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databricks.labs.dqx.anomaly.model_config

Model configuration and record structure for row anomaly detection.

Single responsibility: model record structure (dataclasses) and config identity (compute_config_hash). Persistence lives in model_registry.

ModelIdentity Objects​

@dataclass
class ModelIdentity()

Core model identification (5 fields).

TrainingMetadata Objects​

@dataclass
class TrainingMetadata()

Training configuration and metrics (7 fields).

FeatureEngineering Objects​

@dataclass
class FeatureEngineering()

Feature engineering metadata (5 fields).

SegmentationConfig Objects​

@dataclass
class SegmentationConfig()

Segmentation configuration (5 fields).

AnomalyModelRecord Objects​

@dataclass
class AnomalyModelRecord()

Registry record for a trained anomaly model using composition.

Composed of 4 focused components, each under the 16-attribute limit:

  • identity: Core model identification (5 fields)
  • training: Training configuration and metrics (6 fields)
  • features: Feature engineering metadata (5 fields)
  • segmentation: Segmentation configuration (5 fields)

Stored as nested structs in Delta tables (no flattening needed).

compute_config_hash​

def compute_config_hash(columns: list[str],
                        segment_by: list[str] | None) -> str

Generate stable hash of model configuration.

Arguments:

  • columns - List of column names used for training
  • segment_by - List of columns used for segmentation, or None

Returns:

16-character hex string (first 16 chars of SHA256 hash)

Notes:

This hash uniquely identifies a model configuration based on:

  • Sorted list of columns (order-independent)
  • Sorted list of segment_by columns (order-independent) Used for collision detection when same model_name is reused with different configs.