Metadata

Each Machine is entitled to have Metadata, which can be set at the Machine.metadata level inside the config, but will result in a standardized output of metadata under user_defined and build_metadata. Where user_defined can go arbitrarily deep, depending on the amount of metadata the user wishes to enter.

build_metadata is more predictable. During the course of building a Machine the system will insert certain metadata given about the build time, and model metrics (depending on configuration).

class gordo.machine.metadata.metadata.BuildMetadata(model: gordo.machine.metadata.metadata.ModelBuildMetadata = <factory>, dataset: gordo.machine.metadata.metadata.DatasetBuildMetadata = <factory>)

Bases: object

dataset: DatasetBuildMetadata

classmethod from_dict(kvs: dict | list | str | int | float | bool | None, *, infer_missing=False) → A

classmethod from_json(s: str | bytes | bytearray, *, parse_float=None, parse_int=None, parse_constant=None, infer_missing=False, **kw) → A

model: ModelBuildMetadata

classmethod schema(*, infer_missing: bool = False, only=None, exclude=(), many: bool = False, context=None, load_only=(), dump_only=(), partial: bool = False, unknown=None) → SchemaF[A]

to_dict(encode_json=False) → Dict[str, dict | list | str | int | float | bool | None]

to_json(*, skipkeys: bool = False, ensure_ascii: bool = True, check_circular: bool = True, allow_nan: bool = True, indent: int | str | None = None, separators: Tuple[str, str] | None = None, default: Callable | None = None, sort_keys: bool = False, **kw) → str

class gordo.machine.metadata.metadata.CrossValidationMetaData(scores: Dict[str, Any] = <factory>, cv_duration_sec: Optional[float] = None, splits: Dict[str, Any] = <factory>)

Bases: object

cv_duration_sec: float | None = None

classmethod from_dict(kvs: dict | list | str | int | float | bool | None, *, infer_missing=False) → A

classmethod from_json(s: str | bytes | bytearray, *, parse_float=None, parse_int=None, parse_constant=None, infer_missing=False, **kw) → A

classmethod schema(*, infer_missing: bool = False, only=None, exclude=(), many: bool = False, context=None, load_only=(), dump_only=(), partial: bool = False, unknown=None) → SchemaF[A]

scores: Dict[str, Any]

splits: Dict[str, Any]

to_dict(encode_json=False) → Dict[str, dict | list | str | int | float | bool | None]

to_json(*, skipkeys: bool = False, ensure_ascii: bool = True, check_circular: bool = True, allow_nan: bool = True, indent: int | str | None = None, separators: Tuple[str, str] | None = None, default: Callable | None = None, sort_keys: bool = False, **kw) → str

class gordo.machine.metadata.metadata.DatasetBuildMetadata(query_duration_sec: Optional[float] = None, dataset_meta: Dict[str, Any] = <factory>)

Bases: object

dataset_meta: Dict[str, Any]

classmethod from_dict(kvs: dict | list | str | int | float | bool | None, *, infer_missing=False) → A

classmethod from_json(s: str | bytes | bytearray, *, parse_float=None, parse_int=None, parse_constant=None, infer_missing=False, **kw) → A

query_duration_sec: float | None = None

classmethod schema(*, infer_missing: bool = False, only=None, exclude=(), many: bool = False, context=None, load_only=(), dump_only=(), partial: bool = False, unknown=None) → SchemaF[A]

to_dict(encode_json=False) → Dict[str, dict | list | str | int | float | bool | None]

to_json(*, skipkeys: bool = False, ensure_ascii: bool = True, check_circular: bool = True, allow_nan: bool = True, indent: int | str | None = None, separators: Tuple[str, str] | None = None, default: Callable | None = None, sort_keys: bool = False, **kw) → str

class gordo.machine.metadata.metadata.Metadata(user_defined: Dict[str, Any] = <factory>, build_metadata: gordo.machine.metadata.metadata.BuildMetadata = <factory>)

Bases: object

build_metadata: BuildMetadata

classmethod from_dict(kvs: dict | list | str | int | float | bool | None, *, infer_missing=False) → A

classmethod from_json(s: str | bytes | bytearray, *, parse_float=None, parse_int=None, parse_constant=None, infer_missing=False, **kw) → A

classmethod schema(*, infer_missing: bool = False, only=None, exclude=(), many: bool = False, context=None, load_only=(), dump_only=(), partial: bool = False, unknown=None) → SchemaF[A]

to_dict(encode_json=False) → Dict[str, dict | list | str | int | float | bool | None]

to_json(*, skipkeys: bool = False, ensure_ascii: bool = True, check_circular: bool = True, allow_nan: bool = True, indent: int | str | None = None, separators: Tuple[str, str] | None = None, default: Callable | None = None, sort_keys: bool = False, **kw) → str

user_defined: Dict[str, Any]

class gordo.machine.metadata.metadata.ModelBuildMetadata(model_offset: int = 0, model_creation_date: Optional[str] = None, model_builder_version: str = '5.1.4', cross_validation: gordo.machine.metadata.metadata.CrossValidationMetaData = <factory>, model_training_duration_sec: Optional[float] = None, model_meta: Dict[str, Any] = <factory>)

Bases: object

cross_validation: CrossValidationMetaData

classmethod from_dict(kvs: dict | list | str | int | float | bool | None, *, infer_missing=False) → A

classmethod from_json(s: str | bytes | bytearray, *, parse_float=None, parse_int=None, parse_constant=None, infer_missing=False, **kw) → A

model_builder_version: str = '5.1.4'

model_creation_date: str | None = None

model_meta: Dict[str, Any]

model_offset: int = 0

model_training_duration_sec: float | None = None

classmethod schema(*, infer_missing: bool = False, only=None, exclude=(), many: bool = False, context=None, load_only=(), dump_only=(), partial: bool = False, unknown=None) → SchemaF[A]

to_dict(encode_json=False) → Dict[str, dict | list | str | int | float | bool | None]

to_json(*, skipkeys: bool = False, ensure_ascii: bool = True, check_circular: bool = True, allow_nan: bool = True, indent: int | str | None = None, separators: Tuple[str, str] | None = None, default: Callable | None = None, sort_keys: bool = False, **kw) → str