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
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libs/ai-services/app/models/avm_industrial.py
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libs/ai-services/app/models/avm_industrial.py
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from pydantic import BaseModel, Field
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class IndustrialAVMRequest(BaseModel):
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"""Request schema for industrial property rent estimation."""
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province: str = Field(..., min_length=1, description="Province name (e.g. Bình Dương)")
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region: str = Field(
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..., min_length=1, description="Region: south, north, central, mekong_delta"
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)
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park_occupancy_rate: float = Field(
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..., ge=0, le=1, description="Industrial park occupancy rate (0-1)"
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)
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park_area_ha: float = Field(..., gt=0, description="Total park area in hectares")
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park_age_years: int = Field(..., ge=0, description="Industrial park age in years")
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distance_to_port_km: float = Field(
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..., ge=0, description="Distance to nearest seaport in km"
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)
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distance_to_airport_km: float = Field(
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..., ge=0, description="Distance to nearest airport in km"
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)
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distance_to_highway_km: float = Field(
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..., ge=0, description="Distance to nearest highway in km"
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)
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property_type: str = Field(
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...,
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description="Industrial property type: warehouse, factory, ready_built_factory, "
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"ready_built_warehouse, open_yard, office_in_park",
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)
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area_m2: float = Field(..., gt=0, description="Leasable area in m²")
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ceiling_height_m: float = Field(
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0.0, ge=0, description="Ceiling/clear height in meters"
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)
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floor_load_ton_m2: float = Field(
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0.0, ge=0, description="Floor load capacity in tons/m²"
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)
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power_capacity_kva: float = Field(
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0.0, ge=0, description="Allocated power capacity in kVA"
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)
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industry_demand_index: float = Field(
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0.5, ge=0, le=1, description="Local industry demand index (0-1)"
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)
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fdi_province_musd: float = Field(
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0.0, ge=0, description="Province FDI inflow in million USD (trailing 12 months)"
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)
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labor_cost_province_vnd: float = Field(
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0.0, ge=0, description="Average province labor cost in VND/month"
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)
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logistics_connectivity_score: float = Field(
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0.5, ge=0, le=1, description="Logistics connectivity score (0-1)"
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)
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class IndustrialComparable(BaseModel):
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"""A comparable industrial property used for the estimation."""
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park_name: str
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province: str
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property_type: str
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area_m2: float
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rent_usd_m2: float
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similarity_score: float = Field(..., ge=0, le=1)
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class FeatureImportance(BaseModel):
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"""Feature importance from the model prediction."""
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feature: str
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importance: float = Field(..., ge=0, le=1)
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class IndustrialAVMResponse(BaseModel):
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"""Response schema for industrial property rent estimation."""
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estimated_rent_usd_m2: float = Field(
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..., description="Estimated monthly rent in USD per m²"
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)
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confidence: float = Field(
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..., ge=0, le=1, description="Prediction confidence score"
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)
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rent_range_low_usd_m2: float = Field(
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..., description="Lower bound rent estimate in USD/m²"
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)
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rent_range_high_usd_m2: float = Field(
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..., description="Upper bound rent estimate in USD/m²"
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)
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annual_rent_usd_m2: float = Field(
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..., description="Estimated annual rent in USD/m²"
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)
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total_monthly_rent_usd: float = Field(
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..., description="Total monthly rent for the requested area in USD"
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)
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comparables: list[IndustrialComparable] = Field(
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default_factory=list, description="Similar industrial properties for reference"
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)
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drivers: list[FeatureImportance] = Field(
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default_factory=list,
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description="Top feature drivers for this prediction",
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)
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model_version: str = Field("heuristic-v1", description="Model version used")
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185
libs/ai-services/app/models/avm_v2.py
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libs/ai-services/app/models/avm_v2.py
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"""AVM v2 — Residential multi-model ensemble request/response schemas."""
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from pydantic import BaseModel, Field
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class AVMv2PredictRequest(BaseModel):
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"""Extended feature set for residential AVM v2 ensemble."""
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# ── Location features ──────────────────────────────────
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district: str = Field(..., min_length=1, description="District name")
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city: str = Field(..., min_length=1, description="City name")
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distance_to_cbd_km: float = Field(0.0, ge=0, description="Distance to CBD in km")
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distance_to_metro_km: float = Field(
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0.0, ge=0, description="Distance to nearest metro station in km"
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)
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distance_to_school_km: float = Field(
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0.0, ge=0, description="Distance to nearest school in km"
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)
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distance_to_hospital_km: float = Field(
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0.0, ge=0, description="Distance to nearest hospital in km"
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)
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distance_to_park_km: float = Field(
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0.0, ge=0, description="Distance to nearest park in km"
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)
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distance_to_mall_km: float = Field(
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0.0, ge=0, description="Distance to nearest mall/shopping center in km"
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)
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flood_zone_risk: float = Field(
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0.0, ge=0, le=1, description="Flood zone risk score (0=safe, 1=high risk)"
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)
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# ── Physical features ──────────────────────────────────
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property_type: str = Field(..., description="e.g. apartment, house, villa, land")
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area_m2: float = Field(..., gt=0, description="Property area in m²")
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rooms: int = Field(0, ge=0, description="Total rooms (bedrooms)")
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floor_ratio: float = Field(
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1.0, gt=0, description="Total floor area / land area ratio"
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)
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building_age_years: int = Field(0, ge=0, description="Building age in years")
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has_elevator: bool = Field(False, description="Building has elevator")
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has_parking: bool = Field(False, description="Property has dedicated parking")
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has_pool: bool = Field(False, description="Property has swimming pool")
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has_legal_paper: bool = Field(True, description="Has sổ đỏ/sổ hồng")
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# ── Market features ────────────────────────────────────
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avg_price_district_3m_vnd_m2: float = Field(
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0.0, ge=0,
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description="Avg price per m² in the district over last 3 months (VND)",
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)
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listing_density: float = Field(
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0.0, ge=0,
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description="Number of active listings per km² in the district",
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)
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absorption_rate: float = Field(
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0.0, ge=0, le=1,
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description="Percentage of listings sold in last 30 days (0-1)",
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)
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dom_avg: float = Field(
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0.0, ge=0,
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description="Average days on market in the district",
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)
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price_momentum_30d: float = Field(
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0.0,
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description="Price change percentage in last 30 days (-1 to +1)",
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)
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yoy_change: float = Field(
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0.0,
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description="Year-over-year price change percentage (-1 to +1)",
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)
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# ── LLM-extracted features ─────────────────────────────
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renovation_score: float = Field(
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0.5, ge=0, le=1, description="Renovation quality score (0-1)"
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)
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view_quality: float = Field(
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0.5, ge=0, le=1, description="View quality score (0-1)"
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)
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interior_quality: float = Field(
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0.5, ge=0, le=1, description="Interior quality score (0-1)"
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)
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noise_level: float = Field(
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0.5, ge=0, le=1, description="Noise level score (0=quiet, 1=noisy)"
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)
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natural_light: float = Field(
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0.5, ge=0, le=1, description="Natural light score (0-1)"
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)
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# ── Temporal features ──────────────────────────────────
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month: int = Field(1, ge=1, le=12, description="Transaction month (1-12)")
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quarter: int = Field(1, ge=1, le=4, description="Transaction quarter (1-4)")
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is_year_end: bool = Field(False, description="Whether in Q4 / Tết season")
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class AVMv2Comparable(BaseModel):
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"""A comparable property used for context."""
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district: str
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property_type: str
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area_m2: float
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price_vnd: float
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price_per_m2_vnd: float
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similarity_score: float = Field(..., ge=0, le=1)
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class AVMv2FeatureImportance(BaseModel):
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"""Feature contribution to the prediction."""
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feature: str
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importance: float = Field(..., ge=0, le=1)
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class ModelPrediction(BaseModel):
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"""Individual model prediction within the ensemble."""
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model_name: str
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weight: float
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predicted_price_vnd: float
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predicted_price_per_m2_vnd: float
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class AVMv2PredictResponse(BaseModel):
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"""Multi-model ensemble prediction response."""
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estimated_price_vnd: float = Field(..., description="Weighted ensemble estimated price in VND")
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price_per_m2_vnd: float = Field(..., description="Price per m² in VND")
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confidence: float = Field(
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..., ge=0, le=1,
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description="Confidence = 1 - CV(predictions across 3 models)",
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)
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price_range_low_vnd: float = Field(..., description="Lower bound estimate in VND")
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price_range_high_vnd: float = Field(..., description="Upper bound estimate in VND")
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# Ensemble breakdown
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model_predictions: list[ModelPrediction] = Field(
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default_factory=list,
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description="Individual predictions from each model in the ensemble",
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)
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# Explainability
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drivers: list[AVMv2FeatureImportance] = Field(
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default_factory=list,
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description="Top feature drivers ranked by importance",
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)
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comparables: list[AVMv2Comparable] = Field(
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default_factory=list,
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description="Similar properties for reference",
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)
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# Model metadata
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model_version: str = Field("ensemble-v2-heuristic", description="Ensemble version used")
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ensemble_method: str = Field("weighted_average", description="Ensemble strategy")
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class AVMv2TrainRequest(BaseModel):
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"""Request to trigger model retraining."""
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force: bool = Field(False, description="Force retrain even if recent model exists")
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optuna_trials: int = Field(100, ge=10, le=500, description="Number of Optuna trials")
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test_size: float = Field(0.1, ge=0.05, le=0.3, description="Test split ratio")
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val_size: float = Field(0.1, ge=0.05, le=0.3, description="Validation split ratio")
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class AVMv2TrainResponse(BaseModel):
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"""Training result summary."""
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model_version: str
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metrics: dict = Field(default_factory=dict, description="MAE, MAPE, RMSE, R²")
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district_metrics: dict = Field(
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default_factory=dict,
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description="Per-district breakdown of metrics",
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)
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training_samples: int
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validation_samples: int
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test_samples: int
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best_params: dict = Field(default_factory=dict, description="Optuna best hyperparameters per model")
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class AVMv2ModelInfo(BaseModel):
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"""Model registry entry information."""
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model_version: str
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created_at: str
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metrics: dict
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is_active: bool = Field(True)
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ab_test_traffic_pct: float = Field(0.0, ge=0, le=1)
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