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tfgnn.NodeSet

View source on GitHub

A composite tensor for node set features plus size information.

tfgnn.NodeSet(
    data: Data, spec: 'GraphPieceSpecBase'
)

The items of the node set are subset of graph nodes.

All nodes in a node set have the same features, identified by a string key. Each feature is stored as one tensor and has shape [*graph_shape, num_nodes, *feature_shape]. The num_nodes is the number of nodes in a graph (could be ragged). The feature_shape is the shape of the feature value for each node. NodeSet supports both fixed-size and variable-size features. The fixed-size features must have fully defined feature_shape. They are stored as tf.Tensor if num_nodes is fixed-size or graph_shape.rank = 0. Variable-size node features are always stored as tf.RaggedTensor.

Note that node set features are indexed without regard to graph components. The information which node belong to which graph component is contained in the .sizes tensor which defines the number of nodes in each graph component.

Args

data Nest of Field or subclasses of GraphPieceBase.
spec A subclass of GraphPieceSpecBase with a _data_spec that matches data.

Attributes

features A read-only mapping of feature name to feature specs.
indices_dtype The dtype for graph items indexing. One of tf.int32 or tf.int64.
num_components The number of graph components for each graph.
rank The rank of this Tensor. Guaranteed not to be None.
row_splits_dtype The dtype for ragged row partitions. One of tf.int32 or tf.int64.
shape A possibly-partial shape specification for this Tensor.

The returned tf.TensorShape is guaranteed to have a known rank and no unknown dimensions except possibly the outermost.
sizes The number of items in each graph component.
spec The public type specification of this tensor.
total_num_components The total number of graph components.
total_size The total number of items.

Methods

from_fields

View source

@classmethod
from_fields(
    *_,
    features: Optional[Fields] = None,
    sizes: Field,
    validate: Optional[bool] = None
) -> 'NodeSet'

Constructs a new instance from node set fields.

Example:

tfgnn.NodeSet.from_fields(
    sizes=tf.constant([3]),
    features={
        "tokenized_title": tf.ragged.constant(
            [["Anisotropic", "approximation"],
             ["Better", "bipartite", "bijection", "bounds"],
             ["Convolutional", "convergence", "criteria"]]),
        "embedding": tf.zeros([3, 128]),
        "year": tf.constant([2018, 2019, 2020]),
    })
Args
features A mapping from feature name to feature Tensors or RaggedTensors. All feature tensors must have shape [*graph_shape, num_nodes, *feature_shape], where num_nodes is the number of nodes in the node set (could be ragged) and feature_shape is a shape of the feature value for each node.
sizes A number of nodes in each graph component. Has shape [*graph_shape, num_components], where num_components is the number of graph components (could be ragged).
validate If true, use tf.assert ops to inspect the shapes of each field and check at runtime that they form a valid NodeSet. The default behavior is set by the disable_graph_tensor_validation_at_runtime() and enable_graph_tensor_validation_at_runtime().
Returns
A NodeSet composite tensor.

get_features_dict

View source

get_features_dict() -> Dict[FieldName, Field]

Returns features copy as a dictionary.

replace_features

View source

replace_features(
    features: Mapping[FieldName, Field]
) -> '_NodeOrEdgeSet'

Returns a new instance with a new set of features.

set_shape

View source

set_shape(
    new_shape: ShapeLike
) -> 'GraphPieceBase'

Deprecated. Use with_shape().

with_indices_dtype

View source

with_indices_dtype(
    dtype: tf.dtypes.DType
) -> 'GraphPieceBase'

Returns a copy of this piece with the given indices dtype.

with_row_splits_dtype

View source

with_row_splits_dtype(
    dtype: tf.dtypes.DType
) -> 'GraphPieceBase'

Returns a copy of this piece with the given row splits dtype.

with_shape

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with_shape(
    new_shape: ShapeLike
) -> 'GraphPieceBase'

Enforce the common prefix shape on all the contained features.

__getitem__

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__getitem__(
    feature_name: FieldName
) -> Field

Indexing operator [] to access feature values by their name.