Embedding Maps Workflows

Use embedding-map workflows when embedding structure, trajectories, or scalar value overlays must be inspected, compared, or visualized.

Typical Questions

• Do records cluster by condition, role, or phase?

• Are semantic spaces separable across treatments?

• Which map best preserves interpretable structure and trajectory legibility?

• How do traces move through the same map as value signals change?

Key API Entry Points

• design_research_analysis.embed_records()

• design_research_analysis.build_embedding_map()

• design_research_analysis.compare_embedding_maps()

• design_research_analysis.cluster_embedding_map()

• design_research_analysis.compute_design_space_coverage()

• design_research_analysis.compute_idea_space_trajectory()

• design_research_analysis.compute_divergence_convergence()

• design_research_analysis.plot_embedding_map()

• design_research_analysis.plot_embedding_map_grid()

Map-Space Diagnostics

Coverage and trajectory metrics can be computed on raw embeddings or on a lower-dimensional embedding map. Prefer map-space metrics when you want summaries that line up directly with plotted coordinates and CLI exports. Prefer raw embedding-space metrics when preserving the full geometry matters more than human-readable visuals.

CLI Path

design-research-analysis run-embedding-maps \
  --input data/events.csv \
  --summary-json artifacts/embedding_maps.json \
  --map-csv artifacts/embedding_maps.csv \
  --method pca \
  --method umap \
  --trace-column session_id \
  --order-column timestamp \
  --comparison-png artifacts/embedding_maps.png

The embedding-maps summary JSON includes per-method clustering, coverage, and trajectory diagnostics. When trace and order columns are supplied, trajectories follow those fields; otherwise the CLI falls back to session_id and timestamp when present.