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Helpers

siapy.datasets.helpers

generate_classification_target 

generate_classification_target(
    dataframe: DataFrame, column_names: str | list[str]
) -> ClassificationTarget
Source code in siapy/datasets/helpers.py 
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def generate_classification_target(
    dataframe: pd.DataFrame,
    column_names: str | list[str],
) -> "ClassificationTarget":
    from .schemas import (
        ClassificationTarget,  # Local import to avoid circular dependency
    )

    if isinstance(column_names, str):
        column_names = [column_names]
    # create one column labels from multiple columns
    label = dataframe[column_names].apply(tuple, axis=1)
    # Convert tuples to strings with '__' delimiter
    label = label.apply(lambda x: "__".join(x))
    # encode to numbers
    encoded_np, encoding_np = pd.factorize(label)
    encoded = pd.Series(encoded_np, name="encoded")
    encoding = pd.Series(encoding_np, name="encoding")
    return ClassificationTarget(label=label, value=encoded, encoding=encoding)

generate_regression_target 

generate_regression_target(
    dataframe: DataFrame, column_name: str
) -> RegressionTarget
Source code in siapy/datasets/helpers.py 
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def generate_regression_target(
    dataframe: pd.DataFrame,
    column_name: str,
) -> "RegressionTarget":
    from .schemas import (
        RegressionTarget,
    )  # Local import to avoid circular dependency

    return RegressionTarget(name=column_name, value=dataframe[column_name])

merge_signals_from_multiple_cameras 

merge_signals_from_multiple_cameras(
    data: TabularDatasetData,
) -> None
Source code in siapy/datasets/helpers.py 
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def merge_signals_from_multiple_cameras(data: "TabularDatasetData") -> None:
    # TODO: Implement the function to merge signals from multiple cameras
    pass