Learning Code Consistency

Learning Code Consistency Checker

Objectives

• Verify code logic matches between .py and .ipynb files
• Ensure .md documentation data matches code output
• Validate all screenshots/images are referenced correctly

Instructions

1. Compare .py and .ipynb Files

Check these elements:

• Function definitions (names, parameters, logic)
• Import statements
• Main execution flow
• Output statements (print, plots)
• File paths (especially image save paths)

Common differences to ignore:

• Jupyter cell structure vs sequential execution
• Interactive outputs (plt.show() in .ipynb vs plt.savefig() in .py)
• Cell magic commands (%matplotlib inline)

2. Verify .md Documentation Against Code

Data consistency checks:

• Statistical values (α, R², counts, percentages)
• Top N results (word frequencies, rankings)
• Image filenames match actual generated files
• Screenshot descriptions match code output

How to verify:

1. Run the .py file and capture output
2. Compare printed values with .md content
3. Check [Image Data] markers in output for easy matching
4. Verify image paths are relative and correct

3. Image Reference Validation

Check:

• All images in .md exist in the specified directory
• Image paths are relative (e.g., images/xxx.png)
• No absolute paths (e.g., C:\Users\...)
• No broken image links

4. Report Format

Provide a structured report:

## Consistency Check Report

### ✅ .py vs .ipynb

- Functions: Identical
- Logic: Consistent
- Outputs: Match (accounting for interactive differences)

### ✅ .md vs Code Output

- Statistical values: All match
- Image references: All valid
- Data points verified: [list key values]

### ⚠️ Issues Found

- [Issue 1]: Description and fix
- [Issue 2]: Description and fix

### 📋 Summary

Ready for submission: Yes/No

Validation Steps

1. Code execution test: Run .py file successfully
2. Output comparison: Match printed values with .md
3. Image check: Verify all images exist and display correctly
4. Cross-reference: Ensure .ipynb produces same results as .py

Common Issues

• Different random seeds: Ensure reproducibility with random.seed() or np.random.seed()
• Path differences: Use relative paths consistently
• Rounding differences: Check decimal places match in .md
• Missing images: Verify all plots are saved before referencing