rai-predictive-modeling

Predictive Modeling

Early access. The RAI predictive reasoner (GNN) is in early access — APIs, engine requirements, and behavior may change. Confirm the latest surface with the RelationalAI team before production use.

Summary

What: Data modeling workflow for GNN pipelines -- from imports through graph construction and feature configuration.

When to use:

• Defining concepts and loading data from Snowflake
• Building graph structure (edges, self-references)
• Configuring task relationships (train/val/test splits)
• Setting up PropertyTransformer features

When NOT to use:

• Training, predictions, evaluation, model management -- see rai-predictive-training
• Graph algorithms (centrality, community detection) -- see rai-graph-analysis

Overview: 6 steps: imports -> concepts -> populate -> task relationships -> graph -> features

---

Prerequisites

Experiment schema setup (one-time, ACCOUNTADMIN)

GNN training writes experiment artifacts to a Snowflake schema. Create a database and schema you own, then grant the RELATIONALAI native app the four required privileges:

CREATE DATABASE IF NOT EXISTS <YOUR_DB>;
CREATE SCHEMA   IF NOT EXISTS <YOUR_DB>.<YOUR_SCHEMA>;

GRANT USAGE             ON DATABASE <YOUR_DB>                         TO APPLICATION RELATIONALAI;
GRANT USAGE             ON SCHEMA   <YOUR_DB>.<YOUR_SCHEMA>           TO APPLICATION RELATIONALAI;
GRANT CREATE EXPERIMENT ON SCHEMA   <YOUR_DB>.<YOUR_SCHEMA>           TO APPLICATION RELATIONALAI;
GRANT CREATE MODEL      ON SCHEMA   <YOUR_DB>.<YOUR_SCHEMA>           TO APPLICATION RELATIONALAI;

All four grants are required. Then pass the same database and schema to the GNN constructor:

gnn = GNN(
    exp_database="<YOUR_DB>",
    exp_schema="<YOUR_SCHEMA>",
    # ... other args (graph=, property_transformer=, train=, validation=, task_type=)
)

relationalai package version

The predictive submodule (relationalai.semantics.reasoners.predictive) is not in every published relationalai release — from relationalai.semantics.reasoners.predictive import GNN raises ModuleNotFoundError on releases that pre-date it. Pin a release that ships the submodule (or install from the development branch when iterating against unreleased changes).