team1.ipynb

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    "accelerator": "TPU",
    "gpuClass": "standard"
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  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# 파일 업로드\n",
        "1. 왼쪽에 폴더 모양 버튼을 누른다\n",
        "  ![image.png](data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAC0AAAAzCAYAAAADxoFxAAAAAXNSR0IArs4c6QAAAARnQU1BAACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAAEzSURBVGhD7Za/agJBEIfHP9FGTjR4gQOrNBZiYWfrE6TNC6TMw6T0Dex8grTpUsTGxipyoI0ETJGkMLebNRjOYu+Y/PDg98Hc7XBbfAyzc1sKgmCfIIbLdlsq1Yp0OqHNz40oiuy7bJ8Fo1DScRzbYKVRFFI60/SYjV7cSuTmaeBWeNgeKCiNgtIoKI2C0igojUJNOqx/yjjcpuI/ULvlHX875nHTkodl12U6qEibit5fv7oszeajlsSFy/4yf2vIdHXlMj9Ue/pHLh2GsP51Mm67a+kHO7vHF9VK3z33fiV9MOdgMlzY9jFt5AtHHgpKo6A0ClXpfvPdzlzvSPbnQWVOH+ZtXrLOd7W7hxHPU7ksP5UDatJIeBBRUBoFpVFQGgWlUVAaRaYL07nA9kBBaQwi36kzlVbGgvGUAAAAAElFTkSuQmCC)\n",
        "2. 파일 업로드 버튼을 누른다. ![image.png](data:image/png;base64,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)\n",
        "3. 제공된 데이터 csv 파일들을 업로드 한다.\n",
        "----\n",
        "* 참고사항: 이 과정은 코랩을 새로고침 할 때 마다 해주어야 함. (정확히는 런타임이 다시 실행 될 때 마다)"
      ],
      "metadata": {
        "id": "TSSE6tq4nSk_"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 데이터 전처리"
      ],
      "metadata": {
        "id": "0F4a_7HepYem"
      }
    },
    {
      "cell_type": "code",
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        "outputId": "3495b3f9-cdf1-468e-fde3-bafd0eb4084b"
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      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:TPU system grpc://10.8.117.226:8470 has already been initialized. Reinitializing the TPU can cause previously created variables on TPU to be lost.\n"
          ]
        }
      ],
      "source": [
        "import pandas as pd\n",
        "import tensorflow as tf\n",
        "import os\n",
        "\n",
        "resolver = tf.distribute.cluster_resolver.TPUClusterResolver(tpu='grpc://' + os.environ['COLAB_TPU_ADDR'])\n",
        "\n",
        "tf.config.experimental_connect_to_cluster(resolver)\n",
        "tf.tpu.experimental.initialize_tpu_system(resolver)\n",
        "strategy = tf.distribute.TPUStrategy(resolver)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 데이터셋 불러오기\n",
        "dataset_train = pd.read_csv('new_weather_data_train.csv', index_col=0)\n",
        "dataset_train"
      ],
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        "id": "mv54KdoHodo_",
        "outputId": "4cd86317-1af8-4e4c-8d1f-ad39afc21591"
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      "execution_count": null,
      "outputs": [
        {
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          "data": {
            "text/plain": [
              "      month  day  hour  temperature  temperature_dew_point  relative_humidity  \\\n",
              "0         1    1     0         -1.8                   -3.9                 86   \n",
              "1         1    1     1         -1.8                   -3.7                 87   \n",
              "2         1    1     2         -1.8                   -3.4                 89   \n",
              "3         1    1     3         -1.5                   -3.2                 88   \n",
              "4         1    1     4         -1.5                   -3.3                 88   \n",
              "...     ...  ...   ...          ...                    ...                ...   \n",
              "7022     10   19    14         13.0                   12.1                 94   \n",
              "7023     10   19    15         13.3                   12.1                 92   \n",
              "7024     10   19    16         12.9                   12.3                 96   \n",
              "7025     10   19    17         13.1                   12.1                 94   \n",
              "7026     10   19    18         13.1                   12.1                 94   \n",
              "\n",
              "      wind_speed  visibility  pressure  weather  \n",
              "0              4         8.0    101.24        2  \n",
              "1              4         8.0    101.24        2  \n",
              "2              7         4.0    101.26        0  \n",
              "3              6         4.0    101.27        0  \n",
              "4              7         4.8    101.23        2  \n",
              "...          ...         ...       ...      ...  \n",
              "7022          15         8.0    100.55        0  \n",
              "7023          13         9.7    100.45        0  \n",
              "7024           7         3.2    100.45        0  \n",
              "7025          17         6.4    100.38        0  \n",
              "7026          22        12.9    100.29        0  \n",
              "\n",
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              "      <th>month</th>\n",
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              "      <td>12.1</td>\n",
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              "      <td>15</td>\n",
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              "      <th>7023</th>\n",
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              "      <td>13.3</td>\n",
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              "      <td>12.9</td>\n",
              "      <td>12.3</td>\n",
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              "      <td>6.4</td>\n",
              "      <td>100.38</td>\n",
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          },
          "metadata": {},
          "execution_count": 369
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 데이터셋 정보 확인\n",
        "dataset_train.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "x5u9d2w0ok_D",
        "outputId": "cd512638-0f1f-46c8-8cd2-6b534064ed90"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 7027 entries, 0 to 7026\n",
            "Data columns (total 10 columns):\n",
            " #   Column                 Non-Null Count  Dtype  \n",
            "---  ------                 --------------  -----  \n",
            " 0   month                  7027 non-null   int64  \n",
            " 1   day                    7027 non-null   int64  \n",
            " 2   hour                   7027 non-null   int64  \n",
            " 3   temperature            7027 non-null   float64\n",
            " 4   temperature_dew_point  7027 non-null   float64\n",
            " 5   relative_humidity      7027 non-null   int64  \n",
            " 6   wind_speed             7027 non-null   int64  \n",
            " 7   visibility             7027 non-null   float64\n",
            " 8   pressure               7027 non-null   float64\n",
            " 9   weather                7027 non-null   int64  \n",
            "dtypes: float64(4), int64(6)\n",
            "memory usage: 603.9 KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Feature 데이터와 Target 데이터를 분리\n",
        "* Feature: Target 데이터를 예측하기 위한 데이터 속성\n",
        "* Target: 예측 해야하는 데이터. 여기서는 총 22가지의 카테고리 데이터"
      ],
      "metadata": {
        "id": "rYo4JVKipopE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Feature 데이터 분리\n",
        "dataset_train_x = dataset_train.drop(['weather'], axis=1)\n",
        "dataset_train_x"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "4vPRUEBiozxi",
        "outputId": "92f74a7e-3144-4612-937b-734f44b1771c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      month  day  hour  temperature  temperature_dew_point  relative_humidity  \\\n",
              "0         1    1     0         -1.8                   -3.9                 86   \n",
              "1         1    1     1         -1.8                   -3.7                 87   \n",
              "2         1    1     2         -1.8                   -3.4                 89   \n",
              "3         1    1     3         -1.5                   -3.2                 88   \n",
              "4         1    1     4         -1.5                   -3.3                 88   \n",
              "...     ...  ...   ...          ...                    ...                ...   \n",
              "7022     10   19    14         13.0                   12.1                 94   \n",
              "7023     10   19    15         13.3                   12.1                 92   \n",
              "7024     10   19    16         12.9                   12.3                 96   \n",
              "7025     10   19    17         13.1                   12.1                 94   \n",
              "7026     10   19    18         13.1                   12.1                 94   \n",
              "\n",
              "      wind_speed  visibility  pressure  \n",
              "0              4         8.0    101.24  \n",
              "1              4         8.0    101.24  \n",
              "2              7         4.0    101.26  \n",
              "3              6         4.0    101.27  \n",
              "4              7         4.8    101.23  \n",
              "...          ...         ...       ...  \n",
              "7022          15         8.0    100.55  \n",
              "7023          13         9.7    100.45  \n",
              "7024           7         3.2    100.45  \n",
              "7025          17         6.4    100.38  \n",
              "7026          22        12.9    100.29  \n",
              "\n",
              "[7027 rows x 9 columns]"
            ],
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              "      <th></th>\n",
              "      <th>month</th>\n",
              "      <th>day</th>\n",
              "      <th>hour</th>\n",
              "      <th>temperature</th>\n",
              "      <th>temperature_dew_point</th>\n",
              "      <th>relative_humidity</th>\n",
              "      <th>wind_speed</th>\n",
              "      <th>visibility</th>\n",
              "      <th>pressure</th>\n",
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              "  </thead>\n",
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              "      <td>1</td>\n",
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              "      <td>-3.7</td>\n",
              "      <td>87</td>\n",
              "      <td>4</td>\n",
              "      <td>8.0</td>\n",
              "      <td>101.24</td>\n",
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              "      <td>1</td>\n",
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              "      <td>-1.8</td>\n",
              "      <td>-3.4</td>\n",
              "      <td>89</td>\n",
              "      <td>7</td>\n",
              "      <td>4.0</td>\n",
              "      <td>101.26</td>\n",
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              "      <th>3</th>\n",
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              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>-1.5</td>\n",
              "      <td>-3.2</td>\n",
              "      <td>88</td>\n",
              "      <td>6</td>\n",
              "      <td>4.0</td>\n",
              "      <td>101.27</td>\n",
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              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>-1.5</td>\n",
              "      <td>-3.3</td>\n",
              "      <td>88</td>\n",
              "      <td>7</td>\n",
              "      <td>4.8</td>\n",
              "      <td>101.23</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",
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              "    <tr>\n",
              "      <th>7022</th>\n",
              "      <td>10</td>\n",
              "      <td>19</td>\n",
              "      <td>14</td>\n",
              "      <td>13.0</td>\n",
              "      <td>12.1</td>\n",
              "      <td>94</td>\n",
              "      <td>15</td>\n",
              "      <td>8.0</td>\n",
              "      <td>100.55</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7023</th>\n",
              "      <td>10</td>\n",
              "      <td>19</td>\n",
              "      <td>15</td>\n",
              "      <td>13.3</td>\n",
              "      <td>12.1</td>\n",
              "      <td>92</td>\n",
              "      <td>13</td>\n",
              "      <td>9.7</td>\n",
              "      <td>100.45</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7024</th>\n",
              "      <td>10</td>\n",
              "      <td>19</td>\n",
              "      <td>16</td>\n",
              "      <td>12.9</td>\n",
              "      <td>12.3</td>\n",
              "      <td>96</td>\n",
              "      <td>7</td>\n",
              "      <td>3.2</td>\n",
              "      <td>100.45</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7025</th>\n",
              "      <td>10</td>\n",
              "      <td>19</td>\n",
              "      <td>17</td>\n",
              "      <td>13.1</td>\n",
              "      <td>12.1</td>\n",
              "      <td>94</td>\n",
              "      <td>17</td>\n",
              "      <td>6.4</td>\n",
              "      <td>100.38</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7026</th>\n",
              "      <td>10</td>\n",
              "      <td>19</td>\n",
              "      <td>18</td>\n",
              "      <td>13.1</td>\n",
              "      <td>12.1</td>\n",
              "      <td>94</td>\n",
              "      <td>22</td>\n",
              "      <td>12.9</td>\n",
              "      <td>100.29</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>7027 rows × 9 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-90ea1056-dbbd-4ae0-97bf-73e2660866c6')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "        \n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
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              "          document.querySelector('#df-90ea1056-dbbd-4ae0-97bf-73e2660866c6 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-90ea1056-dbbd-4ae0-97bf-73e2660866c6');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 371
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Target 데이터 분리\n",
        "dataset_train_y = dataset_train[['weather']]\n",
        "dataset_train_y"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "ks6glbN9pDL4",
        "outputId": "8c36fcbe-aa06-4c15-c887-70a122e191f3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      weather\n",
              "0           2\n",
              "1           2\n",
              "2           0\n",
              "3           0\n",
              "4           2\n",
              "...       ...\n",
              "7022        0\n",
              "7023        0\n",
              "7024        0\n",
              "7025        0\n",
              "7026        0\n",
              "\n",
              "[7027 rows x 1 columns]"
            ],
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              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>weather</th>\n",
              "    </tr>\n",
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              "  <tbody>\n",
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              "      <th>0</th>\n",
              "      <td>2</td>\n",
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              "      <th>2</th>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2</td>\n",
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              "    <tr>\n",
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              "    <tr>\n",
              "      <th>7025</th>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>7026</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>7027 rows × 1 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8de1b914-2ec0-4cd6-a20f-fe8fee97b663')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-8de1b914-2ec0-4cd6-a20f-fe8fee97b663 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-8de1b914-2ec0-4cd6-a20f-fe8fee97b663');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 372
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "target 데이터를 원핫 인코딩으로 컴퓨터가 학습하기 좋은 형태로 변형.\n",
        "* 원 핫 인코딩\n",
        "  * 숫자로 표현된 카테고리 데이터를 0과 1로 바꾸는 작업.  \n",
        "    예) 1, 3, 2 -> [1, 0, 0], [0, 0, 1], [0, 1, 0]\n",
        "  * 일반 숫자는 크기에 따라 중요도를 학습 할 수 있으므로, 해당하는 카테고리에만 1을 주어 잘못된 방향으로 학습하는걸 방지."
      ],
      "metadata": {
        "id": "qrT9qe6TjKKM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.keras.utils import to_categorical\n",
        "dataset_train_y = to_categorical(dataset_train_y)\n",
        "dataset_train_y"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zMl57T0eiSJI",
        "outputId": "40dfcc2b-eff0-49b7-e945-0078dfd89dbf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0., 0., 1., 0., 0.],\n",
              "       [0., 0., 1., 0., 0.],\n",
              "       [1., 0., 0., 0., 0.],\n",
              "       ...,\n",
              "       [1., 0., 0., 0., 0.],\n",
              "       [1., 0., 0., 0., 0.],\n",
              "       [1., 0., 0., 0., 0.]], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 373
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**추가된 부분**\n",
        "* 각 데이터를 12시간 이전 데이터까지 포함시킴\n",
        "* 최근 12시간 날씨 데이터를 활용하여 날씨 예측의 정확도를 높이는 과정"
      ],
      "metadata": {
        "id": "0Ke0UghOvTdl"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 하나의 라벨에 최근 12개의 데이터씩 묶어주는 작업\n",
        "# 12시간 전 데이터부터 현재 데이터까지 학습에 활용\n",
        "import numpy as np\n",
        "train_x = np.zeros((len(dataset_train_x) - 12, 12, 9))\n",
        "for i in range(len(dataset_train_x) - 12):\n",
        "  x = dataset_train_x.iloc[i:i + 12, :].values\n",
        "  train_x[i,:, :] = x\n",
        "train_x.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_rEDaFQIuwvQ",
        "outputId": "1b9f5b13-d21d-4c8a-bff7-cb98d73b25c8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(7015, 12, 9)"
            ]
          },
          "metadata": {},
          "execution_count": 374
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 날씨 예측할떄 최근 12시간 로그를 활용하므로 맨 앞 11시간 라벨은 활용할 수 없음\n",
        "# 맨 앞 11시간 데이터는 12개씩 묶어줄 수 없기 때문\n",
        "# 12번째 라벨부터 학습에 사용하도록 변경\n",
        "train_y = dataset_train_y[12:,:]\n",
        "train_y.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "s2oYK22jvtua",
        "outputId": "5d3189c1-81f3-4c6f-c1c7-c3e2d812cd78"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(7015, 5)"
            ]
          },
          "metadata": {},
          "execution_count": 375
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 테스트 데이터는 feature 데이터만 존재\n",
        "dataset_test_x = pd.read_csv('new_weather_data_test_x.csv', index_col=0)\n",
        "dataset_test_x"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "wfchuW3fpHni",
        "outputId": "4b946ee9-ed8b-4795-bbd6-dc6604ea1991"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      month  day  hour  temperature  temperature_dew_point  relative_humidity  \\\n",
              "7027     10   19    19         13.5                   12.4                 93   \n",
              "7028     10   19    20         13.8                   12.9                 94   \n",
              "7029     10   19    21         13.8                   12.6                 92   \n",
              "7030     10   19    22         13.4                   12.3                 93   \n",
              "7031     10   19    23         13.5                   12.3                 92   \n",
              "...     ...  ...   ...          ...                    ...                ...   \n",
              "8779     12   31    19          0.1                   -2.7                 81   \n",
              "8780     12   31    20          0.2                   -2.4                 83   \n",
              "8781     12   31    21         -0.5                   -1.5                 93   \n",
              "8782     12   31    22         -0.2                   -1.8                 89   \n",
              "8783     12   31    23          0.0                   -2.1                 86   \n",
              "\n",
              "      wind_speed  visibility  pressure  \n",
              "7027           7         9.7    100.29  \n",
              "7028          19        12.9    100.13  \n",
              "7029          17        24.1    100.16  \n",
              "7030           7        16.1    100.15  \n",
              "7031          11        16.1    100.10  \n",
              "...          ...         ...       ...  \n",
              "8779          30         9.7    100.13  \n",
              "8780          24         9.7    100.03  \n",
              "8781          28         4.8     99.95  \n",
              "8782          28         9.7     99.91  \n",
              "8783          30        11.3     99.89  \n",
              "\n",
              "[1757 rows x 9 columns]"
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              "      <th></th>\n",
              "      <th>month</th>\n",
              "      <th>day</th>\n",
              "      <th>hour</th>\n",
              "      <th>temperature</th>\n",
              "      <th>temperature_dew_point</th>\n",
              "      <th>relative_humidity</th>\n",
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              "      <th>7027</th>\n",
              "      <td>10</td>\n",
              "      <td>19</td>\n",
              "      <td>19</td>\n",
              "      <td>13.5</td>\n",
              "      <td>12.4</td>\n",
              "      <td>93</td>\n",
              "      <td>7</td>\n",
              "      <td>9.7</td>\n",
              "      <td>100.29</td>\n",
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              "    <tr>\n",
              "      <th>7028</th>\n",
              "      <td>10</td>\n",
              "      <td>19</td>\n",
              "      <td>20</td>\n",
              "      <td>13.8</td>\n",
              "      <td>12.9</td>\n",
              "      <td>94</td>\n",
              "      <td>19</td>\n",
              "      <td>12.9</td>\n",
              "      <td>100.13</td>\n",
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              "    <tr>\n",
              "      <th>7029</th>\n",
              "      <td>10</td>\n",
              "      <td>19</td>\n",
              "      <td>21</td>\n",
              "      <td>13.8</td>\n",
              "      <td>12.6</td>\n",
              "      <td>92</td>\n",
              "      <td>17</td>\n",
              "      <td>24.1</td>\n",
              "      <td>100.16</td>\n",
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              "    <tr>\n",
              "      <th>7030</th>\n",
              "      <td>10</td>\n",
              "      <td>19</td>\n",
              "      <td>22</td>\n",
              "      <td>13.4</td>\n",
              "      <td>12.3</td>\n",
              "      <td>93</td>\n",
              "      <td>7</td>\n",
              "      <td>16.1</td>\n",
              "      <td>100.15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7031</th>\n",
              "      <td>10</td>\n",
              "      <td>19</td>\n",
              "      <td>23</td>\n",
              "      <td>13.5</td>\n",
              "      <td>12.3</td>\n",
              "      <td>92</td>\n",
              "      <td>11</td>\n",
              "      <td>16.1</td>\n",
              "      <td>100.10</td>\n",
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              "    <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",
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              "      <th>8779</th>\n",
              "      <td>12</td>\n",
              "      <td>31</td>\n",
              "      <td>19</td>\n",
              "      <td>0.1</td>\n",
              "      <td>-2.7</td>\n",
              "      <td>81</td>\n",
              "      <td>30</td>\n",
              "      <td>9.7</td>\n",
              "      <td>100.13</td>\n",
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              "    <tr>\n",
              "      <th>8780</th>\n",
              "      <td>12</td>\n",
              "      <td>31</td>\n",
              "      <td>20</td>\n",
              "      <td>0.2</td>\n",
              "      <td>-2.4</td>\n",
              "      <td>83</td>\n",
              "      <td>24</td>\n",
              "      <td>9.7</td>\n",
              "      <td>100.03</td>\n",
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              "    <tr>\n",
              "      <th>8781</th>\n",
              "      <td>12</td>\n",
              "      <td>31</td>\n",
              "      <td>21</td>\n",
              "      <td>-0.5</td>\n",
              "      <td>-1.5</td>\n",
              "      <td>93</td>\n",
              "      <td>28</td>\n",
              "      <td>4.8</td>\n",
              "      <td>99.95</td>\n",
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              "      <th>8782</th>\n",
              "      <td>12</td>\n",
              "      <td>31</td>\n",
              "      <td>22</td>\n",
              "      <td>-0.2</td>\n",
              "      <td>-1.8</td>\n",
              "      <td>89</td>\n",
              "      <td>28</td>\n",
              "      <td>9.7</td>\n",
              "      <td>99.91</td>\n",
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              "      <th>8783</th>\n",
              "      <td>12</td>\n",
              "      <td>31</td>\n",
              "      <td>23</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-2.1</td>\n",
              "      <td>86</td>\n",
              "      <td>30</td>\n",
              "      <td>11.3</td>\n",
              "      <td>99.89</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1757 rows × 9 columns</p>\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-edcca4e5-df88-4981-82d6-42da36a53556');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
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              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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            ]
          },
          "metadata": {},
          "execution_count": 376
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 하나의 라벨에 최근 12개의 데이터씩 묶어주는 작업\n",
        "# Test 데이터는 모든 데이터를 예측해야 하므로\n",
        "# 맨 앞 12시간 전 데이터 까지는 빈 부분은 모두 0으로 채움\n",
        "import numpy as np\n",
        "test_x = np.zeros((len(dataset_test_x), 12, 9))\n",
        "for i in range(1, len(dataset_test_x) + 1):\n",
        "  x = np.zeros((12, 9))\n",
        "  insert_data = dataset_test_x.iloc[(i - 12 if i - 12 > 0 else 0):i, :].values\n",
        "  x[12 - len(insert_data):12,:] = insert_data\n",
        "  test_x[i - 1,:, :] = x\n",
        "test_x.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EY2tJK7kwY_U",
        "outputId": "83609469-2287-4b8c-9c90-af3a329b4203"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1757, 12, 9)"
            ]
          },
          "metadata": {},
          "execution_count": 377
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from keras import backend as K\n",
        "def recall(y_target, y_pred):\n",
        "    y_target_yn = K.round(K.clip(y_target, 0, 1))\n",
        "    y_pred_yn = K.round(K.clip(y_pred, 0, 1))\n",
        "    count_true_positive = K.sum(y_target_yn * y_pred_yn) \n",
        "    count_true_positive_false_negative = K.sum(y_target_yn)\n",
        "    recall = count_true_positive / (count_true_positive_false_negative + K.epsilon())\n",
        "\n",
        "    # return a single tensor value\n",
        "    return recall\n",
        "\n",
        "\n",
        "def precision(y_target, y_pred):\n",
        "    y_pred_yn = K.round(K.clip(y_pred, 0, 1))\n",
        "    y_target_yn = K.round(K.clip(y_target, 0, 1))\n",
        "    count_true_positive = K.sum(y_target_yn * y_pred_yn) \n",
        "    count_true_positive_false_positive = K.sum(y_pred_yn)\n",
        "    precision = count_true_positive / (count_true_positive_false_positive + K.epsilon())\n",
        "    return precision\n",
        "\n",
        "\n",
        "def f1score(y_target, y_pred):\n",
        "    _recall = recall(y_target, y_pred)\n",
        "    _precision = precision(y_target, y_pred)\n",
        "    _f1score = ( 2 * _recall * _precision) / (_recall + _precision+ K.epsilon())\n",
        "    return _f1score"
      ],
      "metadata": {
        "id": "OA4U2Fb3Y4Xp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 모델을 구성할 라이브어리 임포트\n",
        "import tensorflow.keras.layers as layer\n",
        "from tensorflow.keras.models import Sequential\n"
      ],
      "metadata": {
        "id": "DY5YctbNs61k"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 모델 작성\n",
        "# 모델을 계속 바꿔보며 학습하시고 학습 결과를 제출하시면 됩니다.\n",
        "\n",
        "model = Sequential()\n",
        "with strategy.scope(): model.add(layer.Input(shape=(12, 9,))) # 가장 첫 레이어는 반드시 units=9\n",
        "with strategy.scope(): model.add(layer.LSTM(units=64))\n",
        "with strategy.scope(): model.add(layer.Dropout(0.3))\n",
        "with strategy.scope(): model.add(layer.Dense(units=5, activation='softmax')) # 가장 마지막 레이어는 반드시 units=5, activation='softmax'"
      ],
      "metadata": {
        "id": "DzXGveRnUB4E"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.keras.optimizers import Adam\n",
        "from tensorflow.keras.losses import categorical_crossentropy\n",
        "# 모델 컴파일\n",
        "with strategy.scope(): model.compile(loss=categorical_crossentropy, optimizer=Adam(), metrics=[f1score])"
      ],
      "metadata": {
        "id": "-VbFUrFClLey"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# model.summary()"
      ],
      "metadata": {
        "id": "bn9yQqx1kpt6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 학습\n",
        "# ephocs=학습 방법 횟수. ephocs의 값을 바꾸면 정확도가 더욱 좋아지기도 함.\n",
        "with strategy.scope():model.fit(train_x, train_y, epochs=70, batch_size=64)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gVAabCC0ldn8",
        "outputId": "f81fecfd-5bea-479b-c6ca-e506d7b5c8ec"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/70\n",
            "110/110 [==============================] - 7s 24ms/step - loss: 1.1298 - f1score: 0.2860\n",
            "Epoch 2/70\n",
            "110/110 [==============================] - 2s 17ms/step - loss: 0.9464 - f1score: 0.4500\n",
            "Epoch 3/70\n",
            "110/110 [==============================] - 2s 17ms/step - loss: 0.9051 - f1score: 0.4955\n",
            "Epoch 4/70\n",
            "110/110 [==============================] - 2s 17ms/step - loss: 0.8820 - f1score: 0.5212\n",
            "Epoch 5/70\n",
            "110/110 [==============================] - 2s 17ms/step - loss: 0.8718 - f1score: 0.5383\n",
            "Epoch 6/70\n",
            "110/110 [==============================] - 2s 17ms/step - loss: 0.8565 - f1score: 0.5436\n",
            "Epoch 7/70\n",
            "110/110 [==============================] - 2s 17ms/step - loss: 0.8496 - f1score: 0.5562\n",
            "Epoch 8/70\n",
            "110/110 [==============================] - 2s 16ms/step - loss: 0.8459 - f1score: 0.5611\n",
            "Epoch 9/70\n",
            "110/110 [==============================] - 2s 17ms/step - loss: 0.8306 - f1score: 0.5713\n",
            "Epoch 10/70\n",
            "110/110 [==============================] - 2s 18ms/step - loss: 0.8228 - f1score: 0.5780\n",
            "Epoch 11/70\n",
            "110/110 [==============================] - 2s 17ms/step - loss: 0.8200 - f1score: 0.5812\n",
            "Epoch 12/70\n",
            "110/110 [==============================] - 2s 16ms/step - loss: 0.8199 - f1score: 0.5837\n",
            "Epoch 13/70\n",
            "110/110 [==============================] - 2s 16ms/step - loss: 0.8140 - f1score: 0.5823\n",
            "Epoch 14/70\n",
            "110/110 [==============================] - 2s 16ms/step - loss: 0.8083 - f1score: 0.5832\n",
            "Epoch 15/70\n",
            "110/110 [==============================] - 2s 16ms/step - loss: 0.8076 - f1score: 0.5884\n",
            "Epoch 16/70\n",
            "110/110 [==============================] - 2s 17ms/step - loss: 0.8075 - f1score: 0.5928\n",
            "Epoch 17/70\n",
            "110/110 [==============================] - 2s 17ms/step - loss: 0.7986 - f1score: 0.6005\n",
            "Epoch 18/70\n",
            "110/110 [==============================] - 2s 15ms/step - loss: 0.8009 - f1score: 0.5993\n",
            "Epoch 19/70\n",
            "110/110 [==============================] - 2s 16ms/step - loss: 0.7972 - f1score: 0.5986\n",
            "Epoch 20/70\n",
            "110/110 [==============================] - 2s 18ms/step - loss: 0.7909 - f1score: 0.6014\n",
            "Epoch 21/70\n",
            "110/110 [==============================] - 2s 17ms/step - loss: 0.7887 - f1score: 0.6066\n",
            "Epoch 22/70\n",
            "110/110 [==============================] - 2s 18ms/step - loss: 0.7847 - f1score: 0.6066\n",
            "Epoch 23/70\n",
            "110/110 [==============================] - 2s 17ms/step - loss: 0.7800 - f1score: 0.6162\n",
            "Epoch 24/70\n",
            "110/110 [==============================] - 2s 15ms/step - loss: 0.7838 - f1score: 0.6131\n",
            "Epoch 25/70\n",
            "110/110 [==============================] - 2s 16ms/step - loss: 0.7811 - f1score: 0.6139\n",
            "Epoch 26/70\n",
            "110/110 [==============================] - 2s 16ms/step - loss: 0.7706 - f1score: 0.6247\n",
            "Epoch 27/70\n",
            "110/110 [==============================] - 2s 16ms/step - loss: 0.7667 - f1score: 0.6218\n",
            "Epoch 28/70\n",
            "110/110 [==============================] - 2s 16ms/step - loss: 0.7593 - f1score: 0.6281\n",
            "Epoch 29/70\n",
            "110/110 [==============================] - 2s 17ms/step - loss: 0.7686 - f1score: 0.6213\n",
            "Epoch 30/70\n",
            "110/110 [==============================] - 2s 16ms/step - loss: 0.7624 - f1score: 0.6251\n",
            "Epoch 31/70\n",
            "110/110 [==============================] - 2s 16ms/step - loss: 0.7591 - f1score: 0.6285\n",
            "Epoch 32/70\n",
            "110/110 [==============================] - 2s 16ms/step - loss: 0.7547 - f1score: 0.6285\n",
            "Epoch 33/70\n",
            "110/110 [==============================] - 2s 16ms/step - loss: 0.7540 - f1score: 0.6278\n",
            "Epoch 34/70\n",
            "110/110 [==============================] - 2s 17ms/step - loss: 0.7527 - f1score: 0.6358\n",
            "Epoch 35/70\n",
            "110/110 [==============================] - 2s 16ms/step - loss: 0.7505 - f1score: 0.6293\n",
            "Epoch 36/70\n",
            "110/110 [==============================] - 2s 18ms/step - loss: 0.7497 - f1score: 0.6315\n",
            "Epoch 37/70\n",
            "110/110 [==============================] - 2s 16ms/step - loss: 0.7481 - f1score: 0.6340\n",
            "Epoch 38/70\n",
            "110/110 [==============================] - 2s 16ms/step - loss: 0.7424 - f1score: 0.6420\n",
            "Epoch 39/70\n",
            "110/110 [==============================] - 2s 16ms/step - loss: 0.7394 - f1score: 0.6459\n",
            "Epoch 40/70\n",
            "110/110 [==============================] - 2s 16ms/step - loss: 0.7328 - f1score: 0.6473\n",
            "Epoch 41/70\n",
            "110/110 [==============================] - 2s 16ms/step - loss: 0.7304 - f1score: 0.6552\n",
            "Epoch 42/70\n",
            "110/110 [==============================] - 2s 17ms/step - loss: 0.7282 - f1score: 0.6511\n",
            "Epoch 43/70\n",
            "110/110 [==============================] - 2s 16ms/step - loss: 0.7263 - f1score: 0.6554\n",
            "Epoch 44/70\n",
            "110/110 [==============================] - 2s 18ms/step - loss: 0.7350 - f1score: 0.6408\n",
            "Epoch 45/70\n",
            "110/110 [==============================] - 2s 17ms/step - loss: 0.7251 - f1score: 0.6533\n",
            "Epoch 46/70\n",
            "110/110 [==============================] - 2s 18ms/step - loss: 0.7157 - f1score: 0.6603\n",
            "Epoch 47/70\n",
            "110/110 [==============================] - 2s 17ms/step - loss: 0.7158 - f1score: 0.6548\n",
            "Epoch 48/70\n",
            "110/110 [==============================] - 2s 17ms/step - loss: 0.7143 - f1score: 0.6634\n",
            "Epoch 49/70\n",
            "110/110 [==============================] - 2s 18ms/step - loss: 0.7164 - f1score: 0.6548\n",
            "Epoch 50/70\n",
            "110/110 [==============================] - 2s 17ms/step - loss: 0.7177 - f1score: 0.6594\n",
            "Epoch 51/70\n",
            "110/110 [==============================] - 2s 16ms/step - loss: 0.7077 - f1score: 0.6609\n",
            "Epoch 52/70\n",
            "110/110 [==============================] - 2s 16ms/step - loss: 0.7027 - f1score: 0.6692\n",
            "Epoch 53/70\n",
            "110/110 [==============================] - 2s 15ms/step - loss: 0.7004 - f1score: 0.6701\n",
            "Epoch 54/70\n",
            "110/110 [==============================] - 2s 16ms/step - loss: 0.7030 - f1score: 0.6702\n",
            "Epoch 55/70\n",
            "110/110 [==============================] - 2s 17ms/step - loss: 0.7049 - f1score: 0.6648\n",
            "Epoch 56/70\n",
            "110/110 [==============================] - 2s 16ms/step - loss: 0.7039 - f1score: 0.6673\n",
            "Epoch 57/70\n",
            "110/110 [==============================] - 2s 15ms/step - loss: 0.6984 - f1score: 0.6756\n",
            "Epoch 58/70\n",
            "110/110 [==============================] - 2s 15ms/step - loss: 0.6960 - f1score: 0.6700\n",
            "Epoch 59/70\n",
            "110/110 [==============================] - 2s 15ms/step - loss: 0.7004 - f1score: 0.6708\n",
            "Epoch 60/70\n",
            "110/110 [==============================] - 2s 15ms/step - loss: 0.6918 - f1score: 0.6742\n",
            "Epoch 61/70\n",
            "110/110 [==============================] - 2s 15ms/step - loss: 0.6906 - f1score: 0.6748\n",
            "Epoch 62/70\n",
            "110/110 [==============================] - 2s 17ms/step - loss: 0.6868 - f1score: 0.6793\n",
            "Epoch 63/70\n",
            "110/110 [==============================] - 2s 16ms/step - loss: 0.6854 - f1score: 0.6774\n",
            "Epoch 64/70\n",
            "110/110 [==============================] - 2s 15ms/step - loss: 0.6793 - f1score: 0.6807\n",
            "Epoch 65/70\n",
            "110/110 [==============================] - 2s 15ms/step - loss: 0.6824 - f1score: 0.6785\n",
            "Epoch 66/70\n",
            "110/110 [==============================] - 2s 16ms/step - loss: 0.6881 - f1score: 0.6800\n",
            "Epoch 67/70\n",
            "110/110 [==============================] - 2s 15ms/step - loss: 0.6787 - f1score: 0.6807\n",
            "Epoch 68/70\n",
            "110/110 [==============================] - 2s 15ms/step - loss: 0.6779 - f1score: 0.6861\n",
            "Epoch 69/70\n",
            "110/110 [==============================] - 2s 17ms/step - loss: 0.6755 - f1score: 0.6908\n",
            "Epoch 70/70\n",
            "110/110 [==============================] - 2s 16ms/step - loss: 0.6970 - f1score: 0.6769\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 날씨 예측\n",
        "import numpy as np\n",
        "pre_data = pd.DataFrame({'weather': np.argmax(model.predict(test_x), axis=1)})\n",
        "pre_data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 442
        },
        "id": "Np5EVweulson",
        "outputId": "6e13a0e8-a805-4ae0-cd02-8cbab9b77e77"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "55/55 [==============================] - 1s 8ms/step\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      weather\n",
              "0           0\n",
              "1           0\n",
              "2           2\n",
              "3           2\n",
              "4           2\n",
              "...       ...\n",
              "1752        2\n",
              "1753        2\n",
              "1754        1\n",
              "1755        1\n",
              "1756        1\n",
              "\n",
              "[1757 rows x 1 columns]"
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              "\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-832791ca-e1b1-42fa-8543-fb0db6889530');\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",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 384
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 예측 데이터 저장\n",
        "# 저장된 파일을 제출하시면 됩니다.\n",
        "pre_data.to_csv('submission_lstm2.csv')"
      ],
      "metadata": {
        "id": "r-rkMcvKpVt5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "uCAd3rHCq6bF"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}