Diagnosed thesis Makefile and refactor workflow risks

• Day: 2026-06-09
• Time: 11:40 to 11:50
• Project: Dev
• Workspace: WP 2: Operational
• Status: Completed
• Priority: HIGH
• Assignee: Matías Nehuen Iglesias
• Tags: Makefile, Gridsearchcv, Refactoring, Python, Automation, Debugging

Description

Session Goal

Analyze the thesis automation and Python refactor workflow to identify where long-running experiments and module-splitting errors were coming from, and define safer execution patterns for future work.

Key Activities

• Traced the execution path for make thesis-all and isolated run-baseline in thesis-core as the likely source of an ~11 hour GridSearchCV run.
• Evaluated the Makefile target hierarchy and proposed separating targets into fast, canonical, and expensive tiers.
• Recommended adding explicit expensive-run gates, cost estimation, and timing instrumentation so long experiments cannot be triggered accidentally.
• Designed a deterministic AST-based extraction workflow for splitting experiments.py into experiment_frame.py and experiment_artifacts.py.
• Proposed a safe refactor sequence: extract top-level functions with a tiny AST tool, verify imports/compilation, then delete source code only after tests pass.
• Diagnosed missing-symbol issues after module extraction and suggested concrete fixes, including local wrappers for private helpers and proper import placement.
• Identified incorrect Python imports in experiment_frame.py and proposed moving get_split_path to eph_income.splits and importing resolve_project_path from eph_income.dataset.

Achievements

• Clarified the most probable cause of the unexpectedly long thesis run.
• Established a safer Makefile strategy for experiment gating and runtime visibility.
• Produced a refactor plan that reduces risk during module splitting by using deterministic extraction and staged validation.
• Narrowed down import and helper-resolution bugs introduced by the refactor and specified how to repair them.

Pending Tasks

• Implement the Makefile tiering and expensive-run safeguards.
• Add runtime/cost instrumentation for thesis experiment targets.
• Apply the AST-based module split and validate with ruff and py_compile.
• Patch the missing helper/import issues in the extracted Python modules and rerun tests.

Evidence

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• event_ids: []