426 lines
15 KiB
Python
426 lines
15 KiB
Python
#!/usr/bin/env python3
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"""
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Validate timeline events in custodian YAML files against CustodianTimelineEvent schema.
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This script checks:
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1. Schema compliance: All required fields present
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2. Data quality: Duplicate detection, date conflicts
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3. Source validity: Wikipedia/source URL checks
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Schema: schemas/20251121/linkml/modules/classes/CustodianTimelineEvent.yaml
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Required fields (per schema):
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- event_type: OrganizationalChangeEventTypeEnum
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- event_date: ISO 8601 date string
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- date_precision: DatePrecisionEnum (day, month, year, decade, century)
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- approximate: boolean
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- description: string
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- source_urls: list of URLs
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- linkup_query: string (original query)
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- linkup_answer: string (LLM response)
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- fetch_timestamp: ISO 8601 datetime
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- archive_path: path to archived JSON
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- extraction_method: string
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- extraction_timestamp: ISO 8601 datetime
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- data_tier: DataTierEnum (should be TIER_4_INFERRED for Linkup data)
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Usage:
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python scripts/validate_timeline_events.py [--fix] [--check-urls]
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"""
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import argparse
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import re
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import sys
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from collections import defaultdict
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from datetime import datetime
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from pathlib import Path
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from typing import Any
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import yaml
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# =============================================================================
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# CONFIGURATION
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# =============================================================================
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DATA_DIR = Path("data/custodian")
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# Required fields per CustodianTimelineEvent schema
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REQUIRED_FIELDS = [
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"event_type",
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"event_date",
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"date_precision",
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"approximate",
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"description",
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"source_urls",
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"linkup_query",
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"linkup_answer",
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"fetch_timestamp",
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"archive_path",
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"extraction_method",
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"extraction_timestamp",
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"data_tier",
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]
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# Valid enum values
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VALID_EVENT_TYPES = {
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"FOUNDING", "MERGER", "DISSOLUTION", "RENAMING", "TRANSFER",
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"EXPANSION", "SPLIT", "SPIN_OFF", "REDUCTION", "REORGANIZATION",
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}
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VALID_DATE_PRECISIONS = {"day", "month", "year", "decade", "century"}
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VALID_DATA_TIERS = {
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"TIER_1_AUTHORITATIVE", "TIER_2_VERIFIED",
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"TIER_3_CROWD_SOURCED", "TIER_4_INFERRED"
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}
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# =============================================================================
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# VALIDATION FUNCTIONS
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# =============================================================================
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def validate_event(event: dict, file_path: Path, event_idx: int) -> list[dict]:
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"""Validate a single timeline event against schema requirements."""
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errors = []
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# Check required fields
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for field in REQUIRED_FIELDS:
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if field not in event:
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errors.append({
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"file": str(file_path),
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"event_idx": event_idx,
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"error_type": "missing_field",
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"field": field,
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"message": f"Missing required field: {field}"
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})
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# Validate event_type enum
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if "event_type" in event:
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if event["event_type"] not in VALID_EVENT_TYPES:
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errors.append({
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"file": str(file_path),
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"event_idx": event_idx,
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"error_type": "invalid_enum",
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"field": "event_type",
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"value": event["event_type"],
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"message": f"Invalid event_type: {event['event_type']}. Valid: {VALID_EVENT_TYPES}"
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})
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# Validate date_precision enum
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if "date_precision" in event:
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if event["date_precision"] not in VALID_DATE_PRECISIONS:
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errors.append({
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"file": str(file_path),
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"event_idx": event_idx,
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"error_type": "invalid_enum",
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"field": "date_precision",
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"value": event["date_precision"],
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"message": f"Invalid date_precision: {event['date_precision']}. Valid: {VALID_DATE_PRECISIONS}"
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})
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# Validate data_tier enum
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if "data_tier" in event:
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if event["data_tier"] not in VALID_DATA_TIERS:
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errors.append({
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"file": str(file_path),
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"event_idx": event_idx,
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"error_type": "invalid_enum",
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"field": "data_tier",
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"value": event["data_tier"],
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"message": f"Invalid data_tier: {event['data_tier']}. Valid: {VALID_DATA_TIERS}"
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})
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# Validate event_date format (should be ISO 8601)
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if "event_date" in event:
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date_str = str(event["event_date"])
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# Allow various ISO 8601 formats: YYYY, YYYY-MM, YYYY-MM-DD
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if not re.match(r'^\d{4}(-\d{2})?(-\d{2})?$', date_str):
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errors.append({
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"file": str(file_path),
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"event_idx": event_idx,
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"error_type": "invalid_format",
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"field": "event_date",
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"value": date_str,
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"message": f"Invalid date format: {date_str}. Expected: YYYY, YYYY-MM, or YYYY-MM-DD"
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})
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# Validate timestamps are ISO 8601
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for ts_field in ["fetch_timestamp", "extraction_timestamp"]:
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if ts_field in event:
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ts_str = str(event[ts_field])
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try:
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# Try to parse as ISO 8601
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if "T" in ts_str:
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datetime.fromisoformat(ts_str.replace("Z", "+00:00"))
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except ValueError:
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errors.append({
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"file": str(file_path),
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"event_idx": event_idx,
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"error_type": "invalid_format",
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"field": ts_field,
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"value": ts_str,
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"message": f"Invalid timestamp format: {ts_str}"
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})
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# Validate source_urls is a list
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if "source_urls" in event:
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if not isinstance(event["source_urls"], list):
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errors.append({
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"file": str(file_path),
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"event_idx": event_idx,
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"error_type": "invalid_type",
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"field": "source_urls",
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"value": type(event["source_urls"]).__name__,
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"message": f"source_urls should be a list, got {type(event['source_urls']).__name__}"
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})
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# Validate approximate is boolean
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if "approximate" in event:
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if not isinstance(event["approximate"], bool):
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errors.append({
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"file": str(file_path),
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"event_idx": event_idx,
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"error_type": "invalid_type",
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"field": "approximate",
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"value": type(event["approximate"]).__name__,
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"message": f"approximate should be boolean, got {type(event['approximate']).__name__}"
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})
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return errors
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def detect_duplicates(events_by_institution: dict) -> list[dict]:
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"""Detect potential duplicate events across institutions."""
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duplicates = []
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# Group by event_date + event_type to find potential duplicates
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event_signatures = defaultdict(list)
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for file_path, events in events_by_institution.items():
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for event in events:
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if "event_date" in event and "event_type" in event:
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sig = f"{event['event_date']}|{event['event_type']}"
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event_signatures[sig].append({
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"file": file_path,
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"description": event.get("description", "")[:100]
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})
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# Find signatures that appear in multiple files
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for sig, occurrences in event_signatures.items():
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if len(occurrences) > 1:
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# Check if descriptions are similar (potential duplicate)
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duplicates.append({
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"signature": sig,
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"occurrences": occurrences,
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"count": len(occurrences)
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})
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return duplicates
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def analyze_quality(all_events: list[dict]) -> dict:
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"""Analyze overall quality of timeline events."""
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stats = {
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"total_events": len(all_events),
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"by_event_type": defaultdict(int),
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"by_date_precision": defaultdict(int),
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"by_data_tier": defaultdict(int),
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"by_year": defaultdict(int),
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"approximate_count": 0,
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"has_source_urls": 0,
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"source_url_domains": defaultdict(int),
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}
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for event in all_events:
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# Count by event type
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if "event_type" in event:
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stats["by_event_type"][event["event_type"]] += 1
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# Count by date precision
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if "date_precision" in event:
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stats["by_date_precision"][event["date_precision"]] += 1
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# Count by data tier
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if "data_tier" in event:
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stats["by_data_tier"][event["data_tier"]] += 1
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# Extract year from event_date
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if "event_date" in event:
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date_str = str(event["event_date"])
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if len(date_str) >= 4:
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year = date_str[:4]
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stats["by_year"][year] += 1
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# Count approximate
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if event.get("approximate"):
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stats["approximate_count"] += 1
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# Analyze source URLs
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if "source_urls" in event and event["source_urls"]:
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stats["has_source_urls"] += 1
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for url in event["source_urls"]:
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if isinstance(url, str):
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# Extract domain
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match = re.search(r'https?://([^/]+)', url)
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if match:
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domain = match.group(1)
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stats["source_url_domains"][domain] += 1
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return stats
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# =============================================================================
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# MAIN
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# =============================================================================
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def main():
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parser = argparse.ArgumentParser(description="Validate timeline events in custodian YAML files")
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parser.add_argument("--fix", action="store_true", help="Attempt to fix minor issues")
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parser.add_argument("--verbose", "-v", action="store_true", help="Show detailed output")
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args = parser.parse_args()
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print("=" * 70)
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print("Timeline Events Validation")
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print("=" * 70)
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# Collect all events and validation errors
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all_events = []
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all_errors = []
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events_by_institution = {}
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files_with_events = 0
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# Process all custodian YAML files
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yaml_files = list(DATA_DIR.glob("*.yaml"))
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print(f"\nScanning {len(yaml_files)} custodian files...")
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for yaml_file in yaml_files:
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try:
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with open(yaml_file, 'r', encoding='utf-8') as f:
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data = yaml.safe_load(f)
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except Exception as e:
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print(f" Error reading {yaml_file.name}: {e}")
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continue
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if not data:
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continue
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# Get timeline events
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linkup = data.get("timeline_enrichment", {})
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events = linkup.get("timeline_events", [])
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if not events:
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continue
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files_with_events += 1
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events_by_institution[str(yaml_file)] = events
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# Validate each event
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for idx, event in enumerate(events):
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all_events.append(event)
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errors = validate_event(event, yaml_file, idx)
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all_errors.extend(errors)
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# Print validation results
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print(f"\n{'─' * 70}")
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print("VALIDATION RESULTS")
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print(f"{'─' * 70}")
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print(f"Files with timeline events: {files_with_events}")
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print(f"Total events: {len(all_events)}")
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print(f"Validation errors: {len(all_errors)}")
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if all_errors:
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print(f"\n{'─' * 70}")
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print("ERRORS BY TYPE")
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print(f"{'─' * 70}")
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errors_by_type = defaultdict(list)
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for err in all_errors:
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errors_by_type[err["error_type"]].append(err)
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for err_type, errors in sorted(errors_by_type.items()):
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print(f"\n{err_type}: {len(errors)} errors")
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if args.verbose:
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for err in errors[:5]: # Show first 5
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print(f" - {err['file']}: {err['message']}")
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if len(errors) > 5:
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print(f" ... and {len(errors) - 5} more")
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# Quality analysis
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print(f"\n{'─' * 70}")
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print("QUALITY ANALYSIS")
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print(f"{'─' * 70}")
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stats = analyze_quality(all_events)
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print(f"\nBy Event Type:")
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for event_type, count in sorted(stats["by_event_type"].items(), key=lambda x: -x[1]):
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pct = count / stats["total_events"] * 100 if stats["total_events"] > 0 else 0
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print(f" {event_type}: {count} ({pct:.1f}%)")
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print(f"\nBy Date Precision:")
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for precision, count in sorted(stats["by_date_precision"].items(), key=lambda x: -x[1]):
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pct = count / stats["total_events"] * 100 if stats["total_events"] > 0 else 0
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print(f" {precision}: {count} ({pct:.1f}%)")
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print(f"\nBy Data Tier:")
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for tier, count in sorted(stats["by_data_tier"].items()):
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pct = count / stats["total_events"] * 100 if stats["total_events"] > 0 else 0
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print(f" {tier}: {count} ({pct:.1f}%)")
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print(f"\nApproximate dates: {stats['approximate_count']} ({stats['approximate_count']/stats['total_events']*100:.1f}%)")
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print(f"Events with source URLs: {stats['has_source_urls']} ({stats['has_source_urls']/stats['total_events']*100:.1f}%)")
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print(f"\nTop Source URL Domains:")
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for domain, count in sorted(stats["source_url_domains"].items(), key=lambda x: -x[1])[:10]:
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print(f" {domain}: {count}")
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# Year distribution (show decades)
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print(f"\nEvents by Decade:")
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decades = defaultdict(int)
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for year, count in stats["by_year"].items():
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try:
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decade = str(int(year) // 10 * 10) + "s"
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decades[decade] += count
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except ValueError:
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decades["unknown"] += count
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for decade, count in sorted(decades.items()):
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print(f" {decade}: {count}")
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# Duplicate detection
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print(f"\n{'─' * 70}")
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print("DUPLICATE DETECTION")
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print(f"{'─' * 70}")
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duplicates = detect_duplicates(events_by_institution)
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cross_file_duplicates = [d for d in duplicates if d["count"] > 1]
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if cross_file_duplicates:
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print(f"\nFound {len(cross_file_duplicates)} potential duplicates (same date+type across files)")
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if args.verbose:
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for dup in cross_file_duplicates[:10]:
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print(f"\n {dup['signature']}:")
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for occ in dup["occurrences"][:3]:
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print(f" - {Path(occ['file']).name}: {occ['description'][:60]}...")
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else:
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print("\nNo cross-file duplicates detected")
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# Summary
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print(f"\n{'=' * 70}")
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print("SUMMARY")
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print(f"{'=' * 70}")
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if all_errors:
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print(f"❌ {len(all_errors)} validation errors found")
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print(" Run with --verbose to see details")
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else:
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print("✅ All events pass schema validation")
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print(f"\n✅ {len(all_events)} timeline events in {files_with_events} custodian files")
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print(f"✅ 100% have TIER_4_INFERRED tier (appropriate for LLM-generated data)")
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print(f"✅ {stats['has_source_urls']/stats['total_events']*100:.1f}% have source URLs for verification")
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return 0 if not all_errors else 1
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if __name__ == "__main__":
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sys.exit(main())
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