Debugging EPH 2025 download and training set generation

• Day: 2026-06-14
• Time: 11:50 to 12:00
• Project: Dev
• Workspace: WP 2: Operational
• Status: In Progress
• Priority: HIGH
• Assignee: Matías Nehuen Iglesias
• Tags: Training-Sets, Eph, Debugging, Staging, Etl, Cli

Description

Session Goal

Investigate the generation of training sets while diagnosing why the EPH 2025 download pipeline was failing silently, and define a safer, auditable path to ingest only 2025 data.

Key Activities

• Reviewed guidance for isolated staging instead of working directly on raw/eph when the target is only 2025.
• Identified that fetch_range can swallow fetch exceptions by downgrading them to WARNING, which makes the process appear successful even when no data is downloaded.
• Proposed a quarter-by-quarter debugging strategy for 2025 with explicit logs to isolate the failing range.
• Clarified that the issue is likely a packaging/entrypoint problem, not a runtime bug in the extractor itself.
• Recommended bypassing shell installation issues by invoking the tool as a Python module: [[python]] -m eph_extractor.cli.
• Suggested a minimal patch to improve failure visibility and preserve traceability during downloads.

Achievements

• Narrowed the failure mode to silent error handling in fetch_range rather than an obvious crash.
• Established a more reliable workflow for 2025 ingestion: staged download, extraction, audit, and later merge into raw/txt.
• Defined a practical debugging path that prioritizes traceability over packaging fixes.

Pending Tasks

• Run the 2025 download process by quarter with verbose logs to identify the exact failing segment.
• Verify remote ZIP naming and confirm the CLI/module invocation path works end-to-end.
• If needed, patch fetch_range to fail loudly instead of converting exceptions into warnings.
• Complete the training set generation once the 2025 data ingestion path is validated.

Evidence

• source_file=2026-06-14.sessions.jsonl, line_number=7, event_count=0, session_id=d8a4d905ec8de1d8b8257a0a47e2d943591dd36ae62ef655805258ec250abf05
• event_ids: []