▶StackingML_smartFactoryFINAL_Yumin.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "id": "6ab709fe",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6ab709fe",
        "outputId": "8e439ba0-ad62-440e-e1e0-0e6ef05f1ac6"
      },
      "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",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m76.6/76.6 MB\u001b[0m \u001b[31m14.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: scipy in /usr/local/lib/python3.8/dist-packages (from catboost) (1.7.3)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.8/dist-packages (from catboost) (1.15.0)\n",
            "Requirement already satisfied: pandas>=0.24.0 in /usr/local/lib/python3.8/dist-packages (from catboost) (1.3.5)\n",
            "Requirement already satisfied: graphviz in /usr/local/lib/python3.8/dist-packages (from catboost) (0.10.1)\n",
            "Requirement already satisfied: plotly in /usr/local/lib/python3.8/dist-packages (from catboost) (5.5.0)\n",
            "Requirement already satisfied: numpy>=1.16.0 in /usr/local/lib/python3.8/dist-packages (from catboost) (1.22.4)\n",
            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.8/dist-packages (from catboost) (3.5.3)\n",
            "Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.8/dist-packages (from pandas>=0.24.0->catboost) (2022.7.1)\n",
            "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.8/dist-packages (from pandas>=0.24.0->catboost) (2.8.2)\n",
            "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.8/dist-packages (from matplotlib->catboost) (4.38.0)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.8/dist-packages (from matplotlib->catboost) (0.11.0)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.8/dist-packages (from matplotlib->catboost) (1.4.4)\n",
            "Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.8/dist-packages (from matplotlib->catboost) (8.4.0)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.8/dist-packages (from matplotlib->catboost) (23.0)\n",
            "Requirement already satisfied: pyparsing>=2.2.1 in /usr/local/lib/python3.8/dist-packages (from matplotlib->catboost) (3.0.9)\n",
            "Requirement already satisfied: 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"
          ]
        }
      ],
      "source": [
        "# !pip install pytorch_tabnet\n",
        "!pip install catboost\n",
        "!pip install lightgbm\n",
        "# !pip install optuna"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "id": "94682a80",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "94682a80",
        "outputId": "4d03b122-7483-4f43-e1f9-25be66473ec3"
      },
      "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",
      "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'])   "
      ],
      "metadata": {
        "id": "kZ1PM_Zrtzph"
      },
      "id": "kZ1PM_Zrtzph",
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "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": null,
      "id": "e7f4ff7b",
      "metadata": {
        "id": "e7f4ff7b"
      },
      "outputs": [],
      "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",
      "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": 13,
      "id": "20a97788",
      "metadata": {
        "id": "20a97788"
      },
      "outputs": [],
      "source": [
        "import seaborn as sns "
      ]
    },
    {
      "cell_type": "markdown",
      "id": "2fe15f1a",
      "metadata": {
        "id": "2fe15f1a"
      },
      "source": [
        "# 3.VotingClassifier"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "GBC_param= {'n_estimators': 1400, 'learning_rate': 0.007793231258605556, 'max_depth': 6, 'subsample': 0.8, 'max_features': 'sqrt'}\n",
        "CB_param= {'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",
        "\n",
        "models = [\n",
        "    CatBoostClassifier(\n",
        "        # objective='MultiClass', task_type='GPU', one_hot_max_size=2, iterations=4000, learning_rate=0.05, \n",
        "        verbose=False, random_seed=seed_num, \n",
        "        **CB_param\n",
        "                                   ),\n",
        "    # LGBMClassifier(objective='multiclass', random_state=seed_num),\n",
        "    XGBClassifier(random_state=seed_num),\n",
        "    GradientBoostingClassifier(random_state=seed_num),    \n",
        "    # RidgeClassifierCV(),\n",
        "]\n",
        "[x.fit(train_x, train_y) for x in models]\n"
      ],
      "metadata": {
        "id": "Vm8q0p4Hyzg2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c57b7290-a361-4395-945a-3187ac10e332"
      },
      "id": "Vm8q0p4Hyzg2",
      "execution_count": 84,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<catboost.core.CatBoostClassifier at 0x7f77ed586760>,\n",
              " XGBClassifier(objective='multi:softprob', random_state=37),\n",
              " GradientBoostingClassifier(random_state=37)]"
            ]
          },
          "metadata": {},
          "execution_count": 84
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 85,
      "id": "0bcafdb3",
      "metadata": {
        "id": "0bcafdb3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 525
        },
        "outputId": "89a0bd59-9018-4336-a67f-d5f680a6658a"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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          "metadata": {},
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "def 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"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 86,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 334
        },
        "outputId": "fd0e84e4-35d7-4b2e-9681-f5470968d15c",
        "id": "zfIcqXBqymkN"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1    263\n",
              "0     40\n",
              "2      7\n",
              "Name: Y_Class, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 86
        },
        {
          "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\n",
        "submit.to_csv('CparamXG212-smartFactorySubmission.csv', index=False)  \n",
        "sns.countplot(x=preds);\n",
        "submit.Y_Class.value_counts()"
      ],
      "id": "zfIcqXBqymkN"
    },
    {
      "cell_type": "code",
      "source": [
        "aaa = pd.read_csv('CXG212(1)-smartFactorySubmission.csv')\n",
        "sns.countplot(x=aaa.Y_Class);\n",
        "aaa.Y_Class.value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 349
        },
        "id": "rXw6yXcgEzO0",
        "outputId": "0f92d4b5-d47e-47be-bd1a-8f22876fe67a"
      },
      "id": "rXw6yXcgEzO0",
      "execution_count": 93,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1    265\n",
              "0     37\n",
              "2      8\n",
              "Name: Y_Class, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 93
        },
        {
          "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": [
        "# 148  1 2 \n",
        "# 163  2\n",
        "# 166  2 \n",
        "# 295  1 0 \n",
        "# 32/495"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nWXbLTICgP5C",
        "outputId": "950c53c7-5236-4e17-9d06-d9d54f539e59"
      },
      "id": "nWXbLTICgP5C",
      "execution_count": 99,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.06464646464646465"
            ]
          },
          "metadata": {},
          "execution_count": 99
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "aaa = pd.read_csv('/content/drive/MyDrive/GBCminmax-smartFactorySubmission.csv')\n",
        "sns.countplot(x=aaa.Y_Class);\n",
        "aaa.Y_Class.value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 349
        },
        "id": "fj9KCdluffIp",
        "outputId": "8218e2a2-1e66-420e-c292-446528229e24"
      },
      "id": "fj9KCdluffIp",
      "execution_count": 95,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1    253\n",
              "0     42\n",
              "2     15\n",
              "Name: Y_Class, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 95
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAEHCAYAAABBW1qbAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/NK7nSAAAACXBIWXMAAAsTAAALEwEAmpwYAAAPZElEQVR4nO3df6xfdX3H8ecLii4qTlg7xNJZZ6oJOi14w8jYDEqcQLIVjRJYBOZY6hYwshgjmmU6ExKT+WOKk6UMBIwDMfgDN6Kyzs3421vGEMqYVWG0KfQKRlAjWnjvj++5H74pt+Xbwvme297nIzn5nvM5n3Pu++ZL74vz+Z7v56SqkCQJ4KChC5AkLR6GgiSpMRQkSY2hIElqDAVJUrNs6AKeiOXLl9fq1auHLkOS9iubNm36UVWtWGjffh0Kq1evZnZ2dugyJGm/kuSu3e1z+EiS1BgKkqTGUJAkNYaCJKkxFCRJjaEgSWoMBUlSYyhIkpreQiHJqiRfTrI5yW1J3tK1vzvJtiQ3d8upY8e8I8mWJHckeXVftUmSFtbnN5p3Am+tqpuSHApsSnJjt++DVfW+8c5JjgbOAF4EPAf4tyQvqKqHe6xR+4n/e8/vDF3CAe+3/ua7Q5egRaC3K4Wq2l5VN3XrDwK3Ayv3cMg64JqqeqiqfghsAY7rqz5J0mNN5TOFJKuBY4BvdU3nJ7klyeVJDuvaVgJ3jx22lQVCJMn6JLNJZufm5vosW5KWnN5DIckzgOuAC6rqAeAS4PnAWmA78P69OV9VbaiqmaqaWbFiwUn+JEn7qNdQSHIIo0D4RFV9GqCq7q2qh6vqEeBSHh0i2gasGjv8qK5NkjQlfd59FOAy4Paq+sBY+5Fj3V4D3NqtXw+ckeSpSZ4HrAG+3Vd9kqTH6vPuoxOAs4DvJrm5a3sncGaStUABdwJvAqiq25JcC2xmdOfSed55JEnT1VsoVNVXgSyw64Y9HHMRcFFfNUmS9sxvNEuSGkNBktQYCpKkxlCQJDWGgiSpMRQkSY2hIElqDAVJUmMoSJIaQ0GS1BgKkqTGUJAkNYaCJKkxFCRJjaEgSWoMBUlSYyhIkhpDQZLUGAqSpMZQkCQ1hoIkqTEUJEmNoSBJagwFSVJjKEiSGkNBktQYCpKkxlCQJDWGgiSpMRQkSY2hIElqDAVJUtNbKCRZleTLSTYnuS3JW7r2w5PcmOR73ethXXuSfDjJliS3JDm2r9okSQvr80phJ/DWqjoaOB44L8nRwIXAxqpaA2zstgFOAdZ0y3rgkh5rkyQtoLdQqKrtVXVTt/4gcDuwElgHXNl1uxI4rVtfB1xVI98EnpXkyL7qkyQ91lQ+U0iyGjgG+BZwRFVt73bdAxzRra8E7h47bGvXtuu51ieZTTI7NzfXX9GStAT1HgpJngFcB1xQVQ+M76uqAmpvzldVG6pqpqpmVqxY8SRWKknqNRSSHMIoED5RVZ/umu+dHxbqXnd07duAVWOHH9W1SZKmpM+7jwJcBtxeVR8Y23U9cE63fg7wubH2s7u7kI4HfjI2zCRJmoJlPZ77BOAs4LtJbu7a3gm8F7g2ybnAXcDp3b4bgFOBLcDPgTf2WJskaQG9hUJVfRXIbnaftED/As7rqx5J0uPzG82SpMZQkCQ1hoIkqTEUJEmNoSBJagwFSVJjKEiSGkNBktQYCpKkxlCQJDWGgiSpMRQkSY2hIElqDAVJUmMoSJIaQ0GS1BgKkqTGUJAkNYaCJKkxFCRJjaEgSWoMBUlSYyhIkhpDQZLUGAqSpMZQkCQ1hoIkqTEUJEmNoSBJagwFSVJjKEiSGkNBktT0FgpJLk+yI8mtY23vTrItyc3dcurYvnck2ZLkjiSv7qsuSdLu9XmlcAVw8gLtH6yqtd1yA0CSo4EzgBd1x3w0ycE91iZJWkBvoVBVXwHun7D7OuCaqnqoqn4IbAGO66s2SdLChvhM4fwkt3TDS4d1bSuBu8f6bO3aHiPJ+iSzSWbn5ub6rlWSlpRph8IlwPOBtcB24P17e4Kq2lBVM1U1s2LFiie5PEla2qYaClV1b1U9XFWPAJfy6BDRNmDVWNejujZJ0hRNFApJNk7SNsF5jhzbfA0wf2fS9cAZSZ6a5HnAGuDbe3t+SdITs2xPO5P8GvA0YHk3/p9u1zPZzZj/2LFXAyd2x24F3gWcmGQtUMCdwJsAquq2JNcCm4GdwHlV9fC+/UqSpH21x1Bg9Ef7AuA5wCYeDYUHgI/s6cCqOnOB5sv20P8i4KLHqUeS1KM9hkJVfQj4UJI3V9XFU6pJkjSQx7tSAKCqLk7ye8Dq8WOq6qqe6pIkDWCiUEjycUa3kt4MzI/1F2AoSNIBZKJQAGaAo6uq+ixGkjSsSb+ncCvw7D4LkSQNb9IrheXA5iTfBh6ab6yqP+6lKknSICYNhXf3WYQkaXGY9O6j/+y7EEnS8Ca9++hBRncbATwFOAT4WVU9s6/CJEnTN+mVwqHz60nC6PkHx/dVlCRpGHs9S2qNfBbwkZmSdICZdPjotWObBzH63sIveqlIkjSYSe8++qOx9Z2MZjhd96RXI0ka1KSfKbyx70IkScOb9CE7RyX5TJId3XJdkqP6Lk6SNF2TftD8MUZPR3tOt3y+a5MkHUAmDYUVVfWxqtrZLVcAK3qsS5I0gElD4b4kb0hycLe8Abivz8IkSdM3aSj8GXA6cA+wHXgd8Kc91SRJGsikt6S+Bzinqn4MkORw4H2MwkKSdICY9ErhJfOBAFBV9wPH9FOSJGkok4bCQUkOm9/orhQmvcqQJO0nJv3D/n7gG0k+1W2/Hrion5IkSUOZ9BvNVyWZBV7ZNb22qjb3V5YkaQgTDwF1IWAQSNIBbK+nzpYkHbgMBUlSYyhIkhpDQZLUGAqSpMZQkCQ1hoIkqektFJJc3j2l7daxtsOT3Jjke93rYV17knw4yZYktyQ5tq+6JEm71+eVwhXAybu0XQhsrKo1wMZuG+AUYE23rAcu6bEuSdJu9BYKVfUV4P5dmtcBV3brVwKnjbVfVSPfBJ6V5Mi+apMkLWzanykcUVXbu/V7gCO69ZXA3WP9tnZtj5FkfZLZJLNzc3P9VSpJS9BgHzRXVQG1D8dtqKqZqppZscLHREvSk2naoXDv/LBQ97qja98GrBrrd1TXJkmaommHwvXAOd36OcDnxtrP7u5COh74ydgwkyRpSnp7elqSq4ETgeVJtgLvAt4LXJvkXOAu4PSu+w3AqcAW4OfAG/uqS5K0e72FQlWduZtdJy3Qt4Dz+qpFkjQZv9EsSWoMBUlSYyhIkhpDQZLUGAqSpMZQkCQ1hoIkqTEUJEmNoSBJagwFSVJjKEiSGkNBktQYCpKkxlCQJDWGgiSpMRQkSY2hIElqDAVJUmMoSJIaQ0GS1BgKkqTGUJAkNYaCJKkxFCRJjaEgSWoMBUlSYyhIkhpDQZLUGAqSpMZQkCQ1hoIkqTEUJEnNsiF+aJI7gQeBh4GdVTWT5HDgk8Bq4E7g9Kr68RD1SdJSNeSVwiuqam1VzXTbFwIbq2oNsLHbliRN0WIaPloHXNmtXwmcNlwpkrQ0DRUKBXwpyaYk67u2I6pqe7d+D3DEQgcmWZ9kNsns3NzcNGqVpCVjkM8UgN+vqm1JfhO4Mcn/jO+sqkpSCx1YVRuADQAzMzML9pEk7ZtBrhSqalv3ugP4DHAccG+SIwG61x1D1CZJS9nUQyHJ05McOr8O/CFwK3A9cE7X7Rzgc9OuTZKWuiGGj44APpNk/uf/c1V9Icl3gGuTnAvcBZw+QG2StKRNPRSq6gfASxdovw84adr1SJIetZhuSZUkDWyou4+m7mVvu2roEpaETX939tAlSHoCvFKQJDWGgiSpMRQkSY2hIElqDAVJUmMoSJIaQ0GS1BgKkqTGUJAkNYaCJKkxFCRJjaEgSWoMBUlSYyhIkhpDQZLUGAqSpMZQkCQ1hoIkqTEUJEmNoSBJagwFSVKzbOgCJB34Trj4hKFLOOB97c1fe1LO45WCJKkxFCRJjaEgSWoMBUlSYyhIkhpDQZLUGAqSpMZQkCQ1iy4Ukpyc5I4kW5JcOHQ9krSULKpQSHIw8A/AKcDRwJlJjh62KklaOhZVKADHAVuq6gdV9UvgGmDdwDVJ0pKRqhq6hibJ64CTq+rPu+2zgN+tqvPH+qwH1nebLwTumHqh07Mc+NHQRWif+f7tvw709+65VbVioR373YR4VbUB2DB0HdOQZLaqZoauQ/vG92//tZTfu8U2fLQNWDW2fVTXJkmagsUWCt8B1iR5XpKnAGcA1w9ckyQtGYtq+KiqdiY5H/gicDBweVXdNnBZQ1oSw2QHMN+//deSfe8W1QfNkqRhLbbhI0nSgAwFSVJjKCxCTvWxf0tyeZIdSW4duhbtnSSrknw5yeYktyV5y9A1TZufKSwy3VQf/wu8CtjK6I6sM6tq86CFaWJJXg78FLiqql48dD2aXJIjgSOr6qYkhwKbgNOW0r8/rxQWH6f62M9V1VeA+4euQ3uvqrZX1U3d+oPA7cDKYauaLkNh8VkJ3D22vZUl9h+ltBgkWQ0cA3xr4FKmylCQpF0keQZwHXBBVT0wdD3TZCgsPk71IQ0oySGMAuETVfXpoeuZNkNh8XGqD2kgSQJcBtxeVR8Yup4hGAqLTFXtBOan+rgduHaJT/Wx30lyNfAN4IVJtiY5d+iaNLETgLOAVya5uVtOHbqoafKWVElS45WCJKkxFCRJjaEgSWoMBUlSYyhIkhpDQZLUGArSmIx8NckpY22vT/KF3fR/dpJrknw/yaYkNyR5QZLVTp2t/ZHfU5B2keTFwKcYTYa2DPgv4OSq+v4u/QJ8Hbiyqv6xa3sp8ExGkxr+i1Nna3+zbOgCpMWmqm5N8nng7cDTGT0X4fsLdH0F8Kv5QOiO/W9oM2wytv7x7lwA51fV17u5+z/JKESWAX/JKGQuA2aAAi6vqg8+qb+gtAeGgrSwvwVuAn7J6A/0Ql7M6CEsj2cH8Kqq+kWSNcDV3Tn/BPhiVV3UPVzpacBaYOX8FUaSZz2RX0LaW4aCtICq+lmSTwI/raqHnuDpDgE+kmQt8DDwgq79O8Dl3aycn62qm5P8APjtJBcD/wp86Qn+bGmv+EGztHuPdMvu3Aa8bILz/BVwL/BSRlcIT4H2hLaXM5oa/YokZ1fVj7t+/wH8BfBP+1q8tC8MBWnf/Tvw1CTr5xuSvCTJH+zS79eB7VX1CKMZOA/u+j4XuLeqLmX0x//YJMuBg6rqOuCvgWOn8HtIjcNH0j6qqkryGuDvk7wd+AVwJ3DBLl0/ClyX5GzgC8DPuvYTgbcl+RXwU+BsRo9e/ViS+f9he0efv4O0K29JlSQ1Dh9JkhqHj6THkeQ3gI0L7Dqpqu6bdj1Snxw+kiQ1Dh9JkhpDQZLUGAqSpMZQkCQ1/w//zN2zhzYVygAAAABJRU5ErkJggg==\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "aaa = pd.read_csv('/content/drive/MyDrive/GBCoptuna-smartFactorySubmission.csv')\n",
        "sns.countplot(x=aaa.Y_Class);\n",
        "aaa.Y_Class.value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 349
        },
        "id": "21AetCxgST0D",
        "outputId": "b7e56c38-312c-469a-d37c-f6f812433401"
      },
      "id": "21AetCxgST0D",
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1    260\n",
              "0     43\n",
              "2      7\n",
              "Name: Y_Class, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 63
        },
        {
          "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": [
        "aaa = pd.read_csv('/content/drive/MyDrive/voting.catB_GBC-smartFactorySubmission.csv')\n",
        "sns.countplot(x=aaa.Y_Class);\n",
        "aaa.Y_Class.value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 349
        },
        "id": "Mt733EeeHECW",
        "outputId": "9939c6f1-b6ec-4e95-a462-f2e01766acfb"
      },
      "id": "Mt733EeeHECW",
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1    267\n",
              "0     37\n",
              "2      6\n",
              "Name: Y_Class, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 55
        },
        {
          "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": [
        "sdf = pd.read_csv('/content/drive/MyDrive/smartFactory_submission18.csv')\n",
        "sns.countplot(x=sdf.Y_Class);\n",
        "sdf.Y_Class.value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 349
        },
        "id": "Be0Yji5AEiu0",
        "outputId": "6cc41f99-ec6d-4f04-8f91-2ed1da1f69ab"
      },
      "id": "Be0Yji5AEiu0",
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1    274\n",
              "0     29\n",
              "2      7\n",
              "Name: Y_Class, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 54
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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GP8LyWHYDL6mqnyVZDdzQ7fOPgc9U1VXdjys9CTgFWD47wkjytMfzR0gHylCQ5lBVP0nyYeDHVfXw49zdUcB7kpwCPAKc2LV/A9jQPZXz41V1R5LvAs9K8m7gX4HPPs73lg6IJ5qlfftlN+3LncALRtjPXwG7gJMZjBCeAO0X2l7I4NHo1yW5pKp+2PX7d+DPgfcdbPHSwTAUpIP3b8ATk6ybbUjy/CS/v1e/Xwd2VtUvGTyB88iu7zOBXVX1XgZf/qclWQocUVU3A38DnDaBv0NqPHwkHaSqqiQvA/4hyRuAnwH3AFfs1fWfgZuTXAJ8GvhJ134W8PokvwB+DFzC4KdX359k9n/Y3jjOv0Ham5ekSpIaDx9JkhoPH0mPIclvAJvmWHV2VT0w6XqkcfLwkSSp8fCRJKkxFCRJjaEgSWoMBUlS839Nh+UDzdNKigAAAABJRU5ErkJggg==\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "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": 6,
      "outputs": [],
      "id": "Dsbc_zz0KxDp"
    },
    {
      "cell_type": "code",
      "source": [
        "# find weights for each model \n",
        "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": null,
      "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": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "id": "e1e8e853",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        },
        "id": "e1e8e853",
        "outputId": "9dde57df-5ca2-459f-e458-53e0519767c7"
      },
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-10-dc92d8057380>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0msubmit\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'/content/drive/MyDrive/sample_submission.csv'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0msubmit\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Y_Class'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpreds\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m \u001b[0msubmit\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'CXG212-smartFactorySubmission.csv'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0msns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcountplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpreds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m;\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mNameError\u001b[0m: name 'preds' is not defined"
          ]
        }
      ],
      "source": [
        "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);"
      ]
    },
    {
      "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",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 380
        },
        "outputId": "73e07a3c-db64-4fbc-a7d4-fe70504eda59"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "error",
          "ename": "TypeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-9-0bc19587b29d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[0;31m# use X_test as validation set\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 10\u001b[0;31m \u001b[0mstack\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\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[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     11\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     12\u001b[0m train_stack = np.concatenate(\n",
            "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/sklearn/ensemble/_stacking.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y, sample_weight)\u001b[0m\n\u001b[1;32m    486\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_le\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mLabelEncoder\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    487\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclasses_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_le\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclasses_\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 488\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_le\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtransform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msample_weight\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    489\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    490\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mif_delegate_has_method\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdelegate\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"final_estimator_\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/sklearn/ensemble/_stacking.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y, sample_weight)\u001b[0m\n\u001b[1;32m    148\u001b[0m         \u001b[0;31m# all_estimators contains all estimators, the one to be fitted and the\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    149\u001b[0m         \u001b[0;31m# 'drop' string.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 150\u001b[0;31m         \u001b[0mnames\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mall_estimators\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_validate_estimators\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    151\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_validate_final_estimator\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    152\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/sklearn/ensemble/_base.py\u001b[0m in \u001b[0;36m_validate_estimators\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    245\u001b[0m                 \u001b[0;34m\" of (string, estimator) tuples.\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    246\u001b[0m             )\n\u001b[0;32m--> 247\u001b[0;31m         \u001b[0mnames\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mestimators\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mestimators\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    248\u001b[0m         \u001b[0;31m# defined by MetaEstimatorMixin\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    249\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_validate_names\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnames\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mTypeError\u001b[0m: 'CatBoostClassifier' object is not iterable"
          ]
        }
      ],
      "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": null,
      "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": "code",
      "source": [],
      "metadata": {
        "id": "o_5wuIBcSvvu"
      },
      "id": "o_5wuIBcSvvu",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 5.Specific feature "
      ],
      "metadata": {
        "id": "FpAAoohISvYp"
      },
      "id": "FpAAoohISvYp"
    },
    {
      "cell_type": "code",
      "source": [
        "x=train_x.loc[:,['X_1834','X_1945','X_256','X_248','X_258','X_373','X_131','X_2841']]\n",
        "y=train_y\n",
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.3)\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "import sklearn.model_selection as ms\n",
        "\n",
        "model = GradientBoostingClassifier(random_state=37).fit(X_train, y_train)\n",
        "print('Done.')\n",
        "y_pred = model.predict(X_test)\n",
        "# test_x = test_x.drop(columns=['LINE', 'PRODUCT_CODE'])\n",
        "\n",
        "test_x=test_x.loc[:,['X_1834','X_1945','X_256','X_248','X_258','X_373','X_131','X_2841']]\n",
        "preds = model.predict(test_x)\n",
        "sns.countplot(x=preds); \n",
        "# preds.value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "cAqWZJ7NSva4",
        "outputId": "7d690f08-13de-4aae-839b-83ef7a8a413c"
      },
      "id": "cAqWZJ7NSva4",
      "execution_count": 75,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Done.\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"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt # 득점모델 변수 중요도\n",
        "import seaborn as sns\n",
        "from sklearn.ensemble import RandomForestRegressor\n",
        "\n",
        "%matplotlib inline\n",
        "rf_run = RandomForestRegressor(random_state=0, max_depth=5, min_samples_leaf=8, min_samples_split=8,n_estimators=200)\n",
        "rf_run.fit(X_train, y_train)\n",
        "ftr_importances_values = rf_run.feature_importances_\n",
        "ftr_importances = pd.Series(ftr_importances_values, index=X_train.columns)\n",
        "ftr_top = ftr_importances.sort_values(ascending=False)[:20]\n",
        " \n",
        "plt.figure(figsize=(10, 10))\n",
        "sns.barplot(x=ftr_top, y=ftr_top.index)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 592
        },
        "id": "lLsOtCgTSvRA",
        "outputId": "3b53834f-4af7-49cf-da2c-f5003f63c8b8"
      },
      "id": "lLsOtCgTSvRA",
      "execution_count": 80,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x720 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "GdugZkQrSvJS"
      },
      "id": "GdugZkQrSvJS",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "xskVkfufSvDg"
      },
      "id": "xskVkfufSvDg",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "xuPOUxQ7Su9m"
      },
      "id": "xuPOUxQ7Su9m",
      "execution_count": null,
      "outputs": []
    },
    {
      "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
}