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[LifeSci] Move to Independent Repo (#1592) 

* Move LifeSci

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README.md
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...@@ -24,7 +24,7 @@ DGL is an easy-to-use, high performance and scalable Python package for deep lea ...@@ -24,7 +24,7 @@ DGL is an easy-to-use, high performance and scalable Python package for deep lea
## Using DGL ## Using DGL
**A data scientist** may want to apply a pre-trained model to your data right away. For this you can use DGL's [Application packages, formally *Model Zoo*](https://github.com/dmlc/dgl/tree/master/apps). Application packages are developed for domain applications, as is the case for [DGL-LifeScience](https://github.com/dmlc/dgl/tree/master/apps/life_sci). We will soon add model zoo for knowledge graph embedding learning and recommender systems. Here's how you will use a pretrained model: **A data scientist** may want to apply a pre-trained model to your data right away. For this you can use DGL's [Application packages, formally *Model Zoo*](https://github.com/dmlc/dgl/tree/master/apps). Application packages are developed for domain applications, as is the case for [DGL-LifeScience](https://github.com/awslabs/dgl-lifesci). We will soon add model zoo for knowledge graph embedding learning and recommender systems. Here's how you will use a pretrained model:
```python ```python
from dgllife.data import Tox21 from dgllife.data import Tox21
from dgllife.model import load_pretrained from dgllife.model import load_pretrained
...@@ -148,7 +148,7 @@ Overall there are 30+ models implemented by using DGL: ...@@ -148,7 +148,7 @@ Overall there are 30+ models implemented by using DGL:
- [TensorFlow](https://github.com/dmlc/dgl/tree/master/examples/tensorflow) - [TensorFlow](https://github.com/dmlc/dgl/tree/master/examples/tensorflow)
### DGL for domain applications ### DGL for domain applications
- [DGL-LifeSci](https://github.com/dmlc/dgl/tree/master/apps/life_sci), previously DGL-Chem - [DGL-LifeSci](https://github.com/awslabs/dgl-lifesci), previously DGL-Chem
- [DGL-KE](https://github.com/awslabs/dgl-ke) - [DGL-KE](https://github.com/awslabs/dgl-ke)
- DGL-RecSys(coming soon) - DGL-RecSys(coming soon)
......
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# Contributing to DGL-LifeSci
Contribution is always welcome. All contributions must go through pull requests
and code review.
Below is a list of community contributors for this project.
Contributors
------------
* [Chengqiang Lu](https://github.com/geekinglcq): Alchemy dataset; MPNN, MGCN and SchNet
* [Jiajing Hu](https://github.com/jjhu94): Weave
apps/life_sci/Jenkinsfile deleted 100644 → 0
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#!/usr/bin/env groovy
// Adapted from github.com/dmlc/dgl/Jenkinsfile
app_linux_libs = "_deps/dgl*.whl"
app = "dgllife"
def init_git() {
sh "rm -rf *"
checkout scm
sh "git submodule update --recursive --init"
}
// pack libraries for later use
def pack_dgl(name, libs) {
echo "Packing ${libs} into ${name}"
stash includes: libs, name: name
}
// unpack libraries saved before
def unpack_dgl(name, libs) {
unstash name
echo "Unpacked ${libs} from ${name}"
}
def build_linux(dev) {
init_git()
sh "bash tests/scripts/build.sh ${dev}"
pack_dgl("${app}-${dev}-linux", app_linux_libs)
}
def unit_test_linux(backend, dev) {
init_git()
unpack_dgl("${app}-${dev}-linux", app_linux_libs)
timeout(time: 10, unit: 'MINUTES') {
sh "bash tests/scripts/task_unit_test.sh ${backend} ${dev}"
}
}
pipeline {
agent any
stages {
stage("Lint Check") {
agent {
docker {
label "linux-cpu-node"
image "dgllib/dgl-ci-lint"
}
}
steps {
init_git()
sh "bash tests/scripts/task_lint.sh"
}
post {
always {
cleanWs disableDeferredWipeout: true, deleteDirs: true
}
}
}
stage("Build") {
parallel {
stage("CPU Build") {
agent {
docker {
label "linux-cpu-node"
image "dgllib/${app}-ci-cpu"
}
}
steps {
build_linux("cpu")
}
post {
always {
cleanWs disableDeferredWipeout: true, deleteDirs: true
}
}
}
stage("GPU Build") {
agent {
docker {
label "linux-cpu-node"
image "dgllib/${app}-ci-gpu"
//args "-u root"
}
}
steps {
build_linux("gpu")
}
post {
always {
cleanWs disableDeferredWipeout: true, deleteDirs: true
}
}
}
}
}
stage("Test") {
parallel {
stage("Torch CPU") {
agent {
docker {
label "linux-cpu-node"
image "dgllib/${app}-ci-cpu"
}
}
stages {
stage("Unit test") {
steps {
unit_test_linux("pytorch", "cpu")
}
}
}
post {
always {
cleanWs disableDeferredWipeout: true, deleteDirs: true
}
}
}
stage("Torch GPU") {
agent {
docker {
label "linux-gpu-node"
image "dgllib/${app}-ci-gpu"
args "--runtime nvidia"
}
}
stages {
stage("Unit test") {
steps {
sh "nvidia-smi"
unit_test_linux("pytorch", "gpu")
}
}
}
post {
always {
cleanWs disableDeferredWipeout: true, deleteDirs: true
}
}
}
}
}
}
}
\ No newline at end of file
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apps/life_sci/README.md
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# DGL-LifeSci # DGL-LifeSci
[Documentation](https://lifesci.dgl.ai/index.html) | [Discussion Forum](https://discuss.dgl.ai) DGL-LifeSci is moved [here](https://github.com/awslabs/dgl-lifesci).
## Introduction
Deep learning on graphs has been an arising trend in the past few years. There are a lot of graphs in
life science such as molecular graphs and biological networks, making it an import area for applying
deep learning on graphs. DGL-LifeSci is a DGL-based package for various applications in life science
with graph neural networks.
We provide various functionalities, including but not limited to methods for graph construction,
featurization, and evaluation, model architectures, training scripts and pre-trained models.
For a list of community contributors, see [here](CONTRIBUTORS.md).
**For a full list of work implemented in DGL-LifeSci, see [here](examples/README.md).**
## Installation
### Requirements
DGL-LifeSci should work on
* all Linux distributions no earlier than Ubuntu 16.04
* macOS X
* Windows 10
DGL-LifeSci requires python 3.6+, DGL 0.4.3+ and PyTorch 1.2.0+.
Additionally, we require `RDKit 2018.09.3` for cheminformatics. We recommend installing it with
```
conda install -c conda-forge rdkit==2018.09.3
```
For other installation recipes for RDKit, see the [official documentation](https://www.rdkit.org/docs/Install.html).
### Pip installation for DGL-LifeSci
```
pip install dgllife
```
### Conda installation for DGL-LifeSci
```
conda install -c dglteam dgllife
```
### Installation from source
If you want to try experimental features, you can install from source as follows:
```
git clone https://github.com/dmlc/dgl.git
cd apps/life_sci/python
python setup.py install
```
### Verifying successful installation
Once you have installed the package, you can verify the success of installation with
```python
import dgllife
print(dgllife.__version__)
# 0.2.2
```
If you are new to DGL, the first time you import dgl a message will pop up as below:
```
DGL does not detect a valid backend option. Which backend would you like to work with?
Backend choice (pytorch, mxnet or tensorflow):
```
and you need to enter `pytorch`.
## Example Usage
To apply graph neural networks to molecules with DGL, we need to first construct `DGLGraph` --
the graph data structure in DGL and prepare initial node/edge features. Below gives an example of
constructing a bi-directed graph from a molecule and featurizing it with atom and bond features such
as atom type and bond type.
```python
from dgllife.utils import smiles_to_bigraph, CanonicalAtomFeaturizer, CanonicalBondFeaturizer
# Node featurizer
node_featurizer = CanonicalAtomFeaturizer(atom_data_field='h')
# Edge featurizer
edge_featurizer = CanonicalBondFeaturizer(bond_data_field='h')
# SMILES (a string representation for molecule) for Penicillin
smiles = 'CC1(C(N2C(S1)C(C2=O)NC(=O)CC3=CC=CC=C3)C(=O)O)C'
g = smiles_to_bigraph(smiles=smiles,
node_featurizer=node_featurizer,
edge_featurizer=edge_featurizer)
print(g)
"""
DGLGraph(num_nodes=23, num_edges=50,
ndata_schemes={'h': Scheme(shape=(74,), dtype=torch.float32)}
edata_schemes={'h': Scheme(shape=(12,), dtype=torch.float32)})
"""
```
We implement various models that users can import directly. Below gives an example of defining a GCN-based model
for molecular property prediction.
```python
from dgllife.model import GCNPredictor
model = GCNPredictor(in_feats=1)
```
For a full example of applying `GCNPredictor`, run the following command
```bash
python examples/property_prediction/classification.py -m GCN -d Tox21
```
For more examples on molecular property prediction, generative models, protein-ligand binding affinity
prediction and reaction prediction, see `examples`.
We also provide pre-trained models for most examples, which can be used off-shelf without training from scratch.
Below gives an example of loading a pre-trained model for `GCNPredictor` on a molecular property prediction dataset.
```python
from dgllife.data import Tox21
from dgllife.model import load_pretrained
from dgllife.utils import smiles_to_bigraph, CanonicalAtomFeaturizer
dataset = Tox21(smiles_to_bigraph, CanonicalAtomFeaturizer())
model = load_pretrained('GCN_Tox21') # Pretrained model loaded
model.eval()
smiles, g, label, mask = dataset[0]
feats = g.ndata.pop('h')
label_pred = model(g, feats)
print(smiles) # CCOc1ccc2nc(S(N)(=O)=O)sc2c1
print(label_pred[:, mask != 0]) # Mask non-existing labels
# tensor([[ 1.4190, -0.1820, 1.2974, 1.4416, 0.6914,
# 2.0957, 0.5919, 0.7715, 1.7273, 0.2070]])
```
Similarly, we can load a pre-trained model for generating molecules. If possible, we recommend running
the code block below with Jupyter notebook.
```python
from dgllife.model import load_pretrained
model = load_pretrained('DGMG_ZINC_canonical')
model.eval()
smiles = []
for i in range(4):
smiles.append(model(rdkit_mol=True))
print(smiles)
# ['CC1CCC2C(CCC3C2C(NC2=CC(Cl)=CC=C2N)S3(=O)=O)O1',
# 'O=C1SC2N=CN=C(NC(SC3=CC=CC=N3)C1=CC=CO)C=2C1=CCCC1',
# 'CC1C=CC(=CC=1)C(=O)NN=C(C)C1=CC=CC2=CC=CC=C21',
# 'CCN(CC1=CC=CC=C1F)CC1CCCN(C)C1']
```
If you are running the code block above in Jupyter notebook, you can also visualize the molecules generated with
```python
from IPython.display import SVG
from rdkit import Chem
from rdkit.Chem import Draw
mols = [Chem.MolFromSmiles(s) for s in smiles]
SVG(Draw.MolsToGridImage(mols, molsPerRow=4, subImgSize=(180, 150), useSVG=True))
```
![](https://data.dgl.ai/dgllife/dgmg/dgmg_model_zoo_example2.png)
## Speed Reference
Below we provide some reference numbers to show how DGL improves the speed of training models per epoch in seconds.
| Model | Original Implementation | DGL Implementation | Improvement |
| ---------------------------------- | ----------------------- | -------------------------- | ---------------------------- |
| GCN on Tox21 | 5.5 (DeepChem) | 1.0 | 5.5x |
| AttentiveFP on Aromaticity | 6.0 | 1.2 | 5x |
| JTNN on ZINC | 1826 | 743 | 2.5x |
| WLN for reaction center prediction | 11657 | 858 (1 GPU) / 134 (8 GPUs) | 13.6x (1GPU) / 87.0x (8GPUs) |
| WLN for candidate ranking | 40122 | 22268 | 1.8x |
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# Conda Recipe
Build the package with `conda build .`
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$PYTHON setup.py install --single-version-externally-managed --record=record.txt # Python command to install the script.
\ No newline at end of file
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python:
- 3.6
- 3.7
\ No newline at end of file
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package:
name: dgllife{{ environ.get('APP_PACKAGE_SUFFIX', '') }}
version: "0.2.2"
source:
url: https://files.pythonhosted.org/packages/19/e7/7d9890c7ddb303613f8a9c0bafafa40239e65f9c0698e699f19f577412fc/dgllife-0.2.2.tar.gz
sha256: 812a93d54cd5c049a7368aae01d53b5a4a12ca2d0e4e57d74fa4a844a1c01d92
requirements:
build:
- python {{ python }}
- setuptools
- cmake
- git
- cython
run:
- python
- requests
- scikit-learn
- pandas
- tqdm
- numpy
- scipy
- networkx
about:
license: Apache
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From dgllib/dgl-sagemaker-cpu:dgl_0.4_pytorch_1.2.0_rdkit
RUN pip uninstall -y scikit-learn
RUN pip install scikit-learn==0.22.2.post1
RUN pip uninstall -y pandas
RUN pip install pandas==0.24.2
RUN pip uninstall -y dgl
RUN pip install --pre dgl
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From dgllib/dgl-sagemaker-gpu:dgl_0.4_pytorch_1.2.0_rdkit
RUN pip uninstall -y scikit-learn
RUN pip install scikit-learn==0.22.2.post1
RUN pip uninstall -y pandas
RUN pip install pandas==0.24.2
RUN pip uninstall -y dgl-cu100
RUN pip install --pre dgl-cu101
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# Build Docker Image for CI
Docker images are used by the CI and release script. Make sure to install necessary requirements in it.
## To build
```bash
docker build -t dgllib/dgllife-ci-cpu:latest -f Dockerfile.ci_cpu .
```
```bash
docker build -t dgllib/dgllife-ci-gpu:latest -f Dockerfile.ci_gpu .
```
## To push
```bash
docker push dgllib/dgllife-ci-cpu:latest
```
```bash
docker push dgllib/dgllife-ci-gpu:latest
```
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build
# tutorials are auto-generated
source/tutorials
source/generated
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# Minimal makefile for Sphinx documentation
#
# You can set these variables from the command line.
SPHINXOPTS =
SPHINXBUILD = sphinx-build
SOURCEDIR = source
BUILDDIR = build
# Put it first so that "make" without argument is like "make help".
help:
@$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
.PHONY: help Makefile
pytorch:
@echo "##################################################################"
@echo "# #"
@echo "# Step 1: Building PyTorch tutorials #"
@echo "# #"
@echo "##################################################################"
@DGLBACKEND=pytorch $(SPHINXBUILD) -M html "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
html-noexec:
$(SPHINXBUILD) -D plot_gallery=0 -b html "$(SOURCEDIR)" "$(BUILDDIR)/html"
@echo
@echo "Build finished. The HTML pages are in $(BUILDDIR)/html."
html: Makefile pytorch
# Catch-all target: route all unknown targets to Sphinx using the new
# "make mode" option. $(O) is meant as a shortcut for $(SPHINXOPTS).
%: Makefile
@$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
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Documentation and Tutorials
===
Requirements
------------
* sphinx
* sphinx-gallery
* sphinx_rtd_theme
Build documents
---------------
First, clean up existing files:
```
./clean.sh
```
Then build:
```
make html
```
Render locally
--------------
```
cd build/html
python3 -m http.server 8000
```
apps/life_sci/docs/clean.sh deleted 100755 → 0
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#!/bin/sh
make clean
rm -rf build
rm -rf source/tutorials
rm -rf source/generated
apps/life_sci/docs/source/api/data.rst deleted 100644 → 0
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.. _apidata:
Datasets
========
.. contents:: Contents
:local:
Molecular Property Prediction
-----------------------------
Tox21
`````
.. autoclass:: dgllife.data.Tox21
:members: task_pos_weights, __getitem__, __len__
:show-inheritance:
Alchemy for Quantum Chemistry
`````````````````````````````
.. autoclass:: dgllife.data.TencentAlchemyDataset
:members: set_mean_and_std, __getitem__, __len__
Pubmed Aromaticity
``````````````````
.. autoclass:: dgllife.data.PubChemBioAssayAromaticity
:members: __getitem__, __len__
:show-inheritance:
Adapting to New Datasets with CSV
`````````````````````````````````
.. autoclass:: dgllife.data.MoleculeCSVDataset
:members: __getitem__, __len__
Reaction Prediction
-------------------
USPTO
`````
.. autoclass:: dgllife.data.USPTOCenter
:members: __getitem__, __len__
:show-inheritance:
.. autoclass:: dgllife.data.USPTORank
:members: ignore_large, __getitem__, __len__
:show-inheritance:
Adapting to New Datasets for Weisfeiler-Lehman Networks
```````````````````````````````````````````````````````
.. autoclass:: dgllife.data.WLNCenterDataset
:members: __getitem__, __len__
.. autoclass:: dgllife.data.WLNRankDataset
:members: ignore_large, __getitem__, __len__
Protein-Ligand Binding Affinity Prediction
------------------------------------------
PDBBind
```````
.. autoclass:: dgllife.data.PDBBind
:members: __getitem__, __len__
apps/life_sci/docs/source/api/model.gnn.rst deleted 100644 → 0
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.. _apimodelgnn:
Graph Neural Networks for Updating Node/Edge Representations
============================================================
All models based on graph neural networks start with updating node/edge representations.
We introduce various GNN models implemented in DGL-LifeSci for representation update.
.. contents:: Contents
:local:
AttentiveFP
-----------
.. automodule:: dgllife.model.gnn.attentivefp
:members:
GAT
---
.. automodule:: dgllife.model.gnn.gat
:members:
GCN
---
.. automodule:: dgllife.model.gnn.gcn
:members:
MGCN
----
.. automodule:: dgllife.model.gnn.mgcn
:members:
MPNN
----
.. automodule:: dgllife.model.gnn.mpnn
:members:
SchNet
------
.. automodule:: dgllife.model.gnn.schnet
:members:
Weave
-----
.. automodule:: dgllife.model.gnn.weave
:members:
GIN
---
.. automodule:: dgllife.model.gnn.gin
:members:
WLN
---
.. automodule:: dgllife.model.gnn.wln
:members:
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.. _apimodelpretrain:
Pre-trained Models
==================
We provide multiple pre-trained models for users to use without the need of training from scratch.
Example Usage
-------------
Property Prediction
```````````````````
.. code-block:: python
from dgllife.data import Tox21
from dgllife.model import load_pretrained
from dgllife.utils import smiles_to_bigraph, CanonicalAtomFeaturizer
dataset = Tox21(smiles_to_bigraph, CanonicalAtomFeaturizer())
model = load_pretrained('GCN_Tox21') # Pretrained model loaded
model.eval()
smiles, g, label, mask = dataset[0]
feats = g.ndata.pop('h')
label_pred = model(g, feats)
print(smiles) # CCOc1ccc2nc(S(N)(=O)=O)sc2c1
print(label_pred[:, mask != 0]) # Mask non-existing labels
# tensor([[ 1.4190, -0.1820, 1.2974, 1.4416, 0.6914,
# 2.0957, 0.5919, 0.7715, 1.7273, 0.2070]])
Generative Models
.. code-block:: python
from dgllife.model import load_pretrained
model = load_pretrained('DGMG_ZINC_canonical')
model.eval()
smiles = []
for i in range(4):
smiles.append(model(rdkit_mol=True))
print(smiles)
# ['CC1CCC2C(CCC3C2C(NC2=CC(Cl)=CC=C2N)S3(=O)=O)O1',
# 'O=C1SC2N=CN=C(NC(SC3=CC=CC=N3)C1=CC=CO)C=2C1=CCCC1',
# 'CC1C=CC(=CC=1)C(=O)NN=C(C)C1=CC=CC2=CC=CC=C21',
# 'CCN(CC1=CC=CC=C1F)CC1CCCN(C)C1']
If you are running the code block above in Jupyter notebook, you can also visualize the molecules generated with
.. code-block:: python
from IPython.display import SVG
from rdkit import Chem
from rdkit.Chem import Draw
mols = [Chem.MolFromSmiles(s) for s in smiles]
SVG(Draw.MolsToGridImage(mols, molsPerRow=4, subImgSize=(180, 150), useSVG=True))
.. image:: https://data.dgl.ai/dgllife/dgmg/dgmg_model_zoo_example2.png
API
---
.. autofunction:: dgllife.model.load_pretrained
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.. _apimodelreadout:
Readout for Computing Graph Representations
===========================================
After updating node/edge representations with graph neural networks (GNNs), a common operation is to compute
graph representations out of updated node/edge representations. For example, we need to compute molecular
representations out of atom/bond representations in molecular property prediction. We call the various modules
for computing graph-level representations **readout** as in Neural Message Passing for Quantum Chemistry and this
section lists the readout modules implemented in DGL-LifeSci.
.. contents:: Contents
:local:
AttentiveFP Readout
-------------------
.. automodule:: dgllife.model.readout.attentivefp_readout
:members:
MLP Readout
-----------
.. automodule:: dgllife.model.readout.mlp_readout
:members:
Weighted Sum and Max Readout
----------------------------
.. automodule:: dgllife.model.readout.weighted_sum_and_max
:members:
Weave Readout
-------------
.. automodule:: dgllife.model.readout.weave_readout
:members:
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