base.py 文件源码

def _copy_partial_doc(cls):
    for base in cls.mro():
        if base.__module__.startswith('sklearn'):
            break
    lines = base.__doc__.split(os.linesep)
    header, rest = lines[0], lines[1:]

    insert = """

    This class wraps scikit-learn's {classname}. When a dask-array is passed
    to our ``fit`` method, the array is passed block-wise to the scikit-learn
    class' ``partial_fit`` method. This will allow you to fit the estimator
    on larger-than memory datasets sequentially (block-wise), but without an
    parallelism, or any ability to distribute across a cluster.""".format(
        classname=base.__name__)

    doc = '\n'.join([header + insert] + rest)

    cls.__doc__ = doc
    return cls