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Version: 0.93

snorkelflow.sdk.FTDataset

class snorkelflow.sdk.FTDataset(df, dataset_uid, label_schema_uid, model_node_uid)

Bases: object

__init__​

__init__(df, dataset_uid, label_schema_uid, model_node_uid)

Methods

__init__(df, dataset_uid, label_schema_uid, ...)

append(ft_dataset)

Append the given FTDataset to the current FTDataset.

create_annotation_batches([assignees])

Create an annotation batch for the ft dataset.

export_data(format, filepath)

Export the data in the FTDataset to the specified format and write to the provided filepath.

filter([source_uids, splits, x_uids, ...])

Filter the dataset based on the given filters.

get_data()

Get the data associated with the fine tuning dataset.

get_x_uids()

Get the x_uids in the FTDataset.

set_as_dev_set()

Resample the x_uids within the FTDataset as the dev set for the fine tuning application.

append​

append(ft_dataset)

Append the given FTDataset to the current FTDataset.

Parameters​

NameTypeDefaultInfo
ft_datasetFTDatasetThe FTDataset to append to the current FTDataset.

Returns​

The appended FTDataset

Return type​

FTDataset

create_annotation_batches​

create_annotation_batches(assignees=None)

Create an annotation batch for the ft dataset. A batch will be created for each split the x_uids in the dataset are a part of.

Parameters​

NameTypeDefaultInfo
assigneesOptional[List[int]]NoneThe user uids of the assignees of the annotation batch.

Returns​

The created annotation batch

Return type​

List[Dict[str, Any]]

export_data​

export_data(format, filepath)

Export the data in the FTDataset to the specified format and write to the provided filepath.

Parameters​

NameTypeDefaultInfo
formatExportFormatThe format to export the data to.
filepathstrThe filepath to write the exported data to.

Return type​

None

filter​

filter(source_uids=None, splits=None, x_uids=None, feature_hashes=None, has_gt=None)

Filter the dataset based on the given filters.

Parameters​

NameTypeDefaultInfo
source_uidsOptional[List[int]]NoneThe source uids to filter by.
splitsOptional[List[str]]NoneThe splits to filter by.
x_uidsOptional[List[str]]NoneThe x uids to filter by.
feature_hashesOptional[List[str]]NoneThe feature hashes to filter by.
has_gtOptional[bool]NoneFilter by the existence / non-existence of ground truth.

Returns​

The filtered dataset

Return type​

FTDataset

get_data​

get_data()

Get the data associated with the fine tuning dataset.

Returns​

The data associated with the fine tuning dataset

Return type​

pd.DataFrame

get_x_uids​

get_x_uids()

Get the x_uids in the FTDataset.

Returns​

The x_uids in the FTDataset

Return type​

List[str]

set_as_dev_set​

set_as_dev_set()

Resample the x_uids within the FTDataset as the dev set for the fine tuning application. Note that a dev set must only contain x_uids that are in the train set.

Return type​

None