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
@@ -6,7 +6,7 @@ from slowapi.util import get_remote_address
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from app.config import settings
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from app.middleware import verify_api_key
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from app.routers import avm, moderation, nlp
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from app.routers import avm, avm_industrial, avm_v2, moderation, nlp
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limiter = Limiter(key_func=get_remote_address, default_limits=[settings.rate_limit])
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@@ -32,6 +32,8 @@ app.add_middleware(
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)
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app.include_router(avm.router)
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app.include_router(avm_v2.router)
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app.include_router(avm_industrial.router)
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app.include_router(moderation.router)
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app.include_router(nlp.router)
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100
libs/ai-services/app/models/avm_industrial.py
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100
libs/ai-services/app/models/avm_industrial.py
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@@ -0,0 +1,100 @@
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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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185
libs/ai-services/app/models/avm_v2.py
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@@ -0,0 +1,185 @@
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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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22
libs/ai-services/app/routers/avm_industrial.py
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22
libs/ai-services/app/routers/avm_industrial.py
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@@ -0,0 +1,22 @@
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"""Industrial AVM router — rent estimation for industrial parks."""
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from fastapi import APIRouter
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from app.models.avm_industrial import (
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IndustrialAVMRequest,
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IndustrialAVMResponse,
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)
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from app.services.avm_industrial_service import industrial_avm_service
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router = APIRouter(prefix="/avm/industrial", tags=["AVM Industrial"])
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@router.post("/predict", response_model=IndustrialAVMResponse)
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def predict_industrial(req: IndustrialAVMRequest) -> IndustrialAVMResponse:
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"""Estimate industrial property rent using gradient boosting model.
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Returns estimated monthly rent in USD/m² with confidence interval,
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comparable properties, and feature importance drivers.
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Falls back to heuristic when trained model is not available.
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"""
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return industrial_avm_service.predict(req)
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39
libs/ai-services/app/routers/avm_v2.py
Normal file
39
libs/ai-services/app/routers/avm_v2.py
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@@ -0,0 +1,39 @@
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"""AVM v2 ensemble router — residential property valuation."""
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from fastapi import APIRouter
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from app.models.avm_v2 import (
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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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)
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from app.services.avm_v2_service import avm_v2_service
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router = APIRouter(prefix="/avm/v2", tags=["AVM v2 Ensemble"])
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@router.post("/predict", response_model=AVMv2PredictResponse)
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def predict_v2(req: AVMv2PredictRequest) -> AVMv2PredictResponse:
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"""Predict residential property price using the multi-model ensemble.
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Ensemble: XGBoost (0.4) + LightGBM (0.35) + CatBoost (0.25).
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Falls back to heuristic when trained models are not available.
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"""
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return avm_v2_service.predict(req)
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@router.post("/train", response_model=AVMv2TrainResponse)
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def train_v2(req: AVMv2TrainRequest) -> AVMv2TrainResponse:
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"""Trigger model retraining with Optuna hyperparameter optimization.
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Requires training data pipeline (Phase 3). Currently returns scaffold.
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"""
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return avm_v2_service.train(req)
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@router.get("/model-info", response_model=AVMv2ModelInfo)
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def model_info_v2() -> AVMv2ModelInfo:
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"""Get current active ensemble model information."""
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return avm_v2_service.get_model_info()
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318
libs/ai-services/app/services/avm_industrial_service.py
Normal file
318
libs/ai-services/app/services/avm_industrial_service.py
Normal file
@@ -0,0 +1,318 @@
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"""Industrial AVM — Rent estimation service for industrial parks.
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Heuristic fallback when trained models are not available.
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Uses gradient boosting approach similar to residential AVM v2.
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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_industrial import (
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FeatureImportance,
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IndustrialAVMRequest,
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IndustrialAVMResponse,
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IndustrialComparable,
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)
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logger = logging.getLogger(__name__)
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# ── Feature ordering for model input ────────────────────────────
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INDUSTRIAL_FEATURE_NAMES = [
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"region_encoded",
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"park_occupancy_rate",
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"park_area_ha",
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"park_age_years",
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"distance_to_port_km",
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"distance_to_airport_km",
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"distance_to_highway_km",
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"property_type_encoded",
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"area_m2",
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"ceiling_height_m",
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"floor_load_ton_m2",
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"power_capacity_kva",
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"industry_demand_index",
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"fdi_province_musd",
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"labor_cost_province_vnd",
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"logistics_connectivity_score",
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]
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REGION_MAP = {
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"south": 0,
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"north": 1,
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"central": 2,
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"mekong_delta": 3,
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}
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PROPERTY_TYPE_MAP = {
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"warehouse": 0,
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"factory": 1,
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"ready_built_factory": 2,
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"ready_built_warehouse": 3,
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"open_yard": 4,
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"office_in_park": 5,
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}
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# ── Province-level rent baselines (USD/m²/month) ────────────────
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# Based on Vietnamese industrial real estate market data
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PROVINCE_BASELINE: dict[str, float] = {
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# Southern Economic Zone
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"hồ chí minh": 6.5,
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"bình dương": 5.0,
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"đồng nai": 4.5,
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"long an": 3.5,
|
||||
"bà rịa - vũng tàu": 4.0,
|
||||
"tây ninh": 3.0,
|
||||
# Northern Industrial Corridor
|
||||
"hà nội": 5.5,
|
||||
"bắc ninh": 5.0,
|
||||
"hải phòng": 4.8,
|
||||
"hải dương": 4.0,
|
||||
"hưng yên": 3.8,
|
||||
"vĩnh phúc": 3.5,
|
||||
"thái nguyên": 3.2,
|
||||
"bắc giang": 4.2,
|
||||
# Central
|
||||
"đà nẵng": 4.0,
|
||||
"quảng nam": 3.0,
|
||||
# Mekong Delta
|
||||
"cần thơ": 3.0,
|
||||
"tiền giang": 2.8,
|
||||
}
|
||||
DEFAULT_RENT_BASELINE = 3.5
|
||||
|
||||
# ── Comparable industrial parks (synthetic for heuristic) ────────
|
||||
SYNTHETIC_COMPARABLES: list[dict] = [
|
||||
{"park_name": "VSIP I", "province": "Bình Dương", "type": "factory", "area": 5000, "rent": 5.2},
|
||||
{"park_name": "Amata", "province": "Đồng Nai", "type": "factory", "area": 8000, "rent": 4.8},
|
||||
{"park_name": "Long Hậu", "province": "Long An", "type": "warehouse", "area": 3000, "rent": 3.8},
|
||||
{"park_name": "Đình Vũ", "province": "Hải Phòng", "type": "warehouse", "area": 6000, "rent": 4.5},
|
||||
{"park_name": "Yên Phong", "province": "Bắc Ninh", "type": "ready_built_factory", "area": 4000, "rent": 5.0},
|
||||
{"park_name": "Thăng Long", "province": "Hà Nội", "type": "factory", "area": 10000, "rent": 5.8},
|
||||
{"park_name": "VSIP Quảng Ngãi", "province": "Quảng Ngãi", "type": "factory", "area": 5000, "rent": 3.2},
|
||||
{"park_name": "Châu Đức", "province": "Bà Rịa - Vũng Tàu", "type": "warehouse", "area": 4000, "rent": 4.0},
|
||||
]
|
||||
|
||||
|
||||
def _encode_features(req: IndustrialAVMRequest) -> np.ndarray:
|
||||
"""Encode an industrial prediction request into a feature vector."""
|
||||
return np.array(
|
||||
[[
|
||||
REGION_MAP.get(req.region.lower(), 0),
|
||||
req.park_occupancy_rate,
|
||||
req.park_area_ha,
|
||||
req.park_age_years,
|
||||
req.distance_to_port_km,
|
||||
req.distance_to_airport_km,
|
||||
req.distance_to_highway_km,
|
||||
PROPERTY_TYPE_MAP.get(req.property_type.lower(), 1),
|
||||
req.area_m2,
|
||||
req.ceiling_height_m,
|
||||
req.floor_load_ton_m2,
|
||||
req.power_capacity_kva,
|
||||
req.industry_demand_index,
|
||||
req.fdi_province_musd,
|
||||
req.labor_cost_province_vnd,
|
||||
req.logistics_connectivity_score,
|
||||
]],
|
||||
dtype=np.float64,
|
||||
)
|
||||
|
||||
|
||||
def _find_comparables(req: IndustrialAVMRequest) -> list[IndustrialComparable]:
|
||||
"""Find synthetic comparable properties based on similarity."""
|
||||
comparables: list[IndustrialComparable] = []
|
||||
|
||||
for comp in SYNTHETIC_COMPARABLES:
|
||||
# Simple similarity: province match (0.4) + type match (0.3) + area proximity (0.3)
|
||||
province_score = 0.4 if comp["province"].lower() == req.province.lower() else 0.0
|
||||
type_score = 0.3 if comp["type"] == req.property_type.lower() else 0.0
|
||||
area_ratio = min(req.area_m2, comp["area"]) / max(req.area_m2, comp["area"])
|
||||
area_score = area_ratio * 0.3
|
||||
|
||||
similarity = province_score + type_score + area_score
|
||||
|
||||
if similarity >= 0.15:
|
||||
comparables.append(
|
||||
IndustrialComparable(
|
||||
park_name=comp["park_name"],
|
||||
province=comp["province"],
|
||||
property_type=comp["type"],
|
||||
area_m2=comp["area"],
|
||||
rent_usd_m2=comp["rent"],
|
||||
similarity_score=round(similarity, 4),
|
||||
)
|
||||
)
|
||||
|
||||
comparables.sort(key=lambda c: c.similarity_score, reverse=True)
|
||||
return comparables[:5]
|
||||
|
||||
|
||||
class IndustrialAVMService:
|
||||
"""Industrial property rent estimation service.
|
||||
|
||||
Uses gradient boosting when a trained model is available,
|
||||
falls back to heuristic pricing for development/demo.
|
||||
"""
|
||||
|
||||
def __init__(self) -> None:
|
||||
self._model: Any = None
|
||||
self._model_version = "heuristic-v1"
|
||||
self._load_model()
|
||||
|
||||
def _load_model(self) -> None:
|
||||
"""Attempt to load trained industrial AVM model."""
|
||||
try:
|
||||
import xgboost as xgb
|
||||
|
||||
from app.config import settings
|
||||
|
||||
path = os.path.join(settings.model_path, "avm_industrial_xgb.json")
|
||||
if os.path.exists(path):
|
||||
booster = xgb.Booster()
|
||||
booster.load_model(path)
|
||||
self._model = booster
|
||||
self._model_version = "xgb-industrial-v1"
|
||||
logger.info("Loaded industrial AVM model from %s", path)
|
||||
else:
|
||||
logger.info("No trained industrial AVM model — using heuristic")
|
||||
except Exception:
|
||||
logger.info("Industrial AVM model not available — using heuristic")
|
||||
|
||||
def predict(self, req: IndustrialAVMRequest) -> IndustrialAVMResponse:
|
||||
"""Predict industrial property rent."""
|
||||
if self._model is not None:
|
||||
return self._predict_model(req)
|
||||
return self._predict_heuristic(req)
|
||||
|
||||
def _predict_model(self, req: IndustrialAVMRequest) -> IndustrialAVMResponse:
|
||||
"""Predict using trained gradient boosting model."""
|
||||
import xgboost as xgb
|
||||
|
||||
features = _encode_features(req)
|
||||
dmatrix = xgb.DMatrix(features, feature_names=INDUSTRIAL_FEATURE_NAMES)
|
||||
pred_log = self._model.predict(dmatrix)[0]
|
||||
rent = float(np.exp(pred_log))
|
||||
|
||||
comparables = _find_comparables(req)
|
||||
|
||||
# Feature importance
|
||||
try:
|
||||
scores = self._model.get_score(importance_type="gain")
|
||||
total = sum(scores.values()) or 1.0
|
||||
drivers = [
|
||||
FeatureImportance(feature=f, importance=round(s / total, 4))
|
||||
for f, s in sorted(scores.items(), key=lambda x: x[1], reverse=True)
|
||||
][:8]
|
||||
except Exception:
|
||||
drivers = []
|
||||
|
||||
return IndustrialAVMResponse(
|
||||
estimated_rent_usd_m2=round(rent, 2),
|
||||
confidence=0.80,
|
||||
rent_range_low_usd_m2=round(rent * 0.88, 2),
|
||||
rent_range_high_usd_m2=round(rent * 1.12, 2),
|
||||
annual_rent_usd_m2=round(rent * 12, 2),
|
||||
total_monthly_rent_usd=round(rent * req.area_m2, 2),
|
||||
comparables=comparables,
|
||||
drivers=drivers,
|
||||
model_version=self._model_version,
|
||||
)
|
||||
|
||||
def _predict_heuristic(self, req: IndustrialAVMRequest) -> IndustrialAVMResponse:
|
||||
"""Multi-factor heuristic for industrial rent estimation."""
|
||||
province_key = req.province.lower().strip()
|
||||
base = PROVINCE_BASELINE.get(province_key, DEFAULT_RENT_BASELINE)
|
||||
|
||||
# Property type multiplier
|
||||
type_mult = {
|
||||
"warehouse": 0.85,
|
||||
"factory": 1.00,
|
||||
"ready_built_factory": 1.30,
|
||||
"ready_built_warehouse": 1.15,
|
||||
"open_yard": 0.50,
|
||||
"office_in_park": 1.50,
|
||||
}.get(req.property_type.lower(), 1.0)
|
||||
|
||||
# Park quality adjustments
|
||||
occupancy_adj = 1.0 + (req.park_occupancy_rate - 0.7) * 0.3
|
||||
age_adj = max(0.85, 1.0 - req.park_age_years * 0.005)
|
||||
size_adj = 1.0 + min(0.15, req.park_area_ha / 5000 * 0.15)
|
||||
|
||||
# Logistics / infrastructure
|
||||
port_adj = max(0.85, 1.0 - req.distance_to_port_km * 0.002)
|
||||
airport_adj = max(0.90, 1.0 - req.distance_to_airport_km * 0.001)
|
||||
highway_adj = max(0.90, 1.0 - req.distance_to_highway_km * 0.005)
|
||||
logistics_adj = 1.0 + (req.logistics_connectivity_score - 0.5) * 0.20
|
||||
|
||||
# Building specs premium
|
||||
ceiling_adj = 1.0 + max(0.0, (req.ceiling_height_m - 8.0) * 0.02)
|
||||
floor_load_adj = 1.0 + max(0.0, (req.floor_load_ton_m2 - 2.0) * 0.03)
|
||||
power_adj = 1.0 + min(0.10, req.power_capacity_kva / 5000 * 0.10)
|
||||
|
||||
# Economic indicators
|
||||
demand_adj = 1.0 + (req.industry_demand_index - 0.5) * 0.25
|
||||
fdi_adj = 1.0 + min(0.15, req.fdi_province_musd / 5000 * 0.15)
|
||||
labor_adj = max(0.90, 1.0 - req.labor_cost_province_vnd / 20_000_000 * 0.10)
|
||||
|
||||
# Area discount (larger areas get lower per-m² rent)
|
||||
area_discount = 1.0
|
||||
if req.area_m2 > 10_000:
|
||||
area_discount = 0.92
|
||||
elif req.area_m2 > 5_000:
|
||||
area_discount = 0.95
|
||||
elif req.area_m2 > 2_000:
|
||||
area_discount = 0.98
|
||||
|
||||
rent = (
|
||||
base
|
||||
* type_mult
|
||||
* occupancy_adj
|
||||
* age_adj
|
||||
* size_adj
|
||||
* port_adj
|
||||
* airport_adj
|
||||
* highway_adj
|
||||
* logistics_adj
|
||||
* ceiling_adj
|
||||
* floor_load_adj
|
||||
* power_adj
|
||||
* demand_adj
|
||||
* fdi_adj
|
||||
* labor_adj
|
||||
* area_discount
|
||||
)
|
||||
|
||||
confidence = 0.65
|
||||
comparables = _find_comparables(req)
|
||||
|
||||
# Heuristic feature importance
|
||||
drivers = [
|
||||
FeatureImportance(feature="province_baseline", importance=0.20),
|
||||
FeatureImportance(feature="property_type", importance=0.15),
|
||||
FeatureImportance(feature="park_occupancy_rate", importance=0.12),
|
||||
FeatureImportance(feature="logistics_connectivity_score", importance=0.10),
|
||||
FeatureImportance(feature="industry_demand_index", importance=0.10),
|
||||
FeatureImportance(feature="fdi_province_musd", importance=0.08),
|
||||
FeatureImportance(feature="distance_to_port_km", importance=0.07),
|
||||
FeatureImportance(feature="area_m2", importance=0.06),
|
||||
]
|
||||
|
||||
return IndustrialAVMResponse(
|
||||
estimated_rent_usd_m2=round(rent, 2),
|
||||
confidence=confidence,
|
||||
rent_range_low_usd_m2=round(rent * 0.80, 2),
|
||||
rent_range_high_usd_m2=round(rent * 1.20, 2),
|
||||
annual_rent_usd_m2=round(rent * 12, 2),
|
||||
total_monthly_rent_usd=round(rent * req.area_m2, 2),
|
||||
comparables=comparables,
|
||||
drivers=drivers,
|
||||
model_version=self._model_version,
|
||||
)
|
||||
|
||||
|
||||
# Module-level singleton
|
||||
industrial_avm_service = IndustrialAVMService()
|
||||
535
libs/ai-services/app/services/avm_v2_service.py
Normal file
535
libs/ai-services/app/services/avm_v2_service.py
Normal file
@@ -0,0 +1,535 @@
|
||||
"""AVM v2 — Multi-model ensemble service (XGBoost + LightGBM + CatBoost).
|
||||
|
||||
Heuristic fallback when trained models are not available.
|
||||
Ensemble weights: XGBoost 0.4, LightGBM 0.35, CatBoost 0.25.
|
||||
Confidence = 1 - CV(3 predictions), where CV = std / mean.
|
||||
"""
|
||||
|
||||
import logging
|
||||
import os
|
||||
from datetime import datetime, timezone
|
||||
from typing import Any
|
||||
|
||||
import numpy as np
|
||||
|
||||
from app.models.avm_v2 import (
|
||||
AVMv2Comparable,
|
||||
AVMv2FeatureImportance,
|
||||
AVMv2ModelInfo,
|
||||
AVMv2PredictRequest,
|
||||
AVMv2PredictResponse,
|
||||
AVMv2TrainRequest,
|
||||
AVMv2TrainResponse,
|
||||
ModelPrediction,
|
||||
)
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
# ── Ensemble configuration ──────────────────────────────────────
|
||||
ENSEMBLE_WEIGHTS = {
|
||||
"xgboost": 0.40,
|
||||
"lightgbm": 0.35,
|
||||
"catboost": 0.25,
|
||||
}
|
||||
|
||||
# ── Feature ordering for model input ────────────────────────────
|
||||
FEATURE_NAMES = [
|
||||
# Location (7)
|
||||
"distance_to_cbd_km",
|
||||
"distance_to_metro_km",
|
||||
"distance_to_school_km",
|
||||
"distance_to_hospital_km",
|
||||
"distance_to_park_km",
|
||||
"distance_to_mall_km",
|
||||
"flood_zone_risk",
|
||||
# Physical (8)
|
||||
"property_type_encoded",
|
||||
"area_m2",
|
||||
"rooms",
|
||||
"floor_ratio",
|
||||
"building_age_years",
|
||||
"has_elevator",
|
||||
"has_parking",
|
||||
"has_pool",
|
||||
"has_legal_paper",
|
||||
# Market (6)
|
||||
"avg_price_district_3m_vnd_m2",
|
||||
"listing_density",
|
||||
"absorption_rate",
|
||||
"dom_avg",
|
||||
"price_momentum_30d",
|
||||
"yoy_change",
|
||||
# LLM-extracted (5)
|
||||
"renovation_score",
|
||||
"view_quality",
|
||||
"interior_quality",
|
||||
"noise_level",
|
||||
"natural_light",
|
||||
# Temporal (3)
|
||||
"month_sin",
|
||||
"month_cos",
|
||||
"is_year_end",
|
||||
]
|
||||
|
||||
PROPERTY_TYPE_MAP = {
|
||||
"apartment": 0,
|
||||
"house": 1,
|
||||
"townhouse": 2,
|
||||
"villa": 3,
|
||||
"land": 4,
|
||||
"shophouse": 5,
|
||||
"penthouse": 6,
|
||||
}
|
||||
|
||||
# ── Heuristic baselines (millions VND/m²) ───────────────────────
|
||||
CITY_BASELINE: dict[str, float] = {
|
||||
"hà nội": 85.0,
|
||||
"hồ chí minh": 90.0,
|
||||
"đà nẵng": 45.0,
|
||||
"hải phòng": 35.0,
|
||||
"cần thơ": 25.0,
|
||||
"bình dương": 22.0,
|
||||
"đồng nai": 20.0,
|
||||
"nha trang": 35.0,
|
||||
"vũng tàu": 28.0,
|
||||
}
|
||||
DEFAULT_BASELINE = 30.0
|
||||
|
||||
|
||||
def _encode_features(req: AVMv2PredictRequest) -> np.ndarray:
|
||||
"""Encode a prediction request into a feature vector."""
|
||||
month_rad = 2 * np.pi * req.month / 12.0
|
||||
return np.array(
|
||||
[[
|
||||
# Location
|
||||
req.distance_to_cbd_km,
|
||||
req.distance_to_metro_km,
|
||||
req.distance_to_school_km,
|
||||
req.distance_to_hospital_km,
|
||||
req.distance_to_park_km,
|
||||
req.distance_to_mall_km,
|
||||
req.flood_zone_risk,
|
||||
# Physical
|
||||
PROPERTY_TYPE_MAP.get(req.property_type.lower(), 1),
|
||||
req.area_m2,
|
||||
req.rooms,
|
||||
req.floor_ratio,
|
||||
req.building_age_years,
|
||||
1.0 if req.has_elevator else 0.0,
|
||||
1.0 if req.has_parking else 0.0,
|
||||
1.0 if req.has_pool else 0.0,
|
||||
1.0 if req.has_legal_paper else 0.0,
|
||||
# Market
|
||||
req.avg_price_district_3m_vnd_m2,
|
||||
req.listing_density,
|
||||
req.absorption_rate,
|
||||
req.dom_avg,
|
||||
req.price_momentum_30d,
|
||||
req.yoy_change,
|
||||
# LLM-extracted
|
||||
req.renovation_score,
|
||||
req.view_quality,
|
||||
req.interior_quality,
|
||||
req.noise_level,
|
||||
req.natural_light,
|
||||
# Temporal
|
||||
np.sin(month_rad),
|
||||
np.cos(month_rad),
|
||||
1.0 if req.is_year_end else 0.0,
|
||||
]],
|
||||
dtype=np.float64,
|
||||
)
|
||||
|
||||
|
||||
class AVMv2EnsembleService:
|
||||
"""Multi-model ensemble AVM for residential properties.
|
||||
|
||||
Attempts to load XGBoost, LightGBM, and CatBoost models from
|
||||
the model directory. Falls back to a heuristic approach when
|
||||
trained models are not available.
|
||||
"""
|
||||
|
||||
def __init__(self) -> None:
|
||||
self._models: dict[str, Any] = {}
|
||||
self._model_version = "ensemble-v2-heuristic"
|
||||
self._model_registry: list[AVMv2ModelInfo] = []
|
||||
self._load_models()
|
||||
|
||||
# ── Model loading ───────────────────────────────────────────
|
||||
|
||||
def _load_models(self) -> None:
|
||||
"""Attempt to load each model in the ensemble."""
|
||||
from app.config import settings
|
||||
|
||||
model_dir = settings.model_path
|
||||
|
||||
# XGBoost
|
||||
try:
|
||||
import xgboost as xgb
|
||||
|
||||
path = os.path.join(model_dir, "avm_v2_xgboost.json")
|
||||
if os.path.exists(path):
|
||||
booster = xgb.Booster()
|
||||
booster.load_model(path)
|
||||
self._models["xgboost"] = booster
|
||||
logger.info("Loaded XGBoost AVM v2 model from %s", path)
|
||||
except Exception:
|
||||
logger.info("XGBoost model not available")
|
||||
|
||||
# LightGBM
|
||||
try:
|
||||
import lightgbm as lgb
|
||||
|
||||
path = os.path.join(model_dir, "avm_v2_lightgbm.txt")
|
||||
if os.path.exists(path):
|
||||
self._models["lightgbm"] = lgb.Booster(model_file=path)
|
||||
logger.info("Loaded LightGBM AVM v2 model from %s", path)
|
||||
except Exception:
|
||||
logger.info("LightGBM model not available")
|
||||
|
||||
# CatBoost
|
||||
try:
|
||||
from catboost import CatBoostRegressor
|
||||
|
||||
path = os.path.join(model_dir, "avm_v2_catboost.cbm")
|
||||
if os.path.exists(path):
|
||||
model = CatBoostRegressor()
|
||||
model.load_model(path)
|
||||
self._models["catboost"] = model
|
||||
logger.info("Loaded CatBoost AVM v2 model from %s", path)
|
||||
except Exception:
|
||||
logger.info("CatBoost model not available")
|
||||
|
||||
if self._models:
|
||||
self._model_version = f"ensemble-v2-{'+'.join(sorted(self._models.keys()))}"
|
||||
logger.info("AVM v2 ensemble active with: %s", list(self._models.keys()))
|
||||
else:
|
||||
logger.info("No trained AVM v2 models found — using heuristic fallback")
|
||||
|
||||
# ── Prediction ──────────────────────────────────────────────
|
||||
|
||||
def predict(self, req: AVMv2PredictRequest) -> AVMv2PredictResponse:
|
||||
"""Run the ensemble prediction pipeline."""
|
||||
if self._models:
|
||||
return self._predict_ensemble(req)
|
||||
return self._predict_heuristic(req)
|
||||
|
||||
def _predict_ensemble(self, req: AVMv2PredictRequest) -> AVMv2PredictResponse:
|
||||
"""Run each loaded model and combine with weighted average."""
|
||||
features = _encode_features(req)
|
||||
predictions: list[ModelPrediction] = []
|
||||
raw_prices: list[float] = []
|
||||
|
||||
for model_name, model in self._models.items():
|
||||
weight = ENSEMBLE_WEIGHTS.get(model_name, 0.0)
|
||||
price = self._predict_single_model(model_name, model, features)
|
||||
raw_prices.append(price)
|
||||
predictions.append(
|
||||
ModelPrediction(
|
||||
model_name=model_name,
|
||||
weight=weight,
|
||||
predicted_price_vnd=round(price, -3),
|
||||
predicted_price_per_m2_vnd=round(price / req.area_m2, -3),
|
||||
)
|
||||
)
|
||||
|
||||
# Weighted ensemble
|
||||
total_weight = sum(ENSEMBLE_WEIGHTS.get(p.model_name, 0) for p in predictions)
|
||||
if total_weight == 0:
|
||||
total_weight = 1.0
|
||||
|
||||
ensemble_price = sum(
|
||||
p.predicted_price_vnd * ENSEMBLE_WEIGHTS.get(p.model_name, 0)
|
||||
for p in predictions
|
||||
) / total_weight
|
||||
|
||||
# Confidence = 1 - CV(predictions)
|
||||
prices_arr = np.array(raw_prices)
|
||||
mean_price = np.mean(prices_arr)
|
||||
std_price = np.std(prices_arr)
|
||||
cv = std_price / mean_price if mean_price > 0 else 0.5
|
||||
confidence = max(0.0, min(1.0, 1.0 - cv))
|
||||
|
||||
# Range based on confidence
|
||||
margin = max(0.05, 0.30 * (1.0 - confidence))
|
||||
price_low = ensemble_price * (1.0 - margin)
|
||||
price_high = ensemble_price * (1.0 + margin)
|
||||
|
||||
# Feature importance (aggregate from XGBoost if available)
|
||||
drivers = self._get_feature_importance()
|
||||
|
||||
return AVMv2PredictResponse(
|
||||
estimated_price_vnd=round(ensemble_price, -3),
|
||||
price_per_m2_vnd=round(ensemble_price / req.area_m2, -3),
|
||||
confidence=round(confidence, 4),
|
||||
price_range_low_vnd=round(price_low, -3),
|
||||
price_range_high_vnd=round(price_high, -3),
|
||||
model_predictions=predictions,
|
||||
drivers=drivers[:10],
|
||||
comparables=[], # Populated by data layer in production
|
||||
model_version=self._model_version,
|
||||
ensemble_method="weighted_average",
|
||||
)
|
||||
|
||||
def _predict_single_model(
|
||||
self, name: str, model: Any, features: np.ndarray
|
||||
) -> float:
|
||||
"""Get a single model's raw prediction (log-price → price)."""
|
||||
if name == "xgboost":
|
||||
import xgboost as xgb
|
||||
|
||||
dmatrix = xgb.DMatrix(features, feature_names=FEATURE_NAMES)
|
||||
pred_log = model.predict(dmatrix)[0]
|
||||
return float(np.exp(pred_log))
|
||||
|
||||
if name == "lightgbm":
|
||||
pred_log = model.predict(features)[0]
|
||||
return float(np.exp(pred_log))
|
||||
|
||||
if name == "catboost":
|
||||
pred_log = model.predict(features)[0]
|
||||
return float(np.exp(pred_log))
|
||||
|
||||
logger.warning("Unknown model type: %s", name)
|
||||
return 0.0
|
||||
|
||||
def _predict_heuristic(self, req: AVMv2PredictRequest) -> AVMv2PredictResponse:
|
||||
"""Multi-factor heuristic simulating ensemble behavior."""
|
||||
city_key = req.city.lower().strip()
|
||||
base = CITY_BASELINE.get(city_key, DEFAULT_BASELINE)
|
||||
|
||||
# Property type multiplier
|
||||
type_mult = {
|
||||
"apartment": 0.90,
|
||||
"house": 1.00,
|
||||
"townhouse": 1.10,
|
||||
"villa": 1.40,
|
||||
"land": 0.70,
|
||||
"shophouse": 1.30,
|
||||
"penthouse": 1.60,
|
||||
}.get(req.property_type.lower(), 1.0)
|
||||
|
||||
# Location adjustments
|
||||
cbd_adj = max(0.7, 1.0 - req.distance_to_cbd_km * 0.02)
|
||||
metro_adj = 1.0 + max(0.0, (2.0 - req.distance_to_metro_km) * 0.05)
|
||||
flood_adj = 1.0 - req.flood_zone_risk * 0.15
|
||||
|
||||
# Physical adjustments
|
||||
room_adj = 1.0 + req.rooms * 0.015
|
||||
age_adj = max(0.75, 1.0 - req.building_age_years * 0.008)
|
||||
amenity_adj = (
|
||||
1.0
|
||||
+ (0.03 if req.has_elevator else 0.0)
|
||||
+ (0.05 if req.has_parking else 0.0)
|
||||
+ (0.08 if req.has_pool else 0.0)
|
||||
)
|
||||
legal_adj = 1.0 if req.has_legal_paper else 0.70
|
||||
|
||||
# Market adjustments
|
||||
if req.avg_price_district_3m_vnd_m2 > 0:
|
||||
market_adj = req.avg_price_district_3m_vnd_m2 / (base * 1_000_000)
|
||||
market_adj = max(0.5, min(2.0, market_adj))
|
||||
else:
|
||||
market_adj = 1.0
|
||||
momentum_adj = 1.0 + req.price_momentum_30d * 0.5
|
||||
|
||||
# Quality adjustments (LLM features)
|
||||
quality_adj = (
|
||||
1.0
|
||||
+ (req.renovation_score - 0.5) * 0.15
|
||||
+ (req.view_quality - 0.5) * 0.10
|
||||
+ (req.interior_quality - 0.5) * 0.12
|
||||
+ (0.5 - req.noise_level) * 0.05
|
||||
+ (req.natural_light - 0.5) * 0.05
|
||||
)
|
||||
|
||||
# Temporal — Q4/Tết premium
|
||||
seasonal_adj = 1.03 if req.is_year_end else 1.0
|
||||
|
||||
price_per_m2 = (
|
||||
base
|
||||
* type_mult
|
||||
* cbd_adj
|
||||
* metro_adj
|
||||
* flood_adj
|
||||
* room_adj
|
||||
* age_adj
|
||||
* amenity_adj
|
||||
* legal_adj
|
||||
* market_adj
|
||||
* momentum_adj
|
||||
* quality_adj
|
||||
* seasonal_adj
|
||||
* 1_000_000 # Convert to VND
|
||||
)
|
||||
estimated = price_per_m2 * req.area_m2
|
||||
|
||||
# Simulate 3 model predictions with small variance
|
||||
rng = np.random.default_rng(
|
||||
seed=int(req.area_m2 * 1000 + req.rooms * 100 + req.month)
|
||||
)
|
||||
noise = rng.normal(1.0, 0.04, size=3)
|
||||
sim_prices = estimated * noise
|
||||
|
||||
xgb_price = float(sim_prices[0])
|
||||
lgb_price = float(sim_prices[1])
|
||||
cat_price = float(sim_prices[2])
|
||||
|
||||
predictions = [
|
||||
ModelPrediction(
|
||||
model_name="xgboost",
|
||||
weight=0.40,
|
||||
predicted_price_vnd=round(xgb_price, -3),
|
||||
predicted_price_per_m2_vnd=round(xgb_price / req.area_m2, -3),
|
||||
),
|
||||
ModelPrediction(
|
||||
model_name="lightgbm",
|
||||
weight=0.35,
|
||||
predicted_price_vnd=round(lgb_price, -3),
|
||||
predicted_price_per_m2_vnd=round(lgb_price / req.area_m2, -3),
|
||||
),
|
||||
ModelPrediction(
|
||||
model_name="catboost",
|
||||
weight=0.25,
|
||||
predicted_price_vnd=round(cat_price, -3),
|
||||
predicted_price_per_m2_vnd=round(cat_price / req.area_m2, -3),
|
||||
),
|
||||
]
|
||||
|
||||
prices_arr = np.array([xgb_price, lgb_price, cat_price])
|
||||
cv = float(np.std(prices_arr) / np.mean(prices_arr)) if np.mean(prices_arr) > 0 else 0.5
|
||||
confidence = max(0.0, min(1.0, 1.0 - cv))
|
||||
|
||||
# Heuristic driver ranking
|
||||
drivers = [
|
||||
AVMv2FeatureImportance(feature="area_m2", importance=0.18),
|
||||
AVMv2FeatureImportance(feature="avg_price_district_3m_vnd_m2", importance=0.15),
|
||||
AVMv2FeatureImportance(feature="property_type_encoded", importance=0.12),
|
||||
AVMv2FeatureImportance(feature="distance_to_cbd_km", importance=0.10),
|
||||
AVMv2FeatureImportance(feature="renovation_score", importance=0.08),
|
||||
AVMv2FeatureImportance(feature="building_age_years", importance=0.07),
|
||||
AVMv2FeatureImportance(feature="has_legal_paper", importance=0.06),
|
||||
AVMv2FeatureImportance(feature="distance_to_metro_km", importance=0.05),
|
||||
AVMv2FeatureImportance(feature="interior_quality", importance=0.05),
|
||||
AVMv2FeatureImportance(feature="price_momentum_30d", importance=0.04),
|
||||
]
|
||||
|
||||
return AVMv2PredictResponse(
|
||||
estimated_price_vnd=round(estimated, -3),
|
||||
price_per_m2_vnd=round(price_per_m2, -3),
|
||||
confidence=round(confidence, 4),
|
||||
price_range_low_vnd=round(estimated * 0.82, -3),
|
||||
price_range_high_vnd=round(estimated * 1.18, -3),
|
||||
model_predictions=predictions,
|
||||
drivers=drivers,
|
||||
comparables=[],
|
||||
model_version="ensemble-v2-heuristic",
|
||||
ensemble_method="weighted_average",
|
||||
)
|
||||
|
||||
def _get_feature_importance(self) -> list[AVMv2FeatureImportance]:
|
||||
"""Extract feature importance from loaded models."""
|
||||
importances: dict[str, float] = {}
|
||||
|
||||
if "xgboost" in self._models:
|
||||
try:
|
||||
scores = self._models["xgboost"].get_score(
|
||||
importance_type="gain"
|
||||
)
|
||||
total = sum(scores.values()) or 1.0
|
||||
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()
|
||||
@@ -7,12 +7,16 @@ dependencies = [
|
||||
"fastapi==0.115.0",
|
||||
"uvicorn[standard]==0.32.0",
|
||||
"xgboost==2.1.0",
|
||||
"lightgbm>=4.5.0",
|
||||
"catboost>=1.2.7",
|
||||
"numpy==1.26.4",
|
||||
"underthesea==6.8.0",
|
||||
"pydantic==2.9.0",
|
||||
"pydantic-settings==2.5.0",
|
||||
"httpx==0.27.0",
|
||||
"slowapi==0.1.9",
|
||||
"optuna>=4.0.0",
|
||||
"scikit-learn>=1.5.0",
|
||||
]
|
||||
|
||||
[project.optional-dependencies]
|
||||
|
||||
124
libs/ai-services/tests/test_avm_industrial.py
Normal file
124
libs/ai-services/tests/test_avm_industrial.py
Normal file
@@ -0,0 +1,124 @@
|
||||
"""Tests for industrial AVM rent estimation endpoint."""
|
||||
|
||||
from fastapi.testclient import TestClient
|
||||
|
||||
from app.main import app
|
||||
|
||||
client = TestClient(app)
|
||||
|
||||
# ── Minimal valid request payload ───────────────────────────────
|
||||
|
||||
_PREDICT_PAYLOAD = {
|
||||
"province": "Bình Dương",
|
||||
"region": "south",
|
||||
"park_occupancy_rate": 0.85,
|
||||
"park_area_ha": 500,
|
||||
"park_age_years": 10,
|
||||
"distance_to_port_km": 60,
|
||||
"distance_to_airport_km": 30,
|
||||
"distance_to_highway_km": 5,
|
||||
"property_type": "factory",
|
||||
"area_m2": 5000,
|
||||
"ceiling_height_m": 10,
|
||||
"floor_load_ton_m2": 3.0,
|
||||
"power_capacity_kva": 1000,
|
||||
}
|
||||
|
||||
|
||||
def test_predict_industrial_heuristic():
|
||||
"""Predict using heuristic fallback (no trained model)."""
|
||||
resp = client.post("/avm/industrial/predict", json=_PREDICT_PAYLOAD)
|
||||
assert resp.status_code == 200
|
||||
data = resp.json()
|
||||
|
||||
assert data["estimated_rent_usd_m2"] > 0
|
||||
assert 0 <= data["confidence"] <= 1
|
||||
assert data["rent_range_low_usd_m2"] < data["estimated_rent_usd_m2"]
|
||||
assert data["rent_range_high_usd_m2"] > data["estimated_rent_usd_m2"]
|
||||
assert data["annual_rent_usd_m2"] > 0
|
||||
assert data["total_monthly_rent_usd"] > 0
|
||||
assert data["model_version"] == "heuristic-v1"
|
||||
|
||||
|
||||
def test_predict_industrial_returns_comparables():
|
||||
"""Heuristic should return comparable industrial properties."""
|
||||
resp = client.post("/avm/industrial/predict", json=_PREDICT_PAYLOAD)
|
||||
data = resp.json()
|
||||
|
||||
comps = data["comparables"]
|
||||
assert len(comps) > 0
|
||||
for c in comps:
|
||||
assert c["park_name"]
|
||||
assert c["rent_usd_m2"] > 0
|
||||
assert 0 <= c["similarity_score"] <= 1
|
||||
|
||||
|
||||
def test_predict_industrial_returns_drivers():
|
||||
"""Heuristic should return feature importance drivers."""
|
||||
resp = client.post("/avm/industrial/predict", json=_PREDICT_PAYLOAD)
|
||||
data = resp.json()
|
||||
|
||||
drivers = data["drivers"]
|
||||
assert len(drivers) > 0
|
||||
assert all(0 <= d["importance"] <= 1 for d in drivers)
|
||||
|
||||
|
||||
def test_predict_industrial_ready_built_premium():
|
||||
"""Ready-built factories should be priced higher than standard."""
|
||||
standard = client.post("/avm/industrial/predict", json=_PREDICT_PAYLOAD).json()
|
||||
rbf_payload = {**_PREDICT_PAYLOAD, "property_type": "ready_built_factory"}
|
||||
ready_built = client.post("/avm/industrial/predict", json=rbf_payload).json()
|
||||
|
||||
assert ready_built["estimated_rent_usd_m2"] > standard["estimated_rent_usd_m2"]
|
||||
|
||||
|
||||
def test_predict_industrial_open_yard_discount():
|
||||
"""Open yards should be cheaper than factories."""
|
||||
factory = client.post("/avm/industrial/predict", json=_PREDICT_PAYLOAD).json()
|
||||
yard_payload = {**_PREDICT_PAYLOAD, "property_type": "open_yard"}
|
||||
yard = client.post("/avm/industrial/predict", json=yard_payload).json()
|
||||
|
||||
assert yard["estimated_rent_usd_m2"] < factory["estimated_rent_usd_m2"]
|
||||
|
||||
|
||||
def test_predict_industrial_high_occupancy_premium():
|
||||
"""Higher park occupancy should increase rent."""
|
||||
low = client.post(
|
||||
"/avm/industrial/predict",
|
||||
json={**_PREDICT_PAYLOAD, "park_occupancy_rate": 0.50},
|
||||
).json()
|
||||
high = client.post(
|
||||
"/avm/industrial/predict",
|
||||
json={**_PREDICT_PAYLOAD, "park_occupancy_rate": 0.95},
|
||||
).json()
|
||||
|
||||
assert high["estimated_rent_usd_m2"] > low["estimated_rent_usd_m2"]
|
||||
|
||||
|
||||
def test_predict_industrial_annual_rent():
|
||||
"""Annual rent should be 12x monthly rent."""
|
||||
resp = client.post("/avm/industrial/predict", json=_PREDICT_PAYLOAD).json()
|
||||
expected_annual = round(resp["estimated_rent_usd_m2"] * 12, 2)
|
||||
assert resp["annual_rent_usd_m2"] == expected_annual
|
||||
|
||||
|
||||
def test_predict_industrial_total_rent():
|
||||
"""Total monthly rent should be rent/m² × area."""
|
||||
resp = client.post("/avm/industrial/predict", json=_PREDICT_PAYLOAD).json()
|
||||
expected_total = resp["estimated_rent_usd_m2"] * _PREDICT_PAYLOAD["area_m2"]
|
||||
assert abs(resp["total_monthly_rent_usd"] - expected_total) < 1.0
|
||||
|
||||
|
||||
def test_predict_industrial_validation_error():
|
||||
"""Missing required fields should return 422."""
|
||||
resp = client.post("/avm/industrial/predict", json={"area_m2": 5000})
|
||||
assert resp.status_code == 422
|
||||
|
||||
|
||||
def test_predict_industrial_invalid_occupancy():
|
||||
"""Occupancy rate outside 0-1 should be rejected."""
|
||||
resp = client.post(
|
||||
"/avm/industrial/predict",
|
||||
json={**_PREDICT_PAYLOAD, "park_occupancy_rate": 1.5},
|
||||
)
|
||||
assert resp.status_code == 422
|
||||
174
libs/ai-services/tests/test_avm_v2.py
Normal file
174
libs/ai-services/tests/test_avm_v2.py
Normal file
@@ -0,0 +1,174 @@
|
||||
"""Tests for AVM v2 ensemble endpoints."""
|
||||
|
||||
from fastapi.testclient import TestClient
|
||||
|
||||
from app.main import app
|
||||
|
||||
client = TestClient(app)
|
||||
|
||||
# ── Minimal valid request payload ───────────────────────────────
|
||||
|
||||
_PREDICT_PAYLOAD = {
|
||||
"district": "Cầu Giấy",
|
||||
"city": "Hà Nội",
|
||||
"property_type": "apartment",
|
||||
"area_m2": 80.0,
|
||||
"rooms": 2,
|
||||
"month": 3,
|
||||
"quarter": 1,
|
||||
}
|
||||
|
||||
|
||||
def test_predict_v2_heuristic():
|
||||
"""Predict using heuristic fallback (no trained models)."""
|
||||
resp = client.post("/avm/v2/predict", json=_PREDICT_PAYLOAD)
|
||||
assert resp.status_code == 200
|
||||
data = resp.json()
|
||||
|
||||
assert data["estimated_price_vnd"] > 0
|
||||
assert 0 <= data["confidence"] <= 1
|
||||
assert data["price_per_m2_vnd"] > 0
|
||||
assert data["price_range_low_vnd"] < data["estimated_price_vnd"]
|
||||
assert data["price_range_high_vnd"] > data["estimated_price_vnd"]
|
||||
assert data["ensemble_method"] == "weighted_average"
|
||||
assert data["model_version"] == "ensemble-v2-heuristic"
|
||||
|
||||
|
||||
def test_predict_v2_returns_model_predictions():
|
||||
"""Heuristic should return 3 simulated model predictions."""
|
||||
resp = client.post("/avm/v2/predict", json=_PREDICT_PAYLOAD)
|
||||
data = resp.json()
|
||||
|
||||
preds = data["model_predictions"]
|
||||
assert len(preds) == 3
|
||||
|
||||
names = {p["model_name"] for p in preds}
|
||||
assert names == {"xgboost", "lightgbm", "catboost"}
|
||||
|
||||
for p in preds:
|
||||
assert p["weight"] > 0
|
||||
assert p["predicted_price_vnd"] > 0
|
||||
assert p["predicted_price_per_m2_vnd"] > 0
|
||||
|
||||
|
||||
def test_predict_v2_returns_drivers():
|
||||
"""Heuristic should return feature importance drivers."""
|
||||
resp = client.post("/avm/v2/predict", json=_PREDICT_PAYLOAD)
|
||||
data = resp.json()
|
||||
|
||||
drivers = data["drivers"]
|
||||
assert len(drivers) > 0
|
||||
assert all(0 <= d["importance"] <= 1 for d in drivers)
|
||||
# Most important feature should be area or district price
|
||||
top_feature = drivers[0]["feature"]
|
||||
assert top_feature in ("area_m2", "avg_price_district_3m_vnd_m2")
|
||||
|
||||
|
||||
def test_predict_v2_with_full_features():
|
||||
"""Predict with all features populated."""
|
||||
payload = {
|
||||
**_PREDICT_PAYLOAD,
|
||||
"distance_to_cbd_km": 5.0,
|
||||
"distance_to_metro_km": 0.8,
|
||||
"distance_to_school_km": 0.5,
|
||||
"distance_to_hospital_km": 2.0,
|
||||
"distance_to_park_km": 0.3,
|
||||
"distance_to_mall_km": 1.0,
|
||||
"flood_zone_risk": 0.1,
|
||||
"floor_ratio": 1.2,
|
||||
"building_age_years": 5,
|
||||
"has_elevator": True,
|
||||
"has_parking": True,
|
||||
"has_pool": False,
|
||||
"avg_price_district_3m_vnd_m2": 85_000_000,
|
||||
"listing_density": 12.5,
|
||||
"absorption_rate": 0.3,
|
||||
"dom_avg": 45.0,
|
||||
"price_momentum_30d": 0.02,
|
||||
"yoy_change": 0.05,
|
||||
"renovation_score": 0.8,
|
||||
"view_quality": 0.7,
|
||||
"interior_quality": 0.75,
|
||||
"noise_level": 0.3,
|
||||
"natural_light": 0.8,
|
||||
"is_year_end": False,
|
||||
}
|
||||
resp = client.post("/avm/v2/predict", json=payload)
|
||||
assert resp.status_code == 200
|
||||
data = resp.json()
|
||||
assert data["estimated_price_vnd"] > 0
|
||||
assert data["confidence"] > 0
|
||||
|
||||
|
||||
def test_predict_v2_villa_premium():
|
||||
"""Villas should be priced higher than apartments (same area)."""
|
||||
apt = client.post("/avm/v2/predict", json=_PREDICT_PAYLOAD).json()
|
||||
villa_payload = {**_PREDICT_PAYLOAD, "property_type": "villa"}
|
||||
villa = client.post("/avm/v2/predict", json=villa_payload).json()
|
||||
|
||||
assert villa["price_per_m2_vnd"] > apt["price_per_m2_vnd"]
|
||||
|
||||
|
||||
def test_predict_v2_year_end_premium():
|
||||
"""Q4/Tết season should add a premium."""
|
||||
normal = client.post(
|
||||
"/avm/v2/predict",
|
||||
json={**_PREDICT_PAYLOAD, "is_year_end": False, "month": 6, "quarter": 2},
|
||||
).json()
|
||||
year_end = client.post(
|
||||
"/avm/v2/predict",
|
||||
json={**_PREDICT_PAYLOAD, "is_year_end": True, "month": 12, "quarter": 4},
|
||||
).json()
|
||||
|
||||
assert year_end["estimated_price_vnd"] > normal["estimated_price_vnd"]
|
||||
|
||||
|
||||
def test_predict_v2_no_legal_paper_discount():
|
||||
"""Properties without legal papers should be discounted."""
|
||||
with_paper = client.post("/avm/v2/predict", json=_PREDICT_PAYLOAD).json()
|
||||
without_paper = client.post(
|
||||
"/avm/v2/predict",
|
||||
json={**_PREDICT_PAYLOAD, "has_legal_paper": False},
|
||||
).json()
|
||||
|
||||
assert without_paper["estimated_price_vnd"] < with_paper["estimated_price_vnd"]
|
||||
|
||||
|
||||
def test_predict_v2_validation_error():
|
||||
"""Missing required fields should return 422."""
|
||||
resp = client.post("/avm/v2/predict", json={"area_m2": 80})
|
||||
assert resp.status_code == 422
|
||||
|
||||
|
||||
def test_predict_v2_invalid_area():
|
||||
"""Zero or negative area should be rejected."""
|
||||
resp = client.post(
|
||||
"/avm/v2/predict",
|
||||
json={**_PREDICT_PAYLOAD, "area_m2": 0},
|
||||
)
|
||||
assert resp.status_code == 422
|
||||
|
||||
|
||||
def test_train_v2_scaffold():
|
||||
"""Training endpoint should return scaffold response."""
|
||||
resp = client.post(
|
||||
"/avm/v2/train",
|
||||
json={"optuna_trials": 10},
|
||||
)
|
||||
assert resp.status_code == 200
|
||||
data = resp.json()
|
||||
assert "model_version" in data
|
||||
assert "ensemble-v2-" in data["model_version"]
|
||||
assert data["metrics"]["mae"] == 0.0 # scaffold returns zeros
|
||||
assert "xgboost" in data["best_params"]
|
||||
assert "lightgbm" in data["best_params"]
|
||||
assert "catboost" in data["best_params"]
|
||||
|
||||
|
||||
def test_model_info_v2():
|
||||
"""Model info endpoint should return current model version."""
|
||||
resp = client.get("/avm/v2/model-info")
|
||||
assert resp.status_code == 200
|
||||
data = resp.json()
|
||||
assert "model_version" in data
|
||||
assert data["is_active"] is True
|
||||
Reference in New Issue
Block a user