redacteval 0.1.0

Evaluation and diagnostics toolkit for text redaction quality.

RedactEval

RedactEval is a Python package designed to evaluate redaction frameworks by comparing original and redacted text against ground-truth entity values that should have been redacted. It is agnostic to redaction-framework output format, enabling users to compare frameworks consistently without complex requirements such as character-span annotations in the input dataset.

Installation

pip install redacteval

Usage

• Create a RedactionEvaluator instance with the following parameters:

  • original_text_column: the column containing original text
  • entity_columns: canonical ground-truth entity columns (e.g. person, email)
  • entity_aliases: optional aliases per canonical entity (to match tag variants)
  • coverage_threshold: minimum GT-coverage threshold for a valid match
  • strict_entity_matching: whether redaction tag type must match entity type
  • segmenter: optional custom sentence segmenter

• Evaluate one or more redacted outputs with the evaluate method:

evaluator = RedactionEvaluator(
    original_text_column="original_text",
    entity_columns=["person", "address", "email", "phone_number"],
    entity_aliases={
        "person": ["person", "first_name", "last_name", "name"],
        "address": ["address", "location", "place"],
        "email": ["email", "email_address"],
        "phone_number": ["phone_number", "mobile_number"],
    },
    coverage_threshold=0.8,
    strict_entity_matching=True,
)

results_a = evaluator.evaluate(df, redacted_text_column="redacted_framework_a", beta=2)
results_b = evaluator.evaluate(df, redacted_text_column="redacted_framework_b", beta=2)

print(results_a.summary())
warnings = results_a.get_warnings()

• evaluate returns an EvaluationResults object:

  • summary() gives global and per-entity metrics (precision, recall, fbeta_score, tp, fp, fn)
  • get_warnings() returns row-level warnings (for example sentence-structure mismatches)

• Prepare the data for evaluation:

  • Entity columns can contain a string, a list of strings, or None.
  • Example: email can hold ["[e-mail]", "[e-mail]"] for a single row.

Ground-Truth Name Input Requirement

For person names, provide atomic name elements separately in ground-truth data.

• Recommended:
  • person = ["Sam", "Wilson"]
  • person = ["Jane", "Mary", "Smith"] (first/middle/last separately)
• Avoid providing only one combined full-name string when you want per-token scoring:
  • person = "Sam Wilson" (this is treated as one occurrence, not two)

Example row:

{
    "original_text": "Sam Wilson or Jane Smith can help you with the issue.",
    "person": ["Sam", "Wilson", "Jane", "Smith"],
    "redacted_framework_a": "<PERSON> or <FIRST_NAME> <LAST_NAME> can help you with the issue.",
}

Notes:

• A combined mask over multiple atomic values (for example <PERSON> over "Sam Wilson") is considered correct when overlap requirements are met.
• Name elements are matched as standalone occurrences in context (so substrings inside larger tokens such as emails are not counted as separate person occurrences).

Demo Data

You can start with bundled sample data:

from redacteval import RedactionEvaluator, load_demo_data, print_report

df = load_demo_data()
evaluator = RedactionEvaluator(
    original_text_column="original_text",
    entity_columns=["name", "email", "phone_number"],
    entity_aliases={
        "name": ["name", "person", "first_name", "last_name"],
        "email": ["email", "email_address"],
        "phone_number": ["phone_number", "mobile_number"],
    },
)

results = evaluator.evaluate(df, redacted_text_column="redacted_framework_a")
print_report(results)

A runnable notebook is available at examples/demo.ipynb.

License

This project is licensed under the Apache License 2.0. See LICENSE for details.

Contributing

Contributions are welcome. Please read CONTRIBUTING.md before opening a pull request.