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from datasets import load_dataset
from typing import Dict, List, Tuple
import random
from tqdm import tqdm
import json
from pathlib import Path
import weave
def load_ai4privacy_dataset(num_samples: int = 100, split: str = "validation") -> List[Dict]:
"""
Load and prepare samples from the ai4privacy dataset.
Args:
num_samples: Number of samples to evaluate
split: Dataset split to use ("train" or "validation")
Returns:
List of prepared test cases
"""
# Load the dataset
dataset = load_dataset("ai4privacy/pii-masking-400k")
# Get the specified split
data_split = dataset[split]
# Randomly sample entries if num_samples is less than total
if num_samples < len(data_split):
indices = random.sample(range(len(data_split)), num_samples)
samples = [data_split[i] for i in indices]
else:
samples = data_split
# Convert to test case format
test_cases = []
for sample in samples:
# Extract entities from privacy_mask
entities: Dict[str, List[str]] = {}
for entity in sample['privacy_mask']:
label = entity['label']
value = entity['value']
if label not in entities:
entities[label] = []
entities[label].append(value)
test_case = {
"description": f"AI4Privacy Sample (ID: {sample['uid']})",
"input_text": sample['source_text'],
"expected_entities": entities,
"masked_text": sample['masked_text'],
"language": sample['language'],
"locale": sample['locale']
}
test_cases.append(test_case)
return test_cases
@weave.op()
def evaluate_model(guardrail, test_cases: List[Dict]) -> Tuple[Dict, List[Dict]]:
"""
Evaluate a model on the test cases.
Args:
guardrail: Entity recognition guardrail to evaluate
test_cases: List of test cases
Returns:
Tuple of (metrics dict, detailed results list)
"""
metrics = {
"total": len(test_cases),
"passed": 0,
"failed": 0,
"entity_metrics": {} # Will store precision/recall per entity type
}
detailed_results = []
for test_case in tqdm(test_cases, desc="Evaluating samples"):
# Run detection
result = guardrail.guard(test_case['input_text'])
detected = result.detected_entities
expected = test_case['expected_entities']
# Track entity-level metrics
all_entity_types = set(list(detected.keys()) + list(expected.keys()))
entity_results = {}
for entity_type in all_entity_types:
detected_set = set(detected.get(entity_type, []))
expected_set = set(expected.get(entity_type, []))
# Calculate metrics
true_positives = len(detected_set & expected_set)
false_positives = len(detected_set - expected_set)
false_negatives = len(expected_set - detected_set)
precision = true_positives / (true_positives + false_positives) if (true_positives + false_positives) > 0 else 0
recall = true_positives / (true_positives + false_negatives) if (true_positives + false_negatives) > 0 else 0
f1 = 2 * (precision * recall) / (precision + recall) if (precision + recall) > 0 else 0
entity_results[entity_type] = {
"precision": precision,
"recall": recall,
"f1": f1,
"true_positives": true_positives,
"false_positives": false_positives,
"false_negatives": false_negatives
}
# Aggregate metrics
if entity_type not in metrics["entity_metrics"]:
metrics["entity_metrics"][entity_type] = {
"total_true_positives": 0,
"total_false_positives": 0,
"total_false_negatives": 0
}
metrics["entity_metrics"][entity_type]["total_true_positives"] += true_positives
metrics["entity_metrics"][entity_type]["total_false_positives"] += false_positives
metrics["entity_metrics"][entity_type]["total_false_negatives"] += false_negatives
# Store detailed result
detailed_result = {
"id": test_case.get("description", ""),
"language": test_case.get("language", ""),
"locale": test_case.get("locale", ""),
"input_text": test_case["input_text"],
"expected_entities": expected,
"detected_entities": detected,
"entity_metrics": entity_results,
"anonymized_text": result.anonymized_text if result.anonymized_text else None
}
detailed_results.append(detailed_result)
# Update pass/fail counts
if all(entity_results[et]["f1"] == 1.0 for et in entity_results):
metrics["passed"] += 1
else:
metrics["failed"] += 1
# Calculate final entity metrics
for entity_type, counts in metrics["entity_metrics"].items():
tp = counts["total_true_positives"]
fp = counts["total_false_positives"]
fn = counts["total_false_negatives"]
precision = tp / (tp + fp) if (tp + fp) > 0 else 0
recall = tp / (tp + fn) if (tp + fn) > 0 else 0
f1 = 2 * (precision * recall) / (precision + recall) if (precision + recall) > 0 else 0
metrics["entity_metrics"][entity_type].update({
"precision": precision,
"recall": recall,
"f1": f1
})
return metrics, detailed_results
def save_results(metrics: Dict, detailed_results: List[Dict], model_name: str, output_dir: str = "evaluation_results"):
"""Save evaluation results to files"""
output_dir = Path(output_dir)
output_dir.mkdir(exist_ok=True)
# Save metrics summary
with open(output_dir / f"{model_name}_metrics.json", "w") as f:
json.dump(metrics, f, indent=2)
# Save detailed results
with open(output_dir / f"{model_name}_detailed_results.json", "w") as f:
json.dump(detailed_results, f, indent=2)
def print_metrics_summary(metrics: Dict):
"""Print a summary of the evaluation metrics"""
print("\nEvaluation Summary")
print("=" * 80)
print(f"Total Samples: {metrics['total']}")
print(f"Passed: {metrics['passed']}")
print(f"Failed: {metrics['failed']}")
print(f"Success Rate: {(metrics['passed']/metrics['total'])*100:.1f}%")
print("\nEntity-level Metrics:")
print("-" * 80)
print(f"{'Entity Type':<20} {'Precision':>10} {'Recall':>10} {'F1':>10}")
print("-" * 80)
for entity_type, entity_metrics in metrics["entity_metrics"].items():
print(f"{entity_type:<20} {entity_metrics['precision']:>10.2f} {entity_metrics['recall']:>10.2f} {entity_metrics['f1']:>10.2f}")
def main():
"""Main evaluation function"""
weave.init("guardrails-genie-pii-evaluation")
# Load test cases
test_cases = load_ai4privacy_dataset(num_samples=100)
# Initialize models to evaluate
models = {
"regex": RegexEntityRecognitionGuardrail(should_anonymize=True),
"presidio": PresidioEntityRecognitionGuardrail(should_anonymize=True),
"transformers": TransformersEntityRecognitionGuardrail(should_anonymize=True)
}
# Evaluate each model
for model_name, guardrail in models.items():
print(f"\nEvaluating {model_name} model...")
metrics, detailed_results = evaluate_model(guardrail, test_cases)
# Print and save results
print_metrics_summary(metrics)
save_results(metrics, detailed_results, model_name)
if __name__ == "__main__":
from guardrails_genie.guardrails.entity_recognition.regex_entity_recognition_guardrail import RegexEntityRecognitionGuardrail
from guardrails_genie.guardrails.entity_recognition.presidio_entity_recognition_guardrail import PresidioEntityRecognitionGuardrail
from guardrails_genie.guardrails.entity_recognition.transformers_entity_recognition_guardrail import TransformersEntityRecognitionGuardrail
main() |