goodgo-platform/libs/ai-services/app/models/avm_v2.py

"""AVM v2 — Residential multi-model ensemble request/response schemas."""

from pydantic import BaseModel, Field


class AVMv2PredictRequest(BaseModel):
    """Extended feature set for residential AVM v2 ensemble."""

    # ── Location features ──────────────────────────────────
    district: str = Field(..., min_length=1, description="District name")
    city: str = Field(..., min_length=1, description="City name")
    distance_to_cbd_km: float = Field(0.0, ge=0, description="Distance to CBD in km")
    distance_to_metro_km: float = Field(
        0.0, ge=0, description="Distance to nearest metro station in km"
    )
    distance_to_school_km: float = Field(
        0.0, ge=0, description="Distance to nearest school in km"
    )
    distance_to_hospital_km: float = Field(
        0.0, ge=0, description="Distance to nearest hospital in km"
    )
    distance_to_park_km: float = Field(
        0.0, ge=0, description="Distance to nearest park in km"
    )
    distance_to_mall_km: float = Field(
        0.0, ge=0, description="Distance to nearest mall/shopping center in km"
    )
    flood_zone_risk: float = Field(
        0.0, ge=0, le=1, description="Flood zone risk score (0=safe, 1=high risk)"
    )

    # ── Physical features ──────────────────────────────────
    property_type: str = Field(..., description="e.g. apartment, house, villa, land")
    area_m2: float = Field(..., gt=0, description="Property area in m²")
    rooms: int = Field(0, ge=0, description="Total rooms (bedrooms)")
    floor_ratio: float = Field(
        1.0, gt=0, description="Total floor area / land area ratio"
    )
    building_age_years: int = Field(0, ge=0, description="Building age in years")
    has_elevator: bool = Field(False, description="Building has elevator")
    has_parking: bool = Field(False, description="Property has dedicated parking")
    has_pool: bool = Field(False, description="Property has swimming pool")
    has_legal_paper: bool = Field(True, description="Has sổ đỏ/sổ hồng")

    # ── Market features ────────────────────────────────────
    avg_price_district_3m_vnd_m2: float = Field(
        0.0, ge=0,
        description="Avg price per m² in the district over last 3 months (VND)",
    )
    listing_density: float = Field(
        0.0, ge=0,
        description="Number of active listings per km² in the district",
    )
    absorption_rate: float = Field(
        0.0, ge=0, le=1,
        description="Percentage of listings sold in last 30 days (0-1)",
    )
    dom_avg: float = Field(
        0.0, ge=0,
        description="Average days on market in the district",
    )
    price_momentum_30d: float = Field(
        0.0,
        description="Price change percentage in last 30 days (-1 to +1)",
    )
    yoy_change: float = Field(
        0.0,
        description="Year-over-year price change percentage (-1 to +1)",
    )

    # ── LLM-extracted features ─────────────────────────────
    renovation_score: float = Field(
        0.5, ge=0, le=1, description="Renovation quality score (0-1)"
    )
    view_quality: float = Field(
        0.5, ge=0, le=1, description="View quality score (0-1)"
    )
    interior_quality: float = Field(
        0.5, ge=0, le=1, description="Interior quality score (0-1)"
    )
    noise_level: float = Field(
        0.5, ge=0, le=1, description="Noise level score (0=quiet, 1=noisy)"
    )
    natural_light: float = Field(
        0.5, ge=0, le=1, description="Natural light score (0-1)"
    )

    # ── Temporal features ──────────────────────────────────
    month: int = Field(1, ge=1, le=12, description="Transaction month (1-12)")
    quarter: int = Field(1, ge=1, le=4, description="Transaction quarter (1-4)")
    is_year_end: bool = Field(False, description="Whether in Q4 / Tết season")


class AVMv2Comparable(BaseModel):
    """A comparable property used for context."""

    district: str
    property_type: str
    area_m2: float
    price_vnd: float
    price_per_m2_vnd: float
    similarity_score: float = Field(..., ge=0, le=1)


class AVMv2FeatureImportance(BaseModel):
    """Feature contribution to the prediction."""

    feature: str
    importance: float = Field(..., ge=0, le=1)


class ModelPrediction(BaseModel):
    """Individual model prediction within the ensemble."""

    model_name: str
    weight: float
    predicted_price_vnd: float
    predicted_price_per_m2_vnd: float


class AVMv2PredictResponse(BaseModel):
    """Multi-model ensemble prediction response."""

    estimated_price_vnd: float = Field(..., description="Weighted ensemble estimated price in VND")
    price_per_m2_vnd: float = Field(..., description="Price per m² in VND")
    confidence: float = Field(
        ..., ge=0, le=1,
        description="Confidence = 1 - CV(predictions across 3 models)",
    )
    price_range_low_vnd: float = Field(..., description="Lower bound estimate in VND")
    price_range_high_vnd: float = Field(..., description="Upper bound estimate in VND")

    # Ensemble breakdown
    model_predictions: list[ModelPrediction] = Field(
        default_factory=list,
        description="Individual predictions from each model in the ensemble",
    )

    # Explainability
    drivers: list[AVMv2FeatureImportance] = Field(
        default_factory=list,
        description="Top feature drivers ranked by importance",
    )
    comparables: list[AVMv2Comparable] = Field(
        default_factory=list,
        description="Similar properties for reference",
    )

    # Model metadata
    model_version: str = Field("ensemble-v2-heuristic", description="Ensemble version used")
    ensemble_method: str = Field("weighted_average", description="Ensemble strategy")


class AVMv2TrainRequest(BaseModel):
    """Request to trigger model retraining."""

    force: bool = Field(False, description="Force retrain even if recent model exists")
    optuna_trials: int = Field(100, ge=10, le=500, description="Number of Optuna trials")
    test_size: float = Field(0.1, ge=0.05, le=0.3, description="Test split ratio")
    val_size: float = Field(0.1, ge=0.05, le=0.3, description="Validation split ratio")


class AVMv2TrainResponse(BaseModel):
    """Training result summary."""

    model_version: str
    metrics: dict = Field(default_factory=dict, description="MAE, MAPE, RMSE, R²")
    district_metrics: dict = Field(
        default_factory=dict,
        description="Per-district breakdown of metrics",
    )
    training_samples: int
    validation_samples: int
    test_samples: int
    best_params: dict = Field(default_factory=dict, description="Optuna best hyperparameters per model")


class AVMv2ModelInfo(BaseModel):
    """Model registry entry information."""

    model_version: str
    created_at: str
    metrics: dict
    is_active: bool = Field(True)
    ab_test_traffic_pct: float = Field(0.0, ge=0, le=1)