def _sparse_feature_from_feature_spec(spec, name, domains):
"""Returns a representation of a SparseFeature from a feature spec."""
if isinstance(spec.index_key, list):
raise ValueError(
'SparseFeature "{}" had index_key {}, but size and index_key '
'fields should be single values'.format(name, spec.index_key))
if isinstance(spec.size, list):
raise ValueError(
'SparseFeature "{}" had size {}, but size and index_key fields '
'should be single values'.format(name, spec.size))
# Create a index feature.
index_feature = schema_pb2.Feature(name=spec.index_key,
type=schema_pb2.INT,
int_domain=schema_pb2.IntDomain(
min=0, max=spec.size - 1))
# Create a value feature.
value_feature = schema_pb2.Feature(name=spec.value_key)
_set_type(name, value_feature, spec.dtype)
_set_domain(name, value_feature, domains.get(name))
# Create a sparse feature which refers to the index and value features.
index_feature_ref = schema_pb2.SparseFeature.IndexFeature(
name=spec.index_key)
value_feature_ref = schema_pb2.SparseFeature.ValueFeature(
name=spec.value_key)
sparse_feature = schema_pb2.SparseFeature(
name=name,
is_sorted=True if spec.already_sorted else None,
index_feature=[index_feature_ref],
value_feature=value_feature_ref)
return (index_feature, value_feature, sparse_feature)
def _sparse_feature_from_feature_spec(spec, name, domains):
"""Returns a representation of a SparseFeature from a feature spec."""
if isinstance(spec.index_key, list):
assert isinstance(spec.size,
(list, tuple, tf.TensorShape)), type(spec.size)
assert len(spec.index_key) == len(spec.size), (spec.index_key,
spec.size)
spec_size = [
s.value if isinstance(s, tf.compat.v1.Dimension) else s
for s in spec.size
]
int_domains = [
schema_pb2.IntDomain(min=0, max=size -
1) if size is not None else None
for size in spec_size
]
index_feature = [
schema_pb2.Feature(name=key,
type=schema_pb2.INT,
int_domain=int_domain)
for (key, int_domain) in zip(spec.index_key, int_domains)
]
index_feature_ref = [
schema_pb2.SparseFeature.IndexFeature(name=key)
for key in spec.index_key
]
else:
# Create a index feature.
index_feature = [
schema_pb2.Feature(name=spec.index_key,
type=schema_pb2.INT,
int_domain=schema_pb2.IntDomain(min=0,
max=spec.size -
1))
]
index_feature_ref = [
schema_pb2.SparseFeature.IndexFeature(name=spec.index_key)
]
# Create a value feature.
value_feature = schema_pb2.Feature(name=spec.value_key)
_set_type(name, value_feature, spec.dtype)
_set_domain(name, value_feature, domains.get(name))
# Create a sparse feature which refers to the index and value features.
value_feature_ref = schema_pb2.SparseFeature.ValueFeature(
name=spec.value_key)
sparse_feature = schema_pb2.SparseFeature(
name=name,
is_sorted=True if spec.already_sorted else None,
index_feature=index_feature_ref,
value_feature=value_feature_ref)
return (index_feature, value_feature, sparse_feature)