validate()

def validate(
    data: pd.DataFrame,
    return_details: bool = True,
    max_examples_per_rule: int = 6
) -> dict

{
    'total_rows': 100,           # Total rows
    'passed_rows': 85,           # Passed rows
    'failed_rows': 15,           # Failed rows
    'pass_rate': 0.85,           # Pass rate
    'is_fully_compliant': False, # Fully compliant
    'constraint_violations': {   # Violation statistics
        'field_constraints': {
            'Rule 1: age >= 18': {
                'failed_count': 10,
                'fail_rate': 0.1,
                'violation_examples': [0, 5, 12]
            }
        }
    },
    'violation_details': DataFrame  # Violation data (optional)
}

from petsard import Constrainer
import pandas as pd

# Prepare data
df = pd.DataFrame({
    'age': [25, 15, 45, 70, 35],
    'performance': [5, 3, 4, 2, 5]
})

# Validate
constrainer = Constrainer(config)
result = constrainer.validate(df)

print(f"Pass rate: {result['pass_rate']:.2%}")
print(f"Fully compliant: {result['is_fully_compliant']}")

result = constrainer.validate(df, return_details=True)

# Check violation statistics
for constraint_type, violations in result['constraint_violations'].items():
    print(f"\n{constraint_type}:")
    for rule_name, stats in violations.items():
        print(f"  {rule_name}: {stats['failed_count']} violations")

# View violation data
if not result['is_fully_compliant']:
    print(result['violation_details'])

# Validate multiple batches
batches = {'batch_1': df1, 'batch_2': df2, 'batch_3': df3}

for name, data in batches.items():
    result = constrainer.validate(data, return_details=False)
    print(f"{name}: {result['pass_rate']:.1%}")

# Generate synthetic data
synthetic = synthesizer.sample(num_rows=1000)

# Validate quality
result = constrainer.validate(synthetic)

if result['pass_rate'] < 0.95:
    # Poor quality, regenerate
    better_data = constrainer.resample_until_satisfy(
        data=pd.DataFrame(),
        target_rows=1000,
        synthesizer=synthesizer
    )