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config_schemas

Dataclasses to hold the configuration details for the runners.

Classes​

APIKeys​

class APIKeys(access_key_id: str, access_key: str):

API keys for BitfountSession.

Variables​

  • static access_key : str
  • static access_key_id : str

AccessManagerConfig​

class AccessManagerConfig(url: str = 'https://am.hub.bitfount.com'):

Configuration for the access manager.

Variables​

  • static url : str

AggregatorConfig​

class AggregatorConfig(    secure: bool, weights: Optional[dict[str, Union[int, float]]] = None,):

Configuration for the Aggregator.

Variables​

  • static secure : bool
  • static weights : Optional[dict[str, typing.Union[int, float]]]

AlgorithmConfig​

class AlgorithmConfig(name: str, arguments: Optional[Any] = None):

Configuration for the Algorithm.

Subclasses​

Variables​

  • static arguments : Optional[Any]
  • static name : str

BitfountModelReferenceConfig​

class BitfountModelReferenceConfig(    model_ref: Union[Path, str],    model_version: Optional[int] = None,    username: Optional[str] = None,    weights: Optional[str] = None,):

Configuration for BitfountModelReference.

Variables​

  • static model_version : Optional[int]
  • static username : Optional[str]
  • static weights : Optional[str]

CSVReportAlgorithmArgumentsConfig​

class CSVReportAlgorithmArgumentsConfig(    save_path: Optional[Path] = None,    original_cols: Optional[list[str]] = None,    filter: Optional[list[ColumnFilter]] = None,):

Configuration for CSVReportAlgorithm arguments.

Variables​

  • static original_cols : Optional[list[str]]

CSVReportAlgorithmConfig​

class CSVReportAlgorithmConfig(    name: str,    arguments: Optional[CSVReportAlgorithmArgumentsConfig] = CSVReportAlgorithmArgumentsConfig(save_path=None, original_cols=None, filter=None),):

Configuration for CSVReportAlgorithm.

Ancestors​

Variables​

  • static name : str

CSVReportGeneratorOphthalmologyAlgorithmArgumentsConfig​

class CSVReportGeneratorOphthalmologyAlgorithmArgumentsConfig(    save_path: Optional[Path] = None,    trial_name: Optional[str] = None,    original_cols: Optional[list[str]] = None,    rename_columns: Optional[dict[str, str]] = None,    filter: Optional[list[ColumnFilter]] = None,    match_patient_visit: Optional[MatchPatientVisit] = None,    matched_csv_path: Optional[Path] = None,    produce_matched_only: bool = True,    csv_extensions: Optional[list[str]] = None,    produce_trial_notes_csv: bool = False,    sorting_columns: Optional[dict[str, str]] = None,):

Configuration for CSVReportGeneratorOphthalmologyAlgorithm arguments.

Variables​

  • static csv_extensions : Optional[list[str]]
  • static original_cols : Optional[list[str]]
  • static produce_matched_only : bool
  • static produce_trial_notes_csv : bool
  • static rename_columns : Optional[dict[str, str]]
  • static sorting_columns : Optional[dict[str, str]]
  • static trial_name : Optional[str]

CSVReportGeneratorOphthalmologyAlgorithmConfig​

class CSVReportGeneratorOphthalmologyAlgorithmConfig(    name: str,    arguments: Optional[CSVReportGeneratorOphthalmologyAlgorithmArgumentsConfig] = CSVReportGeneratorOphthalmologyAlgorithmArgumentsConfig(save_path=None, trial_name=None, original_cols=None, rename_columns=None, filter=None, match_patient_visit=None, matched_csv_path=None, produce_matched_only=True, csv_extensions=None, produce_trial_notes_csv=False, sorting_columns=None),):

Configuration for CSVReportGeneratorOphthalmologyAlgorithm.

Ancestors​

Variables​

  • static name : str

ConversationArgumentsConfig​

class ConversationArgumentsConfig():

Configuration for the Conversation arguments.

ConversationConfig​

class ConversationConfig(    name: str,    arguments: Optional[ConversationArgumentsConfig] = ConversationArgumentsConfig(),):

Configuration for Conversation.

Ancestors​

Variables​

  • static name : str

DataSplitConfig​

class DataSplitConfig(data_splitter: str = 'percentage', args: _JSONDict = {}):

Configuration for the data splitter.

Variables​

  • static data_splitter : str

DataStructureAssignConfig​

class DataStructureAssignConfig(    target: Optional[Union[str, list[str]]] = None,    image_cols: Optional[list[str]] = None,    image_prefix: Optional[str] = None,):

Configuration for the datastructure assign argument.

Variables​

  • static image_cols : Optional[list[str]]
  • static image_prefix : Optional[str]
  • static target : Union[str, list[str], ForwardRef(None)]

DataStructureConfig​

class DataStructureConfig(    table_config: DataStructureTableConfig,    assign: DataStructureAssignConfig = DataStructureAssignConfig(target=None, image_cols=None, image_prefix=None),    select: DataStructureSelectConfig = DataStructureSelectConfig(include=[], include_prefix=None, exclude=[]),    transform: DataStructureTransformConfig = DataStructureTransformConfig(dataset=None, batch=None, image=None, auto_convert_grayscale_images=True),    data_split: Optional[DataSplitConfig] = None,):

Configuration for the modeller schema and dataset options.

Variables​

DataStructureSelectConfig​

class DataStructureSelectConfig(    include: list[str] = [],    include_prefix: Optional[str] = None,    exclude: list[str] = [],):

Configuration for the datastructure select argument.

Variables​

  • static exclude : list[str]
  • static include : list[str]
  • static include_prefix : Optional[str]

DataStructureTableConfig​

class DataStructureTableConfig(    table: Optional[Union[str, dict[str, str]]] = None,    query: Optional[Union[str, dict[str, str]]] = None,    schema_types_override: Optional[Union[SchemaOverrideMapping, Mapping[str, SchemaOverrideMapping]]] = None,):

Configuration for the datastructure table arguments.

Variables​

  • static query : Union[str, dict[str, str], ForwardRef(None)]
  • static table : Union[str, dict[str, str], ForwardRef(None)]

DataStructureTransformConfig​

class DataStructureTransformConfig(    dataset: Optional[list[dict[str, _JSONDict]]] = None,    batch: Optional[list[dict[str, _JSONDict]]] = None,    image: Optional[list[dict[str, _JSONDict]]] = None,    auto_convert_grayscale_images: bool = True,):

Configuration for the datastructure transform argument.

Variables​

  • static auto_convert_grayscale_images : bool
  • static batch : Optional[list[dict[str, dict[str, typing.Any]]]]
  • static dataset : Optional[list[dict[str, dict[str, typing.Any]]]]
  • static image : Optional[list[dict[str, dict[str, typing.Any]]]]

DatasourceConfig​

class DatasourceConfig(    datasource: str,    data_config: PodDataConfig = PodDataConfig(force_stypes=None, column_descriptions=None, table_descriptions=None, ignore_cols=None, modifiers=None, datasource_args={}, data_split=DataSplitConfig(data_splitter='percentage', args={}), auto_tidy=False),    name: Optional[str] = None,    datasource_details_config: Optional[PodDetailsConfig] = None,    schema: Optional[Path] = None,):

Datasource configuration for a multi-datasource Pod.

Variables​

  • static datasource : str
  • static name : Optional[str]

ETDRSAlgorithmArgumentsConfig​

class ETDRSAlgorithmArgumentsConfig(    laterality: str,    slo_photo_location_prefixes: Optional[SLOSegmentationLocationPrefix] = None,    slo_image_metadata_columns: Optional[SLOImageMetadataColumns] = None,    oct_image_metadata_columns: Optional[OCTImageMetadataColumns] = None,    threshold: float = 0.7,    calculate_on_oct: bool = False,    slo_mm_width: float = 8.8,    slo_mm_height: float = 8.8,):

Configuration for ETDRSAlgorithm arguments.

Variables​

  • static calculate_on_oct : bool
  • static laterality : str
  • static slo_mm_height : float
  • static slo_mm_width : float
  • static threshold : float

ETDRSAlgorithmConfig​

class ETDRSAlgorithmConfig(name: str, arguments: Optional[ETDRSAlgorithmArgumentsConfig]):

Configuration for ETDRSAlgorithm.

Ancestors​

Variables​

  • static name : str

FederatedAveragingProtocolArgumentsConfig​

class FederatedAveragingProtocolArgumentsConfig(    aggregator: Optional[AggregatorConfig] = None,    steps_between_parameter_updates: Optional[int] = None,    epochs_between_parameter_updates: Optional[int] = None,    auto_eval: bool = True,    secure_aggregation: bool = False,):

Configuration for the FedreatedAveraging Protocol arguments.

Variables​

  • static auto_eval : bool
  • static epochs_between_parameter_updates : Optional[int]
  • static secure_aggregation : bool
  • static steps_between_parameter_updates : Optional[int]

FederatedAveragingProtocolConfig​

class FederatedAveragingProtocolConfig(    name: str,    arguments: Optional[FederatedAveragingProtocolArgumentsConfig] = FederatedAveragingProtocolArgumentsConfig(aggregator=None, steps_between_parameter_updates=None, epochs_between_parameter_updates=None, auto_eval=True, secure_aggregation=False),):

Configuration for the FederatedAveraging Protocol.

Ancestors​

Variables​

  • static name : str

FederatedModelTrainingAlgorithmConfig​

class FederatedModelTrainingAlgorithmConfig(    name: str,    arguments: Optional[FederatedModelTrainingArgumentsConfig] = FederatedModelTrainingArgumentsConfig(modeller_checkpointing=True, checkpoint_filename=None),    model: Optional[ModelConfig] = None,    pretrained_file: Optional[Path] = None,):

Configuration for the FederatedModelTraining algorithm.

Ancestors​

Variables​

  • static name : str

FederatedModelTrainingArgumentsConfig​

class FederatedModelTrainingArgumentsConfig(    modeller_checkpointing: bool = True, checkpoint_filename: Optional[str] = None,):

Configuration for the FederatedModelTraining algorithm arguments.

Variables​

  • static checkpoint_filename : Optional[str]
  • static modeller_checkpointing : bool

FoveaCoordinatesAlgorithmArgumentsConfig​

class FoveaCoordinatesAlgorithmArgumentsConfig(    bscan_width_col: str = 'size_width',    location_prefixes: Optional[SLOSegmentationLocationPrefix] = None,):

Configuration for FoveaCoordinatesAlgorithm arguments.

Variables​

  • static bscan_width_col : str

FoveaCoordinatesAlgorithmConfig​

class FoveaCoordinatesAlgorithmConfig(    name: str,    arguments: Optional[FoveaCoordinatesAlgorithmArgumentsConfig] = FoveaCoordinatesAlgorithmArgumentsConfig(bscan_width_col='size_width', location_prefixes=None),):

Configuration for FoveaCoordinatesAlgorithm.

Ancestors​

Variables​

  • static name : str

GAScreeningProtocolAmethystArgumentsConfig​

class GAScreeningProtocolAmethystArgumentsConfig(    aggregator: Optional[AggregatorConfig] = None,    results_notification_email: Optional[bool] = False,    trial_name: Optional[str] = None,    rename_columns: Optional[dict[str, str]] = None,):

Configuration for GAScreeningProtocolAmethyst arguments.

Variables​

  • static rename_columns : Optional[dict[str, str]]
  • static results_notification_email : Optional[bool]
  • static trial_name : Optional[str]

GAScreeningProtocolAmethystConfig​

class GAScreeningProtocolAmethystConfig(    name: str,    arguments: Optional[GAScreeningProtocolAmethystArgumentsConfig] = GAScreeningProtocolAmethystArgumentsConfig(aggregator=None, results_notification_email=False, trial_name=None, rename_columns=None),):

Configuration for GAScreeningProtocolAmethyst.

Ancestors​

Variables​

  • static name : str

GAScreeningProtocolJadeArgumentsConfig​

class GAScreeningProtocolJadeArgumentsConfig(    aggregator: Optional[AggregatorConfig] = None,    results_notification_email: Optional[bool] = False,    trial_name: Optional[str] = None,    rename_columns: Optional[dict[str, str]] = None,):

Configuration for GAScreeningProtocolJade arguments.

Variables​

  • static rename_columns : Optional[dict[str, str]]
  • static results_notification_email : Optional[bool]
  • static trial_name : Optional[str]

GAScreeningProtocolJadeConfig​

class GAScreeningProtocolJadeConfig(    name: str,    arguments: Optional[GAScreeningProtocolJadeArgumentsConfig] = GAScreeningProtocolJadeArgumentsConfig(aggregator=None, results_notification_email=False, trial_name=None, rename_columns=None),):

Configuration for GAScreeningProtocolJade.

Ancestors​

Variables​

  • static name : str

GATrialCalculationAlgorithmJadeArgumentsConfig​

class GATrialCalculationAlgorithmJadeArgumentsConfig(    ga_area_include_segmentations: Optional[list[str]] = None,    ga_area_exclude_segmentations: Optional[list[str]] = None,):

Configuration for GATrialCalculationAlgorithmJade arguments.

Variables​

  • static ga_area_exclude_segmentations : Optional[list[str]]
  • static ga_area_include_segmentations : Optional[list[str]]

GATrialCalculationAlgorithmJadeConfig​

class GATrialCalculationAlgorithmJadeConfig(    name: str,    arguments: Optional[GATrialCalculationAlgorithmJadeArgumentsConfig] = GATrialCalculationAlgorithmJadeArgumentsConfig(ga_area_include_segmentations=None, ga_area_exclude_segmentations=None),):

Configuration for GATrialCalculationAlgorithmJade.

Ancestors​

Variables​

  • static name : str

GATrialPDFGeneratorAlgorithmAmethystArgumentsConfig​

class GATrialPDFGeneratorAlgorithmAmethystArgumentsConfig(    report_metadata: Optional[ReportMetadata] = None,    filename_prefix: Optional[str] = None,    save_path: Optional[Path] = None,    filter: Optional[list[ColumnFilter]] = None,    pdf_filename_columns: Optional[list[str]] = None,    trial_name: Optional[str] = None,):

Configuration for GATrialPDFGeneratorAlgorithmAmethyst arguments.

Variables​

  • static filename_prefix : Optional[str]
  • static pdf_filename_columns : Optional[list[str]]
  • static trial_name : Optional[str]

GATrialPDFGeneratorAlgorithmAmethystConfig​

class GATrialPDFGeneratorAlgorithmAmethystConfig(    name: str,    arguments: Optional[GATrialPDFGeneratorAlgorithmAmethystArgumentsConfig] = GATrialPDFGeneratorAlgorithmAmethystArgumentsConfig(report_metadata=None, filename_prefix=None, save_path=None, filter=None, pdf_filename_columns=None, trial_name=None),):

Configuration for GATrialPDFGeneratorAlgorithmAmethyst.

Ancestors​

Variables​

  • static name : str

GATrialPDFGeneratorAlgorithmJadeArgumentsConfig​

class GATrialPDFGeneratorAlgorithmJadeArgumentsConfig(    report_metadata: Optional[ReportMetadata] = None,    filename_prefix: Optional[str] = None,    save_path: Optional[Path] = None,    filter: Optional[list[ColumnFilter]] = None,    pdf_filename_columns: Optional[list[str]] = None,    trial_name: Optional[str] = None,):

Configuration for GATrialPDFGeneratorAlgorithmJade arguments.

Variables​

  • static filename_prefix : Optional[str]
  • static pdf_filename_columns : Optional[list[str]]
  • static trial_name : Optional[str]

GATrialPDFGeneratorAlgorithmJadeConfig​

class GATrialPDFGeneratorAlgorithmJadeConfig(    name: str,    arguments: Optional[GATrialPDFGeneratorAlgorithmJadeArgumentsConfig] = GATrialPDFGeneratorAlgorithmJadeArgumentsConfig(report_metadata=None, filename_prefix=None, save_path=None, filter=None, pdf_filename_columns=None, trial_name=None),):

Configuration for GATrialPDFGeneratorAlgorithmJade.

Ancestors​

Variables​

  • static name : str

GenericAlgorithmConfig​

class GenericAlgorithmConfig(name: str, arguments: _JSONDict = {}):

Configuration for unspecified algorithm plugins.

Raises

  • ValueError: if the algorithm name starts with bitfount.

Ancestors​

Variables​

  • static name : str

GenericProtocolConfig​

class GenericProtocolConfig(name: str, arguments: _JSONDict = {}):

Configuration for unspecified protocol plugins.

Raises

  • ValueError: if the protocol name starts with bitfount.

Ancestors​

Variables​

  • static name : str

HubConfig​

class HubConfig(url: str = 'https://hub.bitfount.com'):

Configuration for the hub.

Variables​

  • static url : str

HuggingFaceImageClassificationInferenceAlgorithmConfig​

class HuggingFaceImageClassificationInferenceAlgorithmConfig(    name: str,    arguments: Optional[HuggingFaceImageClassificationInferenceArgumentsConfig],):

Configuration for HuggingFaceImageClassificationInference.

Ancestors​

Variables​

  • static name : str

HuggingFaceImageClassificationInferenceArgumentsConfig​

class HuggingFaceImageClassificationInferenceArgumentsConfig(    model_id: str,    image_column_name: str,    apply_softmax_to_predictions: bool = True,    batch_size: int = 1,    seed: int = 42,    top_k: int = 5,):

Configuration for HuggingFaceImageClassificationInference arguments.

Variables​

  • static apply_softmax_to_predictions : bool
  • static batch_size : int
  • static image_column_name : str
  • static model_id : str
  • static seed : int
  • static top_k : int

HuggingFaceImageSegmentationInferenceAlgorithmConfig​

class HuggingFaceImageSegmentationInferenceAlgorithmConfig(    name: str, arguments: Optional[HuggingFaceImageSegmentationInferenceArgumentsConfig],):

Configuration for HuggingFaceImageSegmentationInference.

Ancestors​

Variables​

  • static name : str

HuggingFaceImageSegmentationInferenceArgumentsConfig​

class HuggingFaceImageSegmentationInferenceArgumentsConfig(    model_id: str,    image_column_name: str,    alpha: float = 0.3,    batch_size: int = 1,    dataframe_output: bool = False,    mask_threshold: float = 0.5,    overlap_mask_area_threshold: float = 0.5,    seed: int = 42,    save_path: Optional[str] = None,    subtask: Optional[str] = None,    threshold: float = 0.9,):

Configuration for HuggingFaceImageSegmentationInference arguments.

Variables​

  • static alpha : float
  • static batch_size : int
  • static dataframe_output : bool
  • static image_column_name : str
  • static mask_threshold : float
  • static model_id : str
  • static overlap_mask_area_threshold : float
  • static save_path : Optional[str]
  • static seed : int
  • static subtask : Optional[str]
  • static threshold : float

HuggingFacePerplexityEvaluationAlgorithmConfig​

class HuggingFacePerplexityEvaluationAlgorithmConfig(    name: str, arguments: Optional[HuggingFacePerplexityEvaluationArgumentsConfig],):

Configuration for the HuggingFacePerplexityEvaluation algorithm.

Ancestors​

Variables​

  • static name : str

HuggingFacePerplexityEvaluationArgumentsConfig​

class HuggingFacePerplexityEvaluationArgumentsConfig(    model_id: str, text_column_name: str, stride: int = 512, seed: int = 42,):

Configuration for the HuggingFacePerplexityEvaluation algorithm arguments.

Variables​

  • static model_id : str
  • static seed : int
  • static stride : int
  • static text_column_name : str

HuggingFaceTextClassificationInferenceAlgorithmConfig​

class HuggingFaceTextClassificationInferenceAlgorithmConfig(    name: str, arguments: Optional[HuggingFaceTextClassificationInferenceArgumentsConfig],):

Configuration for HuggingFaceTextClassificationInference.

Ancestors​

Variables​

  • static name : str

HuggingFaceTextClassificationInferenceArgumentsConfig​

class HuggingFaceTextClassificationInferenceArgumentsConfig(    model_id: str,    target_column_name: str,    batch_size: int = 1,    function_to_apply: Optional[str] = None,    seed: int = 42,    top_k: int = 5,):

Configuration for HuggingFaceTextClassificationInference arguments.

Variables​

  • static batch_size : int
  • static function_to_apply : Optional[str]
  • static model_id : str
  • static seed : int
  • static target_column_name : str
  • static top_k : int

HuggingFaceTextGenerationInferenceAlgorithmConfig​

class HuggingFaceTextGenerationInferenceAlgorithmConfig(    name: str, arguments: Optional[HuggingFaceTextGenerationInferenceArgumentsConfig],):

Configuration for the HuggingFaceTextGenerationInference algorithm.

Ancestors​

Variables​

  • static name : str

HuggingFaceTextGenerationInferenceArgumentsConfig​

class HuggingFaceTextGenerationInferenceArgumentsConfig(    model_id: str,    text_column_name: Optional[str] = None,    prompt_format: Optional[str] = None,    max_length: int = 50,    num_return_sequences: int = 1,    seed: int = 42,    min_new_tokens: int = 1,    repetition_penalty: float = 1.0,    num_beams: int = 1,    early_stopping: bool = True,    pad_token_id: Optional[int] = None,    eos_token_id: Optional[int] = None,    device: Optional[str] = None,    torch_dtype: str = 'float32',):

Configuration for the HuggingFaceTextGenerationInference algorithm arguments.

Variables​

  • static device : Optional[str]
  • static early_stopping : bool
  • static eos_token_id : Optional[int]
  • static max_length : int
  • static min_new_tokens : int
  • static model_id : str
  • static num_beams : int
  • static num_return_sequences : int
  • static pad_token_id : Optional[int]
  • static prompt_format : Optional[str]
  • static repetition_penalty : float
  • static seed : int
  • static text_column_name : Optional[str]
  • static torch_dtype : str

HuggingFaceZeroShotImageClassificationInferenceAlgorithmConfig​

class HuggingFaceZeroShotImageClassificationInferenceAlgorithmConfig(    name: str,    arguments: Optional[HuggingFaceZeroShotImageClassificationInferenceArgumentsConfig],):

Configuration for HuggingFaceZeroShotImageClassificationInference.

Ancestors​

Variables​

  • static name : str

HuggingFaceZeroShotImageClassificationInferenceArgumentsConfig​

class HuggingFaceZeroShotImageClassificationInferenceArgumentsConfig(    model_id: str,    image_column_name: str,    candidate_labels: list[str],    batch_size: int = 1,    class_outputs: Optional[list[str]] = None,    hypothesis_template: Optional[str] = None,    seed: int = 42,):

Configuration for HuggingFaceZeroShotImageClassificationInference arguments.

Variables​

  • static batch_size : int
  • static candidate_labels : list[str]
  • static class_outputs : Optional[list[str]]
  • static hypothesis_template : Optional[str]
  • static image_column_name : str
  • static model_id : str
  • static seed : int

InferenceAndCSVReportArgumentsConfig​

class InferenceAndCSVReportArgumentsConfig(aggregator: Optional[AggregatorConfig] = None):

Configuration for InferenceAndCSVReport arguments.

Variables​

InferenceAndCSVReportConfig​

class InferenceAndCSVReportConfig(    name: str,    arguments: Optional[InferenceAndCSVReportArgumentsConfig] = InferenceAndCSVReportArgumentsConfig(aggregator=None),):

Configuration for InferenceAndCSVReport.

Ancestors​

Variables​

  • static name : str

InferenceAndReturnCSVReportArgumentsConfig​

class InferenceAndReturnCSVReportArgumentsConfig(    aggregator: Optional[AggregatorConfig] = None,):

Configuration for InferenceAndReturnCSVReport arguments.

Variables​

InferenceAndReturnCSVReportConfig​

class InferenceAndReturnCSVReportConfig(    name: str,    arguments: Optional[InferenceAndReturnCSVReportArgumentsConfig] = InferenceAndReturnCSVReportArgumentsConfig(aggregator=None),):

Configuration for InferenceAndReturnCSVReport.

Ancestors​

Variables​

  • static name : str

InstrumentedInferenceAndCSVReportArgumentsConfig​

class InstrumentedInferenceAndCSVReportArgumentsConfig(    aggregator: Optional[AggregatorConfig] = None,):

Configuration for InstrumentedInferenceAndCSVReport arguments.

Variables​

InstrumentedInferenceAndCSVReportConfig​

class InstrumentedInferenceAndCSVReportConfig(    name: str,    arguments: Optional[InstrumentedInferenceAndCSVReportArgumentsConfig] = InstrumentedInferenceAndCSVReportArgumentsConfig(aggregator=None),):

Configuration for InstrumentedInferenceAndCSVReport.

Ancestors​

Variables​

  • static name : str

JWT​

class JWT(jwt: str, expires: datetime, get_token: Callable[[], tuple[str, datetime]]):

Externally managed JWT for BitfountSession.

Variables​

  • static jwt : str

ModelAlgorithmConfig​

class ModelAlgorithmConfig(    name: str,    arguments: Optional[Any] = None,    model: Optional[ModelConfig] = None,    pretrained_file: Optional[Path] = None,):

Configuration for the Model algorithms.

Ancestors​

Subclasses​

Variables​

ModelConfig​

class ModelConfig(    name: Optional[str] = None,    structure: Optional[ModelStructureConfig] = None,    bitfount_model: Optional[BitfountModelReferenceConfig] = None,    hyperparameters: _JSONDict = {},    logger_config: Optional[LoggerConfig] = None,    dp_config: Optional[DPModellerConfig] = None,):

Configuration for the model.

Variables​

  • static hyperparameters : dict[str, typing.Any]
  • static name : Optional[str]

ModelEvaluationAlgorithmConfig​

class ModelEvaluationAlgorithmConfig(    name: str,    arguments: Optional[ModelEvaluationArgumentsConfig],    model: Optional[ModelConfig] = None,    pretrained_file: Optional[Path] = None,):

Configuration for the ModelEvaluation algorithm.

Ancestors​

Variables​

  • static name : str

ModelEvaluationArgumentsConfig​

class ModelEvaluationArgumentsConfig():

Configuration for the ModelEvaluation algorithm arguments.

ModelInferenceAlgorithmConfig​

class ModelInferenceAlgorithmConfig(    name: str,    arguments: ModelInferenceArgumentsConfig = ModelInferenceArgumentsConfig(class_outputs=None),    model: Optional[ModelConfig] = None,    pretrained_file: Optional[Path] = None,):

Configuration for the ModelInference algorithm.

Ancestors​

Variables​

  • static name : str

ModelInferenceArgumentsConfig​

class ModelInferenceArgumentsConfig(class_outputs: Optional[list[str]] = None):

Configuration for the ModelInference algorithm arguments.

Variables​

  • static class_outputs : Optional[list[str]]

ModelStructureConfig​

class ModelStructureConfig(name: str, arguments: _JSONDict = {}):

Configuration for the ModelStructure.

Variables​

  • static name : str

ModelTrainingAndEvaluationAlgorithmConfig​

class ModelTrainingAndEvaluationAlgorithmConfig(    name: str,    arguments: Optional[ModelTrainingAndEvaluationArgumentsConfig],    model: Optional[ModelConfig] = None,    pretrained_file: Optional[Path] = None,):

Configuration for the ModelTrainingAndEvaluation algorithm.

Ancestors​

Variables​

  • static name : str

ModelTrainingAndEvaluationArgumentsConfig​

class ModelTrainingAndEvaluationArgumentsConfig():

Configuration for the ModelTrainingAndEvaluation algorithm arguments.

ModellerConfig​

class ModellerConfig(    pods: PodsConfig,    task: TaskConfig,    secrets: Optional[Union[APIKeys, JWT]] = None,    modeller: ModellerUserConfig = ModellerUserConfig(username='_default', identity_verification_method='oidc-device-code', private_key_file=None),    hub: HubConfig = HubConfig(url='https://hub.bitfount.com'),    message_service: MessageServiceConfig = MessageServiceConfig(url='messaging.bitfount.com', port=443, tls=True, use_local_storage=False),    version: Optional[str] = None,    project_id: Optional[str] = None,    run_on_new_data_only: bool = False,    batched_execution: Optional[bool] = None,):

Full configuration for the modeller.

Subclasses​

Variables​

  • static batched_execution : Optional[bool]
  • static project_id : Optional[str]
  • static run_on_new_data_only : bool
  • static secrets : Union[APIKeys, JWT, ForwardRef(None)]
  • static version : Optional[str]

ModellerUserConfig​

class ModellerUserConfig(    username: str = '_default',    identity_verification_method: str = 'oidc-device-code',    private_key_file: Optional[Path] = None,):

Configuration for the modeller.

Arguments

  • username: The username of the modeller. This can be picked up automatically from the session but can be overridden here.
  • identity_verification_method: The method to use for identity verification. Accepts one of the values in IDENTITY_VERIFICATION_METHODS, i.e. one of key-based, oidc-auth-code or oidc-device-code.
  • private_key_file: The path to the private key file for key-based identity verification.

Variables​

  • static identity_verification_method : str
  • static username : str

PathConfig​

class PathConfig(path: Path):

Configuration for the path.

Variables​

PodConfig​

class PodConfig(    name: str,    secrets: Optional[Union[APIKeys, JWT]] = None,    pod_details_config: Optional[PodDetailsConfig] = None,    datasource: Optional[str] = None,    data_config: Optional[PodDataConfig] = None,    schema: Optional[Path] = None,    datasources: Optional[list[DatasourceConfig]] = None,    access_manager: AccessManagerConfig = AccessManagerConfig(url='https://am.hub.bitfount.com'),    hub: HubConfig = HubConfig(url='https://hub.bitfount.com'),    message_service: MessageServiceConfig = MessageServiceConfig(url='messaging.bitfount.com', port=443, tls=True, use_local_storage=False),    differential_privacy: Optional[DPPodConfig] = None,    approved_pods: Optional[list[str]] = None,    username: str = '_default',    update_schema: bool = False,    pod_db: Union[bool, PodDbConfig] = False,    show_datapoints_with_results_in_db: bool = True,    version: Optional[str] = None,):

Full configuration for the pod.

Raises

  • ValueError: If a username is not provided alongside API keys.

Variables​

  • static approved_pods : Optional[list[str]]
  • static datasource : Optional[str]
  • static name : str
  • static secrets : Union[APIKeys, JWT, ForwardRef(None)]
  • static show_datapoints_with_results_in_db : bool
  • static update_schema : bool
  • static username : str
  • static version : Optional[str]
  • pod_id : str - The pod ID of the pod specified.

PodDataConfig​

class PodDataConfig(    force_stypes: Optional[Mapping[str, MutableMapping[Union[_ForceStypeValue, _SemanticTypeValue], list[str]]]] = None,    column_descriptions: Optional[Mapping[str, Mapping[str, str]]] = None,    table_descriptions: Optional[Mapping[str, str]] = None,    ignore_cols: Optional[Mapping[str, list[str]]] = None,    modifiers: Optional[dict[str, DataPathModifiers]] = None,    datasource_args: _JSONDict = {},    data_split: DataSplitConfig = DataSplitConfig(data_splitter='percentage', args={}),    auto_tidy: bool = False,):

Configuration for the Schema, BaseSource and Pod.

Arguments

  • force_stypes: The semantic types to force for the data. This is passed to the BitfountSchema. This should be a mapping from pod name to a mapping from the type to a list of column names (e.g. {"pod_name": {categorical: ["col1", "col2"]}}), or a mapping from each table name in a multitable datasource to a mapping from the type to a list of column names (e.g. {"table1": {categorical: ["col1", "col2"]}, "table2": {continuous: ["col3", "col4"]}}).
  • ignore_cols: The columns to ignore. This is passed to the data source.
  • modifiers: The modifiers to apply to the data. This is passed to the BaseSource.
  • datasource_args: Key-value pairs of arguments to pass to the data source constructor.
  • data_split: The data split configuration. This is passed to the data source.
  • auto_tidy: Whether to automatically tidy the data. This is used by the Pod and will result in removal of NaNs and normalisation of numeric values. Defaults to False.

Variables​

  • static auto_tidy : bool
  • static datasource_args : dict[str, typing.Any]

PodDbConfig​

class PodDbConfig(path: Path):

Configuration of the Pod DB.

Variables​

PodDetailsConfig​

class PodDetailsConfig(display_name: str, description: str = ''):

Configuration for the pod details.

Arguments

  • display_name: The display name of the pod.
  • description: The description of the pod.

Variables​

  • static description : str
  • static display_name : str

PodsConfig​

class PodsConfig(identifiers: list[str]):

Configuration for the pods to use for the modeller.

Variables​

  • static identifiers : list[str]

PrivateSqlQueryAlgorithmConfig​

class PrivateSqlQueryAlgorithmConfig(    name: str, arguments: PrivateSqlQueryArgumentsConfig,):

Configuration for the PrivateSqlQuery algorithm.

Ancestors​

Variables​

  • static name : str

PrivateSqlQueryArgumentsConfig​

class PrivateSqlQueryArgumentsConfig(    query: str,    epsilon: float,    delta: float,    column_ranges: dict[str, Optional[PrivateSqlQueryColumnArgumentsConfig]],    table: Optional[str] = None,    db_schema: Optional[str] = None,):

Configuration for the PrivateSqlQuery algorithm arguments.

Variables​

  • static db_schema : Optional[str]
  • static delta : float
  • static epsilon : float
  • static query : str
  • static table : Optional[str]

PrivateSqlQueryColumnArgumentsConfig​

class PrivateSqlQueryColumnArgumentsConfig(    lower: Optional[int] = None, upper: Optional[int] = None,):

Configuration for the PrivateSqlQuery algorithm column arguments.

Variables​

  • static lower : Optional[int]
  • static upper : Optional[int]

ProtocolConfig​

class ProtocolConfig(name: str, arguments: Optional[Any] = None):

Configuration for the Protocol.

Subclasses​

Variables​

  • static arguments : Optional[Any]
  • static name : str

ResultsOnlyProtocolArgumentsConfig​

class ResultsOnlyProtocolArgumentsConfig(    aggregator: Optional[AggregatorConfig] = None, secure_aggregation: bool = False,):

Configuration for the ResultsOnly Protocol arguments.

Variables​

  • static secure_aggregation : bool

ResultsOnlyProtocolConfig​

class ResultsOnlyProtocolConfig(    name: str,    arguments: Optional[ResultsOnlyProtocolArgumentsConfig] = ResultsOnlyProtocolArgumentsConfig(aggregator=None, secure_aggregation=False),):

Configuration for the ResultsOnly Protocol.

Ancestors​

Variables​

  • static name : str

RetinalDiseaseProtocolCobaltArgumentsConfig​

class RetinalDiseaseProtocolCobaltArgumentsConfig(    aggregator: Optional[AggregatorConfig] = None,):

Configuration for RetinalDiseaseProtocolCobalt arguments.

Variables​

RetinalDiseaseProtocolCobaltConfig​

class RetinalDiseaseProtocolCobaltConfig(    name: str,    arguments: Optional[RetinalDiseaseProtocolCobaltArgumentsConfig] = RetinalDiseaseProtocolCobaltArgumentsConfig(aggregator=None),):

Configuration for RetinalDiseaseProtocolCobalt.

Ancestors​

Variables​

  • static name : str

SqlQueryAlgorithmConfig​

class SqlQueryAlgorithmConfig(name: str, arguments: SqlQueryArgumentsConfig):

Configuration for the SqlQuery algorithm.

Ancestors​

Variables​

  • static name : str

SqlQueryArgumentsConfig​

class SqlQueryArgumentsConfig(query: str, table: Optional[str] = None):

Configuration for the SqlQuery algorithm arguments.

Variables​

  • static query : str
  • static table : Optional[str]

TIMMFineTuningAlgorithmConfig​

class TIMMFineTuningAlgorithmConfig(    name: str, arguments: Optional[TIMMFineTuningArgumentsConfig],):

Configuration for TIMMFineTuning algorithm.

Ancestors​

Variables​

  • static name : str

TIMMFineTuningArgumentsConfig​

class TIMMFineTuningArgumentsConfig(    model_id: str,    args: Optional[TIMMTrainingConfig] = None,    batch_transformations: Optional[Union[list[Union[str, _JSONDict]], dict[str, list[Union[str, _JSONDict]]]]] = None,    image_column_name: Optional[str] = None,    labels: Optional[list[str]] = None,    return_weights: bool = False,    save_path: Optional[Path] = None,    target_column_name: Optional[str] = None,):

Configuration for TIMMFineTuning algorithm arguments.

Variables​

  • static batch_transformations : Union[list[Union[str, dict[str, Any]]], dict[str, list[Union[str, dict[str, Any]]]], ForwardRef(None)]
  • static image_column_name : Optional[str]
  • static labels : Optional[list[str]]
  • static model_id : str
  • static return_weights : bool
  • static target_column_name : Optional[str]

TIMMInferenceAlgorithmConfig​

class TIMMInferenceAlgorithmConfig(    name: str, arguments: Optional[TIMMInferenceArgumentsConfig],):

Configuration for TIMMInference algorithm.

Ancestors​

Variables​

  • static name : str

TIMMInferenceArgumentsConfig​

class TIMMInferenceArgumentsConfig(    model_id: str,    image_column_name: str,    num_classes: Optional[int] = None,    checkpoint_path: Optional[Path] = None,    class_outputs: Optional[list[str]] = None,):

Configuration for TIMMInference algorithm arguments.

Variables​

  • static class_outputs : Optional[list[str]]
  • static image_column_name : str
  • static model_id : str
  • static num_classes : Optional[int]

TaskConfig​

class TaskConfig(    protocol: Union[ProtocolConfig._get_subclasses()],    algorithm: Union[Union[AlgorithmConfig._get_subclasses()], list[Union[AlgorithmConfig._get_subclasses()]]],    data_structure: Optional[DataStructureConfig] = None,    aggregator: Optional[AggregatorConfig] = None,    transformation_file: Optional[Path] = None,):

Configuration for the task.

Variables​

TemplatedModellerConfig​

class TemplatedModellerConfig(    pods: PodsConfig,    task: TaskConfig,    secrets: Optional[Union[APIKeys, JWT]] = None,    modeller: ModellerUserConfig = ModellerUserConfig(username='_default', identity_verification_method='oidc-device-code', private_key_file=None),    hub: HubConfig = HubConfig(url='https://hub.bitfount.com'),    message_service: MessageServiceConfig = MessageServiceConfig(url='messaging.bitfount.com', port=443, tls=True, use_local_storage=False),    version: Optional[str] = None,    project_id: Optional[str] = None,    run_on_new_data_only: bool = False,    batched_execution: Optional[bool] = None,    template: Any = None,):

Schema for task templates.

Ancestors​

Variables​

  • static template : Any

TrialInclusionCriteriaMatchAlgorithmAmethystArgumentsConfig​

class TrialInclusionCriteriaMatchAlgorithmAmethystArgumentsConfig(    cnv_threshold: float = 0.5,    largest_ga_lesion_lower_bound: float = 1.26,    total_ga_area_lower_bound: float = 2.5,    total_ga_area_upper_bound: float = 17.5,):

Configuration for TrialInclusionCriteriaMatchAlgorithmAmethyst arguments.

Variables​

  • static cnv_threshold : float
  • static largest_ga_lesion_lower_bound : float
  • static total_ga_area_lower_bound : float
  • static total_ga_area_upper_bound : float

TrialInclusionCriteriaMatchAlgorithmAmethystConfig​

class TrialInclusionCriteriaMatchAlgorithmAmethystConfig(    name: str,    arguments: Optional[TrialInclusionCriteriaMatchAlgorithmAmethystArgumentsConfig] = TrialInclusionCriteriaMatchAlgorithmAmethystArgumentsConfig(cnv_threshold=0.5, largest_ga_lesion_lower_bound=1.26, total_ga_area_lower_bound=2.5, total_ga_area_upper_bound=17.5),):

Configuration for TrialInclusionCriteriaMatchAlgorithmAmethyst.

Ancestors​

Variables​

  • static name : str

TrialInclusionCriteriaMatchAlgorithmJadeArgumentsConfig​

class TrialInclusionCriteriaMatchAlgorithmJadeArgumentsConfig():

Configuration for TrialInclusionCriteriaMatchAlgorithmJade arguments.

TrialInclusionCriteriaMatchAlgorithmJadeConfig​

class TrialInclusionCriteriaMatchAlgorithmJadeConfig(    name: str,    arguments: Optional[TrialInclusionCriteriaMatchAlgorithmJadeArgumentsConfig] = TrialInclusionCriteriaMatchAlgorithmJadeArgumentsConfig(),):

Configuration for TrialInclusionCriteriaMatchAlgorithmJade.

Ancestors​

Variables​

  • static name : str