results

start_time: Any = None

end_time: Any = None

peak_score: float = 0.0

severity: float = 0.0

score_level: ScoreLevel = ScoreLevel.SAMPLE

affected_features: list[str] = field(default_factory=list)

affected_entities: list[Any] = field(default_factory=list)

observation_ids: list[Any] = field(default_factory=list)

metadata: dict[str, Any] = field(default_factory=dict)

to_dict() -> dict[str, Any]

Returns dict[str, Any]

events: list[AnomalyEvent] = field(default_factory=list)

frame: pd.DataFrame = field(default_factory=pd.DataFrame)

label_column: str = 'is_anomaly'

metadata: dict[str, Any] = field(default_factory=dict)

event_count: int

Returns int

to_dict() -> dict[str, Any]

Returns dict[str, Any]

frame: pd.DataFrame

score_column: str = 'score'

score_level: ScoreLevel = ScoreLevel.SAMPLE

is_calibrated: bool = False

metadata: dict[str, Any] = field(default_factory=dict)

values() -> pd.Series

Returns pd.Series

to_records() -> list[dict[str, Any]]

Returns list[dict[str, Any]]

SAMPLE = 'sample'

TIMESTEP = 'timestep'

WINDOW = 'window'

SEQUENCE = 'sequence'

FEATURE = 'feature'

ENTITY = 'entity'

NODE = 'node'

EDGE = 'edge'

GRAPH = 'graph'