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Phase 1: Data splitting with contract-level leakage prevention
This module implements contract-level train/val/test splitting, ensuring
that no contract text appears in more than one split.
CRITICAL: This prevents data leakage and ensures honest evaluation.
"""
import logging
import random
from typing import List, Tuple, Set, Dict
from pathlib import Path
import json
from data.loaders import Contract
logger = logging.getLogger(__name__)
class ContractLevelSplitter:
"""Split contracts into train/val/test with zero-leakage guarantee."""
def __init__(self, random_seed: int = 42):
"""
Initialize splitter.
Args:
random_seed: Seed for reproducibility
"""
self.random_seed = random_seed
random.seed(random_seed)
def split(
self,
contracts: List[Contract],
train_frac: float = 0.70,
val_frac: float = 0.15,
test_frac: float = 0.15,
) -> Tuple[List[Contract], List[Contract], List[Contract]]:
"""
Split contracts at the contract level (never split a contract across splits).
Args:
contracts: List of contracts to split
train_frac: Fraction for training
val_frac: Fraction for validation
test_frac: Fraction for testing
Returns:
Tuple of (train_contracts, val_contracts, test_contracts)
Raises:
AssertionError: If fractions don't sum to 1.0 or leakage is detected
"""
assert abs(train_frac + val_frac + test_frac - 1.0) < 1e-6, \
f"Fractions must sum to 1.0, got {train_frac + val_frac + test_frac}"
# Shuffle contracts
shuffled = list(contracts)
random.shuffle(shuffled)
n = len(shuffled)
train_end = int(n * train_frac)
val_end = train_end + int(n * val_frac)
train = shuffled[:train_end]
val = shuffled[train_end:val_end]
test = shuffled[val_end:]
# Verify zero leakage
self._verify_no_leakage(train, val, test)
logger.info(f"Split {n} contracts:")
logger.info(f" Train: {len(train)} ({len(train)/n*100:.1f}%)")
logger.info(f" Val: {len(val)} ({len(val)/n*100:.1f}%)")
logger.info(f" Test: {len(test)} ({len(test)/n*100:.1f}%)")
return train, val, test
@staticmethod
def _verify_no_leakage(
train: List[Contract],
val: List[Contract],
test: List[Contract]
) -> None:
"""
Verify that no contract appears in multiple splits.
Raises:
AssertionError: If leakage is detected
"""
train_ids = {c.id for c in train}
val_ids = {c.id for c in val}
test_ids = {c.id for c in test}
# Check for overlaps
train_val_overlap = train_ids & val_ids
val_test_overlap = val_ids & test_ids
train_test_overlap = train_ids & test_ids
assert len(train_val_overlap) == 0, \
f"LEAKAGE: {len(train_val_overlap)} contracts in both train and val: {train_val_overlap}"
assert len(val_test_overlap) == 0, \
f"LEAKAGE: {len(val_test_overlap)} contracts in both val and test: {val_test_overlap}"
assert len(train_test_overlap) == 0, \
f"LEAKAGE: {len(train_test_overlap)} contracts in both train and test: {train_test_overlap}"
# Check for duplicate text (additional verification)
train_texts = {c.text for c in train}
val_texts = {c.text for c in val}
test_texts = {c.text for c in test}
# Duplicates within splits are OK; duplicates across splits are NOT OK
assert len(train_texts & val_texts) == 0, \
"LEAKAGE: Duplicate contract text in train and val"
assert len(val_texts & test_texts) == 0, \
"LEAKAGE: Duplicate contract text in val and test"
assert len(train_texts & test_texts) == 0, \
"LEAKAGE: Duplicate contract text in train and test"
logger.info("✓ Leakage verification passed: zero contract overlap across splits")
class DatasetStatistics:
"""Compute and log dataset statistics."""
@staticmethod
def compute(
datasets: Dict[str, List[Contract]],
output_file: str = None
) -> Dict:
"""
Compute statistics across all datasets.
Args:
datasets: Dict of {name: [contracts]}
output_file: Optional path to save statistics as JSON
Returns:
Dict of statistics
"""
stats = {}
for dataset_name, contracts in datasets.items():
if not contracts:
continue
# Basic counts
num_contracts = len(contracts)
num_clauses = sum(len(c.clauses) for c in contracts)
# Text statistics
text_lengths = [len(c.text) for c in contracts]
clause_counts = [len(c.clauses) for c in contracts]
# Category distribution (for CUAD/MAUD)
category_counts = {}
for contract in contracts:
for clause in contract.clauses:
cat = clause.get("category", "unknown")
category_counts[cat] = category_counts.get(cat, 0) + 1
stats[dataset_name] = {
"num_contracts": num_contracts,
"num_clauses": num_clauses,
"clauses_per_contract": {
"mean": sum(clause_counts) / len(clause_counts),
"median": sorted(clause_counts)[len(clause_counts)//2],
"min": min(clause_counts),
"max": max(clause_counts),
},
"text_length_chars": {
"mean": sum(text_lengths) / len(text_lengths),
"median": sorted(text_lengths)[len(text_lengths)//2],
"min": min(text_lengths),
"max": max(text_lengths),
},
"categories": category_counts,
}
# Log summary
logger.info("\n" + "=" * 70)
logger.info("DATASET STATISTICS")
logger.info("=" * 70)
for dataset_name, ds_stats in stats.items():
logger.info(f"\n{dataset_name}:")
logger.info(f" Contracts: {ds_stats['num_contracts']}")
logger.info(f" Clauses: {ds_stats['num_clauses']}")
logger.info(f" Clauses/contract (mean): {ds_stats['clauses_per_contract']['mean']:.1f}")
logger.info(f" Text length chars (mean): {ds_stats['text_length_chars']['mean']:.0f}")
if ds_stats['categories']:
logger.info(f" Categories: {len(ds_stats['categories'])}")
sorted_cats = sorted(
ds_stats['categories'].items(),
key=lambda x: x[1],
reverse=True
)
for cat, count in sorted_cats[:5]:
logger.info(f" - {cat}: {count}")
logger.info("=" * 70 + "\n")
# Save to file if requested
if output_file:
with open(output_file, "w") as f:
json.dump(stats, f, indent=2)
logger.info(f"Statistics saved to {output_file}")
return stats
if __name__ == "__main__":
logging.basicConfig(level=logging.INFO)
from data.loaders import load_all_datasets
# Load all datasets
datasets = load_all_datasets()
# Example: split CUAD train/test
if "cuad_train" in datasets and datasets["cuad_train"]:
splitter = ContractLevelSplitter(random_seed=42)
train, val, test = splitter.split(
datasets["cuad_train"],
train_frac=0.70,
val_frac=0.15,
test_frac=0.15
)
logger.info(f"✓ Successfully split CUAD: {len(train)} train, {len(val)} val, {len(test)} test")
# Compute statistics
stats = DatasetStatistics.compute(datasets, output_file="data/statistics.json")
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