StackingMLoptuna_smartFactory_Yumin.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 21,
      "id": "6ab709fe",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6ab709fe",
        "outputId": "95bc98be-26c6-4dd3-be78-f400653ab303"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: catboost in /usr/local/lib/python3.8/dist-packages (1.1.1)\n",
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            "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[31m6.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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            "Installing collected packages: Mako, colorlog, cmaes, alembic, optuna\n",
            "Successfully installed Mako-1.2.4 alembic-1.9.4 cmaes-0.9.1 colorlog-6.7.0 optuna-3.1.0\n"
          ]
        }
      ],
      "source": [
        "# !pip install pytorch_tabnet\n",
        "!pip install catboost\n",
        "!pip install optuna"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "id": "94682a80",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "94682a80",
        "outputId": "1cfe609f-0e13-4c16-a954-77f1a9b6cfa5"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ],
      "source": [
        "import pandas as pd\n",
        "import random\n",
        "import os\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt   \n",
        "\n",
        "from sklearn import preprocessing, metrics\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "from sklearn.preprocessing import OneHotEncoder\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "import tensorflow as tf\n",
        "from tensorflow.keras import regularizers\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Dense,Dropout, Activation\n",
        "\n",
        "from sklearn.metrics import f1_score\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "\n",
        "from catboost import CatBoostClassifier\n",
        "\n",
        "import optuna\n",
        "from optuna.samplers import TPESampler\n",
        "from optuna import Trial\n",
        "\n",
        "from sklearn.metrics import f1_score\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",
        "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",
        "from google.colab import drive # GoogleDrive mount\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "wqkW_3EbQxj1",
      "metadata": {
        "id": "wqkW_3EbQxj1"
      },
      "source": [
        "# 1.preprocessing\n",
        "1. Label Encoding: categorical values `LINE`, `PRODUCT_CODE`\n",
        "2. Missing values: fillna(0) \n",
        "3. scaling: StandardScaler"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "id": "c66d3bf4"
      },
      "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'])   \n",
        "\n",
        "# 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",
        "\n",
        "# 2) Missing Values \n",
        "train_x = train_x.fillna(0)\n",
        "test_x = test_x.fillna(0)\n",
        "\n",
        "# 3) MinMaxscaling: only `X_???` values (continuous)\n",
        "from sklearn.preprocessing import StandardScaler\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=37)"
      ],
      "id": "c66d3bf4"
    },
    {
      "cell_type": "markdown",
      "id": "yreBA4JMQPJ9",
      "metadata": {
        "id": "yreBA4JMQPJ9"
      },
      "source": [
        "# 2.catBoost classifier "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "id": "e7f4ff7b",
      "metadata": {
        "id": "e7f4ff7b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 738
        },
        "outputId": "ee6a07a1-8c58-427a-c401-fcb6898ea155"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\u001b[33m[W 2023-02-28 00:23:52,083]\u001b[0m Trial 4 failed with parameters: {'iterations': 731, 'learning_rate': 0.0030293896771850033, 'depth': 9, 'l2_leaf_reg': 2.7282317266243193e-06, 'bootstrap_type': 'Bayesian', 'random_strength': 0.22660775778981115, 'bagging_temperature': 4.956375001506918, 'od_type': 'IncToDec', 'od_wait': 31} 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-24-b3636f5b3e4e>\", line 19, in objective\n",
            "    model.fit(X_train, y_train)\n",
            "  File \"/usr/local/lib/python3.8/dist-packages/catboost/core.py\", line 5128, in fit\n",
            "    self._fit(X, y, cat_features, text_features, embedding_features, None, sample_weight, None, None, None, None, baseline, use_best_model,\n",
            "  File \"/usr/local/lib/python3.8/dist-packages/catboost/core.py\", line 2355, in _fit\n",
            "    self._train(\n",
            "  File \"/usr/local/lib/python3.8/dist-packages/catboost/core.py\", line 1759, in _train\n",
            "    self._object._train(train_pool, test_pool, params, allow_clear_pool, init_model._object if init_model else None)\n",
            "  File \"_catboost.pyx\", line 4623, in _catboost._CatBoost._train\n",
            "  File \"_catboost.pyx\", line 4672, in _catboost._CatBoost._train\n",
            "KeyboardInterrupt\n",
            "\u001b[33m[W 2023-02-28 00:23:52,089]\u001b[0m Trial 4 failed with value None.\u001b[0m\n"
          ]
        },
        {
          "output_type": "error",
          "ename": "KeyboardInterrupt",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-24-b3636f5b3e4e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     26\u001b[0m \u001b[0msampler\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mTPESampler\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mseed\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m316\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     27\u001b[0m \u001b[0mstudy\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0moptuna\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcreate_study\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstudy_name\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"catboost\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdirection\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"maximize\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msampler\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msampler\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 28\u001b[0;31m \u001b[0mstudy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptimize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobjective\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_trials\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\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-24-b3636f5b3e4e>\u001b[0m in \u001b[0;36mobjective\u001b[0;34m(trial)\u001b[0m\n\u001b[1;32m     17\u001b[0m         \u001b[0mverbose\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     18\u001b[0m     )\n\u001b[0;32m---> 19\u001b[0;31m     \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     20\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     21\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mf1_score\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maverage\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'macro'\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/catboost/core.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y, cat_features, text_features, embedding_features, sample_weight, baseline, use_best_model, eval_set, verbose, logging_level, plot, plot_file, column_description, verbose_eval, metric_period, silent, early_stopping_rounds, save_snapshot, snapshot_file, snapshot_interval, init_model, callbacks, log_cout, log_cerr)\u001b[0m\n\u001b[1;32m   5126\u001b[0m             \u001b[0mCatBoostClassifier\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_check_is_compatible_loss\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mparams\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'loss_function'\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   5127\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 5128\u001b[0;31m         self._fit(X, y, cat_features, text_features, embedding_features, None, sample_weight, None, None, None, None, baseline, use_best_model,\n\u001b[0m\u001b[1;32m   5129\u001b[0m                   \u001b[0meval_set\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mverbose\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlogging_level\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mplot\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mplot_file\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolumn_description\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mverbose_eval\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmetric_period\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5130\u001b[0m                   silent, early_stopping_rounds, save_snapshot, snapshot_file, snapshot_interval, init_model, callbacks, log_cout, log_cerr)\n",
            "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/catboost/core.py\u001b[0m in \u001b[0;36m_fit\u001b[0;34m(self, X, y, cat_features, text_features, embedding_features, pairs, sample_weight, group_id, group_weight, subgroup_id, pairs_weight, baseline, use_best_model, eval_set, verbose, logging_level, plot, plot_file, column_description, verbose_eval, metric_period, silent, early_stopping_rounds, save_snapshot, snapshot_file, snapshot_interval, init_model, callbacks, log_cout, log_cerr)\u001b[0m\n\u001b[1;32m   2353\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mlog_fixup\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlog_cout\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlog_cerr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m\\\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2354\u001b[0m             \u001b[0mplot_wrapper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mplot_file\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'Training plots'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0m_get_train_dir\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_params\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[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2355\u001b[0;31m             self._train(\n\u001b[0m\u001b[1;32m   2356\u001b[0m                 \u001b[0mtrain_pool\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2357\u001b[0m                 \u001b[0mtrain_params\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"eval_sets\"\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/catboost/core.py\u001b[0m in \u001b[0;36m_train\u001b[0;34m(self, train_pool, test_pool, params, allow_clear_pool, init_model)\u001b[0m\n\u001b[1;32m   1757\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1758\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_train\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrain_pool\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtest_pool\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparams\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mallow_clear_pool\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minit_model\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-> 1759\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_object\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_train\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain_pool\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtest_pool\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparams\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mallow_clear_pool\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minit_model\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_object\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0minit_model\u001b[0m \u001b[0;32melse\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[0m\u001b[1;32m   1760\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_set_trained_model_attributes\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   1761\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m_catboost.pyx\u001b[0m in \u001b[0;36m_catboost._CatBoost._train\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32m_catboost.pyx\u001b[0m in \u001b[0;36m_catboost._CatBoost._train\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ],
      "source": [
        "X_train, X_test, y_train, y_test =train_test_split(train_x, train_y,\n",
        "                                                   test_size=0.3,\n",
        "                                                   random_state=37)\n",
        "     \n",
        "\n",
        "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=316)\n",
        "study = optuna.create_study(study_name=\"catboost\", direction=\"maximize\", sampler=sampler)\n",
        "study.optimize(objective, n_trials=10)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "4ee90ada",
      "metadata": {
        "id": "4ee90ada"
      },
      "outputs": [],
      "source": [
        "print(\"Number of finished trials: \", len(study.trials))\n",
        "print(\"Best trial:\")\n",
        "trial = study.best_trial\n",
        "print(\"  Value: \", trial.value)\n",
        "print(\"  Params: \",trial.params)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "b32047d8",
      "metadata": {
        "id": "b32047d8"
      },
      "outputs": [],
      "source": [
        "print(\"Best Score:\", study.best_value)\n",
        "print(\"Best trial:\", study.best_trial.params)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "2e5dd359",
      "metadata": {
        "id": "2e5dd359"
      },
      "outputs": [],
      "source": [
        "from sklearn.metrics import classification_report\n",
        "print(classification_report(y_pred, y_test))"
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "TxYDx5PqaCVl"
      },
      "id": "TxYDx5PqaCVl",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# aaa = pd.read_csv('/content/drive/MyDrive/xgb-smartFactorySubmission.csv')\n",
        "aaa = pd.read_csv('ridge-smartFactorySubmission.csv')\n",
        "sns.countplot(x=aaa.Y_Class);\n",
        "aaa.Y_Class.value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 352
        },
        "id": "h8w2FQLraCbg",
        "outputId": "d1f979f2-f1a4-49b7-faac-1e982cb4a720"
      },
      "id": "h8w2FQLraCbg",
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1    281\n",
              "0     22\n",
              "2      7\n",
              "Name: Y_Class, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 42
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "7-OF3FmTaCho"
      },
      "id": "7-OF3FmTaCho",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "91bbd3b7",
      "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",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "fab2a838",
      "metadata": {
        "id": "fab2a838"
      },
      "outputs": [],
      "source": [
        "# 하이퍼파라미터 중요도\n",
        "optuna.visualization.plot_param_importances(study)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "e534f3a3",
      "metadata": {
        "id": "e534f3a3"
      },
      "outputs": [],
      "source": [
        "preds = model.predict(test_x)\n",
        "     \n",
        "\n",
        "preds"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "b687c6a1",
      "metadata": {
        "id": "b687c6a1"
      },
      "outputs": [],
      "source": [
        "submit = pd.read_csv('C:/Users/user/OneDrive/open/sample_submission.csv')\n",
        "submit['Y_Class'] = preds\n",
        "\n",
        "submit.to_csv('C:/Users/user/OneDrive/open/smartFactory_submission_MLP.csv', index=False)\n",
        "# submit.to_csv('smartFactory_submission.csv', index=False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "20a97788",
      "metadata": {
        "id": "20a97788"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "markdown",
      "id": "2fe15f1a",
      "metadata": {
        "id": "2fe15f1a"
      },
      "source": [
        "# 3.VotingClassifier"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "models = [\n",
        "    CatBoostClassifier(\n",
        "        # objective='MultiClass',\n",
        "        #                            task_type='GPU',\n",
        "        #                            one_hot_max_size=2, \n",
        "        #                            iterations=4000, \n",
        "        verbose=False,\n",
        "        #                            learning_rate=0.05,\n",
        "        #                            random_seed=seed_num,\n",
        "                                   ),\n",
        "    # LGBMClassifier(objective='multiclass', random_state=seed_num),\n",
        "    XGBClassifier(random_state=seed_num),\n",
        "    GradientBoostingClassifier(random_state=seed_num),   # GBC: no tuning is the best \n",
        "    # RidgeClassifierCV(),\n",
        "]\n",
        "[x.fit(train_x, train_y) for x in models]\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Vm8q0p4Hyzg2",
        "outputId": "3c04c460-85c0-40e7-cb7a-078394d42a75"
      },
      "id": "Vm8q0p4Hyzg2",
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<catboost.core.CatBoostClassifier at 0x7f1d9b918cd0>,\n",
              " XGBClassifier(objective='multi:softprob', random_state=37),\n",
              " GradientBoostingClassifier(random_state=37)]"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "id": "0bcafdb3",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0bcafdb3",
        "outputId": "89a7c799-df41-4ed3-c96e-fdfa30cbe063"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 2, 0, 0, 0, 0, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0,\n",
              "       0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 0, 1,\n",
              "       0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0,\n",
              "       0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 2, 2, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 2, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 0,\n",
              "       1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1])"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ],
      "source": [
        "def predict(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 = predict(models, test_x, 'hard', \n",
        "                [2,1,2] )\n",
        "\n",
        "# sns.countplot(x=preds);    \n",
        "preds\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "outputId": "8d5fdc3e-3a01-46dd-87d8-9041f99fbbd7",
        "id": "zfIcqXBqymkN"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "import seaborn as sns \n",
        "submit = pd.read_csv('/content/drive/MyDrive/sample_submission.csv')\n",
        "\n",
        "submit['Y_Class'] = preds\n",
        "submit.to_csv('CXG212-smartFactorySubmission.csv', index=False)  \n",
        "sns.countplot(x=preds);"
      ],
      "id": "zfIcqXBqymkN"
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 3.1 StackingClassifier"
      ],
      "metadata": {
        "id": "F4CYiugG2423"
      },
      "id": "F4CYiugG2423"
    },
    {
      "cell_type": "code",
      "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",
        "\n",
        "models = [\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",
        "    LGBMClassifier(objective='multiclass', random_state=seed_num),\n",
        "    XGBClassifier(random_state=seed_num),\n",
        "    GradientBoostingClassifier(random_state=seed_num),   # GBC: no tuning is the best \n",
        "    RidgeClassifierCV(),\n",
        "]\n",
        "\n",
        "# [x.fit(train_x, train_y) for x in models]   "
      ],
      "metadata": {
        "id": "Dsbc_zz0KxDp"
      },
      "execution_count": 99,
      "outputs": [],
      "id": "Dsbc_zz0KxDp"
    },
    {
      "cell_type": "code",
      "source": [
        "model_list = ['catboost', 'lgbm', 'xgb', 'gradient', 'ridge']\n",
        "num=0\n",
        "\n",
        "for model in models: \n",
        "  preds = model.fit(train_x, train_y).predict(test_x)\n",
        "  if num == 0:  # catboostclassifier \n",
        "    preds= preds.reshape(1,test_x.shape[0])  # test_x.shape[0] = number of columns \n",
        "    preds = list(preds[0])\n",
        "    num+=1\n",
        "  else: \n",
        "    preds.reshape(1,test_x.shape[0])\n",
        "    preds=list(preds)\n",
        "  \n",
        "  globals()[f'preds_{model_list[num]}'] = preds   # set variable name for each model prediction results  \n",
        "  \n",
        "  print(model,  globals()[f'preds_{model_list[num]}'])   "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jS9YIWtJIMfn",
        "outputId": "624b944a-20ae-44cd-b71e-cd950dbe7656"
      },
      "id": "jS9YIWtJIMfn",
      "execution_count": 128,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<catboost.core.CatBoostClassifier object at 0x7f17b43af610> [1, 2, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 2, 0, 2, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 2, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 2, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 0, 2, 2, 0, 0, 1, 1, 1, 1, 1, 1, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]\n",
            "LGBMClassifier(objective='multiclass', random_state=37) [1, 1, 1, 1, 1, 1, 1, 0, 0, 2, 1, 1, 2, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]\n",
            "XGBClassifier(objective='multi:softprob', random_state=37) [1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]\n",
            "BaggingClassifier(random_state=37) [1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 2, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 2, 1, 2, 2, 1, 2, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 1, 1, 1, 1, 2, 2, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 1, 2, 2, 1, 2, 1, 1, 1, 1, 1, 1, 1, 2, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 0, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 1, 2, 2, 2, 2, 1, 1, 1, 1, 2, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 1, 2, 2, 2, 2, 2, 1, 1, 1, 1, 2, 2, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 2, 1, 2, 1, 2, 1]\n",
            "GradientBoostingClassifier(random_state=37) [1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 2, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 2, 1, 2, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 2, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 2, 1, 2, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 2, 2, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 0, 0, 0, 0, 0, 2, 2, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 2, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1]\n",
            "RidgeClassifierCV(alphas=array([ 0.1,  1. , 10. ])) [1, 1, 1, 1, 2, 1, 1, 0, 2, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 2, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "preds_catb = [1, 2, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 2, 0, 2, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 2, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 2, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 0, 2, 2, 0, 0, 1, 1, 1, 1, 1, 1, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]\n",
        "preds_lgbm = [1, 1, 1, 1, 1, 1, 1, 0, 0, 2, 1, 1, 2, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]\n",
        "preds_xgb = [1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]\n",
        "preds_bagging = [1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 2, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 2, 1, 2, 2, 1, 2, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 1, 1, 1, 1, 2, 2, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 1, 2, 2, 1, 2, 1, 1, 1, 1, 1, 1, 1, 2, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 0, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 1, 2, 2, 2, 2, 1, 1, 1, 1, 2, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 1, 2, 2, 2, 2, 2, 1, 1, 1, 1, 2, 2, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 2, 1, 2, 1, 2, 1]\n",
        "preds_gradient = [1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 2, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 2, 1, 2, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 2, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 2, 1, 2, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 2, 2, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 0, 0, 0, 0, 0, 2, 2, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 2, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1]\n",
        "preds_ridge = [1, 1, 1, 1, 2, 1, 1, 0, 2, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 2, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]"
      ],
      "metadata": {
        "id": "Q-71TFSqg2aH"
      },
      "id": "Q-71TFSqg2aH",
      "execution_count": 135,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 144,
      "id": "e1e8e853",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "e1e8e853",
        "outputId": "bdc600aa-998f-408a-a941-896324aa6688"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "submit = pd.read_csv('/content/drive/MyDrive/sample_submission.csv')\n",
        "\n",
        "submit['Y_Class'] = preds_ridge\n",
        "submit.to_csv('CXG212-smartFactorySubmission.csv', index=False)  \n",
        "sns.countplot(x=preds);"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "stack = StackingClassifier(\n",
        "    estimators=models,\n",
        "    final_estimator=LogisticRegression(),  \n",
        "    cv=5,\n",
        "    n_jobs=4,\n",
        "    passthrough=True\n",
        ")\n",
        "\n",
        "# use X_test as validation set      \n",
        "stack.fit(train_x, train_y)\n",
        "\n",
        "train_stack = np.concatenate(\n",
        "    [model.predict_proba(train_x) for model in models], axis=1)\n",
        "val_stack = np.concatenate(\n",
        "    [model.predict_proba(X_test) for model in models], axis=1)\n",
        "\n",
        "# meta model training\n",
        "meta_model = LogisticRegression().fit(train_stack, train_y)\n",
        "preds = meta_model.predict(val_stack)\n",
        "\n"
      ],
      "metadata": {
        "id": "X7bzI9lZO4VA"
      },
      "execution_count": null,
      "outputs": [],
      "id": "X7bzI9lZO4VA"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "e2069fcf",
      "metadata": {
        "id": "e2069fcf"
      },
      "outputs": [],
      "source": [
        "# object fit train_x, train_y, random_state=37  "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 92,
      "id": "669e28a5",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "669e28a5",
        "outputId": "49b3ddea-dd79-4b33-83f6-26d30d0243ef"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "seed_num=37\n",
        "\n",
        "GBC_model = GradientBoostingClassifier(random_state=37\n",
        "              ).fit(train_x, train_y)\n",
        "\n",
        "preds = GBC_model.predict(test_x)\n",
        "sns.countplot(x=preds);"
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "bId7aEA7wZm5"
      },
      "id": "bId7aEA7wZm5",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HgRXZthuQD4I"
      },
      "source": [
        "# 4.Sequential MLP"
      ],
      "id": "HgRXZthuQD4I"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 488
        },
        "id": "83ee1fde",
        "outputId": "7b343cb4-ffc3-48a2-8cd6-c2ace659f941",
        "scrolled": false
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/5\n",
            "6/6 [==============================] - 1s 42ms/step - loss: 4.5826 - accuracy: 0.5837 - val_loss: 4.8250 - val_accuracy: 0.5917\n",
            "Epoch 2/5\n",
            "6/6 [==============================] - 0s 13ms/step - loss: 2.7988 - accuracy: 0.5962 - val_loss: 2.6763 - val_accuracy: 0.6833\n",
            "Epoch 3/5\n",
            "6/6 [==============================] - 0s 13ms/step - loss: 2.1086 - accuracy: 0.6360 - val_loss: 2.2737 - val_accuracy: 0.6417\n",
            "Epoch 4/5\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 1.8722 - accuracy: 0.6569 - val_loss: 1.6281 - val_accuracy: 0.5500\n",
            "Epoch 5/5\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 1.5864 - accuracy: 0.6506 - val_loss: 1.6832 - val_accuracy: 0.6833\n",
            "10/10 [==============================] - 0s 2ms/step\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "model = Sequential([\n",
        "    # Input layer: ReLU (decide input size according to dimension)\n",
        "    Dense(512, activation='relu', input_shape=(train_x.shape[-1],)),    \n",
        "    # BatchNormalization(), \n",
        "    Dropout(0.3), \n",
        "\n",
        "    # Output layer: softmax \n",
        "    Dense(3, activation='softmax')  \n",
        "\n",
        "    ])\n",
        "from keras import optimizers, metrics, callbacks\n",
        "import seaborn as sns \n",
        "model.compile(optimizer = optimizers.Adam(1e-3),  # default learning rate=1e-3 \n",
        "            loss='sparse_categorical_crossentropy',  # tow or more label classes (`Y_Class`: 0,1,2)\n",
        "            metrics=['accuracy'])\n",
        "\n",
        "history = model.fit(train_x, train_y, epochs=5,\n",
        "                    batch_size=80,  # batch size: 2^x && half of data size(598/2) \n",
        "                    validation_split=0.2,\n",
        "                    )   \n",
        "\n",
        "preds = model.predict(test_x)\n",
        "a=[]\n",
        "for i in preds: \n",
        "    i = list(i)\n",
        "    a.append(i.index(max(i)))\n",
        "sns.countplot(x=a);"
      ],
      "id": "83ee1fde"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "f5a8c473",
        "outputId": "fcd7e3ac-a62a-4577-db75-1845d8d40c27"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1008x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "epochs = range(1, len(history.history[\"loss\"]) + 1)\n",
        "\n",
        "fig, axes = plt.subplots(1,2,figsize=(14,4))\n",
        "ax= axes.flatten()\n",
        "a, b = ax[0], ax[1]\n",
        "\n",
        "a.plot(epochs, history.history[\"loss\"], \"bo\", label=\"Training loss\")\n",
        "a.plot(epochs, history.history[\"val_loss\"], \"b\", label=\"Validation loss\")\n",
        "a.set_title(\"Training and validation loss\")\n",
        "a.set_xlabel(\"Epochs\")\n",
        "a.set_ylabel(\"Loss\")\n",
        "a.legend()   \n",
        "\n",
        "b.plot(epochs, history.history[\"accuracy\"], \"bo\", label=\"Training accuracy\")\n",
        "b.plot(epochs, history.history[\"val_accuracy\"], \"b\", label=\"Validation accuracy\")\n",
        "b.set_title(\"Training and validation accuracy\")\n",
        "b.set_xlabel(\"Epochs\")\n",
        "b.set_ylabel(\"Accuracy\")\n",
        "b.legend();"
      ],
      "id": "f5a8c473"
    },
    {
      "cell_type": "markdown",
      "source": [
        "# THE END"
      ],
      "metadata": {
        "id": "utf4-GCG5REy"
      },
      "id": "utf4-GCG5REy"
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "gvXsoboM5RL6"
      },
      "id": "gvXsoboM5RL6",
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "provenance": []
    },
    "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
}