rdblearn 0.1.2

Relational Database Learning with Foundation Models

RDBLearn 🚀

Relational Database Learning with Foundation Models.

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📑 Table of Contents

• Introduction
• Installation
• Usage
• Core API Reference
• License

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🎯 Introduction

RDBLearn is a framework designed to apply single-table foundation models to multi-table relational database tasks. It automates the process of flattening relational data into a single feature-rich table using Deep Feature Synthesis (DFS) and then leverages powerful single-table estimators (like TabPFN) for prediction.

Core Components

• 🔧 FastDFS - Efficient Deep Feature Synthesis for automated multi-table flattening.
• 🤖 RDBLearn Estimators - Scikit-learn compatible RDBLearnClassifier and RDBLearnRegressor that integrate DFS and single-table models.
• ⚡ Foundation Models - Seamless integration with TabPFN and other foundation models for single table prediction tasks.

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⚙️ Installation

Requires Python 3.12.

pip install rdblearn

This installs fastdfs and other PyPI dependencies. For flash-attn (CUDA / LimiX-style GPU workloads), install separately — PyPI packages cannot declare direct URL dependencies:

pip install -r requirements-gpu.txt

Or install from source:

git clone https://github.com/HKUSHXLab/rdblearn.git
cd rdblearn
git checkout v0.1.2
pip install -e .
# optional GPU wheel:
pip install -r requirements-gpu.txt

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🚀 Usage

Basic Example (RelBench rel-avito)

RDBLearn includes two features enabled by default that improve prediction quality:

• Target History Augmentation (enable_target_augmentation): Injects the full training data (X and y) as a history table into the RDB before downsampling, allowing DFS to derive entity-level aggregate features from historical target values (e.g., mean past CTR per ad). Temporal cutoffs are respected to prevent data leakage. Requires cutoff_time_column to be provided.
• Temporal Difference Features (temporal_diff): Converts absolute epoch-time columns produced by DFS into relative temporal differences from the cutoff time (i.e., cutoff_time - epochtime), so the model sees how recently events occurred rather than raw timestamps.

from rdblearn.datasets import RDBDataset
from rdblearn.estimator import RDBLearnRegressor
from tabpfn import TabPFNRegressor

# 1. Load RelBench dataset and task
dataset = RDBDataset.from_relbench("rel-avito")
task = dataset.tasks["ad-ctr"]

# 2. Initialize the estimator with a base model (e.g., TabPFN)
#    Both enable_target_augmentation and temporal_diff are enabled by default.
reg = RDBLearnRegressor(
    base_estimator=TabPFNRegressor(device="cpu"), # or "cuda"
    config={
        "dfs": {"max_depth": 2},
        "enable_target_augmentation": True,
        "temporal_diff": {"enabled": True},
        "max_train_samples": 1000
    }
)

# 3. Fit on relational data
X_train = task.train_df.drop(columns=[task.metadata.target_col])
y_train = task.train_df[task.metadata.target_col]

reg.fit(
    X=X_train,
    y=y_train,
    rdb=dataset.rdb,
    key_mappings=task.metadata.key_mappings,
    cutoff_time_column=task.metadata.time_col
)

# 4. Predict
X_test = task.test_df.drop(columns=[task.metadata.target_col])
predictions = reg.predict(X=X_test)

See examples/ for more detailed usage.

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Core API Reference

RDBDataset

The central class for managing relational data and task-specific tables.

• from_relbench(dataset_name: str) -> RDBDataset: Load a dataset from the RelBench benchmark.
• from_4dbinfer(dataset_name: str) -> RDBDataset: Load a dataset from the 4DBInfer benchmark.
• save(path: str): Save the RDB and all associated tasks to disk.
• load(path: str) -> RDBDataset: Load a previously saved dataset from disk.

RDBLearnClassifier / RDBLearnRegressor

Scikit-learn compatible estimators for relational learning.

• __init__(base_estimator, config: Optional[dict] = None):
  • base_estimator: A single-table estimator (e.g., TabPFNClassifier, AutoGluonClassifier).
  • config: Optional dictionary to override default DFS or sampling settings. Key options:
    • dfs: DFS configuration (e.g., {"max_depth": 2}).
    • max_train_samples (int, default 10000): Maximum training samples before downsampling.
    • stratified_sampling (bool, default False): Use stratified sampling for classification tasks.
    • enable_target_augmentation (bool, default True): Augment the RDB with the full training target history table, enabling DFS to derive entity-level target aggregate features (e.g., entity mean). Requires cutoff_time_column to be set during fit.
    • temporal_diff (dict or TemporalDiffConfig, default {"enabled": True}): Convert DFS-generated epoch-time columns into temporal difference features relative to the cutoff time. Supports enabled (bool) and exclude_columns (list of column names to skip).
    • predict_batch_size (int, default 5000): Batch size for prediction.
• fit(X, y, rdb, key_mappings, cutoff_time_column=None, **kwargs):
  • X: Training features (DataFrame).
  • y: Training labels (Series).
  • rdb: The relational database context (fastdfs.RDB).
  • key_mappings: Dictionary mapping columns in X to table.primary_key in the RDB.
  • cutoff_time_column: Optional column name in X representing the time of the observation.
• predict(X, rdb=None, **kwargs):
  • X: Test features.
  • rdb: Optional RDB context (uses the one from fit if not provided).
• predict_proba(X, rdb=None, **kwargs): (Classifier only) Predict class probabilities.

TaskMetadata

Data structure containing task-specific information.

• key_mappings: Dict[str, str]
• target_col: str
• time_col: Optional[str]
• task_type: Optional[str]
• evaluation_metric: Optional[str]

LimiX Integration

rdblearn.utils provides wrappers to adapt LimiX predictors into scikit-learn compatible estimators.

• LimiXWrapperClassifier(predictor): Wrapper for classification tasks.

  • predictor: An initialized LimiXPredictor instance.
  • fit(X, y): Stores training data for in-context inference.
  • predict(X): Returns class labels.
  • predict_proba(X): Returns class probabilities.

• LimiXWrapperRegressor(predictor): Wrapper for regression tasks.

  • predictor: An initialized LimiXPredictor instance.
  • fit(X, y): Stores training data.
  • predict(X): Returns predicted values.

Note: You must install LimiX separately and provide an initialized LimiXPredictor to these wrappers.

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📜 License

This project is licensed under the MIT License.