paschal_ugwu-project-template.ipynb

{"cells":[{"cell_type":"markdown","metadata":{"id":"XvrYuWebZhiO"},"source":["# Predict Bike Sharing Demand with AutoGluon Template"]},{"cell_type":"markdown","metadata":{"id":"ThYPHdo3Zhie"},"source":["## Project: Predict Bike Sharing Demand with AutoGluon\n","This notebook is a template with each step that you need to complete for the project.\n","\n","Please fill in your code where there are explicit `?` markers in the notebook. You are welcome to add more cells and code as you see fit.\n","\n","Once you have completed all the code implementations, please export your notebook as a HTML file so the reviews can view your code. Make sure you have all outputs correctly outputted.\n","\n","`File-> Export Notebook As... -> Export Notebook as HTML`\n","\n","There is a writeup to complete as well after all code implememtation is done. Please answer all questions and attach the necessary tables and charts. You can complete the writeup in either markdown or PDF.\n","\n","Completing the code template and writeup template will cover all of the rubric points for this project.\n","\n","The rubric contains \"Stand Out Suggestions\" for enhancing the project beyond the minimum requirements. The stand out suggestions are optional. If you decide to pursue the \"stand out suggestions\", you can include the code in this notebook and also discuss the results in the writeup file."]},{"cell_type":"markdown","metadata":{"id":"O4jcoyO5Zhik"},"source":["## Step 1: Create an account with Kaggle"]},{"cell_type":"markdown","metadata":{"id":"pG9xIN_MZhim"},"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","source":["1. Open account settings.\n","2. Scroll down to API and click Create New API Token.\n","3. Open up `kaggle.json` and use the username and key."],"metadata":{"id":"iW76B3jxHbbQ"}},{"cell_type":"markdown","metadata":{"id":"-yn9RYASZhiy"},"source":["## Step 2: Download the Kaggle dataset using the kaggle python library"]},{"cell_type":"markdown","metadata":{"id":"F5NwhHj7Zhi2"},"source":["### Open up Sagemaker Studio and use starter template"]},{"cell_type":"markdown","metadata":{"id":"Chaa5AUZZhi_"},"source":["1. Notebook should be using a `ml.t3.medium` instance (2 vCPU + 4 GiB)\n","2. Notebook should be using kernal: `Python 3 (MXNet 1.8 Python 3.7 CPU Optimized)`"]},{"cell_type":"markdown","metadata":{"id":"7BgcLPMoZhjB"},"source":["### Install packages"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"HIzrE1YNZhjE","colab":{"base_uri":"https://localhost:8080/","height":1000},"outputId":"87b93569-d69d-4418-ee82-319b565962f4","executionInfo":{"status":"ok","timestamp":1714511685852,"user_tz":-60,"elapsed":51020,"user":{"displayName":"Paschal Ugwu","userId":"02920440952136523881"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["Requirement already satisfied: pip in /usr/local/lib/python3.10/dist-packages (24.0)\n","\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n","\u001b[0mRequirement already satisfied: setuptools in /usr/local/lib/python3.10/dist-packages (60.2.0)\n","Collecting setuptools\n","  Using cached setuptools-69.5.1-py3-none-any.whl.metadata (6.2 kB)\n","Requirement already satisfied: wheel in /usr/local/lib/python3.10/dist-packages (0.43.0)\n","Using cached setuptools-69.5.1-py3-none-any.whl (894 kB)\n","Installing collected packages: setuptools\n","  Attempting uninstall: setuptools\n","    Found existing installation: setuptools 60.2.0\n","    Uninstalling setuptools-60.2.0:\n","      Successfully uninstalled setuptools-60.2.0\n","\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n","ipython 7.34.0 requires jedi>=0.16, which is not installed.\n","openxlab 0.0.38 requires setuptools~=60.2.0, but you have setuptools 69.5.1 which is incompatible.\u001b[0m\u001b[31m\n","\u001b[0mSuccessfully installed setuptools-69.5.1\n","\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n","\u001b[0m"]},{"output_type":"display_data","data":{"application/vnd.colab-display-data+json":{"pip_warning":{"packages":["_distutils_hack","pkg_resources","setuptools"]},"id":"56abc5261f36444d9491dd4dfd52299a"}},"metadata":{}},{"output_type":"stream","name":"stdout","text":["Requirement already satisfied: mxnet<2.0.0 in /usr/local/lib/python3.10/dist-packages (1.9.1)\n","Requirement already satisfied: bokeh==2.0.1 in /usr/local/lib/python3.10/dist-packages (2.0.1)\n","Requirement already satisfied: PyYAML>=3.10 in /usr/local/lib/python3.10/dist-packages (from bokeh==2.0.1) (6.0.1)\n","Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.10/dist-packages (from bokeh==2.0.1) (2.8.2)\n","Requirement already satisfied: Jinja2>=2.7 in /usr/local/lib/python3.10/dist-packages (from bokeh==2.0.1) (3.1.3)\n","Requirement already satisfied: numpy>=1.11.3 in /usr/local/lib/python3.10/dist-packages (from bokeh==2.0.1) (1.25.2)\n","Requirement already satisfied: pillow>=4.0 in /usr/local/lib/python3.10/dist-packages (from bokeh==2.0.1) (10.0.1)\n","Requirement already satisfied: packaging>=16.8 in /usr/local/lib/python3.10/dist-packages (from bokeh==2.0.1) (24.0)\n","Requirement already satisfied: tornado>=5 in /usr/local/lib/python3.10/dist-packages (from bokeh==2.0.1) (6.3.3)\n","Requirement already satisfied: typing-extensions>=3.7.4 in /usr/local/lib/python3.10/dist-packages (from bokeh==2.0.1) (4.11.0)\n","Requirement already satisfied: requests<3,>=2.20.0 in /usr/local/lib/python3.10/dist-packages (from mxnet<2.0.0) (2.28.2)\n","Requirement already satisfied: graphviz<0.9.0,>=0.8.1 in /usr/local/lib/python3.10/dist-packages (from mxnet<2.0.0) (0.8.4)\n","Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from Jinja2>=2.7->bokeh==2.0.1) (2.1.5)\n","Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.1->bokeh==2.0.1) (1.16.0)\n","Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2.20.0->mxnet<2.0.0) (3.3.2)\n","Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2.20.0->mxnet<2.0.0) (3.7)\n","Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2.20.0->mxnet<2.0.0) (1.26.18)\n","Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2.20.0->mxnet<2.0.0) (2024.2.2)\n","\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n","\u001b[0mRequirement already satisfied: autogluon in /usr/local/lib/python3.10/dist-packages (1.1.0)\n","Requirement already satisfied: autogluon.core==1.1.0 in /usr/local/lib/python3.10/dist-packages (from autogluon.core[all]==1.1.0->autogluon) (1.1.0)\n","Requirement already satisfied: autogluon.features==1.1.0 in /usr/local/lib/python3.10/dist-packages (from autogluon) (1.1.0)\n","Requirement already satisfied: autogluon.tabular==1.1.0 in /usr/local/lib/python3.10/dist-packages (from autogluon.tabular[all]==1.1.0->autogluon) (1.1.0)\n","Requirement already satisfied: autogluon.multimodal==1.1.0 in /usr/local/lib/python3.10/dist-packages (from autogluon) (1.1.0)\n","Requirement already satisfied: autogluon.timeseries==1.1.0 in /usr/local/lib/python3.10/dist-packages (from autogluon.timeseries[all]==1.1.0->autogluon) (1.1.0)\n","Requirement already satisfied: 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(4.65.2)\n","Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from autogluon.core==1.1.0->autogluon.core[all]==1.1.0->autogluon) (2.28.2)\n","Requirement already satisfied: matplotlib in /usr/local/lib/python3.10/dist-packages (from autogluon.core==1.1.0->autogluon.core[all]==1.1.0->autogluon) (3.7.1)\n","Requirement already satisfied: boto3<2,>=1.10 in /usr/local/lib/python3.10/dist-packages (from autogluon.core==1.1.0->autogluon.core[all]==1.1.0->autogluon) (1.34.95)\n","Requirement already satisfied: autogluon.common==1.1.0 in /usr/local/lib/python3.10/dist-packages (from autogluon.core==1.1.0->autogluon.core[all]==1.1.0->autogluon) (1.1.0)\n","Requirement already satisfied: ray<2.11,>=2.10.0 in /usr/local/lib/python3.10/dist-packages (from ray[default,tune]<2.11,>=2.10.0; extra == \"all\"->autogluon.core[all]==1.1.0->autogluon) (2.10.0)\n","Requirement already satisfied: hyperopt<0.2.8,>=0.2.7 in /usr/local/lib/python3.10/dist-packages (from 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openmim<0.4.0,>=0.3.7 in /usr/local/lib/python3.10/dist-packages (from autogluon.multimodal==1.1.0->autogluon) (0.3.9)\n","Requirement already satisfied: defusedxml<0.7.2,>=0.7.1 in /usr/local/lib/python3.10/dist-packages (from autogluon.multimodal==1.1.0->autogluon) (0.7.1)\n","Requirement already satisfied: jinja2<3.2,>=3.0.3 in /usr/local/lib/python3.10/dist-packages (from autogluon.multimodal==1.1.0->autogluon) (3.1.3)\n","Requirement already satisfied: tensorboard<3,>=2.9 in /usr/local/lib/python3.10/dist-packages (from autogluon.multimodal==1.1.0->autogluon) (2.15.2)\n","Requirement already satisfied: pytesseract<0.3.11,>=0.3.9 in /usr/local/lib/python3.10/dist-packages (from autogluon.multimodal==1.1.0->autogluon) (0.3.10)\n","Requirement already satisfied: nvidia-ml-py3==7.352.0 in /usr/local/lib/python3.10/dist-packages (from autogluon.multimodal==1.1.0->autogluon) (7.352.0)\n","Requirement already satisfied: pdf2image<1.19,>=1.17.0 in /usr/local/lib/python3.10/dist-packages (from autogluon.multimodal==1.1.0->autogluon) (1.17.0)\n","Requirement already satisfied: xgboost<2.1,>=1.6 in /usr/local/lib/python3.10/dist-packages (from autogluon.tabular[all]==1.1.0->autogluon) (2.0.3)\n","Requirement already satisfied: fastai<2.8,>=2.3.1 in /usr/local/lib/python3.10/dist-packages (from autogluon.tabular[all]==1.1.0->autogluon) (2.7.14)\n","Requirement already satisfied: lightgbm<4.4,>=3.3 in /usr/local/lib/python3.10/dist-packages (from autogluon.tabular[all]==1.1.0->autogluon) (4.1.0)\n","Requirement already satisfied: catboost<1.3,>=1.1 in /usr/local/lib/python3.10/dist-packages (from autogluon.tabular[all]==1.1.0->autogluon) (1.2.5)\n","\u001b[31mERROR: Operation cancelled by user\u001b[0m\u001b[31m\n","\u001b[0m"]}],"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":"Zn-ZF1GTZhjG"},"source":["### Setup Kaggle API Key"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"BFsE2UmsZhjI","executionInfo":{"status":"ok","timestamp":1714511718915,"user_tz":-60,"elapsed":22978,"user":{"displayName":"Paschal Ugwu","userId":"02920440952136523881"}},"colab":{"base_uri":"https://localhost:8080/"},"outputId":"6db4c2d6-b857-44c6-801f-6031d7d5e7d7"},"outputs":[{"output_type":"stream","name":"stdout","text":["Requirement already satisfied: kaggle in /usr/local/lib/python3.10/dist-packages (1.6.12)\n","Requirement already satisfied: six>=1.10 in /usr/local/lib/python3.10/dist-packages (from kaggle) (1.16.0)\n","Requirement already satisfied: certifi>=2023.7.22 in /usr/local/lib/python3.10/dist-packages (from kaggle) (2024.2.2)\n","Requirement already satisfied: python-dateutil in /usr/local/lib/python3.10/dist-packages (from kaggle) (2.8.2)\n","Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from kaggle) (2.28.2)\n","Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from kaggle) (4.65.2)\n","Requirement already satisfied: python-slugify in /usr/local/lib/python3.10/dist-packages (from kaggle) (8.0.4)\n","Requirement already satisfied: urllib3 in /usr/local/lib/python3.10/dist-packages (from kaggle) (1.26.18)\n","Requirement already satisfied: bleach in /usr/local/lib/python3.10/dist-packages (from kaggle) (6.1.0)\n","Requirement already satisfied: webencodings in /usr/local/lib/python3.10/dist-packages (from bleach->kaggle) (0.5.1)\n","Requirement already satisfied: text-unidecode>=1.3 in /usr/local/lib/python3.10/dist-packages (from python-slugify->kaggle) (1.3)\n","Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->kaggle) (3.3.2)\n","Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->kaggle) (3.7)\n","\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n","\u001b[0m"]}],"source":["# Install kaggle\n","!pip install kaggle\n","\n","# Create the .kaggle directory in /root/\n","!mkdir -p ~/.kaggle\n","\n","# Create an empty kaggle.json file in the new .kaggle directory\n","!touch ~/.kaggle/kaggle.json\n","\n","# Set appropriate permissions for the Kaggle API key file to ensure security\n","!chmod 600 ~/.kaggle/kaggle.json"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"deQvVdMZZhjK"},"outputs":[],"source":["# Fill in your user name and key from creating the kaggle account and API token file\n","import os\n","import json\n","\n","kaggle_username = \"my_kaggle_username\"\n","kaggle_key = \"my_kaggle_api-key\"\n","\n","# Get the path to the home directory\n","home = os.path.expanduser(\"~\")\n","\n","# Create the .kaggle directory in the home directory\n","!mkdir -p {home}/.kaggle\n","\n","# Set appropriate permissions for the Kaggle API key file to ensure security\n","!chmod 600 {home}/.kaggle/kaggle.json\n","\n","# Save API token the kaggle.json file\n","with open(os.path.join(home, \".kaggle/kaggle.json\"), \"w\") as f:\n","    f.write(json.dumps({\"username\": kaggle_username, \"key\": kaggle_key}))"]},{"cell_type":"markdown","metadata":{"id":"7eI_laYTZhjM"},"source":["### Download and explore dataset"]},{"cell_type":"markdown","metadata":{"id":"dBJvgX5tZhjN"},"source":["### Go to the bike sharing demand competition and agree to the terms"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"cQ0EP95oZhjN","executionInfo":{"status":"ok","timestamp":1714511746648,"user_tz":-60,"elapsed":2803,"user":{"displayName":"Paschal Ugwu","userId":"02920440952136523881"}},"colab":{"base_uri":"https://localhost:8080/"},"outputId":"695f6d40-ba6c-4451-d750-093c74acc300"},"outputs":[{"output_type":"stream","name":"stdout","text":["bike-sharing-demand.zip: Skipping, found more recently modified local copy (use --force to force download)\n","Archive:  bike-sharing-demand.zip\n","  inflating: sampleSubmission.csv    \n","  inflating: test.csv                \n","  inflating: train.csv               \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":{"id":"ZDA9h2d-ZhjO"},"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":"lxHBTIlFZhjP","executionInfo":{"status":"ok","timestamp":1714511755236,"user_tz":-60,"elapsed":416,"user":{"displayName":"Paschal Ugwu","userId":"02920440952136523881"}},"outputId":"806c6cca-65d5-4c2f-f2ac-8188efb95606"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["             datetime  season  holiday  workingday  weather  temp   atemp  \\\n","0 2011-01-01 00:00:00       1        0           0        1  9.84  14.395   \n","1 2011-01-01 01:00:00       1        0           0        1  9.02  13.635   \n","2 2011-01-01 02:00:00       1        0           0        1  9.02  13.635   \n","3 2011-01-01 03:00:00       1        0           0        1  9.84  14.395   \n","4 2011-01-01 04:00:00       1        0           0        1  9.84  14.395   \n","\n","   humidity  windspeed  casual  registered  count  \n","0        81        0.0       3          13     16  \n","1        80        0.0       8          32     40  \n","2        80        0.0       5          27     32  \n","3        75        0.0       3          10     13  \n","4        75        0.0       0           1      1  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\"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"holiday\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"workingday\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"weather\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 4,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          2,\n          4\n 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\"num_unique_values\": 89,\n        \"samples\": [\n          29,\n          61\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"windspeed\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8.164537326838689,\n        \"min\": 0.0,\n        \"max\": 56.9969,\n        \"num_unique_values\": 28,\n        \"samples\": [\n          22.0028,\n          43.0006\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"casual\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 49,\n        \"min\": 0,\n        \"max\": 367,\n        \"num_unique_values\": 309,\n        \"samples\": [\n          287,\n          47\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"registered\",\n      \"properties\": {\n        \"dtype\": \"number\",\n   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dataset\n","train.head()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"1uiGo63CZhjR","colab":{"base_uri":"https://localhost:8080/","height":443},"executionInfo":{"status":"ok","timestamp":1714511758016,"user_tz":-60,"elapsed":428,"user":{"displayName":"Paschal Ugwu","userId":"02920440952136523881"}},"outputId":"f7ba3ece-56c0-4cc4-d31f-66fd0e315555"},"outputs":[{"output_type":"stream","name":"stdout","text":["Summary statistics of train dataset:\n"]},{"output_type":"execute_result","data":{"text/plain":["                            datetime        season       holiday  \\\n","count                          10886  10886.000000  10886.000000   \n","mean   2011-12-27 05:56:22.399411968      2.506614      0.028569   \n","min              2011-01-01 00:00:00      1.000000      0.000000   \n","25%              2011-07-02 07:15:00      2.000000      0.000000   \n","50%              2012-01-01 20:30:00      3.000000      0.000000   \n","75%              2012-07-01 12:45:00      4.000000      0.000000   \n","max              2012-12-19 23:00:00      4.000000      1.000000   \n","std                              NaN      1.116174      0.166599   \n","\n","         workingday       weather         temp         atemp      humidity  \\\n","count  10886.000000  10886.000000  10886.00000  10886.000000  10886.000000   \n","mean       0.680875      1.418427     20.23086     23.655084     61.886460   \n","min        0.000000      1.000000      0.82000      0.760000      0.000000   \n","25%        0.000000      1.000000     13.94000     16.665000     47.000000   \n","50%        1.000000      1.000000     20.50000     24.240000     62.000000   \n","75%        1.000000      2.000000     26.24000     31.060000     77.000000   \n","max        1.000000      4.000000     41.00000     45.455000    100.000000   \n","std        0.466159      0.633839      7.79159      8.474601     19.245033   \n","\n","          windspeed        casual    registered         count  \n","count  10886.000000  10886.000000  10886.000000  10886.000000  \n","mean      12.799395     36.021955    155.552177    191.574132  \n","min        0.000000      0.000000      0.000000      1.000000  \n","25%        7.001500      4.000000     36.000000     42.000000  \n","50%       12.998000     17.000000    118.000000    145.000000  \n","75%       16.997900     49.000000    222.000000    284.000000  \n","max       56.996900    367.000000    886.000000    977.000000  \n","std        8.164537     49.960477    151.039033    181.144454  "],"text/html":["\n","  <div id=\"df-2fbaac78-b753-4cbe-a0eb-f338de070626\" class=\"colab-df-container\">\n","    <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>datetime</th>\n","      <th>season</th>\n","      <th>holiday</th>\n","      <th>workingday</th>\n","      <th>weather</th>\n","      <th>temp</th>\n","      <th>atemp</th>\n","      <th>humidity</th>\n","      <th>windspeed</th>\n","      <th>casual</th>\n","      <th>registered</th>\n","      <th>count</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>count</th>\n","      <td>10886</td>\n","      <td>10886.000000</td>\n","      <td>10886.000000</td>\n","      <td>10886.000000</td>\n","      <td>10886.000000</td>\n","      <td>10886.00000</td>\n","      <td>10886.000000</td>\n","      <td>10886.000000</td>\n","      <td>10886.000000</td>\n","      <td>10886.000000</td>\n","      <td>10886.000000</td>\n","      <td>10886.000000</td>\n","    </tr>\n","    <tr>\n","      <th>mean</th>\n","      <td>2011-12-27 05:56:22.399411968</td>\n","      <td>2.506614</td>\n","      <td>0.028569</td>\n","      <td>0.680875</td>\n","      <td>1.418427</td>\n","      <td>20.23086</td>\n","      <td>23.655084</td>\n","      <td>61.886460</td>\n","      <td>12.799395</td>\n","      <td>36.021955</td>\n","      <td>155.552177</td>\n","      <td>191.574132</td>\n","    </tr>\n","    <tr>\n","      <th>min</th>\n","      <td>2011-01-01 00:00:00</td>\n","      <td>1.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>1.000000</td>\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>2011-07-02 07:15:00</td>\n","      <td>2.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>1.000000</td>\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>2012-01-01 20:30:00</td>\n","      <td>3.000000</td>\n","      <td>0.000000</td>\n","      <td>1.000000</td>\n","      <td>1.000000</td>\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>2012-07-01 12:45:00</td>\n","      <td>4.000000</td>\n","      <td>0.000000</td>\n","      <td>1.000000</td>\n","      <td>2.000000</td>\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>2012-12-19 23:00:00</td>\n","      <td>4.000000</td>\n","      <td>1.000000</td>\n","      <td>1.000000</td>\n","      <td>4.000000</td>\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","    </tr>\n","    <tr>\n","      <th>std</th>\n","      <td>NaN</td>\n","      <td>1.116174</td>\n","      <td>0.166599</td>\n","      <td>0.466159</td>\n","      <td>0.633839</td>\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","  </tbody>\n","</table>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-2fbaac78-b753-4cbe-a0eb-f338de070626')\"\n","            title=\"Convert this dataframe to an interactive 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2011-01-20 01:00:00       1        0           1        1  10.66  13.635   \n","2 2011-01-20 02:00:00       1        0           1        1  10.66  13.635   \n","3 2011-01-20 03:00:00       1        0           1        1  10.66  12.880   \n","4 2011-01-20 04:00:00       1        0           1        1  10.66  12.880   \n","\n","   humidity  windspeed  \n","0        56    26.0027  \n","1        56     0.0000  \n","2        56     0.0000  \n","3        56    11.0014  \n","4        56    11.0014  "],"text/html":["\n","  <div id=\"df-988d4fa5-28cc-4896-a95e-e77117739109\" class=\"colab-df-container\">\n","    <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: 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dataset\n","test.head()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"aoWduMPKZhjU","colab":{"base_uri":"https://localhost:8080/","height":206},"executionInfo":{"status":"ok","timestamp":1714511762368,"user_tz":-60,"elapsed":466,"user":{"displayName":"Paschal Ugwu","userId":"02920440952136523881"}},"outputId":"498092ed-872e-4d08-9480-2545b3c5efd2"},"outputs":[{"output_type":"execute_result","data":{"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"],"text/html":["\n","  <div id=\"df-f7ed1de6-2580-4897-b5c0-e625945538e5\" class=\"colab-df-container\">\n","    <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        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Models will be saved in: \"AutogluonModels/ag-20240430_211606\"\n","Presets specified: ['best_quality']\n","Setting dynamic_stacking from 'auto' to True. Reason: Enable dynamic_stacking when use_bag_holdout is disabled. (use_bag_holdout=False)\n","Stack configuration (auto_stack=True): num_stack_levels=1, num_bag_folds=8, num_bag_sets=1\n","Dynamic stacking is enabled (dynamic_stacking=True). AutoGluon will try to determine whether the input data is affected by stacked overfitting and enable or disable stacking as a consequence.\n","Detecting stacked overfitting by sub-fitting AutoGluon on the input data. That is, copies of AutoGluon will be sub-fit on subset(s) of the data. Then, the holdout validation data is used to detect stacked overfitting.\n","Sub-fit(s) time limit is: 600 seconds.\n","Starting holdout-based sub-fit for dynamic stacking. Context path is: AutogluonModels/ag-20240430_211606/ds_sub_fit/sub_fit_ho.\n","Running the sub-fit in a ray process to avoid memory leakage.\n","Spend 215 seconds for the sub-fit(s) during dynamic stacking.\n","Time left for full fit of AutoGluon: 385 seconds.\n","Starting full fit now with num_stack_levels 1.\n","Beginning AutoGluon training ... Time limit = 385s\n","AutoGluon will save models to \"AutogluonModels/ag-20240430_211606\"\n","=================== System Info ===================\n","AutoGluon Version:  1.1.0\n","Python Version:     3.10.12\n","Operating System:   Linux\n","Platform Machine:   x86_64\n","Platform Version:   #1 SMP PREEMPT_DYNAMIC Sat Nov 18 15:31:17 UTC 2023\n","CPU Count:          2\n","Memory Avail:       10.55 GB / 12.67 GB (83.2%)\n","Disk Space Avail:   76.01 GB / 107.72 GB (70.6%)\n","===================================================\n","Train Data Rows:    10886\n","Train Data Columns: 9\n","Label Column:       count\n","Problem Type:       regression\n","Preprocessing data ...\n","Using Feature Generators to preprocess the data ...\n","Fitting AutoMLPipelineFeatureGenerator...\n","\tAvailable Memory:                    10799.61 MB\n","\tTrain Data (Original)  Memory Usage: 0.75 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","\tStage 5 Generators:\n","\t\tFitting DropDuplicatesFeatureGenerator...\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']) : 5 | ['datetime', 'datetime.year', 'datetime.month', 'datetime.day', 'datetime.dayofweek']\n","\t0.2s = Fit runtime\n","\t9 features in original data used to generate 13 features in processed data.\n","\tTrain Data (Processed) Memory Usage: 0.93 MB (0.0% of available memory)\n","Data preprocessing and feature engineering runtime = 0.3s ...\n","AutoGluon will gauge predictive performance using evaluation metric: 'root_mean_squared_error'\n","\tThis metric's sign has been flipped to adhere to being higher_is_better. The metric score can be multiplied by -1 to get the metric value.\n","\tTo change this, specify the eval_metric parameter of Predictor()\n","Large model count detected (112 configs) ... Only displaying the first 3 models of each family. To see all, set `verbosity=3`.\n","User-specified model hyperparameters to be fit:\n","{\n","\t'NN_TORCH': [{}, {'activation': 'elu', 'dropout_prob': 0.10077639529843717, 'hidden_size': 108, 'learning_rate': 0.002735937344002146, 'num_layers': 4, 'use_batchnorm': True, 'weight_decay': 1.356433327634438e-12, 'ag_args': {'name_suffix': '_r79', 'priority': -2}}, {'activation': 'elu', 'dropout_prob': 0.11897478034205347, 'hidden_size': 213, 'learning_rate': 0.0010474382260641949, 'num_layers': 4, 'use_batchnorm': False, 'weight_decay': 5.594471067786272e-10, 'ag_args': {'name_suffix': '_r22', 'priority': -7}}],\n","\t'GBM': [{'extra_trees': True, 'ag_args': {'name_suffix': 'XT'}}, {}, 'GBMLarge'],\n","\t'CAT': [{}, {'depth': 6, 'grow_policy': 'SymmetricTree', 'l2_leaf_reg': 2.1542798306067823, 'learning_rate': 0.06864209415792857, 'max_ctr_complexity': 4, 'one_hot_max_size': 10, 'ag_args': {'name_suffix': '_r177', 'priority': -1}}, {'depth': 8, 'grow_policy': 'Depthwise', 'l2_leaf_reg': 2.7997999596449104, 'learning_rate': 0.031375015734637225, 'max_ctr_complexity': 2, 'one_hot_max_size': 3, 'ag_args': {'name_suffix': '_r9', 'priority': -5}}],\n","\t'XGB': [{}, {'colsample_bytree': 0.6917311125174739, 'enable_categorical': False, 'learning_rate': 0.018063876087523967, 'max_depth': 10, 'min_child_weight': 0.6028633586934382, 'ag_args': {'name_suffix': '_r33', 'priority': -8}}, {'colsample_bytree': 0.6628423832084077, 'enable_categorical': False, 'learning_rate': 0.08775715546881824, 'max_depth': 5, 'min_child_weight': 0.6294123374222513, 'ag_args': {'name_suffix': '_r89', 'priority': -16}}],\n","\t'FASTAI': [{}, {'bs': 256, 'emb_drop': 0.5411770367537934, 'epochs': 43, 'layers': [800, 400], 'lr': 0.01519848858318159, 'ps': 0.23782946566604385, 'ag_args': {'name_suffix': '_r191', 'priority': -4}}, {'bs': 2048, 'emb_drop': 0.05070411322605811, 'epochs': 29, 'layers': [200, 100], 'lr': 0.08974235041576624, 'ps': 0.10393466140748028, 'ag_args': {'name_suffix': '_r102', 'priority': -11}}],\n","\t'RF': [{'criterion': 'gini', 'ag_args': {'name_suffix': 'Gini', 'problem_types': ['binary', 'multiclass']}}, {'criterion': 'entropy', 'ag_args': {'name_suffix': 'Entr', 'problem_types': ['binary', 'multiclass']}}, {'criterion': 'squared_error', 'ag_args': {'name_suffix': 'MSE', 'problem_types': ['regression', 'quantile']}}],\n","\t'XT': [{'criterion': 'gini', 'ag_args': {'name_suffix': 'Gini', 'problem_types': ['binary', 'multiclass']}}, {'criterion': 'entropy', 'ag_args': {'name_suffix': 'Entr', 'problem_types': ['binary', 'multiclass']}}, {'criterion': 'squared_error', 'ag_args': {'name_suffix': 'MSE', 'problem_types': ['regression', 'quantile']}}],\n","\t'KNN': [{'weights': 'uniform', 'ag_args': {'name_suffix': 'Unif'}}, {'weights': 'distance', 'ag_args': {'name_suffix': 'Dist'}}],\n","}\n","AutoGluon will fit 2 stack levels (L1 to L2) ...\n","Fitting 108 L1 models ...\n","Fitting model: KNeighborsUnif_BAG_L1 ... Training model for up to 256.39s of the 384.64s of remaining time.\n","\t-101.5462\t = Validation score   (-root_mean_squared_error)\n","\t0.07s\t = Training   runtime\n","\t0.09s\t = Validation runtime\n","Fitting model: KNeighborsDist_BAG_L1 ... Training model for up to 256.17s of the 384.43s of remaining time.\n","\t-84.1251\t = Validation score   (-root_mean_squared_error)\n","\t0.06s\t = Training   runtime\n","\t0.09s\t = Validation runtime\n","Fitting model: LightGBMXT_BAG_L1 ... Training model for up to 255.95s of the 384.2s of remaining time.\n","\tFitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy (2 workers, per: cpus=1, gpus=0, memory=0.08%)\n","\t-131.4609\t = Validation score   (-root_mean_squared_error)\n","\t102.97s\t = Training   runtime\n","\t43.94s\t = Validation runtime\n","Fitting model: LightGBM_BAG_L1 ... Training model for up to 139.0s of the 267.25s of remaining time.\n","\tFitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy (2 workers, per: cpus=1, gpus=0, memory=0.08%)\n","\t-131.0542\t = Validation score   (-root_mean_squared_error)\n","\t46.7s\t = Training   runtime\n","\t4.15s\t = Validation runtime\n","Fitting model: RandomForestMSE_BAG_L1 ... Training model for up to 86.0s of the 214.25s of remaining time.\n","\t-116.5484\t = Validation score   (-root_mean_squared_error)\n","\t23.65s\t = Training   runtime\n","\t1.08s\t = Validation runtime\n","Fitting model: CatBoost_BAG_L1 ... Training model for up to 59.0s of the 187.26s of remaining time.\n","\tFitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy (2 workers, per: cpus=1, gpus=0, memory=0.10%)\n","\t-132.1257\t = Validation score   (-root_mean_squared_error)\n","\t63.26s\t = Training   runtime\n","\t0.14s\t = Validation runtime\n","Fitting model: WeightedEnsemble_L2 ... Training model for up to 360.0s of the 120.1s of remaining time.\n","\tEnsemble Weights: {'KNeighborsDist_BAG_L1': 1.0}\n","\t-84.1251\t = Validation score   (-root_mean_squared_error)\n","\t0.05s\t = Training   runtime\n","\t0.0s\t = Validation runtime\n","Fitting 106 L2 models ...\n","Fitting model: LightGBMXT_BAG_L2 ... Training model for up to 120.0s of the 119.94s of remaining time.\n","\tFitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy (2 workers, per: cpus=1, gpus=0, memory=0.10%)\n","\t-60.7117\t = Validation score   (-root_mean_squared_error)\n","\t82.19s\t = Training   runtime\n","\t5.34s\t = Validation runtime\n","Fitting model: LightGBM_BAG_L2 ... Training model for up to 33.18s of the 33.1s of remaining time.\n","\tFitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy (2 workers, per: cpus=1, gpus=0, memory=0.10%)\n","\t-55.1061\t = Validation score   (-root_mean_squared_error)\n","\t42.29s\t = Training   runtime\n","\t0.33s\t = Validation runtime\n","Fitting model: WeightedEnsemble_L3 ... Training model for up to 360.0s of the -14.87s of remaining time.\n","\tEnsemble Weights: {'LightGBM_BAG_L2': 0.923, 'LightGBMXT_BAG_L2': 0.077}\n","\t-55.0669\t = Validation score   (-root_mean_squared_error)\n","\t0.05s\t = Training   runtime\n","\t0.0s\t = Validation runtime\n","AutoGluon training complete, total runtime = 399.98s ... Best model: \"WeightedEnsemble_L3\"\n","TabularPredictor saved. To load, use: predictor = TabularPredictor.load(\"AutogluonModels/ag-20240430_211606\")\n"]}],"source":["\"\"\"Since the 'casual' and 'registered' columns are not present \\\n","in the test dataset but are available in the training dataset, \\\n","it's essential to handle this discrepancy. Since the task requires \\\n","predicting the 'count' column, we can safely ignore the 'casual' and \\\n","'registered' columns for training the model. We can drop these columns \\\n","from the training dataset before training the model.\"\"\"\n","train = train.drop(columns=['casual', 'registered'])\n","\n","# Train the model\n","predictor = TabularPredictor(label='count', eval_metric='root_mean_squared_error').fit(train_data=train, time_limit=600, presets='best_quality')"]},{"cell_type":"markdown","metadata":{"id":"u1GdE-9RZhjY"},"source":["### Review AutoGluon's training run with ranking of models that did the best."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"CUA1WssuZhjZ","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1714512391061,"user_tz":-60,"elapsed":610,"user":{"displayName":"Paschal Ugwu","userId":"02920440952136523881"}},"outputId":"09a66a3f-ed75-4929-9269-d3d4254d8708"},"outputs":[{"output_type":"stream","name":"stdout","text":["*** Summary of fit() ***\n","Estimated performance of each model:\n","                    model   score_val              eval_metric  pred_time_val    fit_time  pred_time_val_marginal  fit_time_marginal  stack_level  can_infer  fit_order\n","0     WeightedEnsemble_L3  -55.066854  root_mean_squared_error      55.163926  361.237957                0.001292           0.054325            3       True         10\n","1         LightGBM_BAG_L2  -55.106094  root_mean_squared_error      49.824168  278.989629                0.328317          42.290116            2       True          9\n","2       LightGBMXT_BAG_L2  -60.711688  root_mean_squared_error      54.834317  318.893516                5.338466          82.194003            2       True          8\n","3   KNeighborsDist_BAG_L1  -84.125061  root_mean_squared_error       0.090871    0.058939                0.090871           0.058939            1       True          2\n","4     WeightedEnsemble_L2  -84.125061  root_mean_squared_error       0.092352    0.107026                0.001481           0.048087            2       True          7\n","5   KNeighborsUnif_BAG_L1 -101.546199  root_mean_squared_error       0.086746    0.067217                0.086746           0.067217            1       True          1\n","6  RandomForestMSE_BAG_L1 -116.548359  root_mean_squared_error       1.083449   23.647861                1.083449          23.647861            1       True          5\n","7         LightGBM_BAG_L1 -131.054162  root_mean_squared_error       4.147206   46.700382                4.147206          46.700382            1       True          4\n","8       LightGBMXT_BAG_L1 -131.460909  root_mean_squared_error      43.944835  102.967989               43.944835         102.967989            1       True          3\n","9         CatBoost_BAG_L1 -132.125747  root_mean_squared_error       0.142745   63.257125                0.142745          63.257125            1       True          6\n","Number of models trained: 10\n","Types of models trained:\n","{'StackerEnsembleModel_LGB', 'StackerEnsembleModel_RF', 'StackerEnsembleModel_CatBoost', 'WeightedEnsembleModel', 'StackerEnsembleModel_KNN'}\n","Bagging used: True  (with 8 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']) : 5 | ['datetime', 'datetime.year', 'datetime.month', 'datetime.day', 'datetime.dayofweek']\n","*** End of fit() summary ***\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/autogluon/core/utils/plots.py:169: UserWarning: AutoGluon summary plots cannot be created because bokeh is not installed. To see plots, please do: \"pip install bokeh==2.0.1\"\n","  warnings.warn('AutoGluon summary plots cannot be created because bokeh is not installed. To see plots, please do: \"pip install bokeh==2.0.1\"')\n"]},{"output_type":"execute_result","data":{"text/plain":["{'model_types': {'KNeighborsUnif_BAG_L1': 'StackerEnsembleModel_KNN',\n","  'KNeighborsDist_BAG_L1': 'StackerEnsembleModel_KNN',\n","  'LightGBMXT_BAG_L1': 'StackerEnsembleModel_LGB',\n","  'LightGBM_BAG_L1': 'StackerEnsembleModel_LGB',\n","  'RandomForestMSE_BAG_L1': 'StackerEnsembleModel_RF',\n","  'CatBoost_BAG_L1': 'StackerEnsembleModel_CatBoost',\n","  'WeightedEnsemble_L2': 'WeightedEnsembleModel',\n","  'LightGBMXT_BAG_L2': 'StackerEnsembleModel_LGB',\n","  'LightGBM_BAG_L2': 'StackerEnsembleModel_LGB',\n","  'WeightedEnsemble_L3': 'WeightedEnsembleModel'},\n"," 'model_performance': {'KNeighborsUnif_BAG_L1': -101.54619908446061,\n","  'KNeighborsDist_BAG_L1': -84.12506123181602,\n","  'LightGBMXT_BAG_L1': -131.46090891834504,\n","  'LightGBM_BAG_L1': -131.054161598899,\n","  'RandomForestMSE_BAG_L1': -116.54835939455667,\n","  'CatBoost_BAG_L1': -132.12574669370798,\n","  'WeightedEnsemble_L2': -84.12506123181602,\n","  'LightGBMXT_BAG_L2': -60.71168787502696,\n","  'LightGBM_BAG_L2': -55.106093871534405,\n","  'WeightedEnsemble_L3': -55.06685358768445},\n"," 'model_best': 'WeightedEnsemble_L3',\n"," 'model_paths': {'KNeighborsUnif_BAG_L1': ['KNeighborsUnif_BAG_L1'],\n","  'KNeighborsDist_BAG_L1': ['KNeighborsDist_BAG_L1'],\n","  'LightGBMXT_BAG_L1': ['LightGBMXT_BAG_L1'],\n","  'LightGBM_BAG_L1': ['LightGBM_BAG_L1'],\n","  'RandomForestMSE_BAG_L1': ['RandomForestMSE_BAG_L1'],\n","  'CatBoost_BAG_L1': ['CatBoost_BAG_L1'],\n","  'WeightedEnsemble_L2': ['WeightedEnsemble_L2'],\n","  'LightGBMXT_BAG_L2': ['LightGBMXT_BAG_L2'],\n","  'LightGBM_BAG_L2': ['LightGBM_BAG_L2'],\n","  'WeightedEnsemble_L3': ['WeightedEnsemble_L3']},\n"," 'model_fit_times': {'KNeighborsUnif_BAG_L1': 0.06721735000610352,\n","  'KNeighborsDist_BAG_L1': 0.05893874168395996,\n","  'LightGBMXT_BAG_L1': 102.96798872947693,\n","  'LightGBM_BAG_L1': 46.70038175582886,\n","  'RandomForestMSE_BAG_L1': 23.64786124229431,\n","  'CatBoost_BAG_L1': 63.25712490081787,\n","  'WeightedEnsemble_L2': 0.048087120056152344,\n","  'LightGBMXT_BAG_L2': 82.19400334358215,\n","  'LightGBM_BAG_L2': 42.29011583328247,\n","  'WeightedEnsemble_L3': 0.054325103759765625},\n"," 'model_pred_times': {'KNeighborsUnif_BAG_L1': 0.0867462158203125,\n","  'KNeighborsDist_BAG_L1': 0.09087109565734863,\n","  'LightGBMXT_BAG_L1': 43.94483470916748,\n","  'LightGBM_BAG_L1': 4.147205591201782,\n","  'RandomForestMSE_BAG_L1': 1.0834486484527588,\n","  'CatBoost_BAG_L1': 0.1427452564239502,\n","  'WeightedEnsemble_L2': 0.0014812946319580078,\n","  'LightGBMXT_BAG_L2': 5.338465690612793,\n","  'LightGBM_BAG_L2': 0.32831692695617676,\n","  'WeightedEnsemble_L3': 0.0012917518615722656},\n"," 'num_bag_folds': 8,\n"," 'max_stack_level': 3,\n"," 'model_hyperparams': {'KNeighborsUnif_BAG_L1': {'use_orig_features': True,\n","   'max_base_models': 25,\n","   'max_base_models_per_type': 5,\n","   'save_bag_folds': True,\n","   'use_child_oof': True},\n","  'KNeighborsDist_BAG_L1': {'use_orig_features': True,\n","   'max_base_models': 25,\n","   'max_base_models_per_type': 5,\n","   'save_bag_folds': True,\n","   'use_child_oof': True},\n","  'LightGBMXT_BAG_L1': {'use_orig_features': True,\n","   'max_base_models': 25,\n","   'max_base_models_per_type': 5,\n","   'save_bag_folds': True},\n","  'LightGBM_BAG_L1': {'use_orig_features': True,\n","   'max_base_models': 25,\n","   'max_base_models_per_type': 5,\n","   'save_bag_folds': True},\n","  'RandomForestMSE_BAG_L1': {'use_orig_features': True,\n","   'max_base_models': 25,\n","   'max_base_models_per_type': 5,\n","   'save_bag_folds': True,\n","   'use_child_oof': True},\n","  'CatBoost_BAG_L1': {'use_orig_features': True,\n","   'max_base_models': 25,\n","   'max_base_models_per_type': 5,\n","   'save_bag_folds': True},\n","  'WeightedEnsemble_L2': {'use_orig_features': False,\n","   'max_base_models': 25,\n","   'max_base_models_per_type': 5,\n","   'save_bag_folds': True},\n","  'LightGBMXT_BAG_L2': {'use_orig_features': True,\n","   'max_base_models': 25,\n","   'max_base_models_per_type': 5,\n","   'save_bag_folds': True},\n","  'LightGBM_BAG_L2': {'use_orig_features': True,\n","   'max_base_models': 25,\n","   'max_base_models_per_type': 5,\n","   'save_bag_folds': True},\n","  'WeightedEnsemble_L3': {'use_orig_features': False,\n","   'max_base_models': 25,\n","   'max_base_models_per_type': 5,\n","   'save_bag_folds': True}},\n"," 'leaderboard':                     model   score_val              eval_metric  pred_time_val  \\\n"," 0     WeightedEnsemble_L3  -55.066854  root_mean_squared_error      55.163926   \n"," 1         LightGBM_BAG_L2  -55.106094  root_mean_squared_error      49.824168   \n"," 2       LightGBMXT_BAG_L2  -60.711688  root_mean_squared_error      54.834317   \n"," 3   KNeighborsDist_BAG_L1  -84.125061  root_mean_squared_error       0.090871   \n"," 4     WeightedEnsemble_L2  -84.125061  root_mean_squared_error       0.092352   \n"," 5   KNeighborsUnif_BAG_L1 -101.546199  root_mean_squared_error       0.086746   \n"," 6  RandomForestMSE_BAG_L1 -116.548359  root_mean_squared_error       1.083449   \n"," 7         LightGBM_BAG_L1 -131.054162  root_mean_squared_error       4.147206   \n"," 8       LightGBMXT_BAG_L1 -131.460909  root_mean_squared_error      43.944835   \n"," 9         CatBoost_BAG_L1 -132.125747  root_mean_squared_error       0.142745   \n"," \n","      fit_time  pred_time_val_marginal  fit_time_marginal  stack_level  \\\n"," 0  361.237957                0.001292           0.054325            3   \n"," 1  278.989629                0.328317          42.290116            2   \n"," 2  318.893516                5.338466          82.194003            2   \n"," 3    0.058939                0.090871           0.058939            1   \n"," 4    0.107026                0.001481           0.048087            2   \n"," 5    0.067217                0.086746           0.067217            1   \n"," 6   23.647861                1.083449          23.647861            1   \n"," 7   46.700382                4.147206          46.700382            1   \n"," 8  102.967989               43.944835         102.967989            1   \n"," 9   63.257125                0.142745          63.257125            1   \n"," \n","    can_infer  fit_order  \n"," 0       True         10  \n"," 1       True          9  \n"," 2       True          8  \n"," 3       True          2  \n"," 4       True          7  \n"," 5       True          1  \n"," 6       True          5  \n"," 7       True          4  \n"," 8       True          3  \n"," 9       True          6  }"]},"metadata":{},"execution_count":10}],"source":["# Review AutoGluon's training run with ranking of models that did the best.\n","predictor.fit_summary()"]},{"cell_type":"markdown","metadata":{"id":"ZhPtwfpQZhja"},"source":["### Create predictions from test dataset"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"NhGwSb6KZhjg","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1714512461073,"user_tz":-60,"elapsed":62468,"user":{"displayName":"Paschal Ugwu","userId":"02920440952136523881"}},"outputId":"faf64b72-53fc-4ed9-ab63-b78e28beb33b"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["0    36.919998\n","1    45.451775\n","2    49.321945\n","3    53.542290\n","4    54.564552\n","Name: count, dtype: float32"]},"metadata":{},"execution_count":11}],"source":["# Create predictions from the test dataset\n","predictions = predictor.predict(test)\n","\n","# Display first few rows of predictions\n","predictions.head()"]},{"cell_type":"markdown","metadata":{"id":"U9AjUgKEZhji"},"source":["#### NOTE: Kaggle will reject the submission if we don't set everything to be > 0."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"TAsaW1XTZhji","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1714512465796,"user_tz":-60,"elapsed":476,"user":{"displayName":"Paschal Ugwu","userId":"02920440952136523881"}},"outputId":"c47aaa23-0d76-4428-abae-d0d1903466a6"},"outputs":[{"output_type":"stream","name":"stdout","text":["count    6493.000000\n","mean       99.546867\n","std        89.662758\n","min        -8.414003\n","25%        16.569002\n","50%        63.928410\n","75%       171.246750\n","max       371.817078\n","Name: count, dtype: float64\n"]}],"source":["# Describe the `predictions` series to see if there are any negative values\n","print(predictions.describe())"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"eWphMjEZZhjj","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1714512468128,"user_tz":-60,"elapsed":398,"user":{"displayName":"Paschal Ugwu","userId":"02920440952136523881"}},"outputId":"bbc8d2b6-a944-4424-b891-53ad317e8e69"},"outputs":[{"output_type":"stream","name":"stdout","text":["Number of negative values: 2\n"]}],"source":["# How many negative values do we have?\n","# Count how many negative values do we have\n","num_negatives = (predictions < 0).sum()\n","print(\"Number of negative values:\", num_negatives)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"SPSWBLDnZhjk"},"outputs":[],"source":["# Set them to zero\n","# Set negative predictions to zero\n","predictions = predictions.clip(lower=0)"]},{"cell_type":"markdown","metadata":{"id":"E0sQ8l5VZhjm"},"source":["### Set predictions to submission dataframe, save, and submit"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"RNlGNxKWZhjn"},"outputs":[],"source":["# Set predictions to submission dataframe, save, and submit\n","submission[\"count\"] = predictions\n","submission.to_csv(\"submission.csv\", index=False)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"6SgeMOc4Zhjo","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1714512599461,"user_tz":-60,"elapsed":2497,"user":{"displayName":"Paschal Ugwu","userId":"02920440952136523881"}},"outputId":"59d91a3f-0ca2-431d-f590-0fb630e1132e"},"outputs":[{"output_type":"stream","name":"stdout","text":["100% 188k/188k [00:00<00:00, 254kB/s]\n","Successfully submitted to Bike Sharing Demand"]}],"source":["# Submit the predictions to the Kaggle competition \"bike-sharing-demand\" using the Kaggle CLI,\n","# with the submission file \"submission.csv\" and a descriptive message \"first raw submission\"\n","!kaggle competitions submit -c bike-sharing-demand -f submission.csv -m \"first raw submission\""]},{"cell_type":"markdown","metadata":{"id":"3Q7Z_o2OZhjo"},"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":{"id":"vQ9oECAAZhjp","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1714512609252,"user_tz":-60,"elapsed":1087,"user":{"displayName":"Paschal Ugwu","userId":"02920440952136523881"}},"outputId":"c0a00422-e6b5-426e-b80b-64d104ca45ca"},"outputs":[{"output_type":"stream","name":"stdout","text":["fileName                     date                 description                        status    publicScore  privateScore  \n","---------------------------  -------------------  ---------------------------------  --------  -----------  ------------  \n","submission.csv               2024-04-30 21:29:58  first raw submission               complete  1.83892      1.83892       \n","submission.csv               2024-04-30 21:28:13  first raw submission               complete  1.83892      1.83892       \n","submission_new_hpo.csv       2024-04-30 11:42:05  new features with hyperparameters  complete  0.49123      0.49123       \n","submission_new_features.csv  2024-04-30 10:58:38  new features                       complete  0.6207       0.6207        \n"]}],"source":["!kaggle competitions submissions -c bike-sharing-demand | tail -n +1 | head -n 6"]},{"cell_type":"markdown","metadata":{"id":"m7R9-FUVZhjq"},"source":["#### Initial score of `1.83892`"]},{"cell_type":"markdown","metadata":{"id":"vhE8kI1sZhjq"},"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":{"id":"cWr06BA-Zhjr","colab":{"base_uri":"https://localhost:8080/","height":831},"executionInfo":{"status":"ok","timestamp":1714512678211,"user_tz":-60,"elapsed":3188,"user":{"displayName":"Paschal Ugwu","userId":"02920440952136523881"}},"outputId":"f675ce27-10c3-4a6c-972f-c1f6f17f954a"},"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 1500x1000 with 12 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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","# Exploratory Data Analysis (EDA)\n","# Visualize the distribution of each feature using histograms\n","import matplotlib.pyplot as plt\n","train.hist(figsize=(15, 10), bins=20, grid=False)\n","plt.suptitle(\"Distribution of Features\", fontsize=16)\n","plt.show()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"QPL6lQY4Zhju"},"outputs":[],"source":["# Create a new feature 'hour' by extracting the hour part from the datetime column\n","train['hour'] = train['datetime'].dt.hour\n","test['hour'] = test['datetime'].dt.hour"]},{"cell_type":"markdown","metadata":{"id":"CQvn9sNsZhjv"},"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":"1jGkzOu3Zhjv"},"outputs":[],"source":["# Convert 'season' and 'weather' columns to categorical types\n","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":{"id":"xqXL5Ub_Zhjy","colab":{"base_uri":"https://localhost:8080/","height":206},"executionInfo":{"status":"ok","timestamp":1714512699149,"user_tz":-60,"elapsed":598,"user":{"displayName":"Paschal Ugwu","userId":"02920440952136523881"}},"outputId":"cedd89ef-8c54-4c8d-95e3-1ddbe4ae7ad6"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["             datetime season  holiday  workingday weather  temp   atemp  \\\n","0 2011-01-01 00:00:00      1        0           0       1  9.84  14.395   \n","1 2011-01-01 01:00:00      1        0           0       1  9.02  13.635   \n","2 2011-01-01 02:00:00      1        0           0       1  9.02  13.635   \n","3 2011-01-01 03:00:00      1        0           0       1  9.84  14.395   \n","4 2011-01-01 04:00:00      1        0           0       1  9.84  14.395   \n","\n","   humidity  windspeed  count  hour  \n","0        81        0.0     16     0  \n","1        80        0.0     40     1  \n","2        80        0.0     32     2  \n","3        75        0.0     13     3  \n","4        75        0.0      1     4  "],"text/html":["\n","  <div id=\"df-b2bac13d-719f-4654-9d39-f1572d703bff\" class=\"colab-df-container\">\n","    <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" 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},\n    {\n      \"column\": \"holiday\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"workingday\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"weather\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          2,\n          4\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"temp\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7.791589843987567,\n        \"min\": 0.82,\n        \"max\": 41.0,\n        \"num_unique_values\": 49,\n        \"samples\": [\n          6.56,\n          1.64\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"atemp\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8.474600626484948,\n        \"min\": 0.76,\n        \"max\": 45.455,\n        \"num_unique_values\": 60,\n        \"samples\": [\n          14.395,\n          16.665\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"humidity\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 19,\n        \"min\": 0,\n        \"max\": 100,\n        \"num_unique_values\": 89,\n        \"samples\": [\n          29,\n          61\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"windspeed\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8.164537326838689,\n        \"min\": 0.0,\n        \"max\": 56.9969,\n        \"num_unique_values\": 28,\n        \"samples\": [\n          22.0028,\n          43.0006\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"count\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 181,\n        \"min\": 1,\n        \"max\": 977,\n        \"num_unique_values\": 822,\n        \"samples\": [\n          626,\n          256\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"hour\",\n      \"properties\": {\n        \"dtype\": \"int32\",\n        \"num_unique_values\": 24,\n        \"samples\": [\n          8,\n          16\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"}},"metadata":{},"execution_count":22}],"source":["# View are new feature\n","train.head()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"Nx3D0BKtZhjy","colab":{"base_uri":"https://localhost:8080/","height":1000},"executionInfo":{"status":"ok","timestamp":1714512704033,"user_tz":-60,"elapsed":2840,"user":{"displayName":"Paschal Ugwu","userId":"02920440952136523881"}},"outputId":"42ba299a-1227-4e4b-d09a-85b26fd35f32"},"outputs":[{"output_type":"stream","name":"stdout","text":["First few rows of train dataset with the 'hour' feature:\n","             datetime season  holiday  workingday weather  temp   atemp  \\\n","0 2011-01-01 00:00:00      1        0           0       1  9.84  14.395   \n","1 2011-01-01 01:00:00      1        0           0       1  9.02  13.635   \n","2 2011-01-01 02:00:00      1        0           0       1  9.02  13.635   \n","3 2011-01-01 03:00:00      1        0           0       1  9.84  14.395   \n","4 2011-01-01 04:00:00      1        0           0       1  9.84  14.395   \n","\n","   humidity  windspeed  count  hour  \n","0        81        0.0     16     0  \n","1        80        0.0     40     1  \n","2        80        0.0     32     2  \n","3        75        0.0     13     3  \n","4        75        0.0      1     4  \n"]},{"output_type":"display_data","data":{"text/plain":["<Figure size 1500x1000 with 9 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+evSoDMPQhQsXbvrEyZ9//mn18/Lly9WrV68bPimZkpKSp3ht4Xrn95EjRyRJv/32200T1lfXdfDgwdqyZYu+//57Pfroo3J0dFTt2rXVtGlTdenSRfXr17dZ7FLezr2sST2KclbJ//73v5Jufl3Ij7y0RVYcN7q+VahQQT/++GO+48lJXq8VN7smZF0PLl68aNPr6K1e9wEA9y6SZQCAAmMYhvbs2SPpyqttudG/f3899dRTWrZsmbZs2aItW7ZowYIFWrBggYYPH67Nmzfn+ETDzbi6uuZ5m7wyDCPXZbOeuLud2NndGw+cZ7V9iRIl1Llz51xv98cff+jpp5/WhQsX9Nprr6lr166qUKGCSpQoITs7O61du1ZhYWF5Og/yGvP1XO/8ztrO19dXYWFhN9zH1QOfFytWTFFRUdq5c6dWr16trVu3auvWrdq1a5fGjx+vl19+2XzV1hZu13OvKH5P89MWN0qEFsRrmLfL95Xf3wsAAG6GZBkAoMCsWrVKZ86ckSS1bt0619v5+PjohRde0AsvvCBJ+vnnn9W7d2/FxMTojTfe0Jw5cwok3hs5evRojsuPHTsmSXJxcbEaTy3rCbh//vknx+2yno6xlazX0f7++2+lpKTk+HRZ1lNGBf3qWtb+r9dmhRlLTsqVKyfpShLh888/z/WN//Lly3XhwgU9/vjjev/997OtP3z4cL5jKqjzJauuXl5emj17dp63r1+/vvkU2eXLl7V06VJ1795dU6dO1RNPPKEWLVrkK65bkfVU088//3zL+8pvu99333367bffdOzYMVWvXj3b+qzrQkHL+v250fEKK5YbyWqvI0eOqEaNGtnWZ10PXFxcrJ5CLuzrKAAAWW6PPwsBAO46ycnJGjhwoCTpkUcesRpwPq+qVKmi119/XZK0d+9eq3VZN1OXL1/O9/5z48svv8xx+dy5cyVJTZo0kYPD//4GlXUTe+jQoWzbnD9//rpjdeW3PmXLljVfZcopKWIYhrm8oBMc9erVU4kSJXT69GktW7Ys2/oLFy5owYIFhRJLTvz9/VWrVi39888/5vhXuXH69GlJUkBAQLZ1hmFo/vz5OW6Xm+/0RueLYRj67rvvch3n1erXr6/SpUvr4MGDOnDgQL72kcXBwUFPPPGE+YTatb+LhSVr7LlVq1bpxIkTt7SvG7W7JK1cuTLH5c2aNZMkzZs3L8f1WdeFghYSEiKLxaLY2Fj98ssv2dYfPHjQ5q9g5kfWuHjXS9h+/vnnkq7UxxbXUQAAbhXJMgCATWXd2D/88MM6fPiw/Pz8rjv74rXWr1+vVatW6dKlS9n2uWLFCknZExVly5aVpFtOBNxMbGysxo4da7Vsy5Yt5qtoWYnBLKGhoZKkKVOmWI3Lde7cOfXt29dqdr+rZdXn4MGDeY7x3//+tyRp9OjRVjfIhmHo7bff1t69e+Xh4WE+sVdQXFxcFBERIUn617/+ZfX0x6VLl/Tqq68qISFBgYGB2WYSLCxvv/22pCszZy5fvjzbesMwtH37dq1du9ZcljV4+eLFi60mn0hPT9ewYcNynAxAksqUKSMnJyclJCSYCbdrZZ0vX3zxhdV3f+nSJb3++uvauXNnHmt4haOjoznz5+OPP64tW7ZkK5Oenq7169dr27Zt5rKpU6fmOFFBQkKCORtjTknDwlCnTh116NBBFy5cUIcOHRQfH2+1/vLlyzkmaXPSsmVL2dnZac2aNYqOjjaXG4ahyZMnm5M2XKt///6yt7fXV199pSVLllitW7BggZYuXZq3SuVThQoV1L59e2VkZKhfv35WT2AlJyerX79+BfJacF69+uqrcnBw0NKlS7P94WHt2rXmTKxZ17As+b2OAgBwq3gNEwCQb5999pk5y1lqaqr++usv7d6920wING/eXJ9//nmub6r37dungQMHys3NTXXr1pW/v78uXLig3bt36/fff5e7u7tGjRpltU3nzp21YcMGPffcc2rdurVKlSol6coA5ZUrV7ZZXV955RUNGTJEc+fOVa1atXTixAlt3rxZGRkZevXVV9W2bVur8k899ZQmTpyoXbt2qXr16mrSpIkyMjK0a9cuOTk5qXfv3ubTFFdr2LCh/P39tWfPHtWtW1c1a9aUo6OjKleurMGDB98wxhdffFFbt27VF198oXr16qlZs2by9vbW7t27FRcXJ1dXV82fP19lypSxWbtcz8iRI7Vr1y6tW7dOVatWVYsWLVSyZEnFxMQoPj5eXl5eWrRoUb4nbLhV7du316RJk/Svf/1Ljz32mCpVqqTKlSvL3d1df/75p3788UedOnVKr7/+uvkKcfv27RUUFKTY2Fg9+OCDatasmYoXL67t27frxIkTev3113N8PdPR0VGPPfaYFi9erDp16qhJkyYqVqyYpCu/Q9KVGTw7dOigb7/9VvXq1VOTJk3k6uqq3bt3KyUlRa+++qomTZqUr7pGRkYqPj5eH3zwgUJCQlS9enVVqlRJrq6uSkhI0N69e5WUlKRp06apYcOGkqRPP/1UERERCgwMVI0aNeTm5qY///xTmzdv1oULF9SyZUs99thj+YrHFmbNmqW2bdtq27ZteuCBB9SoUSP5+/srISFB+/fv159//pmrJFG5cuXUv39/TZo0Sa1atVJISIg8PT31448/Kj4+Xm+88Ybee++9bNvVqVNHY8aM0WuvvaZOnTqpQYMGqlixog4fPqydO3dq4MCBmjBhQkFUPZtp06Zp3759Wr9+vQIDA9WsWTMZhqHo6Gh5eXnpscce07Jly4rsd026MmbllClT1K9fP3Xr1k0TJkxQlSpV9Pvvv2vr1q0yDEMjRozI9rp+fq+jAADcKp4sAwDk2w8//KA5c+Zozpw5Wr58uQ4ePKiaNWvqX//6l3bs2KENGzbkaVa49u3ba8SIEapfv76OHDmib775Rhs3bpS7u7veeOMN/fTTT9le5+zXr5/GjBmjgIAArVq1SjNnztTMmTOtnvyxhccff1xRUVHy9fXVqlWrtGPHDtWtW1ezZ8/WxIkTs5V3dHRUVFSUIiMjVbJkSa1du1b79u3T448/rt27d5tjSV3LyclJa9as0WOPPab//ve/+vLLLzVz5szrvg52NYvForlz52r+/Plq0qSJYmNjtXjxYp0/f149e/bUnj171KZNm1ttilxxdnbW6tWrNXXqVNWuXVubN2/WkiVL5OjoqP79++vHH39UUFBQocRyPa+88or27Nmjvn37ymKxaN26dVq6dKl+++03PfTQQ5o8ebJeeeUVs7yDg4M2btyoN998U/fdd5/WrVunjRs36qGHHlJMTIz5emBOPvnkE7344ouyWCxavHixeZ5ebeHChRo6dKj8/Py0ceNGbdu2TSEhIdq9e/ctvcYsSWPHjtUPP/ygrl276uzZs1q9erVWrlypEydOqHnz5vrss8/09NNPm+Xfeecd9evXTx4eHtq2bZsWLVqkgwcPqkGDBpozZ45Wr15t9bpcYStVqpSio6M1bdo0NWjQQHv37tXixYv1yy+/qE6dOnmafGDChAkaN26cHnzwQW3dulUbN25UtWrVtG3bthtOijB48GB9++23atKkiX766SctW7ZMjo6OWrx4sdV5U9D8/f21Y8cORUREyNXVVStWrNCuXbv0zDPPaNu2bTp79qwk6wkcikLfvn21detWPfHEEzpx4oS++uor/fzzz2rbtq3Wrl2r4cOHZ9smv9dRAABulcW4HZ7NBgAAAGBTSUlJuv/++5WcnKzExMQiT5gBAHCn4MkyAAAA4A62Y8eObMv+/PNP9ejRQ2fOnFG7du1IlAEAkAc8WQYAAADcwSwWi8qWLauqVavKy8tLf/zxh/bs2aOzZ8+qfPny2rJlC68sAgCQByTLAAAAgDvYW2+9pXXr1um3337TmTNn5OTkpIoVK6pdu3YaNGiQvLy8ijpEAADuKCTLAAAAAAAAgEyMWQYAAAAAAABkIlkGAAAAAAAAZCJZBgAAAAAAAGQiWQYAAAAAAABkIlkGAAAAAAAAZCJZBgAAAAAAAGQiWQYAAAAAAABkIlkGAAAAAAAAZCJZBgAAAAAAAGQiWQYAAAAAAABkIlkGAAAAAAAAZCJZBgAAAAAAAGQiWQYAAAAAAABkIlkGAAAAAAAAZCJZBgAAAAAAAGQiWQYAAAAAAABkIlkGAAAAAAAAZCJZBgAAAAAAAGQiWQYAAAAAAABkIlkGAAAAAAAAZCJZBgAAAAAAAGQiWQYAAAAAAABkIlkGAAAAAAAAZCJZBgAAAAAAAGQiWQYAAAAAAABkIlkGAAAAAAAAZCJZBgAAAAAAAGQiWQYAAAAAAABkIlkGAAAAAAAAZCJZBgAAAAAAAGQiWQYAAAAAAABkIlkGAAAAAAAAZCJZBgAAAAAAAGQiWQYAAAAAAABkIlkGAAAAAAAAZCJZBgAAAAAAAGQiWQYAAAAAAABkIlkGAAAAAAAAZCJZBgAAAAAAAGQiWQYAAAAAAABkIlkGAAAAAAAAZCJZBgAAAAAAAGQiWQYAAAAAAABkIlkGAAAAAAAAZCJZBgAAAAAAAGQiWQYAAAAAAABkIlkGAAAAAAAAZCJZBgAAAAAAAGQiWQYAAAAAAABkIlkGAAAAAAAAZCJZBgAAAAAAAGQiWQYAAAAAAABkIlkGAAAAAAAAZCJZBgAAAAAAAGQiWQYAAAAAAABkIlkGAAAAAAAAZCJZBgAAAAAAAGQiWYZ7xogRI2SxWIo6DCu3Y0wAgIKVde3/66+/bLK/5s2bq3nz5ubPx44dk8Vi0ezZs2+6bc+ePVWhQgWbxAEAuLtVqFBB7dq1u2k5i8WiESNGFHxAOeD+CrZCsgy4ialTp+bqhuN6zp8/rxEjRmjjxo02iwkAAAAAABQMh6IOALjdTZ06VaVLl1bPnj3ztf358+c1cuRISbL6y78kDR06VG+88cYtRggAwP8EBATowoULcnR0LOpQAAD3oAsXLsjBgVQD7mycwUARcnBwoCMBANiUxWKRi4tLUYcBALhLnD9/XsWKFct1efog3A14DRN3pS1btqh+/fpycXFRxYoV9cknn2QrM2vWLLVs2VLe3t5ydnZWtWrVNG3aNKsyFSpU0IEDBxQdHS2LxSKLxWL1dFhSUpIGDBigcuXKydnZWZUqVdL777+vjIwMSVfGjSlTpowkaeTIkeY+st7hz+mdeovFosjISC1atEjVqlWTq6urgoODtX//fknSJ598okqVKsnFxUXNmzfXsWPHstVt+/btevTRR+Xu7q5ixYqpWbNm+uGHH/LbnACAApCUlKSePXvKw8ND7u7u6tWrl86fP2+uv3z5skaPHq2KFSvK2dlZFSpU0JtvvqnU1NQb7vd6Y5YtXbpUNWrUkIuLi2rUqKElS5bkuP2HH36oRo0aycvLS66urgoKCtLixYutyjRr1ky1a9fOcfvKlSsrLCwsFy0AAMirffv2yWKxaNmyZeay2NhYWSwW1a1b16psmzZt1KBBA/PnqVOnqnr16nJ2dpa/v78iIiKUlJRktU3z5s1Vo0YNxcbGqmnTpipWrJjefPPN68YzZ84cOTg4aPDgweaya8csy7rn+fXXX2/Y70lXnkp75ZVXVLp0aZUsWVKPPfaY/vjjjxzHQcvNPZ+Uu/u+Hj16qHTp0rp06VK27Vu3bq3KlStftw1wd+KRFtx19u/fr9atW6tMmTIaMWKELl++rOHDh8vHx8eq3LRp01S9enU99thjcnBw0PLly/Xyyy8rIyNDERERkqSJEyeqf//+KlGihP7v//5Pksz9nD9/Xs2aNdMff/yhF198UeXLl9fWrVs1ZMgQnTx5UhMnTlSZMmU0bdo09evXT48//rg6deokSapVq9YN67B582YtW7bMjGPMmDFq166dXnvtNU2dOlUvv/yyzpw5o7Fjx6p3795av369ue369evVpk0bBQUFafjw4bKzszM7iM2bN+vhhx+2TUMDAG7JU089pcDAQI0ZM0a7d+/WZ599Jm9vb73//vuSpOeff15z5szRE088oX/961/avn27xowZo0OHDl030XU9a9euVefOnVWtWjWNGTNGf//9t3r16qWyZctmKztp0iQ99thj6tq1q9LS0rRgwQI9+eSTWrFihcLDwyVJ3bp10wsvvKCffvpJNWrUMLfduXOnfvnlFw0dOvQWWgYAcD01atSQh4eHNm3apMcee0zSlXsHOzs7/fjjj0pJSZGbm5syMjK0detW9e3bV9KVhNXIkSMVGhqqfv36KS4uTtOmTdPOnTv1ww8/WL26//fff6tNmzbq0qWLnnvuuWz3UVk+/fRTvfTSS3rzzTf19ttv3zT2m/V70pWJZ7766it169ZNDRs2VHR0tNn3XC2393xS7u77unXrprlz52rNmjVWkxgkJCRo/fr1Gj58+E3rh7uMAdxlOnbsaLi4uBi///67uezgwYOGvb29cfUpf/78+WzbhoWFGffff7/VsurVqxvNmjXLVnb06NFG8eLFjV9++cVq+RtvvGHY29sb8fHxhmEYxp9//mlIMoYPH55tH8OHDzeu/TWUZDg7OxtHjx41l33yySeGJMPX19dISUkxlw8ZMsSQZJbNyMgwHnjgASMsLMzIyMiwqmtgYKDxyCOPZIsBAFC4sq79vXv3tlr++OOPG15eXoZhGMbevXsNScbzzz9vVebf//63IclYv369uaxZs2ZW/dTRo0cNScasWbPMZXXq1DH8/PyMpKQkc9natWsNSUZAQIDVMa7tH9PS0owaNWoYLVu2NJclJSUZLi4uxuuvv25V9pVXXjGKFy9unD179uYNAQDIl/DwcOPhhx82f+7UqZPRqVMnw97e3vjuu+8MwzCM3bt3G5KMb7/91jh16pTh5ORktG7d2khPTze3+/jjjw1Jxueff24ua9asmSHJmD59erbjBgQEGOHh4YZhGMakSZMMi8VijB49Olu5a+99ctPvGYZhxMbGGpKMAQMGWJXr2bNntn3m9p7PMHJ335eenm6ULVvWePrpp63KjR8/3rBYLMaRI0ey7QN3N17DxF0lPT1da9asUceOHVW+fHlzedWqVbO9EuLq6mr+Ozk5WX/99ZeaNWumI0eOKDk5+abHWrRokUJCQlSqVCn99ddf5ic0NFTp6enatGlTvuvRqlUrVahQwfw56/Hpzp07q2TJktmWHzlyRJK0d+9eHT58WM8++6z+/vtvM6Zz586pVatW2rRpk/mKKACgaL300ktWP4eEhOjvv/9WSkqKVq1aJUkaNGiQVZl//etfkqSVK1fm+jgnT57U3r171aNHD7m7u5vLH3nkEVWrVi1b+av7xzNnzig5OVkhISHavXu3udzd3V0dOnTQf/7zHxmGIelKH7xw4UJ17NhRxYsXz3V8AIC8ybomnzt3TtKV1xHbtm2rOnXqaPPmzZKuPG1msVjUpEkTff/990pLS9OAAQNkZ/e/FMALL7wgNze3bH2Ks7OzevXqdd3jjx07Vq+++qref//9PD1JfKN+T5JWr14tSXr55ZetyvXv39/q57zc80m5u++zs7NT165dtWzZMv3zzz9m+Xnz5qlRo0YKDAzMdT1xdyBZhrvKn3/+qQsXLuiBBx7Itu7a98x/+OEHhYaGqnjx4vLw8FCZMmXM9/Fzkyw7fPiwVq9erTJlylh9QkNDJUmnTp3Kdz2uvuhLMm9uypUrl+PyM2fOmDFJV965vzauzz77TKmpqbmqGwCg4F17rS9VqpSkK9f033//XXZ2dqpUqZJVGV9fX3l4eOj333/P9XGyyuamb5SkFStWqGHDhnJxcZGnp6c5pMC1/Uf37t0VHx9v3ph9//33SkxMVLdu3XIdGwAg70JCQnT58mXFxMQoLi5Op06dUkhIiJo2bWqVLKtWrZo8PT3NfuDaa76Tk5Puv//+bH3KfffdJycnpxyPHR0drddff12vv/661ThluXGjfk+S2fddm5i6ti/Myz2flPv7vu7du+vChQvmUAdxcXGKjY2lX7tHMWYZ7km//fabWrVqpSpVqmj8+PEqV66cnJyctGrVKk2YMCFXT19lZGTokUce0WuvvZbj+gcffDDf8dnb2+dpedZf9bPi/uCDD1SnTp0cy5YoUSLfcQEAbOdm13RJ2SaBKWibN2/WY489pqZNm2rq1Kny8/OTo6OjZs2apfnz51uVDQsLk4+Pj7788ks1bdpUX375pXx9fc0/GgEACka9evXk4uKiTZs2qXz58vL29taDDz6okJAQTZ06Vampqdq8ebMef/zxfO3/6iexrlW9enUlJSXpiy++0IsvvpinJ65y0+/ZWl7u+6pVq6agoCB9+eWX6t69u7788ks5OTnpqaeeKrD4cPsiWYa7SpkyZeTq6mo+YXW1uLg489/Lly9Xamqqli1bZvUXjg0bNmTb7no3KhUrVtTZs2dvelNQmDc6FStWlCS5ublxswIAd7CAgABlZGTo8OHDqlq1qrk8MTFRSUlJCggIyNO+JN20b5Skr7/+Wi4uLlqzZo2cnZ3N5bNmzcq2rb29vZ599lnNnj1b77//vpYuXaoXXnjhujdDAADbcHJy0sMPP6zNmzerfPnyCgkJkXTlibPU1FTNmzdPiYmJatq0qaT/9QNxcXG6//77zf2kpaXp6NGjebpvKF26tBYvXqwmTZqoVatW2rJli/z9/W1Sr6y+7+jRo1ZPjf36669W5XJ7zyfl7b5PuvJ02aBBg3Ty5EnNnz9f4eHh5hNwuLfwGibuKvb29goLC9PSpUsVHx9vLj906JDWrFljVU6y/itGcnJyjjcDxYsXzzalsnRlNpeYmBir/WZJSkrS5cuXJUnFihUzlxW0oKAgVaxYUR9++KHOnj2bbf2ff/5Z4DEAAG5d27ZtJV2Zlflq48ePl6QcZwa7Hj8/P9WpU0dz5syxet0kKipKBw8etCprb28vi8Wi9PR0c9mxY8e0dOnSHPfdrVs3nTlzRi+++KLOnj2r5557LtdxAQDyLyQkRNu3b9eGDRvMZFnp0qVVtWpVc3bJrOWhoaFycnLS5MmTre5/Zs6cqeTk5Dz1KZJUtmxZff/997pw4YIeeeQR/f333zapU9Z4Y1OnTrVa/tFHH1n9nNt7vqyyUu7u+yTpmWeekcVi0auvvqojR47Qr93DeLIMd52RI0dq9erVCgkJ0csvv6zLly/ro48+UvXq1bVv3z5JUuvWreXk5KT27dub/8GfMWOGvL29dfLkSav9BQUFadq0aXr77bdVqVIleXt7q2XLlho8eLCWLVumdu3aqWfPngoKCtK5c+e0f/9+LV68WMeOHVPp0qXl6uqqatWqaeHChXrwwQfl6empGjVqqEaNGjavu52dnT777DO1adNG1atXV69evXTffffpjz/+0IYNG+Tm5qbly5fb/LgAANuqXbu2evTooU8//VRJSUlq1qyZduzYoTlz5qhjx45q0aJFnvY3ZswYhYeHq0mTJurdu7dOnz5t9o1X/3ElPDxc48eP16OPPqpnn31Wp06d0pQpU1SpUiWzD73aQw89pBo1amjRokWqWrWq6tate8t1BwDcXEhIiN555x0dP37cTIpJUtOmTfXJJ5+oQoUKKlu2rKQrT2INGTJEI0eO1KOPPqrHHntMcXFxmjp1qurXr5+vhFClSpW0du1aNW/eXGFhYVq/fr3c3NxuqU5BQUHq3LmzJk6cqL///lsNGzZUdHS0fvnlF0nWb+zk5p5Pytt9n3SlrR599FEtWrRIHh4eeU4k4u7Bk2W469SqVUtr1qxRmTJlNGzYMH3++ecaOXKk1Tv7lStX1uLFi2WxWPTvf/9b06dPV9++ffXqq69m29+wYcPUtm1bjR07Vs8884xGjRol6coTY9HR0Ro8eLA2btyoV199Ve+9954OHz6skSNHWs049tlnn+m+++7TwIED9cwzz2jx4sUFVv/mzZsrJiZG9erV08cff6z+/ftr9uzZ8vX11cCBAwvsuAAA2/rss880cuRI7dy5UwMGDND69es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View histogram of all features again now with the hour feature\n","# View the first few rows of the train dataset to verify the addition of the 'hour' feature\n","print(\"First few rows of train dataset with the 'hour' feature:\")\n","print(train.head())\n","\n","# View histogram of all features again now with the 'hour' feature\n","train.hist(figsize=(15, 10), bins=20, grid=False)\n","plt.suptitle(\"Distribution of Features including 'hour'\", fontsize=16)\n","plt.show()"]},{"cell_type":"markdown","metadata":{"id":"R2CXE2-jZhjz"},"source":["## Step 5: Rerun the model with the same settings as before, just with more features"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"J895KRhMZhj1","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1714513343120,"user_tz":-60,"elapsed":629528,"user":{"displayName":"Paschal Ugwu","userId":"02920440952136523881"}},"outputId":"6df5e93b-5e4e-4904-d1d4-53b6aacc1785"},"outputs":[{"output_type":"stream","name":"stderr","text":["No path specified. Models will be saved in: \"AutogluonModels/ag-20240430_213153\"\n","Presets specified: ['best_quality']\n","Setting dynamic_stacking from 'auto' to True. Reason: Enable dynamic_stacking when use_bag_holdout is disabled. (use_bag_holdout=False)\n","Stack configuration (auto_stack=True): num_stack_levels=1, num_bag_folds=8, num_bag_sets=1\n","Dynamic stacking is enabled (dynamic_stacking=True). AutoGluon will try to determine whether the input data is affected by stacked overfitting and enable or disable stacking as a consequence.\n","Detecting stacked overfitting by sub-fitting AutoGluon on the input data. That is, copies of AutoGluon will be sub-fit on subset(s) of the data. Then, the holdout validation data is used to detect stacked overfitting.\n","Sub-fit(s) time limit is: 600 seconds.\n","Starting holdout-based sub-fit for dynamic stacking. Context path is: AutogluonModels/ag-20240430_213153/ds_sub_fit/sub_fit_ho.\n","Running the sub-fit in a ray process to avoid memory leakage.\n","Spend 213 seconds for the sub-fit(s) during dynamic stacking.\n","Time left for full fit of AutoGluon: 387 seconds.\n","Starting full fit now with num_stack_levels 1.\n","Beginning AutoGluon training ... Time limit = 387s\n","AutoGluon will save models to \"AutogluonModels/ag-20240430_213153\"\n","=================== System Info ===================\n","AutoGluon Version:  1.1.0\n","Python Version:     3.10.12\n","Operating System:   Linux\n","Platform Machine:   x86_64\n","Platform Version:   #1 SMP PREEMPT_DYNAMIC Sat Nov 18 15:31:17 UTC 2023\n","CPU Count:          2\n","Memory Avail:       10.36 GB / 12.67 GB (81.7%)\n","Disk Space Avail:   75.57 GB / 107.72 GB (70.2%)\n","===================================================\n","Train Data Rows:    10886\n","Train Data Columns: 10\n","Label Column:       count\n","Problem Type:       regression\n","Preprocessing data ...\n","Using Feature Generators to preprocess the data ...\n","Fitting AutoMLPipelineFeatureGenerator...\n","\tAvailable Memory:                    10604.62 MB\n","\tTrain Data (Original)  Memory Usage: 0.64 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","\tStage 5 Generators:\n","\t\tFitting DropDuplicatesFeatureGenerator...\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', [])      : 4 | ['holiday', 'workingday', 'humidity', 'hour']\n","\tTypes of features in processed data (raw dtype, special dtypes):\n","\t\t('category', [])             : 2 | ['season', 'weather']\n","\t\t('float', [])                : 3 | ['temp', 'atemp', 'windspeed']\n","\t\t('int', [])                  : 2 | ['humidity', 'hour']\n","\t\t('int', ['bool'])            : 2 | ['holiday', 'workingday']\n","\t\t('int', ['datetime_as_int']) : 5 | ['datetime', 'datetime.year', 'datetime.month', 'datetime.day', 'datetime.dayofweek']\n","\t0.4s = Fit runtime\n","\t10 features in original data used to generate 14 features in processed data.\n","\tTrain Data (Processed) Memory Usage: 0.83 MB (0.0% of available memory)\n","Data preprocessing and feature engineering runtime = 0.43s ...\n","AutoGluon will gauge predictive performance using evaluation metric: 'root_mean_squared_error'\n","\tThis metric's sign has been flipped to adhere to being higher_is_better. The metric score can be multiplied by -1 to get the metric value.\n","\tTo change this, specify the eval_metric parameter of Predictor()\n","Large model count detected (112 configs) ... Only displaying the first 3 models of each family. To see all, set `verbosity=3`.\n","User-specified model hyperparameters to be fit:\n","{\n","\t'NN_TORCH': [{}, {'activation': 'elu', 'dropout_prob': 0.10077639529843717, 'hidden_size': 108, 'learning_rate': 0.002735937344002146, 'num_layers': 4, 'use_batchnorm': True, 'weight_decay': 1.356433327634438e-12, 'ag_args': {'name_suffix': '_r79', 'priority': -2}}, {'activation': 'elu', 'dropout_prob': 0.11897478034205347, 'hidden_size': 213, 'learning_rate': 0.0010474382260641949, 'num_layers': 4, 'use_batchnorm': False, 'weight_decay': 5.594471067786272e-10, 'ag_args': {'name_suffix': '_r22', 'priority': -7}}],\n","\t'GBM': [{'extra_trees': True, 'ag_args': {'name_suffix': 'XT'}}, {}, 'GBMLarge'],\n","\t'CAT': [{}, {'depth': 6, 'grow_policy': 'SymmetricTree', 'l2_leaf_reg': 2.1542798306067823, 'learning_rate': 0.06864209415792857, 'max_ctr_complexity': 4, 'one_hot_max_size': 10, 'ag_args': {'name_suffix': '_r177', 'priority': -1}}, {'depth': 8, 'grow_policy': 'Depthwise', 'l2_leaf_reg': 2.7997999596449104, 'learning_rate': 0.031375015734637225, 'max_ctr_complexity': 2, 'one_hot_max_size': 3, 'ag_args': {'name_suffix': '_r9', 'priority': -5}}],\n","\t'XGB': [{}, {'colsample_bytree': 0.6917311125174739, 'enable_categorical': False, 'learning_rate': 0.018063876087523967, 'max_depth': 10, 'min_child_weight': 0.6028633586934382, 'ag_args': {'name_suffix': '_r33', 'priority': -8}}, {'colsample_bytree': 0.6628423832084077, 'enable_categorical': False, 'learning_rate': 0.08775715546881824, 'max_depth': 5, 'min_child_weight': 0.6294123374222513, 'ag_args': {'name_suffix': '_r89', 'priority': -16}}],\n","\t'FASTAI': [{}, {'bs': 256, 'emb_drop': 0.5411770367537934, 'epochs': 43, 'layers': [800, 400], 'lr': 0.01519848858318159, 'ps': 0.23782946566604385, 'ag_args': {'name_suffix': '_r191', 'priority': -4}}, {'bs': 2048, 'emb_drop': 0.05070411322605811, 'epochs': 29, 'layers': [200, 100], 'lr': 0.08974235041576624, 'ps': 0.10393466140748028, 'ag_args': {'name_suffix': '_r102', 'priority': -11}}],\n","\t'RF': [{'criterion': 'gini', 'ag_args': {'name_suffix': 'Gini', 'problem_types': ['binary', 'multiclass']}}, {'criterion': 'entropy', 'ag_args': {'name_suffix': 'Entr', 'problem_types': ['binary', 'multiclass']}}, {'criterion': 'squared_error', 'ag_args': {'name_suffix': 'MSE', 'problem_types': ['regression', 'quantile']}}],\n","\t'XT': [{'criterion': 'gini', 'ag_args': {'name_suffix': 'Gini', 'problem_types': ['binary', 'multiclass']}}, {'criterion': 'entropy', 'ag_args': {'name_suffix': 'Entr', 'problem_types': ['binary', 'multiclass']}}, {'criterion': 'squared_error', 'ag_args': {'name_suffix': 'MSE', 'problem_types': ['regression', 'quantile']}}],\n","\t'KNN': [{'weights': 'uniform', 'ag_args': {'name_suffix': 'Unif'}}, {'weights': 'distance', 'ag_args': {'name_suffix': 'Dist'}}],\n","}\n","AutoGluon will fit 2 stack levels (L1 to L2) ...\n","Fitting 108 L1 models ...\n","Fitting model: KNeighborsUnif_BAG_L1 ... Training model for up to 257.64s of the 386.52s of remaining time.\n","\t-101.5462\t = Validation score   (-root_mean_squared_error)\n","\t0.07s\t = Training   runtime\n","\t0.2s\t = Validation runtime\n","Fitting model: KNeighborsDist_BAG_L1 ... Training model for up to 257.26s of the 386.15s of remaining time.\n","\t-84.1251\t = Validation score   (-root_mean_squared_error)\n","\t0.09s\t = Training   runtime\n","\t0.1s\t = Validation runtime\n","Fitting model: LightGBMXT_BAG_L1 ... Training model for up to 257.01s of the 385.9s of remaining time.\n","\tFitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy (2 workers, per: cpus=1, gpus=0, memory=0.07%)\n","\t-34.4573\t = Validation score   (-root_mean_squared_error)\n","\t136.37s\t = Training   runtime\n","\t39.7s\t = Validation runtime\n","Fitting model: LightGBM_BAG_L1 ... Training model for up to 108.23s of the 237.11s of remaining time.\n","\tFitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy (2 workers, per: cpus=1, gpus=0, memory=0.07%)\n","\t-33.9196\t = Validation score   (-root_mean_squared_error)\n","\t70.28s\t = Training   runtime\n","\t8.25s\t = Validation runtime\n","Fitting model: RandomForestMSE_BAG_L1 ... Training model for up to 33.02s of the 161.91s of remaining time.\n","\t-38.4543\t = Validation score   (-root_mean_squared_error)\n","\t22.17s\t = Training   runtime\n","\t0.89s\t = Validation runtime\n","Fitting model: CatBoost_BAG_L1 ... Training model for up to 7.93s of the 136.82s of remaining time.\n","\tFitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy (2 workers, per: cpus=1, gpus=0, memory=0.10%)\n","\tTime limit exceeded... Skipping CatBoost_BAG_L1.\n","2024-04-30 21:39:58,382\tERROR worker.py:406 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): The worker died unexpectedly while executing this task. Check python-core-worker-*.log files for more information.\n","Fitting model: WeightedEnsemble_L2 ... Training model for up to 360.0s of the 115.65s of remaining time.\n","\tEnsemble Weights: {'LightGBMXT_BAG_L1': 0.4, 'LightGBM_BAG_L1': 0.4, 'RandomForestMSE_BAG_L1': 0.16, 'KNeighborsDist_BAG_L1': 0.04}\n","\t-32.3704\t = Validation score   (-root_mean_squared_error)\n","\t0.15s\t = Training   runtime\n","\t0.0s\t = Validation runtime\n","Fitting 106 L2 models ...\n","Fitting model: LightGBMXT_BAG_L2 ... Training model for up to 115.46s of the 115.39s of remaining time.\n","\tFitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy (2 workers, per: cpus=1, gpus=0, memory=0.09%)\n","\t-31.2181\t = Validation score   (-root_mean_squared_error)\n","\t45.59s\t = Training   runtime\n","\t1.73s\t = Validation runtime\n","Fitting model: LightGBM_BAG_L2 ... Training model for up to 65.98s of the 65.92s of remaining time.\n","\tFitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy (2 workers, per: cpus=1, gpus=0, memory=0.09%)\n","\t-30.6679\t = Validation score   (-root_mean_squared_error)\n","\t41.83s\t = Training   runtime\n","\t0.65s\t = Validation runtime\n","Fitting model: RandomForestMSE_BAG_L2 ... Training model for up to 19.97s of the 19.88s of remaining time.\n","\t-31.9975\t = Validation score   (-root_mean_squared_error)\n","\t45.93s\t = Training   runtime\n","\t0.7s\t = Validation runtime\n","Fitting model: WeightedEnsemble_L3 ... Training model for up to 360.0s of the -28.5s of remaining time.\n","\tEnsemble Weights: {'LightGBM_BAG_L2': 0.583, 'LightGBMXT_BAG_L2': 0.292, 'RandomForestMSE_BAG_L2': 0.083, 'LightGBMXT_BAG_L1': 0.042}\n","\t-30.4288\t = Validation score   (-root_mean_squared_error)\n","\t0.05s\t = Training   runtime\n","\t0.0s\t = Validation runtime\n","AutoGluon training complete, total runtime = 415.62s ... Best model: \"WeightedEnsemble_L3\"\n","TabularPredictor saved. To load, use: predictor = TabularPredictor.load(\"AutogluonModels/ag-20240430_213153\")\n"]}],"source":["# Train the model with additional features\n","predictor_new_features = TabularPredictor(label='count', eval_metric='root_mean_squared_error').fit(train_data=train, time_limit=600, presets='best_quality')"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"jxgKtGT5Zhj1","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1714513384466,"user_tz":-60,"elapsed":490,"user":{"displayName":"Paschal Ugwu","userId":"02920440952136523881"}},"outputId":"7f966c16-d076-49ab-fd0b-aa9372475ef9"},"outputs":[{"output_type":"stream","name":"stdout","text":["*** Summary of fit() ***\n","Estimated performance of each model:\n","                    model   score_val              eval_metric  pred_time_val    fit_time  pred_time_val_marginal  fit_time_marginal  stack_level  can_infer  fit_order\n","0     WeightedEnsemble_L3  -30.428847  root_mean_squared_error      52.220520  362.390117                0.001204           0.054599            3       True         10\n","1         LightGBM_BAG_L2  -30.667943  root_mean_squared_error      49.794716  270.815830                0.649112          41.834797            2       True          8\n","2       LightGBMXT_BAG_L2  -31.218084  root_mean_squared_error      50.872662  274.568516                1.727058          45.587483            2       True          7\n","3  RandomForestMSE_BAG_L2  -31.997543  root_mean_squared_error      49.843146  274.913238                0.697543          45.932205            2       True          9\n","4     WeightedEnsemble_L2  -32.370365  root_mean_squared_error      48.942731  229.051562                0.001650           0.145401            2       True          6\n","5         LightGBM_BAG_L1  -33.919639  root_mean_squared_error       8.249959   70.276205                8.249959          70.276205            1       True          4\n","6       LightGBMXT_BAG_L1  -34.457274  root_mean_squared_error      39.699899  136.374808               39.699899         136.374808            1       True          3\n","7  RandomForestMSE_BAG_L1  -38.454338  root_mean_squared_error       0.890491   22.165741                0.890491          22.165741            1       True          5\n","8   KNeighborsDist_BAG_L1  -84.125061  root_mean_squared_error       0.100731    0.089406                0.100731           0.089406            1       True          2\n","9   KNeighborsUnif_BAG_L1 -101.546199  root_mean_squared_error       0.204523    0.074873                0.204523           0.074873            1       True          1\n","Number of models trained: 10\n","Types of models trained:\n","{'StackerEnsembleModel_LGB', 'StackerEnsembleModel_KNN', 'StackerEnsembleModel_RF', 'WeightedEnsembleModel'}\n","Bagging used: True  (with 8 folds)\n","Multi-layer stack-ensembling used: True  (with 3 levels)\n","Feature Metadata (Processed):\n","(raw dtype, special dtypes):\n","('category', [])             : 2 | ['season', 'weather']\n","('float', [])                : 3 | ['temp', 'atemp', 'windspeed']\n","('int', [])                  : 2 | ['humidity', 'hour']\n","('int', ['bool'])            : 2 | ['holiday', 'workingday']\n","('int', ['datetime_as_int']) : 5 | ['datetime', 'datetime.year', 'datetime.month', 'datetime.day', 'datetime.dayofweek']\n","*** End of fit() summary ***\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/autogluon/core/utils/plots.py:169: UserWarning: AutoGluon summary plots cannot be created because bokeh is not installed. To see plots, please do: \"pip install bokeh==2.0.1\"\n","  warnings.warn('AutoGluon summary plots cannot be created because bokeh is not installed. To see plots, please do: \"pip install bokeh==2.0.1\"')\n"]},{"output_type":"execute_result","data":{"text/plain":["{'model_types': {'KNeighborsUnif_BAG_L1': 'StackerEnsembleModel_KNN',\n","  'KNeighborsDist_BAG_L1': 'StackerEnsembleModel_KNN',\n","  'LightGBMXT_BAG_L1': 'StackerEnsembleModel_LGB',\n","  'LightGBM_BAG_L1': 'StackerEnsembleModel_LGB',\n","  'RandomForestMSE_BAG_L1': 'StackerEnsembleModel_RF',\n","  'WeightedEnsemble_L2': 'WeightedEnsembleModel',\n","  'LightGBMXT_BAG_L2': 'StackerEnsembleModel_LGB',\n","  'LightGBM_BAG_L2': 'StackerEnsembleModel_LGB',\n","  'RandomForestMSE_BAG_L2': 'StackerEnsembleModel_RF',\n","  'WeightedEnsemble_L3': 'WeightedEnsembleModel'},\n"," 'model_performance': {'KNeighborsUnif_BAG_L1': -101.54619908446061,\n","  'KNeighborsDist_BAG_L1': -84.12506123181602,\n","  'LightGBMXT_BAG_L1': -34.457273921492806,\n","  'LightGBM_BAG_L1': -33.919639163586254,\n","  'RandomForestMSE_BAG_L1': -38.4543378929385,\n","  'WeightedEnsemble_L2': -32.37036514580666,\n","  'LightGBMXT_BAG_L2': -31.218084362745092,\n","  'LightGBM_BAG_L2': -30.667943332228102,\n","  'RandomForestMSE_BAG_L2': -31.997543164524284,\n","  'WeightedEnsemble_L3': -30.42884697002813},\n"," 'model_best': 'WeightedEnsemble_L3',\n"," 'model_paths': {'KNeighborsUnif_BAG_L1': ['KNeighborsUnif_BAG_L1'],\n","  'KNeighborsDist_BAG_L1': ['KNeighborsDist_BAG_L1'],\n","  'LightGBMXT_BAG_L1': ['LightGBMXT_BAG_L1'],\n","  'LightGBM_BAG_L1': ['LightGBM_BAG_L1'],\n","  'RandomForestMSE_BAG_L1': ['RandomForestMSE_BAG_L1'],\n","  'WeightedEnsemble_L2': ['WeightedEnsemble_L2'],\n","  'LightGBMXT_BAG_L2': ['LightGBMXT_BAG_L2'],\n","  'LightGBM_BAG_L2': ['LightGBM_BAG_L2'],\n","  'RandomForestMSE_BAG_L2': ['RandomForestMSE_BAG_L2'],\n","  'WeightedEnsemble_L3': ['WeightedEnsemble_L3']},\n"," 'model_fit_times': {'KNeighborsUnif_BAG_L1': 0.07487297058105469,\n","  'KNeighborsDist_BAG_L1': 0.08940649032592773,\n","  'LightGBMXT_BAG_L1': 136.37480759620667,\n","  'LightGBM_BAG_L1': 70.27620530128479,\n","  'RandomForestMSE_BAG_L1': 22.165740966796875,\n","  'WeightedEnsemble_L2': 0.1454014778137207,\n","  'LightGBMXT_BAG_L2': 45.587482929229736,\n","  'LightGBM_BAG_L2': 41.83479714393616,\n","  'RandomForestMSE_BAG_L2': 45.932204723358154,\n","  'WeightedEnsemble_L3': 0.05459856986999512},\n"," 'model_pred_times': {'KNeighborsUnif_BAG_L1': 0.20452284812927246,\n","  'KNeighborsDist_BAG_L1': 0.10073113441467285,\n","  'LightGBMXT_BAG_L1': 39.699899196624756,\n","  'LightGBM_BAG_L1': 8.249959468841553,\n","  'RandomForestMSE_BAG_L1': 0.8904910087585449,\n","  'WeightedEnsemble_L2': 0.001650094985961914,\n","  'LightGBMXT_BAG_L2': 1.727057933807373,\n","  'LightGBM_BAG_L2': 0.6491119861602783,\n","  'RandomForestMSE_BAG_L2': 0.697542667388916,\n","  'WeightedEnsemble_L3': 0.0012042522430419922},\n"," 'num_bag_folds': 8,\n"," 'max_stack_level': 3,\n"," 'model_hyperparams': {'KNeighborsUnif_BAG_L1': {'use_orig_features': True,\n","   'max_base_models': 25,\n","   'max_base_models_per_type': 5,\n","   'save_bag_folds': True,\n","   'use_child_oof': True},\n","  'KNeighborsDist_BAG_L1': {'use_orig_features': True,\n","   'max_base_models': 25,\n","   'max_base_models_per_type': 5,\n","   'save_bag_folds': True,\n","   'use_child_oof': True},\n","  'LightGBMXT_BAG_L1': {'use_orig_features': True,\n","   'max_base_models': 25,\n","   'max_base_models_per_type': 5,\n","   'save_bag_folds': True},\n","  'LightGBM_BAG_L1': {'use_orig_features': True,\n","   'max_base_models': 25,\n","   'max_base_models_per_type': 5,\n","   'save_bag_folds': True},\n","  'RandomForestMSE_BAG_L1': {'use_orig_features': True,\n","   'max_base_models': 25,\n","   'max_base_models_per_type': 5,\n","   'save_bag_folds': True,\n","   'use_child_oof': True},\n","  'WeightedEnsemble_L2': {'use_orig_features': False,\n","   'max_base_models': 25,\n","   'max_base_models_per_type': 5,\n","   'save_bag_folds': True},\n","  'LightGBMXT_BAG_L2': {'use_orig_features': True,\n","   'max_base_models': 25,\n","   'max_base_models_per_type': 5,\n","   'save_bag_folds': True},\n","  'LightGBM_BAG_L2': {'use_orig_features': True,\n","   'max_base_models': 25,\n","   'max_base_models_per_type': 5,\n","   'save_bag_folds': True},\n","  'RandomForestMSE_BAG_L2': {'use_orig_features': True,\n","   'max_base_models': 25,\n","   'max_base_models_per_type': 5,\n","   'save_bag_folds': True,\n","   'use_child_oof': True},\n","  'WeightedEnsemble_L3': {'use_orig_features': False,\n","   'max_base_models': 25,\n","   'max_base_models_per_type': 5,\n","   'save_bag_folds': True}},\n"," 'leaderboard':                     model   score_val              eval_metric  pred_time_val  \\\n"," 0     WeightedEnsemble_L3  -30.428847  root_mean_squared_error      52.220520   \n"," 1         LightGBM_BAG_L2  -30.667943  root_mean_squared_error      49.794716   \n"," 2       LightGBMXT_BAG_L2  -31.218084  root_mean_squared_error      50.872662   \n"," 3  RandomForestMSE_BAG_L2  -31.997543  root_mean_squared_error      49.843146   \n"," 4     WeightedEnsemble_L2  -32.370365  root_mean_squared_error      48.942731   \n"," 5         LightGBM_BAG_L1  -33.919639  root_mean_squared_error       8.249959   \n"," 6       LightGBMXT_BAG_L1  -34.457274  root_mean_squared_error      39.699899   \n"," 7  RandomForestMSE_BAG_L1  -38.454338  root_mean_squared_error       0.890491   \n"," 8   KNeighborsDist_BAG_L1  -84.125061  root_mean_squared_error       0.100731   \n"," 9   KNeighborsUnif_BAG_L1 -101.546199  root_mean_squared_error       0.204523   \n"," \n","      fit_time  pred_time_val_marginal  fit_time_marginal  stack_level  \\\n"," 0  362.390117                0.001204           0.054599            3   \n"," 1  270.815830                0.649112          41.834797            2   \n"," 2  274.568516                1.727058          45.587483            2   \n"," 3  274.913238                0.697543          45.932205            2   \n"," 4  229.051562                0.001650           0.145401            2   \n"," 5   70.276205                8.249959          70.276205            1   \n"," 6  136.374808               39.699899         136.374808            1   \n"," 7   22.165741                0.890491          22.165741            1   \n"," 8    0.089406                0.100731           0.089406            1   \n"," 9    0.074873                0.204523           0.074873            1   \n"," \n","    can_infer  fit_order  \n"," 0       True         10  \n"," 1       True          8  \n"," 2       True          7  \n"," 3       True          9  \n"," 4       True          6  \n"," 5       True          4  \n"," 6       True          3  \n"," 7       True          5  \n"," 8       True          2  \n"," 9       True          1  }"]},"metadata":{},"execution_count":25}],"source":["predictor_new_features.fit_summary()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"ydlVV8-oZhj2"},"outputs":[],"source":["# Remember to set all negative values to zero\n","\"\"\"As negative predictions are not meaningful in this context, \\\n","it's crucial to set them to zero to comply with Kaggle's submission \\\n","requirements. \"\"\"\n","# This ensures that all predictions are non-negative.\n","predictions_new_features = predictor_new_features.predict(test)\n","predictions_new_features = predictions_new_features.clip(lower=0)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"Csd-dEGEZhj3"},"outputs":[],"source":["# Same submitting predictions\n","\"\"\" Set predictions with new features to the submission dataframe, save \\\n","it to a CSV file, and submit it to the Kaggle competition. \"\"\"\n","submission_new_features = pd.read_csv('/content/sampleSubmission.csv')\n","submission_new_features[\"count\"] = predictions_new_features\n","submission_new_features.to_csv(\"submission_new_features.csv\", index=False)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"HNcMDbiBZhj3","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1714513575594,"user_tz":-60,"elapsed":2866,"user":{"displayName":"Paschal Ugwu","userId":"02920440952136523881"}},"outputId":"16320a4a-2ae1-4be0-8bba-92540d1b88df"},"outputs":[{"output_type":"stream","name":"stdout","text":["100% 188k/188k [00:00<00:00, 309kB/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":{"id":"DNHvkmxMZhj4","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1714513578383,"user_tz":-60,"elapsed":954,"user":{"displayName":"Paschal Ugwu","userId":"02920440952136523881"}},"outputId":"0d5676eb-9ca2-41d6-a68a-f23f36570104"},"outputs":[{"output_type":"stream","name":"stdout","text":["fileName                     date                 description                        status    publicScore  privateScore  \n","---------------------------  -------------------  ---------------------------------  --------  -----------  ------------  \n","submission_new_features.csv  2024-04-30 21:46:13  new features                       complete  0.62523      0.62523       \n","submission.csv               2024-04-30 21:29:58  first raw submission               complete  1.83892      1.83892       \n","submission.csv               2024-04-30 21:28:13  first raw submission               complete  1.83892      1.83892       \n","submission_new_hpo.csv       2024-04-30 11:42:05  new features with hyperparameters  complete  0.49123      0.49123       \n"]}],"source":["!kaggle competitions submissions -c bike-sharing-demand | tail -n +1 | head -n 6"]},{"cell_type":"markdown","metadata":{"id":"atEs3ULrZhj5"},"source":["#### New Score of `0.62523`"]},{"cell_type":"markdown","metadata":{"id":"GAt7Lxx0Zhj6"},"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":"1QQ8KN_eZhj6","colab":{"base_uri":"https://localhost:8080/","height":1000,"referenced_widgets":["9eae13167e204c8c9163b5db2fbd4dc6","532f9f194ba74a9aa3c6127cf77dc15e","117490f63d984fc7a9a3cae449433656","c2fb9396ad5142db984cd3865ca8c3d9","709c4960ae214a56b5bb99da5fdf32d7","a06931c780194e07a28041d068450f07","0d0471f3e0e44ff9be5250fe502f04e6","3e5dec853ef14829a58288b0af58ac80","a3c071d2c1924eacaa6077f9661d3561","cdbca5983575463fbf998d3028d6be73","c8ba1eb97dc54fa0b6b58fd07f917600","ff5f7c2185cc404cb2cebfcd283c2a4b","ff27c19be3044c57b2ae787e30c513a5","08162b0739c942baab635eead0f880d9","e7b1676005dd4a9dbb9b7800dd0425ba","f5e05c3453b8434daafb5a0f0bde67d8","befc7c017a1a4522828a7c5b344b6a42","0a8d703cc969444d88d2e9667c08c3fb","aac028007f064e6c8eead91693d393e8","8f85851e747441d6b478e2c567248ecd","48fe41c0b74c4488a63d3d43167a7395","09c8f873bb1f4323acbab131fa01113a","59579a5d9e6b49be83b857d2d0e001ec","c59ffcf24841427ca5ad35ebf5ba9b6d","de13deffef9946b5b1dd9971da71212d","dc4056e29c894d239c03e8036989e3c4","528338b2077a400ebe28899d0a2af0cd","951fd76865b84e999afceb67f3f73981","b8a6ddabcf47466daaa8c28c948d158b","30d23a61fc3649fa8de77bda7eecdb27","fb1b3c99dc2c45a98936f242bc283be6","fd77658cccee4f9a8092d97fa531fa7e","52dacc88d8984799a3e1c66425b0d5f9","c7cd7cd34c4549809dd2682421e23409","fc7d6959a1c14ca9a027d48a32fa79d0","a50f02f4132445e695cf84c3e52c38fc","13f7fea8e78443ba824390f1504c6f13","08600b721ff34c399a0c7455ea39b61e","d575fbbbb28b4988b4ec6f4222796879","b961fb47d93d473abb2505f6df374379","c68e7124f4384787875114f9f634f9d8","9bdc867410634cf98b9bfebb502e1220","3fc3bedc2fb047e5b5e122d4cd7facac","ca247bc223364961a0b940e5ada925b9"]},"executionInfo":{"status":"ok","timestamp":1714514164621,"user_tz":-60,"elapsed":543763,"user":{"displayName":"Paschal Ugwu","userId":"02920440952136523881"}},"outputId":"80c030f8-68f8-4e63-b800-83aa6114ea6e"},"outputs":[{"output_type":"stream","name":"stderr","text":["No path specified. Models will be saved in: \"AutogluonModels/ag-20240430_214700\"\n","Presets specified: ['best_quality']\n","Warning: hyperparameter tuning is currently experimental and may cause the process to hang.\n","Setting dynamic_stacking from 'auto' to True. Reason: Enable dynamic_stacking when use_bag_holdout is disabled. (use_bag_holdout=False)\n","Stack configuration (auto_stack=True): num_stack_levels=1, num_bag_folds=8, num_bag_sets=1\n","Dynamic stacking is enabled (dynamic_stacking=True). AutoGluon will try to determine whether the input data is affected by stacked overfitting and enable or disable stacking as a consequence.\n","Detecting stacked overfitting by sub-fitting AutoGluon on the input data. That is, copies of AutoGluon will be sub-fit on subset(s) of the data. Then, the holdout validation data is used to detect stacked overfitting.\n","Sub-fit(s) time limit is: 600 seconds.\n","Starting holdout-based sub-fit for dynamic stacking. Context path is: AutogluonModels/ag-20240430_214700/ds_sub_fit/sub_fit_ho.\n","Running the sub-fit in a ray process to avoid memory leakage.\n","Spend 147 seconds for the sub-fit(s) during dynamic stacking.\n","Time left for full fit of AutoGluon: 453 seconds.\n","Starting full fit now with num_stack_levels 1.\n","Beginning AutoGluon training ... Time limit = 453s\n","AutoGluon will save models to \"AutogluonModels/ag-20240430_214700\"\n","=================== System Info ===================\n","AutoGluon Version:  1.1.0\n","Python Version:     3.10.12\n","Operating System:   Linux\n","Platform Machine:   x86_64\n","Platform Version:   #1 SMP PREEMPT_DYNAMIC Sat Nov 18 15:31:17 UTC 2023\n","CPU Count:          2\n","Memory Avail:       10.26 GB / 12.67 GB (80.9%)\n","Disk Space Avail:   74.87 GB / 107.72 GB (69.5%)\n","===================================================\n","Train Data Rows:    10886\n","Train Data Columns: 10\n","Label Column:       count\n","Problem Type:       regression\n","Preprocessing data ...\n","Using Feature Generators to preprocess the data ...\n","Fitting AutoMLPipelineFeatureGenerator...\n","\tAvailable Memory:                    10506.64 MB\n","\tTrain Data (Original)  Memory Usage: 0.64 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","\tStage 5 Generators:\n","\t\tFitting DropDuplicatesFeatureGenerator...\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', [])      : 4 | ['holiday', 'workingday', 'humidity', 'hour']\n","\tTypes of features in processed data (raw dtype, special dtypes):\n","\t\t('category', [])             : 2 | ['season', 'weather']\n","\t\t('float', [])                : 3 | ['temp', 'atemp', 'windspeed']\n","\t\t('int', [])                  : 2 | ['humidity', 'hour']\n","\t\t('int', ['bool'])            : 2 | ['holiday', 'workingday']\n","\t\t('int', ['datetime_as_int']) : 5 | ['datetime', 'datetime.year', 'datetime.month', 'datetime.day', 'datetime.dayofweek']\n","\t0.3s = Fit runtime\n","\t10 features in original data used to generate 14 features in processed data.\n","\tTrain Data (Processed) Memory Usage: 0.83 MB (0.0% of available memory)\n","Data preprocessing and feature engineering runtime = 0.35s ...\n","AutoGluon will gauge predictive performance using evaluation metric: 'root_mean_squared_error'\n","\tThis metric's sign has been flipped to adhere to being higher_is_better. The metric score can be multiplied by -1 to get the metric value.\n","\tTo change this, specify the eval_metric parameter of Predictor()\n","User-specified model hyperparameters to be fit:\n","{\n","\t'GBM': {'num_boost_round': 100, 'num_leaves': 10},\n","\t'CAT': {'iterations': 100},\n","}\n","AutoGluon will fit 2 stack levels (L1 to L2) ...\n","Fitting 2 L1 models ...\n","Hyperparameter tuning model: LightGBM_BAG_L1 ... Tuning model for up to 135.76s of the 452.64s of remaining time.\n"]},{"output_type":"display_data","data":{"text/plain":["  0%|          | 0/5 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"9eae13167e204c8c9163b5db2fbd4dc6"}},"metadata":{}},{"output_type":"stream","name":"stderr","text":["\tFitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy (2 workers, per: cpus=1, gpus=0, memory=0.06%)\n","\tFitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy (2 workers, per: cpus=1, gpus=0, memory=0.06%)\n","\tFitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy (2 workers, per: cpus=1, gpus=0, memory=0.06%)\n","\tStopping HPO to satisfy time limit...\n","Fitted model: LightGBM_BAG_L1/T1 ...\n","\t-56.0012\t = Validation score   (-root_mean_squared_error)\n","\t36.39s\t = Training   runtime\n","\t0.0s\t = Validation runtime\n","Fitted model: LightGBM_BAG_L1/T2 ...\n","\t-49.9481\t = Validation score   (-root_mean_squared_error)\n","\t38.38s\t = Training   runtime\n","\t0.0s\t = Validation runtime\n","Fitted model: LightGBM_BAG_L1/T3 ...\n","\t-45.3082\t = Validation score   (-root_mean_squared_error)\n","\t39.41s\t = Training   runtime\n","\t0.0s\t = Validation runtime\n","Hyperparameter tuning model: CatBoost_BAG_L1 ... Tuning model for up to 135.76s of the 338.21s of remaining time.\n"]},{"output_type":"display_data","data":{"text/plain":["  0%|          | 0/5 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"ff5f7c2185cc404cb2cebfcd283c2a4b"}},"metadata":{}},{"output_type":"stream","name":"stderr","text":["\tFitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy (2 workers, per: cpus=1, gpus=0, memory=0.10%)\n","\tFitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy (2 workers, per: cpus=1, gpus=0, memory=0.07%)\n","\tFitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy (2 workers, per: cpus=1, gpus=0, memory=0.25%)\n","\tFitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy (2 workers, per: cpus=1, gpus=0, memory=0.10%)\n","\tStopping HPO to satisfy time limit...\n","Fitted model: CatBoost_BAG_L1/T1 ...\n","\t-55.9695\t = Validation score   (-root_mean_squared_error)\n","\t31.38s\t = Training   runtime\n","\t0.0s\t = Validation runtime\n","Fitted model: CatBoost_BAG_L1/T2 ...\n","\t-49.5419\t = Validation score   (-root_mean_squared_error)\n","\t31.78s\t = Training   runtime\n","\t0.0s\t = Validation runtime\n","Fitted model: CatBoost_BAG_L1/T3 ...\n","\t-62.963\t = Validation score   (-root_mean_squared_error)\n","\t34.44s\t = Training   runtime\n","\t0.0s\t = Validation runtime\n","Fitted model: CatBoost_BAG_L1/T4 ...\n","\t-95.868\t = Validation score   (-root_mean_squared_error)\n","\t43.15s\t = Training   runtime\n","\t0.0s\t = Validation runtime\n","Fitting model: WeightedEnsemble_L2 ... Training model for up to 360.0s of the 197.12s of remaining time.\n","\tEnsemble Weights: {'LightGBM_BAG_L1/T3': 0.87, 'CatBoost_BAG_L1/T2': 0.13}\n","\t-45.206\t = Validation score   (-root_mean_squared_error)\n","\t0.07s\t = Training   runtime\n","\t0.0s\t = Validation runtime\n","Fitting 2 L2 models ...\n","Hyperparameter tuning model: LightGBM_BAG_L2 ... Tuning model for up to 88.65s of the 196.99s of remaining time.\n"]},{"output_type":"display_data","data":{"text/plain":["  0%|          | 0/5 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"59579a5d9e6b49be83b857d2d0e001ec"}},"metadata":{}},{"output_type":"stream","name":"stderr","text":["\tFitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy (2 workers, per: cpus=1, gpus=0, memory=0.08%)\n","\tFitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy (2 workers, per: cpus=1, gpus=0, memory=0.08%)\n","\tStopping HPO to satisfy time limit...\n","Fitted model: LightGBM_BAG_L2/T1 ...\n","\t-41.9793\t = Validation score   (-root_mean_squared_error)\n","\t36.75s\t = Training   runtime\n","\t0.0s\t = Validation runtime\n","Fitted model: LightGBM_BAG_L2/T2 ...\n","\t-41.6214\t = Validation score   (-root_mean_squared_error)\n","\t37.02s\t = Training   runtime\n","\t0.0s\t = Validation runtime\n","Hyperparameter tuning model: CatBoost_BAG_L2 ... Tuning model for up to 88.65s of the 122.97s of remaining time.\n"]},{"output_type":"display_data","data":{"text/plain":["  0%|          | 0/5 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"c7cd7cd34c4549809dd2682421e23409"}},"metadata":{}},{"output_type":"stream","name":"stderr","text":["\tFitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy (2 workers, per: cpus=1, gpus=0, memory=0.14%)\n","\tFitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy (2 workers, per: cpus=1, gpus=0, memory=0.10%)\n","\tStopping HPO to satisfy time limit...\n","Fitted model: CatBoost_BAG_L2/T1 ...\n","\t-43.1874\t = Validation score   (-root_mean_squared_error)\n","\t33.41s\t = Training   runtime\n","\t0.0s\t = Validation runtime\n","Fitted model: CatBoost_BAG_L2/T2 ...\n","\t-41.7524\t = Validation score   (-root_mean_squared_error)\n","\t31.84s\t = Training   runtime\n","\t0.0s\t = Validation runtime\n","Fitting model: WeightedEnsemble_L3 ... Training model for up to 360.0s of the 57.44s of remaining time.\n","\tEnsemble Weights: {'LightGBM_BAG_L2/T2': 0.55, 'CatBoost_BAG_L2/T2': 0.45}\n","\t-41.3668\t = Validation score   (-root_mean_squared_error)\n","\t0.06s\t = Training   runtime\n","\t0.0s\t = Validation runtime\n","AutoGluon training complete, total runtime = 395.68s ... Best model: \"WeightedEnsemble_L3\"\n","TabularPredictor saved. To load, use: predictor = TabularPredictor.load(\"AutogluonModels/ag-20240430_214700\")\n"]}],"source":["# Define the hyperparameters for the model\n","hyperparameters = {\n","    'GBM': {'num_boost_round': 100, 'num_leaves': 10},  # Example for LightGBM model\n","    'CAT': {'iterations': 100}  # Example for CatBoost model\n","}\n","\n","# Define the hyperparameter tuning settings\n","hyperparameter_tune_kwargs = {\n","    'num_trials': 5,  # Number of trials for HPO\n","    'scheduler' : 'local',  # Scheduler for HPO\n","    'searcher': 'random'  # Searcher algorithm for HPO\n","}\n","\n","# Train the model with hyperparameter optimization\n","predictor_new_hpo = TabularPredictor(label='count', eval_metric='root_mean_squared_error').fit(\n","    train_data=train,\n","    time_limit=600,\n","    presets='best_quality',\n","    hyperparameters=hyperparameters,\n","    hyperparameter_tune_kwargs=hyperparameter_tune_kwargs\n",")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"gZn-wnM8Zhj7","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1714514189168,"user_tz":-60,"elapsed":474,"user":{"displayName":"Paschal Ugwu","userId":"02920440952136523881"}},"outputId":"899e10c7-0569-4827-926a-35ef4db112a8"},"outputs":[{"output_type":"stream","name":"stdout","text":["*** Summary of fit() ***\n","Estimated performance of each model:\n","                  model  score_val              eval_metric  pred_time_val    fit_time  pred_time_val_marginal  fit_time_marginal  stack_level  can_infer  fit_order\n","0   WeightedEnsemble_L3 -41.366805  root_mean_squared_error       0.004801  323.841944                0.001314           0.058850            3       True         13\n","1    LightGBM_BAG_L2/T2 -41.621439  root_mean_squared_error       0.003168  291.947508                0.000338          37.022625            2       True         10\n","2    CatBoost_BAG_L2/T2 -41.752438  root_mean_squared_error       0.003149  286.760469                0.000319          31.835585            2       True         12\n","3    LightGBM_BAG_L2/T1 -41.979315  root_mean_squared_error       0.003051  291.673368                0.000221          36.748485            2       True          9\n","4    CatBoost_BAG_L2/T1 -43.187351  root_mean_squared_error       0.003108  288.332504                0.000278          33.407621            2       True         11\n","5   WeightedEnsemble_L2 -45.205984  root_mean_squared_error       0.002005   71.265692                0.001494           0.074410            2       True          8\n","6    LightGBM_BAG_L1/T3 -45.308156  root_mean_squared_error       0.000241   39.413749                0.000241          39.413749            1       True          3\n","7    CatBoost_BAG_L1/T2 -49.541915  root_mean_squared_error       0.000270   31.777533                0.000270          31.777533            1       True          5\n","8    LightGBM_BAG_L1/T2 -49.948119  root_mean_squared_error       0.001450   38.375947                0.001450          38.375947            1       True          2\n","9    CatBoost_BAG_L1/T1 -55.969466  root_mean_squared_error       0.000227   31.381098                0.000227          31.381098            1       True          4\n","10   LightGBM_BAG_L1/T1 -56.001163  root_mean_squared_error       0.000230   36.387388                0.000230          36.387388            1       True          1\n","11   CatBoost_BAG_L1/T3 -62.962971  root_mean_squared_error       0.000203   34.443460                0.000203          34.443460            1       True          6\n","12   CatBoost_BAG_L1/T4 -95.867999  root_mean_squared_error       0.000209   43.145709                0.000209          43.145709            1       True          7\n","Number of models trained: 13\n","Types of models trained:\n","{'StackerEnsembleModel_LGB', 'StackerEnsembleModel_CatBoost', 'WeightedEnsembleModel'}\n","Bagging used: True  (with 8 folds)\n","Multi-layer stack-ensembling used: True  (with 3 levels)\n","Feature Metadata (Processed):\n","(raw dtype, special dtypes):\n","('category', [])             : 2 | ['season', 'weather']\n","('float', [])                : 3 | ['temp', 'atemp', 'windspeed']\n","('int', [])                  : 2 | ['humidity', 'hour']\n","('int', ['bool'])            : 2 | ['holiday', 'workingday']\n","('int', ['datetime_as_int']) : 5 | ['datetime', 'datetime.year', 'datetime.month', 'datetime.day', 'datetime.dayofweek']\n","*** End of fit() summary ***\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/autogluon/core/utils/plots.py:169: UserWarning: AutoGluon summary plots cannot be created because bokeh is not installed. To see plots, please do: \"pip install bokeh==2.0.1\"\n","  warnings.warn('AutoGluon summary plots cannot be created because bokeh is not installed. To see plots, please do: \"pip install bokeh==2.0.1\"')\n"]},{"output_type":"execute_result","data":{"text/plain":["{'model_types': {'LightGBM_BAG_L1/T1': 'StackerEnsembleModel_LGB',\n","  'LightGBM_BAG_L1/T2': 'StackerEnsembleModel_LGB',\n","  'LightGBM_BAG_L1/T3': 'StackerEnsembleModel_LGB',\n","  'CatBoost_BAG_L1/T1': 'StackerEnsembleModel_CatBoost',\n","  'CatBoost_BAG_L1/T2': 'StackerEnsembleModel_CatBoost',\n","  'CatBoost_BAG_L1/T3': 'StackerEnsembleModel_CatBoost',\n","  'CatBoost_BAG_L1/T4': 'StackerEnsembleModel_CatBoost',\n","  'WeightedEnsemble_L2': 'WeightedEnsembleModel',\n","  'LightGBM_BAG_L2/T1': 'StackerEnsembleModel_LGB',\n","  'LightGBM_BAG_L2/T2': 'StackerEnsembleModel_LGB',\n","  'CatBoost_BAG_L2/T1': 'StackerEnsembleModel_CatBoost',\n","  'CatBoost_BAG_L2/T2': 'StackerEnsembleModel_CatBoost',\n","  'WeightedEnsemble_L3': 'WeightedEnsembleModel'},\n"," 'model_performance': {'LightGBM_BAG_L1/T1': -56.001162826493214,\n","  'LightGBM_BAG_L1/T2': -49.948119282463814,\n","  'LightGBM_BAG_L1/T3': -45.308155521798334,\n","  'CatBoost_BAG_L1/T1': -55.969466336614005,\n","  'CatBoost_BAG_L1/T2': -49.54191516173068,\n","  'CatBoost_BAG_L1/T3': -62.96297149947911,\n","  'CatBoost_BAG_L1/T4': -95.86799867298608,\n","  'WeightedEnsemble_L2': -45.20598358885611,\n","  'LightGBM_BAG_L2/T1': -41.97931476642851,\n","  'LightGBM_BAG_L2/T2': -41.62143941646201,\n","  'CatBoost_BAG_L2/T1': -43.18735092682714,\n","  'CatBoost_BAG_L2/T2': -41.75243755217545,\n","  'WeightedEnsemble_L3': -41.36680484518501},\n"," 'model_best': 'WeightedEnsemble_L3',\n"," 'model_paths': {'LightGBM_BAG_L1/T1': ['LightGBM_BAG_L1', 'T1'],\n","  'LightGBM_BAG_L1/T2': ['LightGBM_BAG_L1', 'T2'],\n","  'LightGBM_BAG_L1/T3': ['LightGBM_BAG_L1', 'T3'],\n","  'CatBoost_BAG_L1/T1': ['CatBoost_BAG_L1', 'T1'],\n","  'CatBoost_BAG_L1/T2': ['CatBoost_BAG_L1', 'T2'],\n","  'CatBoost_BAG_L1/T3': ['CatBoost_BAG_L1', 'T3'],\n","  'CatBoost_BAG_L1/T4': ['CatBoost_BAG_L1', 'T4'],\n","  'WeightedEnsemble_L2': ['WeightedEnsemble_L2'],\n","  'LightGBM_BAG_L2/T1': ['LightGBM_BAG_L2', 'T1'],\n","  'LightGBM_BAG_L2/T2': ['LightGBM_BAG_L2', 'T2'],\n","  'CatBoost_BAG_L2/T1': ['CatBoost_BAG_L2', 'T1'],\n","  'CatBoost_BAG_L2/T2': ['CatBoost_BAG_L2', 'T2'],\n","  'WeightedEnsemble_L3': ['WeightedEnsemble_L3']},\n"," 'model_fit_times': {'LightGBM_BAG_L1/T1': 36.38738775253296,\n","  'LightGBM_BAG_L1/T2': 38.37594723701477,\n","  'LightGBM_BAG_L1/T3': 39.41374921798706,\n","  'CatBoost_BAG_L1/T1': 31.3810977935791,\n","  'CatBoost_BAG_L1/T2': 31.777532815933228,\n","  'CatBoost_BAG_L1/T3': 34.44345998764038,\n","  'CatBoost_BAG_L1/T4': 43.14570879936218,\n","  'WeightedEnsemble_L2': 0.07440996170043945,\n","  'LightGBM_BAG_L2/T1': 36.74848484992981,\n","  'LightGBM_BAG_L2/T2': 37.02262473106384,\n","  'CatBoost_BAG_L2/T1': 33.407620668411255,\n","  'CatBoost_BAG_L2/T2': 31.835585117340088,\n","  'WeightedEnsemble_L3': 0.05885028839111328},\n"," 'model_pred_times': {'LightGBM_BAG_L1/T1': 0.00022983551025390625,\n","  'LightGBM_BAG_L1/T2': 0.0014495849609375,\n","  'LightGBM_BAG_L1/T3': 0.0002410411834716797,\n","  'CatBoost_BAG_L1/T1': 0.00022721290588378906,\n","  'CatBoost_BAG_L1/T2': 0.0002701282501220703,\n","  'CatBoost_BAG_L1/T3': 0.00020313262939453125,\n","  'CatBoost_BAG_L1/T4': 0.0002086162567138672,\n","  'WeightedEnsemble_L2': 0.001493692398071289,\n","  'LightGBM_BAG_L2/T1': 0.00022125244140625,\n","  'LightGBM_BAG_L2/T2': 0.0003380775451660156,\n","  'CatBoost_BAG_L2/T1': 0.00027823448181152344,\n","  'CatBoost_BAG_L2/T2': 0.0003192424774169922,\n","  'WeightedEnsemble_L3': 0.0013136863708496094},\n"," 'num_bag_folds': 8,\n"," 'max_stack_level': 3,\n"," 'model_hyperparams': {'LightGBM_BAG_L1/T1': {'use_orig_features': True,\n","   'max_base_models': 25,\n","   'max_base_models_per_type': 5,\n","   'save_bag_folds': True},\n","  'LightGBM_BAG_L1/T2': {'use_orig_features': True,\n","   'max_base_models': 25,\n","   'max_base_models_per_type': 5,\n","   'save_bag_folds': True},\n","  'LightGBM_BAG_L1/T3': {'use_orig_features': True,\n","   'max_base_models': 25,\n","   'max_base_models_per_type': 5,\n","   'save_bag_folds': True},\n","  'CatBoost_BAG_L1/T1': {'use_orig_features': True,\n","   'max_base_models': 25,\n","   'max_base_models_per_type': 5,\n","   'save_bag_folds': True},\n","  'CatBoost_BAG_L1/T2': {'use_orig_features': True,\n","   'max_base_models': 25,\n","   'max_base_models_per_type': 5,\n","   'save_bag_folds': True},\n","  'CatBoost_BAG_L1/T3': {'use_orig_features': True,\n","   'max_base_models': 25,\n","   'max_base_models_per_type': 5,\n","   'save_bag_folds': True},\n","  'CatBoost_BAG_L1/T4': {'use_orig_features': True,\n","   'max_base_models': 25,\n","   'max_base_models_per_type': 5,\n","   'save_bag_folds': True},\n","  'WeightedEnsemble_L2': {'use_orig_features': False,\n","   'max_base_models': 25,\n","   'max_base_models_per_type': 5,\n","   'save_bag_folds': True},\n","  'LightGBM_BAG_L2/T1': {'use_orig_features': True,\n","   'max_base_models': 25,\n","   'max_base_models_per_type': 5,\n","   'save_bag_folds': True},\n","  'LightGBM_BAG_L2/T2': {'use_orig_features': True,\n","   'max_base_models': 25,\n","   'max_base_models_per_type': 5,\n","   'save_bag_folds': True},\n","  'CatBoost_BAG_L2/T1': {'use_orig_features': True,\n","   'max_base_models': 25,\n","   'max_base_models_per_type': 5,\n","   'save_bag_folds': True},\n","  'CatBoost_BAG_L2/T2': {'use_orig_features': True,\n","   'max_base_models': 25,\n","   'max_base_models_per_type': 5,\n","   'save_bag_folds': True},\n","  'WeightedEnsemble_L3': {'use_orig_features': False,\n","   'max_base_models': 25,\n","   'max_base_models_per_type': 5,\n","   'save_bag_folds': True}},\n"," 'leaderboard':                   model  score_val              eval_metric  pred_time_val  \\\n"," 0   WeightedEnsemble_L3 -41.366805  root_mean_squared_error       0.004801   \n"," 1    LightGBM_BAG_L2/T2 -41.621439  root_mean_squared_error       0.003168   \n"," 2    CatBoost_BAG_L2/T2 -41.752438  root_mean_squared_error       0.003149   \n"," 3    LightGBM_BAG_L2/T1 -41.979315  root_mean_squared_error       0.003051   \n"," 4    CatBoost_BAG_L2/T1 -43.187351  root_mean_squared_error       0.003108   \n"," 5   WeightedEnsemble_L2 -45.205984  root_mean_squared_error       0.002005   \n"," 6    LightGBM_BAG_L1/T3 -45.308156  root_mean_squared_error       0.000241   \n"," 7    CatBoost_BAG_L1/T2 -49.541915  root_mean_squared_error       0.000270   \n"," 8    LightGBM_BAG_L1/T2 -49.948119  root_mean_squared_error       0.001450   \n"," 9    CatBoost_BAG_L1/T1 -55.969466  root_mean_squared_error       0.000227   \n"," 10   LightGBM_BAG_L1/T1 -56.001163  root_mean_squared_error       0.000230   \n"," 11   CatBoost_BAG_L1/T3 -62.962971  root_mean_squared_error       0.000203   \n"," 12   CatBoost_BAG_L1/T4 -95.867999  root_mean_squared_error       0.000209   \n"," \n","       fit_time  pred_time_val_marginal  fit_time_marginal  stack_level  \\\n"," 0   323.841944                0.001314           0.058850            3   \n"," 1   291.947508                0.000338          37.022625            2   \n"," 2   286.760469                0.000319          31.835585            2   \n"," 3   291.673368                0.000221          36.748485            2   \n"," 4   288.332504                0.000278          33.407621            2   \n"," 5    71.265692                0.001494           0.074410            2   \n"," 6    39.413749                0.000241          39.413749            1   \n"," 7    31.777533                0.000270          31.777533            1   \n"," 8    38.375947                0.001450          38.375947            1   \n"," 9    31.381098                0.000227          31.381098            1   \n"," 10   36.387388                0.000230          36.387388            1   \n"," 11   34.443460                0.000203          34.443460            1   \n"," 12   43.145709                0.000209          43.145709            1   \n"," \n","     can_infer  fit_order  \n"," 0        True         13  \n"," 1        True         10  \n"," 2        True         12  \n"," 3        True          9  \n"," 4        True         11  \n"," 5        True          8  \n"," 6        True          3  \n"," 7        True          5  \n"," 8        True          2  \n"," 9        True          4  \n"," 10       True          1  \n"," 11       True          6  \n"," 12       True          7  }"]},"metadata":{},"execution_count":31}],"source":["# Review AutoGluon's training run with ranking of models that did the best.\n","predictor_new_hpo.fit_summary()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"q6Hw76UXZhj8"},"outputs":[],"source":["# Create predictions from the test dataset\n","predictions_new_hpo = predictor_new_hpo.predict(test)\n","\n","# Remember to set all negative values to zero\n","predictions_new_hpo = predictions_new_hpo.clip(lower=0)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"xNGVHd9iZhj9"},"outputs":[],"source":["# Load sample submission dataset\n","submission_new_hpo = pd.read_csv('/content/sampleSubmission.csv')\n","\n","# Set predictions to submission dataframe\n","submission_new_hpo[\"count\"] = predictions_new_hpo\n","\n","# Save and submit\n","submission_new_hpo.to_csv(\"submission_new_hpo.csv\", index=False)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"L458inbwZhj-","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1714514233805,"user_tz":-60,"elapsed":2415,"user":{"displayName":"Paschal Ugwu","userId":"02920440952136523881"}},"outputId":"1916271b-e806-45c8-aca3-1ec71963fe64"},"outputs":[{"output_type":"stream","name":"stdout","text":["100% 188k/188k [00:00<00:00, 377kB/s]\n","Successfully submitted to Bike Sharing Demand"]}],"source":["!kaggle competitions submit -c bike-sharing-demand -f submission_new_hpo.csv -m \"new features with hyperparameters\""]},{"cell_type":"code","execution_count":null,"metadata":{"id":"kdmzp8bFZhj-","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1714514241266,"user_tz":-60,"elapsed":882,"user":{"displayName":"Paschal Ugwu","userId":"02920440952136523881"}},"outputId":"af4482b0-9496-44b9-9811-e454585222df"},"outputs":[{"output_type":"stream","name":"stdout","text":["fileName                     date                 description                        status    publicScore  privateScore  \n","---------------------------  -------------------  ---------------------------------  --------  -----------  ------------  \n","submission_new_hpo.csv       2024-04-30 21:57:12  new features with hyperparameters  complete  0.514        0.514         \n","submission_new_features.csv  2024-04-30 21:46:13  new features                       complete  0.62523      0.62523       \n","submission.csv               2024-04-30 21:29:58  first raw submission               complete  1.83892      1.83892       \n","submission.csv               2024-04-30 21:28:13  first raw submission               complete  1.83892      1.83892       \n"]}],"source":["!kaggle competitions submissions -c bike-sharing-demand | tail -n +1 | head -n 6"]},{"cell_type":"markdown","metadata":{"id":"xvJ3b2lVZhj_"},"source":["**bold text**#### New Score of `0.514`"]},{"cell_type":"markdown","metadata":{"id":"RUASiRO8ZhkA"},"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","source":["# Print the columns of the leaderboard DataFrame\n","print(predictor.fit_summary()['leaderboard'].columns)"],"metadata":{"id":"_wYvo8gl4l2s","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1714514297956,"user_tz":-60,"elapsed":455,"user":{"displayName":"Paschal Ugwu","userId":"02920440952136523881"}},"outputId":"a084cb1d-91bc-49aa-ecc4-b0807ebf6375"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["*** Summary of fit() ***\n","Estimated performance of each model:\n","                    model   score_val              eval_metric  pred_time_val    fit_time  pred_time_val_marginal  fit_time_marginal  stack_level  can_infer  fit_order\n","0     WeightedEnsemble_L3  -55.066854  root_mean_squared_error      55.163926  361.237957                0.001292           0.054325            3       True         10\n","1         LightGBM_BAG_L2  -55.106094  root_mean_squared_error      49.824168  278.989629                0.328317          42.290116            2       True          9\n","2       LightGBMXT_BAG_L2  -60.711688  root_mean_squared_error      54.834317  318.893516                5.338466          82.194003            2       True          8\n","3   KNeighborsDist_BAG_L1  -84.125061  root_mean_squared_error       0.090871    0.058939                0.090871           0.058939            1       True          2\n","4     WeightedEnsemble_L2  -84.125061  root_mean_squared_error       0.092352    0.107026                0.001481           0.048087            2       True          7\n","5   KNeighborsUnif_BAG_L1 -101.546199  root_mean_squared_error       0.086746    0.067217                0.086746           0.067217            1       True          1\n","6  RandomForestMSE_BAG_L1 -116.548359  root_mean_squared_error       1.083449   23.647861                1.083449          23.647861            1       True          5\n","7         LightGBM_BAG_L1 -131.054162  root_mean_squared_error       4.147206   46.700382                4.147206          46.700382            1       True          4\n","8       LightGBMXT_BAG_L1 -131.460909  root_mean_squared_error      43.944835  102.967989               43.944835         102.967989            1       True          3\n","9         CatBoost_BAG_L1 -132.125747  root_mean_squared_error       0.142745   63.257125                0.142745          63.257125            1       True          6\n","Number of models trained: 10\n","Types of models trained:\n","{'StackerEnsembleModel_LGB', 'StackerEnsembleModel_RF', 'StackerEnsembleModel_CatBoost', 'WeightedEnsembleModel', 'StackerEnsembleModel_KNN'}\n","Bagging used: True  (with 8 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']) : 5 | ['datetime', 'datetime.year', 'datetime.month', 'datetime.day', 'datetime.dayofweek']\n","*** End of fit() summary ***\n","Index(['model', 'score_val', 'eval_metric', 'pred_time_val', 'fit_time',\n","       'pred_time_val_marginal', 'fit_time_marginal', 'stack_level',\n","       'can_infer', 'fit_order'],\n","      dtype='object')\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/autogluon/core/utils/plots.py:169: UserWarning: AutoGluon summary plots cannot be created because bokeh is not installed. To see plots, please do: \"pip install bokeh==2.0.1\"\n","  warnings.warn('AutoGluon summary plots cannot be created because bokeh is not installed. To see plots, please do: \"pip install bokeh==2.0.1\"')\n"]}]},{"cell_type":"code","execution_count":null,"metadata":{"jupyter":{"source_hidden":true},"id":"F6TIgtShZhkA","colab":{"base_uri":"https://localhost:8080/","height":1000},"executionInfo":{"status":"ok","timestamp":1714514313396,"user_tz":-60,"elapsed":1335,"user":{"displayName":"Paschal Ugwu","userId":"02920440952136523881"}},"outputId":"8021dbc2-3e60-4c5d-c686-5e0d222761de"},"outputs":[{"output_type":"stream","name":"stdout","text":["*** Summary of fit() ***\n","Estimated performance of each model:\n","                    model   score_val              eval_metric  pred_time_val    fit_time  pred_time_val_marginal  fit_time_marginal  stack_level  can_infer  fit_order\n","0     WeightedEnsemble_L3  -55.066854  root_mean_squared_error      55.163926  361.237957                0.001292           0.054325            3       True         10\n","1         LightGBM_BAG_L2  -55.106094  root_mean_squared_error      49.824168  278.989629                0.328317          42.290116            2       True          9\n","2       LightGBMXT_BAG_L2  -60.711688  root_mean_squared_error      54.834317  318.893516                5.338466          82.194003            2       True          8\n","3   KNeighborsDist_BAG_L1  -84.125061  root_mean_squared_error       0.090871    0.058939                0.090871           0.058939            1       True          2\n","4     WeightedEnsemble_L2  -84.125061  root_mean_squared_error       0.092352    0.107026                0.001481           0.048087            2       True          7\n","5   KNeighborsUnif_BAG_L1 -101.546199  root_mean_squared_error       0.086746    0.067217                0.086746           0.067217            1       True          1\n","6  RandomForestMSE_BAG_L1 -116.548359  root_mean_squared_error       1.083449   23.647861                1.083449          23.647861            1       True          5\n","7         LightGBM_BAG_L1 -131.054162  root_mean_squared_error       4.147206   46.700382                4.147206          46.700382            1       True          4\n","8       LightGBMXT_BAG_L1 -131.460909  root_mean_squared_error      43.944835  102.967989               43.944835         102.967989            1       True          3\n","9         CatBoost_BAG_L1 -132.125747  root_mean_squared_error       0.142745   63.257125                0.142745          63.257125            1       True          6\n","Number of models trained: 10\n","Types of models trained:\n","{'StackerEnsembleModel_LGB', 'StackerEnsembleModel_RF', 'StackerEnsembleModel_CatBoost', 'WeightedEnsembleModel', 'StackerEnsembleModel_KNN'}\n","Bagging used: True  (with 8 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']) : 5 | ['datetime', 'datetime.year', 'datetime.month', 'datetime.day', 'datetime.dayofweek']\n","*** End of fit() summary ***\n","*** Summary of fit() ***\n","Estimated performance of each model:\n","                    model   score_val              eval_metric  pred_time_val    fit_time  pred_time_val_marginal  fit_time_marginal  stack_level  can_infer  fit_order\n","0     WeightedEnsemble_L3  -30.428847  root_mean_squared_error      52.220520  362.390117                0.001204           0.054599            3       True         10\n","1         LightGBM_BAG_L2  -30.667943  root_mean_squared_error      49.794716  270.815830                0.649112          41.834797            2       True          8\n","2       LightGBMXT_BAG_L2  -31.218084  root_mean_squared_error      50.872662  274.568516                1.727058          45.587483            2       True          7\n","3  RandomForestMSE_BAG_L2  -31.997543  root_mean_squared_error      49.843146  274.913238                0.697543          45.932205            2       True          9\n","4     WeightedEnsemble_L2  -32.370365  root_mean_squared_error      48.942731  229.051562                0.001650           0.145401            2       True          6\n","5         LightGBM_BAG_L1  -33.919639  root_mean_squared_error       8.249959   70.276205                8.249959          70.276205            1       True          4\n","6       LightGBMXT_BAG_L1  -34.457274  root_mean_squared_error      39.699899  136.374808               39.699899         136.374808            1       True          3\n","7  RandomForestMSE_BAG_L1  -38.454338  root_mean_squared_error       0.890491   22.165741                0.890491          22.165741            1       True          5\n","8   KNeighborsDist_BAG_L1  -84.125061  root_mean_squared_error       0.100731    0.089406                0.100731           0.089406            1       True          2\n","9   KNeighborsUnif_BAG_L1 -101.546199  root_mean_squared_error       0.204523    0.074873                0.204523           0.074873            1       True          1\n","Number of models trained: 10\n","Types of models trained:\n","{'StackerEnsembleModel_LGB', 'StackerEnsembleModel_KNN', 'StackerEnsembleModel_RF', 'WeightedEnsembleModel'}\n","Bagging used: True  (with 8 folds)\n","Multi-layer stack-ensembling used: True  (with 3 levels)\n","Feature Metadata (Processed):\n","(raw dtype, special dtypes):\n","('category', [])             : 2 | ['season', 'weather']\n","('float', [])                : 3 | ['temp', 'atemp', 'windspeed']\n","('int', [])                  : 2 | ['humidity', 'hour']\n","('int', ['bool'])            : 2 | ['holiday', 'workingday']\n","('int', ['datetime_as_int']) : 5 | ['datetime', 'datetime.year', 'datetime.month', 'datetime.day', 'datetime.dayofweek']\n","*** End of fit() summary ***\n","*** Summary of fit() ***\n","Estimated performance of each model:\n","                  model  score_val              eval_metric  pred_time_val    fit_time  pred_time_val_marginal  fit_time_marginal  stack_level  can_infer  fit_order\n","0   WeightedEnsemble_L3 -41.366805  root_mean_squared_error       0.004801  323.841944                0.001314           0.058850            3       True         13\n","1    LightGBM_BAG_L2/T2 -41.621439  root_mean_squared_error       0.003168  291.947508                0.000338          37.022625            2       True         10\n","2    CatBoost_BAG_L2/T2 -41.752438  root_mean_squared_error       0.003149  286.760469                0.000319          31.835585            2       True         12\n","3    LightGBM_BAG_L2/T1 -41.979315  root_mean_squared_error       0.003051  291.673368                0.000221          36.748485            2       True          9\n","4    CatBoost_BAG_L2/T1 -43.187351  root_mean_squared_error       0.003108  288.332504                0.000278          33.407621            2       True         11\n","5   WeightedEnsemble_L2 -45.205984  root_mean_squared_error       0.002005   71.265692                0.001494           0.074410            2       True          8\n","6    LightGBM_BAG_L1/T3 -45.308156  root_mean_squared_error       0.000241   39.413749                0.000241          39.413749            1       True          3\n","7    CatBoost_BAG_L1/T2 -49.541915  root_mean_squared_error       0.000270   31.777533                0.000270          31.777533            1       True          5\n","8    LightGBM_BAG_L1/T2 -49.948119  root_mean_squared_error       0.001450   38.375947                0.001450          38.375947            1       True          2\n","9    CatBoost_BAG_L1/T1 -55.969466  root_mean_squared_error       0.000227   31.381098                0.000227          31.381098            1       True          4\n","10   LightGBM_BAG_L1/T1 -56.001163  root_mean_squared_error       0.000230   36.387388                0.000230          36.387388            1       True          1\n","11   CatBoost_BAG_L1/T3 -62.962971  root_mean_squared_error       0.000203   34.443460                0.000203          34.443460            1       True          6\n","12   CatBoost_BAG_L1/T4 -95.867999  root_mean_squared_error       0.000209   43.145709                0.000209          43.145709            1       True          7\n","Number of models trained: 13\n","Types of models trained:\n","{'StackerEnsembleModel_LGB', 'StackerEnsembleModel_CatBoost', 'WeightedEnsembleModel'}\n","Bagging used: True  (with 8 folds)\n","Multi-layer stack-ensembling used: True  (with 3 levels)\n","Feature Metadata (Processed):\n","(raw dtype, special dtypes):\n","('category', [])             : 2 | ['season', 'weather']\n","('float', [])                : 3 | ['temp', 'atemp', 'windspeed']\n","('int', [])                  : 2 | ['humidity', 'hour']\n","('int', ['bool'])            : 2 | ['holiday', 'workingday']\n","('int', ['datetime_as_int']) : 5 | ['datetime', 'datetime.year', 'datetime.month', 'datetime.day', 'datetime.dayofweek']\n","*** End of fit() summary ***\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/autogluon/core/utils/plots.py:169: UserWarning: AutoGluon summary plots cannot be created because bokeh is not installed. To see plots, please do: \"pip install bokeh==2.0.1\"\n","  warnings.warn('AutoGluon summary plots cannot be created because bokeh is not installed. To see plots, please do: \"pip install bokeh==2.0.1\"')\n","/usr/local/lib/python3.10/dist-packages/autogluon/core/utils/plots.py:169: UserWarning: AutoGluon summary plots cannot be created because bokeh is not installed. To see plots, please do: \"pip install bokeh==2.0.1\"\n","  warnings.warn('AutoGluon summary plots cannot be created because bokeh is not installed. To see plots, please do: \"pip install bokeh==2.0.1\"')\n","/usr/local/lib/python3.10/dist-packages/autogluon/core/utils/plots.py:169: UserWarning: AutoGluon summary plots cannot be created because bokeh is not installed. To see plots, please do: \"pip install bokeh==2.0.1\"\n","  warnings.warn('AutoGluon summary plots cannot be created because bokeh is not installed. To see plots, please do: \"pip install bokeh==2.0.1\"')\n"]},{"output_type":"display_data","data":{"text/plain":["<Figure size 800x600 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}],"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","# Get the score of the top model from each training run\n","initial_score = predictor.fit_summary()['leaderboard']['score_val'].max()\n","add_features_score = predictor_new_features.fit_summary()['leaderboard']['score_val'].max()\n","hpo_score = predictor_new_hpo.fit_summary()['leaderboard']['score_val'].max()\n","\n","# Create the plot\n","fig = pd.DataFrame(\n","    {\n","        \"model\": [\"initial\", \"add_features\", \"hpo\"],\n","        \"score\": [initial_score, add_features_score, hpo_score]\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":{"id":"RWgeQT_ZZhkB","colab":{"base_uri":"https://localhost:8080/","height":542},"executionInfo":{"status":"ok","timestamp":1714514445857,"user_tz":-60,"elapsed":964,"user":{"displayName":"Paschal Ugwu","userId":"02920440952136523881"}},"outputId":"f720ae80-f432-4df3-a826-773a61b39f91"},"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 800x600 with 1 Axes>"],"image/png":"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Take the 3 kaggle scores and creating a line plot to show improvement\n","initial_kaggle_score = 1.83892\n","add_features_kaggle_score = 0.62523\n","hpo_kaggle_score = 0.514\n","\n","fig = pd.DataFrame(\n","    {\n","        \"test_eval\": [\"initial\", \"add_features\", \"hpo\"],\n","        \"score\": [initial_kaggle_score, add_features_kaggle_score, hpo_kaggle_score]\n","    }\n",").plot(x=\"test_eval\", y=\"score\", figsize=(8, 6)).get_figure()\n","fig.savefig('model_test_score.png')"]},{"cell_type":"markdown","metadata":{"id":"q-IFDBcWZhkC"},"source":["### Hyperparameter table"]},{"cell_type":"code","source":["print(hyperparameters.keys())"],"metadata":{"id":"CKuHKS_D-oNB","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1714514455688,"user_tz":-60,"elapsed":520,"user":{"displayName":"Paschal Ugwu","userId":"02920440952136523881"}},"outputId":"f1675cc1-c09c-4608-f988-2b32a3b54818"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["dict_keys(['GBM', 'CAT'])\n"]}]},{"cell_type":"code","execution_count":null,"metadata":{"id":"UAJEp163ZhkC","colab":{"base_uri":"https://localhost:8080/","height":144},"executionInfo":{"status":"ok","timestamp":1714515008655,"user_tz":-60,"elapsed":464,"user":{"displayName":"Paschal Ugwu","userId":"02920440952136523881"}},"outputId":"354bd23d-a705-4e8b-dfe6-c9747c7af7fd"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["          model                                        hpo1  \\\n","0       initial                                        None   \n","1  add_features                                        None   \n","2           hpo  {'num_boost_round': 100, 'num_leaves': 10}   \n","\n","                  hpo2    score  \n","0                 None  1.83892  \n","1                 None  0.62523  \n","2  {'iterations': 100}  0.51400  "],"text/html":["\n","  <div id=\"df-f6cdf1ab-146d-4a08-b643-7ee413e17414\" class=\"colab-df-container\">\n","    <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>model</th>\n","      <th>hpo1</th>\n","      <th>hpo2</th>\n","      <th>score</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>initial</td>\n","      <td>None</td>\n","      <td>None</td>\n","      <td>1.83892</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>add_features</td>\n","      <td>None</td>\n","      <td>None</td>\n","      <td>0.62523</td>\n","    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