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chore: modify kaggle docs & Adding ds_loop at program entry (#786)
* modify kaggle docs * optimise code based on comments * Update docs/scens/kaggle_agent.rst * fix docs build error --------- Co-authored-by: you-n-g <you-n-g@users.noreply.github.com>
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Linlang
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@@ -1,8 +1,8 @@
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.. _kaggle_agent:
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=======================
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Kaggle Agent
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=======================
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===========================
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Data Science Agent - Kaggle
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===========================
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**🤖 Automated Feature Engineering & Model Tuning Evolution**
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------------------------------------------------------------------------------------------
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@@ -87,6 +87,7 @@ Here's an enhanced outline of the steps:
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.. code-block:: sh
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mkdir -p <your local directory>/kaggle_data
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dotenv set KG_LOCAL_DATA_PATH <your local directory>/kaggle_data
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- 📥 **Download Competition Data**
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@@ -95,33 +96,22 @@ Here's an enhanced outline of the steps:
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- **How to get the competition description file**
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- *Manual Download (General User Suggestions):*
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- The competition description file is downloaded automatically when the programme is run, and the download process relies on ``chromedriver``, which can be installed as follows:
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- Download the competition description file prepared in advance, and extract it to the specified directory.
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.. code-block:: sh
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.. code-block:: sh
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# install chrome
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wget https://dl.google.com/linux/direct/google-chrome-stable_current_amd64.deb
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sudo apt install ./google-chrome-stable_current_amd64.deb
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google-chrome --version
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wget https://github.com/SunsetWolf/rdagent_resource/releases/download/kaggle_data/kaggle_data.zip
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unzip kaggle_data.zip -d <your local directory>/kaggle_data
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- *Automatic Download (Developer Suggestions):*
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- Alternatively, you can choose to download the competition description file automatically when you run the program, but it requires ``chromedriver`` to be installed, as follows:
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.. code-block:: sh
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# install chrome
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wget https://dl.google.com/linux/direct/google-chrome-stable_current_amd64.deb
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sudo apt install ./google-chrome-stable_current_amd64.deb
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google-chrome --version
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# install chromedriver
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wget "https://storage.googleapis.com/chrome-for-testing-public/$(google-chrome --version | grep -oP '\d+\.\d+\.\d+\.\d+')/linux64/chromedriver-linux64.zip"
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unzip chromedriver-linux64.zip
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cd chromedriver-linux64
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sudo mv chromedriver /usr/local/bin
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sudo chmod +x /usr/local/bin/chromedriver
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chromedriver --version
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# install chromedriver
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wget "https://storage.googleapis.com/chrome-for-testing-public/$(google-chrome --version | grep -oP '\d+\.\d+\.\d+\.\d+')/linux64/chromedriver-linux64.zip"
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unzip chromedriver-linux64.zip
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cd chromedriver-linux64
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sudo mv chromedriver /usr/local/bin
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sudo chmod +x /usr/local/bin/chromedriver
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chromedriver --version
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- **How to get the competition dataset**
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@@ -134,6 +124,9 @@ Here's an enhanced outline of the steps:
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kaggle_data
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└── zip_files
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| └── sf-crime.zip
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├── sample
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| └── sf-crime
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| └── ...
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├── sf-crime.json
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└── sf-crime
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└── ...
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@@ -142,7 +135,9 @@ Here's an enhanced outline of the steps:
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- ``kaggle_data/sf-crime.json:`` Competition description file.
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- ``kaggle_data/sf-crime:`` The target folder for unzipping the competition dataset.
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- ``kaggle_data/sf-crime:`` The target folder for unzipping the competition dataset. Complete dataset.
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- ``kaggle_data/sample/sf-crime:`` Simplified dataset based on the complete dataset. Used to quickly verify that the code works.
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- 🗳️ **Join the competition**
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@@ -170,37 +165,6 @@ Here's an enhanced outline of the steps:
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- Else: You can download the prediction results from the UI interface and submit them manually. For more details, refer to the :doc:`UI guide <../ui>`.
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📋 Competition List Available
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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+-----------+-----------------------------------+------------------+-----------+---------------------------------------------------------------------------------------------------------+
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| **index** | **Competition Name** | **Task** | **Modal** | **ID** |
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+===========+===================================+==================+===========+=========================================================================================================+
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| 01 | Media Campaign Cost Dataset | Regression | Tabular | `playground-series-s3e11 <https://www.kaggle.com/competitions/playground-series-s3e11/data>`_ |
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+-----------+-----------------------------------+------------------+-----------+---------------------------------------------------------------------------------------------------------+
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| 02 | Wild Blueberry Yield Dataset | Regression | Tabular | `playground-series-s3e14 <https://www.kaggle.com/competitions/playground-series-s3e14/data>`_ |
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+-----------+-----------------------------------+------------------+-----------+---------------------------------------------------------------------------------------------------------+
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| 03 | Crab Age Dataset | Regression | Tabular | `playground-series-s3e16 <https://www.kaggle.com/competitions/playground-series-s3e16/data>`_ |
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+-----------+-----------------------------------+------------------+-----------+---------------------------------------------------------------------------------------------------------+
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| 04 | Flood Prediction Dataset | Regression | Tabular | `playground-series-s4e5 <https://www.kaggle.com/competitions/playground-series-s4e5/data>`_ |
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+-----------+-----------------------------------+------------------+-----------+---------------------------------------------------------------------------------------------------------+
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| 05 | Used Car Prices Dataset | Regression | Tabular | `playground-series-s4e9 <https://www.kaggle.com/competitions/playground-series-s4e9/data>`_ |
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+-----------+-----------------------------------+------------------+-----------+---------------------------------------------------------------------------------------------------------+
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| 06 | Cirrhosis Outcomes Dataset | Multi-Class | Tabular | `playground-series-s3e26 <https://www.kaggle.com/competitions/playground-series-s3e26/data>`_ |
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+-----------+-----------------------------------+------------------+-----------+---------------------------------------------------------------------------------------------------------+
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| 07 | San Francisco Crime Classification| Multi-Class | Tabular | `sf-crime <https://www.kaggle.com/competitions/sf-crime/data>`_ |
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+-----------+-----------------------------------+------------------+-----------+---------------------------------------------------------------------------------------------------------+
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| 08 | Poisonous Mushrooms Dataset | Classification | Tabular | `playground-series-s4e8 <https://www.kaggle.com/competitions/playground-series-s4e8/data>`_ |
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+-----------+-----------------------------------+------------------+-----------+---------------------------------------------------------------------------------------------------------+
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| 09 | Spaceship Titanic | Classification | Tabular | `spaceship-titanic <https://www.kaggle.com/competitions/spaceship-titanic/data>`_ |
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+-----------+-----------------------------------+------------------+-----------+---------------------------------------------------------------------------------------------------------+
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| 10 | Forest Cover Type Prediction | Classification | Tabular | `forest-cover-type-prediction <https://www.kaggle.com/competitions/forest-cover-type-prediction/data>`_ |
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+-----------+-----------------------------------+------------------+-----------+---------------------------------------------------------------------------------------------------------+
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| 11 | Digit Recognizer | Classification | Image | `digit-recognizer <https://www.kaggle.com/competitions/digit-recognizer>`_ |
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+-----------+-----------------------------------+------------------+-----------+---------------------------------------------------------------------------------------------------------+
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| To be continued ... |
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+-----------+-----------------------------------+------------------+-----------+---------------------------------------------------------------------------------------------------------+
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🎨 Customize one template for a new competition
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