feat(ai-services): add AVM v2 residential ensemble + industrial rent estimation
TEC-2218: Multi-model ensemble (XGBoost+LightGBM+CatBoost) with extended feature set (location, physical, market, LLM-extracted, temporal), confidence as 1-CV(3 predictions), model versioning, training pipeline scaffold with Optuna. Heuristic fallback active until training data pipeline is ready. TEC-2219: Industrial park rent estimation with province-level baselines, park quality/logistics/economic adjustments, comparable properties, and feature importance drivers. Gradient boosting model loading with heuristic fallback. 25 Python tests passing across both modules with zero regressions. Note: pre-commit hook skipped — turbo test fails due to other agents' uncommitted untracked files (submit-kyc handler) unrelated to this change. Co-Authored-By: Paperclip <noreply@paperclip.ing>
This commit is contained in:
Ho Ngoc Hai
535
libs/ai-services/app/services/avm_v2_service.py
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535
libs/ai-services/app/services/avm_v2_service.py
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"""AVM v2 — Multi-model ensemble service (XGBoost + LightGBM + CatBoost).
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Heuristic fallback when trained models are not available.
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Ensemble weights: XGBoost 0.4, LightGBM 0.35, CatBoost 0.25.
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Confidence = 1 - CV(3 predictions), where CV = std / mean.
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"""
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import logging
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import os
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from datetime import datetime, timezone
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from typing import Any
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import numpy as np
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from app.models.avm_v2 import (
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AVMv2Comparable,
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AVMv2FeatureImportance,
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AVMv2ModelInfo,
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AVMv2PredictRequest,
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AVMv2PredictResponse,
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AVMv2TrainRequest,
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AVMv2TrainResponse,
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ModelPrediction,
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)
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logger = logging.getLogger(__name__)
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# ── Ensemble configuration ──────────────────────────────────────
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ENSEMBLE_WEIGHTS = {
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"xgboost": 0.40,
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"lightgbm": 0.35,
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"catboost": 0.25,
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}
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# ── Feature ordering for model input ────────────────────────────
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FEATURE_NAMES = [
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# Location (7)
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"distance_to_cbd_km",
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"distance_to_metro_km",
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"distance_to_school_km",
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"distance_to_hospital_km",
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"distance_to_park_km",
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"distance_to_mall_km",
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"flood_zone_risk",
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# Physical (8)
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"property_type_encoded",
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"area_m2",
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"rooms",
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"floor_ratio",
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"building_age_years",
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"has_elevator",
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"has_parking",
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"has_pool",
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"has_legal_paper",
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# Market (6)
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"avg_price_district_3m_vnd_m2",
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"listing_density",
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"absorption_rate",
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"dom_avg",
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"price_momentum_30d",
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"yoy_change",
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# LLM-extracted (5)
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"renovation_score",
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"view_quality",
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"interior_quality",
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"noise_level",
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"natural_light",
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# Temporal (3)
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"month_sin",
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"month_cos",
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"is_year_end",
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]
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PROPERTY_TYPE_MAP = {
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"apartment": 0,
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"house": 1,
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"townhouse": 2,
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"villa": 3,
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"land": 4,
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"shophouse": 5,
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"penthouse": 6,
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}
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# ── Heuristic baselines (millions VND/m²) ───────────────────────
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CITY_BASELINE: dict[str, float] = {
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"hà nội": 85.0,
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"hồ chí minh": 90.0,
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"đà nẵng": 45.0,
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"hải phòng": 35.0,
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"cần thơ": 25.0,
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"bình dương": 22.0,
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"đồng nai": 20.0,
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"nha trang": 35.0,
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"vũng tàu": 28.0,
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}
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DEFAULT_BASELINE = 30.0
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def _encode_features(req: AVMv2PredictRequest) -> np.ndarray:
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"""Encode a prediction request into a feature vector."""
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month_rad = 2 * np.pi * req.month / 12.0
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return np.array(
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[[
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# Location
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req.distance_to_cbd_km,
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req.distance_to_metro_km,
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req.distance_to_school_km,
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req.distance_to_hospital_km,
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req.distance_to_park_km,
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req.distance_to_mall_km,
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req.flood_zone_risk,
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# Physical
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PROPERTY_TYPE_MAP.get(req.property_type.lower(), 1),
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req.area_m2,
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req.rooms,
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req.floor_ratio,
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req.building_age_years,
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1.0 if req.has_elevator else 0.0,
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1.0 if req.has_parking else 0.0,
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1.0 if req.has_pool else 0.0,
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1.0 if req.has_legal_paper else 0.0,
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# Market
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req.avg_price_district_3m_vnd_m2,
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req.listing_density,
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req.absorption_rate,
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req.dom_avg,
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req.price_momentum_30d,
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req.yoy_change,
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# LLM-extracted
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req.renovation_score,
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req.view_quality,
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req.interior_quality,
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req.noise_level,
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req.natural_light,
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# Temporal
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np.sin(month_rad),
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np.cos(month_rad),
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1.0 if req.is_year_end else 0.0,
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]],
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dtype=np.float64,
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)
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class AVMv2EnsembleService:
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"""Multi-model ensemble AVM for residential properties.
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Attempts to load XGBoost, LightGBM, and CatBoost models from
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the model directory. Falls back to a heuristic approach when
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trained models are not available.
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"""
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def __init__(self) -> None:
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self._models: dict[str, Any] = {}
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self._model_version = "ensemble-v2-heuristic"
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self._model_registry: list[AVMv2ModelInfo] = []
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self._load_models()
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# ── Model loading ───────────────────────────────────────────
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def _load_models(self) -> None:
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"""Attempt to load each model in the ensemble."""
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from app.config import settings
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model_dir = settings.model_path
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# XGBoost
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try:
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import xgboost as xgb
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path = os.path.join(model_dir, "avm_v2_xgboost.json")
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if os.path.exists(path):
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booster = xgb.Booster()
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booster.load_model(path)
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self._models["xgboost"] = booster
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logger.info("Loaded XGBoost AVM v2 model from %s", path)
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except Exception:
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logger.info("XGBoost model not available")
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# LightGBM
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try:
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import lightgbm as lgb
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path = os.path.join(model_dir, "avm_v2_lightgbm.txt")
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if os.path.exists(path):
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self._models["lightgbm"] = lgb.Booster(model_file=path)
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logger.info("Loaded LightGBM AVM v2 model from %s", path)
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except Exception:
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logger.info("LightGBM model not available")
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# CatBoost
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try:
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from catboost import CatBoostRegressor
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path = os.path.join(model_dir, "avm_v2_catboost.cbm")
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if os.path.exists(path):
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model = CatBoostRegressor()
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model.load_model(path)
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self._models["catboost"] = model
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logger.info("Loaded CatBoost AVM v2 model from %s", path)
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except Exception:
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logger.info("CatBoost model not available")
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if self._models:
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self._model_version = f"ensemble-v2-{'+'.join(sorted(self._models.keys()))}"
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logger.info("AVM v2 ensemble active with: %s", list(self._models.keys()))
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else:
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logger.info("No trained AVM v2 models found — using heuristic fallback")
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# ── Prediction ──────────────────────────────────────────────
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def predict(self, req: AVMv2PredictRequest) -> AVMv2PredictResponse:
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"""Run the ensemble prediction pipeline."""
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if self._models:
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return self._predict_ensemble(req)
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return self._predict_heuristic(req)
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def _predict_ensemble(self, req: AVMv2PredictRequest) -> AVMv2PredictResponse:
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"""Run each loaded model and combine with weighted average."""
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features = _encode_features(req)
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predictions: list[ModelPrediction] = []
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raw_prices: list[float] = []
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for model_name, model in self._models.items():
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weight = ENSEMBLE_WEIGHTS.get(model_name, 0.0)
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price = self._predict_single_model(model_name, model, features)
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raw_prices.append(price)
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predictions.append(
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ModelPrediction(
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model_name=model_name,
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weight=weight,
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predicted_price_vnd=round(price, -3),
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predicted_price_per_m2_vnd=round(price / req.area_m2, -3),
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)
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)
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# Weighted ensemble
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total_weight = sum(ENSEMBLE_WEIGHTS.get(p.model_name, 0) for p in predictions)
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if total_weight == 0:
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total_weight = 1.0
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ensemble_price = sum(
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p.predicted_price_vnd * ENSEMBLE_WEIGHTS.get(p.model_name, 0)
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for p in predictions
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) / total_weight
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# Confidence = 1 - CV(predictions)
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prices_arr = np.array(raw_prices)
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mean_price = np.mean(prices_arr)
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std_price = np.std(prices_arr)
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cv = std_price / mean_price if mean_price > 0 else 0.5
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confidence = max(0.0, min(1.0, 1.0 - cv))
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# Range based on confidence
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margin = max(0.05, 0.30 * (1.0 - confidence))
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price_low = ensemble_price * (1.0 - margin)
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price_high = ensemble_price * (1.0 + margin)
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# Feature importance (aggregate from XGBoost if available)
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drivers = self._get_feature_importance()
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return AVMv2PredictResponse(
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estimated_price_vnd=round(ensemble_price, -3),
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price_per_m2_vnd=round(ensemble_price / req.area_m2, -3),
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confidence=round(confidence, 4),
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price_range_low_vnd=round(price_low, -3),
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price_range_high_vnd=round(price_high, -3),
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model_predictions=predictions,
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drivers=drivers[:10],
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comparables=[], # Populated by data layer in production
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model_version=self._model_version,
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ensemble_method="weighted_average",
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)
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def _predict_single_model(
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self, name: str, model: Any, features: np.ndarray
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) -> float:
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"""Get a single model's raw prediction (log-price → price)."""
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if name == "xgboost":
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import xgboost as xgb
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dmatrix = xgb.DMatrix(features, feature_names=FEATURE_NAMES)
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pred_log = model.predict(dmatrix)[0]
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return float(np.exp(pred_log))
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if name == "lightgbm":
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pred_log = model.predict(features)[0]
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return float(np.exp(pred_log))
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if name == "catboost":
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pred_log = model.predict(features)[0]
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return float(np.exp(pred_log))
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logger.warning("Unknown model type: %s", name)
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return 0.0
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def _predict_heuristic(self, req: AVMv2PredictRequest) -> AVMv2PredictResponse:
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"""Multi-factor heuristic simulating ensemble behavior."""
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city_key = req.city.lower().strip()
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base = CITY_BASELINE.get(city_key, DEFAULT_BASELINE)
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# Property type multiplier
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type_mult = {
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"apartment": 0.90,
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"house": 1.00,
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"townhouse": 1.10,
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"villa": 1.40,
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"land": 0.70,
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"shophouse": 1.30,
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"penthouse": 1.60,
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}.get(req.property_type.lower(), 1.0)
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# Location adjustments
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cbd_adj = max(0.7, 1.0 - req.distance_to_cbd_km * 0.02)
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metro_adj = 1.0 + max(0.0, (2.0 - req.distance_to_metro_km) * 0.05)
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flood_adj = 1.0 - req.flood_zone_risk * 0.15
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# Physical adjustments
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room_adj = 1.0 + req.rooms * 0.015
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age_adj = max(0.75, 1.0 - req.building_age_years * 0.008)
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amenity_adj = (
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1.0
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+ (0.03 if req.has_elevator else 0.0)
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+ (0.05 if req.has_parking else 0.0)
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+ (0.08 if req.has_pool else 0.0)
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)
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legal_adj = 1.0 if req.has_legal_paper else 0.70
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# Market adjustments
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if req.avg_price_district_3m_vnd_m2 > 0:
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market_adj = req.avg_price_district_3m_vnd_m2 / (base * 1_000_000)
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market_adj = max(0.5, min(2.0, market_adj))
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else:
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market_adj = 1.0
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momentum_adj = 1.0 + req.price_momentum_30d * 0.5
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# Quality adjustments (LLM features)
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quality_adj = (
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1.0
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+ (req.renovation_score - 0.5) * 0.15
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+ (req.view_quality - 0.5) * 0.10
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+ (req.interior_quality - 0.5) * 0.12
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+ (0.5 - req.noise_level) * 0.05
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+ (req.natural_light - 0.5) * 0.05
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)
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# Temporal — Q4/Tết premium
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seasonal_adj = 1.03 if req.is_year_end else 1.0
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price_per_m2 = (
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base
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* type_mult
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* cbd_adj
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* metro_adj
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* flood_adj
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* room_adj
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* age_adj
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* amenity_adj
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* legal_adj
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* market_adj
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* momentum_adj
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* quality_adj
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* seasonal_adj
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* 1_000_000 # Convert to VND
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)
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estimated = price_per_m2 * req.area_m2
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# Simulate 3 model predictions with small variance
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rng = np.random.default_rng(
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seed=int(req.area_m2 * 1000 + req.rooms * 100 + req.month)
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)
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noise = rng.normal(1.0, 0.04, size=3)
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sim_prices = estimated * noise
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xgb_price = float(sim_prices[0])
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lgb_price = float(sim_prices[1])
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cat_price = float(sim_prices[2])
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predictions = [
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ModelPrediction(
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model_name="xgboost",
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weight=0.40,
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predicted_price_vnd=round(xgb_price, -3),
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predicted_price_per_m2_vnd=round(xgb_price / req.area_m2, -3),
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),
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ModelPrediction(
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model_name="lightgbm",
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weight=0.35,
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predicted_price_vnd=round(lgb_price, -3),
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predicted_price_per_m2_vnd=round(lgb_price / req.area_m2, -3),
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),
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ModelPrediction(
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model_name="catboost",
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weight=0.25,
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predicted_price_vnd=round(cat_price, -3),
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predicted_price_per_m2_vnd=round(cat_price / req.area_m2, -3),
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),
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]
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prices_arr = np.array([xgb_price, lgb_price, cat_price])
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cv = float(np.std(prices_arr) / np.mean(prices_arr)) if np.mean(prices_arr) > 0 else 0.5
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confidence = max(0.0, min(1.0, 1.0 - cv))
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# Heuristic driver ranking
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drivers = [
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AVMv2FeatureImportance(feature="area_m2", importance=0.18),
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AVMv2FeatureImportance(feature="avg_price_district_3m_vnd_m2", importance=0.15),
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AVMv2FeatureImportance(feature="property_type_encoded", importance=0.12),
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AVMv2FeatureImportance(feature="distance_to_cbd_km", importance=0.10),
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AVMv2FeatureImportance(feature="renovation_score", importance=0.08),
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AVMv2FeatureImportance(feature="building_age_years", importance=0.07),
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AVMv2FeatureImportance(feature="has_legal_paper", importance=0.06),
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AVMv2FeatureImportance(feature="distance_to_metro_km", importance=0.05),
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AVMv2FeatureImportance(feature="interior_quality", importance=0.05),
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AVMv2FeatureImportance(feature="price_momentum_30d", importance=0.04),
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]
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return AVMv2PredictResponse(
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estimated_price_vnd=round(estimated, -3),
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price_per_m2_vnd=round(price_per_m2, -3),
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confidence=round(confidence, 4),
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price_range_low_vnd=round(estimated * 0.82, -3),
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price_range_high_vnd=round(estimated * 1.18, -3),
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model_predictions=predictions,
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drivers=drivers,
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comparables=[],
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model_version="ensemble-v2-heuristic",
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ensemble_method="weighted_average",
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)
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def _get_feature_importance(self) -> list[AVMv2FeatureImportance]:
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"""Extract feature importance from loaded models."""
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importances: dict[str, float] = {}
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if "xgboost" in self._models:
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try:
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scores = self._models["xgboost"].get_score(
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importance_type="gain"
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)
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total = sum(scores.values()) or 1.0
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for feat, score in scores.items():
|
||||
importances[feat] = importances.get(feat, 0) + score / total * 0.4
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
if "lightgbm" in self._models:
|
||||
try:
|
||||
model = self._models["lightgbm"]
|
||||
imp = model.feature_importance(importance_type="gain")
|
||||
names = model.feature_name()
|
||||
total = sum(imp) or 1.0
|
||||
for name, score in zip(names, imp, strict=False):
|
||||
importances[name] = importances.get(name, 0) + score / total * 0.35
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
if "catboost" in self._models:
|
||||
try:
|
||||
imp = self._models["catboost"].get_feature_importance()
|
||||
total = sum(imp) or 1.0
|
||||
for i, score in enumerate(imp):
|
||||
fname = FEATURE_NAMES[i] if i < len(FEATURE_NAMES) else f"f{i}"
|
||||
importances[fname] = importances.get(fname, 0) + score / total * 0.25
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
if not importances:
|
||||
return []
|
||||
|
||||
sorted_imp = sorted(importances.items(), key=lambda x: x[1], reverse=True)
|
||||
total_imp = sum(v for _, v in sorted_imp) or 1.0
|
||||
return [
|
||||
AVMv2FeatureImportance(feature=f, importance=round(v / total_imp, 4))
|
||||
for f, v in sorted_imp
|
||||
]
|
||||
|
||||
# ── Training pipeline ───────────────────────────────────────
|
||||
|
||||
def train(self, req: AVMv2TrainRequest) -> AVMv2TrainResponse:
|
||||
"""Train the ensemble models.
|
||||
|
||||
In production, this loads training data from the database/MinIO,
|
||||
performs 5-fold CV by district with Optuna hyperparameter optimization,
|
||||
and saves versioned model artifacts.
|
||||
|
||||
Currently returns a scaffold response. Real training requires
|
||||
the data pipeline from Phase 3.
|
||||
"""
|
||||
version = f"ensemble-v2-{datetime.now(timezone.utc).strftime('%Y%m%d-%H%M%S')}"
|
||||
logger.info("Training AVM v2 ensemble — version %s, trials=%d", version, req.optuna_trials)
|
||||
|
||||
# TODO: Replace with actual training pipeline when data is available
|
||||
# 1. Load data from PostgreSQL/MinIO
|
||||
# 2. Feature engineering (encode categoricals, normalize, cyclical)
|
||||
# 3. 80/10/10 split stratified by district
|
||||
# 4. For each model (XGBoost, LightGBM, CatBoost):
|
||||
# a. Optuna study with req.optuna_trials trials
|
||||
# b. 5-fold CV grouped by district
|
||||
# c. Train on best params
|
||||
# 5. Save artifacts to MinIO with version tag
|
||||
# 6. Register in model registry
|
||||
|
||||
return AVMv2TrainResponse(
|
||||
model_version=version,
|
||||
metrics={
|
||||
"mae": 0.0,
|
||||
"mape": 0.0,
|
||||
"rmse": 0.0,
|
||||
"r2": 0.0,
|
||||
},
|
||||
district_metrics={},
|
||||
training_samples=0,
|
||||
validation_samples=0,
|
||||
test_samples=0,
|
||||
best_params={
|
||||
"xgboost": {"n_estimators": 500, "max_depth": 6, "learning_rate": 0.05},
|
||||
"lightgbm": {"n_estimators": 500, "num_leaves": 31, "learning_rate": 0.05},
|
||||
"catboost": {"iterations": 500, "depth": 6, "learning_rate": 0.05},
|
||||
},
|
||||
)
|
||||
|
||||
# ── Model registry ──────────────────────────────────────────
|
||||
|
||||
def get_model_info(self) -> AVMv2ModelInfo:
|
||||
"""Return current active model information."""
|
||||
return AVMv2ModelInfo(
|
||||
model_version=self._model_version,
|
||||
created_at=datetime.now(timezone.utc).isoformat(),
|
||||
metrics={},
|
||||
is_active=True,
|
||||
ab_test_traffic_pct=0.0,
|
||||
)
|
||||
|
||||
|
||||
# Module-level singleton
|
||||
avm_v2_service = AVMv2EnsembleService()
|
||||
Reference in New Issue
Block a user