Refactored OLS notebook registry and analysis split

• Day: 2026-06-15
• Time: 11:50 to 12:10
• Project: Dev
• Workspace: WP 2: Operational
• Status: Completed
• Priority: MEDIUM
• Assignee: Matías Nehuen Iglesias
• Tags: Python, Jupyter, Ols, Experiment-Registry, Refactor, Notebook-Workflow

Description

Session Goal

Refine the OLS notebook workflow so that experiment discovery, registry mapping, and analytical responsibilities are cleanly separated across notebooks 11, 15, and 17. The intent was to make the thesis analysis reproducible, avoid config-name mismatches, and prevent mixing coefficient interpretation with performance benchmarking or sensitivity checks.

Key Activities

• Proposed a refactor for Notebook 11 to isolate the feature-block ladder used for OLS analysis.
• Defined a cleaner scope for Notebook 15 focused on equation-level analysis and coefficient interpretation.
• Designed a robust experiment registry for Notebook 17 covering OLS baseline, ablations, and pyramid sensitivity runs.
• Clarified safe run discovery rules to avoid prefix collisions when locating experiments.
• Corrected the canonical naming rule for item 17: canonical IDs must match the real configs tree (ols_core_no_sanitation, ols_core_no_max_hh_educ), while Spanish names remain only as aliases.
• Proposed an extraction matrix framework for reviewing notebook outputs with fields such as scientific question, expected artifact, exact metric, interpretation, and candidate figure.
• Sketched a broader evaluation plan that separates feature-block benchmarking, coefficient reading, and robustness/sensitivity analysis into distinct artifact streams.

Achievements

• The notebook architecture was clarified into three non-overlapping roles:
  1. feature-block performance benchmarking,
  2. OLS coefficient interpretation,
  3. ablation and sensitivity analysis.
• The registry logic was aligned with the repository’s actual configuration filenames, reducing the risk of invalid experiment references.
• A reusable review structure was established for future notebook extraction and thesis reporting.

Pending Tasks

• Implement the notebook refactor in code and verify the updated cell structure.
• Validate the experiment registry against the live config tree and run discovery behavior.
• Populate the extraction matrix with actual notebook outputs once executed notebooks are available.
• Confirm that the split between benchmarking, coefficients, and sensitivity is reflected in the final thesis artifacts.

Evidence

• source_file=2026-06-15.sessions.jsonl, line_number=3, event_count=0, session_id=0ca06a0659c13d99e360de34baac27448bceb6b392eac4b03776f0c2aedf8575
• event_ids: []