smartFactory_catBoost_optuna_Yumin.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "source": [
        "!pip install catboost\n",
        "!pip install optuna"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hC6xTjdGA-rj",
        "outputId": "22d2c4e7-45f4-4b05-f4a8-26a94ab46e22"
      },
      "id": "hC6xTjdGA-rj",
      "execution_count": 5,
      "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: plotly in /usr/local/lib/python3.8/dist-packages (from catboost) (5.5.0)\n",
            "Requirement already satisfied: graphviz in /usr/local/lib/python3.8/dist-packages (from catboost) (0.10.1)\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: pandas>=0.24.0 in /usr/local/lib/python3.8/dist-packages (from catboost) (1.3.5)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.8/dist-packages (from catboost) (1.15.0)\n",
            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.8/dist-packages (from catboost) (3.5.3)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.8/dist-packages (from catboost) (1.7.3)\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",
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            "Requirement already satisfied: tenacity>=6.2.0 in /usr/local/lib/python3.8/dist-packages (from plotly->catboost) (8.2.1)\n",
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting optuna\n",
            "  Downloading optuna-3.1.0-py3-none-any.whl (365 kB)\n",
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            "\u001b[?25hCollecting cmaes>=0.9.1\n",
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            "Installing collected packages: Mako, colorlog, cmaes, alembic, optuna\n",
            "Successfully installed Mako-1.2.4 alembic-1.9.4 cmaes-0.9.1 colorlog-6.7.0 optuna-3.1.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "id": "94682a80",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "94682a80",
        "outputId": "5d1cb663-d612-47ee-bdee-f72fad212db8"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "import pandas as pd\n",
        "import random\n",
        "import os\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "from sklearn.preprocessing import LabelEncoder\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 catboost import CatBoostClassifier\n",
        "\n",
        "import optuna\n",
        "from optuna.samplers import TPESampler\n",
        "from optuna import Trial\n",
        "\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "id": "6d28a6c9",
      "metadata": {
        "id": "6d28a6c9"
      },
      "outputs": [],
      "source": [
        "# train_df = pd.read_csv('C://Users//user//OneDrive//open//train.csv')\n",
        "# test_df = pd.read_csv('C:/Users/user/OneDrive/open/test.csv')\n",
        "# submit = pd.read_csv('C:/Users/user/OneDrive/open/sample_submission.csv')"
      ]
    },
    {
      "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')"
      ],
      "metadata": {
        "id": "kLBoulM6u2Fb"
      },
      "execution_count": 177,
      "outputs": [],
      "id": "kLBoulM6u2Fb"
    },
    {
      "cell_type": "code",
      "execution_count": 178,
      "id": "f75f9df6",
      "metadata": {
        "id": "f75f9df6"
      },
      "outputs": [],
      "source": [
        "train_x = train_df.drop(columns=['PRODUCT_ID', 'TIMESTAMP', 'Y_Class'])\n",
        "train_y = train_df['Y_Class']\n",
        "\n",
        "test_x = test_df.drop(columns=['PRODUCT_ID', 'TIMESTAMP'])\n",
        "# test_x = test_df"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 1.preprocessing"
      ],
      "metadata": {
        "id": "wqkW_3EbQxj1"
      },
      "id": "wqkW_3EbQxj1"
    },
    {
      "cell_type": "code",
      "execution_count": 179,
      "id": "c66d3bf4",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c66d3bf4",
        "outputId": "e1cc1b69-fd19-4335-8d87-446ec075ec80"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Done.\n"
          ]
        }
      ],
      "source": [
        "# qualitative to quantitative\n",
        "qual_col = ['LINE', 'PRODUCT_CODE']\n",
        "\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",
        "    \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",
        "print('Done.')  \n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 180,
      "id": "5dc3e419",
      "metadata": {
        "id": "5dc3e419"
      },
      "outputs": [],
      "source": [
        "train_x = train_x.fillna(0)\n",
        "test_x = test_x.fillna(0)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 183,
      "id": "f42862a2",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 453
        },
        "id": "f42862a2",
        "outputId": "65baeab8-6aa8-4d55-87ff-c10e0f7d37d1"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:493: FutureWarning: The feature names should match those that were passed during fit. Starting version 1.2, an error will be raised.\n",
            "Feature names seen at fit time, yet now missing:\n",
            "- Y_Quality\n",
            "\n",
            "  warnings.warn(message, FutureWarning)\n"
          ]
        },
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-183-d128655ce46e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mX_train_std\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mstdsc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit_transform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain_x\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0mX_test_std\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mstdsc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtransform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtest_x\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/sklearn/preprocessing/_data.py\u001b[0m in \u001b[0;36mtransform\u001b[0;34m(self, X)\u001b[0m\n\u001b[1;32m    494\u001b[0m         \u001b[0mcheck_is_fitted\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    495\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 496\u001b[0;31m         X = self._validate_data(\n\u001b[0m\u001b[1;32m    497\u001b[0m             \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    498\u001b[0m             \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy\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/base.py\u001b[0m in \u001b[0;36m_validate_data\u001b[0;34m(self, X, y, reset, validate_separately, **check_params)\u001b[0m\n\u001b[1;32m    583\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    584\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mno_val_X\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mcheck_params\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"ensure_2d\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 585\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_check_n_features\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreset\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mreset\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    586\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    587\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mout\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/sklearn/base.py\u001b[0m in \u001b[0;36m_check_n_features\u001b[0;34m(self, X, reset)\u001b[0m\n\u001b[1;32m    398\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    399\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mn_features\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_features_in_\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 400\u001b[0;31m             raise ValueError(\n\u001b[0m\u001b[1;32m    401\u001b[0m                 \u001b[0;34mf\"X has {n_features} features, but {self.__class__.__name__} \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    402\u001b[0m                 \u001b[0;34mf\"is expecting {self.n_features_in_} features as input.\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: X has 2877 features, but MinMaxScaler is expecting 2878 features as input."
          ]
        }
      ],
      "source": [
        "from sklearn.preprocessing import MinMaxScaler\n",
        "\n",
        "stdsc = MinMaxScaler()\n",
        "\n",
        "X_train_std = stdsc.fit_transform(train_x)\n",
        "X_test_std = stdsc.transform(test_x)  "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "11f6d4ca",
      "metadata": {
        "id": "11f6d4ca"
      },
      "outputs": [],
      "source": [
        "# X_train_std = pd.DataFrame(X_train_std)\n",
        "# X_test_std = pd.DataFrame(X_test_std)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "fa987a0c",
      "metadata": {
        "id": "fa987a0c",
        "outputId": "81c4e49c-0e47-4c2a-efbd-1116ef13f72e"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([[ 0.34307569, -0.71324153, -1.17783182, ...,  0.        ,\n",
              "         0.        ,  0.        ],\n",
              "       [ 1.47703624, -0.12575057, -1.17783182, ...,  0.        ,\n",
              "         0.        ,  0.        ],\n",
              "       [ 0.05008771, -0.71324153, -1.17783182, ...,  0.        ,\n",
              "         0.        ,  0.        ],\n",
              "       ...,\n",
              "       [-1.29915998, -0.71324153, -1.17783182, ...,  0.        ,\n",
              "         0.        ,  0.        ],\n",
              "       [ 0.06468338,  0.46174038, -0.1599945 , ...,  0.        ,\n",
              "         0.        ,  0.        ],\n",
              "       [ 0.37935041,  1.04923134, -0.1599945 , ...,  0.        ,\n",
              "         0.        ,  0.        ]])"
            ]
          },
          "execution_count": 91,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# X_train_std"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 130,
      "id": "1a75269c",
      "metadata": {
        "id": "1a75269c"
      },
      "outputs": [],
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X_train_std, train_y,\n",
        "                                                   test_size=0.3,\n",
        "                                                   random_state=305)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 143,
      "id": "83ee1fde",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "83ee1fde",
        "outputId": "c9969549-1e79-4550-9616-854d147e6072"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_6\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " dense_12 (Dense)            (None, 512)               1473536   \n",
            "                                                                 \n",
            " dense_13 (Dense)            (None, 3)                 1539      \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 1,475,075\n",
            "Trainable params: 1,475,075\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "model = Sequential([\n",
        "          Dense(512, activation='relu',input_shape=(2877,)),\n",
        "          Dense(3, activation='softmax')])\n",
        "\n",
        "model.summary()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 132,
      "id": "cb9ab71b",
      "metadata": {
        "scrolled": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cb9ab71b",
        "outputId": "63b7fb49-b994-4c91-d752-1d1582b74678"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0      1\n",
              "1      2\n",
              "2      1\n",
              "3      2\n",
              "4      1\n",
              "      ..\n",
              "593    1\n",
              "594    0\n",
              "595    0\n",
              "596    1\n",
              "597    1\n",
              "Name: Y_Class, Length: 598, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 132
        }
      ],
      "source": [
        "train_y"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 2.catboost Classiifer"
      ],
      "metadata": {
        "id": "HgRXZthuQD4I"
      },
      "id": "HgRXZthuQD4I"
    },
    {
      "cell_type": "code",
      "execution_count": 139,
      "id": "52b8e615",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "52b8e615",
        "outputId": "4ff9beef-8307-46c7-c028-ab9a98e5f19e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "6/6 [==============================] - 1s 76ms/step - loss: 1.9864 - accuracy: 0.5795 - val_loss: 1.4810 - val_accuracy: 0.5583\n",
            "Epoch 2/10\n",
            "6/6 [==============================] - 0s 33ms/step - loss: 1.1174 - accuracy: 0.6444 - val_loss: 1.0912 - val_accuracy: 0.6167\n",
            "Epoch 3/10\n",
            "6/6 [==============================] - 0s 37ms/step - loss: 0.8234 - accuracy: 0.7134 - val_loss: 1.0253 - val_accuracy: 0.6833\n",
            "Epoch 4/10\n",
            "6/6 [==============================] - 0s 41ms/step - loss: 0.7412 - accuracy: 0.7050 - val_loss: 0.9175 - val_accuracy: 0.6667\n",
            "Epoch 5/10\n",
            "6/6 [==============================] - 0s 43ms/step - loss: 0.7208 - accuracy: 0.7197 - val_loss: 0.9714 - val_accuracy: 0.6333\n",
            "Epoch 6/10\n",
            "6/6 [==============================] - 0s 43ms/step - loss: 0.7006 - accuracy: 0.7385 - val_loss: 0.9098 - val_accuracy: 0.6833\n",
            "Epoch 7/10\n",
            "6/6 [==============================] - 0s 40ms/step - loss: 0.7090 - accuracy: 0.7448 - val_loss: 0.9326 - val_accuracy: 0.6167\n",
            "Epoch 8/10\n",
            "6/6 [==============================] - 0s 40ms/step - loss: 0.6731 - accuracy: 0.7427 - val_loss: 0.8759 - val_accuracy: 0.6833\n",
            "Epoch 9/10\n",
            "6/6 [==============================] - 0s 40ms/step - loss: 0.6606 - accuracy: 0.7343 - val_loss: 0.9308 - val_accuracy: 0.6250\n",
            "Epoch 10/10\n",
            "6/6 [==============================] - 0s 44ms/step - loss: 0.6599 - accuracy: 0.7301 - val_loss: 0.9005 - val_accuracy: 0.6833\n"
          ]
        }
      ],
      "source": [
        "model.compile(optimizer='adam',\n",
        "                   loss='sparse_categorical_crossentropy',\n",
        "                   metrics=['accuracy'])\n",
        "     \n",
        "\n",
        "history = model.fit(X_train_std,\n",
        "                    train_y,\n",
        "                    epochs=10,\n",
        "                    batch_size=80,\n",
        "                    validation_split=0.2)"
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "wPPm8TwhTjcY"
      },
      "id": "wPPm8TwhTjcY",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 140,
      "id": "f5a8c473",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "f5a8c473",
        "outputId": "46e1b1b4-40a7-4cf6-8cd0-497b511be412"
      },
      "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": [
        "loss = history.history[\"loss\"]\n",
        "val_loss = history.history[\"val_loss\"]\n",
        "epochs = range(1, len(loss) + 1)\n",
        "plt.plot(epochs, loss, \"bo\", label=\"Training loss\")\n",
        "plt.plot(epochs, val_loss, \"b\", label=\"Validation loss\")\n",
        "plt.title(\"Training and validation loss\")\n",
        "plt.xlabel(\"Epochs\")\n",
        "plt.ylabel(\"Loss\")\n",
        "plt.legend()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 141,
      "id": "026847cf",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "026847cf",
        "outputId": "46bd606a-573f-4e6d-abb3-b2f04837f1ca"
      },
      "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": [
        "plt.clf()\n",
        "acc = history.history[\"accuracy\"]\n",
        "val_acc = history.history[\"val_accuracy\"]\n",
        "plt.plot(epochs, acc, \"bo\", label=\"Training accuracy\")\n",
        "plt.plot(epochs, val_acc, \"b\", label=\"Validation accuracy\")\n",
        "plt.title(\"Training and validation accuracy\")\n",
        "plt.xlabel(\"Epochs\")\n",
        "plt.ylabel(\"Accuracy\")\n",
        "plt.legend()\n",
        "plt.show()\n",
        "     "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 111,
      "id": "27e457a9",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "27e457a9",
        "outputId": "a6a46d97-0495-42e3-b1a1-742e22878bfc"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[-0.85592158, -0.71324153, -1.17783182, ...,  0.        ,\n",
              "         0.        ,  0.        ],\n",
              "       [ 0.18187866,  0.46174038,  0.85784283, ...,  0.        ,\n",
              "         0.        ,  0.        ],\n",
              "       [-0.46119267, -1.88822344, -1.17783182, ...,  0.        ,\n",
              "         0.        ,  0.        ],\n",
              "       ...,\n",
              "       [-0.67347484,  0.46174038, -0.1599945 , ...,  0.        ,\n",
              "         0.        ,  0.        ],\n",
              "       [-0.34120702,  1.04923134,  0.85784283, ...,  0.        ,\n",
              "         0.        ,  0.        ],\n",
              "       [-0.54919633, -1.30073248, -1.17783182, ...,  0.        ,\n",
              "         0.        ,  0.        ]])"
            ]
          },
          "metadata": {},
          "execution_count": 111
        }
      ],
      "source": [
        "X_train"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 144,
      "id": "2fab4505",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 418
        },
        "id": "2fab4505",
        "outputId": "7fb3c89c-bd06-44d7-e2b0-cc05512525fc"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:493: FutureWarning: The feature names should match those that were passed during fit. Starting version 1.2, an error will be raised.\n",
            "Feature names seen at fit time, yet now missing:\n",
            "- Y_Quality\n",
            "\n",
            "  warnings.warn(message, FutureWarning)\n"
          ]
        },
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-144-783911b66b0a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mX_test_std\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mstdsc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtransform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtest_x\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/sklearn/preprocessing/_data.py\u001b[0m in \u001b[0;36mtransform\u001b[0;34m(self, X)\u001b[0m\n\u001b[1;32m    494\u001b[0m         \u001b[0mcheck_is_fitted\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    495\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 496\u001b[0;31m         X = self._validate_data(\n\u001b[0m\u001b[1;32m    497\u001b[0m             \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    498\u001b[0m             \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy\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/base.py\u001b[0m in \u001b[0;36m_validate_data\u001b[0;34m(self, X, y, reset, validate_separately, **check_params)\u001b[0m\n\u001b[1;32m    583\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    584\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mno_val_X\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mcheck_params\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"ensure_2d\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 585\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_check_n_features\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreset\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mreset\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    586\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    587\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mout\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/sklearn/base.py\u001b[0m in \u001b[0;36m_check_n_features\u001b[0;34m(self, X, reset)\u001b[0m\n\u001b[1;32m    398\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    399\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mn_features\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_features_in_\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 400\u001b[0;31m             raise ValueError(\n\u001b[0m\u001b[1;32m    401\u001b[0m                 \u001b[0;34mf\"X has {n_features} features, but {self.__class__.__name__} \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    402\u001b[0m                 \u001b[0;34mf\"is expecting {self.n_features_in_} features as input.\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: X has 2877 features, but MinMaxScaler is expecting 2878 features as input."
          ]
        }
      ],
      "source": [
        "X_test_std = stdsc.transform(test_x) "
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# a = pd.DataFrame(test_x.iloc[:,-1:])\n",
        "# test_x = pd.concat([test_x ,a ])"
      ],
      "metadata": {
        "id": "2afZOBbYFMpy"
      },
      "id": "2afZOBbYFMpy",
      "execution_count": 154,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 160,
      "id": "57a8de60",
      "metadata": {
        "scrolled": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "57a8de60",
        "outputId": "571abbac-43ab-4c8b-f07c-44ca9dce443f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "39/39 [==============================] - 1s 12ms/step\n"
          ]
        }
      ],
      "source": [
        "preds = model.predict(test_x)\n",
        "\n",
        "preds = preds.astype(int)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "submit = pd.read_csv('/content/drive/MyDrive/sample_submission.csv')  \n",
        "\n",
        "submit['Y_Class'] = preds \n",
        "submit.to_csv('smartFactory_submission17.csv', index=False)  "
      ],
      "metadata": {
        "id": "Cyfb21qOt4dn",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "outputId": "bc419ebd-b426-4d50-aec0-0a4fb4fcc9f7"
      },
      "execution_count": 161,
      "outputs": [
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-161-57e51a174d43>\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'smartFactory_submission17.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[0;32m/usr/local/lib/python3.8/dist-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__setitem__\u001b[0;34m(self, key, value)\u001b[0m\n\u001b[1;32m   3610\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3611\u001b[0m             \u001b[0;31m# set column\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3612\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_set_item\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\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   3613\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3614\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_setitem_slice\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mslice\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m_set_item\u001b[0;34m(self, key, value)\u001b[0m\n\u001b[1;32m   3782\u001b[0m         \u001b[0mensure\u001b[0m \u001b[0mhomogeneity\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3783\u001b[0m         \"\"\"\n\u001b[0;32m-> 3784\u001b[0;31m         \u001b[0mvalue\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_sanitize_column\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\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   3785\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3786\u001b[0m         if (\n",
            "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m_sanitize_column\u001b[0;34m(self, value)\u001b[0m\n\u001b[1;32m   4507\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4508\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mis_list_like\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4509\u001b[0;31m             \u001b[0mcom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrequire_length_match\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\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   4510\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0msanitize_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mallow_2d\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4511\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/pandas/core/common.py\u001b[0m in \u001b[0;36mrequire_length_match\u001b[0;34m(data, index)\u001b[0m\n\u001b[1;32m    529\u001b[0m     \"\"\"\n\u001b[1;32m    530\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 531\u001b[0;31m         raise ValueError(\n\u001b[0m\u001b[1;32m    532\u001b[0m             \u001b[0;34m\"Length of values \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    533\u001b[0m             \u001b[0;34mf\"({len(data)}) \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: Length of values (1240) does not match length of index (310)"
          ]
        }
      ],
      "id": "Cyfb21qOt4dn"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "14993522",
      "metadata": {
        "id": "14993522"
      },
      "outputs": [],
      "source": [
        "submit['Y_Class'] = preds\n",
        "     \n",
        "\n",
        "# submit.to_csv('C:/Users/user/OneDrive/open/mid_submission-MLP.csv', index=False)\n",
        "submit.to_csv('mid_submission-MLP.csv', index=False)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 3.optimization"
      ],
      "metadata": {
        "id": "yreBA4JMQPJ9"
      },
      "id": "yreBA4JMQPJ9"
    },
    {
      "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=1)\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",
        "\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": null,
      "id": "20a97788",
      "metadata": {
        "id": "20a97788"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "c9c15ce7",
      "metadata": {
        "id": "c9c15ce7"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "369e67a9",
      "metadata": {
        "id": "369e67a9"
      },
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "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"
    },
    "colab": {
      "provenance": []
    }
  },
  "nbformat": 4,
  "nbformat_minor": 5
}