Validate the Graph

Validate the graph after configuring table metadata and relationships. Validation checks structural compatibility; it cannot determine whether the graph accurately represents the business problem.

Review the complete graph

Inspect table metadata and relationships together:

graph.print_metadata()
graph.print_links()

Confirm the following:

• Each table represents one entity, event, or junction concept.
• Every primary key is non-null and unique.
• Event tables use the correct time column.
• Identifier, feature, text, and timestamp columns have appropriate semantic types.
• Every foreign key points to the intended primary-key table.
• Related key columns use compatible physical types.
• The graph contains the relational paths required by the predictive query.

Run graph validation

graph.validate()

Resolve reported errors before submitting a prediction request. After correcting metadata or relationships, inspect and validate the graph again.

Validation reports invalid edges, incompatible foreign-key and primary-key types, empty discovery results, and primary-key candidates that were declined during inference. For graphs converted from warehouse views, inspect conversion diagnostics as well:

for message in graph.conversion_messages:
    print(message)

Visualize the graph

Generate a Mermaid representation after validation:

graph.visualize(path="graph.html", show_columns=True, height=540)

Omit path to display the visualization in a supported notebook environment. Local HTML and MMD output do not require a rendering service. PNG and SVG rendering require network access to the Mermaid rendering service. The public method does not accept a backend argument.

When validation succeeds and the graph reflects the intended prediction task, continue to Make Predictions.