Add hometitle service: fuzzy matching engine and change detector FRE-5351
- matcher.service.ts: name/address normalization, Levenshtein distance, geocoding proximity, confidence scoring (0.0-1.0) - change-detector.ts: PropertySnapshot diff engine, severity scoring (minor/moderate/major), configurable thresholds, alert triggering - 57 unit tests with 98%+ coverage across all thresholds
This commit is contained in:
309
services/hometitle/src/matcher.service.ts
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309
services/hometitle/src/matcher.service.ts
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import {
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Address,
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MatchResult,
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MatchDetails,
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FieldMatch,
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MatchingConfig,
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NormalizedTokens,
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PropertyType,
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} from './types';
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const DEFAULT_CONFIG: MatchingConfig = {
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nameThreshold: 0.85,
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addressThreshold: 0.9,
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overallThreshold: 0.85,
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geocodingRadiusMeters: 100,
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};
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const COMMON_PREFIXES = new Set([
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'mr', 'mrs', 'ms', 'miss', 'dr', 'prof', 'jr', 'sr', 'junior', 'senior',
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'ii', 'iii', 'iv', 'rev', 'st', 'hon', 'esq',
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]);
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const COMMON_SUFFIXES = new Set([
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'jr', 'sr', 'junior', 'senior', 'ii', 'iii', 'iv', 'v', 'esq',
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'phd', 'md', 'llm', 'cpa',
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]);
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const STREET_TYPE_MAP: Record<string, string> = {
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'st': 'street', 'street': 'street',
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'ave': 'avenue', 'avenue': 'avenue',
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'blvd': 'boulevard', 'boulevard': 'boulevard',
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'dr': 'drive', 'drive': 'drive',
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'ln': 'lane', 'lane': 'lane',
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'ct': 'court', 'court': 'court',
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'pl': 'place', 'place': 'place',
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'rd': 'road', 'road': 'road',
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'way': 'way',
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'trl': 'trail', 'trail': 'trail',
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'hwy': 'highway', 'highway': 'highway',
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'pkwy': 'parkway', 'parkway': 'parkway',
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'cir': 'circle', 'circle': 'circle',
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'sq': 'square', 'square': 'square',
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'ter': 'terrace', 'terrace': 'terrace',
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};
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const PROPERTY_TYPE_CONFIGS: Record<PropertyType, Partial<MatchingConfig>> = {
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'residential': { nameThreshold: 0.85, addressThreshold: 0.9 },
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'commercial': { nameThreshold: 0.8, addressThreshold: 0.9 },
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'land': { nameThreshold: 0.8, addressThreshold: 0.85 },
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'multi-family': { nameThreshold: 0.8, addressThreshold: 0.9 },
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};
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function levenshteinDistance(a: string, b: string): number {
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const matrix: number[][] = Array.from({ length: b.length + 1 }, (_, i) =>
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Array.from({ length: a.length + 1 }, (_, j) => (i === 0 ? j : j === 0 ? i : 0))
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);
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for (let i = 1; i <= b.length; i++) {
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for (let j = 1; j <= a.length; j++) {
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const cost = a[j - 1] === b[i - 1] ? 0 : 1;
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matrix[i][j] = Math.min(
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matrix[i - 1][j] + 1,
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matrix[i][j - 1] + 1,
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matrix[i - 1][j - 1] + cost,
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);
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}
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}
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return matrix[b.length][a.length];
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}
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function similarityScore(distance: number, maxLen: number): number {
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if (maxLen === 0) return 1.0;
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return 1.0 - distance / maxLen;
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}
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function normalizeString(str: string): string {
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return str
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.toLowerCase()
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.replace(/[''']/g, '')
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.replace(/[^a-z0-9\s]/g, ' ')
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.replace(/\s+/g, ' ')
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.trim();
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}
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function parseName(name: string): NormalizedTokens {
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const clean = normalizeString(name);
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const parts = clean.split(' ').filter(Boolean);
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let firstName = '';
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let lastName = '';
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let middleName = '';
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const initials: string[] = [];
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if (parts.length === 0) return { firstName, lastName, middleName, initials };
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let startIdx = 0;
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while (startIdx < parts.length && COMMON_PREFIXES.has(parts[startIdx])) {
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startIdx++;
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}
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let endIdx = parts.length;
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while (endIdx > startIdx + 1 && COMMON_SUFFIXES.has(parts[endIdx - 1])) {
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endIdx--;
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}
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const coreParts = parts.slice(startIdx, endIdx);
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if (coreParts.length === 1) {
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lastName = coreParts[0];
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} else if (coreParts.length === 2) {
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firstName = coreParts[0];
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lastName = coreParts[1];
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} else {
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firstName = coreParts[0];
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lastName = coreParts[coreParts.length - 1];
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middleName = coreParts.slice(1, -1).join(' ');
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}
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if (firstName.length === 1) {
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initials.push(firstName);
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}
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if (middleName) {
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const middleParts = middleName.split(' ');
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for (const mp of middleParts) {
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if (mp.length === 1) initials.push(mp);
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}
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}
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return { firstName, lastName, middleName, initials };
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}
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function normalizeStreetType(type: string): string {
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const clean = normalizeString(type);
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return STREET_TYPE_MAP[clean] || clean;
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}
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function normalizeAddress(addr: Address): string {
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const parts = [
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addr.streetNumber,
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normalizeString(addr.streetName),
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addr.streetType ? normalizeStreetType(addr.streetType) : '',
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addr.unit ? normalizeString(addr.unit) : '',
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normalizeString(addr.city),
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addr.state.toLowerCase(),
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addr.zip,
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].filter(Boolean);
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return parts.join(' ');
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}
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function computeFieldMatch(valueA: string, valueB: string, normalizeFn?: (v: string) => string): FieldMatch {
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const normFn = normalizeFn || normalizeString;
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const normalizedA = normFn(valueA);
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const normalizedB = normFn(valueB);
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if (!normalizedA && !normalizedB) return { valueA, valueB, normalizedA, normalizedB, score: 1.0 };
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if (!normalizedA || !normalizedB) return { valueA, valueB, normalizedA, normalizedB, score: 0.0 };
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if (normalizedA === normalizedB) return { valueA, valueB, normalizedA, normalizedB, score: 1.0 };
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const dist = levenshteinDistance(normalizedA, normalizedB);
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const maxLen = Math.max(normalizedA.length, normalizedB.length);
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const score = similarityScore(dist, maxLen);
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return { valueA, valueB, normalizedA, normalizedB, score: Math.round(score * 1000) / 1000 };
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}
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function haversineDistance(lat1: number, lon1: number, lat2: number, lon2: number): number {
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const R = 6371000;
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const dLat = ((lat2 - lat1) * Math.PI) / 180;
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const dLon = ((lon2 - lon1) * Math.PI) / 180;
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const a =
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Math.sin(dLat / 2) * Math.sin(dLat / 2) +
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Math.cos((lat1 * Math.PI) / 180) *
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Math.cos((lat2 * Math.PI) / 180) *
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Math.sin(dLon / 2) *
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Math.sin(dLon / 2);
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const c = 2 * Math.atan2(Math.sqrt(a), Math.sqrt(1 - a));
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return R * c;
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}
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function computeNameScore(tokensA: NormalizedTokens, tokensB: NormalizedTokens): number {
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const firstScore = computeFieldMatch(tokensA.firstName, tokensB.firstName).score;
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const lastScore = computeFieldMatch(tokensA.lastName, tokensB.lastName).score;
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const middleScore = computeFieldMatch(tokensA.middleName, tokensB.middleName).score;
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let initialMatchScore = 1.0;
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if (tokensA.initials.length > 0 || tokensB.initials.length > 0) {
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const allInitialsA = new Set(tokensA.initials.map(i => i.toLowerCase()));
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const allInitialsB = new Set(tokensB.initials.map(i => i.toLowerCase()));
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let matched = 0;
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for (const init of allInitialsA) {
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if (allInitialsB.has(init)) matched++;
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}
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const total = Math.max(allInitialsA.size, allInitialsB.size);
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initialMatchScore = total > 0 ? matched / total : 1.0;
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}
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const weighted = (lastScore * 0.45) + (firstScore * 0.35) + (middleScore * 0.1) + (initialMatchScore * 0.1);
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return Math.round(weighted * 1000) / 1000;
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}
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function computeAddressScore(addrA: Address, addrB: Address, config: MatchingConfig): { score: number; geocodingDistance?: number } {
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const numberMatch = computeFieldMatch(addrA.streetNumber, addrB.streetNumber).score;
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const streetMatch = computeFieldMatch(addrA.streetName, addrB.streetName, normalizeString).score;
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const typeMatch = computeFieldMatch(
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addrA.streetType ? normalizeStreetType(addrA.streetType) : '',
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addrB.streetType ? normalizeStreetType(addrB.streetType) : '',
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).score;
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const unitMatch = computeFieldMatch(addrA.unit || '', addrB.unit || '').score;
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const cityMatch = computeFieldMatch(addrA.city, addrB.city).score;
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const stateMatch = computeFieldMatch(addrA.state, addrB.state).score;
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const zipMatch = computeFieldMatch(addrA.zip, addrB.zip).score;
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let geocodingDistance: number | undefined;
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let geoScore = 0.0;
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if (addrA.latitude && addrA.longitude && addrB.latitude && addrB.longitude) {
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geocodingDistance = haversineDistance(addrA.latitude, addrA.longitude, addrB.latitude, addrB.longitude);
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const maxDist = config.geocodingRadiusMeters;
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geoScore = geocodingDistance <= maxDist ? 1.0 : Math.max(0, 1.0 - (geocodingDistance - maxDist) / (maxDist * 5));
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}
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const weighted =
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(numberMatch * 0.2) +
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(streetMatch * 0.25) +
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(typeMatch * 0.1) +
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(unitMatch * 0.1) +
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(cityMatch * 0.1) +
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(stateMatch * 0.1) +
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(zipMatch * 0.1) +
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(geoScore * (geocodingDistance !== undefined ? 0.05 : 0));
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return { score: Math.round(weighted * 1000) / 1000, geocodingDistance };
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}
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export function matchRecords(
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nameA: string,
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addressA: Address,
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nameB: string,
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addressB: Address,
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config?: Partial<MatchingConfig>,
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): MatchResult {
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const effectiveConfig = { ...DEFAULT_CONFIG, ...config };
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const tokensA = parseName(nameA);
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const tokensB = parseName(nameB);
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const nameScore = computeNameScore(tokensA, tokensB);
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const { score: addressScore, geocodingDistance } = computeAddressScore(addressA, addressB, effectiveConfig);
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const overallConfidence = Math.round((nameScore * 0.5 + addressScore * 0.5) * 1000) / 1000;
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const firstMatch = computeFieldMatch(tokensA.firstName, tokensB.firstName);
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const lastMatch = computeFieldMatch(tokensA.lastName, tokensB.lastName);
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const middleMatch = computeFieldMatch(tokensA.middleName, tokensB.middleName);
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const numberMatch = computeFieldMatch(addressA.streetNumber, addressB.streetNumber);
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const streetMatch = computeFieldMatch(addressA.streetName, addressB.streetName, normalizeString);
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const typeMatch = computeFieldMatch(
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addressA.streetType ? normalizeStreetType(addressA.streetType) : '',
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addressB.streetType ? normalizeStreetType(addressB.streetType) : '',
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);
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const unitMatch = computeFieldMatch(addressA.unit || '', addressB.unit || '');
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const cityMatch = computeFieldMatch(addressA.city, addressB.city);
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const stateMatch = computeFieldMatch(addressA.state, addressB.state);
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const zipMatch = computeFieldMatch(addressA.zip, addressB.zip);
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const normalizedA = normalizeAddress(addressA);
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const normalizedB = normalizeAddress(addressB);
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const dist = levenshteinDistance(
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normalizeString(nameA),
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normalizeString(nameB),
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);
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const details: MatchDetails = {
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nameNormalized: [normalizeString(nameA), normalizeString(nameB)],
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addressNormalized: [normalizedA, normalizedB],
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levenshteinDistance: dist,
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geocodingDistance,
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fields: {
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firstName: firstMatch,
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lastName: lastMatch,
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middleName: middleMatch,
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streetNumber: numberMatch,
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streetName: streetMatch,
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streetType: typeMatch,
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unit: unitMatch,
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city: cityMatch,
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state: stateMatch,
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zip: zipMatch,
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},
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};
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return {
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nameScore,
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addressScore,
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overallConfidence,
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isMatch: overallConfidence >= effectiveConfig.overallThreshold,
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details,
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};
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}
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export function getConfigForPropertyType(type: PropertyType): MatchingConfig {
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return { ...DEFAULT_CONFIG, ...PROPERTY_TYPE_CONFIGS[type] };
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}
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export { parseName, normalizeString, normalizeStreetType, levenshteinDistance, similarityScore };
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