refactor#7
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WalkthroughThis pull request refactors multiple task modules to streamline the answer extraction process and metadata handling. The changes simplify conditional logic within the evaluation functions, replace intermediate extraction mechanisms with direct function calls to Changes
Sequence Diagram(s)sequenceDiagram
participant Task
participant ExtractionFn as extract_answer()
participant Model
Note over Task: Start evaluation in evaluate_example
Task->>Model: Receive model output
Task->>ExtractionFn: Call extract_answer(model output)
ExtractionFn-->>Task: Return candidate answers
Task->>Task: Check candidate confidence
alt High confidence
Task->>Task: Select best candidate answer
else Low confidence
Task->>Task: Fallback: use last sentence from model output
end
Task->>Task: Update metadata with candidate & alternative details
Task-->>Model: Return TaskResult (includes answer, metadata, and question)
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Actionable comments posted: 0
🧹 Nitpick comments (1)
src/benchpress/tasks/gpqa.py (1)
130-138: Complete metadata with backward compatibility.The metadata includes all necessary fields including backward compatibility keys and the newly added pattern_type. This ensures the refactoring doesn't break existing code.
Consider breaking long lines to improve readability:
- "confidence": float(extracted_answer.confidence), # For backward compatibility + # For backward compatibility + "confidence": float(extracted_answer.confidence),🧰 Tools
🪛 Ruff (0.8.2)
133-133: Line too long (91 > 88)
(E501)
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📒 Files selected for processing (3)
src/benchpress/tasks/aime24.py(2 hunks)src/benchpress/tasks/gpqa.py(4 hunks)src/benchpress/tasks/math500.py(2 hunks)
🧰 Additional context used
🧬 Code Definitions (1)
src/benchpress/tasks/gpqa.py (4)
src/benchpress/extraction/base.py (2)
ExtractedAnswer(55-64)ExtractionContext(44-51)src/benchpress/extraction/core.py (1)
extract_answer(15-72)tests/fixtures/extraction_examples.py (1)
extraction_context(168-175)src/benchpress/tasks/base.py (1)
TaskResult(11-25)
🪛 Ruff (0.8.2)
src/benchpress/tasks/aime24.py
151-151: Line too long (100 > 88)
(E501)
src/benchpress/tasks/math500.py
56-56: Line too long (93 > 88)
(E501)
135-135: Line too long (100 > 88)
(E501)
src/benchpress/tasks/gpqa.py
133-133: Line too long (91 > 88)
(E501)
🔇 Additional comments (15)
src/benchpress/tasks/math500.py (5)
56-56: Improvement: Centralized normalization logic.Good approach to remove the specialized normalization method and rely on the central utility in the extraction processors. This promotes code reuse and consistency across different tasks.
🧰 Tools
🪛 Ruff (0.8.2)
56-56: Line too long (93 > 88)
(E501)
113-118: Clean extraction implementation.The refactored approach directly leverages the extraction system and cleanly handles the candidate selection logic. This is more concise than the previous implementation.
119-120: Streamlined comparison logic.Direct comparison of extracted answer with example.answer simplifies the code while maintaining the same functionality.
133-139: Well-structured metadata update with backward compatibility.Consolidating metadata updates into a single call improves code organization. The inclusion of both current and backward-compatible keys ensures existing code continues to work.
🧰 Tools
🪛 Ruff (0.8.2)
135-135: Line too long (100 > 88)
(E501)
143-150: Enhanced result with alternative answers.Adding alternative answers to the metadata provides more context about potential interpretations, which is valuable for analysis and debugging.
src/benchpress/tasks/aime24.py (4)
129-134: Clean extraction implementation.The refactored extraction approach directly leverages the extraction system and uses a concise ternary operator for candidate selection. This improves code clarity.
149-155: Well-structured metadata update with backward compatibility.Consolidating metadata updates into a single call improves code readability. Including both current and backward-compatible keys ensures existing code continues to work.
🧰 Tools
🪛 Ruff (0.8.2)
151-151: Line too long (100 > 88)
(E501)
157-166: Enhanced result with alternative answers.Adding alternative answers to the metadata provides more context about potential interpretations, limiting to top alternatives keeps the output manageable.
169-169: Improved TaskResult structure.Adding the original question to the TaskResult enhances the completeness of the returned data structure, making it more useful for downstream consumers.
src/benchpress/tasks/gpqa.py (6)
9-9: Updated imports to support refactored extraction.The import statement correctly includes all necessary classes from the extraction module.
19-19: Improved code structure comment.The comment clearly explains the architectural change from using an instance attribute to directly calling the module function.
101-102: Simplified extraction logic.Using the extract_answer function directly simplifies the code and makes it more consistent with other tasks.
104-116: Streamlined fallback mechanism.The simplified fallback logic is cleaner and easier to understand. Using the last sentence as a fallback with a standardized confidence score provides a consistent approach.
141-149: Consistent alternative answers handling.The implementation for alternative answers matches the approach used in other tasks, providing consistency across the codebase.
152-152: Improved TaskResult structure.Adding the original question to the TaskResult enhances the completeness of the returned data structure, consistent with changes in other tasks.
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