Relational α

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How it works

1. Deep feature synthesis. Starting from the table that holds the entity you are predicting for, it follows the schema’s foreign keys outward and aggregates the rows reachable at each depth into features, such as COUNT(results) or MEAN(results.points). This is the recipe popularized by featuretools, and TabPFN-Rel builds on RDBLearn’s implementation of it.
2. Depth selection. How far to traverse is the one hyperparameter that matters, and it is chosen per task rather than fixed.
3. In-context prediction. The resulting flat table goes to TabPFN-3, which predicts the labels of the query rows from the labelled context rows in a single forward pass.

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The relational stack

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Getting Started

pip install relarena

import pandas as pd

import relarena.models  # registers the built-in models, including tabpfn-rel
from relarena.evaluation import compute_leaderboard
from relarena.registry import registry
from relarena.results import summary_to_dataframe
from relarena.runner import run_experiment
from relarena.tasks import list_entity_tasks

frames = []
for spec in list_entity_tasks(["rel-f1"]):
    summary = run_experiment(
        registry.get("tabpfn-rel-client"), spec.dataset, spec.task, seed=0, n_trials=3
    )
    frames.append(summary_to_dataframe(summary))

board = compute_leaderboard(pd.concat(frames, ignore_index=True))

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Key Features

• No task-specific training: features are computed once and the prediction is a single in-context forward pass, so there is no gradient training loop to configure.
• Schema-driven: the aggregations follow the foreign keys already declared in your database, so no manual feature engineering is required to get a first result.
• Almost nothing to tune: aggregation depth is the only hyperparameter searched, in contrast to the graph neural networks typically applied to this setting.
• Text columns included: free-text fields in the entity table are passed to the model rather than dropped, which matters on tasks where the text carries the signal.

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