Team스마일C_CodeSubmission.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "id": "ZJBm2amd3Lb5",
      "metadata": {
        "id": "ZJBm2amd3Lb5"
      },
      "source": [
        "# 팀 스마일C 코드 제출\n",
        "* 팀원: 정우섭, 김유민, 김유진, 장동언, 황정묵\n",
        "* PRIVATE SCORE: 0.65887 \n",
        "* PRIVATE RANKING: 36"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "CwuGPDTVMXHe",
      "metadata": {
        "id": "CwuGPDTVMXHe"
      },
      "source": [
        "# 개발 환경\n",
        "* OS : Windows11\n",
        "* python: 3.10.10\n",
        "* 라이브러리 버전 : requirements.txt 참조"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "QIfodgpc3HP6",
      "metadata": {
        "id": "QIfodgpc3HP6"
      },
      "source": [
        "# 0.Data Load\n",
        "1. install packages\n",
        "2. import libraries and csv data files"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "id": "6ab709fe",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6ab709fe",
        "outputId": "eb9de930-21d8-4a08-e8fe-45367074258a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: catboost in /usr/local/lib/python3.8/dist-packages (1.1.1)\n",
            "Requirement already satisfied: graphviz in /usr/local/lib/python3.8/dist-packages (from catboost) (0.10.1)\n",
            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.8/dist-packages (from catboost) (3.5.3)\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: numpy>=1.16.0 in /usr/local/lib/python3.8/dist-packages (from catboost) (1.22.4)\n",
            "Requirement already satisfied: plotly in /usr/local/lib/python3.8/dist-packages (from catboost) (5.5.0)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.8/dist-packages (from catboost) (1.15.0)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.8/dist-packages (from catboost) (1.10.1)\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: pytz>=2017.3 in /usr/local/lib/python3.8/dist-packages (from pandas>=0.24.0->catboost) (2022.7.1)\n",
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            "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",
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            "Requirement already satisfied: tenacity>=6.2.0 in /usr/local/lib/python3.8/dist-packages (from plotly->catboost) (8.2.2)\n",
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: lightgbm in /usr/local/lib/python3.8/dist-packages (2.2.3)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.8/dist-packages (from lightgbm) (1.10.1)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.8/dist-packages (from lightgbm) (1.22.4)\n",
            "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.8/dist-packages (from lightgbm) (1.2.1)\n",
            "Requirement already satisfied: joblib>=1.1.1 in /usr/local/lib/python3.8/dist-packages (from scikit-learn->lightgbm) (1.2.0)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.8/dist-packages (from scikit-learn->lightgbm) (3.1.0)\n",
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: optuna in /usr/local/lib/python3.8/dist-packages (3.1.0)\n",
            "Requirement already satisfied: cmaes>=0.9.1 in /usr/local/lib/python3.8/dist-packages (from optuna) (0.9.1)\n",
            "Requirement already satisfied: sqlalchemy>=1.3.0 in /usr/local/lib/python3.8/dist-packages (from optuna) (1.4.46)\n",
            "Requirement already satisfied: colorlog in /usr/local/lib/python3.8/dist-packages (from optuna) (6.7.0)\n",
            "Requirement already satisfied: PyYAML in /usr/local/lib/python3.8/dist-packages (from optuna) (6.0)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.8/dist-packages (from optuna) (1.22.4)\n",
            "Requirement already satisfied: alembic>=1.5.0 in /usr/local/lib/python3.8/dist-packages (from optuna) (1.10.1)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.8/dist-packages (from optuna) (4.64.1)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.8/dist-packages (from optuna) (23.0)\n",
            "Requirement already satisfied: importlib-metadata in /usr/local/lib/python3.8/dist-packages (from alembic>=1.5.0->optuna) (6.0.0)\n",
            "Requirement already satisfied: typing-extensions>=4 in /usr/local/lib/python3.8/dist-packages (from alembic>=1.5.0->optuna) (4.5.0)\n",
            "Requirement already satisfied: Mako in /usr/local/lib/python3.8/dist-packages (from alembic>=1.5.0->optuna) (1.2.4)\n",
            "Requirement already satisfied: importlib-resources in /usr/local/lib/python3.8/dist-packages (from alembic>=1.5.0->optuna) (5.12.0)\n",
            "Requirement already satisfied: greenlet!=0.4.17 in /usr/local/lib/python3.8/dist-packages (from sqlalchemy>=1.3.0->optuna) (2.0.2)\n",
            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.8/dist-packages (from importlib-metadata->alembic>=1.5.0->optuna) (3.15.0)\n",
            "Requirement already satisfied: MarkupSafe>=0.9.2 in /usr/local/lib/python3.8/dist-packages (from Mako->alembic>=1.5.0->optuna) (2.1.2)\n",
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: xgboost in /usr/local/lib/python3.8/dist-packages (1.7.4)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.8/dist-packages (from xgboost) (1.10.1)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.8/dist-packages (from xgboost) (1.22.4)\n",
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: torch in /usr/local/lib/python3.8/dist-packages (1.13.1+cu116)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.8/dist-packages (from torch) (4.5.0)\n"
          ]
        }
      ],
      "source": [
        "!pip install catboost\n",
        "!pip install lightgbm\n",
        "!pip install optuna \n",
        "!pip install xgboost\n",
        "!pip install torch"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "id": "94682a80",
      "metadata": {
        "id": "94682a80"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import random\n",
        "import os\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt   \n",
        "import seaborn as sns \n",
        "\n",
        "# model evaluation, preprocessing \n",
        "import sklearn\n",
        "from sklearn import preprocessing, metrics\n",
        "from sklearn.preprocessing import LabelEncoder, OneHotEncoder, StandardScaler, MinMaxScaler\n",
        "from sklearn.model_selection import train_test_split, KFold, cross_validate\n",
        "from sklearn.metrics import accuracy_score, classification_report, f1_score, roc_auc_score, log_loss\n",
        "\n",
        "# sequential model modeling \n",
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "from keras import regularizers\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Dense,Dropout, Activation\n",
        "from keras import backend as K \n",
        "from keras import regularizers\n",
        "from keras.layers import Dense,Dropout, Activation, BatchNormalization, Conv2D, Flatten  \n",
        "from keras import optimizers, metrics, callbacks\n",
        "from keras.backend import clear_session\n",
        "\n",
        "# optuna Hyper-params tuning\n",
        "import optuna\n",
        "from optuna import Trial\n",
        "from optuna.samplers import TPESampler\n",
        "from optuna.visualization import plot_optimization_history, plot_param_importances, plot_parallel_coordinate, plot_contour\n",
        "\n",
        "# ML classification models \n",
        "from lightgbm import LGBMClassifier\n",
        "from catboost import CatBoostClassifier\n",
        "from sklearn.ensemble import BaggingClassifier, GradientBoostingClassifier, RandomForestClassifier\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.linear_model import RidgeClassifierCV\n",
        "from xgboost import XGBClassifier\n",
        "\n",
        "# Google Drive mount\n",
        "# from google.colab import drive \n",
        "# drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "id": "0AdADLxT1DoQ",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0AdADLxT1DoQ",
        "outputId": "fee05437-7143-41f4-8cfd-b3883b85e623"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "37\n"
          ]
        }
      ],
      "source": [
        "# seed fixing for reproduction \n",
        "import torch \n",
        "import random as rn \n",
        "\n",
        "def seed_everything(seed):\n",
        "    random.seed(seed)\n",
        "    os.environ['PYTHONHASHSEED'] = str(seed)\n",
        "    np.random.seed(seed)\n",
        "seed_everything(37) # Seed 고정\n",
        "seed_num=37\n",
        "\n",
        "def seed_everything(seed):\n",
        "    random.seed(seed)\n",
        "    os.environ['PYTHONHASHSEED'] = str(seed)\n",
        "    np.random.seed(seed)\n",
        "    torch.manual_seed(seed)\n",
        "    torch.cuda.manual_seed(seed)  # type: ignore\n",
        "    torch.backends.cudnn.deterministic = True  # type: ignore\n",
        "    torch.backends.cudnn.benchmark = True  # type: ignore\n",
        "seed_everything(37)\n",
        "seed_num=37\n",
        "seed=37\n",
        "\n",
        "np.random.seed(seed_num)\n",
        "rn.seed(seed_num) \n",
        "tf.random.set_seed(seed_num)\n",
        "session_conf = tf.compat.v1.ConfigProto(intra_op_parallelism_threads=1, inter_op_parallelism_threads=1)\n",
        "sess = tf.compat.v1.Session(config=session_conf)\n",
        "K.set_session(sess)\n",
        "\n",
        "class config:   \n",
        "    seed = 37  \n",
        "    device = \"cuda:0\"            \n",
        "\n",
        "def seed_everything(seed: int = 37):\n",
        "    random.seed(seed)\n",
        "    np.random.seed(seed)\n",
        "    os.environ[\"PYTHONHASHSEED\"] = str(seed)\n",
        "    tf.random.set_seed(seed)\n",
        "seed_everything(config.seed)\n",
        "tf.random.set_seed(37) # tensorflow global seed \n",
        "print(config.seed)     "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "id": "lQROx2fcxIW7",
      "metadata": {
        "id": "lQROx2fcxIW7"
      },
      "outputs": [],
      "source": [
        "train_df = pd.read_csv('./train.csv')\n",
        "test_df = pd.read_csv('./test.csv')\n",
        "submit = pd.read_csv('./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'])   "
      ]
    },
    {
      "cell_type": "markdown",
      "id": "wqkW_3EbQxj1",
      "metadata": {
        "id": "wqkW_3EbQxj1"
      },
      "source": [
        "# 1.Data & Features"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "ZK3O2Kd24yP_",
      "metadata": {
        "id": "ZK3O2Kd24yP_"
      },
      "source": [
        "## 1.1 EDA\n",
        "1. feature correlation heatmaps\n",
        "2. feature importance\n",
        "3. feature distribution plots"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "id": "E1vfXTKcK9Dt",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 632
        },
        "id": "E1vfXTKcK9Dt",
        "outputId": "172749db-625b-4237-8f78-7bdfaf0e0c0e"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x720 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# random 20 features list (모두 NaN인 컬럼 제외하고, 랜덤 20개 feature의 상관관계 히트맵)\n",
        "Xs = train_df.iloc[:,4:].columns\n",
        "null_count = train_df.isnull().sum().to_dict()\n",
        "cols = pd.DataFrame({i for i in null_count if null_count[i]<250 and i in Xs})\n",
        "cols_name = list(cols[0])  # not_all_NAN features name in list \n",
        "\n",
        "import random\n",
        "a = ['Y_Quality']\n",
        "for i in range(20):\n",
        "    num = random.randint(1,2875)\n",
        "    a.append(cols_name[i])\n",
        "\n",
        "# heatmap: `random X features` and `Y_Quality` correlation\n",
        "corr = train_df[a].corr()  # correlation matrix\n",
        "fig, ax = plt.subplots(figsize=(15,10))\n",
        "sns.heatmap(corr, annot=True, fmt='.2f', cmap='Blues', linewidths=2);"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "id": "lLsOtCgTSvRA",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 592
        },
        "id": "lLsOtCgTSvRA",
        "outputId": "27014e2c-451d-44b4-929b-ddb95f7b9e34"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x720 with 1 Axes>"
            ],
            "image/png": 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yPQe0JPOT3JVkbtOe07QXjDF2KMkNSW5NcnOSs7q2/XWz34bmNdT075vkb5Pc1Ox3bq+1SpIkDZKeA1pVbQFWAyubrpXASFVtGmP4o8BbqurlwGnARUn269q+oqqGmteGpu9dwLer6mjgBOCPk8zqtV5JkqRB0e8vCawC1idZDiwBzhtrUFXd3vX+niT3AgcCD+7k2AXskyTA3sAPgcf7rFeSJKn1+noGraq2AyvoBLXlTXunkhwLzAK+29X9oWbqc1WSPZu+jwELgXuAjcC7q+rJcY65LMm6JOseefgnfXwiSZKk6TcViwROB7YCR0w0MMk84BLg3K6w9fvA4cArgbnA+5r+U4ENwPOBIeBjSWaPddyqGqmq4aoa3nsfZ0ElSdJg6yugNQ/0nwwsBs5vAth4Y2cDVwPvr6pv7Oivqq3VsQ34OHBss+lc4LJm253AXXSCnCRJ0ozWzyrO0FkksLyqNgMXABeOM3YWcDnwyaq6dNS2eV3HOwO4pdm0GTip2fZc4N8A3+u1XkmSpEHRzx20pcDmqlrbtC8GFiY5foyxbwReBZwz+us0gE8n2UjnObMDgD9q+v8Q+MVm27XA+6rqvj7qlSRJGgg9r+KsqhFgpKv9BHDMOGM/BXxqnG0njtN/D3BKr/VJkiQNKn9JQJIkqWX6/R60p0hyJJ1Vmt22VdWiqTyPJEnSTDalAa2qNtL5SgxJkiT1yClOSZKkljGgSZIktcyUTnG2wYH7H8pvvfmr012GJElSz7yDJkmS1DIGNEmSpJYxoEmSJLWMAU2SJKllZtwigXseuIMP/s2p012GBswH3+jCEklSe3gHTZIkqWUMaJIkSS1jQJMkSWoZA5okSVLLGNAkSZJaxoAmSZLUMgY0SZKklpkwoCWZn+SuJHOb9pymvWCMsUNJbkhya5Kbk5zVte2kJDcm2ZDk60kOGbXvG5JUkuGmvX+Sv0/ySJKP9f1JJUmSBsSEAa2qtgCrgZVN10pgpKo2jTH8UeAtVfVy4DTgoiT7NdtWA79RVUPAZ4AP7NgpyT7Au4Fvdh3rMeA/A++Z/MeRJEkafJOd4lwFLE6yHFgCXDjWoKq6varuaN7fA9wLHLhjMzC7eb8vcE/Xrn8IfJhOKNtxrB9X1de7+yRJknYHk/qpp6ranmQFsAY4paq2T7RPkmOBWcB3m663A19O8q/AQ8DiZtwxwPyquro5xy5LsgxYBrDvAc/q5RCSJEmtsSuLBE4HtgJHTDQwyTzgEuDcqnqy6T4feHVVHQx8HPhokp8DPgr87i5VPUpVjVTVcFUN7zV7Vj+HkiRJmnaTCmhJhoCT6dz1Or8JYOONnQ1cDby/qr7R9B0IHF1VO54x+zzwi8A+dALf15Jsao5/5Y6FApIkSbujyaziDJ0H/JdX1WbgAsZ5Bi3JLOBy4JNVdWnXpgeAfZMc1rRPBm6rqh9V1QFVtaCqFgDfAF5bVet6/kSSJEkDbjLPoC0FNlfV2qZ9MXBukuOr6rpRY98IvArYP8k5Td85VbUhyVLgi0mepBPY/v1EJ27uqs0GZiU5g87zb9+eRM2SJEkDa8KAVlUjwEhX+wngmHHGfgr41DjbLqdzd21n5zphVHvBRPVJkiTNNP6SgCRJUstM6ms2RktyJJ1Vmt22VdWi/kuSJEnavfUU0KpqIzA0taVIkiQJnOKUJElqHQOaJElSy/Q0xdlmz59zKB9841enuwxJkqSeeQdNkiSpZQxokiRJLWNAkyRJahkDmiRJUsvMuEUCdzz4XU7/0humuwwNkK+87ovTXYIkSU/hHTRJkqSWMaBJkiS1jAFNkiSpZQxokiRJLWNAkyRJahkDmiRJUssY0CRJklpmwoCWZH6Su5LMbdpzmvaCccavSfJgkqtG9Z+U5MYkG5J8PckhTf+rmv7Hk5w5ap8XJrkmyW1Jvj3eOSVJkmaSCQNaVW0BVgMrm66VwEhVbRpnlwuAN4/Rvxr4jaoaAj4DfKDp3wyc0/SN9knggqpaCBwL3DtRvZIkSYNusr8ksApYn2Q5sAQ4b7yBVXVtkhPG2gTMbt7vC9zTjN8EkOTJ7sFJXgbsUVVrm3GPTLJWSZKkgTapgFZV25OsANYAp1TV9h7O9Xbgy0n+FXgIWDzB+MOAB5NcBrwY+H+A36uqJ0YPTLIMWAbwrAOf3UNpkiRJ7bEriwROB7YCR/R4rvOBV1fVwcDHgY9OMH4P4DjgPcArgZfQmQr9KVU1UlXDVTU8a/aePZYnSZLUDpMKaEmGgJPp3PU6P8m8XTlJkgOBo6vqm03X54FfnGC3u4ENVfW9qnocuAI4ZlfOK0mSNIgms4ozdB7wX15Vm+ksArhwF8/zALBvksOa9snAbRPs80/Afk24AzgR+PYunleSJGngTOYO2lJg846H9YGLgYVJjh9rcJLrgS8AJyW5O8mpzR2wpcAXk9xEZ5Xnimb8K5PcDfw68OdJbgVonjV7D3Btko1AgL/o9YNKkiQNilTVdNcwpfY9ZE794h+fON1laIB85XVfnO4SJEm7qSTrq2p4dL+/JCBJktQyk/0etKdIciRwyajubVW1qP+SJEmSdm89BbSq2ggMTW0pkiRJAqc4JUmSWseAJkmS1DI9TXG22aH7vdRVeZIkaaB5B02SJKllDGiSJEktY0CTJElqGQOaJElSy8y4RQJ3PLiVV1/+R9Ndhlroy6//wHSXIEnSpHgHTZIkqWUMaJIkSS1jQJMkSWoZA5okSVLLGNAkSZJaxoAmSZLUMgY0SZKkluk5oCWZn+SuJHOb9pymvWCMsS9KcmOSDUluTfKOrm0fSrIlySOj9nlhkr9P8q0kNyd5da+1SpIkDZKeA1pVbQFWAyubrpXASFVtGmP4VuAXqmoIWAT8XpLnN9v+Fjh2jH0+APxNVb0COBu4uNdaJUmSBkm/vySwClifZDmwBDhvrEFV9ZOu5p50BcOq+gZAkp/aDZjdvN8XuKfPWiVJkgZCXwGtqrYnWQGsAU6pqu3jjU0yH7gaOARYUVUTBa4PAtck+W3gOcCv7OTYy4BlAM86cN9d+gySJEltMxWLBE6nM4V5xM4GVdWWqjqKTkB7a5LnTnDcNwF/XVUHA68GLkkyZr1VNVJVw1U1PGv2c3b9E0iSJLVIXwEtyRBwMrAYOD/JvIn2ae6c3QIcN8HQtwF/0+xzA/As4IB+6pUkSRoE/aziDJ1FAsurajNwAXDhOGMPTvLs5v0cOs+rfWeCU2wGTmr2WUgnoP2g13olSZIGRT930JYCm6tqbdO+GFiY5Pgxxi4EvpnkJuA64MKq2giQ5CNJ7gb2SnJ3kg82+/wusLTZ57PAOVVVfdQrSZI0EHpeJFBVI8BIV/sJ4Jhxxq4Fjhpn23uB947R/23gl3qtT5IkaVD5SwKSJEkt0+/3oD1FkiOBS0Z1b6uqRVN5HkmSpJlsSgNa81zZ0FQeU5IkaXfjFKckSVLLGNAkSZJaZkqnONvg0P3m8eXXf2C6y5AkSeqZd9AkSZJaxoAmSZLUMgY0SZKkljGgSZIktYwBTZIkqWVm3CrOOx78Aa+5bPV0l6GWufrX3jndJUiSNGneQZMkSWoZA5okSVLLGNAkSZJaxoAmSZLUMgY0SZKkljGgSZIktUzPAS3J/CR3JZnbtOc07QVjjH1RkhuTbEhya5J3NP37NH07XvcluWjUvm9IUkmGe61VkiRpkPT8PWhVtSXJamAlsKz5c6SqNo0xfCvwC1W1LcnewC1Jrqyqe4ChHYOSrAcu62rvA7wb+GavdUqSJA2afqc4VwGLkywHlgAXjjWoqn5SVdua5p5jnTfJYcBBwPVd3X8IfBh4rM86JUmSBkZfAa2qtgMr6AS15U17TM2U6M3AFuDDzd2zbmcDn6+qasYfA8yvqqsnqiPJsiTrkqz7yY8e6fXjSJIktcJULBI4nc4U5hE7G1RVW6rqKOAQ4K1JnjtqyNnAZwGS/BzwUeB3J1NAVY1U1XBVDc/ad+9drV+SJKlV+gpoSYaAk4HFwPlJ5k20T3Pn7BbguK7jHA3sUVXrm6596AS+ryXZ1Bz/ShcKSJKk3UE/qzgDrKYztbkZuIBxnkFLcnCSZzfv59B5Xu07XUPeRHP3DKCqflRVB1TVgqpaAHwDeG1Vreu1XkmSpEHRzx20pcDmqlrbtC8GFiY5foyxC4FvJrkJuA64sKo2dm1/I10BTZIkaXfWz9dsjAAjXe0ngGPGGbsWOGonx3rJBOc6obcqJUmSBo+/JCBJktQyPd9BG0uSI4FLRnVvq6pFU3keSZKkmWxKA1rzXNnQVB5TkiRpd+MUpyRJUssY0CRJklpmSqc42+DQ/Q7k6l9753SXIUmS1DPvoEmSJLWMAU2SJKllDGiSJEktY0CTJElqGQOaJElSy8y4VZx3PvBD/u2ln57uMtQyV535G9NdgiRJk+YdNEmSpJYxoEmSJLWMAU2SJKllDGiSJEktY0CTJElqGQOaJElSy/Qc0JLMT3JXkrlNe07TXjDG2KEkNyS5NcnNSc4aY8x/T/JIV3vPJJ9PcmeSb451XEmSpJmo54BWVVuA1cDKpmslMFJVm8YY/ijwlqp6OXAacFGS/XZsTDIMzBm1z9uAB6rqEGAV8OFea5UkSRok/U5xrgIWJ1kOLAEuHGtQVd1eVXc07+8B7gUOBEjyDOAC4L2jdnsd8Inm/aXASUnSZ72SJEmt19cvCVTV9iQrgDXAKVW1faJ9khwLzAK+23SdB1xZVVtH5a8XAFua8zye5EfA/sB9/dQsSZLUdlOxSOB0YCtwxEQDk8wDLgHOraonkzwf+HXgT/opIMmyJOuSrPvJQw/1cyhJkqRp11dASzIEnAwsBs5vAth4Y2cDVwPvr6pvNN2vAA4B7kyyCdgryZ3Ntu8D85t99wD2Be4f69hVNVJVw1U1PGv27H4+kiRJ0rTrZxVn6CwSWF5Vm+k8RzbmM2hJZgGXA5+sqkt39FfV1VX1vKpaUFULgEebRQEAVwJvbd6fCfxdVVWv9UqSJA2Kfu6gLQU2V9Xapn0xsDDJ8WOMfSPwKuCcJBua19AEx/8rYP/mjtrvAL/XR62SJEkDo+dFAlU1Aox0tZ8Ajhln7KeAT03imHt3vX+MzvNpkiRJuxV/SUCSJKll+vqajdGSHElnlWa3bVW1aCrPI0mSNJNNaUCrqo3A0FQeU5IkaXfjFKckSVLLGNAkSZJaZkqnONvgkDlzuerM35juMiRJknrmHTRJkqSWMaBJkiS1jAFNkiSpZQxokiRJLWNAkyRJapkZt4rzzgd+xGsv/dvpLkPT7Mozf3W6S5AkqWfeQZMkSWoZA5okSVLLGNAkSZJaxoAmSZLUMgY0SZKkljGgSZIktYwBTZIkqWV6DmhJ5ie5K8ncpj2naS8YY+yLktyYZEOSW5O8o+nfp+nb8bovyUXNtnck2dj0fz3Jy3qtVZIkaZD0/EW1VbUlyWpgJbCs+XOkqjaNMXwr8AtVtS3J3sAtSa6sqnuAoR2DkqwHLmuan6mqP2v6Xwt8FDit13olSZIGRb+/JLAKWJ9kObAEOG+sQVX1k67mnoxx5y7JYcBBwPXNPg91bX4OUH3WKkmSNBD6CmhVtT3JCmANcEpVbR9vbJL5wNXAIcCK5u5Zt7OBz1dVde3zLuB3gFnAiTs59jI6d/F49gEH9vhpJEmS2mEqFgmcTmcK84idDaqqLVV1FJ2A9tYkzx015Gzgs6P2+dOqeinwPuADOzn2SFUNV9XwrNn79vIZJEmSWqOvgJZkCDgZWAycn2TeRPs0d85uAY7rOs7RwB5VtX6c3T4HnNFPrZIkSYOin1WcAVYDy6tqM3ABcOE4Yw9O8uzm/Rw6z6t9p2vImxh19yzJoV3N1wB39FqrJEnSIOnnGbSlwOaqWtu0LwbOTXJ8VV03auxC4I+TFBDgwqra2LX9jcCrR+1zXpJfAbYDDwBv7aNWSZKkgdHP12yMACNd7SeAY8YZuxY4aifHeskYfe/utTZJkqRB5i8JSJIktUy/34P2FEmOBC4Z1b2tqhZN5XkkSZJmsikNaM1zZUNTeUxJkqTdjVOckiRJLWNAkyRJapkpneJsg0Pm7MuVZ/7qdJchSZLUM++gSZIktYwBTZIkqWUMaJIkSS1jQJMkSWqZGbdI4LsPPMLrv/j16S5DT6PL37BkukuQJGlKeQdNkiSpZQxokiRJLWNAkyRJahkDmiRJUssY0CRJklrGgCZJktQyBjRJkqSW6TmgJZmf5K4kc5v2nKa9YIyxQ0luSHJrkpuTnNW17a+b/TY0r6Gmf0VX3y1JnthxLkmSpJms54BWVVuA1cDKpmslMFJVm8YY/ijwlqp6OXAacFGS/bq2r6iqoea1oTn+BTv6gN8HrquqH/ZaryRJ0qDo95cEVgHrkywHlgDnjTWoqm7ven9PknuBA4EHJ3meNwGf7atSSZKkAdHXM2hVtR1YQSeoLW/aO5XkWGAW8N2u7g81U5+rkuw5avxedO66fXEnx1yWZF2SddseerCHTyJJktQeU7FI4HRgK3DERAOTzAMuAc6tqieb7t8HDgdeCcwF3jdqt18F/ufOpjeraqSqhqtqeM/Z++36J5AkSWqRvgJa80D/ycBi4PwmgI03djZwNfD+qvrGjv6q2lod24CPA8eO2vVsnN6UJEm7kX5WcYbOIoHlVbUZuAC4cJyxs4DLgU9W1aWjts3rOt4ZwC1d2/YFjge+1GudkiRJg6afO2hLgc1VtbZpXwwsTHL8GGPfCLwKOGf012kAn06yEdgIHAD8Udd+rweuqaof91GnJEnSQElVTXcNU2rOSw+vEz7yl9Ndhp5Gl79hyXSXIElST5Ksr6rh0f3+koAkSVLL9Ps9aE+R5Eg6qzS7bauqRVN5HkmSpJlsSgNaVW0EhqbymJIkSbsbpzglSZJaxoAmSZLUMlM6xdkGL52zt6v6JEnSQPMOmiRJUssY0CRJklrGgCZJktQyBjRJkqSWmXGLBL734DbOuuzO6S5DPyOf/7VDprsESZJ+5ryDJkmS1DIGNEmSpJYxoEmSJLWMAU2SJKllDGiSJEktY0CTJElqGQOaJElSy/Qc0JLMT3JXkrlNe07TXjDO+DVJHkxy1aj+v0pyU5Kbk1yaZO+mf88kn09yZ5JvjndcSZKkmabngFZVW4DVwMqmayUwUlWbxtnlAuDNY/SfX1VHV9VRwGbgvKb/bcADVXUIsAr4cK+1SpIkDZJ+pzhXAYuTLAeWABeON7CqrgUeHqP/IYAkAZ4NVLPpdcAnmveXAic1YyRJkma0vgJaVW0HVtAJasub9i5L8nHgX4DDgT9pul8AbGnO8zjwI2D/cfZflmRdknXbfvTDXkqQJElqjalYJHA6sBU4otcDVNW5wPOB24Czeth/pKqGq2p4z33n9lqGJElSK/QV0JIMAScDi4Hzk8zr9VhV9QTwOeANTdf3gfnNefYA9gXu76deSZKkQdDPKs7QWSSwvKo201kEMO4zaOMdI8khXcd7LfDPzeYrgbc2788E/q6q6qePIkmSNLPs0ce+S4HNVbW2aV8MnJvk+Kq6bvTgJNfTecZs7yR301mluRb4RJLZQICbgHc2u/wVcEmSO4EfAmf3UaskSdLA6DmgVdUIMNLVfgI4Zifjjxtn0y+NM/4x4Nd7rU+SJGlQ+UsCkiRJLdPPFOdPSXIkcMmo7m1VtWgqzyNJkjSTTWlAq6qNwNBUHlOSJGl34xSnJElSyxjQJEmSWmZKpzjb4CX77cnnf+2Q6S5DkiSpZ95BkyRJahkDmiRJUssY0CRJklrGgCZJktQyM26RwL0PbudPL//f012Gurzr9c+d7hIkSRoo3kGTJElqGQOaJElSyxjQJEmSWsaAJkmS1DIGNEmSpJYxoEmSJLWMAU2SJKllJgxoSeYnuSvJ3KY9p2kvGGf8miQPJrlqVP/1STY0r3uSXDFq+yuTPJ7kzKb9oiQ3NuNvTfKOXj+kJEnSIJnwi2qrakuS1cBKYFnz50hVbRpnlwuAvYDfGnWc43a8T/JF4Etd7WcAHwau6dplK/ALVbUtyd7ALUmurKp7JvPBJEmSBtVkpzhXAYuTLAeWABeON7CqrgUeHm97ktnAicAVXd2/DXwRuLfrOD+pqm1Nc89dqFWSJGmgTeqnnqpqe5IVwBrglKra3sc5zwCuraqHAJK8AHg98MvAK7sHJpkPXA0cAqwY7+5ZkmV07u4x58CD+yhNkiRp+u3KXanT6Uw7HtHnOd8EfLarfRHwvqp6cvTAqtpSVUfRCWhvTTLmjzpW1UhVDVfV8N6z5/ZZniRJ0vSaVEBLMgScDCwGzk8yr5eTJTkAOJbOXbEdhoHPJdkEnAlcnOSM7v2aO2e3AMchSZI0w01mFWeA1cDyqtpMZxHAuM+gTeBM4KqqemxHR1W9uKoWVNUC4FLgP1TVFUkOTvLspoY5dJ59+06P55UkSRoYk7mDthTYXFVrm/bFwMIkx481OMn1wBeAk5LcneTUrs1n89TpzZ1ZCHwzyU3AdcCFVbVxkvtKkiQNrMl8zcYIMNLVfgI4Zifjx52GrKoTJjjXOV3v1wJHTVSfJEnSTONXV0iSJLXMpL5mY7QkRwKXjOreVlWL+i9JkiRp99ZTQGueBRua2lIkSZIETnFKkiS1jgFNkiSpZXqa4myzg/Z7Ju96/Zg/OCBJkjQQvIMmSZLUMgY0SZKkljGgSZIktYwBTZIkqWUMaJIkSS0z41Zx/uiBx/nK5++b7jJ2O6efdcB0lyBJ0ozhHTRJkqSWMaBJkiS1jAFNkiSpZQxokiRJLWNAkyRJahkDmiRJUsv0HNCSzE9yV5K5TXtO014wzvgPJ7mleZ3V1X99kg3N654kV3Qd7/IkNyf5xyRH9FqrJEnSIOk5oFXVFmA1sLLpWgmMVNWm0WOTvAY4BhgCFgHvSTK7Oc5xVTVUVUPADcBlzW7/CdhQVUcBbwH+715rlSRJGiT9TnGuAhYnWQ4sAS4cZ9zLgH+oqser6sfAzcBp3QOawHYicEXXPn8HUFX/DCxI8tw+65UkSWq9vgJaVW0HVtAJasub9lhuAk5LsleSA4BfBuaPGnMGcG1VPdS1z68BJDkWeBFw8FgHT7Isybok6x566P5+PpIkSdK0m4pFAqcDW4FxnxGrqmuALwP/L/BZOlOZT4wa9qZm2w4rgf2SbAB+G/jWGPvsOP5IVQ1X1fDs2fv3+DEkSZLaoa+AlmQIOBlYDJyfZN54Y6vqQ82zZicDAW7vOs4BwLHA1V3jH6qqc5tn094CHAh8r596JUmSBkE/qzhDZ5HA8qraDFzAOM+gJXlGkv2b90cBRwHXdA05E7iqqh7r2me/JLOa5tvpPMP2EJIkSTPcHn3suxTYXFVrm/bFwLlJjq+q60aNfSZwfSfT8RDwm1X1eNf2s/k/q0F3WAh8IkkBtwJv66NWSZKkgdFzQKuqEWCkq/0Ena/SGGvsY3RWZY53rBPG6LsBOKzX+iRJkgaVvyQgSZLUMv1Mcf6UJEcCl4zq3lZVi6byPJIkSTPZlAa0qtpI59cCJEmS1COnOCVJklrGgCZJktQyUzrF2Qb7ztmD0886YLrLkCRJ6pl30CRJklrGgCZJktQyBjRJkqSWMaBJkiS1jAFNkiSpZWbcKs5H73ucb/3lvdNdxm7hFW8/aLpLkCRpRvIOmiRJUssY0CRJklrGgCZJktQyBjRJkqSWMaBJkiS1jAFNkiSpZQxokiRJLTNhQEsyP8ldSeY27TlNe8E449ckeTDJVaP6k+RDSW5PcluS/9j0n5DkR0k2NK//0vQ/K8k/Jrkpya1J/mvfn1aSJGkATPhFtVW1JclqYCWwrPlzpKo2jbPLBcBewG+N6j8HmA8cXlVPJun+ltPrq+rfjhq/DTixqh5J8kzg60m+UlXfmKhmSZKkQTbZKc5VwOIky4ElwIXjDayqa4GHx9j0TuAPqurJZtxOv+6/Oh5pms9sXjXJeiVJkgbWpAJaVW0HVtAJasub9q56KXBWknVJvpLk0K5tv9BMZX4lyct3dCZ5RpINwL3A2qr65lgHTrKsOe66Bx6+v4fSJEmS2mNXFgmcDmwFjujxXHsCj1XVMPAXwP9o+m8EXlRVRwN/AlyxY4eqeqKqhoCDgWOTjHnuqhqpquGqGp6zz/49lidJktQOkwpoSYaAk4HFwPlJ5vVwrruBy5r3lwNHAVTVQzumMqvqy8AzkxzQvWNVPQj8PXBaD+eVJEkaKJNZxRlgNZ2pzc10FgGM+wzaTlwB/HLz/njg9ub4z2vOQZJjm5ruT3Jgkv2a/mfTCYj/3MN5JUmSBsqEqziBpcDmqlrbtC8Gzk1yfFVdN3pwkuuBw4G9k9wNvK2qvkpn9eenk5wPPAK8vdnlTOCdSR4H/hU4u6qquUv3iSTPoBPa/qaqrhp9PkmSpJkmVTNrYeTLFgzVpz9wzXSXsVt4xdsPmniQJEkaV5L1zfP5T+EvCUiSJLXMZKY4f0qSI4FLRnVvq6pF/ZckSZK0e+spoFXVRmBoakuRJEkSOMUpSZLUOgY0SZKklulpirPN9jpgD1cXSpKkgeYdNEmSpJYxoEmSJLWMAU2SJKllDGiSJEktM+MWCWz/l+1s/cj3p7uM3cK8975gukuQJGlG8g6aJElSyxjQJEmSWsaAJkmS1DIGNEmSpJYxoEmSJLWMAU2SJKllDGiSJEktM2FASzI/yV1J5jbtOU17wTjj1yR5MMlVo/o/neQ7SW5J8j+SPLPp3zfJ3ya5KcmtSc7t2ueFSa5JcluSb493TkmSpJlkwoBWVVuA1cDKpmslMFJVm8bZ5QLgzWP0fxo4HDgSeDbw9qb/XcC3q+po4ATgj5PMarZ9ErigqhYCxwL3TlSvJEnSoJvsFOcqYHGS5cAS4MLxBlbVtcDDY/R/uRrAPwIH79gE7JMkwN7AD4HHk7wM2KOq1jb7P1JVj06yXkmSpIE1qYBWVduBFXSC2vKm3ZNmavPNwJqm62PAQuAeYCPw7qp6EjgMeDDJZUm+leSCJM8Y55jLkqxLsu7+H9/fa2mSJEmtsCuLBE4HtgJH9HnOi4F/qKrrm/apwAbg+cAQ8LEks+n8TuhxwHuAVwIvAc4Z64BVNVJVw1U1vP9z9u+zPEmSpOk1qYCWZAg4GVgMnJ9kXi8nS/J/AQcCv9PVfS5wWTP7eSdwF51n1e4GNlTV96rqceAK4JhezitJkjRIJrOKM3QWCSyvqs10FgGM+wzaTo7zdjp3y97UTGHusBk4qRnzXODfAN8D/gnYL8mBzbgTgW/v6nklSZIGzWTuoC0FNu94WJ/OFOXCJMePNTjJ9cAXgJOS3J3k1GbTnwHPBW5IsiHJf2n6/xD4xSQbgWuB91XVfVX1BJ3pzWubbQH+oofPKEmSNFD2mGhAVY0AI13tJ9jJVGNVHTdO/5jnqqp7gFPG2bYWOGqiGiVJkmYSf0lAkiSpZSa8gzaWJEcCl4zq3lZVi/ovSZIkaffWU0Crqo10vhJDkiRJU8wpTkmSpJYxoEmSJLVMT1OcbfbM5z2Tee99wXSXIUmS1DPvoEmSJLWMAU2SJKllDGiSJEktY0CTJElqmRm3SGD7/36U/33R+ukuY2A9d/nPT3cJkiTt9ryDJkmS1DIGNEmSpJYxoEmSJLWMAU2SJKllDGiSJEktY0CTJElqGQOaJElSy0wY0JLMT3JXkrlNe07TXjDO+DVJHkxy1aj+65NsaF73JLmi6T88yQ1JtiV5zxjHe0aSb40+niRJ0kw1YUCrqi3AamBl07USGKmqTePscgHw5jGOc1xVDVXVEHADcFmz6YfAfwQuHOd47wZum6hOSZKkmWKyU5yrgMVJlgNLGD9MUVXXAg+Ptz3JbOBE4Ipm/L1V9U/A9jHGHgy8BvjLSdYpSZI08Cb1U09VtT3JCmANcEpV/VSY2gVnANdW1UOTGHsR8F5gn50NSrIMWAZw8Jzn9VGaJEnS9NuVRQKnA1uBI/o855uAz040KMm/Be6tqgl/WLOqRqpquKqG5z5nTp/lSZIkTa9JBbQkQ8DJwGLg/CTzejlZkgOAY4GrJzH8l4DXJtkEfA44McmnejmvJEnSIJnMKs7QWSSwvKo201kEMO4zaBM4E7iqqh6baGBV/X5VHVxVC4Czgb+rqt/s8bySJEkDYzJ30JYCm6tqbdO+GFiY5PixBie5HvgCcFKSu5Oc2rX5bEZNbyZ5XpK7gd8BPtDsM3tXP4gkSdJMMeEigaoaAUa62k8Ax+xk/HE72XbCGH3/Ahw8QQ1fA742Ua2SJEkzgb8kIEmS1DKT+pqN0ZIcCVwyqntbVS3qvyRJkqTdW08Brao2AkNTW4okSZLAKU5JkqTWMaBJkiS1TE9TnG32zOfuxXOX//x0lyFJktQz76BJkiS1jAFNkiSpZQxokiRJLWNAkyRJapkZt0jg8Xsf4t6PXTPdZbTaQeedMt0lSJKknfAOmiRJUssY0CRJklrGgCZJktQyBjRJkqSWMaBJkiS1jAFNkiSpZQxokiRJLbPTgJZkfpK7ksxt2nOa9oJxxq9J8mCSq0b1J8mHktye5LYk/7Fr2wlJNiS5Ncl1Xf2bkmxstq3r61NKkiQNkJ1+UW1VbUmyGlgJLGv+HKmqTePscgGwF/Bbo/rPAeYDh1fVk0kOAkiyH3AxcFpVbd7R3+WXq+q+yX8cSZKkwTeZKc5VwOIky4ElwIXjDayqa4GHx9j0TuAPqurJZty9Tf+/Ay6rqs2j+iVJknZbEwa0qtoOrKAT1JY37V31UuCsJOuSfCXJoU3/YcCcJF9Lsj7JW7pPDVzT9C/b2cGTLGuOve7+R37UQ3mSJEntMdlFAqcDW4EjejzPnsBjVTUM/AXwP5r+PYCfB14DnAr85ySHNduWVNUxzbnfleRV4x28qkaqariqhvffe98eS5QkSWqHCQNakiHgZGAxcH6SeT2c527gsub95cBRXf1fraofN8+a/QNwNEBVfb/5895mn2N7OK8kSdLAmWgVZ4DVdKY2N9NZBDDuM2g7cQXwy83744Hbm/dfApYk2SPJXsAi4LYkz0myT1PDc4BTgFt6OK8kSdLA2ekqTmApsLmq1jbti4FzkxxfVdeNHpzkeuBwYO8kdwNvq6qv0ln9+ekk5wOPAG8HqKrbkqwBbgaeBP6yqm5J8hLg8k4+ZA/gM1W1pt8PK0mSNAhSVdNdw5QaeuFhdc17PzbdZbTaQeedMt0lSJIkIMn65hn9p/CXBCRJklpmoinOn5LkSOCSUd3bqmrR1JQkSZK0e9vlgFZVG4GhqS9FkiRJ4BSnJElS6xjQJEmSWmaXpzjbbo+DZrtKUZIkDTTvoEmSJLWMAU2SJKllZtwX1SZ5GPjOdNcxwA4A7pvuIgac17B/XsP+eP365zXsn9dwcl5UVQeO7pxxz6AB3xnrG3k1OUnWef364zXsn9ewP16//nkN++c17I9TnJIkSS1jQJMkSWqZmRjQRqa7gAHn9euf17B/XsP+eP365zXsn9ewDzNukYAkSdKgm4l30CRJkgbawAS0JKcl+U6SO5P83hjb90zy+Wb7N5Ms6Nr2+03/d5Kc+rQW3iK9XsMk+yf5+ySPJPnY0154i/RxDU9Osj7JxubPE5/24lugj+t3bJINzeumJK9/2otviX7+Xdhsf2Hzz/J7nraiW6aPv4cLkvxr19/FP3vai2+BPv97fFSSG5Lc2vz78FlPa/GDpKpa/wKeAXwXeAkwC7gJeNmoMf8B+LPm/dnA55v3L2vG7wm8uDnOM6b7Mw3YNXwOsAR4B/Cx6f4sA3oNXwE8v3l/BPD96f48A3b99gL2aN7PA+7d0d6dXv1cw67tlwJfAN4z3Z9n0K4hsAC4Zbo/wwBfvz2Am4Gjm/b+u+N/jyf7GpQ7aMcCd1bV96rqJ8DngNeNGvM64BPN+0uBk5Kk6f9cVW2rqruAO5vj7W56voZV9eOq+jrw2NNXbiv1cw2/VVX3NP23As9OsufTUnV79HP9Hq2qx5v+ZwG768Oz/fy7kCRnAHfR+Tu4u+rrGqqv63cKcHNV3QRQVfdX1RNPU90DZ1AC2guALV3tu5u+Mcc0/yL/EZ10Ppl9dwf9XEN1TNU1fANwY1Vt+xnV2VZ9Xb8ki5LcCmwE3tEV2HYnPV/DJHsD7wP+69NQZ5v1+8/xi5N8K8l1SY77WRfbQv1cv8OASvLVJDcmee/TUO/Amom/JCC1VpKXAx+m83+S2gVV9U3g5UkWAp9I8pWq2t3v6u6KDwKrquoRbwb1bCvwwqq6P8nPA1ckeXlVPTTdhQ2IPeg8LvNK4FHg2iTrq+ra6S2rnQblDtr3gfld7YObvjHHJNkD2Be4f5L77g76uYbq6OsaJjkYuBx4S1V992debftMyd/BqroNeITOs3y7m36u4SLgI0k2AcuB/5TkvJ9xvW3U8zVsHpW5H6Cq1tN5Fuuwn3nF7dLP38G7gX+oqvuq6lHgy8AxP/OKB9SgBLR/Ag5N8uIks+g8dHjlqDFXAm9t3p8J/F11nkK8Eji7WVXyYuBQ4B+fprrbpJ9rqI6er2GS/YCrgd+rqv/5dBXcMv1cvxc3/6InyYuAw4FNT0/ZrdLzNayq46pqQVUtAC4C/ltV7Y6rsvv5e3hgkmcAJHkJnf+efO9pqrst+vlvyVeBI5Ps1fzzfDzw7aep7sEz3asUJvsCXg3cTuf/WN7f9P0B8Nrm/bPorEy6k04Ae0nXvu9v9vsOcPp0f5YBvYabgB/SuXNxN6NW7ewur16vIfAB4MfAhq7XQdP9eQbo+r2ZzoPtG4AbgTOm+7MM2jUcdYwPspuu4uznGtJ5frT77+GvTvdnGaTr12z7zeYa3gJ8ZLo/S5tf/pKAJElSywzKFKckSdJuw4AmSZLUMgY0SZKkljGgSZIktYwBTZIkqWUMaJIkSS1jQJMkSWoZA5okSVLL/H8Vrx3dz5ctzAAAAABJRU5ErkJggg==\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# feature 중요도\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from sklearn.ensemble import RandomForestRegressor\n",
        "\n",
        "qual_col = ['LINE', 'PRODUCT_CODE']\n",
        "for i in qual_col:\n",
        "    le = LabelEncoder()\n",
        "    le = le.fit(train_x[i])\n",
        "    train_x[i] = le.transform(train_x[i])\n",
        "    for label in np.unique(test_x[i]):\n",
        "        if label not in le.classes_:\n",
        "            le.classes_ = np.append(le.classes_, label)\n",
        "    test_x[i] = le.transform(test_x[i])\n",
        "\n",
        "train_x = train_x.fillna(0)\n",
        "test_x = test_x.fillna(0)\n",
        "X_train, X_test, y_train, y_test=train_test_split(train_x,train_y,test_size=0.3,random_state=seed_num)\n",
        "\n",
        "%matplotlib inline\n",
        "RF = RandomForestRegressor(random_state=0, max_depth=5, min_samples_leaf=8, min_samples_split=8,n_estimators=200)\n",
        "RF.fit(X_train, y_train)\n",
        "ftr_importances_values = RF.feature_importances_\n",
        "ftr_importances = pd.Series(ftr_importances_values, index=X_train.columns)\n",
        "ftr_top = ftr_importances.sort_values(ascending=False)[:20]\n",
        " \n",
        "plt.figure(figsize=(10, 10))\n",
        "sns.barplot(x=ftr_top, y=ftr_top.index)\n",
        "plt.show()      "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "id": "GdugZkQrSvJS",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 393
        },
        "id": "GdugZkQrSvJS",
        "outputId": "77f844c8-c2a1-4f2a-9b2d-598b675d6479"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             LINE  PRODUCT_CODE         X_1        X_2    X_3         X_4  \\\n",
              "count  598.000000    598.000000  598.000000  598.00000  598.0  598.000000   \n",
              "mean     3.214047      1.157191    1.406355   55.51505    0.0   26.262542   \n",
              "std      1.703579      0.983298    4.655332   47.03581    0.0   22.201747   \n",
              "min      0.000000      0.000000    0.000000    0.00000    0.0    0.000000   \n",
              "25%      2.000000      0.000000    0.000000    0.00000    0.0    0.000000   \n",
              "50%      4.000000      2.000000    2.000000   91.00000    0.0   45.000000   \n",
              "75%      5.000000      2.000000    2.000000   95.00000    0.0   45.000000   \n",
              "max      5.000000      2.000000  103.000000  102.00000    0.0   45.000000   \n",
              "\n",
              "              X_5    X_6         X_7         X_8  ...      X_2866      X_2867  \\\n",
              "count  598.000000  598.0  598.000000  598.000000  ...  598.000000  598.000000   \n",
              "mean     6.065217    0.0   28.481605    5.864548  ...    8.496204    8.964498   \n",
              "std      5.140970    0.0   24.308159    4.960485  ...   19.189554   20.294317   \n",
              "min      0.000000    0.0    0.000000    0.000000  ...    0.000000    0.000000   \n",
              "25%      0.000000    0.0    0.000000    0.000000  ...    0.000000    0.000000   \n",
              "50%     10.000000    0.0   45.000000   10.000000  ...    0.000000    0.000000   \n",
              "75%     10.000000    0.0   50.000000   10.000000  ...    0.000000    0.000000   \n",
              "max     11.000000    0.0   62.000000   11.000000  ...   60.240000   68.660000   \n",
              "\n",
              "           X_2868      X_2869      X_2870      X_2871  X_2872  X_2873  X_2874  \\\n",
              "count  598.000000  598.000000  598.000000  598.000000   598.0   598.0   598.0   \n",
              "mean     8.295351    8.638763   11.145435    0.165552     0.0     0.0     0.0   \n",
              "std     18.755287   19.632753   24.961071    0.371989     0.0     0.0     0.0   \n",
              "min      0.000000    0.000000    0.000000    0.000000     0.0     0.0     0.0   \n",
              "25%      0.000000    0.000000    0.000000    0.000000     0.0     0.0     0.0   \n",
              "50%      0.000000    0.000000    0.000000    0.000000     0.0     0.0     0.0   \n",
              "75%      0.000000    0.000000    0.000000    0.000000     0.0     0.0     0.0   \n",
              "max     60.410000   59.930000   79.750000    1.000000     0.0     0.0     0.0   \n",
              "\n",
              "       X_2875  \n",
              "count   598.0  \n",
              "mean      0.0  \n",
              "std       0.0  \n",
              "min       0.0  \n",
              "25%       0.0  \n",
              "50%       0.0  \n",
              "75%       0.0  \n",
              "max       0.0  \n",
              "\n",
              "[8 rows x 2877 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-e0281981-95df-4e94-96f1-5f4358b574c5\">\n",
              "    <div class=\"colab-df-container\">\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>LINE</th>\n",
              "      <th>PRODUCT_CODE</th>\n",
              "      <th>X_1</th>\n",
              "      <th>X_2</th>\n",
              "      <th>X_3</th>\n",
              "      <th>X_4</th>\n",
              "      <th>X_5</th>\n",
              "      <th>X_6</th>\n",
              "      <th>X_7</th>\n",
              "      <th>X_8</th>\n",
              "      <th>...</th>\n",
              "      <th>X_2866</th>\n",
              "      <th>X_2867</th>\n",
              "      <th>X_2868</th>\n",
              "      <th>X_2869</th>\n",
              "      <th>X_2870</th>\n",
              "      <th>X_2871</th>\n",
              "      <th>X_2872</th>\n",
              "      <th>X_2873</th>\n",
              "      <th>X_2874</th>\n",
              "      <th>X_2875</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>598.000000</td>\n",
              "      <td>598.000000</td>\n",
              "      <td>598.000000</td>\n",
              "      <td>598.00000</td>\n",
              "      <td>598.0</td>\n",
              "      <td>598.000000</td>\n",
              "      <td>598.000000</td>\n",
              "      <td>598.0</td>\n",
              "      <td>598.000000</td>\n",
              "      <td>598.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>598.000000</td>\n",
              "      <td>598.000000</td>\n",
              "      <td>598.000000</td>\n",
              "      <td>598.000000</td>\n",
              "      <td>598.000000</td>\n",
              "      <td>598.000000</td>\n",
              "      <td>598.0</td>\n",
              "      <td>598.0</td>\n",
              "      <td>598.0</td>\n",
              "      <td>598.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>3.214047</td>\n",
              "      <td>1.157191</td>\n",
              "      <td>1.406355</td>\n",
              "      <td>55.51505</td>\n",
              "      <td>0.0</td>\n",
              "      <td>26.262542</td>\n",
              "      <td>6.065217</td>\n",
              "      <td>0.0</td>\n",
              "      <td>28.481605</td>\n",
              "      <td>5.864548</td>\n",
              "      <td>...</td>\n",
              "      <td>8.496204</td>\n",
              "      <td>8.964498</td>\n",
              "      <td>8.295351</td>\n",
              "      <td>8.638763</td>\n",
              "      <td>11.145435</td>\n",
              "      <td>0.165552</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>1.703579</td>\n",
              "      <td>0.983298</td>\n",
              "      <td>4.655332</td>\n",
              "      <td>47.03581</td>\n",
              "      <td>0.0</td>\n",
              "      <td>22.201747</td>\n",
              "      <td>5.140970</td>\n",
              "      <td>0.0</td>\n",
              "      <td>24.308159</td>\n",
              "      <td>4.960485</td>\n",
              "      <td>...</td>\n",
              "      <td>19.189554</td>\n",
              "      <td>20.294317</td>\n",
              "      <td>18.755287</td>\n",
              "      <td>19.632753</td>\n",
              "      <td>24.961071</td>\n",
              "      <td>0.371989</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>4.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>91.00000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>45.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>45.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>5.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>95.00000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>45.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>50.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>5.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>103.000000</td>\n",
              "      <td>102.00000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>45.000000</td>\n",
              "      <td>11.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>62.000000</td>\n",
              "      <td>11.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>60.240000</td>\n",
              "      <td>68.660000</td>\n",
              "      <td>60.410000</td>\n",
              "      <td>59.930000</td>\n",
              "      <td>79.750000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>8 rows × 2877 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e0281981-95df-4e94-96f1-5f4358b574c5')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-e0281981-95df-4e94-96f1-5f4358b574c5 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-e0281981-95df-4e94-96f1-5f4358b574c5');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ],
      "source": [
        "# feature distribution \n",
        "train_x.describe()    "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "id": "fceMKvgyg2Kg",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 298
        },
        "id": "fceMKvgyg2Kg",
        "outputId": "ec890640-80c9-4ee4-8bd2-d092373efc84"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<AxesSubplot:xlabel='X_592', ylabel='Count'>"
            ]
          },
          "metadata": {},
          "execution_count": 24
        },
        {
          "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": [
        "# feature histogram with KDE (밀도그래프) \n",
        "# 임의의 feature에 대한 histplot + 밀도 곡선 출력\n",
        "num = random.randint(1,2875)\n",
        "sns.histplot(train_x[f'X_{num}'], kde=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "id": "xskVkfufSvDg",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 700
        },
        "id": "xskVkfufSvDg",
        "outputId": "a157b2ce-c9d9-44b9-dcc6-9378ff70a3ee"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1152x864 with 20 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# visualize random 20 features distribution \n",
        "# imbalanced feature distribution (NO Gaussian distribution shape) --> MinMaxscaler\n",
        "fig, axes = plt.subplots(5,4,figsize=(16,12))\n",
        "ax= axes.flatten()\n",
        "\n",
        "for i in range(20): \n",
        "    num = random.randint(1,2875)\n",
        "    ax[i].hist(train_x[f'X_{num}']);    "
      ]
    },
    {
      "cell_type": "markdown",
      "id": "xuPOUxQ7Su9m",
      "metadata": {
        "id": "xuPOUxQ7Su9m"
      },
      "source": [
        "## 1.2 Preprocessing\n",
        "1. Label Encoding: categorical values `LINE`, `PRODUCT_CODE`\n",
        "2. Missing values: fillna(0) \n",
        "3. scaling: StandardScaler"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "id": "kZ1PM_Zrtzph",
      "metadata": {
        "id": "kZ1PM_Zrtzph"
      },
      "outputs": [],
      "source": [
        "train_df = pd.read_csv('./train.csv')\n",
        "test_df = pd.read_csv('./test.csv')\n",
        "submit = pd.read_csv('./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'])   "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "id": "3AbGp2rBuxuW",
      "metadata": {
        "id": "3AbGp2rBuxuW"
      },
      "outputs": [],
      "source": [
        "# 1) qualitative to quantitative\n",
        "qual_col = ['LINE', 'PRODUCT_CODE']\n",
        "for i in qual_col:\n",
        "    le = LabelEncoder()    # one-hot encoding (X): get_dummies(test_df) is not allowed (data leakage)   \n",
        "    le = le.fit(train_x[i])\n",
        "    train_x[i] = le.transform(train_x[i])\n",
        "    for label in np.unique(test_x[i]): \n",
        "        if label not in le.classes_: \n",
        "            le.classes_ = np.append(le.classes_, label)\n",
        "    test_x[i] = le.transform(test_x[i]) \n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "id": "GZ72F04guy3o",
      "metadata": {
        "id": "GZ72F04guy3o"
      },
      "outputs": [],
      "source": [
        "# 2) Missing Values \n",
        "train_x = train_x.fillna(0)\n",
        "test_x = test_x.fillna(0)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "id": "c66d3bf4",
      "metadata": {
        "id": "c66d3bf4"
      },
      "outputs": [],
      "source": [
        "# 3) MinMaxscaling: only `X_???` values (continuous)\n",
        "Xs = train_x.select_dtypes(include=float).iloc[:,1:].columns.tolist()\n",
        "scaler = MinMaxScaler().fit(train_x.loc[:, Xs])\n",
        "train_x.loc[:, Xs] = scaler.transform(train_x.loc[:, Xs])\n",
        "test_x.loc[:, Xs] = scaler.transform(test_x.loc[:, Xs])\n",
        "\n",
        "X_train, X_test, y_train, y_test=train_test_split(train_x,train_y,test_size=0.3,random_state=seed_num)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "hIoaatyGu9Zq",
      "metadata": {
        "id": "hIoaatyGu9Zq"
      },
      "source": [
        "# 2.Modeling"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "GQGMvepMJy87",
      "metadata": {
        "id": "GQGMvepMJy87"
      },
      "source": [
        "## 2.1 ML models\n",
        "- 여러 Machine Learning Classifier 모델들의 test set Accuracy score 도출 \n",
        "    - Ensemble에서 각 모델의 weight(가중치) 정하기 위한 근거로 사용하기 위함\n",
        "1. RandomForestClassifier\n",
        "2. GradientboostingClassiifer\n",
        "3. XGBClassifier\n",
        "4. LGBMClassifier\n",
        "5. CatboostClassifier\n",
        "6. RidgeClassifier\n",
        "7. BaggingClassifier"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "id": "m61QOAHA8lvG",
      "metadata": {
        "id": "m61QOAHA8lvG"
      },
      "outputs": [],
      "source": [
        "from catboost import CatBoostClassifier\n",
        "from sklearn.ensemble import GradientBoostingClassifier\n",
        "from sklearn.ensemble import StackingClassifier\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "\n",
        "models = [\n",
        "    RandomForestClassifier(random_state=seed_num), \n",
        "    GradientBoostingClassifier(random_state=seed_num),      \n",
        "    XGBClassifier(random_state=seed_num),\n",
        "    LGBMClassifier(objective='multiclass', random_state=seed_num),\n",
        "    CatBoostClassifier(objective='MultiClass',\n",
        "                                   task_type='GPU',\n",
        "                                   one_hot_max_size=2, random_seed=seed_num,\n",
        "                                   iterations=4000, verbose=False,\n",
        "                                   learning_rate=0.05\n",
        "                                   ),\n",
        "    RidgeClassifierCV(),  # RidgeClassifier: no seed setting argument  \n",
        "    BaggingClassifier(random_state=seed_num)]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "id": "qfV3aJaS8lvI",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qfV3aJaS8lvI",
        "outputId": "202a289f-45b0-4fc8-a39e-e5de1b97f798"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'RF': 0.7888888888888889, 'GBC': 0.8111111111111111, 'XGB': 0.7722222222222223, 'LGBM': 0.7888888888888889, 'Catboost': 0.8166666666666667, 'Ridge': 0.7611111111111111, 'Bagging': 0.75}\n"
          ]
        }
      ],
      "source": [
        "# find weights for each model \n",
        "model_list = ['RF', 'GBC', 'XGB', 'LGBM', 'Catboost', 'Ridge', 'Bagging']\n",
        "i=0\n",
        "model_acc = {}\n",
        "\n",
        "for model in models:  \n",
        "    if i == 4: \n",
        "        preds = model.fit(X_train, y_train).predict(X_test)\n",
        "        preds = preds.reshape(1,180)\n",
        "        preds = list(preds[0])\n",
        "    else: \n",
        "        preds = model.fit(X_train, y_train).predict(X_test)\n",
        "\n",
        "    globals()[f'preds_{model_list[i]}'] = preds   # set variable name for each model prediction results  \n",
        "  \n",
        "    score = sklearn.metrics.accuracy_score(y_test, preds)\n",
        "    model_acc[model_list[i]] = score\n",
        "    i += 1   \n",
        "\n",
        "print(model_acc)   "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "id": "PihsvBmKhfOd",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 499
        },
        "id": "PihsvBmKhfOd",
        "outputId": "3dee0f98-5d52-4c3f-861c-33396bde6340"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1008x576 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# ML models accuracy comparison plot \n",
        "model_name = list(model_acc.keys())\n",
        "model_acc = list(model_acc.values())\n",
        "acc_dic = {'model': model_name, 'acc': model_acc}\n",
        "acc_df = pd.DataFrame(acc_dic)\n",
        "acc_df = acc_df.set_index('model')\n",
        "\n",
        "acc_df.plot(kind='barh', figsize=(14,8),\n",
        "            title='Model Comparison - accuracy score');"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "_KIk0bpq4IHj",
      "metadata": {
        "id": "_KIk0bpq4IHj"
      },
      "source": [
        "## 2.2 Sequential MLP \n",
        "* Deep Learning Modeling: Sequential MLP model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "id": "8wlnknVPi0NX",
      "metadata": {
        "id": "8wlnknVPi0NX"
      },
      "outputs": [],
      "source": [
        "import torch.nn as nn \n",
        "import torch.nn.functional as F \n",
        "\n",
        "class MultilayerPerceptron(nn.Module):\n",
        "    def __init__(self, input_dim, hidden_dim1, hidden_dim2, hidden_dim3, output_dim):\n",
        "        \"\"\"\n",
        "        parameters: \n",
        "            input_dim (int): 입력 벡터 크기\n",
        "            hidden_dim1 (int): 첫 번째 Linear 층의 출력 크기\n",
        "            hidden_dim2 (int): 두 번째 Linear 층의 출력 크기 \n",
        "            output_dim (int): 세 번째 Linear 층의 출력 크기 \n",
        "        \"\"\"\n",
        "        super(MultilayerPerceptron, self).__init__()\n",
        "        self.fc1 = nn.Linear(input_dim, hidden_dim1)\n",
        "        self.fc2 = nn.Linear(hidden_dim1, hidden_dim2)\n",
        "        self.fc3 = nn.Linear(hidden_dim2, hidden_dim3)\n",
        "        self.fc4 = nn.Linear(hidden_dim3, output_dim)\n",
        "\n",
        "    def forward(self, x_in, apply_softmax=False):\n",
        "        \"\"\"\n",
        "        MLP의 정방향 계산 \n",
        "\n",
        "        parameters:\n",
        "            x_in (torch.Tensor): 입력 데이터 텐서\n",
        "                x_in.shape는 (batch, input_dim)\n",
        "            apply_softmax (multiclasses): softmax activation function \n",
        "        return: \n",
        "            result Tensor\n",
        "            tesnor.shape: (batch, output_dim)\n",
        "        \"\"\"\n",
        "        intermediate1 = F.relu(self.fc1(x_in))   # activation function\n",
        "        intermediate2 = F.relu(self.fc2(intermediate1))   # activation function\n",
        "        intermediate3 = F.relu(self.fc3(intermediate2))   # activation function\n",
        "        output = self.fc4(intermediate3)    \n",
        "\n",
        "\n",
        "        if apply_softmax: \n",
        "            output = F.softmax(output, dim=1)   # output layer: softmax activation function\n",
        "        return output "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "id": "Fp5qXV02kPeC",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Fp5qXV02kPeC",
        "outputId": "d1d61b8a-363a-4d8a-ae40-5748cee6e159"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MultilayerPerceptron(\n",
            "  (fc1): Linear(in_features=2877, out_features=1024, bias=True)\n",
            "  (fc2): Linear(in_features=1024, out_features=512, bias=True)\n",
            "  (fc3): Linear(in_features=512, out_features=256, bias=True)\n",
            "  (fc4): Linear(in_features=256, out_features=3, bias=True)\n",
            ")\n",
            "Type: torch.FloatTensor\n",
            "shape: torch.Size([256, 3])\n"
          ]
        }
      ],
      "source": [
        "def describe(x): \n",
        "    print(f'Type: {x.type()}')\n",
        "    print(f'shape: {x.shape}')\n",
        "    # print(f'value: {x}') \n",
        "\n",
        "batch_size = 256 \n",
        "input_dim = train_x.shape[-1]\n",
        "hidden_dim1 = 1024\n",
        "hidden_dim2 = 512\n",
        "hidden_dim3 = 256\n",
        "output_dim = 3 \n",
        "\n",
        "# model design \n",
        "mlp = MultilayerPerceptron(input_dim, hidden_dim1, hidden_dim2, hidden_dim3, output_dim)\n",
        "print(mlp)\n",
        "x_input = torch.rand(batch_size, input_dim)\n",
        "y_output = mlp(x_input, apply_softmax=True)  # result: probabilities format \n",
        "a = y_output\n",
        "describe(y_output)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "id": "FLHA36Z1iEhW",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "FLHA36Z1iEhW",
        "outputId": "b643b41d-8b45-4225-bdc6-9066a85bcb01"
      },
      "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": [
        "prediction=[]\n",
        "\n",
        "for i in a: \n",
        "    i = list(i)\n",
        "    prediction.append(i.index(max(i)))\n",
        "\n",
        "sns.countplot(x=prediction);    "
      ]
    },
    {
      "cell_type": "markdown",
      "id": "7f3d392b",
      "metadata": {
        "id": "7f3d392b"
      },
      "source": [
        "# 3.Params Optimization\n",
        "1. Optuna\n",
        "2. GridSearch "
      ]
    },
    {
      "cell_type": "markdown",
      "id": "5ee4390b",
      "metadata": {
        "id": "5ee4390b"
      },
      "source": [
        "## 3.1 GradientBoosting\n",
        "1. Optuna\n",
        "    - hyperparameters auto optimization"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 49,
      "id": "29dc0524",
      "metadata": {
        "id": "29dc0524"
      },
      "outputs": [],
      "source": [
        "def objective(trial, X, y, cv, scoring):\n",
        "  params = {\n",
        "    \"n_estimators\": trial.suggest_int(\"n_estimators\", 100, 5000, step = 100),\n",
        "    \"learning_rate\": trial.suggest_float(\"learning_rate\", 1e-4, 0.3, log = True),\n",
        "    \"max_depth\": trial.suggest_int(\"max_depth\", 3, 9),\n",
        "    \"subsample\": trial.suggest_float(\"subsample\", 0.5, 0.9, step = 0.1),\n",
        "    \"max_features\": trial.suggest_categorical(\"max_features\", [\"auto\", \"sqrt\", \"log2\"]),\n",
        "    \"random_state\": 42,\n",
        "    }\n",
        "  # Perform cross validation\n",
        "  gb_class = GradientBoostingClassifier(**params)\n",
        "\n",
        "  # Compute scores\n",
        "  scores = cross_validate(gb_class, X, y, cv = cv, scoring = scoring, n_jobs = -1)\n",
        "  accuracy = scores[\"test_score\"].mean()\n",
        "\n",
        "  return accuracy"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "0c65d30f",
      "metadata": {
        "id": "0c65d30f"
      },
      "source": [
        "- Optimization for params\n",
        "\n",
        "    - K-Fold, trials = 10"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "4d11262c",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4d11262c",
        "outputId": "ce27b287-a331-44b8-bd3b-ab7bc09f492e"
      },
      "outputs": [
        {
          "metadata": {
            "tags": null
          },
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\u001b[32m[I 2023-03-07 04:02:59,375]\u001b[0m A new study created in memory with name: no-name-a9009d1d-0463-4030-b8c7-a1dfa88847be\u001b[0m\n",
            "\u001b[32m[I 2023-03-07 04:03:36,920]\u001b[0m Trial 0 finished with value: 0.7776821572002295 and parameters: {'n_estimators': 2300, 'learning_rate': 0.030363332319684515, 'max_depth': 9, 'subsample': 0.7, 'max_features': 'sqrt'}. Best is trial 0 with value: 0.7776821572002295.\u001b[0m\n",
            "\u001b[32m[I 2023-03-07 04:04:28,659]\u001b[0m Trial 1 finished with value: 0.7609294320137694 and parameters: {'n_estimators': 4500, 'learning_rate': 0.0036449200738671106, 'max_depth': 3, 'subsample': 0.5, 'max_features': 'sqrt'}. Best is trial 0 with value: 0.7776821572002295.\u001b[0m\n",
            "\u001b[32m[I 2023-03-07 04:07:33,645]\u001b[0m Trial 2 finished with value: 0.7419678714859439 and parameters: {'n_estimators': 4300, 'learning_rate': 0.2341301454019806, 'max_depth': 3, 'subsample': 0.9, 'max_features': 'auto'}. Best is trial 0 with value: 0.7776821572002295.\u001b[0m\n",
            "\u001b[32m[I 2023-03-07 04:07:51,068]\u001b[0m Trial 3 finished with value: 0.76815834767642 and parameters: {'n_estimators': 1900, 'learning_rate': 0.07958473192899661, 'max_depth': 7, 'subsample': 0.7, 'max_features': 'log2'}. Best is trial 0 with value: 0.7776821572002295.\u001b[0m\n",
            "\u001b[32m[I 2023-03-07 04:16:10,376]\u001b[0m Trial 4 finished with value: 0.7800917957544464 and parameters: {'n_estimators': 3400, 'learning_rate': 0.044114071655488925, 'max_depth': 6, 'subsample': 0.8, 'max_features': 'auto'}. Best is trial 4 with value: 0.7800917957544464.\u001b[0m\n",
            "\u001b[32m[I 2023-03-07 04:32:34,539]\u001b[0m Trial 5 finished with value: 0.7466150315547907 and parameters: {'n_estimators': 3800, 'learning_rate': 0.0005378673436193717, 'max_depth': 3, 'subsample': 0.8, 'max_features': 'auto'}. Best is trial 4 with value: 0.7800917957544464.\u001b[0m\n"
          ]
        }
      ],
      "source": [
        "study = optuna.create_study(direction = \"maximize\")\n",
        "\n",
        "kf = sklearn.model_selection.StratifiedKFold(n_splits=5, shuffle=True, random_state=37)\n",
        "\n",
        "func = lambda trial: objective(trial, X_train, y_train, \n",
        "                               cv = kf, scoring = \"accuracy\")\n",
        "\n",
        "# %%time\n",
        "# Start optimizing with 100 trials\n",
        "study.optimize(func, n_trials = 10)\n",
        "\n",
        "print(f\"The highest accuracy reached by this study: {(study.best_value) * 100}%.\")\n",
        "print(\"Best params:\")\n",
        "for key, value in study.best_params.items():\n",
        "    print(f\"\\t{key}: {value}\")    "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "7d5b7e95",
      "metadata": {
        "id": "7d5b7e95"
      },
      "outputs": [],
      "source": [
        "import imblearn\n",
        "\n",
        "params= {'n_estimators': 1200, 'learning_rate': 0.009147154102399788, \\\n",
        "        'max_depth': 9, 'subsample': 0.8, 'max_features': 'auto'}\n",
        "GBC =  GradientBoostingClassifier(random_state=37, **params,                              \n",
        "                                  ).fit(X_train, y_train)\n",
        "preds = GBC.predict(X_test)\n",
        "\n",
        "print(classification_report(y_test, preds))\n",
        "print(\"\\n Optuna : \", accuracy_score(y_test, preds))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "8f76630f",
      "metadata": {
        "id": "8f76630f"
      },
      "outputs": [],
      "source": [
        "from optuna.visualization import plot_optimization_history\n",
        "from optuna.visualization import plot_param_importances\n",
        "from optuna.visualization import plot_parallel_coordinate\n",
        "from optuna.visualization import plot_contour\n",
        "plot_param_importances(study)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "38703a67",
      "metadata": {
        "id": "38703a67"
      },
      "source": [
        "### 3.1.1 GridSearch\n",
        "\n",
        "- hyperparameter optimization"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "f34d37d8",
      "metadata": {
        "id": "f34d37d8"
      },
      "outputs": [],
      "source": [
        "# from sklearn.model_selection import GridSearchCV\n",
        "\n",
        "# estimator = GradientBoostingClassifier(random_state = 37)\n",
        "\n",
        "# param_grid = {\n",
        "#             'n_estimators' : [n_estimators for n_estimators in range(100, 5000, 100)],\n",
        "#             'learning_rate' : [lr * 0.0001 for lr in range(1, 10)],\n",
        "#             'max_depth' : [depth for depth in range(3, 9)],\n",
        "#             'subsample' : [subsample * 0.1 for subsample in range(5, 9, 1)],\n",
        "#             'max_features' : ['auto', 'sqrt', 'log2']\n",
        "#             }\n",
        "\n",
        "# # scoring = 'r2' -> 결정계수로 scoring\n",
        "# grid_GBC = GridSearchCV(estimator, param_grid, scoring = 'r2', n_jobs = -1)\n",
        "# grid_GBC.fit(train_x, train_y)\n",
        "\n",
        "# print('best estimator model: \\n{}'.format(grid_GBC.best_estimator_))\n",
        "# print('\\nbest parameter: \\n{}'.format(grid_GBC.best_params_))\n",
        "# print(\"\\nbest score: \\n{}\".format(grid_GBC.best_score_.round(3)))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "7f8add53",
      "metadata": {
        "id": "7f8add53"
      },
      "outputs": [],
      "source": [
        "# # GridSearch best parameter \n",
        "# import imblearn\n",
        "\n",
        "# params= {'n_estimators': 1200, 'learning_rate': 0.009147154102399788, \\\n",
        "#         'max_depth': 9, 'subsample': 0.8, 'max_features': 'auto'}\n",
        "# GBC =  GradientBoostingClassifier(random_state=37, **params,                              \n",
        "#                                   ).fit(X_train, y_train)\n",
        "# preds = GBC.predict(X_test)\n",
        "\n",
        "# print(classification_report(y_test, preds))\n",
        "# print(\"\\n Grid : \", accuracy_score(y_test, preds))"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "-V0IZDs6_S3f",
      "metadata": {
        "id": "-V0IZDs6_S3f"
      },
      "source": [
        "## 3.2 CatBoost\n",
        "1. optuna \n",
        "    - hyperparameters auto optimization"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "4ea05433",
      "metadata": {
        "id": "4ea05433"
      },
      "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)  "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "a8ffdb51",
      "metadata": {
        "id": "a8ffdb51"
      },
      "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"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "1942cb67",
      "metadata": {
        "id": "1942cb67"
      },
      "outputs": [],
      "source": [
        "params=  {'iterations': 857, 'learning_rate': 0.07097207730593516, 'depth': 8, \\\n",
        "        'l2_leaf_reg': 0.00012090525126196811, 'bootstrap_type': 'Bayesian', \\\n",
        "        'random_strength': 5.359110894800412, 'bagging_temperature': 0.7137022569955509, \\\n",
        "        'od_type': 'IncToDec', 'od_wait': 42}\n",
        "CBC = CatBoostClassifier(**params, verbose=False, random_state=37)\n",
        "CBC.fit(X_train, y_train)\n",
        "preds_CBC = CBC.predict(X_test)\n",
        "\n",
        "print(classification_report(y_test, preds_CBC))\n",
        "print(\"\\n Optuna : \", accuracy_score(y_test, preds_CBC))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "eACiKQAXwMSW",
      "metadata": {
        "id": "eACiKQAXwMSW"
      },
      "outputs": [],
      "source": [
        "from optuna.visualization import plot_optimization_history\n",
        "from optuna.visualization import plot_param_importances\n",
        "from optuna.visualization import plot_parallel_coordinate\n",
        "from optuna.visualization import plot_contour"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "b30ec99b",
      "metadata": {
        "id": "b30ec99b"
      },
      "outputs": [],
      "source": [
        "plot_param_importances(study)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "458a5890",
      "metadata": {
        "id": "458a5890"
      },
      "source": [
        "### 3.2.1 GridSearch\n",
        "\n",
        "- hyperparameter optimization"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "c06e0929",
      "metadata": {
        "id": "c06e0929"
      },
      "outputs": [],
      "source": [
        "# from sklearn.model_selection import GridSearchCV\n",
        "\n",
        "# estimator = CatBoostClassifier(random_state = 37)\n",
        "\n",
        "# param_grid = {\n",
        "#             'iterataions' : [iter for iter in range(100, 1000, 100)],\n",
        "#             'learning_rate' : [lr * 0.0001 for lr in range(1, 10)],\n",
        "#             'depth' : [depth for depth in range(4, 10)],\n",
        "#             'l2_leaf_reg' : [leaf * 0.0000001 for leaf in range(1, 100, 10)],\n",
        "#             'bootstrap_type' : ['Bayesian'],\n",
        "#             'random_strength' : [strength * 0.1 for strength in range(1, 100, 10)],\n",
        "#             'bagging_temperature' : [bag * 0.1 for bag in range(1, 10)],\n",
        "#             'od_type' : ['IncToDec', 'Iter'],\n",
        "#             'od_wait' : [wait for wait in range(10, 50, 4)]\n",
        "#             }\n",
        "\n",
        "# # scoring = 'r2' -> 결정계수로 scoring\n",
        "# grid_CBC = GridSearchCV(estimator, param_grid, scoring = 'r2', n_jobs = -1)\n",
        "# grid_CBC.fit(X_train, y_train)\n",
        "\n",
        "# print('best estimator model: \\n{}'.format(grid_CBC.best_estimator_))\n",
        "# print('\\nbest parameter: \\n{}'.format(grid_CBC.best_params_))\n",
        "# print(\"\\nbest score: \\n{}\".format(grid_CBC.best_score_.round(3)))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "092912ed",
      "metadata": {
        "id": "092912ed"
      },
      "outputs": [],
      "source": [
        "# params=  {'iterations': 857, 'learning_rate': 0.07097207730593516, 'depth': 8, \\\n",
        "#         'l2_leaf_reg': 0.00012090525126196811, 'bootstrap_type': 'Bayesian', \\\n",
        "#         'random_strength': 5.359110894800412, 'bagging_temperature': 0.7137022569955509, \\\n",
        "#         'od_type': 'IncToDec', 'od_wait': 42}\n",
        "# CBC = CatBoostClassifier(**params, verbose=False, random_state=37)\n",
        "# CBC.fit(X_train, y_train)\n",
        "# preds_CBC = CBC.predict(X_test)\n",
        "\n",
        "# print(classification_report(y_test, preds_CBC))\n",
        "# print(\"\\n Grid : \", accuracy_score(y_test, preds_CBC))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "o8N-QHuIvNN3"
      },
      "source": [
        "# 4.Ensemble"
      ],
      "id": "o8N-QHuIvNN3"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2fe15f1a"
      },
      "source": [
        "## 4.1 VotingClassifier\n",
        "1. 3-ensemble Modle\n",
        "2. Cross-Validation 구축 후 성능 검증"
      ],
      "id": "2fe15f1a"
    },
    {
      "cell_type": "code",
      "execution_count": 39,
      "metadata": {
        "id": "Vm8q0p4Hyzg2"
      },
      "outputs": [],
      "source": [
        "models = [\n",
        "    # test set accuracy 1위(0.8166) \n",
        "    CatBoostClassifier(verbose=False, random_seed=seed_num),\n",
        "    # test set accuracy 5위(0.7722)\n",
        "    XGBClassifier(random_state=seed_num),\n",
        "    # test set accuracy 2위(0.8111) \n",
        "    GradientBoostingClassifier(random_state=seed_num)]\n",
        "\n",
        "fit = [x.fit(train_x, train_y) for x in models]    "
      ],
      "id": "Vm8q0p4Hyzg2"
    },
    {
      "cell_type": "code",
      "execution_count": 40,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "0bcafdb3",
        "outputId": "5a174ae2-b6ed-4b89-9db3-65abfebfdc80"
      },
      "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": [
        "def prediction(models, test, mode=None, weights=None):\n",
        "    if mode == \"hard\":\n",
        "        preds = np.asarray([x.predict(test).reshape(-1) for x in models]).T\n",
        "        res = np.apply_along_axis(\n",
        "            lambda x: np.argmax(np.bincount(x, weights=weights)),\n",
        "            axis=1,\n",
        "            arr=preds\n",
        "        )\n",
        "    elif mode == \"soft\":  \n",
        "        preds = np.asarray([x.predict_proba(test) for x in models])\n",
        "        res = np.zeros(preds[0].shape)\n",
        "        for pred, weight in zip(preds, weights):\n",
        "            res = res + pred*weight\n",
        "        res = np.argmax(preds, axis=0) \n",
        "    else:\n",
        "        res = models[0].predict(test)\n",
        "    return res\n",
        "\n",
        "preds = prediction(models, test_x, 'hard', [2,1,2] )\n",
        "\n",
        "sns.countplot(x=preds);   "
      ],
      "id": "0bcafdb3"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KUOHpt3z847x"
      },
      "source": [
        "## 4.2 Validation Score\n",
        "1. train.csv에서 split된 test set accuracy 100% 검증"
      ],
      "id": "KUOHpt3z847x"
    },
    {
      "cell_type": "code",
      "execution_count": 47,
      "metadata": {
        "id": "Z3UesEBQ1r8z"
      },
      "outputs": [],
      "source": [
        "cbc = CatBoostClassifier(verbose=False, random_seed=seed_num)  \n",
        "xgb = XGBClassifier(random_state=seed_num) \n",
        "gbc = GradientBoostingClassifier(random_state=seed_num) \n",
        "voting_model = sklearn.ensemble.VotingClassifier(estimators=[\n",
        "                ('CatBoostClassifier', cbc),\n",
        "                ('XGBClassifier', xgb),\n",
        "                ('GradientBoostingClassifier', gbc)], \n",
        "                voting='hard', weights=[2,1,2])"
      ],
      "id": "Z3UesEBQ1r8z"
    },
    {
      "cell_type": "code",
      "execution_count": 48,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TgLWsUI2A25b",
        "outputId": "70c0de76-d1cf-4807-a994-95bd7c207c82"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1.0"
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ],
      "source": [
        "val_preds = prediction(models, X_test, 'hard', [2,1,2])\n",
        "val_score= sklearn.metrics.accuracy_score(y_test, val_preds)\n",
        "val_score  "
      ],
      "id": "TgLWsUI2A25b"
    },
    {
      "cell_type": "markdown",
      "id": "zbNtWowN78mX",
      "metadata": {
        "id": "zbNtWowN78mX"
      },
      "source": [
        "# 5.Submit"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 46,
      "id": "YQHmg0PCJetf",
      "metadata": {
        "id": "YQHmg0PCJetf"
      },
      "outputs": [],
      "source": [
        "submit = pd.read_csv('./sample_submission.csv')\n",
        "submit['Y_Class'] = preds\n",
        "\n",
        "submit.to_csv('smartFactorySubmission.csv', index=False)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "utf4-GCG5REy",
      "metadata": {
        "id": "utf4-GCG5REy"
      },
      "source": [
        "# 6.References\n",
        "PPT에 기재하였습니다\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# THE END"
      ],
      "metadata": {
        "id": "f5sg6EQYMj_P"
      },
      "id": "f5sg6EQYMj_P"
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "machine_shape": "hm",
      "provenance": [],
      "toc_visible": true
    },
    "gpuClass": "standard",
    "kernelspec": {
      "display_name": "Python 3 (ipykernel)",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.9.13"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 5
}