7_Wind_Power_Prediction.ipynb

{
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  {
   "cell_type": "markdown",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "mvOY-8IyQehi",
    "outputId": "067aee07-7894-4af6-cda3-f8bdbd8cde95"
   },
   "source": [
    "## Wind Power Generation Prediction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "CKLHuOlFRz7f",
    "outputId": "62bb972b-b408-4f9f-db41-1763c70deae8"
   },
   "source": [
    "### Data\n",
    "Source: https://dacon.io/competitions/official/236066/data\n",
    "#### Train/Test Dataset\n",
    "* Columns:\n",
    "    * `id`: unique sample ID\n",
    "    * `temperature` (°C)\n",
    "    * `pressure` (hPa)\n",
    "    * `humidity` (%)\n",
    "    * `wind_speed` (m/s)\n",
    "    * `wind_direction` (degree)\n",
    "    * `precipitation` (1 hr, mm)\n",
    "    * `snowing`: indicator whether it is snowing or not (False, True)\n",
    "    * `cloudiness` (%)\n",
    "    * `target`: generated wind power (GW), not included in test data\n",
    "\n",
    "### Score Measures\n",
    "* Evaluated with macro f1 score\n",
    "* Public score: measured with 30% of the test data\n",
    "* Private score: measured with the remaining 70% of the test data\n",
    "    \n",
    "### Final Scores\n",
    "* public: 1.82965 (rank #9/148)\n",
    "* private: 1.84272 (rank #10/147)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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    },
    "id": "iWCOLYwK9VEC",
    "outputId": "bc3aafaa-b01c-424d-ac2a-a64292b7dee3"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "  Attempting uninstall: psutil\n",
      "    Found existing installation: psutil 5.4.8\n",
      "    Uninstalling psutil-5.4.8:\n",
      "      Successfully uninstalled psutil-5.4.8\n",
      "  Attempting uninstall: Pillow\n",
      "    Found existing installation: Pillow 7.1.2\n",
      "    Uninstalling Pillow-7.1.2:\n",
      "      Successfully uninstalled Pillow-7.1.2\n",
      "  Attempting uninstall: networkx\n",
      "    Found existing installation: networkx 3.0\n",
      "    Uninstalling networkx-3.0:\n",
      "      Successfully uninstalled networkx-3.0\n",
      "  Attempting uninstall: jinja2\n",
      "    Found existing installation: Jinja2 2.11.3\n",
      "    Uninstalling Jinja2-2.11.3:\n",
      "      Successfully uninstalled Jinja2-2.11.3\n",
      "  Attempting uninstall: xgboost\n",
      "    Found existing installation: xgboost 0.90\n",
      "    Uninstalling xgboost-0.90:\n",
      "      Successfully uninstalled xgboost-0.90\n",
      "  Attempting uninstall: pandas\n",
      "    Found existing installation: pandas 1.3.5\n",
      "    Uninstalling pandas-1.3.5:\n",
      "      Successfully uninstalled pandas-1.3.5\n",
      "  Attempting uninstall: jsonschema\n",
      "    Found existing installation: jsonschema 4.3.3\n",
      "    Uninstalling jsonschema-4.3.3:\n",
      "      Successfully uninstalled jsonschema-4.3.3\n",
      "  Attempting uninstall: hyperopt\n",
      "    Found existing installation: hyperopt 0.1.2\n",
      "    Uninstalling hyperopt-0.1.2:\n",
      "      Successfully uninstalled hyperopt-0.1.2\n",
      "  Attempting uninstall: statsmodels\n",
      "    Found existing installation: statsmodels 0.12.2\n",
      "    Uninstalling statsmodels-0.12.2:\n",
      "      Successfully uninstalled statsmodels-0.12.2\n",
      "  Attempting uninstall: scikit-image\n",
      "    Found existing installation: scikit-image 0.18.3\n",
      "    Uninstalling scikit-image-0.18.3:\n",
      "      Successfully uninstalled scikit-image-0.18.3\n",
      "  Attempting uninstall: lightgbm\n",
      "    Found existing installation: lightgbm 2.2.3\n",
      "    Uninstalling lightgbm-2.2.3:\n",
      "      Successfully uninstalled lightgbm-2.2.3\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 7.9.0 requires jedi>=0.10, which is not installed.\n",
      "flask 1.1.4 requires Jinja2<3.0,>=2.10.1, but you have jinja2 3.1.2 which is incompatible.\u001b[0m\u001b[31m\n",
      "\u001b[0mSuccessfully installed Pillow-9.4.0 accelerate-0.16.0 antlr4-python3-runtime-4.9.3 autogluon-0.7.0 autogluon.common-0.7.0 autogluon.core-0.7.0 autogluon.features-0.7.0 autogluon.multimodal-0.7.0 autogluon.tabular-0.7.0 autogluon.timeseries-0.7.0 boto3-1.26.79 botocore-1.29.79 catboost-1.1.1 colorama-0.4.6 datasets-2.10.0 deprecated-1.2.13 distlib-0.3.6 evaluate-0.3.0 fairscale-0.4.13 gluonts-0.12.3 huggingface-hub-0.12.1 hyperopt-0.2.7 jinja2-3.1.2 jmespath-1.0.1 jsonschema-4.17.3 lightgbm-3.3.5 lightning-utilities-0.7.1 markdown-it-py-2.2.0 mdurl-0.1.2 model-index-0.1.11 multiprocess-0.70.14 networkx-2.8.8 nlpaug-1.1.11 nptyping-2.4.1 omegaconf-2.2.3 openmim-0.3.6 ordered-set-4.1.0 pandas-1.5.3 pkgutil-resolve-name-1.3.10 pmdarima-1.8.5 psutil-5.9.4 py4j-0.10.9.7 pyDeprecate-0.3.2 pygments-2.14.0 pytesseract-0.3.10 pytorch-lightning-1.9.3 pytorch-metric-learning-1.7.3 ray-2.2.0 responses-0.18.0 rich-13.3.1 s3transfer-0.6.0 scikit-image-0.19.3 sentencepiece-0.1.97 seqeval-1.2.2 sktime-0.15.1 statsforecast-1.4.0 statsmodels-0.13.5 tbats-1.1.2 tensorboardX-2.6 timm-0.6.12 tokenizers-0.13.2 torchmetrics-0.8.2 transformers-4.26.1 ujson-5.7.0 urllib3-1.26.14 virtualenv-20.19.0 xgboost-1.7.4 xxhash-3.2.0\n"
     ]
    }
   ],
   "source": [
    "! pip install autogluon"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "cqLodwMQR32R"
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import time\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "D6O5SNKySdpr"
   },
   "source": [
    "## Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "s9otTGofSOEE"
   },
   "outputs": [],
   "source": [
    "train = pd.read_csv('train.csv')\n",
    "test = pd.read_csv('test.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 270
    },
    "id": "JAReNKYnSV_o",
    "outputId": "52d6916b-9ec3-489b-cf71-0ecdae7107d8"
   },
   "outputs": [
    {
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      "text/plain": [
       "            id  temperature  pressure  humidity  wind_speed  wind_direction  \\\n",
       "0  TRAIN_00000      27.8816    1013.6      46.8         2.4           232.4   \n",
       "1  TRAIN_00001       5.7540    1026.6      76.6         3.6           172.0   \n",
       "2  TRAIN_00002      20.8220    1016.2      64.8         2.2           206.0   \n",
       "3  TRAIN_00003      20.0758    1017.4      72.6         0.8           215.4   \n",
       "4  TRAIN_00004       7.5260    1023.2      82.8         1.2           158.0   \n",
       "\n",
       "   precipitation  snowing  cloudiness  target  \n",
       "0           0.00    False         0.0   1.180  \n",
       "1           0.00    False         8.8   8.581  \n",
       "2           0.06    False        23.0   3.978  \n",
       "3           0.00    False         0.0   8.301  \n",
       "4           0.00    False        15.0   1.692  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "-KQYVAZSSQ0D",
    "outputId": "6bca9151-12a1-4667-ce26-573616ee0a3a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 19275 entries, 0 to 19274\n",
      "Data columns (total 10 columns):\n",
      " #   Column          Non-Null Count  Dtype  \n",
      "---  ------          --------------  -----  \n",
      " 0   id              19275 non-null  object \n",
      " 1   temperature     19275 non-null  float64\n",
      " 2   pressure        19275 non-null  float64\n",
      " 3   humidity        19275 non-null  float64\n",
      " 4   wind_speed      19275 non-null  float64\n",
      " 5   wind_direction  19275 non-null  float64\n",
      " 6   precipitation   19275 non-null  float64\n",
      " 7   snowing         19275 non-null  bool   \n",
      " 8   cloudiness      19275 non-null  float64\n",
      " 9   target          19275 non-null  float64\n",
      "dtypes: bool(1), float64(8), object(1)\n",
      "memory usage: 1.3+ MB\n"
     ]
    }
   ],
   "source": [
    "train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "2h_MDwEgSce3",
    "outputId": "c7818298-43b6-43fe-8242-e0b5e9a33773"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                0\n",
       "temperature       0\n",
       "pressure          0\n",
       "humidity          0\n",
       "wind_speed        0\n",
       "wind_direction    0\n",
       "precipitation     0\n",
       "snowing           0\n",
       "cloudiness        0\n",
       "target            0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "uoBdzaJuShpi",
    "outputId": "c61aaa8f-e77e-4f17-cd52-516f387d1b81"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                0\n",
       "temperature       0\n",
       "pressure          0\n",
       "humidity          0\n",
       "wind_speed        0\n",
       "wind_direction    0\n",
       "precipitation     0\n",
       "snowing           0\n",
       "cloudiness        0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "PoB677rGEods"
   },
   "source": [
    "## EDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 344
    },
    "id": "RP5Jkq2vWdFz",
    "outputId": "ad03eba4-acee-4c82-c0f1-8ca64b261945"
   },
   "outputs": [
    {
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       "      <td>208.0000</td>\n",
       "      <td>338.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>precipitation</th>\n",
       "      <td>19275.0</td>\n",
       "      <td>0.076572</td>\n",
       "      <td>0.193110</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0600</td>\n",
       "      <td>3.180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cloudiness</th>\n",
       "      <td>19275.0</td>\n",
       "      <td>25.749748</td>\n",
       "      <td>17.628612</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>12.000</td>\n",
       "      <td>23.000</td>\n",
       "      <td>37.0000</td>\n",
       "      <td>93.600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>target</th>\n",
       "      <td>19275.0</td>\n",
       "      <td>5.477028</td>\n",
       "      <td>3.217581</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.956</td>\n",
       "      <td>4.837</td>\n",
       "      <td>7.4365</td>\n",
       "      <td>17.344</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>\n",
       "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-7e05dcee-95af-42a6-8675-9f2b5775a005')\"\n",
       "              title=\"Convert this dataframe to an interactive table.\"\n",
       "              style=\"display:none;\">\n",
       "        \n",
       "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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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",
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       "\n",
       "    .colab-df-convert {\n",
       "      background-color: #E8F0FE;\n",
       "      border: none;\n",
       "      border-radius: 50%;\n",
       "      cursor: pointer;\n",
       "      display: none;\n",
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       "    }\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",
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       "\n",
       "      <script>\n",
       "        const buttonEl =\n",
       "          document.querySelector('#df-7e05dcee-95af-42a6-8675-9f2b5775a005 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-7e05dcee-95af-42a6-8675-9f2b5775a005');\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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       "      </script>\n",
       "    </div>\n",
       "  </div>\n",
       "  "
      ],
      "text/plain": [
       "                  count         mean          std         min       25%  \\\n",
       "temperature     19275.0    17.288476     7.192604   -0.208137    11.712   \n",
       "pressure        19275.0  1059.825712  2544.132430  812.600000  1012.600   \n",
       "humidity        19275.0    68.948763    14.856929   22.600000    57.800   \n",
       "wind_speed      19275.0     2.476970     1.359942    0.000000     1.400   \n",
       "wind_direction  19275.0   166.578179    57.313099    0.000000   127.000   \n",
       "precipitation   19275.0     0.076572     0.193110    0.000000     0.000   \n",
       "cloudiness      19275.0    25.749748    17.628612    0.000000    12.000   \n",
       "target          19275.0     5.477028     3.217581    0.000000     2.956   \n",
       "\n",
       "                     50%        75%         max  \n",
       "temperature       16.530    22.5590      37.058  \n",
       "pressure        1016.800  1021.0000  201373.000  \n",
       "humidity          70.800    80.8000     100.000  \n",
       "wind_speed         2.200     3.2000      29.800  \n",
       "wind_direction   166.600   208.0000     338.000  \n",
       "precipitation      0.000     0.0600       3.180  \n",
       "cloudiness        23.000    37.0000      93.600  \n",
       "target             4.837     7.4365      17.344  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.describe().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 283
    },
    "id": "kzDOkRY4w_Dz",
    "outputId": "aa368d1b-3a12-4552-edde-a1c9e4c3f961"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7f4c9b28cfd0>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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iGOBa4AgggY2Z+dmuPmcCXwG+WzR9OTMvG3TMpc4gkDQKZWYM+4H/lJn3RMTBwJaI2JyZ3+rq97eZeU6JcZY0355a0qgNHAyZuQvYVWw/GxHfBo4CuoNBi1DX6woMA0mzKlljiIg1wOuBu3rcfVpE3A88AXwoMx/qs48NwAaA1atXV1HW2PAUkaQmKR0MEfHzwF8CH8jMZ7ruvgd4ZWb+Q0SsB24G1vbaT2ZuBDYCTE1NZdm6mswgkNRkpYIhIl5MKxSuy8wvd9/fHhSZuSki/jgiDsvMJ8uMO47qCIN3nLqaT5z3mpGPK2m8lbkqKYAvAt/OzD/o0+cXgd2ZmRFxMvAi4KlBxxwnzgokjasyM4bTgX8HPBgRs6ul/wVYDZCZVwJvA94bEfuB54DzM3NiTxN5BZGkSVDmqqRvADFPnyuAKwYdYxwYBpImja98HsCow8AgkDRKBsMCOCuQtJQYDH04K5C0VBkMbX75kk38+MBo1sZPP24l1737tJGMJUmLYTAwutmBswJJ42DJBsOo3pPIMJA0bpZcMIxidmAYSBpnSyoYhhkKhoGkSbEkgmEYgWAQSJpUEx8MVYaCYSBpKZjYYKgqEAwDSUvNRAZDFaFgIEhaqiYuGMqEgmEgSRMWDIOGgoEgSf/kRXUXUJVBQmHt4SsMBUnqMhEzhpvvnVn0YwwESeptImYMi3lri8BQkKS5TMSMYaEMBEma30TMGBbCUJCkhVkSwWAoSNLClQqGiFgXEY9ExPaIuLjH/S+JiBuL+++KiDVlxhuEoSBJizNwMETEMuBzwNnACcAFEXFCV7eLgB9k5i8Bfwh8ctDxJEmjUWbGcDKwPTN3ZObzwA3AuV19zgWuKba/BLw5IqLEmJKkISsTDEcBj7fd3lm09eyTmfuBHwIv77WziNgQEdMRMb13794SZUmSymjM4nNmbszMqcycWrVq1aIeu6zPJKRfuySpvzLBMAMc03b76KKtZ5+IWA78AvBUiTF7uuCUYxbVLknqr0wwfBNYGxHHRsRBwPnALV19bgEuLLbfBvyvzMwSY/b0ifNewztOXf3/ZwjLInjHqav5xHmvqXooSZp4UeZ5OiLWA58BlgFXZeblEXEZMJ2Zt0TES4E/BV4P7APOz8wd8+13amoqp6enB65LkpaaiNiSmVNV7KvUW2Jk5iZgU1fbR9u2fwz8mzJjSJJGqzGLz5KkZjAYJEkdDAZJUgeDQZLUodRVScMSEXuB7w348MOAJyssp0pNrg2aXV+Ta4Nm19fk2qDZ9TW5Nuis75WZubhXB/fRyGAoIyKmq7pkq2pNrg2aXV+Ta4Nm19fk2qDZ9TW5NhhefZ5KkiR1MBgkSR0mMRg21l3AHJpcGzS7vibXBs2ur8m1QbPra3JtMKT6Jm6NQZJUziTOGCRJJRgMkqQOExMMEbEuIh6JiO0RcfGIxjwmIv53RHwrIh6KiP9QtH8sImYi4r7ia33bYz5S1PhIRLx12PVHxGMR8WBRx3TRtjIiNkfEtuL7oUV7RMQfFTU8EBEnte3nwqL/toi4sN94i6jr+Lbjc19EPBMRH6jz2EXEVRGxJyK2trVVdqwi4l8Uv4vtxWMX/ElSfWr7VEQ8XIx/U0QcUrSviYjn2o7hlfPV0O/nLFlfZb/LaL29/11F+43Reqv/MrXd2FbXYxFxX43Hrt/zSH1/e5k59l+03vb7UeBVwEHA/cAJIxj3SOCkYvtg4DvACcDHgA/16H9CUdtLgGOLmpcNs37gMeCwrrbfBy4uti8GPllsrwf+GgjgVOCuon0lsKP4fmixfWjFv7/vA6+s89gBbwROArYO41gBdxd9o3js2SVrOwtYXmx/sq22Ne39uvbTs4Z+P2fJ+ir7XQL/g9bb9gNcCby3TG1d938a+GiNx67f80htf3uTMmM4GdiemTsy83ngBuDcYQ+ambsy855i+1ng27zwc6/bnQvckJk/yczvAttp1T7q+s8Frim2rwHOa2u/NlvuBA6JiCOBtwKbM3NfZv4A2Aysq7CeNwOPZuZcr3Yf+rHLzNtpfW5I97ilj1Vx38sy885s/Uu9tm1fA9WWmbdl67PUAe6k9SmKfc1TQ7+fc+D65rCo32Xxv9tfBb40SH1z1Vbs+7eA6+fax5CPXb/nkdr+9iYlGI4CHm+7vZO5n6ArFxFraH0g0V1F0/uLad5VbVPLfnUOs/4EbouILRGxoWg7IjN3FdvfB46osT5offpf+z/Mphw7qO5YHVVsD6vOd9H6n+CsYyPi3oj4m4h4Q1vN/Wro93OWVcXv8uXA020hWOWxewOwOzO3tbXVduy6nkdq+9ublGCoVUT8PPCXwAcy8xng88BxwInALlpT1bqckZknAWcD74uIN7bfWfwPorZrlotzxb8B/EXR1KRj16HuY9VPRFwC7AeuK5p2Aasz8/XAB4E/j4iXLXR/Ff6cjf1dtrmAzv+U1HbsejyPVLLfQUxKMMwAx7TdPrpoG7qIeDGtX+Z1mfllgMzcnZkHMvNnwBdoTZHnqnNo9WfmTPF9D3BTUcvuYno5O0XeU1d9tALrnszcXdTZmGNXqOpYzdB5qqeSOiPincA5wNuLJw+KUzRPFdtbaJ23/+fz1NDv5xxYhb/Lp2idLlne1V5Ksb/fBG5sq7mWY9freWSO/Q7/b28xiyRN/aL1EaU7aC1kzS5avXoE4wat83Wf6Wo/sm37P9I6nwrwajoX3XbQWnAbSv3ACuDgtu3/S2tt4FN0Lmr9frH963Quat2d/7So9V1aC1qHFtsrKzqGNwC/3ZRjR9fiY5XHihcuAK4vWds64FvAqq5+q4BlxfaraD0JzFlDv5+zZH2V/S5pzSjbF5//fZna2o7f39R97Oj/PFLb317lT5Z1fdFaqf8OrYS/ZERjnkFrevcAcF/xtR74U+DBov2Wrn8glxQ1PkLblQHDqL/4w76/+Hpodr+0ztl+HdgG/M+2P54APlfU8CAw1bavd9FaJNxO2xN5yfpW0Prf4C+0tdV27GidUtgF/JTWediLqjxWwBSwtXjMFRTvPFCitu20zinP/u1dWfT918Xv+z7gHuBfzldDv5+zZH2V/S6Lv+W7i5/5L4CXlKmtaL8aeE9X3zqOXb/nkdr+9nxLDElSh0lZY5AkVcRgkCR1MBgkSR0MBklSB4NBktTBYJAkdTAYJEkd/h9kOO1OREyOVAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(range(train.shape[0]), np.sort(train['target'].values))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 890
    },
    "id": "mPe0Z755DchH",
    "outputId": "3107f35b-fd56-469c-ef96-2625d82e8ec7"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 9 Axes>"
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       "      <th></th>\n",
       "      <th>feature</th>\n",
       "      <th>skewness</th>\n",
       "      <th>kurtosis</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>temperature</td>\n",
       "      <td>0.26</td>\n",
       "      <td>-0.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>pressure</td>\n",
       "      <td>76.07</td>\n",
       "      <td>5962.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>humidity</td>\n",
       "      <td>-0.33</td>\n",
       "      <td>-0.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>wind_speed</td>\n",
       "      <td>1.68</td>\n",
       "      <td>10.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>wind_direction</td>\n",
       "      <td>-0.04</td>\n",
       "      <td>-0.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>precipitation</td>\n",
       "      <td>6.73</td>\n",
       "      <td>68.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>cloudiness</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>target</td>\n",
       "      <td>0.77</td>\n",
       "      <td>-0.00</td>\n",
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      ],
      "text/plain": [
       "          feature  skewness  kurtosis\n",
       "0     temperature      0.26     -0.65\n",
       "1        pressure     76.07   5962.79\n",
       "2        humidity     -0.33     -0.75\n",
       "3      wind_speed      1.68     10.55\n",
       "4  wind_direction     -0.04     -0.37\n",
       "5   precipitation      6.73     68.66\n",
       "6      cloudiness      0.70      0.10\n",
       "7          target      0.77     -0.00"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numerics = ['int16', 'int32', 'int64', 'float16', 'float32', 'float64']\n",
    "train.hist(figsize=(10, 10))\n",
    "plt.show()\n",
    "l = []\n",
    "for col in train.select_dtypes(include=numerics):\n",
    "    skewness = round(train[col].skew(),2)\n",
    "    kurtosis = round(train[col].kurt(),2)\n",
    "    a = [col, skewness, kurtosis]\n",
    "    l.append(a)\n",
    "pd.DataFrame(l, columns = ['feature', 'skewness', 'kurtosis'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "hTRDpKel1sbm",
    "outputId": "44505c3d-1fa0-41cc-e97a-87a1c816a4fe"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x1008 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "numerics = ['int16', 'int32', 'int64', 'float16', 'float32', 'float64']\n",
    "num_cols = train.select_dtypes(include=numerics)\n",
    "fig, axes = plt.subplots(nrows = len(num_cols.columns), figsize = (12, 14))\n",
    "for col, ax in zip(num_cols, axes.flat):\n",
    "    sns.boxplot(data = train, x = col, ax = ax)\n",
    "fig.tight_layout()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 300
    },
    "id": "jRf4Tn69Yaae",
    "outputId": "bc31d013-9166-4b23-f3e7-7ab6e2489744"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7faafa3f0970>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(data = train, x = 'snowing')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 207
    },
    "id": "sqJjt-99WnmP",
    "outputId": "60ed078c-8ac6-4ec2-f723-e31988f07186"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "  <div id=\"df-a5fa521d-d467-4ba2-85e9-41878d403ff4\">\n",
       "    <div class=\"colab-df-container\">\n",
       "      <div>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>temperature</th>\n",
       "      <th>pressure</th>\n",
       "      <th>humidity</th>\n",
       "      <th>wind_speed</th>\n",
       "      <th>wind_direction</th>\n",
       "      <th>precipitation</th>\n",
       "      <th>cloudiness</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>snowing</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>False</th>\n",
       "      <td>17.359732</td>\n",
       "      <td>1060.196261</td>\n",
       "      <td>68.936031</td>\n",
       "      <td>2.470339</td>\n",
       "      <td>166.310131</td>\n",
       "      <td>0.075048</td>\n",
       "      <td>25.653943</td>\n",
       "      <td>5.478660</td>\n",
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       "    <tr>\n",
       "      <th>True</th>\n",
       "      <td>6.372144</td>\n",
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       "      <td>0.310080</td>\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",
       "    [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-a5fa521d-d467-4ba2-85e9-41878d403ff4 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-a5fa521d-d467-4ba2-85e9-41878d403ff4');\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",
       "  "
      ],
      "text/plain": [
       "         temperature     pressure   humidity  wind_speed  wind_direction  \\\n",
       "snowing                                                                    \n",
       "False      17.359732  1060.196261  68.936031    2.470339      166.310131   \n",
       "True        6.372144  1003.057600  70.899200    3.492800      207.643200   \n",
       "\n",
       "         precipitation  cloudiness    target  \n",
       "snowing                                       \n",
       "False         0.075048   25.653943  5.478660  \n",
       "True          0.310080   40.427200  5.226992  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(train.groupby('snowing').mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "coH-ciZu0jp4",
    "outputId": "af8c47fb-ba22-4ba8-e9ed-741b479730a3"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ttest_indResult(statistic=0.9873546844734775, pvalue=0.3253596358094196)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# determine if the difference of mean between snowing=True&False is statistically significant\n",
    "import scipy.stats\n",
    "\n",
    "snowing_false = train[train['snowing']==False]['target'].values\n",
    "snowing_true = train[train['snowing']==True]['target'].values\n",
    "\n",
    "scipy.stats.ttest_ind(snowing_false, snowing_true, equal_var=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 957
    },
    "id": "PU859x2-UulR",
    "outputId": "e41b3d09-b20d-4360-ce5a-725e9e0b3d76"
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1728x1080 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cols = ['pressure', 'wind_speed', 'precipitation']\n",
    "fig, axes = plt.subplots(nrows = len(cols), figsize = (24, 5*len(cols)))\n",
    "for col, ax in zip(cols, axes.flat):\n",
    "    plt.sca(ax)\n",
    "    plt.xticks(rotation=90)\n",
    "    min_val = min(train[col])\n",
    "    max_val = max(train[col])\n",
    "    ax.set(title = f'{col} ({min_val}~{max_val})')\n",
    "    sns.countplot(data = train, x = col, ax = ax)\n",
    "\n",
    "fig.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 658
    },
    "id": "nj_ePXb3WRhQ",
    "outputId": "7cd3f69b-32d1-4582-9c46-460a3d0df938"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 936x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (13, 10))\n",
    "colormap = plt.cm.PuBu\n",
    "sns.heatmap(train.corr(), annot = True, cmap = colormap)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "GYDa--KwEq5s"
   },
   "source": [
    "* outliers in pressure, windspeed, precipitation\n",
    "* the difference of mean between snowing=True&False is statistically insignificant\n",
    "* pressure & snowing show low correlation to the target"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "bvU0s5EHWxgp"
   },
   "source": [
    "## Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "csthmvbqXSCA",
    "outputId": "eaa0b14f-91cf-4009-d687-e12a54ba6104"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Before: (19275, 10)\n",
      "After: (19274, 10)\n"
     ]
    }
   ],
   "source": [
    "# delete outliers\n",
    "\n",
    "print(f\"Before: {train.shape}\")\n",
    "# train = train[(train['pressure'] <= 2000) & (train['pressure'] > 940)]\n",
    "train = train[(train['wind_speed']< 20)]\n",
    "# out_cols = ['pressure', 'wind_speed']\n",
    "# for col in out_cols:\n",
    "#   Q1 = train[col].quantile(0.25)\n",
    "#   Q3 = train[col].quantile(0.75)\n",
    "#   IQR = Q3 - Q1    #IQR is interquartile range. \n",
    "\n",
    "#   filter = (train[col] >= Q1 - 1.5 * IQR) & (train[col] <= Q3 + 1.5 *IQR)\n",
    "#   train = train.loc[filter]  \n",
    "print(f\"After: {train.shape}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 297
    },
    "id": "eeqmdufNG1jV",
    "outputId": "27746095-587e-4bfa-8369-2defd37ed2ca"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(nrows=2)\n",
    "sns.boxplot(data = train, x = 'pressure', ax=axes[0])\n",
    "axes[0].set_title(\"train pressure\")\n",
    "sns.boxplot(data = test, x = 'pressure', ax=axes[1])\n",
    "axes[1].set_title(\"test pressure\")\n",
    "fig.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 297
    },
    "id": "2uR8NgV04P-d",
    "outputId": "5bbbcf3b-e767-4edf-9c32-2860bd43ff79"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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D1AhgWcn6cvr5lzaApBbg3cCDTa5KvX0X+BKwscn1aIS3Ac8CP05DmldJGtLsStVDRKwAvg38BVgJvBARdza3Vg2xc0SsTMtPAzs3szINdgpwR7Mr4Zsk6kzSUODnwBci4sVm16deJH0cWBURC5pdlwYZBBwI/CAi3g28TP8aAmqXrr0cTRaUdwWGSDq5ubVqrMj+H2eL+J8cSeeSXaK4rtl1aXSAWgHsVrI+MqX1S5K2IgtO10XELc2uT519APiEpKVkQ7djJf2kuVWqq+XA8ogo9opvJgtY/dGhwJKIeDYi3gBuAd7f5Do1wjOSdgFIP1c1uT51J2kS8HHgpMjBP8k2OkD9AdhT0tskbU12ofWXDa5DQ0gS2fWJxRFxcbPrU28R8ZWIGBkRLWTndX5E9Nu/siPiaWCZpL1S0keBx5tYpXr6C3CwpG3T5/qj9NMbQir8EpiYlicCv2hiXepO0jiyIfpPRMQrza4PNDhApQtw/wr8iuwDfmNELGpkHRroA8CnyHoSD6fXEc2ulNXUZOA6SY8C7wK+3tzq1EfqJd4MPAQsJPveyN1jcTaHpOuB+4G9JC2XdCpwIXCYpCfJepEXNrOOtVSlvd8DtgPmpe+ry5taSfyoIzMzyynfJGFmZrnkAGVmZrnkAGVmZrnkAGVmZrnkAGVmZrnkAGVmZrnkAGVbPElzejK1gKSW0mkKmk3S2mbXwaweBjW7AmbNFhH+B2qzHHIPyvo9Sf8maUpa/g9J89PyWEnXSVoqaXjqGS2WdGWanO9OSYNT3tGSHpH0CHBGF8fbV9Lv03/jPyppz1T2E+l4i9NEh9uWlH2PpAWSflXy/Le3S5qb0u+TtHdKf5uk+yUtlDSjjm+dWVM5QNmW4D7gQ2l5DDA0Pcj3Q8C9FXn3BC6LiH2B54FjU/qPgckR8c5uHO804JKIeFc63vKUvhfw/YjYB3gR+JdUj5nAcRExGrgauCDlvyIdczRwNvD9lH4J2VPU9yeb/sKsX3KAsi3BAmC0pDcBr5E9g2wMWYC6ryLvkoh4uGS/lnR9avs0yRvAtV0c737gq5K+DOweEetS+rKI+G1a/gnwQbKgtR/p+WfANGBkmqbl/cBNKf2HwC5p3w8A13ezLmZ9lq9BWb8XEW9IWgJMAn4HPAocAoxi06dyv1ayvAEY3Ivj/VTSg2SzC8+R9M/AU2w6n1AAAhZFxPtKN6Rg+nzqhXV4mJ7Wy6yvcQ/KthT3kQ2T3ZuWTwP+uztz3qSp3J+X9MGUdFJn+SXtATwVEZeSTdFwQNr0VknFQPRPwG+APwI7FdMlbSVp3zS55RJJx6d0SSoOL/6WbEqTLuti1pc5QNmW4j6yIbL7I+IZ4FU2Hd7rzGeAy9Jwm7rIOwF4LOXdD5id0v8InCFpMTCM7DrS68BxwEXpBoyH+dtkgCcBp6b0RWSz2gJMTeUsBEb0oA1mfYqn2zBrAEktwO0RsV+z62LWV7gHZWZmueQelFkvSfoYcFFF8pKIGN+M+pj1Nw5QZmaWSx7iMzOzXHKAMjOzXHKAMjOzXHKAMjOzXHKAMjOzXHKAMjOzXHKAMjOzXHKAMjOzXHKAMjOzXHKAMuuFNCV8oZf7XpOnqdolFSQt7zqnWWM5QFnuSVoq6dAalDNJ0m9qUaeI2Dci7q5FWWbWMQcoMzPLJQcoyzVJ1wJvBW6TtFbSl1L6wZJ+J+l5SY+UDrelntJTkl6StETSSZL2AS4H3pfKeb6DYx2SJgEsrs+T9IeS9fskfTItt/fqJE2XdKOk2emYiySNKdnv3ZIeSttuALYp2TZc0u2pHWvSMQaUHOMrkh6X9JykH0sq3ffjkh5O+/5O0gEl23aV9HNJz6b3YErJtsFpmPE5SY8D7+n5mTFrgIjwy69cv4ClwKEl6yOA1cARZH9kHZbWdwKGAC8Ce6W8uwD7puVJwG86Oc5gspl2hwNbAc8AK4Dt0rZ1wI6VdQKmp/2OAAYC3wAeSNu2Bv4MnJnKPA54A5iRtn+DLHBulV4f4m+zDCwFHgN2A3Ygm+q9uN+7gVXAe9MxJ6b8f5fekwXAv6fj7wE8BXws7Xsh2WzCO6SyHwOWN/s8++VX5cs9KOuLTgbmRMSciNgYEfOAVrIAAbAR2E/S4IhYGRGLulNoRKwD/gB8GBgNPEIWFD4AHAw8GRGrq+z+m1SfDcC1wDtT+sFkgee7EfFGRNycjlH0BlkQ3T1tvy8iSufA+V5ELIuINcAFwIkp/fPADyPiwYjYEBGzgNfS8d4D7BQRX4uI1yPiKeBK4IS07wTggohYExHLgEu78/6YNZoDlPVFuwPHp6Gt59Nw3QeBXSLiZeAfgdOAlZL+n6S9e1D2PUCBLEjdA9wNfCS97ulkv6dLll8BtpE0CNgVWFERdP5csvwtoA24Mw1LnlNR7rKK/XZNy7sDX6x4D3ZL23cHdq3Y9lVg57Tvrh2Ua5Y7DlDWF1TOqrkMuDYiti95DYmICwEi4lcRcRhZz+QJst5DR+V0pDJA3UP3AlQ1K4ERklSS9tbiQkS8FBFfjIg9gE8AZ0n6aEne3Sr2+9+0vIysF1T6HmwbEdenbUsqtm0XEcUe5soOyjXLHQco6wueIbuOUvQT4ChJH5M0UNI26X95RkraWdLRkoaQDXmtJRvyK5YzUtLWnRzrd8BewEHA79Pw4O5k13ru7UXd7wfWA1MkbSXpmFQ20H6jw6gUwF4ANpTUF+CM1K4dgHOBG1L6lcBpkt6rzBBJR0raDvg98JKkL6cbIgZK2k9S8WaIG4GvSBomaSQwuRftMqs7ByjrC74BTEvDVWen6yZHkw1bPUvWY/g3ss/zAOAssp7GGrKez+mpnPnAIuBpSX/t6EBpiPAhYFFEvJ6S7wf+HBGrelrxVMYxZDdorCEbfrylJMuewF1kgfR+4PsR8euS7T8F7iS7yeFPwIxUbivwOeB7wHNkw4ST0rYNwMeBdwFLgL8CVwFvTmWeTzastySVfW1P22XWCCofGjezvJC0FPhsRNzV7LqYNYN7UGZmlksOUGZmlkse4jMzs1xyD8rMzHJpUE8yDx8+PFpaWupUFTMz2xItWLDgrxGxU2V6jwJUS0sLra2ttauVmZlt8SR1+DQTD/GZmVkuOUCZmVkuOUCZmVkuOUCZmVkuOUCZmVku9eguvr5k5syZtLW1bVYZK1asAGDEiBG1qFKZUaNGMXmyHyJtZlZNvw1QbW1tPPzYYjZsu0Ovyxj4ygsAPP1abd+mga+sqWl5Zmb9Ub8NUAAbtt2BdXsf0XXGKgY/MQdgs8rorFwzM6vO16DMzCyXHKDMzCyXHKDMzCyXHKDMzCyXHKDMzCyXHKDMzCyXHKDMzCyXHKDMzCyXHKDMzCyXHKDMzCyXHKDMzCyXHKDMzCyXHKDMzCyXGh6gZs6cycyZMxt9WGsgn2Mzq4WGT7exuZMIWv75HJtZLXiIz8zMcskByszMcskByszMcskByszMcskByszMcskByszMcskByszMcskByszMcskByszMcskByszMcskByszMcskByuquUCi0vzpa706eyvUJEyZQKBQ48cQTq5Yxfvx4CoUCxx57LAAnn3wyhUKBSZMmte9TWc7pp59OoVBg8uTJAFx66aUUCgUuu+yy9n0q0y666CIKhQLf+c53AGhtbWXs2LEsWLAAyJ5NeOSRR5Y9o7Ay7Re/+AWFQoHbbrut2/usXr2aKVOmsHr16g7Xq6WV6qrMWqlFufWqW7P05fY0qu4OUNYnrVq1CoCVK1dWzfPcc88BtP8SLV++HIClS5dWLWfx4sUALFy4EIBbbrkFgJtuuql9n8q0O+64A6A9uEyfPp2NGzdy3nnnATBjxgxefvllZsyY0V5GZdp3v/tdAC6++OJu7zNr1iwWLlzI7NmzO1yvllaqqzJrpRbl1qtuzdKX29OoujtAWV2V9pKqrXcnT2frJ554Yo/3mTRpEhMmTChLGzt2bNn6+PHjy9Yvu+wyLr300rK0T33qU2XrX/rSl1i7di0Aa9eu5bbbbmsPiEuXLqWtrY22traytKuuuoqIACAi+NGPftTlPgsWLGDu3LlEBHPnzqWtra1sffXq1axevXqTtFJdlVmrv467qkejysiTvtyeRtZdxV+M7hgzZky0trZu1gGPO+441q1bx6hRozarnK60tbXx0uvBy+86oddlDH5iDgDr9j6iVtUCYMjDP2O7rVX396BZ2traGDx4MDfffPMmgWFLI4nS37GWlhagvBfXlY72GTp0KK+++irr169n0KBBjBw5kuXLl7evH3nkkUQEc+bMKUs788wz28uYNGlSp2VW5u+tiy++uNN6NKqMPOnL7alH3SUtiIgxleld9qAkfV5Sq6TWZ599drMqYbalqfwDcOnSpT0KTtX2Wbt2LevXrwdg/fr1LF26tGx93rx53HXXXZukVZbbWZmV+Xurq3o0qow86cvtaWTdu5ywMCKuAK6ArAe1uQccMWIEAJdccsnmFtWpqVOnsuCpZ+p6jN7auM2bGLXHznV/D5pl6tSpza5CbjSrB3XYYYdt0oM67LDDNim3szIr8/fWoYce2mk9GlVGnvTl9jSy7r4GZX3eLrvs0uN9WlpaeMtb3lKWNmBA+a/DsGHDytaPP/54jjnmmLK03XbbrWz9oIMOKls/66yzytanTZvGtGnTytJOPvnksvXK61od7XP++ee313fgwIFMmzatbP3Tn/40EydO3CStstzOyqzM31td1aNRZeRJX25PI+vuAGV1dffdd3e53p08na1ff/31Pd7nmmuu4cYbbyxLmz9/ftn6rbfeWrZ+xhlnMGXKlLK0a6+9tmz9m9/8JkOHDgWyHslRRx3V3gNqaWlh1KhRjBo1qizts5/9LJKArMd16qmndrnP6NGjGTduHJIYN24co0aNKlvfcccd2XHHHTdJK9VVmZX5e6urejSqjDzpy+1pZN0doKxPKvZ+Ous9FXtAxV+gkSNHAn8bMuuonH322QeA/ff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      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(nrows=2)\n",
    "sns.boxplot(data = train, x = 'wind_speed', ax=axes[0])\n",
    "axes[0].set_title(\"train windspeed\")\n",
    "sns.boxplot(data = test, x = 'wind_speed', ax=axes[1])\n",
    "axes[1].set_title(\"test windspeed\")\n",
    "fig.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "D4oNEm4289ua"
   },
   "outputs": [],
   "source": [
    "# snowing dummy variables\n",
    "# train = pd.get_dummies(train, columns=['snowing'])\n",
    "# test = pd.get_dummies(test, columns=['snowing'])\n",
    "\n",
    "# target_val = train['target']\n",
    "# train.drop('target', axis=1, inplace=True)\n",
    "# train['target'] = target_val"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 300
    },
    "id": "UIKaj3BTXSFV",
    "outputId": "e2c88eaf-02b1-47aa-a018-d5a8fe6d6af4"
   },
   "outputs": [
    {
     "data": {
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       "\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>VIF Factor</th>\n",
       "      <th>numeric features</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>11.773572</td>\n",
       "      <td>humidity</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10.960169</td>\n",
       "      <td>wind_direction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.844822</td>\n",
       "      <td>wind_speed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.676083</td>\n",
       "      <td>cloudiness</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4.575988</td>\n",
       "      <td>temperature</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>4.334624</td>\n",
       "      <td>target</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1.757990</td>\n",
       "      <td>precipitation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1.172183</td>\n",
       "      <td>pressure</td>\n",
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       "            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",
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       "  </div>\n",
       "  "
      ],
      "text/plain": [
       "   VIF Factor numeric features\n",
       "0   11.773572         humidity\n",
       "1   10.960169   wind_direction\n",
       "2    4.844822       wind_speed\n",
       "3    4.676083       cloudiness\n",
       "4    4.575988      temperature\n",
       "5    4.334624           target\n",
       "6    1.757990    precipitation\n",
       "7    1.172183         pressure"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# multicollinearity test\n",
    "from statsmodels.stats.outliers_influence import variance_inflation_factor\n",
    "numerics = ['int16', 'int32', 'int64', 'float16', 'float32', 'float64']\n",
    "traindf = train.select_dtypes(include=numerics)\n",
    "vif = pd.DataFrame()\n",
    "vif[\"VIF Factor\"] = [variance_inflation_factor(\n",
    "    traindf.values, i) for i in range(traindf.shape[1])]\n",
    "vif[\"numeric features\"] = traindf.columns\n",
    "vif = vif.sort_values(\"VIF Factor\", ascending=False).reset_index(drop=True)\n",
    "vif\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 269
    },
    "id": "o9AgQQ-C6I09",
    "outputId": "05cd128c-b447-49a0-ec64-dfb897565c41"
   },
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>VIF Factor</th>\n",
       "      <th>numeric features</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7.367865</td>\n",
       "      <td>humidity</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5.520820</td>\n",
       "      <td>wind_speed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.594173</td>\n",
       "      <td>temperature</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.524485</td>\n",
       "      <td>cloudiness</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4.367626</td>\n",
       "      <td>target</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.320886</td>\n",
       "      <td>precipitation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1.171061</td>\n",
       "      <td>pressure</td>\n",
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       "                                                     [key], {});\n",
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       "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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      "text/plain": [
       "   VIF Factor numeric features\n",
       "0    7.367865         humidity\n",
       "1    5.520820       wind_speed\n",
       "2    4.594173      temperature\n",
       "3    4.524485       cloudiness\n",
       "4    4.367626           target\n",
       "5    1.320886    precipitation\n",
       "6    1.171061         pressure"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# for reference\n",
    "traindf = train.select_dtypes(include=numerics).drop(['wind_direction'], axis=1)\n",
    "vif = pd.DataFrame()\n",
    "vif[\"VIF Factor\"] = [variance_inflation_factor(\n",
    "    traindf.values, i) for i in range(traindf.shape[1])]\n",
    "vif[\"numeric features\"] = traindf.columns\n",
    "vif = vif.sort_values(\"VIF Factor\", ascending=False).reset_index(drop=True)\n",
    "vif"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "iCzP3TEM54b6"
   },
   "outputs": [],
   "source": [
    "#drop columns\n",
    "\n",
    "#drop_columns = ['id', 'snowing', 'pressure']\n",
    "drop_columns = ['id', 'snowing']\n",
    "#drop_columns = ['id', 'wind_direction', 'snowing']\n",
    "#drop_columns = ['id']\n",
    "train = train.drop(drop_columns, axis = 1)\n",
    "test = test.drop(drop_columns, axis = 1)\n",
    "\n",
    "# train['snowing'] =train['snowing'].map(lambda x: 1 if x==True else 0)\n",
    "# test['snowing'] =test['snowing'].map(lambda x: 1 if x==True else 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "gMaMY0qu_6tB",
    "outputId": "7f8de0ef-0fe5-4f8c-d390-7d7db9f40060"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.8/dist-packages/pandas/core/indexing.py:719: FutureWarning: Slicing a positional slice with .loc is not supported, and will raise TypeError in a future version.  Use .loc with labels or .iloc with positions instead.\n",
      "  ):\n"
     ]
    }
   ],
   "source": [
    "from sklearn.preprocessing import StandardScaler, Normalizer\n",
    "x = train.drop('target', axis=1)\n",
    "scaler = StandardScaler()\n",
    "#scaler = Normalizer()\n",
    "X_train_scaled = scaler.fit_transform(x)\n",
    "X_test_scaled = scaler.transform(test)\n",
    "\n",
    "train.loc[:,:-1] = X_train_scaled\n",
    "test.loc[:,:] = X_test_scaled"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "qGHXr6pgSr-_"
   },
   "source": [
    "## 모델 정의 및 학습"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "_ezQ0powziau"
   },
   "source": [
    "### AutoGluon"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Performed numerous experiments with AutoGluon using provided data. Since I worked in a Colab environment, the maximum time allowed for training was 3 hours\n",
    "    * delete snowing col, get rid of 1 major outlier in pressure > best performance\n",
    "    * use all cols & values, change snowing to binary var\n",
    "    * delete wind direction and snowing\n",
    "    * log transform highly skewed variables, drop snowing & pressure\n",
    "    * use snowing as dummy variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "u3XumBuMziEV"
   },
   "outputs": [],
   "source": [
    "from autogluon.tabular import TabularDataset, TabularPredictor\n",
    "\n",
    "train_data = TabularDataset(train)\n",
    "test_data = TabularDataset(test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "xbT0NI0z0POv"
   },
   "outputs": [],
   "source": [
    "label = 'target'\n",
    "eval_metric = 'mean_absolute_error'\n",
    "time_limit = 3600*3 # limit training time to 3 hrs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "yIUaAoUz0bUD",
    "outputId": "18443110-5ce0-43f6-f40e-38efaae42aed"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No path specified. Models will be saved in: \"AutogluonModels/ag-20230225_100154/\"\n",
      "Presets specified: ['best_quality']\n",
      "Stack configuration (auto_stack=True): num_stack_levels=1, num_bag_folds=8, num_bag_sets=20\n",
      "Beginning AutoGluon training ... Time limit = 10800s\n",
      "AutoGluon will save models to \"AutogluonModels/ag-20230225_100154/\"\n",
      "AutoGluon Version:  0.7.0\n",
      "Python Version:     3.8.10\n",
      "Operating System:   Linux\n",
      "Platform Machine:   x86_64\n",
      "Platform Version:   #1 SMP Sat Dec 10 16:00:40 UTC 2022\n",
      "Train Data Rows:    19274\n",
      "Train Data Columns: 9\n",
      "Label Column: target\n",
      "Preprocessing data ...\n",
      "AutoGluon infers your prediction problem is: 'regression' (because dtype of label-column == float and many unique label-values observed).\n",
      "\tLabel info (max, min, mean, stddev): (17.344, 0.0, 5.47686, 3.21758)\n",
      "\tIf 'regression' is not the correct problem_type, please manually specify the problem_type parameter during predictor init (You may specify problem_type as one of: ['binary', 'multiclass', 'regression'])\n",
      "Using Feature Generators to preprocess the data ...\n",
      "Fitting AutoMLPipelineFeatureGenerator...\n",
      "\tAvailable Memory:                    10318.53 MB\n",
      "\tTrain Data (Original)  Memory Usage: 1.39 MB (0.0% of available memory)\n",
      "\tInferring data type of each feature based on column values. Set feature_metadata_in to manually specify special dtypes of the features.\n",
      "\tStage 1 Generators:\n",
      "\t\tFitting AsTypeFeatureGenerator...\n",
      "\t\t\tNote: Converting 2 features to boolean dtype as they only contain 2 unique values.\n",
      "\tStage 2 Generators:\n",
      "\t\tFitting FillNaFeatureGenerator...\n",
      "\tStage 3 Generators:\n",
      "\t\tFitting IdentityFeatureGenerator...\n",
      "\tStage 4 Generators:\n",
      "\t\tFitting DropUniqueFeatureGenerator...\n",
      "\tTypes of features in original data (raw dtype, special dtypes):\n",
      "\t\t('float', []) : 9 | ['temperature', 'pressure', 'humidity', 'wind_speed', 'wind_direction', ...]\n",
      "\tTypes of features in processed data (raw dtype, special dtypes):\n",
      "\t\t('float', [])     : 7 | ['temperature', 'pressure', 'humidity', 'wind_speed', 'wind_direction', ...]\n",
      "\t\t('int', ['bool']) : 2 | ['snowing_False', 'snowing_True']\n",
      "\t0.2s = Fit runtime\n",
      "\t9 features in original data used to generate 9 features in processed data.\n",
      "\tTrain Data (Processed) Memory Usage: 1.12 MB (0.0% of available memory)\n",
      "Data preprocessing and feature engineering runtime = 0.23s ...\n",
      "AutoGluon will gauge predictive performance using evaluation metric: 'mean_absolute_error'\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",
      "\tTo change this, specify the eval_metric parameter of Predictor()\n",
      "AutoGluon will fit 2 stack levels (L1 to L2) ...\n",
      "Fitting 11 L1 models ...\n",
      "Fitting model: KNeighborsUnif_BAG_L1 ... Training model for up to 7198.05s of the 10799.76s of remaining time.\n",
      "\t-2.307\t = Validation score   (-mean_absolute_error)\n",
      "\t0.07s\t = Training   runtime\n",
      "\t1.65s\t = Validation runtime\n",
      "Fitting model: KNeighborsDist_BAG_L1 ... Training model for up to 7196.16s of the 10797.88s of remaining time.\n",
      "\t-2.0959\t = Validation score   (-mean_absolute_error)\n",
      "\t0.11s\t = Training   runtime\n",
      "\t1.7s\t = Validation runtime\n",
      "Fitting model: LightGBMXT_BAG_L1 ... Training model for up to 7194.18s of the 10795.89s of remaining time.\n",
      "\tFitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy\n",
      "\t-2.0572\t = Validation score   (-mean_absolute_error)\n",
      "\t177.96s\t = Training   runtime\n",
      "\t66.71s\t = Validation runtime\n",
      "Fitting model: LightGBM_BAG_L1 ... Training model for up to 6938.12s of the 10539.84s of remaining time.\n",
      "\tFitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy\n",
      "\t-2.0199\t = Validation score   (-mean_absolute_error)\n",
      "\t98.91s\t = Training   runtime\n",
      "\t12.77s\t = Validation runtime\n",
      "Fitting model: RandomForestMSE_BAG_L1 ... Training model for up to 6832.23s of the 10433.95s of remaining time.\n",
      "\t-1.9533\t = Validation score   (-mean_absolute_error)\n",
      "\t23.16s\t = Training   runtime\n",
      "\t1.22s\t = Validation runtime\n",
      "Fitting model: CatBoost_BAG_L1 ... Training model for up to 6804.49s of the 10406.21s of remaining time.\n",
      "\tFitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy\n",
      "\t-2.0329\t = Validation score   (-mean_absolute_error)\n",
      "\t1153.19s\t = Training   runtime\n",
      "\t0.28s\t = Validation runtime\n",
      "Fitting model: ExtraTreesMSE_BAG_L1 ... Training model for up to 5648.6s of the 9250.32s of remaining time.\n",
      "\t-2.0047\t = Validation score   (-mean_absolute_error)\n",
      "\t7.96s\t = Training   runtime\n",
      "\t0.89s\t = Validation runtime\n",
      "Fitting model: NeuralNetFastAI_BAG_L1 ... Training model for up to 5637.22s of the 9238.94s of remaining time.\n",
      "\tFitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy\n",
      "\t-2.2926\t = Validation score   (-mean_absolute_error)\n",
      "\t193.72s\t = Training   runtime\n",
      "\t0.67s\t = Validation runtime\n",
      "Fitting model: XGBoost_BAG_L1 ... Training model for up to 5440.83s of the 9042.55s of remaining time.\n",
      "\tFitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy\n",
      "\t-2.0386\t = Validation score   (-mean_absolute_error)\n",
      "\t66.58s\t = Training   runtime\n",
      "\t6.31s\t = Validation runtime\n",
      "Fitting model: NeuralNetTorch_BAG_L1 ... Training model for up to 5370.32s of the 8972.04s of remaining time.\n",
      "\tFitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy\n",
      "\t-2.0915\t = Validation score   (-mean_absolute_error)\n",
      "\t967.01s\t = Training   runtime\n",
      "\t0.35s\t = Validation runtime\n",
      "Fitting model: LightGBMLarge_BAG_L1 ... Training model for up to 4398.53s of the 8000.25s of remaining time.\n",
      "\tFitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy\n",
      "\t-1.9221\t = Validation score   (-mean_absolute_error)\n",
      "\t364.58s\t = Training   runtime\n",
      "\t136.04s\t = Validation runtime\n",
      "Completed 1/20 k-fold bagging repeats ...\n",
      "Fitting model: WeightedEnsemble_L2 ... Training model for up to 719.8s of the 7601.11s of remaining time.\n",
      "\t-1.9022\t = Validation score   (-mean_absolute_error)\n",
      "\t0.65s\t = Training   runtime\n",
      "\t0.0s\t = Validation runtime\n",
      "Fitting 9 L2 models ...\n",
      "Fitting model: LightGBMXT_BAG_L2 ... Training model for up to 7600.14s of the 7600.09s of remaining time.\n",
      "\tFitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy\n",
      "\t-1.8775\t = Validation score   (-mean_absolute_error)\n",
      "\t25.72s\t = Training   runtime\n",
      "\t1.2s\t = Validation runtime\n",
      "Fitting model: LightGBM_BAG_L2 ... Training model for up to 7570.19s of the 7570.14s of remaining time.\n",
      "\tFitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy\n",
      "\t-1.8785\t = Validation score   (-mean_absolute_error)\n",
      "\t23.68s\t = Training   runtime\n",
      "\t0.33s\t = Validation runtime\n",
      "Fitting model: RandomForestMSE_BAG_L2 ... Training model for up to 7543.7s of the 7543.66s of remaining time.\n",
      "\t-1.9014\t = Validation score   (-mean_absolute_error)\n",
      "\t84.47s\t = Training   runtime\n",
      "\t0.98s\t = Validation runtime\n",
      "Fitting model: CatBoost_BAG_L2 ... Training model for up to 7455.9s of the 7455.86s of remaining time.\n",
      "\tFitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy\n",
      "\t-1.8788\t = Validation score   (-mean_absolute_error)\n",
      "\t43.94s\t = Training   runtime\n",
      "\t0.12s\t = Validation runtime\n",
      "Fitting model: ExtraTreesMSE_BAG_L2 ... Training model for up to 7409.34s of the 7409.3s of remaining time.\n",
      "\t-1.8935\t = Validation score   (-mean_absolute_error)\n",
      "\t17.05s\t = Training   runtime\n",
      "\t0.93s\t = Validation runtime\n",
      "Fitting model: NeuralNetFastAI_BAG_L2 ... Training model for up to 7389.5s of the 7389.45s of remaining time.\n",
      "\tFitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy\n",
      "\t-1.8732\t = Validation score   (-mean_absolute_error)\n",
      "\t194.5s\t = Training   runtime\n",
      "\t0.58s\t = Validation runtime\n",
      "Fitting model: XGBoost_BAG_L2 ... Training model for up to 7192.07s of the 7192.01s of remaining time.\n",
      "\tFitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy\n",
      "\t-1.8808\t = Validation score   (-mean_absolute_error)\n",
      "\t30.39s\t = Training   runtime\n",
      "\t0.29s\t = Validation runtime\n",
      "Fitting model: NeuralNetTorch_BAG_L2 ... Training model for up to 7155.74s of the 7155.7s of remaining time.\n",
      "\tFitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy\n",
      "\t-1.8502\t = Validation score   (-mean_absolute_error)\n",
      "\t253.91s\t = Training   runtime\n",
      "\t0.36s\t = Validation runtime\n",
      "Fitting model: LightGBMLarge_BAG_L2 ... Training model for up to 6898.72s of the 6898.68s of remaining time.\n",
      "\tFitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy\n",
      "\t-1.8866\t = Validation score   (-mean_absolute_error)\n",
      "\t44.68s\t = Training   runtime\n",
      "\t0.81s\t = Validation runtime\n",
      "Repeating k-fold bagging: 2/20\n",
      "Fitting model: LightGBMXT_BAG_L2 ... Training model for up to 6848.72s of the 6848.67s of remaining time.\n",
      "\tFitting 8 child models (S2F1 - S2F8) | Fitting with ParallelLocalFoldFittingStrategy\n",
      "\t-1.8757\t = Validation score   (-mean_absolute_error)\n",
      "\t49.68s\t = Training   runtime\n",
      "\t1.74s\t = Validation runtime\n",
      "Fitting model: LightGBM_BAG_L2 ... Training model for up to 6819.48s of the 6819.43s of remaining time.\n",
      "\tFitting 8 child models (S2F1 - S2F8) | Fitting with ParallelLocalFoldFittingStrategy\n",
      "\t-1.8754\t = Validation score   (-mean_absolute_error)\n",
      "\t46.9s\t = Training   runtime\n",
      "\t0.57s\t = Validation runtime\n",
      "Fitting model: CatBoost_BAG_L2 ... Training model for up to 6793.09s of the 6793.04s of remaining time.\n",
      "\tFitting 8 child models (S2F1 - S2F8) | Fitting with ParallelLocalFoldFittingStrategy\n",
      "\t-1.8756\t = Validation score   (-mean_absolute_error)\n",
      "\t87.77s\t = Training   runtime\n",
      "\t0.23s\t = Validation runtime\n",
      "Fitting model: NeuralNetFastAI_BAG_L2 ... Training model for up to 6746.28s of the 6746.23s of remaining time.\n",
      "\tFitting 8 child models (S2F1 - S2F8) | Fitting with ParallelLocalFoldFittingStrategy\n",
      "\t-1.8651\t = Validation score   (-mean_absolute_error)\n",
      "\t393.66s\t = Training   runtime\n",
      "\t1.66s\t = Validation runtime\n",
      "Fitting model: XGBoost_BAG_L2 ... Training model for up to 6544.09s of the 6544.04s of remaining time.\n",
      "\tFitting 8 child models (S2F1 - S2F8) | Fitting with ParallelLocalFoldFittingStrategy\n",
      "\t-1.8767\t = Validation score   (-mean_absolute_error)\n",
      "\t56.39s\t = Training   runtime\n",
      "\t0.48s\t = Validation runtime\n",
      "Fitting model: NeuralNetTorch_BAG_L2 ... Training model for up to 6512.19s of the 6512.15s of remaining time.\n",
      "\tFitting 8 child models (S2F1 - S2F8) | Fitting with ParallelLocalFoldFittingStrategy\n",
      "\t-1.8439\t = Validation score   (-mean_absolute_error)\n",
      "\t499.67s\t = Training   runtime\n",
      "\t0.7s\t = Validation runtime\n",
      "Fitting model: LightGBMLarge_BAG_L2 ... Training model for up to 6262.35s of the 6262.3s of remaining time.\n",
      "\tFitting 8 child models (S2F1 - S2F8) | Fitting with ParallelLocalFoldFittingStrategy\n",
      "\t-1.8812\t = Validation score   (-mean_absolute_error)\n",
      "\t83.85s\t = Training   runtime\n",
      "\t1.41s\t = Validation runtime\n",
      "Repeating k-fold bagging: 3/20\n",
      "Fitting model: LightGBMXT_BAG_L2 ... Training model for up to 6219.57s of the 6219.53s of remaining time.\n",
      "\tFitting 8 child models (S3F1 - S3F8) | Fitting with ParallelLocalFoldFittingStrategy\n",
      "\t-1.8753\t = Validation score   (-mean_absolute_error)\n",
      "\t80.23s\t = Training   runtime\n",
      "\t2.57s\t = Validation runtime\n",
      "Fitting model: LightGBM_BAG_L2 ... Training model for up to 6185.9s of the 6185.85s of remaining time.\n",
      "\tFitting 8 child models (S3F1 - S3F8) | Fitting with ParallelLocalFoldFittingStrategy\n",
      "\t-1.874\t = Validation score   (-mean_absolute_error)\n",
      "\t70.37s\t = Training   runtime\n",
      "\t0.82s\t = Validation runtime\n",
      "Fitting model: CatBoost_BAG_L2 ... Training model for up to 6157.31s of the 6157.27s of remaining time.\n",
      "\tFitting 8 child models (S3F1 - S3F8) | Fitting with ParallelLocalFoldFittingStrategy\n",
      "\t-1.8744\t = Validation score   (-mean_absolute_error)\n",
      "\t132.59s\t = Training   runtime\n",
      "\t0.29s\t = Validation runtime\n",
      "Fitting model: NeuralNetFastAI_BAG_L2 ... Training model for up to 6109.4s of the 6109.35s of remaining time.\n",
      "\tFitting 8 child models (S3F1 - S3F8) | Fitting with ParallelLocalFoldFittingStrategy\n",
      "\t-1.863\t = Validation score   (-mean_absolute_error)\n",
      "\t592.38s\t = Training   runtime\n",
      "\t2.53s\t = Validation runtime\n",
      "Fitting model: XGBoost_BAG_L2 ... Training model for up to 5907.57s of the 5907.52s of remaining time.\n",
      "\tFitting 8 child models (S3F1 - S3F8) | Fitting with ParallelLocalFoldFittingStrategy\n",
      "\t-1.8749\t = Validation score   (-mean_absolute_error)\n",
      "\t82.24s\t = Training   runtime\n",
      "\t0.61s\t = Validation runtime\n",
      "Fitting model: NeuralNetTorch_BAG_L2 ... Training model for up to 5877.75s of the 5877.7s of remaining time.\n",
      "\tFitting 8 child models (S3F1 - S3F8) | Fitting with ParallelLocalFoldFittingStrategy\n",
      "\t-1.8416\t = Validation score   (-mean_absolute_error)\n",
      "\t757.58s\t = Training   runtime\n",
      "\t1.13s\t = Validation runtime\n",
      "Fitting model: LightGBMLarge_BAG_L2 ... Training model for up to 5614.95s of the 5614.9s of remaining time.\n",
      "\tFitting 8 child models (S3F1 - S3F8) | Fitting with ParallelLocalFoldFittingStrategy\n",
      "\t-1.8788\t = Validation score   (-mean_absolute_error)\n",
      "\t119.34s\t = Training   runtime\n",
      "\t1.98s\t = Validation runtime\n",
      "Repeating k-fold bagging: 4/20\n",
      "Fitting model: LightGBMXT_BAG_L2 ... Training model for up to 5574.57s of the 5574.52s of remaining time.\n",
      "\tFitting 8 child models (S4F1 - S4F8) | Fitting with ParallelLocalFoldFittingStrategy\n",
      "\t-1.8749\t = Validation score   (-mean_absolute_error)\n",
      "\t107.76s\t = Training   runtime\n",
      "\t3.33s\t = Validation runtime\n",
      "Fitting model: LightGBM_BAG_L2 ... Training model for up to 5541.68s of the 5541.64s of remaining time.\n",
      "\tFitting 8 child models (S4F1 - S4F8) | Fitting with ParallelLocalFoldFittingStrategy\n",
      "\t-1.8732\t = Validation score   (-mean_absolute_error)\n",
      "\t93.26s\t = Training   runtime\n",
      "\t1.02s\t = Validation runtime\n",
      "Fitting model: CatBoost_BAG_L2 ... Training model for up to 5514.18s of the 5514.13s of remaining time.\n",
      "\tFitting 8 child models (S4F1 - S4F8) | Fitting with ParallelLocalFoldFittingStrategy\n",
      "\t-1.8738\t = Validation score   (-mean_absolute_error)\n",
      "\t178.28s\t = Training   runtime\n",
      "\t0.33s\t = Validation runtime\n",
      "Fitting model: NeuralNetFastAI_BAG_L2 ... Training model for up to 5465.39s of the 5465.35s of remaining time.\n",
      "\tFitting 8 child models (S4F1 - S4F8) | Fitting with ParallelLocalFoldFittingStrategy\n",
      "\t-1.8612\t = Validation score   (-mean_absolute_error)\n",
      "\t794.36s\t = Training   runtime\n",
      "\t3.23s\t = Validation runtime\n",
      "Fitting model: XGBoost_BAG_L2 ... Training model for up to 5260.45s of the 5260.39s of remaining time.\n",
      "\tFitting 8 child models (S4F1 - S4F8) | Fitting with ParallelLocalFoldFittingStrategy\n",
      "\t-1.8737\t = Validation score   (-mean_absolute_error)\n",
      "\t105.41s\t = Training   runtime\n",
      "\t0.72s\t = Validation runtime\n",
      "Fitting model: NeuralNetTorch_BAG_L2 ... Training model for up to 5234.2s of the 5234.15s of remaining time.\n",
      "\tFitting 8 child models (S4F1 - S4F8) | Fitting with ParallelLocalFoldFittingStrategy\n",
      "\t-1.8406\t = Validation score   (-mean_absolute_error)\n",
      "\t1024.47s\t = Training   runtime\n",
      "\t1.62s\t = Validation runtime\n",
      "Fitting model: LightGBMLarge_BAG_L2 ... Training model for up to 4963.23s of the 4963.18s of remaining time.\n",
      "\tFitting 8 child models (S4F1 - S4F8) | Fitting with ParallelLocalFoldFittingStrategy\n",
      "\t-1.8783\t = Validation score   (-mean_absolute_error)\n",
      "\t155.92s\t = Training   runtime\n",
      "\t2.54s\t = Validation runtime\n",
      "Repeating k-fold bagging: 5/20\n",
      "Fitting model: LightGBMXT_BAG_L2 ... Training model for up to 4923.27s of the 4923.22s of remaining time.\n",
      "\tFitting 8 child models (S5F1 - S5F8) | Fitting with ParallelLocalFoldFittingStrategy\n",
      "\t-1.8746\t = Validation score   (-mean_absolute_error)\n",
      "\t136.3s\t = Training   runtime\n",
      "\t3.99s\t = Validation runtime\n",
      "Fitting model: LightGBM_BAG_L2 ... Training model for up to 4889.1s of the 4889.05s of remaining time.\n",
      "\tFitting 8 child models (S5F1 - S5F8) | Fitting with ParallelLocalFoldFittingStrategy\n",
      "\t-1.8725\t = Validation score   (-mean_absolute_error)\n",
      "\t117.01s\t = Training   runtime\n",
      "\t1.25s\t = Validation runtime\n",
      "Fitting model: CatBoost_BAG_L2 ... Training model for up to 4862.21s of the 4862.17s of remaining time.\n",
      "\tFitting 8 child models (S5F1 - S5F8) | Fitting with ParallelLocalFoldFittingStrategy\n",
      "\t-1.8734\t = Validation score   (-mean_absolute_error)\n",
      "\t231.22s\t = Training   runtime\n",
      "\t0.39s\t = Validation runtime\n",
      "Fitting model: NeuralNetFastAI_BAG_L2 ... Training model for up to 4806.1s of the 4806.05s of remaining time.\n",
      "\tFitting 8 child models (S5F1 - S5F8) | Fitting with ParallelLocalFoldFittingStrategy\n",
      "\t-1.8606\t = Validation score   (-mean_absolute_error)\n",
      "\t987.36s\t = Training   runtime\n",
      "\t3.81s\t = Validation runtime\n",
      "Fitting model: XGBoost_BAG_L2 ... Training model for up to 4608.69s of the 4608.64s of remaining time.\n",
      "\tFitting 8 child models (S5F1 - S5F8) | Fitting with ParallelLocalFoldFittingStrategy\n",
      "\t-1.873\t = Validation score   (-mean_absolute_error)\n",
      "\t129.55s\t = Training   runtime\n",
      "\t0.96s\t = Validation runtime\n",
      "Fitting model: NeuralNetTorch_BAG_L2 ... Training model for up to 4578.82s of the 4578.77s of remaining time.\n",
      "\tFitting 8 child models (S5F1 - S5F8) | Fitting with ParallelLocalFoldFittingStrategy\n",
      "\t-1.8403\t = Validation score   (-mean_absolute_error)\n",
      "\t1272.12s\t = Training   runtime\n",
      "\t2.03s\t = Validation runtime\n",
      "Fitting model: LightGBMLarge_BAG_L2 ... Training model for up to 4325.73s of the 4325.68s of remaining time.\n",
      "\tFitting 8 child models (S5F1 - S5F8) | Fitting with ParallelLocalFoldFittingStrategy\n",
      "\t-1.8766\t = Validation score   (-mean_absolute_error)\n",
      "\t192.49s\t = Training   runtime\n",
      "\t3.16s\t = Validation runtime\n",
      "Repeating k-fold bagging: 6/20\n",
      "Fitting model: LightGBMXT_BAG_L2 ... Training model for up to 4285.26s of the 4285.21s of remaining time.\n",
      "\tFitting 8 child models (S6F1 - S6F8) | Fitting with ParallelLocalFoldFittingStrategy\n",
      "\t-1.874\t = Validation score   (-mean_absolute_error)\n",
      "\t161.7s\t = Training   runtime\n",
      "\t4.73s\t = Validation runtime\n",
      "Fitting model: LightGBM_BAG_L2 ... Training model for up to 4254.92s of the 4254.87s of remaining time.\n",
      "\tFitting 8 child models (S6F1 - S6F8) | Fitting with ParallelLocalFoldFittingStrategy\n",
      "\t-1.8719\t = Validation score   (-mean_absolute_error)\n",
      "\t144.84s\t = Training   runtime\n",
      "\t1.62s\t = Validation runtime\n",
      "Fitting model: CatBoost_BAG_L2 ... Training model for up to 4224.05s of the 4224.0s of remaining time.\n",
      "\tFitting 8 child models (S6F1 - S6F8) | Fitting with ParallelLocalFoldFittingStrategy\n",
      "\t-1.8727\t = Validation score   (-mean_absolute_error)\n",
      "\t285.4s\t = Training   runtime\n",
      "\t0.45s\t = Validation runtime\n",
      "Fitting model: NeuralNetFastAI_BAG_L2 ... Training model for up to 4166.59s of the 4166.54s of remaining time.\n",
      "\tFitting 8 child models (S6F1 - S6F8) | Fitting with ParallelLocalFoldFittingStrategy\n",
      "\t-1.8602\t = Validation score   (-mean_absolute_error)\n",
      "\t1178.46s\t = Training   runtime\n",
      "\t4.26s\t = Validation runtime\n",
      "Fitting model: XGBoost_BAG_L2 ... Training model for up to 3972.39s of the 3972.34s of remaining time.\n",
      "\tFitting 8 child models (S6F1 - S6F8) | Fitting with ParallelLocalFoldFittingStrategy\n",
      "\t-1.8723\t = Validation score   (-mean_absolute_error)\n",
      "\t153.6s\t = Training   runtime\n",
      "\t1.15s\t = Validation runtime\n",
      "Fitting model: NeuralNetTorch_BAG_L2 ... Training model for up to 3942.65s of the 3942.59s of remaining time.\n",
      "\tFitting 8 child models (S6F1 - S6F8) | Fitting with ParallelLocalFoldFittingStrategy\n",
      "\t-1.8399\t = Validation score   (-mean_absolute_error)\n",
      "\t1525.54s\t = Training   runtime\n",
      "\t2.5s\t = Validation runtime\n",
      "Fitting model: LightGBMLarge_BAG_L2 ... Training model for up to 3683.93s of the 3683.88s of remaining time.\n",
      "\tFitting 8 child models (S6F1 - S6F8) | Fitting with ParallelLocalFoldFittingStrategy\n",
      "\t-1.8755\t = Validation score   (-mean_absolute_error)\n",
      "\t229.53s\t = Training   runtime\n",
      "\t3.83s\t = Validation runtime\n",
      "Repeating k-fold bagging: 7/20\n",
      "Fitting model: LightGBMXT_BAG_L2 ... Training model for up to 3641.77s of the 3641.72s of remaining time.\n",
      "\tFitting 8 child models (S7F1 - S7F8) | Fitting with ParallelLocalFoldFittingStrategy\n",
      "\t-1.8737\t = Validation score   (-mean_absolute_error)\n",
      "\t187.19s\t = Training   runtime\n",
      "\t5.59s\t = Validation runtime\n",
      "Fitting model: LightGBM_BAG_L2 ... Training model for up to 3610.75s of the 3610.71s of remaining time.\n",
      "\tFitting 8 child models (S7F1 - S7F8) | Fitting with ParallelLocalFoldFittingStrategy\n",
      "\t-1.8717\t = Validation score   (-mean_absolute_error)\n",
      "\t172.88s\t = Training   runtime\n",
      "\t2.05s\t = Validation runtime\n",
      "Fitting model: CatBoost_BAG_L2 ... Training model for up to 3579.74s of the 3579.7s of remaining time.\n",
      "\tFitting 8 child models (S7F1 - S7F8) | Fitting with ParallelLocalFoldFittingStrategy\n",
      "\t-1.8721\t = Validation score   (-mean_absolute_error)\n",
      "\t338.61s\t = Training   runtime\n",
      "\t0.51s\t = Validation runtime\n",
      "Fitting model: NeuralNetFastAI_BAG_L2 ... Training model for up to 3523.46s of the 3523.41s of remaining time.\n",
      "\tFitting 8 child models (S7F1 - S7F8) | Fitting with ParallelLocalFoldFittingStrategy\n",
      "\t-1.8599\t = Validation score   (-mean_absolute_error)\n",
      "\t1361.27s\t = Training   runtime\n",
      "\t4.75s\t = Validation runtime\n",
      "Fitting model: XGBoost_BAG_L2 ... Training model for up to 3335.66s of the 3335.62s of remaining time.\n",
      "\tFitting 8 child models (S7F1 - S7F8) | Fitting with ParallelLocalFoldFittingStrategy\n",
      "\t-1.8719\t = Validation score   (-mean_absolute_error)\n",
      "\t177.21s\t = Training   runtime\n",
      "\t1.41s\t = Validation runtime\n",
      "Fitting model: NeuralNetTorch_BAG_L2 ... Training model for up to 3309.17s of the 3309.12s of remaining time.\n",
      "\tFitting 8 child models (S7F1 - S7F8) | Fitting with ParallelLocalFoldFittingStrategy\n",
      "\t-1.8394\t = Validation score   (-mean_absolute_error)\n",
      "\t1786.43s\t = Training   runtime\n",
      "\t3.04s\t = Validation runtime\n",
      "Fitting model: LightGBMLarge_BAG_L2 ... Training model for up to 3044.94s of the 3044.89s of remaining time.\n",
      "\tFitting 8 child models (S7F1 - S7F8) | Fitting with ParallelLocalFoldFittingStrategy\n",
      "\t-1.875\t = Validation score   (-mean_absolute_error)\n",
      "\t267.15s\t = Training   runtime\n",
      "\t4.43s\t = Validation runtime\n",
      "Repeating k-fold bagging: 8/20\n",
      "Fitting model: LightGBMXT_BAG_L2 ... Training model for up to 3004.23s of the 3004.18s of remaining time.\n",
      "\tFitting 8 child models (S8F1 - S8F8) | Fitting with ParallelLocalFoldFittingStrategy\n",
      "\t-1.8736\t = Validation score   (-mean_absolute_error)\n",
      "\t210.67s\t = Training   runtime\n",
      "\t6.27s\t = Validation runtime\n",
      "Fitting model: LightGBM_BAG_L2 ... Training model for up to 2976.31s of the 2976.27s of remaining time.\n",
      "\tFitting 8 child models (S8F1 - S8F8) | Fitting with ParallelLocalFoldFittingStrategy\n",
      "\t-1.8713\t = Validation score   (-mean_absolute_error)\n",
      "\t200.45s\t = Training   runtime\n",
      "\t2.38s\t = Validation runtime\n",
      "Fitting model: CatBoost_BAG_L2 ... Training model for up to 2945.62s of the 2945.57s of remaining time.\n",
      "\tFitting 8 child models (S8F1 - S8F8) | Fitting with ParallelLocalFoldFittingStrategy\n",
      "\t-1.8722\t = Validation score   (-mean_absolute_error)\n",
      "\t382.62s\t = Training   runtime\n",
      "\t0.59s\t = Validation runtime\n",
      "Fitting model: NeuralNetFastAI_BAG_L2 ... Training model for up to 2895.92s of the 2895.87s of remaining time.\n",
      "\tFitting 8 child models (S8F1 - S8F8) | Fitting with ParallelLocalFoldFittingStrategy\n",
      "\t-1.8598\t = Validation score   (-mean_absolute_error)\n",
      "\t1551.68s\t = Training   runtime\n",
      "\t5.45s\t = Validation runtime\n",
      "Fitting model: XGBoost_BAG_L2 ... Training model for up to 2700.24s of the 2700.2s of remaining time.\n",
      "\tFitting 8 child models (S8F1 - S8F8) | Fitting with ParallelLocalFoldFittingStrategy\n",
      "\t-1.8719\t = Validation score   (-mean_absolute_error)\n",
      "\t199.92s\t = Training   runtime\n",
      "\t1.56s\t = Validation runtime\n",
      "Fitting model: NeuralNetTorch_BAG_L2 ... Training model for up to 2674.69s of the 2674.64s of remaining time.\n",
      "\tFitting 8 child models (S8F1 - S8F8) | Fitting with ParallelLocalFoldFittingStrategy\n",
      "\t-1.8396\t = Validation score   (-mean_absolute_error)\n",
      "\t2062.76s\t = Training   runtime\n",
      "\t3.48s\t = Validation runtime\n",
      "Fitting model: LightGBMLarge_BAG_L2 ... Training model for up to 2394.79s of the 2394.74s of remaining time.\n",
      "\tFitting 8 child models (S8F1 - S8F8) | Fitting with ParallelLocalFoldFittingStrategy\n",
      "\t-1.8749\t = Validation score   (-mean_absolute_error)\n",
      "\t304.59s\t = Training   runtime\n",
      "\t5.1s\t = Validation runtime\n",
      "Repeating k-fold bagging: 9/20\n",
      "Fitting model: LightGBMXT_BAG_L2 ... Training model for up to 2351.89s of the 2351.85s of remaining time.\n",
      "\tFitting 8 child models (S9F1 - S9F8) | Fitting with ParallelLocalFoldFittingStrategy\n",
      "\t-1.8739\t = Validation score   (-mean_absolute_error)\n",
      "\t235.33s\t = Training   runtime\n",
      "\t7.14s\t = Validation runtime\n",
      "Fitting model: LightGBM_BAG_L2 ... Training model for up to 2322.13s of the 2322.08s of remaining time.\n",
      "\tFitting 8 child models (S9F1 - S9F8) | Fitting with ParallelLocalFoldFittingStrategy\n",
      "\t-1.8709\t = Validation score   (-mean_absolute_error)\n",
      "\t223.46s\t = Training   runtime\n",
      "\t2.62s\t = Validation runtime\n",
      "Fitting model: CatBoost_BAG_L2 ... Training model for up to 2295.23s of the 2295.19s of remaining time.\n",
      "\tFitting 8 child models (S9F1 - S9F8) | Fitting with ParallelLocalFoldFittingStrategy\n",
      "\t-1.8722\t = Validation score   (-mean_absolute_error)\n",
      "\t430.25s\t = Training   runtime\n",
      "\t0.64s\t = Validation runtime\n",
      "Fitting model: NeuralNetFastAI_BAG_L2 ... Training model for up to 2244.35s of the 2244.3s of remaining time.\n",
      "\tFitting 8 child models (S9F1 - S9F8) | Fitting with ParallelLocalFoldFittingStrategy\n",
      "\t-1.8594\t = Validation score   (-mean_absolute_error)\n",
      "\t1744.26s\t = Training   runtime\n",
      "\t5.99s\t = Validation runtime\n",
      "Fitting model: XGBoost_BAG_L2 ... Training model for up to 2048.94s of the 2048.89s of remaining time.\n",
      "\tFitting 8 child models (S9F1 - S9F8) | Fitting with ParallelLocalFoldFittingStrategy\n",
      "\t-1.8719\t = Validation score   (-mean_absolute_error)\n",
      "\t223.41s\t = Training   runtime\n",
      "\t1.92s\t = Validation runtime\n",
      "Fitting model: NeuralNetTorch_BAG_L2 ... Training model for up to 2019.71s of the 2019.66s of remaining time.\n",
      "\tFitting 8 child models (S9F1 - S9F8) | Fitting with ParallelLocalFoldFittingStrategy\n",
      "\t-1.8393\t = Validation score   (-mean_absolute_error)\n",
      "\t2325.84s\t = Training   runtime\n",
      "\t3.79s\t = Validation runtime\n",
      "Fitting model: LightGBMLarge_BAG_L2 ... Training model for up to 1753.02s of the 1752.97s of remaining time.\n",
      "\tFitting 8 child models (S9F1 - S9F8) | Fitting with ParallelLocalFoldFittingStrategy\n",
      "\t-1.8747\t = Validation score   (-mean_absolute_error)\n",
      "\t339.45s\t = Training   runtime\n",
      "\t5.67s\t = Validation runtime\n",
      "Repeating k-fold bagging: 10/20\n",
      "Fitting model: LightGBMXT_BAG_L2 ... Training model for up to 1713.19s of the 1713.15s of remaining time.\n",
      "\tFitting 8 child models (S10F1 - S10F8) | Fitting with ParallelLocalFoldFittingStrategy\n",
      "\t-1.8738\t = Validation score   (-mean_absolute_error)\n",
      "\t260.99s\t = Training   runtime\n",
      "\t7.82s\t = Validation runtime\n",
      "Fitting model: LightGBM_BAG_L2 ... Training model for up to 1682.45s of the 1682.4s of remaining time.\n",
      "\tFitting 8 child models (S10F1 - S10F8) | Fitting with ParallelLocalFoldFittingStrategy\n",
      "\t-1.871\t = Validation score   (-mean_absolute_error)\n",
      "\t246.44s\t = Training   runtime\n",
      "\t2.86s\t = Validation runtime\n",
      "Fitting model: CatBoost_BAG_L2 ... Training model for up to 1654.1s of the 1654.06s of remaining time.\n",
      "\tFitting 8 child models (S10F1 - S10F8) | Fitting with ParallelLocalFoldFittingStrategy\n",
      "\t-1.872\t = Validation score   (-mean_absolute_error)\n",
      "\t485.38s\t = Training   runtime\n",
      "\t0.72s\t = Validation runtime\n",
      "Fitting model: NeuralNetFastAI_BAG_L2 ... Training model for up to 1595.86s of the 1595.81s of remaining time.\n",
      "\tFitting 8 child models (S10F1 - S10F8) | Fitting with ParallelLocalFoldFittingStrategy\n",
      "\t-1.859\t = Validation score   (-mean_absolute_error)\n",
      "\t1936.11s\t = Training   runtime\n",
      "\t7.1s\t = Validation runtime\n",
      "Fitting model: XGBoost_BAG_L2 ... Training model for up to 1399.08s of the 1399.04s of remaining time.\n",
      "\tFitting 8 child models (S10F1 - S10F8) | Fitting with ParallelLocalFoldFittingStrategy\n",
      "\t-1.8715\t = Validation score   (-mean_absolute_error)\n",
      "\t247.17s\t = Training   runtime\n",
      "\t2.22s\t = Validation runtime\n",
      "Fitting model: NeuralNetTorch_BAG_L2 ... Training model for up to 1371.83s of the 1371.78s of remaining time.\n",
      "\tFitting 8 child models (S10F1 - S10F8) | Fitting with ParallelLocalFoldFittingStrategy\n",
      "\t-1.8393\t = Validation score   (-mean_absolute_error)\n",
      "\t2591.84s\t = Training   runtime\n",
      "\t4.14s\t = Validation runtime\n",
      "Fitting model: LightGBMLarge_BAG_L2 ... Training model for up to 1102.89s of the 1102.85s of remaining time.\n",
      "\tFitting 8 child models (S10F1 - S10F8) | Fitting with ParallelLocalFoldFittingStrategy\n",
      "\t-1.8742\t = Validation score   (-mean_absolute_error)\n",
      "\t381.55s\t = Training   runtime\n",
      "\t6.31s\t = Validation runtime\n",
      "Repeating k-fold bagging: 11/20\n",
      "Fitting model: LightGBMXT_BAG_L2 ... Training model for up to 1055.62s of the 1055.57s of remaining time.\n",
      "\tFitting 8 child models (S11F1 - S11F8) | Fitting with ParallelLocalFoldFittingStrategy\n",
      "\t-1.8739\t = Validation score   (-mean_absolute_error)\n",
      "\t285.25s\t = Training   runtime\n",
      "\t8.57s\t = Validation runtime\n",
      "Fitting model: LightGBM_BAG_L2 ... Training model for up to 1028.32s of the 1028.27s of remaining time.\n",
      "\tFitting 8 child models (S11F1 - S11F8) | Fitting with ParallelLocalFoldFittingStrategy\n",
      "\t-1.8708\t = Validation score   (-mean_absolute_error)\n",
      "\t269.71s\t = Training   runtime\n",
      "\t3.1s\t = Validation runtime\n",
      "Fitting model: CatBoost_BAG_L2 ... Training model for up to 1000.53s of the 1000.48s of remaining time.\n",
      "\tFitting 8 child models (S11F1 - S11F8) | Fitting with ParallelLocalFoldFittingStrategy\n",
      "\t-1.8722\t = Validation score   (-mean_absolute_error)\n",
      "\t529.25s\t = Training   runtime\n",
      "\t0.77s\t = Validation runtime\n",
      "Fitting model: NeuralNetFastAI_BAG_L2 ... Training model for up to 951.29s of the 951.24s of remaining time.\n",
      "\tFitting 8 child models (S11F1 - S11F8) | Fitting with ParallelLocalFoldFittingStrategy\n",
      "\t-1.859\t = Validation score   (-mean_absolute_error)\n",
      "\t2127.7s\t = Training   runtime\n",
      "\t7.64s\t = Validation runtime\n",
      "Fitting model: XGBoost_BAG_L2 ... Training model for up to 755.61s of the 755.56s of remaining time.\n",
      "\tFitting 8 child models (S11F1 - S11F8) | Fitting with ParallelLocalFoldFittingStrategy\n",
      "\t-1.8714\t = Validation score   (-mean_absolute_error)\n",
      "\t272.08s\t = Training   runtime\n",
      "\t2.38s\t = Validation runtime\n",
      "Fitting model: NeuralNetTorch_BAG_L2 ... Training model for up to 725.82s of the 725.77s of remaining time.\n",
      "\tFitting 8 child models (S11F1 - S11F8) | Fitting with ParallelLocalFoldFittingStrategy\n",
      "\t-1.8391\t = Validation score   (-mean_absolute_error)\n",
      "\t2841.98s\t = Training   runtime\n",
      "\t4.64s\t = Validation runtime\n",
      "Fitting model: LightGBMLarge_BAG_L2 ... Training model for up to 470.4s of the 470.35s of remaining time.\n",
      "\tFitting 8 child models (S11F1 - S11F8) | Fitting with ParallelLocalFoldFittingStrategy\n",
      "\t-1.874\t = Validation score   (-mean_absolute_error)\n",
      "\t417.75s\t = Training   runtime\n",
      "\t7.04s\t = Validation runtime\n",
      "Completed 11/20 k-fold bagging repeats ...\n",
      "Fitting model: WeightedEnsemble_L3 ... Training model for up to 760.01s of the 430.2s of remaining time.\n",
      "\t-1.8373\t = Validation score   (-mean_absolute_error)\n",
      "\t0.62s\t = Training   runtime\n",
      "\t0.0s\t = Validation runtime\n",
      "AutoGluon training complete, total runtime = 10370.58s ... Best model: \"WeightedEnsemble_L3\"\n",
      "TabularPredictor saved. To load, use: predictor = TabularPredictor.load(\"AutogluonModels/ag-20230225_100154/\")\n"
     ]
    }
   ],
   "source": [
    "predictor = TabularPredictor(\n",
    "    label=label,\n",
    "    eval_metric=eval_metric\n",
    ").fit(train_data, auto_stack= True, presets='best_quality', time_limit=time_limit)\n",
    "# setting auto_stack=True allows AutoGluon to automatically determine the best parameters for bagging&stacking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 739
    },
    "id": "QjgisfekF2gB",
    "outputId": "5db08109-7304-4148-a4ee-87c12f122019"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "  <div id=\"df-f55cca15-beae-4cd3-867f-17840ab14893\">\n",
       "    <div class=\"colab-df-container\">\n",
       "      <div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>model</th>\n",
       "      <th>score_val</th>\n",
       "      <th>pred_time_val</th>\n",
       "      <th>fit_time</th>\n",
       "      <th>pred_time_val_marginal</th>\n",
       "      <th>fit_time_marginal</th>\n",
       "      <th>stack_level</th>\n",
       "      <th>can_infer</th>\n",
       "      <th>fit_order</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>WeightedEnsemble_L3</td>\n",
       "      <td>-1.835252</td>\n",
       "      <td>461.249548</td>\n",
       "      <td>11376.241162</td>\n",
       "      <td>0.001002</td>\n",
       "      <td>0.920076</td>\n",
       "      <td>3</td>\n",
       "      <td>True</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NeuralNetTorch_BAG_L2</td>\n",
       "      <td>-1.836335</td>\n",
       "      <td>450.868209</td>\n",
       "      <td>9383.448956</td>\n",
       "      <td>3.855416</td>\n",
       "      <td>2092.667105</td>\n",
       "      <td>2</td>\n",
       "      <td>True</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NeuralNetFastAI_BAG_L2</td>\n",
       "      <td>-1.856861</td>\n",
       "      <td>454.907571</td>\n",
       "      <td>9147.677492</td>\n",
       "      <td>7.894778</td>\n",
       "      <td>1856.895641</td>\n",
       "      <td>2</td>\n",
       "      <td>True</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>XGBoost_BAG_L2</td>\n",
       "      <td>-1.869224</td>\n",
       "      <td>448.932349</td>\n",
       "      <td>7589.399260</td>\n",
       "      <td>1.919555</td>\n",
       "      <td>298.617409</td>\n",
       "      <td>2</td>\n",
       "      <td>True</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CatBoost_BAG_L2</td>\n",
       "      <td>-1.870294</td>\n",
       "      <td>447.624124</td>\n",
       "      <td>7881.888082</td>\n",
       "      <td>0.611331</td>\n",
       "      <td>591.106232</td>\n",
       "      <td>2</td>\n",
       "      <td>True</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>LightGBM_BAG_L2</td>\n",
       "      <td>-1.870384</td>\n",
       "      <td>451.471537</td>\n",
       "      <td>7581.601088</td>\n",
       "      <td>4.458744</td>\n",
       "      <td>290.819237</td>\n",
       "      <td>2</td>\n",
       "      <td>True</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>LightGBMXT_BAG_L2</td>\n",
       "      <td>-1.871373</td>\n",
       "      <td>454.730949</td>\n",
       "      <td>7608.971546</td>\n",
       "      <td>7.718156</td>\n",
       "      <td>318.189695</td>\n",
       "      <td>2</td>\n",
       "      <td>True</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>LightGBMLarge_BAG_L2</td>\n",
       "      <td>-1.873636</td>\n",
       "      <td>453.782311</td>\n",
       "      <td>7791.433186</td>\n",
       "      <td>6.769518</td>\n",
       "      <td>500.651335</td>\n",
       "      <td>2</td>\n",
       "      <td>True</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>ExtraTreesMSE_BAG_L2</td>\n",
       "      <td>-1.895713</td>\n",
       "      <td>448.232694</td>\n",
       "      <td>7313.751678</td>\n",
       "      <td>1.219901</td>\n",
       "      <td>22.969828</td>\n",
       "      <td>2</td>\n",
       "      <td>True</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>WeightedEnsemble_L2</td>\n",
       "      <td>-1.897954</td>\n",
       "      <td>219.997420</td>\n",
       "      <td>2626.383609</td>\n",
       "      <td>0.001352</td>\n",
       "      <td>0.874249</td>\n",
       "      <td>2</td>\n",
       "      <td>True</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>RandomForestMSE_BAG_L2</td>\n",
       "      <td>-1.903503</td>\n",
       "      <td>448.278452</td>\n",
       "      <td>7402.788512</td>\n",
       "      <td>1.265659</td>\n",
       "      <td>112.006661</td>\n",
       "      <td>2</td>\n",
       "      <td>True</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>LightGBMLarge_BAG_L1</td>\n",
       "      <td>-1.906212</td>\n",
       "      <td>214.227776</td>\n",
       "      <td>949.526923</td>\n",
       "      <td>214.227776</td>\n",
       "      <td>949.526923</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>RandomForestMSE_BAG_L1</td>\n",
       "      <td>-1.957112</td>\n",
       "      <td>1.504620</td>\n",
       "      <td>31.105562</td>\n",
       "      <td>1.504620</td>\n",
       "      <td>31.105562</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>LightGBM_BAG_L1</td>\n",
       "      <td>-1.990073</td>\n",
       "      <td>64.204317</td>\n",
       "      <td>289.480377</td>\n",
       "      <td>64.204317</td>\n",
       "      <td>289.480377</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>XGBoost_BAG_L1</td>\n",
       "      <td>-2.006202</td>\n",
       "      <td>12.777771</td>\n",
       "      <td>168.746081</td>\n",
       "      <td>12.777771</td>\n",
       "      <td>168.746081</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>ExtraTreesMSE_BAG_L1</td>\n",
       "      <td>-2.011313</td>\n",
       "      <td>1.115627</td>\n",
       "      <td>10.547890</td>\n",
       "      <td>1.115627</td>\n",
       "      <td>10.547890</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>CatBoost_BAG_L1</td>\n",
       "      <td>-2.013765</td>\n",
       "      <td>0.772668</td>\n",
       "      <td>3343.801695</td>\n",
       "      <td>0.772668</td>\n",
       "      <td>3343.801695</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>LightGBMXT_BAG_L1</td>\n",
       "      <td>-2.048089</td>\n",
       "      <td>144.797652</td>\n",
       "      <td>457.336150</td>\n",
       "      <td>144.797652</td>\n",
       "      <td>457.336150</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>NeuralNetTorch_BAG_L1</td>\n",
       "      <td>-2.071836</td>\n",
       "      <td>0.624409</td>\n",
       "      <td>1644.747708</td>\n",
       "      <td>0.624409</td>\n",
       "      <td>1644.747708</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>KNeighborsDist_BAG_L1</td>\n",
       "      <td>-2.095929</td>\n",
       "      <td>3.639262</td>\n",
       "      <td>0.129166</td>\n",
       "      <td>3.639262</td>\n",
       "      <td>0.129166</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>NeuralNetFastAI_BAG_L1</td>\n",
       "      <td>-2.294922</td>\n",
       "      <td>1.170305</td>\n",
       "      <td>395.320824</td>\n",
       "      <td>1.170305</td>\n",
       "      <td>395.320824</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>KNeighborsUnif_BAG_L1</td>\n",
       "      <td>-2.307021</td>\n",
       "      <td>2.178385</td>\n",
       "      <td>0.039472</td>\n",
       "      <td>2.178385</td>\n",
       "      <td>0.039472</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>\n",
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      "text/plain": [
       "                     model  score_val  pred_time_val      fit_time  \\\n",
       "0      WeightedEnsemble_L3  -1.835252     461.249548  11376.241162   \n",
       "1    NeuralNetTorch_BAG_L2  -1.836335     450.868209   9383.448956   \n",
       "2   NeuralNetFastAI_BAG_L2  -1.856861     454.907571   9147.677492   \n",
       "3           XGBoost_BAG_L2  -1.869224     448.932349   7589.399260   \n",
       "4          CatBoost_BAG_L2  -1.870294     447.624124   7881.888082   \n",
       "5          LightGBM_BAG_L2  -1.870384     451.471537   7581.601088   \n",
       "6        LightGBMXT_BAG_L2  -1.871373     454.730949   7608.971546   \n",
       "7     LightGBMLarge_BAG_L2  -1.873636     453.782311   7791.433186   \n",
       "8     ExtraTreesMSE_BAG_L2  -1.895713     448.232694   7313.751678   \n",
       "9      WeightedEnsemble_L2  -1.897954     219.997420   2626.383609   \n",
       "10  RandomForestMSE_BAG_L2  -1.903503     448.278452   7402.788512   \n",
       "11    LightGBMLarge_BAG_L1  -1.906212     214.227776    949.526923   \n",
       "12  RandomForestMSE_BAG_L1  -1.957112       1.504620     31.105562   \n",
       "13         LightGBM_BAG_L1  -1.990073      64.204317    289.480377   \n",
       "14          XGBoost_BAG_L1  -2.006202      12.777771    168.746081   \n",
       "15    ExtraTreesMSE_BAG_L1  -2.011313       1.115627     10.547890   \n",
       "16         CatBoost_BAG_L1  -2.013765       0.772668   3343.801695   \n",
       "17       LightGBMXT_BAG_L1  -2.048089     144.797652    457.336150   \n",
       "18   NeuralNetTorch_BAG_L1  -2.071836       0.624409   1644.747708   \n",
       "19   KNeighborsDist_BAG_L1  -2.095929       3.639262      0.129166   \n",
       "20  NeuralNetFastAI_BAG_L1  -2.294922       1.170305    395.320824   \n",
       "21   KNeighborsUnif_BAG_L1  -2.307021       2.178385      0.039472   \n",
       "\n",
       "    pred_time_val_marginal  fit_time_marginal  stack_level  can_infer  \\\n",
       "0                 0.001002           0.920076            3       True   \n",
       "1                 3.855416        2092.667105            2       True   \n",
       "2                 7.894778        1856.895641            2       True   \n",
       "3                 1.919555         298.617409            2       True   \n",
       "4                 0.611331         591.106232            2       True   \n",
       "5                 4.458744         290.819237            2       True   \n",
       "6                 7.718156         318.189695            2       True   \n",
       "7                 6.769518         500.651335            2       True   \n",
       "8                 1.219901          22.969828            2       True   \n",
       "9                 0.001352           0.874249            2       True   \n",
       "10                1.265659         112.006661            2       True   \n",
       "11              214.227776         949.526923            1       True   \n",
       "12                1.504620          31.105562            1       True   \n",
       "13               64.204317         289.480377            1       True   \n",
       "14               12.777771         168.746081            1       True   \n",
       "15                1.115627          10.547890            1       True   \n",
       "16                0.772668        3343.801695            1       True   \n",
       "17              144.797652         457.336150            1       True   \n",
       "18                0.624409        1644.747708            1       True   \n",
       "19                3.639262           0.129166            1       True   \n",
       "20                1.170305         395.320824            1       True   \n",
       "21                2.178385           0.039472            1       True   \n",
       "\n",
       "    fit_order  \n",
       "0          22  \n",
       "1          20  \n",
       "2          18  \n",
       "3          19  \n",
       "4          16  \n",
       "5          14  \n",
       "6          13  \n",
       "7          21  \n",
       "8          17  \n",
       "9          12  \n",
       "10         15  \n",
       "11         11  \n",
       "12          5  \n",
       "13          4  \n",
       "14          9  \n",
       "15          7  \n",
       "16          6  \n",
       "17          3  \n",
       "18         10  \n",
       "19          2  \n",
       "20          8  \n",
       "21          1  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictor.leaderboard(silent = True)"
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  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "WOimWP0THZGQ"
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   "outputs": [],
   "source": [
    "predictor.save()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "XpGwzz331YKB"
   },
   "outputs": [],
   "source": [
    "model_to_use = predictor.get_model_best()\n",
    "model_pred = predictor.predict(test_data, model=model_to_use)"
   ]
  },
  {
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       "               id    target\n",
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   "source": [
    "submission = pd.read_csv('sample_submission.csv')\n",
    "submission['target'] = model_pred\n",
    "submission"
   ]
  },
  {
   "cell_type": "code",
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   "metadata": {
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   },
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    "submission.to_csv('sub_autogluon_2.csv', index=False)"
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  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "U06EAUZ0VxHp"
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   "source": [
    "#### Takeaways\n",
    "* AutoGluon provides a simple method for prediction with good results. It automatically goes through effective machine learning models to build the best model and works for other problems including classification, time series, images, and multimodal\n",
    "* Possible ways to improve the score:\n",
    "    * conduct research on wind power and consider more variables with feature engineering (ex. air density, absolute humidity, air pressure)\n",
    "    * work in an environment that allows longer training time\n"
   ]
  }
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