def fit(self, y):
"""Fit label binarizer
Parameters
----------
y : Dask.Array of shape [n_samples,] or [n_samples, n_classes]
chunked by row.
Target values. The 2-d matrix should only contain 0 and 1,
represents multilabel classification.
Returns
-------
self : returns an instance of self.
"""
# Take the unique classes and broadcast them all around the cluster.
futures = self.client.sync(_extract_partitions, y)
unique = [
self.client.submit(LabelBinarizer._func_unique_classes, f)
for w, f in futures
]
classes = self.client.compute(unique, True)
classes = rmm_cupy_ary(cp.unique,
rmm_cupy_ary(cp.stack, classes, axis=0))
self._set_internal_model(LB(**self.kwargs).fit(classes))
return self
def _func_create_model(**kwargs):
return LB(**kwargs)