Source code for stellargraph.layer.gcn

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import warnings
from tensorflow.keras import backend as K
from tensorflow.keras import activations, initializers, constraints, regularizers
from tensorflow.keras.layers import Input, Layer, Lambda, Dropout, Reshape

from ..mapper import FullBatchGenerator
from .misc import SqueezedSparseConversion
from .preprocessing_layer import GraphPreProcessingLayer


[docs]class GraphConvolution(Layer): """ Graph Convolution (GCN) Keras layer. The implementation is based on the keras-gcn github repo https://github.com/tkipf/keras-gcn. Original paper: Semi-Supervised Classification with Graph Convolutional Networks. Thomas N. Kipf, Max Welling, International Conference on Learning Representations (ICLR), 2017 https://github.com/tkipf/gcn 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 three inputs required, the node features, the output indices (the nodes that are to be selected in the final layer) and the normalized graph Laplacian matrix - This class assumes that the normalized Laplacian matrix is passed as input to the Keras methods. - The output indices are used when ``final_layer=True`` and the returned outputs are the final-layer features for the nodes indexed by output indices. - If ``final_layer=False`` all the node features are output in the same ordering as given by the adjacency matrix. Args: units (int): dimensionality of output feature vectors 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): If False the layer returns output for all nodes, if True it returns the subset specified by the indices passed to it. kernel_initializer (str or func): The initialiser to use for the weights; defaults to 'glorot_uniform'. kernel_regularizer (str or func): The regulariser to use for the weights; defaults to None. kernel_constraint (str or func): The constraint to use for the weights; defaults to None. bias_initializer (str or func): The initialiser to use for the bias; defaults to 'zeros'. bias_regularizer (str or func): The regulariser to use for the bias; defaults to None. bias_constraint (str or func): The constraint to use for the bias; defaults to None. """ def __init__( self, units, activation=None, use_bias=True, final_layer=False, **kwargs ): if "input_shape" not in kwargs and "input_dim" in kwargs: kwargs["input_shape"] = (kwargs.get("input_dim"),) self.units = units self.activation = activations.get(activation) self.use_bias = use_bias self.final_layer = final_layer self._get_regularisers_from_keywords(kwargs) super().__init__(**kwargs) def _get_regularisers_from_keywords(self, kwargs): self.kernel_initializer = initializers.get( kwargs.pop("kernel_initializer", "glorot_uniform") ) self.kernel_regularizer = regularizers.get( kwargs.pop("kernel_regularizer", None) ) self.kernel_constraint = constraints.get(kwargs.pop("kernel_constraint", None)) self.bias_initializer = initializers.get( kwargs.pop("bias_initializer", "zeros") ) self.bias_regularizer = regularizers.get(kwargs.pop("bias_regularizer", None)) self.bias_constraint = constraints.get(kwargs.pop("bias_constraint", None))
[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, "final_layer": self.final_layer, "activation": activations.serialize(self.activation), "kernel_initializer": initializers.serialize(self.kernel_initializer), "kernel_regularizer": regularizers.serialize(self.kernel_regularizer), "kernel_constraint": constraints.serialize(self.kernel_constraint), "bias_initializer": initializers.serialize(self.bias_initializer), "bias_regularizer": regularizers.serialize(self.bias_regularizer), "bias_constraint": constraints.serialize(self.bias_constraint), } base_config = super().get_config() return {**base_config, **config}
[docs] def compute_output_shape(self, input_shapes): """ Computes the output shape of the layer. Assumes the following inputs: Args: input_shapes (tuple of ints) Shape tuples can include None for free dimensions, instead of an integer. Returns: An input shape tuple. """ feature_shape, out_shape, *As_shapes = input_shapes batch_dim = feature_shape[0] if self.final_layer: out_dim = out_shape[1] else: 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 and adjacency matrix) """ feat_shape = input_shapes[0] input_dim = int(feat_shape[-1]) self.kernel = self.add_weight( shape=(input_dim, self.units), initializer=self.kernel_initializer, name="kernel", 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 3 input tensors that includes node features (size 1 x N x F), output indices (size 1 x M) graph adjacency matrix (size N x N), where 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, out_indices, *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) out_indices = K.squeeze(out_indices, 0) # Calculate the layer operation of GCN A = As[0] h_graph = K.dot(A, features) output = K.dot(h_graph, self.kernel) # Add optional bias & apply activation if self.bias is not None: output += self.bias output = self.activation(output) # On the final layer we gather the nodes referenced by the indices if self.final_layer: output = K.gather(output, out_indices) # Add batch dimension back if we removed it # print("BATCH DIM:", batch_dim) if batch_dim == 1: output = K.expand_dims(output, 0) return output
[docs]class GCN: """ A stack of Graph Convolutional layers that implement a graph convolution network model as in https://arxiv.org/abs/1609.02907 The model minimally requires specification of the layer sizes as a list of ints 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 pre-processed adjacency matrix should be supplied using either the :class:`FullBatchNodeGenerator` class for node inference or the :class:`FullBatchLinkGenerator` class for link inference. To have the appropriate pre-processing the generator object should be instanciated with the `method='gcn'` argument. Note that currently the GCN class is compatible with both sparse and dense adjacency matrices and the :class:`FullBatchNodeGenerator` will default to sparse. For more details, please see the GCN demo notebook: demos/node-classification/gat/gcn-cora-node-classification-example.ipynb Example: Creating a GCN node classification model from an existing :class:`StellarGraph` object ``G``:: generator = FullBatchNodeGenerator(G, method="gcn") gcn = GCN( layer_sizes=[32, 4], activations=["elu","softmax"], generator=generator, dropout=0.5 ) x_inp, predictions = gcn.build() Notes: - The inputs are tensors with a batch dimension of 1. These are provided by the \ :class:`FullBatchNodeGenerator` object. - This assumes that the normalized Lapalacian matrix is provided as input to Keras methods. When using the :class:`FullBatchNodeGenerator` specify the ``method='gcn'`` argument to do this pre-processing. - The nodes provided to the :class:`FullBatchNodeGenerator.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. Args: layer_sizes (list of int): Output sizes of GCN layers in the stack. generator (FullBatchNodeGenerator): The generator instance. bias (bool): If True, a bias vector is learnt for each layer in the GCN model. dropout (float): Dropout rate applied to input features of each GCN layer. activations (list of str or func): Activations applied to each layer's output; defaults to ['relu', ..., 'relu']. kernel_regularizer (str or func): The regulariser to use for the weights of each layer; defaults to None. """ def __init__( self, layer_sizes, generator, bias=True, dropout=0.0, activations=None, **kwargs ): if not isinstance(generator, FullBatchGenerator): raise TypeError( "Generator should be a instance of FullBatchNodeGenerator or FullBatchLinkGenerator" ) n_layers = len(layer_sizes) self.layer_sizes = layer_sizes self.activations = activations self.bias = bias self.dropout = dropout # Copy required information from generator self.method = generator.method self.multiplicity = generator.multiplicity self.n_nodes = generator.features.shape[0] self.n_features = generator.features.shape[1] # Check if the generator is producing a sparse matrix self.use_sparse = generator.use_sparse if self.method == "none": self.graph_norm_layer = GraphPreProcessingLayer(num_of_nodes=self.n_nodes) # 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 # Optional regulariser, etc. for weights and biases self._get_regularisers_from_keywords(kwargs) # Initialize a stack of GCN layers self._layers = [] for ii in range(n_layers): self._layers.append(Dropout(self.dropout)) self._layers.append( GraphConvolution( self.layer_sizes[ii], activation=self.activations[ii], use_bias=self.bias, final_layer=ii == (n_layers - 1), **self._regularisers ) ) def _get_regularisers_from_keywords(self, kwargs): regularisers = {} for param_name in [ "kernel_initializer", "kernel_regularizer", "kernel_constraint", "bias_initializer", "bias_regularizer", "bias_constraint", ]: param_value = kwargs.pop(param_name, None) if param_value is not None: regularisers[param_name] = param_value self._regularisers = regularisers def __call__(self, x): """ Apply a stack of GCN layers to the inputs. The input tensors are expected to be a list of the following: [ Node features shape (1, N, F), Adjacency indices (1, E, 2), Adjacency values (1, E), Output indices (1, O) ] where N is the number of nodes, F the number of input features, E is the number of edges, O the number of output nodes. 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 a sparse matrix if self.use_sparse: A_indices, A_values = As Ainput = [ SqueezedSparseConversion( shape=(n_nodes, n_nodes), dtype=A_values.dtype )([A_indices, A_values]) ] # Otherwise, create dense matrix from input tensor else: Ainput = [Lambda(lambda A: K.squeeze(A, 0))(A) for A in As] # TODO: Support multiple matrices? if len(Ainput) != 1: raise NotImplementedError( "The GCN method currently only accepts a single matrix" ) h_layer = x_in if self.method == "none": # For GCN, if no preprocessing has been done, we apply the preprocessing layer to perform that. Ainput = [self.graph_norm_layer(Ainput[0])] for layer in self._layers: if isinstance(layer, GraphConvolution): # For a GCN layer add the matrix and output indices # Note that the output indices are only used if `final_layer=True` h_layer = layer([h_layer, out_indices] + Ainput) else: # For other (non-graph) layers only supply the input tensor h_layer = layer(h_layer) return h_layer
[docs] def build(self, multiplicity=None): """ Builds a GCN model for node or link prediction Returns: tuple: `(x_inp, x_out)`, where `x_inp` is a list of Keras/TensorFlow input tensors for the GCN model and `x_out` is a tensor of the GCN model output. """ # Inputs for features x_t = Input(batch_shape=(1, self.n_nodes, self.n_features)) # If not specified use multiplicity from instanciation if multiplicity is None: multiplicity = self.multiplicity # Indices to gather for model output if multiplicity == 1: out_indices_t = Input(batch_shape=(1, None), dtype="int32") else: out_indices_t = Input(batch_shape=(1, None, multiplicity), 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") A_values_t = Input(batch_shape=(1, None)) A_placeholders = [A_indices_t, A_values_t] else: # Placeholders for the dense adjacency matrix A_m = Input(batch_shape=(1, self.n_nodes, self.n_nodes)) A_placeholders = [A_m] # TODO: Support multiple matrices 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
def link_model(self): if self.multiplicity != 2: warnings.warn( "Link model requested but a generator not supporting links was supplied." ) return self.build(multiplicity=2) def node_model(self): if self.multiplicity != 1: warnings.warn( "Node model requested but a generator not supporting nodes was supplied." ) return self.build(multiplicity=1)