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pypipkg:pypi/python-levenshtein@0.27.x
tile.json

tessl/pypi-python-levenshtein

tessl install tessl/pypi-python-levenshtein@0.27.0

Python compatibility wrapper for computing string edit distances and similarities using fast Levenshtein algorithms.

Agent Success

Agent success rate when using this tile

88%

Improvement

Agent success rate improvement when using this tile compared to baseline

1.38x

Baseline

Agent success rate without this tile

64%

rubric.jsonevals/scenario-4/

{
  "context": "This criteria evaluates how well the engineer uses the Levenshtein package's edit operation functions to implement a string transformation system. The focus is on proper usage of editops(), apply_edit(), and inverse() functions to record, apply, and reverse string transformations.",
  "type": "weighted_checklist",
  "checklist": [
    {
      "name": "Uses editops function",
      "description": "Implementation correctly uses Levenshtein.editops() to generate edit operations between source and target strings. The edit operations should be stored for later use.",
      "max_score": 30
    },
    {
      "name": "Uses apply_edit function",
      "description": "Implementation correctly uses Levenshtein.apply_edit() to apply the stored edit operations to a source string, transforming it to the target string.",
      "max_score": 25
    },
    {
      "name": "Uses inverse function",
      "description": "Implementation correctly uses Levenshtein.inverse() to create inverse edit operations that reverse the original transformation.",
      "max_score": 25
    },
    {
      "name": "Correct operation counting",
      "description": "Implementation correctly returns the count of edit operations, typically by using len() on the list returned by editops().",
      "max_score": 10
    },
    {
      "name": "Proper initialization",
      "description": "TransformTracker class correctly initializes by computing and storing edit operations from source and target strings during construction.",
      "max_score": 10
    }
  ]
}