predict_autogluon.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "izOj6i8CtafO",
        "outputId": "c5623ddf-fb38-4ee8-9829-e4128f655980"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: pip in /usr/local/lib/python3.10/dist-packages (23.3.1)\n",
            "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
            "\u001b[0mRequirement already satisfied: autogluon in /usr/local/lib/python3.10/dist-packages (1.0.0)\n",
            "Requirement already satisfied: autogluon.core==1.0.0 in /usr/local/lib/python3.10/dist-packages (from autogluon.core[all]==1.0.0->autogluon) (1.0.0)\n",
            "Requirement already satisfied: autogluon.features==1.0.0 in /usr/local/lib/python3.10/dist-packages (from autogluon) (1.0.0)\n",
            "Requirement already satisfied: autogluon.tabular==1.0.0 in /usr/local/lib/python3.10/dist-packages (from autogluon.tabular[all]==1.0.0->autogluon) (1.0.0)\n",
            "Requirement already satisfied: autogluon.multimodal==1.0.0 in /usr/local/lib/python3.10/dist-packages (from autogluon) (1.0.0)\n",
            "Requirement already satisfied: autogluon.timeseries==1.0.0 in /usr/local/lib/python3.10/dist-packages (from autogluon.timeseries[all]==1.0.0->autogluon) (1.0.0)\n",
            "Requirement already satisfied: numpy<1.29,>=1.21 in /usr/local/lib/python3.10/dist-packages (from autogluon.core==1.0.0->autogluon.core[all]==1.0.0->autogluon) (1.23.5)\n",
            "Requirement already satisfied: scipy<1.13,>=1.5.4 in /usr/local/lib/python3.10/dist-packages (from autogluon.core==1.0.0->autogluon.core[all]==1.0.0->autogluon) (1.11.4)\n",
            "Requirement already satisfied: scikit-learn<1.5,>=1.3.0 in /usr/local/lib/python3.10/dist-packages (from autogluon.core==1.0.0->autogluon.core[all]==1.0.0->autogluon) (1.3.2)\n",
            "Requirement already satisfied: networkx<4,>=3.0 in /usr/local/lib/python3.10/dist-packages (from autogluon.core==1.0.0->autogluon.core[all]==1.0.0->autogluon) (3.2.1)\n",
            "Requirement already satisfied: pandas<2.2.0,>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from autogluon.core==1.0.0->autogluon.core[all]==1.0.0->autogluon) (2.1.3)\n",
            "Requirement already satisfied: tqdm<5,>=4.38 in /usr/local/lib/python3.10/dist-packages (from autogluon.core==1.0.0->autogluon.core[all]==1.0.0->autogluon) (4.65.2)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from autogluon.core==1.0.0->autogluon.core[all]==1.0.0->autogluon) (2.28.2)\n",
            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.10/dist-packages (from autogluon.core==1.0.0->autogluon.core[all]==1.0.0->autogluon) (3.7.1)\n",
            "Requirement already satisfied: boto3<2,>=1.10 in /usr/local/lib/python3.10/dist-packages (from autogluon.core==1.0.0->autogluon.core[all]==1.0.0->autogluon) (1.33.6)\n",
            "Requirement already satisfied: autogluon.common==1.0.0 in /usr/local/lib/python3.10/dist-packages (from autogluon.core==1.0.0->autogluon.core[all]==1.0.0->autogluon) (1.0.0)\n",
            "Requirement already satisfied: ray<2.7,>=2.6.3 in /usr/local/lib/python3.10/dist-packages (from ray[default]<2.7,>=2.6.3; extra == \"all\"->autogluon.core[all]==1.0.0->autogluon) (2.6.3)\n",
            "Requirement already satisfied: async-timeout in /usr/local/lib/python3.10/dist-packages (from autogluon.core[all]==1.0.0->autogluon) (4.0.3)\n",
            "Requirement already satisfied: hyperopt<0.2.8,>=0.2.7 in /usr/local/lib/python3.10/dist-packages (from autogluon.core[all]==1.0.0->autogluon) (0.2.7)\n",
            "Requirement already satisfied: Pillow<11,>=10.0.1 in /usr/local/lib/python3.10/dist-packages (from autogluon.multimodal==1.0.0->autogluon) (10.0.1)\n",
            "Requirement already satisfied: torch<2.1,>=2.0 in /usr/local/lib/python3.10/dist-packages (from autogluon.multimodal==1.0.0->autogluon) (2.0.1)\n",
            "Requirement already satisfied: lightning<2.1,>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from autogluon.multimodal==1.0.0->autogluon) (2.0.9.post0)\n",
            "Requirement already satisfied: jsonschema<4.18,>=4.14 in /usr/local/lib/python3.10/dist-packages (from autogluon.multimodal==1.0.0->autogluon) (4.17.3)\n",
            "Requirement already satisfied: seqeval<1.3.0,>=1.2.2 in /usr/local/lib/python3.10/dist-packages (from autogluon.multimodal==1.0.0->autogluon) (1.2.2)\n",
            "Requirement already satisfied: evaluate<0.5.0,>=0.4.0 in /usr/local/lib/python3.10/dist-packages (from autogluon.multimodal==1.0.0->autogluon) (0.4.1)\n",
            "Requirement already satisfied: accelerate<0.22.0,>=0.21.0 in /usr/local/lib/python3.10/dist-packages (from autogluon.multimodal==1.0.0->autogluon) (0.21.0)\n",
            "Requirement already satisfied: transformers<4.32.0,>=4.31.0 in /usr/local/lib/python3.10/dist-packages (from transformers[sentencepiece]<4.32.0,>=4.31.0->autogluon.multimodal==1.0.0->autogluon) (4.31.0)\n",
            "Requirement already satisfied: timm<0.10.0,>=0.9.5 in /usr/local/lib/python3.10/dist-packages (from autogluon.multimodal==1.0.0->autogluon) (0.9.12)\n",
            "Requirement already satisfied: torchvision<0.16.0,>=0.14.0 in /usr/local/lib/python3.10/dist-packages (from autogluon.multimodal==1.0.0->autogluon) (0.15.2)\n",
            "Requirement already satisfied: scikit-image<0.21.0,>=0.19.1 in /usr/local/lib/python3.10/dist-packages (from autogluon.multimodal==1.0.0->autogluon) (0.19.3)\n",
            "Requirement already satisfied: text-unidecode<1.4,>=1.3 in /usr/local/lib/python3.10/dist-packages (from autogluon.multimodal==1.0.0->autogluon) (1.3)\n",
            "Requirement already satisfied: torchmetrics<1.2.0,>=1.0.0 in /usr/local/lib/python3.10/dist-packages (from autogluon.multimodal==1.0.0->autogluon) (1.1.2)\n",
            "Requirement already satisfied: nptyping<2.5.0,>=1.4.4 in /usr/local/lib/python3.10/dist-packages (from autogluon.multimodal==1.0.0->autogluon) (2.4.1)\n",
            "Requirement already satisfied: omegaconf<2.3.0,>=2.1.1 in /usr/local/lib/python3.10/dist-packages (from autogluon.multimodal==1.0.0->autogluon) (2.2.3)\n",
            "Requirement already satisfied: pytorch-metric-learning<2.0,>=1.3.0 in /usr/local/lib/python3.10/dist-packages (from autogluon.multimodal==1.0.0->autogluon) (1.7.3)\n",
            "Requirement already satisfied: nlpaug<1.2.0,>=1.1.10 in /usr/local/lib/python3.10/dist-packages (from autogluon.multimodal==1.0.0->autogluon) (1.1.11)\n",
            "Requirement already satisfied: nltk<4.0.0,>=3.4.5 in /usr/local/lib/python3.10/dist-packages (from autogluon.multimodal==1.0.0->autogluon) (3.8.1)\n",
            "Requirement already satisfied: openmim<0.4.0,>=0.3.7 in /usr/local/lib/python3.10/dist-packages (from autogluon.multimodal==1.0.0->autogluon) (0.3.9)\n",
            "Requirement already satisfied: defusedxml<0.7.2,>=0.7.1 in /usr/local/lib/python3.10/dist-packages (from autogluon.multimodal==1.0.0->autogluon) (0.7.1)\n",
            "Requirement already satisfied: jinja2<3.2,>=3.0.3 in /usr/local/lib/python3.10/dist-packages (from autogluon.multimodal==1.0.0->autogluon) (3.1.2)\n",
            "Requirement already satisfied: tensorboard<3,>=2.9 in /usr/local/lib/python3.10/dist-packages (from autogluon.multimodal==1.0.0->autogluon) (2.14.1)\n",
            "Requirement already satisfied: pytesseract<0.3.11,>=0.3.9 in /usr/local/lib/python3.10/dist-packages (from autogluon.multimodal==1.0.0->autogluon) (0.3.10)\n",
            "Requirement already satisfied: nvidia-ml-py3==7.352.0 in /usr/local/lib/python3.10/dist-packages (from autogluon.multimodal==1.0.0->autogluon) (7.352.0)\n",
            "Requirement already satisfied: xgboost<2.1,>=1.6 in /usr/local/lib/python3.10/dist-packages (from autogluon.tabular[all]==1.0.0->autogluon) (2.0.2)\n",
            "Requirement already satisfied: fastai<2.8,>=2.3.1 in /usr/local/lib/python3.10/dist-packages (from autogluon.tabular[all]==1.0.0->autogluon) (2.7.13)\n",
            "Requirement already satisfied: lightgbm<4.2,>=3.3 in /usr/local/lib/python3.10/dist-packages (from autogluon.tabular[all]==1.0.0->autogluon) (4.1.0)\n",
            "Requirement already satisfied: catboost<1.3,>=1.1 in /usr/local/lib/python3.10/dist-packages (from autogluon.tabular[all]==1.0.0->autogluon) (1.2.2)\n",
            "Requirement already satisfied: joblib<2,>=1.1 in /usr/local/lib/python3.10/dist-packages (from autogluon.timeseries==1.0.0->autogluon.timeseries[all]==1.0.0->autogluon) (1.3.2)\n",
            "Requirement already satisfied: pytorch-lightning<2.1,>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from autogluon.timeseries==1.0.0->autogluon.timeseries[all]==1.0.0->autogluon) (2.0.9.post0)\n",
            "Requirement already satisfied: statsmodels<0.15,>=0.13.0 in /usr/local/lib/python3.10/dist-packages (from autogluon.timeseries==1.0.0->autogluon.timeseries[all]==1.0.0->autogluon) (0.14.0)\n",
            "Requirement already satisfied: gluonts<0.15,>=0.14.0 in /usr/local/lib/python3.10/dist-packages (from autogluon.timeseries==1.0.0->autogluon.timeseries[all]==1.0.0->autogluon) (0.14.2)\n",
            "Requirement already satisfied: statsforecast<1.5,>=1.4.0 in /usr/local/lib/python3.10/dist-packages (from autogluon.timeseries==1.0.0->autogluon.timeseries[all]==1.0.0->autogluon) (1.4.0)\n",
            "Requirement already satisfied: mlforecast<0.10.1,>=0.10.0 in /usr/local/lib/python3.10/dist-packages (from autogluon.timeseries==1.0.0->autogluon.timeseries[all]==1.0.0->autogluon) (0.10.0)\n",
            "Requirement already satisfied: utilsforecast<0.0.11,>=0.0.10 in /usr/local/lib/python3.10/dist-packages (from autogluon.timeseries==1.0.0->autogluon.timeseries[all]==1.0.0->autogluon) (0.0.10)\n",
            "Requirement already satisfied: orjson~=3.9 in /usr/local/lib/python3.10/dist-packages (from autogluon.timeseries==1.0.0->autogluon.timeseries[all]==1.0.0->autogluon) (3.9.10)\n",
            "Requirement already satisfied: psutil<6,>=5.7.3 in /usr/local/lib/python3.10/dist-packages (from autogluon.common==1.0.0->autogluon.core==1.0.0->autogluon.core[all]==1.0.0->autogluon) (5.9.5)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.10/dist-packages (from autogluon.common==1.0.0->autogluon.core==1.0.0->autogluon.core[all]==1.0.0->autogluon) (60.2.0)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from accelerate<0.22.0,>=0.21.0->autogluon.multimodal==1.0.0->autogluon) (23.2)\n",
            "Requirement already satisfied: pyyaml in /usr/local/lib/python3.10/dist-packages (from accelerate<0.22.0,>=0.21.0->autogluon.multimodal==1.0.0->autogluon) (6.0.1)\n",
            "Requirement already satisfied: botocore<1.34.0,>=1.33.6 in /usr/local/lib/python3.10/dist-packages (from boto3<2,>=1.10->autogluon.core==1.0.0->autogluon.core[all]==1.0.0->autogluon) (1.33.6)\n",
            "Requirement already satisfied: jmespath<2.0.0,>=0.7.1 in /usr/local/lib/python3.10/dist-packages (from boto3<2,>=1.10->autogluon.core==1.0.0->autogluon.core[all]==1.0.0->autogluon) (0.10.0)\n",
            "Requirement already satisfied: s3transfer<0.9.0,>=0.8.2 in /usr/local/lib/python3.10/dist-packages (from boto3<2,>=1.10->autogluon.core==1.0.0->autogluon.core[all]==1.0.0->autogluon) (0.8.2)\n",
            "Requirement already satisfied: graphviz in /usr/local/lib/python3.10/dist-packages (from catboost<1.3,>=1.1->autogluon.tabular[all]==1.0.0->autogluon) (0.20.1)\n",
            "Requirement already satisfied: plotly in /usr/local/lib/python3.10/dist-packages (from catboost<1.3,>=1.1->autogluon.tabular[all]==1.0.0->autogluon) (5.15.0)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.10/dist-packages (from catboost<1.3,>=1.1->autogluon.tabular[all]==1.0.0->autogluon) (1.16.0)\n",
            "Requirement already satisfied: datasets>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from evaluate<0.5.0,>=0.4.0->autogluon.multimodal==1.0.0->autogluon) (2.15.0)\n",
            "Requirement already satisfied: dill in /usr/local/lib/python3.10/dist-packages (from evaluate<0.5.0,>=0.4.0->autogluon.multimodal==1.0.0->autogluon) (0.3.7)\n",
            "Requirement already satisfied: xxhash in /usr/local/lib/python3.10/dist-packages (from evaluate<0.5.0,>=0.4.0->autogluon.multimodal==1.0.0->autogluon) (3.4.1)\n",
            "Requirement already satisfied: multiprocess in /usr/local/lib/python3.10/dist-packages (from evaluate<0.5.0,>=0.4.0->autogluon.multimodal==1.0.0->autogluon) (0.70.15)\n",
            "Requirement already satisfied: fsspec>=2021.05.0 in /usr/local/lib/python3.10/dist-packages (from fsspec[http]>=2021.05.0->evaluate<0.5.0,>=0.4.0->autogluon.multimodal==1.0.0->autogluon) (2023.6.0)\n",
            "Requirement already satisfied: huggingface-hub>=0.7.0 in /usr/local/lib/python3.10/dist-packages (from evaluate<0.5.0,>=0.4.0->autogluon.multimodal==1.0.0->autogluon) (0.19.4)\n",
            "Requirement already satisfied: responses<0.19 in /usr/local/lib/python3.10/dist-packages (from evaluate<0.5.0,>=0.4.0->autogluon.multimodal==1.0.0->autogluon) (0.18.0)\n",
            "Requirement already satisfied: pip in /usr/local/lib/python3.10/dist-packages (from fastai<2.8,>=2.3.1->autogluon.tabular[all]==1.0.0->autogluon) (23.3.1)\n",
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            "Requirement already satisfied: nvidia-cuda-nvrtc-cu11==11.7.99 in /usr/local/lib/python3.10/dist-packages (from torch<2.1,>=2.0->autogluon.multimodal==1.0.0->autogluon) (11.7.99)\n",
            "Requirement already satisfied: nvidia-cuda-runtime-cu11==11.7.99 in /usr/local/lib/python3.10/dist-packages (from torch<2.1,>=2.0->autogluon.multimodal==1.0.0->autogluon) (11.7.99)\n",
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            "Requirement already satisfied: nvidia-cudnn-cu11==8.5.0.96 in /usr/local/lib/python3.10/dist-packages (from torch<2.1,>=2.0->autogluon.multimodal==1.0.0->autogluon) (8.5.0.96)\n",
            "Requirement already satisfied: nvidia-cublas-cu11==11.10.3.66 in /usr/local/lib/python3.10/dist-packages (from torch<2.1,>=2.0->autogluon.multimodal==1.0.0->autogluon) (11.10.3.66)\n",
            "Requirement already satisfied: nvidia-cufft-cu11==10.9.0.58 in /usr/local/lib/python3.10/dist-packages (from torch<2.1,>=2.0->autogluon.multimodal==1.0.0->autogluon) (10.9.0.58)\n",
            "Requirement already satisfied: nvidia-curand-cu11==10.2.10.91 in /usr/local/lib/python3.10/dist-packages (from torch<2.1,>=2.0->autogluon.multimodal==1.0.0->autogluon) (10.2.10.91)\n",
            "Requirement already satisfied: nvidia-cusolver-cu11==11.4.0.1 in /usr/local/lib/python3.10/dist-packages (from torch<2.1,>=2.0->autogluon.multimodal==1.0.0->autogluon) (11.4.0.1)\n",
            "Requirement already satisfied: nvidia-cusparse-cu11==11.7.4.91 in /usr/local/lib/python3.10/dist-packages (from torch<2.1,>=2.0->autogluon.multimodal==1.0.0->autogluon) (11.7.4.91)\n",
            "Requirement already satisfied: nvidia-nccl-cu11==2.14.3 in /usr/local/lib/python3.10/dist-packages (from torch<2.1,>=2.0->autogluon.multimodal==1.0.0->autogluon) (2.14.3)\n",
            "Requirement already satisfied: nvidia-nvtx-cu11==11.7.91 in /usr/local/lib/python3.10/dist-packages (from torch<2.1,>=2.0->autogluon.multimodal==1.0.0->autogluon) (11.7.91)\n",
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            "Requirement already satisfied: wheel in /usr/local/lib/python3.10/dist-packages (from nvidia-cublas-cu11==11.10.3.66->torch<2.1,>=2.0->autogluon.multimodal==1.0.0->autogluon) (0.42.0)\n",
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            "Requirement already satisfied: cffi>=1.12 in /usr/local/lib/python3.10/dist-packages (from cryptography>=2.6.0->aliyun-python-sdk-core>=2.13.12->oss2~=2.17.0->openxlab->opendatalab->openmim<0.4.0,>=0.3.7->autogluon.multimodal==1.0.0->autogluon) (1.16.0)\n",
            "Requirement already satisfied: pycparser in /usr/local/lib/python3.10/dist-packages (from cffi>=1.12->cryptography>=2.6.0->aliyun-python-sdk-core>=2.13.12->oss2~=2.17.0->openxlab->opendatalab->openmim<0.4.0,>=0.3.7->autogluon.multimodal==1.0.0->autogluon) (2.21)\n",
            "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
            "\u001b[0m"
          ]
        }
      ],
      "source": [
        "!python -m pip install --upgrade pip\n",
        "!python -m pip install autogluon"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uBuligMjyFWN",
        "outputId": "6ecfca41-f31f-4677-b1f3-ec621ca1d0c9"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "bxg6ISBXyFbr"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import random\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import datetime\n",
        "from sklearn.preprocessing import LabelEncoder"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "XcTbjH62yFhw"
      },
      "outputs": [],
      "source": [
        "train_df = pd.read_csv('/content/drive/MyDrive/train.csv')\n",
        "test_df = pd.read_csv('/content/drive/MyDrive/test.csv')\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "IS0PapXlyFm2",
        "outputId": "3d860458-4537-4e4e-9253-d234f69fbd4e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                    ID   timestamp item corporation location  supply(kg)  \\\n",
              "0      TG_A_J_20190101  2019-01-01   TG           A        J         0.0   \n",
              "1      TG_A_J_20190102  2019-01-02   TG           A        J         0.0   \n",
              "2      TG_A_J_20190103  2019-01-03   TG           A        J     60601.0   \n",
              "3      TG_A_J_20190104  2019-01-04   TG           A        J     25000.0   \n",
              "4      TG_A_J_20190105  2019-01-05   TG           A        J     32352.0   \n",
              "...                ...         ...  ...         ...      ...         ...   \n",
              "59392  RD_F_J_20230227  2023-02-27   RD           F        J    452440.0   \n",
              "59393  RD_F_J_20230228  2023-02-28   RD           F        J    421980.0   \n",
              "59394  RD_F_J_20230301  2023-03-01   RD           F        J    382980.0   \n",
              "59395  RD_F_J_20230302  2023-03-02   RD           F        J    477220.0   \n",
              "59396  RD_F_J_20230303  2023-03-03   RD           F        J    427520.0   \n",
              "\n",
              "       price(원/kg)  \n",
              "0              0.0  \n",
              "1              0.0  \n",
              "2           1728.0  \n",
              "3           1408.0  \n",
              "4           1250.0  \n",
              "...            ...  \n",
              "59392        468.0  \n",
              "59393        531.0  \n",
              "59394        574.0  \n",
              "59395        523.0  \n",
              "59396        529.0  \n",
              "\n",
              "[59397 rows x 7 columns]"
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              "      <th></th>\n",
              "      <th>ID</th>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>TG_A_J_20190102</td>\n",
              "      <td>2019-01-02</td>\n",
              "      <td>TG</td>\n",
              "      <td>A</td>\n",
              "      <td>J</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>TG_A_J_20190103</td>\n",
              "      <td>2019-01-03</td>\n",
              "      <td>TG</td>\n",
              "      <td>A</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>TG_A_J_20190104</td>\n",
              "      <td>2019-01-04</td>\n",
              "      <td>TG</td>\n",
              "      <td>A</td>\n",
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              "      <td>25000.0</td>\n",
              "      <td>1408.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>TG_A_J_20190105</td>\n",
              "      <td>2019-01-05</td>\n",
              "      <td>TG</td>\n",
              "      <td>A</td>\n",
              "      <td>J</td>\n",
              "      <td>32352.0</td>\n",
              "      <td>1250.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>59392</th>\n",
              "      <td>RD_F_J_20230227</td>\n",
              "      <td>2023-02-27</td>\n",
              "      <td>RD</td>\n",
              "      <td>F</td>\n",
              "      <td>J</td>\n",
              "      <td>452440.0</td>\n",
              "      <td>468.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>59393</th>\n",
              "      <td>RD_F_J_20230228</td>\n",
              "      <td>2023-02-28</td>\n",
              "      <td>RD</td>\n",
              "      <td>F</td>\n",
              "      <td>J</td>\n",
              "      <td>421980.0</td>\n",
              "      <td>531.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>59394</th>\n",
              "      <td>RD_F_J_20230301</td>\n",
              "      <td>2023-03-01</td>\n",
              "      <td>RD</td>\n",
              "      <td>F</td>\n",
              "      <td>J</td>\n",
              "      <td>382980.0</td>\n",
              "      <td>574.0</td>\n",
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              "    <tr>\n",
              "      <th>59395</th>\n",
              "      <td>RD_F_J_20230302</td>\n",
              "      <td>2023-03-02</td>\n",
              "      <td>RD</td>\n",
              "      <td>F</td>\n",
              "      <td>J</td>\n",
              "      <td>477220.0</td>\n",
              "      <td>523.0</td>\n",
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              "    <tr>\n",
              "      <th>59396</th>\n",
              "      <td>RD_F_J_20230303</td>\n",
              "      <td>2023-03-03</td>\n",
              "      <td>RD</td>\n",
              "      <td>F</td>\n",
              "      <td>J</td>\n",
              "      <td>427520.0</td>\n",
              "      <td>529.0</td>\n",
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              "\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-ac3aaca7-68ea-48cf-aa08-979a6c3fb7bf 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-ac3aaca7-68ea-48cf-aa08-979a6c3fb7bf');\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",
              "\n",
              "\n",
              "<div id=\"df-869d4b72-d72a-46a0-a632-618708df3ce3\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-869d4b72-d72a-46a0-a632-618708df3ce3')\"\n",
              "            title=\"Suggest charts\"\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",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-869d4b72-d72a-46a0-a632-618708df3ce3 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ],
      "source": [
        "train_df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "ZHFUR90iyFtT",
        "outputId": "e37457f7-2791-4ec4-bc6e-0add52444300"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                   ID   timestamp item corporation location\n",
              "0     TG_A_J_20230304  2023-03-04   TG           A        J\n",
              "1     TG_A_J_20230305  2023-03-05   TG           A        J\n",
              "2     TG_A_J_20230306  2023-03-06   TG           A        J\n",
              "3     TG_A_J_20230307  2023-03-07   TG           A        J\n",
              "4     TG_A_J_20230308  2023-03-08   TG           A        J\n",
              "...               ...         ...  ...         ...      ...\n",
              "1087  RD_F_J_20230327  2023-03-27   RD           F        J\n",
              "1088  RD_F_J_20230328  2023-03-28   RD           F        J\n",
              "1089  RD_F_J_20230329  2023-03-29   RD           F        J\n",
              "1090  RD_F_J_20230330  2023-03-30   RD           F        J\n",
              "1091  RD_F_J_20230331  2023-03-31   RD           F        J\n",
              "\n",
              "[1092 rows x 5 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-0c259ee5-3efa-4b5e-8a0d-099f3f709984\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>ID</th>\n",
              "      <th>timestamp</th>\n",
              "      <th>item</th>\n",
              "      <th>corporation</th>\n",
              "      <th>location</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>TG_A_J_20230304</td>\n",
              "      <td>2023-03-04</td>\n",
              "      <td>TG</td>\n",
              "      <td>A</td>\n",
              "      <td>J</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>TG_A_J_20230305</td>\n",
              "      <td>2023-03-05</td>\n",
              "      <td>TG</td>\n",
              "      <td>A</td>\n",
              "      <td>J</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>TG_A_J_20230306</td>\n",
              "      <td>2023-03-06</td>\n",
              "      <td>TG</td>\n",
              "      <td>A</td>\n",
              "      <td>J</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>TG_A_J_20230307</td>\n",
              "      <td>2023-03-07</td>\n",
              "      <td>TG</td>\n",
              "      <td>A</td>\n",
              "      <td>J</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>TG_A_J_20230308</td>\n",
              "      <td>2023-03-08</td>\n",
              "      <td>TG</td>\n",
              "      <td>A</td>\n",
              "      <td>J</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1087</th>\n",
              "      <td>RD_F_J_20230327</td>\n",
              "      <td>2023-03-27</td>\n",
              "      <td>RD</td>\n",
              "      <td>F</td>\n",
              "      <td>J</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1088</th>\n",
              "      <td>RD_F_J_20230328</td>\n",
              "      <td>2023-03-28</td>\n",
              "      <td>RD</td>\n",
              "      <td>F</td>\n",
              "      <td>J</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1089</th>\n",
              "      <td>RD_F_J_20230329</td>\n",
              "      <td>2023-03-29</td>\n",
              "      <td>RD</td>\n",
              "      <td>F</td>\n",
              "      <td>J</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1090</th>\n",
              "      <td>RD_F_J_20230330</td>\n",
              "      <td>2023-03-30</td>\n",
              "      <td>RD</td>\n",
              "      <td>F</td>\n",
              "      <td>J</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1091</th>\n",
              "      <td>RD_F_J_20230331</td>\n",
              "      <td>2023-03-31</td>\n",
              "      <td>RD</td>\n",
              "      <td>F</td>\n",
              "      <td>J</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1092 rows × 5 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0c259ee5-3efa-4b5e-8a0d-099f3f709984')\"\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 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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-0c259ee5-3efa-4b5e-8a0d-099f3f709984 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-0c259ee5-3efa-4b5e-8a0d-099f3f709984');\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",
              "\n",
              "\n",
              "<div id=\"df-1dc35ae4-3c8e-4345-98a0-ebae3ae96f3b\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-1dc35ae4-3c8e-4345-98a0-ebae3ae96f3b')\"\n",
              "            title=\"Suggest charts\"\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",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-1dc35ae4-3c8e-4345-98a0-ebae3ae96f3b button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ],
      "source": [
        "test_df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "mL5M9ACuyFyr"
      },
      "outputs": [],
      "source": [
        "# 데이터 전처리\n",
        "# 변수 별 확인하기 위해\n",
        "train_df['item_id'] = train_df['ID'].str[:6]\n",
        "\n",
        "\n",
        "# 시계열로 변환\n",
        "\n",
        "train_df['timestamp'] = pd.to_datetime(train_df['timestamp'])\n",
        "test_df['timestamp'] = pd.to_datetime(test_df['timestamp'])\n",
        "\n",
        "\n",
        "# 날짜 데이터 분리\n",
        "train_df['year'] = train_df['timestamp'].dt.year\n",
        "train_df['month'] = train_df['timestamp'].dt.month\n",
        "train_df['day'] = train_df['timestamp'].dt.day\n",
        "train_df['weekday'] = train_df['timestamp'].dt.weekday\n",
        "\n",
        "test_df['year'] = test_df['timestamp'].dt.year\n",
        "test_df['month'] = test_df['timestamp'].dt.month\n",
        "test_df['day'] = test_df['timestamp'].dt.day\n",
        "test_df['weekday'] = test_df['timestamp'].dt.weekday"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "-54M_NNu24Bd",
        "outputId": "b50bd2e3-0ddc-4c6a-8b6f-0b38fec7619f"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                    ID  timestamp item corporation location  supply(kg)  \\\n",
              "0      TG_A_J_20190101 2019-01-01   TG           A        J         0.0   \n",
              "1      TG_A_J_20190102 2019-01-02   TG           A        J         0.0   \n",
              "2      TG_A_J_20190103 2019-01-03   TG           A        J     60601.0   \n",
              "3      TG_A_J_20190104 2019-01-04   TG           A        J     25000.0   \n",
              "4      TG_A_J_20190105 2019-01-05   TG           A        J     32352.0   \n",
              "...                ...        ...  ...         ...      ...         ...   \n",
              "59392  RD_F_J_20230227 2023-02-27   RD           F        J    452440.0   \n",
              "59393  RD_F_J_20230228 2023-02-28   RD           F        J    421980.0   \n",
              "59394  RD_F_J_20230301 2023-03-01   RD           F        J    382980.0   \n",
              "59395  RD_F_J_20230302 2023-03-02   RD           F        J    477220.0   \n",
              "59396  RD_F_J_20230303 2023-03-03   RD           F        J    427520.0   \n",
              "\n",
              "       price(원/kg) item_id  year  month  day  weekday  \n",
              "0              0.0  TG_A_J  2019      1    1        1  \n",
              "1              0.0  TG_A_J  2019      1    2        2  \n",
              "2           1728.0  TG_A_J  2019      1    3        3  \n",
              "3           1408.0  TG_A_J  2019      1    4        4  \n",
              "4           1250.0  TG_A_J  2019      1    5        5  \n",
              "...            ...     ...   ...    ...  ...      ...  \n",
              "59392        468.0  RD_F_J  2023      2   27        0  \n",
              "59393        531.0  RD_F_J  2023      2   28        1  \n",
              "59394        574.0  RD_F_J  2023      3    1        2  \n",
              "59395        523.0  RD_F_J  2023      3    2        3  \n",
              "59396        529.0  RD_F_J  2023      3    3        4  \n",
              "\n",
              "[59397 rows x 12 columns]"
            ],
            "text/html": [
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              "      <th>0</th>\n",
              "      <td>TG_A_J_20190101</td>\n",
              "      <td>2019-01-01</td>\n",
              "      <td>TG</td>\n",
              "      <td>A</td>\n",
              "      <td>J</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>TG_A_J</td>\n",
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              "      <th>2</th>\n",
              "      <td>TG_A_J_20190103</td>\n",
              "      <td>2019-01-03</td>\n",
              "      <td>TG</td>\n",
              "      <td>A</td>\n",
              "      <td>J</td>\n",
              "      <td>60601.0</td>\n",
              "      <td>1728.0</td>\n",
              "      <td>TG_A_J</td>\n",
              "      <td>2019</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>TG_A_J_20190104</td>\n",
              "      <td>2019-01-04</td>\n",
              "      <td>TG</td>\n",
              "      <td>A</td>\n",
              "      <td>J</td>\n",
              "      <td>25000.0</td>\n",
              "      <td>1408.0</td>\n",
              "      <td>TG_A_J</td>\n",
              "      <td>2019</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>TG_A_J_20190105</td>\n",
              "      <td>2019-01-05</td>\n",
              "      <td>TG</td>\n",
              "      <td>A</td>\n",
              "      <td>J</td>\n",
              "      <td>32352.0</td>\n",
              "      <td>1250.0</td>\n",
              "      <td>TG_A_J</td>\n",
              "      <td>2019</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>59392</th>\n",
              "      <td>RD_F_J_20230227</td>\n",
              "      <td>2023-02-27</td>\n",
              "      <td>RD</td>\n",
              "      <td>F</td>\n",
              "      <td>J</td>\n",
              "      <td>452440.0</td>\n",
              "      <td>468.0</td>\n",
              "      <td>RD_F_J</td>\n",
              "      <td>2023</td>\n",
              "      <td>2</td>\n",
              "      <td>27</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>59393</th>\n",
              "      <td>RD_F_J_20230228</td>\n",
              "      <td>2023-02-28</td>\n",
              "      <td>RD</td>\n",
              "      <td>F</td>\n",
              "      <td>J</td>\n",
              "      <td>421980.0</td>\n",
              "      <td>531.0</td>\n",
              "      <td>RD_F_J</td>\n",
              "      <td>2023</td>\n",
              "      <td>2</td>\n",
              "      <td>28</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>59394</th>\n",
              "      <td>RD_F_J_20230301</td>\n",
              "      <td>2023-03-01</td>\n",
              "      <td>RD</td>\n",
              "      <td>F</td>\n",
              "      <td>J</td>\n",
              "      <td>382980.0</td>\n",
              "      <td>574.0</td>\n",
              "      <td>RD_F_J</td>\n",
              "      <td>2023</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
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              "    <tr>\n",
              "      <th>59395</th>\n",
              "      <td>RD_F_J_20230302</td>\n",
              "      <td>2023-03-02</td>\n",
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              "      <td>J</td>\n",
              "      <td>477220.0</td>\n",
              "      <td>523.0</td>\n",
              "      <td>RD_F_J</td>\n",
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              "      <td>3</td>\n",
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              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>59396</th>\n",
              "      <td>RD_F_J_20230303</td>\n",
              "      <td>2023-03-03</td>\n",
              "      <td>RD</td>\n",
              "      <td>F</td>\n",
              "      <td>J</td>\n",
              "      <td>427520.0</td>\n",
              "      <td>529.0</td>\n",
              "      <td>RD_F_J</td>\n",
              "      <td>2023</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>59397 rows × 12 columns</p>\n",
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              "        document.querySelector('#df-2e41d55e-dcf9-4430-9277-821e1bbce6d5 button.colab-df-convert');\n",
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              "      async function convertToInteractive(key) {\n",
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              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "          + ' to learn more about interactive tables.';\n",
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              "\n",
              "  .colab-df-quickchart:hover {\n",
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              "    fill: var(--button-hover-fill-color);\n",
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              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
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              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
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              "\n",
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              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-251bb40d-5cf1-4fea-96b7-8760df499143 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ],
      "source": [
        "train_df"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "_CVNsQLBFsYS",
        "outputId": "76a3369e-4b36-4fb5-b183-ce62dec0b71a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                   ID  timestamp item corporation location  year  month  day  \\\n",
              "0     TG_A_J_20230304 2023-03-04   TG           A        J  2023      3    4   \n",
              "1     TG_A_J_20230305 2023-03-05   TG           A        J  2023      3    5   \n",
              "2     TG_A_J_20230306 2023-03-06   TG           A        J  2023      3    6   \n",
              "3     TG_A_J_20230307 2023-03-07   TG           A        J  2023      3    7   \n",
              "4     TG_A_J_20230308 2023-03-08   TG           A        J  2023      3    8   \n",
              "...               ...        ...  ...         ...      ...   ...    ...  ...   \n",
              "1087  RD_F_J_20230327 2023-03-27   RD           F        J  2023      3   27   \n",
              "1088  RD_F_J_20230328 2023-03-28   RD           F        J  2023      3   28   \n",
              "1089  RD_F_J_20230329 2023-03-29   RD           F        J  2023      3   29   \n",
              "1090  RD_F_J_20230330 2023-03-30   RD           F        J  2023      3   30   \n",
              "1091  RD_F_J_20230331 2023-03-31   RD           F        J  2023      3   31   \n",
              "\n",
              "      weekday  \n",
              "0           5  \n",
              "1           6  \n",
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              "3           1  \n",
              "4           2  \n",
              "...       ...  \n",
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              "1089        2  \n",
              "1090        3  \n",
              "1091        4  \n",
              "\n",
              "[1092 rows x 9 columns]"
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              "      <td>TG_A_J_20230304</td>\n",
              "      <td>2023-03-04</td>\n",
              "      <td>TG</td>\n",
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              "      <td>2023</td>\n",
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              "      <td>5</td>\n",
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              "      <th>1</th>\n",
              "      <td>TG_A_J_20230305</td>\n",
              "      <td>2023-03-05</td>\n",
              "      <td>TG</td>\n",
              "      <td>A</td>\n",
              "      <td>J</td>\n",
              "      <td>2023</td>\n",
              "      <td>3</td>\n",
              "      <td>5</td>\n",
              "      <td>6</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>TG_A_J_20230306</td>\n",
              "      <td>2023-03-06</td>\n",
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              "      <td>A</td>\n",
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              "      <td>3</td>\n",
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              "      <td>TG_A_J_20230307</td>\n",
              "      <td>2023-03-07</td>\n",
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              "      <td>A</td>\n",
              "      <td>J</td>\n",
              "      <td>2023</td>\n",
              "      <td>3</td>\n",
              "      <td>7</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>TG_A_J_20230308</td>\n",
              "      <td>2023-03-08</td>\n",
              "      <td>TG</td>\n",
              "      <td>A</td>\n",
              "      <td>J</td>\n",
              "      <td>2023</td>\n",
              "      <td>3</td>\n",
              "      <td>8</td>\n",
              "      <td>2</td>\n",
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              "      <th>...</th>\n",
              "      <td>...</td>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1087</th>\n",
              "      <td>RD_F_J_20230327</td>\n",
              "      <td>2023-03-27</td>\n",
              "      <td>RD</td>\n",
              "      <td>F</td>\n",
              "      <td>J</td>\n",
              "      <td>2023</td>\n",
              "      <td>3</td>\n",
              "      <td>27</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1088</th>\n",
              "      <td>RD_F_J_20230328</td>\n",
              "      <td>2023-03-28</td>\n",
              "      <td>RD</td>\n",
              "      <td>F</td>\n",
              "      <td>J</td>\n",
              "      <td>2023</td>\n",
              "      <td>3</td>\n",
              "      <td>28</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1089</th>\n",
              "      <td>RD_F_J_20230329</td>\n",
              "      <td>2023-03-29</td>\n",
              "      <td>RD</td>\n",
              "      <td>F</td>\n",
              "      <td>J</td>\n",
              "      <td>2023</td>\n",
              "      <td>3</td>\n",
              "      <td>29</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1090</th>\n",
              "      <td>RD_F_J_20230330</td>\n",
              "      <td>2023-03-30</td>\n",
              "      <td>RD</td>\n",
              "      <td>F</td>\n",
              "      <td>J</td>\n",
              "      <td>2023</td>\n",
              "      <td>3</td>\n",
              "      <td>30</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1091</th>\n",
              "      <td>RD_F_J_20230331</td>\n",
              "      <td>2023-03-31</td>\n",
              "      <td>RD</td>\n",
              "      <td>F</td>\n",
              "      <td>J</td>\n",
              "      <td>2023</td>\n",
              "      <td>3</td>\n",
              "      <td>31</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1092 rows × 9 columns</p>\n",
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              "    async function quickchart(key) {\n",
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              "        const charts = await google.colab.kernel.invokeFunction(\n",
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              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "        document.querySelector('#df-8c00dbc9-0ae0-400c-8406-2f22bf71acf7 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "    </div>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_df['item_id'] = test_df['item'] + '_' + test_df['corporation'] + '_' + test_df['location']"
      ],
      "metadata": {
        "id": "67eZo170F9eZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "RnynfOtg3U1t"
      },
      "outputs": [],
      "source": [
        "# 모델을 위한 데이터준비\n",
        "from autogluon.timeseries import TimeSeriesDataFrame, TimeSeriesPredictor\n",
        "tr_time_df = TimeSeriesDataFrame(train_df.drop(columns=['ID','corporation','location','item']))\n",
        "test_time_df = TimeSeriesDataFrame(test_df.drop(columns=['ID','corporation','location', 'item']))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "id": "DgeFJJCO368-",
        "outputId": "b81091dc-7b43-4faa-f2b3-3db2413d2c80"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                    supply(kg)  price(원/kg)  year  month  day  weekday\n",
              "item_id timestamp                                                     \n",
              "TG_A_J  2019-01-01         0.0          0.0  2019      1    1        1\n",
              "        2019-01-02         0.0          0.0  2019      1    2        2\n",
              "        2019-01-03     60601.0       1728.0  2019      1    3        3\n",
              "        2019-01-04     25000.0       1408.0  2019      1    4        4\n",
              "        2019-01-05     32352.0       1250.0  2019      1    5        5\n",
              "...                        ...          ...   ...    ...  ...      ...\n",
              "RD_F_J  2023-02-27    452440.0        468.0  2023      2   27        0\n",
              "        2023-02-28    421980.0        531.0  2023      2   28        1\n",
              "        2023-03-01    382980.0        574.0  2023      3    1        2\n",
              "        2023-03-02    477220.0        523.0  2023      3    2        3\n",
              "        2023-03-03    427520.0        529.0  2023      3    3        4\n",
              "\n",
              "[59397 rows x 6 columns]"
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              "    <tr>\n",
              "      <th rowspan=\"5\" valign=\"top\">TG_A_J</th>\n",
              "      <th>2019-01-01</th>\n",
              "      <td>0.0</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-01-02</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2019</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-01-03</th>\n",
              "      <td>60601.0</td>\n",
              "      <td>1728.0</td>\n",
              "      <td>2019</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-01-04</th>\n",
              "      <td>25000.0</td>\n",
              "      <td>1408.0</td>\n",
              "      <td>2019</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-01-05</th>\n",
              "      <td>32352.0</td>\n",
              "      <td>1250.0</td>\n",
              "      <td>2019</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"5\" valign=\"top\">RD_F_J</th>\n",
              "      <th>2023-02-27</th>\n",
              "      <td>452440.0</td>\n",
              "      <td>468.0</td>\n",
              "      <td>2023</td>\n",
              "      <td>2</td>\n",
              "      <td>27</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2023-02-28</th>\n",
              "      <td>421980.0</td>\n",
              "      <td>531.0</td>\n",
              "      <td>2023</td>\n",
              "      <td>2</td>\n",
              "      <td>28</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2023-03-01</th>\n",
              "      <td>382980.0</td>\n",
              "      <td>574.0</td>\n",
              "      <td>2023</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2023-03-02</th>\n",
              "      <td>477220.0</td>\n",
              "      <td>523.0</td>\n",
              "      <td>2023</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2023-03-03</th>\n",
              "      <td>427520.0</td>\n",
              "      <td>529.0</td>\n",
              "      <td>2023</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>59397 rows × 6 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-3d419028-e7b1-482a-87fc-a40241f1ccc2')\"\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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-3d419028-e7b1-482a-87fc-a40241f1ccc2 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-3d419028-e7b1-482a-87fc-a40241f1ccc2');\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",
              "\n",
              "\n",
              "<div id=\"df-1f68e306-9e9e-4d60-b71d-ace90d77866d\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-1f68e306-9e9e-4d60-b71d-ace90d77866d')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "    <g>\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-1f68e306-9e9e-4d60-b71d-ace90d77866d button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ],
      "source": [
        "tr_time_df\n",
        "# 훈련 데이터 완성본"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_time_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "id": "O3OD7sDfD3YR",
        "outputId": "48b75435-dc0f-4007-dac2-b98aafdc32d5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                    year  month  day  weekday\n",
              "item_id timestamp                            \n",
              "TG_A_J  2023-03-04  2023      3    4        5\n",
              "        2023-03-05  2023      3    5        6\n",
              "        2023-03-06  2023      3    6        0\n",
              "        2023-03-07  2023      3    7        1\n",
              "        2023-03-08  2023      3    8        2\n",
              "...                  ...    ...  ...      ...\n",
              "RD_F_J  2023-03-27  2023      3   27        0\n",
              "        2023-03-28  2023      3   28        1\n",
              "        2023-03-29  2023      3   29        2\n",
              "        2023-03-30  2023      3   30        3\n",
              "        2023-03-31  2023      3   31        4\n",
              "\n",
              "[1092 rows x 4 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-c6d0959a-bacb-4ae0-aa30-1bb364c8213f\" 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></th>\n",
              "      <th>year</th>\n",
              "      <th>month</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th rowspan=\"5\" valign=\"top\">TG_A_J</th>\n",
              "      <th>2023-03-04</th>\n",
              "      <td>2023</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2023-03-05</th>\n",
              "      <td>2023</td>\n",
              "      <td>3</td>\n",
              "      <td>5</td>\n",
              "      <td>6</td>\n",
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              "    <tr>\n",
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              "      <td>2023</td>\n",
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              "      <th>2023-03-07</th>\n",
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              "    <tr>\n",
              "      <th>2023-03-08</th>\n",
              "      <td>2023</td>\n",
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              "      <th>...</th>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"5\" valign=\"top\">RD_F_J</th>\n",
              "      <th>2023-03-27</th>\n",
              "      <td>2023</td>\n",
              "      <td>3</td>\n",
              "      <td>27</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2023-03-28</th>\n",
              "      <td>2023</td>\n",
              "      <td>3</td>\n",
              "      <td>28</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2023-03-29</th>\n",
              "      <td>2023</td>\n",
              "      <td>3</td>\n",
              "      <td>29</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2023-03-30</th>\n",
              "      <td>2023</td>\n",
              "      <td>3</td>\n",
              "      <td>30</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2023-03-31</th>\n",
              "      <td>2023</td>\n",
              "      <td>3</td>\n",
              "      <td>31</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1092 rows × 4 columns</p>\n",
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              "\n",
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              "            style=\"display:none;\">\n",
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              "      display:flex;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
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              "      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",
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              "\n",
              "    .colab-df-buttons div {\n",
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              "      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-c6d0959a-bacb-4ae0-aa30-1bb364c8213f 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-c6d0959a-bacb-4ae0-aa30-1bb364c8213f');\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",
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              "      --bg-color: #E8F0FE;\n",
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              "  [theme=dark] .colab-df-quickchart {\n",
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              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
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              "    background-color: var(--disabled-bg-color);\n",
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              "\n",
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              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
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              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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          "metadata": {},
          "execution_count": 13
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      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7rloxfEn37HU",
        "outputId": "d2cffdb6-a8d9-4d45-e668-d5cd98b9f043"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Beginning AutoGluon training... Time limit = 20000s\n",
            "AutoGluon will save models to 'AutogluonModels/ag-20231203_101521'\n",
            "=================== System Info ===================\n",
            "AutoGluon Version:  1.0.0\n",
            "Python Version:     3.10.12\n",
            "Operating System:   Linux\n",
            "Platform Machine:   x86_64\n",
            "Platform Version:   #1 SMP Wed Aug 30 11:19:59 UTC 2023\n",
            "CPU Count:          2\n",
            "GPU Count:          0\n",
            "Memory Avail:       11.39 GB / 12.68 GB (89.9%)\n",
            "Disk Space Avail:   73.54 GB / 107.72 GB (68.3%)\n",
            "===================================================\n",
            "Setting presets to: best_quality\n",
            "\n",
            "Fitting with arguments:\n",
            "{'enable_ensemble': True,\n",
            " 'eval_metric': RMSE,\n",
            " 'hyperparameters': 'default',\n",
            " 'known_covariates_names': ['year', 'month', 'day', 'weekday'],\n",
            " 'num_val_windows': 2,\n",
            " 'prediction_length': 28,\n",
            " 'quantile_levels': [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9],\n",
            " 'random_seed': 42,\n",
            " 'refit_every_n_windows': 1,\n",
            " 'refit_full': False,\n",
            " 'target': 'price(원/kg)',\n",
            " 'time_limit': 20000,\n",
            " 'verbosity': 2}\n",
            "\n",
            "Inferred time series frequency: 'D'\n",
            "Provided train_data has 59397 rows, 39 time series. Median time series length is 1523 (min=1523, max=1523). \n",
            "\n",
            "Provided dataset contains following columns:\n",
            "\ttarget:           'price(원/kg)'\n",
            "\tknown covariates: ['year', 'month', 'day', 'weekday']\n",
            "\tpast covariates:  ['supply(kg)']\n",
            "\n",
            "AutoGluon will gauge predictive performance using evaluation metric: 'RMSE'\n",
            "\tThis metric's sign has been flipped to adhere to being higher_is_better. The metric score can be multiplied by -1 to get the metric value.\n",
            "===================================================\n",
            "\n",
            "Starting training. Start time is 2023-12-03 10:15:23\n",
            "Models that will be trained: ['SeasonalNaive', 'CrostonSBA', 'NPTS', 'AutoETS', 'DynamicOptimizedTheta', 'AutoARIMA', 'RecursiveTabular', 'DirectTabular', 'DeepAR', 'TemporalFusionTransformer', 'PatchTST']\n",
            "Training timeseries model SeasonalNaive. Training for up to 1763.4s of the 19997.8s of remaining time.\n",
            "\t-927.8898     = Validation score (-RMSE)\n",
            "\t0.29    s     = Training runtime\n",
            "\t0.13    s     = Validation (prediction) runtime\n",
            "Training timeseries model CrostonSBA. Training for up to 1939.7s of the 19997.4s of remaining time.\n",
            "\t-1013.0959    = Validation score (-RMSE)\n",
            "\t12.40   s     = Training runtime\n",
            "\t0.13    s     = Validation (prediction) runtime\n",
            "Training timeseries model NPTS. Training for up to 2153.9s of the 19984.8s of remaining time.\n",
            "\t-1083.9432    = Validation score (-RMSE)\n",
            "\t13.74   s     = Training runtime\n",
            "\t12.44   s     = Validation (prediction) runtime\n",
            "Training timeseries model AutoETS. Training for up to 2419.8s of the 19958.6s of remaining time.\n",
            "\t-800.6345     = Validation score (-RMSE)\n",
            "\t47.59   s     = Training runtime\n",
            "\t20.52   s     = Validation (prediction) runtime\n",
            "Training timeseries model DynamicOptimizedTheta. Training for up to 2755.8s of the 19890.5s of remaining time.\n",
            "\t-846.5049     = Validation score (-RMSE)\n",
            "\t111.70  s     = Training runtime\n",
            "\t94.67   s     = Validation (prediction) runtime\n",
            "Training timeseries model AutoARIMA. Training for up to 3180.7s of the 19684.1s of remaining time.\n",
            "\t-817.9428     = Validation score (-RMSE)\n",
            "\t184.15  s     = Training runtime\n",
            "\t271.05  s     = Validation (prediction) runtime\n",
            "Training timeseries model RecursiveTabular. Training for up to 3725.8s of the 19228.9s of remaining time.\n",
            "\t-783.4497     = Validation score (-RMSE)\n",
            "\t139.04  s     = Training runtime\n",
            "\t1.33    s     = Validation (prediction) runtime\n",
            "Training timeseries model DirectTabular. Training for up to 4622.1s of the 19088.5s of remaining time.\n",
            "\t-925.6042     = Validation score (-RMSE)\n",
            "\t5.51    s     = Training runtime\n",
            "\t0.21    s     = Validation (prediction) runtime\n",
            "Training timeseries model DeepAR. Training for up to 6160.9s of the 19082.8s of remaining time.\n",
            "\tWarning: Exception caused DeepAR to fail during training... Skipping this model.\n",
            "\t/usr/local/lib/python3.10/dist-packages/torchaudio/lib/libtorchaudio.so: undefined symbol: _ZN3c104cuda9SetDeviceEi\n",
            "Training timeseries model TemporalFusionTransformer. Training for up to 9240.7s of the 19081.3s of remaining time.\n",
            "\tWarning: Exception caused TemporalFusionTransformer to fail during training... Skipping this model.\n",
            "\t/usr/local/lib/python3.10/dist-packages/torchaudio/lib/libtorchaudio.so: undefined symbol: _ZN3c104cuda9SetDeviceEi\n",
            "Training timeseries model PatchTST. Training for up to 18481.3s of the 19081.3s of remaining time.\n",
            "\tWarning: Exception caused PatchTST to fail during training... Skipping this model.\n",
            "\t/usr/local/lib/python3.10/dist-packages/torchaudio/lib/libtorchaudio.so: undefined symbol: _ZN3c104cuda9SetDeviceEi\n",
            "Fitting simple weighted ensemble.\n",
            "\tEnsemble weights: {'AutoETS': 0.2, 'DirectTabular': 0.29, 'DynamicOptimizedTheta': 0.25, 'RecursiveTabular': 0.22, 'SeasonalNaive': 0.04}\n",
            "\t-732.9589     = Validation score (-RMSE)\n",
            "\t2.46    s     = Training runtime\n",
            "\t116.86  s     = Validation (prediction) runtime\n",
            "Training complete. Models trained: ['SeasonalNaive', 'CrostonSBA', 'NPTS', 'AutoETS', 'DynamicOptimizedTheta', 'AutoARIMA', 'RecursiveTabular', 'DirectTabular', 'WeightedEnsemble']\n",
            "Total runtime: 919.41 s\n",
            "Best model: WeightedEnsemble\n",
            "Best model score: -732.9589\n",
            "Model not specified in predict, will default to the model with the best validation score: WeightedEnsemble\n"
          ]
        }
      ],
      "source": [
        "# TimeSeriesPredictor를 이용하여서 price(원/kg)을 target으로 잡고 eval_metric을 RMSE를 이용한다.\n",
        "# tr_time_df\n",
        "model = TimeSeriesPredictor(prediction_length = 28,target = 'price(원/kg)', known_covariates_names=[\"year\", \"month\", \"day\", \"weekday\"], eval_metric='RMSE')\n",
        "model.fit(tr_time_df,presets=\"best_quality\", time_limit=20000, random_seed=42) # fitting\n",
        "result = model.predict(tr_time_df, known_covariates=test_time_df, random_seed=42) # 예측(테스트 데이터를 이용)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_accuracy_dict = model.evaluate(tr_time_df, display = True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 443
        },
        "id": "XdYLRFwjPR28",
        "outputId": "655995f1-b7e7-481e-c2b0-dafe7733da36"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Model not specified in predict, will default to the model with the best validation score: WeightedEnsemble\n",
            "Evaluations on test data:\n",
            "{\n",
            "    \"RMSE\": -704.7158034233897\n",
            "}\n"
          ]
        },
        {
          "output_type": "error",
          "ename": "KeyError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-18-12b5f28aa6c1>\u001b[0m in \u001b[0;36m<cell line: 2>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mtrain_accuracy_dict\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mevaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtr_time_df\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdisplay\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[0;32m----> 2\u001b[0;31m \u001b[0mtest_accuracy_dict\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mevaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtest_time_df\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdisplay\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[0m",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/autogluon/timeseries/predictor.py\u001b[0m in \u001b[0;36mevaluate\u001b[0;34m(self, data, model, metrics, display, use_cache)\u001b[0m\n\u001b[1;32m    830\u001b[0m             \u001b[0mTo\u001b[0m \u001b[0mget\u001b[0m \u001b[0mthe\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m`\u001b[0m\u001b[0;31m`\u001b[0m\u001b[0meval_metric\u001b[0m\u001b[0;31m`\u001b[0m\u001b[0;31m`\u001b[0m \u001b[0mscore\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdo\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m`\u001b[0m\u001b[0;31m`\u001b[0m\u001b[0moutput\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mpredictor\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0meval_metric\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;31m`\u001b[0m\u001b[0;31m`\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    831\u001b[0m         \"\"\"\n\u001b[0;32m--> 832\u001b[0;31m         \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_check_and_prepare_data_frame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\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    833\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_check_data_for_evaluation\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    834\u001b[0m         \u001b[0mscores_dict\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_learner\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mevaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmetrics\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmetrics\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0muse_cache\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0muse_cache\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/python3.10/dist-packages/autogluon/timeseries/predictor.py\u001b[0m in \u001b[0;36m_check_and_prepare_data_frame\u001b[0;34m(self, data, name)\u001b[0m\n\u001b[1;32m    257\u001b[0m         \"\"\"\n\u001b[1;32m    258\u001b[0m         \u001b[0mdf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_to_data_frame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 259\u001b[0;31m         \u001b[0mdf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtarget\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mfloat\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    260\u001b[0m         \u001b[0;31m# MultiIndex.is_monotonic_increasing checks if index is sorted by [\"item_id\", \"timestamp\"]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    261\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_monotonic_increasing\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/python3.10/dist-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36mastype\u001b[0;34m(self, dtype, copy, errors)\u001b[0m\n\u001b[1;32m   6496\u001b[0m             \u001b[0;32mfor\u001b[0m \u001b[0mcol_name\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mdtype_ser\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[1;32m   6497\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mcol_name\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\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[0;32m-> 6498\u001b[0;31m                     raise KeyError(\n\u001b[0m\u001b[1;32m   6499\u001b[0m                         \u001b[0;34m\"Only a column name can be used for the \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   6500\u001b[0m                         \u001b[0;34m\"key in a dtype mappings argument. \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyError\u001b[0m: \"Only a column name can be used for the key in a dtype mappings argument. 'price(원/kg)' not found in columns.\""
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "yfHOx9c1_dih",
        "outputId": "6479a698-570b-4caf-d671-8f64b69045b7"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     item_id  timestamp         mean          0.1          0.2          0.3  \\\n",
              "0     TG_A_J 2023-03-04  3189.025231  1109.285096  1788.840837  2282.259436   \n",
              "1     TG_A_J 2023-03-05   164.291875 -2247.034648 -1427.928452  -841.350756   \n",
              "2     TG_A_J 2023-03-06  3012.665121   307.855243  1275.357086  1928.127480   \n",
              "3     TG_A_J 2023-03-07  2972.665487   157.090178  1119.084059  1774.824608   \n",
              "4     TG_A_J 2023-03-08  3171.397039   389.107578  1323.051730  2034.337886   \n",
              "...      ...        ...          ...          ...          ...          ...   \n",
              "1087  RD_F_J 2023-03-27   506.155302  -330.265443   -48.088654   154.222608   \n",
              "1088  RD_F_J 2023-03-28   517.558160  -334.906494   -42.441051   167.520853   \n",
              "1089  RD_F_J 2023-03-29   526.026804  -358.080676   -64.095777   163.530254   \n",
              "1090  RD_F_J 2023-03-30   507.294072  -370.062173   -68.341451   145.796282   \n",
              "1091  RD_F_J 2023-03-31   509.046970  -408.380389   -86.752513   129.716127   \n",
              "\n",
              "              0.4          0.5          0.6          0.7          0.8  \\\n",
              "0     2782.405985  3221.265231  3672.751438  4122.645016  4639.532624   \n",
              "1     -366.753660    57.184160   549.659360  1045.290120  1617.419888   \n",
              "2     2491.393148  2984.241932  3450.143283  4005.699570  4635.339817   \n",
              "3     2347.848978  2866.907644  3431.820007  3994.049764  4628.144493   \n",
              "4     2612.563620  3144.907991  3674.739541  4291.992869  5058.483915   \n",
              "...           ...          ...          ...          ...          ...   \n",
              "1087   335.378236   503.620604   660.208721   834.677327  1041.117014   \n",
              "1088   345.033061   520.382665   694.381222   867.606449  1070.853361   \n",
              "1089   343.640480   518.485861   682.779038   872.097919  1095.281036   \n",
              "1090   331.161117   499.228220   668.816755   853.916549  1074.957444   \n",
              "1091   322.354776   494.876622   680.639133   884.225108  1094.043144   \n",
              "\n",
              "              0.9  \n",
              "0     5424.985718  \n",
              "1     2385.152939  \n",
              "2     5517.781280  \n",
              "3     5587.723242  \n",
              "4     6068.252887  \n",
              "...           ...  \n",
              "1087  1339.961309  \n",
              "1088  1360.251372  \n",
              "1089  1384.269651  \n",
              "1090  1372.554618  \n",
              "1091  1384.094944  \n",
              "\n",
              "[1092 rows x 12 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-fd172045-d086-4754-8c41-4d51a02d232c\" class=\"colab-df-container\">\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>item_id</th>\n",
              "      <th>timestamp</th>\n",
              "      <th>mean</th>\n",
              "      <th>0.1</th>\n",
              "      <th>0.2</th>\n",
              "      <th>0.3</th>\n",
              "      <th>0.4</th>\n",
              "      <th>0.5</th>\n",
              "      <th>0.6</th>\n",
              "      <th>0.7</th>\n",
              "      <th>0.8</th>\n",
              "      <th>0.9</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>TG_A_J</td>\n",
              "      <td>2023-03-04</td>\n",
              "      <td>3189.025231</td>\n",
              "      <td>1109.285096</td>\n",
              "      <td>1788.840837</td>\n",
              "      <td>2282.259436</td>\n",
              "      <td>2782.405985</td>\n",
              "      <td>3221.265231</td>\n",
              "      <td>3672.751438</td>\n",
              "      <td>4122.645016</td>\n",
              "      <td>4639.532624</td>\n",
              "      <td>5424.985718</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>TG_A_J</td>\n",
              "      <td>2023-03-05</td>\n",
              "      <td>164.291875</td>\n",
              "      <td>-2247.034648</td>\n",
              "      <td>-1427.928452</td>\n",
              "      <td>-841.350756</td>\n",
              "      <td>-366.753660</td>\n",
              "      <td>57.184160</td>\n",
              "      <td>549.659360</td>\n",
              "      <td>1045.290120</td>\n",
              "      <td>1617.419888</td>\n",
              "      <td>2385.152939</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>TG_A_J</td>\n",
              "      <td>2023-03-06</td>\n",
              "      <td>3012.665121</td>\n",
              "      <td>307.855243</td>\n",
              "      <td>1275.357086</td>\n",
              "      <td>1928.127480</td>\n",
              "      <td>2491.393148</td>\n",
              "      <td>2984.241932</td>\n",
              "      <td>3450.143283</td>\n",
              "      <td>4005.699570</td>\n",
              "      <td>4635.339817</td>\n",
              "      <td>5517.781280</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>TG_A_J</td>\n",
              "      <td>2023-03-07</td>\n",
              "      <td>2972.665487</td>\n",
              "      <td>157.090178</td>\n",
              "      <td>1119.084059</td>\n",
              "      <td>1774.824608</td>\n",
              "      <td>2347.848978</td>\n",
              "      <td>2866.907644</td>\n",
              "      <td>3431.820007</td>\n",
              "      <td>3994.049764</td>\n",
              "      <td>4628.144493</td>\n",
              "      <td>5587.723242</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>TG_A_J</td>\n",
              "      <td>2023-03-08</td>\n",
              "      <td>3171.397039</td>\n",
              "      <td>389.107578</td>\n",
              "      <td>1323.051730</td>\n",
              "      <td>2034.337886</td>\n",
              "      <td>2612.563620</td>\n",
              "      <td>3144.907991</td>\n",
              "      <td>3674.739541</td>\n",
              "      <td>4291.992869</td>\n",
              "      <td>5058.483915</td>\n",
              "      <td>6068.252887</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1087</th>\n",
              "      <td>RD_F_J</td>\n",
              "      <td>2023-03-27</td>\n",
              "      <td>506.155302</td>\n",
              "      <td>-330.265443</td>\n",
              "      <td>-48.088654</td>\n",
              "      <td>154.222608</td>\n",
              "      <td>335.378236</td>\n",
              "      <td>503.620604</td>\n",
              "      <td>660.208721</td>\n",
              "      <td>834.677327</td>\n",
              "      <td>1041.117014</td>\n",
              "      <td>1339.961309</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1088</th>\n",
              "      <td>RD_F_J</td>\n",
              "      <td>2023-03-28</td>\n",
              "      <td>517.558160</td>\n",
              "      <td>-334.906494</td>\n",
              "      <td>-42.441051</td>\n",
              "      <td>167.520853</td>\n",
              "      <td>345.033061</td>\n",
              "      <td>520.382665</td>\n",
              "      <td>694.381222</td>\n",
              "      <td>867.606449</td>\n",
              "      <td>1070.853361</td>\n",
              "      <td>1360.251372</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1089</th>\n",
              "      <td>RD_F_J</td>\n",
              "      <td>2023-03-29</td>\n",
              "      <td>526.026804</td>\n",
              "      <td>-358.080676</td>\n",
              "      <td>-64.095777</td>\n",
              "      <td>163.530254</td>\n",
              "      <td>343.640480</td>\n",
              "      <td>518.485861</td>\n",
              "      <td>682.779038</td>\n",
              "      <td>872.097919</td>\n",
              "      <td>1095.281036</td>\n",
              "      <td>1384.269651</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1090</th>\n",
              "      <td>RD_F_J</td>\n",
              "      <td>2023-03-30</td>\n",
              "      <td>507.294072</td>\n",
              "      <td>-370.062173</td>\n",
              "      <td>-68.341451</td>\n",
              "      <td>145.796282</td>\n",
              "      <td>331.161117</td>\n",
              "      <td>499.228220</td>\n",
              "      <td>668.816755</td>\n",
              "      <td>853.916549</td>\n",
              "      <td>1074.957444</td>\n",
              "      <td>1372.554618</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1091</th>\n",
              "      <td>RD_F_J</td>\n",
              "      <td>2023-03-31</td>\n",
              "      <td>509.046970</td>\n",
              "      <td>-408.380389</td>\n",
              "      <td>-86.752513</td>\n",
              "      <td>129.716127</td>\n",
              "      <td>322.354776</td>\n",
              "      <td>494.876622</td>\n",
              "      <td>680.639133</td>\n",
              "      <td>884.225108</td>\n",
              "      <td>1094.043144</td>\n",
              "      <td>1384.094944</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "<p>1092 rows × 12 columns</p>\n",
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              "\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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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-fd172045-d086-4754-8c41-4d51a02d232c 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-fd172045-d086-4754-8c41-4d51a02d232c');\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",
              "\n",
              "\n",
              "<div id=\"df-701c334a-ebe2-4af3-828e-517f45375319\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-701c334a-ebe2-4af3-828e-517f45375319')\"\n",
              "            title=\"Suggest charts\"\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",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-701c334a-ebe2-4af3-828e-517f45375319 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ],
      "source": [
        "final = pd.DataFrame(result).reset_index()\n",
        "final"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Sg459Vd0_d0P"
      },
      "outputs": [],
      "source": [
        "# 공휴일 삼일절과 일요일 0으로 바꾸기\n",
        "final.loc[final['timestamp']=='2023-03-01','mean'] = 0\n",
        "final.loc[final['timestamp']=='2023-03-05','mean'] = 0\n",
        "final.loc[final['timestamp']=='2023-03-12','mean'] = 0\n",
        "final.loc[final['timestamp']=='2023-03-19','mean'] = 0\n",
        "final.loc[final['timestamp']=='2023-03-26','mean'] = 0"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "final"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "x8x7C1Ehzs10",
        "outputId": "e65a1f5d-0412-48e8-8c11-0814485c87bb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     item_id  timestamp         mean          0.1          0.2          0.3  \\\n",
              "0     TG_A_J 2023-03-04  3189.025231  1109.285096  1788.840837  2282.259436   \n",
              "1     TG_A_J 2023-03-05     0.000000 -2247.034648 -1427.928452  -841.350756   \n",
              "2     TG_A_J 2023-03-06  3012.665121   307.855243  1275.357086  1928.127480   \n",
              "3     TG_A_J 2023-03-07  2972.665487   157.090178  1119.084059  1774.824608   \n",
              "4     TG_A_J 2023-03-08  3171.397039   389.107578  1323.051730  2034.337886   \n",
              "...      ...        ...          ...          ...          ...          ...   \n",
              "1087  RD_F_J 2023-03-27   506.155302  -330.265443   -48.088654   154.222608   \n",
              "1088  RD_F_J 2023-03-28   517.558160  -334.906494   -42.441051   167.520853   \n",
              "1089  RD_F_J 2023-03-29   526.026804  -358.080676   -64.095777   163.530254   \n",
              "1090  RD_F_J 2023-03-30   507.294072  -370.062173   -68.341451   145.796282   \n",
              "1091  RD_F_J 2023-03-31   509.046970  -408.380389   -86.752513   129.716127   \n",
              "\n",
              "              0.4          0.5          0.6          0.7          0.8  \\\n",
              "0     2782.405985  3221.265231  3672.751438  4122.645016  4639.532624   \n",
              "1     -366.753660    57.184160   549.659360  1045.290120  1617.419888   \n",
              "2     2491.393148  2984.241932  3450.143283  4005.699570  4635.339817   \n",
              "3     2347.848978  2866.907644  3431.820007  3994.049764  4628.144493   \n",
              "4     2612.563620  3144.907991  3674.739541  4291.992869  5058.483915   \n",
              "...           ...          ...          ...          ...          ...   \n",
              "1087   335.378236   503.620604   660.208721   834.677327  1041.117014   \n",
              "1088   345.033061   520.382665   694.381222   867.606449  1070.853361   \n",
              "1089   343.640480   518.485861   682.779038   872.097919  1095.281036   \n",
              "1090   331.161117   499.228220   668.816755   853.916549  1074.957444   \n",
              "1091   322.354776   494.876622   680.639133   884.225108  1094.043144   \n",
              "\n",
              "              0.9  \n",
              "0     5424.985718  \n",
              "1     2385.152939  \n",
              "2     5517.781280  \n",
              "3     5587.723242  \n",
              "4     6068.252887  \n",
              "...           ...  \n",
              "1087  1339.961309  \n",
              "1088  1360.251372  \n",
              "1089  1384.269651  \n",
              "1090  1372.554618  \n",
              "1091  1384.094944  \n",
              "\n",
              "[1092 rows x 12 columns]"
            ],
            "text/html": [
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              "    <div>\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>item_id</th>\n",
              "      <th>timestamp</th>\n",
              "      <th>mean</th>\n",
              "      <th>0.1</th>\n",
              "      <th>0.2</th>\n",
              "      <th>0.3</th>\n",
              "      <th>0.4</th>\n",
              "      <th>0.5</th>\n",
              "      <th>0.6</th>\n",
              "      <th>0.7</th>\n",
              "      <th>0.8</th>\n",
              "      <th>0.9</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>TG_A_J</td>\n",
              "      <td>2023-03-04</td>\n",
              "      <td>3189.025231</td>\n",
              "      <td>1109.285096</td>\n",
              "      <td>1788.840837</td>\n",
              "      <td>2282.259436</td>\n",
              "      <td>2782.405985</td>\n",
              "      <td>3221.265231</td>\n",
              "      <td>3672.751438</td>\n",
              "      <td>4122.645016</td>\n",
              "      <td>4639.532624</td>\n",
              "      <td>5424.985718</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>TG_A_J</td>\n",
              "      <td>2023-03-05</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-2247.034648</td>\n",
              "      <td>-1427.928452</td>\n",
              "      <td>-841.350756</td>\n",
              "      <td>-366.753660</td>\n",
              "      <td>57.184160</td>\n",
              "      <td>549.659360</td>\n",
              "      <td>1045.290120</td>\n",
              "      <td>1617.419888</td>\n",
              "      <td>2385.152939</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>TG_A_J</td>\n",
              "      <td>2023-03-06</td>\n",
              "      <td>3012.665121</td>\n",
              "      <td>307.855243</td>\n",
              "      <td>1275.357086</td>\n",
              "      <td>1928.127480</td>\n",
              "      <td>2491.393148</td>\n",
              "      <td>2984.241932</td>\n",
              "      <td>3450.143283</td>\n",
              "      <td>4005.699570</td>\n",
              "      <td>4635.339817</td>\n",
              "      <td>5517.781280</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>TG_A_J</td>\n",
              "      <td>2023-03-07</td>\n",
              "      <td>2972.665487</td>\n",
              "      <td>157.090178</td>\n",
              "      <td>1119.084059</td>\n",
              "      <td>1774.824608</td>\n",
              "      <td>2347.848978</td>\n",
              "      <td>2866.907644</td>\n",
              "      <td>3431.820007</td>\n",
              "      <td>3994.049764</td>\n",
              "      <td>4628.144493</td>\n",
              "      <td>5587.723242</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>TG_A_J</td>\n",
              "      <td>2023-03-08</td>\n",
              "      <td>3171.397039</td>\n",
              "      <td>389.107578</td>\n",
              "      <td>1323.051730</td>\n",
              "      <td>2034.337886</td>\n",
              "      <td>2612.563620</td>\n",
              "      <td>3144.907991</td>\n",
              "      <td>3674.739541</td>\n",
              "      <td>4291.992869</td>\n",
              "      <td>5058.483915</td>\n",
              "      <td>6068.252887</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1087</th>\n",
              "      <td>RD_F_J</td>\n",
              "      <td>2023-03-27</td>\n",
              "      <td>506.155302</td>\n",
              "      <td>-330.265443</td>\n",
              "      <td>-48.088654</td>\n",
              "      <td>154.222608</td>\n",
              "      <td>335.378236</td>\n",
              "      <td>503.620604</td>\n",
              "      <td>660.208721</td>\n",
              "      <td>834.677327</td>\n",
              "      <td>1041.117014</td>\n",
              "      <td>1339.961309</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1088</th>\n",
              "      <td>RD_F_J</td>\n",
              "      <td>2023-03-28</td>\n",
              "      <td>517.558160</td>\n",
              "      <td>-334.906494</td>\n",
              "      <td>-42.441051</td>\n",
              "      <td>167.520853</td>\n",
              "      <td>345.033061</td>\n",
              "      <td>520.382665</td>\n",
              "      <td>694.381222</td>\n",
              "      <td>867.606449</td>\n",
              "      <td>1070.853361</td>\n",
              "      <td>1360.251372</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1089</th>\n",
              "      <td>RD_F_J</td>\n",
              "      <td>2023-03-29</td>\n",
              "      <td>526.026804</td>\n",
              "      <td>-358.080676</td>\n",
              "      <td>-64.095777</td>\n",
              "      <td>163.530254</td>\n",
              "      <td>343.640480</td>\n",
              "      <td>518.485861</td>\n",
              "      <td>682.779038</td>\n",
              "      <td>872.097919</td>\n",
              "      <td>1095.281036</td>\n",
              "      <td>1384.269651</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1090</th>\n",
              "      <td>RD_F_J</td>\n",
              "      <td>2023-03-30</td>\n",
              "      <td>507.294072</td>\n",
              "      <td>-370.062173</td>\n",
              "      <td>-68.341451</td>\n",
              "      <td>145.796282</td>\n",
              "      <td>331.161117</td>\n",
              "      <td>499.228220</td>\n",
              "      <td>668.816755</td>\n",
              "      <td>853.916549</td>\n",
              "      <td>1074.957444</td>\n",
              "      <td>1372.554618</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1091</th>\n",
              "      <td>RD_F_J</td>\n",
              "      <td>2023-03-31</td>\n",
              "      <td>509.046970</td>\n",
              "      <td>-408.380389</td>\n",
              "      <td>-86.752513</td>\n",
              "      <td>129.716127</td>\n",
              "      <td>322.354776</td>\n",
              "      <td>494.876622</td>\n",
              "      <td>680.639133</td>\n",
              "      <td>884.225108</td>\n",
              "      <td>1094.043144</td>\n",
              "      <td>1384.094944</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1092 rows × 12 columns</p>\n",
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              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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-8341ca67-6512-4430-a9de-cbe4bbaf574e 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-8341ca67-6512-4430-a9de-cbe4bbaf574e');\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",
              "\n",
              "\n",
              "<div id=\"df-c4455e88-e325-41cb-8943-52abdb142e60\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-c4455e88-e325-41cb-8943-52abdb142e60')\"\n",
              "            title=\"Suggest charts\"\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",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-c4455e88-e325-41cb-8943-52abdb142e60 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(train_accuracy_dict)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qnTgawOkQ5Zs",
        "outputId": "14e141a7-9f86-41c7-b592-2c57dbbd8a6d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'RMSE': -704.7158034233897}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "submission = pd.read_csv('/content/drive/MyDrive/sample_submission.csv')\n",
        "submission['answer'] = final.reset_index()['mean']\n",
        "submission.loc[submission['answer'] < 0.0, 'answer'] = 0.0\n",
        "submission.to_csv('./final_submission.csv', index=False)"
      ],
      "metadata": {
        "id": "6EufC6gCTnHd"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "submission"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "bO7ogQO8VUAL",
        "outputId": "08ea04ec-31c0-4c48-b7d3-fbdbd565d375"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                   ID       answer\n",
              "0     TG_A_J_20230304  3189.025231\n",
              "1     TG_A_J_20230305     0.000000\n",
              "2     TG_A_J_20230306  3012.665121\n",
              "3     TG_A_J_20230307  2972.665487\n",
              "4     TG_A_J_20230308  3171.397039\n",
              "...               ...          ...\n",
              "1087  RD_F_J_20230327   506.155302\n",
              "1088  RD_F_J_20230328   517.558160\n",
              "1089  RD_F_J_20230329   526.026804\n",
              "1090  RD_F_J_20230330   507.294072\n",
              "1091  RD_F_J_20230331   509.046970\n",
              "\n",
              "[1092 rows x 2 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-d32a40aa-e7be-48de-b528-b95add30c861\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>ID</th>\n",
              "      <th>answer</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>TG_A_J_20230304</td>\n",
              "      <td>3189.025231</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>TG_A_J_20230305</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>TG_A_J_20230306</td>\n",
              "      <td>3012.665121</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>TG_A_J_20230307</td>\n",
              "      <td>2972.665487</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>TG_A_J_20230308</td>\n",
              "      <td>3171.397039</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1087</th>\n",
              "      <td>RD_F_J_20230327</td>\n",
              "      <td>506.155302</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1088</th>\n",
              "      <td>RD_F_J_20230328</td>\n",
              "      <td>517.558160</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1089</th>\n",
              "      <td>RD_F_J_20230329</td>\n",
              "      <td>526.026804</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1090</th>\n",
              "      <td>RD_F_J_20230330</td>\n",
              "      <td>507.294072</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1091</th>\n",
              "      <td>RD_F_J_20230331</td>\n",
              "      <td>509.046970</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1092 rows × 2 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d32a40aa-e7be-48de-b528-b95add30c861')\"\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 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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-d32a40aa-e7be-48de-b528-b95add30c861 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-d32a40aa-e7be-48de-b528-b95add30c861');\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",
              "\n",
              "\n",
              "<div id=\"df-ca9177c1-c19f-46bc-8c4c-82615ee154ec\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-ca9177c1-c19f-46bc-8c4c-82615ee154ec')\"\n",
              "            title=\"Suggest charts\"\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",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-ca9177c1-c19f-46bc-8c4c-82615ee154ec button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "results_frame = pd.concat([tr_time_df.loc[:, 'price(원/kg)'], final.reset_index()['mean']])\n",
        "results_data_frame = pd.concat([train_df['timestamp'], test_df['timestamp']])"
      ],
      "metadata": {
        "id": "U_Cz6EB1YYfy"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "results_frame"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aWBkJVdga8QH",
        "outputId": "595a99de-ae4a-41e3-8085-0a7ec963ae03"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(TG_A_J, 2019-01-01 00:00:00)       0.000000\n",
              "(TG_A_J, 2019-01-02 00:00:00)       0.000000\n",
              "(TG_A_J, 2019-01-03 00:00:00)    1728.000000\n",
              "(TG_A_J, 2019-01-04 00:00:00)    1408.000000\n",
              "(TG_A_J, 2019-01-05 00:00:00)    1250.000000\n",
              "                                    ...     \n",
              "1087                              506.155302\n",
              "1088                              517.558160\n",
              "1089                              526.026804\n",
              "1090                              507.294072\n",
              "1091                              509.046970\n",
              "Length: 60489, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "results_data_frame"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YIYYVp7FbKij",
        "outputId": "4974c90b-7702-4bd0-9b61-b0000301529e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0      2019-01-01\n",
              "1      2019-01-02\n",
              "2      2019-01-03\n",
              "3      2019-01-04\n",
              "4      2019-01-05\n",
              "          ...    \n",
              "1087   2023-03-27\n",
              "1088   2023-03-28\n",
              "1089   2023-03-29\n",
              "1090   2023-03-30\n",
              "1091   2023-03-31\n",
              "Name: timestamp, Length: 60489, dtype: datetime64[ns]"
            ]
          },
          "metadata": {},
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10, 6))\n",
        "plt.plot(results_data_frame, results_frame, label='Actual', marker='o')\n",
        "plt.xlabel('timestamp')\n",
        "plt.ylabel('Price')\n",
        "plt.title('Time Series Prediction')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "JU5A9N9lVUbt",
        "outputId": "6033a09f-4fbd-46c2-bbda-425b502bb201"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "-zEY2FcVXUvL"
      },
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}