set_unlabeled_data_preprocess

code_loader.leap_binder.set_unlabeled_data_preprocess

The leap_binder.set_unlabeled_data_preprocess binding function points to a preprocessing, similar to the Preprocessing Function, but points to unlabeled data lacking the ground-truth values.

This is often used to prioritize samples for labeling, which can be done by using the Fetch Similar analysis.

code_loader.leap_binder.set_unlabeled_data_preprocess(
    function=Callable[[], PreprocessResponse]
)

Args
function	(Callable) This parameter points to the Preprocess Function mentioned above.

Examples

Basic Usage

from code_loader import leap_binder
from code_loader.contract.datasetclasses import PreprocessingResponse

# Preprocessing Function
def unlabeled_preprocessing_func() -> PreprocessingResponse:
...
    return PreprocessingResponse(length=len(unlabeled_df), data=unlabeled_df)

leap_binder.set_unlabeled_data_preprocess(function=unlabeled_preprocessing_func)

Usage within the full script can be found at the Dataset Script.

Guides

Full examples can be found at the Dataset Integration section of the following guides:

• MNIST Guide

• IMDB Guide