bike_sharing_demand_project.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "74JDghANRix4"
      },
      "source": [
        "# Predict Bike Sharing Demand with AutoGluon - Ensiyeh Raoufi"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Xtoh2uvmRix7"
      },
      "source": [
        "## Project: Predict Bike Sharing Demand with AutoGluon\n",
        "This notebook is a complete version of the Kaggle bike sharing demand project.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "59CswROHRix9"
      },
      "source": [
        "## Step 1: Create an account with Kaggle"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3UDjDF65Rix-"
      },
      "source": [
        "### Create Kaggle Account and download API key\n",
        "Below is example of steps to get the API username and key. Each student will have their own username and key."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NkbaBIjiRix_"
      },
      "source": [
        "1. Open account settings.\n",
        "\n",
        "2. Scroll down to API and click Create New API Token.\n",
        "\n",
        "3. Open up `kaggle.json` and use the username and key.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "h_EqA8OlRix_"
      },
      "source": [
        "## Step 2: Download the Kaggle dataset using the kaggle python library"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "54xTqNriRiyC"
      },
      "source": [
        "### Install packages"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oQpkCsnfFuSt",
        "outputId": "d1557c5b-15ce-429d-9789-754a3d1c855e"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "D5ecrUevGSXJ",
        "outputId": "9ec77410-062d-4e06-8d28-f2e3e0f5e0cd"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "/content/drive/My Drive/aws-kaggle-bike-sharing\n"
          ]
        }
      ],
      "source": [
        "cd './drive/My Drive/aws-kaggle-bike-sharing'"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WQSOWakLIVXU",
        "outputId": "59813470-e06e-4324-915b-7bd5d50aabe1"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "/content/drive/My Drive/aws-kaggle-bike-sharing\n"
          ]
        }
      ],
      "source": [
        "!pwd"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fK6K-IwsRiyD",
        "outputId": "fa051b89-3174-4231-eace-e3440051ff63"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
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            "Requirement already satisfied: pyflakes<2.5.0,>=2.4.0 in /usr/local/lib/python3.7/dist-packages (from flake8->autogluon-contrib-nlp==0.0.1b20210201->autogluon.text==0.3.1->autogluon) (2.4.0)\n",
            "Requirement already satisfied: pycodestyle<2.9.0,>=2.8.0 in /usr/local/lib/python3.7/dist-packages (from flake8->autogluon-contrib-nlp==0.0.1b20210201->autogluon.text==0.3.1->autogluon) (2.8.0)\n",
            "Requirement already satisfied: mccabe<0.7.0,>=0.6.0 in /usr/local/lib/python3.7/dist-packages (from flake8->autogluon-contrib-nlp==0.0.1b20210201->autogluon.text==0.3.1->autogluon) (0.6.1)\n",
            "Requirement already satisfied: MarkupSafe>=0.23 in /usr/local/lib/python3.7/dist-packages (from jinja2->distributed>=2.6.0->autogluon.core==0.3.1->autogluon) (2.0.1)\n",
            "Requirement already satisfied: python-slugify in /usr/local/lib/python3.7/dist-packages (from kaggle->d8<1.0,>=0.0.2->autogluon.vision==0.3.1->autogluon) (5.0.2)\n",
            "Requirement already satisfied: retrying>=1.3.3 in /usr/local/lib/python3.7/dist-packages (from plotly->catboost<0.26,>=0.24.0->autogluon.tabular[all]==0.3.1->autogluon) (1.3.3)\n",
            "Requirement already satisfied: colorama in /usr/local/lib/python3.7/dist-packages (from sacrebleu->autogluon-contrib-nlp==0.0.1b20210201->autogluon.text==0.3.1->autogluon) (0.4.4)\n",
            "Requirement already satisfied: tabulate>=0.8.9 in /usr/local/lib/python3.7/dist-packages (from sacrebleu->autogluon-contrib-nlp==0.0.1b20210201->autogluon.text==0.3.1->autogluon) (0.8.9)\n",
            "Requirement already satisfied: pycparser in /usr/local/lib/python3.7/dist-packages (from cffi>=1.1->bcrypt>=3.1.3->paramiko>=2.4->autogluon.core==0.3.1->autogluon) (2.21)\n",
            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata<4.3->flake8->autogluon-contrib-nlp==0.0.1b20210201->autogluon.text==0.3.1->autogluon) (3.6.0)\n",
            "Requirement already satisfied: text-unidecode>=1.3 in /usr/local/lib/python3.7/dist-packages (from python-slugify->kaggle->d8<1.0,>=0.0.2->autogluon.vision==0.3.1->autogluon) (1.3)\n",
            "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\n"
          ]
        }
      ],
      "source": [
        "!pip install -U pip\n",
        "!pip install -U setuptools wheel\n",
        "!pip install -U \"mxnet<2.0.0\" bokeh==2.0.1\n",
        "!pip install autogluon --no-cache-dir\n",
        "# Without --no-cache-dir, smaller aws instances may have trouble installing"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XHTZ8x2cRiyE"
      },
      "source": [
        "### Setup Kaggle API Key"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "m_jjkwg6RiyF"
      },
      "outputs": [],
      "source": [
        "# create the .kaggle directory and an empty kaggle.json file\n",
        "!mkdir -p /root/.kaggle\n",
        "!touch /root/.kaggle/kaggle.json\n",
        "!chmod 600 /root/.kaggle/kaggle.json"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "qHFERvNiRiyG"
      },
      "outputs": [],
      "source": [
        "# Fill in your user name and key from creating the kaggle account and API token file\n",
        "import json\n",
        "kaggle_username = \"FILL_IN_USERNAME\"\n",
        "kaggle_key = \"FILL_IN_KEY\"\n",
        "\n",
        "# Save API token the kaggle.json file\n",
        "with open(\"/root/.kaggle/kaggle.json\", \"w\") as f:\n",
        "    f.write(json.dumps({\"username\": kaggle_username, \"key\": kaggle_key}))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lZeH7mBtRiyG"
      },
      "source": [
        "### Download and explore dataset"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "41CNhCXnRiyH"
      },
      "source": [
        "### Go to the bike sharing demand competition and agree to the terms\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vyLet3J3RiyI",
        "outputId": "cc3e3546-c386-4872-d4b7-d3a500f6c154"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Warning: Looks like you're using an outdated API Version, please consider updating (server 1.5.12 / client 1.5.4)\n",
            "test.csv: Skipping, found more recently modified local copy (use --force to force download)\n",
            "sampleSubmission.csv: Skipping, found more recently modified local copy (use --force to force download)\n",
            "train.csv: Skipping, found more recently modified local copy (use --force to force download)\n",
            "unzip:  cannot find or open bike-sharing-demand.zip, bike-sharing-demand.zip.zip or bike-sharing-demand.zip.ZIP.\n"
          ]
        }
      ],
      "source": [
        "# Download the dataset, it will be in a .zip file so you'll need to unzip it as well.\n",
        "!kaggle competitions download -c bike-sharing-demand\n",
        "# If you already downloaded it you can use the -o command to overwrite the file\n",
        "!unzip -o bike-sharing-demand.zip"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lIT1vituWSDj",
        "outputId": "b31d3fc0-fdd6-419c-c7e2-422fbd13daa9"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\u001b[0m\u001b[01;34mbike-Models-date-time-predictClass\u001b[0m/\n",
            "\u001b[01;34mbike-Models-enhanced-time-new-hpo-predictClass\u001b[0m/\n",
            "\u001b[01;34mbike-Models-independent-date-time-predictClass\u001b[0m/\n",
            "\u001b[01;34mbike-Models-light-new-hpo-predictClass\u001b[0m/\n",
            "\u001b[01;34mbike-Models-new-hpo-predictClass\u001b[0m/\n",
            "\u001b[01;34mbike-Models-predictClass\u001b[0m/\n",
            "model_test_score.png\n",
            "model_train_score.png\n",
            "sampleSubmission.csv\n",
            "submission.csv\n",
            "submission_independent_features.csv\n",
            "submission_new_features.csv\n",
            "submission_new_hpo.csv\n",
            "submission_new_light_hpo.csv\n",
            "test.csv\n",
            "train.csv\n"
          ]
        }
      ],
      "source": [
        "ls "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "t1N56ZA-RiyI"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "from autogluon.tabular import TabularPredictor"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "TCnxRvDARiyJ",
        "outputId": "0ed1df38-3d69-40e1-c968-e4e55e3fc379"
      },
      "outputs": [
        {
          "data": {
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              "\n",
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              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "             datetime  season  holiday  ...  casual  registered  count\n",
              "0 2011-01-01 00:00:00       1        0  ...       3          13     16\n",
              "1 2011-01-01 01:00:00       1        0  ...       8          32     40\n",
              "2 2011-01-01 02:00:00       1        0  ...       5          27     32\n",
              "3 2011-01-01 03:00:00       1        0  ...       3          10     13\n",
              "4 2011-01-01 04:00:00       1        0  ...       0           1      1\n",
              "\n",
              "[5 rows x 12 columns]"
            ]
          },
          "execution_count": 5,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Create the train dataset in pandas by reading the csv\n",
        "# Set the parsing of the datetime column so you can use some of the `dt` features in pandas later\n",
        "import pandas as pd\n",
        "train = pd.read_csv('./train.csv', parse_dates=['datetime'])\n",
        "train.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
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        },
        "id": "fTbz8SLJisIE",
        "outputId": "076cc6be-d6ac-4f26-cd42-c852af89e914"
      },
      "outputs": [
        {
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              "  "
            ],
            "text/plain": [
              "              season   holiday  workingday  ...    casual  registered     count\n",
              "season      1.000000  0.029368   -0.008126  ...  0.096758    0.164011  0.163439\n",
              "holiday     0.029368  1.000000   -0.250491  ...  0.043799   -0.020956 -0.005393\n",
              "workingday -0.008126 -0.250491    1.000000  ... -0.319111    0.119460  0.011594\n",
              "weather     0.008879 -0.007074    0.033772  ... -0.135918   -0.109340 -0.128655\n",
              "temp        0.258689  0.000295    0.029966  ...  0.467097    0.318571  0.394454\n",
              "atemp       0.264744 -0.005215    0.024660  ...  0.462067    0.314635  0.389784\n",
              "humidity    0.190610  0.001929   -0.010880  ... -0.348187   -0.265458 -0.317371\n",
              "windspeed  -0.147121  0.008409    0.013373  ...  0.092276    0.091052  0.101369\n",
              "casual      0.096758  0.043799   -0.319111  ...  1.000000    0.497250  0.690414\n",
              "registered  0.164011 -0.020956    0.119460  ...  0.497250    1.000000  0.970948\n",
              "count       0.163439 -0.005393    0.011594  ...  0.690414    0.970948  1.000000\n",
              "\n",
              "[11 rows x 11 columns]"
            ]
          },
          "execution_count": 6,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "train.corr()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
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        },
        "id": "h1qz8cPjTNkL",
        "outputId": "c28d647c-aa25-4581-f561-645c501e47d5"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 10886 entries, 0 to 10885\n",
            "Data columns (total 12 columns):\n",
            " #   Column      Non-Null Count  Dtype         \n",
            "---  ------      --------------  -----         \n",
            " 0   datetime    10886 non-null  datetime64[ns]\n",
            " 1   season      10886 non-null  int64         \n",
            " 2   holiday     10886 non-null  int64         \n",
            " 3   workingday  10886 non-null  int64         \n",
            " 4   weather     10886 non-null  int64         \n",
            " 5   temp        10886 non-null  float64       \n",
            " 6   atemp       10886 non-null  float64       \n",
            " 7   humidity    10886 non-null  int64         \n",
            " 8   windspeed   10886 non-null  float64       \n",
            " 9   casual      10886 non-null  int64         \n",
            " 10  registered  10886 non-null  int64         \n",
            " 11  count       10886 non-null  int64         \n",
            "dtypes: datetime64[ns](1), float64(3), int64(8)\n",
            "memory usage: 1020.7 KB\n"
          ]
        }
      ],
      "source": [
        "train.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "KhNd36c1RiyJ",
        "outputId": "50ff9343-4e2c-4061-a3d5-ac0b1b93cca3"
      },
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        {
          "data": {
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              "      <td>10886.000000</td>\n",
              "      <td>10886.000000</td>\n",
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              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>20.23086</td>\n",
              "      <td>23.655084</td>\n",
              "      <td>61.886460</td>\n",
              "      <td>12.799395</td>\n",
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              "      <td>155.552177</td>\n",
              "      <td>191.574132</td>\n",
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              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>7.79159</td>\n",
              "      <td>8.474601</td>\n",
              "      <td>19.245033</td>\n",
              "      <td>8.164537</td>\n",
              "      <td>49.960477</td>\n",
              "      <td>151.039033</td>\n",
              "      <td>181.144454</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.82000</td>\n",
              "      <td>0.760000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>13.94000</td>\n",
              "      <td>16.665000</td>\n",
              "      <td>47.000000</td>\n",
              "      <td>7.001500</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>36.000000</td>\n",
              "      <td>42.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>20.50000</td>\n",
              "      <td>24.240000</td>\n",
              "      <td>62.000000</td>\n",
              "      <td>12.998000</td>\n",
              "      <td>17.000000</td>\n",
              "      <td>118.000000</td>\n",
              "      <td>145.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>26.24000</td>\n",
              "      <td>31.060000</td>\n",
              "      <td>77.000000</td>\n",
              "      <td>16.997900</td>\n",
              "      <td>49.000000</td>\n",
              "      <td>222.000000</td>\n",
              "      <td>284.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>41.00000</td>\n",
              "      <td>45.455000</td>\n",
              "      <td>100.000000</td>\n",
              "      <td>56.996900</td>\n",
              "      <td>367.000000</td>\n",
              "      <td>886.000000</td>\n",
              "      <td>977.000000</td>\n",
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              "                                                     [key], {});\n",
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              "\n",
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              "  "
            ],
            "text/plain": [
              "              temp         atemp  ...    registered         count\n",
              "count  10886.00000  10886.000000  ...  10886.000000  10886.000000\n",
              "mean      20.23086     23.655084  ...    155.552177    191.574132\n",
              "std        7.79159      8.474601  ...    151.039033    181.144454\n",
              "min        0.82000      0.760000  ...      0.000000      1.000000\n",
              "25%       13.94000     16.665000  ...     36.000000     42.000000\n",
              "50%       20.50000     24.240000  ...    118.000000    145.000000\n",
              "75%       26.24000     31.060000  ...    222.000000    284.000000\n",
              "max       41.00000     45.455000  ...    886.000000    977.000000\n",
              "\n",
              "[8 rows x 7 columns]"
            ]
          },
          "execution_count": 13,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Simple output of the train dataset to view some of the min/max/varition of the dataset features.\n",
        "import numpy as np\n",
        "train.describe()"
      ]
    },
    {
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        "id": "fxJLm2ucRiyJ",
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              "             datetime  season  holiday  ...   atemp  humidity  windspeed\n",
              "0 2011-01-20 00:00:00       1        0  ...  11.365        56    26.0027\n",
              "1 2011-01-20 01:00:00       1        0  ...  13.635        56     0.0000\n",
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              "4 2011-01-20 04:00:00       1        0  ...  12.880        56    11.0014\n",
              "\n",
              "[5 rows x 9 columns]"
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          "metadata": {},
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        "# Create the test pandas dataframe in pandas by reading the csv, remember to parse the datetime!\n",
        "test =  pd.read_csv('./test.csv', parse_dates=['datetime'])\n",
        "test.head()"
      ]
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      "metadata": {
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        },
        "id": "PPLF2aDLTzZz",
        "outputId": "06773bf7-fcc9-47c2-ea86-1f497fb1fb3a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 6493 entries, 0 to 6492\n",
            "Data columns (total 9 columns):\n",
            " #   Column      Non-Null Count  Dtype         \n",
            "---  ------      --------------  -----         \n",
            " 0   datetime    6493 non-null   datetime64[ns]\n",
            " 1   season      6493 non-null   int64         \n",
            " 2   holiday     6493 non-null   int64         \n",
            " 3   workingday  6493 non-null   int64         \n",
            " 4   weather     6493 non-null   int64         \n",
            " 5   temp        6493 non-null   float64       \n",
            " 6   atemp       6493 non-null   float64       \n",
            " 7   humidity    6493 non-null   int64         \n",
            " 8   windspeed   6493 non-null   float64       \n",
            "dtypes: datetime64[ns](1), float64(3), int64(5)\n",
            "memory usage: 456.7 KB\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",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-125886c8-4cb9-4084-b9a7-2319e6cc3098 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-125886c8-4cb9-4084-b9a7-2319e6cc3098');\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": [
              "             datetime  count\n",
              "0 2011-01-20 00:00:00      0\n",
              "1 2011-01-20 01:00:00      0\n",
              "2 2011-01-20 02:00:00      0\n",
              "3 2011-01-20 03:00:00      0\n",
              "4 2011-01-20 04:00:00      0"
            ]
          },
          "execution_count": 14,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "\n",
        "# Same thing as train and test dataset\n",
        "submission = pd.read_csv('./sampleSubmission.csv', parse_dates=['datetime'])\n",
        "submission.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YfxE6NOiRiyK"
      },
      "source": [
        "## Step 3: Train a model using AutoGluon’s Tabular Prediction"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_ZulwpovRiyT"
      },
      "source": [
        "Requirements:\n",
        "* We are prediting `count`, so it is the label we are setting.\n",
        "* Ignore `casual` and `registered` columns as they are also not present in the test dataset. \n",
        "* Use the `root_mean_squared_error` as the metric to use for evaluation.\n",
        "* Set a time limit of 10 minutes (600 seconds).\n",
        "* Use the preset `best_quality` to focus on creating the best model."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "F_Dc_Zi9RiyU",
        "outputId": "f2e555c7-d963-4992-81ed-f5a0d663491a"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Warning: path already exists! This predictor may overwrite an existing predictor! path=\"bike-Models-predictClass\"\n",
            "Presets specified: ['best_quality']\n",
            "Beginning AutoGluon training ... Time limit = 600s\n",
            "AutoGluon will save models to \"bike-Models-predictClass/\"\n",
            "AutoGluon Version:  0.3.1\n",
            "Train Data Rows:    10886\n",
            "Train Data Columns: 11\n",
            "Preprocessing data ...\n",
            "Using Feature Generators to preprocess the data ...\n",
            "Dropping user-specified ignored columns: ['casual', 'registered']\n",
            "Fitting AutoMLPipelineFeatureGenerator...\n",
            "\tAvailable Memory:                    12564.99 MB\n",
            "\tTrain Data (Original)  Memory Usage: 0.78 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",
            "\t\tFitting DatetimeFeatureGenerator...\n",
            "\tStage 4 Generators:\n",
            "\t\tFitting DropUniqueFeatureGenerator...\n",
            "\tTypes of features in original data (raw dtype, special dtypes):\n",
            "\t\t('datetime', []) : 1 | ['datetime']\n",
            "\t\t('float', [])    : 3 | ['temp', 'atemp', 'windspeed']\n",
            "\t\t('int', [])      : 5 | ['season', 'holiday', 'workingday', 'weather', 'humidity']\n",
            "\tTypes of features in processed data (raw dtype, special dtypes):\n",
            "\t\t('float', [])                : 3 | ['temp', 'atemp', 'windspeed']\n",
            "\t\t('int', [])                  : 3 | ['season', 'weather', 'humidity']\n",
            "\t\t('int', ['bool'])            : 2 | ['holiday', 'workingday']\n",
            "\t\t('int', ['datetime_as_int']) : 1 | ['datetime']\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: 0.63 MB (0.0% of available memory)\n",
            "Data preprocessing and feature engineering runtime = 0.28s ...\n",
            "AutoGluon will gauge predictive performance using evaluation metric: 'root_mean_squared_error'\n",
            "\tTo change this, specify the eval_metric argument of fit()\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 399.71s of the 599.71s of remaining time.\n",
            "\t-160.4129\t = Validation score   (root_mean_squared_error)\n",
            "\t0.03s\t = Training   runtime\n",
            "\t0.1s\t = Validation runtime\n",
            "Fitting model: KNeighborsDist_BAG_L1 ... Training model for up to 398.32s of the 598.32s of remaining time.\n",
            "\t-169.552\t = Validation score   (root_mean_squared_error)\n",
            "\t0.03s\t = Training   runtime\n",
            "\t0.11s\t = Validation runtime\n",
            "Fitting model: LightGBMXT_BAG_L1 ... Training model for up to 397.1s of the 597.1s of remaining time.\n",
            "/usr/local/lib/python3.7/dist-packages/lightgbm/engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.\n",
            "  _log_warning(\"'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. \"\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[1000]\ttrain_set's rmse: 123.806\tvalid_set's rmse: 134.369\n",
            "[2000]\ttrain_set's rmse: 117.412\tvalid_set's rmse: 133.705\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/lightgbm/engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.\n",
            "  _log_warning(\"'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. \"\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[1000]\ttrain_set's rmse: 123.42\tvalid_set's rmse: 141.5\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/lightgbm/engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.\n",
            "  _log_warning(\"'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. \"\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[1000]\ttrain_set's rmse: 125.097\tvalid_set's rmse: 128.797\n",
            "[2000]\ttrain_set's rmse: 119.003\tvalid_set's rmse: 127.909\n",
            "[3000]\ttrain_set's rmse: 114.63\tvalid_set's rmse: 127.431\n",
            "[4000]\ttrain_set's rmse: 111.295\tvalid_set's rmse: 126.943\n",
            "[5000]\ttrain_set's rmse: 108.576\tvalid_set's rmse: 126.844\n",
            "[6000]\ttrain_set's rmse: 106.085\tvalid_set's rmse: 126.71\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/lightgbm/engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.\n",
            "  _log_warning(\"'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. \"\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[1000]\ttrain_set's rmse: 124.131\tvalid_set's rmse: 138.303\n",
            "[2000]\ttrain_set's rmse: 117.833\tvalid_set's rmse: 137.535\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/lightgbm/engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.\n",
            "  _log_warning(\"'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. \"\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[1000]\ttrain_set's rmse: 124.871\tvalid_set's rmse: 128.052\n",
            "[2000]\ttrain_set's rmse: 118.547\tvalid_set's rmse: 127.003\n",
            "[3000]\ttrain_set's rmse: 114.124\tvalid_set's rmse: 126.834\n",
            "[4000]\ttrain_set's rmse: 110.645\tvalid_set's rmse: 126.694\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/lightgbm/engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.\n",
            "  _log_warning(\"'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. \"\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[1000]\ttrain_set's rmse: 124.654\tvalid_set's rmse: 135.095\n",
            "[2000]\ttrain_set's rmse: 118.764\tvalid_set's rmse: 133.849\n",
            "[3000]\ttrain_set's rmse: 114.615\tvalid_set's rmse: 133.509\n",
            "[4000]\ttrain_set's rmse: 111.249\tvalid_set's rmse: 133.472\n",
            "[5000]\ttrain_set's rmse: 108.455\tvalid_set's rmse: 133.281\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/lightgbm/engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.\n",
            "  _log_warning(\"'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. \"\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[1000]\ttrain_set's rmse: 124.01\tvalid_set's rmse: 139.355\n",
            "[2000]\ttrain_set's rmse: 117.819\tvalid_set's rmse: 138.312\n",
            "[3000]\ttrain_set's rmse: 113.4\tvalid_set's rmse: 137.886\n",
            "[4000]\ttrain_set's rmse: 110.032\tvalid_set's rmse: 137.758\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/lightgbm/engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.\n",
            "  _log_warning(\"'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. \"\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[1000]\ttrain_set's rmse: 124.47\tvalid_set's rmse: 135.412\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/lightgbm/engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.\n",
            "  _log_warning(\"'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. \"\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[1000]\ttrain_set's rmse: 124.18\tvalid_set's rmse: 137.579\n",
            "[2000]\ttrain_set's rmse: 117.782\tvalid_set's rmse: 136.664\n",
            "[3000]\ttrain_set's rmse: 113.447\tvalid_set's rmse: 136.246\n",
            "[4000]\ttrain_set's rmse: 109.982\tvalid_set's rmse: 136.227\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/lightgbm/engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.\n",
            "  _log_warning(\"'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. \"\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[1000]\ttrain_set's rmse: 124.641\tvalid_set's rmse: 132.484\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\t-134.0883\t = Validation score   (root_mean_squared_error)\n",
            "\t66.06s\t = Training   runtime\n",
            "\t2.89s\t = Validation runtime\n",
            "Fitting model: LightGBM_BAG_L1 ... Training model for up to 318.15s of the 518.15s of remaining time.\n",
            "/usr/local/lib/python3.7/dist-packages/lightgbm/engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.\n",
            "  _log_warning(\"'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
            "/usr/local/lib/python3.7/dist-packages/lightgbm/engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.\n",
            "  _log_warning(\"'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
            "/usr/local/lib/python3.7/dist-packages/lightgbm/engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.\n",
            "  _log_warning(\"'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. \"\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[1000]\ttrain_set's rmse: 96.0217\tvalid_set's rmse: 123.249\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/lightgbm/engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.\n",
            "  _log_warning(\"'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. \"\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[1000]\ttrain_set's rmse: 94.7479\tvalid_set's rmse: 135.635\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/lightgbm/engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.\n",
            "  _log_warning(\"'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
            "/usr/local/lib/python3.7/dist-packages/lightgbm/engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.\n",
            "  _log_warning(\"'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. \"\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[1000]\ttrain_set's rmse: 95.3055\tvalid_set's rmse: 132.087\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/lightgbm/engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.\n",
            "  _log_warning(\"'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
            "/usr/local/lib/python3.7/dist-packages/lightgbm/engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.\n",
            "  _log_warning(\"'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. \"\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[1000]\ttrain_set's rmse: 95.1635\tvalid_set's rmse: 131.521\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/lightgbm/engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.\n",
            "  _log_warning(\"'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
            "/usr/local/lib/python3.7/dist-packages/lightgbm/engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.\n",
            "  _log_warning(\"'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
            "\t-132.2864\t = Validation score   (root_mean_squared_error)\n",
            "\t13.0s\t = Training   runtime\n",
            "\t0.53s\t = Validation runtime\n",
            "Fitting model: RandomForestMSE_BAG_L1 ... Training model for up to 299.51s of the 499.51s of remaining time.\n",
            "\t-118.4567\t = Validation score   (root_mean_squared_error)\n",
            "\t8.48s\t = Training   runtime\n",
            "\t0.46s\t = Validation runtime\n",
            "Fitting model: CatBoost_BAG_L1 ... Training model for up to 284.36s of the 484.36s of remaining time.\n",
            "\t-132.3497\t = Validation score   (root_mean_squared_error)\n",
            "\t67.81s\t = Training   runtime\n",
            "\t0.07s\t = Validation runtime\n",
            "Fitting model: ExtraTreesMSE_BAG_L1 ... Training model for up to 212.89s of the 412.89s of remaining time.\n",
            "\t-128.7334\t = Validation score   (root_mean_squared_error)\n",
            "\t5.13s\t = Training   runtime\n",
            "\t0.49s\t = Validation runtime\n",
            "Fitting model: NeuralNetFastAI_BAG_L1 ... Training model for up to 202.73s of the 402.73s of remaining time.\n",
            "\tTime limit exceeded... Skipping NeuralNetFastAI_BAG_L1.\n",
            "Fitting model: XGBoost_BAG_L1 ... Training model for up to 181.09s of the 381.09s of remaining time.\n",
            "\t-132.3085\t = Validation score   (root_mean_squared_error)\n",
            "\t15.81s\t = Training   runtime\n",
            "\t0.17s\t = Validation runtime\n",
            "Fitting model: NeuralNetMXNet_BAG_L1 ... Training model for up to 160.24s of the 360.24s of remaining time.\n",
            "\tRan out of time, stopping training early. (Stopping on epoch 11)\n",
            "\tRan out of time, stopping training early. (Stopping on epoch 13)\n",
            "\tRan out of time, stopping training early. (Stopping on epoch 13)\n",
            "\tRan out of time, stopping training early. (Stopping on epoch 14)\n",
            "\tRan out of time, stopping training early. (Stopping on epoch 14)\n",
            "\tRan out of time, stopping training early. (Stopping on epoch 15)\n",
            "\tRan out of time, stopping training early. (Stopping on epoch 16)\n",
            "\tRan out of time, stopping training early. (Stopping on epoch 17)\n",
            "\tRan out of time, stopping training early. (Stopping on epoch 18)\n",
            "\tRan out of time, stopping training early. (Stopping on epoch 23)\n",
            "\t-144.1035\t = Validation score   (root_mean_squared_error)\n",
            "\t149.31s\t = Training   runtime\n",
            "\t1.86s\t = Validation runtime\n",
            "Fitting model: LightGBMLarge_BAG_L1 ... Training model for up to 4.14s of the 204.13s of remaining time.\n",
            "/usr/local/lib/python3.7/dist-packages/lightgbm/engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.\n",
            "  _log_warning(\"'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
            "\tRan out of time, early stopping on iteration 55. Best iteration is:\n",
            "\t[55]\ttrain_set's rmse: 123.55\tvalid_set's rmse: 135.631\n",
            "\tTime limit exceeded... Skipping LightGBMLarge_BAG_L1.\n",
            "Completed 1/20 k-fold bagging repeats ...\n",
            "Fitting model: WeightedEnsemble_L2 ... Training model for up to 360.0s of the 203.27s of remaining time.\n",
            "\t-118.4416\t = Validation score   (root_mean_squared_error)\n",
            "\t0.45s\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 202.23s of the 202.2s of remaining time.\n",
            "/usr/local/lib/python3.7/dist-packages/lightgbm/engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.\n",
            "  _log_warning(\"'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
            "/usr/local/lib/python3.7/dist-packages/lightgbm/engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.\n",
            "  _log_warning(\"'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
            "/usr/local/lib/python3.7/dist-packages/lightgbm/engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.\n",
            "  _log_warning(\"'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
            "/usr/local/lib/python3.7/dist-packages/lightgbm/engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.\n",
            "  _log_warning(\"'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
            "/usr/local/lib/python3.7/dist-packages/lightgbm/engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.\n",
            "  _log_warning(\"'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
            "/usr/local/lib/python3.7/dist-packages/lightgbm/engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.\n",
            "  _log_warning(\"'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
            "/usr/local/lib/python3.7/dist-packages/lightgbm/engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.\n",
            "  _log_warning(\"'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
            "/usr/local/lib/python3.7/dist-packages/lightgbm/engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.\n",
            "  _log_warning(\"'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
            "/usr/local/lib/python3.7/dist-packages/lightgbm/engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.\n",
            "  _log_warning(\"'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
            "/usr/local/lib/python3.7/dist-packages/lightgbm/engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.\n",
            "  _log_warning(\"'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
            "\t-116.0935\t = Validation score   (root_mean_squared_error)\n",
            "\t12.18s\t = Training   runtime\n",
            "\t0.3s\t = Validation runtime\n",
            "Fitting model: LightGBM_BAG_L2 ... Training model for up to 186.35s of the 186.32s of remaining time.\n",
            "/usr/local/lib/python3.7/dist-packages/lightgbm/engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.\n",
            "  _log_warning(\"'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
            "/usr/local/lib/python3.7/dist-packages/lightgbm/engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.\n",
            "  _log_warning(\"'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
            "/usr/local/lib/python3.7/dist-packages/lightgbm/engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.\n",
            "  _log_warning(\"'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
            "/usr/local/lib/python3.7/dist-packages/lightgbm/engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.\n",
            "  _log_warning(\"'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
            "/usr/local/lib/python3.7/dist-packages/lightgbm/engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.\n",
            "  _log_warning(\"'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
            "/usr/local/lib/python3.7/dist-packages/lightgbm/engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.\n",
            "  _log_warning(\"'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
            "/usr/local/lib/python3.7/dist-packages/lightgbm/engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.\n",
            "  _log_warning(\"'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
            "/usr/local/lib/python3.7/dist-packages/lightgbm/engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.\n",
            "  _log_warning(\"'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
            "/usr/local/lib/python3.7/dist-packages/lightgbm/engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.\n",
            "  _log_warning(\"'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
            "/usr/local/lib/python3.7/dist-packages/lightgbm/engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.\n",
            "  _log_warning(\"'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
            "\t-116.8198\t = Validation score   (root_mean_squared_error)\n",
            "\t8.15s\t = Training   runtime\n",
            "\t0.11s\t = Validation runtime\n",
            "Fitting model: RandomForestMSE_BAG_L2 ... Training model for up to 172.3s of the 172.27s of remaining time.\n",
            "\t-119.3966\t = Validation score   (root_mean_squared_error)\n",
            "\t32.73s\t = Training   runtime\n",
            "\t0.62s\t = Validation runtime\n",
            "Fitting model: CatBoost_BAG_L2 ... Training model for up to 135.14s of the 135.11s of remaining time.\n",
            "\t-116.5214\t = Validation score   (root_mean_squared_error)\n",
            "\t35.7s\t = Training   runtime\n",
            "\t0.05s\t = Validation runtime\n",
            "Fitting model: ExtraTreesMSE_BAG_L2 ... Training model for up to 96.44s of the 96.41s of remaining time.\n",
            "\t-117.5264\t = Validation score   (root_mean_squared_error)\n",
            "\t7.49s\t = Training   runtime\n",
            "\t0.57s\t = Validation runtime\n",
            "Fitting model: NeuralNetFastAI_BAG_L2 ... Training model for up to 82.29s of the 82.26s of remaining time.\n",
            "\tRan out of time, stopping training early. (Stopping on epoch 13)\n",
            "\tRan out of time, stopping training early. (Stopping on epoch 10)\n",
            "\tRan out of time, stopping training early. (Stopping on epoch 14)\n",
            "\tRan out of time, stopping training early. (Stopping on epoch 14)\n",
            "\tRan out of time, stopping training early. (Stopping on epoch 14)\n",
            "\tRan out of time, stopping training early. (Stopping on epoch 15)\n",
            "\tRan out of time, stopping training early. (Stopping on epoch 16)\n",
            "\tRan out of time, stopping training early. (Stopping on epoch 17)\n",
            "\tRan out of time, stopping training early. (Stopping on epoch 18)\n",
            "\tRan out of time, stopping training early. (Stopping on epoch 22)\n",
            "\t-116.1575\t = Validation score   (root_mean_squared_error)\n",
            "\t75.46s\t = Training   runtime\n",
            "\t0.41s\t = Validation runtime\n",
            "Fitting model: XGBoost_BAG_L2 ... Training model for up to 1.58s of the 1.55s of remaining time.\n",
            "\tTime limit exceeded... Skipping XGBoost_BAG_L2.\n",
            "Fitting model: NeuralNetMXNet_BAG_L2 ... Training model for up to 1.0s of the 0.97s of remaining time.\n",
            "\tTime limit exceeded... Skipping NeuralNetMXNet_BAG_L2.\n",
            "Fitting model: LightGBMLarge_BAG_L2 ... Training model for up to 0.26s of the 0.23s of remaining time.\n",
            "/usr/local/lib/python3.7/dist-packages/lightgbm/engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.\n",
            "  _log_warning(\"'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
            "\tRan out of time, early stopping on iteration 1. Best iteration is:\n",
            "\t[1]\ttrain_set's rmse: 177.976\tvalid_set's rmse: 174.678\n",
            "\tTime limit exceeded... Skipping LightGBMLarge_BAG_L2.\n",
            "Completed 1/20 k-fold bagging repeats ...\n",
            "Fitting model: WeightedEnsemble_L3 ... Training model for up to 360.0s of the -0.05s of remaining time.\n",
            "\t-115.1407\t = Validation score   (root_mean_squared_error)\n",
            "\t0.36s\t = Training   runtime\n",
            "\t0.0s\t = Validation runtime\n",
            "AutoGluon training complete, total runtime = 601.61s ...\n",
            "TabularPredictor saved. To load, use: predictor = TabularPredictor.load(\"bike-Models-predictClass/\")\n"
          ]
        }
      ],
      "source": [
        "#If eval_metric = None, it is automatically chosen based on problem_type. Defaults to ‘root_mean_squared_error’ for regression.\n",
        "save_path = 'bike-Models-predictClass'\n",
        "predictor = TabularPredictor('count', problem_type='regression', path=save_path, \n",
        "                             learner_kwargs={'ignored_columns':['casual', 'registered']} ).fit(train, time_limit=600, presets='best_quality')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "z_xq1xthrgCQ"
      },
      "outputs": [],
      "source": [
        "#!ls -a 'bike-Models-predictClass'\n",
        "#!zip -r /content/bike-Models-predictClass.zip /content/"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "9E77obw3SqrV"
      },
      "outputs": [],
      "source": [
        "#from google.colab import files\n",
        "#files.download(\"/content/bike-Models-predictClass.zip\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lFhLvmc-RiyU"
      },
      "source": [
        "### Review AutoGluon's training run with ranking of models that did the best."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zdqoQAd0RiyU",
        "outputId": "7e413a45-3b6c-439b-c662-36a98cd27db8"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "*** Summary of fit() ***\n",
            "Estimated performance of each model:\n",
            "                     model   score_val  pred_time_val    fit_time  pred_time_val_marginal  fit_time_marginal  stack_level  can_infer  fit_order\n",
            "0      WeightedEnsemble_L3 -115.140710       8.074546  429.313970                0.000886           0.359521            3       True         17\n",
            "1        LightGBMXT_BAG_L2 -116.093486       6.983480  337.850787                0.302981          12.184751            2       True         11\n",
            "2   NeuralNetFastAI_BAG_L2 -116.157457       7.087236  401.130653                0.406737          75.464617            2       True         16\n",
            "3          CatBoost_BAG_L2 -116.521409       6.727717  361.367364                0.047219          35.701328            2       True         14\n",
            "4          LightGBM_BAG_L2 -116.819839       6.789403  333.818446                0.108904           8.152410            2       True         12\n",
            "5     ExtraTreesMSE_BAG_L2 -117.526351       7.255038  333.152672                0.574539           7.486635            2       True         15\n",
            "6      WeightedEnsemble_L2 -118.441584       2.324525  158.246964                0.000838           0.454913            2       True         10\n",
            "7   RandomForestMSE_BAG_L1 -118.456660       0.461676    8.482607                0.461676           8.482607            1       True          5\n",
            "8   RandomForestMSE_BAG_L2 -119.396641       7.295872  358.397035                0.615373          32.730998            2       True         13\n",
            "9     ExtraTreesMSE_BAG_L1 -128.733445       0.487602    5.126870                0.487602           5.126870            1       True          7\n",
            "10         LightGBM_BAG_L1 -132.286410       0.529377   13.003821                0.529377          13.003821            1       True          4\n",
            "11          XGBoost_BAG_L1 -132.308522       0.169382   15.805181                0.169382          15.805181            1       True          8\n",
            "12         CatBoost_BAG_L1 -132.349692       0.067568   67.813615                0.067568          67.813615            1       True          6\n",
            "13       LightGBMXT_BAG_L1 -134.088334       2.893707   66.064636                2.893707          66.064636            1       True          3\n",
            "14   NeuralNetMXNet_BAG_L1 -144.103512       1.862011  149.309443                1.862011         149.309443            1       True          9\n",
            "15   KNeighborsUnif_BAG_L1 -160.412950       0.103980    0.032147                0.103980           0.032147            1       True          1\n",
            "16   KNeighborsDist_BAG_L1 -169.551983       0.105196    0.027717                0.105196           0.027717            1       True          2\n",
            "Number of models trained: 17\n",
            "Types of models trained:\n",
            "{'StackerEnsembleModel_XT', 'StackerEnsembleModel_CatBoost', 'StackerEnsembleModel_TabularNeuralNet', 'WeightedEnsembleModel', 'StackerEnsembleModel_NNFastAiTabular', 'StackerEnsembleModel_XGBoost', 'StackerEnsembleModel_RF', 'StackerEnsembleModel_KNN', 'StackerEnsembleModel_LGB'}\n",
            "Bagging used: True  (with 10 folds)\n",
            "Multi-layer stack-ensembling used: True  (with 3 levels)\n",
            "Feature Metadata (Processed):\n",
            "(raw dtype, special dtypes):\n",
            "('float', [])                : 3 | ['temp', 'atemp', 'windspeed']\n",
            "('int', [])                  : 3 | ['season', 'weather', 'humidity']\n",
            "('int', ['bool'])            : 2 | ['holiday', 'workingday']\n",
            "('int', ['datetime_as_int']) : 1 | ['datetime']\n",
            "Plot summary of models saved to file: ./bike-Models-predictClass/SummaryOfModels.html\n",
            "*** End of fit() summary ***\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "{'leaderboard':                      model   score_val  ...  can_infer  fit_order\n",
              " 0      WeightedEnsemble_L3 -115.140710  ...       True         17\n",
              " 1        LightGBMXT_BAG_L2 -116.093486  ...       True         11\n",
              " 2   NeuralNetFastAI_BAG_L2 -116.157457  ...       True         16\n",
              " 3          CatBoost_BAG_L2 -116.521409  ...       True         14\n",
              " 4          LightGBM_BAG_L2 -116.819839  ...       True         12\n",
              " 5     ExtraTreesMSE_BAG_L2 -117.526351  ...       True         15\n",
              " 6      WeightedEnsemble_L2 -118.441584  ...       True         10\n",
              " 7   RandomForestMSE_BAG_L1 -118.456660  ...       True          5\n",
              " 8   RandomForestMSE_BAG_L2 -119.396641  ...       True         13\n",
              " 9     ExtraTreesMSE_BAG_L1 -128.733445  ...       True          7\n",
              " 10         LightGBM_BAG_L1 -132.286410  ...       True          4\n",
              " 11          XGBoost_BAG_L1 -132.308522  ...       True          8\n",
              " 12         CatBoost_BAG_L1 -132.349692  ...       True          6\n",
              " 13       LightGBMXT_BAG_L1 -134.088334  ...       True          3\n",
              " 14   NeuralNetMXNet_BAG_L1 -144.103512  ...       True          9\n",
              " 15   KNeighborsUnif_BAG_L1 -160.412950  ...       True          1\n",
              " 16   KNeighborsDist_BAG_L1 -169.551983  ...       True          2\n",
              " \n",
              " [17 rows x 9 columns],\n",
              " 'max_stack_level': 3,\n",
              " 'model_best': 'WeightedEnsemble_L3',\n",
              " 'model_fit_times': {'CatBoost_BAG_L1': 67.8136146068573,\n",
              "  'CatBoost_BAG_L2': 35.70132780075073,\n",
              "  'ExtraTreesMSE_BAG_L1': 5.1268699169158936,\n",
              "  'ExtraTreesMSE_BAG_L2': 7.486635446548462,\n",
              "  'KNeighborsDist_BAG_L1': 0.027716875076293945,\n",
              "  'KNeighborsUnif_BAG_L1': 0.03214716911315918,\n",
              "  'LightGBMXT_BAG_L1': 66.06463623046875,\n",
              "  'LightGBMXT_BAG_L2': 12.18475079536438,\n",
              "  'LightGBM_BAG_L1': 13.003820896148682,\n",
              "  'LightGBM_BAG_L2': 8.152409791946411,\n",
              "  'NeuralNetFastAI_BAG_L2': 75.46461653709412,\n",
              "  'NeuralNetMXNet_BAG_L1': 149.30944347381592,\n",
              "  'RandomForestMSE_BAG_L1': 8.482606649398804,\n",
              "  'RandomForestMSE_BAG_L2': 32.730998277664185,\n",
              "  'WeightedEnsemble_L2': 0.4549133777618408,\n",
              "  'WeightedEnsemble_L3': 0.35952138900756836,\n",
              "  'XGBoost_BAG_L1': 15.805180549621582},\n",
              " 'model_hyperparams': {'CatBoost_BAG_L1': {'max_base_models': 25,\n",
              "   'max_base_models_per_type': 5,\n",
              "   'save_bag_folds': True,\n",
              "   'use_orig_features': True},\n",
              "  'CatBoost_BAG_L2': {'max_base_models': 25,\n",
              "   'max_base_models_per_type': 5,\n",
              "   'save_bag_folds': True,\n",
              "   'use_orig_features': True},\n",
              "  'ExtraTreesMSE_BAG_L1': {'max_base_models': 25,\n",
              "   'max_base_models_per_type': 5,\n",
              "   'save_bag_folds': True,\n",
              "   'use_child_oof': True,\n",
              "   'use_orig_features': True},\n",
              "  'ExtraTreesMSE_BAG_L2': {'max_base_models': 25,\n",
              "   'max_base_models_per_type': 5,\n",
              "   'save_bag_folds': True,\n",
              "   'use_child_oof': True,\n",
              "   'use_orig_features': True},\n",
              "  'KNeighborsDist_BAG_L1': {'max_base_models': 25,\n",
              "   'max_base_models_per_type': 5,\n",
              "   'save_bag_folds': True,\n",
              "   'use_child_oof': True,\n",
              "   'use_orig_features': True},\n",
              "  'KNeighborsUnif_BAG_L1': {'max_base_models': 25,\n",
              "   'max_base_models_per_type': 5,\n",
              "   'save_bag_folds': True,\n",
              "   'use_child_oof': True,\n",
              "   'use_orig_features': True},\n",
              "  'LightGBMXT_BAG_L1': {'max_base_models': 25,\n",
              "   'max_base_models_per_type': 5,\n",
              "   'save_bag_folds': True,\n",
              "   'use_orig_features': True},\n",
              "  'LightGBMXT_BAG_L2': {'max_base_models': 25,\n",
              "   'max_base_models_per_type': 5,\n",
              "   'save_bag_folds': True,\n",
              "   'use_orig_features': True},\n",
              "  'LightGBM_BAG_L1': {'max_base_models': 25,\n",
              "   'max_base_models_per_type': 5,\n",
              "   'save_bag_folds': True,\n",
              "   'use_orig_features': True},\n",
              "  'LightGBM_BAG_L2': {'max_base_models': 25,\n",
              "   'max_base_models_per_type': 5,\n",
              "   'save_bag_folds': True,\n",
              "   'use_orig_features': True},\n",
              "  'NeuralNetFastAI_BAG_L2': {'max_base_models': 25,\n",
              "   'max_base_models_per_type': 5,\n",
              "   'save_bag_folds': True,\n",
              "   'use_orig_features': True},\n",
              "  'NeuralNetMXNet_BAG_L1': {'max_base_models': 25,\n",
              "   'max_base_models_per_type': 5,\n",
              "   'save_bag_folds': True,\n",
              "   'use_orig_features': True},\n",
              "  'RandomForestMSE_BAG_L1': {'max_base_models': 25,\n",
              "   'max_base_models_per_type': 5,\n",
              "   'save_bag_folds': True,\n",
              "   'use_child_oof': True,\n",
              "   'use_orig_features': True},\n",
              "  'RandomForestMSE_BAG_L2': {'max_base_models': 25,\n",
              "   'max_base_models_per_type': 5,\n",
              "   'save_bag_folds': True,\n",
              "   'use_child_oof': True,\n",
              "   'use_orig_features': True},\n",
              "  'WeightedEnsemble_L2': {'max_base_models': 25,\n",
              "   'max_base_models_per_type': 5,\n",
              "   'save_bag_folds': True,\n",
              "   'use_orig_features': False},\n",
              "  'WeightedEnsemble_L3': {'max_base_models': 25,\n",
              "   'max_base_models_per_type': 5,\n",
              "   'save_bag_folds': True,\n",
              "   'use_orig_features': False},\n",
              "  'XGBoost_BAG_L1': {'max_base_models': 25,\n",
              "   'max_base_models_per_type': 5,\n",
              "   'save_bag_folds': True,\n",
              "   'use_orig_features': True}},\n",
              " 'model_paths': {'CatBoost_BAG_L1': './bike-Models-predictClass/models/CatBoost_BAG_L1/',\n",
              "  'CatBoost_BAG_L2': './bike-Models-predictClass/models/CatBoost_BAG_L2/',\n",
              "  'ExtraTreesMSE_BAG_L1': './bike-Models-predictClass/models/ExtraTreesMSE_BAG_L1/',\n",
              "  'ExtraTreesMSE_BAG_L2': './bike-Models-predictClass/models/ExtraTreesMSE_BAG_L2/',\n",
              "  'KNeighborsDist_BAG_L1': './bike-Models-predictClass/models/KNeighborsDist_BAG_L1/',\n",
              "  'KNeighborsUnif_BAG_L1': './bike-Models-predictClass/models/KNeighborsUnif_BAG_L1/',\n",
              "  'LightGBMXT_BAG_L1': './bike-Models-predictClass/models/LightGBMXT_BAG_L1/',\n",
              "  'LightGBMXT_BAG_L2': './bike-Models-predictClass/models/LightGBMXT_BAG_L2/',\n",
              "  'LightGBM_BAG_L1': './bike-Models-predictClass/models/LightGBM_BAG_L1/',\n",
              "  'LightGBM_BAG_L2': './bike-Models-predictClass/models/LightGBM_BAG_L2/',\n",
              "  'NeuralNetFastAI_BAG_L2': './bike-Models-predictClass/models/NeuralNetFastAI_BAG_L2/',\n",
              "  'NeuralNetMXNet_BAG_L1': './bike-Models-predictClass/models/NeuralNetMXNet_BAG_L1/',\n",
              "  'RandomForestMSE_BAG_L1': './bike-Models-predictClass/models/RandomForestMSE_BAG_L1/',\n",
              "  'RandomForestMSE_BAG_L2': './bike-Models-predictClass/models/RandomForestMSE_BAG_L2/',\n",
              "  'WeightedEnsemble_L2': './bike-Models-predictClass/models/WeightedEnsemble_L2/',\n",
              "  'WeightedEnsemble_L3': './bike-Models-predictClass/models/WeightedEnsemble_L3/',\n",
              "  'XGBoost_BAG_L1': './bike-Models-predictClass/models/XGBoost_BAG_L1/'},\n",
              " 'model_performance': {'CatBoost_BAG_L1': -132.3496915713461,\n",
              "  'CatBoost_BAG_L2': -116.52140861379446,\n",
              "  'ExtraTreesMSE_BAG_L1': -128.73344506216944,\n",
              "  'ExtraTreesMSE_BAG_L2': -117.52635112228522,\n",
              "  'KNeighborsDist_BAG_L1': -169.55198317920082,\n",
              "  'KNeighborsUnif_BAG_L1': -160.41294976754526,\n",
              "  'LightGBMXT_BAG_L1': -134.08833420116997,\n",
              "  'LightGBMXT_BAG_L2': -116.09348586162136,\n",
              "  'LightGBM_BAG_L1': -132.28640961443512,\n",
              "  'LightGBM_BAG_L2': -116.8198391288509,\n",
              "  'NeuralNetFastAI_BAG_L2': -116.15745743964773,\n",
              "  'NeuralNetMXNet_BAG_L1': -144.10351221449005,\n",
              "  'RandomForestMSE_BAG_L1': -118.45666016795751,\n",
              "  'RandomForestMSE_BAG_L2': -119.39664058215577,\n",
              "  'WeightedEnsemble_L2': -118.44158358809601,\n",
              "  'WeightedEnsemble_L3': -115.14070971436848,\n",
              "  'XGBoost_BAG_L1': -132.30852184526844},\n",
              " 'model_pred_times': {'CatBoost_BAG_L1': 0.06756806373596191,\n",
              "  'CatBoost_BAG_L2': 0.04721856117248535,\n",
              "  'ExtraTreesMSE_BAG_L1': 0.48760151863098145,\n",
              "  'ExtraTreesMSE_BAG_L2': 0.5745387077331543,\n",
              "  'KNeighborsDist_BAG_L1': 0.10519647598266602,\n",
              "  'KNeighborsUnif_BAG_L1': 0.10398006439208984,\n",
              "  'LightGBMXT_BAG_L1': 2.893707036972046,\n",
              "  'LightGBMXT_BAG_L2': 0.302980899810791,\n",
              "  'LightGBM_BAG_L1': 0.529376745223999,\n",
              "  'LightGBM_BAG_L2': 0.10890412330627441,\n",
              "  'NeuralNetFastAI_BAG_L2': 0.4067373275756836,\n",
              "  'NeuralNetMXNet_BAG_L1': 1.862011194229126,\n",
              "  'RandomForestMSE_BAG_L1': 0.46167588233947754,\n",
              "  'RandomForestMSE_BAG_L2': 0.6153731346130371,\n",
              "  'WeightedEnsemble_L2': 0.0008378028869628906,\n",
              "  'WeightedEnsemble_L3': 0.0008864402770996094,\n",
              "  'XGBoost_BAG_L1': 0.16938185691833496},\n",
              " 'model_types': {'CatBoost_BAG_L1': 'StackerEnsembleModel_CatBoost',\n",
              "  'CatBoost_BAG_L2': 'StackerEnsembleModel_CatBoost',\n",
              "  'ExtraTreesMSE_BAG_L1': 'StackerEnsembleModel_XT',\n",
              "  'ExtraTreesMSE_BAG_L2': 'StackerEnsembleModel_XT',\n",
              "  'KNeighborsDist_BAG_L1': 'StackerEnsembleModel_KNN',\n",
              "  'KNeighborsUnif_BAG_L1': 'StackerEnsembleModel_KNN',\n",
              "  'LightGBMXT_BAG_L1': 'StackerEnsembleModel_LGB',\n",
              "  'LightGBMXT_BAG_L2': 'StackerEnsembleModel_LGB',\n",
              "  'LightGBM_BAG_L1': 'StackerEnsembleModel_LGB',\n",
              "  'LightGBM_BAG_L2': 'StackerEnsembleModel_LGB',\n",
              "  'NeuralNetFastAI_BAG_L2': 'StackerEnsembleModel_NNFastAiTabular',\n",
              "  'NeuralNetMXNet_BAG_L1': 'StackerEnsembleModel_TabularNeuralNet',\n",
              "  'RandomForestMSE_BAG_L1': 'StackerEnsembleModel_RF',\n",
              "  'RandomForestMSE_BAG_L2': 'StackerEnsembleModel_RF',\n",
              "  'WeightedEnsemble_L2': 'WeightedEnsembleModel',\n",
              "  'WeightedEnsemble_L3': 'WeightedEnsembleModel',\n",
              "  'XGBoost_BAG_L1': 'StackerEnsembleModel_XGBoost'},\n",
              " 'num_bag_folds': 10}"
            ]
          },
          "execution_count": 16,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "predictor = TabularPredictor.load('./bike-Models-predictClass')  # how to load previously-trained predictor from file\n",
        "predictor.fit_summary(show_plot=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "w4zPCl4MRiyV"
      },
      "source": [
        "### Create predictions from test dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "S4OxEHq0RiyV",
        "outputId": "13d59525-e3f6-4b39-a689-299f10ac3f9e"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0     97.780212\n",
              "1     92.688980\n",
              "2     92.690201\n",
              "3    108.982773\n",
              "4    108.886246\n",
              "Name: count, dtype: float32"
            ]
          },
          "execution_count": 17,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "predictions = predictor.predict(test)\n",
        "predictions.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "L5xLLolWRetZ"
      },
      "outputs": [],
      "source": [
        "#predictor.leaderboard(test, silent=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6MPNVaYQRiyV"
      },
      "source": [
        "#### NOTE: Kaggle will reject the submission if we don't set everything to be > 0."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FEABdYaURiyV",
        "outputId": "1da06050-d7a2-4a46-8829-b20526596504"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Series([], Name: count, dtype: float32)"
            ]
          },
          "execution_count": 22,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Describe the `predictions` series to see if there are any negative values\n",
        "predictions[predictions<0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "P0zHy6q1RiyW",
        "outputId": "688ddb99-e65a-4988-e6a3-90cd2ad073c7"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "execution_count": 23,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# How many negative values do we have?\n",
        "len(predictions[predictions<0])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "_B3ctYieRiyW"
      },
      "outputs": [],
      "source": [
        "# Set them to zero\n",
        "predictions[predictions<0]=0"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aGFXQRHaRiyW"
      },
      "source": [
        "### Set predictions to submission dataframe, save, and submit"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "vm7FTLxMRiyX"
      },
      "outputs": [],
      "source": [
        "submission[\"count\"] = predictions\n",
        "submission.to_csv(\"submission.csv\", index=False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SYf_rOnbRiyX",
        "outputId": "f07b665e-fc14-4900-923e-aa6922a7a5ff"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Warning: Looks like you're using an outdated API Version, please consider updating (server 1.5.12 / client 1.5.4)\n",
            "100% 188k/188k [00:00<00:00, 458kB/s]\n",
            "Successfully submitted to Bike Sharing Demand"
          ]
        }
      ],
      "source": [
        "!kaggle competitions submit -c bike-sharing-demand -f submission.csv -m \"first raw submission\""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "s8ptltvDRiyX"
      },
      "source": [
        "#### View submission via the command line or in the web browser under the competition's page - `My Submissions`"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hC0TN-K3RiyY",
        "outputId": "88dd1652-ee7c-47c9-d40b-06c046df4ac7"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Warning: Looks like you're using an outdated API Version, please consider updating (server 1.5.12 / client 1.5.4)\n",
            "fileName                             date                 description                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                status    publicScore  privateScore  \n",
            "-----------------------------------  -------------------  -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------  --------  -----------  ------------  \n",
            "submission.csv                       2021-12-28 13:31:26  first raw submission-third time                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            complete  1.39449      1.39449       \n",
            "submission_independent_features.csv  2021-12-27 21:53:35  I used a TabularPredictor to predict the 'count' column in the test file. I chose the problem type to be a Regression problem (because that's it!) and I used ‘root_mean_squared_error’ for regression. I ignored the 'casual' and 'registered' columns because they are not existed in the test file.  During reading the csv files, I remembered to parse the datetime, so that I could create two new features 'date' and 'time. I changed the types of features l'season' and 'weather' to the Category format, because AutoGluon originally sees them as ints, but in reality they are int representations of a category. Setting the dtype to category will classify these as categories in AutoGluon. I gave a time limit of 10 minutes to the model fitting and then I predicted the test file's 'count' column using my predictor. At the end I  described the final predictions series to see if there are any negative values, because Kaggle will reject the submission if we don't set everything to be > 0.  complete  0.47959      0.47959       \n",
            "submission_new_hpo.csv               2021-12-27 20:37:35  new features with light hyperparameters                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    complete  0.49169      0.49169       \n"
          ]
        }
      ],
      "source": [
        "!kaggle competitions submissions -c bike-sharing-demand | tail -n +1 | head -n 6"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "f9t-OkkaRiyY"
      },
      "source": [
        "#### Initial score of `1.39407`"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "G8Bgev69RiyY"
      },
      "source": [
        "## Step 4: Exploratory Data Analysis and Creating an additional feature\n",
        "* Any additional feature will do, but a great suggestion would be to separate out the datetime into hour, day, or month parts."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 845
        },
        "id": "ajDXsPX8RiyY",
        "outputId": "0b0c69e6-5873-4876-da3f-9ccff909dcf9"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f2b23438990>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f2b2465fe10>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f2b24573410>],\n",
              "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f2b24656410>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f2b245fc410>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f2b244f1b10>],\n",
              "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f2c78bcc1d0>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f2c78b7b710>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f2c78b7b750>],\n",
              "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f2c78ba9e50>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f2c78b16990>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f2c78ac9f90>]],\n",
              "      dtype=object)"
            ]
          },
          "execution_count": 28,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 1080x720 with 12 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Create a histogram of all features to show the distribution of each one relative to the data. This is part of the exploritory data analysis\n",
        "train.hist(bins=30, figsize=(15, 10))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "NR04t8J3RiyZ",
        "outputId": "516f7686-dc3c-4e38-909a-51dcda032e58"
      },
      "outputs": [
        {
          "data": {
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-702eaa02-8c05-455a-badb-c942c5c18dbe 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-702eaa02-8c05-455a-badb-c942c5c18dbe');\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": [
              "             datetime  season  holiday  ...  count        date      time\n",
              "0 2011-01-01 00:00:00       1        0  ...     16  2011-01-01  00:00:00\n",
              "1 2011-01-01 01:00:00       1        0  ...     40  2011-01-01  01:00:00\n",
              "2 2011-01-01 02:00:00       1        0  ...     32  2011-01-01  02:00:00\n",
              "3 2011-01-01 03:00:00       1        0  ...     13  2011-01-01  03:00:00\n",
              "4 2011-01-01 04:00:00       1        0  ...      1  2011-01-01  04:00:00\n",
              "\n",
              "[5 rows x 14 columns]"
            ]
          },
          "execution_count": 8,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# create a new feature\n",
        "train['date'] = train['datetime'].dt.date\n",
        "train['time'] = train['datetime'].dt.time\n",
        "test['date'] = test['datetime'].dt.date\n",
        "test['time'] = test['datetime'].dt.time\n",
        "train.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0jQqrIpiRiyZ"
      },
      "source": [
        "## Make category types for these so models know they are not just numbers\n",
        "* AutoGluon originally sees these as ints, but in reality they are int representations of a category.\n",
        "* Setting the dtype to category will classify these as categories in AutoGluon."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "TqtvAK-pRiyZ"
      },
      "outputs": [],
      "source": [
        "train[\"season\"] = train[\"season\"].astype(\"category\")\n",
        "train[\"weather\"] = train[\"weather\"].astype(\"category\")\n",
        "test[\"season\"] = test[\"season\"].astype(\"category\")\n",
        "test[\"weather\"] = test[\"weather\"].astype(\"category\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "h9F8XVRDRiya",
        "outputId": "10f5a1ad-b410-4dcd-8c23-9907da63ab77"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 10886 entries, 0 to 10885\n",
            "Data columns (total 14 columns):\n",
            " #   Column      Non-Null Count  Dtype         \n",
            "---  ------      --------------  -----         \n",
            " 0   datetime    10886 non-null  datetime64[ns]\n",
            " 1   season      10886 non-null  category      \n",
            " 2   holiday     10886 non-null  int64         \n",
            " 3   workingday  10886 non-null  int64         \n",
            " 4   weather     10886 non-null  category      \n",
            " 5   temp        10886 non-null  float64       \n",
            " 6   atemp       10886 non-null  float64       \n",
            " 7   humidity    10886 non-null  int64         \n",
            " 8   windspeed   10886 non-null  float64       \n",
            " 9   casual      10886 non-null  int64         \n",
            " 10  registered  10886 non-null  int64         \n",
            " 11  count       10886 non-null  int64         \n",
            " 12  date        10886 non-null  object        \n",
            " 13  time        10886 non-null  object        \n",
            "dtypes: category(2), datetime64[ns](1), float64(3), int64(6), object(2)\n",
            "memory usage: 1.0+ MB\n"
          ]
        }
      ],
      "source": [
        "# View are new feature\n",
        "train.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 790
        },
        "id": "iUBl6CGkRiya",
        "outputId": "36e65516-8fe3-4cc9-9b28-4c61a278e3a4"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f2c78a3a190>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f2c785a5e10>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f2b25411b90>],\n",
              "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f2b2543c0d0>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f2b253f05d0>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f2b253aec50>],\n",
              "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f2b24672e90>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f2c78b238d0>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f2c78b23a50>]],\n",
              "      dtype=object)"
            ]
          },
          "execution_count": 32,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1080x720 with 9 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "# View histogram of all features again now with the hour feature\n",
        "train.hist(bins=30, figsize=(15, 10))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AYoZ5NJCRiya"
      },
      "source": [
        "## Step 5: Rerun the model with the same settings as before, just with more features"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7O88JCRxRiya",
        "outputId": "455d3425-2b41-46cf-90fc-fc823f6cc8eb"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Warning: path already exists! This predictor may overwrite an existing predictor! path=\"bike-Models-date-time-predictClass\"\n",
            "Presets specified: ['best_quality']\n",
            "Beginning AutoGluon training ... Time limit = 600s\n",
            "AutoGluon will save models to \"bike-Models-date-time-predictClass/\"\n",
            "AutoGluon Version:  0.3.1\n",
            "Train Data Rows:    10886\n",
            "Train Data Columns: 13\n",
            "Preprocessing data ...\n",
            "Using Feature Generators to preprocess the data ...\n",
            "Dropping user-specified ignored columns: ['casual', 'registered']\n",
            "Fitting AutoMLPipelineFeatureGenerator...\n",
            "\tAvailable Memory:                    11293.64 MB\n",
            "\tTrain Data (Original)  Memory Usage: 1.59 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",
            "\t\tFitting CategoryFeatureGenerator...\n",
            "\t\t\tFitting CategoryMemoryMinimizeFeatureGenerator...\n",
            "\t\tFitting DatetimeFeatureGenerator...\n",
            "\tStage 4 Generators:\n",
            "\t\tFitting DropUniqueFeatureGenerator...\n",
            "\tTypes of features in original data (raw dtype, special dtypes):\n",
            "\t\t('category', [])                   : 2 | ['season', 'weather']\n",
            "\t\t('datetime', [])                   : 1 | ['datetime']\n",
            "\t\t('float', [])                      : 3 | ['temp', 'atemp', 'windspeed']\n",
            "\t\t('int', [])                        : 3 | ['holiday', 'workingday', 'humidity']\n",
            "\t\t('object', [])                     : 1 | ['time']\n",
            "\t\t('object', ['datetime_as_object']) : 1 | ['date']\n",
            "\tTypes of features in processed data (raw dtype, special dtypes):\n",
            "\t\t('category', [])             : 3 | ['season', 'weather', 'time']\n",
            "\t\t('float', [])                : 3 | ['temp', 'atemp', 'windspeed']\n",
            "\t\t('int', [])                  : 1 | ['humidity']\n",
            "\t\t('int', ['bool'])            : 2 | ['holiday', 'workingday']\n",
            "\t\t('int', ['datetime_as_int']) : 2 | ['datetime', 'date']\n",
            "\t0.2s = Fit runtime\n",
            "\t11 features in original data used to generate 11 features in processed data.\n",
            "\tTrain Data (Processed) Memory Usage: 0.58 MB (0.0% of available memory)\n",
            "Data preprocessing and feature engineering runtime = 0.24s ...\n",
            "AutoGluon will gauge predictive performance using evaluation metric: 'root_mean_squared_error'\n",
            "\tTo change this, specify the eval_metric argument of fit()\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 399.74s of the 599.75s of remaining time.\n",
            "\t-164.89\t = Validation score   (root_mean_squared_error)\n",
            "\t0.02s\t = Training   runtime\n",
            "\t0.1s\t = Validation runtime\n",
            "Fitting model: KNeighborsDist_BAG_L1 ... Training model for up to 399.46s of the 599.47s of remaining time.\n",
            "\t-179.1334\t = Validation score   (root_mean_squared_error)\n",
            "\t0.02s\t = Training   runtime\n",
            "\t0.1s\t = Validation runtime\n",
            "Fitting model: LightGBMXT_BAG_L1 ... Training model for up to 399.18s of the 599.19s of remaining time.\n",
            "/usr/local/lib/python3.7/dist-packages/lightgbm/engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.\n",
            "  _log_warning(\"'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. \"\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[1000]\ttrain_set's rmse: 29.1144\tvalid_set's rmse: 37.444\n",
            "[2000]\ttrain_set's rmse: 24.5939\tvalid_set's rmse: 37.3866\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/lightgbm/engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.\n",
            "  _log_warning(\"'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. \"\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[1000]\ttrain_set's rmse: 29.1513\tvalid_set's rmse: 41.7151\n",
            "[2000]\ttrain_set's rmse: 24.7195\tvalid_set's rmse: 41.2177\n",
            "[3000]\ttrain_set's rmse: 21.8718\tvalid_set's rmse: 41.0832\n",
            "[4000]\ttrain_set's rmse: 19.7381\tvalid_set's rmse: 41.0397\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/lightgbm/engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.\n",
            "  _log_warning(\"'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. \"\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[1000]\ttrain_set's rmse: 29.6587\tvalid_set's rmse: 35.368\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/lightgbm/engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.\n",
            "  _log_warning(\"'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. \"\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[1000]\ttrain_set's rmse: 28.8509\tvalid_set's rmse: 41.9087\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/lightgbm/engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.\n",
            "  _log_warning(\"'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. \"\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[1000]\ttrain_set's rmse: 29.2035\tvalid_set's rmse: 39.9955\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/lightgbm/engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.\n",
            "  _log_warning(\"'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
            "/usr/local/lib/python3.7/dist-packages/lightgbm/engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.\n",
            "  _log_warning(\"'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. \"\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[1000]\ttrain_set's rmse: 29.5214\tvalid_set's rmse: 38.8051\n",
            "[2000]\ttrain_set's rmse: 25.0027\tvalid_set's rmse: 38.3551\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/lightgbm/engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.\n",
            "  _log_warning(\"'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. \"\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[1000]\ttrain_set's rmse: 29.2048\tvalid_set's rmse: 38.956\n",
            "[2000]\ttrain_set's rmse: 24.7004\tvalid_set's rmse: 38.7517\n",
            "[3000]\ttrain_set's rmse: 21.7576\tvalid_set's rmse: 38.6892\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/lightgbm/engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.\n",
            "  _log_warning(\"'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. \"\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[1000]\ttrain_set's rmse: 28.8345\tvalid_set's rmse: 43.6763\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/lightgbm/engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.\n",
            "  _log_warning(\"'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
            "\t-39.718\t = Validation score   (root_mean_squared_error)\n",
            "\t31.56s\t = Training   runtime\n",
            "\t1.89s\t = Validation runtime\n",
            "Fitting model: LightGBM_BAG_L1 ... Training model for up to 362.09s of the 562.1s of remaining time.\n",
            "/usr/local/lib/python3.7/dist-packages/lightgbm/engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.\n",
            "  _log_warning(\"'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. \"\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[1000]\ttrain_set's rmse: 20.4819\tvalid_set's rmse: 36.9382\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/lightgbm/engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.\n",
            "  _log_warning(\"'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. \"\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[1000]\ttrain_set's rmse: 20.642\tvalid_set's rmse: 40.533\n",
            "[2000]\ttrain_set's rmse: 14.2078\tvalid_set's rmse: 40.26\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/lightgbm/engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.\n",
            "  _log_warning(\"'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. \"\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[1000]\ttrain_set's rmse: 20.5969\tvalid_set's rmse: 36.9997\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/lightgbm/engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.\n",
            "  _log_warning(\"'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. \"\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[1000]\ttrain_set's rmse: 20.3751\tvalid_set's rmse: 40.4555\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/lightgbm/engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.\n",
            "  _log_warning(\"'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. \"\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[1000]\ttrain_set's rmse: 20.4588\tvalid_set's rmse: 39.8934\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/lightgbm/engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.\n",
            "  _log_warning(\"'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
            "/usr/local/lib/python3.7/dist-packages/lightgbm/engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.\n",
            "  _log_warning(\"'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. \"\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[1000]\ttrain_set's rmse: 20.6592\tvalid_set's rmse: 37.6586\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/lightgbm/engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.\n",
            "  _log_warning(\"'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
            "/usr/local/lib/python3.7/dist-packages/lightgbm/engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.\n",
            "  _log_warning(\"'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. \"\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[1000]\ttrain_set's rmse: 20.2382\tvalid_set's rmse: 43.1456\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/lightgbm/engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.\n",
            "  _log_warning(\"'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
            "\t-39.3568\t = Validation score   (root_mean_squared_error)\n",
            "\t20.07s\t = Training   runtime\n",
            "\t0.88s\t = Validation runtime\n",
            "Fitting model: RandomForestMSE_BAG_L1 ... Training model for up to 339.11s of the 539.13s of remaining time.\n",
            "\t-41.8084\t = Validation score   (root_mean_squared_error)\n",
            "\t13.29s\t = Training   runtime\n",
            "\t0.47s\t = Validation runtime\n",
            "Fitting model: CatBoost_BAG_L1 ... Training model for up to 323.54s of the 523.56s of remaining time.\n",
            "\tTime limit exceeded... Skipping CatBoost_BAG_L1.\n",
            "Fitting model: ExtraTreesMSE_BAG_L1 ... Training model for up to 286.7s of the 486.71s of remaining time.\n",
            "\t-41.1851\t = Validation score   (root_mean_squared_error)\n",
            "\t4.06s\t = Training   runtime\n",
            "\t0.48s\t = Validation runtime\n",
            "Fitting model: NeuralNetFastAI_BAG_L1 ... Training model for up to 279.61s of the 479.62s of remaining time.\n",
            "\tRan out of time, stopping training early. (Stopping on epoch 26)\n",
            "\tRan out of time, stopping training early. (Stopping on epoch 21)\n",
            "\t-40.9495\t = Validation score   (root_mean_squared_error)\n",
            "\t172.43s\t = Training   runtime\n",
            "\t0.49s\t = Validation runtime\n",
            "Fitting model: XGBoost_BAG_L1 ... Training model for up to 106.27s of the 306.29s of remaining time.\n",
            "\t-43.7034\t = Validation score   (root_mean_squared_error)\n",
            "\t52.35s\t = Training   runtime\n",
            "\t0.42s\t = Validation runtime\n",
            "Fitting model: NeuralNetMXNet_BAG_L1 ... Training model for up to 51.1s of the 251.11s of remaining time.\n",
            "\tRan out of time, stopping training early. (Stopping on epoch 1)\n",
            "\tRan out of time, stopping training early. (Stopping on epoch 2)\n",
            "\tRan out of time, stopping training early. (Stopping on epoch 2)\n",
            "\tRan out of time, stopping training early. (Stopping on epoch 2)\n",
            "\tRan out of time, stopping training early. (Stopping on epoch 2)\n",
            "\tRan out of time, stopping training early. (Stopping on epoch 2)\n",
            "\tRan out of time, stopping training early. (Stopping on epoch 2)\n",
            "\tRan out of time, stopping training early. (Stopping on epoch 1)\n",
            "\tRan out of time, stopping training early. (Stopping on epoch 1)\n",
            "\tRan out of time, stopping training early. (Stopping on epoch 2)\n",
            "\t-220.4059\t = Validation score   (root_mean_squared_error)\n",
            "\t46.4s\t = Training   runtime\n",
            "\t2.77s\t = Validation runtime\n",
            "Fitting model: LightGBMLarge_BAG_L1 ... Training model for up to 1.69s of the 201.71s of remaining time.\n",
            "/usr/local/lib/python3.7/dist-packages/lightgbm/engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.\n",
            "  _log_warning(\"'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
            "\tRan out of time, early stopping on iteration 1. Best iteration is:\n",
            "\t[1]\ttrain_set's rmse: 176.413\tvalid_set's rmse: 174.761\n",
            "\tTime limit exceeded... Skipping LightGBMLarge_BAG_L1.\n",
            "Completed 1/20 k-fold bagging repeats ...\n",
            "Fitting model: WeightedEnsemble_L2 ... Training model for up to 360.0s of the 201.33s of remaining time.\n",
            "\t-37.9885\t = Validation score   (root_mean_squared_error)\n",
            "\t0.54s\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 200.72s of the 200.69s of remaining time.\n",
            "/usr/local/lib/python3.7/dist-packages/lightgbm/engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.\n",
            "  _log_warning(\"'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
            "/usr/local/lib/python3.7/dist-packages/lightgbm/engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.\n",
            "  _log_warning(\"'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
            "/usr/local/lib/python3.7/dist-packages/lightgbm/engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.\n",
            "  _log_warning(\"'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
            "/usr/local/lib/python3.7/dist-packages/lightgbm/engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.\n",
            "  _log_warning(\"'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
            "/usr/local/lib/python3.7/dist-packages/lightgbm/engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.\n",
            "  _log_warning(\"'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
            "/usr/local/lib/python3.7/dist-packages/lightgbm/engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.\n",
            "  _log_warning(\"'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
            "/usr/local/lib/python3.7/dist-packages/lightgbm/engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.\n",
            "  _log_warning(\"'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
            "/usr/local/lib/python3.7/dist-packages/lightgbm/engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.\n",
            "  _log_warning(\"'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
            "/usr/local/lib/python3.7/dist-packages/lightgbm/engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.\n",
            "  _log_warning(\"'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
            "/usr/local/lib/python3.7/dist-packages/lightgbm/engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.\n",
            "  _log_warning(\"'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
            "\t-39.0885\t = Validation score   (root_mean_squared_error)\n",
            "\t23.87s\t = Training   runtime\n",
            "\t0.27s\t = Validation runtime\n",
            "Fitting model: LightGBM_BAG_L2 ... Training model for up to 176.18s of the 176.15s of remaining time.\n",
            "/usr/local/lib/python3.7/dist-packages/lightgbm/engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.\n",
            "  _log_warning(\"'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
            "/usr/local/lib/python3.7/dist-packages/lightgbm/engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.\n",
            "  _log_warning(\"'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
            "/usr/local/lib/python3.7/dist-packages/lightgbm/engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.\n",
            "  _log_warning(\"'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
            "/usr/local/lib/python3.7/dist-packages/lightgbm/engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.\n",
            "  _log_warning(\"'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
            "/usr/local/lib/python3.7/dist-packages/lightgbm/engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.\n",
            "  _log_warning(\"'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
            "/usr/local/lib/python3.7/dist-packages/lightgbm/engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.\n",
            "  _log_warning(\"'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
            "/usr/local/lib/python3.7/dist-packages/lightgbm/engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.\n",
            "  _log_warning(\"'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
            "/usr/local/lib/python3.7/dist-packages/lightgbm/engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.\n",
            "  _log_warning(\"'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
            "/usr/local/lib/python3.7/dist-packages/lightgbm/engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.\n",
            "  _log_warning(\"'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
            "/usr/local/lib/python3.7/dist-packages/lightgbm/engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.\n",
            "  _log_warning(\"'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
            "\t-38.6289\t = Validation score   (root_mean_squared_error)\n",
            "\t34.17s\t = Training   runtime\n",
            "\t0.19s\t = Validation runtime\n",
            "Fitting model: RandomForestMSE_BAG_L2 ... Training model for up to 141.49s of the 141.47s of remaining time.\n",
            "\t-38.9948\t = Validation score   (root_mean_squared_error)\n",
            "\t36.63s\t = Training   runtime\n",
            "\t0.56s\t = Validation runtime\n",
            "Fitting model: CatBoost_BAG_L2 ... Training model for up to 102.45s of the 102.42s of remaining time.\n",
            "\t-38.5823\t = Validation score   (root_mean_squared_error)\n",
            "\t38.38s\t = Training   runtime\n",
            "\t0.07s\t = Validation runtime\n",
            "Fitting model: ExtraTreesMSE_BAG_L2 ... Training model for up to 63.81s of the 63.79s of remaining time.\n",
            "\t-38.4167\t = Validation score   (root_mean_squared_error)\n",
            "\t7.67s\t = Training   runtime\n",
            "\t0.55s\t = Validation runtime\n",
            "Fitting model: NeuralNetFastAI_BAG_L2 ... Training model for up to 53.96s of the 53.94s of remaining time.\n",
            "\tRan out of time, stopping training early. (Stopping on epoch 7)\n",
            "\tRan out of time, stopping training early. (Stopping on epoch 8)\n",
            "\tRan out of time, stopping training early. (Stopping on epoch 6)\n",
            "\tRan out of time, stopping training early. (Stopping on epoch 9)\n",
            "\tRan out of time, stopping training early. (Stopping on epoch 9)\n",
            "\tRan out of time, stopping training early. (Stopping on epoch 10)\n",
            "\tRan out of time, stopping training early. (Stopping on epoch 10)\n",
            "\tRan out of time, stopping training early. (Stopping on epoch 11)\n",
            "\tRan out of time, stopping training early. (Stopping on epoch 13)\n",
            "\tRan out of time, stopping training early. (Stopping on epoch 16)\n",
            "\t-39.661\t = Validation score   (root_mean_squared_error)\n",
            "\t51.28s\t = Training   runtime\n",
            "\t0.41s\t = Validation runtime\n",
            "Fitting model: XGBoost_BAG_L2 ... Training model for up to 1.91s of the 1.89s of remaining time.\n",
            "\tTime limit exceeded... Skipping XGBoost_BAG_L2.\n",
            "Fitting model: NeuralNetMXNet_BAG_L2 ... Training model for up to 1.65s of the 1.62s of remaining time.\n",
            "\tTime limit exceeded... Skipping NeuralNetMXNet_BAG_L2.\n",
            "Fitting model: LightGBMLarge_BAG_L2 ... Training model for up to 0.94s of the 0.91s of remaining time.\n",
            "/usr/local/lib/python3.7/dist-packages/lightgbm/engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.\n",
            "  _log_warning(\"'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
            "\tRan out of time, early stopping on iteration 1. Best iteration is:\n",
            "\t[1]\ttrain_set's rmse: 176.229\tvalid_set's rmse: 172.891\n",
            "\tTime limit exceeded... Skipping LightGBMLarge_BAG_L2.\n",
            "Completed 1/20 k-fold bagging repeats ...\n",
            "Fitting model: WeightedEnsemble_L3 ... Training model for up to 360.0s of the 0.61s of remaining time.\n",
            "\t-38.0861\t = Validation score   (root_mean_squared_error)\n",
            "\t0.31s\t = Training   runtime\n",
            "\t0.0s\t = Validation runtime\n",
            "AutoGluon training complete, total runtime = 599.79s ...\n",
            "TabularPredictor saved. To load, use: predictor = TabularPredictor.load(\"bike-Models-date-time-predictClass/\")\n"
          ]
        }
      ],
      "source": [
        "save_path = 'bike-Models-date-time-predictClass'\n",
        "predictor_new_features = TabularPredictor('count', problem_type='regression', \n",
        "                                          path=save_path, learner_kwargs={'ignored_columns':['casual' , 'registered']} ).fit(train, time_limit=600, presets='best_quality')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Tjw6rfOHRiyb",
        "outputId": "1365f716-c7db-47ba-e523-0eff1077277e"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "*** Summary of fit() ***\n",
            "Estimated performance of each model:\n",
            "                     model   score_val  pred_time_val    fit_time  pred_time_val_marginal  fit_time_marginal  stack_level  can_infer  fit_order\n",
            "0      WeightedEnsemble_L2  -37.988516       4.207814  241.945342                0.000656           0.538869            2       True         10\n",
            "1      WeightedEnsemble_L3  -38.086125       8.821500  472.011739                0.000638           0.305379            3       True         17\n",
            "2     ExtraTreesMSE_BAG_L2  -38.416738       8.150619  347.873746                0.545762           7.672433            2       True         15\n",
            "3          CatBoost_BAG_L2  -38.582345       7.678370  378.583902                0.073512          38.382589            2       True         14\n",
            "4          LightGBM_BAG_L2  -38.628887       7.791484  374.375236                0.186627          34.173923            2       True         12\n",
            "5   RandomForestMSE_BAG_L2  -38.994778       8.163848  376.835938                0.558991          36.634625            2       True         13\n",
            "6        LightGBMXT_BAG_L2  -39.088493       7.877461  364.074241                0.272604          23.872928            2       True         11\n",
            "7          LightGBM_BAG_L1  -39.356826       0.876003   20.071864                0.876003          20.071864            1       True          4\n",
            "8   NeuralNetFastAI_BAG_L2  -39.660971       8.014961  391.477416                0.410104          51.276103            2       True         16\n",
            "9        LightGBMXT_BAG_L1  -39.717992       1.885879   31.560511                1.885879          31.560511            1       True          3\n",
            "10  NeuralNetFastAI_BAG_L1  -40.949494       0.491884  172.426317                0.491884         172.426317            1       True          7\n",
            "11    ExtraTreesMSE_BAG_L1  -41.185121       0.484667    4.058860                0.484667           4.058860            1       True          6\n",
            "12  RandomForestMSE_BAG_L1  -41.808439       0.468726   13.288921                0.468726          13.288921            1       True          5\n",
            "13          XGBoost_BAG_L1  -43.703402       0.421334   52.345332                0.421334          52.345332            1       True          8\n",
            "14   KNeighborsUnif_BAG_L1 -164.889972       0.103705    0.024777                0.103705           0.024777            1       True          1\n",
            "15   KNeighborsDist_BAG_L1 -179.133407       0.104873    0.020950                0.104873           0.020950            1       True          2\n",
            "16   NeuralNetMXNet_BAG_L1 -220.405893       2.767787   46.403780                2.767787          46.403780            1       True          9\n",
            "Number of models trained: 17\n",
            "Types of models trained:\n",
            "{'StackerEnsembleModel_RF', 'StackerEnsembleModel_NNFastAiTabular', 'StackerEnsembleModel_XGBoost', 'StackerEnsembleModel_LGB', 'StackerEnsembleModel_XT', 'StackerEnsembleModel_KNN', 'WeightedEnsembleModel', 'StackerEnsembleModel_TabularNeuralNet', 'StackerEnsembleModel_CatBoost'}\n",
            "Bagging used: True  (with 10 folds)\n",
            "Multi-layer stack-ensembling used: True  (with 3 levels)\n",
            "Feature Metadata (Processed):\n",
            "(raw dtype, special dtypes):\n",
            "('category', [])             : 3 | ['season', 'weather', 'time']\n",
            "('float', [])                : 3 | ['temp', 'atemp', 'windspeed']\n",
            "('int', [])                  : 1 | ['humidity']\n",
            "('int', ['bool'])            : 2 | ['holiday', 'workingday']\n",
            "('int', ['datetime_as_int']) : 2 | ['datetime', 'date']\n",
            "Plot summary of models saved to file: ./bike-Models-date-time-predictClass/SummaryOfModels.html\n",
            "*** End of fit() summary ***\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "{'leaderboard':                      model   score_val  ...  can_infer  fit_order\n",
              " 0      WeightedEnsemble_L2  -37.988516  ...       True         10\n",
              " 1      WeightedEnsemble_L3  -38.086125  ...       True         17\n",
              " 2     ExtraTreesMSE_BAG_L2  -38.416738  ...       True         15\n",
              " 3          CatBoost_BAG_L2  -38.582345  ...       True         14\n",
              " 4          LightGBM_BAG_L2  -38.628887  ...       True         12\n",
              " 5   RandomForestMSE_BAG_L2  -38.994778  ...       True         13\n",
              " 6        LightGBMXT_BAG_L2  -39.088493  ...       True         11\n",
              " 7          LightGBM_BAG_L1  -39.356826  ...       True          4\n",
              " 8   NeuralNetFastAI_BAG_L2  -39.660971  ...       True         16\n",
              " 9        LightGBMXT_BAG_L1  -39.717992  ...       True          3\n",
              " 10  NeuralNetFastAI_BAG_L1  -40.949494  ...       True          7\n",
              " 11    ExtraTreesMSE_BAG_L1  -41.185121  ...       True          6\n",
              " 12  RandomForestMSE_BAG_L1  -41.808439  ...       True          5\n",
              " 13          XGBoost_BAG_L1  -43.703402  ...       True          8\n",
              " 14   KNeighborsUnif_BAG_L1 -164.889972  ...       True          1\n",
              " 15   KNeighborsDist_BAG_L1 -179.133407  ...       True          2\n",
              " 16   NeuralNetMXNet_BAG_L1 -220.405893  ...       True          9\n",
              " \n",
              " [17 rows x 9 columns],\n",
              " 'max_stack_level': 3,\n",
              " 'model_best': 'WeightedEnsemble_L2',\n",
              " 'model_fit_times': {'CatBoost_BAG_L2': 38.382588624954224,\n",
              "  'ExtraTreesMSE_BAG_L1': 4.058859586715698,\n",
              "  'ExtraTreesMSE_BAG_L2': 7.6724326610565186,\n",
              "  'KNeighborsDist_BAG_L1': 0.0209503173828125,\n",
              "  'KNeighborsUnif_BAG_L1': 0.02477741241455078,\n",
              "  'LightGBMXT_BAG_L1': 31.560510635375977,\n",
              "  'LightGBMXT_BAG_L2': 23.87292790412903,\n",
              "  'LightGBM_BAG_L1': 20.071864128112793,\n",
              "  'LightGBM_BAG_L2': 34.173922538757324,\n",
              "  'NeuralNetFastAI_BAG_L1': 172.4263174533844,\n",
              "  'NeuralNetFastAI_BAG_L2': 51.2761025428772,\n",
              "  'NeuralNetMXNet_BAG_L1': 46.403780460357666,\n",
              "  'RandomForestMSE_BAG_L1': 13.288921117782593,\n",
              "  'RandomForestMSE_BAG_L2': 36.63462495803833,\n",
              "  'WeightedEnsemble_L2': 0.5388689041137695,\n",
              "  'WeightedEnsemble_L3': 0.30537891387939453,\n",
              "  'XGBoost_BAG_L1': 52.34533214569092},\n",
              " 'model_hyperparams': {'CatBoost_BAG_L2': {'max_base_models': 25,\n",
              "   'max_base_models_per_type': 5,\n",
              "   'save_bag_folds': True,\n",
              "   'use_orig_features': True},\n",
              "  'ExtraTreesMSE_BAG_L1': {'max_base_models': 25,\n",
              "   'max_base_models_per_type': 5,\n",
              "   'save_bag_folds': True,\n",
              "   'use_child_oof': True,\n",
              "   'use_orig_features': True},\n",
              "  'ExtraTreesMSE_BAG_L2': {'max_base_models': 25,\n",
              "   'max_base_models_per_type': 5,\n",
              "   'save_bag_folds': True,\n",
              "   'use_child_oof': True,\n",
              "   'use_orig_features': True},\n",
              "  'KNeighborsDist_BAG_L1': {'max_base_models': 25,\n",
              "   'max_base_models_per_type': 5,\n",
              "   'save_bag_folds': True,\n",
              "   'use_child_oof': True,\n",
              "   'use_orig_features': True},\n",
              "  'KNeighborsUnif_BAG_L1': {'max_base_models': 25,\n",
              "   'max_base_models_per_type': 5,\n",
              "   'save_bag_folds': True,\n",
              "   'use_child_oof': True,\n",
              "   'use_orig_features': True},\n",
              "  'LightGBMXT_BAG_L1': {'max_base_models': 25,\n",
              "   'max_base_models_per_type': 5,\n",
              "   'save_bag_folds': True,\n",
              "   'use_orig_features': True},\n",
              "  'LightGBMXT_BAG_L2': {'max_base_models': 25,\n",
              "   'max_base_models_per_type': 5,\n",
              "   'save_bag_folds': True,\n",
              "   'use_orig_features': True},\n",
              "  'LightGBM_BAG_L1': {'max_base_models': 25,\n",
              "   'max_base_models_per_type': 5,\n",
              "   'save_bag_folds': True,\n",
              "   'use_orig_features': True},\n",
              "  'LightGBM_BAG_L2': {'max_base_models': 25,\n",
              "   'max_base_models_per_type': 5,\n",
              "   'save_bag_folds': True,\n",
              "   'use_orig_features': True},\n",
              "  'NeuralNetFastAI_BAG_L1': {'max_base_models': 25,\n",
              "   'max_base_models_per_type': 5,\n",
              "   'save_bag_folds': True,\n",
              "   'use_orig_features': True},\n",
              "  'NeuralNetFastAI_BAG_L2': {'max_base_models': 25,\n",
              "   'max_base_models_per_type': 5,\n",
              "   'save_bag_folds': True,\n",
              "   'use_orig_features': True},\n",
              "  'NeuralNetMXNet_BAG_L1': {'max_base_models': 25,\n",
              "   'max_base_models_per_type': 5,\n",
              "   'save_bag_folds': True,\n",
              "   'use_orig_features': True},\n",
              "  'RandomForestMSE_BAG_L1': {'max_base_models': 25,\n",
              "   'max_base_models_per_type': 5,\n",
              "   'save_bag_folds': True,\n",
              "   'use_child_oof': True,\n",
              "   'use_orig_features': True},\n",
              "  'RandomForestMSE_BAG_L2': {'max_base_models': 25,\n",
              "   'max_base_models_per_type': 5,\n",
              "   'save_bag_folds': True,\n",
              "   'use_child_oof': True,\n",
              "   'use_orig_features': True},\n",
              "  'WeightedEnsemble_L2': {'max_base_models': 25,\n",
              "   'max_base_models_per_type': 5,\n",
              "   'save_bag_folds': True,\n",
              "   'use_orig_features': False},\n",
              "  'WeightedEnsemble_L3': {'max_base_models': 25,\n",
              "   'max_base_models_per_type': 5,\n",
              "   'save_bag_folds': True,\n",
              "   'use_orig_features': False},\n",
              "  'XGBoost_BAG_L1': {'max_base_models': 25,\n",
              "   'max_base_models_per_type': 5,\n",
              "   'save_bag_folds': True,\n",
              "   'use_orig_features': True}},\n",
              " 'model_paths': {'CatBoost_BAG_L2': './bike-Models-date-time-predictClass/models/CatBoost_BAG_L2/',\n",
              "  'ExtraTreesMSE_BAG_L1': './bike-Models-date-time-predictClass/models/ExtraTreesMSE_BAG_L1/',\n",
              "  'ExtraTreesMSE_BAG_L2': './bike-Models-date-time-predictClass/models/ExtraTreesMSE_BAG_L2/',\n",
              "  'KNeighborsDist_BAG_L1': './bike-Models-date-time-predictClass/models/KNeighborsDist_BAG_L1/',\n",
              "  'KNeighborsUnif_BAG_L1': './bike-Models-date-time-predictClass/models/KNeighborsUnif_BAG_L1/',\n",
              "  'LightGBMXT_BAG_L1': './bike-Models-date-time-predictClass/models/LightGBMXT_BAG_L1/',\n",
              "  'LightGBMXT_BAG_L2': './bike-Models-date-time-predictClass/models/LightGBMXT_BAG_L2/',\n",
              "  'LightGBM_BAG_L1': './bike-Models-date-time-predictClass/models/LightGBM_BAG_L1/',\n",
              "  'LightGBM_BAG_L2': './bike-Models-date-time-predictClass/models/LightGBM_BAG_L2/',\n",
              "  'NeuralNetFastAI_BAG_L1': './bike-Models-date-time-predictClass/models/NeuralNetFastAI_BAG_L1/',\n",
              "  'NeuralNetFastAI_BAG_L2': './bike-Models-date-time-predictClass/models/NeuralNetFastAI_BAG_L2/',\n",
              "  'NeuralNetMXNet_BAG_L1': './bike-Models-date-time-predictClass/models/NeuralNetMXNet_BAG_L1/',\n",
              "  'RandomForestMSE_BAG_L1': './bike-Models-date-time-predictClass/models/RandomForestMSE_BAG_L1/',\n",
              "  'RandomForestMSE_BAG_L2': './bike-Models-date-time-predictClass/models/RandomForestMSE_BAG_L2/',\n",
              "  'WeightedEnsemble_L2': './bike-Models-date-time-predictClass/models/WeightedEnsemble_L2/',\n",
              "  'WeightedEnsemble_L3': './bike-Models-date-time-predictClass/models/WeightedEnsemble_L3/',\n",
              "  'XGBoost_BAG_L1': './bike-Models-date-time-predictClass/models/XGBoost_BAG_L1/'},\n",
              " 'model_performance': {'CatBoost_BAG_L2': -38.58234497084629,\n",
              "  'ExtraTreesMSE_BAG_L1': -41.18512146855623,\n",
              "  'ExtraTreesMSE_BAG_L2': -38.41673750378741,\n",
              "  'KNeighborsDist_BAG_L1': -179.1334072231015,\n",
              "  'KNeighborsUnif_BAG_L1': -164.88997203295196,\n",
              "  'LightGBMXT_BAG_L1': -39.71799213617305,\n",
              "  'LightGBMXT_BAG_L2': -39.08849307565603,\n",
              "  'LightGBM_BAG_L1': -39.35682629569128,\n",
              "  'LightGBM_BAG_L2': -38.628887295236744,\n",
              "  'NeuralNetFastAI_BAG_L1': -40.949494207599685,\n",
              "  'NeuralNetFastAI_BAG_L2': -39.66097103418855,\n",
              "  'NeuralNetMXNet_BAG_L1': -220.4058933528486,\n",
              "  'RandomForestMSE_BAG_L1': -41.80843887962418,\n",
              "  'RandomForestMSE_BAG_L2': -38.99477833137597,\n",
              "  'WeightedEnsemble_L2': -37.988515599670954,\n",
              "  'WeightedEnsemble_L3': -38.086125061738834,\n",
              "  'XGBoost_BAG_L1': -43.7034017910046},\n",
              " 'model_pred_times': {'CatBoost_BAG_L2': 0.07351207733154297,\n",
              "  'ExtraTreesMSE_BAG_L1': 0.4846673011779785,\n",
              "  'ExtraTreesMSE_BAG_L2': 0.5457615852355957,\n",
              "  'KNeighborsDist_BAG_L1': 0.1048734188079834,\n",
              "  'KNeighborsUnif_BAG_L1': 0.10370469093322754,\n",
              "  'LightGBMXT_BAG_L1': 1.885878562927246,\n",
              "  'LightGBMXT_BAG_L2': 0.27260375022888184,\n",
              "  'LightGBM_BAG_L1': 0.8760030269622803,\n",
              "  'LightGBM_BAG_L2': 0.18662691116333008,\n",
              "  'NeuralNetFastAI_BAG_L1': 0.4918837547302246,\n",
              "  'NeuralNetFastAI_BAG_L2': 0.41010379791259766,\n",
              "  'NeuralNetMXNet_BAG_L1': 2.767786979675293,\n",
              "  'RandomForestMSE_BAG_L1': 0.46872568130493164,\n",
              "  'RandomForestMSE_BAG_L2': 0.5589907169342041,\n",
              "  'WeightedEnsemble_L2': 0.0006561279296875,\n",
              "  'WeightedEnsemble_L3': 0.0006377696990966797,\n",
              "  'XGBoost_BAG_L1': 0.42133402824401855},\n",
              " 'model_types': {'CatBoost_BAG_L2': 'StackerEnsembleModel_CatBoost',\n",
              "  'ExtraTreesMSE_BAG_L1': 'StackerEnsembleModel_XT',\n",
              "  'ExtraTreesMSE_BAG_L2': 'StackerEnsembleModel_XT',\n",
              "  'KNeighborsDist_BAG_L1': 'StackerEnsembleModel_KNN',\n",
              "  'KNeighborsUnif_BAG_L1': 'StackerEnsembleModel_KNN',\n",
              "  'LightGBMXT_BAG_L1': 'StackerEnsembleModel_LGB',\n",
              "  'LightGBMXT_BAG_L2': 'StackerEnsembleModel_LGB',\n",
              "  'LightGBM_BAG_L1': 'StackerEnsembleModel_LGB',\n",
              "  'LightGBM_BAG_L2': 'StackerEnsembleModel_LGB',\n",
              "  'NeuralNetFastAI_BAG_L1': 'StackerEnsembleModel_NNFastAiTabular',\n",
              "  'NeuralNetFastAI_BAG_L2': 'StackerEnsembleModel_NNFastAiTabular',\n",
              "  'NeuralNetMXNet_BAG_L1': 'StackerEnsembleModel_TabularNeuralNet',\n",
              "  'RandomForestMSE_BAG_L1': 'StackerEnsembleModel_RF',\n",
              "  'RandomForestMSE_BAG_L2': 'StackerEnsembleModel_RF',\n",
              "  'WeightedEnsemble_L2': 'WeightedEnsembleModel',\n",
              "  'WeightedEnsemble_L3': 'WeightedEnsembleModel',\n",
              "  'XGBoost_BAG_L1': 'StackerEnsembleModel_XGBoost'},\n",
              " 'num_bag_folds': 10}"
            ]
          },
          "execution_count": 28,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "predictor_new_features = TabularPredictor.load('./bike-Models-date-time-predictClass')  # how to load previously-trained predictor from file\n",
        "predictor_new_features.fit_summary(show_plot=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1rDelWO5Riyb",
        "outputId": "d90d7bcb-d8aa-4375-aa80-7d3d98a87f69"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "0    13.769157\n",
            "1     4.974021\n",
            "2     1.461930\n",
            "3     2.637503\n",
            "4     2.371739\n",
            "Name: count, dtype: float32\n",
            "27     -0.889317\n",
            "52     -2.928915\n",
            "53     -2.282344\n",
            "75     -3.369709\n",
            "76     -2.374342\n",
            "96     -1.687773\n",
            "97     -1.584855\n",
            "192    -1.414405\n",
            "439    -1.445751\n",
            "440    -1.071522\n",
            "556    -0.011098\n",
            "724    -0.119467\n",
            "867    -2.457613\n",
            "868    -3.089838\n",
            "915    -0.374865\n",
            "916    -1.303040\n",
            "939    -0.350886\n",
            "3014   -3.759709\n",
            "3084   -0.421971\n",
            "4109   -2.817142\n",
            "4112   -5.845719\n",
            "4325   -5.624488\n",
            "4326   -6.408031\n",
            "4328   -7.463823\n",
            "6283   -1.745358\n",
            "Name: count, dtype: float32\n",
            "25\n",
            "0\n"
          ]
        }
      ],
      "source": [
        "# Remember to set all negative values to zero\n",
        "predictions_new = predictor_new_features.predict(test)\n",
        "print(predictions_new.head())\n",
        "print(predictions_new[predictions_new < 0])\n",
        "print(len(predictions_new[predictions_new < 0]))\n",
        "predictions_new[predictions_new < 0] = 0\n",
        "print(len(predictions_new[predictions_new < 0]))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "h_ye_stSRiyb"
      },
      "outputs": [],
      "source": [
        "# Same submitting predictions\n",
        "submission_new_features = pd.read_csv('./sampleSubmission.csv', parse_dates=['datetime'])\n",
        "submission_new_features[\"count\"] = predictions_new\n",
        "submission_new_features.to_csv(\"submission_new_features.csv\", index=False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7H9MNOz9Riyc",
        "outputId": "85fa496d-f7bb-4fcc-817b-f375c39d6b13"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Warning: Looks like you're using an outdated API Version, please consider updating (server 1.5.12 / client 1.5.4)\n",
            "100% 188k/188k [00:02<00:00, 90.7kB/s]\n",
            "Successfully submitted to Bike Sharing Demand"
          ]
        }
      ],
      "source": [
        "!kaggle competitions submit -c bike-sharing-demand -f submission_new_features.csv -m \"new features\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Wb0GVvXPRiyc",
        "outputId": "e3148480-b5ac-41e8-f784-e7761e8d84a2"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Warning: Looks like you're using an outdated API Version, please consider updating (server 1.5.12 / client 1.5.4)\n",
            "fileName                     date                 description           status    publicScore  privateScore  \n",
            "---------------------------  -------------------  --------------------  --------  -----------  ------------  \n",
            "submission_new_features.csv  2021-12-26 09:51:53  new features          complete  0.48168      0.48168       \n",
            "submission.csv               2021-12-26 09:22:09  first raw submission  complete  1.39407      1.39407       \n",
            "submission.csv               2021-12-24 07:45:44  first raw submission  complete  1.39748      1.39748       \n"
          ]
        }
      ],
      "source": [
        "!kaggle competitions submissions -c bike-sharing-demand | tail -n +1 | head -n 6"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "I5aR1E0TL0Wj",
        "outputId": "736aba27-05c9-4b50-fa7d-22bc99ebc79f"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Presets specified: ['best_quality']\n",
            "Beginning AutoGluon training ... Time limit = 600s\n",
            "AutoGluon will save models to \"bike-Models-independent-date-time-predictClass/\"\n",
            "AutoGluon Version:  0.3.1\n",
            "Train Data Rows:    10886\n",
            "Train Data Columns: 13\n",
            "Preprocessing data ...\n",
            "Using Feature Generators to preprocess the data ...\n",
            "Dropping user-specified ignored columns: ['casual', 'datetime', 'registered']\n",
            "Fitting AutoMLPipelineFeatureGenerator...\n",
            "\tAvailable Memory:                    11249.2 MB\n",
            "\tTrain Data (Original)  Memory Usage: 1.5 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",
            "\t\tFitting CategoryFeatureGenerator...\n",
            "\t\t\tFitting CategoryMemoryMinimizeFeatureGenerator...\n",
            "\t\tFitting DatetimeFeatureGenerator...\n",
            "\tStage 4 Generators:\n",
            "\t\tFitting DropUniqueFeatureGenerator...\n",
            "\tTypes of features in original data (raw dtype, special dtypes):\n",
            "\t\t('category', [])                   : 2 | ['season', 'weather']\n",
            "\t\t('float', [])                      : 3 | ['temp', 'atemp', 'windspeed']\n",
            "\t\t('int', [])                        : 3 | ['holiday', 'workingday', 'humidity']\n",
            "\t\t('object', [])                     : 1 | ['time']\n",
            "\t\t('object', ['datetime_as_object']) : 1 | ['date']\n",
            "\tTypes of features in processed data (raw dtype, special dtypes):\n",
            "\t\t('category', [])             : 3 | ['season', 'weather', 'time']\n",
            "\t\t('float', [])                : 3 | ['temp', 'atemp', 'windspeed']\n",
            "\t\t('int', [])                  : 1 | ['humidity']\n",
            "\t\t('int', ['bool'])            : 2 | ['holiday', 'workingday']\n",
            "\t\t('int', ['datetime_as_int']) : 1 | ['date']\n",
            "\t0.2s = Fit runtime\n",
            "\t10 features in original data used to generate 10 features in processed data.\n",
            "\tTrain Data (Processed) Memory Usage: 0.49 MB (0.0% of available memory)\n",
            "Data preprocessing and feature engineering runtime = 0.22s ...\n",
            "AutoGluon will gauge predictive performance using evaluation metric: 'root_mean_squared_error'\n",
            "\tTo change this, specify the eval_metric argument of fit()\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 399.75s of the 599.77s of remaining time.\n",
            "\t-164.89\t = Validation score   (root_mean_squared_error)\n",
            "\t0.02s\t = Training   runtime\n",
            "\t0.1s\t = Validation runtime\n",
            "Fitting model: KNeighborsDist_BAG_L1 ... Training model for up to 399.47s of the 599.49s of remaining time.\n",
            "\t-179.1334\t = Validation score   (root_mean_squared_error)\n",
            "\t0.02s\t = Training   runtime\n",
            "\t0.11s\t = Validation runtime\n",
            "Fitting model: LightGBMXT_BAG_L1 ... Training model for up to 399.16s of the 599.18s of remaining time.\n",
            "/usr/local/lib/python3.7/dist-packages/lightgbm/engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.\n",
            "  _log_warning(\"'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. \"\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[1000]\ttrain_set's rmse: 29.9214\tvalid_set's rmse: 37.9528\n",
            "[2000]\ttrain_set's rmse: 25.3247\tvalid_set's rmse: 37.7707\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/lightgbm/engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.\n",
            "  _log_warning(\"'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. \"\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[1000]\ttrain_set's rmse: 29.5276\tvalid_set's rmse: 41.8083\n",
            "[2000]\ttrain_set's rmse: 25.1539\tvalid_set's rmse: 41.4651\n",
            "[3000]\ttrain_set's rmse: 22.3692\tvalid_set's rmse: 41.339\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/lightgbm/engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.\n",
            "  _log_warning(\"'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. \"\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[1000]\ttrain_set's rmse: 29.875\tvalid_set's rmse: 36.0158\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/lightgbm/engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.\n",
            "  _log_warning(\"'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. \"\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[1000]\ttrain_set's rmse: 29.4633\tvalid_set's rmse: 42.5193\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/lightgbm/engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.\n",
            "  _log_warning(\"'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. \"\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[1000]\ttrain_set's rmse: 29.7326\tvalid_set's rmse: 40.0973\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/lightgbm/engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.\n",
            "  _log_warning(\"'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. \"\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[1000]\ttrain_set's rmse: 29.6363\tvalid_set's rmse: 42.1779\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/lightgbm/engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.\n",
            "  _log_warning(\"'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. \"\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[1000]\ttrain_set's rmse: 29.8896\tvalid_set's rmse: 38.9671\n",
            "[2000]\ttrain_set's rmse: 25.4792\tvalid_set's rmse: 38.7685\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/lightgbm/engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.\n",
            "  _log_warning(\"'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. \"\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[1000]\ttrain_set's rmse: 29.7259\tvalid_set's rmse: 38.7573\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/lightgbm/engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.\n",
            "  _log_warning(\"'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
            "/usr/local/lib/python3.7/dist-packages/lightgbm/engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.\n",
            "  _log_warning(\"'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
            "\t-39.9211\t = Validation score   (root_mean_squared_error)\n",
            "\t30.74s\t = Training   runtime\n",
            "\t1.65s\t = Validation runtime\n",
            "Fitting model: LightGBM_BAG_L1 ... Training model for up to 363.78s of the 563.8s of remaining time.\n",
            "/usr/local/lib/python3.7/dist-packages/lightgbm/engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.\n",
            "  _log_warning(\"'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
            "/usr/local/lib/python3.7/dist-packages/lightgbm/engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.\n",
            "  _log_warning(\"'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
            "/usr/local/lib/python3.7/dist-packages/lightgbm/engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.\n",
            "  _log_warning(\"'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. \"\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[1000]\ttrain_set's rmse: 20.741\tvalid_set's rmse: 36.7353\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/lightgbm/engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.\n",
            "  _log_warning(\"'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. \"\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[1000]\ttrain_set's rmse: 20.5858\tvalid_set's rmse: 40.8092\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/lightgbm/engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.\n",
            "  _log_warning(\"'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. \"\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[1000]\ttrain_set's rmse: 20.7831\tvalid_set's rmse: 40.1068\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/lightgbm/engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.\n",
            "  _log_warning(\"'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
            "/usr/local/lib/python3.7/dist-packages/lightgbm/engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.\n",
            "  _log_warning(\"'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. \"\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[1000]\ttrain_set's rmse: 20.7425\tvalid_set's rmse: 37.5717\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/lightgbm/engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.\n",
            "  _log_warning(\"'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
            "/usr/local/lib/python3.7/dist-packages/lightgbm/engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.\n",
            "  _log_warning(\"'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
            "/usr/local/lib/python3.7/dist-packages/lightgbm/engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.\n",
            "  _log_warning(\"'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
            "\t-39.907\t = Validation score   (root_mean_squared_error)\n",
            "\t26.89s\t = Training   runtime\n",
            "\t0.91s\t = Validation runtime\n",
            "Fitting model: RandomForestMSE_BAG_L1 ... Training model for up to 334.02s of the 534.04s of remaining time.\n",
            "\t-41.9775\t = Validation score   (root_mean_squared_error)\n",
            "\t9.47s\t = Training   runtime\n",
            "\t0.53s\t = Validation runtime\n",
            "Fitting model: CatBoost_BAG_L1 ... Training model for up to 321.14s of the 521.16s of remaining time.\n",
            "\tTime limit exceeded... Skipping CatBoost_BAG_L1.\n",
            "Fitting model: ExtraTreesMSE_BAG_L1 ... Training model for up to 239.1s of the 439.12s of remaining time.\n",
            "\t-41.4853\t = Validation score   (root_mean_squared_error)\n",
            "\t6.74s\t = Training   runtime\n",
            "\t0.72s\t = Validation runtime\n",
            "Fitting model: NeuralNetFastAI_BAG_L1 ... Training model for up to 229.26s of the 429.28s of remaining time.\n",
            "\tRan out of time, stopping training early. (Stopping on epoch 11)\n",
            "\tRan out of time, stopping training early. (Stopping on epoch 16)\n",
            "\t-41.9107\t = Validation score   (root_mean_squared_error)\n",
            "\t169.27s\t = Training   runtime\n",
            "\t0.7s\t = Validation runtime\n",
            "Fitting model: XGBoost_BAG_L1 ... Training model for up to 58.82s of the 258.84s of remaining time.\n",
            "\t-43.7494\t = Validation score   (root_mean_squared_error)\n",
            "\t33.44s\t = Training   runtime\n",
            "\t0.35s\t = Validation runtime\n",
            "Fitting model: NeuralNetMXNet_BAG_L1 ... Training model for up to 22.45s of the 222.47s of remaining time.\n",
            "\tRan out of time, stopping training early. (Stopping on epoch 0)\n",
            "\tRan out of time, stopping training early. (Stopping on epoch 0)\n",
            "\tRan out of time, stopping training early. (Stopping on epoch 0)\n",
            "\tRan out of time, stopping training early. (Stopping on epoch 0)\n",
            "\tRan out of time, stopping training early. (Stopping on epoch 0)\n",
            "\tRan out of time, stopping training early. (Stopping on epoch 0)\n",
            "\tRan out of time, stopping training early. (Stopping on epoch 0)\n",
            "\tRan out of time, stopping training early. (Stopping on epoch 0)\n",
            "\tRan out of time, stopping training early. (Stopping on epoch 0)\n",
            "\tRan out of time, stopping training early. (Stopping on epoch 0)\n",
            "\t-276.946\t = Validation score   (root_mean_squared_error)\n",
            "\t18.32s\t = Training   runtime\n",
            "\t2.6s\t = Validation runtime\n",
            "Fitting model: LightGBMLarge_BAG_L1 ... Training model for up to 1.29s of the 201.31s of remaining time.\n",
            "/usr/local/lib/python3.7/dist-packages/lightgbm/engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.\n",
            "  _log_warning(\"'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
            "\tRan out of time, early stopping on iteration 1. Best iteration is:\n",
            "\t[1]\ttrain_set's rmse: 176.402\tvalid_set's rmse: 174.792\n",
            "\tTime limit exceeded... Skipping LightGBMLarge_BAG_L1.\n",
            "Completed 1/20 k-fold bagging repeats ...\n",
            "Fitting model: WeightedEnsemble_L2 ... Training model for up to 360.0s of the 201.0s of remaining time.\n",
            "\t-38.3291\t = Validation score   (root_mean_squared_error)\n",
            "\t0.42s\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 200.54s of the 200.52s of remaining time.\n",
            "/usr/local/lib/python3.7/dist-packages/lightgbm/engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.\n",
            "  _log_warning(\"'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
            "/usr/local/lib/python3.7/dist-packages/lightgbm/engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.\n",
            "  _log_warning(\"'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
            "/usr/local/lib/python3.7/dist-packages/lightgbm/engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.\n",
            "  _log_warning(\"'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
            "/usr/local/lib/python3.7/dist-packages/lightgbm/engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.\n",
            "  _log_warning(\"'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
            "/usr/local/lib/python3.7/dist-packages/lightgbm/engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.\n",
            "  _log_warning(\"'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
            "/usr/local/lib/python3.7/dist-packages/lightgbm/engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.\n",
            "  _log_warning(\"'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
            "/usr/local/lib/python3.7/dist-packages/lightgbm/engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.\n",
            "  _log_warning(\"'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
            "/usr/local/lib/python3.7/dist-packages/lightgbm/engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.\n",
            "  _log_warning(\"'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
            "/usr/local/lib/python3.7/dist-packages/lightgbm/engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.\n",
            "  _log_warning(\"'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
            "/usr/local/lib/python3.7/dist-packages/lightgbm/engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.\n",
            "  _log_warning(\"'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
            "\t-39.5039\t = Validation score   (root_mean_squared_error)\n",
            "\t11.14s\t = Training   runtime\n",
            "\t0.2s\t = Validation runtime\n",
            "Fitting model: LightGBM_BAG_L2 ... Training model for up to 188.87s of the 188.85s of remaining time.\n",
            "/usr/local/lib/python3.7/dist-packages/lightgbm/engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.\n",
            "  _log_warning(\"'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
            "/usr/local/lib/python3.7/dist-packages/lightgbm/engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.\n",
            "  _log_warning(\"'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
            "/usr/local/lib/python3.7/dist-packages/lightgbm/engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.\n",
            "  _log_warning(\"'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
            "/usr/local/lib/python3.7/dist-packages/lightgbm/engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.\n",
            "  _log_warning(\"'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
            "/usr/local/lib/python3.7/dist-packages/lightgbm/engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.\n",
            "  _log_warning(\"'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
            "/usr/local/lib/python3.7/dist-packages/lightgbm/engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.\n",
            "  _log_warning(\"'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
            "/usr/local/lib/python3.7/dist-packages/lightgbm/engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.\n",
            "  _log_warning(\"'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
            "/usr/local/lib/python3.7/dist-packages/lightgbm/engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.\n",
            "  _log_warning(\"'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
            "/usr/local/lib/python3.7/dist-packages/lightgbm/engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.\n",
            "  _log_warning(\"'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
            "/usr/local/lib/python3.7/dist-packages/lightgbm/engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.\n",
            "  _log_warning(\"'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
            "\t-39.0093\t = Validation score   (root_mean_squared_error)\n",
            "\t7.57s\t = Training   runtime\n",
            "\t0.16s\t = Validation runtime\n",
            "Fitting model: RandomForestMSE_BAG_L2 ... Training model for up to 180.84s of the 180.81s of remaining time.\n",
            "\t-39.1037\t = Validation score   (root_mean_squared_error)\n",
            "\t31.32s\t = Training   runtime\n",
            "\t0.56s\t = Validation runtime\n",
            "Fitting model: CatBoost_BAG_L2 ... Training model for up to 147.13s of the 147.11s of remaining time.\n",
            "\t-38.8431\t = Validation score   (root_mean_squared_error)\n",
            "\t59.29s\t = Training   runtime\n",
            "\t0.15s\t = Validation runtime\n",
            "Fitting model: ExtraTreesMSE_BAG_L2 ... Training model for up to 87.49s of the 87.47s of remaining time.\n",
            "\t-38.6093\t = Validation score   (root_mean_squared_error)\n",
            "\t7.27s\t = Training   runtime\n",
            "\t0.57s\t = Validation runtime\n",
            "Fitting model: NeuralNetFastAI_BAG_L2 ... Training model for up to 77.8s of the 77.77s of remaining time.\n",
            "\tRan out of time, stopping training early. (Stopping on epoch 12)\n",
            "\tRan out of time, stopping training early. (Stopping on epoch 10)\n",
            "\tRan out of time, stopping training early. (Stopping on epoch 13)\n",
            "\tRan out of time, stopping training early. (Stopping on epoch 13)\n",
            "\tRan out of time, stopping training early. (Stopping on epoch 13)\n",
            "\tRan out of time, stopping training early. (Stopping on epoch 14)\n",
            "\tRan out of time, stopping training early. (Stopping on epoch 15)\n",
            "\tRan out of time, stopping training early. (Stopping on epoch 16)\n",
            "\tRan out of time, stopping training early. (Stopping on epoch 18)\n",
            "\tRan out of time, stopping training early. (Stopping on epoch 23)\n",
            "\t-39.6051\t = Validation score   (root_mean_squared_error)\n",
            "\t74.14s\t = Training   runtime\n",
            "\t0.45s\t = Validation runtime\n",
            "Fitting model: XGBoost_BAG_L2 ... Training model for up to 2.81s of the 2.79s of remaining time.\n",
            "\tTime limit exceeded... Skipping XGBoost_BAG_L2.\n",
            "Fitting model: NeuralNetMXNet_BAG_L2 ... Training model for up to 2.48s of the 2.46s of remaining time.\n",
            "\tTime limit exceeded... Skipping NeuralNetMXNet_BAG_L2.\n",
            "Fitting model: LightGBMLarge_BAG_L2 ... Training model for up to 1.76s of the 1.74s of remaining time.\n",
            "/usr/local/lib/python3.7/dist-packages/lightgbm/engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.\n",
            "  _log_warning(\"'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
            "\tRan out of time, early stopping on iteration 1. Best iteration is:\n",
            "\t[1]\ttrain_set's rmse: 176.233\tvalid_set's rmse: 172.873\n",
            "\tTime limit exceeded... Skipping LightGBMLarge_BAG_L2.\n",
            "Completed 1/20 k-fold bagging repeats ...\n",
            "Fitting model: WeightedEnsemble_L3 ... Training model for up to 360.0s of the 1.39s of remaining time.\n",
            "\t-38.3445\t = Validation score   (root_mean_squared_error)\n",
            "\t0.31s\t = Training   runtime\n",
            "\t0.0s\t = Validation runtime\n",
            "AutoGluon training complete, total runtime = 599.02s ...\n",
            "TabularPredictor saved. To load, use: predictor = TabularPredictor.load(\"bike-Models-independent-date-time-predictClass/\")\n"
          ]
        }
      ],
      "source": [
        "save_path = 'bike-Models-independent-date-time-predictClass'\n",
        "predictor_independent_features = TabularPredictor('count', problem_type='regression', \n",
        "                                          path=save_path, learner_kwargs={'ignored_columns':['casual', 'datetime' , 'registered']} ).fit(train, time_limit=600, presets='best_quality')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "L61QaVfQMVIz",
        "outputId": "872cdcbf-951c-41a1-d0d4-44714a64a72e"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "*** Summary of fit() ***\n",
            "Estimated performance of each model:\n",
            "                     model   score_val  pred_time_val    fit_time  pred_time_val_marginal  fit_time_marginal  stack_level  can_infer  fit_order\n",
            "0      WeightedEnsemble_L2  -38.329149       4.514402  243.526106                0.000648           0.420050            2       True         10\n",
            "1      WeightedEnsemble_L3  -38.344466       9.565711  474.794088                0.000664           0.309178            3       True         17\n",
            "2     ExtraTreesMSE_BAG_L2  -38.609262       8.244282  302.176109                0.572876           7.266946            2       True         15\n",
            "3          CatBoost_BAG_L2  -38.843059       7.825600  354.195723                0.154194          59.286559            2       True         14\n",
            "4          LightGBM_BAG_L2  -39.009257       7.828121  302.474573                0.156715           7.565409            2       True         12\n",
            "5   RandomForestMSE_BAG_L2  -39.103720       8.232015  326.225291                0.560609          31.316128            2       True         13\n",
            "6        LightGBMXT_BAG_L2  -39.503934       7.868667  306.051218                0.197262          11.142054            2       True         11\n",
            "7   NeuralNetFastAI_BAG_L2  -39.605067       8.120652  369.049868                0.449246          74.140704            2       True         16\n",
            "8          LightGBM_BAG_L1  -39.907034       0.914273   26.889561                0.914273          26.889561            1       True          4\n",
            "9        LightGBMXT_BAG_L1  -39.921141       1.647788   30.738657                1.647788          30.738657            1       True          3\n",
            "10    ExtraTreesMSE_BAG_L1  -41.485346       0.716418    6.740607                0.716418           6.740607            1       True          6\n",
            "11  NeuralNetFastAI_BAG_L1  -41.910654       0.702527  169.265331                0.702527         169.265331            1       True          7\n",
            "12  RandomForestMSE_BAG_L1  -41.977468       0.532748    9.471900                0.532748           9.471900            1       True          5\n",
            "13          XGBoost_BAG_L1  -43.749400       0.348515   33.438892                0.348515          33.438892            1       True          8\n",
            "14   KNeighborsUnif_BAG_L1 -164.889972       0.103878    0.023440                0.103878           0.023440            1       True          1\n",
            "15   KNeighborsDist_BAG_L1 -179.133407       0.105193    0.020574                0.105193           0.020574            1       True          2\n",
            "16   NeuralNetMXNet_BAG_L1 -276.945962       2.600066   18.320202                2.600066          18.320202            1       True          9\n",
            "Number of models trained: 17\n",
            "Types of models trained:\n",
            "{'StackerEnsembleModel_XT', 'StackerEnsembleModel_CatBoost', 'StackerEnsembleModel_TabularNeuralNet', 'WeightedEnsembleModel', 'StackerEnsembleModel_NNFastAiTabular', 'StackerEnsembleModel_XGBoost', 'StackerEnsembleModel_RF', 'StackerEnsembleModel_KNN', 'StackerEnsembleModel_LGB'}\n",
            "Bagging used: True  (with 10 folds)\n",
            "Multi-layer stack-ensembling used: True  (with 3 levels)\n",
            "Feature Metadata (Processed):\n",
            "(raw dtype, special dtypes):\n",
            "('category', [])             : 3 | ['season', 'weather', 'time']\n",
            "('float', [])                : 3 | ['temp', 'atemp', 'windspeed']\n",
            "('int', [])                  : 1 | ['humidity']\n",
            "('int', ['bool'])            : 2 | ['holiday', 'workingday']\n",
            "('int', ['datetime_as_int']) : 1 | ['date']\n",
            "Plot summary of models saved to file: ./bike-Models-independent-date-time-predictClass/SummaryOfModels.html\n",
            "*** End of fit() summary ***\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "{'leaderboard':                      model   score_val  ...  can_infer  fit_order\n",
              " 0      WeightedEnsemble_L2  -38.329149  ...       True         10\n",
              " 1      WeightedEnsemble_L3  -38.344466  ...       True         17\n",
              " 2     ExtraTreesMSE_BAG_L2  -38.609262  ...       True         15\n",
              " 3          CatBoost_BAG_L2  -38.843059  ...       True         14\n",
              " 4          LightGBM_BAG_L2  -39.009257  ...       True         12\n",
              " 5   RandomForestMSE_BAG_L2  -39.103720  ...       True         13\n",
              " 6        LightGBMXT_BAG_L2  -39.503934  ...       True         11\n",
              " 7   NeuralNetFastAI_BAG_L2  -39.605067  ...       True         16\n",
              " 8          LightGBM_BAG_L1  -39.907034  ...       True          4\n",
              " 9        LightGBMXT_BAG_L1  -39.921141  ...       True          3\n",
              " 10    ExtraTreesMSE_BAG_L1  -41.485346  ...       True          6\n",
              " 11  NeuralNetFastAI_BAG_L1  -41.910654  ...       True          7\n",
              " 12  RandomForestMSE_BAG_L1  -41.977468  ...       True          5\n",
              " 13          XGBoost_BAG_L1  -43.749400  ...       True          8\n",
              " 14   KNeighborsUnif_BAG_L1 -164.889972  ...       True          1\n",
              " 15   KNeighborsDist_BAG_L1 -179.133407  ...       True          2\n",
              " 16   NeuralNetMXNet_BAG_L1 -276.945962  ...       True          9\n",
              " \n",
              " [17 rows x 9 columns],\n",
              " 'max_stack_level': 3,\n",
              " 'model_best': 'WeightedEnsemble_L2',\n",
              " 'model_fit_times': {'CatBoost_BAG_L2': 59.28655934333801,\n",
              "  'ExtraTreesMSE_BAG_L1': 6.7406065464019775,\n",
              "  'ExtraTreesMSE_BAG_L2': 7.2669456005096436,\n",
              "  'KNeighborsDist_BAG_L1': 0.020573854446411133,\n",
              "  'KNeighborsUnif_BAG_L1': 0.023439884185791016,\n",
              "  'LightGBMXT_BAG_L1': 30.738657236099243,\n",
              "  'LightGBMXT_BAG_L2': 11.142054319381714,\n",
              "  'LightGBM_BAG_L1': 26.88956093788147,\n",
              "  'LightGBM_BAG_L2': 7.565409421920776,\n",
              "  'NeuralNetFastAI_BAG_L1': 169.26533126831055,\n",
              "  'NeuralNetFastAI_BAG_L2': 74.14070439338684,\n",
              "  'NeuralNetMXNet_BAG_L1': 18.320201873779297,\n",
              "  'RandomForestMSE_BAG_L1': 9.47189974784851,\n",
              "  'RandomForestMSE_BAG_L2': 31.31612777709961,\n",
              "  'WeightedEnsemble_L2': 0.42004990577697754,\n",
              "  'WeightedEnsemble_L3': 0.30917787551879883,\n",
              "  'XGBoost_BAG_L1': 33.438892126083374},\n",
              " 'model_hyperparams': {'CatBoost_BAG_L2': {'max_base_models': 25,\n",
              "   'max_base_models_per_type': 5,\n",
              "   'save_bag_folds': True,\n",
              "   'use_orig_features': True},\n",
              "  'ExtraTreesMSE_BAG_L1': {'max_base_models': 25,\n",
              "   'max_base_models_per_type': 5,\n",
              "   'save_bag_folds': True,\n",
              "   'use_child_oof': True,\n",
              "   'use_orig_features': True},\n",
              "  'ExtraTreesMSE_BAG_L2': {'max_base_models': 25,\n",
              "   'max_base_models_per_type': 5,\n",
              "   'save_bag_folds': True,\n",
              "   'use_child_oof': True,\n",
              "   'use_orig_features': True},\n",
              "  'KNeighborsDist_BAG_L1': {'max_base_models': 25,\n",
              "   'max_base_models_per_type': 5,\n",
              "   'save_bag_folds': True,\n",
              "   'use_child_oof': True,\n",
              "   'use_orig_features': True},\n",
              "  'KNeighborsUnif_BAG_L1': {'max_base_models': 25,\n",
              "   'max_base_models_per_type': 5,\n",
              "   'save_bag_folds': True,\n",
              "   'use_child_oof': True,\n",
              "   'use_orig_features': True},\n",
              "  'LightGBMXT_BAG_L1': {'max_base_models': 25,\n",
              "   'max_base_models_per_type': 5,\n",
              "   'save_bag_folds': True,\n",
              "   'use_orig_features': True},\n",
              "  'LightGBMXT_BAG_L2': {'max_base_models': 25,\n",
              "   'max_base_models_per_type': 5,\n",
              "   'save_bag_folds': True,\n",
              "   'use_orig_features': True},\n",
              "  'LightGBM_BAG_L1': {'max_base_models': 25,\n",
              "   'max_base_models_per_type': 5,\n",
              "   'save_bag_folds': True,\n",
              "   'use_orig_features': True},\n",
              "  'LightGBM_BAG_L2': {'max_base_models': 25,\n",
              "   'max_base_models_per_type': 5,\n",
              "   'save_bag_folds': True,\n",
              "   'use_orig_features': True},\n",
              "  'NeuralNetFastAI_BAG_L1': {'max_base_models': 25,\n",
              "   'max_base_models_per_type': 5,\n",
              "   'save_bag_folds': True,\n",
              "   'use_orig_features': True},\n",
              "  'NeuralNetFastAI_BAG_L2': {'max_base_models': 25,\n",
              "   'max_base_models_per_type': 5,\n",
              "   'save_bag_folds': True,\n",
              "   'use_orig_features': True},\n",
              "  'NeuralNetMXNet_BAG_L1': {'max_base_models': 25,\n",
              "   'max_base_models_per_type': 5,\n",
              "   'save_bag_folds': True,\n",
              "   'use_orig_features': True},\n",
              "  'RandomForestMSE_BAG_L1': {'max_base_models': 25,\n",
              "   'max_base_models_per_type': 5,\n",
              "   'save_bag_folds': True,\n",
              "   'use_child_oof': True,\n",
              "   'use_orig_features': True},\n",
              "  'RandomForestMSE_BAG_L2': {'max_base_models': 25,\n",
              "   'max_base_models_per_type': 5,\n",
              "   'save_bag_folds': True,\n",
              "   'use_child_oof': True,\n",
              "   'use_orig_features': True},\n",
              "  'WeightedEnsemble_L2': {'max_base_models': 25,\n",
              "   'max_base_models_per_type': 5,\n",
              "   'save_bag_folds': True,\n",
              "   'use_orig_features': False},\n",
              "  'WeightedEnsemble_L3': {'max_base_models': 25,\n",
              "   'max_base_models_per_type': 5,\n",
              "   'save_bag_folds': True,\n",
              "   'use_orig_features': False},\n",
              "  'XGBoost_BAG_L1': {'max_base_models': 25,\n",
              "   'max_base_models_per_type': 5,\n",
              "   'save_bag_folds': True,\n",
              "   'use_orig_features': True}},\n",
              " 'model_paths': {'CatBoost_BAG_L2': './bike-Models-independent-date-time-predictClass/models/CatBoost_BAG_L2/',\n",
              "  'ExtraTreesMSE_BAG_L1': './bike-Models-independent-date-time-predictClass/models/ExtraTreesMSE_BAG_L1/',\n",
              "  'ExtraTreesMSE_BAG_L2': './bike-Models-independent-date-time-predictClass/models/ExtraTreesMSE_BAG_L2/',\n",
              "  'KNeighborsDist_BAG_L1': './bike-Models-independent-date-time-predictClass/models/KNeighborsDist_BAG_L1/',\n",
              "  'KNeighborsUnif_BAG_L1': './bike-Models-independent-date-time-predictClass/models/KNeighborsUnif_BAG_L1/',\n",
              "  'LightGBMXT_BAG_L1': './bike-Models-independent-date-time-predictClass/models/LightGBMXT_BAG_L1/',\n",
              "  'LightGBMXT_BAG_L2': './bike-Models-independent-date-time-predictClass/models/LightGBMXT_BAG_L2/',\n",
              "  'LightGBM_BAG_L1': './bike-Models-independent-date-time-predictClass/models/LightGBM_BAG_L1/',\n",
              "  'LightGBM_BAG_L2': './bike-Models-independent-date-time-predictClass/models/LightGBM_BAG_L2/',\n",
              "  'NeuralNetFastAI_BAG_L1': './bike-Models-independent-date-time-predictClass/models/NeuralNetFastAI_BAG_L1/',\n",
              "  'NeuralNetFastAI_BAG_L2': './bike-Models-independent-date-time-predictClass/models/NeuralNetFastAI_BAG_L2/',\n",
              "  'NeuralNetMXNet_BAG_L1': './bike-Models-independent-date-time-predictClass/models/NeuralNetMXNet_BAG_L1/',\n",
              "  'RandomForestMSE_BAG_L1': './bike-Models-independent-date-time-predictClass/models/RandomForestMSE_BAG_L1/',\n",
              "  'RandomForestMSE_BAG_L2': './bike-Models-independent-date-time-predictClass/models/RandomForestMSE_BAG_L2/',\n",
              "  'WeightedEnsemble_L2': './bike-Models-independent-date-time-predictClass/models/WeightedEnsemble_L2/',\n",
              "  'WeightedEnsemble_L3': './bike-Models-independent-date-time-predictClass/models/WeightedEnsemble_L3/',\n",
              "  'XGBoost_BAG_L1': './bike-Models-independent-date-time-predictClass/models/XGBoost_BAG_L1/'},\n",
              " 'model_performance': {'CatBoost_BAG_L2': -38.843059426801986,\n",
              "  'ExtraTreesMSE_BAG_L1': -41.48534608455675,\n",
              "  'ExtraTreesMSE_BAG_L2': -38.60926243079764,\n",
              "  'KNeighborsDist_BAG_L1': -179.1334072231015,\n",
              "  'KNeighborsUnif_BAG_L1': -164.88997203295196,\n",
              "  'LightGBMXT_BAG_L1': -39.921141011208654,\n",
              "  'LightGBMXT_BAG_L2': -39.50393355137847,\n",
              "  'LightGBM_BAG_L1': -39.90703383271498,\n",
              "  'LightGBM_BAG_L2': -39.009257012880454,\n",
              "  'NeuralNetFastAI_BAG_L1': -41.91065383951648,\n",
              "  'NeuralNetFastAI_BAG_L2': -39.605066810880366,\n",
              "  'NeuralNetMXNet_BAG_L1': -276.94596235191193,\n",
              "  'RandomForestMSE_BAG_L1': -41.97746831638079,\n",
              "  'RandomForestMSE_BAG_L2': -39.10371988435538,\n",
              "  'WeightedEnsemble_L2': -38.329149230697915,\n",
              "  'WeightedEnsemble_L3': -38.34446554134446,\n",
              "  'XGBoost_BAG_L1': -43.749400474675284},\n",
              " 'model_pred_times': {'CatBoost_BAG_L2': 0.15419387817382812,\n",
              "  'ExtraTreesMSE_BAG_L1': 0.7164182662963867,\n",
              "  'ExtraTreesMSE_BAG_L2': 0.5728757381439209,\n",
              "  'KNeighborsDist_BAG_L1': 0.1051931381225586,\n",
              "  'KNeighborsUnif_BAG_L1': 0.10387802124023438,\n",
              "  'LightGBMXT_BAG_L1': 1.6477878093719482,\n",
              "  'LightGBMXT_BAG_L2': 0.19726157188415527,\n",
              "  'LightGBM_BAG_L1': 0.9142730236053467,\n",
              "  'LightGBM_BAG_L2': 0.15671539306640625,\n",
              "  'NeuralNetFastAI_BAG_L1': 0.7025270462036133,\n",
              "  'NeuralNetFastAI_BAG_L2': 0.4492464065551758,\n",
              "  'NeuralNetMXNet_BAG_L1': 2.6000664234161377,\n",
              "  'RandomForestMSE_BAG_L1': 0.5327475070953369,\n",
              "  'RandomForestMSE_BAG_L2': 0.5606091022491455,\n",
              "  'WeightedEnsemble_L2': 0.0006482601165771484,\n",
              "  'WeightedEnsemble_L3': 0.0006642341613769531,\n",
              "  'XGBoost_BAG_L1': 0.3485145568847656},\n",
              " 'model_types': {'CatBoost_BAG_L2': 'StackerEnsembleModel_CatBoost',\n",
              "  'ExtraTreesMSE_BAG_L1': 'StackerEnsembleModel_XT',\n",
              "  'ExtraTreesMSE_BAG_L2': 'StackerEnsembleModel_XT',\n",
              "  'KNeighborsDist_BAG_L1': 'StackerEnsembleModel_KNN',\n",
              "  'KNeighborsUnif_BAG_L1': 'StackerEnsembleModel_KNN',\n",
              "  'LightGBMXT_BAG_L1': 'StackerEnsembleModel_LGB',\n",
              "  'LightGBMXT_BAG_L2': 'StackerEnsembleModel_LGB',\n",
              "  'LightGBM_BAG_L1': 'StackerEnsembleModel_LGB',\n",
              "  'LightGBM_BAG_L2': 'StackerEnsembleModel_LGB',\n",
              "  'NeuralNetFastAI_BAG_L1': 'StackerEnsembleModel_NNFastAiTabular',\n",
              "  'NeuralNetFastAI_BAG_L2': 'StackerEnsembleModel_NNFastAiTabular',\n",
              "  'NeuralNetMXNet_BAG_L1': 'StackerEnsembleModel_TabularNeuralNet',\n",
              "  'RandomForestMSE_BAG_L1': 'StackerEnsembleModel_RF',\n",
              "  'RandomForestMSE_BAG_L2': 'StackerEnsembleModel_RF',\n",
              "  'WeightedEnsemble_L2': 'WeightedEnsembleModel',\n",
              "  'WeightedEnsemble_L3': 'WeightedEnsembleModel',\n",
              "  'XGBoost_BAG_L1': 'StackerEnsembleModel_XGBoost'},\n",
              " 'num_bag_folds': 10}"
            ]
          },
          "execution_count": 33,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "predictor_independent_features = TabularPredictor.load('./bike-Models-independent-date-time-predictClass')  # how to load previously-trained predictor from file\n",
        "predictor_independent_features.fit_summary(show_plot=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JVuGALQUMrmx",
        "outputId": "9c2f1376-2f07-4155-bafa-b02ff66156b4"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "0    13.721030\n",
            "1     4.746763\n",
            "2     1.662209\n",
            "3     2.931067\n",
            "4     2.695409\n",
            "Name: count, dtype: float32\n",
            "52     -1.965939\n",
            "53     -2.598184\n",
            "75     -2.425157\n",
            "76     -1.982363\n",
            "96     -0.780175\n",
            "97     -0.692139\n",
            "192    -0.898183\n",
            "439    -0.198847\n",
            "556    -0.398775\n",
            "557    -0.215835\n",
            "558    -0.653328\n",
            "581    -0.036772\n",
            "723    -0.137002\n",
            "724    -0.530939\n",
            "867    -2.385391\n",
            "868    -3.782177\n",
            "916    -1.082697\n",
            "3014   -4.183029\n",
            "3179   -0.220892\n",
            "3395   -0.577769\n",
            "4109   -3.827591\n",
            "4112   -6.421842\n",
            "4325   -6.719227\n",
            "4326   -6.822623\n",
            "4327   -0.522034\n",
            "4328   -7.908324\n",
            "Name: count, dtype: float32\n",
            "26\n",
            "0\n"
          ]
        }
      ],
      "source": [
        "# Remember to set all negative values to zero\n",
        "predictions_independent = predictor_independent_features.predict(test)\n",
        "print(predictions_independent.head())\n",
        "print(predictions_independent[predictions_independent < 0])\n",
        "print(len(predictions_independent[predictions_independent < 0]))\n",
        "predictions_independent[predictions_independent < 0] = 0\n",
        "print(len(predictions_independent[predictions_independent < 0]))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iMjVtxlJLV4f",
        "outputId": "f34fdac8-27b2-41ea-f2fd-b204f9b32676"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "AutoGluon infers problem type is:  regression\n",
            "AutoGluon identified the following types of features:\n",
            "('category', [])             : 3 | ['season', 'weather', 'time']\n",
            "('float', [])                : 3 | ['temp', 'atemp', 'windspeed']\n",
            "('int', [])                  : 1 | ['humidity']\n",
            "('int', ['bool'])            : 2 | ['holiday', 'workingday']\n",
            "('int', ['datetime_as_int']) : 1 | ['date']\n"
          ]
        }
      ],
      "source": [
        "print(\"AutoGluon infers problem type is: \", predictor_independent_features.problem_type)\n",
        "print(\"AutoGluon identified the following types of features:\")\n",
        "print(predictor_independent_features.feature_metadata)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "IiwvYpH6NDZk"
      },
      "outputs": [],
      "source": [
        "# Same submitting predictions\n",
        "submission_independent_features = pd.read_csv('./sampleSubmission.csv', parse_dates=['datetime'])\n",
        "submission_independent_features[\"count\"] = predictions_independent\n",
        "submission_independent_features.to_csv(\"submission_independent_features.csv\", index=False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RWl-CZivNQkO",
        "outputId": "901c7c06-8ac1-47a2-8d69-cd72846f4403"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Warning: Looks like you're using an outdated API Version, please consider updating (server 1.5.12 / client 1.5.4)\n",
            "100% 188k/188k [00:01<00:00, 131kB/s]\n",
            "Successfully submitted to Bike Sharing Demand"
          ]
        }
      ],
      "source": [
        "!kaggle competitions submit -c bike-sharing-demand -f submission_independent_features.csv -m \"new features\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FKdy_I2iNYDx",
        "outputId": "4bbb7d32-1fa8-458c-efd7-7fe93e2d256d"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Warning: Looks like you're using an outdated API Version, please consider updating (server 1.5.12 / client 1.5.4)\n",
            "fileName                             date                 description           status    publicScore  privateScore  \n",
            "-----------------------------------  -------------------  --------------------  --------  -----------  ------------  \n",
            "submission_independent_features.csv  2021-12-27 05:26:29  new features          complete  0.47959      0.47959       \n",
            "submission_independent_features.csv  2021-12-26 10:13:42  new features          complete  0.47959      0.47959       \n",
            "submission_new_features.csv          2021-12-26 09:51:53  new features          complete  0.48168      0.48168       \n"
          ]
        }
      ],
      "source": [
        "!kaggle competitions submissions -c bike-sharing-demand | tail -n +1 | head -n 6"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BsdIuU_bRiyc"
      },
      "source": [
        "#### New Score of `0.47959`"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gXEKAqWeRiyc"
      },
      "source": [
        "## Step 6: Hyper parameter optimization\n",
        "* There are many options for hyper parameter optimization.\n",
        "* Options are to change the AutoGluon higher level parameters or the individual model hyperparameters.\n",
        "* The hyperparameters of the models themselves that are in AutoGluon. Those need the `hyperparameter` and `hyperparameter_tune_kwargs` arguments."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "mNXo8Hb4fiHC"
      },
      "outputs": [],
      "source": [
        "train[\"workingday\"] = train[\"workingday\"].astype(\"category\")\n",
        "train[\"holiday\"] = train[\"holiday\"].astype(\"category\")\n",
        "test[\"workingday\"] = test[\"workingday\"].astype(\"category\")\n",
        "test[\"holiday\"] = test[\"holiday\"].astype(\"category\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "id": "H79qw--QjMX3",
        "outputId": "ac892d76-ed23-455f-a1d0-1758c8fbaffd"
      },
      "outputs": [
        {
          "data": {
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              "                temp     atemp  humidity  ...    casual  registered     count\n",
              "temp        1.000000  0.984948 -0.064949  ...  0.467097    0.318571  0.394454\n",
              "atemp       0.984948  1.000000 -0.043536  ...  0.462067    0.314635  0.389784\n",
              "humidity   -0.064949 -0.043536  1.000000  ... -0.348187   -0.265458 -0.317371\n",
              "windspeed  -0.017852 -0.057473 -0.318607  ...  0.092276    0.091052  0.101369\n",
              "casual      0.467097  0.462067 -0.348187  ...  1.000000    0.497250  0.690414\n",
              "registered  0.318571  0.314635 -0.265458  ...  0.497250    1.000000  0.970948\n",
              "count       0.394454  0.389784 -0.317371  ...  0.690414    0.970948  1.000000\n",
              "\n",
              "[7 rows x 7 columns]"
            ]
          },
          "execution_count": 12,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "train.corr()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 790
        },
        "id": "_a9EteHsfneX",
        "outputId": "8477ddba-3bfc-4ad7-d8f3-8f90846828e8"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f2b23da8150>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f2b210ff750>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f2b243a7510>],\n",
              "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f2b244bfd50>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f2b210f6790>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f2b20fb2c10>],\n",
              "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f2b20f5b890>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f2b240ef250>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f2b240ef210>]],\n",
              "      dtype=object)"
            ]
          },
          "execution_count": 60,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1080x720 with 9 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "# View histogram of all features again now with the hour feature\n",
        "train.hist(bins=30, figsize=(15, 10))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
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            "b2560b483f544c96bb1be7565d1cc76f",
            "3da3f662acfa460ebef4b877e8f0afff",
            "6a9605c8666e4ce3b8fe95c7ac710fe7",
            "0d882cb1ccda43f88f99f7813ea0a2f7"
          ]
        },
        "id": "AgZr4lO9fqBE",
        "outputId": "960631b6-6008-4b05-ae55-3a3f5572a213"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Presets specified: ['best_quality']\n",
            "Warning: hyperparameter tuning is currently experimental and may cause the process to hang.\n",
            "Beginning AutoGluon training ... Time limit = 600s\n",
            "AutoGluon will save models to \"bike-Models-all-category-predictClass/\"\n",
            "AutoGluon Version:  0.3.1\n",
            "Train Data Rows:    10886\n",
            "Train Data Columns: 13\n",
            "Preprocessing data ...\n",
            "Using Feature Generators to preprocess the data ...\n",
            "Dropping user-specified ignored columns: ['casual', 'datetime', 'registered']\n",
            "Fitting AutoMLPipelineFeatureGenerator...\n",
            "\tAvailable Memory:                    10966.54 MB\n",
            "\tTrain Data (Original)  Memory Usage: 1.35 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",
            "\t\tFitting CategoryFeatureGenerator...\n",
            "\t\t\tFitting CategoryMemoryMinimizeFeatureGenerator...\n",
            "\t\tFitting DatetimeFeatureGenerator...\n",
            "\tStage 4 Generators:\n",
            "\t\tFitting DropUniqueFeatureGenerator...\n",
            "\tTypes of features in original data (raw dtype, special dtypes):\n",
            "\t\t('category', [])                   : 4 | ['season', 'holiday', 'workingday', 'weather']\n",
            "\t\t('float', [])                      : 3 | ['temp', 'atemp', 'windspeed']\n",
            "\t\t('int', [])                        : 1 | ['humidity']\n",
            "\t\t('object', [])                     : 1 | ['time']\n",
            "\t\t('object', ['datetime_as_object']) : 1 | ['date']\n",
            "\tTypes of features in processed data (raw dtype, special dtypes):\n",
            "\t\t('category', [])             : 3 | ['season', 'weather', 'time']\n",
            "\t\t('float', [])                : 3 | ['temp', 'atemp', 'windspeed']\n",
            "\t\t('int', [])                  : 1 | ['humidity']\n",
            "\t\t('int', ['bool'])            : 2 | ['holiday', 'workingday']\n",
            "\t\t('int', ['datetime_as_int']) : 1 | ['date']\n",
            "\t0.5s = Fit runtime\n",
            "\t10 features in original data used to generate 10 features in processed data.\n",
            "\tTrain Data (Processed) Memory Usage: 0.49 MB (0.0% of available memory)\n",
            "Data preprocessing and feature engineering runtime = 0.6s ...\n",
            "AutoGluon will gauge predictive performance using evaluation metric: 'root_mean_squared_error'\n",
            "\tTo change this, specify the eval_metric argument of fit()\n",
            "AutoGluon will fit 2 stack levels (L1 to L2) ...\n",
            "Excluded Model Types: ['KNN', 'NN']\n",
            "\tFound 'NN' model in hyperparameters, but 'NN' is present in `excluded_model_types` and will be removed.\n",
            "\tFound 'KNN' model in hyperparameters, but 'KNN' is present in `excluded_model_types` and will be removed.\n",
            "\tFound 'KNN' model in hyperparameters, but 'KNN' is present in `excluded_model_types` and will be removed.\n",
            "Fitting 8 L1 models ...\n",
            "Hyperparameter tuning model: LightGBMXT_BAG_L1 ...\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "b55c3b7f88724908b3ae38fd56389082",
              "version_major": 2,
              "version_minor": 0
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          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/lightgbm/engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.\n",
            "  _log_warning(\"'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. \"\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[1000]\ttrain_set's rmse: 29.6045\tvalid_set's rmse: 37.7798\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\tRan out of time, early stopping on iteration 1587. Best iteration is:\n",
            "\t[1577]\ttrain_set's rmse: 26.7148\tvalid_set's rmse: 37.5429\n",
            "\tTime limit exceeded\n",
            "Fitted model: LightGBMXT_BAG_L1/T0 ...\n",
            "\t-37.5429\t = Validation score   (root_mean_squared_error)\n",
            "\t3.8s\t = Training   runtime\n",
            "\t0.21s\t = Validation runtime\n",
            "Hyperparameter tuning model: LightGBM_BAG_L1 ...\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "13f460be56404c3daf7444970b8e31db",
              "version_major": 2,
              "version_minor": 0
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          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/lightgbm/engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.\n",
            "  _log_warning(\"'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
            "/usr/local/lib/python3.7/dist-packages/lightgbm/engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.\n",
            "  _log_warning(\"'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
            "\tRan out of time, early stopping on iteration 656. Best iteration is:\n",
            "\t[656]\ttrain_set's rmse: 33.3206\tvalid_set's rmse: 42.3605\n",
            "\tTime limit exceeded\n",
            "Fitted model: LightGBM_BAG_L1/T0 ...\n",
            "\t-38.1139\t = Validation score   (root_mean_squared_error)\n",
            "\t1.36s\t = Training   runtime\n",
            "\t0.05s\t = Validation runtime\n",
            "Fitted model: LightGBM_BAG_L1/T1 ...\n",
            "\t-42.3605\t = Validation score   (root_mean_squared_error)\n",
            "\t2.3s\t = Training   runtime\n",
            "\t0.09s\t = Validation runtime\n",
            "Hyperparameter tuning model: RandomForestMSE_BAG_L1 ...\n",
            "Fitted model: RandomForestMSE_BAG_L1/T0 ...\n",
            "\t-41.9775\t = Validation score   (root_mean_squared_error)\n",
            "\t9.69s\t = Training   runtime\n",
            "\t0.51s\t = Validation runtime\n",
            "Hyperparameter tuning model: CatBoost_BAG_L1 ...\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "6ac20c2a523244d0b4b36c8d05aa0ade",
              "version_major": 2,
              "version_minor": 0
            },
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          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\tTime limit exceeded\n",
            "Fitted model: CatBoost_BAG_L1/T0 ...\n",
            "\t-82.5272\t = Validation score   (root_mean_squared_error)\n",
            "\t2.78s\t = Training   runtime\n",
            "\t0.17s\t = Validation runtime\n",
            "Hyperparameter tuning model: ExtraTreesMSE_BAG_L1 ...\n",
            "Fitted model: ExtraTreesMSE_BAG_L1/T0 ...\n",
            "\t-41.4853\t = Validation score   (root_mean_squared_error)\n",
            "\t6.71s\t = Training   runtime\n",
            "\t0.54s\t = Validation runtime\n",
            "Hyperparameter tuning model: NeuralNetFastAI_BAG_L1 ...\n",
            "\tRan out of time, stopping training early. (Stopping on epoch 5)\n",
            "Fitted model: NeuralNetFastAI_BAG_L1/T0 ...\n",
            "\t-145.3358\t = Validation score   (root_mean_squared_error)\n",
            "\t3.38s\t = Training   runtime\n",
            "\t0.05s\t = Validation runtime\n",
            "Hyperparameter tuning model: XGBoost_BAG_L1 ...\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "145a36404013443cbfd47753a9db27ce",
              "version_major": 2,
              "version_minor": 0
            },
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          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\tTime limit exceeded\n",
            "Fitted model: XGBoost_BAG_L1/T0 ...\n",
            "\t-41.3599\t = Validation score   (root_mean_squared_error)\n",
            "\t3.88s\t = Training   runtime\n",
            "\t0.03s\t = Validation runtime\n",
            "Fitting model: LightGBMLarge_BAG_L1 ... Training model for up to 4.49s of the 540.99s of remaining time.\n",
            "/usr/local/lib/python3.7/dist-packages/lightgbm/engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.\n",
            "  _log_warning(\"'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
            "\t-37.4132\t = Validation score   (root_mean_squared_error)\n",
            "\t3.92s\t = Training   runtime\n",
            "\t0.13s\t = Validation runtime\n",
            "Fitting model: LightGBMXT_BAG_L1/T0 ... Training model for up to 336.45s of the 536.32s of remaining time.\n",
            "/usr/local/lib/python3.7/dist-packages/lightgbm/engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.\n",
            "  _log_warning(\"'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. \"\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[1000]\ttrain_set's rmse: 29.5276\tvalid_set's rmse: 41.8083\n",
            "[2000]\ttrain_set's rmse: 25.1539\tvalid_set's rmse: 41.4651\n",
            "[3000]\ttrain_set's rmse: 22.3692\tvalid_set's rmse: 41.339\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/lightgbm/engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.\n",
            "  _log_warning(\"'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. \"\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[1000]\ttrain_set's rmse: 29.875\tvalid_set's rmse: 36.0158\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/lightgbm/engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.\n",
            "  _log_warning(\"'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. \"\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[1000]\ttrain_set's rmse: 29.4633\tvalid_set's rmse: 42.5193\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/lightgbm/engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.\n",
            "  _log_warning(\"'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. \"\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[1000]\ttrain_set's rmse: 29.7326\tvalid_set's rmse: 40.0973\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/lightgbm/engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.\n",
            "  _log_warning(\"'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. \"\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[1000]\ttrain_set's rmse: 29.6363\tvalid_set's rmse: 42.1779\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/lightgbm/engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.\n",
            "  _log_warning(\"'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. \"\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[1000]\ttrain_set's rmse: 29.8896\tvalid_set's rmse: 38.9671\n",
            "[2000]\ttrain_set's rmse: 25.4792\tvalid_set's rmse: 38.7685\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/lightgbm/engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.\n",
            "  _log_warning(\"'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. \"\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[1000]\ttrain_set's rmse: 29.7259\tvalid_set's rmse: 38.7573\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/lightgbm/engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.\n",
            "  _log_warning(\"'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
            "/usr/local/lib/python3.7/dist-packages/lightgbm/engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.\n",
            "  _log_warning(\"'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
            "\t-39.9023\t = Validation score   (root_mean_squared_error)\n",
            "\t37.87s\t = Training   runtime\n",
            "\t1.86s\t = Validation runtime\n",
            "Fitting model: LightGBM_BAG_L1/T0 ... Training model for up to 297.96s of the 497.83s of remaining time.\n",
            "/usr/local/lib/python3.7/dist-packages/lightgbm/engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.\n",
            "  _log_warning(\"'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
            "/usr/local/lib/python3.7/dist-packages/lightgbm/engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.\n",
            "  _log_warning(\"'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. \"\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[1000]\ttrain_set's rmse: 20.741\tvalid_set's rmse: 36.7353\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/lightgbm/engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.\n",
            "  _log_warning(\"'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. \"\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[1000]\ttrain_set's rmse: 20.5858\tvalid_set's rmse: 40.8092\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/lightgbm/engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.\n",
            "  _log_warning(\"'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. \"\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[1000]\ttrain_set's rmse: 20.7831\tvalid_set's rmse: 40.1068\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/lightgbm/engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.\n",
            "  _log_warning(\"'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
            "/usr/local/lib/python3.7/dist-packages/lightgbm/engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.\n",
            "  _log_warning(\"'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. \"\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[1000]\ttrain_set's rmse: 20.7425\tvalid_set's rmse: 37.5717\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/lightgbm/engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.\n",
            "  _log_warning(\"'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
            "/usr/local/lib/python3.7/dist-packages/lightgbm/engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.\n",
            "  _log_warning(\"'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
            "/usr/local/lib/python3.7/dist-packages/lightgbm/engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.\n",
            "  _log_warning(\"'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
            "\t-39.907\t = Validation score   (root_mean_squared_error)\n",
            "\t17.17s\t = Training   runtime\n",
            "\t0.92s\t = Validation runtime\n",
            "Fitting model: LightGBM_BAG_L1/T1 ... Training model for up to 279.34s of the 479.21s of remaining time.\n",
            "/usr/local/lib/python3.7/dist-packages/lightgbm/engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.\n",
            "  _log_warning(\"'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
            "/usr/local/lib/python3.7/dist-packages/lightgbm/engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.\n",
            "  _log_warning(\"'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. \"\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[1000]\ttrain_set's rmse: 20.741\tvalid_set's rmse: 36.7353\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/lightgbm/engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.\n",
            "  _log_warning(\"'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. \"\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[1000]\ttrain_set's rmse: 20.5858\tvalid_set's rmse: 40.8092\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/lightgbm/engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.\n",
            "  _log_warning(\"'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. \"\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[1000]\ttrain_set's rmse: 20.7831\tvalid_set's rmse: 40.1068\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/lightgbm/engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.\n",
            "  _log_warning(\"'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
            "/usr/local/lib/python3.7/dist-packages/lightgbm/engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.\n",
            "  _log_warning(\"'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. \"\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[1000]\ttrain_set's rmse: 20.7425\tvalid_set's rmse: 37.5717\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/lightgbm/engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.\n",
            "  _log_warning(\"'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
            "/usr/local/lib/python3.7/dist-packages/lightgbm/engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.\n",
            "  _log_warning(\"'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
            "/usr/local/lib/python3.7/dist-packages/lightgbm/engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.\n",
            "  _log_warning(\"'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
            "\t-40.3331\t = Validation score   (root_mean_squared_error)\n",
            "\t24.73s\t = Training   runtime\n",
            "\t1.01s\t = Validation runtime\n",
            "Fitting model: RandomForestMSE_BAG_L1/T0 ... Training model for up to 254.09s of the 453.95s of remaining time.\n",
            "\t-41.9775\t = Validation score   (root_mean_squared_error)\n",
            "\t9.69s\t = Training   runtime\n",
            "\t0.51s\t = Validation runtime\n",
            "Fitting model: CatBoost_BAG_L1/T0 ... Training model for up to 254.02s of the 453.89s of remaining time.\n",
            "\tTime limit exceeded... Skipping CatBoost_BAG_L1/T0.\n",
            "Fitting model: ExtraTreesMSE_BAG_L1/T0 ... Training model for up to 196.48s of the 396.34s of remaining time.\n",
            "\t-41.4853\t = Validation score   (root_mean_squared_error)\n",
            "\t6.71s\t = Training   runtime\n",
            "\t0.54s\t = Validation runtime\n",
            "Fitting model: NeuralNetFastAI_BAG_L1/T0 ... Training model for up to 196.44s of the 396.3s of remaining time.\n",
            "\tRan out of time, stopping training early. (Stopping on epoch 28)\n",
            "\tRan out of time, stopping training early. (Stopping on epoch 19)\n",
            "\tRan out of time, stopping training early. (Stopping on epoch 23)\n",
            "\tRan out of time, stopping training early. (Stopping on epoch 23)\n",
            "\tRan out of time, stopping training early. (Stopping on epoch 23)\n",
            "\tRan out of time, stopping training early. (Stopping on epoch 10)\n",
            "\tRan out of time, stopping training early. (Stopping on epoch 21)\n",
            "\tRan out of time, stopping training early. (Stopping on epoch 16)\n",
            "\t-61.1832\t = Validation score   (root_mean_squared_error)\n",
            "\t189.59s\t = Training   runtime\n",
            "\t0.93s\t = Validation runtime\n",
            "Fitting model: XGBoost_BAG_L1/T0 ... Training model for up to 8.77s of the 208.64s of remaining time.\n",
            "\tTime limit exceeded... Skipping XGBoost_BAG_L1/T0.\n",
            "Fitting model: LightGBMLarge_BAG_L1 ... Training model for up to 7.72s of the 207.58s of remaining time.\n",
            "/usr/local/lib/python3.7/dist-packages/lightgbm/engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.\n",
            "  _log_warning(\"'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
            "\tRan out of time, early stopping on iteration 101. Best iteration is:\n",
            "\t[101]\ttrain_set's rmse: 34.9642\tvalid_set's rmse: 49.3308\n",
            "\tTime limit exceeded... Skipping LightGBMLarge_BAG_L1.\n",
            "Completed 1/20 k-fold bagging repeats ...\n",
            "Fitting model: WeightedEnsemble_L2 ... Training model for up to 360.0s of the 206.58s of remaining time.\n",
            "\t-38.5012\t = Validation score   (root_mean_squared_error)\n",
            "\t0.62s\t = Training   runtime\n",
            "\t0.0s\t = Validation runtime\n",
            "Excluded Model Types: ['KNN', 'NN']\n",
            "\tFound 'NN' model in hyperparameters, but 'NN' is present in `excluded_model_types` and will be removed.\n",
            "\tFound 'KNN' model in hyperparameters, but 'KNN' is present in `excluded_model_types` and will be removed.\n",
            "\tFound 'KNN' model in hyperparameters, but 'KNN' is present in `excluded_model_types` and will be removed.\n",
            "Fitting 8 L2 models ...\n",
            "Hyperparameter tuning model: LightGBMXT_BAG_L2 ...\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "ab094f2eb95549d59e8e371ced8cf458",
              "version_major": 2,
              "version_minor": 0
            },
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          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/lightgbm/engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.\n",
            "  _log_warning(\"'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
            "\tRan out of time, early stopping on iteration 272. Best iteration is:\n",
            "\t[124]\ttrain_set's rmse: 36.519\tvalid_set's rmse: 41.3393\n",
            "\tTime limit exceeded\n",
            "Fitted model: LightGBMXT_BAG_L2/T0 ...\n",
            "\t-41.3393\t = Validation score   (root_mean_squared_error)\n",
            "\t1.6s\t = Training   runtime\n",
            "\t0.03s\t = Validation runtime\n",
            "Hyperparameter tuning model: LightGBM_BAG_L2 ...\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "ba95a29354ed47a4be2e711616c89ef3",
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              "version_minor": 0
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          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/lightgbm/engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.\n",
            "  _log_warning(\"'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
            "\tTime limit exceeded\n",
            "Fitted model: LightGBM_BAG_L2/T0 ...\n",
            "\t-39.7814\t = Validation score   (root_mean_squared_error)\n",
            "\t1.07s\t = Training   runtime\n",
            "\t0.02s\t = Validation runtime\n",
            "Hyperparameter tuning model: RandomForestMSE_BAG_L2 ...\n",
            "Fitted model: RandomForestMSE_BAG_L2/T0 ...\n",
            "\t-39.262\t = Validation score   (root_mean_squared_error)\n",
            "\t30.24s\t = Training   runtime\n",
            "\t1.15s\t = Validation runtime\n",
            "Hyperparameter tuning model: CatBoost_BAG_L2 ...\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "47d97db39af44225b1d340c56ebbcce1",
              "version_major": 2,
              "version_minor": 0
            },
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          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\tTime limit exceeded\n",
            "Fitted model: CatBoost_BAG_L2/T0 ...\n",
            "\t-39.823\t = Validation score   (root_mean_squared_error)\n",
            "\t1.75s\t = Training   runtime\n",
            "\t0.02s\t = Validation runtime\n",
            "Hyperparameter tuning model: ExtraTreesMSE_BAG_L2 ...\n",
            "Fitted model: ExtraTreesMSE_BAG_L2/T0 ...\n",
            "\t-38.8205\t = Validation score   (root_mean_squared_error)\n",
            "\t8.61s\t = Training   runtime\n",
            "\t0.58s\t = Validation runtime\n",
            "Hyperparameter tuning model: NeuralNetFastAI_BAG_L2 ...\n",
            "\tRan out of time, stopping training early. (Stopping on epoch 2)\n",
            "Fitted model: NeuralNetFastAI_BAG_L2/T0 ...\n",
            "\t-45.6402\t = Validation score   (root_mean_squared_error)\n",
            "\t1.82s\t = Training   runtime\n",
            "\t0.05s\t = Validation runtime\n",
            "Hyperparameter tuning model: XGBoost_BAG_L2 ...\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "dc678f5feb52426b9a2dd788cc6a8bd5",
              "version_major": 2,
              "version_minor": 0
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          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\tTime limit exceeded\n",
            "Fitted model: XGBoost_BAG_L2/T0 ...\n",
            "\t-39.446\t = Validation score   (root_mean_squared_error)\n",
            "\t1.25s\t = Training   runtime\n",
            "\t0.01s\t = Validation runtime\n",
            "Fitting model: LightGBMLarge_BAG_L2 ... Training model for up to 2.32s of the 142.85s of remaining time.\n",
            "/usr/local/lib/python3.7/dist-packages/lightgbm/engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.\n",
            "  _log_warning(\"'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
            "\tRan out of time, early stopping on iteration 256. Best iteration is:\n",
            "\t[118]\ttrain_set's rmse: 25.0559\tvalid_set's rmse: 40.0328\n",
            "\t-40.0328\t = Validation score   (root_mean_squared_error)\n",
            "\t2.02s\t = Training   runtime\n",
            "\t0.03s\t = Validation runtime\n",
            "Fitting model: LightGBMXT_BAG_L2/T0 ... Training model for up to 140.58s of the 140.51s of remaining time.\n",
            "/usr/local/lib/python3.7/dist-packages/lightgbm/engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.\n",
            "  _log_warning(\"'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
            "/usr/local/lib/python3.7/dist-packages/lightgbm/engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.\n",
            "  _log_warning(\"'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
            "/usr/local/lib/python3.7/dist-packages/lightgbm/engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.\n",
            "  _log_warning(\"'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
            "/usr/local/lib/python3.7/dist-packages/lightgbm/engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.\n",
            "  _log_warning(\"'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
            "/usr/local/lib/python3.7/dist-packages/lightgbm/engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.\n",
            "  _log_warning(\"'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
            "/usr/local/lib/python3.7/dist-packages/lightgbm/engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.\n",
            "  _log_warning(\"'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
            "/usr/local/lib/python3.7/dist-packages/lightgbm/engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.\n",
            "  _log_warning(\"'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
            "/usr/local/lib/python3.7/dist-packages/lightgbm/engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.\n",
            "  _log_warning(\"'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
            "/usr/local/lib/python3.7/dist-packages/lightgbm/engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.\n",
            "  _log_warning(\"'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
            "\t-39.5339\t = Validation score   (root_mean_squared_error)\n",
            "\t10.75s\t = Training   runtime\n",
            "\t0.25s\t = Validation runtime\n",
            "Fitting model: LightGBM_BAG_L2/T0 ... Training model for up to 130.83s of the 130.77s of remaining time.\n",
            "/usr/local/lib/python3.7/dist-packages/lightgbm/engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.\n",
            "  _log_warning(\"'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
            "/usr/local/lib/python3.7/dist-packages/lightgbm/engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.\n",
            "  _log_warning(\"'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
            "/usr/local/lib/python3.7/dist-packages/lightgbm/engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.\n",
            "  _log_warning(\"'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
            "/usr/local/lib/python3.7/dist-packages/lightgbm/engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.\n",
            "  _log_warning(\"'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
            "/usr/local/lib/python3.7/dist-packages/lightgbm/engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.\n",
            "  _log_warning(\"'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
            "/usr/local/lib/python3.7/dist-packages/lightgbm/engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.\n",
            "  _log_warning(\"'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
            "/usr/local/lib/python3.7/dist-packages/lightgbm/engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.\n",
            "  _log_warning(\"'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
            "/usr/local/lib/python3.7/dist-packages/lightgbm/engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.\n",
            "  _log_warning(\"'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
            "/usr/local/lib/python3.7/dist-packages/lightgbm/engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.\n",
            "  _log_warning(\"'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
            "\t-38.961\t = Validation score   (root_mean_squared_error)\n",
            "\t11.0s\t = Training   runtime\n",
            "\t0.18s\t = Validation runtime\n",
            "Fitting model: RandomForestMSE_BAG_L2/T0 ... Training model for up to 120.43s of the 120.37s of remaining time.\n",
            "\t-39.262\t = Validation score   (root_mean_squared_error)\n",
            "\t30.24s\t = Training   runtime\n",
            "\t1.15s\t = Validation runtime\n",
            "Fitting model: CatBoost_BAG_L2/T0 ... Training model for up to 120.38s of the 120.32s of remaining time.\n",
            "\t-38.6279\t = Validation score   (root_mean_squared_error)\n",
            "\t43.46s\t = Training   runtime\n",
            "\t0.09s\t = Validation runtime\n",
            "Fitting model: ExtraTreesMSE_BAG_L2/T0 ... Training model for up to 78.39s of the 78.33s of remaining time.\n",
            "\t-38.8205\t = Validation score   (root_mean_squared_error)\n",
            "\t8.61s\t = Training   runtime\n",
            "\t0.58s\t = Validation runtime\n",
            "Fitting model: NeuralNetFastAI_BAG_L2/T0 ... Training model for up to 78.34s of the 78.28s of remaining time.\n",
            "\tRan out of time, stopping training early. (Stopping on epoch 10)\n",
            "\tRan out of time, stopping training early. (Stopping on epoch 11)\n",
            "\tRan out of time, stopping training early. (Stopping on epoch 11)\n",
            "\tRan out of time, stopping training early. (Stopping on epoch 12)\n",
            "\tRan out of time, stopping training early. (Stopping on epoch 11)\n",
            "\tRan out of time, stopping training early. (Stopping on epoch 13)\n",
            "\tRan out of time, stopping training early. (Stopping on epoch 14)\n",
            "\tRan out of time, stopping training early. (Stopping on epoch 15)\n",
            "\tRan out of time, stopping training early. (Stopping on epoch 19)\n",
            "\t-40.0718\t = Validation score   (root_mean_squared_error)\n",
            "\t76.38s\t = Training   runtime\n",
            "\t0.51s\t = Validation runtime\n",
            "Fitting model: XGBoost_BAG_L2/T0 ... Training model for up to 2.93s of the 2.87s of remaining time.\n",
            "\tTime limit exceeded... Skipping XGBoost_BAG_L2/T0.\n",
            "Fitting model: LightGBMLarge_BAG_L2 ... Training model for up to 2.54s of the 2.48s of remaining time.\n",
            "/usr/local/lib/python3.7/dist-packages/lightgbm/engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.\n",
            "  _log_warning(\"'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
            "\tRan out of time, early stopping on iteration 1. Best iteration is:\n",
            "\t[1]\ttrain_set's rmse: 175.136\tvalid_set's rmse: 182.732\n",
            "\tTime limit exceeded... Skipping LightGBMLarge_BAG_L2.\n",
            "Completed 1/20 k-fold bagging repeats ...\n",
            "Fitting model: WeightedEnsemble_L3 ... Training model for up to 360.0s of the 2.09s of remaining time.\n",
            "\t-38.3545\t = Validation score   (root_mean_squared_error)\n",
            "\t0.33s\t = Training   runtime\n",
            "\t0.0s\t = Validation runtime\n",
            "AutoGluon training complete, total runtime = 598.36s ...\n",
            "TabularPredictor saved. To load, use: predictor = TabularPredictor.load(\"bike-Models-all-category-predictClass/\")\n"
          ]
        }
      ],
      "source": [
        "num_trials = 5  # try at most 5 different hyperparameter configurations for each type of model\n",
        "search_strategy = 'auto'  # to tune hyperparameters using Bayesian optimization routine with a local scheduler\n",
        "\n",
        "hyperparameter_tune_kwargs = {  # HPO is not performed unless hyperparameter_tune_kwargs is specified\n",
        "    'num_trials': num_trials,\n",
        "    'scheduler' : 'local',\n",
        "    'searcher': search_strategy,\n",
        "}\n",
        "excluded_model_types = ['KNN', 'NN']\n",
        "\n",
        "save_path = 'bike-Models-all-category-predictClass'\n",
        "predictor_category_features = TabularPredictor('count', problem_type='regression', \n",
        "                                          path=save_path, learner_kwargs={'ignored_columns':['casual', 'datetime' , 'registered']} ).fit(\n",
        "                                          train, time_limit=600, hyperparameter_tune_kwargs=hyperparameter_tune_kwargs,\n",
        "                                          excluded_model_types=excluded_model_types, presets='best_quality')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "r-san_YjgJ-V",
        "outputId": "e0fe8c54-edca-4e7e-ca26-2bc737dccdcb"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "*** Summary of fit() ***\n",
            "Estimated performance of each model:\n",
            "                        model  score_val  pred_time_val    fit_time  pred_time_val_marginal  fit_time_marginal  stack_level  can_infer  fit_order\n",
            "0        LightGBMLarge_BAG_L1 -37.413172       0.130720    3.921357                0.130720           3.921357            1       True          9\n",
            "1         WeightedEnsemble_L3 -38.354510       7.145235  425.522444                0.000836           0.331050            3       True         19\n",
            "2         WeightedEnsemble_L2 -38.501170       3.835718   72.052474                0.001297           0.618256            2       True         10\n",
            "3          CatBoost_BAG_L2/T0 -38.627875       5.874132  329.207324                0.094300          43.458205            2       True         14\n",
            "4     ExtraTreesMSE_BAG_L2/T0 -38.820537       6.359997  294.354189                0.580164           8.605070            2       True         15\n",
            "5          LightGBM_BAG_L2/T0 -38.960980       5.958716  296.744196                0.178884          10.995077            2       True         12\n",
            "6   RandomForestMSE_BAG_L2/T0 -39.262049       6.931088  315.992291                1.151256          30.243172            2       True         13\n",
            "7           XGBoost_BAG_L2/T0 -39.445994       5.792697  286.998614                0.012865           1.249495            2       True         17\n",
            "8        LightGBMXT_BAG_L2/T0 -39.533922       6.033673  296.494733                0.253840          10.745614            2       True         11\n",
            "9        LightGBMXT_BAG_L1/T0 -39.902278       1.863356   37.868054                1.863356          37.868054            1       True          1\n",
            "10         LightGBM_BAG_L1/T0 -39.907034       0.923686   17.169391                0.923686          17.169391            1       True          2\n",
            "11       LightGBMLarge_BAG_L2 -40.032759       5.808518  287.771568                0.028686           2.022449            2       True         18\n",
            "12  NeuralNetFastAI_BAG_L2/T0 -40.071753       6.291051  362.133042                0.511219          76.383923            2       True         16\n",
            "13         LightGBM_BAG_L1/T1 -40.333098       1.012545   24.726175                1.012545          24.726175            1       True          3\n",
            "14          XGBoost_BAG_L1/T0 -41.359877       0.033182    3.876535                0.033182           3.876535            1       True          8\n",
            "15    ExtraTreesMSE_BAG_L1/T0 -41.485346       0.541971    6.709701                0.541971           6.709701            1       True          6\n",
            "16  RandomForestMSE_BAG_L1/T0 -41.977468       0.505409    9.687072                0.505409           9.687072            1       True          4\n",
            "17  NeuralNetFastAI_BAG_L1/T0 -61.183225       0.932866  189.588726                0.932866         189.588726            1       True          7\n",
            "18         CatBoost_BAG_L1/T0 -82.527193       0.167046    2.780956                0.167046           2.780956            1       True          5\n",
            "Number of models trained: 19\n",
            "Types of models trained:\n",
            "{'StackerEnsembleModel_XT', 'StackerEnsembleModel_CatBoost', 'WeightedEnsembleModel', 'StackerEnsembleModel_NNFastAiTabular', 'StackerEnsembleModel_XGBoost', 'StackerEnsembleModel_RF', 'StackerEnsembleModel_LGB'}\n",
            "Bagging used: True  (with 10 folds)\n",
            "Multi-layer stack-ensembling used: True  (with 3 levels)\n",
            "Feature Metadata (Processed):\n",
            "(raw dtype, special dtypes):\n",
            "('category', [])             : 3 | ['season', 'weather', 'time']\n",
            "('float', [])                : 3 | ['temp', 'atemp', 'windspeed']\n",
            "('int', [])                  : 1 | ['humidity']\n",
            "('int', ['bool'])            : 2 | ['holiday', 'workingday']\n",
            "('int', ['datetime_as_int']) : 1 | ['date']\n",
            "Plot summary of models saved to file: bike-Models-all-category-predictClass/SummaryOfModels.html\n",
            "*** End of fit() summary ***\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "{'leaderboard':                         model  score_val  ...  can_infer  fit_order\n",
              " 0        LightGBMLarge_BAG_L1 -37.413172  ...       True          9\n",
              " 1         WeightedEnsemble_L3 -38.354510  ...       True         19\n",
              " 2         WeightedEnsemble_L2 -38.501170  ...       True         10\n",
              " 3          CatBoost_BAG_L2/T0 -38.627875  ...       True         14\n",
              " 4     ExtraTreesMSE_BAG_L2/T0 -38.820537  ...       True         15\n",
              " 5          LightGBM_BAG_L2/T0 -38.960980  ...       True         12\n",
              " 6   RandomForestMSE_BAG_L2/T0 -39.262049  ...       True         13\n",
              " 7           XGBoost_BAG_L2/T0 -39.445994  ...       True         17\n",
              " 8        LightGBMXT_BAG_L2/T0 -39.533922  ...       True         11\n",
              " 9        LightGBMXT_BAG_L1/T0 -39.902278  ...       True          1\n",
              " 10         LightGBM_BAG_L1/T0 -39.907034  ...       True          2\n",
              " 11       LightGBMLarge_BAG_L2 -40.032759  ...       True         18\n",
              " 12  NeuralNetFastAI_BAG_L2/T0 -40.071753  ...       True         16\n",
              " 13         LightGBM_BAG_L1/T1 -40.333098  ...       True          3\n",
              " 14          XGBoost_BAG_L1/T0 -41.359877  ...       True          8\n",
              " 15    ExtraTreesMSE_BAG_L1/T0 -41.485346  ...       True          6\n",
              " 16  RandomForestMSE_BAG_L1/T0 -41.977468  ...       True          4\n",
              " 17  NeuralNetFastAI_BAG_L1/T0 -61.183225  ...       True          7\n",
              " 18         CatBoost_BAG_L1/T0 -82.527193  ...       True          5\n",
              " \n",
              " [19 rows x 9 columns],\n",
              " 'max_stack_level': 3,\n",
              " 'model_best': 'WeightedEnsemble_L3',\n",
              " 'model_fit_times': {'CatBoost_BAG_L1/T0': 2.7809560298919678,\n",
              "  'CatBoost_BAG_L2/T0': 43.45820474624634,\n",
              "  'ExtraTreesMSE_BAG_L1/T0': 6.709700584411621,\n",
              "  'ExtraTreesMSE_BAG_L2/T0': 8.605070114135742,\n",
              "  'LightGBMLarge_BAG_L1': 3.9213571548461914,\n",
              "  'LightGBMLarge_BAG_L2': 2.022449016571045,\n",
              "  'LightGBMXT_BAG_L1/T0': 37.868054151535034,\n",
              "  'LightGBMXT_BAG_L2/T0': 10.745613813400269,\n",
              "  'LightGBM_BAG_L1/T0': 17.1693913936615,\n",
              "  'LightGBM_BAG_L1/T1': 24.72617530822754,\n",
              "  'LightGBM_BAG_L2/T0': 10.99507737159729,\n",
              "  'NeuralNetFastAI_BAG_L1/T0': 189.58872604370117,\n",
              "  'NeuralNetFastAI_BAG_L2/T0': 76.3839225769043,\n",
              "  'RandomForestMSE_BAG_L1/T0': 9.687071561813354,\n",
              "  'RandomForestMSE_BAG_L2/T0': 30.24317216873169,\n",
              "  'WeightedEnsemble_L2': 0.6182560920715332,\n",
              "  'WeightedEnsemble_L3': 0.33105039596557617,\n",
              "  'XGBoost_BAG_L1/T0': 3.876535415649414,\n",
              "  'XGBoost_BAG_L2/T0': 1.249495029449463},\n",
              " 'model_hyperparams': {'CatBoost_BAG_L1/T0': {'max_base_models': 25,\n",
              "   'max_base_models_per_type': 5,\n",
              "   'save_bag_folds': True,\n",
              "   'use_orig_features': True},\n",
              "  'CatBoost_BAG_L2/T0': {'max_base_models': 25,\n",
              "   'max_base_models_per_type': 5,\n",
              "   'save_bag_folds': True,\n",
              "   'use_orig_features': True},\n",
              "  'ExtraTreesMSE_BAG_L1/T0': {'max_base_models': 25,\n",
              "   'max_base_models_per_type': 5,\n",
              "   'save_bag_folds': True,\n",
              "   'use_child_oof': True,\n",
              "   'use_orig_features': True},\n",
              "  'ExtraTreesMSE_BAG_L2/T0': {'max_base_models': 25,\n",
              "   'max_base_models_per_type': 5,\n",
              "   'save_bag_folds': True,\n",
              "   'use_child_oof': True,\n",
              "   'use_orig_features': True},\n",
              "  'LightGBMLarge_BAG_L1': {'max_base_models': 25,\n",
              "   'max_base_models_per_type': 5,\n",
              "   'save_bag_folds': True,\n",
              "   'use_orig_features': True},\n",
              "  'LightGBMLarge_BAG_L2': {'max_base_models': 25,\n",
              "   'max_base_models_per_type': 5,\n",
              "   'save_bag_folds': True,\n",
              "   'use_orig_features': True},\n",
              "  'LightGBMXT_BAG_L1/T0': {'max_base_models': 25,\n",
              "   'max_base_models_per_type': 5,\n",
              "   'save_bag_folds': True,\n",
              "   'use_orig_features': True},\n",
              "  'LightGBMXT_BAG_L2/T0': {'max_base_models': 25,\n",
              "   'max_base_models_per_type': 5,\n",
              "   'save_bag_folds': True,\n",
              "   'use_orig_features': True},\n",
              "  'LightGBM_BAG_L1/T0': {'max_base_models': 25,\n",
              "   'max_base_models_per_type': 5,\n",
              "   'save_bag_folds': True,\n",
              "   'use_orig_features': True},\n",
              "  'LightGBM_BAG_L1/T1': {'max_base_models': 25,\n",
              "   'max_base_models_per_type': 5,\n",
              "   'save_bag_folds': True,\n",
              "   'use_orig_features': True},\n",
              "  'LightGBM_BAG_L2/T0': {'max_base_models': 25,\n",
              "   'max_base_models_per_type': 5,\n",
              "   'save_bag_folds': True,\n",
              "   'use_orig_features': True},\n",
              "  'NeuralNetFastAI_BAG_L1/T0': {'max_base_models': 25,\n",
              "   'max_base_models_per_type': 5,\n",
              "   'save_bag_folds': True,\n",
              "   'use_orig_features': True},\n",
              "  'NeuralNetFastAI_BAG_L2/T0': {'max_base_models': 25,\n",
              "   'max_base_models_per_type': 5,\n",
              "   'save_bag_folds': True,\n",
              "   'use_orig_features': True},\n",
              "  'RandomForestMSE_BAG_L1/T0': {'max_base_models': 25,\n",
              "   'max_base_models_per_type': 5,\n",
              "   'save_bag_folds': True,\n",
              "   'use_child_oof': True,\n",
              "   'use_orig_features': True},\n",
              "  'RandomForestMSE_BAG_L2/T0': {'max_base_models': 25,\n",
              "   'max_base_models_per_type': 5,\n",
              "   'save_bag_folds': True,\n",
              "   'use_child_oof': True,\n",
              "   'use_orig_features': True},\n",
              "  'WeightedEnsemble_L2': {'max_base_models': 25,\n",
              "   'max_base_models_per_type': 5,\n",
              "   'save_bag_folds': True,\n",
              "   'use_orig_features': False},\n",
              "  'WeightedEnsemble_L3': {'max_base_models': 25,\n",
              "   'max_base_models_per_type': 5,\n",
              "   'save_bag_folds': True,\n",
              "   'use_orig_features': False},\n",
              "  'XGBoost_BAG_L1/T0': {'max_base_models': 25,\n",
              "   'max_base_models_per_type': 5,\n",
              "   'save_bag_folds': True,\n",
              "   'use_orig_features': True},\n",
              "  'XGBoost_BAG_L2/T0': {'max_base_models': 25,\n",
              "   'max_base_models_per_type': 5,\n",
              "   'save_bag_folds': True,\n",
              "   'use_orig_features': True}},\n",
              " 'model_paths': {'CatBoost_BAG_L1/T0': 'bike-Models-all-category-predictClass/models/CatBoost_BAG_L1/T0/',\n",
              "  'CatBoost_BAG_L2/T0': 'bike-Models-all-category-predictClass/models/CatBoost_BAG_L2/T0/',\n",
              "  'ExtraTreesMSE_BAG_L1/T0': 'bike-Models-all-category-predictClass/models/ExtraTreesMSE_BAG_L1/T0/',\n",
              "  'ExtraTreesMSE_BAG_L2/T0': 'bike-Models-all-category-predictClass/models/ExtraTreesMSE_BAG_L2/T0/',\n",
              "  'LightGBMLarge_BAG_L1': 'bike-Models-all-category-predictClass/models/LightGBMLarge_BAG_L1/',\n",
              "  'LightGBMLarge_BAG_L2': 'bike-Models-all-category-predictClass/models/LightGBMLarge_BAG_L2/',\n",
              "  'LightGBMXT_BAG_L1/T0': 'bike-Models-all-category-predictClass/models/LightGBMXT_BAG_L1/T0/',\n",
              "  'LightGBMXT_BAG_L2/T0': 'bike-Models-all-category-predictClass/models/LightGBMXT_BAG_L2/T0/',\n",
              "  'LightGBM_BAG_L1/T0': 'bike-Models-all-category-predictClass/models/LightGBM_BAG_L1/T0/',\n",
              "  'LightGBM_BAG_L1/T1': 'bike-Models-all-category-predictClass/models/LightGBM_BAG_L1/T1/',\n",
              "  'LightGBM_BAG_L2/T0': 'bike-Models-all-category-predictClass/models/LightGBM_BAG_L2/T0/',\n",
              "  'NeuralNetFastAI_BAG_L1/T0': 'bike-Models-all-category-predictClass/models/NeuralNetFastAI_BAG_L1/T0/',\n",
              "  'NeuralNetFastAI_BAG_L2/T0': 'bike-Models-all-category-predictClass/models/NeuralNetFastAI_BAG_L2/T0/',\n",
              "  'RandomForestMSE_BAG_L1/T0': 'bike-Models-all-category-predictClass/models/RandomForestMSE_BAG_L1/T0/',\n",
              "  'RandomForestMSE_BAG_L2/T0': 'bike-Models-all-category-predictClass/models/RandomForestMSE_BAG_L2/T0/',\n",
              "  'WeightedEnsemble_L2': 'bike-Models-all-category-predictClass/models/WeightedEnsemble_L2/',\n",
              "  'WeightedEnsemble_L3': 'bike-Models-all-category-predictClass/models/WeightedEnsemble_L3/',\n",
              "  'XGBoost_BAG_L1/T0': 'bike-Models-all-category-predictClass/models/XGBoost_BAG_L1/T0/',\n",
              "  'XGBoost_BAG_L2/T0': 'bike-Models-all-category-predictClass/models/XGBoost_BAG_L2/T0/'},\n",
              " 'model_performance': {'CatBoost_BAG_L1/T0': -82.52719324376544,\n",
              "  'CatBoost_BAG_L2/T0': -38.62787484622592,\n",
              "  'ExtraTreesMSE_BAG_L1/T0': -41.48534608455675,\n",
              "  'ExtraTreesMSE_BAG_L2/T0': -38.82053676133196,\n",
              "  'LightGBMLarge_BAG_L1': -37.413172210515896,\n",
              "  'LightGBMLarge_BAG_L2': -40.032758529571595,\n",
              "  'LightGBMXT_BAG_L1/T0': -39.90227754367527,\n",
              "  'LightGBMXT_BAG_L2/T0': -39.533921833764786,\n",
              "  'LightGBM_BAG_L1/T0': -39.90703383271498,\n",
              "  'LightGBM_BAG_L1/T1': -40.33309777145715,\n",
              "  'LightGBM_BAG_L2/T0': -38.96098007305627,\n",
              "  'NeuralNetFastAI_BAG_L1/T0': -61.183225433158185,\n",
              "  'NeuralNetFastAI_BAG_L2/T0': -40.07175290304351,\n",
              "  'RandomForestMSE_BAG_L1/T0': -41.97746831638079,\n",
              "  'RandomForestMSE_BAG_L2/T0': -39.262049017261994,\n",
              "  'WeightedEnsemble_L2': -38.501169526234854,\n",
              "  'WeightedEnsemble_L3': -38.35450954149592,\n",
              "  'XGBoost_BAG_L1/T0': -41.35987728400493,\n",
              "  'XGBoost_BAG_L2/T0': -39.44599439365959},\n",
              " 'model_pred_times': {'CatBoost_BAG_L1/T0': 0.16704630851745605,\n",
              "  'CatBoost_BAG_L2/T0': 0.0942997932434082,\n",
              "  'ExtraTreesMSE_BAG_L1/T0': 0.5419712066650391,\n",
              "  'ExtraTreesMSE_BAG_L2/T0': 0.5801644325256348,\n",
              "  'LightGBMLarge_BAG_L1': 0.1307201385498047,\n",
              "  'LightGBMLarge_BAG_L2': 0.0286862850189209,\n",
              "  'LightGBMXT_BAG_L1/T0': 1.8633556365966797,\n",
              "  'LightGBMXT_BAG_L2/T0': 0.25384044647216797,\n",
              "  'LightGBM_BAG_L1/T0': 0.9236860275268555,\n",
              "  'LightGBM_BAG_L1/T1': 1.0125446319580078,\n",
              "  'LightGBM_BAG_L2/T0': 0.17888402938842773,\n",
              "  'NeuralNetFastAI_BAG_L1/T0': 0.932866096496582,\n",
              "  'NeuralNetFastAI_BAG_L2/T0': 0.5112185478210449,\n",
              "  'RandomForestMSE_BAG_L1/T0': 0.505408525466919,\n",
              "  'RandomForestMSE_BAG_L2/T0': 1.1512563228607178,\n",
              "  'WeightedEnsemble_L2': 0.0012965202331542969,\n",
              "  'WeightedEnsemble_L3': 0.0008356571197509766,\n",
              "  'XGBoost_BAG_L1/T0': 0.03318190574645996,\n",
              "  'XGBoost_BAG_L2/T0': 0.012865066528320312},\n",
              " 'model_types': {'CatBoost_BAG_L1/T0': 'StackerEnsembleModel_CatBoost',\n",
              "  'CatBoost_BAG_L2/T0': 'StackerEnsembleModel_CatBoost',\n",
              "  'ExtraTreesMSE_BAG_L1/T0': 'StackerEnsembleModel_XT',\n",
              "  'ExtraTreesMSE_BAG_L2/T0': 'StackerEnsembleModel_XT',\n",
              "  'LightGBMLarge_BAG_L1': 'StackerEnsembleModel_LGB',\n",
              "  'LightGBMLarge_BAG_L2': 'StackerEnsembleModel_LGB',\n",
              "  'LightGBMXT_BAG_L1/T0': 'StackerEnsembleModel_LGB',\n",
              "  'LightGBMXT_BAG_L2/T0': 'StackerEnsembleModel_LGB',\n",
              "  'LightGBM_BAG_L1/T0': 'StackerEnsembleModel_LGB',\n",
              "  'LightGBM_BAG_L1/T1': 'StackerEnsembleModel_LGB',\n",
              "  'LightGBM_BAG_L2/T0': 'StackerEnsembleModel_LGB',\n",
              "  'NeuralNetFastAI_BAG_L1/T0': 'StackerEnsembleModel_NNFastAiTabular',\n",
              "  'NeuralNetFastAI_BAG_L2/T0': 'StackerEnsembleModel_NNFastAiTabular',\n",
              "  'RandomForestMSE_BAG_L1/T0': 'StackerEnsembleModel_RF',\n",
              "  'RandomForestMSE_BAG_L2/T0': 'StackerEnsembleModel_RF',\n",
              "  'WeightedEnsemble_L2': 'WeightedEnsembleModel',\n",
              "  'WeightedEnsemble_L3': 'WeightedEnsembleModel',\n",
              "  'XGBoost_BAG_L1/T0': 'StackerEnsembleModel_XGBoost',\n",
              "  'XGBoost_BAG_L2/T0': 'StackerEnsembleModel_XGBoost'},\n",
              " 'num_bag_folds': 10}"
            ]
          },
          "execution_count": 63,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "predictor_category_features = TabularPredictor.load('bike-Models-all-category-predictClass')\n",
        "predictor_category_features.fit_summary(show_plot=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "CJimlU1thN5m"
      },
      "outputs": [],
      "source": [
        "predictions_category_features = predictor_category_features.predict(test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Cfbo8RzThEWy",
        "outputId": "3c7c3be5-3937-4683-bb83-5bdfeba04bce"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "0    11.305120\n",
            "1     5.503338\n",
            "2     4.174058\n",
            "3     4.982790\n",
            "4     4.804758\n",
            "Name: count, dtype: float32\n",
            "Series([], Name: count, dtype: float32)\n",
            "0\n",
            "0\n"
          ]
        }
      ],
      "source": [
        "# Remember to set all negative values to zero\n",
        "print(predictions_category_features.head())\n",
        "print(predictions_category_features[predictions_category_features < 0])\n",
        "print(len(predictions_category_features[predictions_category_features < 0]))\n",
        "predictions_category_features[predictions_category_features < 0] = 0\n",
        "print(len(predictions_category_features[predictions_category_features < 0]))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "v2MIUoiNhink"
      },
      "outputs": [],
      "source": [
        "# Same submitting predictions\n",
        "submission_category_features = pd.read_csv('./sampleSubmission.csv', parse_dates=['datetime'])\n",
        "submission_category_features[\"count\"] = predictions_category_features\n",
        "submission_category_features.to_csv(\"submission_category_features.csv\", index=False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LgLss6rRhvOK",
        "outputId": "60370803-49ae-4d80-bf86-c786b6e8a276"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Warning: Looks like you're using an outdated API Version, please consider updating (server 1.5.12 / client 1.5.4)\n",
            "100% 188k/188k [00:00<00:00, 460kB/s]\n",
            "Successfully submitted to Bike Sharing Demand"
          ]
        }
      ],
      "source": [
        "!kaggle competitions submit -c bike-sharing-demand -f submission_category_features.csv -m \"prediction_all_category_features\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "b-jlG4Vih5Mm",
        "outputId": "6601bd0d-482c-486e-b0a0-5b9ca1230de0"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Warning: Looks like you're using an outdated API Version, please consider updating (server 1.5.12 / client 1.5.4)\n",
            "fileName                             date                 description                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                status    publicScore  privateScore  \n",
            "-----------------------------------  -------------------  -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------  --------  -----------  ------------  \n",
            "submission_category_features.csv     2021-12-28 14:56:47  prediction_all_category_features                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                           complete  0.45586      0.45586       \n",
            "submission_searcher_new_hpo.csv      2021-12-28 14:05:53  searcher_new_hpo                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                           complete  0.46345      0.46345       \n",
            "submission_new_hpo.csv               2021-12-28 14:04:51  searcher_new_hpo                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                           complete  0.49169      0.49169       \n",
            "close failed in file object destructor:\n",
            "sys.excepthook is missing\n",
            "lost sys.stderr\n"
          ]
        }
      ],
      "source": [
        "!kaggle competitions submissions -c bike-sharing-demand | tail -n +1 | head -n 6"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "NhSBANk-pWHi"
      },
      "outputs": [],
      "source": [
        "predictor_new_features = predictor_independent_features\n",
        "predictor_new_hpo = predictor_category_features"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2ObSKW-ORiye"
      },
      "source": [
        "#### New Score of ` 0.45586 `"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "a0hudZBDRiye"
      },
      "source": [
        "## Step 7: Write a Report\n",
        "### Refer to the markdown file for the full report\n",
        "### Creating plots and table for report"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 389
        },
        "id": "94JENWcmRiyf",
        "jupyter": {
          "source_hidden": true
        },
        "outputId": "bdf54a80-fffe-42ca-f4ac-527a9cd94db2"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Taking the top model score from each training run and creating a line plot to show improvement\n",
        "# You can create these in the notebook and save them to PNG or use some other tool (e.g. google sheets, excel)\n",
        "fig = pd.DataFrame(\n",
        "    {\n",
        "        \"model\": [ \"initial\", \"add_features\", \"hpo\"],\n",
        "        \"score\": [-predictor.leaderboard( silent=True)['score_val'][0],\n",
        "                  -predictor_new_features.leaderboard( silent=True)['score_val'][0],\n",
        "                  -predictor_new_hpo.leaderboard( silent=True)['score_val'][0]]\n",
        "    }\n",
        ").plot(x=\"model\", y=\"score\", figsize=(8, 6)).get_figure()\n",
        "fig.savefig('model_train_score.png')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 390
        },
        "id": "1EfX4szkRiyf",
        "outputId": "e12b6e11-8c55-42fa-89c1-2cad0b0f990f"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Take the 3 kaggle scores and creating a line plot to show improvement\n",
        "\n",
        "fig = pd.DataFrame(\n",
        "    {\n",
        "        \"test_eval\": [\"initial\", \"add_features\", \"hpo\"],\n",
        "        \"score\": [1.39407,\n",
        "                  0.47959,\n",
        "                  0.45586]\n",
        "    }\n",
        ").plot(x=\"test_eval\", y=\"score\", figsize=(8, 6)).get_figure()\n",
        "fig.savefig('model_test_score.png')\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
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      "source": [
        "### Hyperparameter table"
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              "      <th>model</th>\n",
              "      <th>hpo1</th>\n",
              "      <th>hpo2</th>\n",
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              "      <th>0</th>\n",
              "      <td>initial</td>\n",
              "      <td>problem_type='regression'</td>\n",
              "      <td>learner_kwargs={'ignored_columns':['casual' , 'registered']}</td>\n",
              "      <td>time_limit=600</td>\n",
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              "      <td>add_features</td>\n",
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              "      <td>Changing the format of 'season' and 'weather' to category</td>\n",
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              "      <td>Changing the format of 'workingday' and 'holiday' to category</td>\n",
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              "          model  ...    score\n",
              "0       initial  ...  1.39407\n",
              "1  add_features  ...  0.47959\n",
              "2           hpo  ...  0.45586\n",
              "\n",
              "[3 rows x 5 columns]"
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        "# The 3 hyperparameters we tuned with the kaggle score as the result\n",
        "pd.DataFrame({\n",
        "    \"model\": [\"initial\", \"add_features\", \"hpo\"],\n",
        "    \"hpo1\": [\"problem_type='regression'\", \n",
        "             \"Added two distinct features 'date' and 'time' to train data\",\n",
        "             \"hyperparameter_tune_kwargs={'num_trials':5, 'scheduler' : 'local',  'searcher':'auto}\"],\n",
        "    \"hpo2\": [\"learner_kwargs={'ignored_columns':['casual' , 'registered']}\", \n",
        "             \"learner_kwargs={'ignored_columns':['casual', 'datetime' , 'registered']}\",\n",
        "             \"Changing the format of 'workingday' and 'holiday' to category\"],\n",
        "    \"hpo3\": [\"time_limit=600\", \n",
        "             \"Changing the format of 'season' and 'weather' to category\",\n",
        "             \"excluded_model_types = ['KNN', 'NN']\"],\n",
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