def _fit_compute_node(self, node, Xs, ys, cache, **fit_params):
# TODO: same as _compute_node TODO?
if ys:
output_data = node.fit_compute_func(
unlistify(Xs), unlistify(ys), **fit_params
)
else:
output_data = node.fit_compute_func(unlistify(Xs), **fit_params)
output_data = listify(output_data)
self._update_cache(cache, output_data, node)
def _compute_node(self, node, Xs, cache):
# TODO: Raise warning if computed output is already in cache.
# This happens when recomputing a step that had a subset of its outputs already passed in the inputs.
# TODO: Some regressors have extra options in their predict method, and they return a tuple of arrays.
# https://scikit-learn.org/stable/glossary.html#term-predict
output_data = node.compute_func(unlistify(Xs))
output_data = listify(output_data)
self._update_cache(cache, output_data, node)
def _fit_node(self, node, Xs, ys, **fit_params):
if ys:
node.fit_func(unlistify(Xs), unlistify(ys), **fit_params)
else:
node.fit_func(unlistify(Xs), **fit_params)
def test_unlistify(x, expected, raises):
with raises:
assert unlistify(x) == expected