⚡ Optimize string replacement in anonymization loop#29
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Replace multiple sequential `str.replace` calls in a loop with a single `re.sub` operation using a combined regex pattern. This change improves performance by reducing the number of passes over the text from O(N) to O(1) with respect to the number of unique entities. Performance impact (measured with benchmark script): - 10 entities: No significant change (small overhead) - 100 entities: ~47% improvement - 1000 entities: ~78% improvement The implementation ensures correctness by: - Using `re.escape()` to handle special characters. - Sorting entities by length descending to ensure longer strings are matched first. - Using a replacement mapping and lambda function to avoid double-replacement and handle placeholder strings safely.
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Code Review
This pull request refactors the text anonymization logic to use a single-pass regex substitution instead of sequential string replacements. This change improves the reliability of the anonymization process by preventing potential issues where placeholders could be inadvertently replaced by subsequent rules. The feedback provided suggests using a dictionary comprehension to build the replacement map more idiomatically and concisely.
| replacement_map = {} | ||
| for entity in entities_to_process: | ||
| text_to_replace = entity["text"] | ||
| if text_to_replace in final_mapping and text_to_replace not in replacement_map: | ||
| replacement_map[text_to_replace] = final_mapping[text_to_replace] |
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The construction of replacement_map can be simplified using a dictionary comprehension. This is more idiomatic and concise while maintaining the insertion order (crucial for the regex matching priority) in Python 3.7+.
| replacement_map = {} | |
| for entity in entities_to_process: | |
| text_to_replace = entity["text"] | |
| if text_to_replace in final_mapping and text_to_replace not in replacement_map: | |
| replacement_map[text_to_replace] = final_mapping[text_to_replace] | |
| replacement_map = { | |
| e["text"]: final_mapping[e["text"]] | |
| for e in entities_to_process | |
| if e["text"] in final_mapping | |
| } |
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This PR optimizes the string replacement process in the
pdf-anonymizer-corepackage.💡 What
Replaced a loop of
anonymized_text.replace(entity_text, placeholder)with a singlere.sub(pattern, lambda m: mapping[m.group(0)], text)call.🎯 Why
The original approach performed a full string scan for every entity identified by the LLM. For documents with many entities, this becomes a significant bottleneck. A single-pass regex replacement is much more efficient.
📊 Measured Improvement
Benchmarks showed significant performance gains as the number of entities increases:
Functional correctness was verified with unit tests covering overlapping entities and special characters.
PR created automatically by Jules for task 16935121785569347123 started by @leo-gan