▶smartFactoryFINAL_teamShare (5).ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZJBm2amd3Lb5"
      },
      "source": [
        "# 팀 스마일C 코드 제출\n",
        "* 팀원: 정우섭, 김유민, 김유진, 장동언, 황정묵\n",
        "* PRIVATE SCORE: 0.65887 \n",
        "* PRIVATE RANKING: 36"
      ],
      "id": "ZJBm2amd3Lb5"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CwuGPDTVMXHe"
      },
      "source": [
        "# 개발 환경\n",
        "* OS : \n",
        "* 라이브러리 버전 : "
      ],
      "id": "CwuGPDTVMXHe"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QIfodgpc3HP6"
      },
      "source": [
        "# 0.Data Load"
      ],
      "id": "QIfodgpc3HP6"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "6ab709fe",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4040357a-cb08-478b-ca9e-73a61bfe515a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting catboost\n",
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            "Requirement already satisfied: pyparsing>=2.2.1 in /usr/local/lib/python3.8/dist-packages (from matplotlib->catboost) (3.0.9)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.8/dist-packages (from matplotlib->catboost) (0.11.0)\n",
            "Requirement already satisfied: tenacity>=6.2.0 in /usr/local/lib/python3.8/dist-packages (from plotly->catboost) (8.2.2)\n",
            "Installing collected packages: catboost\n",
            "Successfully installed catboost-1.1.1\n",
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: lightgbm in /usr/local/lib/python3.8/dist-packages (2.2.3)\n",
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            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.8/dist-packages (from scikit-learn->lightgbm) (3.1.0)\n",
            "Requirement already satisfied: joblib>=1.1.1 in /usr/local/lib/python3.8/dist-packages (from scikit-learn->lightgbm) (1.2.0)\n",
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting optuna\n",
            "  Downloading optuna-3.1.0-py3-none-any.whl (365 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m365.3/365.3 KB\u001b[0m \u001b[31m7.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting cmaes>=0.9.1\n",
            "  Downloading cmaes-0.9.1-py3-none-any.whl (21 kB)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.8/dist-packages (from optuna) (4.64.1)\n",
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            "  Downloading colorlog-6.7.0-py2.py3-none-any.whl (11 kB)\n",
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            "Collecting alembic>=1.5.0\n",
            "  Downloading alembic-1.10.1-py3-none-any.whl (212 kB)\n",
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            "Installing collected packages: Mako, colorlog, cmaes, alembic, optuna\n",
            "Successfully installed Mako-1.2.4 alembic-1.10.1 cmaes-0.9.1 colorlog-6.7.0 optuna-3.1.0\n"
          ]
        }
      ],
      "source": [
        "!pip install catboost\n",
        "!pip install lightgbm\n",
        "!pip install optuna   "
      ],
      "id": "6ab709fe"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "94682a80",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "616d4428-407e-46df-ad1f-89703c0cad97"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "import pandas as pd\n",
        "import random\n",
        "import os\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt   \n",
        "import seaborn as sns \n",
        "\n",
        "# model evaluation, preprocessing \n",
        "import sklearn\n",
        "from sklearn import preprocessing, metrics\n",
        "from sklearn.preprocessing import LabelEncoder, OneHotEncoder, StandardScaler, MinMaxScaler\n",
        "from sklearn.model_selection import train_test_split, KFold, cross_validate\n",
        "from sklearn.metrics import accuracy_score, classification_report, f1_score, roc_auc_score, log_loss\n",
        "\n",
        "# sequential model modeling \n",
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "from keras import regularizers\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Dense,Dropout, Activation\n",
        "from keras import backend as K \n",
        "from keras import regularizers\n",
        "from keras.layers import Dense,Dropout, Activation, BatchNormalization, Conv2D, Flatten  \n",
        "from keras import optimizers, metrics, callbacks\n",
        "from keras.backend import clear_session\n",
        "\n",
        "# optuna Hyper-params tuning\n",
        "import optuna\n",
        "from optuna import Trial\n",
        "from optuna.samplers import TPESampler\n",
        "from optuna.visualization import plot_optimization_history, plot_param_importances, plot_parallel_coordinate, plot_contour\n",
        "\n",
        "# ML classification models \n",
        "from lightgbm import LGBMClassifier\n",
        "from catboost import CatBoostClassifier\n",
        "from sklearn.ensemble import BaggingClassifier, GradientBoostingClassifier, RandomForestClassifier\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.linear_model import RidgeClassifierCV\n",
        "from xgboost import XGBClassifier\n",
        "\n",
        "# Google Drive mount\n",
        "from google.colab import drive \n",
        "drive.mount('/content/drive')"
      ],
      "id": "94682a80"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "0AdADLxT1DoQ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "889b4c44-2974-4ef0-c604-ef571149dfbf"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "37\n"
          ]
        }
      ],
      "source": [
        "# seed fixing for reproduction \n",
        "import torch \n",
        "import random as rn \n",
        "\n",
        "def seed_everything(seed):\n",
        "    random.seed(seed)\n",
        "    os.environ['PYTHONHASHSEED'] = str(seed)\n",
        "    np.random.seed(seed)\n",
        "seed_everything(37) # Seed 고정\n",
        "seed_num=37\n",
        "\n",
        "def seed_everything(seed):\n",
        "    random.seed(seed)\n",
        "    os.environ['PYTHONHASHSEED'] = str(seed)\n",
        "    np.random.seed(seed)\n",
        "    torch.manual_seed(seed)\n",
        "    torch.cuda.manual_seed(seed)  # type: ignore\n",
        "    torch.backends.cudnn.deterministic = True  # type: ignore\n",
        "    torch.backends.cudnn.benchmark = True  # type: ignore\n",
        "seed_everything(37)\n",
        "seed_num=37\n",
        "seed=37\n",
        "\n",
        "np.random.seed(seed_num)\n",
        "rn.seed(seed_num) \n",
        "tf.random.set_seed(seed_num)\n",
        "session_conf = tf.compat.v1.ConfigProto(intra_op_parallelism_threads=1, inter_op_parallelism_threads=1)\n",
        "sess = tf.compat.v1.Session(config=session_conf)\n",
        "K.set_session(sess)\n",
        "\n",
        "class config:   \n",
        "    seed = 37  \n",
        "    device = \"cuda:0\"            \n",
        "\n",
        "def seed_everything(seed: int = 37):\n",
        "    random.seed(seed)\n",
        "    np.random.seed(seed)\n",
        "    os.environ[\"PYTHONHASHSEED\"] = str(seed)\n",
        "    tf.random.set_seed(seed)\n",
        "seed_everything(config.seed)\n",
        "tf.random.set_seed(37) # tensorflow global seed \n",
        "print(config.seed)     "
      ],
      "id": "0AdADLxT1DoQ"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "lQROx2fcxIW7"
      },
      "outputs": [],
      "source": [
        "train_df = pd.read_csv('/content/drive/MyDrive/train.csv')\n",
        "test_df = pd.read_csv('/content/drive/MyDrive/test.csv')\n",
        "submit = pd.read_csv('/content/drive/MyDrive/sample_submission.csv')\n",
        "\n",
        "train_x = train_df.drop(columns=['PRODUCT_ID', 'TIMESTAMP', 'Y_Class','Y_Quality'])\n",
        "train_y = train_df['Y_Class']\n",
        "test_x = test_df.drop(columns=['PRODUCT_ID', 'TIMESTAMP'])   "
      ],
      "id": "lQROx2fcxIW7"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wqkW_3EbQxj1"
      },
      "source": [
        "# 1.Data & Features"
      ],
      "id": "wqkW_3EbQxj1"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZK3O2Kd24yP_"
      },
      "source": [
        "## 1.1 EDA"
      ],
      "id": "ZK3O2Kd24yP_"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 632
        },
        "id": "E1vfXTKcK9Dt",
        "outputId": "d5050ba1-2758-4481-9cd7-f2d9e1336207"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x720 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# random 20 features list (모두 NaN인 컬럼 제외하고, 랜덤 20개 feature의 상관관계 히트맵)\n",
        "Xs = train_df.iloc[:,4:].columns\n",
        "null_count = train_df.isnull().sum().to_dict()\n",
        "cols = pd.DataFrame({i for i in null_count if null_count[i]<250 and i in Xs})\n",
        "cols_name = list(cols[0])  # not_all_NAN features name in list \n",
        "\n",
        "import random\n",
        "a = ['Y_Quality']\n",
        "for i in range(20):\n",
        "    num = random.randint(1,2875)\n",
        "    a.append(cols_name[i])\n",
        "\n",
        "# heatmap: `random X features` and `Y_Quality` correlation\n",
        "corr = train_df[a].corr()  # correlation matrix\n",
        "fig, ax = plt.subplots(figsize=(15,10))\n",
        "sns.heatmap(corr, annot=True, fmt='.2f', cmap='Blues', linewidths=2);"
      ],
      "id": "E1vfXTKcK9Dt"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 592
        },
        "id": "lLsOtCgTSvRA",
        "outputId": "4eaafee2-3227-473e-e26d-9558a341d2f6"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x720 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# feature 중요도\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from sklearn.ensemble import RandomForestRegressor\n",
        "\n",
        "qual_col = ['LINE', 'PRODUCT_CODE']\n",
        "for i in qual_col:\n",
        "    le = LabelEncoder()\n",
        "    le = le.fit(train_x[i])\n",
        "    train_x[i] = le.transform(train_x[i])\n",
        "    for label in np.unique(test_x[i]):\n",
        "        if label not in le.classes_:\n",
        "            le.classes_ = np.append(le.classes_, label)\n",
        "    test_x[i] = le.transform(test_x[i])\n",
        "\n",
        "train_x = train_x.fillna(0)\n",
        "test_x = test_x.fillna(0)\n",
        "X_train, X_test, y_train, y_test=train_test_split(train_x,train_y,test_size=0.3,random_state=seed_num)\n",
        "\n",
        "%matplotlib inline\n",
        "RF = RandomForestRegressor(random_state=0, max_depth=5, min_samples_leaf=8, min_samples_split=8,n_estimators=200)\n",
        "RF.fit(X_train, y_train)\n",
        "ftr_importances_values = RF.feature_importances_\n",
        "ftr_importances = pd.Series(ftr_importances_values, index=X_train.columns)\n",
        "ftr_top = ftr_importances.sort_values(ascending=False)[:20]\n",
        " \n",
        "plt.figure(figsize=(10, 10))\n",
        "sns.barplot(x=ftr_top, y=ftr_top.index)\n",
        "plt.show()      "
      ],
      "id": "lLsOtCgTSvRA"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 244
        },
        "id": "cAqWZJ7NSva4",
        "outputId": "a66fa826-c342-4706-de37-e023b24f081f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Done.\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-9-565d2ac6028a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0my_pred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[0mtest_x\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtest_x\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mftr_top_idx\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 10\u001b[0;31m \u001b[0mpreds\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtest_x\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\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     11\u001b[0m \u001b[0msns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcountplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpreds\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;31mTypeError\u001b[0m: predict() takes 2 positional arguments but 3 were given"
          ]
        }
      ],
      "source": [
        "ftr_top = pd.DataFrame(ftr_top)\n",
        "ftr_top_idx = list(ftr_top.index)\n",
        "x=train_x.loc[:,ftr_top_idx]\n",
        "y=train_y\n",
        "\n",
        "model = GradientBoostingClassifier(random_state=37).fit(X_train, y_train)\n",
        "print('Done.')\n",
        "y_pred = model.predict(X_test)\n",
        "test_x=test_x.loc[:,ftr_top_idx]\n",
        "preds = model.predict(test_x, y_pred)\n",
        "sns.countplot(x=preds);      "
      ],
      "id": "cAqWZJ7NSva4"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 394
        },
        "id": "GdugZkQrSvJS",
        "outputId": "233242ad-180e-4fce-aef9-e9724169ce2d"
      },
      "outputs": [
        {
          "data": {
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              "      <th></th>\n",
              "      <th>LINE</th>\n",
              "      <th>PRODUCT_CODE</th>\n",
              "      <th>X_1</th>\n",
              "      <th>X_2</th>\n",
              "      <th>X_3</th>\n",
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              "      <th>X_7</th>\n",
              "      <th>X_8</th>\n",
              "      <th>...</th>\n",
              "      <th>X_2866</th>\n",
              "      <th>X_2867</th>\n",
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              "      <th>X_2869</th>\n",
              "      <th>X_2870</th>\n",
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              "      <th>X_2875</th>\n",
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              "  </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",
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              "      <td>598.000000</td>\n",
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              "      <td>598.0</td>\n",
              "      <td>598.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>3.214047</td>\n",
              "      <td>1.157191</td>\n",
              "      <td>1.406355</td>\n",
              "      <td>0.544265</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.583612</td>\n",
              "      <td>0.551383</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.459381</td>\n",
              "      <td>0.533141</td>\n",
              "      <td>...</td>\n",
              "      <td>0.141039</td>\n",
              "      <td>0.130564</td>\n",
              "      <td>0.137318</td>\n",
              "      <td>0.144148</td>\n",
              "      <td>0.139755</td>\n",
              "      <td>0.165552</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>1.703579</td>\n",
              "      <td>0.983298</td>\n",
              "      <td>4.655332</td>\n",
              "      <td>0.461135</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.493372</td>\n",
              "      <td>0.467361</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.392067</td>\n",
              "      <td>0.450953</td>\n",
              "      <td>...</td>\n",
              "      <td>0.318552</td>\n",
              "      <td>0.295577</td>\n",
              "      <td>0.310467</td>\n",
              "      <td>0.327595</td>\n",
              "      <td>0.312991</td>\n",
              "      <td>0.371989</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
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              "      <td>0.000000</td>\n",
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              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
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              "      <td>0.0</td>\n",
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              "      <td>2.000000</td>\n",
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              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
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              "      <td>0.000000</td>\n",
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              "      <td>0.0</td>\n",
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              "      <td>0.000000</td>\n",
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              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "      <th>max</th>\n",
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              "      <td>2.000000</td>\n",
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              "      <td>1.000000</td>\n",
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              "      <td>1.000000</td>\n",
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              "</table>\n",
              "<p>8 rows × 2877 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-3f6e4f9b-c31c-43fe-8317-2818b5de14e5')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
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              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
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              "      border-radius: 50%;\n",
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              "      fill: #1967D2;\n",
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              "    }\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-3f6e4f9b-c31c-43fe-8317-2818b5de14e5 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-3f6e4f9b-c31c-43fe-8317-2818b5de14e5');\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",
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              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "             LINE  PRODUCT_CODE         X_1         X_2    X_3         X_4  \\\n",
              "count  598.000000    598.000000  598.000000  598.000000  598.0  598.000000   \n",
              "mean     3.214047      1.157191    1.406355    0.544265    0.0    0.583612   \n",
              "std      1.703579      0.983298    4.655332    0.461135    0.0    0.493372   \n",
              "min      0.000000      0.000000    0.000000    0.000000    0.0    0.000000   \n",
              "25%      2.000000      0.000000    0.000000    0.000000    0.0    0.000000   \n",
              "50%      4.000000      2.000000    2.000000    0.892157    0.0    1.000000   \n",
              "75%      5.000000      2.000000    2.000000    0.931373    0.0    1.000000   \n",
              "max      5.000000      2.000000  103.000000    1.000000    0.0    1.000000   \n",
              "\n",
              "              X_5    X_6         X_7         X_8  ...      X_2866      X_2867  \\\n",
              "count  598.000000  598.0  598.000000  598.000000  ...  598.000000  598.000000   \n",
              "mean     0.551383    0.0    0.459381    0.533141  ...    0.141039    0.130564   \n",
              "std      0.467361    0.0    0.392067    0.450953  ...    0.318552    0.295577   \n",
              "min      0.000000    0.0    0.000000    0.000000  ...    0.000000    0.000000   \n",
              "25%      0.000000    0.0    0.000000    0.000000  ...    0.000000    0.000000   \n",
              "50%      0.909091    0.0    0.725806    0.909091  ...    0.000000    0.000000   \n",
              "75%      0.909091    0.0    0.806452    0.909091  ...    0.000000    0.000000   \n",
              "max      1.000000    0.0    1.000000    1.000000  ...    1.000000    1.000000   \n",
              "\n",
              "           X_2868      X_2869      X_2870      X_2871  X_2872  X_2873  X_2874  \\\n",
              "count  598.000000  598.000000  598.000000  598.000000   598.0   598.0   598.0   \n",
              "mean     0.137318    0.144148    0.139755    0.165552     0.0     0.0     0.0   \n",
              "std      0.310467    0.327595    0.312991    0.371989     0.0     0.0     0.0   \n",
              "min      0.000000    0.000000    0.000000    0.000000     0.0     0.0     0.0   \n",
              "25%      0.000000    0.000000    0.000000    0.000000     0.0     0.0     0.0   \n",
              "50%      0.000000    0.000000    0.000000    0.000000     0.0     0.0     0.0   \n",
              "75%      0.000000    0.000000    0.000000    0.000000     0.0     0.0     0.0   \n",
              "max      1.000000    1.000000    1.000000    1.000000     0.0     0.0     0.0   \n",
              "\n",
              "       X_2875  \n",
              "count   598.0  \n",
              "mean      0.0  \n",
              "std       0.0  \n",
              "min       0.0  \n",
              "25%       0.0  \n",
              "50%       0.0  \n",
              "75%       0.0  \n",
              "max       0.0  \n",
              "\n",
              "[8 rows x 2877 columns]"
            ]
          },
          "execution_count": 56,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# feature distribution \n",
        "train_x.describe()    "
      ],
      "id": "GdugZkQrSvJS"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "fceMKvgyg2Kg",
        "outputId": "7af724ba-e6cd-47be-d0ac-4d921e97d9fb"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<AxesSubplot:xlabel='X_1712', ylabel='Count'>"
            ]
          },
          "execution_count": 117,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# feature histogram with KDE (밀도그래프) \n",
        "# 임의의 feature에 대한 histplot + 밀도 곡선 출력\n",
        "num = random.randint(1,2875)\n",
        "sns.histplot(train_x[f'X_{num}'], kde=True)"
      ],
      "id": "fceMKvgyg2Kg"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 700
        },
        "id": "xskVkfufSvDg",
        "outputId": "11175f8c-e10f-4c58-fbbc-8a3a8d2db3ba"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1152x864 with 20 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# visualize random 20 features distribution \n",
        "# imbalanced feature distribution (NO Gaussian distribution shape) --> MinMaxscaler\n",
        "fig, axes = plt.subplots(5,4,figsize=(16,12))\n",
        "ax= axes.flatten()\n",
        "\n",
        "for i in range(20): \n",
        "    num = random.randint(1,2875)\n",
        "    ax[i].hist(train_x[f'X_{num}']);    "
      ],
      "id": "xskVkfufSvDg"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xuPOUxQ7Su9m"
      },
      "source": [
        "## 1.2 Preprocessing\n",
        "1. Label Encoding: categorical values `LINE`, `PRODUCT_CODE`\n",
        "2. Missing values: fillna(0) \n",
        "3. scaling: StandardScaler "
      ],
      "id": "xuPOUxQ7Su9m"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "kZ1PM_Zrtzph"
      },
      "outputs": [],
      "source": [
        "train_df = pd.read_csv('/content/drive/MyDrive/train.csv')\n",
        "test_df = pd.read_csv('/content/drive/MyDrive/test.csv')\n",
        "submit = pd.read_csv('/content/drive/MyDrive/sample_submission.csv')\n",
        "\n",
        "train_x = train_df.drop(columns=['PRODUCT_ID', 'TIMESTAMP', 'Y_Class','Y_Quality'])\n",
        "train_y = train_df['Y_Class']\n",
        "test_x = test_df.drop(columns=['PRODUCT_ID', 'TIMESTAMP'])   "
      ],
      "id": "kZ1PM_Zrtzph"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "3AbGp2rBuxuW"
      },
      "outputs": [],
      "source": [
        "# 1) qualitative to quantitative\n",
        "qual_col = ['LINE', 'PRODUCT_CODE']\n",
        "for i in qual_col:\n",
        "    le = LabelEncoder()    # one-hot encoding (X): get_dummies(test_df) is not allowed (data leakage)   \n",
        "    le = le.fit(train_x[i])\n",
        "    train_x[i] = le.transform(train_x[i])\n",
        "    for label in np.unique(test_x[i]): \n",
        "        if label not in le.classes_: \n",
        "            le.classes_ = np.append(le.classes_, label)\n",
        "    test_x[i] = le.transform(test_x[i]) \n"
      ],
      "id": "3AbGp2rBuxuW"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "GZ72F04guy3o"
      },
      "outputs": [],
      "source": [
        "# 2) Missing Values \n",
        "train_x = train_x.fillna(0)\n",
        "test_x = test_x.fillna(0)"
      ],
      "id": "GZ72F04guy3o"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "c66d3bf4"
      },
      "outputs": [],
      "source": [
        "# 3) MinMaxscaling: only `X_???` values (continuous)\n",
        "Xs = train_x.select_dtypes(include=float).iloc[:,1:].columns.tolist()\n",
        "scaler = MinMaxScaler().fit(train_x.loc[:, Xs])\n",
        "train_x.loc[:, Xs] = scaler.transform(train_x.loc[:, Xs])\n",
        "test_x.loc[:, Xs] = scaler.transform(test_x.loc[:, Xs])\n",
        "\n",
        "X_train, X_test, y_train, y_test=train_test_split(train_x,train_y,test_size=0.3,random_state=seed_num)"
      ],
      "id": "c66d3bf4"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hIoaatyGu9Zq"
      },
      "source": [
        "# 2.Modeling"
      ],
      "id": "hIoaatyGu9Zq"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GQGMvepMJy87"
      },
      "source": [
        "## 2.1 ML models\n",
        "- 여러 Machine Learning Classifier 모델들의 test set Accuracy score 도출 \n",
        "    - Ensemble에서 각 모델의 weight(가중치) 정하기 위한 근거로 사용하기 위함\n",
        "1. RandomForestClassifier\n",
        "2. GradientboostingClassiifer\n",
        "3. XGBClassifier\n",
        "4. LGBMClassifier\n",
        "5. CatboostClassifier\n",
        "6. RidgeClassifier\n",
        "7. BaggingClassifier"
      ],
      "id": "GQGMvepMJy87"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "m61QOAHA8lvG"
      },
      "outputs": [],
      "source": [
        "from catboost import CatBoostClassifier\n",
        "from sklearn.ensemble import GradientBoostingClassifier\n",
        "from sklearn.ensemble import StackingClassifier\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "\n",
        "models = [\n",
        "    RandomForestClassifier(random_state=seed_num), \n",
        "    GradientBoostingClassifier(random_state=seed_num),      \n",
        "    XGBClassifier(random_state=seed_num),\n",
        "    LGBMClassifier(objective='multiclass', random_state=seed_num),\n",
        "    CatBoostClassifier(objective='MultiClass',\n",
        "                                   task_type='GPU',\n",
        "                                   one_hot_max_size=2, random_seed=seed_num,\n",
        "                                   iterations=4000, verbose=False,\n",
        "                                   learning_rate=0.05\n",
        "                                   ),\n",
        "    RidgeClassifierCV(),  # RidgeClassifier: no seed setting argument  \n",
        "    BaggingClassifier(random_state=seed_num)]"
      ],
      "id": "m61QOAHA8lvG"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "qfV3aJaS8lvI",
        "outputId": "0fc7966a-e003-42c3-ff8f-0dab005be719"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "{'RF': 0.7888888888888889, 'GBC': 0.8111111111111111, 'XGB': 0.7722222222222223, 'LGBM': 0.7888888888888889, 'Catboost': 0.8166666666666667, 'Ridge': 0.7611111111111111, 'Bagging': 0.75}\n"
          ]
        }
      ],
      "source": [
        "# find weights for each model \n",
        "model_list = ['RF', 'GBC', 'XGB', 'LGBM', 'Catboost', 'Ridge', 'Bagging']\n",
        "i=0\n",
        "model_acc = {}\n",
        "\n",
        "for model in models:  \n",
        "    if i == 4: \n",
        "        preds = model.fit(X_train, y_train).predict(X_test)\n",
        "        preds = preds.reshape(1,180)\n",
        "        preds = list(preds[0])\n",
        "    else: \n",
        "        preds = model.fit(X_train, y_train).predict(X_test)\n",
        "\n",
        "    globals()[f'preds_{model_list[i]}'] = preds   # set variable name for each model prediction results  \n",
        "  \n",
        "    score = sklearn.metrics.accuracy_score(y_test, preds)\n",
        "    model_acc[model_list[i]] = score\n",
        "    i += 1   \n",
        "\n",
        "print(model_acc)   "
      ],
      "id": "qfV3aJaS8lvI"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "PihsvBmKhfOd",
        "outputId": "8679b2e3-3e5a-429e-8eff-e9642220d0ac"
      },
      "outputs": [
        {
          "data": {
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\n",
            "text/plain": [
              "<Figure size 1008x576 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# ML models accuracy comparison plot \n",
        "model_name = list(model_acc.keys())\n",
        "model_acc = list(model_acc.values())\n",
        "acc_dic = {'model': model_name, 'acc': model_acc}\n",
        "acc_df = pd.DataFrame(acc_dic)\n",
        "acc_df = acc_df.set_index('model')\n",
        "\n",
        "acc_df.plot(kind='barh', figsize=(14,8),\n",
        "            title='Model Comparison - accuracy score');"
      ],
      "id": "PihsvBmKhfOd"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_KIk0bpq4IHj"
      },
      "source": [
        "## 2.2 sequential MLP "
      ],
      "id": "_KIk0bpq4IHj"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "8wlnknVPi0NX"
      },
      "outputs": [],
      "source": [
        "import torch.nn as nn \n",
        "import torch.nn.functional as F \n",
        "\n",
        "class MultilayerPerceptron(nn.Module):\n",
        "    def __init__(self, input_dim, hidden_dim1, hidden_dim2, hidden_dim3, output_dim):\n",
        "        \"\"\"\n",
        "        parameters: \n",
        "            input_dim (int): 입력 벡터 크기\n",
        "            hidden_dim1 (int): 첫 번째 Linear 층의 출력 크기\n",
        "            hidden_dim2 (int): 두 번째 Linear 층의 출력 크기 \n",
        "            output_dim (int): 세 번째 Linear 층의 출력 크기 \n",
        "        \"\"\"\n",
        "        super(MultilayerPerceptron, self).__init__()\n",
        "        self.fc1 = nn.Linear(input_dim, hidden_dim1)\n",
        "        self.fc2 = nn.Linear(hidden_dim1, hidden_dim2)\n",
        "        self.fc3 = nn.Linear(hidden_dim2, hidden_dim3)\n",
        "        self.fc4 = nn.Linear(hidden_dim3, output_dim)\n",
        "\n",
        "    def forward(self, x_in, apply_softmax=False):\n",
        "        \"\"\"\n",
        "        MLP의 정방향 계산 \n",
        "\n",
        "        parameters:\n",
        "            x_in (torch.Tensor): 입력 데이터 텐서\n",
        "                x_in.shape는 (batch, input_dim)\n",
        "            apply_softmax (multiclasses): softmax activation function \n",
        "        return: \n",
        "            result Tensor\n",
        "            tesnor.shape: (batch, output_dim)\n",
        "        \"\"\"\n",
        "        intermediate1 = F.relu(self.fc1(x_in))   # activation function\n",
        "        intermediate2 = F.relu(self.fc2(intermediate1))   # activation function\n",
        "        intermediate3 = F.relu(self.fc3(intermediate2))   # activation function\n",
        "        output = self.fc4(intermediate3)    \n",
        "\n",
        "\n",
        "        if apply_softmax: \n",
        "            output = F.softmax(output, dim=1)   # output layer: softmax activation function\n",
        "        return output "
      ],
      "id": "8wlnknVPi0NX"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Fp5qXV02kPeC",
        "outputId": "e94a2e80-08ac-4713-854f-ab987cb017c3"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "MultilayerPerceptron(\n",
            "  (fc1): Linear(in_features=2877, out_features=1024, bias=True)\n",
            "  (fc2): Linear(in_features=1024, out_features=512, bias=True)\n",
            "  (fc3): Linear(in_features=512, out_features=256, bias=True)\n",
            "  (fc4): Linear(in_features=256, out_features=3, bias=True)\n",
            ")\n",
            "Type: torch.FloatTensor\n",
            "shape: torch.Size([256, 3])\n"
          ]
        }
      ],
      "source": [
        "def describe(x): \n",
        "    print(f'Type: {x.type()}')\n",
        "    print(f'shape: {x.shape}')\n",
        "    # print(f'value: {x}') \n",
        "\n",
        "batch_size = 256 \n",
        "input_dim = train_x.shape[-1]\n",
        "hidden_dim1 = 1024\n",
        "hidden_dim2 = 512\n",
        "hidden_dim3 = 256\n",
        "output_dim = 3 \n",
        "\n",
        "# model design \n",
        "mlp = MultilayerPerceptron(input_dim, hidden_dim1, hidden_dim2, hidden_dim3, output_dim)\n",
        "print(mlp)\n",
        "x_input = torch.rand(batch_size, input_dim)\n",
        "y_output = mlp(x_input, apply_softmax=True)  # result: probabilities format \n",
        "a = y_output\n",
        "describe(y_output)"
      ],
      "id": "Fp5qXV02kPeC"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "FLHA36Z1iEhW",
        "outputId": "e608dd99-322b-4bbb-d2b2-e480ed26f890"
      },
      "outputs": [
        {
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "prediction=[]\n",
        "\n",
        "for i in a: \n",
        "    i = list(i)\n",
        "    prediction.append(i.index(max(i)))\n",
        "\n",
        "sns.countplot(x=prediction);    "
      ],
      "id": "FLHA36Z1iEhW"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "k1KBJb0mIYUI"
      },
      "outputs": [],
      "source": [
        "submit = pd.read_csv('/content/drive/MyDrive/sample_submission.csv')\n",
        "submit['Y_Class'] = prediction\n",
        "\n",
        "submit.to_csv('MLP-ANN-smartFactorySubmission.csv', index=False)     "
      ],
      "id": "k1KBJb0mIYUI"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wE74J0d04SNc"
      },
      "source": [
        "# 3.Params Optimization\n",
        "1. GridSearch \n",
        "2. Optuna\n",
        "3. RandomizedSearchCV"
      ],
      "id": "wE74J0d04SNc"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Fn79mJ8rn4Jn"
      },
      "source": [
        "## 3.1 GradientBoosting"
      ],
      "id": "Fn79mJ8rn4Jn"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "zHCvGdc9H067"
      },
      "outputs": [],
      "source": [
        "gb_class = GradientBoostingClassifier(random_state = 37) \n",
        "\n",
        "kf = KFold(n_splits=5, shuffle=True, random_state=37)\n",
        "\n",
        "scores = cross_validate(gb_class, X_train, y_train, \n",
        "                        cv = kf, scoring = \"accuracy\", \n",
        "                        return_train_score = True, n_jobs = -1) "
      ],
      "id": "zHCvGdc9H067"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Vsgw8TQrtpmQ",
        "outputId": "0da07f57-e8e8-4849-bf04-8e59c889e06c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "{'fit_time': array([0.40023923, 0.41092181, 0.39718652, 0.42171001, 0.30059123]), 'score_time': array([0.00424504, 0.00317955, 0.00248575, 0.00245333, 0.00216508]), 'test_score': array([0.75      , 0.71428571, 0.69047619, 0.71084337, 0.69879518]), 'train_score': array([0.90419162, 0.90718563, 0.91317365, 0.92238806, 0.90746269])}\n",
            "Best Accuracy: 71.0\n"
          ]
        }
      ],
      "source": [
        "print(scores) \n",
        "scoring = scores['test_score'].mean()\n",
        "print(f'Best Accuracy: {round(scoring, 2)*100}')  "
      ],
      "id": "Vsgw8TQrtpmQ"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "p3-9ibAcp96Q"
      },
      "outputs": [],
      "source": [
        "def objective(trial, X, y, cv, scoring):\n",
        "  params = {\n",
        "    \"n_estimators\": trial.suggest_int(\"n_estimators\", 100, 5000, step = 100),\n",
        "    \"learning_rate\": trial.suggest_float(\"learning_rate\", 1e-4, 0.3, log = True),\n",
        "    \"max_depth\": trial.suggest_int(\"max_depth\", 3, 9),\n",
        "    \"subsample\": trial.suggest_float(\"subsample\", 0.5, 0.9, step = 0.1),\n",
        "    \"max_features\": trial.suggest_categorical(\"max_features\", [\"auto\", \"sqrt\", \"log2\"]),\n",
        "    \"random_state\": 42,\n",
        "    }\n",
        "  # Perform cross validation\n",
        "  gb_class = GradientBoostingClassifier(**params)\n",
        "\n",
        "  # Compute scores\n",
        "  scores = cross_validate(gb_class, X, y, cv = cv, scoring = scoring, n_jobs = -1)\n",
        "  accuracy = scores[\"test_score\"].mean()\n",
        "\n",
        "  return accuracy"
      ],
      "id": "p3-9ibAcp96Q"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 800
        },
        "id": "gerDxJOEt9hE",
        "outputId": "ff277c69-8a47-4e9e-9648-41a776f2e041"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\u001b[32m[I 2023-03-01 15:15:54,013]\u001b[0m A new study created in memory with name: no-name-6926a03d-1efe-4862-95f6-591a38f14305\u001b[0m\n",
            "\u001b[33m[W 2023-03-01 15:16:03,251]\u001b[0m Trial 0 failed with parameters: {'n_estimators': 4000, 'learning_rate': 0.23414076793735694, 'max_depth': 3, 'subsample': 0.8, 'max_features': 'log2'} because of the following error: KeyboardInterrupt().\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-66-03e1837b5db3>\", line 5, in <lambda>\n",
            "    func = lambda trial: objective(trial, X_train, y_train,\n",
            "  File \"<ipython-input-65-8a203aa46cbb>\", line 26, in objective\n",
            "    scores = cross_validate(gb_class, X, y, cv = cv, scoring = scoring, n_jobs = -1)\n",
            "  File \"/usr/local/lib/python3.8/dist-packages/sklearn/model_selection/_validation.py\", line 267, in cross_validate\n",
            "    results = parallel(\n",
            "  File \"/usr/local/lib/python3.8/dist-packages/joblib/parallel.py\", line 1098, in __call__\n",
            "    self.retrieve()\n",
            "  File \"/usr/local/lib/python3.8/dist-packages/joblib/parallel.py\", line 975, in retrieve\n",
            "    self._output.extend(job.get(timeout=self.timeout))\n",
            "  File \"/usr/local/lib/python3.8/dist-packages/joblib/_parallel_backends.py\", line 567, in wrap_future_result\n",
            "    return future.result(timeout=timeout)\n",
            "  File \"/usr/lib/python3.8/concurrent/futures/_base.py\", line 439, in result\n",
            "    self._condition.wait(timeout)\n",
            "  File \"/usr/lib/python3.8/threading.py\", line 302, in wait\n",
            "    waiter.acquire()\n",
            "KeyboardInterrupt\n",
            "\u001b[33m[W 2023-03-01 15:16:03,256]\u001b[0m Trial 0 failed with value None.\u001b[0m\n"
          ]
        },
        {
          "ename": "KeyboardInterrupt",
          "evalue": "ignored",
          "output_type": "error",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-66-03e1837b5db3>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0;31m# %%time\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[0;31m# Start optimizing with 100 trials\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 10\u001b[0;31m \u001b[0mstudy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptimize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_trials\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m10\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     11\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     12\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"The highest accuracy reached by this study: {(study.best_value) * 100}%.\"\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-66-03e1837b5db3>\u001b[0m in \u001b[0;36m<lambda>\u001b[0;34m(trial)\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mkf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msklearn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodel_selection\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mStratifiedKFold\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn_splits\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mshuffle\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\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[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m func = lambda trial: objective(trial, X_train, y_train, \n\u001b[0m\u001b[1;32m      6\u001b[0m                                cv = kf, scoring = \"accuracy\")\n\u001b[1;32m      7\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m<ipython-input-65-8a203aa46cbb>\u001b[0m in \u001b[0;36mobjective\u001b[0;34m(trial, X, y, cv, scoring)\u001b[0m\n\u001b[1;32m     24\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     25\u001b[0m   \u001b[0;31m# Compute scores\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 26\u001b[0;31m   \u001b[0mscores\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcross_validate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgb_class\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[0mcv\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcv\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mscoring\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mscoring\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_jobs\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     27\u001b[0m   \u001b[0maccuracy\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mscores\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"test_score\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean\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     28\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/sklearn/model_selection/_validation.py\u001b[0m in \u001b[0;36mcross_validate\u001b[0;34m(estimator, X, y, groups, scoring, cv, n_jobs, verbose, fit_params, pre_dispatch, return_train_score, return_estimator, error_score)\u001b[0m\n\u001b[1;32m    265\u001b[0m     \u001b[0;31m# independent, and that it is pickle-able.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    266\u001b[0m     \u001b[0mparallel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mParallel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn_jobs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mn_jobs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mverbose\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mverbose\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpre_dispatch\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpre_dispatch\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 267\u001b[0;31m     results = parallel(\n\u001b[0m\u001b[1;32m    268\u001b[0m         delayed(_fit_and_score)(\n\u001b[1;32m    269\u001b[0m             \u001b[0mclone\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mestimator\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/joblib/parallel.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, iterable)\u001b[0m\n\u001b[1;32m   1096\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1097\u001b[0m             \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mretrieval_context\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-> 1098\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mretrieve\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   1099\u001b[0m             \u001b[0;31m# Make sure that we get a last message telling us we are done\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1100\u001b[0m             \u001b[0melapsed_time\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_start_time\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/joblib/parallel.py\u001b[0m in \u001b[0;36mretrieve\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    973\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    974\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'supports_timeout'\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[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 975\u001b[0;31m                     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_output\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mextend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mjob\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtimeout\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    976\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    977\u001b[0m                     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_output\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mextend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mjob\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\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/joblib/_parallel_backends.py\u001b[0m in \u001b[0;36mwrap_future_result\u001b[0;34m(future, timeout)\u001b[0m\n\u001b[1;32m    565\u001b[0m         AsyncResults.get from multiprocessing.\"\"\"\n\u001b[1;32m    566\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--> 567\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfuture\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtimeout\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    568\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mCfTimeoutError\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    569\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mTimeoutError\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/lib/python3.8/concurrent/futures/_base.py\u001b[0m in \u001b[0;36mresult\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m    437\u001b[0m                     \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__get_result\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    438\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 439\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_condition\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwait\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\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    440\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    441\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_state\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mCANCELLED\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mCANCELLED_AND_NOTIFIED\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/lib/python3.8/threading.py\u001b[0m in \u001b[0;36mwait\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m    300\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m    \u001b[0;31m# restore state no matter what (e.g., KeyboardInterrupt)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    301\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mtimeout\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--> 302\u001b[0;31m                 \u001b[0mwaiter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0macquire\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    303\u001b[0m                 \u001b[0mgotit\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    304\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;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ],
      "source": [
        "study = optuna.create_study(direction = \"maximize\")\n",
        "\n",
        "kf = sklearn.model_selection.StratifiedKFold(n_splits=5, shuffle=True, random_state=37)\n",
        "\n",
        "func = lambda trial: objective(trial, X_train, y_train, \n",
        "                               cv = kf, scoring = \"accuracy\")\n",
        "\n",
        "# %%time\n",
        "# Start optimizing with 10 trials\n",
        "study.optimize(func, n_trials = 10)\n",
        "\n",
        "print(f\"The highest accuracy reached by this study: {(study.best_value) * 100}%.\")\n",
        "print(\"Best params:\")\n",
        "for key, value in study.best_params.items():\n",
        "    print(f\"\\t{key}: {value}\")    "
      ],
      "id": "gerDxJOEt9hE"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 574
        },
        "id": "S4zxDMJ4YGGd",
        "outputId": "b3732692-a4ad-4cac-fdc6-0c5c63fcb791"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:493: FutureWarning: The feature names should match those that were passed during fit. Starting version 1.2, an error will be raised.\n",
            "Feature names seen at fit time, yet now missing:\n",
            "- LINE\n",
            "- PRODUCT_CODE\n",
            "- X_1\n",
            "- X_10\n",
            "- X_100\n",
            "- ...\n",
            "\n",
            "  warnings.warn(message, FutureWarning)\n"
          ]
        },
        {
          "ename": "ValueError",
          "evalue": "ignored",
          "output_type": "error",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-67-58403fe7bb6d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      9\u001b[0m GBC =  GradientBoostingClassifier(random_state=37, **param,                              \n\u001b[1;32m     10\u001b[0m                                   ).fit(train_x, train_y)\n\u001b[0;32m---> 11\u001b[0;31m \u001b[0mpreds\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mGBC\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtest_x\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     12\u001b[0m \u001b[0msns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcountplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpreds\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     13\u001b[0m \u001b[0mpreds\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/sklearn/ensemble/_gb.py\u001b[0m in \u001b[0;36mpredict\u001b[0;34m(self, X)\u001b[0m\n\u001b[1;32m   1357\u001b[0m             \u001b[0mThe\u001b[0m \u001b[0mpredicted\u001b[0m \u001b[0mvalues\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1358\u001b[0m         \"\"\"\n\u001b[0;32m-> 1359\u001b[0;31m         \u001b[0mraw_predictions\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdecision_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\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   1360\u001b[0m         \u001b[0mencoded_labels\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloss_\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_raw_prediction_to_decision\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mraw_predictions\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1361\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclasses_\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtake\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mencoded_labels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\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[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/sklearn/ensemble/_gb.py\u001b[0m in \u001b[0;36mdecision_function\u001b[0;34m(self, X)\u001b[0m\n\u001b[1;32m   1310\u001b[0m             \u001b[0marray\u001b[0m \u001b[0mof\u001b[0m \u001b[0mshape\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mn_samples\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   1311\u001b[0m         \"\"\"\n\u001b[0;32m-> 1312\u001b[0;31m         X = self._validate_data(\n\u001b[0m\u001b[1;32m   1313\u001b[0m             \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mDTYPE\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morder\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"C\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maccept_sparse\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"csr\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreset\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1314\u001b[0m         )\n",
            "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/sklearn/base.py\u001b[0m in \u001b[0;36m_validate_data\u001b[0;34m(self, X, y, reset, validate_separately, **check_params)\u001b[0m\n\u001b[1;32m    583\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    584\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mno_val_X\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mcheck_params\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"ensure_2d\"\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[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 585\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_check_n_features\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreset\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mreset\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    586\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    587\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mout\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/sklearn/base.py\u001b[0m in \u001b[0;36m_check_n_features\u001b[0;34m(self, X, reset)\u001b[0m\n\u001b[1;32m    398\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    399\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mn_features\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_features_in_\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 400\u001b[0;31m             raise ValueError(\n\u001b[0m\u001b[1;32m    401\u001b[0m                 \u001b[0;34mf\"X has {n_features} features, but {self.__class__.__name__} \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    402\u001b[0m                 \u001b[0;34mf\"is expecting {self.n_features_in_} features as input.\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: X has 8 features, but GradientBoostingClassifier is expecting 2877 features as input."
          ]
        }
      ],
      "source": [
        "import imblearn\n",
        "\n",
        "params= {'n_estimators': 4700, 'learning_rate': 0.004955208213721993, \\\n",
        "        'max_depth': 6, 'subsample': 0.7, 'max_features': 'auto'}\n",
        "GBC =  GradientBoostingClassifier(random_state=37, **params,                              \n",
        "                                  ).fit(train_x, train_y)\n",
        "preds = GBC.predict(test_x)\n",
        "sns.countplot(x=preds);\n",
        "preds"
      ],
      "id": "S4zxDMJ4YGGd"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "HLeS0rZUO_q_"
      },
      "outputs": [],
      "source": [
        "submit = pd.read_csv('/content/drive/MyDrive/sample_submission.csv')\n",
        "submit['Y_Class'] = preds\n",
        "\n",
        "submit.to_csv('GBCoptuna-smartFactorySubmission.csv', index=False)"
      ],
      "id": "HLeS0rZUO_q_"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gtHQZO6-qs_Z"
      },
      "source": [
        "### GridSearch"
      ],
      "id": "gtHQZO6-qs_Z"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "128TYZYZqrbY"
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import GridSearchCV\n",
        "\n",
        "estimator = GradientBoostingClassifier(random_state = 37)\n",
        "\n",
        "param_grid = {\n",
        "            'n_estimators' : [n_estimators for n_estimators in range(100, 5000, 100)],\n",
        "            'learning_rate' : [lr * 0.0001 for lr in range(1, 10)],\n",
        "            'max_depth' : [depth for depth in range(3, 9)],\n",
        "            'subsample' : [subsample * 0.1 for subsample in range(5, 9, 1)],\n",
        "            'max_features' : ['auto', 'sqrt', 'log2']\n",
        "            }\n",
        "\n",
        "# scoring = 'r2' -> 결정계수로 scoring\n",
        "grid_GBC = GridSearchCV(estimator, param_grid, scoring = 'r2', n_jobs = -1)\n",
        "grid_GBC.fit(train_x, train_y)\n",
        "\n",
        "print('best estimator model: \\n{}'.format(grid_GBC.best_estimator_))\n",
        "print('\\nbest parameter: \\n{}'.format(grid_GBC.best_params_))\n",
        "print(\"\\nbest score: \\n{}\".format(grid_GBC.best_score_.round(3)))"
      ],
      "id": "128TYZYZqrbY"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-V0IZDs6_S3f"
      },
      "source": [
        "## 3.2 CatBoost"
      ],
      "id": "-V0IZDs6_S3f"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "e7f4ff7b"
      },
      "outputs": [],
      "source": [
        "def objective(trial):\n",
        "    model = CatBoostClassifier(\n",
        "        iterations=trial.suggest_int(\"iterations\", 100, 1000),\n",
        "        learning_rate=trial.suggest_float(\"learning_rate\", 1e-3, 1e-1, log=True),\n",
        "        depth=trial.suggest_int(\"depth\", 4, 10),\n",
        "        l2_leaf_reg=trial.suggest_float(\"l2_leaf_reg\", 1e-8, 100.0, log=True),\n",
        "        bootstrap_type=trial.suggest_categorical(\"bootstrap_type\", [\"Bayesian\"]),\n",
        "        random_strength=trial.suggest_float(\"random_strength\", 1e-8, 10.0, log=True),\n",
        "        bagging_temperature=trial.suggest_float(\"bagging_temperature\", 0.0, 10.0),\n",
        "        od_type=trial.suggest_categorical(\"od_type\", [\"IncToDec\", \"Iter\"]),\n",
        "        od_wait=trial.suggest_int(\"od_wait\", 10, 50),\n",
        "        verbose=False\n",
        "    )\n",
        "    model.fit(X_train, y_train)\n",
        "    y_pred = model.predict(X_test)\n",
        "    return f1_score(y_test, y_pred, average='macro')\n",
        "     \n",
        "# hyper-parameter tuning with OPTUNA  \n",
        "optuna.logging.set_verbosity(optuna.logging.WARNING)\n",
        "\n",
        "sampler = TPESampler(seed=37)\n",
        "study = optuna.create_study(study_name=\"catboost\", direction=\"maximize\", sampler=sampler)\n",
        "study.optimize(objective, n_trials=10)  "
      ],
      "id": "e7f4ff7b"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "4ee90ada"
      },
      "outputs": [],
      "source": [
        "print(\"Number of trials: \", len(study.trials))\n",
        "print(\"Best trial:\")\n",
        "trial = study.best_trial\n",
        "print(\"  Value: \", trial.value)\n",
        "print(\"  Params: \",trial.params)\n",
        "\n",
        "model = CatBoostClassifier(**trial.params, verbose=False, random_state = 37)\n",
        "model.fit(X_train, y_train)\n",
        "y_pred = model.predict(X_test)\n"
      ],
      "id": "4ee90ada"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "OYWFMNT9ycd7"
      },
      "outputs": [],
      "source": [
        "params=  {'iterations': 857, 'learning_rate': 0.07097207730593516, 'depth': 8, \\\n",
        "        'l2_leaf_reg': 0.00012090525126196811, 'bootstrap_type': 'Bayesian', \\\n",
        "        'random_strength': 5.359110894800412, 'bagging_temperature': 0.7137022569955509, \\\n",
        "        'od_type': 'IncToDec', 'od_wait': 42}\n",
        "CBC = CatBoostClassifier(**params, verbose=False, random_state=37)\n",
        "CBC.fit(X_train, y_train)\n",
        "y_pred = model.predict(test_x)\n",
        "sns.countplot(x = y_pred);\n",
        "preds"
      ],
      "id": "OYWFMNT9ycd7"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "eACiKQAXwMSW"
      },
      "outputs": [],
      "source": [
        "from optuna.visualization import plot_optimization_history\n",
        "from optuna.visualization import plot_param_importances\n",
        "from optuna.visualization import plot_parallel_coordinate\n",
        "from optuna.visualization import plot_contour\n",
        "\n",
        "plot_optimization_history(study)"
      ],
      "id": "eACiKQAXwMSW"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "zumvj87hzLVy"
      },
      "outputs": [],
      "source": [
        "plot_param_importances(study)"
      ],
      "id": "zumvj87hzLVy"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "91bbd3b7"
      },
      "outputs": [],
      "source": [
        "import pickle\n",
        "pickle.dump(model, open(\"catboost_model.pkl\", \"wb\"))\n",
        "     \n",
        "\n",
        "optuna.visualization.plot_optimization_history(study)\n",
        "     \n",
        "\n",
        "optuna.visualization.plot_parallel_coordinate(study)\n",
        "     \n",
        "\n",
        "# 각 파라미터들의 상관관계\n",
        "optuna.visualization.plot_contour(\n",
        "    study,\n",
        "    params=[\n",
        "        \"iterations\",\n",
        "        \"learning_rate\",\n",
        "        \"depth\",\n",
        "        \"l2_leaf_reg\",\n",
        "        \"bootstrap_type\",\n",
        "        \"random_strength\",\n",
        "        \"bagging_temperature\",\n",
        "        \"od_type\",\n",
        "        \"od_wait\"\n",
        "        ],\n",
        ")"
      ],
      "id": "91bbd3b7"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "fab2a838"
      },
      "outputs": [],
      "source": [
        "# 하이퍼파라미터 중요도\n",
        "optuna.visualization.plot_param_importances(study)"
      ],
      "id": "fab2a838"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "wi_Ll5YCzLbr"
      },
      "outputs": [],
      "source": [
        "from sklearn.ensemble import GradientBoostingClassifier\n",
        "\n",
        "GBC_model = GradientBoostingClassifier(n_estimators= 200, \n",
        "                                       learning_rate=0.21851112508358236, \n",
        "\t\t\t\t\t\t\t\t\tmax_depth= 6,\n",
        "\t\t\t\t\t\t\t\t\tsubsample= 0.7, \n",
        "\t\t\t\t\t\t\t\t\tmax_features= 'auto').fit(X_train, y_train) "
      ],
      "id": "wi_Ll5YCzLbr"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "kvIWDIg5UiQ_"
      },
      "outputs": [],
      "source": [
        "preds = GBC_model.predict(test_x)\n",
        "print('Done.')\n",
        "sns.countplot(x=preds); "
      ],
      "id": "kvIWDIg5UiQ_"
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import GridSearchCV\n",
        "\n",
        "estimator = CatBoostClassifier(random_state = 37)\n",
        "\n",
        "param_grid = {\n",
        "            'iterataions' : [iter for iter in range(100, 1000, 100)],\n",
        "            'learning_rate' : [lr * 0.0001 for lr in range(1, 10)],\n",
        "            'depth' : [depth for depth in range(4, 10)],\n",
        "            'l2_leaf_reg' : [leaf * 0.0000001 for leaf in range(1, 100, 10)],\n",
        "            'bootstrap_type' : ['Bayesian'],\n",
        "            'random_strength' : [strength * 0.1 for strength in range(1, 100, 10)],\n",
        "            'bagging_temperature' : [bag * 0.1 for bag in range(1, 10)],\n",
        "            'od_type' : ['IncToDec', 'Iter'],\n",
        "            'od_wait' : [wait for wait in range(10, 50, 4)]\n",
        "            }\n",
        "\n",
        "# scoring = 'r2' -> 결정계수로 scoring\n",
        "grid_CBC = GridSearchCV(estimator, param_grid, scoring = 'r2', n_jobs = -1)\n",
        "grid_CBC.fit(X_train, y_train)\n",
        "\n",
        "print('best estimator model: \\n{}'.format(grid_CBC.best_estimator_))\n",
        "print('\\nbest parameter: \\n{}'.format(grid_CBC.best_params_))\n",
        "print(\"\\nbest score: \\n{}\".format(grid_CBC.best_score_.round(3)))"
      ],
      "metadata": {
        "id": "Jvh98irBNKcL"
      },
      "id": "Jvh98irBNKcL",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gNzAotw2Jlr-"
      },
      "source": [
        "## 3.3 MLP"
      ],
      "id": "gNzAotw2Jlr-"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "pHd-qrBQ3REB"
      },
      "outputs": [],
      "source": [
        "import torch\n",
        "\n",
        "class Dataset(torch.utils.data.Dataset):\n",
        "\n",
        "    def __init__(self, df):\n",
        "        self.labels = [0 if label == 0 else 1 for label in df['HeartDisease']]\n",
        "        self.features = df.drop(columns=['HeartDisease'], axis=1).values.tolist()\n",
        "\n",
        "    def classes(self):\n",
        "        return self.labels\n",
        "\n",
        "    def __len__(self):\n",
        "        return len(self.labels)\n",
        "\n",
        "    def get_batch_labels(self, idx):\n",
        "        return np.array(self.labels[idx])\n",
        "\n",
        "    def get_batch_features(self, idx):\n",
        "        return np.array(self.features[idx])\n",
        "\n",
        "    def __getitem__(self, idx):\n",
        "        batch_features = self.get_batch_features(idx)\n",
        "        batch_y = self.get_batch_labels(idx)\n",
        "\n",
        "        return batch_features, batch_y"
      ],
      "id": "pHd-qrBQ3REB"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ieivpz673RO-"
      },
      "outputs": [],
      "source": [
        "def objective(trial):\n",
        "\n",
        "    params = {\n",
        "              'learning_rate': trial.suggest_loguniform('learning_rate', 1e-5, 1e-1),\n",
        "              'optimizer': trial.suggest_categorical(\"optimizer\", [\"Adam\", \"RMSprop\", \"SGD\"]),\n",
        "              'n_unit': trial.suggest_int(\"n_unit\", 4, 18)\n",
        "              }\n",
        "    \n",
        "    model = build_model(params)\n",
        "    \n",
        "    accuracy = train_and_evaluate(params, model)\n",
        "\n",
        "    return accuracy"
      ],
      "id": "ieivpz673RO-"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "tsZzHRcx3RKB"
      },
      "outputs": [],
      "source": [
        "study = optuna.create_study(direction=\"maximize\", sampler=optuna.samplers.TPESampler())\n",
        "study.optimize(objective, n_trials=30)"
      ],
      "id": "tsZzHRcx3RKB"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ybk-xTzF3nT_"
      },
      "outputs": [],
      "source": [
        "def build_model(params):\n",
        "    \n",
        "    in_features = train_x.shape[-1]\n",
        "    \n",
        "    return nn.Sequential(\n",
        "    \n",
        "        nn.Linear(in_features, params['n_unit']),\n",
        "        nn.LeakyReLU(),\n",
        "\n",
        "        nn.Linear(params['n_unit'], 2),\n",
        "        nn.LeakyReLU()\n",
        "        \n",
        "    )\n",
        " "
      ],
      "id": "ybk-xTzF3nT_"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ITsy_mGB3RUf"
      },
      "outputs": [],
      "source": [
        "\n",
        "# Train and evaluate the accuarcy of neural network model\n",
        "def train_and_evaluate(param, model):\n",
        "    \n",
        "    df = pd.read_csv('heart.csv')\n",
        "    df = pd.get_dummies(df)\n",
        "    \n",
        "    train_data, val_data = train_test_split(df, test_size = 0.2, random_state = 42)\n",
        "    train, val = Dataset(train_data), Dataset(val_data)\n",
        "\n",
        "    train_dataloader = torch.utils.data.DataLoader(train, batch_size=2, shuffle=True)\n",
        "    val_dataloader = torch.utils.data.DataLoader(val, batch_size=2)\n",
        "\n",
        "    use_cuda = torch.cuda.is_available()\n",
        "    device = torch.device(\"cuda\" if use_cuda else \"cpu\")\n",
        "\n",
        "    criterion = nn.CrossEntropyLoss()\n",
        "    optimizer = getattr(optim, param['optimizer'])(model.parameters(), lr= param['learning_rate'])\n",
        "\n",
        "    if use_cuda:\n",
        "\n",
        "            model = model.cuda()\n",
        "            criterion = criterion.cuda()\n",
        "\n",
        "    for epoch_num in range(EPOCHS):\n",
        "\n",
        "            total_acc_train = 0\n",
        "            total_loss_train = 0\n",
        "\n",
        "            for train_input, train_label in train_dataloader:\n",
        "\n",
        "                train_label = train_label.to(device)\n",
        "                train_input = train_input.to(device)\n",
        "\n",
        "                output = model(train_input.float())\n",
        "                \n",
        "                batch_loss = criterion(output, train_label.long())\n",
        "                total_loss_train += batch_loss.item()\n",
        "                \n",
        "                acc = (output.argmax(dim=1) == train_label).sum().item()\n",
        "                total_acc_train += acc\n",
        "\n",
        "                model.zero_grad()\n",
        "                batch_loss.backward()\n",
        "                optimizer.step()\n",
        "            \n",
        "            total_acc_val = 0\n",
        "            total_loss_val = 0\n",
        "\n",
        "            with torch.no_grad():\n",
        "\n",
        "                for val_input, val_label in val_dataloader:\n",
        "\n",
        "                    val_label = val_label.to(device)\n",
        "                    val_input = val_input.to(device)\n",
        "\n",
        "                    output = model(val_input.float())\n",
        "\n",
        "                    batch_loss = criterion(output, val_label.long())\n",
        "                    total_loss_val += batch_loss.item()\n",
        "                    \n",
        "                    acc = (output.argmax(dim=1) == val_label).sum().item()\n",
        "                    total_acc_val += acc\n",
        "            \n",
        "            accuracy = total_acc_val/len(val_data)\n",
        "\n",
        "    return accuracy\n",
        "  \n",
        " # Define a set of hyperparameter values, build the model, train the model, and evaluate the accuracy \n",
        "def objective(trial):\n",
        "\n",
        "     params = {\n",
        "              'learning_rate': trial.suggest_loguniform('learning_rate', 1e-5, 1e-1),\n",
        "              'optimizer': trial.suggest_categorical(\"optimizer\", [\"Adam\", \"RMSprop\", \"SGD\"]),\n",
        "              'n_unit': trial.suggest_int(\"n_unit\", 4, 18)\n",
        "              }\n",
        "    \n",
        "     model = build_model(params)\n",
        "    \n",
        "     accuracy = train_and_evaluate(params, model)\n",
        "\n",
        "     return accuracy"
      ],
      "id": "ITsy_mGB3RUf"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "o8N-QHuIvNN3"
      },
      "source": [
        "# 4.Ensemble"
      ],
      "id": "o8N-QHuIvNN3"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2fe15f1a"
      },
      "source": [
        "## 4.1 VotingClassifier\n",
        "1. Cross-Validation 구축 후 성능 비교"
      ],
      "id": "2fe15f1a"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Vm8q0p4Hyzg2"
      },
      "outputs": [],
      "source": [
        "models = [\n",
        "    # test set accuracy 1위(0.8166) \n",
        "    CatBoostClassifier(verbose=False, random_seed=seed_num),\n",
        "    # test set accuracy 5위(0.7722)\n",
        "    XGBClassifier(random_state=seed_num),\n",
        "    # test set accuracy 2위(0.8111) \n",
        "    GradientBoostingClassifier(random_state=seed_num)]\n",
        "\n",
        "fit = [x.fit(train_x, train_y) for x in models]    "
      ],
      "id": "Vm8q0p4Hyzg2"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "0bcafdb3",
        "outputId": "046b71ae-0d1b-40c5-d860-e005cb986422"
      },
      "outputs": [
        {
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "def prediction(models, test, mode=None, weights=None):\n",
        "    if mode == \"hard\":\n",
        "        preds = np.asarray([x.predict(test).reshape(-1) for x in models]).T\n",
        "        res = np.apply_along_axis(\n",
        "            lambda x: np.argmax(np.bincount(x, weights=weights)),\n",
        "            axis=1,\n",
        "            arr=preds\n",
        "        )\n",
        "    elif mode == \"soft\":  \n",
        "        preds = np.asarray([x.predict_proba(test) for x in models])\n",
        "        res = np.zeros(preds[0].shape)\n",
        "        for pred, weight in zip(preds, weights):\n",
        "            res = res + pred*weight\n",
        "        res = np.argmax(preds, axis=0) \n",
        "    else:\n",
        "        res = models[0].predict(test)\n",
        "    return res\n",
        "\n",
        "preds = prediction(models, test_x, 'hard', [2,1,2] )\n",
        "\n",
        "sns.countplot(x=preds);   "
      ],
      "id": "0bcafdb3"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KUOHpt3z847x"
      },
      "source": [
        "## 4.2 Score"
      ],
      "id": "KUOHpt3z847x"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Z3UesEBQ1r8z",
        "outputId": "3670348a-887b-4854-ff18-d825090c0132"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array(VotingClassifier(estimators=[('CatBoostClassifier',\n",
              "                              <catboost.core.CatBoostClassifier object at 0x7f3f517db0d0>),\n",
              "                             ('XGBClassifier',\n",
              "                              XGBClassifier(base_score=None, booster=None,\n",
              "                                            callbacks=None,\n",
              "                                            colsample_bylevel=None,\n",
              "                                            colsample_bynode=None,\n",
              "                                            colsample_bytree=None,\n",
              "                                            early_stopping_rounds=None,\n",
              "                                            enable_categorical=False,\n",
              "                                            eval_metric=None,\n",
              "                                            feature_types=None, gamma=None,...\n",
              "                                            learning_rate=None, max_bin=None,\n",
              "                                            max_cat_threshold=None,\n",
              "                                            max_cat_to_onehot=None,\n",
              "                                            max_delta_step=None, max_depth=None,\n",
              "                                            max_leaves=None,\n",
              "                                            min_child_weight=None, missing=nan,\n",
              "                                            monotone_constraints=None,\n",
              "                                            n_estimators=100, n_jobs=None,\n",
              "                                            num_parallel_tree=None,\n",
              "                                            predictor=None, random_state=37, ...)),\n",
              "                             ('GradientBoostingClassifier',\n",
              "                              GradientBoostingClassifier(random_state=37))],\n",
              "                 weights=[2, 1, 2]), dtype=object)"
            ]
          },
          "execution_count": 127,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "cbc = CatBoostClassifier(verbose=False, random_seed=seed_num)  \n",
        "xgb = XGBClassifier(random_state=seed_num) \n",
        "gbc = GradientBoostingClassifier(random_state=seed_num) \n",
        "voting_model = VotingClassifier(estimators=[\n",
        "                ('CatBoostClassifier', cbc),\n",
        "                ('XGBClassifier', xgb),\n",
        "                ('GradientBoostingClassifier', gbc)], \n",
        "                voting='hard', weights=[2,1,2])\n",
        "# fitted = np.asarray(voting_model.fit(X_train, y_train).reshape(-1))\n",
        "np.asarray(fitted)   "
      ],
      "id": "Z3UesEBQ1r8z"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TgLWsUI2A25b",
        "outputId": "2f84e426-fd37-4026-b502-e32b193122c9"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/sklearn/ensemble/_voting.py:68: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n",
            "  return np.asarray([est.predict(X) for est in self.estimators_]).T\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/model_selection/_validation.py:778: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.8/dist-packages/sklearn/model_selection/_validation.py\", line 767, in _score\n",
            "    scores = scorer(estimator, X_test, y_test)\n",
            "  File \"/usr/local/lib/python3.8/dist-packages/sklearn/metrics/_scorer.py\", line 234, in __call__\n",
            "    return self._score(\n",
            "  File \"/usr/local/lib/python3.8/dist-packages/sklearn/metrics/_scorer.py\", line 276, in _score\n",
            "    y_pred = method_caller(estimator, \"predict\", X)\n",
            "  File \"/usr/local/lib/python3.8/dist-packages/sklearn/metrics/_scorer.py\", line 73, in _cached_call\n",
            "    return getattr(estimator, method)(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.8/dist-packages/sklearn/ensemble/_voting.py\", line 366, in predict\n",
            "    predictions = self._predict(X)\n",
            "  File \"/usr/local/lib/python3.8/dist-packages/sklearn/ensemble/_voting.py\", line 68, in _predict\n",
            "    return np.asarray([est.predict(X) for est in self.estimators_]).T\n",
            "ValueError: could not broadcast input array from shape (120,1) into shape (120,)\n",
            "\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/ensemble/_voting.py:68: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n",
            "  return np.asarray([est.predict(X) for est in self.estimators_]).T\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/model_selection/_validation.py:778: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.8/dist-packages/sklearn/model_selection/_validation.py\", line 767, in _score\n",
            "    scores = scorer(estimator, X_test, y_test)\n",
            "  File \"/usr/local/lib/python3.8/dist-packages/sklearn/metrics/_scorer.py\", line 234, in __call__\n",
            "    return self._score(\n",
            "  File \"/usr/local/lib/python3.8/dist-packages/sklearn/metrics/_scorer.py\", line 276, in _score\n",
            "    y_pred = method_caller(estimator, \"predict\", X)\n",
            "  File \"/usr/local/lib/python3.8/dist-packages/sklearn/metrics/_scorer.py\", line 73, in _cached_call\n",
            "    return getattr(estimator, method)(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.8/dist-packages/sklearn/ensemble/_voting.py\", line 366, in predict\n",
            "    predictions = self._predict(X)\n",
            "  File \"/usr/local/lib/python3.8/dist-packages/sklearn/ensemble/_voting.py\", line 68, in _predict\n",
            "    return np.asarray([est.predict(X) for est in self.estimators_]).T\n",
            "ValueError: could not broadcast input array from shape (120,1) into shape (120,)\n",
            "\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/ensemble/_voting.py:68: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n",
            "  return np.asarray([est.predict(X) for est in self.estimators_]).T\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/model_selection/_validation.py:778: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.8/dist-packages/sklearn/model_selection/_validation.py\", line 767, in _score\n",
            "    scores = scorer(estimator, X_test, y_test)\n",
            "  File \"/usr/local/lib/python3.8/dist-packages/sklearn/metrics/_scorer.py\", line 234, in __call__\n",
            "    return self._score(\n",
            "  File \"/usr/local/lib/python3.8/dist-packages/sklearn/metrics/_scorer.py\", line 276, in _score\n",
            "    y_pred = method_caller(estimator, \"predict\", X)\n",
            "  File \"/usr/local/lib/python3.8/dist-packages/sklearn/metrics/_scorer.py\", line 73, in _cached_call\n",
            "    return getattr(estimator, method)(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.8/dist-packages/sklearn/ensemble/_voting.py\", line 366, in predict\n",
            "    predictions = self._predict(X)\n",
            "  File \"/usr/local/lib/python3.8/dist-packages/sklearn/ensemble/_voting.py\", line 68, in _predict\n",
            "    return np.asarray([est.predict(X) for est in self.estimators_]).T\n",
            "ValueError: could not broadcast input array from shape (120,1) into shape (120,)\n",
            "\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/ensemble/_voting.py:68: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n",
            "  return np.asarray([est.predict(X) for est in self.estimators_]).T\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/model_selection/_validation.py:778: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.8/dist-packages/sklearn/model_selection/_validation.py\", line 767, in _score\n",
            "    scores = scorer(estimator, X_test, y_test)\n",
            "  File \"/usr/local/lib/python3.8/dist-packages/sklearn/metrics/_scorer.py\", line 234, in __call__\n",
            "    return self._score(\n",
            "  File \"/usr/local/lib/python3.8/dist-packages/sklearn/metrics/_scorer.py\", line 276, in _score\n",
            "    y_pred = method_caller(estimator, \"predict\", X)\n",
            "  File \"/usr/local/lib/python3.8/dist-packages/sklearn/metrics/_scorer.py\", line 73, in _cached_call\n",
            "    return getattr(estimator, method)(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.8/dist-packages/sklearn/ensemble/_voting.py\", line 366, in predict\n",
            "    predictions = self._predict(X)\n",
            "  File \"/usr/local/lib/python3.8/dist-packages/sklearn/ensemble/_voting.py\", line 68, in _predict\n",
            "    return np.asarray([est.predict(X) for est in self.estimators_]).T\n",
            "ValueError: could not broadcast input array from shape (119,1) into shape (119,)\n",
            "\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/ensemble/_voting.py:68: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n",
            "  return np.asarray([est.predict(X) for est in self.estimators_]).T\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/model_selection/_validation.py:778: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.8/dist-packages/sklearn/model_selection/_validation.py\", line 767, in _score\n",
            "    scores = scorer(estimator, X_test, y_test)\n",
            "  File \"/usr/local/lib/python3.8/dist-packages/sklearn/metrics/_scorer.py\", line 234, in __call__\n",
            "    return self._score(\n",
            "  File \"/usr/local/lib/python3.8/dist-packages/sklearn/metrics/_scorer.py\", line 276, in _score\n",
            "    y_pred = method_caller(estimator, \"predict\", X)\n",
            "  File \"/usr/local/lib/python3.8/dist-packages/sklearn/metrics/_scorer.py\", line 73, in _cached_call\n",
            "    return getattr(estimator, method)(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.8/dist-packages/sklearn/ensemble/_voting.py\", line 366, in predict\n",
            "    predictions = self._predict(X)\n",
            "  File \"/usr/local/lib/python3.8/dist-packages/sklearn/ensemble/_voting.py\", line 68, in _predict\n",
            "    return np.asarray([est.predict(X) for est in self.estimators_]).T\n",
            "ValueError: could not broadcast input array from shape (119,1) into shape (119,)\n",
            "\n",
            "  warnings.warn(\n"
          ]
        }
      ],
      "source": [
        "val_preds = prediction(models, X_test, 'hard', [2,1,2])\n",
        "val_score= sklearn.metrics.accuracy_score(y_test, val_preds)\n",
        "val_score  "
      ],
      "id": "TgLWsUI2A25b"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zbNtWowN78mX"
      },
      "source": [
        "# 5.Submit"
      ],
      "id": "zbNtWowN78mX"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "zfIcqXBqymkN"
      },
      "outputs": [],
      "source": [
        "submit = pd.read_csv('/content/drive/MyDrive/sample_submission.csv')\n",
        "\n",
        "submit['Y_Class'] = preds\n",
        "submit.to_csv('CXG212(1)-smartFactorySubmission.csv', index=False)  \n",
        "sns.countplot(x=preds);\n",
        "submit.Y_Class.value_counts()"
      ],
      "id": "zfIcqXBqymkN"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "gvXsoboM5RL6"
      },
      "outputs": [],
      "source": [],
      "id": "gvXsoboM5RL6"
    }
  ],
  "metadata": {
    "colab": {
      "machine_shape": "hm",
      "provenance": [],
      "toc_visible": true
    },
    "gpuClass": "standard",
    "kernelspec": {
      "display_name": "Python 3 (ipykernel)",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.9.7"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 5
}