smartFactory_GBC_CatBoost_Yumin.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Setup"
      ],
      "metadata": {
        "id": "zrHSs3fQqYSV"
      },
      "id": "zrHSs3fQqYSV"
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install catboost\n",
        "!pip install optuna"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iNgxjQveVeIk",
        "outputId": "8a1bc3e6-9365-4e45-a6bc-581ff20ef68b"
      },
      "id": "iNgxjQveVeIk",
      "execution_count": null,
      "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",
            "  Downloading catboost-1.1.1-cp38-none-manylinux1_x86_64.whl (76.6 MB)\n",
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            "Requirement already satisfied: tenacity>=6.2.0 in /usr/local/lib/python3.8/dist-packages (from plotly->catboost) (8.2.1)\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",
            "Collecting optuna\n",
            "  Downloading optuna-3.1.0-py3-none-any.whl (365 kB)\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"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 3.GradientBoostingClassifier"
      ],
      "metadata": {
        "id": "-v3orlbv2uZu"
      },
      "id": "-v3orlbv2uZu"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "rNmh3-PM9EFM"
      },
      "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()    # TRY one-hot encoding \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) scaling: 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",
        "\n",
        "# train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split(train_x, train_y, \n",
        "                                                    random_state=37, test_size=0.3)\n",
        "\n",
        "\n",
        "from sklearn.ensemble import GradientBoostingClassifier\n",
        "# GBC =  GradientBoostingClassifier(random_state=37).fit(train_x, train_y)\n",
        "# preds = GBC.predict(test_x)\n",
        "# sns.countplot(x=preds);\n",
        "# preds"
      ],
      "id": "rNmh3-PM9EFM"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "TEp_s2vw2SGN"
      },
      "outputs": [],
      "source": [
        "submit = pd.read_csv('/content/drive/MyDrive/sample_submission.csv')\n",
        "submit['Y_Class'] = preds\n",
        "\n",
        "submit.to_csv('GBC_MMs_trainx-smartFactorySubmission.csv', index=False)\n",
        "# sns.countplot(x=preds);"
      ],
      "id": "TEp_s2vw2SGN"
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import KFold\n",
        "from sklearn.model_selection import cross_val_score\n",
        "from sklearn.model_selection import cross_validate\n",
        "\n",
        "def objective(trial, X, y, cv, scoring):\n",
        "  \"\"\"\n",
        "  An objective function to tune hyperparameters of Gradient Boosting Classifier.\n",
        "  Args:\n",
        "    trial: an Optuna trial\n",
        "    X: DataFrame object, features\n",
        "    y: Series object, Labels\n",
        "    cv: k folds to cross-validate\n",
        "    scoring: String, evaluation metric\n",
        "  Return:\n",
        "    Mean test accuracy\n",
        "  \"\"\"\n",
        "\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\": 37,\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"
      ],
      "metadata": {
        "id": "YDFU6Eh4GR4u"
      },
      "id": "YDFU6Eh4GR4u",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "study = optuna.create_study(direction = \"maximize\")\n",
        "\n",
        "kf = KFold(n_splits = 5, shuffle = True, random_state = 37)\n",
        "\n",
        "func = lambda trial: objective(trial, train_x, train_y, cv = kf, scoring = \"accuracy\")\n",
        "\n",
        "study.optimize(func, n_trials = 30)\n",
        "print(\"Best params:\")\n",
        "for key, value in study.best_params.items():\n",
        "    print(f\"\\t{key}: {value}\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "q4gWwfRWGRZ1",
        "outputId": "9ee4d12a-8f9e-47a8-a1d6-ef502c571ceb"
      },
      "id": "q4gWwfRWGRZ1",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\u001b[32m[I 2023-02-26 05:16:54,586]\u001b[0m A new study created in memory with name: no-name-ebd42908-1b5f-4809-88ea-9d8a58a133ef\u001b[0m\n",
            "\u001b[32m[I 2023-02-26 06:12:00,648]\u001b[0m Trial 0 finished with value: 0.7506582633053223 and parameters: {'n_estimators': 3600, 'learning_rate': 0.00021479973315201132, 'max_depth': 9, 'subsample': 0.8, 'max_features': 'auto'}. Best is trial 0 with value: 0.7506582633053223.\u001b[0m\n",
            "\u001b[32m[I 2023-02-26 06:12:43,290]\u001b[0m Trial 1 finished with value: 0.8026610644257703 and parameters: {'n_estimators': 1500, 'learning_rate': 0.029761872801184145, 'max_depth': 8, 'subsample': 0.6, 'max_features': 'sqrt'}. Best is trial 1 with value: 0.8026610644257703.\u001b[0m\n",
            "\u001b[32m[I 2023-02-26 06:12:49,179]\u001b[0m Trial 2 finished with value: 0.6854621848739496 and parameters: {'n_estimators': 600, 'learning_rate': 0.0006573138814954677, 'max_depth': 3, 'subsample': 0.9, 'max_features': 'log2'}. Best is trial 1 with value: 0.8026610644257703.\u001b[0m\n",
            "\u001b[32m[I 2023-02-26 06:12:50,600]\u001b[0m Trial 3 finished with value: 0.7491036414565826 and parameters: {'n_estimators': 100, 'learning_rate': 0.10888479437174528, 'max_depth': 5, 'subsample': 0.5, 'max_features': 'log2'}. Best is trial 1 with value: 0.8026610644257703.\u001b[0m\n",
            "\u001b[32m[I 2023-02-26 06:13:03,986]\u001b[0m Trial 4 finished with value: 0.7758823529411765 and parameters: {'n_estimators': 1300, 'learning_rate': 0.009606745406463854, 'max_depth': 4, 'subsample': 0.6, 'max_features': 'log2'}. Best is trial 1 with value: 0.8026610644257703.\u001b[0m\n",
            "\u001b[32m[I 2023-02-26 06:13:10,554]\u001b[0m Trial 5 finished with value: 0.6871428571428572 and parameters: {'n_estimators': 200, 'learning_rate': 0.0019048233535640868, 'max_depth': 9, 'subsample': 0.8, 'max_features': 'log2'}. Best is trial 1 with value: 0.8026610644257703.\u001b[0m\n",
            "\u001b[32m[I 2023-02-26 06:13:13,013]\u001b[0m Trial 6 finished with value: 0.7490476190476192 and parameters: {'n_estimators': 100, 'learning_rate': 0.022654502035674047, 'max_depth': 7, 'subsample': 0.9, 'max_features': 'log2'}. Best is trial 1 with value: 0.8026610644257703.\u001b[0m\n",
            "\u001b[32m[I 2023-02-26 06:13:23,617]\u001b[0m Trial 7 finished with value: 0.7507563025210084 and parameters: {'n_estimators': 800, 'learning_rate': 0.0037235960120209073, 'max_depth': 5, 'subsample': 0.6, 'max_features': 'log2'}. Best is trial 1 with value: 0.8026610644257703.\u001b[0m\n",
            "\u001b[32m[I 2023-02-26 06:14:05,457]\u001b[0m Trial 8 finished with value: 0.7758543417366947 and parameters: {'n_estimators': 900, 'learning_rate': 0.0030328069291390386, 'max_depth': 9, 'subsample': 0.9, 'max_features': 'sqrt'}. Best is trial 1 with value: 0.8026610644257703.\u001b[0m\n",
            "\u001b[32m[I 2023-02-26 06:44:37,190]\u001b[0m Trial 9 finished with value: 0.7775210084033614 and parameters: {'n_estimators': 3000, 'learning_rate': 0.002244076907279764, 'max_depth': 9, 'subsample': 0.5, 'max_features': 'auto'}. Best is trial 1 with value: 0.8026610644257703.\u001b[0m\n",
            "\u001b[32m[I 2023-02-26 06:45:09,131]\u001b[0m Trial 10 finished with value: 0.7842436974789916 and parameters: {'n_estimators': 5000, 'learning_rate': 0.2932043816657719, 'max_depth': 7, 'subsample': 0.7, 'max_features': 'sqrt'}. Best is trial 1 with value: 0.8026610644257703.\u001b[0m\n",
            "\u001b[32m[I 2023-02-26 06:45:38,966]\u001b[0m Trial 11 finished with value: 0.7942857142857143 and parameters: {'n_estimators': 4600, 'learning_rate': 0.20399403521891676, 'max_depth': 7, 'subsample': 0.7, 'max_features': 'sqrt'}. Best is trial 1 with value: 0.8026610644257703.\u001b[0m\n",
            "\u001b[32m[I 2023-02-26 06:46:06,567]\u001b[0m Trial 12 finished with value: 0.7993417366946778 and parameters: {'n_estimators': 2000, 'learning_rate': 0.062539412824165, 'max_depth': 7, 'subsample': 0.7, 'max_features': 'sqrt'}. Best is trial 1 with value: 0.8026610644257703.\u001b[0m\n",
            "\u001b[32m[I 2023-02-26 06:46:47,198]\u001b[0m Trial 13 finished with value: 0.7976470588235294 and parameters: {'n_estimators': 1900, 'learning_rate': 0.033216826794753344, 'max_depth': 8, 'subsample': 0.6, 'max_features': 'sqrt'}. Best is trial 1 with value: 0.8026610644257703.\u001b[0m\n",
            "\u001b[32m[I 2023-02-26 06:47:17,308]\u001b[0m Trial 14 finished with value: 0.7942717086834733 and parameters: {'n_estimators': 2100, 'learning_rate': 0.0575992808869687, 'max_depth': 6, 'subsample': 0.7, 'max_features': 'sqrt'}. Best is trial 1 with value: 0.8026610644257703.\u001b[0m\n",
            "\u001b[32m[I 2023-02-26 06:48:37,214]\u001b[0m Trial 15 finished with value: 0.8009943977591035 and parameters: {'n_estimators': 2800, 'learning_rate': 0.014158319465959319, 'max_depth': 8, 'subsample': 0.6, 'max_features': 'sqrt'}. Best is trial 1 with value: 0.8026610644257703.\u001b[0m\n",
            "\u001b[32m[I 2023-02-26 06:50:02,961]\u001b[0m Trial 16 finished with value: 0.8010084033613445 and parameters: {'n_estimators': 3300, 'learning_rate': 0.014463923820272942, 'max_depth': 8, 'subsample': 0.6, 'max_features': 'sqrt'}. Best is trial 1 with value: 0.8026610644257703.\u001b[0m\n",
            "\u001b[33m[W 2023-02-26 07:04:43,464]\u001b[0m Trial 17 failed with parameters: {'n_estimators': 3600, 'learning_rate': 0.008409785025942265, 'max_depth': 8, 'subsample': 0.5, 'max_features': 'auto'} 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-12-e1e9b65c3122>\", line 5, in <lambda>\n",
            "    func = lambda trial: objective(trial, train_x, train_y, cv = kf, scoring = \"accuracy\")\n",
            "  File \"<ipython-input-11-d6775ae286fb>\", line 30, 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 2023-02-26 07:04:43,466]\u001b[0m Trial 17 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-12-e1e9b65c3122>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mfunc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mlambda\u001b[0m \u001b[0mtrial\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mobjective\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrial\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrain_x\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrain_y\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcv\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mkf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mscoring\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"accuracy\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\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;36m30\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      8\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Best params:\"\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;32mfor\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mstudy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbest_params\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\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/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-12-e1e9b65c3122>\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[0mKFold\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 \u001b[0mfunc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mlambda\u001b[0m \u001b[0mtrial\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mobjective\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrial\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrain_x\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrain_y\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcv\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mkf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mscoring\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"accuracy\"\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      6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m \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;36m30\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-11-d6775ae286fb>\u001b[0m in \u001b[0;36mobjective\u001b[0;34m(trial, X, y, cv, scoring)\u001b[0m\n\u001b[1;32m     28\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     29\u001b[0m   \u001b[0;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---> 30\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     31\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     32\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: "
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import imblearn\n",
        "\n",
        "# Trial 8 finished with value: 0.7976750700280112 and \n",
        "# param = {'n_estimators': 2500, 'learning_rate': 0.24437231062646464, 'max_depth': 7, 'subsample': 0.9, 'max_features': 'sqrt'}\n",
        "\n",
        "# value: 0.7926050420168067 \n",
        "param= {'n_estimators': 1400, 'learning_rate': 0.007793231258605556, 'max_depth': 6, 'subsample': 0.8, 'max_features': 'sqrt'}\n",
        "\n",
        "GBC =  GradientBoostingClassifier(random_state=37, **param,                              \n",
        "                                  ).fit(train_x, train_y)\n",
        "preds = GBC.predict(test_x)\n",
        "sns.countplot(x=preds);\n",
        "preds"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 525
        },
        "id": "S4zxDMJ4YGGd",
        "outputId": "996ee95b-34b7-4777-9d4e-f809f1c57065"
      },
      "id": "S4zxDMJ4YGGd",
      "execution_count": null,
      "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, 1, 0, 0, 0, 0, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0,\n",
              "       0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 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, 1, 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, 1, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 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, 1, 0, 0,\n",
              "       0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1])"
            ]
          },
          "metadata": {},
          "execution_count": 30
        },
        {
          "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": [
        "# Best is trial 0 with value: 0.8026750700280111.\n",
        "params= {'n_estimators': 4700, 'learning_rate': 0.007080906403563565, 'max_depth': 4, 'subsample': 0.8, 'max_features': 'sqrt'}\n",
        "\n",
        "GBC =  GradientBoostingClassifier(random_state=37,\n",
        "                                  **params,                              \n",
        "                                  ).fit(train_x, train_y)\n",
        "preds = GBC.predict(test_x)\n",
        "sns.countplot(x=preds);\n",
        "preds"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 525
        },
        "id": "DbxqiKTwSsO2",
        "outputId": "4aeb79a1-1ad9-4b6b-b28e-6d87c09418ab"
      },
      "id": "DbxqiKTwSsO2",
      "execution_count": null,
      "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",
              "       0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 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, 1, 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, 0, 0, 1, 1, 2, 1, 2, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 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",
              "       0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1])"
            ]
          },
          "metadata": {},
          "execution_count": 23
        },
        {
          "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",
      "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": "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",
      "source": [
        "params=  {'iterations': 857, 'learning_rate': 0.07097207730593516, 'depth': 8, 'l2_leaf_reg': 0.00012090525126196811, 'bootstrap_type': 'Bayesian', 'random_strength': 5.359110894800412, 'bagging_temperature': 0.7137022569955509, 'od_type': 'IncToDec', 'od_wait': 42}\n",
        "a = CatBoostClassifier(**params, verbose=False, random_state=37)\n",
        "a.fit(X_train, y_train)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OYWFMNT9ycd7",
        "outputId": "80c11b86-e20b-4ea5-c191-98d09c28f7d6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<catboost.core.CatBoostClassifier at 0x7fda25fe50d0>"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ],
      "id": "OYWFMNT9ycd7"
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred1 = a.predict(X_test) \n",
        "print(classification_report(y_pred1, y_test))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "atWXIsggyz-I",
        "outputId": "1f4df3b9-92cc-4e61-fbbd-6262563da6ed"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.38      0.67      0.48        12\n",
            "           1       0.97      0.79      0.87       160\n",
            "           2       0.29      1.00      0.44         8\n",
            "\n",
            "    accuracy                           0.79       180\n",
            "   macro avg       0.55      0.82      0.60       180\n",
            "weighted avg       0.90      0.79      0.83       180\n",
            "\n"
          ]
        }
      ],
      "id": "atWXIsggyz-I"
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import classification_report\n",
        "print(classification_report(y_pred, y_test))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pKk-l7EFwsPu",
        "outputId": "d48cc3a4-52cc-4235-f594-53d78005eb18"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.38      0.67      0.48        12\n",
            "           1       0.97      0.79      0.87       160\n",
            "           2       0.29      1.00      0.44         8\n",
            "\n",
            "    accuracy                           0.79       180\n",
            "   macro avg       0.55      0.82      0.60       180\n",
            "weighted avg       0.90      0.79      0.83       180\n",
            "\n"
          ]
        }
      ],
      "id": "pKk-l7EFwsPu"
    },
    {
      "cell_type": "code",
      "source": [
        "preds = a.predict(test_x)\n",
        "preds.reshape([1,310])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "G8qc4i2oQrv1",
        "outputId": "3a9f4a28-b90e-4d79-e300-80212c0f2b50"
      },
      "execution_count": null,
      "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, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0,\n",
              "        0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 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, 1, 1,\n",
              "        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "        1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0,\n",
              "        1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "        1, 1]])"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ],
      "id": "G8qc4i2oQrv1"
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 4.Ensemble\n",
        "- 35~42"
      ],
      "metadata": {
        "id": "sSGqsbK39jpn"
      },
      "id": "sSGqsbK39jpn"
    },
    {
      "cell_type": "code",
      "source": [
        "from catboost import CatBoostClassifier\n",
        "from sklearn.ensemble import GradientBoostingClassifier\n",
        "from sklearn.ensemble import VotingClassifier\n",
        "\n",
        "models = [\n",
        "   \n",
        "    ('gb', GradientBoostingClassifier(random_state=37)),\n",
        "    ('catB', CatBoostClassifier( \n",
        "    **params, verbose=False, random_state=37\n",
        ")),\n",
        "   \n",
        "]"
      ],
      "metadata": {
        "id": "Dsbc_zz0KxDp"
      },
      "execution_count": null,
      "outputs": [],
      "id": "Dsbc_zz0KxDp"
    },
    {
      "cell_type": "code",
      "source": [
        "vote = VotingClassifier(models, voting='soft', weights=[3,1]).fit(train_x, train_y)   \n",
        "\n",
        "# preds = vote.predict_proba(test_x).reshape([1,310])\n",
        "preds "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "r7sF5QxUV2DX",
        "outputId": "8b2b5324-9d33-46e6-a845-106ce0e64e39"
      },
      "execution_count": null,
      "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, 2, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 2, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 2, 1, 1, 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, 2, 1, 1, 1, 1, 2, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 2, 0, 0, 0,\n",
              "       1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1,\n",
              "       1, 1])"
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ],
      "id": "r7sF5QxUV2DX"
    },
    {
      "cell_type": "code",
      "source": [
        "preds = vote.predict(test_x)\n",
        "preds\n",
        "sns.countplot(x=preds);"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "nQkCQ2Ez_qTX",
        "outputId": "1c9b7cb6-f1a8-40db-d965-667f40bcdad6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAD4CAYAAAAD6PrjAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/NK7nSAAAACXBIWXMAAAsTAAALEwEAmpwYAAANaUlEQVR4nO3df6jd9X3H8efLH+3YtFTJXZYm2SIl60j3I3YXJ3MMV9mqwhZbOlGoZs6R/qFFoQxs/1ilIBRWLa3bhBStcThdhjqzIdtckElLq72RoDGZa2h1JkRzq6W6lTni3vvjfu+nB71JT9Tv+d6b+3zA4X7P53zPyVuu+sz3nO85J1WFJEkAJw09gCRp8TAKkqTGKEiSGqMgSWqMgiSpOWXoAd6OFStW1Lp164YeQ5KWlF27dn2/qqYWum1JR2HdunXMzMwMPYYkLSlJnjvabT59JElqjIIkqTEKkqTGKEiSGqMgSWqMgiSpMQqSpMYoSJIaoyBJapb0O5q1fPzn539l6BFOeD//Z08NPYIWAY8UJEmNUZAkNb1FIcnaJI8k2Zvk6STXdes3JjmYZHd3uXjkPp9Jsj/JM0k+0tdskqSF9fmawhHg01X1RJLTgV1JHu5u+1JVfXF05yQbgMuADwLvA/41yS9W1es9zihJGtHbkUJVHaqqJ7rtV4F9wOpj3GUTcG9VvVZV3wP2A+f0NZ8k6c0m8ppCknXA2cBj3dK1SZ5MckeSM7q11cDzI3c7wAIRSbIlyUySmdnZ2T7HlqRlp/coJDkNuA+4vqpeAW4D3g9sBA4BNx/P41XV1qqarqrpqakFvzhIkvQW9RqFJKcyF4S7q+p+gKp6saper6r/A77Kj58iOgisHbn7mm5NkjQhfZ59FOB2YF9V3TKyvmpkt48Ce7rtHcBlSd6d5CxgPfB4X/NJkt6sz7OPzgOuAJ5Ksrtb+yxweZKNQAHPAp8EqKqnk2wH9jJ35tI1nnkkSZPVWxSq6utAFrjpoWPc5ybgpr5mkiQdm+9oliQ1RkGS1BgFSVJjFCRJjVGQJDVGQZLUGAVJUmMUJEmNUZAkNUZBktQYBUlSYxQkSY1RkCQ1RkGS1BgFSVJjFCRJjVGQJDVGQZLUGAVJUmMUJEmNUZAkNUZBktQYBUlSYxQkSY1RkCQ1RkGS1BgFSVJjFCRJjVGQJDVGQZLUGAVJUtNbFJKsTfJIkr1Jnk5yXbd+ZpKHk3yn+3lGt54kX0myP8mTST7U12ySpIX1eaRwBPh0VW0AzgWuSbIBuAHYWVXrgZ3ddYCLgPXdZQtwW4+zSZIW0FsUqupQVT3Rbb8K7ANWA5uAbd1u24BLuu1NwF0151vAe5Os6ms+SdKbTeQ1hSTrgLOBx4CVVXWou+kFYGW3vRp4fuRuB7q1Nz7WliQzSWZmZ2f7G1qSlqHeo5DkNOA+4PqqemX0tqoqoI7n8apqa1VNV9X01NTUOzipJKnXKCQ5lbkg3F1V93fLL84/LdT9PNytHwTWjtx9TbcmSZqQPs8+CnA7sK+qbhm5aQewudveDDw4sn5ldxbSucAPR55mkiRNwCk9PvZ5wBXAU0l2d2ufBb4AbE9yNfAccGl320PAxcB+4EfAVT3OJklaQG9RqKqvAznKzRcssH8B1/Q1jyTpJ/MdzZKkxihIkhqjIElqjIIkqTEKkqTGKEiSGqMgSWqMgiSpMQqSpMYoSJIaoyBJaoyCJKkxCpKkxihIkhqjIElqjIIkqTEKkqTGKEiSGqMgSWqMgiSpMQqSpMYoSJIaoyBJaoyCJKkxCpKkxihIkhqjIElqjIIkqTEKkqTGKEiSGqMgSWp6i0KSO5IcTrJnZO3GJAeT7O4uF4/c9pkk+5M8k+Qjfc0lSTq6Po8U7gQuXGD9S1W1sbs8BJBkA3AZ8MHuPn+V5OQeZ5MkLaC3KFTVo8DLY+6+Cbi3ql6rqu8B+4Fz+ppNkrSwIV5TuDbJk93TS2d0a6uB50f2OdCtvUmSLUlmkszMzs72PaskLStjRSHJznHWxnAb8H5gI3AIuPl4H6CqtlbVdFVNT01NvYURJElHc8qxbkzyU8BPAyu6v9Wnu+k9HOVv8sdSVS+OPPZXgX/srh4E1o7suqZbkyRN0E86UvgksAv4pe7n/OVB4C+O9w9Lsmrk6keB+TOTdgCXJXl3krOA9cDjx/v4kqS355hHClX1ZeDLST5VVbcezwMnuQc4n7mjjAPA54Dzk2wECniWuehQVU8n2Q7sBY4A11TV68f3jyJJeruOGYV5VXVrkt8E1o3ep6ruOsZ9Ll9g+fZj7H8TcNM480iS+jFWFJL8NXMvEO8G5v8GX8BRoyBJWnrGigIwDWyoqupzGEnSsMZ9n8Ie4Of6HESSNLxxjxRWAHuTPA68Nr9YVX/Qy1SSpEGMG4Ub+xxCkrQ4jHv20b/1PYgkaXjjnn30KnNnGwG8CzgV+O+qek9fg0mSJm/cI4XT57eThLlPNT23r6EkScM47k9JrTl/D/hFOJJ0ghn36aOPjVw9ibn3LfxPLxNJkgYz7tlHvz+yfYS5zy3a9I5PI0ka1LivKVzV9yCSpOGN+yU7a5I8kORwd7kvyZq+h5MkTda4LzR/jbnvPHhfd/mHbk2SdAIZNwpTVfW1qjrSXe4E/C5MSTrBjBuFl5J8IsnJ3eUTwEt9DiZJmrxxo/DHwKXAC8Ah4OPAH/U0kyRpIOOekvp5YHNV/QAgyZnAF5mLhSTpBDHukcKvzgcBoKpeBs7uZyRJ0lDGjcJJSc6Yv9IdKYx7lCFJWiLG/R/7zcA3k/xdd/0PgZv6GUmSNJRx39F8V5IZ4MPd0seqam9/Y0mShjD2U0BdBAyBJJ3AjvujsyVJJy6jIElqjIIkqTEKkqTGKEiSGqMgSWqMgiSpMQqSpKa3KCS5o/vqzj0ja2cmeTjJd7qfZ3TrSfKVJPuTPJnkQ33NJUk6uj6PFO4ELnzD2g3AzqpaD+zsrgNcBKzvLluA23qcS5J0FL1FoaoeBV5+w/ImYFu3vQ24ZGT9rprzLeC9SVb1NZskaWGTfk1hZVUd6rZfAFZ226uB50f2O9CtSZImaLAXmquqgDre+yXZkmQmyczs7GwPk0nS8jXpKLw4/7RQ9/Nwt34QWDuy35pu7U2qamtVTVfV9NTUVK/DStJyM+ko7AA2d9ubgQdH1q/szkI6F/jhyNNMkqQJ6e0rNZPcA5wPrEhyAPgc8AVge5KrgeeAS7vdHwIuBvYDPwKu6msuSdLR9RaFqrr8KDddsMC+BVzT1yySpPH4jmZJUmMUJEmNUZAkNUZBktQYBUlSYxQkSY1RkCQ1RkGS1BgFSVJjFCRJjVGQJDVGQZLUGAVJUmMUJEmNUZAkNUZBktQYBUlSYxQkSY1RkCQ1RkGS1BgFSVJjFCRJjVGQJDVGQZLUGAVJUmMUJEmNUZAkNUZBktQYBUlSYxQkSY1RkCQ1RkGS1JwyxB+a5FngVeB14EhVTSc5E/hbYB3wLHBpVf1giPkkabka8kjhd6pqY1VNd9dvAHZW1XpgZ3ddkjRBi+npo03Atm57G3DJcKNI0vI0VBQK+Jcku5Js6dZWVtWhbvsFYOVCd0yyJclMkpnZ2dlJzCpJy8YgrykAv1VVB5P8LPBwkn8fvbGqKkktdMeq2gpsBZienl5wH0nSWzPIkUJVHex+HgYeAM4BXkyyCqD7eXiI2SRpOZt4FJL8TJLT57eB3wP2ADuAzd1um4EHJz2bJC13Qzx9tBJ4IMn8n/83VfVPSb4NbE9yNfAccOkAs0nSsjbxKFTVd4FfW2D9JeCCSc8jSfqxxXRKqiRpYEZBktQYBUlSYxQkSc1Qb16buF//07uGHmFZ2PXnVw49gqS3wSMFSVJjFCRJjVGQJDVGQZLUGAVJUmMUJEmNUZAkNUZBktQYBUlSYxQkSY1RkCQ1RkGS1BgFSVJjFCRJjVGQJDVGQZLUGAVJUrNsvnlN0nDOu/W8oUc44X3jU994Rx7HIwVJUmMUJEmNUZAkNUZBktQYBUlSYxQkSY1RkCQ1RkGS1Cy6KCS5MMkzSfYnuWHoeSRpOVlUUUhyMvCXwEXABuDyJBuGnUqSlo9FFQXgHGB/VX23qv4XuBfYNPBMkrRspKqGnqFJ8nHgwqr6k+76FcBvVNW1I/tsAbZ0Vz8APDPxQSdnBfD9oYfQW+bvb+k60X93v1BVUwvdsOQ+EK+qtgJbh55jEpLMVNX00HPorfH3t3Qt59/dYnv66CCwduT6mm5NkjQBiy0K3wbWJzkrybuAy4AdA88kScvGonr6qKqOJLkW+GfgZOCOqnp64LGGtCyeJjuB+ftbupbt725RvdAsSRrWYnv6SJI0IKMgSWqMwiLkR30sbUnuSHI4yZ6hZ9HxSbI2ySNJ9iZ5Osl1Q880ab6msMh0H/XxH8DvAgeYOyPr8qraO+hgGluS3wb+C7irqn556Hk0viSrgFVV9USS04FdwCXL6b8/jxQWHz/qY4mrqkeBl4eeQ8evqg5V1RPd9qvAPmD1sFNNllFYfFYDz49cP8Ay+5dSWgySrAPOBh4beJSJMgqS9AZJTgPuA66vqleGnmeSjMLi40d9SANKcipzQbi7qu4fep5JMwqLjx/1IQ0kSYDbgX1VdcvQ8wzBKCwyVXUEmP+oj33A9mX+UR9LTpJ7gG8CH0hyIMnVQ8+ksZ0HXAF8OMnu7nLx0ENNkqekSpIajxQkSY1RkCQ1RkGS1BgFSVJjFCRJjVGQJDVGQZLU/D+agT1i1aVFpwAAAABJRU5ErkJggg==\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "id": "nQkCQ2Ez_qTX"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "F5rv_sf2hgHN"
      },
      "outputs": [],
      "source": [
        "submit = pd.read_csv('/content/drive/MyDrive/sample_submission.csv')\n",
        "submit['Y_Class'] = preds\n",
        "\n",
        "submit.to_csv('voting.catB_GBC.w3:1-smartFactorySubmission.csv', index=False)\n"
      ],
      "id": "F5rv_sf2hgHN"
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "ujxoQYI6CPN3"
      },
      "execution_count": null,
      "outputs": [],
      "id": "ujxoQYI6CPN3"
    },
    {
      "cell_type": "code",
      "source": [
        "#   Params:  {'iterations': 848, 'learning_rate': 0.030969090455030726, 'depth': 8, 'l2_leaf_reg': 6.761650264581644e-08, 'bootstrap_type': 'Bayesian', 'random_strength': 5.097812627692342e-07, 'bagging_temperature': 7.206905770161392, 'od_type': 'IncToDec', 'od_wait': 49}\n",
        "\n",
        "model = CatBoostClassifier( \n",
        "    iterations= 960, \n",
        "                                       learning_rate= 0.0022686226693627966,\n",
        "                                       depth= 4, \n",
        "                                       l2_leaf_reg= 0.00025235896782569236, \n",
        "                                       bootstrap_type= 'Bayesian', \n",
        "                                       random_strength= 0.07344055855215284, \n",
        "                                       bagging_temperature= 0.5154099595769013, \n",
        "                                       od_type= 'IncToDec', \n",
        "                                       od_wait=34)\n"
      ],
      "metadata": {
        "id": "s1O2zhvUR5Rb"
      },
      "execution_count": null,
      "outputs": [],
      "id": "s1O2zhvUR5Rb"
    },
    {
      "cell_type": "code",
      "source": [
        "# b = pd.read_csv('catB_noSMOTE-smartFactorySubmission.csv')\n",
        "# sns.countplot(x = b['Y_Class']);"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 280
        },
        "id": "STiqtVSlxuPt",
        "outputId": "9c0c1a98-bad9-4eb9-c236-2f431d799a21"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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        }
      ],
      "id": "STiqtVSlxuPt"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "91bbd3b7",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "faabe348-6338-4406-eb6f-6682773a3589"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\u001b[33m[W 2023-02-25 16:04:26,811]\u001b[0m Param bootstrap_type unique value length is less than 2.\u001b[0m\n",
            "\u001b[33m[W 2023-02-25 16:04:26,813]\u001b[0m Param bootstrap_type unique value length is less than 2.\u001b[0m\n",
            "\u001b[33m[W 2023-02-25 16:04:26,815]\u001b[0m Param bootstrap_type unique value length is less than 2.\u001b[0m\n",
            "\u001b[33m[W 2023-02-25 16:04:26,816]\u001b[0m Param bootstrap_type unique value length is less than 2.\u001b[0m\n",
            "\u001b[33m[W 2023-02-25 16:04:26,817]\u001b[0m Param bootstrap_type unique value length is less than 2.\u001b[0m\n",
            "\u001b[33m[W 2023-02-25 16:04:26,819]\u001b[0m Param bootstrap_type unique value length is less than 2.\u001b[0m\n",
            "\u001b[33m[W 2023-02-25 16:04:26,820]\u001b[0m Param bootstrap_type unique value length is less than 2.\u001b[0m\n",
            "\u001b[33m[W 2023-02-25 16:04:26,821]\u001b[0m Param bootstrap_type unique value length is less than 2.\u001b[0m\n",
            "\u001b[33m[W 2023-02-25 16:04:26,822]\u001b[0m Param bootstrap_type unique value length is less than 2.\u001b[0m\n",
            "\u001b[33m[W 2023-02-25 16:04:26,824]\u001b[0m Param bootstrap_type unique value length is less than 2.\u001b[0m\n",
            "\u001b[33m[W 2023-02-25 16:04:26,826]\u001b[0m Param bootstrap_type unique value length is less than 2.\u001b[0m\n",
            "\u001b[33m[W 2023-02-25 16:04:26,827]\u001b[0m Param bootstrap_type unique value length is less than 2.\u001b[0m\n",
            "\u001b[33m[W 2023-02-25 16:04:26,835]\u001b[0m Param bootstrap_type unique value length is less than 2.\u001b[0m\n",
            "\u001b[33m[W 2023-02-25 16:04:26,836]\u001b[0m Param bootstrap_type unique value length is less than 2.\u001b[0m\n",
            "\u001b[33m[W 2023-02-25 16:04:26,838]\u001b[0m Param bootstrap_type unique value length is less than 2.\u001b[0m\n",
            "\u001b[33m[W 2023-02-25 16:04:26,840]\u001b[0m Param bootstrap_type unique value length is less than 2.\u001b[0m\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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              "                            \n",
              "var gd = document.getElementById('eec493e0-6b36-4513-b0b7-4bb8905d91dc');\n",
              "var x = new MutationObserver(function (mutations, observer) {{\n",
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              "        if (!display || display === 'none') {{\n",
              "            console.log([gd, 'removed!']);\n",
              "            Plotly.purge(gd);\n",
              "            observer.disconnect();\n",
              "        }}\n",
              "}});\n",
              "\n",
              "// Listen for the removal of the full notebook cells\n",
              "var notebookContainer = gd.closest('#notebook-container');\n",
              "if (notebookContainer) {{\n",
              "    x.observe(notebookContainer, {childList: true});\n",
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              "    x.observe(outputEl, {childList: true});\n",
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              "\n",
              "                        })                };                            </script>        </div>\n",
              "</body>\n",
              "</html>"
            ]
          },
          "metadata": {}
        }
      ],
      "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",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "outputId": "e41cddf6-41b7-40bb-efd4-6860389a697a"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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      ],
      "source": [
        "# 하이퍼파라미터 중요도\n",
        "optuna.visualization.plot_param_importances(study)"
      ],
      "id": "fab2a838"
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      "id": "UGXZF-80lBAP"
    },
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