evidently.pipeline

Submodules

column_mapping module

class ColumnMapping(target: Optional[str] = 'target', prediction: Union[str, int, Sequence[str], Sequence[int], NoneType] = 'prediction', datetime: Optional[str] = 'datetime', id: Optional[str] = None, numerical_features: Optional[List[str]] = None, categorical_features: Optional[List[str]] = None, datetime_features: Optional[List[str]] = None, target_names: Optional[List[str]] = None, task: Optional[str] = None, pos_label: Union[str, int, NoneType] = 1)

Bases: object

Attributes:

categorical_features : Optional[List[str]] = None

datetime : Optional[str] = 'datetime'

datetime_features : Optional[List[str]] = None

id : Optional[str] = None

numerical_features : Optional[List[str]] = None

pos_label : Optional[Union[str, int]] = 1

prediction : Optional[Union[str, int, Sequence[str], Sequence[int]]] = 'prediction'

target : Optional[str] = 'target'

target_names : Optional[List[str]] = None

task : Optional[str] = None

Methods:

is_classification_task()

is_regression_task()

class TaskType()

Bases: object

Attributes:

CLASSIFICATION_TASK : str = 'classification'

REGRESSION_TASK : str = 'regression'

pipeline module

class Pipeline(stages: Sequence[PipelineStage], options: list)

Bases: object

Attributes:

analyzers_results : Dict[Type[Analyzer], object]

options_provider : OptionsProvider

stages : Sequence[PipelineStage]

Methods:

execute(reference_data: DataFrame, current_data: Optional[DataFrame] = None, column_mapping: Optional[ColumnMapping] = None)

get_analyzers()

stage module

class PipelineStage()

Bases: object

Attributes:

options_provider : OptionsProvider

Methods:

add_analyzer(analyzer_type: Type[Analyzer])

analyzers()

abstract calculate(reference_data: DataFrame, current_data: DataFrame, column_mapping: ColumnMapping, analyzers_results: Dict[Type[Analyzer], Any])