",
            "Column X_17 does not exist in test set\n",
            "X_18 6\n",
            "Column X_18 does not exist in test set\n",
            "X_19 2\n",
            "Column X_19 does not exist in test set\n",
            "X_20 68\n",
            "Column X_20 does not exist in test set\n",
            "X_21 73\n",
            "Column X_21 does not exist in test set\n",
            "X_22 67\n",
            "Column X_22 does not exist in test set\n",
            "X_23 2\n",
            "Column X_23 does not exist in test set\n",
            "X_24 3\n",
            "Column X_24 does not exist in test set\n",
            "X_25 2\n",
            "Column X_25 does not exist in test set\n",
            "X_26 2\n",
            "Column X_26 does not exist in test set\n",
            "X_27 2\n",
            "Column X_27 does not exist in test set\n",
            "X_28 2\n",
            "Column X_28 does not exist in test set\n",
            "X_29 2\n",
            "Column X_29 does not exist in test set\n",
            "X_30 2\n",
            "Column X_30 does not exist in test set\n",
            "X_31 2\n",
            "Column X_31 does not exist in test set\n",
            "X_32 2\n",
            "Column X_32 does not exist in test set\n",
            "X_33 2\n",
            "Column X_33 does not exist in test set\n",
            "X_34 2\n",
            "Column X_34 does not exist in test set\n",
            "X_35 2\n",
            "Column X_35 does not exist in test set\n",
            "X_36 2\n",
            "Column X_36 does not exist in test set\n",
            "X_37 2\n",
            "Column X_37 does not exist in test set\n",
            "X_38 6\n",
            "Column X_38 does not exist in test set\n",
            "X_39 3\n",
            "X_40 11\n",
            "Column X_40 does not exist in test set\n",
            "X_41 13\n",
            "Column X_41 does not exist in test set\n",
            "X_42 12\n",
            "Column X_42 does not exist in test set\n",
            "X_43 13\n",
            "Column X_43 does not exist in test set\n",
            "X_44 12\n",
            "Column X_44 does not exist in test set\n",
            "X_45 10\n",
            "Column X_45 does not exist in test set\n",
            "X_46 15\n",
            "Column X_46 does not exist in test set\n",
            "X_47 13\n",
            "Column X_47 does not exist in test set\n",
            "X_48 13\n",
            "Column X_48 does not exist in test set\n",
            "X_49 14\n",
            "Column X_49 does not exist in test set\n",
            "X_50 13\n",
            "Column X_50 does not exist in test set\n",
            "X_51 15\n",
            "Column X_51 does not exist in test set\n",
            "X_52 13\n",
            "Column X_52 does not exist in test set\n",
            "X_53 14\n",
            "Column X_53 does not exist in test set\n",
            "X_54 16\n",
            "Column X_54 does not exist in test set\n",
            "X_55 12\n",
            "Column X_55 does not exist in test set\n",
            "X_56 15\n",
            "Column X_56 does not exist in test set\n",
            "X_57 16\n",
            "Column X_57 does not exist in test set\n",
            "X_58 17\n",
            "Column X_58 does not exist in test set\n",
            "X_59 14\n",
            "Column X_59 does not exist in test set\n",
            "X_60 20\n",
            "Column X_60 does not exist in test set\n",
            "X_61 12\n",
            "Column X_61 does not exist in test set\n",
            "X_62 20\n",
            "Column X_62 does not exist in test set\n",
            "X_63 9\n",
            "Column X_63 does not exist in test set\n",
            "X_64 14\n",
            "Column X_64 does not exist in test set\n",
            "X_65 14\n",
            "Column X_65 does not exist in test set\n",
            "X_66 11\n",
            "Column X_66 does not exist in test set\n",
            "X_67 2\n",
            "Column X_67 does not exist in test set\n",
            "X_68 2\n",
            "Column X_68 does not exist in test set\n",
            "X_69 2\n",
            "X_70 1\n",
            "X_71 2\n",
            "Column X_71 does not exist in test set\n",
            "X_72 2\n",
            "Column X_72 does not exist in test set\n",
            "X_73 24\n",
            "Column X_73 does not exist in test set\n",
            "X_74 1\n",
            "X_75 1\n",
            "X_76 1\n",
            "X_77 1\n",
            "X_78 1\n",
            "X_79 1\n",
            "X_80 2\n",
            "Column X_80 does not exist in test set\n",
            "X_81 2\n",
            "Column X_81 does not exist in test set\n",
            "X_82 1\n",
            "X_83 1\n",
            "X_84 1\n",
            "X_85 1\n",
            "X_86 3\n",
            "Column X_86 does not exist in test set\n",
            "X_87 3\n",
            "Column X_87 does not exist in test set\n",
            "X_88 3\n",
            "Column X_88 does not exist in test set\n",
            "X_89 2\n",
            "Column X_89 does not exist in test set\n",
            "X_90 5\n",
            "Column X_90 does not exist in test set\n",
            "X_91 2\n",
            "Column X_91 does not exist in test set\n",
            "X_92 2\n",
            "Column X_92 does not exist in test set\n",
            "X_93 2\n",
            "Column X_93 does not exist in test set\n",
            "X_94 3\n",
            "Column X_94 does not exist in test set\n",
            "X_95 3\n",
            "Column X_95 does not exist in test set\n",
            "X_96 2\n",
            "Column X_96 does not exist in test set\n",
            "X_97 14\n",
            "Column X_97 does not exist in test set\n",
            "X_98 16\n",
            "Column X_98 does not exist in test set\n",
            "X_99 17\n",
            "Column X_99 does not exist in test set\n",
            "X_100 2\n",
            "Column X_100 does not exist in test set\n",
            "X_101 43\n",
            "Column X_101 does not exist in test set\n",
            "X_102 53\n",
            "Column X_102 does not exist in test set\n",
            "X_103 34\n",
            "Column X_103 does not exist in test set\n",
            "X_104 2\n",
            "Column X_104 does not exist in test set\n",
            "X_105 50\n",
            "Column X_105 does not exist in test set\n",
            "X_106 55\n",
            "Column X_106 does not exist in test set\n",
            "X_107 48\n",
            "Column X_107 does not exist in test set\n",
            "X_108 2\n",
            "Column X_108 does not exist in test set\n",
            "X_109 47\n",
            "Column X_109 does not exist in test set\n",
            "X_110 82\n",
            "Column X_110 does not exist in test set\n",
            "X_111 40\n",
            "Column X_111 does not exist in test set\n",
            "X_112 2\n",
            "Column X_112 does not exist in test set\n",
            "X_113 3\n",
            "Column X_113 does not exist in test set\n",
            "X_114 3\n",
            "Column X_114 does not exist in test set\n",
            "X_115 3\n",
            "Column X_115 does not exist in test set\n",
            "X_116 2\n",
            "Column X_116 does not exist in test set\n",
            "X_117 30\n",
            "Column X_117 does not exist in test set\n",
            "X_118 32\n",
            "Column X_118 does not exist in test set\n",
            "X_119 49\n",
            "Column X_119 does not exist in test set\n",
            "X_120 25\n",
            "Column X_120 does not exist in test set\n",
            "X_121 24\n",
            "Column X_121 does not exist in test set\n",
            "X_122 2\n",
            "Column X_122 does not exist in test set\n",
            "X_124 176\n",
            "Column X_124 does not exist in test set\n",
            "X_125 176\n",
            "Column X_125 does not exist in test set\n",
            "X_126 175\n",
            "Column X_126 does not exist in test set\n",
            "X_127 175\n",
            "Column X_127 does not exist in test set\n",
            "X_128 79\n",
            "Column X_128 does not exist in test set\n",
            "X_129 79\n",
            "Column X_129 does not exist in test set\n",
            "X_130 43\n",
            "Column X_130 does not exist in test set\n",
            "X_131 43\n",
            "Column X_131 does not exist in test set\n",
            "X_132 4\n",
            "Column X_132 does not exist in test set\n",
            "X_133 3\n",
            "Column X_133 does not exist in test set\n",
            "X_134 4\n",
            "Column X_134 does not exist in test set\n",
            "X_135 2\n",
            "Column X_135 does not exist in test set\n",
            "X_136 68\n",
            "Column X_136 does not exist in test set\n",
            "X_137 68\n",
            "Column X_137 does not exist in test set\n",
            "X_138 74\n",
            "Column X_138 does not exist in test set\n",
            "X_139 100\n",
            "Column X_139 does not exist in test set\n",
            "X_140 94\n",
            "Column X_140 does not exist in test set\n",
            "X_141 108\n",
            "Column X_141 does not exist in test set\n",
            "X_142 1\n",
            "X_143 2\n",
            "X_144 5\n",
            "Column X_144 does not exist in test set\n",
            "X_145 2\n",
            "Column X_145 does not exist in test set\n",
            "X_146 109\n",
            "Column X_146 does not exist in test set\n",
            "X_147 2\n",
            "Column X_147 does not exist in test set\n",
            "X_148 32\n",
            "Column X_148 does not exist in test set\n",
            "X_149 33\n",
            "Column X_149 does not exist in test set\n",
            "X_150 36\n",
            "Column X_150 does not exist in test set\n",
            "X_151 3\n",
            "Column X_151 does not exist in test set\n",
            "X_152 4\n",
            "Column X_152 does not exist in test set\n",
            "X_153 4\n",
            "Column X_153 does not exist in test set\n",
            "X_154 2\n",
            "Column X_154 does not exist in test set\n",
            "X_155 3\n",
            "Column X_155 does not exist in test set\n",
            "X_156 4\n",
            "Column X_156 does not exist in test set\n",
            "X_157 4\n",
            "Column X_157 does not exist in test set\n",
            "X_158 2\n",
            "Column X_158 does not exist in test set\n",
            "X_159 3\n",
            "Column X_159 does not exist in test set\n",
            "X_160 3\n",
            "Column X_160 does not exist in test set\n",
            "X_161 4\n",
            "Column X_161 does not exist in test set\n",
            "X_162 2\n",
            "Column X_162 does not exist in test set\n",
            "X_163 3\n",
            "Column X_163 does not exist in test set\n",
            "X_164 3\n",
            "Column X_164 does not exist in test set\n",
            "X_165 3\n",
            "Column X_165 does not exist in test set\n",
            "X_166 2\n",
            "Column X_166 does not exist in test set\n",
            "X_167 3\n",
            "Column X_167 does not exist in test set\n",
            "X_168 4\n",
            "Column X_168 does not exist in test set\n",
            "X_169 3\n",
            "Column X_169 does not exist in test set\n",
            "X_170 2\n",
            "Column X_170 does not exist in test set\n",
            "X_171 3\n",
            "Column X_171 does not exist in test set\n",
            "X_172 3\n",
            "Column X_172 does not exist in test set\n",
            "X_173 4\n",
            "Column X_173 does not exist in test set\n",
            "X_174 2\n",
            "Column X_174 does not exist in test set\n",
            "X_175 3\n",
            "Column X_175 does not exist in test set\n",
            "X_176 3\n",
            "Column X_176 does not exist in test set\n",
            "X_177 3\n",
            "Column X_177 does not exist in test set\n",
            "X_178 2\n",
            "Column X_178 does not exist in test set\n",
            "X_179 3\n",
            "Column X_179 does not exist in test set\n",
            "X_180 3\n",
            "Column X_180 does not exist in test set\n",
            "X_181 3\n",
            "Column X_181 does not exist in test set\n",
            "X_182 2\n",
            "Column X_182 does not exist in test set\n",
            "X_183 3\n",
            "Column X_183 does not exist in test set\n",
            "X_184 4\n",
            "Column X_184 does not exist in test set\n",
            "X_185 3\n",
            "Column X_185 does not exist in test set\n",
            "X_186 2\n",
            "Column X_186 does not exist in test set\n",
            "X_187 2\n",
            "Column X_187 does not exist in test set\n",
            "X_188 2\n",
            "Column X_188 does not exist in test set\n",
            "X_189 28\n",
            "Column X_189 does not exist in test set\n",
            "X_190 1\n",
            "X_191 1\n",
            "X_192 1\n",
            "X_193 1\n",
            "X_194 1\n",
            "X_195 1\n",
            "X_196 9\n",
            "Column X_196 does not exist in test set\n",
            "X_197 9\n",
            "Column X_197 does not exist in test set\n",
            "X_198 9\n",
            "Column X_198 does not exist in test set\n",
            "X_199 9\n",
            "Column X_199 does not exist in test set\n",
            "X_200 8\n",
            "Column X_200 does not exist in test set\n",
            "X_201 8\n",
            "Column X_201 does not exist in test set\n",
            "X_202 10\n",
            "Column X_202 does not exist in test set\n",
            "X_203 10\n",
            "Column X_203 does not exist in test set\n",
            "X_204 10\n",
            "Column X_204 does not exist in test set\n",
            "X_205 10\n",
            "Column X_205 does not exist in test set\n",
            "X_206 9\n",
            "Column X_206 does not exist in test set\n",
            "X_207 9\n",
            "Column X_207 does not exist in test set\n",
            "X_208 10\n",
            "Column X_208 does not exist in test set\n",
            "X_209 9\n",
            "Column X_209 does not exist in test set\n",
            "X_210 9\n",
            "Column X_210 does not exist in test set\n",
            "X_211 10\n",
            "Column X_211 does not exist in test set\n",
            "X_212 9\n",
            "Column X_212 does not exist in test set\n",
            "X_213 9\n",
            "Column X_213 does not exist in test set\n",
            "X_214 9\n",
            "Column X_214 does not exist in test set\n",
            "X_215 9\n",
            "Column X_215 does not exist in test set\n",
            "X_216 8\n",
            "Column X_216 does not exist in test set\n",
            "X_217 14\n",
            "Column X_217 does not exist in test set\n",
            "X_218 13\n",
            "Column X_218 does not exist in test set\n",
            "X_219 13\n",
            "Column X_219 does not exist in test set\n",
            "X_220 9\n",
            "Column X_220 does not exist in test set\n",
            "X_221 8\n",
            "Column X_221 does not exist in test set\n",
            "X_222 11\n",
            "Column X_222 does not exist in test set\n",
            "X_223 11\n",
            "Column X_223 does not exist in test set\n",
            "X_224 8\n",
            "Column X_224 does not exist in test set\n",
            "X_225 14\n",
            "Column X_225 does not exist in test set\n",
            "X_226 9\n",
            "Column X_226 does not exist in test set\n",
            "X_227 7\n",
            "Column X_227 does not exist in test set\n",
            "X_228 9\n",
            "Column X_228 does not exist in test set\n",
            "X_229 9\n",
            "Column X_229 does not exist in test set\n",
            "X_230 9\n",
            "Column X_230 does not exist in test set\n",
            "X_231 8\n",
            "Column X_231 does not exist in test set\n",
            "X_232 1\n",
            "X_233 2\n",
            "Column X_233 does not exist in test set\n",
            "X_234 1\n",
            "X_235 1\n",
            "X_236 5\n",
            "Column X_236 does not exist in test set\n",
            "X_237 4\n",
            "Column X_237 does not exist in test set\n",
            "X_238 10\n",
            "Column X_238 does not exist in test set\n",
            "X_239 2\n",
            "Column X_239 does not exist in test set\n",
            "X_240 38\n",
            "Column X_240 does not exist in test set\n",
            "X_241 2\n",
            "Column X_241 does not exist in test set\n",
            "X_242 28\n",
            "Column X_242 does not exist in test set\n",
            "X_243 28\n",
            "Column X_243 does not exist in test set\n",
            "X_244 2\n",
            "Column X_244 does not exist in test set\n",
            "X_245 3\n",
            "Column X_245 does not exist in test set\n",
            "X_246 5\n",
            "Column X_246 does not exist in test set\n",
            "X_247 11\n",
            "Column X_247 does not exist in test set\n",
            "X_248 48\n",
            "Column X_248 does not exist in test set\n",
            "X_249 2\n",
            "Column X_249 does not exist in test set\n",
            "X_250 3\n",
            "Column X_250 does not exist in test set\n",
            "X_251 45\n",
            "Column X_251 does not exist in test set\n",
            "X_252 2\n",
            "Column X_252 does not exist in test set\n",
            "X_253 3\n",
            "Column X_253 does not exist in test set\n",
            "X_254 3\n",
            "Column X_254 does not exist in test set\n",
            "X_255 2\n",
            "Column X_255 does not exist in test set\n",
            "X_256 109\n",
            "Column X_256 does not exist in test set\n",
            "X_257 95\n",
            "Column X_257 does not exist in test set\n",
            "X_258 89\n",
            "Column X_258 does not exist in test set\n",
            "X_259 1\n",
            "X_260 1\n",
            "X_261 18\n",
            "Column X_261 does not exist in test set\n",
            "X_262 16\n",
            "Column X_262 does not exist in test set\n",
            "X_263 20\n",
            "Column X_263 does not exist in test set\n",
            "X_264 2\n",
            "Column X_264 does not exist in test set\n",
            "X_265 50\n",
            "Column X_265 does not exist in test set\n",
            "X_266 48\n",
            "Column X_266 does not exist in test set\n",
            "X_267 50\n",
            "Column X_267 does not exist in test set\n",
            "X_268 3\n",
            "Column X_268 does not exist in test set\n",
            "X_269 3\n",
            "Column X_269 does not exist in test set\n",
            "X_270 3\n",
            "Column X_270 does not exist in test set\n",
            "X_271 3\n",
            "Column X_271 does not exist in test set\n",
            "X_272 3\n",
            "Column X_272 does not exist in test set\n",
            "X_273 3\n",
            "Column X_273 does not exist in test set\n",
            "X_274 2\n",
            "Column X_274 does not exist in test set\n",
            "X_275 3\n",
            "Column X_275 does not exist in test set\n",
            "X_276 3\n",
            "Column X_276 does not exist in test set\n",
            "X_277 3\n",
            "Column X_277 does not exist in test set\n",
            "X_278 2\n",
            "Column X_278 does not exist in test set\n",
            "X_279 3\n",
            "Column X_279 does not exist in test set\n",
            "X_280 3\n",
            "Column X_280 does not exist in test set\n",
            "X_281 3\n",
            "Column X_281 does not exist in test set\n",
            "X_282 2\n",
            "Column X_282 does not exist in test set\n",
            "X_283 9\n",
            "Column X_283 does not exist in test set\n",
            "X_284 3\n",
            "X_285 10\n",
            "Column X_285 does not exist in test set\n",
            "X_286 13\n",
            "Column X_286 does not exist in test set\n",
            "X_287 11\n",
            "Column X_287 does not exist in test set\n",
            "X_288 11\n",
            "Column X_288 does not exist in test set\n",
            "X_289 12\n",
            "Column X_289 does not exist in test set\n",
            "X_290 10\n",
            "Column X_290 does not exist in test set\n",
            "X_291 11\n",
            "Column X_291 does not exist in test set\n",
            "X_292 11\n",
            "Column X_292 does not exist in test set\n",
            "X_293 10\n",
            "Column X_293 does not exist in test set\n",
            "X_294 10\n",
            "Column X_294 does not exist in test set\n",
            "X_295 11\n",
            "Column X_295 does not exist in test set\n",
            "X_296 9\n",
            "Column X_296 does not exist in test set\n",
            "X_297 9\n",
            "Column X_297 does not exist in test set\n",
            "X_298 8\n",
            "Column X_298 does not exist in test set\n",
            "X_299 9\n",
            "Column X_299 does not exist in test set\n",
            "X_300 8\n",
            "Column X_300 does not exist in test set\n",
            "X_301 12\n",
            "Column X_301 does not exist in test set\n",
            "X_302 9\n",
            "Column X_302 does not exist in test set\n",
            "X_303 15\n",
            "Column X_303 does not exist in test set\n",
            "X_304 14\n",
            "Column X_304 does not exist in test set\n",
            "X_305 16\n",
            "Column X_305 does not exist in test set\n",
            "X_306 11\n",
            "Column X_306 does not exist in test set\n",
            "X_307 15\n",
            "Column X_307 does not exist in test set\n",
            "X_308 9\n",
            "Column X_308 does not exist in test set\n",
            "X_309 10\n",
            "Column X_309 does not exist in test set\n",
            "X_310 14\n",
            "Column X_310 does not exist in test set\n",
            "X_311 12\n",
            "Column X_311 does not exist in test set\n",
            "X_312 2\n",
            "Column X_312 does not exist in test set\n",
            "X_313 2\n",
            "Column X_313 does not exist in test set\n",
            "X_314 2\n",
            "Column X_314 does not exist in test set\n",
            "X_315 1\n",
            "X_316 2\n",
            "Column X_316 does not exist in test set\n",
            "X_317 2\n",
            "Column X_317 does not exist in test set\n",
            "X_318 26\n",
            "Column X_318 does not exist in test set\n",
            "X_319 1\n",
            "X_320 2\n",
            "Column X_320 does not exist in test set\n",
            "X_321 2\n",
            "Column X_321 does not exist in test set\n",
            "X_322 1\n",
            "X_323 1\n",
            "X_324 1\n",
            "X_325 2\n",
            "Column X_325 does not exist in test set\n",
            "X_326 2\n",
            "Column X_326 does not exist in test set\n",
            "X_327 1\n",
            "X_328 1\n",
            "X_329 1\n",
            "X_330 1\n",
            "X_331 4\n",
            "Column X_331 does not exist in test set\n",
            "X_332 4\n",
            "Column X_332 does not exist in test set\n",
            "X_333 4\n",
            "Column X_333 does not exist in test set\n",
            "X_334 2\n",
            "Column X_334 does not exist in test set\n",
            "X_335 6\n",
            "Column X_335 does not exist in test set\n",
            "X_336 2\n",
            "Column X_336 does not exist in test set\n",
            "X_337 27\n",
            "Column X_337 does not exist in test set\n",
            "X_338 25\n",
            "Column X_338 does not exist in test set\n",
            "X_339 26\n",
            "Column X_339 does not exist in test set\n",
            "X_340 2\n",
            "Column X_340 does not exist in test set\n",
            "X_341 3\n",
            "Column X_341 does not exist in test set\n",
            "X_342 2\n",
            "Column X_342 does not exist in test set\n",
            "X_343 2\n",
            "Column X_343 does not exist in test set\n",
            "X_344 40\n",
            "Column X_344 does not exist in test set\n",
            "X_345 21\n",
            "Column X_345 does not exist in test set\n",
            "X_346 43\n",
            "Column X_346 does not exist in test set\n",
            "X_347 2\n",
            "Column X_347 does not exist in test set\n",
            "X_348 24\n",
            "Column X_348 does not exist in test set\n",
            "X_349 16\n",
            "Column X_349 does not exist in test set\n",
            "X_350 29\n",
            "Column X_350 does not exist in test set\n",
            "X_351 2\n",
            "Column X_351 does not exist in test set\n",
            "X_352 15\n",
            "Column X_352 does not exist in test set\n",
            "X_353 24\n",
            "Column X_353 does not exist in test set\n",
            "X_354 29\n",
            "Column X_354 does not exist in test set\n",
            "X_355 2\n",
            "Column X_355 does not exist in test set\n",
            "X_356 22\n",
            "Column X_356 does not exist in test set\n",
            "X_357 22\n",
            "Column X_357 does not exist in test set\n",
            "X_358 25\n",
            "Column X_358 does not exist in test set\n",
            "X_359 2\n",
            "Column X_359 does not exist in test set\n",
            "X_360 3\n",
            "Column X_360 does not exist in test set\n",
            "X_361 3\n",
            "Column X_361 does not exist in test set\n",
            "X_362 3\n",
            "Column X_362 does not exist in test set\n",
            "X_363 2\n",
            "Column X_363 does not exist in test set\n",
            "X_364 29\n",
            "Column X_364 does not exist in test set\n",
            "X_365 31\n",
            "Column X_365 does not exist in test set\n",
            "X_366 35\n",
            "Column X_366 does not exist in test set\n",
            "X_367 26\n",
            "Column X_367 does not exist in test set\n",
            "X_368 26\n",
            "Column X_368 does not exist in test set\n",
            "X_369 2\n",
            "Column X_369 does not exist in test set\n",
            "X_370 130\n",
            "Column X_370 does not exist in test set\n",
            "X_371 60\n",
            "Column X_371 does not exist in test set\n",
            "X_372 60\n",
            "Column X_372 does not exist in test set\n",
            "X_373 71\n",
            "Column X_373 does not exist in test set\n",
            "X_374 71\n",
            "Column X_374 does not exist in test set\n",
            "X_375 1\n",
            "X_376 1\n",
            "X_377 1\n",
            "X_378 1\n",
            "X_379 31\n",
            "Column X_379 does not exist in test set\n",
            "X_380 21\n",
            "Column X_380 does not exist in test set\n",
            "X_381 2\n",
            "Column X_381 does not exist in test set\n",
            "X_382 2\n",
            "Column X_382 does not exist in test set\n",
            "X_383 2\n",
            "Column X_383 does not exist in test set\n",
            "X_384 14\n",
            "Column X_384 does not exist in test set\n",
            "X_386 14\n",
            "Column X_386 does not exist in test set\n",
            "X_387 49\n",
            "Column X_387 does not exist in test set\n",
            "X_389 40\n",
            "Column X_389 does not exist in test set\n",
            "X_390 2\n",
            "X_391 2\n",
            "X_392 2\n",
            "X_393 3\n",
            "Column X_393 does not exist in test set\n",
            "X_394 10\n",
            "Column X_394 does not exist in test set\n",
            "X_395 14\n",
            "Column X_395 does not exist in test set\n",
            "X_396 7\n",
            "Column X_396 does not exist in test set\n",
            "X_397 140\n",
            "Column X_397 does not exist in test set\n",
            "X_398 94\n",
            "Column X_398 does not exist in test set\n",
            "X_399 188\n",
            "Column X_399 does not exist in test set\n",
            "X_400 28\n",
            "Column X_400 does not exist in test set\n",
            "X_402 26\n",
            "Column X_402 does not exist in test set\n",
            "X_403 6\n",
            "Column X_403 does not exist in test set\n",
            "X_404 156\n",
            "Column X_404 does not exist in test set\n",
            "X_405 8\n",
            "Column X_405 does not exist in test set\n",
            "X_406 8\n",
            "Column X_406 does not exist in test set\n",
            "X_408 9\n",
            "Column X_408 does not exist in test set\n",
            "X_409 2\n",
            "X_410 2\n",
            "X_411 2\n",
            "X_412 11\n",
            "Column X_412 does not exist in test set\n",
            "X_414 8\n",
            "Column X_414 does not exist in test set\n",
            "X_415 31\n",
            "Column X_415 does not exist in test set\n",
            "X_417 26\n",
            "Column X_417 does not exist in test set\n",
            "X_418 11\n",
            "Column X_418 does not exist in test set\n",
            "X_420 11\n",
            "Column X_420 does not exist in test set\n",
            "X_421 30\n",
            "Column X_421 does not exist in test set\n",
            "X_423 31\n",
            "Column X_423 does not exist in test set\n",
            "X_424 2\n",
            "X_425 2\n",
            "X_426 2\n",
            "X_427 1\n",
            "X_429 2\n",
            "X_430 2\n",
            "X_431 2\n",
            "X_432 2\n",
            "X_433 2\n",
            "X_434 2\n",
            "X_435 51\n",
            "Column X_435 does not exist in test set\n",
            "X_436 47\n",
            "Column X_436 does not exist in test set\n",
            "X_437 108\n",
            "Column X_437 does not exist in test set\n",
            "X_438 112\n",
            "Column X_438 does not exist in test set\n",
            "X_439 47\n",
            "Column X_439 does not exist in test set\n",
            "X_441 58\n",
            "Column X_441 does not exist in test set\n",
            "X_442 32\n",
            "Column X_442 does not exist in test set\n",
            "X_443 10\n",
            "Column X_443 does not exist in test set\n",
            "X_444 4\n",
            "Column X_444 does not exist in test set\n",
            "X_445 28\n",
            "Column X_445 does not exist in test set\n",
            "X_446 28\n",
            "Column X_446 does not exist in test set\n",
            "X_447 17\n",
            "Column X_447 does not exist in test set\n",
            "X_448 33\n",
            "Column X_448 does not exist in test set\n",
            "X_449 6\n",
            "Column X_449 does not exist in test set\n",
            "X_450 3\n",
            "Column X_450 does not exist in test set\n",
            "X_451 10\n",
            "Column X_451 does not exist in test set\n",
            "X_453 179\n",
            "Column X_453 does not exist in test set\n",
            "X_454 92\n",
            "Column X_454 does not exist in test set\n",
            "X_456 138\n",
            "Column X_456 does not exist in test set\n",
            "X_457 126\n",
            "Column X_457 does not exist in test set\n",
            "X_459 156\n",
            "Column X_459 does not exist in test set\n",
            "X_460 97\n",
            "Column X_460 does not exist in test set\n",
            "X_461 166\n",
            "Column X_461 does not exist in test set\n",
            "X_462 28\n",
            "Column X_462 does not exist in test set\n",
            "X_463 10\n",
            "Column X_463 does not exist in test set\n",
            "X_464 5\n",
            "Column X_464 does not exist in test set\n",
            "X_465 26\n",
            "Column X_465 does not exist in test set\n",
            "X_466 1\n",
            "X_467 1\n",
            "X_468 35\n",
            "Column X_468 does not exist in test set\n",
            "X_469 8\n",
            "Column X_469 does not exist in test set\n",
            "X_470 4\n",
            "Column X_470 does not exist in test set\n",
            "X_471 9\n",
            "Column X_471 does not exist in test set\n",
            "X_472 8\n",
            "Column X_472 does not exist in test set\n",
            "X_473 6\n",
            "Column X_473 does not exist in test set\n",
            "X_474 19\n",
            "Column X_474 does not exist in test set\n",
            "X_475 16\n",
            "Column X_475 does not exist in test set\n",
            "X_476 16\n",
            "Column X_476 does not exist in test set\n",
            "X_477 7\n",
            "Column X_477 does not exist in test set\n",
            "X_478 15\n",
            "Column X_478 does not exist in test set\n",
            "X_479 3\n",
            "Column X_479 does not exist in test set\n",
            "X_480 2\n",
            "Column X_480 does not exist in test set\n",
            "X_481 8\n",
            "Column X_481 does not exist in test set\n",
            "X_482 145\n",
            "Column X_482 does not exist in test set\n",
            "X_483 151\n",
            "Column X_483 does not exist in test set\n",
            "X_484 131\n",
            "Column X_484 does not exist in test set\n",
            "X_485 117\n",
            "Column X_485 does not exist in test set\n",
            "X_486 16\n",
            "Column X_486 does not exist in test set\n",
            "X_487 2\n",
            "X_488 8\n",
            "Column X_488 does not exist in test set\n",
            "X_489 68\n",
            "Column X_489 does not exist in test set\n",
            "X_492 94\n",
            "Column X_492 does not exist in test set\n",
            "X_493 91\n",
            "Column X_493 does not exist in test set\n",
            "X_495 60\n",
            "Column X_495 does not exist in test set\n",
            "X_496 33\n",
            "Column X_496 does not exist in test set\n",
            "X_497 166\n",
            "Column X_497 does not exist in test set\n",
            "X_503 2\n",
            "X_504 8\n",
            "Column X_504 does not exist in test set\n",
            "X_505 1\n",
            "X_506 19\n",
            "Column X_506 does not exist in test set\n",
            "X_507 14\n",
            "Column X_507 does not exist in test set\n",
            "X_508 1\n",
            "Column X_508 does not exist in test set\n",
            "X_509 2\n",
            "X_510 13\n",
            "Column X_510 does not exist in test set\n",
            "X_511 3\n",
            "Column X_511 does not exist in test set\n",
            "X_512 2\n",
            "Column X_512 does not exist in test set\n",
            "X_513 11\n",
            "Column X_513 does not exist in test set\n",
            "X_514 138\n",
            "Column X_514 does not exist in test set\n",
            "X_515 180\n",
            "Column X_515 does not exist in test set\n",
            "X_516 141\n",
            "Column X_516 does not exist in test set\n",
            "X_517 130\n",
            "Column X_517 does not exist in test set\n",
            "X_518 17\n",
            "Column X_518 does not exist in test set\n",
            "X_519 2\n",
            "X_522 4\n",
            "Column X_522 does not exist in test set\n",
            "X_523 112\n",
            "Column X_523 does not exist in test set\n",
            "X_524 179\n",
            "Column X_524 does not exist in test set\n",
            "X_525 140\n",
            "Column X_525 does not exist in test set\n",
            "X_526 58\n",
            "Column X_526 does not exist in test set\n",
            "X_527 94\n",
            "Column X_527 does not exist in test set\n",
            "X_528 188\n",
            "Column X_528 does not exist in test set\n",
            "X_529 76\n",
            "X_530 81\n",
            "X_531 86\n",
            "X_532 83\n",
            "X_533 83\n",
            "X_536 30\n",
            "Column X_536 does not exist in test set\n",
            "X_537 12\n",
            "Column X_537 does not exist in test set\n",
            "X_538 5\n",
            "Column X_538 does not exist in test set\n",
            "X_539 16\n",
            "Column X_539 does not exist in test set\n",
            "X_540 28\n",
            "Column X_540 does not exist in test set\n",
            "X_541 17\n",
            "Column X_541 does not exist in test set\n",
            "X_542 4\n",
            "Column X_542 does not exist in test set\n",
            "X_543 11\n",
            "Column X_543 does not exist in test set\n",
            "X_544 34\n",
            "Column X_544 does not exist in test set\n",
            "X_545 9\n",
            "Column X_545 does not exist in test set\n",
            "X_546 13\n",
            "Column X_546 does not exist in test set\n",
            "X_547 6\n",
            "Column X_547 does not exist in test set\n",
            "X_548 8\n",
            "Column X_548 does not exist in test set\n",
            "X_549 16\n",
            "Column X_549 does not exist in test set\n",
            "X_550 3\n",
            "Column X_550 does not exist in test set\n",
            "X_551 2\n",
            "Column X_551 does not exist in test set\n",
            "X_552 9\n",
            "Column X_552 does not exist in test set\n",
            "X_553 117\n",
            "Column X_553 does not exist in test set\n",
            "X_554 183\n",
            "Column X_554 does not exist in test set\n",
            "X_555 154\n",
            "Column X_555 does not exist in test set\n",
            "X_556 132\n",
            "Column X_556 does not exist in test set\n",
            "X_557 15\n",
            "Column X_557 does not exist in test set\n",
            "X_558 1\n",
            "X_559 10\n",
            "Column X_559 does not exist in test set\n",
            "X_560 9\n",
            "Column X_560 does not exist in test set\n",
            "X_561 1\n",
            "X_562 29\n",
            "Column X_562 does not exist in test set\n",
            "X_563 5\n",
            "Column X_563 does not exist in test set\n",
            "X_564 5\n",
            "Column X_564 does not exist in test set\n",
            "X_565 5\n",
            "Column X_565 does not exist in test set\n",
            "X_566 19\n",
            "Column X_566 does not exist in test set\n",
            "X_567 1\n",
            "X_568 11\n",
            "Column X_568 does not exist in test set\n",
            "X_569 5\n",
            "Column X_569 does not exist in test set\n",
            "X_570 4\n",
            "Column X_570 does not exist in test set\n",
            "X_571 11\n",
            "Column X_571 does not exist in test set\n",
            "X_572 22\n",
            "Column X_572 does not exist in test set\n",
            "X_573 12\n",
            "Column X_573 does not exist in test set\n",
            "X_574 20\n",
            "Column X_574 does not exist in test set\n",
            "X_575 2\n",
            "Column X_575 does not exist in test set\n",
            "X_576 5\n",
            "Column X_576 does not exist in test set\n",
            "X_577 11\n",
            "Column X_577 does not exist in test set\n",
            "X_578 11\n",
            "Column X_578 does not exist in test set\n",
            "X_579 3\n",
            "Column X_579 does not exist in test set\n",
            "X_580 5\n",
            "Column X_580 does not exist in test set\n",
            "X_581 18\n",
            "Column X_581 does not exist in test set\n",
            "X_582 15\n",
            "Column X_582 does not exist in test set\n",
            "X_583 1\n",
            "X_584 14\n",
            "Column X_584 does not exist in test set\n",
            "X_585 5\n",
            "X_586 6\n",
            "X_587 13\n",
            "Column X_587 does not exist in test set\n",
            "X_588 8\n",
            "Column X_588 does not exist in test set\n",
            "X_589 19\n",
            "Column X_589 does not exist in test set\n",
            "X_590 16\n",
            "Column X_590 does not exist in test set\n",
            "X_591 5\n",
            "X_592 8\n",
            "Column X_592 does not exist in test set\n",
            "X_593 9\n",
            "Column X_593 does not exist in test set\n",
            "X_594 7\n",
            "Column X_594 does not exist in test set\n",
            "X_595 8\n",
            "Column X_595 does not exist in test set\n",
            "X_596 9\n",
            "Column X_596 does not exist in test set\n",
            "X_597 8\n",
            "Column X_597 does not exist in test set\n",
            "X_598 9\n",
            "Column X_598 does not exist in test set\n",
            "X_599 8\n",
            "Column X_599 does not exist in test set\n",
            "X_600 32\n",
            "X_601 35\n",
            "X_602 28\n",
            "X_603 24\n",
            "X_604 32\n",
            "X_605 10\n",
            "Column X_605 does not exist in test set\n",
            "X_606 11\n",
            "Column X_606 does not exist in test set\n",
            "X_607 5\n",
            "X_608 16\n",
            "Column X_608 does not exist in test set\n",
            "X_609 12\n",
            "Column X_609 does not exist in test set\n",
            "X_610 2\n",
            "Column X_610 does not exist in test set\n",
            "X_611 4\n",
            "X_612 3\n",
            "X_613 12\n",
            "Column X_613 does not exist in test set\n",
            "X_614 20\n",
            "Column X_614 does not exist in test set\n",
            "X_615 10\n",
            "Column X_615 does not exist in test set\n",
            "X_616 23\n",
            "Column X_616 does not exist in test set\n",
            "X_617 1\n",
            "X_618 6\n",
            "Column X_618 does not exist in test set\n",
            "X_619 41\n",
            "Column X_619 does not exist in test set\n",
            "X_620 1\n",
            "X_621 20\n",
            "Column X_621 does not exist in test set\n",
            "X_622 5\n",
            "Column X_622 does not exist in test set\n",
            "X_623 5\n",
            "Column X_623 does not exist in test set\n",
            "X_624 5\n",
            "Column X_624 does not exist in test set\n",
            "X_625 2\n",
            "Column X_625 does not exist in test set\n",
            "X_626 5\n",
            "Column X_626 does not exist in test set\n",
            "X_627 11\n",
            "Column X_627 does not exist in test set\n",
            "X_628 11\n",
            "Column X_628 does not exist in test set\n",
            "X_629 3\n",
            "Column X_629 does not exist in test set\n",
            "X_630 5\n",
            "Column X_630 does not exist in test set\n",
            "X_631 1\n",
            "X_632 18\n",
            "Column X_632 does not exist in test set\n",
            "X_633 1\n",
            "X_634 1\n",
            "X_635 10\n",
            "Column X_635 does not exist in test set\n",
            "X_636 1\n",
            "X_637 11\n",
            "Column X_637 does not exist in test set\n",
            "X_638 1\n",
            "X_639 1\n",
            "X_640 1\n",
            "X_641 1\n",
            "X_642 1\n",
            "X_643 33\n",
            "Column X_643 does not exist in test set\n",
            "X_644 166\n",
            "Column X_644 does not exist in test set\n",
            "X_650 2\n",
            "X_653 91\n",
            "Column X_653 does not exist in test set\n",
            "X_656 91\n",
            "Column X_656 does not exist in test set\n",
            "X_658 25\n",
            "Column X_658 does not exist in test set\n",
            "X_659 122\n",
            "Column X_659 does not exist in test set\n",
            "X_660 22\n",
            "Column X_660 does not exist in test set\n",
            "X_661 124\n",
            "Column X_661 does not exist in test set\n",
            "X_662 86\n",
            "Column X_662 does not exist in test set\n",
            "X_663 66\n",
            "Column X_663 does not exist in test set\n",
            "X_664 11\n",
            "Column X_664 does not exist in test set\n",
            "X_666 3\n",
            "Column X_666 does not exist in test set\n",
            "X_667 76\n",
            "Column X_667 does not exist in test set\n",
            "X_670 9\n",
            "Column X_670 does not exist in test set\n",
            "X_671 13\n",
            "Column X_671 does not exist in test set\n",
            "X_672 1\n",
            "X_673 1\n",
            "X_674 42\n",
            "Column X_674 does not exist in test set\n",
            "X_675 34\n",
            "Column X_675 does not exist in test set\n",
            "X_676 1\n",
            "X_677 9\n",
            "Column X_677 does not exist in test set\n",
            "X_678 5\n",
            "Column X_678 does not exist in test set\n",
            "X_679 9\n",
            "Column X_679 does not exist in test set\n",
            "X_680 6\n",
            "X_681 12\n",
            "X_682 5\n",
            "X_683 3\n",
            "Column X_683 does not exist in test set\n",
            "X_684 6\n",
            "Column X_684 does not exist in test set\n",
            "X_685 2\n",
            "X_686 10\n",
            "Column X_686 does not exist in test set\n",
            "X_687 30\n",
            "Column X_687 does not exist in test set\n",
            "X_688 18\n",
            "Column X_688 does not exist in test set\n",
            "X_689 3\n",
            "Column X_689 does not exist in test set\n",
            "X_690 4\n",
            "Column X_690 does not exist in test set\n",
            "X_691 1\n",
            "X_692 1\n",
            "X_693 2\n",
            "Column X_693 does not exist in test set\n",
            "X_694 2\n",
            "Column X_694 does not exist in test set\n",
            "X_695 1\n",
            "X_699 4\n",
            "Column X_699 does not exist in test set\n",
            "X_705 197\n",
            "Column X_705 does not exist in test set\n",
            "X_706 193\n",
            "Column X_706 does not exist in test set\n",
            "X_708 196\n",
            "Column X_708 does not exist in test set\n",
            "X_715 1\n",
            "X_717 102\n",
            "Column X_717 does not exist in test set\n",
            "X_718 115\n",
            "Column X_718 does not exist in test set\n",
            "X_719 32\n",
            "Column X_719 does not exist in test set\n",
            "X_720 28\n",
            "Column X_720 does not exist in test set\n",
            "X_721 10\n",
            "Column X_721 does not exist in test set\n",
            "X_722 179\n",
            "Column X_722 does not exist in test set\n",
            "X_723 10\n",
            "Column X_723 does not exist in test set\n",
            "X_724 4\n",
            "Column X_724 does not exist in test set\n",
            "X_725 92\n",
            "Column X_725 does not exist in test set\n",
            "X_726 5\n",
            "Column X_726 does not exist in test set\n",
            "X_727 28\n",
            "Column X_727 does not exist in test set\n",
            "X_728 138\n",
            "Column X_728 does not exist in test set\n",
            "X_729 1\n",
            "Column X_729 does not exist in test set\n",
            "X_730 17\n",
            "Column X_730 does not exist in test set\n",
            "X_731 126\n",
            "Column X_731 does not exist in test set\n",
            "X_732 1\n",
            "Column X_732 does not exist in test set\n",
            "X_733 33\n",
            "Column X_733 does not exist in test set\n",
            "X_734 35\n",
            "Column X_734 does not exist in test set\n",
            "X_735 10\n",
            "Column X_735 does not exist in test set\n",
            "X_736 166\n",
            "Column X_736 does not exist in test set\n",
            "X_737 9\n",
            "Column X_737 does not exist in test set\n",
            "X_738 3\n",
            "Column X_738 does not exist in test set\n",
            "X_739 97\n",
            "Column X_739 does not exist in test set\n",
            "X_740 4\n",
            "Column X_740 does not exist in test set\n",
            "X_741 6\n",
            "Column X_741 does not exist in test set\n",
            "X_742 68\n",
            "Column X_742 does not exist in test set\n",
            "X_743 1\n",
            "X_744 16\n",
            "Column X_744 does not exist in test set\n",
            "X_746 14\n",
            "Column X_746 does not exist in test set\n",
            "X_747 16\n",
            "Column X_747 does not exist in test set\n",
            "X_748 94\n",
            "Column X_748 does not exist in test set\n",
            "X_749 1\n",
            "Column X_749 does not exist in test set\n",
            "X_750 3\n",
            "Column X_750 does not exist in test set\n",
            "X_751 60\n",
            "Column X_751 does not exist in test set\n",
            "X_752 3\n",
            "Column X_752 does not exist in test set\n",
            "X_753 10\n",
            "Column X_753 does not exist in test set\n",
            "X_755 2\n",
            "X_756 7\n",
            "Column X_756 does not exist in test set\n",
            "X_758 5\n",
            "Column X_758 does not exist in test set\n",
            "X_759 1\n",
            "X_760 1\n",
            "X_761 1\n",
            "X_762 29\n",
            "Column X_762 does not exist in test set\n",
            "X_763 80\n",
            "Column X_763 does not exist in test set\n",
            "X_764 1\n",
            "X_765 3\n",
            "Column X_765 does not exist in test set\n",
            "X_766 59\n",
            "Column X_766 does not exist in test set\n",
            "X_767 4\n",
            "Column X_767 does not exist in test set\n",
            "X_768 4\n",
            "Column X_768 does not exist in test set\n",
            "X_769 65\n",
            "Column X_769 does not exist in test set\n",
            "X_770 3\n",
            "Column X_770 does not exist in test set\n",
            "X_771 4\n",
            "Column X_771 does not exist in test set\n",
            "X_772 60\n",
            "Column X_772 does not exist in test set\n",
            "X_773 2\n",
            "X_774 41\n",
            "Column X_774 does not exist in test set\n",
            "X_775 41\n",
            "Column X_775 does not exist in test set\n",
            "X_776 1\n",
            "X_777 1\n",
            "X_778 1\n",
            "X_779 4\n",
            "Column X_779 does not exist in test set\n",
            "X_781 3\n",
            "Column X_781 does not exist in test set\n",
            "X_782 4\n",
            "Column X_782 does not exist in test set\n",
            "X_783 189\n",
            "Column X_783 does not exist in test set\n",
            "X_784 3\n",
            "Column X_784 does not exist in test set\n",
            "X_785 7\n",
            "Column X_785 does not exist in test set\n",
            "X_787 7\n",
            "Column X_787 does not exist in test set\n",
            "X_788 17\n",
            "Column X_788 does not exist in test set\n",
            "X_789 198\n",
            "Column X_789 does not exist in test set\n",
            "X_790 16\n",
            "Column X_790 does not exist in test set\n",
            "X_791 10\n",
            "Column X_791 does not exist in test set\n",
            "X_792 122\n",
            "Column X_792 does not exist in test set\n",
            "X_793 9\n",
            "Column X_793 does not exist in test set\n",
            "X_794 15\n",
            "Column X_794 does not exist in test set\n",
            "X_796 13\n",
            "Column X_796 does not exist in test set\n",
            "X_797 11\n",
            "Column X_797 does not exist in test set\n",
            "X_798 8\n",
            "Column X_798 does not exist in test set\n",
            "X_799 12\n",
            "Column X_799 does not exist in test set\n",
            "X_800 10\n",
            "Column X_800 does not exist in test set\n",
            "X_801 12\n",
            "Column X_801 does not exist in test set\n",
            "X_802 8\n",
            "Column X_802 does not exist in test set\n",
            "X_803 12\n",
            "Column X_803 does not exist in test set\n",
            "X_804 9\n",
            "Column X_804 does not exist in test set\n",
            "X_805 11\n",
            "Column X_805 does not exist in test set\n",
            "X_806 9\n",
            "Column X_806 does not exist in test set\n",
            "X_807 10\n",
            "Column X_807 does not exist in test set\n",
            "X_808 8\n",
            "Column X_808 does not exist in test set\n",
            "X_809 12\n",
            "Column X_809 does not exist in test set\n",
            "X_810 10\n",
            "Column X_810 does not exist in test set\n",
            "X_811 10\n",
            "Column X_811 does not exist in test set\n",
            "X_812 8\n",
            "Column X_812 does not exist in test set\n",
            "X_813 22\n",
            "Column X_813 does not exist in test set\n",
            "X_814 16\n",
            "Column X_814 does not exist in test set\n",
            "X_815 16\n",
            "Column X_815 does not exist in test set\n",
            "X_816 14\n",
            "Column X_816 does not exist in test set\n",
            "X_817 25\n",
            "Column X_817 does not exist in test set\n",
            "X_818 24\n",
            "Column X_818 does not exist in test set\n",
            "X_819 23\n",
            "Column X_819 does not exist in test set\n",
            "X_820 23\n",
            "Column X_820 does not exist in test set\n",
            "X_821 16\n",
            "Column X_821 does not exist in test set\n",
            "X_822 14\n",
            "Column X_822 does not exist in test set\n",
            "X_823 4\n",
            "Column X_823 does not exist in test set\n",
            "X_824 46\n",
            "Column X_824 does not exist in test set\n",
            "X_825 3\n",
            "X_826 5\n",
            "Column X_826 does not exist in test set\n",
            "X_827 51\n",
            "Column X_827 does not exist in test set\n",
            "X_828 4\n",
            "Column X_828 does not exist in test set\n",
            "X_829 7\n",
            "Column X_829 does not exist in test set\n",
            "X_830 6\n",
            "Column X_830 does not exist in test set\n",
            "X_831 29\n",
            "Column X_831 does not exist in test set\n",
            "X_833 24\n",
            "Column X_833 does not exist in test set\n",
            "X_834 16\n",
            "Column X_834 does not exist in test set\n",
            "X_835 77\n",
            "Column X_835 does not exist in test set\n",
            "X_836 1\n",
            "X_837 4\n",
            "Column X_837 does not exist in test set\n",
            "X_838 77\n",
            "Column X_838 does not exist in test set\n",
            "X_839 3\n",
            "Column X_839 does not exist in test set\n",
            "X_840 6\n",
            "Column X_840 does not exist in test set\n",
            "X_841 155\n",
            "Column X_841 does not exist in test set\n",
            "X_842 6\n",
            "Column X_842 does not exist in test set\n",
            "X_843 1\n",
            "X_844 1\n",
            "X_845 16\n",
            "Column X_845 does not exist in test set\n",
            "X_846 13\n",
            "Column X_846 does not exist in test set\n",
            "X_847 2\n",
            "Column X_847 does not exist in test set\n",
            "X_848 26\n",
            "Column X_848 does not exist in test set\n",
            "X_849 1\n",
            "X_850 5\n",
            "Column X_850 does not exist in test set\n",
            "X_851 6\n",
            "Column X_851 does not exist in test set\n",
            "X_852 42\n",
            "Column X_852 does not exist in test set\n",
            "X_853 39\n",
            "Column X_853 does not exist in test set\n",
            "X_854 29\n",
            "Column X_854 does not exist in test set\n",
            "X_855 185\n",
            "Column X_855 does not exist in test set\n",
            "X_856 34\n",
            "Column X_856 does not exist in test set\n",
            "X_857 2\n",
            "Column X_857 does not exist in test set\n",
            "X_858 4\n",
            "Column X_858 does not exist in test set\n",
            "X_859 1\n",
            "X_860 6\n",
            "Column X_860 does not exist in test set\n",
            "X_861 112\n",
            "Column X_861 does not exist in test set\n",
            "X_862 4\n",
            "Column X_862 does not exist in test set\n",
            "X_863 10\n",
            "Column X_863 does not exist in test set\n",
            "X_864 179\n",
            "Column X_864 does not exist in test set\n",
            "X_865 11\n",
            "Column X_865 does not exist in test set\n",
            "X_866 8\n",
            "Column X_866 does not exist in test set\n",
            "X_867 9\n",
            "Column X_867 does not exist in test set\n",
            "X_868 5\n",
            "Column X_868 does not exist in test set\n",
            "X_869 5\n",
            "Column X_869 does not exist in test set\n",
            "X_870 6\n",
            "Column X_870 does not exist in test set\n",
            "X_871 6\n",
            "Column X_871 does not exist in test set\n",
            "X_872 2\n",
            "Column X_872 does not exist in test set\n",
            "X_873 2\n",
            "Column X_873 does not exist in test set\n",
            "X_874 2\n",
            "Column X_874 does not exist in test set\n",
            "X_875 14\n",
            "Column X_875 does not exist in test set\n",
            "X_876 14\n",
            "Column X_876 does not exist in test set\n",
            "X_877 13\n",
            "Column X_877 does not exist in test set\n",
            "X_878 12\n",
            "Column X_878 does not exist in test set\n",
            "X_879 2\n",
            "X_880 2\n",
            "X_881 2\n",
            "X_882 10\n",
            "Column X_882 does not exist in test set\n",
            "X_884 8\n",
            "Column X_884 does not exist in test set\n",
            "X_885 8\n",
            "Column X_885 does not exist in test set\n",
            "X_886 1\n",
            "X_887 1\n",
            "X_888 1\n",
            "X_889 1\n",
            "X_890 183\n",
            "Column X_890 does not exist in test set\n",
            "X_891 185\n",
            "Column X_891 does not exist in test set\n",
            "X_892 186\n",
            "Column X_892 does not exist in test set\n",
            "X_893 181\n",
            "Column X_893 does not exist in test set\n",
            "X_894 135\n",
            "Column X_894 does not exist in test set\n",
            "X_895 142\n",
            "Column X_895 does not exist in test set\n",
            "X_896 136\n",
            "Column X_896 does not exist in test set\n",
            "X_897 143\n",
            "Column X_897 does not exist in test set\n",
            "X_898 7\n",
            "Column X_898 does not exist in test set\n",
            "X_899 121\n",
            "Column X_899 does not exist in test set\n",
            "X_900 5\n",
            "X_901 8\n",
            "Column X_901 does not exist in test set\n",
            "X_902 3\n",
            "Column X_902 does not exist in test set\n",
            "X_903 13\n",
            "Column X_903 does not exist in test set\n",
            "X_904 3\n",
            "Column X_904 does not exist in test set\n",
            "X_905 2\n",
            "X_906 5\n",
            "Column X_906 does not exist in test set\n",
            "X_907 97\n",
            "Column X_907 does not exist in test set\n",
            "X_908 4\n",
            "Column X_908 does not exist in test set\n",
            "X_909 5\n",
            "Column X_909 does not exist in test set\n",
            "X_910 2\n",
            "Column X_910 does not exist in test set\n",
            "X_911 33\n",
            "Column X_911 does not exist in test set\n",
            "X_912 2\n",
            "Column X_912 does not exist in test set\n",
            "X_913 8\n",
            "Column X_913 does not exist in test set\n",
            "X_914 166\n",
            "Column X_914 does not exist in test set\n",
            "X_915 11\n",
            "Column X_915 does not exist in test set\n",
            "X_916 145\n",
            "Column X_916 does not exist in test set\n",
            "X_918 138\n",
            "Column X_918 does not exist in test set\n",
            "X_919 151\n",
            "Column X_919 does not exist in test set\n",
            "X_921 180\n",
            "Column X_921 does not exist in test set\n",
            "X_922 131\n",
            "Column X_922 does not exist in test set\n",
            "X_924 141\n",
            "Column X_924 does not exist in test set\n",
            "X_925 117\n",
            "Column X_925 does not exist in test set\n",
            "X_927 130\n",
            "Column X_927 does not exist in test set\n",
            "X_928 16\n",
            "Column X_928 does not exist in test set\n",
            "X_930 17\n",
            "Column X_930 does not exist in test set\n",
            "X_931 5\n",
            "Column X_931 does not exist in test set\n",
            "X_932 114\n",
            "Column X_932 does not exist in test set\n",
            "X_933 4\n",
            "Column X_933 does not exist in test set\n",
            "X_934 1\n",
            "Column X_934 does not exist in test set\n",
            "X_935 1\n",
            "Column X_935 does not exist in test set\n",
            "X_936 1\n",
            "Column X_936 does not exist in test set\n",
            "X_937 1\n",
            "Column X_937 does not exist in test set\n",
            "X_938 87\n",
            "Column X_938 does not exist in test set\n",
            "X_939 179\n",
            "Column X_939 does not exist in test set\n",
            "X_940 86\n",
            "Column X_940 does not exist in test set\n",
            "X_941 32\n",
            "Column X_941 does not exist in test set\n",
            "X_942 177\n",
            "Column X_942 does not exist in test set\n",
            "X_943 32\n",
            "Column X_943 does not exist in test set\n",
            "X_944 29\n",
            "Column X_944 does not exist in test set\n",
            "X_945 198\n",
            "Column X_945 does not exist in test set\n",
            "X_946 31\n",
            "Column X_946 does not exist in test set\n",
            "X_947 98\n",
            "Column X_947 does not exist in test set\n",
            "X_949 43\n",
            "Column X_949 does not exist in test set\n",
            "X_950 2\n",
            "X_951 2\n",
            "X_952 2\n",
            "X_953 69\n",
            "Column X_953 does not exist in test set\n",
            "X_954 61\n",
            "Column X_954 does not exist in test set\n",
            "X_955 71\n",
            "Column X_955 does not exist in test set\n",
            "X_956 61\n",
            "Column X_956 does not exist in test set\n",
            "X_957 136\n",
            "Column X_957 does not exist in test set\n",
            "X_958 98\n",
            "Column X_958 does not exist in test set\n",
            "X_960 43\n",
            "Column X_960 does not exist in test set\n",
            "X_962 37\n",
            "Column X_962 does not exist in test set\n",
            "X_963 39\n",
            "Column X_963 does not exist in test set\n",
            "X_965 41\n",
            "Column X_965 does not exist in test set\n",
            "X_966 23\n",
            "Column X_966 does not exist in test set\n",
            "X_968 20\n",
            "Column X_968 does not exist in test set\n",
            "X_969 2\n",
            "X_970 2\n",
            "X_971 2\n",
            "X_972 26\n",
            "Column X_972 does not exist in test set\n",
            "X_974 20\n",
            "Column X_974 does not exist in test set\n",
            "X_975 97\n",
            "Column X_975 does not exist in test set\n",
            "X_977 78\n",
            "Column X_977 does not exist in test set\n",
            "X_978 22\n",
            "Column X_978 does not exist in test set\n",
            "X_979 144\n",
            "Column X_979 does not exist in test set\n",
            "X_980 17\n",
            "Column X_980 does not exist in test set\n",
            "X_981 17\n",
            "Column X_981 does not exist in test set\n",
            "X_982 143\n",
            "Column X_982 does not exist in test set\n",
            "X_983 16\n",
            "Column X_983 does not exist in test set\n",
            "X_984 38\n",
            "Column X_984 does not exist in test set\n",
            "X_986 36\n",
            "Column X_986 does not exist in test set\n",
            "X_987 2\n",
            "X_988 2\n",
            "X_989 2\n",
            "X_990 3\n",
            "Column X_990 does not exist in test set\n",
            "X_991 15\n",
            "Column X_991 does not exist in test set\n",
            "X_992 1\n",
            "X_1003 2\n",
            "X_1004 2\n",
            "X_1005 2\n",
            "X_1006 2\n",
            "X_1007 2\n",
            "X_1008 2\n",
            "X_1009 3\n",
            "Column X_1009 does not exist in test set\n",
            "X_1010 29\n",
            "Column X_1010 does not exist in test set\n",
            "X_1011 3\n",
            "Column X_1011 does not exist in test set\n",
            "X_1012 29\n",
            "Column X_1012 does not exist in test set\n",
            "X_1013 40\n",
            "Column X_1013 does not exist in test set\n",
            "X_1014 59\n",
            "Column X_1014 does not exist in test set\n",
            "X_1015 43\n",
            "Column X_1015 does not exist in test set\n",
            "X_1016 49\n",
            "Column X_1016 does not exist in test set\n",
            "X_1017 35\n",
            "Column X_1017 does not exist in test set\n",
            "X_1018 131\n",
            "Column X_1018 does not exist in test set\n",
            "X_1019 109\n",
            "Column X_1019 does not exist in test set\n",
            "X_1020 1\n",
            "X_1021 1\n",
            "X_1022 1\n",
            "X_1023 1\n",
            "X_1024 1\n",
            "X_1025 1\n",
            "X_1026 76\n",
            "Column X_1026 does not exist in test set\n",
            "X_1027 11\n",
            "Column X_1027 does not exist in test set\n",
            "X_1028 17\n",
            "Column X_1028 does not exist in test set\n",
            "X_1029 78\n",
            "Column X_1029 does not exist in test set\n",
            "X_1030 20\n",
            "Column X_1030 does not exist in test set\n",
            "X_1031 15\n",
            "Column X_1031 does not exist in test set\n",
            "X_1032 43\n",
            "Column X_1032 does not exist in test set\n",
            "X_1034 13\n",
            "Column X_1034 does not exist in test set\n",
            "X_1035 14\n",
            "Column X_1035 does not exist in test set\n",
            "X_1036 45\n",
            "Column X_1036 does not exist in test set\n",
            "X_1037 57\n",
            "Column X_1037 does not exist in test set\n",
            "X_1038 61\n",
            "Column X_1038 does not exist in test set\n",
            "X_1039 39\n",
            "Column X_1039 does not exist in test set\n",
            "X_1041 8\n",
            "Column X_1041 does not exist in test set\n",
            "X_1042 12\n",
            "Column X_1042 does not exist in test set\n",
            "X_1044 157\n",
            "Column X_1044 does not exist in test set\n",
            "X_1045 184\n",
            "Column X_1045 does not exist in test set\n",
            "X_1047 171\n",
            "Column X_1047 does not exist in test set\n",
            "X_1048 183\n",
            "Column X_1048 does not exist in test set\n",
            "X_1051 139\n",
            "Column X_1051 does not exist in test set\n",
            "X_1052 148\n",
            "Column X_1052 does not exist in test set\n",
            "X_1058 156\n",
            "Column X_1058 does not exist in test set\n",
            "X_1059 157\n",
            "Column X_1059 does not exist in test set\n",
            "X_1060 30\n",
            "Column X_1060 does not exist in test set\n",
            "X_1061 33\n",
            "Column X_1061 does not exist in test set\n",
            "X_1062 18\n",
            "Column X_1062 does not exist in test set\n",
            "X_1063 84\n",
            "Column X_1063 does not exist in test set\n",
            "X_1064 14\n",
            "Column X_1064 does not exist in test set\n",
            "X_1065 15\n",
            "Column X_1065 does not exist in test set\n",
            "X_1066 37\n",
            "Column X_1066 does not exist in test set\n",
            "X_1068 14\n",
            "Column X_1068 does not exist in test set\n",
            "X_1069 14\n",
            "Column X_1069 does not exist in test set\n",
            "X_1070 1\n",
            "X_1071 23\n",
            "Column X_1071 does not exist in test set\n",
            "X_1072 66\n",
            "Column X_1072 does not exist in test set\n",
            "X_1073 41\n",
            "Column X_1073 does not exist in test set\n",
            "X_1075 7\n",
            "Column X_1075 does not exist in test set\n",
            "X_1076 8\n",
            "Column X_1076 does not exist in test set\n",
            "X_1078 60\n",
            "Column X_1078 does not exist in test set\n",
            "X_1079 15\n",
            "Column X_1079 does not exist in test set\n",
            "X_1080 23\n",
            "Column X_1080 does not exist in test set\n",
            "X_1081 38\n",
            "Column X_1081 does not exist in test set\n",
            "X_1082 28\n",
            "Column X_1082 does not exist in test set\n",
            "X_1083 81\n",
            "Column X_1083 does not exist in test set\n",
            "X_1084 15\n",
            "Column X_1084 does not exist in test set\n",
            "X_1085 15\n",
            "Column X_1085 does not exist in test set\n",
            "X_1086 23\n",
            "Column X_1086 does not exist in test set\n",
            "X_1087 4\n",
            "Column X_1087 does not exist in test set\n",
            "X_1090 20\n",
            "Column X_1090 does not exist in test set\n",
            "X_1091 59\n",
            "Column X_1091 does not exist in test set\n",
            "X_1092 1\n",
            "X_1093 8\n",
            "Column X_1093 does not exist in test set\n",
            "X_1094 43\n",
            "Column X_1094 does not exist in test set\n",
            "X_1095 45\n",
            "Column X_1095 does not exist in test set\n",
            "X_1096 14\n",
            "Column X_1096 does not exist in test set\n",
            "X_1097 13\n",
            "Column X_1097 does not exist in test set\n",
            "X_1098 199\n",
            "Column X_1098 does not exist in test set\n",
            "X_1099 196\n",
            "Column X_1099 does not exist in test set\n",
            "X_1101 175\n",
            "Column X_1101 does not exist in test set\n",
            "X_1102 26\n",
            "Column X_1102 does not exist in test set\n",
            "X_1103 2\n",
            "X_1105 60\n",
            "Column X_1105 does not exist in test set\n",
            "X_1106 194\n",
            "Column X_1106 does not exist in test set\n",
            "X_1107 198\n",
            "Column X_1107 does not exist in test set\n",
            "X_1112 164\n",
            "Column X_1112 does not exist in test set\n",
            "X_1114 77\n",
            "Column X_1114 does not exist in test set\n",
            "X_1117 133\n",
            "Column X_1117 does not exist in test set\n",
            "X_1119 1\n",
            "X_1120 140\n",
            "Column X_1120 does not exist in test set\n",
            "X_1123 154\n",
            "Column X_1123 does not exist in test set\n",
            "X_1130 2\n",
            "X_1132 60\n",
            "Column X_1132 does not exist in test set\n",
            "X_1133 15\n",
            "Column X_1133 does not exist in test set\n",
            "X_1134 28\n",
            "Column X_1134 does not exist in test set\n",
            "X_1135 38\n",
            "Column X_1135 does not exist in test set\n",
            "X_1136 34\n",
            "Column X_1136 does not exist in test set\n",
            "X_1137 1\n",
            "X_1138 16\n",
            "Column X_1138 does not exist in test set\n",
            "X_1139 16\n",
            "Column X_1139 does not exist in test set\n",
            "X_1140 25\n",
            "Column X_1140 does not exist in test set\n",
            "X_1141 5\n",
            "Column X_1141 does not exist in test set\n",
            "X_1144 17\n",
            "Column X_1144 does not exist in test set\n",
            "X_1145 84\n",
            "Column X_1145 does not exist in test set\n",
            "X_1146 1\n",
            "X_1147 9\n",
            "Column X_1147 does not exist in test set\n",
            "X_1148 44\n",
            "Column X_1148 does not exist in test set\n",
            "X_1149 40\n",
            "Column X_1149 does not exist in test set\n",
            "X_1150 13\n",
            "Column X_1150 does not exist in test set\n",
            "X_1151 16\n",
            "Column X_1151 does not exist in test set\n",
            "X_1152 194\n",
            "Column X_1152 does not exist in test set\n",
            "X_1153 198\n",
            "Column X_1153 does not exist in test set\n",
            "X_1155 184\n",
            "Column X_1155 does not exist in test set\n",
            "X_1156 34\n",
            "Column X_1156 does not exist in test set\n",
            "X_1157 2\n",
            "X_1159 131\n",
            "Column X_1159 does not exist in test set\n",
            "X_1160 11\n",
            "Column X_1160 does not exist in test set\n",
            "X_1161 163\n",
            "Column X_1161 does not exist in test set\n",
            "X_1162 160\n",
            "Column X_1162 does not exist in test set\n",
            "X_1163 136\n",
            "Column X_1163 does not exist in test set\n",
            "X_1164 109\n",
            "Column X_1164 does not exist in test set\n",
            "X_1165 98\n",
            "Column X_1165 does not exist in test set\n",
            "X_1167 89\n",
            "X_1168 89\n",
            "X_1169 90\n",
            "X_1170 90\n",
            "X_1171 88\n",
            "X_1174 11\n",
            "Column X_1174 does not exist in test set\n",
            "X_1175 6\n",
            "Column X_1175 does not exist in test set\n",
            "X_1176 72\n",
            "Column X_1176 does not exist in test set\n",
            "X_1177 25\n",
            "Column X_1177 does not exist in test set\n",
            "X_1178 7\n",
            "Column X_1178 does not exist in test set\n",
            "X_1179 34\n",
            "Column X_1179 does not exist in test set\n",
            "X_1180 14\n",
            "Column X_1180 does not exist in test set\n",
            "X_1181 11\n",
            "Column X_1181 does not exist in test set\n",
            "X_1182 31\n",
            "Column X_1182 does not exist in test set\n",
            "X_1183 10\n",
            "Column X_1183 does not exist in test set\n",
            "X_1184 13\n",
            "Column X_1184 does not exist in test set\n",
            "X_1185 18\n",
            "Column X_1185 does not exist in test set\n",
            "X_1186 45\n",
            "Column X_1186 does not exist in test set\n",
            "X_1187 58\n",
            "Column X_1187 does not exist in test set\n",
            "X_1188 11\n",
            "Column X_1188 does not exist in test set\n",
            "X_1189 12\n",
            "Column X_1189 does not exist in test set\n",
            "X_1190 39\n",
            "Column X_1190 does not exist in test set\n",
            "X_1191 24\n",
            "Column X_1191 does not exist in test set\n",
            "X_1192 9\n",
            "Column X_1192 does not exist in test set\n",
            "X_1193 6\n",
            "Column X_1193 does not exist in test set\n",
            "X_1194 69\n",
            "Column X_1194 does not exist in test set\n",
            "X_1195 8\n",
            "Column X_1195 does not exist in test set\n",
            "X_1196 20\n",
            "Column X_1196 does not exist in test set\n",
            "X_1197 17\n",
            "Column X_1197 does not exist in test set\n",
            "X_1198 81\n",
            "Column X_1198 does not exist in test set\n",
            "X_1199 8\n",
            "Column X_1199 does not exist in test set\n",
            "X_1200 3\n",
            "Column X_1200 does not exist in test set\n",
            "X_1201 4\n",
            "X_1202 32\n",
            "Column X_1202 does not exist in test set\n",
            "X_1203 17\n",
            "Column X_1203 does not exist in test set\n",
            "X_1204 7\n",
            "Column X_1204 does not exist in test set\n",
            "X_1205 44\n",
            "Column X_1205 does not exist in test set\n",
            "X_1206 1\n",
            "X_1207 22\n",
            "Column X_1207 does not exist in test set\n",
            "X_1208 44\n",
            "Column X_1208 does not exist in test set\n",
            "X_1209 10\n",
            "Column X_1209 does not exist in test set\n",
            "X_1210 12\n",
            "Column X_1210 does not exist in test set\n",
            "X_1211 164\n",
            "Column X_1211 does not exist in test set\n",
            "X_1212 154\n",
            "Column X_1212 does not exist in test set\n",
            "X_1213 185\n",
            "Column X_1213 does not exist in test set\n",
            "X_1214 162\n",
            "Column X_1214 does not exist in test set\n",
            "X_1215 18\n",
            "Column X_1215 does not exist in test set\n",
            "X_1216 1\n",
            "X_1217 37\n",
            "Column X_1217 does not exist in test set\n",
            "X_1218 18\n",
            "Column X_1218 does not exist in test set\n",
            "X_1219 1\n",
            "X_1220 35\n",
            "Column X_1220 does not exist in test set\n",
            "X_1221 82\n",
            "Column X_1221 does not exist in test set\n",
            "X_1222 33\n",
            "Column X_1222 does not exist in test set\n",
            "X_1223 53\n",
            "Column X_1223 does not exist in test set\n",
            "X_1224 38\n",
            "Column X_1224 does not exist in test set\n",
            "X_1225 16\n",
            "X_1226 11\n",
            "X_1227 31\n",
            "Column X_1227 does not exist in test set\n",
            "X_1228 73\n",
            "Column X_1228 does not exist in test set\n",
            "X_1229 22\n",
            "Column X_1229 does not exist in test set\n",
            "X_1230 18\n",
            "X_1231 23\n",
            "Column X_1231 does not exist in test set\n",
            "X_1232 21\n",
            "Column X_1232 does not exist in test set\n",
            "X_1233 8\n",
            "X_1234 5\n",
            "Column X_1234 does not exist in test set\n",
            "X_1235 2\n",
            "X_1236 10\n",
            "Column X_1236 does not exist in test set\n",
            "X_1237 3\n",
            "Column X_1237 does not exist in test set\n",
            "X_1238 24\n",
            "Column X_1238 does not exist in test set\n",
            "X_1239 12\n",
            "Column X_1239 does not exist in test set\n",
            "X_1240 10\n",
            "Column X_1240 does not exist in test set\n",
            "X_1241 2\n",
            "Column X_1241 does not exist in test set\n",
            "X_1242 5\n",
            "Column X_1242 does not exist in test set\n",
            "X_1243 36\n",
            "Column X_1243 does not exist in test set\n",
            "X_1244 14\n",
            "Column X_1244 does not exist in test set\n",
            "X_1245 22\n",
            "Column X_1245 does not exist in test set\n",
            "X_1246 3\n",
            "X_1247 25\n",
            "X_1248 1\n",
            "X_1249 1\n",
            "X_1250 1\n",
            "X_1251 1\n",
            "X_1252 1\n",
            "X_1253 1\n",
            "X_1254 7\n",
            "Column X_1254 does not exist in test set\n",
            "X_1255 1\n",
            "X_1256 15\n",
            "Column X_1256 does not exist in test set\n",
            "X_1257 14\n",
            "X_1258 29\n",
            "Column X_1258 does not exist in test set\n",
            "X_1259 6\n",
            "X_1260 31\n",
            "Column X_1260 does not exist in test set\n",
            "X_1261 6\n",
            "Column X_1261 does not exist in test set\n",
            "X_1262 52\n",
            "Column X_1262 does not exist in test set\n",
            "X_1263 10\n",
            "Column X_1263 does not exist in test set\n",
            "X_1264 22\n",
            "Column X_1264 does not exist in test set\n",
            "X_1265 48\n",
            "Column X_1265 does not exist in test set\n",
            "X_1266 6\n",
            "Column X_1266 does not exist in test set\n",
            "X_1267 5\n",
            "Column X_1267 does not exist in test set\n",
            "X_1268 5\n",
            "Column X_1268 does not exist in test set\n",
            "X_1269 5\n",
            "Column X_1269 does not exist in test set\n",
            "X_1270 6\n",
            "Column X_1270 does not exist in test set\n",
            "X_1271 5\n",
            "Column X_1271 does not exist in test set\n",
            "X_1272 5\n",
            "Column X_1272 does not exist in test set\n",
            "X_1273 5\n",
            "Column X_1273 does not exist in test set\n",
            "X_1274 25\n",
            "X_1275 32\n",
            "X_1276 28\n",
            "X_1277 28\n",
            "X_1278 24\n",
            "X_1279 21\n",
            "Column X_1279 does not exist in test set\n",
            "X_1280 20\n",
            "Column X_1280 does not exist in test set\n",
            "X_1281 8\n",
            "X_1282 25\n",
            "Column X_1282 does not exist in test set\n",
            "X_1283 23\n",
            "Column X_1283 does not exist in test set\n",
            "X_1284 5\n",
            "Column X_1284 does not exist in test set\n",
            "X_1285 23\n",
            "Column X_1285 does not exist in test set\n",
            "X_1286 48\n",
            "Column X_1286 does not exist in test set\n",
            "X_1287 11\n",
            "Column X_1287 does not exist in test set\n",
            "X_1288 94\n",
            "Column X_1288 does not exist in test set\n",
            "X_1289 140\n",
            "Column X_1289 does not exist in test set\n",
            "X_1290 8\n",
            "Column X_1290 does not exist in test set\n",
            "X_1291 22\n",
            "Column X_1291 does not exist in test set\n",
            "X_1292 34\n",
            "Column X_1292 does not exist in test set\n",
            "X_1293 1\n",
            "X_1294 37\n",
            "Column X_1294 does not exist in test set\n",
            "X_1295 11\n",
            "Column X_1295 does not exist in test set\n",
            "X_1296 10\n",
            "Column X_1296 does not exist in test set\n",
            "X_1297 110\n",
            "Column X_1297 does not exist in test set\n",
            "X_1298 1\n",
            "X_1299 5\n",
            "Column X_1299 does not exist in test set\n",
            "X_1300 6\n",
            "Column X_1300 does not exist in test set\n",
            "X_1301 5\n",
            "Column X_1301 does not exist in test set\n",
            "X_1302 4\n",
            "Column X_1302 does not exist in test set\n",
            "X_1303 3\n",
            "Column X_1303 does not exist in test set\n",
            "X_1304 24\n",
            "Column X_1304 does not exist in test set\n",
            "X_1305 12\n",
            "Column X_1305 does not exist in test set\n",
            "X_1306 10\n",
            "Column X_1306 does not exist in test set\n",
            "X_1307 2\n",
            "Column X_1307 does not exist in test set\n",
            "X_1308 5\n",
            "Column X_1308 does not exist in test set\n",
            "X_1309 1\n",
            "X_1310 36\n",
            "Column X_1310 does not exist in test set\n",
            "X_1311 1\n",
            "X_1312 1\n",
            "X_1313 21\n",
            "Column X_1313 does not exist in test set\n",
            "X_1314 1\n",
            "X_1315 20\n",
            "Column X_1315 does not exist in test set\n",
            "X_1316 1\n",
            "X_1317 1\n",
            "X_1318 1\n",
            "X_1319 1\n",
            "X_1320 1\n",
            "X_1321 154\n",
            "Column X_1321 does not exist in test set\n",
            "X_1328 2\n",
            "X_1329 165\n",
            "Column X_1329 does not exist in test set\n",
            "X_1336 180\n",
            "Column X_1336 does not exist in test set\n",
            "X_1337 168\n",
            "Column X_1337 does not exist in test set\n",
            "X_1338 192\n",
            "Column X_1338 does not exist in test set\n",
            "X_1339 139\n",
            "Column X_1339 does not exist in test set\n",
            "X_1340 65\n",
            "Column X_1340 does not exist in test set\n",
            "X_1341 99\n",
            "Column X_1341 does not exist in test set\n",
            "X_1342 136\n",
            "Column X_1342 does not exist in test set\n",
            "X_1343 188\n",
            "Column X_1343 does not exist in test set\n",
            "X_1344 148\n",
            "Column X_1344 does not exist in test set\n",
            "X_1345 75\n",
            "Column X_1345 does not exist in test set\n",
            "X_1346 86\n",
            "Column X_1346 does not exist in test set\n",
            "X_1347 149\n",
            "Column X_1347 does not exist in test set\n",
            "X_1348 193\n",
            "Column X_1348 does not exist in test set\n",
            "X_1349 139\n",
            "Column X_1349 does not exist in test set\n",
            "X_1350 70\n",
            "Column X_1350 does not exist in test set\n",
            "X_1351 108\n",
            "Column X_1351 does not exist in test set\n",
            "X_1353 51\n",
            "Column X_1353 does not exist in test set\n",
            "X_1355 191\n",
            "Column X_1355 does not exist in test set\n",
            "X_1356 156\n",
            "Column X_1356 does not exist in test set\n",
            "X_1357 164\n",
            "Column X_1357 does not exist in test set\n",
            "X_1360 2\n",
            "Column X_1360 does not exist in test set\n",
            "X_1361 1\n",
            "X_1362 1\n",
            "X_1363 1\n",
            "X_1364 1\n",
            "X_1365 111\n",
            "Column X_1365 does not exist in test set\n",
            "X_1366 89\n",
            "Column X_1366 does not exist in test set\n",
            "X_1367 1\n",
            "X_1368 5\n",
            "Column X_1368 does not exist in test set\n",
            "X_1369 51\n",
            "Column X_1369 does not exist in test set\n",
            "X_1370 22\n",
            "Column X_1370 does not exist in test set\n",
            "X_1371 21\n",
            "Column X_1371 does not exist in test set\n",
            "X_1372 2\n",
            "Column X_1372 does not exist in test set\n",
            "X_1373 2\n",
            "Column X_1373 does not exist in test set\n",
            "X_1374 46\n",
            "Column X_1374 does not exist in test set\n",
            "X_1375 23\n",
            "Column X_1375 does not exist in test set\n",
            "X_1376 20\n",
            "Column X_1376 does not exist in test set\n",
            "X_1377 2\n",
            "Column X_1377 does not exist in test set\n",
            "X_1378 2\n",
            "Column X_1378 does not exist in test set\n",
            "X_1379 44\n",
            "Column X_1379 does not exist in test set\n",
            "X_1380 22\n",
            "Column X_1380 does not exist in test set\n",
            "X_1381 21\n",
            "Column X_1381 does not exist in test set\n",
            "X_1382 2\n",
            "Column X_1382 does not exist in test set\n",
            "X_1383 2\n",
            "Column X_1383 does not exist in test set\n",
            "X_1384 22\n",
            "Column X_1384 does not exist in test set\n",
            "X_1385 4\n",
            "Column X_1385 does not exist in test set\n",
            "X_1386 24\n",
            "Column X_1386 does not exist in test set\n",
            "X_1387 20\n",
            "Column X_1387 does not exist in test set\n",
            "X_1388 46\n",
            "Column X_1388 does not exist in test set\n",
            "X_1389 51\n",
            "Column X_1389 does not exist in test set\n",
            "X_1390 45\n",
            "Column X_1390 does not exist in test set\n",
            "X_1391 35\n",
            "Column X_1391 does not exist in test set\n",
            "X_1392 1\n",
            "X_1393 1\n",
            "X_1394 1\n",
            "X_1395 1\n",
            "X_1396 1\n",
            "X_1397 5\n",
            "Column X_1397 does not exist in test set\n",
            "X_1398 5\n",
            "Column X_1398 does not exist in test set\n",
            "X_1399 1\n",
            "X_1402 184\n",
            "Column X_1402 does not exist in test set\n",
            "X_1407 6\n",
            "Column X_1407 does not exist in test set\n",
            "X_1409 121\n",
            "Column X_1409 does not exist in test set\n",
            "X_1410 198\n",
            "Column X_1410 does not exist in test set\n",
            "X_1411 190\n",
            "Column X_1411 does not exist in test set\n",
            "X_1412 184\n",
            "Column X_1412 does not exist in test set\n",
            "X_1413 199\n",
            "Column X_1413 does not exist in test set\n",
            "X_1414 192\n",
            "Column X_1414 does not exist in test set\n",
            "X_1415 185\n",
            "Column X_1415 does not exist in test set\n",
            "X_1416 182\n",
            "Column X_1416 does not exist in test set\n",
            "X_1417 187\n",
            "Column X_1417 does not exist in test set\n",
            "X_1425 152\n",
            "Column X_1425 does not exist in test set\n",
            "X_1426 1\n",
            "X_1431 13\n",
            "Column X_1431 does not exist in test set\n",
            "X_1432 13\n",
            "Column X_1432 does not exist in test set\n",
            "X_1433 76\n",
            "Column X_1433 does not exist in test set\n",
            "X_1435 30\n",
            "Column X_1435 does not exist in test set\n",
            "X_1436 11\n",
            "Column X_1436 does not exist in test set\n",
            "X_1437 157\n",
            "Column X_1437 does not exist in test set\n",
            "X_1438 33\n",
            "Column X_1438 does not exist in test set\n",
            "X_1439 78\n",
            "Column X_1439 does not exist in test set\n",
            "X_1440 84\n",
            "Column X_1440 does not exist in test set\n",
            "X_1441 20\n",
            "Column X_1441 does not exist in test set\n",
            "X_1442 171\n",
            "Column X_1442 does not exist in test set\n",
            "X_1443 14\n",
            "Column X_1443 does not exist in test set\n",
            "X_1444 15\n",
            "Column X_1444 does not exist in test set\n",
            "X_1445 183\n",
            "Column X_1445 does not exist in test set\n",
            "X_1446 15\n",
            "Column X_1446 does not exist in test set\n",
            "X_1449 13\n",
            "Column X_1449 does not exist in test set\n",
            "X_1450 139\n",
            "Column X_1450 does not exist in test set\n",
            "X_1451 14\n",
            "Column X_1451 does not exist in test set\n",
            "X_1452 14\n",
            "Column X_1452 does not exist in test set\n",
            "X_1453 148\n",
            "Column X_1453 does not exist in test set\n",
            "X_1454 14\n",
            "Column X_1454 does not exist in test set\n",
            "X_1455 45\n",
            "Column X_1455 does not exist in test set\n",
            "X_1457 1\n",
            "X_1458 57\n",
            "Column X_1458 does not exist in test set\n",
            "X_1460 23\n",
            "Column X_1460 does not exist in test set\n",
            "X_1461 17\n",
            "Column X_1461 does not exist in test set\n",
            "X_1462 18\n",
            "Column X_1462 does not exist in test set\n",
            "X_1463 61\n",
            "Column X_1463 does not exist in test set\n",
            "X_1464 66\n",
            "Column X_1464 does not exist in test set\n",
            "X_1465 12\n",
            "Column X_1465 does not exist in test set\n",
            "X_1466 157\n",
            "Column X_1466 does not exist in test set\n",
            "X_1467 8\n",
            "Column X_1467 does not exist in test set\n",
            "X_1468 8\n",
            "Column X_1468 does not exist in test set\n",
            "X_1469 156\n",
            "Column X_1469 does not exist in test set\n",
            "X_1470 7\n",
            "Column X_1470 does not exist in test set\n",
            "X_1476 15\n",
            "Column X_1476 does not exist in test set\n",
            "X_1477 194\n",
            "Column X_1477 does not exist in test set\n",
            "X_1478 15\n",
            "Column X_1478 does not exist in test set\n",
            "X_1479 23\n",
            "Column X_1479 does not exist in test set\n",
            "X_1480 198\n",
            "Column X_1480 does not exist in test set\n",
            "X_1481 28\n",
            "Column X_1481 does not exist in test set\n",
            "X_1482 28\n",
            "Column X_1482 does not exist in test set\n",
            "X_1484 34\n",
            "Column X_1484 does not exist in test set\n",
            "X_1485 81\n",
            "Column X_1485 does not exist in test set\n",
            "X_1487 1\n",
            "X_1488 15\n",
            "Column X_1488 does not exist in test set\n",
            "X_1489 164\n",
            "Column X_1489 does not exist in test set\n",
            "X_1490 16\n",
            "Column X_1490 does not exist in test set\n",
            "X_1491 4\n",
            "Column X_1491 does not exist in test set\n",
            "X_1492 77\n",
            "Column X_1492 does not exist in test set\n",
            "X_1493 5\n",
            "Column X_1493 does not exist in test set\n",
            "X_1500 59\n",
            "Column X_1500 does not exist in test set\n",
            "X_1501 84\n",
            "Column X_1501 does not exist in test set\n",
            "X_1502 1\n",
            "X_1503 1\n",
            "X_1504 1\n",
            "X_1505 43\n",
            "Column X_1505 does not exist in test set\n",
            "X_1507 44\n",
            "Column X_1507 does not exist in test set\n",
            "X_1508 45\n",
            "Column X_1508 does not exist in test set\n",
            "X_1510 40\n",
            "Column X_1510 does not exist in test set\n",
            "X_1511 20\n",
            "Column X_1511 does not exist in test set\n",
            "X_1512 133\n",
            "Column X_1512 does not exist in test set\n",
            "X_1513 17\n",
            "Column X_1513 does not exist in test set\n",
            "X_1514 8\n",
            "Column X_1514 does not exist in test set\n",
            "X_1515 140\n",
            "Column X_1515 does not exist in test set\n",
            "X_1516 9\n",
            "Column X_1516 does not exist in test set\n",
            "X_1517 36\n",
            "Column X_1517 does not exist in test set\n",
            "X_1518 120\n",
            "Column X_1518 does not exist in test set\n",
            "X_1519 3\n",
            "Column X_1519 does not exist in test set\n",
            "X_1520 18\n",
            "Column X_1520 does not exist in test set\n",
            "X_1521 178\n",
            "Column X_1521 does not exist in test set\n",
            "X_1522 1\n",
            "X_1523 2\n",
            "Column X_1523 does not exist in test set\n",
            "X_1524 2\n",
            "Column X_1524 does not exist in test set\n",
            "X_1525 2\n",
            "Column X_1525 does not exist in test set\n",
            "X_1529 82\n",
            "Column X_1529 does not exist in test set\n",
            "X_1531 1\n",
            "X_1532 133\n",
            "Column X_1532 does not exist in test set\n",
            "X_1533 5\n",
            "Column X_1533 does not exist in test set\n",
            "X_1534 152\n",
            "Column X_1534 does not exist in test set\n",
            "X_1535 17\n",
            "X_1536 75\n",
            "Column X_1536 does not exist in test set\n",
            "X_1537 1\n",
            "X_1540 51\n",
            "Column X_1540 does not exist in test set\n",
            "X_1542 68\n",
            "Column X_1542 does not exist in test set\n",
            "X_1543 78\n",
            "Column X_1543 does not exist in test set\n",
            "X_1547 16\n",
            "Column X_1547 does not exist in test set\n",
            "X_1548 85\n",
            "Column X_1548 does not exist in test set\n",
            "X_1549 18\n",
            "Column X_1549 does not exist in test set\n",
            "X_1550 18\n",
            "Column X_1550 does not exist in test set\n",
            "X_1551 92\n",
            "Column X_1551 does not exist in test set\n",
            "X_1552 19\n",
            "Column X_1552 does not exist in test set\n",
            "X_1553 31\n",
            "Column X_1553 does not exist in test set\n",
            "X_1554 63\n",
            "Column X_1554 does not exist in test set\n",
            "X_1555 5\n",
            "X_1556 18\n",
            "Column X_1556 does not exist in test set\n",
            "X_1558 12\n",
            "Column X_1558 does not exist in test set\n",
            "X_1559 15\n",
            "Column X_1559 does not exist in test set\n",
            "X_1561 12\n",
            "X_1562 19\n",
            "Column X_1562 does not exist in test set\n",
            "X_1564 9\n",
            "Column X_1564 does not exist in test set\n",
            "X_1565 30\n",
            "Column X_1565 does not exist in test set\n",
            "X_1566 95\n",
            "Column X_1566 does not exist in test set\n",
            "X_1567 28\n",
            "Column X_1567 does not exist in test set\n",
            "X_1568 20\n",
            "Column X_1568 does not exist in test set\n",
            "X_1569 166\n",
            "Column X_1569 does not exist in test set\n",
            "X_1570 21\n",
            "Column X_1570 does not exist in test set\n",
            "X_1571 1\n",
            "X_1572 1\n",
            "X_1573 1\n",
            "X_1574 1\n",
            "X_1575 1\n",
            "X_1576 1\n",
            "X_1577 1\n",
            "X_1578 1\n",
            "X_1579 1\n",
            "X_1580 1\n",
            "X_1581 1\n",
            "X_1582 1\n",
            "X_1583 15\n",
            "Column X_1583 does not exist in test set\n",
            "X_1584 168\n",
            "Column X_1584 does not exist in test set\n",
            "X_1585 12\n",
            "Column X_1585 does not exist in test set\n",
            "X_1586 9\n",
            "Column X_1586 does not exist in test set\n",
            "X_1587 129\n",
            "Column X_1587 does not exist in test set\n",
            "X_1588 20\n",
            "Column X_1588 does not exist in test set\n",
            "X_1589 121\n",
            "Column X_1589 does not exist in test set\n",
            "X_1590 121\n",
            "Column X_1590 does not exist in test set\n",
            "X_1591 23\n",
            "Column X_1591 does not exist in test set\n",
            "X_1592 22\n",
            "Column X_1592 does not exist in test set\n",
            "X_1593 23\n",
            "Column X_1593 does not exist in test set\n",
            "X_1594 22\n",
            "Column X_1594 does not exist in test set\n",
            "X_1595 22\n",
            "Column X_1595 does not exist in test set\n",
            "X_1596 20\n",
            "Column X_1596 does not exist in test set\n",
            "X_1597 24\n",
            "Column X_1597 does not exist in test set\n",
            "X_1598 24\n",
            "Column X_1598 does not exist in test set\n",
            "X_1599 23\n",
            "Column X_1599 does not exist in test set\n",
            "X_1600 23\n",
            "Column X_1600 does not exist in test set\n",
            "X_1601 26\n",
            "Column X_1601 does not exist in test set\n",
            "X_1602 24\n",
            "Column X_1602 does not exist in test set\n",
            "X_1603 19\n",
            "Column X_1603 does not exist in test set\n",
            "X_1604 19\n",
            "Column X_1604 does not exist in test set\n",
            "X_1605 23\n",
            "Column X_1605 does not exist in test set\n",
            "X_1606 22\n",
            "Column X_1606 does not exist in test set\n",
            "X_1609 27\n",
            "Column X_1609 does not exist in test set\n",
            "X_1610 32\n",
            "Column X_1610 does not exist in test set\n",
            "X_1611 50\n",
            "Column X_1611 does not exist in test set\n",
            "X_1612 55\n",
            "Column X_1612 does not exist in test set\n",
            "X_1613 44\n",
            "Column X_1613 does not exist in test set\n",
            "X_1614 50\n",
            "Column X_1614 does not exist in test set\n",
            "X_1615 53\n",
            "Column X_1615 does not exist in test set\n",
            "X_1616 54\n",
            "Column X_1616 does not exist in test set\n",
            "X_1617 98\n",
            "Column X_1617 does not exist in test set\n",
            "X_1618 37\n",
            "Column X_1618 does not exist in test set\n",
            "X_1619 24\n",
            "Column X_1619 does not exist in test set\n",
            "X_1620 27\n",
            "Column X_1620 does not exist in test set\n",
            "X_1621 29\n",
            "Column X_1621 does not exist in test set\n",
            "X_1622 170\n",
            "Column X_1622 does not exist in test set\n",
            "X_1623 26\n",
            "Column X_1623 does not exist in test set\n",
            "X_1624 8\n",
            "Column X_1624 does not exist in test set\n",
            "X_1625 88\n",
            "Column X_1625 does not exist in test set\n",
            "X_1626 7\n",
            "Column X_1626 does not exist in test set\n",
            "X_1627 12\n",
            "Column X_1627 does not exist in test set\n",
            "X_1628 11\n",
            "Column X_1628 does not exist in test set\n",
            "X_1629 36\n",
            "Column X_1629 does not exist in test set\n",
            "X_1631 28\n",
            "Column X_1631 does not exist in test set\n",
            "X_1632 29\n",
            "Column X_1632 does not exist in test set\n",
            "X_1633 194\n",
            "Column X_1633 does not exist in test set\n",
            "X_1634 33\n",
            "Column X_1634 does not exist in test set\n",
            "X_1635 47\n",
            "Column X_1635 does not exist in test set\n",
            "X_1636 197\n",
            "Column X_1636 does not exist in test set\n",
            "X_1637 42\n",
            "Column X_1637 does not exist in test set\n",
            "X_1638 96\n",
            "Column X_1638 does not exist in test set\n",
            "X_1640 95\n",
            "Column X_1640 does not exist in test set\n",
            "X_1641 12\n",
            "Column X_1641 does not exist in test set\n",
            "X_1642 110\n",
            "Column X_1642 does not exist in test set\n",
            "X_1643 13\n",
            "Column X_1643 does not exist in test set\n",
            "X_1644 1\n",
            "X_1645 1\n",
            "X_1646 31\n",
            "Column X_1646 does not exist in test set\n",
            "X_1647 32\n",
            "Column X_1647 does not exist in test set\n",
            "X_1648 6\n",
            "Column X_1648 does not exist in test set\n",
            "X_1649 93\n",
            "Column X_1649 does not exist in test set\n",
            "X_1650 7\n",
            "Column X_1650 does not exist in test set\n",
            "X_1651 57\n",
            "Column X_1651 does not exist in test set\n",
            "X_1652 81\n",
            "Column X_1652 does not exist in test set\n",
            "X_1655 10\n",
            "Column X_1655 does not exist in test set\n",
            "X_1656 10\n",
            "Column X_1656 does not exist in test set\n",
            "X_1657 15\n",
            "Column X_1657 does not exist in test set\n",
            "X_1658 98\n",
            "Column X_1658 does not exist in test set\n",
            "X_1659 17\n",
            "Column X_1659 does not exist in test set\n",
            "X_1660 5\n",
            "Column X_1660 does not exist in test set\n",
            "X_1661 11\n",
            "Column X_1661 does not exist in test set\n",
            "X_1662 5\n",
            "Column X_1662 does not exist in test set\n",
            "X_1663 34\n",
            "Column X_1663 does not exist in test set\n",
            "X_1664 163\n",
            "Column X_1664 does not exist in test set\n",
            "X_1665 33\n",
            "Column X_1665 does not exist in test set\n",
            "X_1666 10\n",
            "Column X_1666 does not exist in test set\n",
            "X_1667 160\n",
            "Column X_1667 does not exist in test set\n",
            "X_1668 11\n",
            "Column X_1668 does not exist in test set\n",
            "X_1669 8\n",
            "Column X_1669 does not exist in test set\n",
            "X_1670 8\n",
            "Column X_1670 does not exist in test set\n",
            "X_1671 15\n",
            "Column X_1671 does not exist in test set\n",
            "X_1672 13\n",
            "Column X_1672 does not exist in test set\n",
            "X_1673 7\n",
            "Column X_1673 does not exist in test set\n",
            "X_1674 6\n",
            "Column X_1674 does not exist in test set\n",
            "X_1675 2\n",
            "Column X_1675 does not exist in test set\n",
            "X_1676 2\n",
            "Column X_1676 does not exist in test set\n",
            "X_1677 2\n",
            "Column X_1677 does not exist in test set\n",
            "X_1678 34\n",
            "Column X_1678 does not exist in test set\n",
            "X_1679 30\n",
            "Column X_1679 does not exist in test set\n",
            "X_1680 2\n",
            "Column X_1680 does not exist in test set\n",
            "X_1681 2\n",
            "Column X_1681 does not exist in test set\n",
            "X_1682 16\n",
            "Column X_1682 does not exist in test set\n",
            "X_1683 2\n",
            "Column X_1683 does not exist in test set\n",
            "X_1684 2\n",
            "Column X_1684 does not exist in test set\n",
            "X_1685 2\n",
            "Column X_1685 does not exist in test set\n",
            "X_1686 24\n",
            "Column X_1686 does not exist in test set\n",
            "X_1687 2\n",
            "Column X_1687 does not exist in test set\n",
            "X_1688 1\n",
            "X_1689 1\n",
            "X_1690 18\n",
            "Column X_1690 does not exist in test set\n",
            "X_1691 2\n",
            "Column X_1691 does not exist in test set\n",
            "X_1692 1\n",
            "X_1693 1\n",
            "X_1694 108\n",
            "Column X_1694 does not exist in test set\n",
            "X_1695 2\n",
            "Column X_1695 does not exist in test set\n",
            "X_1696 28\n",
            "Column X_1696 does not exist in test set\n",
            "X_1697 38\n",
            "Column X_1697 does not exist in test set\n",
            "X_1698 2\n",
            "X_1699 2\n",
            "X_1700 2\n",
            "X_1701 2\n",
            "Column X_1701 does not exist in test set\n",
            "X_1702 1\n",
            "X_1703 30\n",
            "Column X_1703 does not exist in test set\n",
            "X_1704 121\n",
            "Column X_1704 does not exist in test set\n",
            "X_1705 11\n",
            "Column X_1705 does not exist in test set\n",
            "X_1706 121\n",
            "Column X_1706 does not exist in test set\n",
            "X_1707 2\n",
            "Column X_1707 does not exist in test set\n",
            "X_1708 40\n",
            "Column X_1708 does not exist in test set\n",
            "X_1709 2\n",
            "Column X_1709 does not exist in test set\n",
            "X_1710 43\n",
            "Column X_1710 does not exist in test set\n",
            "X_1711 2\n",
            "Column X_1711 does not exist in test set\n",
            "X_1712 110\n",
            "Column X_1712 does not exist in test set\n",
            "X_1713 58\n",
            "Column X_1713 does not exist in test set\n",
            "X_1714 121\n",
            "Column X_1714 does not exist in test set\n",
            "X_1715 4\n",
            "Column X_1715 does not exist in test set\n",
            "X_1716 37\n",
            "Column X_1716 does not exist in test set\n",
            "X_1717 3\n",
            "Column X_1717 does not exist in test set\n",
            "X_1718 70\n",
            "Column X_1718 does not exist in test set\n",
            "X_1719 2\n",
            "Column X_1719 does not exist in test set\n",
            "X_1720 118\n",
            "Column X_1720 does not exist in test set\n",
            "X_1721 2\n",
            "Column X_1721 does not exist in test set\n",
            "X_1722 1\n",
            "X_1723 1\n",
            "X_1724 1\n",
            "X_1725 98\n",
            "Column X_1725 does not exist in test set\n",
            "X_1726 121\n",
            "Column X_1726 does not exist in test set\n",
            "X_1727 2\n",
            "X_1728 2\n",
            "X_1729 2\n",
            "X_1730 75\n",
            "Column X_1730 does not exist in test set\n",
            "X_1731 2\n",
            "Column X_1731 does not exist in test set\n",
            "X_1732 121\n",
            "Column X_1732 does not exist in test set\n",
            "X_1733 2\n",
            "Column X_1733 does not exist in test set\n",
            "X_1734 76\n",
            "Column X_1734 does not exist in test set\n",
            "X_1735 120\n",
            "Column X_1735 does not exist in test set\n",
            "X_1736 98\n",
            "Column X_1736 does not exist in test set\n",
            "X_1737 120\n",
            "Column X_1737 does not exist in test set\n",
            "X_1738 98\n",
            "Column X_1738 does not exist in test set\n",
            "X_1739 38\n",
            "Column X_1739 does not exist in test set\n",
            "X_1740 121\n",
            "Column X_1740 does not exist in test set\n",
            "X_1741 3\n",
            "Column X_1741 does not exist in test set\n",
            "X_1742 19\n",
            "X_1743 103\n",
            "Column X_1743 does not exist in test set\n",
            "X_1744 121\n",
            "Column X_1744 does not exist in test set\n",
            "X_1745 3\n",
            "Column X_1745 does not exist in test set\n",
            "X_1746 96\n",
            "Column X_1746 does not exist in test set\n",
            "X_1747 66\n",
            "Column X_1747 does not exist in test set\n",
            "X_1748 121\n",
            "Column X_1748 does not exist in test set\n",
            "X_1749 2\n",
            "Column X_1749 does not exist in test set\n",
            "X_1750 121\n",
            "Column X_1750 does not exist in test set\n",
            "X_1751 44\n",
            "Column X_1751 does not exist in test set\n",
            "X_1752 121\n",
            "Column X_1752 does not exist in test set\n",
            "X_1753 2\n",
            "Column X_1753 does not exist in test set\n",
            "X_1754 1\n",
            "X_1755 3\n",
            "Column X_1755 does not exist in test set\n",
            "X_1756 121\n",
            "Column X_1756 does not exist in test set\n",
            "X_1757 32\n",
            "Column X_1757 does not exist in test set\n",
            "X_1758 121\n",
            "Column X_1758 does not exist in test set\n",
            "X_1759 2\n",
            "Column X_1759 does not exist in test set\n",
            "X_1760 1\n",
            "X_1761 4\n",
            "Column X_1761 does not exist in test set\n",
            "X_1762 121\n",
            "Column X_1762 does not exist in test set\n",
            "X_1763 35\n",
            "Column X_1763 does not exist in test set\n",
            "X_1764 121\n",
            "Column X_1764 does not exist in test set\n",
            "X_1765 2\n",
            "Column X_1765 does not exist in test set\n",
            "X_1766 1\n",
            "X_1767 3\n",
            "Column X_1767 does not exist in test set\n",
            "X_1768 104\n",
            "Column X_1768 does not exist in test set\n",
            "X_1769 28\n",
            "Column X_1769 does not exist in test set\n",
            "X_1770 121\n",
            "Column X_1770 does not exist in test set\n",
            "X_1771 2\n",
            "Column X_1771 does not exist in test set\n",
            "X_1772 1\n",
            "X_1773 3\n",
            "Column X_1773 does not exist in test set\n",
            "X_1774 120\n",
            "Column X_1774 does not exist in test set\n",
            "X_1775 36\n",
            "Column X_1775 does not exist in test set\n",
            "X_1776 121\n",
            "Column X_1776 does not exist in test set\n",
            "X_1777 2\n",
            "Column X_1777 does not exist in test set\n",
            "X_1778 1\n",
            "X_1779 3\n",
            "Column X_1779 does not exist in test set\n",
            "X_1780 119\n",
            "Column X_1780 does not exist in test set\n",
            "X_1781 34\n",
            "Column X_1781 does not exist in test set\n",
            "X_1782 121\n",
            "Column X_1782 does not exist in test set\n",
            "X_1783 2\n",
            "Column X_1783 does not exist in test set\n",
            "X_1784 1\n",
            "X_1785 3\n",
            "Column X_1785 does not exist in test set\n",
            "X_1786 121\n",
            "Column X_1786 does not exist in test set\n",
            "X_1787 23\n",
            "Column X_1787 does not exist in test set\n",
            "X_1788 121\n",
            "Column X_1788 does not exist in test set\n",
            "X_1789 2\n",
            "Column X_1789 does not exist in test set\n",
            "X_1790 1\n",
            "X_1791 3\n",
            "Column X_1791 does not exist in test set\n",
            "X_1792 116\n",
            "Column X_1792 does not exist in test set\n",
            "X_1793 27\n",
            "Column X_1793 does not exist in test set\n",
            "X_1794 121\n",
            "Column X_1794 does not exist in test set\n",
            "X_1795 2\n",
            "Column X_1795 does not exist in test set\n",
            "X_1796 1\n",
            "X_1797 3\n",
            "Column X_1797 does not exist in test set\n",
            "X_1798 121\n",
            "Column X_1798 does not exist in test set\n",
            "X_1799 34\n",
            "Column X_1799 does not exist in test set\n",
            "X_1800 121\n",
            "Column X_1800 does not exist in test set\n",
            "X_1801 2\n",
            "Column X_1801 does not exist in test set\n",
            "X_1802 1\n",
            "X_1803 2\n",
            "Column X_1803 does not exist in test set\n",
            "X_1804 68\n",
            "Column X_1804 does not exist in test set\n",
            "X_1805 11\n",
            "Column X_1805 does not exist in test set\n",
            "X_1806 121\n",
            "Column X_1806 does not exist in test set\n",
            "X_1807 2\n",
            "Column X_1807 does not exist in test set\n",
            "X_1808 1\n",
            "X_1809 20\n",
            "Column X_1809 does not exist in test set\n",
            "X_1810 121\n",
            "Column X_1810 does not exist in test set\n",
            "X_1811 2\n",
            "Column X_1811 does not exist in test set\n",
            "X_1812 120\n",
            "Column X_1812 does not exist in test set\n",
            "X_1813 5\n",
            "Column X_1813 does not exist in test set\n",
            "X_1814 121\n",
            "Column X_1814 does not exist in test set\n",
            "X_1815 4\n",
            "Column X_1815 does not exist in test set\n",
            "X_1816 115\n",
            "Column X_1816 does not exist in test set\n",
            "X_1817 65\n",
            "Column X_1817 does not exist in test set\n",
            "X_1818 121\n",
            "Column X_1818 does not exist in test set\n",
            "X_1819 3\n",
            "Column X_1819 does not exist in test set\n",
            "X_1820 56\n",
            "Column X_1820 does not exist in test set\n",
            "X_1821 87\n",
            "Column X_1821 does not exist in test set\n",
            "X_1822 121\n",
            "Column X_1822 does not exist in test set\n",
            "X_1823 2\n",
            "Column X_1823 does not exist in test set\n",
            "X_1824 68\n",
            "Column X_1824 does not exist in test set\n",
            "X_1825 11\n",
            "Column X_1825 does not exist in test set\n",
            "X_1826 121\n",
            "Column X_1826 does not exist in test set\n",
            "X_1827 2\n",
            "Column X_1827 does not exist in test set\n",
            "X_1828 1\n",
            "X_1829 20\n",
            "Column X_1829 does not exist in test set\n",
            "X_1830 121\n",
            "Column X_1830 does not exist in test set\n",
            "X_1831 3\n",
            "Column X_1831 does not exist in test set\n",
            "X_1832 102\n",
            "Column X_1832 does not exist in test set\n",
            "X_1833 41\n",
            "Column X_1833 does not exist in test set\n",
            "X_1834 121\n",
            "Column X_1834 does not exist in test set\n",
            "X_1835 1\n",
            "X_1836 1\n",
            "X_1837 1\n",
            "X_1838 1\n",
            "X_1839 1\n",
            "X_1840 1\n",
            "X_1841 1\n",
            "X_1842 1\n",
            "X_1843 31\n",
            "X_1844 1\n",
            "X_1845 1\n",
            "X_1846 1\n",
            "X_1847 1\n",
            "X_1848 1\n",
            "X_1849 34\n",
            "Column X_1849 does not exist in test set\n",
            "X_1850 121\n",
            "Column X_1850 does not exist in test set\n",
            "X_1851 1\n",
            "X_1852 1\n",
            "X_1853 9\n",
            "Column X_1853 does not exist in test set\n",
            "X_1854 121\n",
            "Column X_1854 does not exist in test set\n",
            "X_1855 5\n",
            "Column X_1855 does not exist in test set\n",
            "X_1856 121\n",
            "Column X_1856 does not exist in test set\n",
            "X_1857 4\n",
            "Column X_1857 does not exist in test set\n",
            "X_1858 120\n",
            "Column X_1858 does not exist in test set\n",
            "X_1859 4\n",
            "Column X_1859 does not exist in test set\n",
            "X_1860 121\n",
            "Column X_1860 does not exist in test set\n",
            "X_1861 5\n",
            "Column X_1861 does not exist in test set\n",
            "X_1862 121\n",
            "Column X_1862 does not exist in test set\n",
            "X_1863 5\n",
            "Column X_1863 does not exist in test set\n",
            "X_1864 121\n",
            "Column X_1864 does not exist in test set\n",
            "X_1865 5\n",
            "Column X_1865 does not exist in test set\n",
            "X_1866 121\n",
            "Column X_1866 does not exist in test set\n",
            "X_1867 3\n",
            "Column X_1867 does not exist in test set\n",
            "X_1868 9\n",
            "Column X_1868 does not exist in test set\n",
            "X_1869 1\n",
            "X_1870 1\n",
            "X_1871 1\n",
            "X_1872 1\n",
            "X_1873 12\n",
            "Column X_1873 does not exist in test set\n",
            "X_1874 5\n",
            "X_1875 15\n",
            "Column X_1875 does not exist in test set\n",
            "X_1876 5\n",
            "X_1877 15\n",
            "Column X_1877 does not exist in test set\n",
            "X_1878 5\n",
            "X_1879 12\n",
            "Column X_1879 does not exist in test set\n",
            "X_1880 5\n",
            "X_1881 9\n",
            "Column X_1881 does not exist in test set\n",
            "X_1882 5\n",
            "X_1883 11\n",
            "Column X_1883 does not exist in test set\n",
            "X_1884 5\n",
            "X_1885 10\n",
            "Column X_1885 does not exist in test set\n",
            "X_1886 5\n",
            "X_1887 11\n",
            "Column X_1887 does not exist in test set\n",
            "X_1888 5\n",
            "X_1889 9\n",
            "Column X_1889 does not exist in test set\n",
            "X_1890 5\n",
            "X_1891 9\n",
            "Column X_1891 does not exist in test set\n",
            "X_1892 5\n",
            "X_1893 9\n",
            "Column X_1893 does not exist in test set\n",
            "X_1894 5\n",
            "X_1895 9\n",
            "Column X_1895 does not exist in test set\n",
            "X_1896 5\n",
            "X_1897 10\n",
            "Column X_1897 does not exist in test set\n",
            "X_1898 5\n",
            "X_1899 8\n",
            "Column X_1899 does not exist in test set\n",
            "X_1900 5\n",
            "X_1901 8\n",
            "Column X_1901 does not exist in test set\n",
            "X_1902 5\n",
            "X_1903 11\n",
            "Column X_1903 does not exist in test set\n",
            "X_1904 5\n",
            "X_1905 10\n",
            "Column X_1905 does not exist in test set\n",
            "X_1906 5\n",
            "X_1907 11\n",
            "Column X_1907 does not exist in test set\n",
            "X_1908 5\n",
            "X_1909 11\n",
            "Column X_1909 does not exist in test set\n",
            "X_1910 5\n",
            "X_1911 9\n",
            "Column X_1911 does not exist in test set\n",
            "X_1912 5\n",
            "X_1913 12\n",
            "Column X_1913 does not exist in test set\n",
            "X_1914 5\n",
            "X_1915 9\n",
            "Column X_1915 does not exist in test set\n",
            "X_1916 5\n",
            "X_1917 9\n",
            "Column X_1917 does not exist in test set\n",
            "X_1918 5\n",
            "X_1919 11\n",
            "Column X_1919 does not exist in test set\n",
            "X_1920 5\n",
            "X_1921 10\n",
            "Column X_1921 does not exist in test set\n",
            "X_1922 5\n",
            "X_1923 10\n",
            "Column X_1923 does not exist in test set\n",
            "X_1924 4\n",
            "X_1925 9\n",
            "Column X_1925 does not exist in test set\n",
            "X_1926 4\n",
            "X_1927 9\n",
            "Column X_1927 does not exist in test set\n",
            "X_1928 5\n",
            "X_1929 10\n",
            "Column X_1929 does not exist in test set\n",
            "X_1930 4\n",
            "X_1931 15\n",
            "Column X_1931 does not exist in test set\n",
            "X_1932 5\n",
            "X_1933 15\n",
            "Column X_1933 does not exist in test set\n",
            "X_1934 5\n",
            "X_1935 9\n",
            "Column X_1935 does not exist in test set\n",
            "X_1936 5\n",
            "X_1937 10\n",
            "Column X_1937 does not exist in test set\n",
            "X_1938 5\n",
            "X_1939 11\n",
            "Column X_1939 does not exist in test set\n",
            "X_1940 5\n",
            "X_1941 11\n",
            "Column X_1941 does not exist in test set\n",
            "X_1942 5\n",
            "X_1943 9\n",
            "Column X_1943 does not exist in test set\n",
            "X_1944 5\n",
            "X_1945 41\n",
            "Column X_1945 does not exist in test set\n",
            "X_1946 78\n",
            "Column X_1946 does not exist in test set\n",
            "X_1947 71\n",
            "Column X_1947 does not exist in test set\n",
            "X_1948 109\n",
            "Column X_1948 does not exist in test set\n",
            "X_1949 104\n",
            "Column X_1949 does not exist in test set\n",
            "X_1950 102\n",
            "Column X_1950 does not exist in test set\n",
            "X_1951 70\n",
            "Column X_1951 does not exist in test set\n",
            "X_1952 80\n",
            "Column X_1952 does not exist in test set\n",
            "X_1953 92\n",
            "Column X_1953 does not exist in test set\n",
            "X_1954 98\n",
            "Column X_1954 does not exist in test set\n",
            "X_1955 80\n",
            "Column X_1955 does not exist in test set\n",
            "X_1956 92\n",
            "Column X_1956 does not exist in test set\n",
            "X_1957 31\n",
            "Column X_1957 does not exist in test set\n",
            "X_1958 96\n",
            "Column X_1958 does not exist in test set\n",
            "X_1959 98\n",
            "Column X_1959 does not exist in test set\n",
            "X_1960 92\n",
            "Column X_1960 does not exist in test set\n",
            "X_1961 93\n",
            "Column X_1961 does not exist in test set\n",
            "X_1962 91\n",
            "Column X_1962 does not exist in test set\n",
            "X_1963 69\n",
            "Column X_1963 does not exist in test set\n",
            "X_1964 87\n",
            "Column X_1964 does not exist in test set\n",
            "X_1965 94\n",
            "Column X_1965 does not exist in test set\n",
            "X_1966 92\n",
            "Column X_1966 does not exist in test set\n",
            "X_1967 81\n",
            "Column X_1967 does not exist in test set\n",
            "X_1968 87\n",
            "Column X_1968 does not exist in test set\n",
            "X_1969 34\n",
            "X_1970 32\n",
            "X_1971 34\n",
            "X_1972 32\n",
            "X_1973 31\n",
            "X_1974 31\n",
            "X_1975 14\n",
            "X_1976 32\n",
            "X_1977 30\n",
            "X_1978 32\n",
            "X_1979 34\n",
            "X_1980 34\n",
            "X_1981 34\n",
            "X_1982 25\n",
            "X_1983 34\n",
            "X_1984 33\n",
            "X_1985 31\n",
            "X_1986 31\n",
            "X_1987 16\n",
            "X_1988 30\n",
            "X_1989 28\n",
            "X_1990 32\n",
            "X_1991 32\n",
            "X_1992 33\n",
            "X_1993 34\n",
            "X_1994 32\n",
            "X_1995 34\n",
            "X_1996 32\n",
            "X_1997 31\n",
            "X_1998 31\n",
            "X_1999 14\n",
            "X_2000 32\n",
            "X_2001 30\n",
            "X_2002 32\n",
            "X_2003 34\n",
            "X_2004 34\n",
            "X_2005 34\n",
            "X_2006 32\n",
            "X_2007 34\n",
            "X_2008 32\n",
            "X_2009 31\n",
            "X_2010 31\n",
            "X_2011 14\n",
            "X_2012 32\n",
            "X_2013 30\n",
            "X_2014 32\n",
            "X_2015 34\n",
            "X_2016 34\n",
            "X_2017 119\n",
            "Column X_2017 does not exist in test set\n",
            "X_2018 80\n",
            "Column X_2018 does not exist in test set\n",
            "X_2019 121\n",
            "Column X_2019 does not exist in test set\n",
            "X_2020 110\n",
            "Column X_2020 does not exist in test set\n",
            "X_2021 90\n",
            "Column X_2021 does not exist in test set\n",
            "X_2022 106\n",
            "Column X_2022 does not exist in test set\n",
            "X_2023 43\n",
            "Column X_2023 does not exist in test set\n",
            "X_2024 90\n",
            "Column X_2024 does not exist in test set\n",
            "X_2025 83\n",
            "Column X_2025 does not exist in test set\n",
            "X_2026 103\n",
            "Column X_2026 does not exist in test set\n",
            "X_2027 110\n",
            "Column X_2027 does not exist in test set\n",
            "X_2028 107\n",
            "Column X_2028 does not exist in test set\n",
            "X_2029 34\n",
            "X_2030 26\n",
            "X_2031 34\n",
            "X_2032 33\n",
            "X_2033 31\n",
            "X_2034 31\n",
            "X_2035 16\n",
            "X_2036 30\n",
            "X_2037 30\n",
            "X_2038 32\n",
            "X_2039 32\n",
            "X_2040 33\n",
            "X_2041 23\n",
            "Column X_2041 does not exist in test set\n",
            "X_2042 4\n",
            "Column X_2042 does not exist in test set\n",
            "X_2043 4\n",
            "Column X_2043 does not exist in test set\n",
            "X_2044 128\n",
            "Column X_2044 does not exist in test set\n",
            "X_2045 76\n",
            "Column X_2045 does not exist in test set\n",
            "X_2046 129\n",
            "Column X_2046 does not exist in test set\n",
            "X_2047 129\n",
            "Column X_2047 does not exist in test set\n",
            "X_2048 115\n",
            "Column X_2048 does not exist in test set\n",
            "X_2049 115\n",
            "Column X_2049 does not exist in test set\n",
            "X_2050 116\n",
            "Column X_2050 does not exist in test set\n",
            "X_2051 117\n",
            "Column X_2051 does not exist in test set\n",
            "X_2052 1\n",
            "X_2053 1\n",
            "X_2054 1\n",
            "X_2055 1\n",
            "X_2056 8\n",
            "Column X_2056 does not exist in test set\n",
            "X_2057 7\n",
            "Column X_2057 does not exist in test set\n",
            "X_2058 7\n",
            "Column X_2058 does not exist in test set\n",
            "X_2059 4\n",
            "Column X_2059 does not exist in test set\n",
            "X_2060 3\n",
            "Column X_2060 does not exist in test set\n",
            "X_2061 3\n",
            "Column X_2061 does not exist in test set\n",
            "X_2062 5\n",
            "Column X_2062 does not exist in test set\n",
            "X_2063 62\n",
            "X_2064 51\n",
            "X_2065 63\n",
            "X_2066 49\n",
            "X_2067 57\n",
            "X_2068 58\n",
            "X_2069 39\n",
            "X_2070 62\n",
            "X_2071 51\n",
            "X_2072 63\n",
            "X_2073 49\n",
            "X_2074 57\n",
            "X_2075 58\n",
            "X_2076 12\n",
            "X_2077 39\n",
            "X_2078 5\n",
            "X_2079 57\n",
            "X_2080 52\n",
            "X_2081 62\n",
            "X_2082 52\n",
            "X_2083 59\n",
            "X_2084 57\n",
            "X_2085 35\n",
            "X_2086 57\n",
            "X_2087 52\n",
            "X_2088 62\n",
            "X_2089 52\n",
            "X_2090 59\n",
            "X_2091 57\n",
            "X_2092 35\n",
            "X_2093 110\n",
            "Column X_2093 does not exist in test set\n",
            "X_2094 84\n",
            "Column X_2094 does not exist in test set\n",
            "X_2095 119\n",
            "Column X_2095 does not exist in test set\n",
            "X_2096 82\n",
            "Column X_2096 does not exist in test set\n",
            "X_2097 103\n",
            "Column X_2097 does not exist in test set\n",
            "X_2098 102\n",
            "Column X_2098 does not exist in test set\n",
            "X_2099 106\n",
            "Column X_2099 does not exist in test set\n",
            "X_2100 62\n",
            "X_2101 51\n",
            "X_2102 63\n",
            "X_2103 49\n",
            "X_2104 57\n",
            "X_2105 58\n",
            "X_2106 57\n",
            "X_2107 52\n",
            "X_2108 62\n",
            "X_2109 52\n",
            "X_2110 59\n",
            "X_2111 57\n",
            "X_2112 7\n",
            "X_2113 7\n",
            "X_2114 10\n",
            "X_2115 10\n",
            "X_2116 10\n",
            "X_2117 10\n",
            "X_2118 7\n",
            "X_2119 7\n",
            "X_2120 6\n",
            "X_2121 6\n",
            "X_2122 6\n",
            "X_2123 6\n",
            "X_2124 6\n",
            "X_2125 6\n",
            "X_2126 7\n",
            "X_2127 7\n",
            "X_2128 6\n",
            "X_2129 6\n",
            "X_2130 8\n",
            "X_2131 8\n",
            "X_2132 8\n",
            "X_2133 8\n",
            "X_2134 7\n",
            "X_2135 7\n",
            "X_2136 7\n",
            "X_2137 7\n",
            "X_2138 7\n",
            "X_2139 7\n",
            "X_2140 7\n",
            "X_2141 7\n",
            "X_2142 7\n",
            "X_2143 7\n",
            "X_2144 7\n",
            "X_2145 7\n",
            "X_2146 8\n",
            "X_2147 8\n",
            "X_2148 8\n",
            "X_2149 8\n",
            "X_2150 7\n",
            "X_2151 7\n",
            "X_2152 7\n",
            "X_2153 7\n",
            "X_2154 7\n",
            "X_2155 7\n",
            "X_2156 7\n",
            "X_2157 7\n",
            "X_2158 7\n",
            "X_2159 7\n",
            "X_2160 7\n",
            "X_2161 7\n",
            "X_2162 7\n",
            "X_2163 7\n",
            "X_2164 7\n",
            "X_2165 7\n",
            "X_2166 7\n",
            "X_2167 7\n",
            "X_2168 6\n",
            "X_2169 6\n",
            "X_2170 10\n",
            "X_2171 10\n",
            "X_2172 10\n",
            "X_2173 10\n",
            "X_2174 7\n",
            "X_2175 7\n",
            "X_2176 6\n",
            "X_2177 6\n",
            "X_2178 10\n",
            "X_2179 10\n",
            "X_2180 10\n",
            "X_2181 10\n",
            "X_2182 6\n",
            "X_2183 6\n",
            "X_2184 3\n",
            "X_2185 3\n",
            "X_2186 3\n",
            "X_2187 3\n",
            "X_2188 2\n",
            "X_2189 2\n",
            "X_2190 55\n",
            "X_2191 25\n",
            "X_2192 7\n",
            "X_2193 7\n",
            "X_2194 10\n",
            "X_2195 10\n",
            "X_2196 10\n",
            "X_2197 10\n",
            "X_2198 7\n",
            "X_2199 7\n",
            "X_2200 6\n",
            "X_2201 6\n",
            "X_2202 6\n",
            "X_2203 6\n",
            "X_2204 6\n",
            "X_2205 6\n",
            "X_2206 7\n",
            "X_2207 7\n",
            "X_2208 6\n",
            "X_2209 6\n",
            "X_2210 8\n",
            "X_2211 8\n",
            "X_2212 8\n",
            "X_2213 8\n",
            "X_2214 7\n",
            "X_2215 7\n",
            "X_2216 7\n",
            "X_2217 7\n",
            "X_2218 7\n",
            "X_2219 7\n",
            "X_2220 7\n",
            "X_2221 7\n",
            "X_2222 7\n",
            "X_2223 7\n",
            "X_2224 7\n",
            "X_2225 7\n",
            "X_2226 8\n",
            "X_2227 8\n",
            "X_2228 8\n",
            "X_2229 8\n",
            "X_2230 7\n",
            "X_2231 7\n",
            "X_2232 7\n",
            "X_2233 7\n",
            "X_2234 7\n",
            "X_2235 7\n",
            "X_2236 7\n",
            "X_2237 7\n",
            "X_2238 7\n",
            "X_2239 7\n",
            "X_2240 7\n",
            "X_2241 7\n",
            "X_2242 7\n",
            "X_2243 7\n",
            "X_2244 7\n",
            "X_2245 7\n",
            "X_2246 7\n",
            "X_2247 7\n",
            "X_2248 6\n",
            "X_2249 6\n",
            "X_2250 10\n",
            "X_2251 10\n",
            "X_2252 10\n",
            "X_2253 10\n",
            "X_2254 7\n",
            "X_2255 7\n",
            "X_2256 6\n",
            "X_2257 6\n",
            "X_2258 10\n",
            "X_2259 10\n",
            "X_2260 10\n",
            "X_2261 10\n",
            "X_2262 6\n",
            "X_2263 6\n",
            "X_2264 7\n",
            "X_2265 7\n",
            "X_2266 10\n",
            "X_2267 10\n",
            "X_2268 10\n",
            "X_2269 10\n",
            "X_2270 7\n",
            "X_2271 7\n",
            "X_2272 6\n",
            "X_2273 6\n",
            "X_2274 6\n",
            "X_2275 6\n",
            "X_2276 6\n",
            "X_2277 6\n",
            "X_2278 7\n",
            "X_2279 7\n",
            "X_2280 6\n",
            "X_2281 6\n",
            "X_2282 8\n",
            "X_2283 8\n",
            "X_2284 8\n",
            "X_2285 8\n",
            "X_2286 7\n",
            "X_2287 7\n",
            "X_2288 7\n",
            "X_2289 7\n",
            "X_2290 7\n",
            "X_2291 7\n",
            "X_2292 7\n",
            "X_2293 7\n",
            "X_2294 7\n",
            "X_2295 7\n",
            "X_2296 7\n",
            "X_2297 7\n",
            "X_2298 8\n",
            "X_2299 8\n",
            "X_2300 8\n",
            "X_2301 8\n",
            "X_2302 7\n",
            "X_2303 7\n",
            "X_2304 7\n",
            "X_2305 7\n",
            "X_2306 7\n",
            "X_2307 7\n",
            "X_2308 7\n",
            "X_2309 7\n",
            "X_2310 7\n",
            "X_2311 7\n",
            "X_2312 7\n",
            "X_2313 7\n",
            "X_2314 7\n",
            "X_2315 7\n",
            "X_2316 7\n",
            "X_2317 7\n",
            "X_2318 7\n",
            "X_2319 7\n",
            "X_2320 6\n",
            "X_2321 6\n",
            "X_2322 10\n",
            "X_2323 10\n",
            "X_2324 10\n",
            "X_2325 10\n",
            "X_2326 7\n",
            "X_2327 7\n",
            "X_2328 6\n",
            "X_2329 6\n",
            "X_2330 10\n",
            "X_2331 10\n",
            "X_2332 10\n",
            "X_2333 10\n",
            "X_2334 6\n",
            "X_2335 6\n",
            "X_2336 7\n",
            "X_2337 7\n",
            "X_2338 10\n",
            "X_2339 10\n",
            "X_2340 10\n",
            "X_2341 10\n",
            "X_2342 7\n",
            "X_2343 7\n",
            "X_2344 6\n",
            "X_2345 6\n",
            "X_2346 6\n",
            "X_2347 6\n",
            "X_2348 6\n",
            "X_2349 6\n",
            "X_2350 7\n",
            "X_2351 7\n",
            "X_2352 6\n",
            "X_2353 6\n",
            "X_2354 8\n",
            "X_2355 8\n",
            "X_2356 8\n",
            "X_2357 8\n",
            "X_2358 7\n",
            "X_2359 7\n",
            "X_2360 7\n",
            "X_2361 7\n",
            "X_2362 7\n",
            "X_2363 7\n",
            "X_2364 7\n",
            "X_2365 7\n",
            "X_2366 7\n",
            "X_2367 7\n",
            "X_2368 7\n",
            "X_2369 7\n",
            "X_2370 8\n",
            "X_2371 8\n",
            "X_2372 8\n",
            "X_2373 8\n",
            "X_2374 7\n",
            "X_2375 7\n",
            "X_2376 7\n",
            "X_2377 7\n",
            "X_2378 7\n",
            "X_2379 7\n",
            "X_2380 7\n",
            "X_2381 7\n",
            "X_2382 7\n",
            "X_2383 7\n",
            "X_2384 7\n",
            "X_2385 7\n",
            "X_2386 7\n",
            "X_2387 7\n",
            "X_2388 7\n",
            "X_2389 7\n",
            "X_2390 7\n",
            "X_2391 7\n",
            "X_2392 6\n",
            "X_2393 6\n",
            "X_2394 10\n",
            "X_2395 10\n",
            "X_2396 10\n",
            "X_2397 10\n",
            "X_2398 7\n",
            "X_2399 7\n",
            "X_2400 6\n",
            "X_2401 6\n",
            "X_2402 10\n",
            "X_2403 10\n",
            "X_2404 10\n",
            "X_2405 10\n",
            "X_2406 6\n",
            "X_2407 6\n",
            "X_2408 8\n",
            "Column X_2408 does not exist in test set\n",
            "X_2409 119\n",
            "Column X_2409 does not exist in test set\n",
            "X_2410 8\n",
            "Column X_2410 does not exist in test set\n",
            "X_2411 4\n",
            "Column X_2411 does not exist in test set\n",
            "X_2412 36\n",
            "Column X_2412 does not exist in test set\n",
            "X_2413 4\n",
            "Column X_2413 does not exist in test set\n",
            "X_2414 3\n",
            "Column X_2414 does not exist in test set\n",
            "X_2415 32\n",
            "Column X_2415 does not exist in test set\n",
            "X_2416 3\n",
            "Column X_2416 does not exist in test set\n",
            "X_2417 2\n",
            "Column X_2417 does not exist in test set\n",
            "X_2418 83\n",
            "Column X_2418 does not exist in test set\n",
            "X_2419 2\n",
            "Column X_2419 does not exist in test set\n",
            "X_2420 1\n",
            "X_2421 98\n",
            "Column X_2421 does not exist in test set\n",
            "X_2422 120\n",
            "Column X_2422 does not exist in test set\n",
            "X_2423 99\n",
            "Column X_2423 does not exist in test set\n",
            "X_2424 2\n",
            "X_2425 2\n",
            "Column X_2425 does not exist in test set\n",
            "X_2426 97\n",
            "Column X_2426 does not exist in test set\n",
            "X_2427 47\n",
            "Column X_2427 does not exist in test set\n",
            "X_2428 120\n",
            "Column X_2428 does not exist in test set\n",
            "X_2429 120\n",
            "Column X_2429 does not exist in test set\n",
            "X_2430 37\n",
            "Column X_2430 does not exist in test set\n",
            "X_2431 119\n",
            "Column X_2431 does not exist in test set\n",
            "X_2432 36\n",
            "Column X_2432 does not exist in test set\n",
            "X_2433 3\n",
            "Column X_2433 does not exist in test set\n",
            "X_2434 87\n",
            "Column X_2434 does not exist in test set\n",
            "X_2435 3\n",
            "Column X_2435 does not exist in test set\n",
            "X_2436 4\n",
            "Column X_2436 does not exist in test set\n",
            "X_2437 77\n",
            "Column X_2437 does not exist in test set\n",
            "X_2438 4\n",
            "Column X_2438 does not exist in test set\n",
            "X_2439 3\n",
            "Column X_2439 does not exist in test set\n",
            "X_2440 77\n",
            "Column X_2440 does not exist in test set\n",
            "X_2441 4\n",
            "Column X_2441 does not exist in test set\n",
            "X_2442 3\n",
            "Column X_2442 does not exist in test set\n",
            "X_2443 56\n",
            "Column X_2443 does not exist in test set\n",
            "X_2444 3\n",
            "Column X_2444 does not exist in test set\n",
            "X_2445 4\n",
            "Column X_2445 does not exist in test set\n",
            "X_2446 83\n",
            "Column X_2446 does not exist in test set\n",
            "X_2447 3\n",
            "Column X_2447 does not exist in test set\n",
            "X_2448 3\n",
            "Column X_2448 does not exist in test set\n",
            "X_2449 81\n",
            "Column X_2449 does not exist in test set\n",
            "X_2450 4\n",
            "Column X_2450 does not exist in test set\n",
            "X_2451 3\n",
            "Column X_2451 does not exist in test set\n",
            "X_2452 69\n",
            "Column X_2452 does not exist in test set\n",
            "X_2453 3\n",
            "Column X_2453 does not exist in test set\n",
            "X_2454 3\n",
            "Column X_2454 does not exist in test set\n",
            "X_2455 71\n",
            "Column X_2455 does not exist in test set\n",
            "X_2456 3\n",
            "Column X_2456 does not exist in test set\n",
            "X_2457 4\n",
            "Column X_2457 does not exist in test set\n",
            "X_2458 93\n",
            "Column X_2458 does not exist in test set\n",
            "X_2459 4\n",
            "Column X_2459 does not exist in test set\n",
            "X_2460 2\n",
            "Column X_2460 does not exist in test set\n",
            "X_2461 2\n",
            "Column X_2461 does not exist in test set\n",
            "X_2462 1\n",
            "X_2463 3\n",
            "Column X_2463 does not exist in test set\n",
            "X_2464 26\n",
            "Column X_2464 does not exist in test set\n",
            "X_2465 17\n",
            "Column X_2465 does not exist in test set\n",
            "X_2466 41\n",
            "Column X_2466 does not exist in test set\n",
            "X_2467 34\n",
            "Column X_2467 does not exist in test set\n",
            "X_2468 9\n",
            "Column X_2468 does not exist in test set\n",
            "X_2469 9\n",
            "Column X_2469 does not exist in test set\n",
            "X_2470 14\n",
            "Column X_2470 does not exist in test set\n",
            "X_2471 14\n",
            "Column X_2471 does not exist in test set\n",
            "X_2472 14\n",
            "Column X_2472 does not exist in test set\n",
            "X_2473 14\n",
            "Column X_2473 does not exist in test set\n",
            "X_2474 9\n",
            "Column X_2474 does not exist in test set\n",
            "X_2475 9\n",
            "Column X_2475 does not exist in test set\n",
            "X_2476 7\n",
            "Column X_2476 does not exist in test set\n",
            "X_2477 7\n",
            "Column X_2477 does not exist in test set\n",
            "X_2478 9\n",
            "Column X_2478 does not exist in test set\n",
            "X_2479 9\n",
            "Column X_2479 does not exist in test set\n",
            "X_2480 9\n",
            "Column X_2480 does not exist in test set\n",
            "X_2481 9\n",
            "Column X_2481 does not exist in test set\n",
            "X_2482 9\n",
            "Column X_2482 does not exist in test set\n",
            "X_2483 9\n",
            "Column X_2483 does not exist in test set\n",
            "X_2484 8\n",
            "Column X_2484 does not exist in test set\n",
            "X_2485 8\n",
            "Column X_2485 does not exist in test set\n",
            "X_2486 9\n",
            "Column X_2486 does not exist in test set\n",
            "X_2487 9\n",
            "Column X_2487 does not exist in test set\n",
            "X_2488 9\n",
            "Column X_2488 does not exist in test set\n",
            "X_2489 9\n",
            "Column X_2489 does not exist in test set\n",
            "X_2490 9\n",
            "Column X_2490 does not exist in test set\n",
            "X_2491 9\n",
            "Column X_2491 does not exist in test set\n",
            "X_2492 9\n",
            "Column X_2492 does not exist in test set\n",
            "X_2493 9\n",
            "Column X_2493 does not exist in test set\n",
            "X_2494 8\n",
            "Column X_2494 does not exist in test set\n",
            "X_2495 8\n",
            "Column X_2495 does not exist in test set\n",
            "X_2496 8\n",
            "Column X_2496 does not exist in test set\n",
            "X_2497 8\n",
            "Column X_2497 does not exist in test set\n",
            "X_2498 10\n",
            "Column X_2498 does not exist in test set\n",
            "X_2499 10\n",
            "Column X_2499 does not exist in test set\n",
            "X_2500 10\n",
            "Column X_2500 does not exist in test set\n",
            "X_2501 10\n",
            "Column X_2501 does not exist in test set\n",
            "X_2502 10\n",
            "Column X_2502 does not exist in test set\n",
            "X_2503 10\n",
            "Column X_2503 does not exist in test set\n",
            "X_2504 10\n",
            "Column X_2504 does not exist in test set\n",
            "X_2505 10\n",
            "Column X_2505 does not exist in test set\n",
            "X_2506 9\n",
            "Column X_2506 does not exist in test set\n",
            "X_2507 9\n",
            "Column X_2507 does not exist in test set\n",
            "X_2508 10\n",
            "Column X_2508 does not exist in test set\n",
            "X_2509 10\n",
            "Column X_2509 does not exist in test set\n",
            "X_2510 9\n",
            "Column X_2510 does not exist in test set\n",
            "X_2511 9\n",
            "Column X_2511 does not exist in test set\n",
            "X_2512 9\n",
            "Column X_2512 does not exist in test set\n",
            "X_2513 9\n",
            "Column X_2513 does not exist in test set\n",
            "X_2514 10\n",
            "Column X_2514 does not exist in test set\n",
            "X_2515 10\n",
            "Column X_2515 does not exist in test set\n",
            "X_2516 9\n",
            "Column X_2516 does not exist in test set\n",
            "X_2517 9\n",
            "Column X_2517 does not exist in test set\n",
            "X_2518 9\n",
            "Column X_2518 does not exist in test set\n",
            "X_2519 9\n",
            "Column X_2519 does not exist in test set\n",
            "X_2520 9\n",
            "Column X_2520 does not exist in test set\n",
            "X_2521 9\n",
            "Column X_2521 does not exist in test set\n",
            "X_2522 9\n",
            "Column X_2522 does not exist in test set\n",
            "X_2523 9\n",
            "Column X_2523 does not exist in test set\n",
            "X_2524 8\n",
            "Column X_2524 does not exist in test set\n",
            "X_2525 8\n",
            "Column X_2525 does not exist in test set\n",
            "X_2526 13\n",
            "Column X_2526 does not exist in test set\n",
            "X_2527 13\n",
            "Column X_2527 does not exist in test set\n",
            "X_2528 13\n",
            "Column X_2528 does not exist in test set\n",
            "X_2529 13\n",
            "Column X_2529 does not exist in test set\n",
            "X_2530 9\n",
            "Column X_2530 does not exist in test set\n",
            "X_2531 9\n",
            "Column X_2531 does not exist in test set\n",
            "X_2532 8\n",
            "Column X_2532 does not exist in test set\n",
            "X_2533 8\n",
            "Column X_2533 does not exist in test set\n",
            "X_2534 11\n",
            "Column X_2534 does not exist in test set\n",
            "X_2535 11\n",
            "Column X_2535 does not exist in test set\n",
            "X_2536 11\n",
            "Column X_2536 does not exist in test set\n",
            "X_2537 11\n",
            "Column X_2537 does not exist in test set\n",
            "X_2538 8\n",
            "Column X_2538 does not exist in test set\n",
            "X_2539 8\n",
            "Column X_2539 does not exist in test set\n",
            "X_2540 3\n",
            "X_2541 24\n",
            "X_2542 4\n",
            "X_2543 3\n",
            "X_2544 3\n",
            "X_2545 3\n",
            "X_2546 3\n",
            "X_2547 3\n",
            "X_2548 3\n",
            "X_2549 2\n",
            "X_2550 2\n",
            "X_2551 20\n",
            "X_2552 53\n",
            "X_2553 55\n",
            "X_2554 26\n",
            "X_2555 25\n",
            "X_2556 7\n",
            "X_2557 7\n",
            "X_2558 10\n",
            "X_2559 10\n",
            "X_2560 10\n",
            "X_2561 10\n",
            "X_2562 7\n",
            "X_2563 7\n",
            "X_2564 6\n",
            "X_2565 6\n",
            "X_2566 6\n",
            "X_2567 6\n",
            "X_2568 6\n",
            "X_2569 6\n",
            "X_2570 7\n",
            "X_2571 7\n",
            "X_2572 6\n",
            "X_2573 6\n",
            "X_2574 8\n",
            "X_2575 8\n",
            "X_2576 8\n",
            "X_2577 8\n",
            "X_2578 7\n",
            "X_2579 7\n",
            "X_2580 7\n",
            "X_2581 7\n",
            "X_2582 7\n",
            "X_2583 7\n",
            "X_2584 7\n",
            "X_2585 7\n",
            "X_2586 7\n",
            "X_2587 7\n",
            "X_2588 7\n",
            "X_2589 7\n",
            "X_2590 8\n",
            "X_2591 8\n",
            "X_2592 8\n",
            "X_2593 8\n",
            "X_2594 7\n",
            "X_2595 7\n",
            "X_2596 7\n",
            "X_2597 7\n",
            "X_2598 7\n",
            "X_2599 7\n",
            "X_2600 7\n",
            "X_2601 7\n",
            "X_2602 7\n",
            "X_2603 7\n",
            "X_2604 7\n",
            "X_2605 7\n",
            "X_2606 7\n",
            "X_2607 7\n",
            "X_2608 7\n",
            "X_2609 7\n",
            "X_2610 7\n",
            "X_2611 7\n",
            "X_2612 6\n",
            "X_2613 6\n",
            "X_2614 10\n",
            "X_2615 10\n",
            "X_2616 10\n",
            "X_2617 10\n",
            "X_2618 7\n",
            "X_2619 7\n",
            "X_2620 6\n",
            "X_2621 6\n",
            "X_2622 10\n",
            "X_2623 10\n",
            "X_2624 10\n",
            "X_2625 10\n",
            "X_2626 6\n",
            "X_2627 6\n",
            "X_2628 1\n",
            "Column X_2628 does not exist in test set\n",
            "X_2629 1\n",
            "Column X_2629 does not exist in test set\n",
            "X_2630 1\n",
            "Column X_2630 does not exist in test set\n",
            "X_2631 1\n",
            "Column X_2631 does not exist in test set\n",
            "X_2632 1\n",
            "Column X_2632 does not exist in test set\n",
            "X_2633 1\n",
            "Column X_2633 does not exist in test set\n",
            "X_2634 1\n",
            "Column X_2634 does not exist in test set\n",
            "X_2635 1\n",
            "Column X_2635 does not exist in test set\n",
            "X_2636 1\n",
            "Column X_2636 does not exist in test set\n",
            "X_2637 1\n",
            "Column X_2637 does not exist in test set\n",
            "X_2638 1\n",
            "Column X_2638 does not exist in test set\n",
            "X_2639 1\n",
            "Column X_2639 does not exist in test set\n",
            "X_2640 1\n",
            "Column X_2640 does not exist in test set\n",
            "X_2641 1\n",
            "Column X_2641 does not exist in test set\n",
            "X_2642 1\n",
            "Column X_2642 does not exist in test set\n",
            "X_2643 1\n",
            "Column X_2643 does not exist in test set\n",
            "X_2644 1\n",
            "Column X_2644 does not exist in test set\n",
            "X_2645 1\n",
            "Column X_2645 does not exist in test set\n",
            "X_2646 1\n",
            "Column X_2646 does not exist in test set\n",
            "X_2647 1\n",
            "Column X_2647 does not exist in test set\n",
            "X_2648 1\n",
            "Column X_2648 does not exist in test set\n",
            "X_2649 1\n",
            "Column X_2649 does not exist in test set\n",
            "X_2650 1\n",
            "Column X_2650 does not exist in test set\n",
            "X_2651 1\n",
            "Column X_2651 does not exist in test set\n",
            "X_2652 1\n",
            "Column X_2652 does not exist in test set\n",
            "X_2653 1\n",
            "Column X_2653 does not exist in test set\n",
            "X_2654 1\n",
            "Column X_2654 does not exist in test set\n",
            "X_2655 1\n",
            "Column X_2655 does not exist in test set\n",
            "X_2656 1\n",
            "Column X_2656 does not exist in test set\n",
            "X_2657 1\n",
            "Column X_2657 does not exist in test set\n",
            "X_2658 1\n",
            "Column X_2658 does not exist in test set\n",
            "X_2659 1\n",
            "Column X_2659 does not exist in test set\n",
            "X_2660 1\n",
            "Column X_2660 does not exist in test set\n",
            "X_2661 1\n",
            "Column X_2661 does not exist in test set\n",
            "X_2662 1\n",
            "Column X_2662 does not exist in test set\n",
            "X_2663 1\n",
            "Column X_2663 does not exist in test set\n",
            "X_2664 1\n",
            "Column X_2664 does not exist in test set\n",
            "X_2665 1\n",
            "Column X_2665 does not exist in test set\n",
            "X_2666 1\n",
            "Column X_2666 does not exist in test set\n",
            "X_2667 1\n",
            "Column X_2667 does not exist in test set\n",
            "X_2668 1\n",
            "Column X_2668 does not exist in test set\n",
            "X_2669 1\n",
            "Column X_2669 does not exist in test set\n",
            "X_2670 1\n",
            "Column X_2670 does not exist in test set\n",
            "X_2671 1\n",
            "Column X_2671 does not exist in test set\n",
            "X_2672 1\n",
            "Column X_2672 does not exist in test set\n",
            "X_2673 1\n",
            "Column X_2673 does not exist in test set\n",
            "X_2674 1\n",
            "Column X_2674 does not exist in test set\n",
            "X_2675 1\n",
            "Column X_2675 does not exist in test set\n",
            "X_2676 1\n",
            "Column X_2676 does not exist in test set\n",
            "X_2677 1\n",
            "Column X_2677 does not exist in test set\n",
            "X_2678 1\n",
            "Column X_2678 does not exist in test set\n",
            "X_2679 1\n",
            "Column X_2679 does not exist in test set\n",
            "X_2680 1\n",
            "Column X_2680 does not exist in test set\n",
            "X_2681 1\n",
            "Column X_2681 does not exist in test set\n",
            "X_2682 1\n",
            "Column X_2682 does not exist in test set\n",
            "X_2683 1\n",
            "Column X_2683 does not exist in test set\n",
            "X_2684 1\n",
            "Column X_2684 does not exist in test set\n",
            "X_2685 1\n",
            "Column X_2685 does not exist in test set\n",
            "X_2686 1\n",
            "Column X_2686 does not exist in test set\n",
            "X_2687 1\n",
            "Column X_2687 does not exist in test set\n",
            "X_2688 1\n",
            "Column X_2688 does not exist in test set\n",
            "X_2689 1\n",
            "Column X_2689 does not exist in test set\n",
            "X_2690 1\n",
            "Column X_2690 does not exist in test set\n",
            "X_2691 1\n",
            "Column X_2691 does not exist in test set\n",
            "X_2692 1\n",
            "Column X_2692 does not exist in test set\n",
            "X_2693 1\n",
            "Column X_2693 does not exist in test set\n",
            "X_2694 1\n",
            "Column X_2694 does not exist in test set\n",
            "X_2695 1\n",
            "Column X_2695 does not exist in test set\n",
            "X_2696 1\n",
            "Column X_2696 does not exist in test set\n",
            "X_2697 1\n",
            "Column X_2697 does not exist in test set\n",
            "X_2698 1\n",
            "Column X_2698 does not exist in test set\n",
            "X_2699 1\n",
            "Column X_2699 does not exist in test set\n",
            "X_2700 28\n",
            "Column X_2700 does not exist in test set\n",
            "X_2701 87\n",
            "Column X_2701 does not exist in test set\n",
            "X_2702 34\n",
            "Column X_2702 does not exist in test set\n",
            "X_2703 81\n",
            "Column X_2703 does not exist in test set\n",
            "X_2704 85\n",
            "Column X_2704 does not exist in test set\n",
            "X_2705 88\n",
            "Column X_2705 does not exist in test set\n",
            "X_2706 88\n",
            "Column X_2706 does not exist in test set\n",
            "X_2707 121\n",
            "Column X_2707 does not exist in test set\n",
            "X_2708 87\n",
            "Column X_2708 does not exist in test set\n",
            "X_2709 71\n",
            "Column X_2709 does not exist in test set\n",
            "X_2710 117\n",
            "Column X_2710 does not exist in test set\n",
            "X_2711 80\n",
            "Column X_2711 does not exist in test set\n",
            "X_2712 94\n",
            "Column X_2712 does not exist in test set\n",
            "X_2713 105\n",
            "Column X_2713 does not exist in test set\n",
            "X_2714 103\n",
            "Column X_2714 does not exist in test set\n",
            "X_2715 107\n",
            "Column X_2715 does not exist in test set\n",
            "X_2716 107\n",
            "Column X_2716 does not exist in test set\n",
            "X_2717 111\n",
            "Column X_2717 does not exist in test set\n",
            "X_2718 6\n",
            "Column X_2718 does not exist in test set\n",
            "X_2719 96\n",
            "Column X_2719 does not exist in test set\n",
            "X_2720 8\n",
            "Column X_2720 does not exist in test set\n",
            "X_2721 4\n",
            "Column X_2721 does not exist in test set\n",
            "X_2722 32\n",
            "Column X_2722 does not exist in test set\n",
            "X_2723 5\n",
            "Column X_2723 does not exist in test set\n",
            "X_2724 4\n",
            "Column X_2724 does not exist in test set\n",
            "X_2725 33\n",
            "Column X_2725 does not exist in test set\n",
            "X_2726 4\n",
            "Column X_2726 does not exist in test set\n",
            "X_2727 3\n",
            "Column X_2727 does not exist in test set\n",
            "X_2728 17\n",
            "Column X_2728 does not exist in test set\n",
            "X_2729 3\n",
            "Column X_2729 does not exist in test set\n",
            "X_2730 2\n",
            "Column X_2730 does not exist in test set\n",
            "X_2731 64\n",
            "Column X_2731 does not exist in test set\n",
            "X_2732 2\n",
            "Column X_2732 does not exist in test set\n",
            "X_2733 1\n",
            "X_2734 97\n",
            "Column X_2734 does not exist in test set\n",
            "X_2735 120\n",
            "Column X_2735 does not exist in test set\n",
            "X_2736 99\n",
            "Column X_2736 does not exist in test set\n",
            "X_2737 2\n",
            "X_2738 2\n",
            "Column X_2738 does not exist in test set\n",
            "X_2739 59\n",
            "Column X_2739 does not exist in test set\n",
            "X_2740 4\n",
            "Column X_2740 does not exist in test set\n",
            "X_2741 120\n",
            "Column X_2741 does not exist in test set\n",
            "X_2742 120\n",
            "Column X_2742 does not exist in test set\n",
            "X_2743 37\n",
            "Column X_2743 does not exist in test set\n",
            "X_2744 114\n",
            "Column X_2744 does not exist in test set\n",
            "X_2745 37\n",
            "Column X_2745 does not exist in test set\n",
            "X_2746 3\n",
            "Column X_2746 does not exist in test set\n",
            "X_2747 64\n",
            "Column X_2747 does not exist in test set\n",
            "X_2748 4\n",
            "Column X_2748 does not exist in test set\n",
            "X_2749 4\n",
            "Column X_2749 does not exist in test set\n",
            "X_2750 60\n",
            "Column X_2750 does not exist in test set\n",
            "X_2751 4\n",
            "Column X_2751 does not exist in test set\n",
            "X_2752 3\n",
            "Column X_2752 does not exist in test set\n",
            "X_2753 63\n",
            "Column X_2753 does not exist in test set\n",
            "X_2754 3\n",
            "Column X_2754 does not exist in test set\n",
            "X_2755 3\n",
            "Column X_2755 does not exist in test set\n",
            "X_2756 36\n",
            "Column X_2756 does not exist in test set\n",
            "X_2757 3\n",
            "Column X_2757 does not exist in test set\n",
            "X_2758 3\n",
            "Column X_2758 does not exist in test set\n",
            "X_2759 56\n",
            "Column X_2759 does not exist in test set\n",
            "X_2760 4\n",
            "Column X_2760 does not exist in test set\n",
            "X_2761 3\n",
            "Column X_2761 does not exist in test set\n",
            "X_2762 56\n",
            "Column X_2762 does not exist in test set\n",
            "X_2763 3\n",
            "Column X_2763 does not exist in test set\n",
            "X_2764 3\n",
            "Column X_2764 does not exist in test set\n",
            "X_2765 55\n",
            "Column X_2765 does not exist in test set\n",
            "X_2766 3\n",
            "Column X_2766 does not exist in test set\n",
            "X_2767 3\n",
            "Column X_2767 does not exist in test set\n",
            "X_2768 48\n",
            "Column X_2768 does not exist in test set\n",
            "X_2769 3\n",
            "Column X_2769 does not exist in test set\n",
            "X_2770 3\n",
            "Column X_2770 does not exist in test set\n",
            "X_2771 67\n",
            "Column X_2771 does not exist in test set\n",
            "X_2772 4\n",
            "Column X_2772 does not exist in test set\n",
            "X_2773 2\n",
            "Column X_2773 does not exist in test set\n",
            "X_2774 2\n",
            "Column X_2774 does not exist in test set\n",
            "X_2775 1\n",
            "X_2776 3\n",
            "Column X_2776 does not exist in test set\n",
            "X_2777 8\n",
            "Column X_2777 does not exist in test set\n",
            "X_2778 7\n",
            "Column X_2778 does not exist in test set\n",
            "X_2779 43\n",
            "Column X_2779 does not exist in test set\n",
            "X_2780 35\n",
            "Column X_2780 does not exist in test set\n",
            "X_2781 8\n",
            "Column X_2781 does not exist in test set\n",
            "X_2782 96\n",
            "Column X_2782 does not exist in test set\n",
            "X_2783 8\n",
            "Column X_2783 does not exist in test set\n",
            "X_2784 5\n",
            "Column X_2784 does not exist in test set\n",
            "X_2785 86\n",
            "Column X_2785 does not exist in test set\n",
            "X_2786 4\n",
            "Column X_2786 does not exist in test set\n",
            "X_2787 4\n",
            "Column X_2787 does not exist in test set\n",
            "X_2788 81\n",
            "Column X_2788 does not exist in test set\n",
            "X_2789 4\n",
            "Column X_2789 does not exist in test set\n",
            "X_2790 5\n",
            "Column X_2790 does not exist in test set\n",
            "X_2791 85\n",
            "Column X_2791 does not exist in test set\n",
            "X_2792 4\n",
            "Column X_2792 does not exist in test set\n",
            "X_2793 5\n",
            "Column X_2793 does not exist in test set\n",
            "X_2794 89\n",
            "Column X_2794 does not exist in test set\n",
            "X_2795 5\n",
            "Column X_2795 does not exist in test set\n",
            "X_2796 5\n",
            "Column X_2796 does not exist in test set\n",
            "X_2797 78\n",
            "Column X_2797 does not exist in test set\n",
            "X_2798 4\n",
            "Column X_2798 does not exist in test set\n",
            "X_2799 6\n",
            "Column X_2799 does not exist in test set\n",
            "X_2800 93\n",
            "Column X_2800 does not exist in test set\n",
            "X_2801 6\n",
            "Column X_2801 does not exist in test set\n",
            "X_2802 9\n",
            "Column X_2802 does not exist in test set\n",
            "X_2803 14\n",
            "Column X_2803 does not exist in test set\n",
            "X_2804 14\n",
            "Column X_2804 does not exist in test set\n",
            "X_2805 9\n",
            "Column X_2805 does not exist in test set\n",
            "X_2806 7\n",
            "Column X_2806 does not exist in test set\n",
            "X_2807 9\n",
            "Column X_2807 does not exist in test set\n",
            "X_2808 9\n",
            "Column X_2808 does not exist in test set\n",
            "X_2809 9\n",
            "Column X_2809 does not exist in test set\n",
            "X_2810 8\n",
            "Column X_2810 does not exist in test set\n",
            "X_2811 9\n",
            "Column X_2811 does not exist in test set\n",
            "X_2812 9\n",
            "Column X_2812 does not exist in test set\n",
            "X_2813 9\n",
            "Column X_2813 does not exist in test set\n",
            "X_2814 9\n",
            "Column X_2814 does not exist in test set\n",
            "X_2815 8\n",
            "Column X_2815 does not exist in test set\n",
            "X_2816 8\n",
            "Column X_2816 does not exist in test set\n",
            "X_2817 10\n",
            "Column X_2817 does not exist in test set\n",
            "X_2818 10\n",
            "Column X_2818 does not exist in test set\n",
            "X_2819 10\n",
            "Column X_2819 does not exist in test set\n",
            "X_2820 10\n",
            "Column X_2820 does not exist in test set\n",
            "X_2821 9\n",
            "Column X_2821 does not exist in test set\n",
            "X_2822 10\n",
            "Column X_2822 does not exist in test set\n",
            "X_2823 9\n",
            "Column X_2823 does not exist in test set\n",
            "X_2824 9\n",
            "Column X_2824 does not exist in test set\n",
            "X_2825 9\n",
            "Column X_2825 does not exist in test set\n",
            "X_2826 9\n",
            "Column X_2826 does not exist in test set\n",
            "X_2827 9\n",
            "Column X_2827 does not exist in test set\n",
            "X_2828 9\n",
            "Column X_2828 does not exist in test set\n",
            "X_2829 8\n",
            "Column X_2829 does not exist in test set\n",
            "X_2830 13\n",
            "Column X_2830 does not exist in test set\n",
            "X_2831 13\n",
            "Column X_2831 does not exist in test set\n",
            "X_2832 9\n",
            "Column X_2832 does not exist in test set\n",
            "X_2833 8\n",
            "Column X_2833 does not exist in test set\n",
            "X_2834 11\n",
            "Column X_2834 does not exist in test set\n",
            "X_2835 11\n",
            "Column X_2835 does not exist in test set\n",
            "X_2836 8\n",
            "Column X_2836 does not exist in test set\n",
            "X_2837 31\n",
            "Column X_2837 does not exist in test set\n",
            "X_2838 1\n",
            "Column X_2838 does not exist in test set\n",
            "X_2839 15\n",
            "Column X_2839 does not exist in test set\n",
            "X_2840 15\n",
            "Column X_2840 does not exist in test set\n",
            "X_2841 74\n",
            "Column X_2841 does not exist in test set\n",
            "X_2842 85\n",
            "Column X_2842 does not exist in test set\n",
            "X_2843 92\n",
            "Column X_2843 does not exist in test set\n",
            "X_2844 1\n",
            "Column X_2844 does not exist in test set\n",
            "X_2845 14\n",
            "Column X_2845 does not exist in test set\n",
            "X_2846 154\n",
            "Column X_2846 does not exist in test set\n",
            "X_2847 13\n",
            "Column X_2847 does not exist in test set\n",
            "X_2848 13\n",
            "Column X_2848 does not exist in test set\n",
            "X_2850 16\n",
            "Column X_2850 does not exist in test set\n",
            "X_2851 199\n",
            "Column X_2851 does not exist in test set\n",
            "X_2853 194\n",
            "Column X_2853 does not exist in test set\n",
            "X_2854 196\n",
            "Column X_2854 does not exist in test set\n",
            "X_2856 198\n",
            "Column X_2856 does not exist in test set\n",
            "X_2860 175\n",
            "Column X_2860 does not exist in test set\n",
            "X_2862 184\n",
            "Column X_2862 does not exist in test set\n",
            "X_2863 26\n",
            "Column X_2863 does not exist in test set\n",
            "X_2865 34\n",
            "Column X_2865 does not exist in test set\n",
            "X_2866 96\n",
            "Column X_2866 does not exist in test set\n",
            "X_2867 98\n",
            "Column X_2867 does not exist in test set\n",
            "X_2868 92\n",
            "Column X_2868 does not exist in test set\n",
            "X_2869 93\n",
            "Column X_2869 does not exist in test set\n",
            "X_2870 91\n",
            "Column X_2870 does not exist in test set\n",
            "X_2871 2\n",
            "X_2872 1\n",
            "Column X_2872 does not exist in test set\n",
            "X_2873 1\n",
            "Column X_2873 does not exist in test set\n",
            "X_2874 1\n",
            "Column X_2874 does not exist in test set\n",
            "X_2875 1\n",
            "Column X_2875 does not exist in test set\n",
            "line_pcode 8\n",
            "Set 2\n",
            "Column Set does not exist in test set\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "clf = TabNetClassifier(n_steps=1, \n",
        "                       cat_idxs=cat_idxs,\n",
        "                       cat_dims=cat_dims,\n",
        "                       cat_emb_dim=1,\n",
        "                       optimizer_fn=torch.optim.Adam,\n",
        "                       optimizer_params=dict(lr=2e-2),\n",
        "                       scheduler_params={\"step_size\":50,\n",
        "                                         \"gamma\":0.9},\n",
        "                       scheduler_fn=torch.optim.lr_scheduler.StepLR,\n",
        "                       mask_type='entmax', # sparsemax \n",
        "                       lambda_sparse=0)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        },
        "id": "f6vV30JP9xzr",
        "outputId": "05bdce91-03aa-4bfe-cdd6-50e236bebcce"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "TypeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-35-f64303b4936c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m clf = TabNetClassifier(n_steps=1, \n\u001b[0m\u001b[1;32m      2\u001b[0m                        \u001b[0mcat_idxs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcat_idxs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m                        \u001b[0mcat_dims\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcat_dims\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m                        \u001b[0mcat_emb_dim\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m                        \u001b[0moptimizer_fn\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptim\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mAdam\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mTypeError\u001b[0m: __init__() missing 2 required positional arguments: 'feature_columns' and 'num_classes'"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "clf = TabNetClassifier(\n",
        "    n_d=64, n_a=64, n_steps=5,\n",
        "    gamma=1.5, n_independent=2, n_shared=2,\n",
        "    cat_idxs=cat_idxs,\n",
        "    cat_dims=cat_dims,\n",
        "    cat_emb_dim=1,\n",
        "    lambda_sparse=1e-4, momentum=0.3, clip_value=2.,\n",
        "    optimizer_fn=torch.optim.Adam,\n",
        "    optimizer_params=dict(lr=2e-2),\n",
        "    scheduler_params = {\"gamma\": 0.95,\n",
        "                     \"step_size\": 20},\n",
        "    scheduler_fn=torch.optim.lr_scheduler.StepLR, epsilon=1e-15\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        },
        "id": "9JR-sVcjCIlu",
        "outputId": "abb51362-6f3d-450a-a6fd-829a064beabe"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "TypeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-40-b8cb75e20b2d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m clf = TabNetClassifier(\n\u001b[0m\u001b[1;32m      2\u001b[0m     \u001b[0mn_d\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m64\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_a\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m64\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_steps\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m     \u001b[0mgamma\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1.5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_independent\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_shared\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m     \u001b[0mcat_idxs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcat_idxs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m     \u001b[0mcat_dims\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcat_dims\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mTypeError\u001b[0m: __init__() missing 2 required positional arguments: 'feature_columns' and 'num_classes'"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "TabNetClassifier"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RxfsdDNDCRHM",
        "outputId": "5629e5d4-f9db-4800-c7b3-bd2b2ddd0984"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tabnet.tabnet.TabNetClassifier"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "categorical_columns = train_df['line_pcode']\n",
        "categorical_dims =  {}\n",
        "\n",
        "unused_feat = ['Set']\n",
        "\n",
        "features = [ col for col in train_df.columns] \n",
        "\n",
        "cat_idxs = [ i for i, f in enumerate(features) if f in categorical_columns]\n",
        "\n",
        "cat_dims = [ categorical_dims[f] for i, f in enumerate(features) if f in categorical_columns]"
      ],
      "metadata": {
        "id": "UxlNX7r_uHPf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Network parameters\n",
        "max_epochs = 20 if not os.getenv(\"CI\", False) else 2\n",
        "batch_size = 1024\n",
        "clf = TabNetClassifier(cat_idxs=cat_idxs,\n",
        "                       cat_dims=cat_dims,\n",
        "                       cat_emb_dim=1,\n",
        "                       optimizer_fn=torch.optim.Adam, # Any optimizer works here\n",
        "                       optimizer_params=dict(lr=2e-2),\n",
        "                       scheduler_fn=torch.optim.lr_scheduler.OneCycleLR,\n",
        "                       scheduler_params={\"is_batch_level\":True,\n",
        "                                         \"max_lr\":5e-2,\n",
        "                                         \"steps_per_epoch\":int(train_df.shape[0] / batch_size)+1,\n",
        "                                         \"epochs\":max_epochs\n",
        "                                          },\n",
        "                       mask_type='entmax', # \"sparsemax\",\n",
        "                      )"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        },
        "id": "R8arV-6kvbfv",
        "outputId": "9c59d9ca-feb6-4902-bfb7-21a2ccd42410"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "TypeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-62-dc462a724972>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mmax_epochs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m20\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgetenv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"CI\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mbatch_size\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m1024\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m clf = TabNetClassifier(cat_idxs=cat_idxs,\n\u001b[0m\u001b[1;32m      5\u001b[0m                        \u001b[0mcat_dims\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcat_dims\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m                        \u001b[0mcat_emb_dim\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mTypeError\u001b[0m: __init__() missing 2 required positional arguments: 'feature_columns' and 'num_classes'"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def my_loss_fn(y_pred, y_true):\n",
        "    \"\"\"\n",
        "    Dummy example similar to using default torch.nn.functional.cross_entropy\n",
        "    \"\"\"\n",
        "    softmax_pred = torch.nn.Softmax(dim=-1)(y_pred)\n",
        "    logloss = (1-y_true)*torch.log(softmax_pred[:,0])\n",
        "    logloss += y_true*torch.log(softmax_pred[:,1])\n",
        "    return -torch.mean(logloss)"
      ],
      "metadata": {
        "id": "AW-soTdovtx3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class my_metric(Metric):\n",
        "    \"\"\"\n",
        "    2xAUC.\n",
        "    \"\"\"\n",
        "\n",
        "    def __init__(self):\n",
        "        self._name = \"custom\" # write an understandable name here\n",
        "        self._maximize = True\n",
        "\n",
        "    def __call__(self, y_true, y_score):\n",
        "        \"\"\"\n",
        "        Compute AUC of predictions.\n",
        "\n",
        "        Parameters\n",
        "        ----------\n",
        "        y_true: np.ndarray\n",
        "            Target matrix or vector\n",
        "        y_score: np.ndarray\n",
        "            Score matrix or vector\n",
        "\n",
        "        Returns\n",
        "        -------\n",
        "            float\n",
        "            AUC of predictions vs targets.\n",
        "        \"\"\"\n",
        "        return 2*roc_auc_score(y_true, y_score[:, 1])"
      ],
      "metadata": {
        "id": "Xog92ODWv36t"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "0EDRhjr018eE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "clf.fit(\n",
        "    X_train=X_train, y_train=y_train,\n",
        "    eval_set=[(X_train, y_train), (X_test, y_test)],\n",
        "    eval_name=['train', 'val'],\n",
        "    eval_metric=[\"auc\", my_metric],\n",
        "    max_epochs=max_epochs , patience=0,\n",
        "    batch_size=batch_size,\n",
        "    virtual_batch_size=128,\n",
        "    num_workers=0,\n",
        "    weights=1,\n",
        "    drop_last=False,\n",
        "    loss_fn=my_loss_fn)   \n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 520
        },
        "id": "GbC9cOvCxjR-",
        "outputId": "f0e7d805-b012-4145-e91c-c1e01b593f13"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "No early stopping will be performed, last training weights will be used.\n"
          ]
        },
        {
          "output_type": "error",
          "ename": "KeyError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
            "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   3360\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3361\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3362\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/pandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/pandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;31mKeyError\u001b[0m: 282",
            "\nThe above exception was the direct cause of the following exception:\n",
            "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-45-7af29e8766d6>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m clf.fit(\n\u001b[0m\u001b[1;32m      2\u001b[0m     \u001b[0mX_train\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0my_train\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m     \u001b[0meval_set\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mX_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m     \u001b[0meval_name\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'train'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'val'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m     \u001b[0meval_metric\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"auc\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmy_metric\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/pytorch_tabnet/abstract_model.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X_train, y_train, eval_set, eval_name, eval_metric, loss_fn, weights, max_epochs, patience, batch_size, virtual_batch_size, num_workers, drop_last, callbacks, pin_memory, from_unsupervised)\u001b[0m\n\u001b[1;32m    221\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_callback_container\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_epoch_begin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mepoch_idx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    222\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 223\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_train_epoch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain_dataloader\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    224\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    225\u001b[0m             \u001b[0;31m# Apply predict epoch to all eval sets\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/pytorch_tabnet/abstract_model.py\u001b[0m in \u001b[0;36m_train_epoch\u001b[0;34m(self, train_loader)\u001b[0m\n\u001b[1;32m    429\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnetwork\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    430\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 431\u001b[0;31m         \u001b[0;32mfor\u001b[0m \u001b[0mbatch_idx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain_loader\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    432\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_callback_container\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_batch_begin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch_idx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    433\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/torch/utils/data/dataloader.py\u001b[0m in \u001b[0;36m__next__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    626\u001b[0m                 \u001b[0;31m# TODO(https://github.com/pytorch/pytorch/issues/76750)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    627\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_reset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# type: ignore[call-arg]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 628\u001b[0;31m             \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_next_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    629\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_num_yielded\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    630\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_dataset_kind\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0m_DatasetKind\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mIterable\u001b[0m \u001b[0;32mand\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m\\\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/torch/utils/data/dataloader.py\u001b[0m in \u001b[0;36m_next_data\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    669\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_next_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    670\u001b[0m         \u001b[0mindex\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_next_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# may raise StopIteration\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 671\u001b[0;31m         \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_dataset_fetcher\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfetch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# may raise StopIteration\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    672\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_pin_memory\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    673\u001b[0m             \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_utils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpin_memory\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpin_memory\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_pin_memory_device\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/torch/utils/data/_utils/fetch.py\u001b[0m in \u001b[0;36mfetch\u001b[0;34m(self, possibly_batched_index)\u001b[0m\n\u001b[1;32m     56\u001b[0m                 \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__getitems__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpossibly_batched_index\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     57\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 58\u001b[0;31m                 \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0midx\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0midx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mpossibly_batched_index\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     59\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     60\u001b[0m             \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mpossibly_batched_index\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/torch/utils/data/_utils/fetch.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m     56\u001b[0m                 \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__getitems__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpossibly_batched_index\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     57\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 58\u001b[0;31m                 \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0midx\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0midx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mpossibly_batched_index\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     59\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     60\u001b[0m             \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mpossibly_batched_index\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/pytorch_tabnet/utils.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, index)\u001b[0m\n\u001b[1;32m     28\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     29\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__getitem__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 30\u001b[0;31m         \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     31\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     32\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   3456\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnlevels\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3457\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3458\u001b[0;31m             \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3459\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mis_integer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3460\u001b[0m                 \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   3361\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3362\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3363\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3364\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3365\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mis_scalar\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0misna\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhasnans\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyError\u001b[0m: 282"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from pytorch_tabnet.tab_model import TabNetClassifier, TabNetRegressor\n",
        "\n",
        "clf.fit(X_train, y_train, eval_set=[(X_test, y_test)])\n",
        "# preds = clf.predict(X_test)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 520
        },
        "id": "j95CRxzZpJJJ",
        "outputId": "6f4a0a39-519a-4295-d714-f5c04b1c4ea5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Device used : cpu\n"
          ]
        },
        {
          "output_type": "error",
          "ename": "KeyError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
            "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   3360\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3361\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3362\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/pandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/pandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;31mKeyError\u001b[0m: 218",
            "\nThe above exception was the direct cause of the following exception:\n",
            "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-36-126c403e80f8>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mclf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mTabNetClassifier\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m#TabNetRegressor()\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mclf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0meval_set\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      5\u001b[0m \u001b[0;31m# preds = clf.predict(X_test)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/pytorch_tabnet/abstract_model.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X_train, y_train, eval_set, eval_name, eval_metric, loss_fn, weights, max_epochs, patience, batch_size, virtual_batch_size, num_workers, drop_last, callbacks, pin_memory, from_unsupervised)\u001b[0m\n\u001b[1;32m    221\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_callback_container\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_epoch_begin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mepoch_idx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    222\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 223\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_train_epoch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain_dataloader\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    224\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    225\u001b[0m             \u001b[0;31m# Apply predict epoch to all eval sets\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/pytorch_tabnet/abstract_model.py\u001b[0m in \u001b[0;36m_train_epoch\u001b[0;34m(self, train_loader)\u001b[0m\n\u001b[1;32m    429\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnetwork\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    430\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 431\u001b[0;31m         \u001b[0;32mfor\u001b[0m \u001b[0mbatch_idx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain_loader\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    432\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_callback_container\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_batch_begin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch_idx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    433\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/torch/utils/data/dataloader.py\u001b[0m in \u001b[0;36m__next__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    626\u001b[0m                 \u001b[0;31m# TODO(https://github.com/pytorch/pytorch/issues/76750)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    627\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_reset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# type: ignore[call-arg]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 628\u001b[0;31m             \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_next_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    629\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_num_yielded\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    630\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_dataset_kind\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0m_DatasetKind\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mIterable\u001b[0m \u001b[0;32mand\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m\\\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/torch/utils/data/dataloader.py\u001b[0m in \u001b[0;36m_next_data\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    669\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_next_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    670\u001b[0m         \u001b[0mindex\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_next_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# may raise StopIteration\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 671\u001b[0;31m         \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_dataset_fetcher\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfetch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# may raise StopIteration\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    672\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_pin_memory\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    673\u001b[0m             \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_utils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpin_memory\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpin_memory\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_pin_memory_device\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/torch/utils/data/_utils/fetch.py\u001b[0m in \u001b[0;36mfetch\u001b[0;34m(self, possibly_batched_index)\u001b[0m\n\u001b[1;32m     56\u001b[0m                 \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__getitems__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpossibly_batched_index\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     57\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 58\u001b[0;31m                 \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0midx\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0midx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mpossibly_batched_index\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     59\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     60\u001b[0m             \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mpossibly_batched_index\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/torch/utils/data/_utils/fetch.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m     56\u001b[0m                 \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__getitems__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpossibly_batched_index\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     57\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 58\u001b[0;31m                 \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0midx\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0midx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mpossibly_batched_index\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     59\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     60\u001b[0m             \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mpossibly_batched_index\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/pytorch_tabnet/utils.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, index)\u001b[0m\n\u001b[1;32m     28\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     29\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__getitem__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 30\u001b[0;31m         \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     31\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     32\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   3456\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnlevels\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3457\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3458\u001b[0;31m             \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3459\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mis_integer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3460\u001b[0m                 \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   3361\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3362\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3363\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3364\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3365\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mis_scalar\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0misna\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhasnans\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyError\u001b[0m: 218"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "qbf8yZtMkZK7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "zu5E6Bbgczr7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Rm-5mQ9wczxF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "YaAMKsqTcz1C"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "URHZftwqcz6F"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 4.Model Comparison"
      ],
      "metadata": {
        "id": "1YwpCO-LHGWh"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "_51zBFdKHGcp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 4.1 Ensemble\n",
        "1. [K-fold CV + stacking Ensemble](https://www.kaggle.com/code/crownsando/kakr-4th-seminar-model-validation-ensemble)\n",
        "\n",
        "2. [VotingClassifier](https://dacon.io/competitions/official/236035/codeshare/7430?page=1&dtype=recent)"
      ],
      "metadata": {
        "id": "3s2Na48HHGhp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n"
      ],
      "metadata": {
        "id": "gSZlZQe8HGno"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 5.Inference\n",
        "- finally, performs perdiction for test set"
      ],
      "metadata": {
        "id": "ex7768-Ktt2u"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# test_x.describe()"
      ],
      "metadata": {
        "id": "o6wlxZ63-aor"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# preds = RF_model.predict(test_x)\n",
        "# print('Done.')"
      ],
      "metadata": {
        "id": "sUcI9qjRtyU2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# test_x['Y_Class']=preds\n",
        "# preds"
      ],
      "metadata": {
        "id": "IYsJ59R8DvKQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "OhYKzO279lgJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 6.Submit"
      ],
      "metadata": {
        "id": "zZCIp_9ct2bQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "submit = pd.read_csv('/content/drive/MyDrive/sample_submission.csv')  \n",
        "\n",
        "submit['Y_Class'] = preds \n",
        "submit.to_csv('smartFactory_submission.csv', index=False)  "
      ],
      "metadata": {
        "id": "Cyfb21qOt4dn"
      },
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sns.countplot(x=preds, order=[0,1,2]);"
      ],
      "metadata": {
        "id": "l4x3CNe09oiT",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "outputId": "35ad2e9e-2ef0-4076-c4d2-2a5047c2106e"
      },
      "execution_count": 31,
      "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"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# THE END\n"
      ],
      "metadata": {
        "id": "hcNFz5zguHMQ"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "5asYFRqp7j0f"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}