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Extracts sample features, neighbor features, and neighbor weights.
nsl.lib.unpack_neighbor_features(
features, neighbor_config, keep_rank=False
)
For example, suppose features
contains a single sample feature named
'F0', the batch size is 2, and each sample has 3 neighbors. Then features
might look like the following:
features = {
'F0': tf.constant(11.0, shape=[2, 4]),
'NL_nbr_0_F0': tf.constant(22.0, shape=[2, 4]),
'NL_nbr_0_weight': tf.constant(0.25, shape=[2, 1]),
'NL_nbr_1_F0': tf.constant(33.0, shape=[2, 4]),
'NL_nbr_1_weight': tf.constant(0.75, shape=[2, 1]),
'NL_nbr_2_F0': tf.constant(44.0, shape=[2, 4]),
'NL_nbr_2_weight': tf.constant(1.0, shape=[2, 1]),
},
where NL_nbr_<i>_F0
represents the corresponding neighbor features for the
sample feature 'F0', and NL_nbr_<i>_weight
represents its neighbor weights.
The specific values for each key (tensors) in this dictionary are for
illustrative purposes only. The first dimension of all tensors is the batch
size.
Example invocation:
neighbor_config = nsl.configs.make_graph_reg_config(max_neighbors=3)
sample_features, nbr_features, nbr_weights = nsl.lib.unpack_neighbor_features(
features, neighbor_config)
After performing these calls, we would have sample_features
set to:
{ 'F0': tf.constant(11.0, shape=[2, 4]) },
neighbor_features
set to:
# The key in this dictionary will contain the original sample's feature name.
# The shape of the corresponding tensor will be 6x4, which is the result of
# doing an interleaved merge of three 2x4 tensors along axis 0.
{
'F0': tf.constant([[22, 22, 22, 22], [33, 33, 33, 33], [44, 44, 44, 44],
[22, 22, 22, 22], [33, 33, 33, 33], [44, 44, 44, 44]]),
},
and neighbor_weights
set to:
# The shape of this tensor is 6x1, which is the result of doing an
# interleaved merge of three 2x1 tensors along axis 0.
tf.constant([[0.25], [0.75], [1.0], [0.25], [0.75], [1.0]])
Args  

features

Dictionary of tensors mapping feature names (sample features,
neighbor features, and neighbor weights) to tensors. For each sample
feature, all its corresponding neighbor features and neighbor weights must
be included. All tensors should have a rank that is at least 2, where the
first dimension is the batch size. The shape of every sample feature
tensor should be identical to each of its corresponding neighbor feature
tensors. The shape of each neighbor weight tensor is expected to be [B,
1] , where B is the batch size. Neighbor weight tensors cannot be sparse
tensors.

neighbor_config

An instance of nsl.configs.GraphNeighborConfig .

keep_rank

Whether to preserve the neighborhood size dimension. Defaults to
False .

Returns  

sample_features

a dictionary mapping feature names to tensors. The shape of these tensors remains unchanged from the input. 
neighbor_features

a dictionary mapping feature names to tensors, where
these feature names are identical to the corresponding feature names in
sample_features . Further, for each feature in this dictionary, the
resulting tensor represents an interleaved concatenated version of all
corresponding neighbor feature tensors that exist. So, if the original
sample feature has a shape [B, D_1, D_2, ...., D_d] , then the shape of
the returned neighbor_features will be [(BxN), D_1, D_2, ..., D_d] if
keep_rank is True , and [B, N, D_1, D_2, ..., D_d] if keep_rank is
False . If num_neighbors is 0, then an empty dictionary is returned.

neighbor_weights

a tensor containing floating point weights. If keep_rank
is True, neighbor_weights will have shape [(BxN), 1] . Otherwise, it
will have shape [B, N, 1] This also represents an interleaved
concatenation of neighbor weight values across all neighbors. The rank of
this tensor remains unchanged. If num_neighbors is 0, then a value of
None is returned.

Raises  

KeyError

If the input does not contain all corresponding neighbor features for every sample feature. 
ValueError

If the tensors of samples and corresponding neighbors don't have the same shape. 