docs: add documentation for kaggle scen (#448)

* 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>

docs/scens/catalog.rst

@@ -34,13 +34,14 @@ The supported scenarios are listed below:
 
 
 .. toctree::
-   :maxdepth: 1
-   :caption: Doctree:
-   :hidden:
-
-   data_agent_fin
-   data_copilot_fin
-   model_agent_fin
-   model_agent_med
-   model_copilot_general
+    :maxdepth: 1
+    :caption: Doctree:
+    :hidden:
+
+    data_agent_fin
+    data_copilot_fin
+    model_agent_fin
+    model_agent_med
+    model_copilot_general
+    kaggle_agent
 

docs/scens/kaggle_agent.rst

@@ -0,0 +1,143 @@
+.. _kaggle_agent:
+
+=======================
+Kaggle Agent
+=======================
+
+**🤖 Automated Feature Engineering & Model Tuning Evolution**
+------------------------------------------------------------------------------------------
+
+📖 Background
+~~~~~~~~~~~~~~
+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.
+The Kaggle Agent stands as a pivotal tool, empowering participants to seamlessly integrate cutting-edge models and datasets, transforming raw data into actionable insights.
+
+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.
+
+
+🌟 Introduction
+~~~~~~~~~~~~~~~~
+
+In this scenario, our automated system proposes hypothesis, choose action, implements code, conducts validation, and utilizes feedback in a continuous, iterative process.
+
+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.
+
+Here's an enhanced outline of the steps:
+
+**Step 1 : Hypothesis Generation 🔍**
+
+- Generate and propose initial hypotheses based on previous experiment analysis and domain expertise, with thorough reasoning and financial justification.
+
+**Step 2 : Experiment Creation ✨**
+
+- Transform the hypothesis into a task.
+- Choose a specific action within feature engineering or model tuning.
+- Develop, define, and implement a new feature or model, including its name, description, and formulation.
+
+**Step 3 : Model/Feature Implementation 👨‍💻**
+
+- Implement the model code based on the detailed description.
+- Evolve the model iteratively as a developer would, ensuring accuracy and efficiency.
+
+**Step 4 : Validation on Test Set or Kaggle 📉**
+
+- Validate the newly developed model using the test set or Kaggle dataset.
+- Assess the model's effectiveness and performance based on the validation results.
+
+**Step 5: Feedback Analysis 🔍**
+
+- Analyze validation results to assess performance.
+- Use insights to refine hypotheses and enhance the model.
+
+**Step 6: Hypothesis Refinement ♻️**
+
+- Adjust hypotheses based on validation feedback.
+- Iterate the process to continuously improve the model.
+
+⚡ Quick Start
+~~~~~~~~~~~~~~~~~
+
+Please refer to the installation part in :doc:`../installation_and_configuration` to prepare your system dependency.
+
+You can try our demo by running the following command:
+
+- 🐍 Create a Conda Environment
+
+  - Create a new conda environment with Python (3.10 and 3.11 are well tested in our CI):
+
+    .. code-block:: sh
+    
+        conda create -n rdagent python=3.10
+
+  - Activate the environment:
+
+    .. code-block:: sh
+
+        conda activate rdagent
+
+- 📦 Install the RDAgent
+
+  - You can install the RDAgent package from PyPI:
+
+    .. code-block:: sh
+
+        pip install rdagent
+
+- 🚀 Run the Application
+
+  - You can directly run the application by using the following command:
+
+    .. code-block:: sh
+
+        python3 rdagent/app/kaggle/loop.py --competition [your competition name]
+
+🛠️ Usage of modules
+~~~~~~~~~~~~~~~~~~~~~
+
+.. _Env Config: 
+
+- **Env Config**
+
+The following environment variables can be set in the `.env` file to customize the application's behavior:
+
+.. autopydantic_settings:: rdagent.app.kaggle.conf.KaggleBasePropSetting
+    :settings-show-field-summary: False
+    :exclude-members: Config
+
+.. autopydantic_settings:: rdagent.components.coder.factor_coder.config.FactorImplementSettings
+    :settings-show-field-summary: False
+    :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
+    :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
+    :no-index:
+
+📋 Competition List Available
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
++-----------------------------------+------------------+-----------+-------------------------------+
+| **Competition Name**              | **Task**         | **Modal** | **ID**                        |
++===================================+==================+===========+===============================+
+| Media Campaign Cost Dataset       | Regression       | Tabular   | playground-series-s3e11       |
++-----------------------------------+------------------+-----------+-------------------------------+
+| Wild Blueberry Yield Dataset      | Regression       | Tabular   | playground-series-s3e14       |
++-----------------------------------+------------------+-----------+-------------------------------+
+| Crab Age Dataset                  | Regression       | Tabular   | playground-series-s3e16       |
++-----------------------------------+------------------+-----------+-------------------------------+
+| Flood Prediction Dataset          | Regression       | Tabular   | playground-series-s4e5        |
++-----------------------------------+------------------+-----------+-------------------------------+
+| Used Car Prices                   | Regression       | Tabular   | playground-series-s4e9        |
++-----------------------------------+------------------+-----------+-------------------------------+
+| Cirrhosis Outcomes                | Multi-Class      | Tabular   | playground-series-s3e26       |
++-----------------------------------+------------------+-----------+-------------------------------+
+| San Francisco Crime Classification| Multi-Class      | Tabular   | sf-crime                      |
++-----------------------------------+------------------+-----------+-------------------------------+
+| Poisonous Mushrooms               | Classification   | Tabular   | playground-series-s4e8        |
++-----------------------------------+------------------+-----------+-------------------------------+
+| Spaceship Titanic                 | Classification   | Tabular   | spaceship-titanic             |
++-----------------------------------+------------------+-----------+-------------------------------+
+| Forest Cover Type Prediction      | Classification   | Tabular   | forest-cover-type-prediction  |
++-----------------------------------+------------------+-----------+-------------------------------+
+| Digit Recognizer                  | Classification   | Image     | digit-recognizer              |
++-----------------------------------+------------------+-----------+-------------------------------+
+| To be continued ...                                                                              |
++-----------------------------------+------------------+-----------+-------------------------------+
+

rdagent/app/kaggle/conf.py

@@ -1,5 +1,3 @@
-from pathlib import Path
-
 from pydantic_settings import BaseSettings
 
 from rdagent.components.workflow.conf import BasePropSetting
@@ -16,13 +14,6 @@ class Config:
     scen: str = "rdagent.scenarios.kaggle.experiment.scenario.KGScenario"
     """Scenario class for data mining model"""
 
-    knowledge_base: str = ""  # TODO enable this line to use the knowledge base
-    # knowledge_base: str = "rdagent.scenarios.kaggle.knowledge_management.graph.KGKnowledgeGraph"
-    """Knowledge base class"""
-
-    knowledge_base_path: str = "kg_graph.pkl"
-    """Knowledge base path"""
-
     hypothesis_gen: str = "rdagent.scenarios.kaggle.proposal.proposal.KGHypothesisGen"
     """Hypothesis generation class"""
 
@@ -51,22 +42,40 @@ class Config:
     """Number of evolutions"""
 
     competition: str = ""
+    """Kaggle competition name, e.g., 'sf-crime'"""
 
     local_data_path: str = "/data/userdata/share/kaggle"
+    """Folder storing Kaggle competition data"""
+
+    if_action_choosing_based_on_UCB: bool = False
+    """Enable decision mechanism based on UCB algorithm"""
 
     domain_knowledge_path: str = "/data/userdata/share/kaggle/domain_knowledge"
+    """Folder storing domain knowledge files in .case format"""
 
-    rag_path: str = "git_ignore_folder/rag"
+    rag_path: str = "git_ignore_folder/kaggle_vector_base.pkl"
+    """Base version of vector-based RAG"""
 
-    if_action_choosing_based_on_UCB: bool = False
+    if_using_vector_rag: bool = False
+    """Enable basic vector-based RAG"""
 
     if_using_graph_rag: bool = False
+    """Enable advanced graph-based RAG"""
 
-    if_using_vector_rag: bool = False
+    # Conditionally set the knowledge_base based on the use of graph RAG
+    knowledge_base: str = (
+        "rdagent.scenarios.kaggle.knowledge_management.graph.KGKnowledgeGraph" if if_using_graph_rag else ""
+    )
+    """Knowledge base class, uses 'KGKnowledgeGraph' when advanced graph-based RAG is enabled, otherwise empty."""
+
+    knowledge_base_path: str = "kg_graph.pkl"
+    """Advanced version of graph-based RAG"""
 
     auto_submit: bool = True
+    """Automatically upload and submit each experiment result to Kaggle platform"""
 
     mini_case: bool = False
+    """Enable mini-case study for experiments"""
 
 
 KAGGLE_IMPLEMENT_SETTING = KaggleBasePropSetting()