In this notebook, we will explore the use of TensorGraph to create graph convolutional models with DeepChem. In particular, we will build a graph convolutional network on the Tox21 dataset.
Attempting to run xgboost will throw runtime errors
Warning: No pyGPGO.covfunc installed on your system
Attempting to run pyGPGO.covfunc will throw runtime errors
Warning: No pyGPGO.acquisition installed on your system
Attempting to run pyGPGO.acquisition will throw runtime errors
Warning: No pyGPGO.surrogates.GaussianProcess installed on your system
Attempting to run pyGPGO.surrogates.GaussianProcess will throw runtime errors
Warning: No pyGPGO.GPGO installed on your system
Attempting to run pyGPGO.GPGO will throw runtime errors
%% Cell type:markdown id: tags:
Now, let's use MoleculeNet to load the Tox21 dataset. We need to make sure to process the data in a way that graph convolutional networks can use For that, we make sure to set the featurizer option to 'GraphConv'. The MoleculeNet call will return a training set, an validation set, and a test set for us to use. The call also returns `transformers`, a list of data transformations that were applied to preprocess the dataset. (Most deep networks are quite finicky and require a set of data transformations to ensure that training proceeds stably.)
Let's now train a graph convolutional network on this dataset. DeepChem has the class `GraphConvTensorGraph` that wraps a standard graph convolutional architecture underneath the hood for user convenience. Let's instantiate an object of this class and train it on our dataset.
/home/rbharath/anaconda2/lib/python2.7/site-packages/tensorflow/python/ops/gradients_impl.py:91: UserWarning: Converting sparse IndexedSlices to a dense Tensor of unknown shape. This may consume a large amount of memory.
"Converting sparse IndexedSlices to a dense Tensor of unknown shape. "
Starting epoch 0
Starting epoch 1
Starting epoch 2
Starting epoch 3
Starting epoch 4
Starting epoch 5
Starting epoch 6
Starting epoch 7
Ending global_step 999: Average loss 471.091
Starting epoch 8
Starting epoch 9
Ending global_step 1290: Average loss 386.859
TIMING: model fitting took 135.723 s
%% Cell type:markdown id: tags:
Let's try to evaluate the accuracy of the model we've trained. For this, we need to define a metric, a measure of model accuracy. `dc.metrics` holds a collection of metrics already. For this dataset, it is standard to use the ROC-AUC score, the area under the receiver operating characteristic curve (which measures the tradeoff between precision and recall). Luckily, the ROC-AUC score is already available in DeepChem.
To measure the accuracy of the model under this metric, we can use the convenience function `model.evaluate()`.
What's going on under the hood? Could we build `GraphConvTensorGraph` ourselves? Of course! The first step is to create a `TensorGraph` object. This object will hold the "computational graph" that defines the computation that a graph convolutional network will perform.
Let's now define the inputs to our model. Conceptually, graph convolutions just requires a the structure of the molecule in question and a vector of features for every atom that describes the local chemical environment. However in practice, due to TensorFlow's limitations as a general programming environment, we have to have some auxiliary information as well preprocessed.
`atom_features` holds a feature vector of length 75 for each atom. The other feature inputs are required to support minibatching in TensorFlow. `degree_slice` is an indexing convenience that makes it easy to locate atoms from all molecules with a given degree. `membership` determines the membership of atoms in molecules (atom `i` belongs to molecule `membership[i]`). `deg_adjs` is a list that contains adjacency lists grouped by atom degree For more details, check out the [code](https://github.com/deepchem/deepchem/blob/master/deepchem/feat/mol_graphs.py).
To define feature inputs in `TensorGraph`, we use the `Feature` layer. Conceptually, a `TensorGraph` is a mathematical graph composed of layer objects. `Features` layers have to be the root nodes of the graph since they consitute inputs.
Let's now implement the body of the graph convolutional network. `TensorGraph` has a number of layers that encode various graph operations. Namely, the `GraphConv`, `GraphPool` and `GraphGather` layers. We will also apply standard neural network layers such as `Dense` and `BatchNorm`.
The layers we're adding effect a "feature transformation" that will create one vector for each molecule.
Let's now make predictions from the `TensorGraph` model. Tox21 is a multitask dataset. That is, there are 12 different datasets grouped together, which share many common molecules, but with different outputs for each. As a result, we have to add a separate output layer for each task. We will use a `for` loop over the `tox21_tasks` list to make this happen. We need to add labels for each
We also have to define a loss for the model which tells the network the objective to minimize during training.
We have to tell `TensorGraph` which layers are outputs with `TensorGraph.add_output(layer)`. Similarly, we tell the network its loss with `TensorGraph.set_loss(loss)`.
Now that we've successfully defined our graph convolutional model in `TensorGraph`, we need to train it. We can call `fit()`, but we need to make sure that each minibatch of data populates all four `Feature` objects that we've created. For this, we need to create a Python generator that given a batch of data generates a dictionary whose keys are the `Feature` layers and whose values are Numpy arrays we'd like to use for this step of training.
Now that we have trained our graph convolutional method, let's evaluate its performance. We again have to use our defined generator to evaluate model performance.
Success! The model we've constructed behaves nearly identically to `GraphConvTensorGraph`. If you're looking to build your own custom models, you can follow the example we've provided here to do so. We hope to see exciting constructions from your end soon!
