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WinstonLiyt
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* init for bg & quickstart for kaggle docs * Add documentation for the environment configuration in the Kaggle scenario. * add some descriptions in documents * remove useless docs * ci issue --------- Co-authored-by: TPLin22 <tplin2@163.com>
144 lines
6.9 KiB
ReStructuredText
144 lines
6.9 KiB
ReStructuredText
.. _kaggle_agent:
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=======================
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Kaggle Agent
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=======================
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**🤖 Automated Feature Engineering & Model Tuning Evolution**
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------------------------------------------------------------------------------------------
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📖 Background
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~~~~~~~~~~~~~~
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In the landscape of data science competitions, Kaggle serves as the ultimate arena where data enthusiasts harness the power of algorithms to tackle real-world challenges.
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The Kaggle Agent stands as a pivotal tool, empowering participants to seamlessly integrate cutting-edge models and datasets, transforming raw data into actionable insights.
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By utilizing the **Kaggle Agent**, data scientists can craft innovative solutions that not only uncover hidden patterns but also drive significant advancements in predictive accuracy and model robustness.
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🌟 Introduction
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~~~~~~~~~~~~~~~~
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In this scenario, our automated system proposes hypothesis, choose action, implements code, conducts validation, and utilizes feedback in a continuous, iterative process.
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The goal is to automatically optimize performance metrics within the validation set or Kaggle Leaderboard, ultimately discovering the most efficient features and models through autonomous research and development.
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Here's an enhanced outline of the steps:
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**Step 1 : Hypothesis Generation 🔍**
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- Generate and propose initial hypotheses based on previous experiment analysis and domain expertise, with thorough reasoning and financial justification.
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**Step 2 : Experiment Creation ✨**
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- Transform the hypothesis into a task.
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- Choose a specific action within feature engineering or model tuning.
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- Develop, define, and implement a new feature or model, including its name, description, and formulation.
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**Step 3 : Model/Feature Implementation 👨💻**
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- Implement the model code based on the detailed description.
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- Evolve the model iteratively as a developer would, ensuring accuracy and efficiency.
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**Step 4 : Validation on Test Set or Kaggle 📉**
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- Validate the newly developed model using the test set or Kaggle dataset.
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- Assess the model's effectiveness and performance based on the validation results.
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**Step 5: Feedback Analysis 🔍**
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- Analyze validation results to assess performance.
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- Use insights to refine hypotheses and enhance the model.
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**Step 6: Hypothesis Refinement ♻️**
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- Adjust hypotheses based on validation feedback.
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- Iterate the process to continuously improve the model.
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⚡ Quick Start
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~~~~~~~~~~~~~~~~~
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Please refer to the installation part in :doc:`../installation_and_configuration` to prepare your system dependency.
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You can try our demo by running the following command:
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- 🐍 Create a Conda Environment
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- Create a new conda environment with Python (3.10 and 3.11 are well tested in our CI):
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.. code-block:: sh
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conda create -n rdagent python=3.10
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- Activate the environment:
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.. code-block:: sh
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conda activate rdagent
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- 📦 Install the RDAgent
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- You can install the RDAgent package from PyPI:
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.. code-block:: sh
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pip install rdagent
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- 🚀 Run the Application
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- You can directly run the application by using the following command:
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.. code-block:: sh
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python3 rdagent/app/kaggle/loop.py --competition [your competition name]
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🛠️ Usage of modules
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~~~~~~~~~~~~~~~~~~~~~
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.. _Env Config:
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- **Env Config**
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The following environment variables can be set in the `.env` file to customize the application's behavior:
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.. autopydantic_settings:: rdagent.app.kaggle.conf.KaggleBasePropSetting
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:settings-show-field-summary: False
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:exclude-members: Config
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.. autopydantic_settings:: rdagent.components.coder.factor_coder.config.FactorImplementSettings
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:settings-show-field-summary: False
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:members: coder_use_cache, data_folder, data_folder_debug, file_based_execution_timeout, select_method, select_threshold, max_loop, knowledge_base_path, new_knowledge_base_path
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:exclude-members: Config, fail_task_trial_limit, v1_query_former_trace_limit, v1_query_similar_success_limit, v2_query_component_limit, v2_query_error_limit, v2_query_former_trace_limit, v2_error_summary, v2_knowledge_sampler, v2_add_fail_attempt_to_latest_successful_execution
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:no-index:
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📋 Competition List Available
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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+-----------------------------------+------------------+-----------+-------------------------------+
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| **Competition Name** | **Task** | **Modal** | **ID** |
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+===================================+==================+===========+===============================+
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| Media Campaign Cost Dataset | Regression | Tabular | playground-series-s3e11 |
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+-----------------------------------+------------------+-----------+-------------------------------+
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| Wild Blueberry Yield Dataset | Regression | Tabular | playground-series-s3e14 |
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+-----------------------------------+------------------+-----------+-------------------------------+
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| Crab Age Dataset | Regression | Tabular | playground-series-s3e16 |
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+-----------------------------------+------------------+-----------+-------------------------------+
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| Flood Prediction Dataset | Regression | Tabular | playground-series-s4e5 |
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+-----------------------------------+------------------+-----------+-------------------------------+
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| Used Car Prices | Regression | Tabular | playground-series-s4e9 |
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+-----------------------------------+------------------+-----------+-------------------------------+
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| Cirrhosis Outcomes | Multi-Class | Tabular | playground-series-s3e26 |
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+-----------------------------------+------------------+-----------+-------------------------------+
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| San Francisco Crime Classification| Multi-Class | Tabular | sf-crime |
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+-----------------------------------+------------------+-----------+-------------------------------+
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| Poisonous Mushrooms | Classification | Tabular | playground-series-s4e8 |
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+-----------------------------------+------------------+-----------+-------------------------------+
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| Spaceship Titanic | Classification | Tabular | spaceship-titanic |
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+-----------------------------------+------------------+-----------+-------------------------------+
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| Forest Cover Type Prediction | Classification | Tabular | forest-cover-type-prediction |
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+-----------------------------------+------------------+-----------+-------------------------------+
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| Digit Recognizer | Classification | Image | digit-recognizer |
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+-----------------------------------+------------------+-----------+-------------------------------+
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| To be continued ... |
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+-----------------------------------+------------------+-----------+-------------------------------+
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