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[Feature] Add Modern Evaluation Metrics Framework #108

@anivar

Description

@anivar

Problem Statement

any-guardrail currently has no evaluation framework or metrics. This makes it impossible to:

  • Measure guardrail performance scientifically
  • Compare different guardrail models
  • Validate improvements
  • Benchmark against competitors

Current State

  • No evaluation metrics (checked codebase)
  • Only basic pass/fail in GuardrailOutput.valid
  • No benchmarking system
  • No performance measurement

Proposed Solution

Add comprehensive evaluation framework with modern metrics:

from any_guardrail.evaluation import Evaluator

evaluator = Evaluator()
results = evaluator.evaluate(
    guardrail,
    test_data,
    metrics=['accuracy', 'f1', 'latency']
)

print(results)
# {
#   'accuracy': 0.92,
#   'f1_score': 0.89,
#   'latency_ms': 45,
#   'confusion_matrix': [[...]]
# }

Features to Add

  1. Basic Metrics:

    • Accuracy, Precision, Recall, F1
    • Confusion matrices
    • ROC curves
  2. Advanced Metrics:

    • Semantic similarity (BERTScore)
    • Cross-lingual consistency
    • Latency and throughput
  3. Benchmarking:

    • Standard test sets
    • Comparison reports
    • CI/CD integration

Implementation Plan

# src/any_guardrail/evaluation/
├── metrics.py       # Core metrics
├── benchmarks.py    # Standard datasets
├── reporter.py      # Generate reports
└── __init__.py

Why This Matters

  • Can't improve what you can't measure
  • Users need to compare guardrail performance
  • Required for research papers and publications
  • Essential for production deployment decisions

References

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