Autogluon_김민석_코드.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "izOj6i8CtafO",
        "outputId": "953ed133-135f-4d1d-8fe4-e675db50a0cd"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
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            "\u001b[?25hDownloading aliyun_python_sdk_kms-2.16.2-py2.py3-none-any.whl (94 kB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m94.0/94.0 kB\u001b[0m \u001b[31m6.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hBuilding wheels for collected packages: nvidia-ml-py3, antlr4-python3-runtime, seqeval, gpustat, lit, oss2, aliyun-python-sdk-core, crcmod\n",
            "  Building wheel for nvidia-ml-py3 (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for nvidia-ml-py3: filename=nvidia_ml_py3-7.352.0-py3-none-any.whl size=19171 sha256=26a9be28c55c31ec2118251650e044cd7bd513673a702990a30b22d7a76bd362\n",
            "  Stored in directory: /root/.cache/pip/wheels/5c/d8/c0/46899f8be7a75a2ffd197a23c8797700ea858b9b34819fbf9e\n",
            "  Building wheel for antlr4-python3-runtime (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for antlr4-python3-runtime: filename=antlr4_python3_runtime-4.9.3-py3-none-any.whl size=144554 sha256=eb644701fafe985ddfdf212cf1d415cd19dac8a00eca53e74318adcae0e37184\n",
            "  Stored in directory: /root/.cache/pip/wheels/12/93/dd/1f6a127edc45659556564c5730f6d4e300888f4bca2d4c5a88\n",
            "  Building wheel for seqeval (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for seqeval: filename=seqeval-1.2.2-py3-none-any.whl size=16162 sha256=7eec4e9351cc7387113ef4aa8a1640d41a577628b34bbd635205d40367d6fb8a\n",
            "  Stored in directory: /root/.cache/pip/wheels/1a/67/4a/ad4082dd7dfc30f2abfe4d80a2ed5926a506eb8a972b4767fa\n",
            "  Building wheel for gpustat (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for gpustat: filename=gpustat-1.1.1-py3-none-any.whl size=26535 sha256=cb03394c505f086150aa129912fe3aa88b197076b157d01fe218a779635aa6d8\n",
            "  Stored in directory: /root/.cache/pip/wheels/ec/d7/80/a71ba3540900e1f276bcae685efd8e590c810d2108b95f1e47\n",
            "  Building wheel for lit (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for lit: filename=lit-17.0.6-py3-none-any.whl size=93255 sha256=b1dc42b31b563e81b27008e1fe727b4b85018303082f0a86705acd71ae5e182b\n",
            "  Stored in directory: /root/.cache/pip/wheels/30/dd/04/47d42976a6a86dc2ab66d7518621ae96f43452c8841d74758a\n",
            "  Building wheel for oss2 (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for oss2: filename=oss2-2.17.0-py3-none-any.whl size=112371 sha256=6ed846b3aa0f9beb136acf0c87d63f438227ba97211957eaa956a830c11b5ffe\n",
            "  Stored in directory: /root/.cache/pip/wheels/87/04/7b/7e61b8157fdf211c5131375240d0d86ca82e2a88ead9672c88\n",
            "  Building wheel for aliyun-python-sdk-core (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for aliyun-python-sdk-core: filename=aliyun_python_sdk_core-2.14.0-py3-none-any.whl size=535289 sha256=1bec702e01b1c628748e70ffe006952db0ccb24956d66d5c2e704881c33d8cd3\n",
            "  Stored in directory: /root/.cache/pip/wheels/16/3c/68/b7eab618d9f1d5e7d386296f1e07e2cf36aaa1eb5161885038\n",
            "  Building wheel for crcmod (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for crcmod: filename=crcmod-1.7-cp310-cp310-linux_x86_64.whl size=31407 sha256=5d97d67b2b4d4234d0d928b2fc8e446c8eb6e367a937ce53daaf888014d7ed7d\n",
            "  Stored in directory: /root/.cache/pip/wheels/85/4c/07/72215c529bd59d67e3dac29711d7aba1b692f543c808ba9e86\n",
            "Successfully built nvidia-ml-py3 antlr4-python3-runtime seqeval gpustat lit oss2 aliyun-python-sdk-core crcmod\n",
            "Installing collected packages: types-python-dateutil, tokenizers, sentencepiece, python-editor, py-spy, opencensus-context, nvidia-ml-py3, nvidia-ml-py, lit, distlib, crcmod, colorful, antlr4-python3-runtime, websockets, urllib3, tzdata, typing-extensions, tqdm, tensorboardX, setuptools, python-multipart, pyrsistent, pycryptodome, pyarrow-hotfix, platformdirs, Pillow, orjson, ordered-set, omegaconf, nvidia-nccl-cu11, nvidia-cufft-cu11, nvidia-cuda-nvrtc-cu11, nptyping, jmespath, h11, dill, colorama, blessed, backoff, window-ops, virtualenv, uvicorn, starlette, scikit-learn, rich, requests, readchar, pytesseract, pandas, nvidia-nvtx-cu11, nvidia-cusparse-cu11, nvidia-curand-cu11, nvidia-cuda-runtime-cu11, nvidia-cuda-cupti-cu11, nvidia-cublas-cu11, multiprocess, model-index, lightning-utilities, jsonschema, gpustat, deepdiff, dateutils, croniter, botocore, arrow, utilsforecast, starsessions, seqeval, s3transfer, responses, ray, nvidia-cusolver-cu11, nvidia-cudnn-cu11, inquirer, gluonts, fastapi, catboost, aliyun-python-sdk-core, aiohttp-cors, transformers, statsforecast, opencensus, mlforecast, datasets, boto3, aliyun-python-sdk-kms, oss2, nlpaug, lightning-cloud, evaluate, autogluon.common, openxlab, autogluon.features, autogluon.core, opendatalab, autogluon.tabular, openmim, triton, torch, torchmetrics, pytorch-lightning, torchvision, lightning, timm, pytorch-metric-learning, autogluon.timeseries, accelerate, autogluon.multimodal, autogluon\n",
            "  Attempting uninstall: tokenizers\n",
            "    Found existing installation: tokenizers 0.15.0\n",
            "    Uninstalling tokenizers-0.15.0:\n",
            "      Successfully uninstalled tokenizers-0.15.0\n",
            "  Attempting uninstall: urllib3\n",
            "    Found existing installation: urllib3 2.0.7\n",
            "    Uninstalling urllib3-2.0.7:\n",
            "      Successfully uninstalled urllib3-2.0.7\n",
            "  Attempting uninstall: typing-extensions\n",
            "    Found existing installation: typing_extensions 4.5.0\n",
            "    Uninstalling typing_extensions-4.5.0:\n",
            "      Successfully uninstalled typing_extensions-4.5.0\n",
            "  Attempting uninstall: tqdm\n",
            "    Found existing installation: tqdm 4.66.1\n",
            "    Uninstalling tqdm-4.66.1:\n",
            "      Successfully uninstalled tqdm-4.66.1\n",
            "  Attempting uninstall: setuptools\n",
            "    Found existing installation: setuptools 67.7.2\n",
            "    Uninstalling setuptools-67.7.2:\n",
            "      Successfully uninstalled setuptools-67.7.2\n",
            "  Attempting uninstall: platformdirs\n",
            "    Found existing installation: platformdirs 4.0.0\n",
            "    Uninstalling platformdirs-4.0.0:\n",
            "      Successfully uninstalled platformdirs-4.0.0\n",
            "  Attempting uninstall: Pillow\n",
            "    Found existing installation: Pillow 9.4.0\n",
            "    Uninstalling Pillow-9.4.0:\n",
            "      Successfully uninstalled Pillow-9.4.0\n",
            "  Attempting uninstall: scikit-learn\n",
            "    Found existing installation: scikit-learn 1.2.2\n",
            "    Uninstalling scikit-learn-1.2.2:\n",
            "      Successfully uninstalled scikit-learn-1.2.2\n",
            "  Attempting uninstall: rich\n",
            "    Found existing installation: rich 13.7.0\n",
            "    Uninstalling rich-13.7.0:\n",
            "      Successfully uninstalled rich-13.7.0\n",
            "  Attempting uninstall: requests\n",
            "    Found existing installation: requests 2.31.0\n",
            "    Uninstalling requests-2.31.0:\n",
            "      Successfully uninstalled requests-2.31.0\n",
            "  Attempting uninstall: pandas\n",
            "    Found existing installation: pandas 1.5.3\n",
            "    Uninstalling pandas-1.5.3:\n",
            "      Successfully uninstalled pandas-1.5.3\n",
            "  Attempting uninstall: jsonschema\n",
            "    Found existing installation: jsonschema 4.19.2\n",
            "    Uninstalling jsonschema-4.19.2:\n",
            "      Successfully uninstalled jsonschema-4.19.2\n",
            "  Attempting uninstall: transformers\n",
            "    Found existing installation: transformers 4.35.2\n",
            "    Uninstalling transformers-4.35.2:\n",
            "      Successfully uninstalled transformers-4.35.2\n",
            "  Attempting uninstall: triton\n",
            "    Found existing installation: triton 2.1.0\n",
            "    Uninstalling triton-2.1.0:\n",
            "      Successfully uninstalled triton-2.1.0\n",
            "  Attempting uninstall: torch\n",
            "    Found existing installation: torch 2.1.0+cu118\n",
            "    Uninstalling torch-2.1.0+cu118:\n",
            "      Successfully uninstalled torch-2.1.0+cu118\n",
            "  Attempting uninstall: torchvision\n",
            "    Found existing installation: torchvision 0.16.0+cu118\n",
            "    Uninstalling torchvision-0.16.0+cu118:\n",
            "      Successfully uninstalled torchvision-0.16.0+cu118\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "ipython 7.34.0 requires jedi>=0.16, which is not installed.\n",
            "lida 0.0.10 requires kaleido, which is not installed.\n",
            "cvxpy 1.3.2 requires setuptools>65.5.1, but you have setuptools 60.2.0 which is incompatible.\n",
            "google-colab 1.0.0 requires pandas==1.5.3, but you have pandas 2.1.3 which is incompatible.\n",
            "google-colab 1.0.0 requires requests==2.31.0, but you have requests 2.28.2 which is incompatible.\n",
            "tensorflow-probability 0.22.0 requires typing-extensions<4.6.0, but you have typing-extensions 4.8.0 which is incompatible.\n",
            "torchaudio 2.1.0+cu118 requires torch==2.1.0, but you have torch 2.0.1 which is incompatible.\n",
            "torchdata 0.7.0 requires torch==2.1.0, but you have torch 2.0.1 which is incompatible.\n",
            "torchtext 0.16.0 requires torch==2.1.0, but you have torch 2.0.1 which is incompatible.\n",
            "yfinance 0.2.32 requires requests>=2.31, but you have requests 2.28.2 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0mSuccessfully installed Pillow-10.0.1 accelerate-0.21.0 aiohttp-cors-0.7.0 aliyun-python-sdk-core-2.14.0 aliyun-python-sdk-kms-2.16.2 antlr4-python3-runtime-4.9.3 arrow-1.3.0 autogluon-1.0.0 autogluon.common-1.0.0 autogluon.core-1.0.0 autogluon.features-1.0.0 autogluon.multimodal-1.0.0 autogluon.tabular-1.0.0 autogluon.timeseries-1.0.0 backoff-2.2.1 blessed-1.20.0 boto3-1.33.6 botocore-1.33.6 catboost-1.2.2 colorama-0.4.6 colorful-0.5.5 crcmod-1.7 croniter-1.4.1 datasets-2.15.0 dateutils-0.6.12 deepdiff-6.7.1 dill-0.3.7 distlib-0.3.7 evaluate-0.4.1 fastapi-0.104.1 gluonts-0.14.2 gpustat-1.1.1 h11-0.14.0 inquirer-3.1.4 jmespath-0.10.0 jsonschema-4.17.3 lightning-2.0.9.post0 lightning-cloud-0.5.57 lightning-utilities-0.10.0 lit-17.0.6 mlforecast-0.10.0 model-index-0.1.11 multiprocess-0.70.15 nlpaug-1.1.11 nptyping-2.4.1 nvidia-cublas-cu11-11.10.3.66 nvidia-cuda-cupti-cu11-11.7.101 nvidia-cuda-nvrtc-cu11-11.7.99 nvidia-cuda-runtime-cu11-11.7.99 nvidia-cudnn-cu11-8.5.0.96 nvidia-cufft-cu11-10.9.0.58 nvidia-curand-cu11-10.2.10.91 nvidia-cusolver-cu11-11.4.0.1 nvidia-cusparse-cu11-11.7.4.91 nvidia-ml-py-12.535.133 nvidia-ml-py3-7.352.0 nvidia-nccl-cu11-2.14.3 nvidia-nvtx-cu11-11.7.91 omegaconf-2.2.3 opencensus-0.11.3 opencensus-context-0.1.3 opendatalab-0.0.10 openmim-0.3.9 openxlab-0.0.29 ordered-set-4.1.0 orjson-3.9.10 oss2-2.17.0 pandas-2.1.3 platformdirs-3.11.0 py-spy-0.3.14 pyarrow-hotfix-0.6 pycryptodome-3.19.0 pyrsistent-0.20.0 pytesseract-0.3.10 python-editor-1.0.4 python-multipart-0.0.6 pytorch-lightning-2.0.9.post0 pytorch-metric-learning-1.7.3 ray-2.6.3 readchar-4.0.5 requests-2.28.2 responses-0.18.0 rich-13.4.2 s3transfer-0.8.2 scikit-learn-1.3.2 sentencepiece-0.1.99 seqeval-1.2.2 setuptools-60.2.0 starlette-0.27.0 starsessions-1.3.0 statsforecast-1.4.0 tensorboardX-2.6.2.2 timm-0.9.12 tokenizers-0.13.3 torch-2.0.1 torchmetrics-1.1.2 torchvision-0.15.2 tqdm-4.65.2 transformers-4.31.0 triton-2.0.0 types-python-dateutil-2.8.19.14 typing-extensions-4.8.0 tzdata-2023.3 urllib3-1.26.18 utilsforecast-0.0.10 uvicorn-0.24.0.post1 virtualenv-20.21.0 websockets-12.0 window-ops-0.0.14\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": "8e70fe1b-104d-4899-e8ef-0348d9d693bb"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive\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/train.csv')\n",
        "test_df = pd.read_csv('/content/test.csv')\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "IS0PapXlyFm2",
        "outputId": "2a6c4132-4598-4ba5-b81b-217ad0bb12cd"
      },
      "outputs": [
        {
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              "      <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",
              "    </tr>\n",
              "    <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",
              "    </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",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>59397 rows × 7 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
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              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-e70e5fe0-651a-4771-9cc0-b9dcf5e8d55a');\n",
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              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "          + ' 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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              "        element.appendChild(docLink);\n",
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              "\n",
              "\n",
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              "            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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              "  </button>\n",
              "\n",
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              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
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              "\n",
              "  .colab-df-quickchart {\n",
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              "    border: none;\n",
              "    border-radius: 50%;\n",
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              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
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              "    width: 32px;\n",
              "  }\n",
              "\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",
              "    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",
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              "    20% {\n",
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              "      border-left-color: var(--fill-color);\n",
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              "    40% {\n",
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              "      border-right-color: var(--fill-color);\n",
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              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
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              "    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-01958664-fc07-49f3-98c1-708ed01fa2dc button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "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]"
            ]
          },
          "execution_count": 5,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "train_df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "ZHFUR90iyFtT",
        "outputId": "fd519d9c-a303-434e-db5f-6288a0592768"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-ae3186ff-31f9-41e8-8068-ba494156ead0\" 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",
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              "</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",
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              "                                                    [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",
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              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-a6408f19-400d-4fb0-971e-1cc9ee35a786')\"\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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              "  </button>\n",
              "\n",
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              "  .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",
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              "      --hover-bg-color: #434B5C;\n",
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              "      --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",
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              "  }\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",
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              "    }\n",
              "    90% {\n",
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              "      border-bottom-color: var(--fill-color);\n",
              "    }\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-a6408f19-400d-4fb0-971e-1cc9ee35a786 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "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]"
            ]
          },
          "execution_count": 6,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "test_df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "mL5M9ACuyFyr"
      },
      "outputs": [],
      "source": [
        "# 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": 424
        },
        "id": "-54M_NNu24Bd",
        "outputId": "958a9cd5-4256-4eba-f2d4-8a2b8dddb276"
      },
      "outputs": [
        {
          "data": {
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              "      <th>0</th>\n",
              "      <td>TG_A_J_20190101</td>\n",
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              "      <td>J</td>\n",
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              "      <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",
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              "      <td>...</td>\n",
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              "    </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",
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              "    <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",
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              "    <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",
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              "      <td>J</td>\n",
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              "  }\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-19f57ba1-fdb9-47de-9727-4b1aca38a67a button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "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]"
            ]
          },
          "execution_count": 8,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "train_df"
      ]
    },
    {
      "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"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "id": "DgeFJJCO368-",
        "outputId": "5cecd9fd-df3f-4155-ff30-2d867677590f"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th>supply(kg)</th>\n",
              "      <th>price(원/kg)</th>\n",
              "      <th>year</th>\n",
              "      <th>month</th>\n",
              "      <th>day</th>\n",
              "      <th>weekday</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>item_id</th>\n",
              "      <th>timestamp</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th rowspan=\"5\" valign=\"top\">TG_A_J</th>\n",
              "      <th>2019-01-01</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2019</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </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",
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              "      padding: 0 0 0 0;\n",
              "      width: 32px;\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",
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              "\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",
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              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-6147cee3-1172-43b6-a5c6-3e351e07b0ca 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-6147cee3-1172-43b6-a5c6-3e351e07b0ca');\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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              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
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              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
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              "      --disabled-fill-color: #666;\n",
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              "\n",
              "  .colab-df-quickchart {\n",
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              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
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              "    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",
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              "      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-92b4e0fc-8158-41dd-83c0-ce28b2c2dd94 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "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]"
            ]
          },
          "execution_count": 10,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "tr_time_df\n",
        "# 훈련 데이터 완성본"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true,
          "base_uri": "https://localhost:8080/"
        },
        "id": "7rloxfEn37HU",
        "outputId": "3bc6c9fa-6bdc-4954-deab-820a28dca995"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "No path specified. Models will be saved in: \"AutogluonModels/ag-20231202_131133\"\n",
            "Beginning AutoGluon training... Time limit = 20000s\n",
            "AutoGluon will save models to 'AutogluonModels/ag-20231202_131133'\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:       11.34 GB / 12.68 GB (89.4%)\n",
            "Disk Space Avail:   73.60 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': [],\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",
            "\tpast covariates:  ['supply(kg)', 'year', 'month', 'day', 'weekday']\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 2023-12-02 13:11:33\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 1763.6s of the 19999.9s of remaining time.\n",
            "\t-927.8898     = Validation score (-RMSE)\n",
            "\t0.31    s     = Training runtime\n",
            "\t0.11    s     = Validation (prediction) runtime\n",
            "Training timeseries model CrostonSBA. Training for up to 1939.9s of the 19999.4s of remaining time.\n",
            "\t-1013.0959    = Validation score (-RMSE)\n",
            "\t0.26    s     = Training runtime\n",
            "\t0.14    s     = Validation (prediction) runtime\n",
            "Training timeseries model NPTS. Training for up to 2155.4s of the 19999.0s of remaining time.\n",
            "\t-1083.9432    = Validation score (-RMSE)\n",
            "\t13.53   s     = Training runtime\n",
            "\t13.02   s     = Validation (prediction) runtime\n",
            "Training timeseries model AutoETS. Training for up to 2421.6s of the 19972.5s of remaining time.\n",
            "\t-800.6345     = Validation score (-RMSE)\n",
            "\t22.68   s     = Training runtime\n",
            "\t21.69   s     = Validation (prediction) runtime\n",
            "Training timeseries model DynamicOptimizedTheta. Training for up to 2761.2s of the 19928.1s of remaining time.\n",
            "\t-846.5049     = Validation score (-RMSE)\n",
            "\t105.73  s     = Training runtime\n",
            "\t100.33  s     = Validation (prediction) runtime\n",
            "Training timeseries model AutoARIMA. Training for up to 3187.0s of the 19722.0s of remaining time.\n",
            "\t-817.9428     = Validation score (-RMSE)\n",
            "\t197.88  s     = Training runtime\n",
            "\t285.70  s     = Validation (prediction) runtime\n",
            "Training timeseries model RecursiveTabular. Training for up to 3727.7s of the 19238.4s of remaining time.\n",
            "\t-798.1943     = Validation score (-RMSE)\n",
            "\t166.97  s     = Training runtime\n",
            "\t1.04    s     = Validation (prediction) runtime\n",
            "Training timeseries model DirectTabular. Training for up to 4617.6s of the 19070.4s of remaining time.\n",
            "\t-929.9025     = Validation score (-RMSE)\n",
            "\t6.43    s     = Training runtime\n",
            "\t0.26    s     = Validation (prediction) runtime\n",
            "Training timeseries model DeepAR. Training for up to 6154.6s of the 19063.7s 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 9230.5s of the 19061.1s 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 18461.0s of the 19061.0s 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': 0.25, 'DirectTabular': 0.31, 'DynamicOptimizedTheta': 0.24, 'RecursiveTabular': 0.14, 'SeasonalNaive': 0.07}\n",
            "\t-734.6666     = Validation score (-RMSE)\n",
            "\t2.64    s     = Training runtime\n",
            "\t123.43  s     = Validation (prediction) runtime\n",
            "Training complete. Models trained: ['SeasonalNaive', 'CrostonSBA', 'NPTS', 'AutoETS', 'DynamicOptimizedTheta', 'AutoARIMA', 'RecursiveTabular', 'DirectTabular', 'WeightedEnsemble']\n",
            "Total runtime: 941.66 s\n",
            "Best model: WeightedEnsemble\n",
            "Best model score: -734.6666\n",
            "Model not specified in predict, will default to the model with the best validation score: WeightedEnsemble\n"
          ]
        }
      ],
      "source": [
        "model = TimeSeriesPredictor(prediction_length = 28,target = 'price(원/kg)',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,random_seed=42) # 예측(테스트 데이터를 이용)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 461
        },
        "id": "yfHOx9c1_dih",
        "outputId": "11c493b7-5e47-40bf-a86c-731c91c4cba9"
      },
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        {
          "output_type": "execute_result",
          "data": {
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              "     item_id  timestamp         mean          0.1          0.2          0.3  \\\n",
              "0     TG_A_J 2023-03-04  3201.040658  1014.224691  1731.805451  2252.516932   \n",
              "1     TG_A_J 2023-03-05   171.283886 -2303.072663 -1462.010363  -859.451167   \n",
              "2     TG_A_J 2023-03-06  3015.722557   282.972795  1258.627271  1918.931381   \n",
              "3     TG_A_J 2023-03-07  2913.598619    94.526271  1057.887903  1716.013714   \n",
              "4     TG_A_J 2023-03-08  3137.002247   367.848673  1298.064049  2005.265355   \n",
              "...      ...        ...          ...          ...          ...          ...   \n",
              "1087  RD_F_J 2023-03-27   505.578259  -296.407113   -25.868709   168.093178   \n",
              "1088  RD_F_J 2023-03-28   512.398206  -304.512460   -24.245767   176.954149   \n",
              "1089  RD_F_J 2023-03-29   520.977722  -325.894451   -44.340816   173.744366   \n",
              "1090  RD_F_J 2023-03-30   505.552488  -334.315351   -45.483644   159.486516   \n",
              "1091  RD_F_J 2023-03-31   510.114801  -369.334474   -60.990631   146.469141   \n",
              "\n",
              "              0.4          0.5          0.6          0.7          0.8  \\\n",
              "0     2773.087008  3232.091247  3703.256648  4175.427551  4718.742285   \n",
              "1     -370.216772    68.127300   572.480453  1081.972594  1670.616836   \n",
              "2     2488.431536  2988.347841  3462.310786  4024.385954  4662.413173   \n",
              "3     2290.607035  2811.742070  3377.039288  3941.236202  4578.514693   \n",
              "4     2581.090523  3111.490290  3639.469931  4252.882991  5013.252716   \n",
              "...           ...          ...          ...          ...          ...   \n",
              "1087   341.808831   503.137052   653.240940   820.516249  1018.454268   \n",
              "1088   347.049375   515.118519   681.886455   847.852844  1042.585479   \n",
              "1089   346.233046   513.714945   671.033904   852.391538  1066.197703   \n",
              "1090   336.929758   497.784155   660.103860   837.291838  1048.910507   \n",
              "1091   331.126950   496.467179   674.559634   869.760929  1070.815434   \n",
              "\n",
              "              0.9  \n",
              "0     5538.313916  \n",
              "1     2462.201111  \n",
              "2     5556.145089  \n",
              "3     5539.550287  \n",
              "4     6016.495786  \n",
              "...           ...  \n",
              "1087  1305.045324  \n",
              "1088  1319.897897  \n",
              "1089  1342.939363  \n",
              "1090  1333.770781  \n",
              "1091  1348.748021  \n",
              "\n",
              "[1092 rows x 12 columns]"
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              "      <th></th>\n",
              "      <th>item_id</th>\n",
              "      <th>timestamp</th>\n",
              "      <th>mean</th>\n",
              "      <th>0.1</th>\n",
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              "      <th>0.3</th>\n",
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              "      <th>0</th>\n",
              "      <td>TG_A_J</td>\n",
              "      <td>2023-03-04</td>\n",
              "      <td>3201.040658</td>\n",
              "      <td>1014.224691</td>\n",
              "      <td>1731.805451</td>\n",
              "      <td>2252.516932</td>\n",
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              "      <th>1</th>\n",
              "      <td>TG_A_J</td>\n",
              "      <td>2023-03-05</td>\n",
              "      <td>171.283886</td>\n",
              "      <td>-2303.072663</td>\n",
              "      <td>-1462.010363</td>\n",
              "      <td>-859.451167</td>\n",
              "      <td>-370.216772</td>\n",
              "      <td>68.127300</td>\n",
              "      <td>572.480453</td>\n",
              "      <td>1081.972594</td>\n",
              "      <td>1670.616836</td>\n",
              "      <td>2462.201111</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>TG_A_J</td>\n",
              "      <td>2023-03-06</td>\n",
              "      <td>3015.722557</td>\n",
              "      <td>282.972795</td>\n",
              "      <td>1258.627271</td>\n",
              "      <td>1918.931381</td>\n",
              "      <td>2488.431536</td>\n",
              "      <td>2988.347841</td>\n",
              "      <td>3462.310786</td>\n",
              "      <td>4024.385954</td>\n",
              "      <td>4662.413173</td>\n",
              "      <td>5556.145089</td>\n",
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              "      <th>3</th>\n",
              "      <td>TG_A_J</td>\n",
              "      <td>2023-03-07</td>\n",
              "      <td>2913.598619</td>\n",
              "      <td>94.526271</td>\n",
              "      <td>1057.887903</td>\n",
              "      <td>1716.013714</td>\n",
              "      <td>2290.607035</td>\n",
              "      <td>2811.742070</td>\n",
              "      <td>3377.039288</td>\n",
              "      <td>3941.236202</td>\n",
              "      <td>4578.514693</td>\n",
              "      <td>5539.550287</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>TG_A_J</td>\n",
              "      <td>2023-03-08</td>\n",
              "      <td>3137.002247</td>\n",
              "      <td>367.848673</td>\n",
              "      <td>1298.064049</td>\n",
              "      <td>2005.265355</td>\n",
              "      <td>2581.090523</td>\n",
              "      <td>3111.490290</td>\n",
              "      <td>3639.469931</td>\n",
              "      <td>4252.882991</td>\n",
              "      <td>5013.252716</td>\n",
              "      <td>6016.495786</td>\n",
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              "      <td>...</td>\n",
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              "      <th>1087</th>\n",
              "      <td>RD_F_J</td>\n",
              "      <td>2023-03-27</td>\n",
              "      <td>505.578259</td>\n",
              "      <td>-296.407113</td>\n",
              "      <td>-25.868709</td>\n",
              "      <td>168.093178</td>\n",
              "      <td>341.808831</td>\n",
              "      <td>503.137052</td>\n",
              "      <td>653.240940</td>\n",
              "      <td>820.516249</td>\n",
              "      <td>1018.454268</td>\n",
              "      <td>1305.045324</td>\n",
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              "    <tr>\n",
              "      <th>1088</th>\n",
              "      <td>RD_F_J</td>\n",
              "      <td>2023-03-28</td>\n",
              "      <td>512.398206</td>\n",
              "      <td>-304.512460</td>\n",
              "      <td>-24.245767</td>\n",
              "      <td>176.954149</td>\n",
              "      <td>347.049375</td>\n",
              "      <td>515.118519</td>\n",
              "      <td>681.886455</td>\n",
              "      <td>847.852844</td>\n",
              "      <td>1042.585479</td>\n",
              "      <td>1319.897897</td>\n",
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              "    <tr>\n",
              "      <th>1089</th>\n",
              "      <td>RD_F_J</td>\n",
              "      <td>2023-03-29</td>\n",
              "      <td>520.977722</td>\n",
              "      <td>-325.894451</td>\n",
              "      <td>-44.340816</td>\n",
              "      <td>173.744366</td>\n",
              "      <td>346.233046</td>\n",
              "      <td>513.714945</td>\n",
              "      <td>671.033904</td>\n",
              "      <td>852.391538</td>\n",
              "      <td>1066.197703</td>\n",
              "      <td>1342.939363</td>\n",
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              "    <tr>\n",
              "      <th>1090</th>\n",
              "      <td>RD_F_J</td>\n",
              "      <td>2023-03-30</td>\n",
              "      <td>505.552488</td>\n",
              "      <td>-334.315351</td>\n",
              "      <td>-45.483644</td>\n",
              "      <td>159.486516</td>\n",
              "      <td>336.929758</td>\n",
              "      <td>497.784155</td>\n",
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              "      <td>837.291838</td>\n",
              "      <td>1048.910507</td>\n",
              "      <td>1333.770781</td>\n",
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              "    <tr>\n",
              "      <th>1091</th>\n",
              "      <td>RD_F_J</td>\n",
              "      <td>2023-03-31</td>\n",
              "      <td>510.114801</td>\n",
              "      <td>-369.334474</td>\n",
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              "      <td>496.467179</td>\n",
              "      <td>674.559634</td>\n",
              "      <td>869.760929</td>\n",
              "      <td>1070.815434</td>\n",
              "      <td>1348.748021</td>\n",
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              "      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-2d6306ad-a48f-4669-9a98-d39c4ee5264c 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-2d6306ad-a48f-4669-9a98-d39c4ee5264c');\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-24ab3820-e9a0-4c83-bc49-dbc482fc22c3\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-24ab3820-e9a0-4c83-bc49-dbc482fc22c3')\"\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-24ab3820-e9a0-4c83-bc49-dbc482fc22c3 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": 16
        }
      ],
      "source": [
        "final = pd.DataFrame(result).reset_index()\n",
        "final"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "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": 461
        },
        "id": "x8x7C1Ehzs10",
        "outputId": "55b10e84-5bcc-4e4f-f70d-6d09c37f0f4b"
      },
      "execution_count": 22,
      "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  3201.040658  1014.224691  1731.805451  2252.516932   \n",
              "1     TG_A_J 2023-03-05     0.000000 -2303.072663 -1462.010363  -859.451167   \n",
              "2     TG_A_J 2023-03-06  3015.722557   282.972795  1258.627271  1918.931381   \n",
              "3     TG_A_J 2023-03-07  2913.598619    94.526271  1057.887903  1716.013714   \n",
              "4     TG_A_J 2023-03-08  3137.002247   367.848673  1298.064049  2005.265355   \n",
              "...      ...        ...          ...          ...          ...          ...   \n",
              "1087  RD_F_J 2023-03-27   505.578259  -296.407113   -25.868709   168.093178   \n",
              "1088  RD_F_J 2023-03-28   512.398206  -304.512460   -24.245767   176.954149   \n",
              "1089  RD_F_J 2023-03-29   520.977722  -325.894451   -44.340816   173.744366   \n",
              "1090  RD_F_J 2023-03-30   505.552488  -334.315351   -45.483644   159.486516   \n",
              "1091  RD_F_J 2023-03-31   510.114801  -369.334474   -60.990631   146.469141   \n",
              "\n",
              "              0.4          0.5          0.6          0.7          0.8  \\\n",
              "0     2773.087008  3232.091247  3703.256648  4175.427551  4718.742285   \n",
              "1     -370.216772    68.127300   572.480453  1081.972594  1670.616836   \n",
              "2     2488.431536  2988.347841  3462.310786  4024.385954  4662.413173   \n",
              "3     2290.607035  2811.742070  3377.039288  3941.236202  4578.514693   \n",
              "4     2581.090523  3111.490290  3639.469931  4252.882991  5013.252716   \n",
              "...           ...          ...          ...          ...          ...   \n",
              "1087   341.808831   503.137052   653.240940   820.516249  1018.454268   \n",
              "1088   347.049375   515.118519   681.886455   847.852844  1042.585479   \n",
              "1089   346.233046   513.714945   671.033904   852.391538  1066.197703   \n",
              "1090   336.929758   497.784155   660.103860   837.291838  1048.910507   \n",
              "1091   331.126950   496.467179   674.559634   869.760929  1070.815434   \n",
              "\n",
              "              0.9  \n",
              "0     5538.313916  \n",
              "1     2462.201111  \n",
              "2     5556.145089  \n",
              "3     5539.550287  \n",
              "4     6016.495786  \n",
              "...           ...  \n",
              "1087  1305.045324  \n",
              "1088  1319.897897  \n",
              "1089  1342.939363  \n",
              "1090  1333.770781  \n",
              "1091  1348.748021  \n",
              "\n",
              "[1092 rows x 12 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-0163cb72-4d0e-42ec-bb2e-9c886ad953c5\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "        text-align: right;\n",
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              "</style>\n",
              "<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>3201.040658</td>\n",
              "      <td>1014.224691</td>\n",
              "      <td>1731.805451</td>\n",
              "      <td>2252.516932</td>\n",
              "      <td>2773.087008</td>\n",
              "      <td>3232.091247</td>\n",
              "      <td>3703.256648</td>\n",
              "      <td>4175.427551</td>\n",
              "      <td>4718.742285</td>\n",
              "      <td>5538.313916</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>-2303.072663</td>\n",
              "      <td>-1462.010363</td>\n",
              "      <td>-859.451167</td>\n",
              "      <td>-370.216772</td>\n",
              "      <td>68.127300</td>\n",
              "      <td>572.480453</td>\n",
              "      <td>1081.972594</td>\n",
              "      <td>1670.616836</td>\n",
              "      <td>2462.201111</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>TG_A_J</td>\n",
              "      <td>2023-03-06</td>\n",
              "      <td>3015.722557</td>\n",
              "      <td>282.972795</td>\n",
              "      <td>1258.627271</td>\n",
              "      <td>1918.931381</td>\n",
              "      <td>2488.431536</td>\n",
              "      <td>2988.347841</td>\n",
              "      <td>3462.310786</td>\n",
              "      <td>4024.385954</td>\n",
              "      <td>4662.413173</td>\n",
              "      <td>5556.145089</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>TG_A_J</td>\n",
              "      <td>2023-03-07</td>\n",
              "      <td>2913.598619</td>\n",
              "      <td>94.526271</td>\n",
              "      <td>1057.887903</td>\n",
              "      <td>1716.013714</td>\n",
              "      <td>2290.607035</td>\n",
              "      <td>2811.742070</td>\n",
              "      <td>3377.039288</td>\n",
              "      <td>3941.236202</td>\n",
              "      <td>4578.514693</td>\n",
              "      <td>5539.550287</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>TG_A_J</td>\n",
              "      <td>2023-03-08</td>\n",
              "      <td>3137.002247</td>\n",
              "      <td>367.848673</td>\n",
              "      <td>1298.064049</td>\n",
              "      <td>2005.265355</td>\n",
              "      <td>2581.090523</td>\n",
              "      <td>3111.490290</td>\n",
              "      <td>3639.469931</td>\n",
              "      <td>4252.882991</td>\n",
              "      <td>5013.252716</td>\n",
              "      <td>6016.495786</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>505.578259</td>\n",
              "      <td>-296.407113</td>\n",
              "      <td>-25.868709</td>\n",
              "      <td>168.093178</td>\n",
              "      <td>341.808831</td>\n",
              "      <td>503.137052</td>\n",
              "      <td>653.240940</td>\n",
              "      <td>820.516249</td>\n",
              "      <td>1018.454268</td>\n",
              "      <td>1305.045324</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1088</th>\n",
              "      <td>RD_F_J</td>\n",
              "      <td>2023-03-28</td>\n",
              "      <td>512.398206</td>\n",
              "      <td>-304.512460</td>\n",
              "      <td>-24.245767</td>\n",
              "      <td>176.954149</td>\n",
              "      <td>347.049375</td>\n",
              "      <td>515.118519</td>\n",
              "      <td>681.886455</td>\n",
              "      <td>847.852844</td>\n",
              "      <td>1042.585479</td>\n",
              "      <td>1319.897897</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1089</th>\n",
              "      <td>RD_F_J</td>\n",
              "      <td>2023-03-29</td>\n",
              "      <td>520.977722</td>\n",
              "      <td>-325.894451</td>\n",
              "      <td>-44.340816</td>\n",
              "      <td>173.744366</td>\n",
              "      <td>346.233046</td>\n",
              "      <td>513.714945</td>\n",
              "      <td>671.033904</td>\n",
              "      <td>852.391538</td>\n",
              "      <td>1066.197703</td>\n",
              "      <td>1342.939363</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1090</th>\n",
              "      <td>RD_F_J</td>\n",
              "      <td>2023-03-30</td>\n",
              "      <td>505.552488</td>\n",
              "      <td>-334.315351</td>\n",
              "      <td>-45.483644</td>\n",
              "      <td>159.486516</td>\n",
              "      <td>336.929758</td>\n",
              "      <td>497.784155</td>\n",
              "      <td>660.103860</td>\n",
              "      <td>837.291838</td>\n",
              "      <td>1048.910507</td>\n",
              "      <td>1333.770781</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1091</th>\n",
              "      <td>RD_F_J</td>\n",
              "      <td>2023-03-31</td>\n",
              "      <td>510.114801</td>\n",
              "      <td>-369.334474</td>\n",
              "      <td>-60.990631</td>\n",
              "      <td>146.469141</td>\n",
              "      <td>331.126950</td>\n",
              "      <td>496.467179</td>\n",
              "      <td>674.559634</td>\n",
              "      <td>869.760929</td>\n",
              "      <td>1070.815434</td>\n",
              "      <td>1348.748021</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1092 rows × 12 columns</p>\n",
              "</div>\n",
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              "\n",
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              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "      width: 32px;\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-0163cb72-4d0e-42ec-bb2e-9c886ad953c5 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-0163cb72-4d0e-42ec-bb2e-9c886ad953c5');\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-84d649c5-2ec8-483c-ad6e-7f399b98a80e\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-84d649c5-2ec8-483c-ad6e-7f399b98a80e')\"\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-84d649c5-2ec8-483c-ad6e-7f399b98a80e 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": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 마지막 제출할때 음수값을 모두 0으로 바꿔준다.\n",
        "submission = pd.read_csv('/content/sample_submission.csv')\n",
        "submission['answer'] = final.reset_index()['mean']\n",
        "submission.loc[ submission['answer'] < 0.0, 'answer'] = 0.0\n",
        "\n",
        "\n",
        "submission.to_csv('./final_submission.csv', index=False)"
      ],
      "metadata": {
        "id": "PMu26sa6zzQr"
      },
      "execution_count": 24,
      "outputs": []
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}