Source code for stellargraph.layer.rgcn

# -*- coding: utf-8 -*-
# Copyright 2019-2020 Data61, CSIRO
# Licensed under the Apache License, Version 2.0 (the "License");
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import tensorflow as tf
from tensorflow.keras import backend as K
from tensorflow.keras.layers import Layer, Lambda, Dropout, Input
from tensorflow.keras import activations, initializers, constraints, regularizers
from .misc import SqueezedSparseConversion, deprecated_model_function, GatherIndices
from ..mapper.full_batch_generators import RelationalFullBatchNodeGenerator

[docs]class RelationalGraphConvolution(Layer): """ Relational Graph Convolution (RGCN) Keras layer. Original paper: Modeling Relational Data with Graph Convolutional Networks. Thomas N. Kipf, Michael Schlichtkrull (2017). Notes: - The inputs are tensors with a batch dimension of 1: Keras requires this batch dimension, and for full-batch methods we only have a single "batch". - There are 1 + R inputs required (where R is the number of relationships): the node features, and a normalized adjacency matrix for each relationship .. seealso:: :class:`.RGCN` combines several of these layers. Args: units (int): dimensionality of output feature vectors num_relationships (int): the number of relationships in the graph num_bases (int): the number of basis matrices to use for parameterizing the weight matrices as described in the paper; defaults to 0. ``num_bases < 0`` triggers the default behaviour of ``num_bases = 0`` activation (str or func): nonlinear activation applied to layer's output to obtain output features use_bias (bool): toggles an optional bias final_layer (bool): Deprecated, use ``tf.gather`` or :class:`.GatherIndices` kernel_initializer (str or func): The initialiser to use for the self kernel and also relational kernels if ``num_bases=0``. kernel_regularizer (str or func): The regulariser to use for the self kernel and also relational kernels if ``num_bases=0``. kernel_constraint (str or func): The constraint to use for the self kernel and also relational kernels if ``num_bases=0``. basis_initializer (str or func): The initialiser to use for the basis matrices. basis_regularizer (str or func): The regulariser to use for the basis matrices. basis_constraint (str or func): The constraint to use for the basis matrices. coefficient_initializer (str or func): The initialiser to use for the coefficients. coefficient_regularizer (str or func): The regulariser to use for the coefficients. coefficient_constraint (str or func): The constraint to use for the coefficients. bias_initializer (str or func): The initialiser to use for the bias. bias_regularizer (str or func): The regulariser to use for the bias. bias_constraint (str or func): The constraint to use for the bias. input_dim (int, optional): the size of the input shape, if known. kwargs: any additional arguments to pass to :class:`tensorflow.keras.layers.Layer` """ def __init__( self, units, num_relationships, num_bases=0, activation=None, use_bias=True, final_layer=None, input_dim=None, kernel_initializer="glorot_uniform", kernel_regularizer=None, kernel_constraint=None, bias_initializer="zeros", bias_regularizer=None, bias_constraint=None, basis_initializer="glorot_uniform", basis_regularizer=None, basis_constraint=None, coefficient_initializer="glorot_uniform", coefficient_regularizer=None, coefficient_constraint=None, **kwargs ): if "input_shape" not in kwargs and input_dim is not None: kwargs["input_shape"] = (input_dim,) super().__init__(**kwargs) if not isinstance(num_bases, int): raise TypeError("num_bases should be an int") if not isinstance(units, int): raise TypeError("units should be an int") if units <= 0: raise ValueError("units should be positive") if not isinstance(num_relationships, int): raise TypeError("num_relationships should be an int") if num_relationships <= 0: raise ValueError("num_relationships should be positive") self.units = units self.num_relationships = num_relationships self.num_bases = num_bases self.activation = activations.get(activation) self.use_bias = use_bias self.kernel_initializer = initializers.get(kernel_initializer) self.kernel_regularizer = regularizers.get(kernel_regularizer) self.kernel_constraint = constraints.get(kernel_constraint) self.bias_initializer = initializers.get(bias_initializer) self.bias_regularizer = regularizers.get(bias_regularizer) self.bias_constraint = constraints.get(bias_constraint) self.basis_initializer = initializers.get(basis_initializer) self.basis_regularizer = regularizers.get(basis_regularizer) self.basis_constraint = constraints.get(basis_constraint) self.coefficient_initializer = initializers.get(coefficient_initializer) self.coefficient_regularizer = regularizers.get(coefficient_regularizer) self.coefficient_constraint = constraints.get(coefficient_constraint) if final_layer is not None: raise ValueError( "'final_layer' is not longer supported, use 'tf.gather' or 'GatherIndices' separately" ) super().__init__(**kwargs)
[docs] def get_config(self): """ Gets class configuration for Keras serialization. Used by Keras model serialization. Returns: A dictionary that contains the config of the layer """ config = { "units": self.units, "use_bias": self.use_bias, "activation": activations.serialize(self.activation), "kernel_initializer": initializers.serialize(self.kernel_initializer), "basis_initializer": initializers.serialize(self.basis_initializer), "coefficient_initializer": initializers.serialize( self.coefficient_initializer ), "bias_initializer": initializers.serialize(self.bias_initializer), "kernel_regularizer": regularizers.serialize(self.kernel_regularizer), "basis_regularizer": regularizers.serialize(self.basis_regularizer), "coefficient_regularizer": regularizers.serialize( self.coefficient_regularizer ), "bias_regularizer": regularizers.serialize(self.bias_regularizer), "kernel_constraint": constraints.serialize(self.kernel_constraint), "basis_constraint": constraints.serialize(self.basis_constraint), "coefficient_constraint": constraints.serialize( self.coefficient_constraint ), "bias_constraint": constraints.serialize(self.bias_constraint), "num_relationships": self.num_relationships, "num_bases": self.num_bases, } base_config = super().get_config() return {**base_config, **config}
[docs] def compute_output_shape(self, input_shapes): """ Computes the output shape of the layer. Args: input_shapes (tuple of int) Shape tuples can include None for free dimensions, instead of an integer. Returns: An input shape tuple. """ feature_shape, A_shape = input_shapes batch_dim = feature_shape[0] out_dim = feature_shape[1] return batch_dim, out_dim, self.units
[docs] def build(self, input_shapes): """ Builds the layer Args: input_shapes (list of int): shapes of the layer's inputs (node features, node_indices, and adjacency matrices) """ feat_shape = input_shapes[0] input_dim = int(feat_shape[-1]) if self.num_bases > 0: # creates a kernel for each edge type/relationship in the graph # each kernel is a linear combination of basis matrices # the basis matrices are shared for all edge types/relationships # each edge type has a different set of learnable coefficients # initialize the shared basis matrices self.bases = self.add_weight( shape=(input_dim, self.units, self.num_bases), initializer=self.basis_initializer, name="bases", regularizer=self.basis_regularizer, constraint=self.basis_constraint, ) # initialize the coefficients for each edge type/relationship self.coefficients = [ self.add_weight( shape=(self.num_bases,), initializer=self.coefficient_initializer, name="coeff", regularizer=self.coefficient_regularizer, constraint=self.coefficient_constraint, ) for _ in range(self.num_relationships) ] # To support eager TF the relational_kernels need to be explicitly calculated # each time the layer is called self.relational_kernels = None else: self.bases = None self.coefficients = None self.relational_kernels = [ self.add_weight( shape=(input_dim, self.units), name="relational_kernels", initializer=self.kernel_initializer, regularizer=self.kernel_regularizer, constraint=self.kernel_constraint, ) for _ in range(self.num_relationships) ] self.self_kernel = self.add_weight( shape=(input_dim, self.units), name="self_kernel", initializer=self.kernel_initializer, regularizer=self.kernel_regularizer, constraint=self.kernel_constraint, ) if self.use_bias: self.bias = self.add_weight( shape=(self.units,), initializer=self.bias_initializer, name="bias", regularizer=self.bias_regularizer, constraint=self.bias_constraint, ) else: self.bias = None self.built = True
[docs] def call(self, inputs): """ Applies the layer. Args: inputs (list): a list of 2 + R input tensors that includes node features (size 1 x N x F), and a graph adjacency matrix (size N x N) for each relationship. R is the number of relationships in the graph (edge type), N is the number of nodes in the graph, and F is the dimensionality of node features. Returns: Keras Tensor that represents the output of the layer. """ features, *As = inputs batch_dim, n_nodes, _ = K.int_shape(features) if batch_dim != 1: raise ValueError( "Currently full-batch methods only support a batch dimension of one" ) # Remove singleton batch dimension features = K.squeeze(features, 0) # Calculate the layer operation of RGCN output =, self.self_kernel) if self.relational_kernels is None: # explicitly calculate the relational kernels if basis matrices are used relational_kernels = [ tf.einsum("ijk,k->ij", self.bases, coeff) for coeff in self.coefficients ] else: relational_kernels = self.relational_kernels for i in range(self.num_relationships): h_graph =[i], features) output +=, relational_kernels[i]) # Add optional bias & apply activation if self.bias is not None: output += self.bias output = self.activation(output) # Add batch dimension back if we removed it if batch_dim == 1: output = K.expand_dims(output, 0) return output
[docs]class RGCN: """ A stack of Relational Graph Convolutional layers that implement a relational graph convolution neural network model as in The model minimally requires specification of the layer sizes as a list of int corresponding to the feature dimensions for each hidden layer, activation functions for each hidden layers, and a generator object. To use this class as a Keras model, the features and preprocessed adjacency matrix should be supplied using the :class:`.RelationalFullBatchNodeGenerator` class. The generator object should be instantiated as follows:: generator = RelationalFullBatchNodeGenerator(G) Note that currently the RGCN class is compatible with both sparse and dense adjacency matrices and the :class:`.RelationalFullBatchNodeGenerator` will default to sparse. Notes: - The inputs are tensors with a batch dimension of 1. These are provided by the \ :class:`.RelationalFullBatchNodeGenerator` object. - The nodes provided to the :meth:`.RelationalFullBatchNodeGenerator.flow` method are used by the final layer to select the predictions for those nodes in order. However, the intermediate layers before the final layer order the nodes in the same way as the adjacency matrix. Examples: Creating a RGCN node classification model from an existing :class:`.StellarGraph` object ``G``:: generator = RelationalFullBatchNodeGenerator(G) rgcn = RGCN( layer_sizes=[32, 4], activations=["elu","softmax"], bases=10, generator=generator, dropout=0.5 ) x_inp, predictions = rgcn.in_out_tensors() .. seealso:: Examples using RGCN: - `node classification <>`__ - `unsupervised representation learning with Deep Graph Infomax <>`__ Appropriate data generator: :class:`.RelationalFullBatchNodeGenerator`. Related model: :class:`.GCN` is a specialisation for a single edge type. :class:`.RelationalGraphConvolution` is the base layer out of which an RGCN model is built. Args: layer_sizes (list of int): Output sizes of RGCN layers in the stack. generator (RelationalFullBatchNodeGenerator): The generator instance. num_bases (int): Specifies number of basis matrices to use for the weight matrices of the RGCN layer as in the paper. Defaults to 0 which specifies that no basis decomposition is used. bias (bool): If True, a bias vector is learnt for each layer in the RGCN model. dropout (float): Dropout rate applied to input features of each RGCN layer. activations (list of str or func): Activations applied to each layer's output; defaults to ``['relu', ..., 'relu']``. kernel_initializer (str or func, optional): The initialiser to use for the weights of each layer. kernel_regularizer (str or func, optional): The regulariser to use for the weights of each layer. kernel_constraint (str or func, optional): The constraint to use for the weights of each layer. bias_initializer (str or func, optional): The initialiser to use for the bias of each layer. bias_regularizer (str or func, optionalx): The regulariser to use for the bias of each layer. bias_constraint (str or func, optional): The constraint to use for the bias of each layer. """ def __init__( self, layer_sizes, generator, bias=True, num_bases=0, dropout=0.0, activations=None, kernel_initializer="glorot_uniform", kernel_regularizer=None, kernel_constraint=None, bias_initializer="zeros", bias_regularizer=None, bias_constraint=None, ): if not isinstance(generator, RelationalFullBatchNodeGenerator): raise TypeError( "Generator should be a instance of RelationalFullBatchNodeGenerator" ) n_layers = len(layer_sizes) self.layer_sizes = layer_sizes self.activations = activations self.bias = bias self.num_bases = num_bases self.dropout = dropout # Copy required information from generator self.multiplicity = generator.multiplicity self.n_nodes = generator.features.shape[0] self.n_features = generator.features.shape[1] self.n_edge_types = len(generator.As) # Check if the generator is producing a sparse matrix self.use_sparse = generator.use_sparse # Activation function for each layer if activations is None: activations = ["relu"] * n_layers elif len(activations) != n_layers: raise ValueError( "Invalid number of activations; require one function per layer" ) self.activations = activations self.num_bases = num_bases # Initialize a stack of RGCN layers self._layers = [] for ii in range(n_layers): self._layers.append(Dropout(self.dropout)) self._layers.append( RelationalGraphConvolution( self.layer_sizes[ii], num_relationships=len(generator.As), num_bases=self.num_bases, activation=self.activations[ii], use_bias=self.bias, kernel_initializer=kernel_initializer, kernel_regularizer=kernel_regularizer, kernel_constraint=kernel_constraint, bias_initializer=bias_initializer, bias_regularizer=bias_regularizer, bias_constraint=bias_constraint, ) ) def __call__(self, x): """ Apply a stack of RGCN layers to the inputs. The input tensors are expected to be a list of the following: [Node features shape (1, N, F), Output indices (1, Z)] + [Adjacency indices for each relationship (1, E, 2) for _ in range(R)] [Adjacency values for each relationshiop (1, E) for _ in range(R)] where N is the number of nodes, F the number of input features, E is the number of edges, Z the number of output nodes, R is the number of relationships in the graph (edge types). Args: x (Tensor): input tensors Returns: Output tensor """ x_in, out_indices, *As = x # Currently we require the batch dimension to be one for full-batch methods batch_dim, n_nodes, _ = K.int_shape(x_in) if batch_dim != 1: raise ValueError( "Currently full-batch methods only support a batch dimension of one" ) # Convert input indices & values to sparse matrices if self.use_sparse: As_indices = As[: self.n_edge_types] As_values = As[self.n_edge_types :] Ainput = [ SqueezedSparseConversion( shape=(n_nodes, n_nodes), dtype=As_values[i].dtype )([As_indices[i], As_values[i]]) for i in range(self.n_edge_types) ] # Otherwise, create dense matrices from input tensor else: Ainput = [Lambda(lambda A: K.squeeze(A, 0))(A_) for A_ in As] h_layer = x_in for layer in self._layers: if isinstance(layer, RelationalGraphConvolution): # For an RGCN layer add the adjacency matrices h_layer = layer([h_layer] + Ainput) else: # For other (non-graph) layers only supply the input tensor h_layer = layer(h_layer) # only return data for the requested nodes h_layer = GatherIndices(batch_dims=1)([h_layer, out_indices]) return h_layer def _node_model(self): """ Builds a RGCN model for node prediction Returns: tuple: ``(x_inp, x_out)``, where ``x_inp`` is a list of Keras input tensors for the RGCN model (containing node features, node indices, and the indices and values for the sparse adjacency matrices for each relationship), and ``x_out`` is a Keras tensor for the RGCN model output. """ # Inputs for features & target indices x_t = Input(batch_shape=(1, self.n_nodes, self.n_features)) out_indices_t = Input(batch_shape=(1, None), dtype="int32") # Create inputs for sparse or dense matrices if self.use_sparse: # Placeholders for the sparse adjacency matrix A_indices_t = [ Input(batch_shape=(1, None, 2), dtype="int64") for i in range(self.n_edge_types) ] A_values_t = [ Input(batch_shape=(1, None)) for i in range(self.n_edge_types) ] A_placeholders = A_indices_t + A_values_t else: # Placeholders for the dense adjacency matrix A_placeholders = [ Input(batch_shape=(1, self.n_nodes, self.n_nodes)) for i in range(self.n_edge_types) ] x_inp = [x_t, out_indices_t] + A_placeholders x_out = self(x_inp) # Flatten output by removing singleton batch dimension if x_out.shape[0] == 1: self.x_out_flat = Lambda(lambda x: K.squeeze(x, 0))(x_out) else: self.x_out_flat = x_out return x_inp, x_out
[docs] def in_out_tensors(self, multiplicity=None): """ Builds a RGCN model for node prediction. Link/node pair prediction will added in the future. Returns: tuple: ``(x_inp, x_out)``, where ``x_inp`` is a list of Keras input tensors for the specified RGCN model and ``x_out`` contains model output tensor(s) of shape ``(batch_size, layer_sizes[-1])`` """ if multiplicity is None: multiplicity = self.multiplicity if multiplicity == 1: return self._node_model() else: raise NotImplementedError( "Currently only node prediction if supported for RGCN." )
build = deprecated_model_function(in_out_tensors, "build")