General Base Layers for Graph Convolutions with tensorflow.keras
Project description
Keras Graph Convolution Neural Networks
A set of layers for graph convolutions in TensorFlow Keras that use RaggedTensors.
- General
- Requirements
- Installation
- Documentation
- Implementation details
- Literature
- Datasets
- Examples
- Issues
- Citing
- References
General
The package in kgcnn contains several layer classes to build up graph convolution models. Some models are given as an example. A documentation is generated in docs. This repo is still under construction. Any comments, suggestions or help are very welcome!
Requirements
For kgcnn, usually the latest version of tensorflow is required, but is listed as extra requirements in the setup.py
for simplicity.
Additional python packages are placed in the setup.py
requirements and are installed automatically.
Packages which must be installed manually for full functionality:
- tensorflow>=2.4.1
- rdkit>=2020.03.4
- openbabel>=3.0.1
- pymatgen>=??.??.??
Installation
Clone repository https://github.com/aimat-lab/gcnn_keras and install with editable mode:
pip install -e ./gcnn_keras
or latest release via Python Package Index.
pip install kgcnn
Documentation
Auto-documentation is generated at https://kgcnn.readthedocs.io/en/latest/index.html .
Implementation details
Representation
The most frequent usage for graph convolutions is either node or graph classification. As for their size, either a single large graph, e.g. citation network or small (batched) graphs like molecules have to be considered. Graphs can be represented by an index list of connections plus feature information. Typical quantities in tensor format to describe a graph are listed below.
nodes
: Node-list of shape(batch, [N], F)
whereN
is the number of nodes andF
is the node feature dimension.edges
: Edge-list of shape(batch, [M], F)
whereM
is the number of edges andF
is the edge feature dimension.indices
: Connection-list of shape(batch, [M], 2)
whereM
is the number of edges. The indices denote a connection of incoming or receiving nodei
and outgoing or sending nodej
as(i, j)
.state
: Graph state information of shape(batch, F)
whereF
denotes the feature dimension.
A major issue for graphs is their flexible size and shape, when using mini-batches. Here, for a graph implementation in the spirit of keras, the batch dimension should be kept also in between layers. This is realized by using RaggedTensor
s.
Input
Here, for ragged tensors, the nodelist of shape (batch, None, F)
and edgelist of shape (batch, None, F')
have one ragged dimension (None, )
.
The graph structure is represented by an index-list of shape (batch, None, 2)
with index of incoming or receiving node i
and outgoing or sending node j
as (i, j)
.
The first index of incoming node i
is usually sorted for faster pooling operations, but can also be unsorted.
Furthermore, the graph is directed, so an additional edge with (j, i)
is required for undirected graphs.
A ragged constant can be directly obtained from a list of numpy arrays: tf.ragged.constant(indices, ragged_rank=1, inner_shape=(2, ))
which yields shape (batch, None, 2)
.
Model
Models can be set up in a functional way. Example message passing from fundamental operations:
import tensorflow.keras as ks
from kgcnn.layers.gather import GatherNodes
from kgcnn.layers.modules import DenseEmbedding, LazyConcatenate # ragged support
from kgcnn.layers.pooling import PoolingLocalMessages, PoolingNodes
n = ks.layers.Input(shape=(None, 3), name='node_input', dtype="float32", ragged=True)
ei = ks.layers.Input(shape=(None, 2), name='edge_index_input', dtype="int64", ragged=True)
n_in_out = GatherNodes()([n, ei])
node_messages = DenseEmbedding(10, activation='relu')(n_in_out)
node_updates = PoolingLocalMessages()([n, node_messages, ei])
n_node_updates = LazyConcatenate(axis=-1)([n, node_updates])
n_embedd = DenseEmbedding(1)(n_node_updates)
g_embedd = PoolingNodes()(n_embedd)
message_passing = ks.models.Model(inputs=[n, ei], outputs=g_embedd)
or via sub-classing of the message passing base layer. Where only message_function
and update_nodes
must be implemented:
from kgcnn.layers.conv.message import MessagePassingBase
from kgcnn.layers.modules import DenseEmbedding, LazyAdd
class MyMessageNN(MessagePassingBase):
def __init__(self, units, **kwargs):
super(MyMessageNN, self).__init__(**kwargs)
self.dense = DenseEmbedding(units)
self.add = LazyAdd(axis=-1)
def message_function(self, inputs, **kwargs):
n_in, n_out, edges = inputs
return self.dense(n_out)
def update_nodes(self, inputs, **kwargs):
nodes, nodes_update = inputs
return self.add([nodes, nodes_update])
Literature
A version of the following models are implemented in literature:
- GCN: Semi-Supervised Classification with Graph Convolutional Networks by Kipf et al. (2016)
- INorp: Interaction Networks for Learning about Objects,Relations and Physics by Battaglia et al. (2016)
- Megnet: Graph Networks as a Universal Machine Learning Framework for Molecules and Crystals by Chen et al. (2019)
- NMPN: Neural Message Passing for Quantum Chemistry by Gilmer et al. (2017)
- Schnet: SchNet – A deep learning architecture for molecules and materials by Schütt et al. (2017)
- Unet: Graph U-Nets by H. Gao and S. Ji (2019)
- GNNExplainer: GNNExplainer: Generating Explanations for Graph Neural Networks by Ying et al. (2019)
- GraphSAGE: Inductive Representation Learning on Large Graphs by Hamilton et al. (2017)
- GAT: Graph Attention Networks by Veličković et al. (2018)
- GATv2: How Attentive are Graph Attention Networks? by Brody et al. (2021)
- DimeNetPP: Fast and Uncertainty-Aware Directional Message Passing for Non-Equilibrium Molecules by Klicpera et al. (2020)
- AttentiveFP: Pushing the Boundaries of Molecular Representation for Drug Discovery with the Graph Attention Mechanism by Xiong et al. (2019)
- GIN: How Powerful are Graph Neural Networks? by Xu et al. (2019)
- PAiNN: Equivariant message passing for the prediction of tensorial properties and molecular spectra by Schütt et al. (2020)
- DMPNN: Analyzing Learned Molecular Representations for Property Prediction by Yang et al. (2019)
Datasets
The base class is MemoryGraphDataset
which holds a lists of graph properties in tensor-like numpy arrays.
Each property that starts with node_
, edge_
, graph_
holds a list with length of the dataset that fit into memory.
Furthermore, file information on disk can be provided in the constructor, that points to a data_directory
for the dataset.
├── data_directory
├── file_directory
│ ├── *.*
│ └── ...
├── file_name
└── dataset_name.pickle
Create and store a general dataset via:
from kgcnn.data.base import MemoryGraphDataset
import numpy as np
dataset = MemoryGraphDataset(data_directory="ExampleDir", dataset_name="Example")
dataset.assign_property("edge_indices", [np.array([[1, 0], [0, 1]])])
dataset.assign_property("edge_labels", [np.array([[0], [1]])])
The subclasses QMDataset
, MoleculeNetDataset
and GraphTUDataset
further have functions required for the specific dataset to convert and load files such as ".txt", ".sdf", ".xyz" etc. via prepare_data()
and read_in_memory()
.
from kgcnn.data.qm import QMDataset
dataset = QMDataset(data_directory="ExampleDir", dataset_name="methane",
file_name="geom.xyz", file_directory=None)
dataset.prepare_data() # Also make .sdf
dataset.read_in_memory()
In data.datasets there are graph learning datasets as subclasses which are being downloaded from e.g.
TUDatasets or MoleculeNet and directly processed and loaded. They are stored at ~/.kgcnn/datasets
.
from kgcnn.data.datasets.MUTAGDataset import MUTAGDataset
dataset = MUTAGDataset()
print(dataset.edge_indices[0])
Examples
A set of example training can be found in training.
Issues
Some known issues to be aware of, if using and making new models or layers with kgcnn
.
- RaggedTensor can not yet be used as a keras model output (https://github.com/tensorflow/tensorflow/issues/42320), which means only padded tensors can be used for batched node embedding tasks.
- Using
RaggedTensor
's for arbitrary ragged rank apart fromkgcnn.layers.modules
can cause significant performance decrease. This is due to shape check during add, multiply or concatenate (we think). We therefore use lazy add and concat in thekgcnn.layers.modules
layers or directly operate on the value tensor for possible rank. - With tensorflow version <=2.5 there is a problem with numpy version >=1.20 also affect
kgcnn
(https://github.com/tensorflow/tensorflow/issues/47691)
Citing
If you want to cite this repo, refer to our paper:
@article{REISER2021100095,
title = {Graph neural networks in TensorFlow-Keras with RaggedTensor representation (kgcnn)},
journal = {Software Impacts},
pages = {100095},
year = {2021},
issn = {2665-9638},
doi = {https://doi.org/10.1016/j.simpa.2021.100095},
url = {https://www.sciencedirect.com/science/article/pii/S266596382100035X},
author = {Patrick Reiser and Andre Eberhard and Pascal Friederich}
}
References
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